736 citations · 835 across the 10 of their papers we have counts for
12 papers
LSTMem: Hierarchical Long Short-Term Online Memory for Large Language Models
Xianglong Shi, Ruijie Yang, Sirui Zhao +4
Large language models increasingly serve as long-horizon assistants and agents, where they must both accumulate information across interactions and make the relevant parts availabl…
SpeechParaling-Bench: A Comprehensive Benchmark for Paralinguistic-Aware Speech Generation
Ruohan Liu, Shukang Yin, Tao Wang +6
Paralinguistic cues are essential for natural human-computer interaction, yet their evaluation in Large Audio-Language Models (LALMs) remains limited by coarse feature coverage and…
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…
Skywork-R1V4: Toward Agentic Multimodal Intelligence through Interleaved Thinking with Images and DeepResearch
Yifan Zhang, Liang Hu, Haofeng Sun +12
Despite recent progress in multimodal agentic systems, existing approaches often treat image manipulation and web search as disjoint capabilities, rely heavily on costly reinforcem…
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…
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…
QuoTA: Query-oriented Token Assignment via CoT Query Decouple for Long Video Comprehension
Yongdong Luo, Wang Chen, Xiawu Zheng +8
Recent advances in long video understanding typically mitigate visual redundancy through visual token pruning based on attention distribution. However, while existing methods emplo…
MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs
Chaoyou Fu, Yi-Fan Zhang, Shukang Yin +9
As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Build…
Sparrow: Data-Efficient Video-LLM with Text-to-Image Augmentation
Shukang Yin, Chaoyou Fu, Sirui Zhao +7
Recent years have seen the success of Multimodal Large Language Models (MLLMs) in the domain of vision understanding. The success of these models can largely be attributed to the d…
Woodpecker: Hallucination Correction for Multimodal Large Language Models
Shukang Yin, Chaoyou Fu, Sirui Zhao +7
Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with t…
A Survey on Multimodal Large Language Models
Shukang Yin, Chaoyou Fu, Sirui Zhao +4
Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perfor…
AU-aware graph convolutional network for Macro- and Micro-expression spotting
Shukang Yin, Shiwei Wu, Tong Xu +3
Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solvin…