# Active Object Localization with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1511.06015 · HTML: https://mlsanity.com/paper/1511.06015/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2015.

## Built on by (most cited first, 30 of 42)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Optimal Boxes: Boosting End-to-End Scene Text Recognition by Adjusting Annotated Bounding Boxes via Reinforcement Learning](https://mlsanity.com/paper/2207.11934/) (2022)
- [Reinforced Attention for Few-Shot Learning and Beyond](https://mlsanity.com/paper/2104.04192/) (2021)
- [Sequential Voting with Relational Box Fields for Active Object Detection](https://mlsanity.com/paper/2110.11524/) (2021)
- [Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification](https://mlsanity.com/paper/2103.08741/) (2021)
- [AdaZoom: Adaptive Zoom Network for Multi-Scale Object Detection in Large Scenes](https://mlsanity.com/paper/2106.10409/) (2021)
- [Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2103.14502/) (2021)
- [IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenes](https://mlsanity.com/paper/2112.05298/) (2021)
- [Estimating Visual Information From Audio Through Manifold Learning](https://mlsanity.com/paper/2208.02337/) (2022)
- [AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale](https://mlsanity.com/paper/2404.03482/) (2024)
- [Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model](https://mlsanity.com/paper/2202.02212/) (2022)
- [Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-Rescue](https://mlsanity.com/paper/2209.03694/) (2022)
- [RL-LOGO: Deep Reinforcement Learning Localization for Logo Recognition](https://mlsanity.com/paper/2312.16792/) (2023)
- [Stochastic Actor-Executor-Critic for Image-to-Image Translation](https://mlsanity.com/paper/2112.07403/) (2021)
- [Towards Two-Stream Foveation-based Active Vision Learning](https://mlsanity.com/paper/2403.15977/) (2024)
- [Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision](https://mlsanity.com/paper/2505.13191/) (2025)
- [MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation](https://mlsanity.com/paper/2506.18679/) (2025)
- [Visual Attention in Imaginative Agents](https://mlsanity.com/paper/2104.00177/) (2021)
- [Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images](https://mlsanity.com/paper/2104.06782/) (2021)
- [Do Neural Network Weights account for Classes Centers?](https://mlsanity.com/paper/2104.07004/) (2021)
- [Dynamic Multi-Scale Loss Optimization for Object Detection](https://mlsanity.com/paper/2108.04014/) (2021)
- [Object Detection with Deep Reinforcement Learning](https://mlsanity.com/paper/2208.04511/) (2022)
- [Adaptive Remote Sensing Image Attribute Learning for Active Object Detection](https://mlsanity.com/paper/2101.06438/) (2021)
- [Where to Look: A Unified Attention Model for Visual Recognition with Reinforcement Learning](https://mlsanity.com/paper/2111.07169/) (2021)
- [Multi-Agent Active Search using Detection and Location Uncertainty](https://mlsanity.com/paper/2203.04524/) (2022)
- [Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object Detection](https://mlsanity.com/paper/2408.06803/) (2024)
- [Autonomous Curiosity for Real-Time Training Onboard Robotic Agents](https://mlsanity.com/paper/2109.00927/) (2021)
- [Reinforcement Explanation Learning](https://mlsanity.com/paper/2111.13406/) (2021)
- [Weakly Supervised Scene Text Detection using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.04866/) (2022)
- [Learning Transferable Reward for Query Object Localization with Policy Adaptation](https://mlsanity.com/paper/2202.12403/) (2022)

## Abstract (arXiv)

We present an active detection model for localizing objects in scenes. The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object. This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects following top-down reasoning. The proposed localization agent is trained using deep reinforcement learning, and evaluated on the Pascal VOC 2007 dataset. We show that agents guided by the proposed model are able to localize a single instance of an object after analyzing only between 11 and 25 regions in an image, and obtain the best detection results among systems that do not use object proposals for object localization.