5 research outputs found
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Weakly supervised point cloud segmentation, i.e. semantically segmenting a
point cloud with only a few labeled points in the whole 3D scene, is highly
desirable due to the heavy burden of collecting abundant dense annotations for
the model training. However, existing methods remain challenging to accurately
segment 3D point clouds since limited annotated data may lead to insufficient
guidance for label propagation to unlabeled data. Considering the
smoothness-based methods have achieved promising progress, in this paper, we
advocate applying the consistency constraint under various perturbations to
effectively regularize unlabeled 3D points. Specifically, we propose a novel
DAT (\textbf{D}ual \textbf{A}daptive \textbf{T}ransformations) model for weakly
supervised point cloud segmentation, where the dual adaptive transformations
are performed via an adversarial strategy at both point-level and region-level,
aiming at enforcing the local and structural smoothness constraints on 3D point
clouds. We evaluate our proposed DAT model with two popular backbones on the
large-scale S3DIS and ScanNet-V2 datasets. Extensive experiments demonstrate
that our model can effectively leverage the unlabeled 3D points and achieve
significant performance gains on both datasets, setting new state-of-the-art
performance for weakly supervised point cloud segmentation.Comment: ECCV 202
Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection
Fully supervised object detection has achieved great success in recent years.
However, abundant bounding boxes annotations are needed for training a detector
for novel classes. To reduce the human labeling effort, we propose a novel
webly supervised object detection (WebSOD) method for novel classes which only
requires the web images without further annotations. Our proposed method
combines bottom-up and top-down cues for novel class detection. Within our
approach, we introduce a bottom-up mechanism based on the well-trained fully
supervised object detector (i.e. Faster RCNN) as an object region estimator for
web images by recognizing the common objectiveness shared by base and novel
classes. With the estimated regions on the web images, we then utilize the
top-down attention cues as the guidance for region classification. Furthermore,
we propose a residual feature refinement (RFR) block to tackle the domain
mismatch between web domain and the target domain. We demonstrate our proposed
method on PASCAL VOC dataset with three different novel/base splits. Without
any target-domain novel-class images and annotations, our proposed webly
supervised object detection model is able to achieve promising performance for
novel classes. Moreover, we also conduct transfer learning experiments on large
scale ILSVRC 2013 detection dataset and achieve state-of-the-art performance