Exploring Context Information for Accurate and Fast Object Detection

Object detection, visual relationship detection, and image captioning, which are the three main visual tasks in scene understanding, are highly correlated and correspond to different semantic levels of scene image. However, the existing captioning methods convert the extracted image features into description text, and the obtained results are not satisfactory. In this work, we propose a Multi-level Semantic Context Information (MSCI) network with an overall symmetrical structure to leverage the mutual connections across the three different semantic layers and extract the context information between them, to solve jointly the three vision tasks for achieving the accurate and comprehensive description of the scene image. The model uses a feature refining structure to mutual connections and iteratively updates the different semantic features of the image. Then a context information extraction network is used to extract the context information between the three different semantic layers, and an attention mechanism is introduced to improve the accuracy of image captioning while using the context information between the different semantic layers to improve the accuracy of object detection and relationship detection. Experiments on the VRD and COCO datasets demonstrate that our proposed model can leverage the context information between semantic layers to improve the accuracy of those visual tasks generation.

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Multiscale Feature Filtering Network for Image Recognition System in Unmanned Aerial Vehicle

Complexity ◽

10.1155/2021/6663851 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xianghua Ma ◽

Zhenkun Yang ◽

Shining Chen

Keyword(s):

Object Detection ◽

Unmanned Aerial Vehicle ◽

Image Recognition ◽

Visual Recognition ◽

Recognition System ◽

Attention Mechanism ◽

Feature Representation ◽

Context Information ◽

Aerial Vehicle ◽

Feature Filtering

For unmanned aerial vehicle (UAV), object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks (CNNs) have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network (MFFNet) is proposed in this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering (MFF) module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks.

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CP-SSD: Context Information Scene Perception Object Detection Based on SSD

Applied Sciences ◽

10.3390/app9142785 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2785 ◽

Cited By ~ 2

Author(s):

Yun Jiang ◽

Tingting Peng ◽

Ning Tan

Keyword(s):

Object Detection ◽

Semantic Information ◽

Detection Method ◽

Scene Perception ◽

Context Information ◽

Single Shot ◽

Deep Layers ◽

Feature Information ◽

The Mean ◽

Detection Effect

Single Shot MultiBox Detector (SSD) has achieved good results in object detection but there are problems such as insufficient understanding of context information and loss of features in deep layers. In order to alleviate these problems, we propose a single-shot object detection network Context Perception-SSD (CP-SSD). CP-SSD promotes the network’s understanding of context information by using context information scene perception modules, so as to capture context information for objects of different scales. Deep layer feature map used semantic activation module, through self-supervised learning to adjust the context feature information and channel interdependence, and enhance useful semantic information. CP-SSD was validated on benchmark dataset PASCAL VOC 2007. The experimental results show that, compared with SSD, the mean Average Precision (mAP) of the CP-SSD detection method reaches 77.8%, which is 0.6% higher than that of SSD, and the detection effect was significantly improved in images with difficult to distinguish the object from the background.

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ℱ3-Net: Feature Fusion and Filtration Network for Object Detection in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs12244027 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4027

Author(s):

Xinhai Ye ◽

Fengchao Xiong ◽

Jianfeng Lu ◽

Jun Zhou ◽

Yuntao Qian

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Multiple Scales ◽

Feature Fusion ◽

Dimensional Subspace ◽

Context Information ◽

Optical Remote Sensing ◽

Feature Maps ◽

Residual Structure ◽

Cluttered Background

Object detection in remote sensing (RS) images is a challenging task due to the difficulties of small size, varied appearance, and complex background. Although a lot of methods have been developed to address this problem, many of them cannot fully exploit multilevel context information or handle cluttered background in RS images either. To this end, in this paper, we propose a feature fusion and filtration network (F3-Net) to improve object detection in RS images, which has higher capacity of combining the context information at multiple scales while suppressing the interference from the background. Specifically, F3-Net leverages a feature adaptation block with a residual structure to adjust the backbone network in an end-to-end manner, better considering the characteristics of RS images. Afterward, the network learns the context information of the object at multiple scales by hierarchically fusing the feature maps from different layers. In order to suppress the interference from cluttered background, the fused feature is then projected into a low-dimensional subspace by an additional feature filtration module. As a result, more relevant and accurate context information is extracted for further detection. Extensive experiments on DOTA, NWPU VHR-10, and UCAS AOD datasets demonstrate that the proposed detector achieves very promising detection performance.

