Learning Dynamic Evidence Routes for Vision Transformer Probing

arXiv:2605.00915 · cs.CV, cs.AI · Submitted 2026-04-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-04-29

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Ablation — 18× in this paper · explained in 1 episode
• Transformer — 13× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Memory Decay — 8× in this paper · explained in 3 episodes
• Optimal Transport — 8× in this paper · explained in 5 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Linear Time-Invariant — 7× in this paper
• State Space Model — 7× in this paper
• Core — 4× in this paper · explained in 2 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Representation Learning — 4× in this paper · explained in 2 episodes
• Global Average Pooling — 3× in this paper
• In International Conference on Learning Representations — 3× in this paper
• Pruning — 3× in this paper · explained in 4 episodes
• AdamW Optimizer — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 2× in this paper
• Generalization — 2× in this paper · explained in 13 episodes
• Sample Efficiency — 2× in this paper · explained in 4 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• BEiT: BERT Pre-Training of Image Transformers
• Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Mamba: Linear-Time Sequence Modeling with Selective State Spaces
• Mamba-3: Improved Sequence Modeling using State Space Principles
• Efficiently Modeling Long Sequences with Structured State Spaces
• DINOv2: Learning Robust Visual Features without Supervision
• PPT: Token Pruning and Pooling for Efficient Vision Transformers
• Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

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