set matching

16 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

DETRs with Hybrid Matching

HDETR/H-Deformable-DETR CVPR 2023

One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to remove duplicate detections.

An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans

lauziere/ehgm 20 Apr 2021

Finding an optimal correspondence between point sets is a common task in computer vision.

Exchangeable deep neural networks for set-to-set matching and learning

st-tech/zozo-shift15m ECCV 2020

Matching two different sets of items, called heterogeneous set-to-set matching problem, has recently received attention as a promising problem.

More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data

luisoala/non-iid-ssdl 20 Apr 2021

In this work, we demonstrate the limits of semantic data set matching.

SHIFT15M: Fashion-specific dataset for set-to-set matching with several distribution shifts

st-tech/zozo-shift15m 30 Aug 2021

This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images.

DETRs with Collaborative Hybrid Assignments Training

open-mmlab/mmdetection ICCV 2023

This new training scheme can easily enhance the encoder's learning ability in end-to-end detectors by training the multiple parallel auxiliary heads supervised by one-to-many label assignments such as ATSS and Faster RCNN.

Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

yunshengb/GraphSim 10 Sep 2018

Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed.

Convolutional Set Matching for Graph Similarity

snap-stanford/neural-subgraph-learning-gnn 23 Oct 2018

We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs.

FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase

infinitecold/FreebaseQA NAACL 2019

In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase.

The CAT SET on the MAT: Cross Attention for Set Matching in Bipartite Hypergraphs

govindjsk/catsetmat 30 Oct 2021

Usual relations between entities could be captured using graphs; but those of a higher-order -- more so between two different types of entities (which we term "left" and "right") -- calls for a "bipartite hypergraph".