Spatial Uncertainty-Aware Semi-Supervised Crowd Counting

Meng, Yanda; Zhang, Hongrun; Zhao, Yitian; Yang, Xiaoyun; Qian, Xuesheng; Huang, Xiaowei; Zheng, Yalin

doi:10.1109/iccv48922.2021.01526

Cited by 68 publications

(24 citation statements)

References 57 publications

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“…IRAST [17] introduces surrogate tasks and develops a self-training method with much fewer density map annotations. SUA [19] proposes a spatial uncertainty-aware semi-supervised approach, where only some images in the training sets are labeled. Xu et al [33] proposes Partial Annotation Learning only using partial annotations in each image as training data.…”

Section: Semi-supervised Methodsmentioning

confidence: 99%

“…Our method decreases the MAE metric by 9.6%, 15.8% and 25.9% respectively on the three CNN backbones, which verifies the effectiveness of our proposed method. This per- [27] 52.74 85.06 6.25 9.9 85.32 154.5 77.44 362 --IRAST(2020) [17] 86.9 148.9 14.7 22.9 135.6 233.4 ----SUA(2021) [19] 68.5 121. 9 [12,7] as the backbone.…”

Section: Ablation Studymentioning

confidence: 99%

“…There is a significant reduction in the annotation amount in semi-supervised methods [20,17,19,33], where only limited location annotations are used in the training process, e.g., annotations of limited images [20,17,19] or partial annotations in each image [33]. However, the location annotations are still required to supervise the network training.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting

Chen¹,

Lu²

2022

Preprint

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Fully-supervised crowd counting is a laborious task due to the large amounts of annotations. Few works focus on weekly-supervised crowd counting, where only the global crowd numbers are available for training. The main challenge of weekly-supervised crowd counting is the lack of local supervision information. To address this problem, we propose a self-adaptive feature similarity learning (SFSL) network and a global-local consistency (GLC) loss to reinforce local feature representation. We introduce a feature vector which represents the unbiased feature estimation of persons. The network updates the feature vector self-adaptively and utilizes the feature similarity for the regression of crowd numbers. Besides, the proposed GLC loss leverages the consistency between the network estimations from global and local areas. The experimental results demonstrate that our proposed method based on different backbones narrows the gap between weakly-supervised and fully-supervised dense crowd counting.

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Section: Semi-supervised Methodsmentioning

confidence: 99%

Section: Ablation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting

Chen¹,

Lu²

2022

Preprint

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“…However, the enormous sizes of WSIs bring along substantial burden for pixel-level annotation. This problem in turn encourages researchers to develop deep learning based models trained with limited annotations, termed as "Weakly Supervised" or "Semi-Supervised" [22,26,35,41]. A large proportion of existing weakly supervised works for WSI classification are characterized as "multiple instance learning" (MIL) [1,5,8,25].…”

Section: Introductionmentioning

confidence: 99%

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Zhang¹,

Meng²,

Zhao³

et al. 2022

Preprint

Self Cite

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Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classification problem still face unique challenges, particularly those related to small sample cohorts. In these, there are limited number of WSI slides (bags), while the resolution of a single WSI is huge, which leads to a large number of patches (instances) cropped from this slide. To address this issue, we propose to virtually enlarge the number of bags by introducing the concept of pseudo-bags, on which a double-tier MIL framework is built to effectively use the intrinsic features. Besides, we also contribute to deriving the instance probability under the framework of attentionbased MIL, and utilize the derivation to help construct and analyze the proposed framework. The proposed method outperforms other latest methods on the CAMELYON-16 by substantially large margins, and is also better in performance on the TCGA lung cancer dataset. The proposed framework is ready to be extended for wider MIL applications. The code is available at: https://github. com/hrzhang1123/DTFD-MIL

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“…Fortunately, semi-supervised methods leverage not only labeled data but also unlabeled data [5], freeing the researchers from labeling work. Therefore, semi-supervised learning has attracted great attention [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation

Xu¹,

Chong²,

Xie³

et al. 2021

Preprint

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Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we proposed a novel uncertainty-aware scheme to make models learn regions purposefully. Specifically, we employ Monte Carlo Sampling as an estimation method to attain an uncertainty map, which can serve as a weight for losses to force the models to focus on the valuable region according to the characteristics of supervised learning and unsupervised learning. Simultaneously, in the backward process, we joint unsupervised and supervised losses to accelerate the convergence of the network via enhancing the gradient flow between different tasks. Quantitatively, we conduct extensive experiments on three challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts.

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Spatial Uncertainty-Aware Semi-Supervised Crowd Counting

Cited by 68 publications

References 57 publications

Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting

Reinforcing Local Feature Representation for Weakly-Supervised Dense Crowd Counting

DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation

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