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Introduction In the realm of document processing and image enhancement, the significance of clear, legible, and high-resolution documents cannot be overstated. However, the presence of shadows in scanned or photographed documents can often pose a significant challenge. Enter LP-IOANet – an innovative solution designed for Efficient High-Resolution Document Shadow Removal. In this article, we will …

 LP-IOANet: Illuminating the Future of Document Enhancement with Efficient High-Resolution Shadow Removal Read More »

Introduction The Conference on Computer Vision and Pattern Recognition (CVPR) 2022 showcased a diverse range of cutting-edge research in the fields of computer vision and artificial intelligence. Among the intriguing topics presented, one that captured considerable attention was Probabilistic Procedure Planning in Instructional Videos. In this article, we delve into the profound significance and the …

[CVPR 2022 Series #1] Probabilistic Procedure Planning in Instructional Videos Read More »

Introduction In the ever-evolving landscape of natural language processing and computer vision, the fusion of various modalities has given rise to innovative approaches to tackle complex tasks. Visual Word Sense Disambiguation (VWSD), often abbreviated as VWSD, is one such task where the goal is to determine the correct sense of a word in a given …

Enhancing Visual Word Sense Disambiguation through Prompt-Based and Cross-Modal Retrieval Read More »

Introduction In the dynamic field of audio analysis and machine learning, acoustic scene classification (ASC) is pivotal, serving tasks such as automatically categorizing audio recordings based on the environment or context in which they were captured. One of the challenges in ASC is the scarcity of labeled data, making data augmentation techniques crucial for improving …

RandMasking Augment: Enhancing Acoustic Scene Classification with Effective Data Augmentation Read More »

introductory The vast tapestry of accents, dialects, and regional subtleties that make up language is quite remarkable. While research on speech recognition and natural language processing (NLP) frequently focuses heavily on major languages, accents and dialects with limited resources are sometimes disregarded. But developments in self-supervised learning are altering the rules. In this paper, we …

 Self-Supervised Accent Education: Helping Under-Resourced Accents Close the Gap Using Native Language Information Read More »

Introduction Welcome to the CVPR 2023 Series, where we embark on a journey through the latest breakthroughs in computer vision and pattern recognition. Our first stop is the captivating world of SPIn-NeRF – a groundbreaking technology that combines Multiview Segmentation and Perceptual Inpainting through the lens of Neural Radiance Fields. Join us as we unravel …

 [CVPR 2023 Series #1] SPIn-NeRF: Bridging the Gap with Multiview Segmentation and Perceptual Inpainting Using Neural Radiance Fields Read More »

introductory In the domains of self-supervised learning and video-based learning, the Computer Vision and Pattern Recognition (CVPR) 2023 conference remains a hub for cutting-edge research and invention. We delve further into StepFormer, an incredible advancement, in this episode of the CVPR 2023 Series. This cutting-edge technology has the potential to revolutionize the field of education …

 II (CVPR 2023 Series) StepFormer: Automating Video Learning through Self-supervised Localization and Step Discovery Read More »

Introduction Few-shot learning, a challenging subfield of machine learning, addresses the formidable task of training models to make accurate predictions when provided with very limited data. This scenario is particularly pertinent in cases where acquiring extensive training data is impractical or cost-prohibitive. Over recent years, a robust pipeline encompassing pre-training, meta-training, and fine-tuning has emerged …

 The Pre-Training, Meta-Training, and Fine-Tuning Pipeline for Few-Shot Learning Read More »

Introduction The internet is a vast repository of information, encompassing billions of websites covering a multitude of topics. As the volume of web content continues to grow exponentially, the challenge of effectively organizing and categorizing these websites becomes increasingly complex. Traditional methods of website clustering often rely on manual categorization or keyword-based approaches, which are …

 Transforming Website Clustering with Transformer Context Models Read More »

Introduction In the ever-evolving field of computer vision and deep learning, the quest for more efficient and compact models without compromising performance has led to numerous innovations. One such groundbreaking development is GOHSP, short for Graph and Optimization-based Heterogeneous Structured Pruning. This unified framework represents a significant leap forward in the world of Vision Transformers …

GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer Read More »