5 citations · 6 across the 8 of their papers we have counts for
8 papers
Advancing WordArt-Oriented Scene Text Recognition: Datasets and Methods
Xingsong Ye, Yongkun Du, Jiaxin Zhang +5
WordArt (artistic text) features highly customized fonts, textures, and layouts, making WordArt-oriented scene TExt Recognition (WATER) substantially more challenging than general…
Video Understanding Reward Modeling: A Robust Benchmark and Performant Reward Models
Yuancheng Wei, Linli Yao, Lei Li +4
Multimodal reward models have advanced substantially in text and image domains, yet progress in video understanding reward modeling remains severely limited by the lack of robust e…
MSD-Score: Multi-Scale Distributional Scoring for Reference-Free Image Caption Evaluation
Shichao Kan, Xuyang Zhang, Haojie Zhang +7
Evaluating image captions without references remains challenging because global embedding similarity often misses fine-grained mismatches such as hallucinated objects, missing attr…
DiffCap-Bench: A Comprehensive, Challenging, Robust Benchmark for Image Difference Captioning
Yuancheng Wei, Haojie Zhang, Linli Yao +7
Image Difference Captioning (IDC) generates natural language descriptions that precisely identify differences between two images, serving as a key benchmark for fine-grained change…
MuSS: A Large-Scale Dataset and Cinematic Narrative Benchmark for Multi-Shot Subject-to-Video Generation
Haojie Zhang, Di Wu, Bingyan Liu +5
While video foundation models excel at single-shot generation, real-world cinematic storytelling inherently relies on complex multi-shot sequencing. Further progress is constrained…
Patch-as-Decodable-Token: Towards Unified Multi-Modal Vision Tasks in MLLMs
Yongyi Su, Haojie Zhang, Shijie Li +11
Multimodal large language models (MLLMs) have advanced rapidly in recent years. However, existing approaches for vision tasks often rely on indirect representations, such as genera…
PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images
Nanqing Liu, Xun Xu, Yongyi Su +2
Segment Anything Model (SAM) is an advanced foundational model for image segmentation, which is gradually being applied to remote sensing images (RSIs). Due to the domain gap betwe…
Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation
Haojie Zhang, Yongyi Su, Xun Xu +1
The success of large language models has inspired the computer vision community to explore image segmentation foundation model that is able to zero/few-shot generalize through prom…