LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training

Learning object segmentation in image and video datasets without human supervision is a challenging problem. Humans easily identify moving salient objects in videos using the gestalt principle of common fate, which suggests that what moves together belongs together. Building upon this idea, we propose a self-supervised object discovery approach that leverages motion and appearance information to produce high-quality object segmentation masks. Specifically, we redesign the traditional graph cut on images to include motion information in a linear combination with appearance information to produce edge weights. Remarkably, this step produces object segmentation masks comparable to the current state-of-the-art on multiple benchmarks. To further improve performance, we bootstrap a segmentation network trained on these preliminary masks as pseudo-ground truths to learn from its own outputs via self-training. We demonstrate the effectiveness of our approach, named LOCATE, on multiple standard video object segmentation, image saliency detection, and object segmentation benchmarks, achieving results on par with and, in many cases surpassing state-of-the-art methods. We also demonstrate the transferability of our approach to novel domains through a qualitative study on in-the-wild images. Additionally, we present extensive ablation analysis to support our design choices and highlight the contribution of each component of our proposed method.Comment: Accepted to the British Machine Vision Conference (BMVC) 202

Altered Fractional Anisostropy in Early Huntington’s Disease

Huntington's disease (HD) is a dominantly inherited neurodegenerative disease best known for chorea. The disorder includes numerous other clinical features including mood disorder, eye movement abnormalities, cognitive disturbance, pendular knee reflexes, motor impersistence, and postural instability. We describe a mild case of HD early in the disease course with depression and subtle neurological manifestations. In addition, we review MRI and diffusion tensor imaging features in this patient. The bicaudate ratio, a measure of caudate atrophy, was increased. Fractional anisotropy values of the bilateral caudate and putamen were increased, signifying neurodegeneration of these structures in HD

Archaeological studies at Dholavira using GPR

A new area at an existing archaeological site of Harappan civilization at Dholavira, Gujarat, India has been studied using ground penetrating radar (GPR). An area of 12,276 m2 was surveyed using 200 MHz antenna at grid spacing of 2–3 m. The soil strata was found to extend mainly up to 3.5–4 m. The survey was conducted during the dry season to collect good signals. Post-processing was carried out to map the bedrock as well as archaeological features. A number of linear features were observed from the 3D image of the subsurface created from the acquired GPR profiles. Unlike residential structures, the large dimensions of these features indicate the likely existence of a series of water structures that may have partly collapsed due to floods at some point. There were some areas full of rubble next to the damaged walls that appeared to be orthogonal to the direction of possible flood from Manhar River.by Silky Agrawal, Mantu Majumder, Ravindra Singh Bisht and Amit Prashan

FODVid: Flow-guided Object Discovery in Videos

Segmentation of objects in a video is challenging due to the nuances such as motion blurring, parallax, occlusions, changes in illumination, etc. Instead of addressing these nuances separately, we focus on building a generalizable solution that avoids overfitting to the individual intricacies. Such a solution would also help us save enormous resources involved in human annotation of video corpora. To solve Video Object Segmentation (VOS) in an unsupervised setting, we propose a new pipeline (FODVid) based on the idea of guiding segmentation outputs using flow-guided graph-cut and temporal consistency. Basically, we design a segmentation model incorporating intra-frame appearance and flow similarities, and inter-frame temporal continuation of the objects under consideration. We perform an extensive experimental analysis of our straightforward methodology on the standard DAVIS16 video benchmark. Though simple, our approach produces results comparable (within a range of ~2 mIoU) to the existing top approaches in unsupervised VOS. The simplicity and effectiveness of our technique opens up new avenues for research in the video domain.Comment: CVPR 2023 (L3D-IVU workshop