IEEE Transactions on Circuits and Systems for Video Technology
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Year: 2023 | Volume: 33 | Issue: 3
IEEE Transactions on Circuits and Systems for Video Technology publication information
Publication Year: 2023,Page(s):C2 - C2
IEEE Transactions on Circuits and Systems for Video Technology publication information
Year: 2023 | Volume: 33 | Issue: 3
A Progressive Difference Method for Capturing Visual Tempos on Action Recognition
Publication Year: 2023,Page(s):977 - 987
Cited by: Papers (15)
Visual tempos show the dynamics of action instances, characterizing the diversity of the actions, such as walking slowly and running quickly. To facilitate action recognition, it is essential to capture visual tempos. To this end, previous methods sample raw videos at multiple frame rates or integrate multi-scale temporal features. These methods inevitably introduce two-stream networks or feature-...Show More
A Perception-Aware Decomposition and Fusion Framework for Underwater Image Enhancement
Yaozu Kang;Qiuping Jiang;Chongyi Li;Wenqi Ren;Hantao Liu;Pengjun Wang
Publication Year: 2023,Page(s):988 - 1002
Cited by: Papers (77)
This paper presents a perception-aware decomposition and fusion framework for underwater image enhancement (UIE). Specifically, a general structural patch decomposition and fusion (SPDF) approach is introduced. SPDF is built upon the fusion of two complementary pre-processed inputs in a perception-aware and conceptually independent image space. First, a raw underwater image is pre-processed to pro...Show More
Enabling Large-Capacity Reversible Data Hiding Over Encrypted JPEG Bitstreams
Zhongyun Hua;Ziyi Wang;Yifeng Zheng;Yongyong Chen;Yuanman Li
Publication Year: 2023,Page(s):1003 - 1018
Cited by: Papers (11)
Cloud computing offers advantages in handling the exponential growth of images but also entails privacy concerns on outsourced private images. Reversible data hiding (RDH) over encrypted images has emerged as an effective technique for securely storing and managing confidential images in the cloud. Most existing schemes only work on uncompressed images. However, almost all images are transmitted a...Show More
Language-Augmented Pixel Embedding for Generalized Zero-Shot Learning
Publication Year: 2023,Page(s):1019 - 1030
Cited by: Papers (9)
Zero-shot Learning (ZSL) aims to recognize novel classes through seen knowledge. The canonical approach to ZSL leverages a visual-to-semantic embedding to map the global features of an image sample to its semantic representation. These global features usually overlook the fine-grained information which is vital for knowledge transfer between seen and unseen classes, rendering these features sub-op...Show More
Learning Spatiotemporal Interactions for User-Generated Video Quality Assessment
Publication Year: 2023,Page(s):1031 - 1042
Cited by: Papers (14)
Distortions from spatial and temporal domains have been identified as the dominant factors that govern the visual quality. Though both have been studied independently in deep learning-based user-generated content (UGC) video quality assessment (VQA) by frame-wise distortion estimation and temporal quality aggregation, much less work has been dedicated to the integration of them with deep represent...Show More
Quality Assessment of UGC Videos Based on Decomposition and Recomposition
Publication Year: 2023,Page(s):1043 - 1054
Cited by: Papers (9)
The prevalence of short-video applications imposes more requirements for video quality assessment (VQA). User-generated content (UGC) videos are captured under an unprofessional environment, thus suffering from various dynamic degradations, such as camera shaking. To cover the dynamic degradations, existing recurrent neural network-based UGC-VQA methods can only provide implicit modeling, which is...Show More
Counting Varying Density Crowds Through Density Guided Adaptive Selection CNN and Transformer Estimation
Publication Year: 2023,Page(s):1055 - 1068
Cited by: Papers (24)
In real-world crowd counting applications, the crowd densities in an image vary greatly. When facing density variation, humans tend to locate and count the targets in low-density regions, and reason the number in high-density regions. We observe that CNN focus on the local information correlation using a fixed-size convolution kernel and the Transformer could effectively extract the semantic crowd...Show More
