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Human-Object Interaction (HOI) detection lies at the core of action understanding. Besides 2D information such as human/object appearance and locations, 3D pose is also usually utilized in HOI learning since its view-independence. However, rough 3D body joints just carry sparse body information and are not sufficient to understand complex interactions. Thus, we need detailed 3D body shape to go further. Meanwhile, the interacted object in 3D is also not fully studied in HOI learning. In light of these, we propose a detailed 2D-3D joint representation learning method. First, we utilize the single-view human body capture method to obtain detailed 3D body, face and hand shapes. Next, we estimate the 3D object location and size with reference to the 2D human-object spatial configuration and object category priors. Finally, a joint learning framework and cross-modal consistency tasks are proposed to learn the joint HOI representation. To better evaluate the 2D ambiguity processing capacity of models, we propose a new benchmark named Ambiguous-HOI consisting of hard ambiguous images. Extensive experiments in large-scale HOI benchmark and Ambiguous-HOI show impressive effectiveness of our method. Code and data are available at https://github.com/DirtyHarryLYL/DJ-RN., Accepted to CVPR 2020, supplementary materials included, code available:https://github.com/DirtyHarryLYL/DJ-RN

Existing image-based activity understanding methods mainly adopt direct mapping, i.e. from image to activity concepts, which may encounter performance bottleneck since the huge gap. In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics. Human Body Part States (PaSta) are fine-grained action semantic tokens, e.g. , which can compose the activities and help us step toward human activity knowledge engine. To fully utilize the power of PaSta, we build a large-scale knowledge base PaStaNet, which contains 7M+ PaSta annotations. And two corresponding models are proposed: first, we design a model named Activity2Vec to extract PaSta features, which aim to be general representations for various activities. Second, we use a PaSta-based Reasoning method to infer activities. Promoted by PaStaNet, our method achieves significant improvements, e.g. 6.4 and 13.9 mAP on full and one-shot sets of HICO in supervised learning, and 3.2 and 4.2 mAP on V-COCO and images-based AVA in transfer learning. Code and data are available at http://hake-mvig.cn/., Comment: Accepted to CVPR 2020, supplementary materials included, code available: http://hake-mvig.cn/

The great increase in the resolution of remote sensing images comes with the problem of high computational complexity. Besides, the irregular circular shape of the tanks and the difficulty to establish the dataset make the detection task a big challenge. This paper proposes a co-feature and shape prior based saliency analysis model to extract the candidates. Two kinds of saliency maps are considered including the co-feature spatial residual map which is extracted as the common parts of different feature maps and the shape saliency map which provides the complementary information with the shape prior knowledge of circular targets. Besides, the curvature driven active contour model is used to insure the precise of the extracted contour. The quantitative evaluation of the experiments is conducted considering the detection precision and the geometric error. Results show that the proposed method is superior both in the pixel level performance and the geometric shape.

Traditional target detection methods are usually based on prior knowledge by template matching and classification. Nowadays, remote sensing images contain richer and richer information. It will cause high computation complexity if we still apply traditional target detection methods to remote sensing images. This paper proposes an airport detection model based on superpixel segmentation and saliency analysis. First, the input image is segmented into superpixels. Then saliency analysis is performed by calculating differences between superpixels and corresponding weights in R, G and B color channels to get the saliency map. Finally we utilize the limitation in the ratio of perimeter and area and morphology operation to eliminate the interference. Experiments compare the proposed model with three saliency analysis models qualitatively and quantitatively. Results show that the proposed model is better than the three comparative models in keeping clear boundaries, eliminating interference and maintaining intact targets.

Discrete digital image can achieve exact histogram specification through the strict ordering algorithm in [1]. However, some edge information will be weakened. In this paper, a nonlinear filter algorithm in wavelet domain is proposed, which can solve the problem significantly. Firstly, the proposed approach utilizes wavelet transform to decompose the image and extract the wavelet coefficients. Secondly, exact histogram specification is implemented to low frequency coefficients, and a nonlinear filter algorithm is applied to enhance high frequency wavelet coefficients of different directions. Finally, the image fulfilled with exact histogram specification and enhancement of edge information is obtained through the inverse wavelet transform. Experimental results show that this method is effective for exact histogram specification and enhancing edge information simultaneously.