433 citations · 771 across the 52 of their papers we have counts for
13 papers · 1 filter
Modeling Multimodal Aleatoric Uncertainty in Segmentation with Mixture of Stochastic Experts
Zhitong Gao, Yucong Chen, Chuyu Zhang +1
Equipping predicted segmentation with calibrated uncertainty is essential for safety-critical applications. In this work, we focus on capturing the data-inherent uncertainty (aka a…
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
Jorg Bornschein, Alexandre Galashov, Ross Hemsley +17
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly…
Generative Negative Text Replay for Continual Vision-Language Pretraining
Shipeng Yan, Lanqing Hong, Hang Xu +4
Vision-language pre-training (VLP) has attracted increasing attention recently. With a large amount of image-text pairs, VLP models trained with contrastive loss have achieved impr…
CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention
Ziyu Guo, Renrui Zhang, Longtian Qiu +4
Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with great transferability, which achieves promising accuracy for zero-shot classifica…
Part-aware Prototypical Graph Network for One-shot Skeleton-based Action Recognition
Tailin Chen, Desen Zhou, Jian Wang +6
In this paper, we study the problem of one-shot skeleton-based action recognition, which poses unique challenges in learning transferable representation from base classes to novel…
Learning Semantic Correspondence with Sparse Annotations
Shuaiyi Huang, Luyu Yang, Bo He +3
Finding dense semantic correspondence is a fundamental problem in computer vision, which remains challenging in complex scenes due to background clutter, extreme intra-class variat…