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C-SAW: Self-Supervised Prompt Learning for image Generalization in Remote Sensing 14

C-SAW: Self-Supervised Prompt Learning for Image Generalizat...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Bhattacharya, Avigyan Singha, Mainak Jha, Ankit Banerjee, Biplab Indian Institute of Technology Bombay Maharashtra Mumbai India

ISBN: (纸本)9798400716256

We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrastively trained VLMs show impressive zero-shot generalization performance, their effectiveness is limited when dealing with diverse domains during training and testing. Existing prompt learning techniques overlook the importance of incorporating domain and content information into the prompts, which results in a drop in performance while dealing with such multi-domain data. To address these challenges, we propose a solution that ensures domain-invariant prompt learning while enhancing the expressiveness of visual features. We observe that CLIP's vision encoder struggles to identify contextual image information, particularly when image patches are jumbled up. This issue is especially severe in optical remote sensing images, where land-cover classes exhibit well-defined contextual appearances. To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features. We keep the CLIP backbone frozen and introduce a small set of projectors for both the CLIP encoders to train C-SAW contrastively. Experimental results demonstrate the superiority of C-SAW across multiple remote sensing benchmarks and different generalization tasks. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Optical remote sensing

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A Novel Approach for Neuromorphic vision Data Compression based on Deep Belief Network 14

A Novel Approach for Neuromorphic Vision Data Compression ba...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Khaidem, Sally Nevatia, Abhipraay Sharma, Mansi Indian Institute of Technology Madras TN Chennai India

ISBN: (纸本)9798400716256

A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the time, location, and polarity of brightness change. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Despite yielding a lower bit rate compared to conventional video capture, the present approach of event capture demonstrates enhanced compressibility. Hence, we introduce a novel deep learning-based compression methodology tailored for event data. The proposed technique employs a deep belief network (DBN) to condense the high-dimensional event data into a latent representation, which is subsequently encoded utilising an entropy-based coding method. Notably, our proposed scheme represents one of the initial endeavours to integrate deep learning methodologies for event compression. It achieves a high compression ratio while maintaining good reconstruction quality outperforming state-of-the-art event data coders and other lossless benchmark techniques. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cameras

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Mandala as computational art: Vectorization and beyond 14

Mandala as computational art: Vectorization and beyond

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Sarkar, Tusita Bhowmick, Partha Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798400716256

This paper introduces a novel technique of computational art with mandala-an iconic heritage of indian folk art. Its novelty lies in several fundamental steps. The first one is fixing the asymmetries and the imperfections in a hand-drawn piece of art based on the notion of a primitive map. The primitive map is described using a novel concept of geometric salience-a set of well-defined salient points on the frontier polygon of a primitive-characterizing the concavities and convexities present in the primitive. The primitive map is also used for the vectorization of a mandala and its succinct representation as a mandala sector graph (MSG), which eventually results in efficient graph operations on an existing artwork to create a new piece of art. The use of frontier polygons in different steps of the algorithm makes it robust and efficient. Experimental results on various datasets demonstrate the potential and versatility of the proposed technique. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Arts computing

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Advancements in Fisheye Lens image Stitching for Enhanced ADAS in Automated Vehicles 7

Advancements in Fisheye Lens Image Stitching for Enhanced AD...

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7th IEEE International conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Harsha Vardhan, K. Hegde, Vinay Rv College of Engineering Department of Computer Science Bangalore India

ISBN: (纸本)9798350343144

As Advanced Driver Assistance Systems (ADAS) become integral to the functionality of automated vehicles, the demand for comprehensive and distortion-free visual information is paramount. This survey paper meticulously examines the realm of image stitching utilizing fisheye lenses to cater to the unique needs of ADAS applications. Drawing insights from an extensive analysis of pertinent research papers, this survey delves into the diverse spectrum of techniques devised to enhance fisheye lens image stitching. Key focal points include the rectification of fisheye lens distortion, mitigation of image duplication artifacts, and the seamless synthesis of stitched images. Embracing advancements in computer vision, image processing, and the utilization of OpenGL-based rendering techniques, this survey encapsulates the evolving landscape of image stitching for ADAS in automated vehicles. By dissecting the challenges inherent to distortion correction and object duplication removal, this paper uncovers the progressive strategies employed to elevate the accuracy and visual fidelity of stitched images in the context of ADAS. With a comprehensive synthesis of existing research, this survey serves as an indispensable compass for researchers, engineers, and practitioners navigating the intricate domain of image stitching within the realm of automated vehicle system. © 2023 IEEE.

