15 papers
Deep LoRA-Unfolding Networks for Image Restoration
Xiangming Wang, Haijin Zeng, Benteng Sun +4
Deep unfolding networks (DUNs), combining conventional iterative optimization algorithms and deep neural networks into a multi-stage framework, have achieved remarkable accomplishm…
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…
ADSeeker: A Knowledge-Grounded Reasoning Framework for Industry Anomaly Detection and Reasoning
Kai Zhang, Zekai Zhang, Xihe Sun +6
Automatic vision inspection holds significant importance in industry inspection. While multimodal large language models (MLLMs) exhibit strong language understanding capabilities a…
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…
Post-Training Quantization for Video Matting
Tianrui Zhu, Houyuan Chen, Ruihao Gong +3
Video matting is crucial for applications such as film production and virtual reality, yet deploying its computationally intensive models on resource-constrained devices presents c…
Accelerating Video Diffusion Models via Distribution Matching
Yuanzhi Zhu, Hanshu Yan, Huan Yang +2
Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, curre…
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
Yuanzhi Zhu, Ruiqing Wang, Shilin Lu +3
Recent advances in diffusion and flow-based generative models have demonstrated remarkable success in image restoration tasks, achieving superior perceptual quality compared to tra…
Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint
Sixiang Chen, Tian Ye, Kai Zhang +3
Recent advancements in adverse weather restoration have shown potential, yet the unpredictable and varied combinations of weather degradations in the real world pose significant ch…
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution
Tianyi Xu, Yiji Zhou, Xiaotao Hu +4
Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the…
From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision
Eashan Adhikarla, Kai Zhang, Gong Chen +2
Low-light image enhancement remains a persistent challenge in computer vision, where state-of-the-art models are often hampered by hardware constraints and computational inefficien…
Subjective Face Transform using Human First Impressions
Chaitanya Roygaga, Joshua Krinsky, Kai Zhang +2
Humans tend to form quick subjective first impressions of non-physical attributes when seeing someone's face, such as perceived trustworthiness or attractiveness. To understand wha…