# Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection

arXiv: https://arxiv.org/abs/2004.04725 · HTML: https://mlsanity.com/paper/2004.04725/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 30 of 80)

- [Localizing Objects with Self-Supervised Transformers and no Labels](https://mlsanity.com/paper/2109.14279/) (2021)
- [Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut](https://mlsanity.com/paper/2202.11539/) (2022)
- [TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization](https://mlsanity.com/paper/2103.14862/) (2021)
- [TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut](https://mlsanity.com/paper/2209.00383/) (2022)
- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification](https://mlsanity.com/paper/2106.06133/) (2021)
- [ASM-Loc: Action-aware Segment Modeling for Weakly-Supervised Temporal Action Localization](https://mlsanity.com/paper/2203.15187/) (2022)
- [Weakly Supervised Temporal Adjacent Network for Language Grounding](https://mlsanity.com/paper/2106.16136/) (2021)
- [3D Spatial Recognition without Spatially Labeled 3D](https://mlsanity.com/paper/2105.06461/) (2021)
- [Point-to-Box Network for Accurate Object Detection via Single Point Supervision](https://mlsanity.com/paper/2207.06827/) (2022)
- [Self-supervised object detection from audio-visual correspondence](https://mlsanity.com/paper/2104.06401/) (2021)
- [DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering](https://mlsanity.com/paper/2212.05853/) (2022)
- [Taming Self-Training for Open-Vocabulary Object Detection](https://mlsanity.com/paper/2308.06412/) (2023)
- [Fine-grained Visual Classification with High-temperature Refinement and Background Suppression](https://mlsanity.com/paper/2303.06442/) (2023)
- [Boosting Weakly Supervised Object Detection via Learning Bounding Box Adjusters](https://mlsanity.com/paper/2108.01499/) (2021)
- [What Can Human Sketches Do for Object Detection?](https://mlsanity.com/paper/2303.15149/) (2023)
- [Localized Vision-Language Matching for Open-vocabulary Object Detection](https://mlsanity.com/paper/2205.06160/) (2022)
- [Active Learning Strategies for Weakly-supervised Object Detection](https://mlsanity.com/paper/2207.12112/) (2022)
- [Object Discovery via Contrastive Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/2208.07576/) (2022)
- [Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations](https://mlsanity.com/paper/2303.16891/) (2023)
- [What data do we need for training an AV motion planner?](https://mlsanity.com/paper/2105.12337/) (2021)
- [Knowledge Combination to Learn Rotated Detection Without Rotated Annotation](https://mlsanity.com/paper/2304.02199/) (2023)
- [Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity](https://mlsanity.com/paper/2110.14191/) (2021)
- [A Novel Plug-in Module for Fine-Grained Visual Classification](https://mlsanity.com/paper/2202.03822/) (2022)
- [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)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition](https://mlsanity.com/paper/2402.14812/) (2024)
- [W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection](https://mlsanity.com/paper/2207.12104/) (2022)

## Abstract (arXiv)

Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training. However, major challenges remain: (1) differentiation of object instances can be ambiguous; (2) detectors tend to focus on discriminative parts rather than entire objects; (3) without ground truth, object proposals have to be redundant for high recalls, causing significant memory consumption. Addressing these challenges is difficult, as it often requires to eliminate uncertainties and trivial solutions. To target these issues we develop an instance-aware and context-focused unified framework. It employs an instance-aware self-training algorithm and a learnable Concrete DropBlock while devising a memory-efficient sequential batch back-propagation. Our proposed method achieves state-of-the-art results on COCO (12.1% ~AP, 24.8% ~AP50), VOC 2007 (54.9% ~AP), and VOC 2012 (52.1% ~AP), improving baselines by great margins. In addition, the proposed method is the first to benchmark ResNet based models and weakly supervised video object detection. Code, models, and more details will be made available at: https://github.com/NVlabs/wetectron.