_ | are | _ |
3D Measurements from Imaging Laser Radars: How Good | are | They? |
5 Questions for Missy Cummings: The Former Fighter Pilot on why Autonomous Vehicles | are | so Risky |
Adversarial Attacks | are | Reversible with Natural Supervision |
Agriculture drones | are | finally cleared for takeoff [News] |
All | are | Worth Words: A ViT Backbone for Diffusion Models |
All Burglaries | are | Not the Same: Predicting Near-Repeat Burglaries in Cities Using Modus Operandi |
All Iris Code Bits | are | Not Created Equal |
All Iris Filters | are | Not Created Equal |
All Labels | are | Not Created Equal: Enhancing Semi-supervision via Label Grouping and Co-training |
All vehicles | are | cars: subclass preferences in container concepts |
All you need | are | a few pixels: semantic segmentation with PixelPick |
Application of Virtual Environments for Infantry Soldier Skills Training: We | are | Doing it Wrong |
Applying Signal Processing to Opposite Sides of Imaging: Separate European research projects | are | focusing on aspects of completely real and entirely fake images |
| are | 2D-LSTM really dead for offline text recognition? |
| are | 3D convolutional networks inherently biased towards appearance? |
| are | Adversarial Robustness and Common Perturbation Robustness Independent Attributes ? |
| are | All Combinations Equal? Combining Textual and Visual Features with Multiple Space Learning for Text-based Video Retrieval |
| are | all objects equal? Deep spatio-temporal importance prediction in driving videos |
| are | All Users Treated Fairly in Federated Learning Systems? |
| are | Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-based Active Learning |
| are | Cars Just 3D Boxes? Jointly Estimating the 3D Shape of Multiple Objects |
| are | Characters Objects? |
| are | City Features Influencing the Behavior of Photographers? An Analysis of Geo-referenced Photos Shooting Orientation |
| are | Climate-Dependent Impacts of Soil Constraints on Crop Growth Evident in Remote-Sensing Data? |
| are | Commercially Implemented Adaptive Cruise Control Systems String Stable? |
| are | Correlation Filters Useful for Human Action Recognition? |
| are | current long-term video understanding datasets long-term? |
| are | Current Monocular Computer Vision Systems for Human Action Recognition Suitable for Visual Surveillance Applications? |
| are | Data-Driven Explanations Robust Against Out-of-Distribution Data? |
| are | Deep Models Robust against Real Distortions? A Case Study on Document Image Classification |
| are | Deep Neural Networks SMARTer Than Second Graders? |
| are | Digraphs Good for Free-Text Keystroke Dynamics? |
| are | Edges Incomplete? |
| are | Edges Sufficient for Object Recognition |
| are | Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners' Dwellings and Their Interaction with Urban Spaces |
| are | emotional objects visually salient? The Emotional Maps Database |
| are | Emotional Robots Deceptive? |
| are | External Face Features Useful for Automatic Face Classification? |
| are | Face Detection Models Biased? |
| are | face recognition methods useful for classifying ships? |
| are | facial attributes adversarially robust? |
| are | French Really That Different? Recognizing Europeans from Faces Using Data-Driven Learning |
| are | Fuzzy Definitions of Basic Attributes of Image Objects Really Useful? |
| are | Gaussian spectra a viable perceptual assumption in color appearance? |
| are | Gibbs-Type Priors the Most Natural Generalization of the Dirichlet Process? |
| are | Graph Convolutional Networks With Random Weights Feasible? |
| are | Haar-Like Rectangular Features for Biometric Recognition Reducible? |
| are | Impossible Figures Possible? |
| are | Indices of Polarimetric Purity Excellent Metrics for Object Identification in Scattering Media? |
| are | IoBT services accessible to everyone? |
| are | Iterations and Curvature Useful for Tensor Voting? |
| are | Labels Always Necessary for Classifier Accuracy Evaluation? |
| are | Labels Necessary for Neural Architecture Search? |
| are | Labels Needed for Incremental Instance Learning? |
| are | Large-Scale 3D Models Really Necessary for Accurate Visual Localization? |
| are | Local Features All You Need for Cross-Domain Visual Place Recognition? |
| are | Measured Ground Control Points Still Required In UAV Based Large Scale Mapping? Assessing the Positional Accuracy of An RTK Multi-rotor Platform |
| are | metrics measuring what they should? An evaluation of Image Captioning task metrics |
| are | mid-air dynamic gestures applicable to user identification? |
| are | MSER Features Really Interesting? |
| are | Multifractal Multipermuted Multinomial Measures Good Enough for Unsupervised Image Segmentation? |
| are | Multilayer Perceptrons Adequate for Pattern-Recognition and Verification |
| are | Multimodal Transformers Robust to Missing Modality? |
| are | Multiple Cross-Correlation Identities better than just Two? Improving the Estimate of Time Differences-of-Arrivals from Blind Audio Signals |
| are | Natural Domain Foundation Models Useful for Medical Image Classification? |
| are | Object Detection Assessment Criteria Ready for Maritime Computer Vision? |
| are | Performance Differences of Interest Operators Statistically Significant? |
| are | Reactions to Ego Vehicles Predictable Without Data?: A Semi-Supervised Approach |
