| _ | make | _ |
|---|
| Accurate Compensation | make | s the World More Clear for the Visually Impaired |
| Anode | make | and Break Excitation Mechanisms and Strength-Interval Curves: Bidomain Simulations in 3D Rotational Anisotropy |
| Attributes | make | Sense on Segmented Objects |
| Automatic | make | and model recognition from frontal images of cars |
| Big S small 3D: What | make | s stereoscopic video so compelling? |
| Break and | make | : Interactive Structural Understanding Using LEGO Bricks |
| Can domain adaptation | make | object recognition work for everyone? |
| Can Multiple Views | make | up for Lack of Camera Registration? |
| Car | make | and Model recognition combining global and local cues |
| Car | make | and model recognition using 3D curve alignment |
| Classification and identification of vehicle type and | make | by cortex-like image descriptor HMAX |
| Classification of Vehicle | make | by Combined Features and Random Subspace Ensemble |
| Classification of vehicle type and | make | by combined features and random subspace ensemble |
| Content | make | s the Difference in Compression Standard Quality Assessment |
| Convolutional Embedding | make | s Hierarchical Vision Transformer Stronger |
| Coupling | make | s Better: An Intertwined Neural Network for Taxi and Ridesourcing Demand Co-Prediction |
| Decomposition | make | s Better Rain Removal: An Improved Attention-Guided Deraining Network |
| Decoupling | make | s Weakly Supervised Local Feature Better |
| Deep Learning for Road Traffic Forecasting: Does it | make | a Difference? |
| Deep vanishing point detection: Geometric priors | make | dataset variations vanish |
| DeepCar 5.0: Vehicle | make | and Model Recognition Under Challenging Conditions |
| Designing Color Filters That | make | Cameras More Colorimetric |
| Digital Retina: A Way to | make | the City Brain More Efficient by Visual Coding |
| Discretizing Space to | make | a Dictionary Matrix for Bistatic Compressive Sensing Detection |
| Diverse Cotraining | make | s Strong Semi-Supervised Segmentor |
| Do Gradient Inversion Attacks | make | Federated Learning Unsafe? |
| Does colorspace transformation | make | any difference on skin detection? |
| Does gender | make | a difference to performing in-vehicle tasks? |
| Effectiveness of Camouflage | make | -Up Patterns Against Face Detection Algorithms |
| Efficient alignment for vehicle | make | and model recognition |
| Fake it till you | make | it: face analysis in the wild using synthetic data alone |
| Fake it Till You | make | it: Learning Transferable Representations from Synthetic ImageNet Clones |
| Fast Fashion Guided Clothing Image Retrieval: Delving Deeper into What Feature | make | s Fashion |
| Feature Super-Resolution: | make | Machine See More Clearly |
| Finding a Colour Filter to | make | a Camera Colorimetric by Optimisation |
| Five Guiding Principles to | make | Jupyter Notebooks Fit for Earth Observation Data Education |
| Gaussian Mixture Distribution | make | s Data Uncertainty Learning Better |
| Good Fences | make | Good Neighbours |
| Happy patrons | make | better tippers: creating a robot waiter using Perseus and the Animate Agent architecture |
| Hard to Track Objects with Irregular Motions and Similar Appearances? | make | It Easier by Buffering the Matching Space |
| Hierarchical Scheme for Vehicle | make | and Model Recognition From Frontal Images of Vehicles, A |
| How Does DCNN | make | Decisions ? |
| How Intelligent Cars Will | make | Driving Easier and Greener |
| How Many Events | make | an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset |
| How Much Chemistry Does a Deep Neural Network Need to Know to | make | Accurate Predictions? |
| How to | make | a BLT Sandwich? Learning VQA towards Understanding Web Instructional Videos |
| How to | make | a Pizza: Learning a Compositional Layer-Based GAN Model |
| How to | make | AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code |
| How to | make | an RGBD Tracker? |
| How to | make | Business with Computer Vision Technology |
| How to | make | iris recognition easier? |
| How to | make | local image features more efficient and distinctive |
| How to | make | n-D Plain Maps Defined on Discrete Surfaces Alexandrov-Well-Composed in a Self-Dual Way |
