6 papers
ViPER: Empowering the Self-Evolution of Visual Perception Abilities in Vision-Language Model
Juntian Zhang, Song Jin, Chuanqi Cheng +8
The limited capacity for fine-grained visual perception presents a critical bottleneck for Vision-Language Models (VLMs) in real-world applications. Addressing this is challenging…
Weaving Context Across Images: Improving Vision-Language Models through Focus-Centric Visual Chains
Juntian Zhang, Chuanqi cheng, Yuhan Liu +3
Vision-language models (VLMs) achieve remarkable success in single-image tasks. However, real-world scenarios often involve intricate multi-image inputs, leading to a notable perfo…
Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
Chuanqi Cheng, Jian Guan, Wei Wu +1
Long-form video processing fundamentally challenges vision-language models (VLMs) due to the high computational costs of handling extended temporal sequences. Existing token prunin…
A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
Jian Guan, Junfei Wu, Jia-Nan Li +2
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to ind…
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis
Chuanqi Cheng, Jian Guan, Wei Wu +1
We explore multi-step reasoning in vision-language models (VLMs). The problem is challenging, as reasoning data consisting of multiple steps of visual and language processing are b…
"In Dialogues We Learn": Towards Personalized Dialogue Without Pre-defined Profiles through In-Dialogue Learning
Chuanqi Cheng, Quan Tu, Shuo Shang +4
Personalized dialogue systems have gained significant attention in recent years for their ability to generate responses in alignment with different personas. However, most existing…