Object Detection is the task of classification and localization of objects in an image or video.
It has gained prominence in recent years due to its widespread applications. This article …

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

The current modus operandi in adapting pre-trained models involves updating all the
backbone parameters, ie., full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) …

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 …

We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical
computer vision tasks, including pose estimation, object detection, depth estimation and …

Abstract Out-of-distribution (OOD) detection is a critical task for deploying machine learning
models in the open world. Distance-based methods have demonstrated promise, where …

Pre-trained vision-language models (eg, CLIP) have shown promising zero-shot
generalization in many downstream tasks with properly designed text prompts. Instead of …

Detecting out-of-distribution inputs is critical for the safe deployment of machine learning
models in the real world. However, neural networks are known to suffer from the …

Recent work has shown that self-supervised pre-training leads to improvements over
supervised learning on challenging visual recognition tasks. CLIP, an exciting new …

Large pre-trained vision-language models like CLIP have shown great potential in learning
representations that are transferable across a wide range of downstream tasks. Different …