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Found 9 papers, 1 papers with code
Garment3DGen: 3D Garment Stylization and Texture Generation
We present a plethora of quantitative and qualitative comparisons on various assets both real and generated and provide use-cases of how one can generate simulation-ready 3D garments.
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity
In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance.
Taming Mode Collapse in Score Distillation for Text-to-3D Generation
In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.
MMG-Ego4D: Multi-Modal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
MMG-Ego4D: Multimodal Generalization in Egocentric Action Recognition
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
PyTorchVideo: A Deep Learning Library for Video Understanding
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing.
Improving Efficiency in Neural Network Accelerator Using Operands Hamming Distance optimization
Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric.
A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications
Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on.
A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things
To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed.
