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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21-30 订阅

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No Prompting Frozen Foundation Models: Interactive Medical Volume Segmentation using Continual Test Time Adaptation of Compact Models 24

No Prompting Frozen Foundation Models: Interactive Medical V...

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15th indian conference on computer vision graphics and image processing

作者： Borkar, Kushal Reen, Abhilaksh Singh Jawahar, C. V. Arora, Chetan Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Delhi Delhi India IIIT Hyderabad Hyderabad India

ISBN: (纸本)9798400710759

Automated segmentation of medical image volumes promises to reduce costly medical experts' time for annotation. However, using machine learning for the task is challenging due to variations in imaging modalities and scarcity of patient data. While interactive image segmentation methods and foundational models incorporating user-provided prompts to refine segmentation masks have shown promise, they overlook crucial sequential information between the slices in 3D medical image volumes and videos, resulting in discontinuities in the segmentation results. This paper proposes a new framework that dynamically updates model parameters during inference in a test time training framework using user-provided scribbles. Our framework preserves acquired knowledge from the previous slices of the current medical volume and the training dataset via student-teacher learning. We evaluate our method on diverse CT, MRI, and microscopic cell datasets. Our framework significantly reduces user annotation time by a factor of 6.72x. Compared to other interactive segmentation methods, we reduce the time by a factor of 2.64x. Our method also outperforms prompting foundation models for segmentation by achieving a dice score of 0.9 in 3-4 interactions compared to 5-8 user interactions for the foundation model, significantly reducing annotation time for the CT and MRI volumes.

关键词： Medical image Analysis image Segmentation Human-computer Interaction Continual Learning

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Transferring Appearance with Mask-Guided Attention 24

Transferring Appearance with Mask-Guided Attention

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15th indian conference on computer vision graphics and image processing

作者： Bandla, Manikanta IISc Bangalore Comp Sci & Automat Bangalore Karnataka India

ISBN: (纸本)9798400710759

Despite significant advancements in large-scale text-to-image generation and text-conditioned image editing, appearance transfer remains relatively unexplored. Transferring appearance aims to transfer an object's appearance in an appearance image to an object in the structure image so that background details are preserved and accurately reflect the transferred object's characteristics. Appearance transfer has practical applications in areas like virtual try-on and e-commerce product placement. Existing methods often require fine-tuning text-to-image diffusion models or are not applicable to virtual try-on and e-commerce scenarios, which is not ideal. In this paper, we introduce a Mask-Guided attention mechanism that replaces the existing self-attention in U-net architecture of Stable diffusion [29]. This approach can be easily integrated into the Masactrl [6] framework, enabling appearance transfer without model fine-tuning and suitable for a wide range of applications. Our method uses masks of objects in images to guide the appearance transfer process, with these masks obtained from the Segment Anything Model (SAM) [17]. This integration of SAM-generated masks allows for precise object localization and more accurate appearance transfer. We have conducted comprehensive experiments on transferring various clothing items (shirts, jeans, t-shirts) onto people, as well as transferring sofas into living spaces.

关键词： Appearance transfer image editing virtual try-on sofa placement masked attention segmentation

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NCVPRIPG 2023 Summer Challenge on Writer Verification 8th

NCVPRIPG 2023 Summer Challenge on Writer Verification

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Prabhat, Gyan Shahid, Abu Gatti, Prajwal Shah, Manan Chutia, Girin Sharma, Arvind Kumar Mishra, Anand Indian Inst Technol Jodhpur Jodhpur Rajasthan India Cashfree Payments India Pvt Ltd Bengaluru India HCL Technol Ltd Chennai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

This report presents the outcomes of the Summer Challenge on Writer Verification, hosted as part of the Eighth National conference on computer vision, Pattern Recognition, image processing, and graphics (NCVPRIPG) held at IIT Jodhpur on July 21-23, 2023. This challenge introduces a novel dataset comprising images of handwritten text contributed by 1,352 unique writers. Predominantly, these images feature handwritten Hindi text, but they also encompass a variety of elements such as numbers, mathematical symbols, and English text. Participants were tasked with developing a model capable of automatically determining whether the text in a given pair of images is authored by the same writer or different writers. The primary objective of this challenge was to advance research in the realm of handwritten text recognition. Throughout the competition, we registered 108 teams, with 18 teams submitting results for the validation dataset. Out of these, 13 teams provided submissions for the semi-final dataset. The top six teams from the semi-finals were subsequently invited to compete in the finals. Additional details about the challenge can be found at https://***/challenges/wv2023/.

