Research map / Video / Action recognition
Two-Stream Networks for Weakly-Supervised Temporal Action Localization With Semantic-Aware Mechanisms
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add semantic-aware dictionary loss (Lsmt) to the two-stream model (ablation) | two-stream WS-TAL model · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 43.0 to 46.2 (+3.2) | Helped |
| add semantic-aware dictionary loss (Lsmt) to the two-stream model (ablation) | two-stream WS-TAL model · ActivityNet-1.3 | mAP@0.5:0.05:0.95 Avg (%): 25.0 to 26.3 (+1.3) | Helped |
| add guide loss (Lgui) (ablation) | two-stream WS-TAL model · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 41.0 to 43.0 (+2.0) | Helped |
| add context classification loss (Lct_cls) (ablation) | two-stream WS-TAL model · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 37.5 to 41.0 (+3.5) | Helped |
| add context classification loss (Lct_cls) (ablation) | two-stream WS-TAL model · ActivityNet-1.3 | mAP@0.5:0.05:0.95 Avg (%): 23.9 to 24.6 (+0.7) | Helped |
| add background classification loss (Lbg_cls) (ablation) | two-stream WS-TAL model · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 36.3 to 37.5 (+1.2) | Helped |
| add background classification loss (Lbg_cls) (ablation) | two-stream WS-TAL model · ActivityNet-1.3 | mAP@0.5:0.05:0.95 Avg (%): 23.5 to 23.9 (+0.4) | Helped |
| replace two-stream with attention-only branch (ablation) | two-stream WS-TAL model · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 41.0 to 30.8 (-10.2) | Hurt |
| replace two-stream with MIL-only branch (ablation) | two-stream WS-TAL model · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 41.0 to 36.0 (-5.0) | Hurt |
| replace two-stream with attention-only branch (ablation) | two-stream WS-TAL model · ActivityNet-1.3 | mAP@0.5:0.05:0.95 Avg (%): 24.6 to 15.6 (-9.0) | Hurt |
| replace two-stream with MIL-only branch (ablation) | two-stream WS-TAL model · ActivityNet-1.3 | mAP@0.5:0.05:0.95 Avg (%): 24.6 to 22.7 (-1.9) | Hurt |
| two-stream attention+MIL model with semantic-aware loss vs prior SOTA | WS-TAL on THUMOS-14 · THUMOS-14 | mAP@0.1:0.1:0.7 Avg (%): 46.2 vs 45.1 (ASM-Loc), +1.1 | Helped |
Closest papers by meaning
- Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature (AAAI 2024 · similarity 0.93)
- Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation (CVPR 2022 · similarity 0.93)
- Completeness Modeling and Context Separation for Weakly Supervised Temporal Action Localization (CVPR 2019 · similarity 0.92)
- A Hybrid Attention Mechanism for Weakly-Supervised Temporal Action Localization (AAAI 2021 · similarity 0.92)
- ACSNet: Action-Context Separation Network for Weakly Supervised Temporal Action Localization (AAAI 2021 · similarity 0.92)
- Similar Modality Enhancement and Action Consistency Learning for Weakly Supervised Temporal Action Localization (AAAI 2025 · similarity 0.90)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
