9 papers
Soft Tail-dropping for Adaptive Visual Tokenization
Zeyuan Chen, Kai Zhang, Zhuowen Tu +1
We present Soft Tail-dropping Adaptive Tokenizer (STAT), a 1D discrete visual tokenizer that adaptively chooses the number of output tokens per image according to its structural co…
Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration
Yongcong Ye, Kai Zhang, Yanghai Zhang +3
Zero-shot composed image retrieval (ZS-CIR) is a rapidly growing area with significant practical applications, allowing users to retrieve a target image by providing a reference im…
Toward Generalizable Deblurring: Leveraging Massive Blur Priors with Linear Attention for Real-World Scenarios
Yuanting Gao, Shuo Cao, Xiaohui Li +3
Image deblurring has advanced rapidly with deep learning, yet most methods exhibit poor generalization beyond their training datasets, with performance dropping significantly in re…
Self-Evaluation Unlocks Any-Step Text-to-Image Generation
Xin Yu, Xiaojuan Qi, Zhengqi Li +6
We introduce the Self-Evaluating Model (Self-E), a novel, from-scratch training approach for text-to-image generation that supports any-step inference. Self-E learns from data simi…
L-STEC: Learned Video Compression with Long-term Spatio-Temporal Enhanced Context
Tiange Zhang, Zhimeng Huang, Xiandong Meng +3
Neural Video Compression has emerged in recent years, with condition-based frameworks outperforming traditional codecs. However, most existing methods rely solely on the previous f…
COLI: A Hierarchical Efficient Compressor for Large Images
Haoran Wang, Hanyu Pei, Yang Lyu +3
The escalating adoption of high-resolution, large-field-of-view imagery amplifies the need for efficient compression methodologies. Conventional techniques frequently fail to prese…