| are | Reducts and Typical Testors the Same? |
| are | reflectance field renderings appropriate for optical flow evaluation? |
| are | Robotic-Assisted Catheter Ablation Lesions Different from Standard Catheter Ablation in Paroxysmal AF Patients? Novel CMRI Findings Made Possible with Semi-automatic 3-D Visualisation |
| are | sparse representations really relevant for image classification? |
| are | spatial and global constraints really necessary for segmentation? |
| are | spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors? |
| are | Straight-Through gradients and Soft-Thresholding all you need for Sparse Training? |
| are | Textureless Scenes Recoverable? |
| are | the Latest GSMaP Satellite Precipitation Products Feasible for Daily and Hourly Discharge Simulations in the Yellow River Source Region? |
| are | the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data |
| are | the Significant Ionospheric Anomalies Associated with the 2007 Great Deep-Focus Undersea Jakarta-Java Earthquake? |
| are | the Wavelet Transforms the Best Filter Banks for Image Compression? |
| are | There One or More Geophysical Coupling Mechanisms before Earthquakes? The Case Study of Lushan (China) 2013 |
| are | There Sufficient Landsat Observations for Retrospective and Continuous Monitoring of Land Cover Changes in China? |
| are | These Birds Similar: Learning Branched Networks for Fine-grained Representations |
| are | These from the Same Place? Seeing the Unseen in Cross-View Image Geo-Localization |
| are | They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text |
| are | They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior |
| are | They Paying Attention? A Model-Based Method to Identify Individuals' Mental States |
| are | Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles? |
| are | twin hyperplanes necessary? |
| are | two rotational flows sufficient to calibrate a smooth non-parametric sensor? |
| are | Vision Transformers Robust to Patch Perturbations? |
| are | Vision Transformers Robust to Spurious Correlations? |
| are | Visual Informatics Actually Useful in Practice: A Study in a Film Studies Context |
| are | we Asking the Right Questions in MovieQA? |
| are | we certain it's anomalous? |
| are | We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods |
| are | We in Boswash Yet? A Multi-Source Geodata Approach to Spatially Delimit Urban Corridors |
| are | we in sync during turn switch? |
| are | we making real progress in computer vision today? |
| are | we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection? |
| are | we ready for autonomous driving? The KITTI vision benchmark suite |
| are | We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset |
| are | We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark |
| are | You Confident That You Have Successfully Generated Adversarial Examples? |
| are | you eligible? Predicting adulthood from face images via Class Specific Mean Autoencoder |
| are | You Really Looking at Me? A Feature-Extraction Framework for Estimating Interpersonal Eye Gaze From Conventional Video |
| are | You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles |
| are | You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension |
| are | You Smiling as a Celebrity? Latent Smile and Gender Recognition |
| are | You Talking to Me? Reasoned Visual Dialog Generation Through Adversarial Learning |
| are | You Tampering with My Data? |
| are | You There? A Study on Measuring Presence in Immersive Virtual Reality |
| are | you using the right approximate nearest neighbor algorithm? |
| are | you what you look like? Exploring correlations in personality type and their wearing |
| are | Younger People More Difficult to Identify or Just a Peer-to-Peer Effect |
| are | S: On Adversarial Robustness Enhancement for Image Steganographic Cost Learning |
Arithmetic Discrete Planes | are | Quasicrystals |
Assessment of video tone-mapping: | are | cameras' S-shaped tone-curves good enough? |
Asymptotic error rates of the W and Z statistics when the training observations | are | dependent |
AutoEval: | are | Labels Always Necessary for Classifier Accuracy Evaluation? |
Automated assessment: How confident | are | we? |
Automated Registration Evaluation System ( | are | S) |
Automatic surface classification for retrieving | are | as which are highly endangered by extreme rain |
Back to the Feature: Classical 3D Features | are | (Almost) All You Need for 3D Anomaly Detection |
Balanced Datasets | are | Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations |
Because better detections | are | still possible: Multi-aspect Object Detection with Boosted Hough Forest |
Because not all displays | are | lists |
Believe It or Not, We Know What You | are | Looking At! |
Bias from the Wild Industry 4.0: | are | We Really Classifying the Quality or Shotgun Series? |
Bikers | are | Like Tobacco Shops, Formal Dressers Are Like Suits: Recognizing Urban Tribes with Caffe |
Bikers | are | Like Tobacco Shops, Formal Dressers Are Like Suits: Recognizing Urban Tribes with Caffe |
Biologically Significant Facial Landmarks: How Significant | are | They for Gender Classification? |
Biometric Recognition: How Do I Know Who You | are | ? |
biorthogonal wavelets that | are | redundant-free and nearly shift-insensitive, The |
Bitstream-Corrupted JPEG Images | are | Restorable: Two-stage Compensation and Alignment Framework for Image Restoration |
