3 citations · 5 across the 7 of their papers we have counts for
7 papers
Latent Noise Mask for Reducing Visual Redundancy in Multimodal Large Language Models
Kai Jiang, Ruishu Zhu, Siqi Huang +2
Multimodal large language models (MLLMs) often fail in fine-grained visual reasoning, as question-relevant visual cues are diluted by dense and redundant image tokens. Recent multi…
Laytrol: Preserving Pretrained Knowledge in Layout Control for Multimodal Diffusion Transformers
Sida Huang, Siqi Huang, Ping Luo +1
With the development of diffusion models, enhancing spatial controllability in text-to-image generation has become a vital challenge. As a representative task for addressing this c…
CoLM: Collaborative Large Models via A Client-Server Paradigm
Siqi Huang, Sida Huang, Hongyuan Zhang
Large models have achieved remarkable performance across a range of reasoning and understanding tasks. Prior work often utilizes model ensembles or multi-agent systems to collabora…
Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives
Kai Jiang, Siqi Huang, Xiangyu Chen +4
Multimodal large language models (MLLMs) deployed on devices must adapt to continuously changing visual scenarios such as variations in background and perspective, to effectively p…
AI Flow: Perspectives, Scenarios, and Approaches
Hongjun An, Wenhan Hu, Sida Huang +11
Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and c…
Learn Beneficial Noise as Graph Augmentation
Siqi Huang, Yanchen Xu, Hongyuan Zhang +1
Although graph contrastive learning (GCL) has been widely investigated, it is still a challenge to generate effective and stable graph augmentations. Existing methods often apply h…
Why Does Dropping Edges Usually Outperform Adding Edges in Graph Contrastive Learning?
Yanchen Xu, Siqi Huang, Hongyuan Zhang +1
Graph contrastive learning (GCL) has been widely used as an effective self-supervised learning method for graph representation learning. However, how to apply adequate and stable g…