5 accepted papers
Open-vocabulary indoor three-dimensional object detection (OVI3DOD) is used to detect any class of objects in indoor scenes with prompts. Owing to the relatively limited three-dimensional (3D) data, most of the OVI3DOD algorithms perform training with pseudo labels transformed from the openvocabular…
For open-vocabulary indoor three-dimensional (3D) object detection (OVI3DOD), there is a gap between the image and the point cloud for indoor scenes, especially on distant objects. However, existing algorithms ignore this problem, which weakens the detection performance. Therefore, we propose Compen
"This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, thei…
Out-of-Distribution (OOD) detection aims to address the excessive confidence prediction by neural networks by triggering an alert when the input sample deviates significantly from the training distribution (in-distribution) indicating that the output may not be reliable. Current OOD detection approa…
Convolutional networks are large linear systems divided into layers and connected by non-linear units. These units are the "articulations" that allow the network to adapt to the input. To understand how a network manages to solve a problem we must look at the articulated decisions in entirety. If we…