12 papers
Tango: Taming Visual Signals for Efficient Video Large Language Models
Shukang Yin, Sirui Zhao, Hanchao Wang +4
Token pruning has emerged as a mainstream approach for developing efficient Video Large Language Models (Video LLMs). This work revisits and advances the two predominant token-prun…
ActFER: Agentic Facial Expression Recognition via Active Tool-Augmented Visual Reasoning
Shifeng Liu, Zhengye Zhang, Sirui Zhao +7
Recent advances in Multimodal Large Language Models (MLLMs) have created new opportunities for facial expression recognition (FER), moving it beyond pure label prediction toward re…
PersonaVLM: Long-Term Personalized Multimodal LLMs
Chang Nie, Chaoyou Fu, Yifan Zhang +2
Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. P…
BABE: Biology Arena BEnchmark
Junting Zhou, Jin Chen, Linfeng Hao +10
The rapid evolution of large language models (LLMs) has expanded their capabilities from basic dialogue to advanced scientific reasoning. However, existing benchmarks in biology of…
MME-CC: A Challenging Multi-Modal Evaluation Benchmark of Cognitive Capacity
Kaiyuan Zhang, Chenghao Yang, Zhoufutu Wen +19
As reasoning models scale rapidly, the essential role of multimodality in human cognition has come into sharp relief, driving a growing need to probe vision-centric cognitive behav…
BaseReward: A Strong Baseline for Multimodal Reward Model
Yi-Fan Zhang, Haihua Yang, Huanyu Zhang +11
The rapid advancement of Multimodal Large Language Models (MLLMs) has made aligning them with human preferences a critical challenge. Reward Models (RMs) are a core technology for…
OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing
Zhihong Chen, Xuehai Bai, Yang Shi +9
The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data. While existi…
RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark
Yang Shi, Yuhao Dong, Yue Ding +22
The integration of visual understanding and generation into unified multimodal models represents a significant stride toward general-purpose AI. However, a fundamental question rem…
Thyme: Think Beyond Images
Yi-Fan Zhang, Xingyu Lu, Shukang Yin +17
Following OpenAI's introduction of the ``thinking with images'' concept, recent efforts have explored stimulating the use of visual information in the reasoning process to enhance…
R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning
Yi-Fan Zhang, Xingyu Lu, Xiao Hu +13
Multimodal Reward Models (MRMs) play a crucial role in enhancing the performance of Multimodal Large Language Models (MLLMs). While recent advancements have primarily focused on im…
MME-VideoOCR: Evaluating OCR-Based Capabilities of Multimodal LLMs in Video Scenarios
Yang Shi, Huanqian Wang, Wulin Xie +20
Multimodal Large Language Models (MLLMs) have achieved considerable accuracy in Optical Character Recognition (OCR) from static images. However, their efficacy in video OCR is sign…
MELLM: A Flow-Guided Large Language Model for Micro-Expression Understanding
Sirui Zhao, Zhengye Zhang, Shifeng Liu +5
Micro-expressions (MEs), brief and low-intensity facial movements revealing concealed emotions, are crucial for affective computing. Despite notable progress in ME recognition, exi…