| _ | need | _ |
|---|
| 15 Keypoints Is All You | need | |
| AdderNet: Do We Really | need | Multiplications in Deep Learning? |
| Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information | need | s |
| AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing | need | s |
| ALAN: Self-Attention Is Not All You | need | for Image Super-Resolution |
| Alignment Relation is What You | need | for Diagram Parsing |
| All Keypoints You | need | : Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes |
| All the attention you | need | : Global-local, spatial-channel attention for image retrieval |
| All you | need | are a few pixels: semantic segmentation with PixelPick |
| All You | need | Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification |
| All You | need | Is a Second Look: Towards Arbitrary-Shaped Text Detection |
| All You | need | is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation |
| All You | need | Is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines |
| Analysing User | need | s for a Unified 3D Metadata Recording and Exploitation of Cultural Heritage Monuments System |
| Analysis of User | need | in Image Archives |
| Are Local Features All You | need | for Cross-Domain Visual Place Recognition? |
| Are Straight-Through gradients and Soft-Thresholding all you | need | for Sparse Training? |
| AutoENP: An Auto Rating Pipeline for Expressing | need | s via Pointing Protocol |
| Autonomous Cognitive Robots | need | Emotional Modulations: Introducing the eMODUL Model |
| Autonomous trucks | need | people |
| B-cos Networks: Alignment is All We | need | for Interpretability |
| Back to the Feature: Classical 3D Features are (Almost) All You | need | for 3D Anomaly Detection |
| Backbone is All Your | need | : A Simplified Architecture for Visual Object Tracking |
| Beyond one-to-one feature correspondence: The | need | for many-to-many matching and image abstraction |
| Bosnia Cantonment Area Monitoring System (BCAMS): Rapid Response to the | need | s of the U.S. Army Europe |
| Bounding Boxes Are All We | need | : Street View Image Classification via Context Encoding of Detected Buildings |
| Breaking the Cycle: Colleagues Are All You | need | |
| Breaking the millisecond barrier: Robots and self-driving cars will | need | completely reengineered networks |
| Change Detection | need | s Neighborhood Interaction in Transformer |
| Classification of small lesions in dynamic breast MRI: eliminating the | need | for precise lesion segmentation through spatio-temporal analysis of contrast enhancement |
| Cognitive Vision | need | s Attention to Link Sensing with Recognition |
| COMET's Education and Training for the Worldwide Meteorological Satellite User Community: Meeting Evolving | need | s with Innovative Instruction |
| Comprehension of City Map Pictograms Designed for Specific Tourists' | need | s |
| Computer vision | need | s a core and foundations |
| Computer Vision | need | s More Experiments and Applications |
| Data Mining Meets the | need | s of Disaster Information Management |
| Deep Multimodal Learning Approach to Perceive Basic | need | s of Humans From Instagram Profile, A |
| Depth is all you | need | : Single-Stage Weakly Supervised Semantic Segmentation From Image-Level Supervision |
| DETR Does Not | need | Multi-Scale or Locality Design |
| Development of a Radar Reflector Kit for Older Adults to Use to Signal Their Location and | need | s in a Large-Scale Earthquake Disaster |
| Direct Georeferencing on Small Unmanned Aerial Platforms for Improved Reliability and Accuracy of Mapping without the | need | for Ground Control Points |
| Do Charitable Foundations Spend Money Where People | need | It Most? A Spatial Analysis of China |
| Do Not Mask What You Do Not | need | to Mask: A Parser-free Virtual Try-on |
| Do We | need | a Higher Resolution? Case Study: Sentinel-1-Based Change Detection of the 2018 Hokkaido Landslides, Japan |
| Do We | need | Binary Features for 3D Reconstruction? |
| Do We | need | Depth in State-Of-The-Art Face Authentication? |
| Do We | need | Large Annotated Training Data for Detection Applications in Biomedical Imaging? A Case Study in Renal Glomeruli Detection |
| Do We | need | More Training Data or Better Models for Object Detection? |
| Do We | need | More Training Data? |
