V-Nutri fuses final-dish features with cooking-process keyframes from egocentric videos to improve dish-level calorie and macronutrient estimation over single-image baselines.
Vivit: A video vision transformer
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VGA-Bench creates a three-tier taxonomy, 1,016-prompt dataset of 60k+ videos, and three multi-task neural models (VAQA-Net, VTag-Net, VGQA-Net) that align with human judgments for video aesthetics and generation quality.
A three-stage pipeline uses few-shot VLM action parsing, sliding-window segmentation, and LLM sequence classification with peer context to measure student engagement from classroom videos.
Future-privileged supervision during training improves causal egocentric gaze estimation, with optimal gains at 1.7-3.3 seconds look-ahead on EGTEA Gaze+ and 2.7 seconds on Ego4D.
citing papers explorer
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V-Nutri: Dish-Level Nutrition Estimation from Egocentric Cooking Videos
V-Nutri fuses final-dish features with cooking-process keyframes from egocentric videos to improve dish-level calorie and macronutrient estimation over single-image baselines.
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VGA-Bench: A Unified Benchmark and Multi-Model Framework for Video Aesthetics and Generation Quality Evaluation
VGA-Bench creates a three-tier taxonomy, 1,016-prompt dataset of 60k+ videos, and three multi-task neural models (VAQA-Net, VTag-Net, VGQA-Net) that align with human judgments for video aesthetics and generation quality.
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Context Matters: Peer-Aware Student Behavioral Engagement Measurement via VLM Action Parsing and LLM Sequence Classification
A three-stage pipeline uses few-shot VLM action parsing, sliding-window segmentation, and LLM sequence classification with peer context to measure student engagement from classroom videos.
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How Much Future Helps? A Controlled Study of Future-Privileged Supervision for Causal Egocentric Gaze Estimation
Future-privileged supervision during training improves causal egocentric gaze estimation, with optimal gains at 1.7-3.3 seconds look-ahead on EGTEA Gaze+ and 2.7 seconds on Ego4D.