# ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization

arXiv: https://arxiv.org/abs/1609.04331 · HTML: https://mlsanity.com/paper/1609.04331/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2016.

## Built on by (most cited first, 30 of 39)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Towards Generic Image Manipulation Detection with Weakly-Supervised Self-Consistency Learning](https://mlsanity.com/paper/2309.01246/) (2023)
- [Weakly-supervised segmentation of referring expressions](https://mlsanity.com/paper/2205.04725/) (2022)
- [Robust Object Detection With Inaccurate Bounding Boxes](https://mlsanity.com/paper/2207.09697/) (2022)
- [ViTOL: Vision Transformer for Weakly Supervised Object Localization](https://mlsanity.com/paper/2204.06772/) (2022)
- [CTRL-O: Language-Controllable Object-Centric Visual Representation Learning](https://mlsanity.com/paper/2503.21747/) (2025)
- [Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations](https://mlsanity.com/paper/2206.00274/) (2022)
- [Weakly Supervised Open-Vocabulary Object Detection](https://mlsanity.com/paper/2312.12437/) (2023)
- [WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition](https://mlsanity.com/paper/2402.14812/) (2024)
- [Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes](https://mlsanity.com/paper/2307.12101/) (2023)
- [DAP: Detection-Aware Pre-training with Weak Supervision](https://mlsanity.com/paper/2103.16651/) (2021)
- [WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection](https://mlsanity.com/paper/2105.11293/) (2021)
- [Scaling Novel Object Detection with Weakly Supervised Detection Transformers](https://mlsanity.com/paper/2207.05205/) (2022)
- [Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey](https://mlsanity.com/paper/2105.12694/) (2021)
- [Complete Instances Mining for Weakly Supervised Instance Segmentation](https://mlsanity.com/paper/2402.07633/) (2024)
- [DETR with Additional Global Aggregation for Cross-domain Weakly Supervised Object Detection](https://mlsanity.com/paper/2304.07082/) (2023)
- [Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling](https://mlsanity.com/paper/2402.11985/) (2024)
- [Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection](https://mlsanity.com/paper/2308.05991/) (2023)
- [LOCL: Learning Object-Attribute Composition using Localization](https://mlsanity.com/paper/2210.03780/) (2022)
- [Assessing Domain Gap for Continual Domain Adaptation in Object Detection](https://mlsanity.com/paper/2302.10396/) (2023)
- [Distributed Attention for Grounded Image Captioning](https://mlsanity.com/paper/2108.01056/) (2021)
- [Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection](https://mlsanity.com/paper/2108.01785/) (2021)
- [Self-Supervised Object Detection via Generative Image Synthesis](https://mlsanity.com/paper/2110.09848/) (2021)
- [Learning to Detect Semantic Boundaries with Image-level Class Labels](https://mlsanity.com/paper/2212.07579/) (2022)
- [Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes](https://mlsanity.com/paper/2308.12017/) (2023)
- [Online Active Proposal Set Generation for Weakly Supervised Object Detection](https://mlsanity.com/paper/2101.07929/) (2021)
- [Contrastive Proposal Extension with LSTM Network for Weakly Supervised Object Detection](https://mlsanity.com/paper/2110.07511/) (2021)
- [Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2110.09110/) (2021)
- [Weakly Semi-supervised Tool Detection in Minimally Invasive Surgery Videos](https://mlsanity.com/paper/2401.02791/) (2024)
- [Two-phase weakly supervised object detection with pseudo ground truth mining](https://mlsanity.com/paper/2104.00231/) (2021)

## Abstract (arXiv)

We aim to localize objects in images using image-level supervision only. Previous approaches to this problem mainly focus on discriminative object regions and often fail to locate precise object boundaries. We address this problem by introducing two types of context-aware guidance models, additive and contrastive models, that leverage their surrounding context regions to improve localization. The additive model encourages the predicted object region to be supported by its surrounding context region. The contrastive model encourages the predicted object region to be outstanding from its surrounding context region. Our approach benefits from the recent success of convolutional neural networks for object recognition and extends Fast R-CNN to weakly supervised object localization. Extensive experimental evaluation on the PASCAL VOC 2007 and 2012 benchmarks shows hat our context-aware approach significantly improves weakly supervised localization and detection.