6 citations · 8 across the 13 of their papers we have counts for
13 papers
Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation
Yishuo Cai, Jiahui Liu, Yuanxin Liu +9
''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoning over crops, models can ac…
JTok: On Token Embedding as another Axis of Scaling Law via Joint Token Self-modulation
Yebin Yang, Huaijin Wu, Fu Guo +5
LLMs have traditionally scaled along dense dimensions, where performance is coupled with near-linear increases in computational cost. While MoE decouples capacity from compute, it…
Innovator-VL: A Multimodal Large Language Model for Scientific Discovery
Zichen Wen, Boxue Yang, Shuang Chen +30
We present Innovator-VL, a scientific multimodal large language model designed to advance understanding and reasoning across diverse scientific domains while maintaining excellent…
Conan: Progressive Learning to Reason Like a Detective over Multi-Scale Visual Evidence
Kun Ouyang, Yuanxin Liu, Linli Yao +5
Video reasoning, which requires multi-step deduction across frames, remains a major challenge for multimodal large language models (MLLMs). While reinforcement learning (RL)-based…
RICO: Improving Accuracy and Completeness in Image Recaptioning via Visual Reconstruction
Yuchi Wang, Yishuo Cai, Shuhuai Ren +6
Image recaptioning is widely used to generate training datasets with enhanced quality for various multimodal tasks. Existing recaptioning methods typically rely on powerful multimo…
RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection
Yixin Yang, Qingxiu Dong, Linli Yao +2
Data selection for instruction tuning is crucial for improving the performance of large language models (LLMs) while reducing training costs. In this paper, we propose Refined Cont…
TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
Linli Yao, Yicheng Li, Yuancheng Wei +11
The rapid growth of online video platforms, particularly live streaming services, has created an urgent need for real-time video understanding systems. These systems must process c…
Generative Frame Sampler for Long Video Understanding
Linli Yao, Haoning Wu, Kun Ouyang +5
Despite recent advances in Video Large Language Models (VideoLLMs), effectively understanding long-form videos remains a significant challenge. Perceiving lengthy videos containing…
Temporal Reasoning Transfer from Text to Video
Lei Li, Yuanxin Liu, Linli Yao +6
Video Large Language Models (Video LLMs) have shown promising capabilities in video comprehension, yet they struggle with tracking temporal changes and reasoning about temporal rel…
DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models
Linli Yao, Lei Li, Shuhuai Ren +4
The visual projector, which bridges the vision and language modalities and facilitates cross-modal alignment, serves as a crucial component in MLLMs. However, measuring the effecti…
LaDiC: Are Diffusion Models Really Inferior to Autoregressive Counterparts for Image-to-Text Generation?
Yuchi Wang, Shuhuai Ren, Rundong Gao +5
Diffusion models have exhibited remarkable capabilities in text-to-image generation. However, their performance in image-to-text generation, specifically image captioning, has lagg…
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
Shuhuai Ren, Linli Yao, Shicheng Li +2
This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contr…
Edit As You Wish: Video Caption Editing with Multi-grained User Control
Linli Yao, Yuanmeng Zhang, Ziheng Wang +5
Automatically narrating videos in natural language complying with user requests, i.e. Controllable Video Captioning task, can help people manage massive videos with desired intenti…