Re-Ranking
287 papers with code • 2 benchmarks • 2 datasets
Libraries
Use these libraries to find Re-Ranking models and implementations
SciDocs
AskUbuntu
Most implemented papers
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
stanford-futuredata/ColBERT
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ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
Particular object retrieval with integral max-pooling of CNN activations
almazan/deep-image-retrieval
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Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations.
Passage Re-ranking with BERT
Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference.
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning
kwang2049/pytorch-bertflow
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Learning sentence embeddings often requires a large amount of labeled data.
Document Expansion by Query Prediction
One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content. From the perspective of a question answering system, this might comprise questions the document can potentially answer.
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows.
Automatic Check-Out via Prototype-based Classifier Learning from Single-Product Exemplars
hao-chen-njust/psp
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The European Conference on Computer Vision (ECCV) 2022
Automatic Check-Out (ACO) aims to accurately predict the presence and count of each category of products in check-out images, where a major challenge is the significant domain gap between training data (single-product exemplars) and test data (check-out images).
Visual Re-ranking with Natural Language Understanding for Text Spotting
ahmedssabir/Visual-Semantic-Relatedness-with-Word-Embedding
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We propose a post-processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text.
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
UKPLab/beir
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To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval.
