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cs.CV2026

GeoBalance: Geometry-Aware Monitoring and Reconstruction with Asymmetric Optimization for Balanced Multimodal Learning

Zechang Xiong, Da Li, Rong Yin +7

Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of others. Existing balancing meth…

cs.CV2026

Interpretability Transfer from Language to Vision via Sparse Autoencoders

Alexey Kravets, Da Li, Chuan Li +2

Recent advances in language model interpretability using sparse autoencoders (SAEs) have yet to effectively translate to the visual domain, mainly due to the difficulty and ambigui…

cs.CV2026

MERGETUNE: Continued Fine-Tuning of Vision-Language Models

Wenqing Wang, Da Li, Xiatian Zhu +1

Fine-tuning vision-language models (VLMs) such as CLIP often leads to catastrophic forgetting of pretrained knowledge. Prior work primarily aims to mitigate forgetting during adapt…

cs.CV2026

CREM: Compression-Driven Representation Enhancement for Multimodal Retrieval and Comprehension

Lihao Liu, Yan Wang, Biao Yang +10

Multimodal Large Language Models (MLLMs) have shown remarkable success in comprehension tasks such as visual description and visual question answering. However, their direct applic…

cs.CV2026

GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking

Zixu Cheng, Da Li, Jian Hu +4

Video reasoning requires a fine-grained understanding of the temporal dependencies and event-level relations between objects and events in videos. Current Multimodal Large Language…