# Learning Pixel-Level Distinctions for Video Highlight Detection

arXiv: https://arxiv.org/abs/2204.04615 · HTML: https://mlsanity.com/paper/2204.04615/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 56 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 22 of 22)

- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?](https://mlsanity.com/paper/1711.09577/) (2017)
- [Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification](https://mlsanity.com/paper/1712.04851/) (2017)
- [Video Summarization with Long Short-term Memory](https://mlsanity.com/paper/1605.08110/) (2016)
- [Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward](https://mlsanity.com/paper/1801.00054/) (2017)
- [Revisiting Video Saliency: A Large-scale Benchmark and a New Model](https://mlsanity.com/paper/1801.07424/) (2018)
- [Rethinking the Evaluation of Video Summaries](https://mlsanity.com/paper/1903.11328/) (2019)
- [Less is More: Learning Highlight Detection from Video Duration](https://mlsanity.com/paper/1903.00859/) (2019)
- [TASED-Net: Temporally-Aggregating Spatial Encoder-Decoder Network for Video Saliency Detection](https://mlsanity.com/paper/1908.05786/) (2019)
- [Hierarchical Recurrent Neural Network for Video Summarization](https://mlsanity.com/paper/1904.12251/) (2019)
- [MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection](https://mlsanity.com/paper/2007.09833/) (2020)
- [Video2GIF: Automatic Generation of Animated GIFs from Video](https://mlsanity.com/paper/1605.04850/) (2016)
- [STAViS: Spatio-Temporal AudioVisual Saliency Network](https://mlsanity.com/paper/2001.03063/) (2020)
- [Cross-category Video Highlight Detection via Set-based Learning](https://mlsanity.com/paper/2108.11770/) (2021)
- [Reconstructive Sequence-Graph Network for Video Summarization](https://mlsanity.com/paper/2105.04066/) (2021)
- [Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders](https://mlsanity.com/paper/1510.01442/) (2015)
- [Query-adaptive Video Summarization via Quality-aware Relevance Estimation](https://mlsanity.com/paper/1705.00581/) (2017)
- [Adaptive Video Highlight Detection by Learning from User History](https://mlsanity.com/paper/2007.09598/) (2020)
- [PHD-GIFs: Personalized Highlight Detection for Automatic GIF Creation](https://mlsanity.com/paper/1804.06604/) (2018)
- [Collaborative Summarization of Topic-Related Videos](https://mlsanity.com/paper/1706.03114/) (2017)
- [Recurrent Mixture Density Network for Spatiotemporal Visual Attention](https://mlsanity.com/paper/1603.08199/) (2016)
- [Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation](https://mlsanity.com/paper/2005.11724/) (2020)

## Built on by (most cited first, 16 of 16)

- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [R2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding](https://mlsanity.com/paper/2404.00801/) (2024)
- [Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2404.09263/) (2024)
- [FlashVTG: Feature Layering and Adaptive Score Handling Network for Video Temporal Grounding](https://mlsanity.com/paper/2412.13441/) (2024)
- [Multimodal Action Quality Assessment](https://mlsanity.com/paper/2402.09444/) (2024)
- [LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2501.10787/) (2025)
- [STAT: Towards Generalizable Temporal Action Localization](https://mlsanity.com/paper/2404.13311/) (2024)
- [CVA: Context-aware Video-text Alignment for Video Temporal Grounding](https://mlsanity.com/paper/2603.24934/) (2026)
- [VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval](https://mlsanity.com/paper/2412.01558/) (2024)
- [The Devil is in the Spurious Correlations: Boosting Moment Retrieval with Dynamic Learning](https://mlsanity.com/paper/2501.07305/) (2025)
- [ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding](https://mlsanity.com/paper/2408.06622/) (2024)
- [Decouple and Cache: KV Cache Construction for Streaming Video Understanding](https://mlsanity.com/paper/2605.01858/) (2026)
- [An Effective-Efficient Approach for Dense Multi-Label Action Detection](https://mlsanity.com/paper/2406.06187/) (2024)
- [MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding](https://mlsanity.com/paper/2506.08512/) (2025)
- [See, Rank, and Filter: Important Word-Aware Clip Filtering via Scene Understanding for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2511.22906/) (2025)
- [TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection](https://mlsanity.com/paper/2608.29577/) (2026)

## Abstract (arXiv)

The goal of video highlight detection is to select the most attractive segments from a long video to depict the most interesting parts of the video. Existing methods typically focus on modeling relationship between different video segments in order to learning a model that can assign highlight scores to these segments; however, these approaches do not explicitly consider the contextual dependency within individual segments. To this end, we propose to learn pixel-level distinctions to improve the video highlight detection. This pixel-level distinction indicates whether or not each pixel in one video belongs to an interesting section. The advantages of modeling such fine-level distinctions are two-fold. First, it allows us to exploit the temporal and spatial relations of the content in one video, since the distinction of a pixel in one frame is highly dependent on both the content before this frame and the content around this pixel in this frame. Second, learning the pixel-level distinction also gives a good explanation to the video highlight task regarding what contents in a highlight segment will be attractive to people. We design an encoder-decoder network to estimate the pixel-level distinction, in which we leverage the 3D convolutional neural networks to exploit the temporal context information, and further take advantage of the visual saliency to model the spatial distinction. State-of-the-art performance on three public benchmarks clearly validates the effectiveness of our framework for video highlight detection.