Papers Zero-Shot Image Classification
“Zero-Shot Image Classification” 태그가 달린 논문 128편 · 필터 해제
G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification
Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fail in complementary ways.When CLIP's top-1 prediction is wrong, the c…
Zero-Shot Image ClassificationVisual ReasoningFrom Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model
Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference. Although significant efforts are devoted to adapting VLMs at test…
Zero-Shot Image ClassificationContrastive LearningClimateVID -- Social Media Videos Analysis and Challenges Involved
The pervasive growth of digital content, specifically short videos on social media platforms, has significantly altered how topics are discussed and understood in public discourse. In this work, we advance automated visu…
Zero-Shot Image ClassificationBeyond Standard Benchmarks: A Systematic Audit of Vision-Language Model's Robustness to Natural Semantic Variation Across Diverse Tasks
Recent advances in vision-language models (VLMs) trained on web-scale image-text pairs have enabled impressive zero-shot transfer across a diverse range of visual tasks. However, comprehensive and independent evaluation …
Zero-Shot Image ClassificationVisual Question AnsweringSemantic SegmentationA Closed-Form Solution for Debiasing Vision-Language Models with Utility Guarantees Across Modalities and Tasks
While Vision-Language Models (VLMs) have achieved remarkable performance across diverse downstream tasks, recent studies have shown that they can inherit social biases from the training data and further propagate them in…
Zero-Shot Image ClassificationText-to-Image GenerationImage RetrievalBeyond Heuristic Prompting: A Concept-Guided Bayesian Framework for Zero-Shot Image Recognition
Vision-Language Models (VLMs), such as CLIP, have significantly advanced zero-shot image recognition. However, their performance remains limited by suboptimal prompt engineering and poor adaptability to target classes. W…
Zero-Shot Image ClassificationPrompt EngineeringDiscovering the Hidden Role of Gini Index In Prompt-based Classification
In classification tasks, the long-tailed minority classes usually offer the predictions that are most important. Yet these classes consistently exhibit low accuracies, whereas a few high-performing classes dominate the g…
Zero-Shot Image ClassificationGMAIL: Generative Modality Alignment for generated Image Learning
Generative models have made it possible to synthesize highly realistic images, potentially providing an abundant data source for training machine learning models. Despite the advantages of these synthesizable data source…
Zero-Shot Image ClassificationImage CaptioningImage RetrievalReinPath: A Multimodal Reinforcement Learning Approach for Pathology
Interpretability is significant in computational pathology, leading to the development of multimodal information integration from histopathological image and corresponding text data.However, existing multimodal methods h…
Zero-Shot Image ClassificationVisual Question AnsweringReinforcement LearningZero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling
Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods. Vision-language models (VLMs) enable strong zero-shot image classification, but existing VLM-based distracted …
Zero-Shot Image ClassificationCan Synthetic Images Serve as Effective and Efficient Class Prototypes?
Vision-Language Models (VLMs) have shown strong performance in zero-shot image classification tasks. However, existing methods, including Contrastive Language-Image Pre-training (CLIP), all rely on annotated text-to-imag…
Zero-Shot Image ClassificationD3G: Diverse Demographic Data Generation Increases Zero-Shot Image Classification Accuracy within Multimodal Models
Image classification is a task essential for machine perception to achieve human-level image understanding. Multimodal models such as CLIP have been able to perform well on this task by learning semantic similarities acr…
Fine-Grained Image ClassificationZero-Shot Image ClassificationLGCA: Enhancing Semantic Representation via Progressive Expansion
Recent advancements in large-scale pretraining in natural language processing have enabled pretrained vision-language models such as CLIP to effectively align images and text, significantly improving performance in zero-…
Zero-Shot Image ClassificationVL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set
The alignment of vision-language representations endows current Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities. However, the interpretability of the alignment component remains uninvestigate…
Zero-Shot Image ClassificationSemantic SimilarityTernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge
Recent years have witnessed an increasing interest in image-text contrastive modeling, exemplified by models such as Contrastive Language-Image Pretraining (CLIP). In this paper, we propose the TernaryCLIP, a lightweight…
Zero-Shot Image ClassificationText RetrievalNo Labels Needed: Zero-Shot Image Classification with Collaborative Self-Learning
While deep learning, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), has significantly advanced classification performance, its typical reliance on extensive annotated datasets presents a m…
Zero-Shot Image ClassificationTransfer LearningNoise Matters: Optimizing Matching Noise for Diffusion Classifiers
Although today's pretrained discriminative vision-language models (e.g., CLIP) have demonstrated strong perception abilities, such as zero-shot image classification, they also suffer from the bag-of-words problem and spu…
Zero-Shot Image ClassificationCIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success in cross-modal tasks such as zero-shot image classification and text-image retrieval by effectively aligning visual and textual representation…
Contrastive Learningimage-classificationImage ClassificationImage Retrieval+5LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
This study addresses the technical bottlenecks in handling long text and the "hallucination" issue caused by insufficient short text information in remote sensing vision-language foundation models (VLFM). We propose a no…
Cross-Modal RetrievalHallucinationimage-classificationImage Classification+4Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation
Vision-language models for Earth observation (EO) typically rely on the visual spectrum of data as the only model input, thus failing to leverage the rich spectral information available in the multispectral channels reco…
Contrastive LearningEarth Observationimage-classificationImage Classification+2