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检索条件"任意字段=Proceedings of the Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing"

共 1468 条记录，以下是11-20 订阅

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GraphVL: Graph-Enhanced Semantic Modeling via vision-Language Models for Generalized Class Discovery 24

GraphVL: Graph-Enhanced Semantic Modeling via Vision-Languag...

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15th indian conference on computer vision graphics and image processing

作者： Solanki, Bhupendra Nair, Ashwin R. Singha, Mainak Mukhopadhyay, Souradeep Jha, Ankit Banerjee, Biplab Indian Inst Technol Mumbai Maharashtra India Indian Inst Sci Educ & Res Thiruvananthapuram Thiruvananthapuram Kerala India Indian Inst Sci Bangalore Karnataka India LNM Inst Informat Technol Jaipur Rajasthan India

ISBN: (纸本)9798400710759

Generalized Category Discovery (GCD) aims to cluster unlabeled images into known and novel categories using labeled images from known classes. To address the challenge of transferring features from known to unknown classes while mitigating model bias, we introduce GraphVL, a novel approach for vision-language modeling in GCD, leveraging CLIP. Our method integrates a graph convolutional network (GCN) with CLIP's text encoder to preserve class neighborhood structure. We also employ a lightweight visual projector for image data, ensuring discriminative features through margin-based contrastive losses for image-text mapping. This neighborhood preservation criterion effectively regulates the semantic space, making it less sensitive to known classes. Additionally, we learn textual prompts from known classes and align them to create a more contextually meaningful semantic feature space for the GCN layer using a contextual similarity loss. Finally, we represent unlabeled samples based on their semantic distance to class prompts from the GCN, enabling semi-supervised clustering for class discovery and minimizing errors. Our experiments on seven benchmark datasets consistently demonstrate the superiority of GraphVL when integrated with the CLIP backbone.

关键词： Class Discovery Contrastive Learning Graph Convolutional Networks Unsupervised learning vision-Language models

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MoCoMER: A Self Supervised Representation Learning for Handwritten Mathematical Expression Recognition 24

MoCoMER: A Self Supervised Representation Learning for Handw...

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15th indian conference on computer vision graphics and image processing

作者： Pramanik, Sandip Mitra, Shree Das, Nibaran Jadavpur Univ Dept CSE Kolkata W Bengal India Indian Inst Informat Technol Guwahati Gauhati India

ISBN: (纸本)9798400710759

This paper presents a self-supervised learning (SSL) based framework, specifically designed for Handwritten Mathematical Expression Recognition (HMER). The proposed approach incorporates a momentum encoding technique and a non-linear projection head into the image encoder to effectively address the common issue of dimensional collapse in self-supervised learning (SSL) methods. Our approach consists of two main steps: first, we use self-supervised pre-training to train the image encoder to obtain strong feature representations from HME images. Subsequently, we fine-tune the model using a Transformer network to predict LaTeX sequences from HME images. The assessment demonstrates that our SSL framework surpasses other existing SSL frameworks as well as several supervised methods in terms of performance. The findings indicate that our approach is highly advanced, emphasizing its potential to enhance the robustness and efficiency of feature representations in HMER tasks. The integration of momentum encoding and a non-linear projection head in the image encoder is shown to enhance the durability and effectiveness of feature representations, leading to superior performance in HMER tasks. Our experiments reveal that our approaches achieve an expression recognition rate (ExpRate) of 62. 17%, 61. 03%, 64. 8% on the CROHME 2014,2016,2019 test datasets respectively. The CROHME 2019 test data achieves the highest ExpRate, which is state-of-the-art (SOTA). This success is achieved by overcoming the challenges of dimensional collapse and leveraging the advantages of both self-supervised and supervised learning.(1)

关键词： HMER SSL Contrastive Learning Momentum Contrast (MoCo) Non-linear projection head Transformer

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Addressing Diffusion Model Based Counter-Forensic image Manipulation for Synthetic image Detection 24

Addressing Diffusion Model Based Counter-Forensic Image Mani...

