71 citations · 87 across the 8 of their papers we have counts for
8 papers
VEGAS: Human-Aligned Video Caption Evaluation via Gaze
Shenghui Chen, Po-han Li, Ximeng Sun +5
Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose VEGAS (Video caption Evaluation…
Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution
Monishwaran Maheswaran, Leon Lakhani, Zhongzhu Zhou +16
We show that verifier-free evolution is bottlenecked by both diversity and efficiency: without external correction, repeated evolution accelerates collapse toward narrow modes, whi…
CaptionQA: Is Your Caption as Useful as the Image Itself?
Shijia Yang, Yunong Liu, Bohan Zhai +5
Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic inference pipelines. Yet current eva…
Law of Vision Representation in MLLMs
Shijia Yang, Bohan Zhai, Quanzeng You +3
We present the "Law of Vision Representation" in multimodal large language models (MLLMs). It reveals a strong correlation between the combination of cross-modal alignment, corresp…
HallE-Control: Controlling Object Hallucination in Large Multimodal Models
Bohan Zhai, Shijia Yang, Chenfeng Xu +4
Current Large Multimodal Models (LMMs) achieve remarkable progress, yet there remains significant uncertainty regarding their ability to accurately apprehend visual details, that i…
Multitask Vision-Language Prompt Tuning
Sheng Shen, Shijia Yang, Tianjun Zhang +4
Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language mo…
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
Jinhyung Park, Chenfeng Xu, Shijia Yang +4
While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object…
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
Chenfeng Xu, Shijia Yang, Tomer Galanti +7
3D point-clouds and 2D images are different visual representations of the physical world. While human vision can understand both representations, computer vision models designed fo…