# Temporal Coherence for Active Learning in Videos

arXiv: https://arxiv.org/abs/1908.11757 · HTML: https://mlsanity.com/paper/1908.11757/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 24 of 24)

- [ActiveNeRF: Learning where to See with Uncertainty Estimation](https://mlsanity.com/paper/2209.08546/) (2022)
- [Synthetic Datasets for Autonomous Driving: A Survey](https://mlsanity.com/paper/2304.12205/) (2023)
- [Reducing Label Effort: Self-Supervised meets Active Learning](https://mlsanity.com/paper/2108.11458/) (2021)
- [Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels](https://mlsanity.com/paper/2301.12689/) (2023)
- [Active Learning for Deep Visual Tracking](https://mlsanity.com/paper/2110.13259/) (2021)
- [Enhancing Diffusion Models with 3D Perspective Geometry Constraints](https://mlsanity.com/paper/2312.00944/) (2023)
- [Class-Balanced Active Learning for Image Classification](https://mlsanity.com/paper/2110.04543/) (2021)
- [ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data](https://mlsanity.com/paper/2309.00832/) (2023)
- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [Semi-supervised Active Learning for Video Action Detection](https://mlsanity.com/paper/2312.07169/) (2023)
- [Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications](https://mlsanity.com/paper/2107.11750/) (2021)
- [FOCAL: A Cost-Aware Video Dataset for Active Learning](https://mlsanity.com/paper/2311.10591/) (2023)
- [When Deep Learners Change Their Mind: Learning Dynamics for Active Learning](https://mlsanity.com/paper/2107.14707/) (2021)
- [Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss](https://mlsanity.com/paper/2309.05517/) (2023)
- [Correlation-aware active learning for surgery video segmentation](https://mlsanity.com/paper/2311.08811/) (2023)
- [OmViD: Omni-supervised active learning for video action detection](https://mlsanity.com/paper/2508.13983/) (2025)
- [Active Safety Envelopes using Light Curtains with Probabilistic Guarantees](https://mlsanity.com/paper/2107.04000/) (2021)
- [WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS](https://mlsanity.com/paper/2107.11736/) (2021)
- [SurGe: Improved Surface Geometry in Point Maps](https://mlsanity.com/paper/2605.31577/) (2026)
- [Mutual-GAN: Towards Unsupervised Cross-Weather Adaptation with Mutual Information Constraint](https://mlsanity.com/paper/2106.16000/) (2021)
- [Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset](https://mlsanity.com/paper/2302.10679/) (2023)
- [MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain](https://mlsanity.com/paper/2501.04950/) (2025)
- [MutualNeRF: Improve the Performance of NeRF under Limited Samples with Mutual Information Theory](https://mlsanity.com/paper/2505.11386/) (2025)
- [Probing Association Instability with Track-State Perturbations for Clip-Level Active Learning in Query-Propagation Multi-Object Tracking](https://mlsanity.com/paper/2608.17224/) (2026)

## Abstract (arXiv)

Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets.