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image Captioning with visual Positional Embedding and Bi-linear Pooling 8th

Image Captioning with Visual Positional Embedding and Bi-lin...

引用

8th International conference on Computer Vision and image processing (CVIP)

作者： Nair, Sidharth Guha, Prithwijit HCLTech Chennai Tamil Nadu India Indian Insitute Technol Guwahati Dept Elect & Elect Engn Gauhati India

ISBN: (纸本)9783031581809;9783031581816

Recent approaches to image captioning typically follow an encoder-decoder architecture. The feature vectors extracted from the region proposals obtained from an object detector network serve as input to encoder. Without any explicit spatial information about the visual regions, the caption synthesis model is limited to learn relationship from captions only. However, the structure between the semantic units in images and sentences is different. This work introduces a grid based spatial position encoding scheme to learn relationship from both domains. Furthermore, bi-linear pooling is used with attention for exploiting spatial and channel-wise attention distribution to capture second order interaction between multi-modal inputs. These are integrated within the Transformer architecture achieving a competitive CIDEr score.

关键词： Transformer Positional Embedding image Captioning Bi-linear Pooling

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H.266/VVC Time Complexity Reduction by Learned Models and image Statistical Features

H.266/VVC Time Complexity Reduction by Learned Models and Im...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Chou, Yel-Guan Chen, Jiann-Jone National Taiwan University of Science and Technology Dept. Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798350359855

The newest video coding standard, Versatile Video Coding (VVC), adopts a quad-Tree (QT) plus multi-Type tree (QTMT) block partition structure and improves the compression performance by about 30%∼50%, compared with the HEVC, at the cost of higher time complexity. To reduce VVC time complexity, we proposed to use a learned model to predict Coding Unit (CU) split modes and setup thresholds based on statistical image features to eliminate unnecessary Rate-Distortion Optimization (RDO) operations. Experiments showed that, compared with the default VVC intra-coding, the proposed method saves 46.73% of encoding time, with Bjontegaard Delta Bit Rate (BDBR) increment of 1.16%. After retraining the learned model with a specified Quantization Parameter (QP), the time reduction rate can achieve 51.79%, and the BDBR slightly increased to 2.07%. The proposed speedup coding scheme effectively reduced the VVC time complexity to make it feasible for practical application. © 2023 ieee.

关键词： image coding

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Attention Unveiled: Revolutionizing image Captioning through visual Attention

Attention Unveiled: Revolutionizing Image Captioning through...

引用

2023 ieee Global conference on Information Technologies and communications, GCITC 2023

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

ISBN: (纸本)9798350308167

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. © 2023 ieee.

关键词： CNN GRU image captioning NLP Transformers

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Histogram Guided image Binning Based Plug-In Module for Low-Light HDR Reconstruction 8

Histogram Guided Image Binning Based Plug-In Module for Low-...

引用

8th International conference on Imaging, Signal processing and communications (ICISPC)

作者： Li, Zheyi Zhao, Fengshan Jiang, Haorong Liu, Qin Ikenaga, Takeshi Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan Nanjing Univ Software Inst Nanjing Japan

ISBN: (纸本)9798350367164;9798350367157

Single image high dynamic range image reconstruction has been receiving much attention for recovering image details and showing the possibility of simulating brightness distribution in the real world. While most current works focus on recovering overexposed areas, this work is more focused on underexposed regions and the brightness adjustment of the whole image. This paper proposes an additional plug-in module with histogram guided image binning method for low-light image high dynamic range restoration. This plug-in module is mainly designed with histogram feature extraction and image binning based brightness restoration, enhancing the recovery for the darker regions. Extensive experimentation demonstrates the effectiveness of the approach in enhancing the visual quality of low-light images and preserving details in underexposed areas. At an extremely low-light condition, networks using this plug-in module achieve up to a 0.8227 PSNR improvement and a 0.8278 PU21-PSNR improvement.

