Papers Zero-Shot Image Classification
“Zero-Shot Image Classification” 태그가 달린 논문 128편 · 필터 해제
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that le…
image-classificationImage ClassificationKnowledge GraphsQuestion Answering+2CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot classification through their ability generate embeddings for each class based on their (natural language) names. Prior work has focused on imp…
Classificationimage-classificationImage ClassificationPrompt Engineering+3Language-Driven Anchors for Zero-Shot Adversarial Robustness
Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting, leaving the challenging domain of zero…
Adversarial DefenseAdversarial RobustnessImage ClassificationZero-Shot Image Classification+1Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities
We explore the extent to which zero-shot vision-language models exhibit gender bias for different vision tasks. Vision models traditionally required task-specific labels for representing concepts, as well as finetuning; …
image-classificationImage Classificationobject-detectionObject Detection+3RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training
Contrastive Language-Image Pre-training (CLIP) is attracting increasing attention for its impressive zero-shot recognition performance on different down-stream tasks. However, training CLIP is data-hungry and require…
Classificationimage-classificationImage Classificationobject-detection+4LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse Retrieval
Image-text retrieval (ITR) aims to retrieve images or texts that match a query originating from the other modality. The conventional dense retrieval paradigm relies on encoding images and texts into dense representat…
image-classificationImage ClassificationImage-text RetrievalRetrieval+2DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning
Inspired by strategies like Active Learning, it is intuitive that intelligently selecting the training classes from a dataset for Zero-Shot Learning (ZSL) can improve the performance of existing ZSL methods. In this work…
Active LearningAttributeClassificationDiversity+4When are Lemons Purple? The Concept Association Bias of Vision-Language Models
Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval. However, such performance does not realize in tasks that require a finer-gr…
Attributeimage-classificationImage ClassificationImage to text+9CLIPPO: Image-and-Language Understanding from Pixels Only
Multimodal models are becoming increasingly effective, in part due to unified components, such as the Transformer architecture. However, multimodal models still often consist of many task- and modality-specific pieces an…
Contrastive Learningimage-classificationImage ClassificationLanguage Modelling+7Reproducible scaling laws for contrastive language-image learning
Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offe…
Image ClassificationOpen Vocabulary Attribute DetectionRetrievalzero-shot-classification+3I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification
Recent works have shown that unstructured text (documents) from online sources can serve as useful auxiliary information for zero-shot image classification. However, these methods require access to a high-quality source …
Classificationimage-classificationImage ClassificationLanguage Modeling+3AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model. Starting from the pre-trained multimodal representation model CLIP released by…
Contrastive LearningCross-Modal RetrievalImage ClassificationImage Retrieval+9Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pair…
Contrastive Learningimage-classificationImage ClassificationImage Retrieval+7Generative Negative Text Replay for Continual Vision-Language Pretraining
Vision-language pre-training (VLP) has attracted increasing attention recently. With a large amount of image-text pairs, VLP models trained with contrastive loss have achieved impressive performance in various tasks, esp…
Continual Learningimage-classificationImage ClassificationImage-text Retrieval+5Text2Model: Text-based Model Induction for Zero-shot Image Classification
We address the challenge of building task-agnostic classifiers using only text descriptions, demonstrating a unified approach to image classification, 3D point cloud classification, and action recognition from scenes. Un…
3D Point Cloud ClassificationAction RecognitionClassificationimage-classification+7General Image Descriptors for Open World Image Retrieval using ViT CLIP
The Google Universal Image Embedding (GUIE) Challenge is one of the first competitions in multi-domain image representations in the wild, covering a wide distribution of objects: landmarks, artwork, food, etc. This is a …
Image RetrievalRetrievalZero-Shot Image ClassificationZero-shot Image Retrieval+1Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss
Learning visual and textual representations in the shared space from web-scale image-text pairs improves the performance of diverse vision-and-language tasks, as well as modality-specific tasks. Many attempts in this fra…
image-classificationImage ClassificationImage-text RetrievalRetrieval+7I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Despite the tremendous progress in zero-shot learning(ZSL), the majority of existing methods still rely on human-annotated attributes, which are difficult to annotate and scale. An unsupervised alternative is to represen…
Generalized Zero-Shot Learningimage-classificationImage ClassificationWord Embeddings+2PaLI: A Jointly-Scaled Multilingual Language-Image Model
Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI (Pathways Language and Image model), a model that extends this approach to the joint modeling of langua…
DecoderFew-Shot Image ClassificationImage CaptioningImage Classification+7What does a platypus look like? Generating customized prompts for zero-shot image classification
Open-vocabulary models are a promising new paradigm for image classification. Unlike traditional classification models, open-vocabulary models classify among any arbitrary set of categories specified with natural languag…
Descriptiveimage-classificationImage ClassificationZero-Shot Image Classification