With the growing emphasis on accessibility-oriented technologies and inclusive intelligent systems, Continuous Sign Language Recognition (CSLR) has attracted increasing attention as a key technique for bridging communication between Deaf and hearing communities. However, existing methods still suffer from insufficient exploitation of visual information, weak temporal alignment supervision, and inconsistency between training and inference, making it difficult to jointly improve recognition accuracy and model robustness. To address these issues, we propose TPA-Seq2Seq (Tri-Prior Aligned Seq2Seq), a tri-prior enhanced Seq2Seq framework for character-level CSLR. Specifically, we introduce PGF (Pose-Guided Fusion) to extract hand-arm-face keypoint descriptors offline through the MediaPipe pipeline and fuse them with RGB-based temporal semantics in a lightweight manner, thereby supplementing fine-grained visual priors. We further design ATAL (Auxiliary Temporal Alignment Loss), a CTC-based auxiliary constraint imposed on the encoder side to strengthen explicit temporal alignment supervision. In addition, we propose SSF (Scheduled Semantic Forcing), a piecewise teacher-forcing decay strategy that alleviates exposure bias and improves generalization. Experiments on the CSL dataset show that TPA-Seq2Seq reduces WER compared with the ResNet18-LSTM baseline and maintains consistent performance across different random seeds.
Ya-Han Yang, Rui Wang, Xiao-Fang Li et al.· International Conferences on...· 0 citations
As AI agents become participants in the development of their successors, they reshape both the production of intelligence and the role of human researchers. We introduce Atria Dawn Preview, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world. This model is trained via a Verifiable Experience Pipeline that connects tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning real-world research, engineering, and digital work, Atria Dawn Preview is competitive with frontier agents and achieves the highest reported score on five of them. Beyond standalone performance, we examine the real research-and-development process behind this model as a case study of human--AI collaboration, analyzing 769 task records from 56 participants together with agent logs. When asked to evaluate completed tasks under comparable conditions, participants rated about one-third of completed AI-assisted tasks as infeasible without AI. More strikingly, agents frequently propose methods and implement revisions, while humans retain most final decisions and guide exploration through judgment and feedback. These observations indicate a shift from task-level execution to project-level partnership, with human effort concentrating on what is worth pursuing and how evidence should guide research. Progress toward more autonomous AI research must therefore advance both the capacity for discovery and the capacity for meaningful human oversight, preserving accountable human authority over the risks and direction of continued development.
Hong-Lin Guo, Tao Gui, Yicheng Chen et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
