7 accepted papers
Long Video Temporal Grounding (LVTG) aims at identifying specific moments within lengthy videos based on user-provided text queries for effective content retrieval. The approach taken by existing methods of dividing video into clips and processing each clip via a full-scale expert encoder is challen…
Procedural videos are critical for learning new tasks. Temporal action segmentation (TAS), which classifies the action in every video frame, has become essential for understanding procedural videos. Existing TAS models, however, learn a fixed-set of tasks at training and unable to adapt to novel tas…
We present a new egocentric procedural error dataset containing videos with various types of errors as well as normal videos and propose a new framework for procedural error detection using error-free training videos only. Our framework consists of an action segmentation model and a contrastive step…
We study supervised action segmentation whose goal is to predict framewise action labels of a video. To capture temporal dependencies over long horizons prior works either improve framewise features with transformer or refine framewise predictions with learned action features. However they are compu…
In this paper we propose a novel concept of path consistency to learn robust object matching without using manual object identity supervision. Our key idea is that to track a object through frames we can obtain multiple different association results from a model by varying the frames it can observe…
We address the problem of set-supervised action learning, whose goal is to learn an action segmentation model using weak supervision in the form of sets of actions occurring in training videos. Our key observation is that videos within the same task have similar ordering of actions, which can be lev…
We address the problem of learning to segment actions from weakly-annotated videos, i.e., videos accompanied by transcripts (ordered list of actions). We propose a framework in which we model actions with a union of low-dimensional subspaces, learn the subspaces using transcripts and refine video fe…