Papers Zero-Shot Image Classification
“Zero-Shot Image Classification” 태그가 달린 논문 128편 · 필터 해제
CoAPT: Context Attribute words for Prompt Tuning
We propose a novel prompt tuning method called CoAPT(Context Attribute words in Prompt Tuning) for few/zero-shot image classification. The core motivation is that attributes are descriptive words with rich information ab…
AttributeDescriptiveDomain Generalizationimage-classification+2Unconstrained Open Vocabulary Image Classification: Zero-Shot Transfer from Text to Image via CLIP Inversion
We introduce NOVIC, an innovative real-time uNconstrained Open Vocabulary Image Classifier that uses an autoregressive transformer to generatively output classification labels as language. Leveraging the extensive knowle…
image-classificationImage ClassificationOpen Vocabulary Image ClassificationOpen Vocabulary Object Detection+2Semantic Compositions Enhance Vision-Language Contrastive Learning
In the field of vision-language contrastive learning, models such as CLIP capitalize on matched image-caption pairs as positive examples and leverage within-batch non-matching pairs as negatives. This approach has led to…
ClassificationContrastive LearningCross-Modal Retrievalimage-classification+6PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration
Vision Language Models (VLMs) like CLIP have attracted substantial attention in pathology, serving as backbones for applications such as zero-shot image classification and Whole Slide Image (WSI) analysis. Additionally, …
image-classificationImage ClassificationZero-Shot Image ClassificationMitigate the Gap: Investigating Approaches for Improving Cross-Modal Alignment in CLIP
Contrastive Language--Image Pre-training (CLIP) has manifested remarkable improvements in zero-shot classification and cross-modal vision-language tasks. Yet, from a geometrical point of view, the CLIP embedding space ha…
cross-modal alignmentImage Classificationtext similarityzero-shot-classification+2WATT: Weight Average Test-Time Adaptation of CLIP
Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performance for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts…
image-classificationImage ClassificationOverall - TestTest-time Adaptation+1BaFTA: Backprop-Free Test-Time Adaptation For Zero-Shot Vision-Language Models
Large-scale pretrained vision-language models like CLIP have demonstrated remarkable zero-shot image classification capabilities across diverse domains. To enhance CLIP's performance while preserving the zero-shot paradi…
image-classificationImage ClassificationOnline ClusteringTest-time Adaptation+1Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning
Decoding images from non-invasive electroencephalographic (EEG) signals has been a grand challenge in understanding how the human brain process visual information in real-world scenarios. To cope with the issues of signa…
Contrastive LearningEEGimage-classificationImage Classification+2Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships
Pre-trained vision-language (VL) models are highly vulnerable to adversarial attacks. However, existing defense methods primarily focus on image classification, overlooking two key aspects of VL tasks: multimodal attacks…
Adversarial DefenseAdversarial Robustnessimage-classificationImage Classification+3It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap
Multi-modal contrastive models such as CLIP achieve state-of-the-art performance in zero-shot classification by embedding input images and texts on a joint representational space. Recently, a modality gap has been report…
image-classificationImage Classificationzero-shot-classificationZero-Shot Image Classification+1What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language Models
Large language models (LLMs) have been effectively used for many computer vision tasks, including image classification. In this paper, we present a simple yet effective approach for zero-shot image classification using m…
Classificationimage-classificationImage ClassificationPrompt Engineering+3Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp
As training datasets become increasingly drawn from unstructured, uncontrolled environments such as the web, researchers and industry practitioners have increasingly relied upon data filtering techniques to "filter out t…
image-classificationImage ClassificationZero-Shot Image ClassificationMulti-method Integration with Confidence-based Weighting for Zero-shot Image Classification
This paper introduces a novel framework for zero-shot learning (ZSL), i.e., to recognize new categories that are unseen during training, by using a multi-model and multi-alignment integration method. Specifically, we pro…
image-classificationImage ClassificationImage GenerationZero-Shot Image Classification+1MoDE: CLIP Data Experts via Clustering
The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions, which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) …
Clusteringimage-classificationImage ClassificationZero-Shot Image ClassificationA Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene
Pre-trained vision-language (V-L) models such as CLIP have shown excellent performance in many downstream cross-modal tasks. However, most of them are only applicable to the English context. Subsequent research has focus…
image-classificationImage ClassificationKnowledge DistillationLanguage Modelling+1Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBMs). While SOTA approaches to Image Classification task work as a black box, there is a growing demand for models…
Contrastive Learningimage-classificationImage ClassificationInterpretable Machine Learning+1Learn "No" to Say "Yes" Better: Improving Vision-Language Models via Negations
Existing vision-language models (VLMs) treat text descriptions as a unit, confusing individual concepts in a prompt and impairing visual semantic matching and reasoning. An important aspect of reasoning in logic and lang…
image-classificationImage ClassificationZero-Shot Image ClassificationBridge the Modality and Capability Gaps in Vision-Language Model Selection
Vision Language Models (VLMs) excel in zero-shot image classification by pairing images with textual category names. The expanding variety of Pre-Trained VLMs enhances the likelihood of identifying a suitable VLM for spe…
Capacity Estimationimage-classificationImage ClassificationLanguage Modeling+3Can We Talk Models Into Seeing the World Differently?
Unlike traditional vision-only models, vision language models (VLMs) offer an intuitive way to access visual content through language prompting by combining a large language model (LLM) with a vision encoder. However, bo…
Image CaptioningImage ClassificationLarge Language ModelQuestion Answering+2PromptKD: Unsupervised Prompt Distillation for Vision-Language Models
Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of pr…
Knowledge DistillationPrompt EngineeringPrompt LearningZero-Shot Image Classification