Results 1 to 10 of about 569,009 (312)
Time series classification through visual pattern recognition
Journal of King Saud University: Computer and Information Sciences, 2022 In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment).
Agnieszka Jastrzebska
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Optimal Transport Aggregation for Visual Place Recognition [PDF]
Computer Vision and Pattern Recognition, 2023 The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues.
Sergio Izquierdo, Javier Civera
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Improved Baselines with Visual Instruction Tuning [PDF]
Computer Vision and Pattern Recognition, 2023 Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this paper, we present the first systematic study to investigate the design choices of LMMs in a controlled setting under the LLaVA framework.
Haotian Liu +3 more
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Multimodal Prompting with Missing Modalities for Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2023 In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on ...
Yi-Lun Lee +3 more
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Bottleneck Transformers for Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2021 We present BoTNet, a conceptually simple yet powerful backbone architecture that incorporates self-attention for multiple computer vision tasks including image classification, object detection and instance segmentation.
A. Srinivas +5 more
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Balanced Contrastive Learning for Long-Tailed Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2022 Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples.
Jianggang Zhu +4 more
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VOLO: Vision Outlooker for Visual Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Recently, Vision Transformers (ViTs) have been broadly explored in visual recognition. With low efficiency in encoding fine-level features, the performance of ViTs is still inferior to the state-of-the-art CNNs when trained from scratch on a midsize ...
Li Yuan +4 more
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An Empirical Study on the Effects of Temporal Trends in Spatial Patterns on Animated Choropleth Maps
ISPRS International Journal of Geo-Information, 2022 Animated cartographic visualization incorporates the concept of geomedia presented in this Special Issue. The presented study aims to examine the effectiveness of spatial pattern and temporal trend recognition on animated choropleth maps. In a controlled
Paweł Cybulski
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Involution: Inverting the Inherence of Convolution for Visual Recognition [PDF]
Computer Vision and Pattern Recognition, 2021 Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard convolution for vision tasks, specifically spatial-agnostic and channel ...
Duo Li +7 more
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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014 Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224$\times$ 224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale.
Kaiming He +3 more
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