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15th indian conference on computer vision graphics and image processing

作者： Herur, Aryan N. Santhosh, Vaibhav Shetty, Nishanth Seelamantula, Chandra Sekhar Natl Inst Technol Karnataka Surathkal Elect & Commun Engn Mangalore Karnataka India Indian Inst Sci Elect Engn Bangalore Karnataka India IISc Bangalore Elect Engn Bangalore Karnataka India

ISBN: (纸本)9798400710759

With the rapid development of modern generative models, the need for an automated synthetic image detection process has never been greater. Recent works in the field of synthetic image detection focus on improving out-of-distribution (OoD) classification performance and robustness to common image pre-processing techniques. However, in this work, we intend to explore the nature of an intricate counter-forensic attack, i.e., the reconstruction of real images with Diffusion Model autoencoders, which could be used to adversely affect the performance of modern synthetic image detection algorithms. We present a variety of experiments to study the nature of this counter-forensic attack and use the inferences from these experiments to develop multiple algorithms to detect such reconstructed images while attempting to detect real and purely synthetic images accurately. To do so, we make use of trained classifiers that can detect real images, autoencoder-reconstructed images, and purely synthetic images. Furthermore, we combine these techniques to build a novel ensemble algorithm that competes with state-of-the-art (SoTA) algorithms in the 'Real vs. Fake' image detection task, while detecting autoencoder reconstructed images accurately, attaining an accuracy of 99.2% in the multiclass setting.

关键词： image Generative Models Convolutional Neural Networks (CNNs) Contrastive Language image Pretraining (CLIP)

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Enhancing Generalization Ability in Deepfake Detection via Continual Learning 24

Enhancing Generalization Ability in Deepfake Detection via C...

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15th indian conference on computer vision graphics and image processing

作者： Usmani, Shaheen Kumar, Sunil Sadhya, Debanjan ABV Indian Inst Informat Technol & Management Gwa Gwalior India

ISBN: (纸本)9798400710759

Deepfake technology generates counterfeit facial images by replicating the characteristics of genuine ones. Sophisticated manipulation tactics have enabled criminals to incite social panic or accrue illegal gains by creating deceptive media, such as forged facial images. Consequently, numerous techniques for detecting deepfakes have been developed to evaluate the authenticity of images. The existing deepfake detection techniques perform well if training and testing datasets exhibit similarity. However, then they do not provide good results if training and testing datasets are dissimilar, thereby not providing generalization ability. In this work, we have proposed the Continual Learning Based Enhanced vision Transformer (CLEViT) model for deepfake detection. The CLEViT model leverages the continual learning mechanism to enhance the model's generalization ability. Continual learning enables deepfake detection models to continuously adapt and update their knowledge based on new data, allowing them to identify manipulation techniques effectively. After that, a vision transformer is used to extract rich contextual information from images using self-attention mechanisms. To validate the efficacy of the model, we have created a multi-task dataset comprising FakeAVCeleb, RFF, RFFD, and the DFDC datasets. The proposed CLEViT model outperforms existing deepfake detection techniques, yielding superior results and simultaneously providing generalization ability.

关键词： Continual learning vision transformer Deepfake Convolutional neural network

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Semi-Decoupled Distillation for Brain Tumor Segmentation using multimodal MRI Scans 24

Semi-Decoupled Distillation for Brain Tumor Segmentation usi...

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15th indian conference on computer vision graphics and image processing

作者： Kundu, Surajit Chatterjee, Ankita Mukhopadhyay, Jayanta Chakravorty, Nishant Indian Inst Technol kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400710759

Brain tumour segmentation is a fundamental task in medical image analysis where each year a number of deep learning models are introduced to delineate the tumour regions with high precision. However, most of these works rely on large number of parameters and higher computational cost, thus, rendering them ineffective in real world application. Therefore, it is important to devise efficient models that can be easily deployed on resource-constrained devices and perform at par with the existing large models. In this paper, we propose a semi-decoupled distillation technique which trains a lightweight "student" model using the features extracted from the decoder of the nnU-Net "teacher" model and its predictions on these features using a single point-wise convolution layer. The final classification layer remains the same for both the models and is kept frozen while training the "student" network. Our approach follows a two stage training procedure where the tumour regions are detected and extracted in the first step, and then sent for second stage training to segment fine-grain classes, including edema, enhanced tumour and tumour core. Our extensive experimentation shows that a lightweight distilled model performs competitively with large models on brain tumour segmentation.

