1 code implementation • 26 Dec 2021 • Wenchi Ma, Tianxiao Zhang, Guanghui Wang
Object Detection with Transformers (DETR) and related works reach or even surpass the highly-optimized Faster-RCNN baseline with self-attention network architectures.
no code implementations • 25 Dec 2021 • Wenchi Ma, Xuemin Tu, Bo Luo, Guanghui Wang
The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains.
no code implementations • 3 Jan 2021 • Tianxiao Zhang, Wenchi Ma, Guanghui Wang
If we train the detector using the data from one domain, it cannot perform well on the data from another domain due to domain shift, which is one of the big challenges of most object detection models.
no code implementations • 15 Oct 2020 • Wenchi Ma, Miao Yu, Kaidong Li, Guanghui Wang
This paper, for the first time, reveals the fundamental reason that impedes the scale-up of layer-wise learning is due to the relatively poor separability of the feature space in shallow layers.
no code implementations • 4 Oct 2020 • Usman Sajid, Wenchi Ma, Guanghui Wang
The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance.
no code implementations • 13 Jul 2020 • Wenchi Ma, Kaidong Li, Guanghui Wang
In this paper, we aim at single-shot object detectors and propose a location-aware anchor-based reasoning (LAAR) for the bounding boxes.
no code implementations • 5 Jan 2020 • Ziming Zhang, Wenchi Ma, Yuanwei Wu, Guanghui Wang
In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively.
no code implementations • 10 Dec 2019 • Wenchi Ma, Yuanwei Wu, Feng Cen, Guanghui Wang
Compared with features produced in earlier layers, the deep features are better at expressing semantic and contextual information.
no code implementations • 4 Dec 2019 • Kaidong Li, Wenchi Ma, Usman Sajid, Yuanwei Wu, Guanghui Wang
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN).
no code implementations • arXiv 2019 • Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang
In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations.
no code implementations • 4 Oct 2019 • Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang
In this paper, we address the problem of weakly supervised object localization (WSL), which trains a detection network on the dataset with only image-level annotations.
no code implementations • 6 Sep 2018 • Wenchi Ma, Yuanwei Wu, Zongbo Wang, Guanghui Wang
To better handle these challenges, the paper proposes a novel framework, multi-scale, deep inception convolutional neural network (MDCN), which focuses on wider and broader object regions by activating feature maps produced in the deep part of the network.