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Found 4 papers, 0 papers with code
Learning from Noisy Labels with Noise Modeling Network
Multi-label image classification has generated significant interest in recent years and the performance of such systems often suffers from the not so infrequent occurrence of incorrect or missing labels in the training data.
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks
Experimental results demonstrate that our approach can achieve state-of-the-art average precision (AP) performances on the InfAR dataset: (1) the proposed two-stream 3D CNN achieves the best reported 77. 5% AP, and (2) our 3D CNN model applied to the optical flow fields achieves the best reported single stream 75. 42% AP.
Seeded Graph Matching Via Joint Optimization of Fidelity and Commensurability
We present a novel approximate graph matching algorithm that incorporates seeded data into the graph matching paradigm.
Seeded Graph Matching
Given two graphs, the graph matching problem is to align the two vertex sets so as to minimize the number of adjacency disagreements between the two graphs.