关键词： Glioblastoma Knowledge Distillation Brain Tumor Segmentation nnU-Net

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IDA-UIE: An Iterative Framework for Deep Network based Degradation Aware Underwater image Enhancement 24

IDA-UIE: An Iterative Framework for Deep Network based Degra...

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15th indian conference on computer vision graphics and image processing

作者： Singh, Pranjali Guha, Prithwijit Indian Inst Technol Guwahati Ctr Intelligent Cyber Phys Syst Gauhati Assam India Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati Assam India

ISBN: (纸本)9798400710759

Underwater image quality is often compromised due to factors like fluorescence, low illumination, absorption, and scattering. Recent advancements in underwater image enhancement have introduced various deep network architectures to address these issues. Typically, these methods employ a single network to tackle all degradation challenges. However, we hypothesize that deep networks trained for specific conditions outperform those trained for multiple degradation cases. Consequently, we propose an iterative framework that individually identifies and resolves a dominant degradation condition. We focus on eight specific degradation conditions: low illumination, low contrast, haziness, blurriness, noise, and color imbalances across three channels. Our approach involves designing a deep network capable of detecting the dominant degradation condition and selecting an appropriate deep network tailored for that specific condition. We further propose that our work is the creation of condition-specific datasets derived from high-quality images in two standard datasets, UIEB and EUVP. These datasets facilitate the training of enhancement networks specific to each degradation condition. Our proposed method performs better than the nine baseline methods on both UIEB and EUVP datasets.

关键词： Underwater Imaging Model Deep Learning Underwater image Enhancement

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Lost in Context: The Influence of Context on Feature Attribution Methods for Object Recognition 24

Lost in Context: The Influence of Context on Feature Attribu...

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15th indian conference on computer vision graphics and image processing

作者： Adhikari, Sayanta Kumar, Rishav Mopuri, Konda Reddy Pachamuthu, Rajalakshmi Indian Inst Technol Hyderabad Artificial Intelligence & Machine Learning Hyderabad Telangana India Indian Inst Technol Hyderabad Elect Engn Hyderabad Telangana India

ISBN: (纸本)9798400710759

Contextual information plays a critical role in object recognition models within computer vision, where changes in context can significantly affect accuracy, underscoring models' dependence on contextual cues. This study investigates how context manipulation influences both model accuracy and feature attribution, providing insights into the reliance of object recognition models on contextual information as understood through the lens of feature attribution methods. We employ a range of feature attribution techniques to decipher the reliance of deep neural networks on context in object recognition tasks. Using the imageNet-9 and our curated imageNet-CS datasets, we conduct experiments to evaluate the impact of contextual variations, analyzed through feature attribution methods. Our findings reveal several key insights: (a) Correctly classified images predominantly emphasize object volume attribution over context volume attribution. (b) The dependence on context remains relatively stable across different context modifications, irrespective of classification accuracy. (c) Context change exerts a more pronounced effect on model performance than Context perturbations. (d) Surprisingly, context attribution in 'no-information' scenarios is non-trivial. Our research moves beyond traditional methods by assessing the implications of broad-level modifications on object recognition, either in the object or its context. Code available at https://***/nineRishav/Lost-In-Context

关键词： Context Explainable AI (XAI) imageNet Feature Attribution Object Recognition

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Dementia Disease Progression Analysis from 2D Cellular Automata Architecture 24

Dementia Disease Progression Analysis from 2D Cellular Autom...

