Re-Ranking
226 papers with code • 2 benchmarks • 2 datasets
Libraries
Use these libraries to find Re-Ranking models and implementations
SciDocs
AskUbuntu
Most implemented papers
A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking
pse-ecn/pose-sensitive-embedding
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CVPR 2018
In contrast to the recent direction of explicitly modeling body parts or correcting for misalignment based on these, we show that a rather straightforward inclusion of acquired camera view and/or the detected joint locations into a convolutional neural network helps to learn a very effective representation.
SphereReID: Deep Hypersphere Manifold Embedding for Person Re-Identification
layumi/Person_reID_baseline_pytorch
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In this paper, we use a modified softmax function, termed Sphere Softmax, to solve the classification problem and learn a hypersphere manifold embedding simultaneously.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
facebookresearch/ReAgent
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ICLR 2019
Ranking is a central task in machine learning and information retrieval.
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
Diffusion is commonly used as a ranking or re-ranking method in retrieval tasks to achieve higher retrieval performance, and has attracted lots of attention in recent years.
Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
We propose a novel method for selecting coherent and diverse responses for a given dialogue context.
Finding Moments in Video Collections Using Natural Language
We evaluate our approach on two recently proposed datasets for temporal localization of moments in video with natural language (DiDeMo and Charades-STA) extended to our video corpus moment retrieval setting.
Matching Images and Text with Multi-modal Tensor Fusion and Re-ranking
Wangt-CN/MTFN-RR-PyTorch-Code
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We propose a novel framework that achieves remarkable matching performance with acceptable model complexity.
Deep Graph Matching Consensus
rusty1s/deep-graph-matching-consensus
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ICLR 2020
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval
cvdfoundation/google-landmark
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Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity.
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
heshuting555/AICITY2020_DMT_VehicleReID
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Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID.
