Joe Mathai
YOU?
Author Swipe
View article: Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies Open
Deepfake videos present an increasing threat to society with potentially negative impact on criminal justice, democracy, and personal safety and privacy. Meanwhile, detecting deepfakes, at scale, remains a very challenging task that often …
View article: In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision Open
Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for seve…
View article: P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking Open
Today's high resolution, high frame rate cameras in autonomous vehicles generate a large volume of data that needs to be transferred and processed by a downstream processor or machine learning (ML) accelerator to enable intelligent computi…
View article: P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications Open
The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in analog voltages by a sensor…
View article: P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications Open
The demand to process vast amounts of data generated from state-of-the-art high resolution cameras has motivated novel energy-efficient on-device AI solutions. Visual data in such cameras are usually captured in the form of analog voltages…
View article: Detection and Continual Learning of Novel Face Presentation Attacks
Detection and Continual Learning of Novel Face Presentation Attacks Open
Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research. However, state-of-the-art face antispoofing systems are still vulnerable to novel …
View article: Multispectral Biometrics System Framework: Application to Presentation Attack Detection
Multispectral Biometrics System Framework: Application to Presentation Attack Detection Open
In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources. The framework unifies the system design for diffe…
View article: Towards Learning Structure via Consensus for Face Segmentation and Parsing
Towards Learning Structure via Consensus for Face Segmentation and Parsing Open
Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather e…
View article: Towards Learning Structure via Consensus for Face Segmentation and\n Parsing
Towards Learning Structure via Consensus for Face Segmentation and\n Parsing Open
Face segmentation is the task of densely labeling pixels on the face\naccording to their semantics. While current methods place an emphasis on\ndeveloping sophisticated architectures, use conditional random fields for\nsmoothness, or rathe…
View article: Learning Structure via Consensus for Face Segmentation and Parsing.
Learning Structure via Consensus for Face Segmentation and Parsing. Open
Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather e…
View article: Does Generative Face Completion Help Face Recognition?
Does Generative Face Completion Help Face Recognition? Open
Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstr…