7 papers
ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval
Yuhan Liu, Pei Fu, Hang Li +8
Leveraging Multimodal Large Language Models (MLLMs) via contrastive learning has become a mainstream paradigm for improving the performance of Universal Multimodal Retrieval (UMR).…
SocialPersona: Benchmarking Personalized Profiling and Response with Multimodal Social-Media Context
Qinkai Zhang, Yanyan Zhao, Xin Lu +3
Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogue? More comprehensive person…
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
Xiang An, Yin Xie, Feilong Tang +27
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of mu…
PatchCue: Enhancing Vision-Language Model Reasoning with Patch-Based Visual Cues
Yukun Qi, Pei Fu, Hang Li +5
Vision-Language Models (VLMs) have achieved remarkable progress on a wide range of challenging multimodal understanding and reasoning tasks. However, existing reasoning paradigms,…
DeepSight: Bridging Depth Maps and Language with a Depth-Driven Multimodal Model
Hao Yang, Hongbo Zhang, Yanyan Zhao +1
Multimodal large language models (MLLMs) have achieved impressive performance across various tasks such as image captioning and visual question answer(VQA); however, they often str…
OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence
Feilong Tang, Xiang An, Yunyao Yan +16
Hypothesis. Artificial general intelligence is, at its core, a compression problem. Effective compression demands resonance: deep learning scales best when its architecture aligns…