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Anchoring Vision and Language Knowledge for Weakly Supervised Group Activity Recognition

Anchoring Vision and Language Knowledge for Weakly Supervise...

引用

IEEE visual communications and image processing (VCIP)

作者： Muhammad Adi Nugroho Jinyoung Park Donguk Kim Changick Kim Korea Advanced Institute of Science and Technology (KAIST)

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The emergence of Foundation Vision-Language Models (VLMs) has ignited a surge of research in the computer vision field due to their robust baseline performance. Inspired by this, we propose the Anchoring Vision-Language Network (AnViL-Net), which integrates a vision language model for the challenging task of Weakly-Supervised Group Activity Recognition (WSGAR). Our network effectively incorporates VLMs into WSGAR, addressing the challenges posed by dynamic actor motions and domain-specific activity classes. AnViL-Net leverages highly generalized VLM vision features as anchors for extracting visual features. Additionally, semantically meaningful VLM language features serve as anchors for inferring the semantic relationships between actors and their activities. We demonstrate the effectiveness of AnViL-Net on multiple group activity datasets, achieving competitive state-of-the-art results.

关键词： visualization Computer vision image recognition visual communication Computational modeling Semantics Dynamics Activity recognition Feature extraction Surges

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Lookup Register-Tables with Interpolation for Effective image Transformation on x86/64 CPUs

Lookup Register-Tables with Interpolation for Effective Imag...

引用

IEEE visual communications and image processing (VCIP)

作者： Hirokazu Kamei Soichiro Honda Kohei Hayashi Yoshihiro Maeda Norishige Fukushima Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan College of Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Lookup tables (LUTs) are commonly used to speed up image processing by handling complex mathematical functions like sine and exponential calculations. They are used in various applications such as camera image processing, high-dynamic range imaging, and edge-preserving filtering. However, due to the increasing gap between computing and input/output performance, LUTs are becoming less effective. Even though specific circuits like SIMD can improve LUT efficiency, they still need to bridge the performance gap fully. The gap makes it difficult to choose between direct numerical and LUT calculations. For this problem, a register-LUTs method with the nearest neighbor was proposed; however, it is limited for functions with narrow-range values approaching zero. In this paper, we propose a method for using register LUTs to process images efficiently over a wide range of values. Our contributions include proposing register-LUT with linear interpolation for efficient computation, using a smaller data type for further efficiency, and suggesting an efficient data retrieving method.

关键词： Interpolation image transformation Accuracy visual communication Single instruction multiple data Filtering image edge detection Stacking Table lookup Registers

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Multi-View image Rectification for UAV-Captured image Sequences

Multi-View Image Rectification for UAV-Captured Image Sequen...

引用

IEEE visual communications and image processing (VCIP)

作者： Shun Zou Xin Jin Yihui Fan Shenzhen International Graduate School Tsinghua University Shenzhen China

The image sequences captured by Unmanned Aerial Vehicles (UAVs) can be applied to many computer vision tasks. However, due to the instability of UAV flight, the captured image sequences will deviate from the preset trajectory and pose, which reduce the quality of subsequent applications such as panoramic image stitching. In this paper, a novel method is proposed to rectify UAV-captured image sequences by transforming the images to a regular trajectory with the uniform pose. First, to minimize the total transformation deviation, virtual regular camera trajectory is derived by minimizing the global error of coordinates between actual and virtual camera trajectories. Then, camera-pose-relevant local homography is proposed by inserting the camera pose into local homography to transform the images to the derived virtual trajectory with the uniform pose and correct translation parallax. The experimental results demonstrate the effectiveness of the proposed rectification algorithm from both theoretical and application levels.

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Context-Aware Seam Restoration for image Extension

Context-Aware Seam Restoration for Image Extension

引用

IEEE visual communications and image processing (VCIP)

作者： Yuk Heo Yeong Jun Koh Chang-Su Kim Korea University Seoul South Korea Chungnam National University Daejeon South Korea

A seam is a set of pixels with minimum energy forming a continuous line in an image. By eliminating or duplicating seams iteratively, an input image can be retargeted. However, this process often results in blurring, stretching, or distortion problems around the seams, especially when extending a target image. We propose a novel approach for image extension using content-aware seam restoration to solve this problem. First, we design CSR-Net, which employs features from the horizontal region of target pixels to restore the seams. Second, we develop an image extension scenario based on the seam restoration and the training methodology of CSR-Net. Experimental results demonstrate that the proposed algorithm provides more accurate expanded results at seam pixels the seams than conventional algorithms.

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Codec-agnostic Lenslet Video Coding with Smoothing Transform

Codec-agnostic Lenslet Video Coding with Smoothing Transform

引用

IEEE visual communications and image processing (VCIP)

作者： Eline Soetens Gauthier Lafruit Mehrdad Teratani Laboratory of Image Synthesis and Analysis Université Libre de Bruxelles Brussels Belgium

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Plenoptic cameras are light field capturing devices able to acquire large amounts of angular and spatial information. The lenslet video produced by such cameras presents on each frame a distinctive hexagonal pattern of micro-images. Due to the particular structure of lenslet images, traditional video codecs perform poorly on lenslet video. Previous works have proposed a preprocessing scheme that cuts and realigns the micro-images on each lenslet frame. While effective, this method introduces high frequency components into the processed image. In this paper, we propose an additional step to the aforementioned scheme by applying an invertible smoothing transform. We evaluate the enhanced scheme on lenslet video sequences captured with single-focused and multi-focused plenoptic cameras. On average, the enhanced scheme achieves 9.85% bitrate reduction compared to the existing scheme.

