53 research outputs found
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving
Autonomous driving has harsh requirements of small model size and energy
efficiency, in order to enable the embedded system to achieve real-time
on-board object detection. Recent deep convolutional neural network based
object detectors have achieved state-of-the-art accuracy. However, such models
are trained with numerous parameters and their high computational costs and
large storage prohibit the deployment to memory and computation resource
limited systems. Low-precision neural networks are popular techniques for
reducing the computation requirements and memory footprint. Among them, binary
weight neural network (BWN) is the extreme case which quantizes the float-point
into just bit. BWNs are difficult to train and suffer from accuracy
deprecation due to the extreme low-bit representation. To address this problem,
we propose a knowledge transfer (KT) method to aid the training of BWN using a
full-precision teacher network. We built DarkNet- and MobileNet-based binary
weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car,
pedestrian and cyclist detection. The experimental results show that the
proposed method maintains high detection accuracy while reducing the model size
of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB.Comment: Accepted by ICRA 201
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras : A Comparison
Altres ajuts: DGT (SPIP2014-01352)Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and nighttime. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images; (b) just infrared images; and (c) both of them. In order to obtain results for the last item, we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset that we have built for this purpose as well as on the publicly available KAIST multispectral dataset
AniPortraitGAN: Animatable 3D Portrait Generation from 2D Image Collections
Previous animatable 3D-aware GANs for human generation have primarily focused
on either the human head or full body. However, head-only videos are relatively
uncommon in real life, and full body generation typically does not deal with
facial expression control and still has challenges in generating high-quality
results. Towards applicable video avatars, we present an animatable 3D-aware
GAN that generates portrait images with controllable facial expression, head
pose, and shoulder movements. It is a generative model trained on unstructured
2D image collections without using 3D or video data. For the new task, we base
our method on the generative radiance manifold representation and equip it with
learnable facial and head-shoulder deformations. A dual-camera rendering and
adversarial learning scheme is proposed to improve the quality of the generated
faces, which is critical for portrait images. A pose deformation processing
network is developed to generate plausible deformations for challenging regions
such as long hair. Experiments show that our method, trained on unstructured 2D
images, can generate diverse and high-quality 3D portraits with desired control
over different properties.Comment: SIGGRAPH Asia 2023. Project Page:
https://yuewuhkust.github.io/AniPortraitGAN
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