Contrastive Reinforcement Learning (CRL) learns representations that estimate goal reachability, yet its policy remains conditioned on raw goals and therefore does not directly exploit the geometry encoded by its critic. We introduce Direction-Conditioned Policies (DCP), a method built around a small modification to CR...
S. Swaminathan, Damiya Gondha, Theyanesh Eswaramoorthy Rajahkrishnan et al.· 0 citations
Generating immersive, synchronized and cinematic audio for long-form textual narratives remains a significant challenge in multi-modal AI. While current Text-to-Audio (TTA) frameworks successfully synthesize isolated sound effects, they struggle with narrative cohesion, temporal alignment, and cinematic emotional depth...
A prominence-aware, iterative post-hoc rectification framework that overcomes both limitations without modifying the underlying captioning model's parameters, and position prominence-guided iterative rectification as a lightweight, scalable, and model-agnostic route to more complete and trustworthy video captioning.
Debjyoti Das Adhikary, Aritra Hazra, P. P. Chakrabarti· 0 citations
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