230 citations · 508 across the 30 of their papers we have counts for
6 papers · 1 filter
Detecting Human-Object Interaction with Mixed Supervision
Suresh Kirthi Kumaraswamy, Miaojing Shi, Ewa Kijak
Human object interaction (HOI) detection is an important task in image understanding and reasoning. It is in a form of HOI triplet <human; verb; object>, requiring bounding boxes f…
Fast Fourier Intrinsic Network
Yanlin Qian, Miaojing Shi, Joni-Kristian Kämäräinen +1
We address the problem of decomposing an image into albedo and shading. We propose the Fast Fourier Intrinsic Network, FFI-Net in short, that operates in the spectral domain, split…
Restoring Negative Information in Few-Shot Object Detection
Yukuan Yang, Fangyun Wei, Miaojing Shi +1
Few-shot learning has recently emerged as a new challenge in the deep learning field: unlike conventional methods that train the deep neural networks (DNNs) with a large number of…
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
Yuting Liu, Zheng Wang, Miaojing Shi +3
Unsupervised crowd counting is a challenging yet not largely explored task. In this paper, we explore it in a transfer learning setting where we learn to detect and count persons i…
Defending Adversarial Examples via DNN Bottleneck Reinforcement
Wenqing Liu, Miaojing Shi, Teddy Furon +1
This paper presents a DNN bottleneck reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks. Typical DNN classifiers encode t…
Active Crowd Counting with Limited Supervision
Zhen Zhao, Miaojing Shi, Xiaoxiao Zhao +1
To learn a reliable people counter from crowd images, head center annotations are normally required. Annotating head centers is however a laborious and tedious process in dense cro…