Search Results for author:
Found 15 papers, 7 papers with code
LAC: Latent Action Composition for Skeleton-based Action Segmentation
In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
How Much Temporal Long-Term Context is Needed for Action Segmentation?
In this work, we try to answer how much long-term temporal context is required for temporal action segmentation by introducing a transformer-based model that leverages sparse attention to capture the full context of a video.
Ranked #1 on
Action Segmentation
on Assembly101
Self-Supervised Video Representation Learning via Latent Time Navigation
Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time.
Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection
Video anomaly detection in surveillance systems with only video-level labels (i. e. weakly-supervised) is challenging.
LAC - Latent Action Composition for Skeleton-based Action Segmentation
In this context, we propose Latent Action Composition (LAC), a novel self-supervised framework aiming at learning from synthesized composable motions for skeleton-based action segmentation.
Robust Action Segmentation from Timestamp Supervision
As obtaining annotations to train an approach for action segmentation in a fully supervised way is expensive, various approaches have been proposed to train action segmentation models using different forms of weak supervision, e. g., action transcripts, action sets, or more recently timestamps.
ViA: View-invariant Skeleton Action Representation Learning via Motion Retargeting
Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings.
TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction
While recurrent neural networks (RNNs) demonstrate outstanding capabilities for future video frame prediction, they model dynamics in a discrete time space, i. e., they predict the frames sequentially with a fixed temporal step.
FIFA: Fast Inference Approximation for Action Segmentation
We apply FIFA on top of state-of-the-art approaches for weakly supervised action segmentation and alignment as well as fully supervised action segmentation.
Segmentation
Weakly Supervised Action Segmentation (Transcript)
UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition
This is achieved by learning an optimal dependency matrix from the uniform distribution based on a multi-head attention mechanism.
Ranked #1 on
Skeleton Based Action Recognition
on UPenn Action
PDAN: Pyramid Dilated Attention Network for Action Detection
Previous action detection methods fail in selecting the key temporal information in long videos.
Ranked #1 on
Action Detection
on TSU
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos
Taking advantage of human pose data for understanding human activities has attracted much attention these days.
Toyota Smarthome Untrimmed: Real-World Untrimmed Videos for Activity Detection
Therefore, we propose a new baseline method for activity detection to tackle the novel challenges provided by our dataset.
Fast Weakly Supervised Action Segmentation Using Mutual Consistency
Action segmentation is the task of predicting the actions for each frame of a video.
Segmentation
Weakly Supervised Action Segmentation (Transcript)
Deep-Temporal LSTM for Daily Living Action Recognition
In this paper, we propose to improve the traditional use of RNNs by employing a many to many model for video classification.
