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  1. Book ; Online ; E-Book: Image Processing and Intelligent Computing Systems

    Singhal, Prateek / Verma, Abhishek / Srivastava, Prabhat Kumar / Ranga, Virender / Kumar, Ram

    2022  

    Abstract: During the Covid-19 pandemic, we observed that the images helped doctors immensely in fast detection of Covid-19 infection in patients. There are many critical applications which use image processing to extract some useful information from raw image data. ...

    Abstract During the Covid-19 pandemic, we observed that the images helped doctors immensely in fast detection of Covid-19 infection in patients. There are many critical applications which use image processing to extract some useful information from raw image data.
    Subject code 616.07/54
    Language English
    Size 1 online resource (321 pages)
    Publisher Taylor & Francis Group
    Publishing place Milton
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 9781000822953 ; 9781032213149 ; 1000822958 ; 1032213140
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online: Language Grounded QFormer for Efficient Vision Language Understanding

    Choraria, Moulik / Sekhar, Nitesh / Wu, Yue / Zhang, Xu / Singhal, Prateek / Varshney, Lav R.

    2023  

    Abstract: Large-scale pretraining and instruction tuning have been successful for training general-purpose language models with broad competencies. However, extending to general-purpose vision-language models is challenging due to the distributional diversity in ... ...

    Abstract Large-scale pretraining and instruction tuning have been successful for training general-purpose language models with broad competencies. However, extending to general-purpose vision-language models is challenging due to the distributional diversity in visual inputs. A recent line of work explores vision-language instruction tuning, taking inspiration from the Query Transformer (QFormer) approach proposed in BLIP-2 models for bridging frozen modalities. However, these approaches rely heavily on large-scale multi-modal pretraining for representation learning before eventual finetuning, incurring a huge computational overhead, poor scaling, and limited accessibility. To that end, we propose a more efficient method for QFormer-based vision-language alignment and demonstrate the effectiveness of our strategy compared to existing baselines in improving the efficiency of vision-language pretraining.

    Comment: Preprint Under Review
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2023-11-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  3. Article ; Online: Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models.

    Kumar, Sarvesh / Wajeed, Mohammed Abdul / Kunabeva, Rajashekhar / Dwivedi, Nripendra / Singhal, Prateek / Jamal, Sajjad Shaukat / Akwafo, Reynah

    Computational intelligence and neuroscience

    2022  Volume 2022, Page(s) 3564436

    Abstract: It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) ...

    Abstract It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient's criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.
    MeSH term(s) Computer Security ; Confidentiality ; Deep Learning ; Health Records, Personal ; Humans ; Information Storage and Retrieval
    Language English
    Publishing date 2022-03-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2388208-6
    ISSN 1687-5273 ; 1687-5273
    ISSN (online) 1687-5273
    ISSN 1687-5273
    DOI 10.1155/2022/3564436
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Avoiding spurious correlations via logit correction

    Liu, Sheng / Zhang, Xu / Sekhar, Nitesh / Wu, Yue / Singhal, Prateek / Fernandez-Granda, Carlos

    2022  

    Abstract: Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for ... ...

    Abstract Empirical studies suggest that machine learning models trained with empirical risk minimization (ERM) often rely on attributes that may be spuriously correlated with the class labels. Such models typically lead to poor performance during inference for data lacking such correlations. In this work, we explicitly consider a situation where potential spurious correlations are present in the majority of training data. In contrast with existing approaches, which use the ERM model outputs to detect the samples without spurious correlations and either heuristically upweight or upsample those samples, we propose the logit correction (LC) loss, a simple yet effective improvement on the softmax cross-entropy loss, to correct the sample logit. We demonstrate that minimizing the LC loss is equivalent to maximizing the group-balanced accuracy, so the proposed LC could mitigate the negative impacts of spurious correlations. Our extensive experimental results further reveal that the proposed LC loss outperforms state-of-the-art solutions on multiple popular benchmarks by a large margin, an average 5.5\% absolute improvement, without access to spurious attribute labels. LC is also competitive with oracle methods that make use of the attribute labels. Code is available at https://github.com/shengliu66/LC.

    Comment: 17 pages, 6 figures
    Keywords Computer Science - Machine Learning ; Computer Science - Computation and Language ; Computer Science - Computer Vision and Pattern Recognition ; Electrical Engineering and Systems Science - Image and Video Processing ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2022-12-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Semantic Motion Segmentation Using Dense CRF Formulation

    Reddy, N. Dinesh / Singhal, Prateek / Krishna, K. Madhava

    2015  

    Abstract: While the literature has been fairly dense in the areas of scene understanding and semantic labeling there have been few works that make use of motion cues to embellish semantic performance and vice versa. In this paper, we address the problem of ... ...

    Abstract While the literature has been fairly dense in the areas of scene understanding and semantic labeling there have been few works that make use of motion cues to embellish semantic performance and vice versa. In this paper, we address the problem of semantic motion segmentation, and show how semantic and motion priors augments performance. We pro- pose an algorithm that jointly infers the semantic class and motion labels of an object. Integrating semantic, geometric and optical ow based constraints into a dense CRF-model we infer both the object class as well as motion class, for each pixel. We found improvement in performance using a fully connected CRF as compared to a standard clique-based CRFs. For inference, we use a Mean Field approximation based algorithm. Our method outperforms recently pro- posed motion detection algorithms and also improves the semantic labeling compared to the state-of-the-art Automatic Labeling Environment algorithm on the challenging KITTI dataset especially for object classes such as pedestrians and cars that are critical to an outdoor robotic navigation scenario.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 629
    Publishing date 2015-04-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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