3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in set-matching
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This paper proposes SHIFT15M, a dataset that can be used to properly evaluate set-to-set matching models in situations where the distribution of data changes between training and testing.
A novel collaborative hybrid assignments training scheme, namely Co - DETR, to learn more efficient and effective DETR-based detectors from versatile label assignment manners to enhance the encoder’s learning ability in end-to-end detectors.
The proposed algorithm, Exact Hypergraph Matching (EHGM), adapts the classical branch-and-bound paradigm to dynamically identify a globally optimal correspondence between point-sets under an arbitrarily intricate hypergraphical model, enabling rich characterizations of relationships between objects via hypergraphs.
The model, Graph-Sim, achieves the state-of-the-art performance on four real-world graph datasets under six out of eight settings, compared to existing popular methods for approximate Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) computation.
A novel deep learning architecture to address the abovementioned difficulties and also an efficient training framework for set-to-set matching is proposed and evaluated through experiments based on two industrial applications: fashion set recommendation and group re-identification.
This work introduces GSimCNN (Graph Similarity Computation via Convolutional Neural Networks) for predicting the similarity score between two graphs and demonstrates that the model achieves the state-of-the-art performance on graph similarity search.
This data set, named FreebaseQA, is generated by matching trivia-type question-answer pairs with subject-predicate-object triples in Freebase to generate over 54K matches from about 28K unique questions with minimal cost.
This work poses bipartite hyperedge link prediction as a set-matching (SETMAT) problem and proposes a novel neural network architecture called CATSETMAT for the same, which is compared with multiple techniques from the state-of-the-art.
It is demonstrated that simple density based dissimilarity measures in the feature space of a generic classifier offer a promising and more reliable quantitative matching criterion to select unlabelled data before SSDL training.
A simple yet effective method based on a hybrid matching scheme that combines the original one-to-one matching branch with an auxiliary one- to-many matching branch during training that shows that a wide range of representative DETR methods can be consistently improved across a widerange of visual tasks.
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