set matching
19 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in set matching
Most implemented papers
DETRs with Hybrid Matching
HDETR/H-Deformable-DETR
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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.
DETRs with Collaborative Hybrid Assignments Training
open-mmlab/mmdetection
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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.
An Exact Hypergraph Matching Algorithm for Nuclear Identification in Embryonic Caenorhabditis elegans
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
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
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
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.
ESM+: Modern Insights into Perspective on Text-to-SQL Evaluation in the Age of Large Language Models
Our results show that EXE and ESM have high false positive and negative rates of 11. 3% and 13. 9%, while ESM+ gives those of 0. 1% and 2. 6% respectively, providing a significantly more stable evaluation.
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching
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
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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
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.
