Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Deep generative models are becoming increasingly powerful, now generating diverse high
fidelity photo-realistic samples given text prompts. Have they reached the point where …

Massive web datasets play a key role in the success of large vision-language models like
CLIP and Flamingo. However, the raw web data is noisy, and existing filtering methods to …

Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Benefiting from prompt tuning, recent years have witnessed the promising performance of
pre-trained vision-language models, eg, CLIP, on versatile downstream tasks. In this paper …

Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …

Semantic segmentation has witnessed tremendous progress due to the proposal of various
advanced network architectures. However, they are extremely hungry for delicate …

Recent advancements in Multimodal Large Language Models (MLLMs) have been utilizing
Visual Prompt Generators (VPGs) to convert visual features into tokens that LLMs can …

The visual classification performance of vision-language models such as CLIP has been
shown to benefit from additional semantic knowledge from large language models (LLMs) …

In this work, we investigate the problem of Model-Agnostic Zero-Shot Classification (MA-
ZSC), which refers to training non-specific classification architectures (downstream models) …