Action Localization
135 papers with code • 0 benchmarks • 3 datasets
Action Localization is finding the spatial and temporal co ordinates for an action in a video. An action localization model will identify which frame an action start and ends in video and return the x,y coordinates of an action. Further the co ordinates will change when the object performing action undergoes a displacement.
Benchmarks
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Libraries
Use these libraries to find Action Localization models and implementations
COIN
HACS
CVB
Latest papers with no code
Temporal Action Localization for Inertial-based Human Activity Recognition
Our results show that state-of-the-art TAL models are able to outperform popular inertial models on 4 out of 6 wearable activity recognition benchmark datasets, with improvements ranging as much as 25% in F1-score.
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.
Proposal-based Temporal Action Localization with Point-level Supervision
Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos where only a single point (frame) within every action instance is annotated in training data.
Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localization
Temporal Action Localization (TAL) aims to identify actions' start, end, and class labels in untrimmed videos.
Boundary-Aware Proposal Generation Method for Temporal Action Localization
More importantly, few works consider the background frames that are similar to action frames in pixels but dissimilar in semantics, which also leads to inaccurate temporal boundaries.
Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives
Overall, this survey provides a valuable resource for researchers interested in the field of action scene understanding in soccer.
Revisiting Kernel Temporal Segmentation as an Adaptive Tokenizer for Long-form Video Understanding
While most modern video understanding models operate on short-range clips, real-world videos are often several minutes long with semantically consistent segments of variable length.
Sub-action Prototype Learning for Point-level Weakly-supervised Temporal Action Localization
Point-level weakly-supervised temporal action localization (PWTAL) aims to localize actions with only a single timestamp annotation for each action instance.
Cross-Video Contextual Knowledge Exploration and Exploitation for Ambiguity Reduction in Weakly Supervised Temporal Action Localization
Further, the GKSA module is used to efficiently summarize and propagate the cross-video representative action knowledge in a learnable manner to promote holistic action patterns understanding, which in turn allows the generation of high-confidence pseudo-labels for self-learning, thus alleviating ambiguity in temporal localization.
Benchmarking Data Efficiency and Computational Efficiency of Temporal Action Localization Models
This work explores and measures how current deep temporal action localization models perform in settings constrained by the amount of data or computational power.
