# Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning

arXiv: https://arxiv.org/abs/1802.09129 · HTML: https://mlsanity.com/paper/1802.09129/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2018.

## Built on by (most cited first, 30 of 33)

- [Weakly Supervised Object Localization and Detection: A Survey](https://mlsanity.com/paper/2104.07918/) (2021)
- [Texts as Images in Prompt Tuning for Multi-Label Image Recognition](https://mlsanity.com/paper/2211.12739/) (2022)
- [Transformer-based Dual Relation Graph for Multi-label Image Recognition](https://mlsanity.com/paper/2110.04722/) (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)
- [Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark](https://mlsanity.com/paper/2103.10895/) (2021)
- [Rethinking Query-based Transformer for Continual Image Segmentation](https://mlsanity.com/paper/2507.07831/) (2025)
- [Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation](https://mlsanity.com/paper/2108.07413/) (2021)
- [Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier](https://mlsanity.com/paper/2407.04036/) (2024)
- [Deep Image Harmonization by Bridging the Reality Gap](https://mlsanity.com/paper/2103.17104/) (2021)
- [Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity](https://mlsanity.com/paper/2110.14191/) (2021)
- [Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification](https://mlsanity.com/paper/2411.17350/) (2024)
- [Why LVLMs Are More Prone to Hallucinations in Longer Responses: The Role of Context](https://mlsanity.com/paper/2510.20229/) (2025)
- [Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey](https://mlsanity.com/paper/2105.12694/) (2021)
- [Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark](https://mlsanity.com/paper/2404.06859/) (2024)
- [Multi-label Image Classification using Adaptive Graph Convolutional Networks: from a Single Domain to Multiple Domains](https://mlsanity.com/paper/2301.04494/) (2023)
- [Semantic Contrastive Bootstrapping for Single-positive Multi-label Recognition](https://mlsanity.com/paper/2307.07680/) (2023)
- [Learning Discriminative Representations for Multi-Label Image Recognition](https://mlsanity.com/paper/2107.11159/) (2021)
- [A Multi-label Continual Learning Framework to Scale Deep Learning Approaches for Packaging Equipment Monitoring](https://mlsanity.com/paper/2208.04227/) (2022)
- [Weakly-supervised Micro- and Macro-expression Spotting Based on Multi-level Consistency](https://mlsanity.com/paper/2305.02734/) (2023)
- [Weakly Supervised Volumetric Image Segmentation with Deformed Templates](https://mlsanity.com/paper/2106.03987/) (2021)
- [Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision](https://mlsanity.com/paper/2211.12716/) (2022)
- [Rethinking Voxelization and Classification for 3D Object Detection](https://mlsanity.com/paper/2301.04058/) (2023)
- [No More Sibling Rivalry: Debiasing Human-Object Interaction Detection](https://mlsanity.com/paper/2509.00760/) (2025)
- [CRISP: Contrastive Residual Injection and Semantic Prompting for Continual Video Instance Segmentation](https://mlsanity.com/paper/2508.10432/) (2025)
- [Fine-Grained AutoAugmentation for Multi-Label Classification](https://mlsanity.com/paper/2107.05384/) (2021)
- [Two-phase weakly supervised object detection with pseudo ground truth mining](https://mlsanity.com/paper/2104.00231/) (2021)
- [PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement](https://mlsanity.com/paper/2108.11439/) (2021)
- [Discovery-and-Selection: Towards Optimal Multiple Instance Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/2110.09060/) (2021)
- [Online progressive instance-balanced sampling for weakly supervised object detection](https://mlsanity.com/paper/2206.10324/) (2022)

## Abstract (arXiv)

Supervised object detection and semantic segmentation require object or even pixel level annotations. When there exist image level labels only, it is challenging for weakly supervised algorithms to achieve accurate predictions. The accuracy achieved by top weakly supervised algorithms is still significantly lower than their fully supervised counterparts. In this paper, we propose a novel weakly supervised curriculum learning pipeline for multi-label object recognition, detection and semantic segmentation. In this pipeline, we first obtain intermediate object localization and pixel labeling results for the training images, and then use such results to train task-specific deep networks in a fully supervised manner. The entire process consists of four stages, including object localization in the training images, filtering and fusing object instances, pixel labeling for the training images, and task-specific network training. To obtain clean object instances in the training images, we propose a novel algorithm for filtering, fusing and classifying object instances collected from multiple solution mechanisms. In this algorithm, we incorporate both metric learning and density-based clustering to filter detected object instances. Experiments show that our weakly supervised pipeline achieves state-of-the-art results in multi-label image classification as well as weakly supervised object detection and very competitive results in weakly supervised semantic segmentation on MS-COCO, PASCAL VOC 2007 and PASCAL VOC 2012.