blood is here: Zipline's medical delivery drones | are | changing the game in Rwanda, The |
Bounding Boxes | are | All We Need: Street View Image Classification via Context Encoding of Detected Buildings |
Breaking the Cycle: Colleagues | are | All You Need |
Bridging Remote Sensing and GIS: Which | are | the main supportive pillars? |
CaCo: Both Positive and Negative Samples | are | Directly Learnable via Cooperative-Adversarial Contrastive Learning |
Can we trust computer with body-cam vidio? Police departments | are | being led to believe AI will help, but they should be wary |
Captioning Images Taken by People Who | are | Blind |
Cardinal axes | are | not independent in color discrimination |
Classifier for Feature Vectors Whose Prototypes | are | a Function of Multiple Continuous Parameters, A |
Climate Sensitivity and Drought Legacy of Tree Growth in Plantation Forests in Northeast China | are | Species- and Age-Dependent |
Clinical Application of a Semiautomatic 3D Fusion Tool Where Automatic Fusion Techniques | are | Difficult to Use |
Clipped Hyperbolic Classifiers | are | Super-Hyperbolic Classifiers |
CNN-Generated Images | are | Surprisingly Easy to Spot… for Now |
Color categories only affect post-perceptual processes when same- and different-category colors | are | equally discriminable |
Color Vision and Image Intensities: When | are | Changes Material? |
Color-motion feature-binding errors | are | mediated by a higher-order chromatic representation |
Common Diffusion Noise Schedules and Sample Steps | are | Flawed |
Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which | are | Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery, A |
Comparative study to analyze the effect of aging on microvascular blood flow by processing laser speckle contrast images when Lorentzian and Gaussian velocity profiles | are | assumed for moving scatterers |
Comparing classifiers when the misallocation costs | are | uncertain |
Connected Pictures | are | not Recognizable by Deterministic Two Dimensional On-Line Tessellation Acceptors |
Constructing Face Image Logs that | are | Both Complete and Concise |
Construction of a System for Defining | are | as Which Are Not Obtained Data from Stereo Images |
Contour Tracking When Two Gray-Level Discontinuities | are | Close to Each Other |
Contrastive Losses | are | Natural Criteria for Unsupervised Video Summarization |
Contrastive Masked Autoencoders | are | Stronger Vision Learners |
ConvNets vs. Transformers: Whose Visual Representations | are | More Transferable? |
Copycat CNN: | are | random non-Labeled data enough to steal knowledge from black-box models? |
Correspondence Matrices | are | Underrated |
Covert Attentional Shoulder Surfing: Human Adversaries | are | More Powerful Than Expected |
Creation of real images which | are | valid for the assumptions made in shape from shading |
Cross-validation and bootstrapping | are | unreliable in small sample classification |
Cues in Dependent Multiple Cue Integration for Robust Tracking | are | Independent, The |
DatasetEquity: | are | All Samples Created Equal? In The Quest For Equity Within Datasets |
Deblurring subject to nonnegativity constraints when known functions | are | present with application to object-constrained computerized tomography |
Deep networks | are | efficient for circular manifolds |
Deep neural networks | are | easily fooled: High confidence predictions for unrecognizable images |
Deep Prototypical-Parts Ease Morphological Kidney Stone Identification and | are | Competitively Robust to Photometric Perturbations |
Deeply learned face representations | are | sparse, selective, and robust |
Deformable part models | are | convolutional neural networks |
delivery drones | are | coming, The |
Denoising Diffusion Autoencoders | are | Unified Self-supervised Learners |
Dense Matching Using Correlation: New Measures That | are | Robust Near Occlusions |
Depth from Defocus vs. Stereo: How Different Really | are | They? |
Descriptive and Prescriptive Languages for Mobility Tasks: | are | They Different? |
Detecting Avocados to Zucchinis: What Have We Done, and Where | are | We Going? |
Determining Which Touch Gestures | are | Commonly Used When Visualizing Physics Problems in Augmented Reality |
DetMatch: Two Teachers | are | Better than One for Joint 2D and 3D Semi-Supervised Object Detection |
Directions of Motion Fields | are | Hardly Ever Ambiguous |
Discriminant Interest Points | are | Stable |
Drivers or Pedestrians, Whose Dynamic Perceptions | are | More Effective to Explain Street Vitality? A Case Study in Guangzhou |
Dynamics | are | Important for the Recognition of Equine Pain in Video |
Efficiency of discriminant analysis when initial samples | are | classified stochastically |
Emotions | are | the Great Captains of Our Lives: Measuring Moods Through the Power of Physiological and Environmental Sensing |
Estimating benefits of C-ITS deployment, when legacy roadside systems | are | present |
Exploration of Location-Aw | are | You-Are-Here Maps on a Pin-Matrix Display |
Expressive Body Movement Responses to Music | are | Coherent, Consistent, and Low Dimensional |
Extensions of Karger's Algorithm: Why They Fail in Theory and How They | are | Useful in Practice |
Face gender classification: A statistical study when neutral and distorted faces | are | combined for training and testing purposes |
