Detailed audio-video joint captioning is essential for multimodal video understanding and generation. However, prior works are constrained by three main limitations: (1) the scarcity of high-quality public datasets with fine-grained audio-visual joint captions; (2) reinforcement-learning methods that rely on coarse rew...
Ming-Yang Wu, Kai-Tuo Feng, Bohao Li et al.· 0 citations
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, specific identities, physical consistency, or ordered events. In this paper, we present VideoGen-Agent, a multimodal agent...
Bin-Xu Li, Hao-Yi Duan, Yu-Hui Zhang et al.· 0 citations
Visual perception is conventionally formulated as a one-shot prediction from a single glance at the image, under the assumption that the image content and the model's parametric knowledge suffice to resolve the query. This assumption often fails in real-world scenarios that hinge on fine-grained visual details or requi...
Kai-Xuan Fan, Kai-Tuo Feng, Tian-Shuo Peng et al.· 0 citations
This work presents Hybrid Stratified Replay (HSR), a novel mechanism that restructures batches by stratifying rollouts based on Path Entropy, a rollout-level confidence proxy, and outcome reward, offering a principled solution for stable and efficient RL fine-tuning.
Yi-Meng Ye, Shuang Chen, Wen-Xuan Huang et al.· 0 citations
JarvisHub is introduced, a canvas-native creative agent harness for long-horizon multimodal creation, where agents can progressively plan, generate, revise, and organize multimodal projects while users remain able to inspect, guide, and intervene throughout the process.
Experiments show that SearchEyes achieves state-of-the-art performance among open-source multimodal search agents, with SearchEyes-27B improving over the strongest open-source baseline by 6.2 points on average.
Zhengbo Jiao, YiMing Cheng, Yilei Jiang et al.· 1 citation
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