NUST-Machine-Intelligence-Laboratory
ExpertPKINet. Python
114HVC. Python
88KTSE. KTSE: a novel approach for weakly supervised semantic segmentation, addressing under-activation and over-expansion issues by simulating inter-image erasing and enhancing network localization
65weblyFG-dataset. Python
43FECANET. Python
33nsrom. Python
32HFAN. Python
31hsi_road. Python
22VideoMAC. Python
19SED. A self-adaptive and class-balanced approach to improve deep neural network performance in the presence of noisy labels
18PNP. The source code and models for our paper PNP: Robust Learning from Noisy Labels by Probabilistic Noise Prediction
14RCTNet. Python
13UNIALIGN. Python
12SSC. Python
12LTFormer. Python
9HPAN. Python
8HCPN. Code for paper: Hierarchical Co-attention Propagation Network for Zero-Shot Video Object Segmentation
8prune_and_merge. Python
8Jo-SRC. Python
7MCCL. Python
7Softly-Update-Drop. Web-Supervised Network with Softly Update-Drop Training for Fine-Grained Visual Classification
6I2CRC. Python
5Anti-Collapse-Loss. Python
5AMG. Python
5CoLDL. Python
4WSNFG. This is the source code for our paper Web-Supervised Network for Fine-Grained Visual Classification
4MMSI. Python
3HGPU. Python
3Co-mining. Python
3MDBA. Python
3Dataset-Purification. This is the source code for our paper Learning from Noisy Web Images for Fine-Grained Visual Recognition through Dataset Purification
3UO-SAM. Python
3Advanced-Softly-Update-Drop. This is the source code for our paper Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Open-set Noise
3ccd. This is the source code for our paper Classification Constrained Discriminator for Domain Adaptive Semantic Segmentation
3Jo-SNC. Python
3CRSSC. Python
3UncertainBEV.
3SAFGCMHN. This is the Pytorch implementation for our paper: Self-Attention based Fine-Grained Cross-Media Hybrid Network
2DDTAS. Python
2TorchSemiSeg2. Python
2EFA. Python
1CLAR-CRSSC. Python
1SMCP. This is the source code for our paper Semantically Meaningful Class Prototype Learning for One-Shot Image Segmentation
1GRIP. Category Regularization and Instance Noise-Cleaning for Webly Supervised Fine-Grained Recognition
1GMFG. This is the source code for our paper Guided by Meta-set: A Data-driven Method for Fine-Grained Visual Recognition
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