2023-03-22 更新
Commonsense Knowledge Assisted Deep Learning for Resource-constrained and Fine-grained Object Detection
Authors:Pu Zhang, Bin Liu
In this paper, we consider fine-grained image object detection in resource-constrained cases such as edge computing. Deep learning (DL), namely learning with deep neural networks (DNNs), has become the dominating approach to object detection. To achieve accurate fine-grained detection, one needs to employ a large enough DNN model and a vast amount of data annotations, which brings a challenge for using modern DL object detectors in resource-constrained cases. To this end, we propose an approach, which leverages commonsense knowledge to assist a coarse-grained object detector to get accurate fine-grained detection results. Specifically, we introduce a commonsense knowledge inference module (CKIM) to translate coarse-grained labels given by a backbone lightweight coarse-grained DL detector to fine-grained labels. We consider both crisp-rule and fuzzy-rule based inference in our CKIM; the latter is used to handle ambiguity in the target semantic labels. We implement our method based on several modern DL detectors, namely YOLOv4, Mobilenetv3-SSD and YOLOv7-tiny. Experiment results show that our approach outperforms benchmark detectors remarkably in terms of accuracy, model size and processing latency.
PDF 7 pages
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Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models
Authors:Divya Kothandaraman, Tianyi Zhou, Ming Lin, Dinesh Manocha
We present a novel method, Aerial Diffusion, for generating aerial views from a single ground-view image using text guidance. Aerial Diffusion leverages a pretrained text-image diffusion model for prior knowledge. We address two main challenges corresponding to domain gap between the ground-view and the aerial view and the two views being far apart in the text-image embedding manifold. Our approach uses a homography inspired by inverse perspective mapping prior to finetuning the pretrained diffusion model. Additionally, using the text corresponding to the ground-view to finetune the model helps us capture the details in the ground-view image at a relatively low bias towards the ground-view image. Aerial Diffusion uses an alternating sampling strategy to compute the optimal solution on complex high-dimensional manifold and generate a high-fidelity (w.r.t. ground view) aerial image. We demonstrate the quality and versatility of Aerial Diffusion on a plethora of images from various domains including nature, human actions, indoor scenes, etc. We qualitatively prove the effectiveness of our method with extensive ablations and comparisons. To the best of our knowledge, Aerial Diffusion is the first approach that performs ground-to-aerial translation in an unsupervised manner.
PDF Code: https://github.com/divyakraman/AerialDiffusion
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Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
Authors:Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim
Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control between different domain features using a single model is still challenging. Existing methods often require additional models, which is computationally demanding and leads to unsatisfactory visual quality. In addition, they have restricted control steps, which prevents a smooth transition. In this paper, we propose a new approach for high-quality domain translation with better controllability. The key idea is to preserve source features within a disentangled subspace of a target feature space. This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model. Our extensive experiments show that the proposed method can produce more consistent and realistic images than previous works and maintain precise controllability over different levels of transformation. The code is available at https://github.com/LeeDongYeun/FixNoise.
PDF Accepted by CVPR 2023. The code is available at https://github.com/LeeDongYeun/FixNoise. Extended from arXiv:2204.14079 (AICC workshop at CVPR 2022)
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Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark
Authors:Muyao Niu, Zhuoxiao Li, Zhihang Zhong, Yinqiang Zheng
Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios. Existing arts can be mainly divided into two threads: 1) RGB-dependent methods restore information using degraded RGB inputs only (\eg, low-light enhancement), 2) RGB-independent methods translate images captured under auxiliary near-infrared (NIR) illuminants into RGB domain (\eg, NIR2RGB translation). The latter is very attractive since it works in complete darkness and the illuminants are visually friendly to naked eyes, but tends to be unstable due to its intrinsic ambiguities. In this paper, we try to robustify NIR2RGB translation by designing the optimal spectrum of auxiliary illumination in the wide-band VIS-NIR range, while keeping visual friendliness. Our core idea is to quantify the visibility constraint implied by the human vision system and incorporate it into the design pipeline. By modeling the formation process of images in the VIS-NIR range, the optimal multiplexing of a wide range of LEDs is automatically designed in a fully differentiable manner, within the feasible region defined by the visibility constraint. We also collect a substantially expanded VIS-NIR hyperspectral image dataset for experiments by using a customized 50-band filter wheel. Experimental results show that the task can be significantly improved by using the optimized wide-band illumination than using NIR only. Codes Available: https://github.com/MyNiuuu/VCSD.
PDF Accepted to CVPR 2023
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