The recent maturation of single-cell RNA sequencing (scRNA-seq) technologies has
coincided with transformative new methods to profile genetic, epigenetic, spatial, proteomic …

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of
millions of cells across species and diseases. These data have spurred the development of …

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work
tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons …

We study the use of deep features extracted from a pretrained Vision Transformer (ViT) as
dense visual descriptors. We observe and empirically demonstrate that such features, when …

Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments,
laboratories and technologies can uncover biological insights, but current methods for …

We present DINO-Tracker–a new framework for long-term dense tracking in video. The pillar
of our approach is combining test-time training on a single video, with the powerful localized …

Visual object tracking is an important area in computer vision, and many tracking algorithms
have been proposed with promising results. Existing object tracking approaches can be …

Feed-forward CNNs trained for image transformation problems rely on loss functions that
measure the similarity between the generated image and a target image. Most of the …

Nearly all existing counting methods are designed for a specific object class. Our work,
however, aims to create a counting model able to count any class of object. To achieve this …

Do different neural networks, trained for various vision tasks, share some common
representations? In this paper, we demonstrate the existence of common features we call" …