Papers Zero-Shot Image Classification
“Zero-Shot Image Classification” 태그가 달린 논문 128편 · 필터 해제
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning
Most advances in medical image recognition supporting clinical auxiliary diagnosis meet challenges due to the low-resource situation in the medical field, where annotations are highly expensive and professional. This low…
Few-Shot Learningimage-classificationImage ClassificationLanguage Modeling+4Image-Caption Encoding for Improving Zero-Shot Generalization
Recent advances in vision-language models have combined contrastive approaches with generative methods to achieve state-of-the-art (SOTA) on downstream inference tasks like zero-shot image classification. However, a pers…
image-classificationImage ClassificationZero-shot GeneralizationZero-Shot Image ClassificationSegment Any Change
Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. In this paper, we propose the segment any change model…
Change Detectionimage-classificationImage ClassificationZero-Shot Image ClassificationCLAMP: Contrastive LAnguage Model Prompt-tuning
Large language models (LLMs) have emerged as powerful general-purpose interfaces for many machine learning problems. Recent work has adapted LLMs to generative visual tasks like image captioning, visual question answerin…
Contrastive LearningImage Captioningimage-classificationImage Classification+6LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models
Vision-language pre-training like CLIP has shown promising performance on various downstream tasks such as zero-shot image classification and image-text retrieval. Most of the existing CLIP-alike works usually adopt rela…
image-classificationImage ClassificationImage-text RetrievalLanguage Modeling+4Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language Models
Vision-language models (VLMs) like CLIP have demonstrated remarkable applicability across a variety of downstream tasks, including zero-shot image classification. Recently, the use of prompts or adapters for efficient tr…
General Knowledgeimage-classificationImage ClassificationTransfer Learning+1Efficient Model-Agnostic Multi-Group Equivariant Networks
Constructing model-agnostic group equivariant networks, such as equitune (Basu et al., 2023b) and its generalizations (Kim et al., 2023), can be computationally expensive for large product groups. We address this problem…
Fairnessimage-classificationImage Classificationmodel+2CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
Open-vocabulary dense prediction tasks including object detection and image segmentation have been advanced by the success of Contrastive Language-Image Pre-training (CLIP). CLIP models, particularly those incorporating …
image-classificationImage ClassificationImage Segmentationobject-detection+10Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models
Recent advances in large-scale vision-language models have achieved impressive performance in various zero-shot image classification tasks. While prior studies have demonstrated significant improvements by introducing fe…
image-classificationImage ClassificationKnowledge DistillationPseudo Label+1GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Cross-modal pre-training has shown impressive performance on a wide range of downstream tasks, benefiting from massive image-text pairs collected from the Internet. In practice, online data are growing constantly, highli…
image-classificationImage ClassificationImage RetrievalImage to text+2PerceptionCLIP: Visual Classification by Inferring and Conditioning on Contexts
Vision-language models like CLIP are widely used in zero-shot image classification due to their ability to understand various visual concepts and natural language descriptions. However, how to fully leverage CLIP's unpre…
Classificationimage-classificationImage ClassificationLanguage Modelling+4PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization
In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Also, a recent study has demonstrated the cross-modal tran…
Domain GeneralizationImage ClassificationMulti-modal ClassificationMultimodal Deep Learning+4Distilling Large Vision-Language Model with Out-of-Distribution Generalizability
Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distilla…
Few-Shot Image ClassificationImage ClassificationKnowledge DistillationLanguage Modeling+7RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
General-purpose foundation models have led to recent breakthroughs in artificial intelligence. In remote sensing, self-supervised learning (SSL) and Masked Image Modeling (MIM) have been adopted to build foundation model…
ClassificationCross-Modal Retrievalimage-classificationImage Classification+8Contrasting Intra-Modal and Ranking Cross-Modal Hard Negatives to Enhance Visio-Linguistic Compositional Understanding
Vision-Language Models (VLMs), such as CLIP, exhibit strong image-text comprehension abilities, facilitating advances in several downstream tasks such as zero-shot image classification, image-text retrieval, and text-to-…
Contrastive Learningimage-classificationImage ClassificationImage Generation+7Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language Representations
Vision-and-language (VL) models with separate encoders for each modality (e.g., CLIP) have become the go-to models for zero-shot image classification and image-text retrieval. They are, however, mostly evaluated in Engli…
image-classificationImage ClassificationImage-text RetrievalMachine Translation+3Semantically-Prompted Language Models Improve Visual Descriptions
Language-vision models like CLIP have made significant strides in vision tasks, such as zero-shot image classification (ZSIC). However, generating specific and expressive visual descriptions remains challenging; descript…
ClassificationDescriptiveFine-Grained Image Classificationimage-classification+5Learning from Children: Improving Image-Caption Pretraining via Curriculum
Image-caption pretraining has been quite successfully used for downstream vision tasks like zero-shot image classification and object detection. However, image-caption pretraining is still a hard problem -- it requires m…
image-classificationImage Classificationobject-detectionObject Detection+1CamDiff: Camouflage Image Augmentation via Diffusion Model
The burgeoning field of camouflaged object detection (COD) seeks to identify objects that blend into their surroundings. Despite the impressive performance of recent models, we have identified a limitation in their robus…
Dataset GenerationImage Augmentationimage-classificationImage Classification+4Text-to-Image Diffusion Models are Zero-Shot Classifiers
The excellent generative capabilities of text-to-image diffusion models suggest they learn informative representations of image-text data. However, what knowledge their representations capture is not fully understood, an…
AttributeContrastive Learningimage-classificationImage Classification+1