Recent Advances in Out-of-Distribution Detection with CLIP-Like Models: A Survey
Out-of-distribution detection (OOD) is a pivotal task for real-world applications that trains models to identify samples that are distributionally different from the in-distribution (ID) data during testing. Recent advances in AI, particularly Vision-Language Models (VLMs) like CLIP, have revolutionized OOD detection by shifting from traditional unimodal image detectors to multimodal image-text detectors. This shift has inspired extensive research; however, existing categorization schemes (e.g., few- or zero-shot types) still rely solely on the availability of ID images, adhering to a unimodal paradigm. To better align with CLIP's cross-modal nature, we propose a new categorization framework rooted in both image and text modalities. Specifically, we categorize existing methods based on how visual and textual information of OOD data is utilized within image + text modalities, and further divide them into four groups: OOD Images (i.e., outliers) Seen or Unseen, and OOD Texts (i.e., learnable vectors or class names) Known or Unknown, across two training strategies (i.e., train-free or training-required). More importantly, we discuss open problems in CLIP-like OOD detection and highlight promising directions for future research, including cross-domain integration, practical applications, and theoretical understanding.
Code (0)
등록된 구현이 없습니다.
Tasks
Out-of-Distribution DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
CLIPScope: Enhancing Zero-Shot OOD Detection with Bayesian Scoring
Detection of out-of-distribution (OOD) samples is crucial for safe real-world deployment of machine learning models. Recent advances in vision language foundation models have made them capable of detecting OOD samples wi…
On-the-fly Object Detection using StyleGAN with CLIP Guidance
We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention. We achieve this by leveraging the combined power of modern generative mode…
Objectobject-detectionObject DetectionZero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier. In testing, the classifier can (1) classify the test samples of kno…
Image DescriptionOut-of-Distribution DetectionOut of Distribution (OOD) DetectionRepresentation Learning+2ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples, especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. H…
Few-Shot LearningOut-of-Distribution DetectionOut of Distribution (OOD) DetectionPrompt LearningCLIPSym: Delving into Symmetry Detection with CLIP
Symmetry is one of the most fundamental geometric cues in computer vision, and detecting it has been an ongoing challenge. With the recent advances in vision-language models,~i.e., CLIP, we investigate whether a pre-trai…
Symmetry Detection