Face re-identification challenge: | are | face recognition models good enough? |
Facial Dynamics Interpreter Network: What | are | the Important Relations Between Local Dynamics for Facial Trait Estimation? |
Factorial coding of natural images: how effective | are | linear models in removing higher-order dependencies? |
Feature Selection When Limited Numbers of Training Samples | are | Available |
Federated Learning Over Images: Vertical Decompositions and Pre-Trained Backbones | are | Difficult to Beat |
Few Labels | are | Enough! Semi-supervised Graph Learning for Social Interaction |
Few shots | are | all you need: A progressive learning approach for low resource handwritten text recognition |
Few-Shot Image Classification Benchmarks | are | Too Far From Reality: Build Back Better with Semantic Task Sampling |
Fiducial Reference Measurements (FRMs): What | are | They? |
Find where you | are | : a new try in place recognition |
Fine-tuned CLIP Models | are | Efficient Video Learners |
First-Person Activity Recognition: What | are | They Doing to Me? |
Fitting of Straight Lines if Both Variables | are | Subject to Error |
Fitting of Straight Lines when Both Variables | are | Subject to Error, The |
Floors | are | Flat: Leveraging Semantics for Real-Time Surface Normal Prediction |
Forest and Land Fires | are | Mainly Associated with Deforestation in Riau Province, Indonesia |
From TRMM to GPM: How Reliable | are | Satellite-Based Precipitation Data across Nigeria? |
Frozen CLIP Models | are | Efficient Video Learners |
Full Affine Wavelets | are | Scale-Space with a Twist |
GANORCON: | are | Generative Models Useful for Few-shot Segmentation? |
Generalized Cylinders: What | are | They? |
Generated Distributions | are | All You Need for Membership Inference Attacks Against Generative Models |
Global Features | are | All You Need for Image Retrieval and Reranking |
Gramian Attention Heads | are | Strong yet Efficient Vision Learners |
Hard Negative Examples | are | Hard, but Useful |
Here We | are | ! Where Are We? Locating Mixed Reality in The Age of the Smartphone |
Here We | are | ! Where Are We? Locating Mixed Reality in The Age of the Smartphone |
Hollywood 3D: What | are | the Best 3D Features for Action Recognition? |
How | are | attributes expressed in face DCNNs? |
How | are | LED Illumination Based Multispectral Imaging Systems Influenced by Different Factors? |
How | are | Macro-Scale and Micro-Scale Built Environments Associated with Running Activity? The Application of Strava Data and Deep Learning in Inner London |
How | are | You Doing? Enabling Older Adults to Enrich Sensor Data with Subjective Input |
How | are | you feeling? Multimodal Emotion Learning for Socially-Assistive Robot Navigation |
How Can You Tell if Two Line Drawings | are | the Same? |
How close | are | we to solving the problem of automated visual surveillance?: A review of real-world surveillance, scientific progress and evaluative mechanisms |
How Complementary | are | SRTM-X and -C Band Digital Elevation Models? |
How effective | are | landmarks and their geometry for face recognition? |
How Efficient | are | Today's Continual Learning Algorithms? |
How Far | are | We from Solving Pedestrian Detection? |
How Far | are | We from Solving the 2D 3D Face Alignment Problem? (and a Dataset of 230,000 3D Facial Landmarks) |
How Far Pre-trained Models | are | from Neural Collapse on the Target Dataset Informs their Transferability |
How good | are | detection proposals, really? |
How Good | are | Local Features for Classes of Geometric Objects |
How Important | are | Deformable Parts in the Deformable Parts Model? |
How important | are | faces for person re-identification? |
How many clusters | are | best? - An experiment |
How many dimensions | are | required to find an adversarial example? |
How Many Facilities | are | Needed? Evaluating Configurations of Subway Security Check Systems via a Hybrid Queueing Model |
How many Observations | are | Enough? Knowledge Distillation for Trajectory Forecasting |
How Many Pan-Arctic Lakes | are | Observed by ICESat-2 in Space and Time? |
How many planar viewing surfaces | are | there in noncentral catadioptric cameras? Towards singe-image localization of space lines |
How often | are | changes made to the bibliography? |
How old | are | you?: Age Estimation with Tensors of Binary Gaussian Receptive Maps |
How Privacy-Preserving | are | Line Clouds? Recovering Scene Details from 3D Lines |
How robust | are | discriminatively trained zero-shot learning models? |
How Robust | are | Randomized Smoothing based Defenses to Data Poisoning? |
How Smart | are | Smart Classrooms? A Review of Smart Classroom Technologies |
How Stable | are | Transferability Metrics Evaluations? |
How to Build an Average Model When Samples | are | Variably Incomplete? Application to Fossil Data |
How Transferable | are | Reasoning Patterns in VQA? |
How Trustworthy | are | Performance Evaluations for Basic Vision Tasks? |
I Bet You | are | Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation |
Image classification in natural scenes: | are | a few selective spectral channels sufficient? |
Image classification: | are | rule-based systems effective when classes are fixed and known? |
Image classification: | are | rule-based systems effective when classes are fixed and known? |
