arXiv:2605.12491v2 Announce Type: replace
Abstract: Vision Transformers (ViTs) learn rich visual-semantic representations through all-to-all self-attention among patch tokens. However, this design im...
By Alan Z. Song, Yinjie Chen, Mu Nan, Deva Ramanan, Michael J. Tarr, Andrew F. Luo
The paper introduces Prune-and-Merge ViT, a training‑free token compression framework for Vision Transformers that combines class‑attention pruning with similarity‑based merging to reduce computational cost while preserving semantic information. It evaluates robustness using two new metrics—Relative Accuracy Drop (RAD) and Prediction Flip Rate (PFR)—and shows that the method maintains or improves accuracy on pretrained ViT and DeiT models compared to existing token reduction techniques. The authors also argue that the hybrid approach does not induce hallucination‑like behavior and keeps predictions stable under common image transformations.
By Vatsal, Soumil Malhotra, Priyanshu Chahal, Sanjeev Kumar
arXiv:2607.09086v2 Announce Type: replace
Abstract: We present Subtoken Vision Transformer (SubViT), a selective image tokenization method for fine-grained visual recognition. Standard Vision Transfo...
By Jie Zhu, Ivy Zhang, Minchul Kim, Xiaoming Liu
arXiv:2607. 02612v1 Announce Type: cross Abstract: Vision Transformers achieve strong image classification accuracy but process all image regions with nearly the same computation, even when many regions are redundant or uninformative.
By Aravind Pradeep, Samira Nazari, Mahdi Taheri, Christian Herglotz
DART is a fully differentiable Dynamic Adaptive Region Tokenizer that replaces fixed‑grid tokenizers in vision models. It uses learnable region scores and quantile‑based partitioning to create content‑aware patches of varying sizes, allocating more tokens to information‑rich areas. In experiments, a DART‑equipped DeiT‑Small matches the performance of a larger DeiT‑Base while nearly doubling inference speed, and the approach also benefits dense prediction and video tasks.
By Shicheng Yin, Kaixuan Yin, Yang Liu, Weixing Chen, Liang Lin
arXiv:2606. 01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expensive and largely overlooked from an efficiency perspective.
By Siyi Chen, Weiming Zhuang, Jingtao Li, Lingjuan Lv