
Chuanbin LiuUniversity of Science and Technology of China | USTC · Department of Electronic Engineering and Information Science
Chuanbin Liu
Doctor of Engineering
About
30
Publications
3,892
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494
Citations
Introduction
Publications
Publications (30)
Existing face forgery detection methods attempt to identify low-level forgery artifacts (
e.g.
, blending boundary, flickering) in spatial-temporal domains or high-level semantic inconsistencies (
e.g.
, abnormal lip movements) between visual-auditory modalities for generalized face forgery detection. However, they still suffer from significant p...
The performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) persists due to the lack of inductive bias, notably when training from scratch with limited datasets. This paper identifies two crucial shortcomings in ViTs:
spatial relevance
and
diverse channel representation
. Thus, ViTs struggle to grasp fine-gra...
Layout generation is an emerging computer vision task that incorporates the challenges of object localization and aesthetic evaluation, widely used in advertisements, posters, and slides design. An ideal layout should consider both the intra-domain relationship within layout elements and the inter-domain relationship between layout elements and the...
Text design is one of the most critical procedures in poster design, as it relies heavily on the creativity and expertise of humans to design text images considering the visual harmony and text-semantic. This study introduces TextPainter, a novel multimodal approach that leverages contextual visual information and corresponding text semantics to ge...
Fine-grained visual categorization (FGVC) is a challenging task in the image analysis field which requires comprehensive discriminative feature extraction and representation. To get around this problem, previous works focus on designing complex modules, the so-called necks and heads, over simple backbones, while bringing a huge computational burden...
Objective
To construct an artificial intelligence system to measure acetabular index and evaluate its accuracy in clinical application.
Methods
A total of 10,219 standard anteroposterior pelvic radiographs were collected retrospectively from April 2014 to December 2018 in our hospital. Of these, 9,219 radiographs were randomly selected to train an...
There still remains an extreme performance gap between Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) when training from scratch on small datasets, which is concluded to the lack of inductive bias. In this paper, we further consider this problem and point out two weaknesses of ViTs in inductive biases, that is, the spatial rele...
Developmental dysplasia of the hip (DDH) is one of the most common diseases in children. Due to the experience-requiring medical image analysis work, online automatic diagnosis of DDH has intrigued the researchers. Traditional implementation of online diagnosis faces challenges with reliability and interpretability. In this paper, we establish an o...
Semantic segmentation is a fundamental problem in multimedia which requires delicate per-pixel predictions of object categories. Recently, many researchers strive to refine the pixel-wise feature with
spatial
-contextual information. However, many of them still neglect the invisible hand of cross-
channel
information which provides inherent sema...
Weakly-supervised video object segmentation is an emerging video task to track and segment the target given a simple bounding box label, which requires the method to fully catch and utilize the target information. Most existing approaches only rely on the guidance of a single frame and ignore the interaction between different frames when gathering...
With the widespread application of artificial intelligence, many scenarios require fewer parameters network. Mobile devices require networks with faster inference speed. However, current networks are becoming more and more complex. Network pruning is an important way to solve this contradiction. Current data dependent network pruning methods use a...
Genu valgus and varus (GVV) are common orthopedic deformities for children. The fundamental step for GVV diagnosis is to locate and identify anatomical landmarks in X-rays. However, it is quite challenging for both humans and computers to accurately detect the landmarks, due to the lack of distinctive position clues. In this paper, we develop a dee...
Developmental dysplasia of the hip (DDH) is a common and serious disease in infants. Hip landmark detection plays a critical role in diagnosing the development of neonatal hip in the ultrasound image. However, the local confusion and the regional weakening make this task challenging. To solve these challenges, we explore the stable hip structure an...
Pediatric bone age assessment (BAA) is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. Different specific bone parts are extracted as anatomical Regions of Interest (RoIs) during this task, since their morphological characters have important biological identification in skeletal maturity. Following...
Bone Age Assessment (BAA) is a challenging clinical practice in pediatrics, which requires rich attention on multiple anatomical Regions of Interest (RoIs). Recently developed deep learning methods address the challenge in BAA with a hard-crop attention mechanism, which segments or detects the discriminative RoIs for meticulous analysis. Great stri...
Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurately detecting and identifying the misshapen anatomical landmarks plays a crucial role in the diagnosis of DDH. However, the diversity during the calcification and the deformity due to the dislocation lead it a difficult task...
Delicate attention of the discriminative regions plays a critical role in Fine-Grained Visual Categorization (FGVC). Unfortunately, most of the existing attention models perform poorly in FGVC, due to the pivotal limitations in discriminative regions proposing and region-based feature learning. 1) The discriminative regions are predominantly locate...
Landmark detection plays a critical role in diagnosis of Developmental Dysplasia of the Hip (DDH). Heatmap and anchor-based object detection techniques could obtain reasonable results. However, they have limitations in both robustness and precision given the complexities and inhomogeneity of hip X-ray images. In this paper, we propose a much simple...
Nowadays, the computer network plays as the most important medium in the transmitting of multimedia. Correspondingly, the orderliness of network is the protection of multimedia transmission. However, due to the Packet Switching design, the network can only provide best-effort service, in which the multimedia applications compete for its network res...
Fine-grained object classification (FGOC) is a challenging research topic in multimedia computing with machine learning, which faces two pivotal conundrums: focusing
attention
on the discriminate part regions, and then processing
recognition
with the part-based features. Existing approaches generally adopt a unidirectional two-step structure, t...
Pediatric bone age assessment (BAA) is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. The morphological characters of different specific bone parts, such as wrist and phalanx, have important reference significance in BAA. Previous deep learning approaches can be divided into two branches, (1) the...
Developmental dysplasia of the hip (DDH) refers to an abnormal development of the hip joint in infants. Accurately detecting and identifying the pelvis landmarks is a crucial step in the diagnosis of DDH. Due to the temporal diversity and pathological deformity, it is a difficult task to detect the misshapen landmark and diagnose the DDH illness co...
Deep neural network (DNN) can extract high- dimensional feature of images for computer vision tasks in- cluding Optical Coherence Tomography (OCT) images classi- fication. However, OCT images are usually processed by DNN just like natural images, thus the performance of DNN is not satisfactory. We present an end-to-end DNN targeting OCT images clas...