RECIPE improves visual procedural planners by rewarding plans according to their grounding quality in ASR transcripts via GRPO, yielding +7–8 in-domain and up to +16 zero-shot macro-accuracy gains over base models and outperforming supervised fine-tuning on seven benchmarks.
PDPP: Projected diffusion for procedure planning in instructional videos
2 Pith papers cite this work. Polarity classification is still indexing.
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TrajPilot predicts candidate future trajectories from egocentric context and uses them to condition action prediction in an embedding space, outperforming VLM and planner baselines on Ego-Exo4D, Ego4D, and other datasets with gains increasing at longer horizons.
citing papers explorer
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RECIPE: Procedural Planning via Grounding in Instructional Video
RECIPE improves visual procedural planners by rewarding plans according to their grounding quality in ASR transcripts via GRPO, yielding +7–8 in-domain and up to +16 zero-shot macro-accuracy gains over base models and outperforming supervised fine-tuning on seven benchmarks.
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How You Move Tells What You'll Do: Trajectory-Conditioned Egocentric Prediction
TrajPilot predicts candidate future trajectories from egocentric context and uses them to condition action prediction in an embedding space, outperforming VLM and planner baselines on Ego-Exo4D, Ego4D, and other datasets with gains increasing at longer horizons.