Content-Aware Unsupervised Deep Homography Estimation

Homography estimation is a basic image alignment method in many applications. It is usually conducted by extracting and matching sparse feature points, which are error-prone in low-light and low-texture images. On the other hand, previous deep homography approaches use either synthetic images for supervised learning or aerial images for unsupervised learning, both ignoring the importance of handling depth disparities and moving objects in real world applications. To overcome these problems, in this work we propose an unsupervised deep homography method with a new architecture design. In the spirit of the RANSAC procedure in traditional methods, we specifically learn an outlier mask to only select reliable regions for homography estimation. We calculate loss with respect to our learned deep features instead of directly comparing image content as did previously. To achieve the unsupervised training, we also formulate a novel triplet loss customized for our network. We verify our method by conducting comprehensive comparisons on a new dataset that covers a wide range of scenes with varying degrees of difficulties for the task. Experimental results reveal that our method outperforms the state-of-the-art including deep solutions and feature-based solutions.Comment: Accepted by ECCV 2020 (Oral, Top 2%, 3 over 3 Strong Accepts). Jirong Zhang and Chuan Wang are joint first authors, and Shuaicheng Liu is the corresponding autho

Rad51 and DNA-PKcs are involved in the generation of specific telomere aberrations induced by the quadruplex ligand 360A that impair mitotic cell progression and lead to cell death

Functional telomeres are protected from non-homologous end-joining (NHEJ) and homologous recombination (HR) DNA repair pathways. Replication is a critical period for telomeres because of the requirement for reconstitution of functional protected telomere conformations, a process that involves DNA repair proteins. Using knockdown of DNA-PKcs and Rad51 expression in three different cell lines, we demonstrate the respective involvement of NHEJ and HR in the formation of telomere aberrations induced by the G-quadruplex ligand 360A during or after replication. HR contributed to specific chromatid-type aberrations (telomere losses and doublets) affecting the lagging strand telomeres, whereas DNA-PKcs-dependent NHEJ was responsible for sister telomere fusions as a direct consequence of G-quadruplex formation and/or stabilization induced by 360A on parental telomere G strands. NHEJ and HR activation at telomeres altered mitotic progression in treated cells. In particular, NHEJ-mediated sister telomere fusions were associated with altered metaphase-anaphase transition and anaphase bridges and resulted in cell death during mitosis or early G1. Collectively, these data elucidate specific molecular and cellular mechanisms triggered by telomere targeting by the G-quadruplex ligand 360A, leading to cancer cell death

Attentive Semantic Alignment with Offset-Aware Correlation Kernels

Semantic correspondence is the problem of establishing correspondences across images depicting different instances of the same object or scene class. One of recent approaches to this problem is to estimate parameters of a global transformation model that densely aligns one image to the other. Since an entire correlation map between all feature pairs across images is typically used to predict such a global transformation, noisy features from different backgrounds, clutter, and occlusion distract the predictor from correct estimation of the alignment. This is a challenging issue, in particular, in the problem of semantic correspondence where a large degree of image variations is often involved. In this paper, we introduce an attentive semantic alignment method that focuses on reliable correlations, filtering out distractors. For effective attention, we also propose an offset-aware correlation kernel that learns to capture translation-invariant local transformations in computing correlation values over spatial locations. Experiments demonstrate the effectiveness of the attentive model and offset-aware kernel, and the proposed model combining both techniques achieves the state-of-the-art performance.N