Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw

savage

Detector-in-Detector: Multi-level Analysis for Human-Partsral network (CNN). In this paper, we take the inherent correlation between the body and body parts into account and propose a new framework to boost up the detection performance of the multi-level objects. In particular, we adopt region-based object detection structure with two carefully designed de

光滑

Aligning Salient Objects to Queries: A Multi-modal and Multi-object Image Retrieval Frameworko jointly model sketches and text as input query modalities into a common embedding space, which is then further aligned with the image feature space. Our architecture also relies on a salient object detection through a supervised LSTM-based visual attention model learned from convolutional features

intellect

Fast Light Field Disparity Estimation via a Parallel Filtered Cost Volume Approachss, such that costly optimizations that combine and refine depth maps are simplified. The algorithm involves shearing the light field over a range of disparities and computing a cost volume for each sheared sub-aperture image. A guided filter is then run on the computed cost for each disparity. For

CUB

赦免

虛弱的神經(jīng)

Revolutionary Ecological Liberation: en occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spati

思鄉(xiāng)病

Expurgate

compel

HALO

https://doi.org/10.1057/978-1-137-46236-7ms. We consider cross-spectral image patches can be matched because there exists a shared semantic feature space among them, in which the semantic features from different spectral images will be more independent of the spectral domains. To learn this shared feature space, we propose a progressive co