Minimum Noticeable Difference-Based Adversarial Privacy Preserving Image Generation
Publication Year: 2023,Page(s):1069 - 1081
Cited by: Papers (12)
Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the existing works of adversarial image generation try to achieve attacks for most models, while few of them make efforts on guaranteeing the perceptual quality of the adversarial examples. High quality adversarial examples mat...Show More
G2LP-Net: Global to Local Progressive Video Inpainting Network
Publication Year: 2023,Page(s):1082 - 1092
Cited by: Papers (5)
The self-attention based video inpainting methods have achieved promising progress by establishing long-range correlation over the whole video. However, existing methods generally relied on the global self-attention that directly searches missing contents among all reference frames but lacks accurate matching and effective organization on contents, which often blurs the result owing to the loss of...Show More
CrossDet++: Growing Crossline Representation for Object Detection
Heqian Qiu;Hongliang Li;Qingbo Wu;Jianhua Cui;Zichen Song;Lanxiao Wang;Minjian Zhang
Publication Year: 2023,Page(s):1093 - 1108
Cited by: Papers (12)
In object detection, precise object representation is a key factor to successfully classify and locate objects of an image. Existing methods usually use rectangular anchor boxes or a set of points to represent objects. However, these methods either introduce background noise or miss the continuous appearance information inside the object, and thus cause incorrect detection results. In this paper, ...Show More
Hybrid CNN-Transformer Features for Visual Place Recognition
Publication Year: 2023,Page(s):1109 - 1122
Cited by: Papers (24)
Visual place recognition is a challenging problem in robotics and autonomous systems because the scene undergoes appearance and viewpoint changes in a changing world. Existing state-of-the-art methods heavily rely on CNN-based architectures. However, CNN cannot effectively model image spatial structure information due to the inherent locality. To address this issue, this paper proposes a novel Tra...Show More
Generation-Based Joint Luminance-Chrominance Learning for Underwater Image Quality Assessment
Zheyin Wang;Liquan Shen;Zhengyong Wang;Yufei Lin;Yanliang Jin
Publication Year: 2023,Page(s):1123 - 1139
Cited by: Papers (17)
Underwater enhanced images (UEIs) are affected by not only the color cast and haze effect due to light attenuation and scattering, but also the over-enhancement and texture distortion caused by enhancement algorithms. However, existing underwater image quality assessment (UIQA) methods mainly focus on the inherent distortion caused by underwater optical imaging, and ignore the widespread artificia...Show More
A Format Compliant Framework for HEVC Selective Encryption After Encoding
Publication Year: 2023,Page(s):1140 - 1156
Cited by: Papers (5)
The security protection of Ultra-High-Definition (UHD) video is facing grand challenges due to changeable application scenarios. The video business is highly dependent on the video format structure, which makes the format compliance of the encryption algorithm essential. Existing HEVC Selective Encryption (SE) algorithms are difficult to encrypt with format compliance while independent from the en...Show More
Deep Texture-Aware Features for Camouflaged Object Detection
Jingjing Ren;Xiaowei Hu;Lei Zhu;Xuemiao Xu;Yangyang Xu;Weiming Wang;Zijun Deng;Pheng-Ann Heng
Publication Year: 2023,Page(s):1157 - 1167
Cited by: Papers (64)
Camouflaged object detection is a challenging task that aims to identify objects having similar texture to the surroundings. This paper presents to amplify the subtle texture difference between camouflaged objects and the background for camouflaged object detection by formulating multiple texture-aware refinement modules to learn the texture-aware features in a deep convolutional neural network. T...Show More
Consistent Intra-Video Contrastive Learning With Asynchronous Long-Term Memory Bank
Publication Year: 2023,Page(s):1168 - 1180
Cited by: Papers (4)
Unsupervised representation learning for videos has recently achieved remarkable performance owing to the effectiveness of contrastive learning. Most works on video contrastive learning (VCL) pull all snippets from the same video into the same category, even if some of them are from different actions, leading to temporal collapse, i.e., the snippet representations of a video are invariable with th...Show More
