Weakly Supervised Object Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

17 leaderboard tables shown for this task, 17 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 17 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

14 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 58 papers with code (142 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Weakly Supervised Deep Detection Networks
  9 Nov 2015 5 repositories listed Syntology ran 1 of 3 samples · 2 unverified · 1 pointer-only (licence)
  Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution.
• PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
  9 Jul 2018 4 repositories listed Syntology ran 1 of 7 samples · 6 unverified
  The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement…
• Multiple Instance Detection Network with Online Instance Classifier Refinement
  1 Apr 2017 4 repositories listed
  We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level…
• Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
  30 Mar 2018 3 repositories listed Syntology ran 1 of 11 samples · 10 unverified
  Can we detect common objects in a variety of image domains without instance-level annotations?
• Text-image Alignment for Diffusion-based Perception
  29 Sep 2023 2 repositories listed
  Our cross-domain segmentation method, trained on Cityscapes, achieves SOTA results on Dark Zurich-val and Nighttime Driving.
• Domain-Adaptive Self-Supervised Pre-Training for Face & Body Detection in Drawings
  19 Nov 2022 2 repositories listed
  Drawings are powerful means of pictorial abstraction and communication.
• Multiple instance learning on deep features for weakly supervised object detection with extreme domain shifts
  3 Aug 2020 2 repositories listed
  Weakly supervised object detection (WSOD) using only image-level annotations has attracted a growing attention over the past few years.
• Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
  9 Apr 2020 2 repositories listed Syntology ran 3 of 5 samples · 2 unverified
  Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
• Weakly Supervised Object Detection in Artworks
  5 Oct 2018 2 repositories listed
  We propose a method for the weakly supervised detection of objects in paintings.
• WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
  1 Jul 2017 2 repositories listed
  This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
• Weakly Supervised Object Detection for Automatic Tooth-marked Tongue Recognition
  29 Aug 2024 1 repository listed
  We propose a novel fully automated Weakly Supervised method using Vision transformer and Multiple instance learning WSVM for tongue extraction and tooth-marked tongue recognition.
• Disentangling spatio-temporal knowledge for weakly supervised object detection and segmentation in surgical video
  22 Jul 2024 1 repository listed
  Weakly supervised video object segmentation (WSVOS) enables the identification of segmentation maps without requiring an extensive training dataset of object masks, relying instead on coarse video labels indicating…
• HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
  27 Jun 2024 1 repository listed
  Most WSOD methods rely on traditional object proposals to generate candidate regions and are confronted with unstable training, which easily gets stuck in a poor local optimum.
• Smart Feature is What You Need
  22 Jun 2024 1 repository listed
  Lack of shape guidance and label jitter caused by information deficiency of weak label are the main problems in 3D weakly-supervised object detection.
• Sparse Generation: Making Pseudo Labels Sparse for Point Weakly Supervised Object Detection on Low Data Volume
  28 Mar 2024 1 repository listed
  Existing pseudo label generation methods for point weakly supervised object detection are inadequate in low data volume and dense object detection tasks.
• WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition
  22 Feb 2024 1 repository listed
  Weakly supervised visual recognition using inexact supervision is a critical yet challenging learning problem.
• Weakly Supervised Object Detection in Chest X-Rays with Differentiable ROI Proposal Networks and Soft ROI Pooling
  19 Feb 2024 1 repository listed
  Weakly supervised object detection (WSup-OD) increases the usefulness and interpretability of image classification algorithms without requiring additional supervision.
• LEOD: Label-Efficient Object Detection for Event Cameras
  29 Nov 2023 1 repository listed Syntology ran 4 of 4 samples · 0 unverified
  On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
• Identifying Light-curve Signals with a Deep Learning Based Object Detection Algorithm. II. A General Light Curve Classification Framework
  14 Nov 2023 1 repository listed
  We present a novel deep learning framework for classifying light curves using a weakly supervised object detection model.
• A Weakly Supervised Learning Method for Cell Detection and Tracking Using Incomplete Initial Annotations
  7 Nov 2023 1 repository listed
  After training the model on the two datasets, with labels that comprise 10% cell markers, the DET improved from 0.
• ALWOD: Active Learning for Weakly-Supervised Object Detection
  14 Sep 2023 1 repository listed
  In this work, we propose ALWOD, a new framework that addresses this problem by fusing active learning (AL) with weakly and semi-supervised object detection paradigms.
• Gall Bladder Cancer Detection from US Images with Only Image Level Labels
  11 Sep 2023 1 repository listed
  We posit that even when we have only the image level label, still formulating the problem as object detection (with bounding box output) helps a deep neural network (DNN) model focus on the relevant region of interest.
• Anatomy-Driven Pathology Detection on Chest X-rays
  5 Sep 2023 1 repository listed
  Pathology detection and delineation enables the automatic interpretation of medical scans such as chest X-rays while providing a high level of explainability to support radiologists in making informed decisions.
• PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
  19 Aug 2023 1 repository listed
  Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags.
• Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection
  11 Aug 2023 1 repository listed
  These inaccurate high-scoring region proposals will mislead the training of subsequent refinement modules and thus hamper the detection performance.
• Max Pooling with Vision Transformers reconciles class and shape in weakly supervised semantic segmentation
  31 Oct 2022 1 repository listed Syntology ran 0 of 5 samples · 5 unverified
  On the other hand, WSSS methods based on Vision Transformers (ViT) have not yet explored valid alternatives to CAM.
• ImaginaryNet: Learning Object Detectors without Real Images and Annotations
  13 Oct 2022 1 repository listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model deployed to generate a photo-realistic image.
• Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
  16 Aug 2022 1 repository listed
  Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.
• Active Learning Strategies for Weakly-supervised Object Detection
  25 Jul 2022 1 repository listed
  On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast…
• W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection
  25 Jul 2022 1 repository listed
  Generally, with given pseudo ground-truths generated from the well-trained WSOD network, we propose a two-module iterative training algorithm to refine pseudo labels and supervise better object detector progressively.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.