5 accepted papers
Inference-time scaling offers a versatile paradigm for aligning visual generative models with downstream objectives without parameter updates. However, existing approaches that optimize the high-dimensional initial noise suffer from severe inefficiency, as many search directions exert negligible inf…
Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the strong generalization capabilities of diffusion models can inadvertently lead to the generation of not-safe-for-work (NSFW) content, posing significant…
Recently, advancements in video synthesis have attracted significant attention. Video synthesis models have demonstrated the practical applicability of diffusion models in creating dynamic visual content. Despite these advancements, the extension of video lengths remains constrained by computational
Recent development in Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) have achieved superior performance and generalization capabilities, covered extensive areas of traditional tasks. However, existing large model training frameworks support only a limited number of models…
Despite existing multimodal language models showing impressive performance on the video understanding task, extremely long videos still pose significant challenges to language model’s context length, memory consumption, and computational complexity. To address these issues, we propose a vision-langu…