124 citations · 253 across the 20 of their papers we have counts for
16 papers · 1 filter
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
Yeying Jin, Beibei Lin, Wending Yan +3
Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources.…
Counting Like Human: Anthropoid Crowd Counting on Modeling the Similarity of Objects
Qi Wang, Juncheng Wang, Junyu Gao +2
The mainstream crowd counting methods regress density map and integrate it to obtain counting results. Since the density representation to one head accords to its adjacent distribu…
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
Xin Yang, Michael Bi Mi, Yuan Yuan +2
Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, renderin…
DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity
Yeying Jin, Wei Ye, Wenhan Yang +2
Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing metho…
Text Growing on Leaf
Chuang. Yang, Mulin. Chen, Yuan. Yuan +1
Irregular-shaped texts bring challenges to Scene Text Detection (STD). Although existing contour point sequence-based approaches achieve comparable performances, they fail to cover…
MAFNet: A Multi-Attention Fusion Network for RGB-T Crowd Counting
Pengyu Chen, Junyu Gao, Yuan Yuan +1
RGB-Thermal (RGB-T) crowd counting is a challenging task, which uses thermal images as complementary information to RGB images to deal with the decreased performance of unimodal RG…
Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teacher
Juncheng Wang, Junyu Gao, Yuan Yuan +1
Crowd localization is to predict each instance head position in crowd scenarios. Since the distance of instances being to the camera are variant, there exists tremendous gaps among…
Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut
Yangtao Wang, Xi Shen, Shell Hu +3
Transformers trained with self-supervised learning using self-distillation loss (DINO) have been shown to produce attention maps that highlight salient foreground objects. In this…
Adaptive Shrink-Mask for Text Detection
Chuang Yang, Mulin Chen, Yuan Yuan +2
Existing real-time text detectors reconstruct text contours by shrink-masks directly, which simplifies the framework and can make the model run fast. However, the strong dependence…
Unsupervised Domain Adaptive Learning via Synthetic Data for Person Re-identification
Qi Wang, Sikai Bai, Junyu Gao +2
Person re-identification (re-ID) has gained more and more attention due to its widespread applications in intelligent video surveillance. Unfortunately, the mainstream deep learnin…
LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds
Qi wang, Tao Han, Junyu Gao +2
The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. It also enlightens us back…
Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image
Qi Wang, Yanling Miao, Mulin Chen +1
Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral pixels into clusters, has drawn significant attention in practical applications. Recently, many graph-bas…
BiP-Net: Bidirectional Perspective Strategy based Arbitrary-Shaped Text Detection Network
Chuang Yang, Mulin Chen, Yuan Yuan +1
Detecting irregular-shaped text instances is the main challenge for text detection. Existing approaches can be roughly divided into top-down and bottom-up perspective methods. The…
CM-Net: Concentric Mask based Arbitrary-Shaped Text Detection
Chuang Yang, Mulin Chen, Zhitong Xiong +2
Recently fast arbitrary-shaped text detection has become an attractive research topic. However, most existing methods are non-real-time, which may fall short in intelligent systems…
Domain-adaptive Crowd Counting via High-quality Image Translation and Density Reconstruction
Junyu Gao, Tao Han, Qi Wang +1
Recently, crowd counting using supervised learning achieves a remarkable improvement. Nevertheless, most counters rely on a large amount of manually labeled data. With the release…
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Yuan Yuan, Siyuan Liu, Jiawei Zhang +3
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from tradi…