The local binary pattern (LBP) is an effective feature, describing the size relationship between the neighboring pixels and the current pixel. While individual LBP-based methods yield good results, co-occurrence LBP-based methods exhibit a better ability to extract structural information. However, most of the co-occurrence LBP-based methods excel mainly in dealing with rotated images, exhibiting limitations

StyleGAN models show editing capabilities via their semantically interpretable latent organizations which require successful GAN inversion methods to edit real images. Many works have been proposed for inverting images into StyleGAN’s latent space. However, their results either suffer from low fidelity to the input image or poor editing qualities, especially for edits that require large transformations

Domain-adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, existing methods primarily focus on directly learning categorically discriminative target features for segmenting target images, which is challenging in the absence of target labels. This work provides a new perspective. We ob serve that the features learned with source

This paper studies graph clustering with application to feature matching and proposes an effective method, termed as GC-LAC, that can establish reliable feature correspondences and simultaneously discover all potential visual patterns. In particular, we regard each putative match as a node and encode the geometric relationships into edges where a visual pattern sharing similar motion behaviors corresponds

This paper tackles the limitation of a closed-world object detection model that was trained on one species. The expectation for this model is that it will not generalize well to recognize the instances of new species if they were present in the incoming data stream. We propose a novel object detection framework for this open-world setting that is suitable for applications that monitor wildlife, ocean