关键词： Writer verification Handwritten text Indic language Document analysis Handwritten text recognition

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Robust Speech Recognition with Unsupervised Frame and Character Level Adversarial Domain Matching 24

Robust Speech Recognition with Unsupervised Frame and Charac...

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15th indian conference on computer vision graphics and image processing

作者： Paul, Soumen Das, Partha Pratim Rao, Krothapalli Sreenivasa Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur W Bengal India

ISBN: (纸本)9798400710759

End-to-end automatic speech recognition (ASR) systems achieve promising performance for large-scale speech datasets. However, these systems experience performance degradation when a domain mismatch exists between training and test datasets. This paper addresses the domain adaptation problem by employing adversarial learning in an unsupervised manner, along with the ASR training. We propose frame level and character level domain adversarial training, which reduces the domain shift between source and target data. Frame-level adversarial training selects all source and target speech frames and tries to classify them into two domains. On the contrary, character-level training generates pseudo-labels for source and target batches and finds the feature distribution for each pseudo-character label. A random feature is selected for each character from the source and target domains. This feature set of all characters is used in the domain classification. Experiments on the Libriadapt and Librispeech clean dataset show that our approaches achieve similar word error rate (WER) reduction as for the state-of-the-art approaches with lower time complexities. The proposed approaches expect promising results for other speech adaptation applications, which will be analyzed in the future.

关键词： Adversarial learning Unsupervised domain adaptation Speech recognition

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Med-SeAM: Medical Context Aware Self-Supervised Learning Framework for Anomaly Classification in Knee MRI 24

Med-SeAM: Medical Context Aware Self-Supervised Learning Fra...

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15th indian conference on computer vision graphics and image processing

作者： Daydar, Akshay Reddy, Ajay Kumar Kumar, Sonal Sur, Arijit Laskar, Hanif Indian Inst Technol Guwahati Kamrup India Guwahati Neurol Res Ctr GNRC Kamrup India

ISBN: (纸本)9798400710759

In deep learning-based medical applications involving musculoskeletal image classification, obtaining high-quality and expert-level annotations of Magnetic Resonance Imaging (MRI) slices has been a significant challenge. This problem is magnified while classifying anomalies in MRI volumes without manual annotations. In recent years, Self-Supervised Learning (SSL) methods have addressed this issue by aligning the slices with pseudo labels obtained using unsupervised computer vision tasks like the Jigsaw puzzle. Yet these assignments are weak and have limited performance in medical imaging tasks due to the absence of clinical manifestation. To overcome this issue, a Medical Context Aware Self-Supervised Learning framework named Med-SeAM is proposed in the present study for anomaly classification. The proposed framework includes a novel Disease Context Aware Learning (DCL) pretext task to classify the critical MRI slices within the Region of Interest in Knee MRI. In addition, the Multi-Domain Feature Cross-Attention (MDFCA) module is attached with intra- and inter-slice learning modules for learning the domain-aware representations. The downstream task focuses on the class-imbalanced binary classification of three anomaly conditions in the knee, namely abnormality, Anterior Cruciate Ligament (ACL) tear, and meniscal tear in the MRNet dataset. The experimental results reveal that the proposed framework has produced excellent results with improvement of 2% and 6% for abnormality and meniscal tear diagnosis, respectively. The experimental results suggest that the amalgamation of deep learning with the domain information achieves excellent performance, surpassing existing supervised and self-supervised schemes by a substantial margin.

关键词： Musculoskeletal image classification Self-Supervised Learning Disease Context Aware Learning Multi-Domain Feature Cross-Attention Knee MRI

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Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation 24

Exploring the Limits of VLMs: A Dataset for Evaluating Text-...