关键词： Video coding Performance evaluation Smoothing methods visual communication Bit rate Video sequences Transforms Cameras Light fields Video codecs

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Fully Aligned Network for Referring image Segmentation

Fully Aligned Network for Referring Image Segmentation

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IEEE visual communications and image processing (VCIP)

作者： Yong Liu Ruihao Xu Yansong Tang Tsinghua Shenzhen International Graduate School Tsinghua University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

This paper focuses on the Referring image Segmentation (RIS) task, which aims to segment objects from an image based on a given language description, having significant potential in practical applications such as food safety detection. Recent advances using the attention mechanism for cross-modal interaction have achieved excellent progress. However, current methods tend to lack explicit principles of interaction design as guidelines, leading to inadequate cross-modal comprehension. Additionally, most previous works use a single-modal mask decoder for prediction, losing the advantage of full cross-modal alignment. To address these challenges, we present a Fully Aligned Network (FAN) that follows four cross-modal interaction principles. Under the guidance of reasonable rules, our FAN achieves state-of-the-art performance on the prevalent RIS benchmarks (RefCOCO, RefCOCO+, G-Ref) with a simple architecture.

关键词： image segmentation Fans visualization Attention mechanisms image coding visual communication Benchmark testing Food safety Decoding Guidelines

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Gait Recognition with DensePose Energy images 28th

Gait Recognition with DensePose Energy Images

引用

28th International conference on Systems, Signals and image processing (IWSSIP)

作者： Schwarz, Philipp Scharinger, Josef Hofer, Philipp Johannes Kepler Univ Linz LIT Secure & Correct Syst Lab Linz Austria Johannes Kepler Univ Linz Inst Computat Percept Linz Austria Johannes Kepler Univ Linz Inst Networks & Secur Linz Austria

ISBN: (纸本)9783030968786;9783030968779

In the recent years, special emphasis has been placed on visual-based gait recognition due to its unique characteristics such as not requiring a special user action, or its long-distance recognizability. In general, there exist two methods - model-based and appearance-based methods - both of which come with their own advantages and disadvantages. In an effort to harness the best of both worlds we create a compact 3D human model-based gait representation out of 2D images with the help of the DensePose algorithm. We design a simple CNN and train several instances to show that the obtained gait representation can in fact be used to improve gait recognition accuracy. Experimental results are based on the publicly available CASIA-B dataset.

关键词： Biometrics Gait recognition DensePose Energy image

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visual Data Enciphering via DNA Encoding, S-Box, and Tent Mapping 5

Visual Data Enciphering via DNA Encoding, S-Box, and Tent Ma...

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5th IEEE International conference on image processing Applications and Systems (IPAS)

作者： Gabr, Mohamed Younis, Hana Ibrahim, Marwa Alajmy, Sara Alexan, Wassim German Univ Cairo Fac MET Cairo Egypt German Univ Cairo Fac IET Cairo Egypt

ISBN: (纸本)9781665462198

The ever-evolving nature of the Internet and wireless communications, as well as the production of huge amounts of multimedia every day has created a dire need for their security. In this paper, an image encryption technique that is based on 3 stages is proposed. The first stage makes use of DNA encoding. The second stage proposed and utilizes a novel S-box that is based on the Mersenne Twister and a linear descent algorithm. The third stage employs the Tent chaotic map. The computed performance evaluation metrics exhibit a high level of achieved security.

关键词： Chaotic maps cryptography image encryption Mersenne Twister S-box Tent map

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Fidelity-preserving Learning-Based image Compression: Loss Function and Subjective Evaluation Methodology

Fidelity-preserving Learning-Based Image Compression: Loss F...

引用

IEEE visual communications and image processing (VCIP)

作者： Shima Mohammadi Yaojun Wu João Ascenso Instituto Superior Técnico Instituto de Telecomunicações Bytedance

Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion between the decoded and the original ground truth images. This paper focuses on perceptual optimization of learning-based image compression solutions and proposes: i) novel loss function to be used during training and ii) novel subjective test methodology that aims to evaluate the decoded image fidelity. According to experimental results from the subjective test taken with the new methodology, the optimization procedure can enhance image quality for low-rates while offering no advantage for high-rates.

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Deep Learning: A Detailed Analysis of Various image Augmentation Techniques 1

Deep Learning: A Detailed Analysis of Various Image Augmenta...

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1st IEEE International conference on Networking and communications, ICNWC 2023

作者： Swathi, S. Rajalakshmi, M. Senniappan, Vijayalakshmi Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Coimbatore Institute of Technology Department of Information Technology Coimbatore India Park College of Engineering Tech Department of Information Technology Coimbatore India

ISBN: (纸本)9798350336009

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses. © 2023 IEEE.

关键词： Computer vision

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