Image databases | are | not databases with images |
Image Labels | are | All You Need for Coarse Seagrass Segmentation |
Image Manifolds which | are | Isometric to Euclidean Space |
Image-based human re-identification: Which covariates | are | actually (the most) important? |
Images Loci | are | Ridges in Geometric Spaces |
independent components of natural scenes | are | edge filters, The |
Individual differences in simultaneous color constancy | are | related to working memory |
Instance and Category Supervision | are | Alternate Learners for Continual Learning |
Intelligent Vision Systems | are | Set to Take-Off |
Interactions Between Large-Scale Functional Brain Networks | are | Captured by Sparse Coupled HMMs |
Iris presentation attack detection: Where | are | we now? |
Just a Few Points | are | All You Need for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo |
Kolmogorov-Smirnov and ROC curve metrics for binary classification performance assessment | are | equivalent |
Labels | are | Not Perfect: Inferring Spatial Uncertainty in Object Detection |
Language Models | are | Causal Knowledge Extractors for Zero-shot Video Question Answering |
Learning to Look at Humans: What | are | the Parts of a Moving Body? |
Level-5 Autonomous Driving: | are | We There Yet? A Review of Research Literature |
LGANet: Local and global attention | are | both you need for action recognition |
Local features | are | not lonely: Laplacian sparse coding for image classification |
Machine Learning Paradigm for Studying Pictorial Realism: How Accurate | are | Constable's Clouds?, A |
Machine vision to curb pig pugnacity: 3D cameras can help predict when pigs | are | about to nip each other's tails |
Manitest: | are | classifiers really invariant? |
Mapping the Twilight Zone: What We | are | Missing between Clouds and Aerosols |
Masked Autoencoders | are | Efficient Class Incremental Learners |
Masked Autoencoders | are | Scalable Vision Learners |
Masked Autoencoders | are | Stronger Knowledge Distillers |
Masked Images | are | Counterfactual Samples for Robust Fine-Tuning |
Masked Motion Predictors | are | Strong 3D Action Representation Learners |
MATE: Masked Autoencoders | are | Online 3D Test-Time Learners |
Membership Inference Attacks | are | Easier on Difficult Problems |
Messages | are | Never Propagated Alone: Collaborative Hypergraph Neural Network for Time-Series Forecasting |
Metamer Mismatching and Its Consequences for Predicting How Colours | are | Affected by the Illuminant |
Method and apparatus for upscaling video images when pixel data used for upscaling a source video image | are | unavailable |
Method of producing a high quality, high resolution image from a sequence of low quality, low resolution images that | are | undersampled and subject to jitter |
Military Tests that Jam and Spoof GPS Signals | are | an Accident Waiting to Happen |
Minds vs. Machines: How Far | are | We From the Common Sense of a Toddler? |
Minor Surfaces | are | Boundaries of Mode-Based Clusters |
MixMix: All You Need for Data-Free Compression | are | Feature and Data Mixing |
Model Comparison Metrics Require Adaptive Correction if Parameters | are | Discretized: Proof-of-Concept Applied to Transient Signals in Dynamic PET |
More Photos | are | All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval |
Morphological Amoebas | are | Self-snakes |
Motion Estimation: The Proper Formulation for when 3 or 4 Frames | are | Available |
Motion Fields | are | Hardly Ever Ambiguous |
Multi-task learning for natural language processing in the 2020s: Where | are | we going? |
Multiple Heads | are | Better than One: Few-shot Font Generation with Multiple Localized Experts |
Multispectral Remote Sensing Data | are | Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China |
Myope Models: | are | face presentation attack detection models short-sighted? |
Nanostructures Transform Tiny Cameras: Thin Semiconductor Metalenses | are | Finally Moving into Consumers' Hands |
Negative Samples | are | at Large: Leveraging Hard-Distance Elastic Loss for Re-identification |
Net2Vec: Quantifying and Explaining How Concepts | are | Encoded by Filters in Deep Neural Networks |
Neural Networks | are | More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation From a Blackbox Model |
Neural Sign Language Synthesis: Words | are | Our Glosses |
New implementation of Ogc Web Processing Service in Python programming language. Pywps-4 and issues we | are | facing with processing of large raster data using Ogc Wps |
New Moneyball: How Ballpark Sensors | are | Changing Baseball, The |
No Matter Where You | are | : Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion |
Not All | are | as Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection |
Not all domains | are | equally complex: Adaptive Multi-Domain Learning |
Not All Frames | are | Equal: Weakly-Supervised Video Grounding With Contextual Similarity and Visual Clustering Losses |
Not All Labels | are | Equal: Rationalizing The Labeling Costs for Training Object Detection |
Not All Models | are | Equal: Predicting Model Transferability in a Self-challenging Fisher Space |
Not All Negatives | are | Equal: Learning to Track With Multiple Background Clusters |
Not All Parts | are | Created Equal: 3D Pose Estimation by Modeling Bi-Directional Dependencies of Body Parts |
Not All Patches | are | Equal: Hierarchical Dataset Condensation for Single Image Super-Resolution |