| How to | make | nD Functions Digitally Well-Composed in a Self-dual Way |
| How to | make | nD images well-composed without interpolation |
| How to | make | Sammon's mapping useful for multidimensional data structures analysis |
| Human Uncertainty | make | s Classification More Robust |
| Implicit Surfaces | make | for Better Silhouettes |
| IoU-Adaptive Deformable R-CNN: | make | Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery |
| Is Neuron Coverage Needed to | make | Person Detection More Robust? |
| LabelRS: An Automated Toolbox to | make | Deep Learning Samples from Remote Sensing Images |
| Large and Diverse Dataset for Improved Vehicle | make | and Model Recognition, A |
| Learning What | make | s a Difference from Counterfactual Examples and Gradient Supervision |
| LIDAR that will | make | self-driving cars affordable [News] |
| log square average case algorithm to | make | insertions in fast similarity search, A |
| make | a Face: Towards Arbitrary High Fidelity Face Manipulation |
| make | Encoder Great Again in 3D GAN Inversion through Geometry and Occlusion-Aware Encoding |
| make | It Easier: An Empirical Simplification of a Deep 3D Segmentation Network for Human Body Parts |
| make | It Move: Controllable Image-to-Video Generation with Text Descriptions |
| make | It Simple: Effective Road Selection for Small-Scale Map Design Using Decision-Tree-Based Models |
| make | Landscape Flatter in Differentially Private Federated Learning |
| make | my day: High-fidelity color denoising with Near-Infrared |
| make | -A-Scene: Scene-Based Text-to-Image Generation with Human Priors |
| make | -A-Story: Visual Memory Conditioned Consistent Story Generation |
| make | -An-Animation: Large-Scale Text-conditional 3D Human Motion Generation |
| make | -It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior |
| make | UpMirror: mirroring make-ups and verifying faces post make-up |
| make | UpMirror: mirroring make-ups and verifying faces post make-up |
| Many Hands | make | Light Work: Transferring Knowledge from Auxiliary Tasks for Video-Text Retrieval |
| method to | make | multiple hypotheses with high cumulative recognition rate using SVMs, A |
| Methodology for Accessing the Local Arrangement of the Sheetlets that | make | up the Extracellular Heart Tissue, A |
| Mid-level-Representation Based Lexicon for Vehicle | make | and Model Recognition |
| Multiresolution cooperation | make | s easier document structure recognition |
| Negatives | make | a Positive: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning |
| Non-Maximum Suppression of Gradient Magnitudes | make | s Them Easier to Threshold |
| Novel Spatial Position Prediction Navigation System | make | s Surgery More Accurate, A |
| Obstacle Avoidance in Highly Automated Cars: Can Progressive Haptic Shared Control | make | it Safer and Smoother? |
| Octuplet Loss: | make | Face Recognition Robust to Image Resolution |
| On the Intra-Category Clustering to | make | Multidictionary Patterns for Multidictionary Template Matching Method |
| Ongoing Conflict | make | s Yemen Dark: From the Perspective of Nighttime Light |
| Overlooked Poses Actually | make | Sense: Distilling Privileged Knowledge for Human Motion Prediction |
| OxfordTVG-HIC: Can Machine | make | Humorous Captions from Images? |
| Part-based recognition of vehicle | make | and model |
| pattern recognition approach to | make | accessible the geographic images for blind and visually impaired, A |
| Practice | make | s Perfect or Does It? Practice Effect in Flying HUD Localizer-Guided Low Visibility Takeoffs |
| Prompt, Generate, Then Cache: Cascade of Foundation Models | make | s Strong Few-Shot Learners |
| Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning | make | a Difference |
| Real-Time Vehicle | make | and Model Recognition Based on a Bag of SURF Features |
| Real-Time Vehicle | make | and Model Recognition Using Unsupervised Feature Learning |
| Recognition of Car | make | s and Models From a Single Traffic-Camera Image |
| Recognizing faces like humans: A new approach to facial identification | make | s automated surveillance easier |
| Research on the Problems of Equipment Dynamic Maintenance Dispatch to | make | the Amount of Restoration Maximum |
| Revisiting Adversarial Robustness Distillation: Robust Soft Labels | make | Student Better |
