Unsupervised localization and segmentation are long-standing computer vision challenges
that involve decomposing an image into semantically-meaningful segments without any …

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 …

Image-level weakly supervised semantic segmentation is a challenging problem that has
been deeply studied in recent years. Most of advanced solutions exploit class activation map …

We consider the problem of predicting the 3D shape, articulation, viewpoint, texture, and
lighting of an articulated animal like a horse given a single test image as input. We present a …

We propose novel motion representations for animating articulated objects consisting of
distinct parts. In a completely unsupervised manner, our method identifies object parts …

Progress in self-supervised learning has brought strong general image representation
learning methods. Yet so far, it has mostly focused on image-level learning. In turn, tasks …

Contrastive self-supervised learning has emerged as a promising approach to unsupervised
visual representation learning. In general, these methods learn global (image-level) …

We learn a self-supervised, single-view 3D reconstruction model that predicts the 3D mesh
shape, texture and camera pose of a target object with a collection of 2D images and …

Creating high-quality articulated 3D models of animals is challenging either via manual
creation or using 3D scanning tools. Therefore, techniques to reconstruct articulated 3D …

While GANs have shown success in realistic image generation, the idea of using GANs for
other tasks unrelated to synthesis is underexplored. Do GANs learn meaningful structural …