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Found 13 papers, 5 papers with code
Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB
Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image.
HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
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.
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.
HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Estimating 6D object poses is a major challenge in 3D computer vision.
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.
PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects
Object pose estimation is crucial for robotic applications and augmented reality.
DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstration
We first present a small sequence of RGB-D images displaying a human-object interaction.
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
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.
