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Found 7 papers, 1 papers with code
Learning to Segment Referred Objects from Narrated Egocentric Videos
In contrast we propose ROSA a weakly-supervised pixel-level grounding framework learning alignments between referred objects and segmentation mask proposals.
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge.
Learning to Ground Instructional Articles in Videos through Narrations
To deal with the scarcity of labeled data at scale, we source the step descriptions from a language knowledge base (wikiHow) containing instructional articles for a large variety of procedural tasks.
MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences.
Weakly-Supervised Generation and Grounding of Visual Descriptions With Conditional Generative Models
Given weak supervision from image- or video-caption pairs, we address the problem of grounding (localizing) each object word of a ground-truth or generated sentence describing a visual input.
Representation Learning on Visual-Symbolic Graphs for Video Understanding
To capture this rich visual and semantic context, we propose using two graphs: (1) an attributed spatio-temporal visual graph whose nodes correspond to actors and objects and whose edges encode different types of interactions, and (2) a symbolic graph that models semantic relationships.
Ranked #10 on
Action Detection
on Charades
(using extra training data)
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding
We introduce an end-to-end algorithm for jointly learning the weights of the CRF model, which include action classification and action transition costs, as well as an overcomplete dictionary of mid-level action primitives.
Ranked #5 on
Action Segmentation
on JIGSAWS
