Vision Transformer
Vision Transformers (ViTs) adapt the transformer architecture, initially designed for natural language processing, to image analysis by treating images as sequences of patches. Current research focuses on improving ViT efficiency and robustness through techniques like token pruning, attention engineering, and hybrid models combining ViTs with convolutional neural networks or other architectures (e.g., Mamba). These advancements are driving progress in various applications, including medical image analysis, object detection, and spatiotemporal prediction, by offering improved accuracy and efficiency compared to traditional convolutional neural networks in specific tasks.
Papers
Weight Copy and Low-Rank Adaptation for Few-Shot Distillation of Vision Transformers
Diana-Nicoleta Grigore, Mariana-Iuliana Georgescu, Jon Alvarez Justo, Tor Johansen, Andreea Iuliana Ionescu, Radu Tudor Ionescu
Arena: A Patch-of-Interest ViT Inference Acceleration System for Edge-Assisted Video Analytics
Haosong Peng, Wei Feng, Hao Li, Yufeng Zhan, Ren Jin, Yuanqing Xia
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers
Jinyang Liu, Wondmgezahu Teshome, Sandesh Ghimire, Mario Sznaier, Octavia Camps
Adapting LLaMA Decoder to Vision Transformer
Jiahao Wang, Wenqi Shao, Mengzhao Chen, Chengyue Wu, Yong Liu, Taiqiang Wu, Kaipeng Zhang, Songyang Zhang, Kai Chen, Ping Luo