Sorry, you need to enable JavaScript to visit this website.

Two-dimensional singular value decomposition (2DSVD) is an important dimensionality reduction algorithm which has inherent advantage in preserving the structure of 2D images. However, 2DSVD algorithm is based on the squared error loss, which may exaggerate the projection errors in the presence of outliers. To solve this problem, we propose a generalized kernel risk sensitive loss for measuring the projection error in 2DSVD(GKRSL-2DSVD). The outliers information will be automatically eliminated during optimization.

Categories:
17 Views

Robust video scene classification models should capture the spatial (pixel-wise) and temporal (frame-wise) characteristics of a video effectively. Transformer models with self-attention which are designed to get contextualized representations for individual tokens given a sequence of tokens, are becoming increasingly popular in many computer vision tasks. However, the use of Transformer based models for video under-standing is still relatively unexplored.

Categories:
8 Views

When training an anchor-based object detector with a sparsely annotated dataset, the effort required to locate positive examples can cause performance degradation. Because anchor-based object detection models collect positive examples under IoU between anchors and ground-truth bounding boxes, in a sparsely annotated image, some objects that are not annotated can be assigned as negative examples, such as backgrounds.

Categories:
56 Views

The present Multi-view stereo (MVS) methods with supervised learning-based networks have an impressive performance comparing with traditional MVS methods. However, the ground-truth depth maps for training are hard to be obtained and are within limited kinds of scenarios. In this paper, we propose a novel unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud reconstruction without any supervision.

Categories:
45 Views

Pages