Not all pixels | are | created equal |
Not All Pixels | are | Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade |
Not All Points | are | Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds |
Not All Relations | are | Equal: Mining Informative Labels for Scene Graph Generation |
Not All Samples | are | Born Equal: Towards Effective Clean-Label Backdoor Attacks |
Not All Samples | are | Trustworthy: Towards Deep Robust SVP Prediction |
Not All Steps | are | Created Equal: Selective Diffusion Distillation for Image Manipulation |
Not All Swear Words | are | Used Equal: Attention over Word n-grams for Abusive Language Identification |
Not All Tokens | are | Equal: Human-centric Visual Analysis via Token Clustering Transformer |
Note-Taker: An assistive technology that allows students who | are | legally blind to take notes in the classroom, The |
Objects | are | Different: Flexible Monocular 3D Object Detection |
Occlusions | are | Fleeting - Texture is Forever: Moving Past Brightness Constancy |
OGC Consensus: How Successful Standards | are | Made |
On design and optimization of face verification systems that | are | smart-card based |
On Fast Trackers that | are | Robust to Partial Occlusions |
On Russian Farms, the Robotic Revolution Has Begun: Hundreds of Aftermarket AIs | are | Harvesting Grain |
On the construction of morphological operators which | are | self-dual and activity-extensive |
On the Recovery of Motion and Structure when Cameras | are | not Calibrated |
Parked Cars | are | Excellent Roadside Units |
Patch Attack Invariance: How Sensitive | are | Patch Attacks to 3D Pose? |
Pattern recognition in stained HEp-2 cells: Where | are | we now? |
Patterns in Poor Learning Engagement in Students While They | are | Solving Mathematics Exercises in an Affective Tutoring System Related to Frustration |
Peekaboo-Where | are | the Objects? Structure Adjusting Superpixels |
Person Re-identification: What Features | are | Important? |
Pictures We Like | are | Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits, The |
Poisons that | are | learned faster are more effective |
Poisons that | are | learned faster are more effective |
Polarimetric SAR Calibration and Residual Error Estimation When Corner Reflectors | are | Unavailable |
Pondering the Ugly Underbelly, and Whether Images | are | Real |
Pose Versus State: | are | Sensor Position and Attitude Sufficient for Modern Photogrammetry and Remote Sensing? |
Precipitation and Minimum Temperature | are | Primary Climatic Controls of Alpine Grassland Autumn Phenology on the Qinghai-Tibet Plateau |
Predicting When Saliency Maps | are | Accurate and Eye Fixations Consistent |
Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy | are | a Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands |
Privacy-Preserving Representations | are | not Enough: Recovering Scene Content from Camera Poses |
Psumnet: Unified Modality Part Streams | are | All You Need for Efficient Pose-based Action Recognition |
Ramps | are | better than stairs to reduce cybersickness in applications based on a HMD and a Gamepad |
Random Boxes | are | Open-world Object Detectors |
Rank Minimization or Nuclear-Norm Minimization: | are | We Solving the Right Problem? |
Rapid Adaptation in Online Continual Learning: | are | We Evaluating It Right? |
Recognizing Patterns: | are | there Processes that Precede Feature Analysis? |
Recursions | are | All You Need: Towards Efficient Deep Unfolding Networks |
Recursive photometric stereo when multiple shadows and highlights | are | present |
Reflecting on How Artworks | are | Processed and Analyzed by Computer Vision |
Regularization Algorithms for Learning That | are | Equivalent to Multilayer Networks |
Reliability in Semantic Segmentation: | are | we on the Right Track? |
Remote Biometric Verification for eLearning Applications: Where We | are | |
Robots | are | Coming |
Rolling Shutter and Radial Distortion | are | Features for High Frame Rate Multi-camera Tracking |
Roses | are | Red, Violets are Blue… But Should VQA expect Them To? |
Roses | are | Red, Violets are Blue… But Should VQA expect Them To? |
Scaling functions for landscape pattern metrics derived from remotely sensed data: | are | their subpixel estimates really accurate? |
Search to Distill: Pearls | are | Everywhere but Not the Eyes |
Secant Cylinders | are | Evil: A Case Study on the Standard Lines of the Universal Transverse Mercator and Universal Polar Stereographic Projections |
Seeing What is Not There: Learning Context to Determine Where Objects | are | Missing |
Segmentation Versus Object Representation: | are | They Separable? |
Segmenting a page of a document into | are | as which are text and areas which are halftone |
Segmenting a page of a document into | are | as which are text and areas which are halftone |
Self-Reported Symptoms of Depression and PTSD | are | Associated with Reduced Vowel Space in Screening Interviews |
Self-Supervised Encoders | are | Better Transfer Learners in Remote Sensing Applications |
Self-Supervised Models | are | Continual Learners |
Shape-biased CNNs | are | Not Always Superior in Out-of-Distribution Robustness |
Shift-Enabled Graphs: Graphs Where Shift-Invariant Filters | are | Representable as Polynomials of Shift Operations |
