ORAV Bench for Omni Reference Audio-Video Generation is introduced, comprising 380 task instances with 2-10 references, 9 semantic roles, and 30 role compositions, and a reference-aware pairwise protocol that prepares visual and auditory evidence, compares the intended contribution of each reference, and checks the ove...
Jia-Cheng Hua, Xiao-Kun Feng, Jia-Qi Hua et al.· 0 citations
Current multi-view gaze estimation remains limited by existing datasets, insufficient exploitation of complementary cross-view information, and evaluation focused primarily on average gaze error. We address these limitations through a more systematic study of multi-view gaze estimation. First, we introduce PrismGaze, a...
Chang Liu, Jia-Qi Liu, Cheng-Wen Zhang et al.· 1 citation
In human-LLM content co-generation, AI-generated information can enter final artifacts without being adequately processed by users, creating risks when artifacts are shared or acted upon. We study whether recognition-level uptake of atomic information units can be assessed in open-ended co-generation and predicted from...
Yu Mei, Feng-You Zu, Rui-Wen Zhang et al.· 0 citations
The architectural improvements and novel training recipe allow PaGE to achieve state-of-the-art performance on several gaze estimation tasks, outperforming humans in 7 out of 9 metrics while reducing the human-AI gap by at least 60% in the remaining 2.
Zhou-Tong Ye, Cheng-Wen Zhang, Zhai-Bin Cui et al.· arXiv.org· 0 citations
U-Lens improves verification efficiency and effort allocation, reduced perceived workload, and strengthened support across all three stages of uncertainty management, and reframes uncertainty support for generative AI from text-centered cues to a user-centered process of interpreting, evaluating, and acting on uncertai...
Yu Mei, Qingyue Zhuang, Jie Cai et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
