LARE: Low-Attention Region Encoding for Text-Image Retrieval

arXiv:2606.18885 · cs.CV, cs.IR · Submitted 2026-06-17 · Read on arXiv

cs.CV, cs.IR

Submitted: 2026-06-17

Updated: 2026-06-17

Code: https://github.com/AbdulmalikDS/LARE

Terminology

• Low-Attention Region Encoding — 18× in this paper
• Embeddings — 15× in this paper · explained in 2 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Transformer — 8× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Representation Learning — 4× in this paper · explained in 2 episodes
• Contrastive Learning — 2× in this paper · explained in 5 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Training-Free Framework — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation Studies — 1× in this paper · explained in 1 episode
• Alignment Methods — 1× in this paper · explained in 1 episode
• Distillation — 1× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 1× in this paper · explained in 4 episodes

Sources

• Global-to-Local or Local-to-Global? Enhancing Image Retrieval with Efficient Local Search and Effective Global Re-ranking
• Look Beyond Saliency: Low-Attention Guided Dual Encoding for Video Semantic Search
• YOLOv4: Optimal Speed and Accuracy of Object Detection
• UNITER-Based Situated Coreference Resolution with Rich Multimodal Input
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference
• FILIP: Fine-grained Interactive Language-Image Pre-Training
• Sigmoid Loss for Language Image Pre-Training

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