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Image Retrieval On Cirr

Metrics

(Recall@5+Recall_subset@1)/2

Results

Performance results of various models on this benchmark

Paper TitleRepository
SPN4CIR82.69Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
SPN4CIR (SPRC)82.69Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
SPRC282.66Sentence-level Prompts Benefit Composed Image Retrieval
SPRC81.39Sentence-level Prompts Benefit Composed Image Retrieval
Candidate Set Re-ranking80.9Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder
CaLa78.74CaLa: Complementary Association Learning for Augmenting Composed Image Retrieval
CASE (Pre-trained on LaSCo.Ca)78.25Data Roaming and Quality Assessment for Composed Image Retrieval
CASE77.5Data Roaming and Quality Assessment for Composed Image Retrieval
VISTA (base)75.9VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
TG-CIR (Wen et al., 2023)75.6Target-Guided Composed Image Retrieval-
CLIP4Cir (v3)75.10Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features
BLIP4CIR+Bi72.59Bi-directional Training for Composed Image Retrieval via Text Prompt Learning
CLIP4Cir (v2)69.09Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based Features-
CLIP4Cir63.87Effective Conditioned and Composed Image Retrieval Combining CLIP-Based Features-
CIRPLANT45.88Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models
ARTEMIS43.05ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similarity
MMRet-MLLM-MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
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Image Retrieval On Cirr | SOTA | HyperAI