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Language-person search is an essential technique for applications like criminal searching, where it is more feasible for a witness to provide language descriptions of a suspect than providing a photo. Most existing works treat the language-person pair as a black-box, neither considering the inner structure in a person picture, nor the correlations between image regions and referring words. In this work, we propose a transformer-based language-person search framework with matching conducted between words and image regions, where a person picture is vertically separated into multiple regions using two different ways, including the overlapped slicing and the key-point-based slicing. The co-attention between linguistic referring words and visual features are evaluated via transformer blocks. Besides the obtained outstanding searching performance, the proposed method enables to provide interpretability by visualizing the co-attention between image parts in the person picture and the corresponding referring words. Without bells and whistles, we achieve the state-of-the-art performance on the CUHK-PEDES dataset with Rank-1 score of 57.67% and the PA100K dataset with mAP of 22.88%, with simple yet elegant design. Code is available on https://github.com/detectiveli/T-MRS. [ABSTRACT FROM AUTHOR]

Correlation filter has been proven to be an effective tool for a number of approaches in visual tracking, particularly for seeking a good balance between tracking accuracy and speed. However, correlation filter-based models are susceptible to wrong updates stemming from inaccurate tracking results. To date, very little effort has been devoted towards handling the correlation filter update problem. In this paper, we propose a novel approach to address the correlation filter update problem. In our approach, we update and maintain multiple correlation filter models in parallel, and we use deep reinforcement learning for the selection of an optimal correlation filter model among them. To facilitate the decision process in an efficient manner, we propose a decision-net to deal with target appearance modeling, which is trained through hundreds of challenging videos using proximal policy optimization and a lightweight learning network. An exhaustive evaluation of the proposed approach on the OTB100 and OTB2013 benchmarks shows that the approach is effective enough to achieve the average success rate of 62.3% and the average precision score of 81.2%, both exceeding the performance of traditional correlation filter-based trackers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a depth map down-sampling and coding scheme that minimizes the view synthesis distortion. Moreover, a solution for the optimal depth map down-sampling problem that minimizes the depth-caused distortion in the virtual view by exploiting the depth map and the associated texture information along with the up-sampling method to be used in the decoder side is derived. Furthermore, to enhance compression performance, the synthesized view distortion, which is evaluated by emulating the interpolation and the virtual view synthesis process, is used in the optimization objective function for coding mode selection in the video encoder. Experimental results show that both the proposed depth map down-sampling and encoding methods lead to good performance, and the average bit rate reduction is 2.62$\%$ compared with 3D-AVC. [ABSTRACT FROM PUBLISHER]

The depth-image-based-rendering is a key technique to realize free viewpoint television. However, one critical problem in these systems is filling the disocclusion due to the 3-D warping process. This paper exploits the temporal correlation of texture and depth information to generate a background reference image. This is then used to fill the holes associated with the dynamic parts of the scene, whereas for static parts the traditional inpainting method is used. To generate the background reference image, the Gaussian mixture model is employed on the texture information, whereas, depth maps information are used to detect moving objects so as to enhance the background reference image. The proposed holes filling approach is particularly useful for the single-view-plus-depth format, where, contrary to the multi-view-plus-depth format, only information of one view could be used for this task. The experimental results show that objective and subjective gains can be achieved, and the gain ranges from 1 to 3 dB over the inpainting method. [ABSTRACT FROM PUBLISHER]

Coding artifacts are annoying in highly compressed signals. Most of the existing artifact reduction methods are designed for one specific type of artifacts, codecs, and bitrates, which are complex and exclusive for one type of artifact reduction. Since both the compressed image/video and the coding error contain information of the original signal, they are highly correlated. Therefore, we try to recover some lost data based on the correlation between the compressed signal and the coding error, and introduce a novel and universal artifact reduction method. Firstly, according to the spatial correlation among pixels, a pixel-adaptive anisotropic filter is designed to reconstruct the distorted signal. Next, a globally optimal filter is designed to further recover the coding loss. Experimental results demonstrate that within an extensive range of bitrates, the proposed method achieves about 0.8 dB, 0.45 dB, 0.3 dB, and 0.2 dB on average of PSNR improvement for JPEG, MPEG4, H.264/AVC, and HEVC compressed signals, respectively. [ABSTRACT FROM AUTHOR]