Papers Text Matching
“Text Matching” 태그가 달린 논문 364편 · 필터 해제
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retrieval and zero-shot classification. However, conventional cross-modal contr…
Contrastive LearningImage-text matchingImage to textImage-to-Text Retrieval+5TNG-CLIP:Training-Time Negation Data Generation for Negation Awareness of CLIP
Vision-language models (VLMs), such as CLIP, have demonstrated strong performance across a range of downstream tasks. However, CLIP is still limited in negation understanding: the ability to recognize the absence or excl…
Image CaptioningImage GenerationImage RetrievalImage-text matching+6Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis
Graphical user interface (GUI) grounding, the ability to map natural language instructions to specific actions on graphical user interfaces, remains a critical bottleneck in computer use agent development. Current benchm…
Text MatchingDescriptive Image-Text Matching with Graded Contextual Similarity
Image-text matching aims to build correspondences between visual and textual data by learning their pairwise similarities. Most existing approaches have adopted sparse binary supervision, indicating whether a pair of ima…
DescriptiveImage-text matchingSentenceText MatchingCompositional Image-Text Matching and Retrieval by Grounding Entities
Vision-language pretraining on large datasets of images-text pairs is one of the main building blocks of current Vision-Language Models. While with additional training, these models excel in various downstream tasks, inc…
Image CaptioningImage-text matchingQuestion AnsweringRetrieval+3LGD: Leveraging Generative Descriptions for Zero-Shot Referring Image Segmentation
Zero-shot referring image segmentation aims to locate and segment the target region based on a referring expression, with the primary challenge of aligning and matching semantics across visual and textual modalities with…
AttributeImage SegmentationReferring ExpressionSemantic Segmentation+2Instruction-augmented Multimodal Alignment for Image-Text and Element Matching
With the rapid advancement of text-to-image (T2I) generation models, assessing the semantic alignment between generated images and text descriptions has become a significant research challenge. Current methods, including…
Image AugmentationImage GenerationImage-text matchingQuestion Answering+5Dependency Structure Augmented Contextual Scoping Framework for Multimodal Aspect-Based Sentiment Analysis
Multimodal Aspect-Based Sentiment Analysis (MABSA) seeks to extract fine-grained information from image-text pairs to identify aspect terms and determine their sentiment polarity. However, existing approaches often fall …
Aspect-Based Sentiment AnalysisDependency ParsingImage-text matchingSentiment Analysis+1PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction
The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for user queries whereas the latter forecast…
Binary ClassificationClick-Through Rate PredictionText MatchingCodeReviewQA: The Code Review Comprehension Assessment for Large Language Models
State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising source code to address code reviews, hinde…
Code GenerationMultiple-choiceText MatchingAligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
Enabling Visual Semantic Models to effectively handle multi-view description matching has been a longstanding challenge. Existing methods typically learn a set of embeddings to find the optimal match for each view's text…
Image-text matchingText MatchingCLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP
Despite its prevalent use in image-text matching tasks in a zero-shot manner, CLIP has been shown to be highly vulnerable to adversarial perturbations added onto images. Recent studies propose to finetune the vision enco…
Adversarial RobustnessImage-text matchingText MatchingMedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations
Despite significant progress in Vision-Language Pre-training (VLP), current approaches predominantly emphasize feature extraction and cross-modal comprehension, with limited attention to generating or transforming visual…
image-classificationImage ClassificationImage GenerationImage-text matching+7IteRPrimE: Zero-shot Referring Image Segmentation with Iterative Grad-CAM Refinement and Primary Word Emphasis
Zero-shot Referring Image Segmentation (RIS) identifies the instance mask that best aligns with a specified referring expression without training and fine-tuning, significantly reducing the labor-intensive annotation pro…
Image SegmentationImage-text matchingReferring ExpressionSemantic Segmentation+1CLIP Under the Microscope: A Fine-Grained Analysis of Multi-Object Representation
Contrastive Language-Image Pre-training (CLIP) models excel in zero-shot classification, yet face challenges in complex multi-object scenarios. This study offers a comprehensive analysis of CLIP's limitations in these co…
Image-text matchingObjectText Matchingzero-shot-classification+1Object-centric Binding in Contrastive Language-Image Pretraining
Recent advances in vision language models (VLM) have been driven by contrastive models such as CLIP, which learn to associate visual information with their corresponding text descriptions. However, these models have limi…
Image-text matchingObjectText MatchingCGI: Identifying Conditional Generative Models with Example Images
Generative models have achieved remarkable performance recently, and thus model hubs have emerged. Existing model hubs typically assume basic text matching is sufficient to search for models. However, in reality, due to …
Text MatchingMASS: Overcoming Language Bias in Image-Text Matching
Pretrained visual-language models have made significant advancements in multimodal tasks, including image-text retrieval. However, a major challenge in image-text matching lies in language bias, where models predominantl…
Image-text matchingImage-text RetrievalMultimodal AssociationRetrieval+2FiLo++: Zero-/Few-Shot Anomaly Detection by Fused Fine-Grained Descriptions and Deformable Localization
Anomaly detection methods typically require extensive normal samples from the target class for training, limiting their applicability in scenarios that require rapid adaptation, such as cold start. Zero-shot and few-shot…
Anomaly DetectionImage-text matchingPatch MatchingPosition+2Learning Textual Prompts for Open-World Semi-Supervised Learning
Traditional semi-supervised learning achieves significant success in closed-world scenarios. To better align with the openness of the real world, researchers propose open-world semi-supervised learning (OWSSL), which…
Image-text matchingOpen-World Semi-Supervised LearningPrompt LearningText Matching