关键词： computer vision

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Accelerating Native Inference Model Performance in Edge Devices using TensorRT 6

Accelerating Native Inference Model Performance in Edge Devi...

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6th IEEE International conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Gopiktrishna, P.B. Marion Lincy G, Rubell Rishekeeshan, Abhishek Deekshitha Indian Institute of Information Technology Computer Science and Engineering Kottayam India AiRender Technology Pvt Ltd Bangalore India

ISBN: (纸本)9798350381689

In this era, deep learning is becoming increasingly popular for solving real-world problems. Due to the extremely high processing power required to execute the most complex deep learning models, graphics processing Units(GPUs) and Neural processing Units(NPUs) are essential for optimal execution. However, it is challenging to incorporate these high-performance processors in every edge device like Internet of Things gadgets, embedded systems, and cell phones to execute complex inference models. Hence, deep learning models must be optimized to perform well on edge devices with limited memory, power, and computing capabilities. Optimizing the inference models to accelerate performance on edge devices is one of the significant issues facing, especially in complex computation domains like computer vision. TensorRT, introduced by NVIDIA, is one of the best approaches used to optimize inference models, by accelerating their performance on edge devices without compromising the model accuracy. It presents a variety of optimization approaches, including layer fusion, quantization, dynamic tensor memory management, kernel auto tuning, and precision calibration. Every optimization technique is contingent upon the problem that have selected and the use of all optimization approaches has the potential to significantly speed up the performance of an inference model. This paper explores how TensorRT accelerates the performance of inference models like ResNet-50, VGG16, and Inception V3 in an image classification problem, and the results are analyzed with its native inference models. The utilization of TensorRT in edge devices is expanding significantly, reflecting a growing scope for enhanced performance and efficiency in processing complex neural networks at the edge. © 2024 IEEE.

关键词： graphics processing unit

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Automatic Weed Detection and Killing Robot 1

Automatic Weed Detection and Killing Robot

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1st International conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Billa, Poornaiah Sudabathula, Seva Tharun Kasi, Divya Sree Ramichetty, Mounika Electronics and Communication Engineering Andhra Pradesh India Affiliated to JNTUK Kakinada India

ISBN: (纸本)9798350339727

Though becoming a pillar of the indian economy, agriculture is currently having a production crisis. The issue of weeds in agriculture is a significant problem that can re- sult in substantial financial losses for farmers. Conventional methods of weed control, such as manual labor or chemical applications, can be time-consuming, demanding, and potentially harmful to the environment. To tackle these challenges, we have developed and implemented an automatic weed detection and killing robot. The robot employs computer vision algorithms to detect and categorize weeds instantly. It comprises a camera and a processing unit that captures images of the crop field and examines them to recognize weeds based on their visual features. The robot then uses a mechanical arm to administer a precise amount of herbicide to the weed, decreasing the use of chemicals and minimizing the possibility of harming non-target crops. This is accomplished by periodically taking pictures of the field, processing them on a Raspberry Pi board and using an image processing algorithm to distinguish the desired plants from the weeds based on a number of characteristics such as the size, colour, and cut. The Raspberry Pi chip sends a signal to the pesticide spraying pump to turn on when the weeds have been accurately discovered and identified through image analysis. © 2023 IEEE.

关键词： computer vision

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Binary image Classification Using Machine Learning and Deep Quantum Neural Networks 14

Binary Image Classification Using Machine Learning and Deep ...

