3 citations · 7 across the 13 of their papers we have counts for
14 papers
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
Zeju Li, Ziyang Zheng, Yizhou Zhou +1
Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-…
Latent Context Compilation: Distilling Long Context into Compact Portable Memory
Zeju Li, Yizhou Zhou, Qiang Xu
Efficient long-context LLM deployment is stalled by a dichotomy between amortized compression, which struggles with out-of-distribution generalization, and Test-Time Training, whic…
PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training
Cong Chen, Mingyu Liu, Chenchen Jing +5
This paper aims to address the challenge of hallucinations in Multimodal Large Language Models (MLLMs) particularly for dense image captioning tasks. To tackle the challenge, we id…
Number it: Temporal Grounding Videos like Flipping Manga
Yongliang Wu, Xinting Hu, Yuyang Sun +5
Video Large Language Models (Vid-LLMs) have made remarkable advancements in comprehending video content for QA dialogue. However, they struggle to extend this visual understanding…
MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling
Jian Yang, Dacheng Yin, Yizhou Zhou +4
Recent advancements in multi-modal large language models have propelled the development of joint probabilistic models capable of both image understanding and generation. However, w…
EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model
Feipeng Ma, Yizhou Zhou, Zheyu Zhang +7
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated satisfactory performance across various vision-language tasks. Current approaches for vision and l…
Visual Perception by Large Language Model's Weights
Feipeng Ma, Hongwei Xue, Guangting Wang +7
Existing Multimodal Large Language Models (MLLMs) follow the paradigm that perceives visual information by aligning visual features with the input space of Large Language Models (L…
Multi-Modal Generative Embedding Model
Feipeng Ma, Hongwei Xue, Guangting Wang +7
Most multi-modal tasks can be formulated into problems of either generation or embedding. Existing models usually tackle these two types of problems by decoupling language modules…
ReGenNet: Towards Human Action-Reaction Synthesis
Liang Xu, Yizhou Zhou, Yichao Yan +5
Humans constantly interact with their surrounding environments. Current human-centric generative models mainly focus on synthesizing humans plausibly interacting with static scenes…
Inter-X: Towards Versatile Human-Human Interaction Analysis
Liang Xu, Xintao Lv, Yichao Yan +10
The analysis of the ubiquitous human-human interactions is pivotal for understanding humans as social beings. Existing human-human interaction datasets typically suffer from inaccu…
Unsupervised Visual Representation Learning by Tracking Patches in Video
Guangting Wang, Yizhou Zhou, Chong Luo +3
Inspired by the fact that human eyes continue to develop tracking ability in early and middle childhood, we propose to use tracking as a proxy task for a computer vision system to…
VAE^2: Preventing Posterior Collapse of Variational Video Predictions in the Wild
Yizhou Zhou, Chong Luo, Xiaoyan Sun +2
Predicting future frames of video sequences is challenging due to the complex and stochastic nature of the problem. Video prediction methods based on variational auto-encoders (VAE…
Spatiotemporal Fusion in 3D CNNs: A Probabilistic View
Yizhou Zhou, Xiaoyan Sun, Chong Luo +2
Despite the success in still image recognition, deep neural networks for spatiotemporal signal tasks (such as human action recognition in videos) still suffers from low efficacy an…
Posterior-Guided Neural Architecture Search
Yizhou Zhou, Xiaoyan Sun, Chong Luo +2
The emergence of neural architecture search (NAS) has greatly advanced the research on network design. Recent proposals such as gradient-based methods or one-shot approaches signif…