open world

model train feature -> ReconNet_dropout -> drop.feature cat[ drop.feature, init.feature] cat at dim=1 ReconNet_dropout dropout ​ shortcut = self.conv1(x) ​ shortcut.features = self.bn0(shortcut.features) ​ shortcut.features = self.act1(shortcut.features) ​ shortcut.features = self.dropout1(shortcut.features) eval metric ???predict_labels = torch.mean(outputs, dim=0, keepdim=True) uncertainty_scores = torch.var(outputs, dim=0, keepdim=True) uncertainty_scores = torch.mean(uncertainty_scores, dim=1)

跳过train直接val 注释以下两个地方： 150到156行： 125到170行 Pointcept is a powerful and flexible codebase for point cloud perception research. It is also an official implementation of the following paper: Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning Xiaoyang Wu, Xin Wen, Xihui Liu, Hengshuang Zhao IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2023 [ Pretrain ] [ MSC ] - [ arXiv ] [ Bib ] → here...

Background & Method few-shot learning(doesn’t really matter in OOD task) Everything you need to know about Few-Shot Learning prototype clustering Apply in Stratified Transformer dmlnet method train crit = nn.NLLLoss(ignore_index=-1) CE_loss = self.crit(pred, feed_dict[‘seg_label’] batch_data -> feed_dict train.py->main net_encoder = ModelBuilder.build_encoder( arch=cfg.MODEL.arch_encoder.lower(), fc_dim=cfg.MODEL.fc_dim, weights=cfg.MODEL.weights_encoder) net_decoder = ModelBuilder.build_decoder( arch=cfg.MODEL.arch_decoder.lower(), fc_dim=cfg.MODEL.fc_dim, num_class=cfg.DATASET.num_class, weights=cfg.MODEL.weights_decoder) Where is prototype? No prototype in Loss Prototype should be in classifier( probability procedure ) self.centers should be prototypes why prototype is 3?...

sparse conv

提出 REAL Related Work 传统 closed-set LIDAR语义分割 point-based methods: point-net / point-net++ 但是有性能限制 voxel-based methods: Cylinder3D，三维数据集SOTA，本文的base architecture Cylinder3D论文解读及代码略解 open-set 但是2D： uncertainty-based methods： generative model-based methods： Stochastic Gradient Langevin Dynamics (SGLD) Open-world Methodology REAL: Redundancy Classifier Framework Oseg: Open-set Semantic Segmentation 将模型从$\mathcal{M}_c$训练到$\mathcal{M}_o$ ，提出两个训练策略：Unknown Object Synthesis，Predictive Distribution Calibration Unknown Object Synthesis Predictive Distribution Calibration deep metric learning open-world 和 open-set 有何不同 不必太关注open-world 与 open-set 差异，但可以多留意生成式模型的后续研究 为什么uncertainty-based methods: the network predicts the novel classes as old classes with high confidence scores...

论文链接： Abstract 提出： Open World Object Detection 问题 模型能够在无明确监督的情形下将无标签物体识别为’unknown' 当逐步输入相应的标签时，能够学习到已识别的’unknown’类别中去 Open World Object Detector ORE 基于Contrastive clustering和Energy based unknown identification 发现： we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort. 我们发现识别和表征未知实例有助于减少incremental object detection设置中的混淆，在这种设置中我们实现了SOTA，而无需额外的方法论。 Intro 问题对比： Open set learning / Incremental Learning / Open world object Detection 方法 ORE Faster R-CNN 作为detector...