Improving Vision Transformers by Revisiting High-Frequency Components
Recurrent Bilinear Optimization for Binary Neural Networks
Neural Architecture Search for Spiking Neural Networks
Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification
DaViT: Dual Attention Vision Transformers
Optimal Transport for Label-Efficient Visible-Infrared Person Re-identification
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Neighborhood Collective Estimation for Noisy Label Identification and Correction
Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay
Anti-Retroactive Interference for Lifelong Learning
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning
Dynamic Metric Learning with Cross-Level Concept Distillation
MENet: A Memory-Based Network with Dual-Branch for Efficient Event Stream Processing
Out-of-Distribution Detection with Boundary Aware Learning
Learning Hierarchy Aware Features for Reducing Mistake Severity
Learning to Detect Every Thing in an Open World
KVT: k-NN Attention for Boosting Vision Transformers
Registration Based Few-Shot Anomaly Detection
Improving Robustness by Enhancing Weak Subnets
Learning Invariant Visual Representations for Compositional Zero-Shot Learning
Improving Covariance Conditioning of the SVD Meta-Layer by Orthogonality
Out-of-Distribution Detection with Semantic Mismatch under Masking
Data-Free Neural Architecture Search via Recursive Label Calibration
Learning from Multiple Annotator Noisy Labels via Sample-Wise Label Fusion
Acknowledging the Unknown for Multi-Label Learning with Single Positive Labels
AutoMix: Unveiling the Power of Mixup for Stronger Classifiers
MaxViT: Multi-axis Vision Transformer
ScalableViT: Rethinking the Context-Oriented Generalization of Vision Transformer
Three Things Everyone Should Know about Vision Transformers
DeiT III: Revenge of the ViT
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
Self-Feature Distillation with Uncertainty Modeling for Degraded Image Recognition
Novel Class Discovery without Forgetting
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification
Negative Samples Are at Large: Leveraging Hard-Distance Elastic Loss for Re-identification
Discrete-Constrained Regression for Local Counting Models
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed Recognition
Chairs Can Be Stood On: Overcoming Object Bias in Human-Object Interaction Detection
A Fast Knowledge Distillation Framework for Visual Recognition
DICE: Leveraging Sparsification for Out-of-Distribution Detection
Invariant Feature Learning for Generalized Long-Tailed Classification
Sliced Recursive Transformer.