164 citations · 761 across the 22 of their papers we have counts for
23 papers
Vision-Language Guided Hyperspectral Object Tracking via Semantics Fusion and Contextual Template Updating
Rui Yao, Yuhong Zhang, Kunyang Sun +4
Hyperspectral object tracking (HOT) leverages the rich spectral information provided by hyperspectral videos (HSVs), offering substantial potential for object tracking. However, ef…
DTTNet: Improving Video Shadow Detection via Dark-Aware Guidance and Tokenized Temporal Modeling
Zhicheng Li, Kunyang Sun, Rui Yao +5
Video shadow detection confronts two entwined difficulties: distinguishing shadows from complex backgrounds and modeling dynamic shadow deformations under varying illumination. To…
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution
Zhiwen Shao, Yifan Cheng, Feiran Li +4
Facial micro-expression recognition (MER) is a challenging problem, due to transient and subtle micro-expression (ME) actions. Most existing methods depend on hand-crafted features…
Modality-Guided Dynamic Graph Fusion and Temporal Diffusion for Self-Supervised RGB-T Tracking
Shenglan Li, Rui Yao, Yong Zhou +5
To reduce the reliance on large-scale annotations, self-supervised RGB-T tracking approaches have garnered significant attention. However, the omission of the object region by erro…
CurvNet: Latent Contour Representation and Iterative Data Engine for Curvature Angle Estimation
Zhiwen Shao, Yichen Yuan, Lizhuang Ma +1
Curvature angle is a quantitative measurement of a curve, in which Cobb angle is customized for spinal curvature. Automatic Cobb angle measurement from X-ray images is crucial for…
Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample
Zhiwen Shao, Hancheng Zhu, Yong Zhou +4
Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal…
SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes
Yiran Song, Qianyu Zhou, Xuequan Lu +2
Segment anything model (SAM) has demonstrated excellent generalizability in common vision scenarios, yet falling short of the ability to understand specialized data. Recently, seve…
CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer
Zhiwen Shao, Yuchen Su, Yong Zhou +4
Contour based scene text detection methods have rapidly developed recently, but still suffer from inaccurate frontend contour initialization, multi-stage error accumulation, or def…
LRANet: Towards Accurate and Efficient Scene Text Detection with Low-Rank Approximation Network
Yuchen Su, Zhineng Chen, Zhiwen Shao +5
Recently, regression-based methods, which predict parameterized text shapes for text localization, have gained popularity in scene text detection. However, the existing parameteriz…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask
Yuchen Su, Zhiwen Shao, Yong Zhou +4
Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation. Most existing regression based methods resort…
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks
Habtamu Fanta, Zhiwen Shao, Lizhuang Ma
Abnormal event detection is a challenging task that requires effectively handling intricate features of appearance and motion. In this paper, we present an approach of detecting an…
Fine-Grained Expression Manipulation via Structured Latent Space
Junshu Tang, Zhiwen Shao, Lizhuang Ma
Fine-grained facial expression manipulation is a challenging problem, as fine-grained expression details are difficult to be captured. Most existing expression manipulation methods…
SiTGRU: Single-Tunnelled Gated Recurrent Unit for Abnormality Detection
Habtamu Fanta, Zhiwen Shao, Lizhuang Ma
Abnormality detection is a challenging task due to the dependence on a specific context and the unconstrained variability of practical scenarios. In recent years, it has benefited…
GeoConv: Geodesic Guided Convolution for Facial Action Unit Recognition
Yuedong Chen, Guoxian Song, Zhiwen Shao +3
Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroug…
JA-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention
Zhiwen Shao, Zhilei Liu, Jianfei Cai +1
Facial action unit (AU) detection and face alignment are two highly correlated tasks, since facial landmarks can provide precise AU locations to facilitate the extraction of meanin…
Facial Action Unit Detection via Adaptive Attention and Relation
Zhiwen Shao, Yong Zhou, Jianfei Cai +2
Facial action unit (AU) detection is challenging due to the difficulty in capturing correlated information from subtle and dynamic AUs. Existing methods often resort to the localiz…
Explicit Facial Expression Transfer via Fine-Grained Representations
Zhiwen Shao, Hengliang Zhu, Junshu Tang +2
Facial expression transfer between two unpaired images is a challenging problem, as fine-grained expression is typically tangled with other facial attributes. Most existing methods…
Unconstrained Facial Action Unit Detection via Latent Feature Domain
Zhiwen Shao, Jianfei Cai, Tat-Jen Cham +2
Facial action unit (AU) detection in the wild is a challenging problem, due to the unconstrained variability in facial appearances and the lack of accurate annotations. Most existi…
Recurrence time correlations in random walks with preferential relocation to visited places
Daniel Campos, Vicenç Méndez
Random walks with memory typically involve rules where a preference for either revisiting or avoiding those sites visited in the past are introduced somehow. Such effects have a di…
Facial Action Unit Detection Using Attention and Relation Learning
Zhiwen Shao, Zhilei Liu, Jianfei Cai +2
Attention mechanism has recently attracted increasing attentions in the field of facial action unit (AU) detection. By finding the region of interest of each AU with the attention…
Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment
Zhiwen Shao, Zhilei Liu, Jianfei Cai +1
Facial action unit (AU) detection and face alignment are two highly correlated tasks since facial landmarks can provide precise AU locations to facilitate the extraction of meaning…
Learning deep representation from coarse to fine for face alignment
Zhiwen Shao, Shouhong Ding, Yiru Zhao +2
In this paper, we propose a novel face alignment method that trains deep convolutional network from coarse to fine. It divides given landmarks into principal subset and elaborate s…