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Found 8 papers, 0 papers with code
ADM-Loc: Actionness Distribution Modeling for Point-supervised Temporal Action Localization
This paper addresses the challenge of point-supervised temporal action detection, in which only one frame per action instance is annotated in the training set.
POTLoc: Pseudo-Label Oriented Transformer for Point-Supervised Temporal Action Localization
This paper tackles the challenge of point-supervised temporal action detection, wherein only a single frame is annotated for each action instance in the training set.
Deep Learning-based Action Detection in Untrimmed Videos: A Survey
The task of temporal activity detection in untrimmed videos aims to localize the temporal boundary of actions and classify the action categories.
Cross-Modal Center Loss for 3D Cross-Modal Retrieval
Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities.
Cross-modal Center Loss
Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities.
An Isolated-Signing RGBD Dataset of 100 American Sign Language Signs Produced by Fluent ASL Signers
We have collected a new dataset consisting of color and depth videos of fluent American Sign Language (ASL) signers performing sequences of 100 ASL signs from a Kinect v2 sensor.
Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos
Our system is able to recognize grammatical elements on ASL-HW-RGBD from manual gestures, facial expressions, and head movements and successfully detect 8 ASL grammatical mistakes.
Recognizing American Sign Language Manual Signs from RGB-D Videos
In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs (consisting of movements of the hands, as well as non-manual face movements in some cases) in real-time from RGB-D videos, by fusing multimodality features including hand gestures, facial expressions, and body poses from multi-channels (RGB, depth, motion, and skeleton joints).
