Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Gera, Darshan"'
Autor:
Balasubramanian, S, Subramaniam, M Sai, Talasu, Sai Sriram, Krishna, P Yedu, Sai, Manepalli Pranav Phanindra, Mukkamala, Ravi, Gera, Darshan
Deep neural networks (DNNS) excel at learning from static datasets but struggle with continual learning, where data arrives sequentially. Catastrophic forgetting, the phenomenon of forgetting previously learned knowledge, is a primary challenge. This
Externí odkaz:
http://arxiv.org/abs/2410.23751
Autor:
Chinagudaba, SeshaSai Nath, Gera, Darshan, Dasu, Krishna Kiran Vamsi, S, Uma Shankar, K, Kiran, Singarajpure, Anil, U, Shivayogappa., N, Somashekar, Chadda, Vineet Kumar, N, Sharath B
Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated wit
Externí odkaz:
http://arxiv.org/abs/2403.08834
The hindering problem in facial expression recognition (FER) is the presence of inaccurate annotations referred to as noisy annotations in the datasets. These noisy annotations are present in the datasets inherently because the labeling is subjective
Externí odkaz:
http://arxiv.org/abs/2305.01884
Autor:
Sola, Sridhar, Gera, Darshan
Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scena
Externí odkaz:
http://arxiv.org/abs/2304.03867
The fifth Affective Behavior Analysis in-the-wild (ABAW) competition has multiple challenges such as Valence-Arousal Estimation Challenge, Expression Classification Challenge, Action Unit Detection Challenge, Emotional Reaction Intensity Estimation C
Externí odkaz:
http://arxiv.org/abs/2303.09785
Dynamic Adaptive Threshold based Learning for Noisy Annotations Robust Facial Expression Recognition
The real-world facial expression recognition (FER) datasets suffer from noisy annotations due to crowd-sourcing, ambiguity in expressions, the subjectivity of annotators and inter-class similarity. However, the recent deep networks have strong capaci
Externí odkaz:
http://arxiv.org/abs/2208.10221
Automatic affect recognition has applications in many areas such as education, gaming, software development, automotives, medical care, etc. but it is non trivial task to achieve appreciable performance on in-the-wild data sets. In-the-wild data sets
Externí odkaz:
http://arxiv.org/abs/2207.09012
Autor:
Chinagudaba, Seshasai Nath, Gera, Darshan, Vamsi Dasu, Krishna Kiran, Shankar S, Uma, K, Kiran, Singarajpure, Anil, U, Shivayogappa, N, Somashekar, Kumar Chadha, Vineet, B N, Sharath
Publikováno v:
In Next Research October 2024 1(1)
Autor:
Gera, Darshan, Balasubramanian, S.
Publikováno v:
International Journal of Engineering Trends and Technology 69.7(2021):244-254
Presence of noise in the labels of large scale facial expression datasets has been a key challenge towards Facial Expression Recognition (FER) in the wild. During early learning stage, deep networks fit on clean data. Then, eventually, they start ove
Externí odkaz:
http://arxiv.org/abs/2107.04746
Autor:
Gera, Darshan, Balasubramanian, S
Publikováno v:
International Journal of Engineering Trends and Technology 69.7(2021):244-254
Facial expression recognition (FER) in the wild is crucial for building reliable human-computer interactive systems. However, annotations of large scale datasets in FER has been a key challenge as these datasets suffer from noise due to various facto
Externí odkaz:
http://arxiv.org/abs/2107.05736