Signal Processing Advances the Quest for Better and Safer Medical Imaging: Imaging Breakthroughs | are | Saving Lives By Giving Radiologists and Physicians Sharper and Safer Views Inside the Human Body |
Smart Home Technologies | are | Saving Money and Lives: Reaching out in new directions, signal processing-supported smart technologies are rapidly changing - and improving - everyday life |
Smart Home Technologies | are | Saving Money and Lives: Reaching out in new directions, signal processing-supported smart technologies are rapidly changing - and improving - everyday life |
Smiling faces | are | better for face recognition |
SOBS algorithm: What | are | the limits?, The |
Some Faces | are | More Equal than Others: Hierarchical Organization for Accurate and Efficient Large-Scale Identity-Based Face Retrieval |
Some Objects | are | More Equal Than Others: Measuring and Predicting Importance |
Space-Time Behavior-Based Correlation - OR - How to Tell If Two Underlying Motion Fields | are | Similar Without Computing Them? |
Sparse Algorithms | are | Not Stable: A No-Free-Lunch Theorem |
Strike (With) a Pose: Neural Networks | are | Easily Fooled by Strange Poses of Familiar Objects |
Supporting video library exploratory search: When storyboards | are | not enough |
Supporting Virtual Collaboration in Spatial Design Tasks: | are | Surrogate or Natural Gestures More Effective? |
Survey: How good | are | the current advances in image set based face identification?: Experiments on three popular benchmarks with a naive approach |
Symmetric 3D Objects | are | an Easy Case for 2D Object Recognition |
Symmetric Objects | are | Hardly Ambiguous |
Symmetry-Based Graph Fourier Transforms: | are | They Optimal for Image Compression? |
Synthetic Expressions | are | Better Than Real for Learning to Detect Facial Actions |
System for Controlling How C | are | fully Surgeons Are Cleaning Their Hands, A |
Systematic Requirements Analysis and Development of an Assistive Device to Enhance the Social Interaction of People Who | are | Blind or Visually Impaired, A |
Television in 3-D: What | are | the Prospects? |
Tell Me What You Like and I'll Tell You What You | are | : Discriminating Visual Preferences on Flickr Data |
Template Estimation in Computational Anatomy: Frechet Means Top and Quotient Spaces | are | Not Consistent |
Template-based paper reconstruction from a single image is well posed when the rulings | are | parallel |
Temporal Changes in Coupled Vegetation Phenology and Productivity | are | Biome-Specific in the Northern Hemisphere |
Text2Video-Zero: Text-to-Image Diffusion Models | are | Zero-Shot Video Generators |
Texture classification: | are | filter banks necessary? |
They | are | Not Completely Useless: Towards Recycling Transferable Unlabeled Data for Class-Mismatched Semi-Supervised Learning |
They | are | Not Equally Reliable: Semantic Event Search Using Differentiated Concept Classifiers |
Three New Imaging Technologies That | are | Worth a Look: Aided by Signal Processing, Advanced Imaging Research Projects Are Opening Doors to New Vistas |
Three New Imaging Technologies That | are | Worth a Look: Aided by Signal Processing, Advanced Imaging Research Projects Are Opening Doors to New Vistas |
Top ten tech cars 2019: Self-driving and electric technologies | are | infiltrating everyday cars, slowly |
Towards Comprehensive Monocular Depth Estimation: Multiple Heads | are | Better Than One |
Towards correlation-based matching algorithms that | are | robust near occlusions |
Towards Few-Annotation Learning for Object Detection: | are | Transformer-based Models More Efficient? |
Transformaly: Two (Feature Spaces) | are | Better Than One |
Transformation systems | are | more economical and informative class descriptions than formal grammars |
Two motion-blurred images | are | better than one |
Two simply connected sets that have the same | are | a are IP-equivalent |
Two thresholds | are | better than one |
UAV Remote Sensing for Biodiversity Monitoring: | are | Forest Canopy Gaps Good Covariates? |
Unmanned Drones | are | Flying High in the Military/Aerospace Sector |
Unsupervised visual feature learning with spike-timing-dependent plasticity: How far | are | we from traditional feature learning approaches? |
USA Crop Yield Estimation with MODIS NDVI: | are | Remotely Sensed Models Better than Simple Trend Analyses? |
Use of Sub-Ensembles and Multi-Template Observers to Evaluate Detection Task Performance for Data That | are | Not Multivariate Normal |
Using Discrimination Graphs to Represent Visual Interpretations that | are | Hypothetical and Ambiguous |
Using Galois Theory to Prove Structure from Motion Algorithms | are | Optimal |
Vanishing Points | are | Meaningful Gestalts |
Video captioning: A comparative review of where we | are | and which could be the route |
Video2vec Embeddings Recognize Events When Examples | are | Scarce |
Vision Transformers | are | Good Mask Auto-Labelers |
Vision Transformers | are | Parameter-Efficient Audio-Visual Learners |
Warming Has Accelerated the Melting of Glaciers on the Tibetan Plateau, but the Debris-Covered Glaciers | are | Rapidly Expanding |
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks | are | Failing to Reproduce Spectral Distributions |
Wavelet Diffusion Models | are | fast and scalable Image Generators |
We | are | Family: Joint Pose Estimation of Multiple Persons |
We | are | Humor Beings: Understanding and Predicting Visual Humor |
