Techniques

Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition AAAI 2021 Audio-Oriented Multimodal Machine Comprehension via Dynamic Inter- and Intra-modality Attention AAAI 2021 AugMax: Adversarial Composition of Random Augmentations for Robust Training NeurIPS 2021 Augmented Shortcuts for Vision Transformers NeurIPS 2021 AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models EMNLP 2021 A Unified Framework for Compressive Video Recovery From Coded Exposure Techniques WACV 2021 A unified framework for Hamiltonian deep neural networks L4DC 2021 AutoAttend: Automated Attention Representation Search ICML 2021 Autobahn: Automorphism-based Graph Neural Nets NeurIPS 2021 AutoDropout: Learning Dropout Patterns to Regularize Deep Networks AAAI 2021 Auto-Exposure Fusion for Single-Image Shadow Removal CVPR 2021 AutoFormer: Searching Transformers for Visual Recognition ICCV 2021 Automated Log-Scale Quantization for Low-Cost Deep Neural Networks CVPR 2021 Automated Model Design and Benchmarking of Deep Learning Models for COVID-19 Detection with Chest CT Scans AAAI 2021 Automated Symbolic Law Discovery: A Computer Vision Approach AAAI 2021 Automatic Quantification of Plant Disease From Field Image Data Using Deep Learning WACV 2021 Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators ICML 2021 AutoRC: Improving BERT Based Relation Classification Models via Architecture Search ACL 2021 A Vector-Based Representation to Enhance Head Pose Estimation WACV 2021 A Wigner-Eckart Theorem for Group Equivariant Convolution Kernels ICLR 2021 A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness NeurIPS 2021 Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks ICML 2021 BASE Layers: Simplifying Training of Large, Sparse Models ICML 2021 Batavia asked for advice. Pretrained language models for Named Entity Recognition in historical texts. EMNLP 2021 Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective ICCV 2021