Search Results for author:
Found 7 papers, 3 papers with code
Syn-Att: Synthetic Speech Attribution via Semi-Supervised Unknown Multi-Class Ensemble of CNNs
In this paper, a novel strategy is proposed to attribute a synthetic speech track to the generator that is used to synthesize it.
Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation
However, the scarcity of semantic information in datasets poses challenges.
CIFF-Net: Contextual Image Feature Fusion for Melanoma Diagnosis
In this paper, based on contextual image feature fusion (CIFF), a deep neural network (CIFF-Net) is proposed, which integrates patient-level contextual information into the traditional approaches for improved Melanoma diagnosis by concurrent multi-image comparative method.
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation
In this paper, a dual window transformer-based network, namely DwinFormer, is proposed, which utilizes both local and global features for end-to-end monocular depth estimation.
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security.
CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans
Moreover, a multi-scale fusion module is introduced with a pyramid fusion scheme to reduce the semantic gaps between subsequent encoder/decoder modules while facilitating the parallel optimization for efficient gradient propagation.
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization
Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients.
