Instruction-guided image editors have become highly capable, yet improving them further still depends on human-edited training pairs or external reward models. Such supervision is costly to obtain and can reward plausible failures: a realistic output may leave the requested change undone or alter content that should be...
Ritesh Thawkar, Shubham Patle, Shravan Venkatraman et al.· 0 citations
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by their static knowledge and susceptibility to hallucinations, especially in domains requiring up to date or attribute grounded information. Retrieval Augmented Generation (R...
Meghana Sunil, V. Shravya, Shravan Venkatraman et al.· 0 citations
This work introduces S$^3$T (Self-Supervised Self-Distillation over Time), which is the first fully self-contained framework for continuous video state tracking and treats temporal sampling density as privileged information, based on the hypothesis that a denser view of the same clip recovers the running state more acc...
Shravan Venkatraman, Wen-Shuai Zhao, Mohammad Hassan Vali et al.· 1 citation
This work introduces CVPD (Contrastive Counterfactual Visual Process Distillation), which is the first fully self-contained framework for dense, on-policy, token-level visual self-distillation for MLLMs, and proposes a three-gate Counterfactual Criterion that identifies visual blind spots where zooming into a region ch...
Shravan Venkatraman, Omkar Thawakar, Ritesh Thawkar et al.· 2 citations
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