Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual foundation …

Abstract Large Vision & Language models pretrained on web-scale data provide
representations that are invaluable for numerous V &L problems. However, it is unclear how …

Vision-language models (VLMs) that use contrastive language-image pre-training have
shown promising zero-shot classification performance. However, their performance on …

Abstract We present Lazy Visual Grounding for open-vocabulary semantic segmentation,
which decouples unsupervised object mask discovery from object grounding. Plenty of the …

Foundation models like CLIP allow zero-shot transfer on various tasks without additional
training data. Yet, the zero-shot performance is less competitive than a fully supervised one …

Multi-expert ensemble models for long-tailed learning typically either learn diverse
generalists from the whole dataset or aggregate specialists on different subsets. However …

Contrastive Language-Image Pre-training (CLIP) learns rich representations via readily
available supervision of natural language. It improves the performance of downstream vision …

The" pre-training and fine-tuning" paradigm in addressing long-tailed recognition tasks has
sparked significant interest since the emergence of large vision-language models like the …

Labeling large image datasets with attributes such as facial age or object type is tedious and
sometimes infeasible. Supervised machine learning methods provide a highly accurate …

Without the demand of training in reality, humans can easily detect a known concept simply
based on its language description. Empowering deep learning with this ability undoubtedly …