More This repo will update the survey of box-supervised instance segmentation, we highly welcome the user to develop more algorithms in this toolbox. If this rep is helpful for your work, please give me a star.
Official pytorch implementation of SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation (CVPR 2023) - lslrh/SIM
This paper presents a weakly supervised image segmentation method that adopts tight bounding box annotations. It proposes generalized multiple instance learning (MIL) and smooth maximum approximation to integrate the bounding box tightness prior into the deep neural network in an end-to-end manner. In...
Pig instance segmentation is a critical component of smart pig farming, serving as the basis for advanced applications such as health monitoring and weight estimation. However, existing methods typically rely on large volumes of precisely labeled mask data, which are both difficult and costly to ...
Abstract: Instance segmentation on 3D point clouds (3DIS) is a longstanding challenge in computer vision, where state-of-the-art methods are mainly based on full supervision. As annotating ground truth dense instance masks is tedious and expensive, solving 3DIS with weak supervision has become ...
BoxTeacher presents a novel perspective,i.e., leveraging high-quality masks, to address box-supervised instance segmentation. BoxTeacher explores a self-training framework with consistency training, pseudo labeling, and noise-aware losses. It's effective and can largely bridge the gap between fully-...
Moreover, as a regularization strategy, multi-scale regularization can be applied to emerging technologies such as Transformer and its variants in contrastive language-image learning, human action recognition, semantic segmentation, and so on. While bounding box re-prediction can be combined with other...
We propose a weakly supervised approach to semantic segmentation using bounding box annotations. Bounding boxes are treated as noisy labels for the foreground objects. We predict a per-class attention map that saliently guides the per-pixel cross entropy
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C). We observe that object candidates mined through current multiple instance learning methods are usually trapped to ...
Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes - jchibane/Box2Mask