arXiv Machine Learning
1d ago

ASAP: Attention Sink Anchored Pruning

ASAP (Attention Sink Anchored Pruning) is a training‑free framework designed to reduce the computational burden of Vision Transformers by addressing the attention sink problem. It models ViT information flow as a Lazy Random Walk, identifies the sink as a dominant probability accumulator, and uses diffusion distance to partition tokens via Radial Diffusion Clustering. The method then compresses background redundancy through Transition Weight Pooling, achieving up to 48% throughput acceleration while maintaining or improving accuracy across image, video, and vision‑language tasks.

By Jaehyuk Lee, Hanyoung Kim, Yanggee Kim, Donghun Lee
Hugging Face Trending Papers
Aug 6

MAVISEG: Manifold Propagation and Visual Prototypes for Zero-Shot Open-Vocabulary Segmentation in Diffusion Transformers

Text-to-image diffusion transformers learn about objects and scenes by learning to generate them, making them strong candidates for training-free zero-shot open-vocabulary semantic segmentation. State-of-the-art attribution methods score each pixel independently, comparing its features against a fixed text-derived class representation, whether as an output-space similarity or as a cross-attention weight.

arXiv Computer Vision
Sep 4

FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation

FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.

By Ming Hu, Jianfu Yin, Mingyu Dou, Miaomiao Zhang, Yao Wang, Cong Hu, Bingliang Hu, Quan Wang
arXiv Computer Vision
Oct 26

SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction

SetCon reformulates open-ended referring segmentation as set-level concept prediction, using LVLM-generated natural-language concepts instead of segmentation-specific tokens. It employs a hierarchical semantic decomposition that first predicts a shared set-level concept and then refines it into fine-grained concept groups aligned with target subsets. The method, supported by a two-stage annotation pipeline adding hierarchical semantic supervision, achieves state-of-the-art results on multiple image and video benchmarks, with performance gains increasing as the number of referred targets grows.

By Zhixiong Zhang, Yizhuo Li, Shuangrui Ding, Yuhang Zang, Shengyuan Ding, Long Xing, Yibin Wang, Qiaosheng Zhang, Jiaqi Wang