The task of 3D single object tracking (SOT) with LiDAR point clouds is crucial for various applications, such as autonomous driving and robotics.
3D object detection with surround-view images is an essential task for autonomous driving.
Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors.
In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames given an object template.
3D object detection using point clouds has attracted increasing attention due to its wide applications in autonomous driving and robotics.
Despite its success, the said paradigm is still constrained by several factors, such as (i) low-quality region proposals for novel classes and (ii) negligence of the inter-class correlation among different classes.
The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection.
First, it projects object queries into the same embedding space as encoded image features, where the matching can be accomplished efficiently with aligned semantics.
Specifically, we design GenCo, a Generative Co-training network that mitigates the discriminator over-fitting issue by introducing multiple complementary discriminators that provide diverse supervision from multiple distinctive views in training.
In addition, existing 3D domain adaptive detection methods often assume prior access to the target domain annotations, which is rarely feasible in the real world.
Generative Adversarial Networks (GANs) have become the de-facto standard in image synthesis.
In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully.
DA-DETR introduces a novel CNN-Transformer Blender (CTBlender) that fuses the CNN features and Transformer features ingeniously for effective feature alignment and knowledge transfer across domains.
In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.
Few-shot object detection has been extensively investigated by incorporating meta-learning into region-based detection frameworks.
Ranked #7 on Few-Shot Object Detection on MS-COCO (30-shot)
This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images.