Cross Modal Retrieval On Coco 2014

المقاييس

Image-to-text R@1

Image-to-text R@10

Image-to-text R@5

Text-to-image R@1

Text-to-image R@10

Text-to-image R@5

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	Image-to-text R@1	Image-to-text R@10	Image-to-text R@5	Text-to-image R@1	Text-to-image R@10	Text-to-image R@5	Paper Title	Repository
PVSE	45.2	84.5	74.3	32.4	75.0	63.0	Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval
3SHNet	67.9	95.4	90.5	50.3	87.7	79.3	3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting
XFM (base)	84.2	98.4	96.4	67.0	92.4	87.2	Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks
METER	76.16	96.82	93.16	57.08	90.07	82.66	An Empirical Study of Training End-to-End Vision-and-Language Transformers
SGRAF	57.8	91.6	84.9	41.9	81.3	70.7	Similarity Reasoning and Filtration for Image-Text Matching
LILE	55.6	91.0	82.4	41.5	82.2	72.1	LILE: Look In-Depth before Looking Elsewhere -- A Dual Attention Network using Transformers for Cross-Modal Information Retrieval in Histopathology Archives	-
ViLT-B/32	61.5	92.7	86.3	42.7	83.1	72.9	ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
PTP-BLIP (14M)	81.5	97.9	95.9	64.9	92.2	87.4	Position-guided Text Prompt for Vision-Language Pre-training
Dual-Path (ResNet)	41.2	81.1	70.5	25.3	66.4	53.4	Deep Visual-Semantic Alignments for Generating Image Descriptions
VSE-Gradient	81.4	97.9	95.6	63.6	91.5	86.0	Dissecting Deep Metric Learning Losses for Image-Text Retrieval
IMRAM	53.7	91.0	83.2	39.7	79.8	69.1	IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
VSRN	53.0	89.4	81.1	40.5	81.1	70.6	Visual Semantic Reasoning for Image-Text Matching
Florence	81.8	-	95.2	63.2	-	85.7	Florence: A New Foundation Model for Computer Vision
DSMD	48.0	84.5	75.6	62.1	92.0	85.9	Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning
ALBEF	77.6	97.2	94.3	60.7	90.5	84.3	Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
X2-VLM (base)	83.5	98.5	96.3	66.2	92.2	87.1	X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
OmniVL (14M)	82.1	98.1	95.9	64.8	91.6	86.1	OmniVL:One Foundation Model for Image-Language and Video-Language Tasks	-
X-VLM (base)	81.2	98.2	95.6	63.4	91.5	85.8	Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
X2-VLM (large)	84.4	98.5	96.5	67.7	92.5	87.5	X$^2$-VLM: All-In-One Pre-trained Model For Vision-Language Tasks
ERNIE-ViL 2.0	77.4	97.1	93.6	59.5	90.1	83.4	ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training

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