Key papers

• Beyond Single-Source Cognitive Taskonomy:Multi-Source Task Relations through fMRI Transfer Learning (2026)
  Junfeng Xia et al.
  4.95
• PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition (2026)
  Dana A Abdullah
  4.39
• Do Transformer Temporal Heads and Post-Pooling Motion Gates Help CorrNet-based CSLR? An Empirical Study (2026)
  Lisi Wang et al.
  4.39
• Vertical Fusion: Condensing Internal Representations for Robust ViT Classification (2026)
  Francesco Di Salvo et al.
  4.39
• AVQ-Attention: Adaptive Vector-Quantized Attention (2026)
  Winfried van den dool et al.
  4.39
• Human-like Object Grouping in Self-supervised Vision Transformers (2026)
  Hossein Adeli et al.
  1.78
• The Laplacian Mechanism Improves Transformers by Reshaping Token Geometry (2026)
  Yuchong Zhang and Vardan Papyan
  1.72
• Do Transformers Understand Ancient Roman Coin Motifs Better than CNNs? (2026)
  David Reid and Ognjen Arandjelovic
  1.67
• Unsupervised Transformer Pre-Training for Images: Self-Distillation, Mean Teachers, and Random Crops (2025)
  Mattia Scardecchia
  1.50
• Stimulus Motion Perception Studies Imply Specific Neural Computations in Human Visual Stabilization (2025)
  David W Arathorn et al.
  1.28
• Disentangling Visual Transformers: Patch-level Interpretability for Image Classification (2025)
  Guillaume Jeanneret and Lo\"ic Simon and Fr\'ed\'eric Jurie
  1.06
• PETAH: Parameter Efficient Task Adaptation For Hybrid Transformers In A Resource-limited Context (2024)
  Maximilian Augustin, Syed Shakib Sarwar, Mostafa Elhoushi, et al.
  0.00
• Probabilistic Model of Visual Segmentation (2018)
  Jonathan Vacher et al.
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• Recurrent Transformer Networks for Semantic Correspondence (2018)
  Seungryong Kim et al.
  —
• On the Relationship between Self-Attention and Convolutional Layers (2019)
  Jean-Baptiste Cordonnier et al.
  —
• Transform-Invariant Convolutional Neural Networks for Image Classification and Search (2019)
  Xu Shen et al.
  —
• Relation Transformer Network (2020)
  Rajat Koner et al.
  —
• A Universal Representation Transformer Layer for Few-Shot Image Classification (2020)
  Lu Liu et al.
  —
• CrossTransformers: spatially-aware few-shot transfer (2020)
  Carl Doersch et al.
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• General Multi-label Image Classification with Transformers (2020)
  Jack Lanchantin et al.
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• Training data-efficient image transformers & distillation through attention (2020)
  Hugo Touvron et al.
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• Translational Equivariance in Kernelizable Attention (2021)
  Max Horn et al.
  —
• Scalable Vision Transformers with Hierarchical Pooling (2021)
  Zizheng Pan et al.
  —
• Attention-Based Multimodal Image Matching (2021)
  Aviad Moreshet et al.
  —
• End-to-End Trainable Multi-Instance Pose Estimation with Transformers (2021)
  Lucas Stoffl and Maxime Vidal and Alexander Mathis
  —
• Understanding Robustness of Transformers for Image Classification (2021)
  Srinadh Bhojanapalli et al.
  —
• Face Transformer for Recognition (2021)
  Yaoyao Zhong and Weihong Deng
  —
• Recurrent Attention Models with Object-centric Capsule Representation for Multi-object Recognition (2021)
  Hossein Adeli et al.
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• Surface Analysis with Vision Transformers (2022)
  Simon Dahan et al.
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• Learning high-level visual representations from a child's perspective without strong inductive biases (2023)
  A. Emin Orhan et al.
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• Soft-TransFormers for Continual Learning (2024)
  Haeyong Kang et al.
  —