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Learning Cross-View Object Correspondence via Cycle-Consistent Mask Prediction
Shannan Yan, Leqi Zheng, Keyu Lv +7
We study the task of establishing object-level visual correspondence across different viewpoints in videos, focusing on the challenging egocentric-to-exocentric and exocentric-to-e…
MMhops-R1: Multimodal Multi-hop Reasoning
Tao Zhang, Ziqi Zhang, Zongyang Ma +7
The ability to perform multi-modal multi-hop reasoning by iteratively integrating information across various modalities and external knowledge is critical for addressing complex re…
HQ-CLIP: Leveraging Large Vision-Language Models to Create High-Quality Image-Text Datasets and CLIP Models
Zhixiang Wei, Guangting Wang, Xiaoxiao Ma +4
Large-scale but noisy image-text pair data have paved the way for the success of Contrastive Language-Image Pretraining (CLIP). As the foundation vision encoder, CLIP in turn serve…
Visual Perception by Large Language Model's Weights
Feipeng Ma, Hongwei Xue, Guangting Wang +7
Existing Multimodal Large Language Models (MLLMs) follow the paradigm that perceives visual information by aligning visual features with the input space of Large Language Models (L…
Multi-Modal Generative Embedding Model
Feipeng Ma, Hongwei Xue, Guangting Wang +7
Most multi-modal tasks can be formulated into problems of either generation or embedding. Existing models usually tackle these two types of problems by decoupling language modules…