IAUnet: Global Context-Aware Feature Learning for Person Reidentification

Hou, Ruibing; Ma, Bingpeng; Chang, Hong; Gu, Xinqian; Shan, Shiguang; Chen, Xilin

doi:10.1109/tnnls.2020.3017939

Cited by 40 publications

(13 citation statements)

References 83 publications

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“…The model has the capability to capture the local contextual information by stacking multi-scale convolutions in each layer of their architecture. In [13], an integration-aggregation-update (IAU) block has been proposed for improving person reID performance. It introduces a spatial-temporal IAU by combining two different types of contextual information into a CNN model for target feature learning: a) spatial interactions, to capture contextual dependencies between different body parts in a single frame, and b) temporal interactions, to capture contextual dependencies between the same body parts across all frames.…”

Section: A Context-aware Models For Large-scale Image Classificationmentioning

confidence: 99%

MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Senousy

Abdelsamea

Gaber

et al. 2022

IEEE Trans. Biomed. Eng.

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Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model. MCUa model consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model. MCUa model has achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.

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Section: A Context-aware Models For Large-scale Image Classificationmentioning

confidence: 99%

MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Senousy

Abdelsamea

Gaber

et al. 2022

IEEE Trans. Biomed. Eng.

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“…Video person re-identification. Most existing video person re-id methods [11,19,21,22,45] focus on clothesconsistent setting. Some research [5] demonstrates that appearance feature plays a more important role than motion feature in this setting.…”

Section: Related Workmentioning

confidence: 99%

Clothes-Changing Person Re-identification with RGB Modality Only

Gu¹,

Chen²,

Ma³

et al. 2022

Preprint

Self Cite

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The key to address clothes-changing person reidentification (re-id) is to extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and gait. Most current works mainly focus on modeling body shape from multimodality information (e.g., silhouettes and sketches), but do not make full use of the clothes-irrelevant information in the original RGB images. In this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine clothesirrelevant features from the original RGB images by penalizing the predictive power of re-id model w.r.t. clothes. Extensive experiments demonstrate that using RGB images only, CAL outperforms all state-of-the-art methods on widely-used clothes-changing person re-id benchmarks. Besides, compared with images, videos contain richer appearance and additional temporal information, which can be used to model proper spatiotemporal patterns to assist clothes-changing re-id. Since there is no publicly available clothes-changing video re-id dataset, we contribute a new dataset named CCVID and show that there exists much room for improvement in modeling spatiotemporal information. The code and new dataset are available at: https: //github.com/guxinqian/Simple-CCReID.

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“…In addition, pixel-wise modeling techniques (Fu et al 2019;Hou et al 2020) may inevitably introduce more background noise in feature maps, making them not suitable for ultrasound image processing.…”

Section: Related Workmentioning

confidence: 99%

Region-aware Global Context Modeling for Automatic Nerve Segmentation from Ultrasound Images

Liu

Wang

et al. 2021

AAAI

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We present a novel deep learning model equipped with a new region-aware global context modeling technique for automatic nerve segmentation from ultrasound images, which is a challenging task due to (1) the large variation and blurred boundaries of targets, (2) the large amount of speckle noise in ultrasound images, and (3) the inherent real-time requirement of this task. It is essential to efficiently capture long-range dependencies by global context modeling for a segmentation network to overcome these challenges. Traditional global context modeling techniques usually explore pixel-aware correlations to establish long-range dependencies, which are usually computation-intensive and greatly degrade time performance. In addition, in this application, pixel-aware modeling may inevitably introduce much speckle noise in the computation and potentially degrade segmentation performance. In this paper, we propose a novel region-aware modeling technique to establish long-range dependencies based on different regions to improve segmentation accuracy while maintaining real-time performance; we call it region-aware pyramid aggregation (RPA) module. In order to adaptively divide the feature maps into a set of semantic-independent regions, we develop an attention mechanism and integrate it into the spatial pyramid network to evaluate the semantic similarity of different regions. We further develop an adaptive pyramid fusion (APF) module to dynamically fuse the multi-level features generated from the decoder to refining the segmentation results. We conducted extensive experiments on a famous public ultrasound nerve image segmentation dataset. Experimental results demonstrate that our method consistently outperforms our rivals in terms of segmentation accuracy. The code is available at https://github.com/jsonliu-szu/RAGCM.

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IAUnet: Global Context-Aware Feature Learning for Person Reidentification

Cited by 40 publications

References 83 publications

MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

MCUa: Multi-Level Context and Uncertainty Aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

Clothes-Changing Person Re-identification with RGB Modality Only

Region-aware Global Context Modeling for Automatic Nerve Segmentation from Ultrasound Images

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