StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

Woo, Sanghyun; Hwang, Soonmin; Kweon, In So

doi:10.1109/wacv.2018.00125

Cited by 74 publications

(43 citation statements)

References 21 publications

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“…This approach has been found to be effective for segmentation [177,241] and human pose estimation [194], has been widely exploited by both one-stage and two-stage detectors to alleviate problems of scale variation across object instances. Representative methods include SharpMask [214], Deconvolutional Single Shot Detector (DSSD) [77], Feature Pyramid Network (FPN) [167], Top Down Modulation (TDM) [247], Reverse connection with Objectness prior Network (RON) [136], ZIP [156], Scale Transfer Detection Network (STDN) [321], RefineDet [308], StairNet [283], Path Aggregation Network (PANet) [174], Feature Pyramid Reconfiguration (FPR) [137], DetNet [164], Scale Aware Network (SAN) [133], Multiscale Location aware Kernel Representation (MLKP) [278] and M2Det [315], as shown in Table 7 and contrasted in Fig. 17.…”

Section: Stdn [321]mentioning

confidence: 99%

Deep Learning for Generic Object Detection: A Survey

et al. 2019

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Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

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Section: Stdn [321]mentioning

confidence: 99%

Deep Learning for Generic Object Detection: A Survey

et al. 2019

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“…Corner-based methods There is a need to train a robust and discriminative feature embedding of objects to obtain a good detection performance. In Some techniques such as dilated/atrous convolutions [97,52] were proposed to avoid downsampling, and used the high reso- were later developed [109,110,109,111,112,92,113,114,115,116,117,118,119], with modifications to the feature pyramid block (see Fig. 8).…”

Section: Keypoints-based Methodsmentioning

confidence: 99%

Recent advances in deep learning for object detection

2020

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Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades.Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, we systematically analyze the existing object detection frameworks and organize the survey into three major parts: (i) detection components, (ii) learning strategies, and (iii) applications & benchmarks. In the survey, we cover a variety of factors affecting the detection performance in detail, such as detector architectures, feature learning, proposal generation, sampling strategies, etc.Finally, we discuss several future directions to facilitate and spur future research for visual object detection with deep learning.

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“…In consideration of the small-object detection problem, the Deconvolutional Single-Shot Detector (DSSD) [20] uses an additional deconvolution layer to increase the resolution of the feature mapping layer and to fuse the context information. StairNet [21] introduced a combined feature module that enhances contextual semantic information in a top-down manner, further inferring the combined information. Feature-Fused SSD (Fast Detection for Small Objects) [22] improve accuracy by fusing feature maps with adjacent layers.…”

Section: Related Studiesmentioning

confidence: 99%

Object Detection Algorithm Based on Multiheaded Attention

Jiang

Zhang

et al. 2019

Applied Sciences

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This study proposes a multiheaded object detection algorithm referred to as MANet. The main purpose of the study is to integrate feature layers of different scales based on the attention mechanism and to enhance contextual connections. To achieve this, we first replaced the feed-forward base network of the single-shot detector with the ResNet–101 (inspired by the Deconvolutional Single-Shot Detector) and then applied linear interpolation and the attention mechanism. The information of the feature layers at different scales was fused to improve the accuracy of target detection. The primary contributions of this study are the propositions of (a) a fusion attention mechanism, and (b) a multiheaded attention fusion method. Our final MANet detector model effectively unifies the feature information among the feature layers at different scales, thus enabling it to detect objects with different sizes and with higher precision. We used the 512 × 512 input MANet (the backbone is ResNet–101) to obtain a mean accuracy of 82.7% based on the PASCAL visual object class 2007 test. These results demonstrated that our proposed method yielded better accuracy than those provided by the conventional Single-shot detector (SSD) and other advanced detectors.

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StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection

Cited by 74 publications

References 21 publications

Deep Learning for Generic Object Detection: A Survey

Deep Learning for Generic Object Detection: A Survey

Recent advances in deep learning for object detection

Object Detection Algorithm Based on Multiheaded Attention

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