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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 219 papers

TitleStatusHype
Condensing Action Segmentation Datasets via Generative Network Inversion—0
Continuous Human Action Recognition for Human-Machine Interaction: A Review—0
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation—0
Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera—0
Depthwise Separable Temporal Convolutional Network for Action Segmentation—0
Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data—0
DIR-AS: Decoupling Individual Identification and Temporal Reasoning for Action Segmentation—0
Distill and Collect for Semi-Supervised Temporal Action Segmentation—0
DPMix: Mixture of Depth and Point Cloud Video Experts for 4D Action Segmentation—0
End-to-End Action Segmentation Transformer—0
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding—0
Enhancing Transformer Backbone for Egocentric Video Action Segmentation—0
Error Detection in Egocentric Procedural Task Videos—0
Exploring Temporally Dynamic Data Augmentation for Video Recognition—0
Fast and Unsupervised Action Boundary Detection for Action Segmentation—0
FIFA: Fast Inference Approximation for Action Segmentation—0
Fine-grained Action Segmentation using the Semi-Supervised Action GAN—0
Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks—0
Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition—0
Grasp Type Revisited: A Modern Perspective on a Classical Feature for Vision—0
Hand Guided High Resolution Feature Enhancement for Fine-Grained Atomic Action Segmentation within Complex Human Assemblies—0
HOIST-Former: Hand-held Objects Identification, Segmentation, and Tracking in the Wild—0
HOIST-Former: Hand-held Objects Identification Segmentation and Tracking in the Wild—0
Human Action Segmentation With Hierarchical Supervoxel Consistency—0
Human Action Sequence Classification—0
Improving action segmentation via explicit similarity measurement—0
Improving Action Segmentation via Graph-Based Temporal Reasoning—0
Joint Visual-Temporal Embedding for Unsupervised Learning of Actions in Untrimmed Sequences—0
Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery—0
Robotic Imitation of Human Actions—0
Robust Action Segmentation from Timestamp Supervision—0
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation—0
Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding—0
Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos—0
Set-Constrained Viterbi for Set-Supervised Action Segmentation—0
SFGANS Self-supervised Future Generator for human ActioN Segmentation—0
SF-TMN: SlowFast Temporal Modeling Network for Surgical Phase Recognition—0
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation—0
Stitch Contrast and Segment_Learning a Human Action Segmentation Model Using Trimmed Skeleton Videos—0
Surgical Phase Recognition in Laparoscopic Cholecystectomy—0
TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment—0
TAEC: Unsupervised Action Segmentation with Temporal-Aware Embedding and Clustering—0
Temporal2Seq: A Unified Framework for Temporal Video Understanding Tasks—0
Temporal Action Segmentation with High-level Complex Activity Labels—0
Temporal Context Consistency Above All: Enhancing Long-Term Anticipation by Learning and Enforcing Temporal Constraints—0
Temporal Deformable Residual Networks for Action Segmentation in Videos—0
Temporal Segment Transformer for Action Segmentation—0
Therbligs in Action: Video Understanding through Motion Primitives—0
TimeLogic: A Temporal Logic Benchmark for Video QA—0
Timestamp-Supervised Action Segmentation with Graph Convolutional Networks—0
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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