# CaT: Weakly Supervised Object Detection with Category Transfer

arXiv: https://arxiv.org/abs/2108.07487 · HTML: https://mlsanity.com/paper/2108.07487/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 28 of 28)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [Zero-Shot Object Detection](https://mlsanity.com/paper/1804.04340/) (2018)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition](https://mlsanity.com/paper/1908.07325/) (2019)
- [PCL: Proposal Cluster Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1807.03342/) (2018)
- [Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection](https://mlsanity.com/paper/2004.04725/) (2020)
- [Learning to Segment Every Thing](https://mlsanity.com/paper/1711.10370/) (2017)
- [WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection](https://mlsanity.com/paper/1909.04972/) (2019)
- [Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection](https://mlsanity.com/paper/2010.12023/) (2020)
- [LSDA: Large Scale Detection Through Adaptation](https://mlsanity.com/paper/1407.5035/) (2014)
- [Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer](https://mlsanity.com/paper/2007.07986/) (2020)
- [Revisiting knowledge transfer for training object class detectors](https://mlsanity.com/paper/1708.06128/) (2017)
- [Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection](https://mlsanity.com/paper/1801.03145/) (2018)
- [Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN](https://mlsanity.com/paper/2002.07417/) (2020)
- [Mixed Supervised Object Detection with Robust Objectness Transfer](https://mlsanity.com/paper/1802.09778/) (2018)
- [Weakly Supervised Object Localization Using Things and Stuff Transfer](https://mlsanity.com/paper/1703.08000/) (2017)
- [Scaling Object Detection by Transferring Classification Weights](https://mlsanity.com/paper/1909.06804/) (2019)
- [Universal Bounding Box Regression and Its Applications](https://mlsanity.com/paper/1904.06805/) (2019)

## Built on by (most cited first, 4 of 4)

- [W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection](https://mlsanity.com/paper/2207.12104/) (2022)
- [Scaling Novel Object Detection with Weakly Supervised Detection Transformers](https://mlsanity.com/paper/2207.05205/) (2022)
- [Weakly Supervised Test-Time Domain Adaptation for Object Detection](https://mlsanity.com/paper/2407.05607/) (2024)
- [HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection](https://mlsanity.com/paper/2406.19394/) (2024)

## Abstract (arXiv)

A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fully-supervised dataset. But these methods do not fully exploit discriminative category information in the fully-supervised dataset, thus causing low mAP. To solve this issue, we propose a novel category transfer framework for weakly supervised object detection. The intuition is to fully leverage both visually-discriminative and semantically-correlated category information in the fully-supervised dataset to enhance the object-classification ability of a weakly-supervised detector. To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets. To handle non-overlapping category transfer, we propose a semantic graph convolutional network to promote the aggregation of semantic features between correlated categories. Experiments are conducted with Pascal VOC 2007 as the target weakly-supervised dataset and COCO as the source fully-supervised dataset. Our category transfer framework achieves 63.5% mAP and 80.3% CorLoc with 5 overlapping categories between two datasets, which outperforms the state-of-the-art methods. Codes are avaliable at https://github.com/MediaBrain-SJTU/CaT.