We | are | More than Our Joints: Predicting how 3D Bodies Move |
We | are | not contortionists: Coupled adaptive learning for head and body orientation estimation in surveillance video |
What Actions | are | Needed for Understanding Human Actions in Videos? |
What | are | Contemporary Mexican Conifers Telling Us? A Perspective Offered from Tree Rings Linked to Climate and the NDVI along a Spatial Gradient |
What | are | customers looking at? |
What | are | good apertures for defocus deblurring? |
What | are | Good Design Gestures? |
What | are | good parts for hair shape modeling? |
What | are | Soft Biometrics and How Can They Be Used? |
What | are | Textons? |
What | are | the analytical conditions for which a blind equalizer will loose the convergence state? |
What | are | the high-level concepts with small semantic gaps? |
What | are | the Limits to Time Series Based Recognition of Semantic Concepts? |
What | are | the online references |
What | are | the true clusters? |
What | are | the Visual Features Underlying Human Versus Machine Vision? |
What | are | they doing?: Collective activity classification using spatio-temporal relationship among people |
What | are | we looking for: Towards statistical modeling of saccadic eye movements and visual saliency |
What | are | we missing here? Brain imaging evidence for higher cognitive functions in primary visual cortex V1 |
What | are | We Missing? Occlusion in Laser Scanning Point Clouds and Its Impact on the Detection of Single-Tree Morphologies and Stand Structural Variables |
What | are | We Tracking: A Unified Approach of Tracking and Recognition |
What | are | you doing while answering your smartphone? |
What | are | You Looking at?: Improving Visual Gaze Estimation by Saliency |
What | are | You Talking About? Text-to-Image Coreference |
What parts of a shape | are | discriminative? |
What Shape | are | Dolphins? Building 3D Morphable Models from 2D Images |
What to Expect When You | are | Expecting on the Grassmannian |
When and Why Static Images | are | More Effective Than Videos |
When | are | Simple LS Estimators Enough? An Empirical Study of LS, TLS, and GTLS |
When Faces | are | Combined with Palmprints: A Novel Biometric Fusion Strategy |
When Occlusions | are | Outliers |
When Two Cameras | are | a Crowd |
Where and Why | are | They Looking? Jointly Inferring Human Attention and Intentions in Complex Tasks |
Where | are | Focused Places of a Photo? |
Where | are | Linear Feature Extraction Methods Applicable? |
Where | are | my clothes? A multi-level approach for evaluating deep instance segmentation architectures on fashion images |
Where | are | the ball and players? Soccer game analysis with color-based tracking and image mosaick |
Where | are | the Blobs: Counting by Localization with Point Supervision |
Where | are | They Going? Clustering Event Camera Data to Detect and Track Moving Objects |
Where | are | they looking in the 3D space? |
Where | are | we with Human Pose Estimation in Real-World Surveillance? |
Where | are | you going? Using human locomotion models for target estimation |
Where | are | you heading? Dynamic Trajectory Prediction with Expert Goal Examples |
Where | are | You Looking At? - Feature-Based Eye Tracking on Unmodified Tablets |
Where We | are | and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes |
Which | are | the factors affecting the performance of audio surveillance systems? |
Which Components | are | Important for Interactive Image Searching? |
Which side of the focal plane | are | you on? |
Which Way | are | You Going? Imitative Decision Learning for Path Forecasting in Dynamic Scenes |
Who | are | My Family Members? A Solution Based on Image Processing and Machine Learning |
Who | are | you referring to? Coreference resolution in image narrations |
Who | are | you with and where are you going? |
Who | are | you with and where are you going? |
Who | are | you? |
Who | are | you? - Learning person specific classifiers from video |
Who | are | you? Real-time person identification |
Who | are | Your Real Friends: Analyzing and Distinguishing Between Offline and Online Friendships From Social Multimedia Data |
Whose Hands | are | These? Hand Detection and Hand-Body Association in the Wild |
Why | are | Deep Representations Good Perceptual Quality Features? |
Why | are | Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps |
Why | are | several different people listed together? |
Why Aspect Graphs | are | Not (Yet) Practical for Computer Vision |
Why linear arrays | are | better image processors |
Why Some things | are | Darker When Wet? |
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles | are | More Efficient than Single Models |
With Signal Processing Support, Prosthetics | are | Becoming Safer, More Natural, and Increasingly Sensitive: Ongoing Prosthetics Research Is Leading to Systems That Adapt to Users Rather Than Forcing Users to Accommodate the Prosthesis [Special Reports] |
You | are | Catching My Attention: Are Vision Transformers Bad Learners under Backdoor Attacks? |
You | are | Catching My Attention: Are Vision Transformers Bad Learners under Backdoor Attacks? |
You | are | Here: Geolocation by Embedding Maps and Images |
You | are | Here: Mimicking the Human Thinking Process in Reading Floor-Plans |
You | are | What You Eat: Exploring Rich Recipe Information for Cross-Region Food Analysis |
511 for are