关键词： Deep Learning HDR Reconstruction Underexposed Regions Low-Light images

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Overfitting NN loop-filters in video coding

Overfitting NN loop-filters in video coding

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Yang, Ruiying Santamaria, Maria Cricri, Francesco Zhang, Honglei Lainema, Jani Youvalari, Ramin G. Hannuksela, Miska M. Elomaa, Tapio Nokia Technologies Tampere Finland Tampere University Tampere Finland

ISBN: (纸本)9798350359855

Overfitting is usually regarded as a negative condition since it impairs the generalisation power of a model. Nevertheless, overfitting a Neural Network (NN) on test data may be advantageous to improve the compression efficiency of image/video coding tools and systems. Previous research has demonstrated the benefits of NN overfitting for post-processing operations, i.e. post-filters, but not yet for actual decoding tools. Generally, the NN is overfitted on test data at the encoder end, and the weight update is coded and sent to the decoder end along the image/video bitstream. The proposed approach follows this strategy. In particular, the overfitting of the Low Operation Point (LOP) loop-filter in NN-based Video Coding (NNVC) software is studied. The overall approach yields Bjontegaard Delta rate (BD-rate) of -7.74%, -13.73% and -12.49%, for the Y, U and V components, respectively. Out of these coding gains, 1.21%, 6.43% and 5.52%, for the Y, U and V components, are attributed to the overfitting. The boost in the coding gains comes with only 1.5% more complexity, due to the multiplier parameters introduced during the overfitting. © 2023 ieee.

关键词： image enhancement

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Rate Adaptation for Learned Two-layer B-frame Coding without Signaling Motion Information

Rate Adaptation for Learned Two-layer B-frame Coding without...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Xie, Hong-Sheng Chen, Yi-Hsin Peng, Wen-Hsiao Benjak, Martin Ostermann, Jorn National Yang Ming Chiao Tung University Taiwan Leibniz Universität Hannover Germany

ISBN: (纸本)9798350359855

This paper explores the potential of a learned two-layer B-frame codec, known as TLZMC. TLZMC is one of the few early attempts that deviate from the hybrid-based coding architecture by skipping motion coding. With TLZMC, a low-resolution base layer is utilized to encode temporally unpredictable information. We address the question of whether adapting the base-layer bitrate can achieve better rate-distortion performance. We apply the feature map modulation technique to enable per-frame bitrate adaptation of the base layer. We then propose and compare three online search strategies for determining the base-layer rate parameter: per-level brute-force search, per-level greedy search, and per-frame greedy search. Experimental results show that our top-performing search strategy achieves 0.6%-15.8% Bjontegaard-Delta rate savings over TLZMC. © 2023 ieee.

关键词： image compression

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Focal Modulation Based End-to-End Multi-Label Classification for Chest X-ray image Classification 31

Focal Modulation Based End-to-End Multi-Label Classification...

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31st ieee conference on Signal processing and communications Applications (SIU)

作者： Ozturk, Saban Cukur, Tolga Bilkent Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkiye Amasya Univ Elekt & Elekt Muhendisligi Bolumu Amasya Turkiye Bilkent Univ Ulusal Manyet Rezonans Arastirma Merkezi UMRAM Ankara Turkiye

ISBN: (纸本)9798350343557

Chest X-ray imaging is of critical importance in order to effectively diagnose chest diseases, which are increasing today due to various environmental and hereditary factors. Although chest X-ray is the most commonly used device for detecting pathological abnormalities, it can be quite challenging for specialists due to misleading locations and sizes of pathological abnormalities, visual similarities, and complex backgrounds. Traditional deep learning (DL) architectures fall short due to relatively small areas of pathological abnormalities and similarities between diseased and healthy areas. In addition, DL structures with standard classification approaches are not ideal for dealing with problems involving multiple diseases. In order to overcome the aforementioned problems, firstly, background-independent feature maps were created using a conventional convolutional neural network (CNN). Then, the relationships between objects in the feature maps are made suitable for multi-label classification tasks using the focal modulation network (FMA), an innovative attention module that is more effective than the self-attention approach. Experiments using a Chest x-ray dataset containing both single and multiple labels for a total of 14 different diseases show that the proposed approach can provide superior performance for multi-label datasets.

