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20232026
most citedPPT: Token Pruning and Pooling for Efficient Vision Transformers

6 citations · 6 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV2026

UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning

Lei Xin, Zeheng Wang, Jiayin Zhu +6

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder for which early and accurate diagnosis is critical to improving long-term developmental outcomes. However, e…

cs.CV2026

LiST: Local-Simplex Test-Time LoRA Fusion

Yihua Shao, Jia Li, Siyu Chen +12

Task-specific LoRA adapters offer a modular way to specialize large language and vision-language models. However, existing adapter composition methods are mostly static and cannot…

cs.CV2026

MambaRaw: Selective State Space Modeling for Efficient 4K Raw Image Reconstruction

Peize Li, Fanhu Zeng, Tongda Xu +5

In-camera JPEG previews are ubiquitous in raw image formats and provide an sRGB reference at negligible storage cost. Although existing metadata-based reconstruction frameworks can…

cs.CV2025

Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration

Fanhu Zeng, Deli Yu, Zhenglun Kong +1

Vision transformers have been widely explored in various vision tasks. Due to heavy computational cost, much interest has aroused for compressing vision transformer dynamically in…

cs.CV2025

EventVAD: Training-Free Event-Aware Video Anomaly Detection

Yihua Shao, Haojin He, Sijie Li +11

Video Anomaly Detection~(VAD) focuses on identifying anomalies within videos. Supervised methods require an amount of in-domain training data and often struggle to generalize to un…

cs.CV2025

MambaIC: State Space Models for High-Performance Learned Image Compression

Fanhu Zeng, Hao Tang, Yihua Shao +3

A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields. Despite rapid progress in image compression, computational…

cs.CV2023★ 6 cited

PPT: Token Pruning and Pooling for Efficient Vision Transformers

Xinjian Wu, Fanhu Zeng, Xiudong Wang +1

Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computati…