InCoM achieves 23-28% higher success rates in mobile manipulation tasks by inferring motion intent for adaptive perception and decoupling base-arm action generation.
Pyramid Scene Parsing Network
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abstract
Scene parsing is challenging for unrestricted open vocabulary and diverse scenes. In this paper, we exploit the capability of global context information by different-region-based context aggregation through our pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet). Our global prior representation is effective to produce good quality results on the scene parsing task, while PSPNet provides a superior framework for pixel-level prediction tasks. The proposed approach achieves state-of-the-art performance on various datasets. It came first in ImageNet scene parsing challenge 2016, PASCAL VOC 2012 benchmark and Cityscapes benchmark. A single PSPNet yields new record of mIoU accuracy 85.4% on PASCAL VOC 2012 and accuracy 80.2% on Cityscapes.
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Separable convLSTMs cut parameters and FLOPs in video segmentation, delivering up to 15% faster GPU inference with similar or slightly lower accuracy.
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.
Synthetic data can partially substitute for real data in object detection training, with performance tied to domain similarity and the volume of real data included.
citing papers explorer
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InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
InCoM achieves 23-28% higher success rates in mobile manipulation tasks by inferring motion intent for adaptive perception and decoupling base-arm action generation.
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Separable Convolutional LSTMs for Faster Video Segmentation
Separable convLSTMs cut parameters and FLOPs in video segmentation, delivering up to 15% faster GPU inference with similar or slightly lower accuracy.
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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.
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How much real data do we actually need: Analyzing object detection performance using synthetic and real data
Synthetic data can partially substitute for real data in object detection training, with performance tied to domain similarity and the volume of real data included.