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Image-text matching

Image-Text Matching is a subtask within Cross-Modal Retrieval (CMR) that involves establishing associations between images and corresponding textual descriptions. The goal is to retrieve an image given a textual query or, conversely, retrieve a textual description given an image query. This task is challenging due to the heterogeneity gap between image and text data representations. Image-text matching is used in applications such as content-based image search, visual question answering, and multimodal summarization.

Assessing Brittleness of Image-Text Retrieval Benchmarks from Vision-Language Models Perspective

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Showing 126–150 of 188 papers

TitleStatusHype
EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoE—0
Towards Grounded Visual Spatial Reasoning in Multi-Modal Vision Language Models—0
Grounded Image Text Matching with Mismatched Relation Reasoning—0
MALM: Mask Augmentation based Local Matching for Food-Recipe RetrievalCode0
Probing the Role of Positional Information in Vision-Language Models—0
Scene Text Recognition with Image-Text Matching-guided Dictionary—0
Vision Meets Definitions: Unsupervised Visual Word Sense Disambiguation Incorporating Gloss InformationCode0
RoCOCO: Robustness Benchmark of MS-COCO to Stress-test Image-Text Matching ModelsCode0
Multi-Modal Representation Learning with Text-Driven Soft Masks—0
Integrating Language Guidance Into Image-Text Matching for Correcting False NegativesCode0
Increasing Textual Context Size Boosts Medical Image-Text MatchingCode0
Refined Vision-Language Modeling for Fine-grained Multi-modal Pre-training—0
Selectively Hard Negative Mining for Alleviating Gradient Vanishing in Image-Text Matching—0
VL-Match: Enhancing Vision-Language Pretraining with Token-Level and Instance-Level Matching—0
Weakly Supervised Referring Image Segmentation with Intra-Chunk and Inter-Chunk Consistency—0
Multimodal Matching-aware Co-attention Networks with Mutual Knowledge Distillation for Fake News Detection—0
Uniform Masking Prevails in Vision-Language Pretraining—0
UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance—0
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?Code0
Dissecting Deep Metric Learning Losses for Image-Text RetrievalCode0
AdsCVLR: Commercial Visual-Linguistic Representation Modeling in Sponsored Search—0
ALADIN: Distilling Fine-grained Alignment Scores for Efficient Image-Text Matching and RetrievalCode0
Don't Stop Learning: Towards Continual Learning for the CLIP Model—0
GR-GAN: Gradual Refinement Text-to-image GenerationCode0
Uncertainty-based Cross-Modal Retrieval with Probabilistic Representations—0
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