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Attention Unveiled: Revolutionizing image Captioning through visual Attention

Attention Unveiled: Revolutionizing Image Captioning through...

引用

Information Technologies and communications (GCITC), Global conference on

作者： Teja Kolla Harsh Kumar Vashisth Manpreet Kaur Computer Science Department Manav Rachna University Faridabad India

image captioning models are a type of "Natural Language processing" (NLP) models that are designed to generate textual descriptions of images. These models are trained on large datasets of images and captions, and use a combination of deep learning models and natural language processing techniques to generate accurate and informative captions. These models have proven to be effective in improving the quality of the generated captions. In recent years, researchers have also been exploring the use of reinforcement learning, adversarial training, and other techniques to improve the performance of image captioning models. Specifically, techniques like Reinforcement Learning from Human Feedback (RLHF), where human-provided captions guide model training, Generative Adversarial Networks (GANs), which generate captions through a competition between a generator and discriminator network, and Self-Critical Sequence Training (SCST), which optimizes model performance based on its own generated captions, have gained attention. These approaches aim to enhance the quality and relevance of captions generated by image captioning models. visual attention models use a transformer and attention models typically consist of two main components: an image encoder that extracts features from the image and a language decoder that generates the textual description. This paper focuses on techniques that can be used for image Captioning using visual attention models.

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CRC-DPCGC: Conditional Residual Coding for Dynamic Point Cloud Geometry Compression

CRC-DPCGC: Conditional Residual Coding for Dynamic Point Clo...

引用

IEEE visual communications and image processing (VCIP)

作者： Bing-Han Wu Monyneath Yim Jui-Chiu Chiang Department of Electrical Engineering National Chung Cheng University Chia-Yi Taiwan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Dynamic point cloud compression faces significant challenges due to the inherent unstructured and sparse nature of point clouds, requiring efficient modeling of temporal context information. Current approaches often fall short in effectively capturing and utilizing inter-frame dependencies. This paper presents a novel conditional residual coding technique for dynamic point cloud geometry compression, specifically designed to address this challenge. Our method exploits spatial-temporal features by a predictor module and a Transformer-based entropy model. The predictor module transfers features from previous point cloud frames to the current frame, efficiently generating residual features. The Transformer entropy model employs a conditional coding strategy, utilizing cross-attention between the current frame and its predecessors, leading to a more precise bit estimation. Extensive experimental results demonstrate that our proposed method outperforms existing dynamic point cloud geometry compression techniques, including rule-based and learningbased approaches, showcasing its effectiveness in capturing and utilizing temporal information.

关键词： Point cloud compression Geometry image coding visual communication Estimation Predictive models Performance gain Transformers Entropy Faces

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FRIDAY: Mitigating Unintentional Facial Identity in Deepfake Detectors Guided by Facial Recognizers

FRIDAY: Mitigating Unintentional Facial Identity in Deepfake...

引用

IEEE visual communications and image processing (VCIP)

作者： Younghun Kim Myung-Joon Kwon Wonjun Lee Changick Kim Graduate School of Green Growth and Sustainability KAIST Daejeon South Korea School of Electrical Engineering KAIST Daejeon South Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Previous Deepfake detection methods perform well within their training domains, but their effectiveness diminishes significantly with new synthesis techniques. Recent studies have revealed that detection models make decision boundaries based on facial identity instead of synthetic artifacts, leading to poor cross-domain performance. To address this issue, we propose FRIDAY, a novel training method that attenuates facial identity utilizing a face recognizer. To be specific, we first train a face recognizer using the same backbone as the Deepfake detector. We then freeze the recognizer and use it during the detector’s training to mitigate facial identity information. This is achieved by feeding input images into both the recognizer and the detector, then minimizing the similarity of their feature embeddings using our Facial Identity Attenuating loss. This process encourages the detector to produce embeddings distinct from the recognizer, effectively attenuating facial identity. Comprehensive experiments demonstrate that our approach significantly improves detection performance on both in-domain and cross-domain datasets.

关键词： Training Deepfakes image recognition visual communication Face recognition Forensics Detectors Feature extraction Artificial intelligence

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PET-NeRV: Bridging Generalized Video Codec and Content-Specific Neural Representation

PET-NeRV: Bridging Generalized Video Codec and Content-Speci...

