Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Chhipa, Prakash Chandra"'
Autor:
Chippa, Meenakshi Subhash, Chhipa, Prakash Chandra, De, Kanjar, Liwicki, Marcus, Saini, Rajkumar
Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining camera intrinsic and extrinsic parameters is challenging, making it h
Externí odkaz:
http://arxiv.org/abs/2410.03686
Autor:
Chhipa, Prakash Chandra, De, Kanjar, Chippa, Meenakshi Subhash, Saini, Rajkumar, Liwicki, Marcus
The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Vision-Language Models (VLMs) have recently achieved groundbreaking results. VLM-based open-vocabulary object detection extends the
Externí odkaz:
http://arxiv.org/abs/2405.14874
Autor:
Chhipa, Prakash Chandra, Chippa, Meenakshi Subhash, De, Kanjar, Saini, Rajkumar, Liwicki, Marcus, Shah, Mubarak
Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating camera intrinsic and extrinsic parameters is a challenging task that prev
Externí odkaz:
http://arxiv.org/abs/2405.02296
Autor:
Chhipa, Prakash Chandra
This thesis investigates the possibility of efficiently adapting self-supervised representation learning on visual domains beyond natural scenes, e.g., medical imagining and non-RGB sensory images. The thesis contributes to i) formalizing the self-su
Externí odkaz:
http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-95425
Autor:
Chhipa, Prakash Chandra, Holmgren, Johan Rodahl, De, Kanjar, Saini, Rajkumar, Liwicki, Marcus
Self-supervised learning in computer vision aims to leverage the inherent structure and relationships within data to learn meaningful representations without explicit human annotation, enabling a holistic understanding of visual scenes. Robustness in
Externí odkaz:
http://arxiv.org/abs/2308.02525
This work presents a novel label-efficient selfsupervised representation learning-based approach for classifying diabetic retinopathy (DR) images in cross-domain settings. Most of the existing DR image classification methods are based on supervised l
Externí odkaz:
http://arxiv.org/abs/2304.11168
This work presents a novel domain adaption paradigm for studying contrastive self-supervised representation learning and knowledge transfer using remote sensing satellite data. Major state-of-the-art remote sensing visual domain efforts primarily foc
Externí odkaz:
http://arxiv.org/abs/2304.09874
Autor:
Chhipa, Prakash Chandra, Chopra, Muskaan, Mengi, Gopal, Gupta, Varun, Upadhyay, Richa, Chippa, Meenakshi Subhash, De, Kanjar, Saini, Rajkumar, Uchida, Seiichi, Liwicki, Marcus
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization of self-supervised learning ps
Externí odkaz:
http://arxiv.org/abs/2304.01354
Autor:
Pihlgren, Gustav Grund, Nikolaidou, Konstantina, Chhipa, Prakash Chandra, Abid, Nosheen, Saini, Rajkumar, Sandin, Fredrik, Liwicki, Marcus
In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation, and autoencoding. Deep perceptual loss is a type of loss function fo
Externí odkaz:
http://arxiv.org/abs/2302.04032
Autor:
Chhipa, Prakash Chandra, Upadhyay, Richa, Saini, Rajkumar, Lindqvist, Lars, Nordenskjold, Richard, Uchida, Seiichi, Liwicki, Marcus
This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utiliz
Externí odkaz:
http://arxiv.org/abs/2210.10633