activity
20142024
most citedBird Species Categorization Using Pose Normalized Deep Convolutional Nets

414 citations · 630 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CV2024

Labeled Data Selection for Category Discovery

Bingchen Zhao, Nico Lang, Serge Belongie +1

Category discovery methods aim to find novel categories in unlabeled visual data. At training time, a set of labeled and unlabeled images are provided, where the labels correspond…

cs.CV2024

Coarse-To-Fine Tensor Trains for Compact Visual Representations

Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen +3

The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstructio…

cs.CV2024

Rethinking Few-shot 3D Point Cloud Semantic Segmentation

Zhaochong An, Guolei Sun, Yun Liu +5

This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS), with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point dist…

cs.CL2023

Prompt, Condition, and Generate: Classification of Unsupported Claims with In-Context Learning

Peter Ebert Christensen, Srishti Yadav, Serge Belongie

Unsupported and unfalsifiable claims we encounter in our daily lives can influence our view of the world. Characterizing, summarizing, and -- more generally -- making sense of such…

cs.CV2023

Fashionpedia-Ads: Do Your Favorite Advertisements Reveal Your Fashion Taste?

Mengyun Shi, Claire Cardie, Serge Belongie

Consumers are exposed to advertisements across many different domains on the internet, such as fashion, beauty, car, food, and others. On the other hand, fashion represents second…

cs.CV2023

Fashionpedia-Taste: A Dataset towards Explaining Human Fashion Taste

Mengyun Shi, Serge Belongie, Claire Cardie

Existing fashion datasets do not consider the multi-facts that cause a consumer to like or dislike a fashion image. Even two consumers like a same fashion image, they could like th…