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15th indian conference on computer vision graphics and image processing

作者： Srivastava, Avnish Sista, Raviteja Chakrabarti, Partha P. Sheet, Debdoot Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400710759

vision language models (VLMs) integrate vision and text by learning from images and their textual descriptions for generating text from images and vice-versa. VLMs are used for various tasks such as image captioning, and visual question answering. As VLMs extend to video generation tasks, significant challenges arise. The generated videos often lack temporal consistency, and there exist issues with alignment between the generated video content and the input text. This work proposes a set of prompts for systematically evaluating VLMs on text-to-video generation focused on object coherence and temporal consistency. This dataset of prompt covers two categories: first, the complexity of prompts, and second, the number of objects and actions. The first category consists of four levels of prompt complexity: simple, mid-level, high-level, and unique and rare object prompts. The second category also consists of four levels: single object, single object in action, multiple objects, and multiple objects in action. Thus, the dataset is a combination of 16 different prompt scenarios where each scenarios has 10 prompts resulting in a dataset of 160 prompts. This work explores and evaluates the outputs of three models from the family of diffusion-based VLMs for the task of text to video generation. The videos generated by the models were assessed by five participants on a 0-5 Likert scale. VLMs under study were able to generate temporally consistent videos and proper object in video in only 33.63% and 39.43% of the total evaluation scenario prompts. Most of these prompts belong to the categories of single object and simple prompts. The performance of models drops with increase in objects, actions and prompt complexity. The scenarios of actions, with rare prompts are where all models perform poorly. Thus, reflecting on limitations of VLMs in generating videos. The proposed dataset can be extended to other video generation models to benchmark their performances on the basic aspects

关键词： Benchmark dataset Diffusion models Temporal consistency Video generation vision language models

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APMSA: Crossmodal Remote Sensing image Retrieval using Attention Pooling and Multimodal Semantic Alignment 24

APMSA: Crossmodal Remote Sensing Image Retrieval using Atten...

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15th indian conference on computer vision graphics and image processing

作者： Aparna, H. Hati, Avik Natl Inst Technol Tiruchirappalli Tiruchirappalli India NIT Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9798400710759

Crossmodal image retrieval can help in crucial remote sensing (RS) applications like land cover monitoring and disaster management by retrieving complementary information from different data sources. Such retrieval techniques aid in the search and identification images captured by different satellite sensors like optical, thermal, SAR and MS over different geo-locations and time period. However, multi-modal RS data present a variety of challenges like intermodal relationship modelling and modality alignment. To this end, we propose a novel crossmodal image retrieval algorithm designed to deal with multiple RS image modalities like optical, multi-spectral and panchromatic. We make use of a modified version of residual networks (ResNets) which consists of stem convolution to capture salient features and attention pooling replacing the global average pooling to enhance the modelling of relation between image pixels. A combination of objective functions - the triplet loss which helps in identifying the latent details and features present in the images of individual modalities, and the novel multimodal semantic alignment loss which aids in orientation of extracted features in a shared vector space, thereby prompting intermodal comprehension and alignment - are used. We tested the algorithm on EuroSAT and DSRSID datasets, and the proposed method achieved 95.92% and 98.86% top-50 retrieval precision, respectively. Experimental results show that the proposed method exhibits superior performance in comparison to existing techniques in literature.

关键词： Crossmodal retrieval Multimodal semantic alignment Attention pooling Remote Sensing

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A Challenge on 3D Reconstruction and Restoration of indian Heritage from Partial and Noisy Scans 8th

A Challenge on 3D Reconstruction and Restoration of Indian H...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Pandey, Atharva Yadav, Vishal Karthikeyan, Akash Solanki, Deepanshu Nagar, Rajendra IIT Jodhpur Jodhpur Rajasthan India Thiagarajar Coll Engn Madurai Tamil Nadu India

