# Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action Localization

arXiv: https://arxiv.org/abs/2412.19418 · HTML: https://mlsanity.com/paper/2412.19418/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 27 Dec 2024.

## Builds on (most cited first, 13 of 13)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [BSN: Boundary Sensitive Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/1806.02964/) (2018)
- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [Gaussian Temporal Awareness Networks for Action Localization](https://mlsanity.com/paper/1909.03877/) (2019)
- [ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2203.15187/) (2022)
- [Cross-modal Consensus Network for Weakly Supervised Temporal Action Localization](https://mlsanity.com/paper/2107.12589/) (2021)
- [Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization](https://mlsanity.com/paper/2203.16800/) (2022)
- [Weakly Supervised Temporal Action Localization via Representative Snippet Knowledge Propagation](https://mlsanity.com/paper/2203.02925/) (2022)
- [Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2305.17861/) (2023)
- [An evidential classifier based on Dempster-Shafer theory and deep learning](https://mlsanity.com/paper/2103.13549/) (2021)
- [Boosting Weakly-Supervised Temporal Action Localization with Text Information](https://mlsanity.com/paper/2305.00607/) (2023)
- [Forcing the Whole Video as Background: An Adversarial Learning Strategy for Weakly Temporal Action Localization](https://mlsanity.com/paper/2207.06659/) (2022)

## Built on by (most cited first, 8 of 8)

- [Complementarity-driven Representation Learning for Multi-modal Knowledge Graph Completion](https://mlsanity.com/paper/2507.20620/) (2025)
- [Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2503.14343/) (2025)
- [Mutual Evidential Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.12418/) (2025)
- [Co-Evidential Fusion with Information Volume for Medical Image Segmentation](https://mlsanity.com/paper/2506.02492/) (2025)
- [DEFNet: Multitasks-based Deep Evidential Fusion Network for Blind Image Quality Assessment](https://mlsanity.com/paper/2507.19418/) (2025)
- [Adaptive Fuzzy Time Series Forecasting via Partially Asymmetric Convolution and Sub-Sliding Window Fusion](https://mlsanity.com/paper/2507.20641/) (2025)
- [Negation-Aware Weighted Information Fusion for Reliable Evidential Reasoning](https://mlsanity.com/paper/2509.00754/) (2025)
- [AdaMixT: Adaptive Weighted Mixture of Multi-Scale Expert Transformers for Time Series Forecasting](https://mlsanity.com/paper/2509.18107/) (2025)

## Abstract (arXiv)

Weakly supervised temporal action localization (WS-TAL) is a task of targeting at localizing complete action instances and categorizing them with video-level labels. Action-background ambiguity, primarily caused by background noise resulting from aggregation and intra-action variation, is a significant challenge for existing WS-TAL methods. In this paper, we introduce a hybrid multi-head attention (HMHA) module and generalized uncertainty-based evidential fusion (GUEF) module to address the problem. The proposed HMHA effectively enhances RGB and optical flow features by filtering redundant information and adjusting their feature distribution to better align with the WS-TAL task. Additionally, the proposed GUEF adaptively eliminates the interference of background noise by fusing snippet-level evidences to refine uncertainty measurement and select superior foreground feature information, which enables the model to concentrate on integral action instances to achieve better action localization and classification performance. Experimental results conducted on the THUMOS14 dataset demonstrate that our method outperforms state-of-the-art methods. Our code is available in urlhttps://github.com/heyuanpengpku/GUEF/tree/main.