12 papers
IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation
Yuan-Ming Li, Qize Yang, Nan Lei +5
Recent advances in motion-aware large language models have shown remarkable promise for jointly learning motion understanding and generation knowledge. However, these models typica…
ObjEmbed: Towards Universal Multimodal Object Embeddings
Shenghao Fu, Yukun Su, Fengyun Rao +3
Aligning objects with corresponding textual descriptions is a fundamental challenge and a realistic requirement in vision-language understanding. While recent multimodal embedding…
MERIT: Multi-domain Efficient RAW Image Translation
Wenjun Huang, Shenghao Fu, Yian Jin +10
RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their d…
WeDetect: Fast Open-Vocabulary Object Detection as Retrieval
Shenghao Fu, Yukun Su, Fengyun Rao +3
Open-vocabulary object detection aims to detect arbitrary classes via text prompts. Methods without cross-modal fusion layers (non-fusion) offer faster inference by treating recogn…
LOVE-R1: Advancing Long Video Understanding with an Adaptive Zoom-in Mechanism via Multi-Step Reasoning
Shenghao Fu, Qize Yang, Yuan-Ming Li +3
Long video understanding is still challenging for recent Large Video-Language Models (LVLMs) due to the conflict between long-form temporal understanding and detailed spatial perce…
HumanOmniV2: From Understanding to Omni-Modal Reasoning with Context
Qize Yang, Shimin Yao, Weixuan Chen +7
With the rapid evolution of multimodal large language models, the capacity to deeply understand and interpret human intentions has emerged as a critical capability, which demands d…