| Saliency Aggregation: Does Unity | make | Strength? |
| Semi-automatic Training of a Vehicle | make | and Model Recognition System |
| Show and Recall: Learning What | make | s Videos Memorable |
| Simple Techniques | make | Sense: Feature Pooling and Normalization for Image Classification |
| Simple Way to | make | Neural Networks Robust Against Diverse Image Corruptions, A |
| Single-image Depth Prediction | make | s Feature Matching Easier |
| SparseFool: A Few Pixels | make | a Big Difference |
| Spherical Eye from Multiple Cameras ( | make | s Better Models of the World), A |
| ST++: | make | Self-trainingWork Better for Semi-supervised Semantic Segmentation |
| StrongSORT: | make | DeepSORT Great Again |
| Symmetrical SURF and Its Applications to Vehicle Detection and Vehicle | make | and Model Recognition |
| Synthesis of Facial Images with Foundation | make | -Up |
| Taking a HINT: Leveraging Explanations to | make | Vision and Language Models More Grounded |
| Three Steps to | make | Shape from Shading Work Consistently on Real Scenes |
| To | make | yourself invisible with Adversarial Semantic Contours |
| Towards understanding what | make | s 3D objects appear simple or complex |
| TransCL: Transformer | make | s Strong and Flexible Compressive Learning |
| truth is hard to | make | : Validation of medical image registration, The |
| Using scale space filtering to | make | thinning algorithms robust against noise in sketch images |
| Vehicle joint | make | and model recognition with multiscale attention windows |
| Vehicle | make | and model recognition using local features and logo detection |
| Vehicle | make | and model recognition using sparse representation and symmetrical SURFs |
| Vehicle | make | and model recognition using symmetrical SURF |
| Vehicle | make | or Model or Type Recogniton |
| Vehicle subtype, | make | and model classification from side profile video |
| Wearables-Fashion With a Purpose: A New Generation of Wearable Devices Uses Signal Processing to | make | Life Easier, Healthier, and More Secure [Special Reports] |
| Weighted loss functions to | make | risk-based language identification fused decisions |
| What Convnets | make | for Image Captioning? |
| What | make | s a chair a chair? |
| What | make | s a Gesture a Gesture? Neural Signatures Involved in Gesture Recognition |
| What | make | s a Good Data Augmentation for Few-Shot Unsupervised Image Anomaly Detection? |
| What | make | s a Good Detector?: Structured Priors for Learning from Few Examples |
| What | make | s a Good Feature? |
| What | make | s a good model of natural images? |
| What | make | s a Patch Distinct? |
| What | make | s a Photograph Memorable? |
| What | make | s a Professional Video? A Computational Aesthetics Approach |
| What | make | s a Style: Experimental Analysis of Fashion Prediction |
| What | make | s a Video a Video: Analyzing Temporal Information in Video Understanding Models and Datasets |
| What | make | s an image memorable? |
| What | make | s an Object Memorable? |
| What | make | s an on-road object important? |
| What | make | s Fake Images Detectable? Understanding Properties that Generalize |
| What | make | s First Steps Users Rave About Virtual Reality? An Explorative Qualitative Study of Consumers' First VR Experience |
| What | make | s for Effective Detection Proposals? |
| What | make | s for Effective Few-shot Point Cloud Classification? |
| What | make | s for Good Tokenizers in Vision Transformer? |
| What | make | s for Hierarchical Vision Transformer? |
| What | make | s Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation? |
| What | make | s Motion Meaningful? Affective Properties of Abstract Motion |
| What | make | s Objects Similar: A Unified Multi-Metric Learning Approach |
| What | make | s Paris Look Like Paris? |
| What | make | s Tom Hanks Look Like Tom Hanks |
| What | make | s Training Multi-Modal Classification Networks Hard? |
| What | make | s Transfer Learning Work for Medical Images: Feature Reuse & Other Factors |
| What | make | s you, you? Analyzing Recognition by Swapping Face Parts |
| When music | make | s a scene |
| You Never Get a Second Chance To | make | a Good First Impression: Seeding Active Learning for 3D Semantic Segmentation |
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