arXiv Machine Learning

Extreme Meta-Classification for Large-Scale Zero-Shot Retrieval

arXiv:2606. 25237v1 Announce Type: cross Abstract: We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace.

arXiv AI

Jun 8

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

arXiv:2606. 07032v1 Announce Type: cross Abstract: Zero-Shot Composed Image Retrieval (ZS-CIR) aims to retrieve a target image based on a query composed of a reference image and a relative caption without training samples.

By Zhenyu Yang, Zemin Du, Shengsheng Qian, Changsheng Xu

llms benchmarks

arXiv Computer Vision

Sep 22

Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale

The paper introduces GradCIR, a method for training composed image retrieval (CIR) systems on graded relevance rather than binary relevance. It uses a vision‑language model to generate queries and 4‑level relevance labels, an iterative feedback loop to mine hard negatives, and a hierarchy‑aware angular objective to directly optimize graded labels. Experiments on a Walmart catalog and FashionIQ show significant NDCG improvements and the system is deployed in Walmart’s live visual‑search traffic.

By Anubhav Gupta, Hrushikesh Mohapatra, Prijith Chandra, Asish Mohapatra, Anuj Garg, Arvind Maan, Sudip Datta, Venkat Bulusu, Sitesh Kumar Jalan

computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 31

OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

arXiv:2607. 27475v1 Announce Type: cross Abstract: In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking.

By Ziwei Li, Shuyao Li, Xufeng Cai, Xue Zou, Yiming Ma, Huiting Lu, Wujie Yan, Zhichen Zhao, Yang Lu, Zhe Wang, Rui Luo, Zhengyu Su, Dan Zhang, Ji Liu

safety

Hugging Face Trending Papers

Sep 21

Graded-Relevance Composed Multimodal Retrieval for E-commerce Visual Search at Scale

Visual search on large e-commerce catalogs must serve both "similarity" queries that ask for items resembling an uploaded image and "modifier" queries that comprise an image and text describing a desi...

computer-vision fine-tuning multimodal benchmarks

arXiv AI

Jul 8

Universal Algorithm-Implicit Learning

arXiv:2602. 14761v2 Announce Type: replace-cross Abstract: Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability.

By Stefano Woerner, Seong Joon Oh, Christian F. Baumgartner

llms rag nlp multimodal benchmarks

arXiv AI

Jun 4

ANN Search: Recall What Matters

arXiv:2606. 04522v1 Announce Type: cross Abstract: Approximate nearest neighbor (ANN) search has become a core primitive in information retrieval and modern machine learning tasks, from classification to retrieval-augmented generation.

By Dimitris Dimitropoulos, Nikos Mamoulis

llms rag efficiency benchmarks

arXiv AI

Jun 16

Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings

arXiv:2606. 15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched.

By Shubhang Bhatnagar, Dheeraj Baiju, Narendra Ahuja

llms rag efficiency multimodal benchmarks

arXiv Machine Learning

Jun 26

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.

By Daria Grushina, Kseniia Kuvshinova, Alina Kostromina, Aziz Temirkhanov, Mile Mitrovic, Dmitry Simakov

llms benchmarks

arXiv Machine Learning

Jun 30

The Voronoi Bottleneck: Capacity-Aware Dense Retrieval for Product Search

arXiv:2606. 28359v1 Announce Type: cross Abstract: Dense embedding retrieval compresses all relevance information into a single inner product, imposing a fundamental geometric limit -- the Voronoi Bottleneck -- on the number of query-document relevance patterns expressible at fixed embedding dimension (d).

By Charith Chandra Sai Balne, Rithwik Maramraju, Siddharth Pratap Singh, Rohit Upadhyay, Aditya Singh, Chittaranjan Tripathy, Yogananda Domlur Seetharama

rag

arXiv AI

Jun 11

TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning

arXiv:2606. 11640v1 Announce Type: cross Abstract: Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult.

By Ruxue Shi, Yili Wang, Mengnan Du, Hangting Ye, Yi Chang, Xin Wang

llms benchmarks safety

arXiv AI

Jun 26

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

arXiv:2606. 26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items.

By Chenghao Liu, Yu Zhang, Zhongtao Jiang, Kun Xu, Zhenwei An, Renzhi Wang, Zhao Wang, Jiachen Zhang, Yuxiao Zhang, Kun Xu, Songfang Huang

llms rag diffusion fine-tuning benchmarks

arXiv Computer Vision

Sep 10

Beyond Similarity: Foundation Models as an Efficient Backbone for Training-Free Composed Video Retrieval

arXiv:2609.10008v1 Announce Type: new Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gal...

By Dmitry Demidov, Muhammad Zaigham Zaheer, Omkar Thawakar, Abdelrahman Mohamed Shaker, Rao Anwer

rag multimodal benchmarks

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