Multimodal classifiers can converge to modality-dominant solutions in which one modality dominates the joint prediction, suppressing the learning of others. Existing balancing methods mainly adjust losses, gradients, or modality contributions, largely treating modality imbalance as an optimization problem while implici...
Ze-Chang Xiong, Da Li, Rong Yin et al.· 0 citations
Long-Video Question Answering (LVQA) requires locating decisive evidence in hour-scale videos under a limited frame budget. Most training-free methods apply the same frame-selection strategy to all questions, despite substantial variation in the evidence required by different question types. Our analysis shows that the...
This work proposes SHIFT, a retrieval training framework based on LLMs that transfers LLMs into reasoning-efficient retrievers with residual projection and task-oriented bidirectional attention aggregation in the latent space, and alleviates the mismatch between contrastive learning and implicit reasoning using fine-gr...
Yuxiao Luo, Da Li, Mingjie Zhang et al.· arXiv.org· 0 citations
This work constructed Illusion-Reasoning, a benchmark of illusion images collected from the real world, incorporating diverse annotated question-answer pairs, and shows that the reasoning capabilities of a wide range of LVLMs are not as advanced as claimed.
