# Weak Supervision with Arbitrary Single Frame for Micro- and Macro-expression Spotting

arXiv: https://arxiv.org/abs/2403.14240 · HTML: https://mlsanity.com/paper/2403.14240/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 92 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 30 of 49)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [Exploring Cross-Image Pixel Contrast for Semantic Segmentation](https://mlsanity.com/paper/2101.11939/) (2021)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [Analysing Affective Behavior in the First ABAW 2020 Competition](https://mlsanity.com/paper/2001.11409/) (2020)
- [UntrimmedNets for Weakly Supervised Action Recognition and Detection](https://mlsanity.com/paper/1703.03329/) (2017)
- [Single Shot Temporal Action Detection](https://mlsanity.com/paper/1710.06236/) (2017)
- [Aligning Pretraining for Detection via Object-Level Contrastive Learning](https://mlsanity.com/paper/2106.02637/) (2021)
- [Weakly Supervised Action Localization by Sparse Temporal Pooling Network](https://mlsanity.com/paper/1712.05080/) (2017)
- [Contrastive Learning for Label-Efficient Semantic Segmentation](https://mlsanity.com/paper/2012.06985/) (2020)
- [W-TALC: Weakly-supervised Temporal Activity Localization and Classification](https://mlsanity.com/paper/1807.10418/) (2018)
- [Weakly Supervised Contrastive Learning](https://mlsanity.com/paper/2110.04770/) (2021)
- [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)
- [SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild](https://mlsanity.com/paper/1901.02839/) (2019)
- [SF-Net: Single-Frame Supervision for Temporal Action Localization](https://mlsanity.com/paper/2003.06845/) (2020)
- [Weakly-Supervised Action Localization by Generative Attention Modeling](https://mlsanity.com/paper/2003.12424/) (2020)
- [Proposal Learning for Semi-Supervised Object Detection](https://mlsanity.com/paper/2001.05086/) (2020)
- [Weakly-supervised Action Localization with Background Modeling](https://mlsanity.com/paper/1908.06552/) (2019)
- [3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization](https://mlsanity.com/paper/1908.08216/) (2019)
- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)

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

- [PESFormer: Boosting Macro- and Micro-expression Spotting with Direct Timestamp Encoding](https://mlsanity.com/paper/2410.18695/) (2024)
- [Point-Supervised Facial Expression Spotting with Gaussian-Based Instance-Adaptive Intensity Modeling](https://mlsanity.com/paper/2511.16952/) (2025)

## Abstract (arXiv)

Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation. Meanwhile, video-level spotting lacks sufficient information about the location and number of expressions during training, resulting in significantly inferior performance compared with fully-supervised spotting. To bridge this gap, we propose a point-level weakly-supervised expression spotting (PWES) framework, where each expression requires to be annotated with only one random frame (i.e., a point). To mitigate the issue of sparse label distribution, the prevailing solution is pseudo-label mining, which, however, introduces new problems: localizing contextual background snippets results in inaccurate boundaries and discarding foreground snippets leads to fragmentary predictions. Therefore, we design the strategies of multi-refined pseudo label generation (MPLG) and distribution-guided feature contrastive learning (DFCL) to address these problems. Specifically, MPLG generates more reliable pseudo labels by merging class-specific probabilities, attention scores, fused features, and point-level labels. DFCL is utilized to enhance feature similarity for the same categories and feature variability for different categories while capturing global representations across the entire datasets. Extensive experiments on the CAS(ME)^2, CAS(ME)^3, and SAMM-LV datasets demonstrate PWES achieves promising performance comparable to that of recent fully-supervised methods.