Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling

• URL: http://arxiv.org/abs/2312.12000v1
• Date: Tue, 19 Dec 2023 09:47:18 GMT
• Title: Diffusing More Objects for Semi-Supervised Domain Adaptation with Less Labeling
• Authors: Leander van den Heuvel, Gertjan Burghouts, David W. Zhang, Gwenn Englebienne, Sabina B. van Rooij
• Abstract summary: For object detection, it is possible to view the prediction of bounding boxes as a reverse diffusion process. Using a diffusion model, the random bounding boxes are iteratively refined in a denoising step, conditioned on the image. We propose a accumulator function that starts each run with random bounding boxes and combines the slightly different predictions.
• Score: 2.941832525496685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For object detection, it is possible to view the prediction of bounding boxes as a reverse diffusion process. Using a diffusion model, the random bounding boxes are iteratively refined in a denoising step, conditioned on the image. We propose a stochastic accumulator function that starts each run with random bounding boxes and combines the slightly different predictions. We empirically verify that this improves detection performance. The improved detections are leveraged on unlabelled images as weighted pseudo-labels for semi-supervised learning. We evaluate the method on a challenging out-of-domain test set. Our method brings significant improvements and is on par with human-selected pseudo-labels, while not requiring any human involvement.

Related papers

• Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection [14.315287192621662]
  Video anomaly detection (VAD) often learns the distribution of normal samples and detects the anomaly through measuring significant deviations. Most VADs cannot cope with cross-dataset validation for new target domains. We propose a novel VAD method with a motion-guided memory module to achieve cross-dataset validation with zero-shot.
  arXiv  Detail & Related papers  (2024-09-26T07:48:20Z)
• TSBP: Improving Object Detection in Histology Images via Test-time Self-guided Bounding-box Propagation [14.771640666631557]
  A global threshold (e.g., 0.5) is often applied to determine which bounding boxes should be included in the final results for an object detection task. A lower threshold can increase detection recall but may also result in more false positives. Because of this, using a preset global threshold (e.g., 0.5) applied to all the bounding box candidates may lead to suboptimal solutions.
  arXiv  Detail & Related papers  (2024-09-25T07:09:04Z)
• False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening [1.7924920920347915]
  We introduce a nodewise variable selection approach to graph learning and provably control the false discovery rate of the selected edge set at a self-estimated level. A novel fusion method of the individual neighborhoods outputs an undirected graph estimate.
  arXiv  Detail & Related papers  (2024-01-18T13:46:41Z)
• Decoupled Prototype Learning for Reliable Test-Time Adaptation [50.779896759106784]
  Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. We propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation.
  arXiv  Detail & Related papers  (2024-01-15T03:33:39Z)
• DiffusionDet: Diffusion Model for Object Detection [56.48884911082612]
  DiffusionDet is a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. Our work possesses an appealing property of flexibility, which enables the dynamic number of boxes and iterative evaluation.
  arXiv  Detail & Related papers  (2022-11-17T18:56:19Z)
• Is your noise correction noisy? PLS: Robustness to label noise with two stage detection [16.65296285599679]
  This paper proposes to improve the correction accuracy of noisy samples once they have been detected. In many state-of-the-art contributions, a two phase approach is adopted where the noisy samples are detected before guessing a corrected pseudo-label. We propose the pseudo-loss, a simple metric that we find to be strongly correlated with pseudo-label correctness on noisy samples.
  arXiv  Detail & Related papers  (2022-10-10T11:32:28Z)
• Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images [57.42492501915773]
  In-vitro tests are an alternative to animal testing for the toxicity of medical devices. Human fatigue plays a role in error making, making the use of deep learning appealing. We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI) Our method successfully provides an adaptive early learning correction technique for object detection.
  arXiv  Detail & Related papers  (2022-08-05T18:52:20Z)
• Saliency Grafting: Innocuous Attribution-Guided Mixup with Calibrated Label Mixing [104.630875328668]
  Mixup scheme suggests mixing a pair of samples to create an augmented training sample. We present a novel, yet simple Mixup-variant that captures the best of both worlds.
  arXiv  Detail & Related papers  (2021-12-16T11:27:48Z)
• Label, Verify, Correct: A Simple Few Shot Object Detection Method [93.84801062680786]
  We introduce a simple pseudo-labelling method to source high-quality pseudo-annotations from a training set. We present two novel methods to improve the precision of the pseudo-labelling process. Our method achieves state-of-the-art or second-best performance compared to existing approaches.
  arXiv  Detail & Related papers  (2021-12-10T18:59:06Z)
• Minimax Active Learning [61.729667575374606]
  Active learning aims to develop label-efficient algorithms by querying the most representative samples to be labeled by a human annotator. Current active learning techniques either rely on model uncertainty to select the most uncertain samples or use clustering or reconstruction to choose the most diverse set of unlabeled examples. We develop a semi-supervised minimax entropy-based active learning algorithm that leverages both uncertainty and diversity in an adversarial manner.
  arXiv  Detail & Related papers  (2020-12-18T19:03:40Z)
• DANCE: Enhancing saliency maps using decoys [35.46266461621123]
  We propose a framework that improves the robustness of saliency methods by following a two-step procedure. First, we introduce a perturbation mechanism that subtly varies the input sample without changing its intermediate representations. Second, we compute saliency maps for perturbed samples and propose a new method to aggregate saliency maps.
  arXiv  Detail & Related papers  (2020-02-03T01:21:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.