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14th International conference on Cloud Computing, Data Science and Engineering, Confluence 2024

作者： Mehta, Vaishali Kumar, Avinash Nahar, Kanchan Poonam Sharma, Dolly Tiwari, Rohak MMDU Mullana Cse Dept. Haryana Ambala India DCRUST Murthal Cse Dept. Haryana Sonepat India Geeta University Cse Dept. Haryana Panipat India Lntce Cse Dept. Haryana Panipat India Amity University Cse Dept. Greater Noida India DPS Rkpuram Greater Noida India

ISBN: (纸本)9798350344837

This paper helps to explore the intricate task of natural and precise image grouping for effective organization and retrieval. High precision in image classification is challenging due to the complexity of images and the vast array of defining features. Deep learning-based artificial intelligence, a rapidly progressing field, plays a crucial role in various industries such as image classification, computer vision, text mining, voice recognition, and medical scan analysis. Deep convolutional neural networks (CNNs) excel in advanced image categorization and processing, particularly for high-resolution images. This study introduces a deep quantum neural network (QNN) technique for binary image categorization, delving into the latest research on image classification using cross-entropy functions, deep learning, and convolutional neural networks. © 2024 IEEE.

关键词： Deep neural networks

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image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International conference on computer vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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Aggregated Co-attention based Visual Question Answering 14

Aggregated Co-attention based Visual Question Answering

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Mishra, Aakansha Anand, Ashish Guha, Prithwijit Indian Institute of Technology Guwahati Assam Guwahati India

ISBN: (纸本)9798400716256

Recent developments in the field of Visual Question Answering (VQA) have witnessed promising improvements in performance through contributions in attention based networks. Most such approaches have focused on unidirectional attention that leverage over attention from textual domain (question) on visual space. This work proposes a multistage co-attention framework. Here, co-attention framework performs both image and text attention. The co-attention mechanism is repeated in multiple stages. Attention on different stages may capture some significant and distinct features for learning better contextual information. Thus, aggregation of attention is performed to preserve the information from different stages. The proposed architecture with multiple stage network could suffer from vanishing or exploding gradients. To prevent this, loss at each of the different stages is computed. Extensive experiments and analysis are performed for validating the effects of aggregated attention and stage-wise loss. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Co-attention Multimodal Interaction VQA

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A Novel Lightweight Multi-Attentive General Ship Detection model for Detection of Ships in Optical and SAR Satellite imagery

A Novel Lightweight Multi-Attentive General Ship Detection m...

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conference on Real-Time processing of image, Depth, and Video Information

作者： Bhattacharjee, Shovakar Shanmugam, Palanisamy Das, Sukhendu Indian Inst Technol Madras Ocean Engn Dept Chennai 600036 Tamil Nadu India Indian Inst Technol Madras Comp Sci & Engn Dept Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781510673199;9781510673182

Satellite imagery-based ship detection is indispensable in maritime surveillance and monitoring the naval activities. Machine learning is an effective approach that enables the process to be automatic and more accurate as compared to many other approaches. Generally, optical and synthetic aperture radar satellite images are often employed for detecting/locating various marine activities using different methods. However, models trained on one set of images often yield large uncertainties when testing on other sets of images due to the complex scene characteristics. This study proposes a novel lightweight computationally efficient deep learning-based general ship detection model called the Multi-Attentive General Ship Detector (MAGSD) for detecting ships in both optical and SAR satellite images. The model is trained with the SAR Ship Dataset (SDD), which has ship instances from Gaofen-3 and Sentinel-1 SAR satellite images, and the MASATI dataset that contains ship instances from the Microsoft Bing Map. The proposed model focuses on the attention-guided convolutional neural network for extracting feature maps for detection, which bridges the gap between SAR and optical image characteristics constraints by focusing on different levels of convolutional features in the network. The model is built with a novel feature extractor that has fourteen convolutional layers with six max pool layers and six attention layers, connecting several convolutional points to focus on local features in different depth maps which serve as the backbone of the model. The comparative analysis showed the robustness of the proposed model over the state-of-the-art baseline model YOLOv5s, with a precision of 8.2% and a recall of 9.63%. These results indicate that the proposed model holds the potential to serve as an efficient tool for ship detection in any satellite images and contributes to the enhanced coastal surveillance and bolsters global naval security.

关键词： Synthetic aperture radar Ocean optics Satellite imaging Satellites Object detection computer vision technology Deep learning Machine learning

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