6 accepted papers
Handling haphazard streaming data, such as data from edge devices, presents a challenging problem. Over time, the incoming data becomes inconsistent, with missing, faulty, or new inputs reappearing. Therefore, it requires models that are reliable. Recent methods to solve this problem depend on a hed…
Image retrieval has garnered a growing interest in recent times. The current approaches are either supervised or self-supervised. These methods do not exploit the benefits of hybrid learning using both supervision and self-supervision. We present a novel Master Assistant Buddy Network (MAB-Net) for…
Object trackers deployed on low-power devices need to be light-weight, however, most of the current state-of-the-art (SOTA) methods rely on using compute-heavy backbones built using CNNs or Transformers. Large sizes of such models do not allow their deployment in low-power conditions and designing c…
Detecting and analyzing nanoscale motion patterns of vesicles, smaller than the microscope resolution ( 250 nm), inside living biological cells is a challenging problem. State-of-the-art CV approaches based on detection, tracking, optical flow or deep learning perform poorly for this problem. We pro…
We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first…
Robotic manipulation of objects requires a fast recognition from image stream. For many cylindrical object (e.g., cans, cups, pipes, bottles, etc.) this is possible through detection of ellipse depicting the circular top of the cylinder. Growing industrial and warehouse applications of robots drive