Search Results for author: Wenchi Ma

Found 12 papers, 1 papers with code

Miti-DETR: Object Detection based on Transformers with Mitigatory Self-Attention Convergence

1 code implementation26 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.

Inductive Bias object-detection +1

Semantic Clustering based Deduction Learning for Image Recognition and Classification

no code implementations25 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.


Six-channel Image Representation for Cross-domain Object Detection

no code implementations3 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.

object-detection Object Detection +2

Why Layer-Wise Learning is Hard to Scale-up and a Possible Solution via Accelerated Downsampling

no code implementations15 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.

Image Classification

Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting

no code implementations4 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.

Crowd Counting regression

Location-Aware Box Reasoning for Anchor-Based Single-Shot Object Detection

no code implementations13 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.

General Classification object-detection +2

Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters

no code implementations5 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.

MDFN: Multi-Scale Deep Feature Learning Network for Object Detection

no code implementations10 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.

object-detection Object Detection

Object Detection with Convolutional Neural Networks

no code implementations4 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).

object-detection Object Detection

Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization

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.

Denoising Multiple Instance Learning +2

Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization

no code implementations4 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.

Denoising Multiple Instance Learning +1

MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection

no code implementations6 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.

object-detection Object Detection

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