5 papers
Beyond Global Similarity: Towards Fine-Grained, Multi-Condition Multimodal Retrieval
Xuan Lu, Kangle Li, Haohang Huang +3
Recent advances in multimodal large language models (MLLMs) have substantially expanded the capabilities of multimodal retrieval, enabling systems to align and retrieve information…
What Do Visual Tokens Really Encode? Uncovering Sparsity and Redundancy in Multimodal Large Language Models
Yingqi Fan, Junlong Tong, Anhao Zhao +1
Multimodal large language models (MLLMs) project visual tokens into the embedding space of language models, yet the internal structuring and processing of visual semantics remain p…
Speak While Watching: Unleashing TRUE Real-Time Video Understanding Capability of Multimodal Large Language Models
Junyan Lin, Junlong Tong, Hao Wu +4
Multimodal Large Language Models (MLLMs) have achieved strong performance across many tasks, yet most systems remain limited to offline inference, requiring complete inputs before…
Multimodal Language Models See Better When They Look Shallower
Haoran Chen, Junyan Lin, Xinghao Chen +6
Multimodal large language models (MLLMs) typically extract visual features from the final layers of a pretrained Vision Transformer (ViT). This widespread deep-layer bias, however,…
Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practices
Junyan Lin, Haoran Chen, Yue Fan +5
Multimodal Large Language Models (MLLMs) have made significant advancements in recent years, with visual features playing an increasingly critical role in enhancing model performan…