ISBN: (纸本)9789819752119;9789819752126

The problem of 3D Reconstruction and restoration of indian heritage sites is a classical 3D computer vision problem which has been addressed in the indian vision and graphics research communities. The major focus of these approaches has been to reconstruct a point cloud model of the heritage sites from multiple colored images captured from different viewpoints. This essentially requires solving the structure from motion problem to be solved. This becomes a challenging problem to solve for large scale architectural sites. With the emergence of 3D scanners, we can obtain partial 3D scans of a site from different viewpoints. Then, themain problem to solve is to fuse these noisy and partial 3Dscans to obtain the complete 3D model of the underlying surface of the heritage site. The problem of reconstructing the complete 3D model from the partial 3D scan can be solved using classical approaches as well as learning based approaches. Using machine learning based approaches in the context of indian heritage may suffer from the limited training dataset availability. Also, the order of points representing the 3D model of such sites can be in millions. Therefore, reconstructing such high resolution 3D models using neural network may demand high-end parallel computing facility.

关键词： Heritage site restoration Point cloud registration Feature based registration

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Enhancing Face Quality Assessment through Age and Expression Analysis 24

Enhancing Face Quality Assessment through Age and Expression...

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15th indian conference on computer vision graphics and image processing

作者： Jaiswal, Prateek Namboodiri, Anoop Int Inst Informat Technol Hyderabad CVIT Hyderabad Telangana India IIIT Hyderabad CVIT Hyderabad Telangana India

ISBN: (纸本)9798400710759

In facial recognition systems, quality extends beyond conventional perceptual quality to features incorporating identity information. Most facial image datasets embark on factors such as illumination and pose, making current systems robust enough to these factors with impressive recognition performance. Still, it is imperative to acknowledge that age variation and emotional similarity significantly influence identity. Variations in these features might significantly deceive the FR systems. These features also serve as easy channels for adversarial attacks on FR systems that alter facial features, such as morphing. Hence, making FR systems sensitive to the variations introduced over the range of these features is critical. We propose that the Unified Tri-Feature Quality Metric (U3FQ) be incorporated. This novel assessment framework integrates three critical elements: age variance, facial expression similarity, and congruence scores from state-of-the-art recognition models such as VGG-Face, ArcFace, FaceNet, and OpenFace. The weighting U3FQ utilizes an advanced learning paradigm, employing a Regression Network model for facial image quality assessment. U3FQ was rigorously evaluated against general IQA techniques-BRISQUE, BLINDS-II, RankIQA, and specialized FIQA methodologies like PFE, SER-FIQA, and SDD-FIQA. Results are backed up with qualitative analysis on the effectiveness of the generated quality scores through DET plots of FNMR on different age ranges, expression matches heat maps, and Expected Verification Rate (EVRC) curves on various datasets.

关键词： Fingerprint image Quality Fingerprint Recognition System image Quality Assessment Weakly Supervised Learning

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Novel Unsupervised Disease Segmentation Framework Based on Clustering 8th

Novel Unsupervised Disease Segmentation Framework Based on C...

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8th National conference on computer vision, Pattern Recognition, image processing and graphics-NCVPRIPG

作者： Singh, Rama Kant Aggarwal, Monika Lall, Brejesh Indian Inst Technol Delhi Bharti Sch New Delhi India Indian Inst Technol Delhi CARE New Delhi India Indian Inst Technol Delhi Elect Engg Dept New Delhi India

ISBN: (纸本)9789819752119;9789819752126

Plant disease is a major limiting factor in yield production. Traditionally, biologists rely on manual screening (localisation) of disease symptoms exhibited on plant leaves. However, this is a highly subjective and error-prone analysis. With the advancement of imaging devices, image-based analysis permits high-throughput disease localisation. In this context, state-of-the-art supervised deep learning-based methods have been presented. However, annotation of disease symptoms is time-consuming and is a current bottleneck. To relieve this limitation, we propose a novel unsupervised framework that utilises Cluster weight as well as Group-local Feature-weight learning using iteration in Fuzzy C-Means (CGFFCM) guided via differentiable clustering algorithm for accurate disease localisation. It is to be noted that the proposed framework does not rely on annotated image data. In this paper, we also performed an exhaustive comparison based on different clustering methods to show the efficacy of the proposed framework.

关键词： Disease phenotyping Unsupervised segmentation Differentiable clustering Fuzzy C-means CGFFCM

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