The semantic segmentation of agricultural aerial images is very important for the recognition and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency, etc. Methods for general semantic ...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
The semantic segmentation of agricultural aerial images is very important for the recognition and analysis of farmland anomaly patterns, such as drydown, endrow, nutrient deficiency, etc. Methods for general semantic segmentation such as Fully Convolutional Networks can extract rich semantic features, but are difficult to exploit the long-range information. Recently, vision Transformer architectures have made outstanding performances in image segmentation tasks, but transformer-based models have not been fully explored in the field of ***, we propose a novel architecture called Agricultural Aerial Transformer (AAFormer) to solve the semantic segmentation of aerial farmland images. We adopt Mix Transformer (MiT) in the encoder stage to enhance the ability of field anomaly patternrecognition and leverage the Squeeze-and-Excitation (SE) module in the decoder stage to improve the effectiveness of key channels. The boundary maps of farmland are introduced into the decoder. Evaluated on the Agriculture-vision validation set, the mIoU of our proposed model reaches 45.44%.
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set ...
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ISBN:
(纸本)9781665448994
Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set as little as one image or one video. We propose a two-branch discriminator, with content and layout branches designed to judge the internal content separately from the scene layout realism. This allows synthesis of visually plausible, novel compositions of a scene, with varying content and layout, while preserving the context of the original sample. Compared to previous single-image GAN models, One-Shot GAN achieves higher diversity and quality of synthesis. It is also not restricted to the single image setting, successfully learning in the introduced setting of a single video.
Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, exis...
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ISBN:
(纸本)9798350302493
Image completion is widely used in photo restoration and editing applications, e.g. for object removal. Recently, there has been a surge of research on generating diverse completions for missing regions. However, existing methods require large training sets from a specific domain of interest, and often fail on general-content images. In this paper, we propose a diverse completion method that does not require a training set and can thus treat arbitrary images from any domain. Our internal diverse completion (IDC) approach draws inspiration from recent single-image generative models that are trained on multiple scales of a single image, adapting them to the extreme setting in which only a small portion of the image is available for training. We illustrate the strength of IDC on several datasets, using both user studies and quantitative comparisons.
Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect use...
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ISBN:
(纸本)9781665448994
Nowadays, video conference solutions are widely adopted for companies, education, and government. People segmentation is crucial for supporting virtual background, an essential video conference function to protect users' privacy. This paper demonstrated a people segmentation framework called CE-PeopleSeg, which employed an efficient segmentation method, structural pruning, and dynamic frame skipping techniques, leading to a fast inference speed on CPU. Our extensive experiments show that the proposed CE-PeopleSeg can achieve a high prediction mIoU of 87.9% on Supervised People Dataset while reaching a real-time inference speed of 32.40 fps on CPU with very low usage of 10%. Our code would be released at https://***/geekJZY/***.
Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including ...
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ISBN:
(纸本)9781665448994
Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the computervision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding "enforcement" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.
Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the met...
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ISBN:
(纸本)9781665448994
Few-shot learning features the capability of generalizing from a few examples. In this paper, we first identify that a discriminative feature space, namely a rectified metric space, that is learned to maintain the metric consistency from training to testing, is an essential component to the success of metric-based few-shot learning. Numerous analyses indicate that a simple modification of the objective can yield substantial performance gains. The resulting approach, called rectified metric propagation (ReMP), further optimizes an attentive prototype propagation network, and applies a repulsive force to make confident predictions. Extensive experiments demonstrate that the proposed ReMP is effective and efficient, and outperforms the state of the arts on various standard few-shot learning datasets.
Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an int...
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ISBN:
(纸本)9781665448994
Line art plays a fundamental role in illustration and design, and allows for iteratively polishing designs. However, as they lack color, they can have issues in conveying final designs. In this work, we propose an interactive colorization approach based on a conditional generative adversarial network that takes both the line art and color hints as inputs to produce a high-quality colorized image. Our approach is based on a U-net architecture with a multi-discriminator framework. We propose a Concatenation and Spatial Attention module that is able to generate more consistent and higher quality of line art colorization from user given hints. We evaluate on a large-scale illustration dataset and comparison with existing approaches corroborate the effectiveness of our approach.
We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed t...
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ISBN:
(纸本)9781665448994
We study event-based sensors in the context of spacecraft guidance and control during a descent on Moon-like terrains. For this purpose, we develop a simulator reproducing the event-based camera outputs when exposed to synthetic images of a space environment. We find that it is possible to reconstruct, in this context, the divergence of optical flow vectors (and therefore the time to contact) and use it in a simple control feedback scheme during simulated descents. The results obtained are very encouraging, albeit insufficient to meet the stringent safety constraints and modelling accuracy imposed upon space missions. We thus conclude by discussing future work aimed at addressing these limitations.
Despite the rapid progress in deep visual recognition, modern computervision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Despite the rapid progress in deep visual recognition, modern computervision datasets significantly overrepresent the developed world and models trained on such datasets underperform on images from unseen geographies. We investigate the effectiveness of unsupervised domain adaptation (UDA) of such models across geographies at closing this performance gap. To do so, we first curate two shifts from existing datasets to study the Geographical DA problem, and discover new challenges beyond data distribution shift: context shift, wherein object surroundings may change significantly across geographies, and subpopulation shift, wherein the intra-category distributions may shift. We demonstrate the inefficacy of standard DA methods at Geographical DA, highlighting the need for specialized geographical adaptation solutions to address the challenge of making object recognition work for everyone.
Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture re...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.
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