# CLIP-AE: CLIP-assisted Cross-view Audio-Visual Enhancement for Unsupervised Temporal Action Localization

arXiv: https://arxiv.org/abs/2505.23524 · HTML: https://mlsanity.com/paper/2505.23524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 10 of 10)

- [Weakly Supervised Action Localization by Sparse Temporal Pooling Network](https://mlsanity.com/paper/1712.05080/) (2017)
- [Weakly-supervised Action Localization with Background Modeling](https://mlsanity.com/paper/1908.06552/) (2019)
- [Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2010.11594/) (2020)
- [Distilling Semantic Priors from SAM to Efficient Image Restoration Models](https://mlsanity.com/paper/2403.16368/) (2024)
- [Weakly-Supervised Temporal Action Localization by Inferring Salient Snippet-Feature](https://mlsanity.com/paper/2303.12332/) (2023)
- [Proposal-Based Multiple Instance Learning for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2305.17861/) (2023)
- [GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization](https://mlsanity.com/paper/2406.16531/) (2024)
- [Revisiting Foreground and Background Separation in Weakly-supervised Temporal Action Localization: A Clustering-based Approach](https://mlsanity.com/paper/2312.14138/) (2023)
- [Rethinking Pseudo-Label Guided Learning for Weakly Supervised Temporal Action Localization from the Perspective of Noise Correction](https://mlsanity.com/paper/2501.11124/) (2025)
- [IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning](https://mlsanity.com/paper/2502.02454/) (2025)

## Built on by (most cited first, 2 of 2)

- [FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learning](https://mlsanity.com/paper/2603.22969/) (2026)
- [MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization](https://mlsanity.com/paper/2607.00902/) (2026)

## Abstract (arXiv)

Temporal Action Localization (TAL) has garnered significant attention in information retrieval. Existing supervised or weakly supervised methods heavily rely on labeled temporal boundaries and action categories, which are labor-intensive and time-consuming. Consequently, unsupervised temporal action localization (UTAL) has gained popularity. However, current methods face two main challenges: 1) Classification pre-trained features overly focus on highly discriminative regions; 2) Solely relying on visual modality information makes it difficult to determine contextual boundaries. To address these issues, we propose a CLIP-assisted cross-view audiovisual enhanced UTAL method. Specifically, we introduce visual language pre-training (VLP) and classification pre-training-based collaborative enhancement to avoid excessive focus on highly discriminative regions; we also incorporate audio perception to provide richer contextual boundary information. Finally, we introduce a self-supervised cross-view learning paradigm to achieve multi-view perceptual enhancement without additional annotations. Extensive experiments on two public datasets demonstrate our model's superiority over several state-of-the-art competitors.