Text Retrieval
239 papers with code • 5 benchmarks • 14 datasets
Benchmarks
These leaderboards are used to track progress in Text Retrieval
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Libraries
Use these libraries to find Text Retrieval models and implementationsDatasets
Reuters-21578
MTEB
RSICD
Image-Chat
TripClick
DIOR-RSVG
BSARD
Most implemented papers
Variational Deep Semantic Hashing for Text Documents
unsuthee/VariationalDeepSemanticHashing
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Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling.
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
vacancy/NSCL-PyTorch-Release
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ICLR 2019
To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation.
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
zhegan27/VILLA
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NeurIPS 2020
We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning.
GLoRIA: A Multimodal Global-Local Representation Learning Framework for Label-Efficient Medical Image Recognition
marshuang80/gloria
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ICCV 2021
In recent years, the growing number of medical imaging studies is placing an ever-increasing burden on radiologists.
Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits
oaqa/FlexNeuART
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We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end.
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval
intersun/LightningDOT
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NAACL 2021
Multimodal pre-training has propelled great advancement in vision-and-language research.
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.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
starmemda/camow
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In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity.
VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
microsoft/unilm
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We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network.
Bridging Video-text Retrieval with Multiple Choice Questions
As an additional benefit, our method achieves competitive results with much shorter pre-training videos on single-modality downstream tasks, e. g., action recognition with linear evaluation.