引用

IEEE visual communications and image processing (VCIP)

作者： Hao Li Lu Yu Yiyi Liao Zhejiang University HangZhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Generalized models dominate neural video compression methods to compress arbitrary video with the same model. However, obtaining a universal neural codec with high compression efficiency on all videos is challenging. Existing work attempts to adapt the decoder-side model per video content through full parameter tuning, but this requires a large Group of Pictures (GOP) to compensate for the cost of transmitting updated parameters. To tackle this challenge, we propose to tune generalized video codecs per video content in a parameter-efficient manner. The per-content tuned parameters are further compressed with entropy coding using adaptive distribution estimations. This allows for enhancing the compression efficiency while maintaining a normal GOP size for random access capabilities. To validate the generality and validity of our approach, we apply it to two representative methods: CNN-based, DCVC-HEM, and Transformer-based, VCT. Our results demonstrate that introducing content-specific representation leads to a notable improvement in compression efficiency compared to the original methods.

关键词： Adaptation models image coding Costs visual communication Estimation Video compression Transformers Entropy coding Video codecs Tuning

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Flexible Coding Order for Learned image Compression

Flexible Coding Order for Learned Image Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Yuqi Li Haotian Zhang Dong Liu University of Science and Technology of China Hefei China

Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however, may be neither optimal nor efficient. We conduct an elaborate study on coding orders in LIC entropy models, shedding light on the potential for improving compression performance by adapting the coding order. We present Mask Modeling Context Model (MMCM), a transformer-based context model designed with a Patch-restricted Iterative Mask Modeling (PIMM) training strategy. Through training to predict the probability distribution of randomly masked tokens, we are able to use a patch-level arbitrary coding order to encode/decode the latent space in a few iterative steps. Additionally, we employ offline and online rate-distortion optimization to adaptively select the appropriate coding order for each image. Our extensive experimental results demonstrate that the proposed method achieves a better rate-distortion performance than the autoregressive context model while requiring less compression complexity.

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Advancements in Lenslet Video Coding: Insights from MPEG LVC

Advancements in Lenslet Video Coding: Insights from MPEG LVC

引用

IEEE visual communications and image processing (VCIP)

作者： Xin Jin Mehrdad Teratani Byeungwoo Jeon Toshiaki Fujii Ruibo Zhao Eline Soetens Yuqing Yang Shenzhen International Graduate School Tsinghua University Shenzhen China Université Libre de Bruxelles Brussels Belgium Sungkyunkwan University Seoul South Korea Nagoya University Nagoya Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Being a general representation format of the dense light field, lenslet video, where each frame consists of a 2D grid of micro-images, shows high potential for applications in immersive media such as glasses-free 3D displays and virtual reality. However, its distinct spatial-temporal-angular distribution places a significant challenge on conventional video coding. In July 2021, the Moving Picture Experts Group (MPEG) established an Ad-Hoc group, Lenslet Video Coding (LVC), to explore use cases, efficient compression methods, testing sequences, conversion tools, and coding architectures towards a new compression standard. This paper provides an overview of recent progress in the LVC Ad Hoc group and presents the compression efficiency of state-of-the-art codec agnostic coding tools to encourage contributions in the future.

关键词： Video coding MPEG standards image coding Three-dimensional displays visual communication Transform coding Virtual reality Media Standards Testing

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UFI: A Unified Feature Interaction Framework for Multi-Label image Classification

UFI: A Unified Feature Interaction Framework for Multi-Label...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Li, Zhuohua Zhang, Weibo Yang, Ziang Zhu, Fuqing Han, Jizhong Hu, Songlin Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Multi-label image classification (MLIC) is a more challenging task compared with single-label image classification due to multiple concepts targets, and complex visual relationships should be formulated. Convolutional Neural Network (CNN) and visual Transformer (ViT) have shown superior performance in local and global feature representations, respectively. However, the interactions between local and global features are neglected in current works. To further formulate the critical interactions, this paper designs a Unified Feature Interaction (UFI) framework, aiming to integrate the selected local features with global features based on CNN and ViT, simultaneously. The proposed UFI includes two key modules: Class-Related Feature Selection (CRFS) and Feature Interaction Attention (FIA) modules. Specifically, according to the activation map, CRFS selects target regions by the preliminary calculation of predicted scores. FIA enables the significant local-global feature interaction based on the selected target regions and whole image. We initially attempted to interact with local and global features for multi-label image classification. UFI provides a stable improvement over the baseline and produces a new state-of-the-art result on MS-COCO and VOC2007. © 2022 IEEE.

