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Found 12 papers, 1 papers with code
The 8th AI City Challenge
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (ITS), presenting significant research opportunities.
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.
DeCo: Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-To-Fine Contrastive Ranking
Compositional temporal grounding is the task of localizing dense action by using known words combined in novel ways in the form of novel query sentences for the actual grounding.
Efficient and Accurate Skeleton-Based Two-Person Interaction Recognition Using Inter- and Intra-body Graphs
Skeleton-based two-person interaction recognition has been gaining increasing attention as advancements are made in pose estimation and graph convolutional networks.
Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks
We present Mask Atari, a new benchmark to help solve partially observable Markov decision process (POMDP) problems with Deep Reinforcement Learning (DRL)-based approaches.
Robust Unsupervised Multi-Object Tracking in Noisy Environments
We resolve this problem by introducing a robust unsupervised multi-object tracking (MOT) model: AttU-Net.
Cycle-Contrast for Self-Supervised Video Representation Learning
We present Cycle-Contrastive Learning (CCL), a novel self-supervised method for learning video representation.
MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding
Unlike vision modalities, body-worn sensors or passive sensing can avoid the failure of action understanding in vision related challenges, e. g. occlusion and appearance variation.
Active Generative Adversarial Network for Image Classification
Sufficient supervised information is crucial for any machine learning models to boost performance.