| Do We | need | Sound for Sound Source Localization? |
| Do We | need | Video Systems Beyond HDTV? |
| Do We Really | need | All These Neurons? |
| Do we really | need | an accurate calibration pattern to achieve a reliable camera calibration? |
| Do We Really | need | Gold Samples for Sample Weighting under Label Noise? |
| Do we really | need | more training data for object localization |
| Do We Really | need | Robust and Alternative Inference Methods for Brain MRI? |
| Do We Really | need | Scene-specific Pose Encoders? |
| Do We Really | need | to Calibrate All the Parameters? Variance-Based Sensitivity Analysis to Simplify Microscopic Traffic Flow Models |
| Do We Really | need | to Collect Millions of Faces for Effective Face Recognition? |
| Do We Still | need | Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration |
| Dry Granular Flows | need | Special Tools |
| Dynamic Neural Network is All You | need | : Understanding the Robustness of Dynamic Mechanisms in Neural Networks |
| Efficient Reference-based Video Super-Resolution (ERVSR): Single Reference Image Is All You | need | |
| Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research | need | s, An |
| Even big data is not enough: | need | for a novel reference modelling for forensic document authentication |
| Everybody | need | s somebody: Modeling social and grouping behavior on a linear programming multiple people tracker |
| Exploiting Reflectance Properties to Analyze Images of Moving Objects | need | s Local Constraints |
| Face Models: How Good Does My Data | need | To Be? |
| Fairness and the | need | for Regulation of AI in Medicine, Teaching, and Recruiting |
| Feature Extraction with Intrinsic Distortion Correction in Celiac Disease Imagery: No | need | for Rasterization |
| Few shots are all you | need | : A progressive learning approach for low resource handwritten text recognition |
| Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You | need | ? |
| Finding the Subspace Mean or Median to Fit Your | need | |
| Focus Is All You | need | : Loss Functions for Event-Based Vision |
| framework of three-dimensional object recognition which | need | s only a few reference images, A |
| GAIA: A Transfer Learning System of Object Detection that Fits Your | need | s |
| Generated Distributions Are All You | need | for Membership Inference Attacks Against Generative Models |
| Geospatial IoT: The | need | for Event-Driven Architectures in Contemporary Spatial Data Infrastructures |
| Global Digital Elevation Model Comparison Criteria: An Evident | need | to Consider Their Application |
| Global Features are All You | need | for Image Retrieval and Reranking |
| GMML is All You | need | |
| Good Data Augmentation Policy is not All You | need | : A Multi-Task Learning Perspective, A |
| Graphical Interface Design for Chatbots for the | need | s of Artificial Intelligence Support in Web and Mobile Applications |
| Healthy Park | need | s Healthy Vegetation: The Story of Gorongosa National Park in the 21st Century, A |
| How Many Annotations Do We | need | for Generalizing New-Coming Shadow Images? |
| How many classifiers do I | need | ? |
| How many more images do we | need | ? Performance prediction of bootstrapping for image classification |
| How Many Vehicles Do We | need | ? Fleet Sizing for Shared Autonomous Vehicles With Ridesharing |
| How Much Chemistry Does a Deep Neural Network | need | to Know to Make Accurate Predictions? |
| How Much Deep Learning does Neural Style Transfer Really | need | ? An Ablation Study |
| How Much More Data Do I | need | ? Estimating Requirements for Downstream Tasks |
| How Much of a Pixel | need | s to Burn to Be Detected by Satellites? A Spectral Modeling Experiment Based on Ecosystem Data from Yellowstone National Park, USA |
| Identifying tree crown delineation shapes and | need | for remediation on high resolution imagery using an evidence based approach |
| Image Labels Are All You | need | for Coarse Seagrass Segmentation |
| Image Quality Evaluation in Professional HDR/WCG Production Questions the | need | for HDR Metrics |
| Improving Segmentation of Breast Arterial Calcifications from Digital Mammography: Good Annotation is All You | need | |
| Industry | need | s for Computer Vision and Pattern Recognition: Panel |
