The Power and Pitfalls of Vector-Based Image Search

Image search with 🤗 datasets

Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)

The article titled "Computer Vision: SIFT algorithm (Scale Invariant Feature Transform)" discusses the SIFT algorithm, a method for matching objects across different viewpoints. It highlights the elegance of this approach in handling variations in scale and orientation. The post was originally published on Towards Data Science.

Replication in Visual Diffusion Models: A Survey and Outlook

arXiv:2408. 00001v2 Announce Type: replace-cross Abstract: Visual diffusion models have revolutionized the field of creative AI, producing high-quality and diverse content.

Exploring the context of online images with Backstory

New experimental AI tool helps people explore the context and origin of images seen online.

Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval

arXiv:2607. 04605v1 Announce Type: cross Abstract: Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expensive.

Making a PDF’s Images Searchable for RAG, Without Paying to Read Them All

Enterprise Document Intelligence [Vol. 1 #5sexies] - image_df tells you where every picture is.

PailitaoGR: Latent Think-with-Images for Generative Image Retrieval

PailitaoGR is a generative image retrieval method that incorporates target-focused perception and selective auxiliary-evidence utilization. It uses a target Enhancer and on-policy distillation to highlight search-target regions, and an auxiliary enhancer with incremental contrastive distillation to exploit auxiliary evidence. Trained on real-world online image-search logs, it achieves an average 13.8% improvement over existing baselines.

PailitaoGR: Latent Think-with-Images for Generative Image Retrieval

PailitaoGR is a generative image retrieval model that incorporates a latent think-with-images approach to better handle real‑world query images. It uses a target‑focused perception mechanism—comprising a target enhancer and on‑policy distillation—to highlight the search target, and a selective auxiliary‑evidence mechanism—using an auxiliary enhancer and incremental contrastive distillation—to exploit useful side information. Trained on real‑world online image‑search logs, the method achieves an average 13.8 % improvement over existing baselines.

ColGraphRAG: Late-Interaction Evidence Retrieval for Multimodal GraphRAG

arXiv:2607. 16208v1 Announce Type: new Abstract: Graph-grounded multimodal question answering organizes text, tables, and images in a structured evidence graph, yet end-to-end accuracy depends on which multimodal assets are ranked highly enough to enter downstream reasoning; for graph-linked images, single-vector bi-encoder similarity can discard patch- and token-level structure needed for fine-grained alignment.

Health-specific embedding tools for dermatology and pathology

Posted by Dave Steiner, Clinical Research Scientist, Google Health, and Rory Pilgrim, Product Manager, Google Research There’s a worldwide shortage of access to medical imaging expert interpretation across specialties including radiology , dermatology and pathology . Machine learning (ML) technology can help ease this burden by powering tools that enable doctors to interpret these images more accurately and efficiently.

Do All Visual Tokens Matter Equally? Object-Evidence Preserving Token Merging for Vision-Language Retrieval

Multi-vector vision-language retrieval preserves fine-grained visual evidence through maximum-similarity late interaction, but dense image-side tokens make storage and scoring expensive. Existing token compression methods reduce this cost, yet they can remove or collapse object- and region-level evidence that future query tokens may need to select.

MAGIC: Marginal-Guided Compression with Optimal Transport for Efficient Visual Document Retrieval

arXiv:2609.21018v1 Announce Type: new Abstract: Recent visual document retrieval (VDR) systems such as ColPali use multi-vector page embeddings, in which patch-level vectors enable fine-grained evide...