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20242026
most citedVisual Perception by Large Language Model's Weights

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Interpolated Policy Distillation: A Controllable Continuum Between Off-Policy and On-Policy Distillation

Youxu Shi, Yifan Sun, Dacheng Yin +5

Off-policy and on-policy distillation have traditionally been formulated as separate paradigms, each favoring a different property of distillation trajectories. Teacher-generated (…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024★ 1 cited

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…

cs.CV2024

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…