| Information | need | s of Next-Generation Forest Carbon Models: Opportunities for Remote Sensing Science |
| Iris recognition: the | need | to recognise the iris as a dynamic biological system: Response to Daugman and Downing |
| Is context all you | need | ? Scaling Neural Sign Language Translation to Large Domains of Discourse |
| Is GPT-3 All You | need | for Visual Question Answering in Cultural Heritage? |
| Is Imitation All You | need | ? Generalized Decision-Making with Dual-Phase Training |
| Is Industrial Tomography Ready for Augmented Reality? A | need | -finding Study of How Augmented Reality Can Be Adopted by Industrial Tomography Experts |
| Is there any | need | for rough clustering? |
| Just a Few Points are All You | need | for Multi-view Stereo: A Novel Semi-supervised Learning Method for Multi-view Stereo |
| Large Class Separation is Not What You | need | for Relational Reasoning-Based OOD Detection |
| Learning Science Using AR Book: A Preliminary Study on Visual | need | s of Deaf Learners |
| LGANet: Local and global attention are both you | need | for action recognition |
| Lifecycle Management, Monitoring and Assessment for Safe Large-scale Infrastructures: Challenges and | need | s |
| Look ATME: The Discriminator Mean Entropy | need | s Attention |
| Low-bit Quantization | need | s Good Distribution |
| Manifold convex hull (MACH): Satisfying a | need | for SPD |
| Matching clinical and biological | need | s with emerging imaging technologies |
| MetaFormer is Actually What You | need | for Vision |
| MixMix: All You | need | for Data-Free Compression Are Feature and Data Mixing |
| More Photos are All You | need | : Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval |
| MTSAT-1R Visible Imager Point Spread Correction Function, Part I: The | need | for, Validation of, and Calibration With |
| Multi-criteria GIS Analyses with the Use of UAVs For The | need | s Of Spatial Planning |
| National Map - A Continuing, Critical | need | for the Nation, The |
| need | And Keys For A New Generation Network Adjustment Software, The |
| need | for a Standardized Methodology for Quantitative Assessment of Natural and Anthropogenic Land Subsidence: The Agosta (Italy) Gas Field Case, The |
| need | for Accuracy Verification of Machine Vision Algorithms and Systems, The |
| need | for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo: A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data, The |
| need | for Information Metrics: With Examples from Document Analysis |
| need | for Reliable Sensor Calibration from the Perspective of a National Mapping Agency |
| need | for Speed: A Benchmark for Higher Frame Rate Object Tracking |
| need | for Universal Design of eXtended Reality (XR) Technology in Primary and Secondary Education, The |
| need | Of Nested Grids For Aerial And Satellite Images And Digital Elevation Models, The |
| need | Only One More Point (NOOMP): Perspective Adaptation Crowd Counting in Complex Scenes |
| need | to integrate geospatial & research data policy activities, The |
| need | s and challenges in human computer interaction for processing social emotional information |
| need | s and Seeds in Character Recognition |
| New camera chip captures only what it | need | s |
| New Wildfire Watchdog: Alerts About Forest Fires Shouldn't Depend on Pets Smelling Smoke. We | need | Smart Infrastructure, and that Needs Zero-Power Sensors, A |
| New Wildfire Watchdog: Alerts About Forest Fires Shouldn't Depend on Pets Smelling Smoke. We | need | Smart Infrastructure, and that Needs Zero-Power Sensors, A |
| No | need | for Landmarks: An Embodied Neural Controller for Robust Insect-Like Navigation Behaviors |
| No | need | for Speed: More Signal Processing Innovation Is Required Before Adopting Automated Vehicles |
| Object Categorization and the | need | for Many-to-Many Matching |
| OCR in the United States Postal Service: Present Status and Future | need | s |
| One Style is All You | need | to Generate a Video |
| One-bit Flip is All You | need | : When Bit-flip Attack Meets Model Training |
| Persee: Addressing the | need | s of the Digitalisation and Online Accessibility of Back Collections through Robust and Integrated Tools |
| Plugging the Gaps in the Global PhenoCam Monitoring of Forests: The | need | for a PhenoCam Network across Indian Forests |