关键词： image classification

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Adaptive Block-Level Quality Parameter Adjustment Towards Low Video Bit-Rate Fluctuation

Adaptive Block-Level Quality Parameter Adjustment Towards Lo...

引用

IEEE visual communications and image processing (VCIP)

作者： Longtao Feng Qian Yin Huiwen Ren Zhao Wang Siwei Ma Yuwen He National Engineering Research Center of Visual Technology School of Computer Science Peking University China Advanced Institute of Information Technology Peking University China Bytedance Inc. San Diego CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Existing quantization parameter (QP) adjustment methods in video coding often focus solely on coding efficiency and ignore the impact of bit-rate fluctuations on video transmission and bandwidth waste. This is mainly because intra pictures, in a hierarchical coding structure, are allocated smaller QP and thus consume more bits. To address this issue, we propose an adaptive block-level QP adjustment method. Specifically, intra picture importance (IPI) is first introduced to evaluate the adjustability of intra picture QP. For intra pictures whose QP can be adjusted, we further propose block importance (BI) to determine their optimal block-level QP adjustment. Experimental results show that our proposed method reduce the bit-rate fluctuations while basically maintaining the coding performance. Notably, significant improvements can be observed in high-resolution videos, with a reduction of approximately 11% in bit-rate fluctuations.

关键词： Video coding Fluctuations image coding Quantization (signal) visual communication Bandwidth Encoding Motion compensation

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image-Prompt Integration Network with Self-Ranking and Inter-Ranking Loss for AI-Generated image Quality Assessment

Image-Prompt Integration Network with Self-Ranking and Inter...

引用

IEEE visual communications and image processing (VCIP)

作者： Tianwei Zhou Xizhang Yao Songbai Tan Xiaoying Ding Guanghui Yue College of Management Shenzhen University Shenzhen China School of Information Engineering Zhongnan University of Economics and Law Wuhan China School of Biomedical Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

AI-generated images (AGIs) are increasingly utilized across diverse domains due to their ability to quickly produce high-quality visuals. However, assessing the quality of AGIs re-mains challenging due to their inherent variability and distinctive distortions. To address these challenges, we propose a novel AGI quality assessment method named SIRQA, which enhances feature representation by integrating visual features with textual prompts, effectively measureing the alignment between the generated images and the described content to improve the precision of quality assessment. Specifically, SIRQA employs self-ranking and inter-ranking mechanisms to refine feature representation. The self-ranking mechanism maintains consistency between feature distances and sampling scales, making sure that features from similar sampling scales are positioned closer together. Addition-ally, inter-ranking mechanism sorts the weighted similarity scores between images and prompts to align with the ranking in the label space. Extensive experiments on the AGIQA3K and PKUI2IQA datasets show that our SIRQA outperforms eight state-of-the-art algorithms in terms of both Spearman’s rank correlation coefficient (SRCC) and Pearson linear correlation coefficient (PLCC).

关键词： image quality Correlation coefficient visualization visual communication image synthesis Benchmark testing Distortion Quality assessment Reliability Distortion measurement

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Development of GPU-based High-speed Frame Rate Convertor Software for International TV Program Material Exchange

Development of GPU-based High-speed Frame Rate Convertor Sof...

引用

IEEE visual communications and image processing (VCIP)

作者： Ryoichi Kawada Manabu Iguchi KDDI Research Inc. Saitama Japan KDDI Corporation Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Traditionally, when exchanging TV program materials between countries with different standard frame rates, frame rate conversion (FRC) is performed through a real-time TV standards convertor equipment. Recent years have seen an increasing need to perform FRC by software as handling materials as electronic files, rather than VTR tapes, has become more popular. Then it will be more convenient, especially when handling many materials, if the conversion can be performed in a shorter time than the actual length of the material. To meet this requirement while maintaining picture quality, the authors have developed a prototype GPU software of motion- compensated FRC, the algorithm of which is the one used in the current TV standards convertor equipment, without any simplification. It can convert HDTV material files at 6.8 times faster than real time on AWS g4dn.12xlarge, while maintaining the required picture quality. It is expected to make a significant contribution to improving the work efficiency of broadcasters and other operators.

关键词： TV image coding visual communication Software algorithms Prototypes Converters Software Real-time systems Standards HDTV

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