3 accepted papers
Large Language Models have significantly advanced autonomous agents through their sophisticated perception and execution capabilities. Despite effective, agents still struggle with robust decision-making due to passive learning from similar experiences that often confound correlation with causality.…
Vector quantization has emerged as a powerful tool in large-scale multimodal models, unifying heterogeneous representations through discrete token encoding. However, its effectiveness hinges on robust codebook design. Current prototype-based approaches relying on trainable vectors or clustered centr
Vision-language models bridge visual and linguistic understanding and have proven to be powerful for video recognition tasks. Existing methods primarily rely on parameter-efficient fine-tuning of pre-trained image-text models, suffering from limited interpretability and poor generalization due to in