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Found 6 papers, 0 papers with code
Overview of the TREC 2023 Product Product Search Track
The focus this year was the creation of a reusable collection and evaluation of the impact of the use of metadata and multi-modal data on retrieval accuracy.
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking.
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymmetry between the context and query encoders.
A Multi-task Learning Framework for Product Ranking with BERT
The proposed model utilizes domain-specific BERT with fine-tuning to bridge the vocabulary gap and employs multi-task learning to optimize multiple objectives simultaneously, which yields a general end-to-end learning framework for product search.
Image Matters: Scalable Detection of Offensive and Non-Compliant Content / Logo in Product Images
In this paper, we present a computer vision driven offensive and non-compliant image detection system for extremely large image datasets.
Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce
Classifying products into categories precisely and efficiently is a major challenge in modern e-commerce.
