Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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Reinforcement learning models trained only in simulation using automatic domain randomization solve Rubik's cube with a real robot hand.
Introduces IA network with SIA and CIA modules to adaptively model spatial and channel feature interdependencies for improved person re-identification on benchmarks.
Deep learning model for logical code segmentation using an approximated ground truth dataset construction technique.
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.
citing papers explorer
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Deep Residual Learning for Image Recognition
Residual networks reformulate layers to learn residual functions, enabling effective training of up to 152-layer models that achieve 3.57% error on ImageNet and win ILSVRC 2015.
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Solving Rubik's Cube with a Robot Hand
Reinforcement learning models trained only in simulation using automatic domain randomization solve Rubik's cube with a real robot hand.
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Interaction-and-Aggregation Network for Person Re-identification
Introduces IA network with SIA and CIA modules to adaptively model spatial and channel feature interdependencies for improved person re-identification on benchmarks.
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Logical Segmentation of Source Code
Deep learning model for logical code segmentation using an approximated ground truth dataset construction technique.
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Rethinking Atrous Convolution for Semantic Image Segmentation
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.