#image-retrieval News & Analysis

4 articles tagged with #image-retrieval. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AINeutralarXiv – CS AI · Jun 86/10

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Never Seen Before: Benchmarking Genuine Zero-Shot Composed Image Retrieval with Consistent Video-Sourced Datasets

Researchers introduce ZeroSight, a new benchmark for Zero-Shot Composed Image Retrieval that addresses critical flaws in existing datasets by using video-sourced data published after CLIP's training cutoff and proposing SC4CIR, a training-free method that reveals current ZS-CIR performance metrics significantly overestimate actual model capabilities.

AINeutralarXiv – CS AI · May 126/10

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Decoupling Endpoint and Semantic Transition Learning for Zero-Shot Composed Image Retrieval

Researchers propose DeCIR, a new approach to zero-shot composed image retrieval that separates endpoint matching from semantic transition learning to overcome limitations in projection-based methods. The technique uses decoupled text adapters and low-rank directional merging to improve performance on image retrieval tasks without increasing computational complexity at inference time.

AINeutralarXiv – CS AI · Mar 95/10

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Visual Words Meet BM25: Sparse Auto-Encoder Visual Word Scoring for Image Retrieval

Researchers introduce BM25-V, a new image retrieval method that combines sparse visual-word activations from Vision Transformers with BM25 scoring for efficient and interpretable image search. The approach achieves 99.3%+ recall across seven benchmarks while offering explainable results and serving as an efficient first-stage retriever for dense reranking systems.

AINeutralarXiv – CS AI · Mar 54/10

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DQE-CIR: Distinctive Query Embeddings through Learnable Attribute Weights and Target Relative Negative Sampling in Composed Image Retrieval

Researchers propose DQE-CIR, a new method for composed image retrieval that improves AI's ability to find images based on reference images and text modifications. The approach addresses limitations in current contrastive learning frameworks by using learnable attribute weights and target relative negative sampling to create more distinctive query embeddings.