# Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization

arXiv: https://arxiv.org/abs/2305.17861 · HTML: https://mlsanity.com/paper/2305.17861/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 68 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 30 of 48)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TVQA: Localized, Compositional Video Question Answering](https://mlsanity.com/paper/1809.01696/) (2018)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [BMN: Boundary-Matching Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1907.09702/) (2019)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [G-TAD: Sub-Graph Localization for Temporal Action Detection](https://mlsanity.com/paper/1911.11462/) (2019)
- [The THUMOS Challenge on Action Recognition for Videos "in the Wild"](https://mlsanity.com/paper/1604.06182/) (2016)
- [Graph Convolutional Networks for Temporal Action Localization](https://mlsanity.com/paper/1909.03252/) (2019)
- [Rethinking the Faster R-CNN Architecture for Temporal Action Localization](https://mlsanity.com/paper/1804.07667/) (2018)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [R-C3D: Region Convolutional 3D Network for Temporal Activity Detection](https://mlsanity.com/paper/1703.07814/) (2017)
- [Gaussian Temporal Awareness Networks for Action Localization](https://mlsanity.com/paper/1909.03877/) (2019)
- [UntrimmedNets for Weakly Supervised Action Recognition and Detection](https://mlsanity.com/paper/1703.03329/) (2017)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [Weakly Supervised Action Localization by Sparse Temporal Pooling Network](https://mlsanity.com/paper/1712.05080/) (2017)
- [W-TALC: Weakly-supervised Temporal Activity Localization and Classification](https://mlsanity.com/paper/1807.10418/) (2018)
- [Weakly Supervised Video Moment Retrieval From Text Queries](https://mlsanity.com/paper/1904.03282/) (2019)
- [TALLFormer: Temporal Action Localization with a Long-memory Transformer](https://mlsanity.com/paper/2204.01680/) (2022)
- [AutoLoc: Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/1807.08333/) (2018)
- [Revisiting Anchor Mechanisms for Temporal Action Localization](https://mlsanity.com/paper/2008.09837/) (2020)
- [Background Suppression Network for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/1911.09963/) (2019)
- [CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning](https://mlsanity.com/paper/2103.16392/) (2021)
- [Enriching Local and Global Contexts for Temporal Action Localization](https://mlsanity.com/paper/2107.12960/) (2021)

## Built on by (most cited first, 20 of 20)

- [Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models](https://mlsanity.com/paper/2411.08466/) (2024)
- [Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action Localization](https://mlsanity.com/paper/2412.19418/) (2024)
- [Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer](https://mlsanity.com/paper/2504.14860/) (2025)
- [Weakly-supervised Audio Temporal Forgery Localization via Progressive Audio-language Co-learning Network](https://mlsanity.com/paper/2505.01880/) (2025)
- [STAT: Towards Generalizable Temporal Action Localization](https://mlsanity.com/paper/2404.13311/) (2024)
- [ComPose: A Unified Completion-Pose Framework for Robust Category-Level Object Pose Estimation](https://mlsanity.com/paper/2605.25553/) (2026)
- [Full-Stage Pseudo Label Quality Enhancement for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2407.08971/) (2024)
- [Geometric Visual Fusion Graph Neural Networks for Multi-Person Human-Object Interaction Recognition in Videos](https://mlsanity.com/paper/2506.03440/) (2025)
- [Towards Weakly Supervised End-to-end Learning for Long-video Action Recognition](https://mlsanity.com/paper/2311.17118/) (2023)
- [CLIP-AE: CLIP-assisted Cross-view Audio-Visual Enhancement for Unsupervised Temporal Action Localization](https://mlsanity.com/paper/2505.23524/) (2025)
- [Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning](https://mlsanity.com/paper/2509.04023/) (2025)
- [POTLoc: Pseudo-Label Oriented Transformer for Point-Supervised Temporal Action Localization](https://mlsanity.com/paper/2310.13585/) (2023)
- [Face-Guided Sentiment Boundary Enhancement for Weakly-Supervised Temporal Sentiment Localization](https://mlsanity.com/paper/2603.14750/) (2026)
- [ADM-Loc: Actionness Distribution Modeling for Point-supervised Temporal Action Localization](https://mlsanity.com/paper/2311.15916/) (2023)
- [Boosting Point-supervised Temporal Action Localization via Text Refinement and Alignment](https://mlsanity.com/paper/2602.01257/) (2026)
- [Exploring the Temporal Consistency for Point-Level Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2602.05718/) (2026)
- [Masked Diffusion Vision-Language Models for Temporal Action Localization](https://mlsanity.com/paper/2605.29858/) (2026)
- [Audio-Text Cross-Attention with Psycholinguistic Support Features for Ambivalence/Hesitancy Recognition](https://mlsanity.com/paper/2607.13345/) (2026)
- [CoRE: Weakly Supervised Coarse-to-Fine Risk Evidence Learning in Driving Videos](https://mlsanity.com/paper/2608.25344/) (2026)
- [Joint Class-Time Learning for Video Classification with Multi-Instance Partial-Label Learning](https://mlsanity.com/paper/2610.06234/) (2026)

## Abstract (arXiv)

Weakly-supervised temporal action localization aims to localize and recognize actions in untrimmed videos with only video-level category labels during training. Without instance-level annotations, most existing methods follow the Segment-based Multiple Instance Learning (S-MIL) framework, where the predictions of segments are supervised by the labels of videos. However, the objective for acquiring segment-level scores during training is not consistent with the target for acquiring proposal-level scores during testing, leading to suboptimal results. To deal with this problem, we propose a novel Proposal-based Multiple Instance Learning (P-MIL) framework that directly classifies the candidate proposals in both the training and testing stages, which includes three key designs: 1) a surrounding contrastive feature extraction module to suppress the discriminative short proposals by considering the surrounding contrastive information, 2) a proposal completeness evaluation module to inhibit the low-quality proposals with the guidance of the completeness pseudo labels, and 3) an instance-level rank consistency loss to achieve robust detection by leveraging the complementarity of RGB and FLOW modalities. Extensive experimental results on two challenging benchmarks including THUMOS14 and ActivityNet demonstrate the superior performance of our method.