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computer-vision-2

Retrieval

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in retrieval-6

Trend

Dataset

Best Model

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OK-VQA

MVK

InfoSeek

Libraries

Use these libraries to find retrieval-6 models and implementations

faceonlive/ai-research

9 papers 133

Datasets

ARO

Subtasks

Deep Hashing Table Retrieval

Most implemented papers

Modeling Relational Data with Graph Convolutional Networks

M. Welling, Rianne van den Berg, Ivan Titov, Thomas Kipf, M. Schlichtkrull, Peter Bloem•Thu Mar 16 2017

It is shown that factorization models for link prediction such as DistMult can be significantly improved through the use of an R-GCN encoder model to accumulate evidence over multiple inference steps in the graph, demonstrating a large improvement of 29.8% on FB15k-237 over a decoder-only baseline.

5621

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Polyvore

towhee-io/towhee

7 papers 2,977

huggingface/transformers

6 papers 124,650

microsoft/simxns

6 papers 96

stanford-futuredata/ColBERT

5 papers 2,450

paddlepaddle/rocketqa

5 papers 740

NVIDIA/Megatron-LM

4 papers 8,485

modelscope/adaseq

4 papers 356

cordercorder/knn-models

4 papers 28

QAMPARI

TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents

Thomas Wolf, Victor Sanh, Julien Chaumond, Clement Delangue•Tue Jan 22 2019

A new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo is introduced which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model which shows strong improvements over the current state-of-the-art end-to-end conversational models.

514 0

Paper Graph

Dense Passage Retrieval for Open-Domain Question Answering

Danqi Chen, Sewon Min, Wen-tau Yih, Patrick Lewis, Barlas Oğuz, Sergey Edunov, Ledell Yu Wu, Vladimir Karpukhin•Thu Apr 09 2020

This work shows that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework.

4987 0

Paper Graph

NetVLAD: CNN Architecture for Weakly Supervised Place Recognition

T. Pajdla, Josef Sivic, A. Torii, Relja Arandjelović, Petr Gronát•Sun Nov 22 2015

A convolutional neural network architecture that is trainable in an end-to-end manner directly for the place recognition task, and significantly outperforms non-learnt image representations and off-the-shelf CNN descriptors on two challenging place recognition benchmarks.

2996 0

Paper Graph

CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Marc Brockschmidt, Miltiadis Allamanis, Hamel Husain, Hongqiu Wu, Tiferet Gazit•Thu Sep 19 2019

The methodology used to obtain the corpus and expert labels, as well as a number of simple baseline solutions for the task are described.

1292 0

Paper Graph

Fine-Tuning CNN Image Retrieval with No Human Annotation

Giorgos Tolias, Ondřej Chum, Filip Radenovic•Thu Nov 02 2017

It is shown that both hard-positive and hard-negative examples, selected by exploiting the geometry and the camera positions available from the 3D models, enhance the performance of particular-object retrieval.

1475 0

Paper Graph

Large-Scale Image Retrieval with Attentive Deep Local Features

Hyeonwoo Noh, Bohyung Han, Tobias Weyand, Jack Sim, A. Araújo•Sun Dec 18 2016

An attentive local feature descriptor suitable for large-scale image retrieval, referred to as DELE (DEep Local Feature), based on convolutional neural networks, which are trained only with image-level annotations on a landmark image dataset.

835 0

Paper Graph

Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors

Artem Babenko, Dmitry Baranchuk, Yury Malkov•Tue Feb 06 2018

It is argued that the potential of the simple inverted index was not fully exploited in previous works and advocate its usage both for the highly-entangled deep descriptors and relatively disentangled SIFT descriptors.

115 0

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Retrieval | State-of-the-Art