SOTAVerified

Action Segmentation

Action Segmentation is a challenging problem in high-level video understanding. In its simplest form, Action Segmentation aims to segment a temporally untrimmed video by time and label each segmented part with one of pre-defined action labels. The results of Action Segmentation can be further used as input to various applications, such as video-to-text and action localization.

Source: TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation

Papers

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Showing 101–150 of 219 papers

TitleStatusHype
Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence TranslationCode1
A Circular Window-based Cascade Transformer for Online Action Detection—0
A Generalized & Robust Framework For Timestamp Supervision in Temporal Action Segmentation—0
An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation—0
Leveraging Action Affinity and Continuity for Semi-supervised Temporal Action Segmentation—0
Turning to a Teacher for Timestamp Supervised Temporal Action Segmentation—0
Timestamp-Supervised Action Segmentation with Graph Convolutional Networks—0
Exploring Temporally Dynamic Data Augmentation for Video Recognition—0
Surgical Phase Recognition in Laparoscopic Cholecystectomy—0
RF-Next: Efficient Receptive Field Search for Convolutional Neural NetworksCode1
Do we really need temporal convolutions in action segmentation?Code0
A Wireless-Vision Dataset for Privacy Preserving Human Activity Recognition—0
Action parsing using context features—0
Cross-Enhancement Transformer for Action SegmentationCode0
Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction—0
Temporal Alignment Networks for Long-term VideoCode1
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural ActivitiesCode0
Bridge-Prompt: Towards Ordinal Action Understanding in Instructional VideosCode1
Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos—0
HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object InteractionCode1
Continuous Human Action Recognition for Human-Machine Interaction: A Review—0
Skeleton-Based Action Segmentation with Multi-Stage Spatial-Temporal Graph Convolutional Neural NetworksCode1
Transformers in Action: Weakly Supervised Action Segmentation—0
Semi-Weakly-Supervised Learning of Complex Actions From Instructional Task VideosCode0
Set-Supervised Action Learning in Procedural Task Videos via Pairwise Order ConsistencyCode1
Fast and Unsupervised Action Boundary Detection for Action Segmentation—0
You Can Wash Hands Better: Accurate Daily Handwashing Assessment with a SmartwatchCode0
Iterative Contrast-Classify For Semi-supervised Temporal Action SegmentationCode1
Towards Tokenized Human Dynamics RepresentationCode1
Few-Shot Temporal Action Localization with Query Adaptive TransformerCode1
ASFormer: Transformer for Action SegmentationCode1
Hierarchical Modeling for Task Recognition and Action Segmentation in Weakly-Labeled Instructional VideosCode0
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text UnderstandingCode0
Long Short View Feature Decomposition via Contrastive Video Representation Learning—0
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment—0
Temporal Action Segmentation with High-level Complex Activity Labels—0
FIFA: Fast Inference Approximation for Action Segmentation—0
Unsupervised Action Segmentation for Instructional Videos—0
SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation—0
Unsupervised Action Segmentation by Joint Representation Learning and Online ClusteringCode1
Coarse to Fine Multi-Resolution Temporal Convolutional NetworkCode1
VLM: Task-agnostic Video-Language Model Pre-training for Video UnderstandingCode0
Efficient Two-Step Networks for Temporal Action SegmentationCode1
Unsupervised Discriminative Embedding for Sub-Action Learning in Complex Activities—0
Action in Mind: A Neural Network Approach to Action Recognition and Segmentation—0
Action Segmentation with Mixed Temporal Domain Adaptation—0
Anchor-Constrained Viterbi for Set-Supervised Action Segmentation—0
Action Shuffle Alternating Learning for Unsupervised Action Segmentation—0
Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networksCode1
Temporally-Weighted Hierarchical Clustering for Unsupervised Action SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AdaFocus (newly extracted I3D-features, LT-Context model)Average F176.2—Unverified
2FACT (efficient hybrid of convolution and transformer model)Average F174.7—Unverified
3ASQueryAverage F174.6—Unverified
4BITAverage F173.7—Unverified
5DiffActAverage F173.6—Unverified
6BaFormerAverage F172.4—Unverified
7CETNetAverage F171.8—Unverified
8SF-TMN(ASFormer)Average F171.6—Unverified
9RF++-SSTDAAcc70.8—Unverified
10ASPnetAverage F170.6—Unverified
#ModelMetricClaimedVerifiedStatus
1Br-Prompt+ASPnet (RGB, flow, accelerometer)F1@50%88.5—Unverified
2Semantic2GraphF1@50%87.3—Unverified
3BaFormerF1@50%83.9—Unverified
4DiffActF1@50%83.7—Unverified
5SF-TMN(ASFormer)F1@50%82.9—Unverified
6LTContextF1@50%82—Unverified
7UVASTF1@50%81.7—Unverified
8Br-Prompt+ASFormerF1@50%81.3—Unverified
9EUTF1@50%81—Unverified
10CETNetF1@50%80.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Semantic2GraphF1@50%91.3—Unverified
2FACTF1@50%87.5—Unverified
3DiffActF1@50%84.7—Unverified
4BaFormerF1@50%83.5—Unverified
5SF-TMN(ASFormer)F1@50%83.1—Unverified
6Br-Prompt+ASFormerF1@50%83—Unverified
7DPRNF1@50%82.9—Unverified
8BITF1@50%82.6—Unverified
9CETNetF1@50%81.3—Unverified
10UVASTF1@50%81—Unverified
#ModelMetricClaimedVerifiedStatus
1UnLoc-LFrame accuracy72.8—Unverified
2UnivlFrame accuracy70—Unverified
3NortonFrame accuracy69.8—Unverified
4VideoClipFrame accuracy68.7—Unverified
5TACoFrame accuracy68.4—Unverified
6VLMFrame accuracy68.4—Unverified
7MIL-NCEFrame accuracy61—Unverified
8ActBERTFrame accuracy57—Unverified
9CBTFrame accuracy53.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ASQueryF1@10%37.8—Unverified
2LTContextF1@10%33.9—Unverified
3ASFormerF1@10%33.4—Unverified
4C2F-TCNF1@10%33.3—Unverified
5UVASTF1@10%32.1—Unverified
6MS-TCN++F1@10%31.6—Unverified
7ProTAS(Offline)F1@10%28.7—Unverified
#ModelMetricClaimedVerifiedStatus
1RL+TreeEdit Distance88.53—Unverified
2RL (full)Edit Distance87.96—Unverified
3TricorNetEdit Distance86.8—Unverified
4SDL+SC-CRFEdit Distance86.21—Unverified
5TCNEdit Distance83.1—Unverified
6ST-CNN+SegEdit Distance66.56—Unverified
#ModelMetricClaimedVerifiedStatus
1TSA (FINCH)Acc62.4—Unverified
2TSA (Kmeans)Acc59.7—Unverified
#ModelMetricClaimedVerifiedStatus
1EUTAcc87.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Unsup. TW-FINCH (K=avg/activity)Accuracy42—Unverified