关键词： chest x-ray deep learning focal modulation networks multi-label classification

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Adaptive and Collaborative Multi-scale Alignment for Text-Based Person Search

Adaptive and Collaborative Multi-scale Alignment for Text-Ba...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Yang, Xinxin Pan, Renjie Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute China

ISBN: (纸本)9798350359855

Text-To-image person search is challenging due to the cross-scale correspondences and information inequality between modalities. Specifically, images and text are complexly linked at different scales and images are usually more informative and complete than text. It is crucial to establish semantic correlations between modalities and focus on task-relevant information in images. In this paper, we propose a novel Adaptive and Collaborative Multi-scale Alignment network (ACMA) for text-based person search that learns semantically consistent and information-Aligned multi-modal representations. Firstly, we introduce a novel joint embedding module that adaptively integrates features of different pixels and words, thereby extracting semantically consistent multi-modal features at different scales. Second, we design a cross-modal fusion feature-based auxiliary visual branch to guide the extraction of key visual features that are beneficial for cross-modal matching. Extensive experiments validate that ACMA outperforms the state-of-The-Art method. © 2023 ieee.

关键词： Embeddings

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Hybrid Light Field image Denoising Network using 4D-DCT Separated Transform

Hybrid Light Field Image Denoising Network using 4D-DCT Sepa...

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2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9798350359855

This paper proposes a novel hybrid light field (LF) denoising method which is based on a convolutional neural network (CNN) designed to reflect the characteristic of LF image in both pixel and frequency domains. Noting that the image noise usually has much high-frequency energy, the proposed network is designed to operate in a transform domain in two stages. At the first stage, energy compaction of spatial-angular information of LF image is sought by 4D-DCT separated transform which can achieve better energy compaction than 2D-DCT applied separately in the spatial and angular domain. The transformed LF is decomposed into different frequency components and each frequency component is recovered progressively. Subsequently, we reshape and convert different frequency components into pixel domain to perform the next refinement step for which a residual spatial-angular block (RSAB) is proposed to handle the 4D LF structure in the pixel domain. Extensive experimental results on different noisy datasets confirm the effectiveness of our proposed method compared to state-of-the-art methods in both objective and subjective quality. © 2023 ieee.

关键词： image denoising

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TRANSFORMER-BASED CLIPPED CONTRASTIVE QUANTIZATION LEARNING FOR UNSUPERVISED image RETRIEVAL 31

TRANSFORMER-BASED CLIPPED CONTRASTIVE QUANTIZATION LEARNING ...

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2024 International conference on image processing

作者： Dubey, Ayush Dubey, Shiv Ram Singh, Satish Kumar Chu, Wei-Ta Indian Inst Informat Technol Comp Vision & Biometr Lab Allahabad Uttar Pradesh India Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9798350349405;9798350349399

Unsupervised image retrieval aims to learn the important visual characteristics without any given level to retrieve the similar images for a given query image. The Convolutional Neural Network (CNN)-based approaches have been extensively exploited with self-supervised contrastive learning for image hashing. However, the existing approaches suffer due to lack of effective utilization of global features by CNNs and biased-ness created by false negative pairs in the contrastive learning. In this paper, we propose a TransClippedCLR model by encoding the global context of an image using Transformer having local context through patch based processing, by generating the hash codes through product quantization and by avoiding the potential false negative pairs through clipped contrastive learning. The proposed model is tested with superior performance for unsupervised image retrieval on benchmark datasets, including CIFAR10, NUS-Wide and Flickr25K, as compared to the recent state-of-the-art deep models. The results using the proposed clipped contrastive learning are greatly improved on all datasets as compared to same backbone network with vanilla contrastive learning.

关键词： Unsupervised Learning image Retrieval Contrastive Learning Transformer Clipping

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