# Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding

arXiv: https://arxiv.org/abs/2110.08063 · HTML: https://mlsanity.com/paper/2110.08063/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 90 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions](https://mlsanity.com/paper/2006.02903/) (2020)
- [Sequence to Sequence -- Video to Text](https://mlsanity.com/paper/1505.00487/) (2015)
- [Multi-level Semantic Feature Augmentation for One-shot Learning](https://mlsanity.com/paper/1804.05298/) (2018)
- [A Discriminative CNN Video Representation for Event Detection](https://mlsanity.com/paper/1411.4006/) (2014)
- [Beyond Gaussian Pyramid: Multi-skip Feature Stacking for Action Recognition](https://mlsanity.com/paper/1411.6660/) (2014)
- [Deep Self-Paced Learning for Person Re-Identification](https://mlsanity.com/paper/1710.05711/) (2017)
- [Learning and Interpreting Multi-Multi-Instance Learning Networks](https://mlsanity.com/paper/1810.11514/) (2018)
- [Exploiting Image-trained CNN Architectures for Unconstrained Video Classification](https://mlsanity.com/paper/1503.04144/) (2015)
- [MMALFM: Explainable Recommendation by Leveraging Reviews and Images](https://mlsanity.com/paper/1811.05318/) (2018)
- [Self-Weighted Robust LDA for Multiclass Classification with Edge Classes](https://mlsanity.com/paper/2009.12362/) (2020)
- [Semi-supervised Feature Analysis by Mining Correlations among Multiple Tasks](https://mlsanity.com/paper/1411.6232/) (2014)
- [Simple to Complex Cross-modal Learning to Rank](https://mlsanity.com/paper/1702.01229/) (2017)

## Abstract (arXiv)

Multimedia event detection is the task of detecting a specific event of interest in an user-generated video on websites. The most fundamental challenge facing this task lies in the enormously varying quality of the video as well as the high-level semantic abstraction of event inherently. In this paper, we decompose the video into several segments and intuitively model the task of complex event detection as a multiple instance learning problem by representing each video as a "bag" of segments in which each segment is referred to as an instance. Instead of treating the instances equally, we associate each instance with a reliability variable to indicate its importance and then select reliable instances for training. To measure the reliability of the varying instances precisely, we propose a visual-semantic guided loss by exploiting low-level feature from visual information together with instance-event similarity based high-level semantic feature. Motivated by curriculum learning, we introduce a negative elastic-net regularization term to start training the classifier with instances of high reliability and gradually taking the instances with relatively low reliability into consideration. An alternative optimization algorithm is developed to solve the proposed challenging non-convex non-smooth problem. Experimental results on standard datasets, i.e., TRECVID MEDTest 2013 and TRECVID MEDTest 2014, demonstrate the effectiveness and superiority of the proposed method to the baseline algorithms.