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Class-Specific Anchor Based and Context-Guided Multi-Class Object Detection in High Resolution Remote Sensing Imagery with a Convolutional Neural Network

Remote Sensing ◽

10.3390/rs11030272 ◽

2019 ◽

Vol 11 (3) ◽

pp. 272 ◽

Cited By ~ 4

Author(s):

Nan Mo ◽

Li Yan ◽

Ruixi Zhu ◽

Hong Xie

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Object Detection ◽

Convolutional Neural Network ◽

Large Scale ◽

Shape Change ◽

Context Information ◽

False Detection ◽

Multi Scale

In this paper, the problem of multi-scale geospatial object detection in High Resolution Remote Sensing Images (HRRSI) is tackled. The different flight heights, shooting angles and sizes of geographic objects in the HRRSI lead to large scale variance in geographic objects. The inappropriate anchor size to propose the objects and the indiscriminative ability of features for describing the objects are the main causes of missing detection and false detection in multi-scale geographic object detection. To address these challenges, we propose a class-specific anchor based and context-guided multi-class object detection method with a convolutional neural network (CNN), which can be divided into two parts: a class-specific anchor based region proposal network (RPN) and a discriminative feature with a context information classification network. A class-specific anchor block providing better initial values for RPN is proposed to generate the anchor of the most suitable scale for each category in order to increase the recall ratio. Meanwhile, we proposed to incorporate the context information into the original convolutional feature to improve the discriminative ability of the features and increase classification accuracy. Considering the quality of samples for classification, the soft filter is proposed to select effective boxes to improve the diversity of the samples for the classifier and avoid missing or false detection to some extent. We also introduced the focal loss in order to improve the classifier in classifying the hard samples. The proposed method is tested on a benchmark dataset of ten classes to prove the superiority. The proposed method outperforms some state-of-the-art methods with a mean average precision (mAP) of 90.4% and better detects the multi-scale objects, especially when objects show a minor shape change.

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Automatic Stereotype Activation Is Context Dependent

Social Psychology ◽

10.1027/1864-9335/a000019 ◽

2010 ◽

Vol 41 (3) ◽

pp. 131-136 ◽

Cited By ~ 35

Author(s):

Catharina Casper ◽

Klaus Rothermund ◽

Dirk Wentura

Keyword(s):

Lexical Decision ◽

Context Information ◽

Decision Task ◽

Social Categories ◽

Priming Effects ◽

Stereotype Activation ◽

Social Stereotypes ◽

Priming Paradigm ◽

Automatic Activation ◽

Mental Schemas

Processes involving an automatic activation of stereotypes in different contexts were investigated using a priming paradigm with the lexical decision task. The names of social categories were combined with background pictures of specific situations to yield a compound prime comprising category and context information. Significant category priming effects for stereotypic attributes (e.g., Bavarians – beer) emerged for fitting contexts (e.g., in combination with a picture of a marquee) but not for nonfitting contexts (e.g., in combination with a picture of a shop). Findings indicate that social stereotypes are organized as specific mental schemas that are triggered by a combination of category and context information.

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Impact of Context Information on Metaphor Elaboration

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000422 ◽

2018 ◽

Vol 65 (6) ◽

pp. 370-384 ◽

Cited By ~ 5

Author(s):

Veronika Lerche ◽

Ursula Christmann ◽

Andreas Voss

Keyword(s):

Diffusion Model ◽

Processing Speed ◽

A Priori ◽

Model Fit ◽

Verbal Task ◽

Context Information ◽

Speed Of Processing ◽

Good Account ◽

Dependent Variables ◽

Decision Settings

Abstract. In experiments by Gibbs, Kushner, and Mills (1991) , sentences were supposedly either authored by poets or by a computer. Gibbs et al. (1991) concluded from their results that the assumed source of the text influences speed of processing, with a higher speed for metaphorical sentences in the Poet condition. However, the dependent variables used (e.g., mean RTs) do not allow clear conclusions regarding processing speed. It is also possible that participants had prior biases before the presentation of the stimuli. We conducted a conceptual replication and applied the diffusion model ( Ratcliff, 1978 ) to disentangle a possible effect on processing speed from a prior bias. Our results are in accordance with the interpretation by Gibbs et al. (1991) : The context information affected processing speed, not a priori decision settings. Additionally, analyses of model fit revealed that the diffusion model provided a good account of the data of this complex verbal task.

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