3.2k citations · 5.9k across the 33 of their papers we have counts for
11 papers · 1 filter
Learning to Augment via Implicit Differentiation for Domain Generalization
Tingwei Wang, Da Li, Kaiyang Zhou +2
Machine learning models are intrinsically vulnerable to domain shift between training and testing data, resulting in poor performance in novel domains. Domain generalization (DG) a…
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Jingkang Yang, Pengyun Wang, Dejian Zou +13
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the lit…
Unified Vision and Language Prompt Learning
Yuhang Zang, Wei Li, Kaiyang Zhou +2
Prompt tuning, a parameter- and data-efficient transfer learning paradigm that tunes only a small number of parameters in a model's input space, has become a trend in the vision co…
On-Device Domain Generalization
Kaiyang Zhou, Yuanhan Zhang, Yuhang Zang +3
We present a systematic study of domain generalization (DG) for tiny neural networks. This problem is critical to on-device machine learning applications but has been overlooked in…
Panoptic Scene Graph Generation
Jingkang Yang, Yi Zhe Ang, Zujin Guo +3
Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected usin…
Detecting Humans in RGB-D Data with CNNs
Kaiyang Zhou, Adeline Paiement, Majid Mirmehdi
We address the problem of people detection in RGB-D data where we leverage depth information to develop a region-of-interest (ROI) selection method that provides proposals to two c…
Neural Prompt Search
Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu
The size of vision models has grown exponentially over the last few years, especially after the emergence of Vision Transformer. This has motivated the development of parameter-eff…
Full-Spectrum Out-of-Distribution Detection
Jingkang Yang, Kaiyang Zhou, Ziwei Liu
Existing out-of-distribution (OOD) detection literature clearly defines semantic shift as a sign of OOD but does not have a consensus over covariate shift. Samples experiencing cov…
Open-Vocabulary DETR with Conditional Matching
Yuhang Zang, Wei Li, Kaiyang Zhou +2
Open-vocabulary object detection, which is concerned with the problem of detecting novel objects guided by natural language, has gained increasing attention from the community. Ide…
Conditional Prompt Learning for Vision-Language Models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy +1
With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. A recently proposed m…
Dynamic Instance Domain Adaptation
Zhongying Deng, Kaiyang Zhou, Da Li +3
Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's training samples come with domain labels (e.g., painting, photo). Samples from each domain a…