Search Results for author:
Found 18 papers, 12 papers with code
GMTR: Graph Matching Transformers
Meanwhile, on Pascal VOC, QueryTrans improves the accuracy of NGMv2 from $80. 1\%$ to $\mathbf{83. 3\%}$, and BBGM from $79. 0\%$ to $\mathbf{84. 5\%}$.
Ranked #1 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Rethinking and Benchmarking Predict-then-Optimize Paradigm for Combinatorial Optimization Problems
Numerous web applications rely on solving combinatorial optimization problems, such as energy cost-aware scheduling, budget allocation on web advertising, and graph matching on social networks.
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
To address these challenges under open-world assumptions, we design an \textbf{A}daptive \textbf{M}ulti-\textbf{I}nterest \textbf{D}ebiasing framework for cross-domain sequential recommendation (\textbf{AMID}), which consists of a multi-interest information module (\textbf{MIM}) and a doubly robust estimator (\textbf{DRE}).
Deep Learning of Partial Graph Matching via Differentiable Top-K
Graph matching (GM) aims at discovering node matching between graphs, by maximizing the node- and edge-wise affinities between the matched elements.
Ranked #1 on
Graph Matching
on Willow Object Class
(F1 score metric)
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization
However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.
Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond
We first show that an adversarial attack on keypoint localities and the hidden graphs can cause significant accuracy drop to deep GM models.
Ranked #6 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research.
On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK
Cardinality constrained combinatorial optimization requires selecting an optimal subset of $k$ elements, and it will be appealing to design data-driven algorithms that perform TopK selection over a probability distribution predicted by a neural network.
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature.
Learning Latent Topology for Graph Matching
Graph matching (GM) has been traditionally modeled as a deterministic optimization problem characterized by an affinity matrix under pre-defined graph topology.
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i. e. the number of inliers is needed to avoid matching outliers.
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning
This paper considers the setting of jointly matching and clustering multiple graphs belonging to different groups, which naturally rises in many realistic problems.
Ranked #2 on
Graph Matching
on Willow Object Class
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.
Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach.
Our approach enjoys flexibility in that the permutation loss is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference.
Ranked #2 on
Graph Matching
on CUB
Learning deep graph matching with channel-independent embedding and Hungarian attention
Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete.
Ranked #15 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching
We also show how to extend our network to hypergraph matching, and matching of multiple graphs.
Ranked #6 on
Graph Matching
on SPair-71k
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35. 7 mAP to 37. 9 mAP without modifying the backbone or network structure.
Ranked #78 on
Instance Segmentation
on COCO test-dev
Learning Combinatorial Embedding Networks for Deep Graph Matching
In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises.