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15th indian conference on computer vision graphics and image processing

作者： Kesu, Siva Manohar Reddy Sinha, Neelam Ramasangu, Hariharan Inst Sci Campus Ctr Brain Res Indian Bangalore Karnataka India Relecura Bangalore Karnataka India

ISBN: (纸本)9798400710759

The detection of change in mild cognitive impairment (MCI) is vital between one visit and another visit for the early-stage detection of Alzheimer's disease (AD). It is associated with changes in the structural and functional aspects. Diagnosis of dementia progression is a challenging but essential step for the planning of early treatment. This paper proposes the cellular automata (CA) framework to identify the changes when the disease progresses from one visit to another. An updated rule using the conduction function of anisotropic diffusion has been proposed to assess the earliest signs of dementia manifestation and progression. The progression of dementia disease in multi-visit subjects for healthy and unhealthy patients has been analyzed by examining the entropy from evolved 2D CA images. The subjects below the reference line in healthy cases lead to the prognosis of the disease, whereas subjects above the reference line in unhealthy subjects are in improved condition. The proposed CA architecture shows the transitions and progressive patterns, thereby offering a method for tracking the evolution of dementia over time.

关键词： Dementia Functional Connectivity FMRI Cellular Automata Disease Progression

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Accurate and Real-time LiDAR Point Cloud Forecasting for Autonomous Driving 24

Accurate and Real-time LiDAR Point Cloud Forecasting for Aut...

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15th indian conference on computer vision graphics and image processing

作者： Dasgupta, Soham Aphale, Kshitij Pal, Kaustab Sharma, Avinash IIT Jodhpur Jodhpur Rajasthan India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Point cloud forecasting is a crucial task for the success of motion planning and state estimation problems. However, due to its complex nature and challenges in integrating with existing architectures, it remains an extremely difficult and interesting problem. LiDAR-based 3D sensing is one of the key components in modern autonomous driving systems and leads to scalability challenges as it yields large-scale point cloud data at a high frame rate. Range image representation of point cloud offers a compact representation of LiDAR data which enables applying powerful convolution network architectures for predicting future range images (and thereby point clouds). In this paper, we use range image representation and propose two different non-recurrent deep network models for point cloud forecasting. More specifically, we predict future point clouds from past observed point clouds by introducing a spatio temporal convolution (STC) block in the latent space of range images, thereby avoiding the use of RNNs to achieve much faster inference. The STC has two variants that use the temporal attention and Inception-Net blocks, respectively. We perform experiments on two publicly available datasets, namely KITTI and Nuscenes and report superior quantitative and qualitative results in comparison to SOTA methods while offering compact model with faster inference speeds.

关键词： Range Sensing 3D computer vision Robotics

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Contextual Self-Attention Based UNet Architecture for Fluid Segmentation in Retinal OCT B-scans 24

Contextual Self-Attention Based UNet Architecture for Fluid ...

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15th indian conference on computer vision graphics and image processing

作者： Kalita, Himashree Dandapat, Samarendra Bora, Prabin Kumar Indian Inst Technol Guwahati Elect & Elect Engn North Guwahati Assam India

ISBN: (纸本)9798400710759

Retinal edema caused by fluid buildups has links to vision threatening retinal diseases. Presently, spectral-domain optical coherence tomography (SD-OCT) is the imaging method of choice for evaluating retinal health and tracking treatment progress. Automated methods for fluid segmentation in retinal OCT B-scans are critical for ocular disease diagnosis. The segmentation task in retinal OCT B-scans with pathological manifestations is challenging as the retinal layered structure varies significantly with the severity of the underlying disease condition. In this paper, we present a Contextual Self-Attention based U-Net (CoSAUNet) architecture for fluid segmentation in pathological OCT B-scans. The proposed model replaces the basic convolutional layers in the standard U-Net architecture with contextual self-attention layers. The contextual self-attention mechanism integrates contextual information mining among neighboring keys and conventional self-attention into a unified architecture to learn the long-range feature dependencies. The robustness of the proposed CoSAUNet model is evaluated through a 3-fold cross-validation setup on the RETOUCH and the AROI datasets, yielding average Dice scores (DS) of 88.16% and 76.67%, respectively. Furthermore, the CoSAUNet performs comparably to the state-of-the-art methods while using only half of the parameters.

关键词： Optical coherence tomography retinal fluid segmentation deep learning semantic segmentation self-attention mechanism

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