Výsledky vyhledávání - "Onoe, Naoyuki"

Report

Fiducial Focus Augmentation for Facial Landmark Detection

Autor: Kar, Purbayan, Chudasama, Vishal, Onoe, Naoyuki, Wasnik, Pankaj, Balasubramanian, Vineeth

Deep learning methods have led to significant improvements in the performance on the facial landmark detection (FLD) task. However, detecting landmarks in challenging settings, such as head pose changes, exaggerated expressions, or uneven illuminatio

Externí odkaz: http://arxiv.org/abs/2402.15044

Zobrazit plný text záznamu

Report

Estimation of individual causal effects in network setup for multiple treatments

Autor: Thorat, Abhinav, Kolla, Ravi, Pedanekar, Niranjan, Onoe, Naoyuki

We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and networked observational data. Leveraging the network information, we aim to utilize hidden confounders that may not be directly accessi

Externí odkaz: http://arxiv.org/abs/2312.11573

Zobrazit plný text záznamu

Report

Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection

Autor: Kar, Purbayan, Chudasama, Vishal, Onoe, Naoyuki, Wasnik, Pankaj

Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. However, many of these methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-

Externí odkaz: http://arxiv.org/abs/2306.02268

Zobrazit plný text záznamu

Report

Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing

Autor: Shah, Nirmesh, Singh, Mayank Kumar, Takahashi, Naoya, Onoe, Naoyuki

Primary goal of an emotional voice conversion (EVC) system is to convert the emotion of a given speech signal from one style to another style without modifying the linguistic content of the signal. Most of the state-of-the-art approaches convert emot

Externí odkaz: http://arxiv.org/abs/2302.10536

Zobrazit plný text záznamu

Report

M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversation

Autor: Chudasama, Vishal, Kar, Purbayan, Gudmalwar, Ashish, Shah, Nirmesh, Wasnik, Pankaj, Onoe, Naoyuki

Emotion Recognition in Conversations (ERC) is crucial in developing sympathetic human-machine interaction. In conversational videos, emotion can be present in multiple modalities, i.e., audio, video, and transcript. However, due to the inherent chara

Externí odkaz: http://arxiv.org/abs/2206.02187

Zobrazit plný text záznamu

Report

A Unified Model for Fingerprint Authentication and Presentation Attack Detection

Autor: Popli, Additya, Tandon, Saraansh, Engelsma, Joshua J., Onoe, Naoyuki, Okubo, Atsushi, Namboodiri, Anoop

Typical fingerprint recognition systems are comprised of a spoof detection module and a subsequent recognition module, running one after the other. In this paper, we reformulate the workings of a typical fingerprint recognition system. In particular,

Externí odkaz: http://arxiv.org/abs/2104.03255

Zobrazit plný text záznamu