Towards Zero-Shot Learning: A Brief Review and an Attention-Based Embedding Network
Publication Year: 2023,Page(s):1181 - 1197
Cited by: Papers (18)
Zero-shot learning (ZSL), an emerging topic in recent years, targets at distinguishing unseen class images by taking images from seen classes for training the classifier. Existing works often build embeddings between global feature space and attribute space, which, however, neglect the treasure in image parts. Discrimination information is usually contained in the image parts, e.g., black and whit...Show More
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
Publication Year: 2023,Page(s):1198 - 1208
Cited by: Papers (26)
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the challenging problem of 2D image-to-3D point cloud registration, dubbed CorrI2P. CorrI2P is mainly composed of three modules, i.e., feature embedding, sym...Show More
Local-to-Global Cost Aggregation for Semantic Correspondence
Publication Year: 2023,Page(s):1209 - 1222
Establishing visual correspondences across semantically similar images is challenging due to intra-class variations, viewpoint changes, repetitive patterns, and background clutter. Recent approaches focus on cost aggregation to achieve promising performance. However, these methods fail to jointly utilize local and global cues to suppress unreliable matches. In this paper, we propose a cost aggrega...Show More
RGB-T Semantic Segmentation With Location, Activation, and Sharpening
Publication Year: 2023,Page(s):1223 - 1235
Cited by: Papers (51)
Semantic segmentation is important for scene understanding. To address the scenes of adverse illumination conditions of natural images, thermal infrared (TIR) images are introduced. Most existing RGB-T semantic segmentation methods follow three cross-modal fusion paradigms, i. e., encoder fusion, decoder fusion, and feature fusion. Some methods, unfortunately, ignore the properties of RGB and TIR ...Show More
Iterative Class Prototype Calibration for Transductive Zero-Shot Learning
Hairui Yang;Baoli Sun;Baopu Li;Caifei Yang;Zhihui Wang;Jenhui Chen;Lei Wang;Haojie Li
Publication Year: 2023,Page(s):1236 - 1246
Cited by: Papers (13)
Zero-shot learning (ZSL) typically suffers from the domain shift issue since the projected feature embedding of unseen samples mismatch with the corresponding class semantic prototypes, making it very challenging to fine-tune an optimal visual-semantic mapping for the unseen domain. Some existing transductive ZSL methods solve this problem by introducing unlabeled samples of the unseen domain, in ...Show More
Audio-Driven Dubbing for User Generated Contents via Style-Aware Semi-Parametric Synthesis
Publication Year: 2023,Page(s):1247 - 1261
Cited by: Papers (4)
Existing automated dubbing methods are usually designed for Professionally Generated Content (PGC) production, which requires massive training data and training time to learn a person-specific audio-video mapping. In this paper, we investigate an audio-driven dubbing method that is more feasible for User Generated Content (UGC) production. There are two unique challenges to design a method for UGC...Show More
Fast CNN-Based Single-Person 2D Human Pose Estimation for Autonomous Systems
Publication Year: 2023,Page(s):1262 - 1275
Cited by: Papers (14)
This paper presents a novel Convolutional Neural Network (CNN) architecture for 2D human pose estimation from RGB images that balances between high 2D human pose/skeleton estimation accuracy and rapid inference. Thus, it is suitable for safety-critical embedded AI scenarios in autonomous systems, where computational resources are typically limited and fast execution is often required, but accuracy...Show More
PointFilterNet: A Filtering Network for Point Cloud Denoising
Publication Year: 2023,Page(s):1276 - 1290
Cited by: Papers (7)
Point clouds obtained by 3D scanning or reconstruction are usually accompanied by noise. Filtering-based point cloud denoising methods are simple and effective, but they are limited by the manually defined coefficients. Deep learning has shown excellent ability in automatically learning parameters. In this paper, a filtering network named PointFilterNet (PFN for short) is proposed to denoise point...Show More
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