CoEvoAD, a co-evolutionary framework for discrete prompt selection and a Cross-Category Transfer Objective (CCTO), which treats held-out source categories as proxies for unseen categories and scores prompt rules based on their estimated cross-category transferability, effectively improving cross-category generalization...
Si-Si Zhu, Chang-Wei Yu, Renshuai Tao et al.· 0 citations
Modality-Contrastive Preference Optimization (MCPO), a highly sample-efficient two-stage length-compression method that requires fewer than 900 training samples, and adopts a highly nonlinear odds-ratio formulation that provides steep gradients in the with-image context to reinforce length constraints for preferred tra...
Guang-Heng Yang, Zhen-Liang Ni, Zhen-Kai Wu et al.· 0 citations
A reinforcement-learning training set enriched with Chinese and adversarial instructions is constructed, which significantly improves model performance on MM-IFEval-Pro and transfers effectively to other mainstream multimodal benchmarks, demonstrating strong cross-task and cross-language generalization.
Chang-Ming Xiao, Zhen-Liang Ni, Jin-Hui He et al.· 0 citations
This work introduces an adaptive visual token selection strategy for VLMs that leverages average text-to-visual attention scores to assess the importance of visual tokens, removing redundant ones during pruning based on a set threshold, thereby optimizing the importance calculation.
Yaozhi Wen, Jialong Guo, Zhen-Liang Ni et al.· 0 citations
This work proposes a lightweight spiking neural network (SNN)-based event-image fusion network (Spike-EIFNet) that leverages the complementary strengths of multimodal fusion and energy-efficient spike-driven computation, and significantly reduces energy consumption.
Siyu Chen, Qie Liu, Xianlei Long et al.· IEEE Transactions on Neural...· 0 citations
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