If you agree, we’ll also use cookies to complement your shopping experience across the Amazon stores as described in our Cookie Notice. We also use these cookies to understand how customers use our services (for example, by measuring site visits) so we can make improvements. You can also generate support files on your own by controlling dtrain_dtest_split in oshp_loader.py, however, the training and validation list might be different from our paper.We use cookies and similar tools that are necessary to enable you to make purchases, to enhance your shopping experiences and to provide our services, as detailed in our Cookie Notice. pkl files from source, which contains each class's image IDs.
Please also download our generated support. $ ln -s YOUR_CIHP_PATH/Category_rev_ids/* YOUR_PROJECT_ROOT/CIHP_OS/Category_rev_ids $ ln -s YOUR_CIHP_PATH/Validation/Category_ids/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_classes $ ln -s YOUR_CIHP_PATH/Training/Category_ids/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_classes $ ln -s YOUR_CIHP_PATH/Validation/Images/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_images $ ln -s YOUR_CIHP_PATH/Training/Images/* YOUR_PROJECT_ROOT/CIHP_OS/trainval_images $ ln -s YOUR_LIP_PATH/val_segmentations_reversed/* YOUR_PROJECT_ROOT/LIP_OS/Category_rev_ids $ ln -s YOUR_LIP_PATH/Train_parsing_reversed_labels/TrainVal_parsing_annotations/* YOUR_PROJECT_ROOT/LIP_OS/Category_rev_ids $ ln -s YOUR_LIP_PATH/TrainVal_parsing_annotations/TrainVal_parsing_annotations/val_segmentations/* YOUR_PROJECT_ROOT/LIP_OS/trainval_classes $ ln -s YOUR_LIP_PATH/TrainVal_parsing_annotations/TrainVal_parsing_annotations/train_segmentations/* YOUR_PROJECT_ROOT/LIP_OS/trainval_classes $ ln -s YOUR_LIP_PATH/TrainVal_images/TrainVal_images/val_images/* YOUR_PROJECT_ROOT/LIP_OS/trainval_images $ ln -s YOUR_LIP_PATH/TrainVal_images/TrainVal_images/train_images/* YOUR_PROJECT_ROOT/LIP_OS/trainval_images $ ln -s YOUR_ATR_PATH/SegmentationClassAug_rev/* YOUR_PROJECT_ROOT/ATR_OS/Category_rev_ids $ ln -s YOUR_ATR_PATH/SegmentationClassAug/* YOUR_PROJECT_ROOT/ATR_OS/trainval_classes $ ln -s YOUR_ATR_PATH/JPEGImages/* YOUR_PROJECT_ROOT/ATR_OS/trainval_images Please also remember to download the atr flipped labels and cihp flipped labels. Then, use the following commands to link the data into our project folder. EOPNetįirst, please download ATR, LIP and CIHP dataset from source. You can find the well-trained models together with the performance in the following table. Progressive One-shot Human Parsing (AAAI 2021) applies a progressive training scheme and is separated into three stages.Įnd-to-end One-shot Human Parsing (journal version) is a one-stage end-to-end training method, which has higher performance and FPS. This new task mainly aims to accommodate human parsing into a wider range of applications that seek to parse flexible fashion/clothing classes that are not pre-defined in previous large-scale datasets. OSHP requires parsing humans in a query image into an open set of reference classes defined by any single reference example (i.e., a support image) during testing, no matter whether they have been annotated during training (base classes) or not (novel classes). In the two papers, we propose a new task named One-shot Human Parsing (OSHP). End-to-end One-shot Human Parsing (journal version).Progressive One-shot Human Parsing (AAAI 2021).This is the official repository for our two papers: