Tracking and following objects of interest is critical to several robotics use cases, ranging
from industrial automation to logistics and warehousing, to healthcare and security. In this …

Accurately quantifying cellular morphology at scale could substantially empower existing
single-cell approaches. However, measuring cell morphology remains an active field of …

Previous works concerning single-view hand-held object reconstruction typically rely on
supervision from 3D ground-truth models which are hard to collect in real world. In contrast …

Abstract A Neural Radiance Field (NeRF) encodes the specific relation of 3D geometry and
appearance of a scene. We here ask the question whether we can transfer the appearance …

This paper propels creative control in generative visual AI by allowing users to “select”.
Departing from traditional text or sketch-based methods, we for the first time allow users to …

Despite promising advancements in deep learning in medical domains, challenges still
remain owing to data scarcity, compounded by privacy concerns and data ownership …

We show that useful video representations can be learned from synthetic videos and natural
images, without incorporating natural videos in the training. We propose a progression of …

Our brains represent the ever-changing environment with neurons in a highly dynamic
fashion. The temporal features of visual pixels in dynamic natural scenes are entrapped in …

Animal re-identification (ReID) has become an indispensable tool in ecological research,
playing a critical role in tracking population dynamics, analyzing behavioral patterns, and …

Abstract Acute Lymphoblastic Leukemia (ALL) is a disease that is caused by the
uncontrollable growth of immature and malignant White Blood Cells (WBCs) which are …