| Precrash Dipping Nose (PCDN) | need | s Pedestrian Recognition |
| Privacy and Civilian Drone Use: The | need | for Further Regulation |
| Producing global land cover maps consistent over time to respond the | need | s of the climate modelling community |
| Psumnet: Unified Modality Part Streams Are All You | need | for Efficient Pose-based Action Recognition |
| Recursions Are All You | need | : Towards Efficient Deep Unfolding Networks |
| Refreshment | need | metrics for improved shape and texture object-based resilient video coding |
| Rethinking Few-shot Image Classification: A Good Embedding is All You | need | ? |
| Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You | need | |
| Satellite-Based Models | need | Improvements on Simulating Annual Gross Primary Productivity: A Comparison of Six Models for Regional Modeling of Deciduous Broadleaf Forests |
| Segmenting across places: The | need | for fair transfer learning with satellite imagery |
| Set of Visualization Data | need | s in Urban Environmental Planning and Design for Photogrammetric Data, A |
| Shape Prior is Not All You | need | : Discovering Balance Between Texture and Shape Bias in CNN |
| Shape refreshment | need | metric for object-based resilient video coding |
| Shaping-Up Multimedia Analytics: | need | s and Expectations of Media Professionals |
| Shore Construction Detection by Automotive Radar for the | need | s of Autonomous Surface Vehicle Navigation |
| Spatial Allocation Based on Physiological | need | s and Land Suitability Using the Combination of Ecological Footprint and SVM (Case Study: Java Island, Indonesia) |
| Speed Is All You | need | : On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations |
| Survey on how computer vision can response to urgent | need | to contribute in COVID-19 pandemics, A |
| Survey on Smart Homes for Aging in Place: Toward Solutions to the Specific | need | s of the Elderly, A |
| Temperate Grassland Afforestation Dynamics in the Aguapey Valuable Grassland Area between 1999 and 2020: Identifying the | need | for Protection |
| Temporal Interpolation is all You | need | for Dynamic Neural Radiance Fields |
| Test-Time Adaptation for Super-Resolution: You Only | need | to Overfit on a Few More Images |
| To each according to its | need | : kernel class specific classifiers |
| To Frontalize or Not to Frontalize: Do We Really | need | Elaborate Pre-processing to Improve Face Recognition? |
| Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency is All You | need | |
| Traction Substation State Estimator for Integrating Smart Loads in Transportation Grids Without the | need | for Additional Sensors, A |
| Transboundary Basins | need | More Attention: Anthropogenic Impacts on Land Cover Changes in Aras River Basin, Monitoring and Prediction |
| TransGeo: Transformer Is All You | need | for Cross-view Image Geo-localization |
| Unmanned Aerial System Imagery, Land Data and User | need | s: A Socio-Technical Assessment in Rwanda |
| Use of a Multilayer Perceptron to Automate Terrain Assessment for the | need | s of the Armed Forces |
| Versioning of 3D City Models for Municipality Applications: | need | s, Obstacles and Recommendations |
| We Don't | need | No Bounding-Boxes: Training Object Class Detectors Using Only Human Verification |
| We don't | need | Thousand Proposals: Single Shot Actor-Action Detection in Videos |
| We | need | to Communicate: Communicating Attention Network for Semantic Segmentation of High-Resolution Remote Sensing Images |
| What is distance and why do we | need | the metric model for pattern learning? |
| What Is the | need | for Building Parts?: A Comparison of CityGML, Inspire Building and a Swedish Building Standard |
| What is the Real | need | for Scene Text Removal? Exploring the Background Integrity and Erasure Exhaustivity Properties |
| Why Accuracy is Not Enough: The | need | for Consistency in Object Detection |
| You Do Not | need | Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement |
| You | need | Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model |
| You Only | need | 80k Parameters to Enhance Image: Learning Periodic Features for Image Enhancement |
| You Only | need | The Image: Unsupervised Few-Shot Semantic Segmentation With Co-Guidance Network |
185 for need