Zobrazeno 1 - 10
of 348
pro vyhledávání: '"Kim, Youngseok"'
Recent advancements in camera-based 3D object detection have introduced cross-modal knowledge distillation to bridge the performance gap with LiDAR 3D detectors, leveraging the precise geometric information in LiDAR point clouds. However, existing cr
Externí odkaz:
http://arxiv.org/abs/2407.10164
Autor:
Kim, Youngseok, Govia, Luke C. G., Dane, Andrew, Berg, Ewout van den, Zajac, David M., Mitchell, Bradley, Liu, Yinyu, Balakrishnan, Karthik, Keefe, George, Stabile, Adam, Pritchett, Emily, Stehlik, Jiri, Kandala, Abhinav
Pre-fault tolerant quantum computers have already demonstrated the ability to estimate observable values accurately, at a scale beyond brute-force classical computation. This has been enabled by error mitigation techniques that often rely on a repres
Externí odkaz:
http://arxiv.org/abs/2407.02467
The advent of scalable deep models and large datasets has improved the performance of Neural Machine Translation. Knowledge Distillation (KD) enhances efficiency by transferring knowledge from a teacher model to a more compact student model. However,
Externí odkaz:
http://arxiv.org/abs/2403.01479
Chiral edge states in quantum Hall effect are the paradigmatic example of the quasi-particle with chirality. In even space-time dimensions, the Nielsen-Ninomiya theorem strictly forbids the chiral states in physical isolation. The exceptions to this
Externí odkaz:
http://arxiv.org/abs/2312.02979
Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth estimation error. Despite improved detection performance, prior works re
Externí odkaz:
http://arxiv.org/abs/2306.08528
We introduce a mini-batch stochastic variance-reduced algorithm to solve finite-sum scale invariant problems which cover several examples in machine learning and statistics such as principal component analysis (PCA) and estimation of mixture proporti
Externí odkaz:
http://arxiv.org/abs/2304.11268
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation. Although recent low-cost camera-based approaches have shown promising results, they are susceptible to poor illuminat
Externí odkaz:
http://arxiv.org/abs/2304.00670
Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to reduce the enormous burden of 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level p
Externí odkaz:
http://arxiv.org/abs/2211.11950
Recent advances in monocular 3D detection leverage a depth estimation network explicitly as an intermediate stage of the 3D detection network. Depth map approaches yield more accurate depth to objects than other methods thanks to the depth estimation
Externí odkaz:
http://arxiv.org/abs/2210.16574
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR. Existing fusion methods often fuse the outputs of single modalities at the result-level, called the late fusion strategy. This can benefit f
Externí odkaz:
http://arxiv.org/abs/2209.06535