Zero-shot Text-to-Image Retrieval

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

7 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (15 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Learning Transferable Visual Models From Natural Language Supervision
  26 Feb 2021 82 repositories listed Syntology ran 16 of 20 samples · 4 unverified · 16 pointer-only (licence)
  State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
• BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
  30 Jan 2023 17 repositories listed Syntology ran 4 of 8 samples · 4 unverified · 1 pointer-only (licence)
  The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models.
• Sigmoid Loss for Language Image Pre-Training
  27 Mar 2023 11 repositories listed Syntology ran 12 of 29 samples · 17 unverified · 25 pointer-only (licence)
  We propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP).
• FLAVA: A Foundational Language And Vision Alignment Model
  8 Dec 2021 4 repositories listed
  State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks.
• ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
  18 May 2023 2 repositories listed Syntology ran 2 of 7 samples · 5 unverified
  In this work, we explore a scalable way for building a general representation model toward unlimited modalities.
• MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
  28 Mar 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  MagicLens is built on a key novel insight: image pairs that naturally occur on the same web pages contain a wide range of implicit relations (e.
• M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
  29 Jan 2024 1 repository listed
  Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence.
• Linguistic-Aware Patch Slimming Framework for Fine-grained Cross-Modal Alignment
  1 Jan 2024 1 repository listed
  We propose a novel Linguistic-Aware Patch Slimming (LAPS) framework for fine-grained alignment which explicitly identifies redundant visual patches with language supervision and rectifies their semantic and spatial…
• Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
  2 Nov 2022 1 repository listed Syntology ran 4 of 7 samples · 3 unverified
  The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining.
• ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training
  30 Sep 2022 1 repository listed
  They attempt to learn cross-modal representation using contrastive learning on image-text pairs, however, the built inter-modal correlations only rely on a single view for each modality.
• ZSCRGAN: A GAN-based Expectation Maximization Model for Zero-Shot Retrieval of Images from Textual Descriptions
  23 Jul 2020 1 repository listed
  Most existing algorithms for cross-modal Information Retrieval are based on a supervised train-test setup, where a model learns to align the mode of the query (e.

Syntology lines on 6 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.