Search Results for author:
Found 3 papers, 2 papers with code
Approaching human 3D shape perception with neurally mappable models
Finally, we find that while the models trained with multi-view learning objectives are able to partially generalize to new object categories, they fall short of human alignment.
Unsupervised Segmentation in Real-World Images via Spelke Object Inference
Self-supervised, category-agnostic segmentation of real-world images is a challenging open problem in computer vision.
Identifying concept libraries from language about object structure
Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
