Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics

• URL: http://arxiv.org/abs/2311.06654v1
• Date: Sat, 11 Nov 2023 19:47:16 GMT
• Title: Unsupervised and semi-supervised co-salient object detection via segmentation frequency statistics
• Authors: Souradeep Chakraborty, Shujon Naha, Muhammet Bastan, Amit Kumar K C, Dimitris Samaras
• Abstract summary: We develop an unsupervised method to detect co-occurring salient objects in an image group. For the first time, we show that a large unlabeled dataset e.g. ImageNet-1k can be effectively leveraged to improve unsupervised CoSOD performance. To avoid propagating erroneous signals from predictions on unlabeled data, we propose a confidence estimation module.
• Score: 24.397029434125873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we address the detection of co-occurring salient objects (CoSOD) in an image group using frequency statistics in an unsupervised manner, which further enable us to develop a semi-supervised method. While previous works have mostly focused on fully supervised CoSOD, less attention has been allocated to detecting co-salient objects when limited segmentation annotations are available for training. Our simple yet effective unsupervised method US-CoSOD combines the object co-occurrence frequency statistics of unsupervised single-image semantic segmentations with salient foreground detections using self-supervised feature learning. For the first time, we show that a large unlabeled dataset e.g. ImageNet-1k can be effectively leveraged to significantly improve unsupervised CoSOD performance. Our unsupervised model is a great pre-training initialization for our semi-supervised model SS-CoSOD, especially when very limited labeled data is available for training. To avoid propagating erroneous signals from predictions on unlabeled data, we propose a confidence estimation module to guide our semi-supervised training. Extensive experiments on three CoSOD benchmark datasets show that both of our unsupervised and semi-supervised models outperform the corresponding state-of-the-art models by a significant margin (e.g., on the Cosal2015 dataset, our US-CoSOD model has an 8.8% F-measure gain over a SOTA unsupervised co-segmentation model and our SS-CoSOD model has an 11.81% F-measure gain over a SOTA semi-supervised CoSOD model).

Related papers

• FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection [0.0]
  This paper proposes a semi-supervised learning model named FPMT within the framework of MixText. The data augmentation module introduces Generative Adversarial Networks to balance and expand the dataset. In terms of training strategy, it initiates with unsupervised training on all data, followed by supervised fine-tuning on a subset of labeled data, and ultimately completing the goal of semi-supervised training.
  arXiv  Detail & Related papers  (2024-09-12T08:38:42Z)
• Self-supervised co-salient object detection via feature correspondence at multiple scales [27.664016341526988]
  This paper introduces a novel two-stage self-supervised approach for detecting co-occurring salient objects (CoSOD) in image groups without requiring segmentation annotations. We train a self-supervised network that detects co-salient regions by computing local patch-level feature correspondences across images. In experiments on three CoSOD benchmark datasets, our model outperforms the corresponding state-of-the-art models by a huge margin.
  arXiv  Detail & Related papers  (2024-03-17T06:21:21Z)
• Benchmarking Zero-Shot Robustness of Multimodal Foundation Models: A Pilot Study [61.65123150513683]
  multimodal foundation models, such as CLIP, produce state-of-the-art zero-shot results. It is reported that these models close the robustness gap by matching the performance of supervised models trained on ImageNet. We show that CLIP leads to a significant robustness drop compared to supervised ImageNet models on our benchmark.
  arXiv  Detail & Related papers  (2024-03-15T17:33:49Z)
• A Large-scale Evaluation of Pretraining Paradigms for the Detection of Defects in Electroluminescence Solar Cell Images [3.729242965449096]
  This work is a large-scale evaluation and benchmarking of various pretraining methods for Solar Cell Defect Detection. We cover supervised training with semantic segmentation, semi-supervised learning, and two self-supervised techniques. We achieve a new state-of-the-art for SCDD and demonstrate that certain pretraining schemes result in superior performance on underrepresented classes.
  arXiv  Detail & Related papers  (2024-02-27T15:37:15Z)
• A semi-supervised Teacher-Student framework for surgical tool detection and localization [2.41710192205034]
  We introduce a semi-supervised learning (SSL) framework in surgical tool detection paradigm. In the proposed work, we train a model with labeled data which initialises the Teacher-Student joint learning. Our results on m2cai16-tool-locations dataset indicate the superiority of our approach on different supervised data settings.
  arXiv  Detail & Related papers  (2022-08-21T17:21:31Z)
• Generalised Co-Salient Object Detection [50.876864826216924]
  We propose a new setting that relaxes an assumption in the conventional Co-Salient Object Detection (CoSOD) setting. We call this new setting Generalised Co-Salient Object Detection (GCoSOD) We propose a novel random sampling based Generalised CoSOD Training (GCT) strategy to distill the awareness of inter-image absence of co-salient objects into CoSOD models.
  arXiv  Detail & Related papers  (2022-08-20T12:23:32Z)
• Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation [90.2445084743881]
  We present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance. Inspired by the recent contrastive loss in self-supervised tasks, we propose the guided point contrastive loss to enhance the feature representation and model generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T16:38:54Z)
• Weakly Supervised Person Search with Region Siamese Networks [65.76237418040071]
  Supervised learning is dominant in person search, but it requires elaborate labeling of bounding boxes and identities. We present a weakly supervised setting where only bounding box annotations are available. Our model achieves the rank-1 of 87.1% and mAP of 86.0% on CUHK-SYSU benchmark.
  arXiv  Detail & Related papers  (2021-09-13T16:33:27Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Semi-supervised Left Atrium Segmentation with Mutual Consistency Training [60.59108570938163]
  We propose a novel Mutual Consistency Network (MC-Net) for semi-supervised left atrium segmentation from 3D MR images. Our MC-Net consists of one encoder and two slightly different decoders, and the prediction discrepancies of two decoders are transformed as an unsupervised loss. We evaluate our MC-Net on the public Left Atrium (LA) database and it obtains impressive performance gains by exploiting the unlabeled data effectively.
  arXiv  Detail & Related papers  (2021-03-04T09:34:32Z)
• Robust Disentanglement of a Few Factors at a Time [5.156484100374058]
  We introduce population-based training (PBT) for improving consistency in training variational autoencoders (VAEs) We then use Unsupervised Disentanglement Ranking (UDR) as an unsupervised to score models in our PBT-VAE training and show how models trained this way tend to consistently disentangle only a subset of the generative factors. We show striking improvement in state-of-the-art unsupervised disentanglement performance and robustness across multiple datasets and metrics.
  arXiv  Detail & Related papers  (2020-10-26T12:34:23Z)

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.