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Farm Parcel Delineation Using Spatio-temporal Convolutional Networks

Farm Parcel Delineation Using Spatio-temporal Convolutional ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Han Lin Aung Burak Uzkent Marshall Burke David Lobell Stefano Ermon Department of Computer Science Stanford University Department of Earth Systems Science Stanford University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Farm parcel delineation (delineation of boundaries of farmland parcels/segmentation of farmland areas) provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in downstream governmental policies of land allocation, irrigation, fertilization, green-house gases (GHG's), etc. This data can also be useful for the agricultural insurance sector for assessing compensations following damages associated with extreme weather events - a growing trend related to climate change [4]. Using satellite imaging can be a scalable and cost-effective manner to perform the task of farm parcel delineation to collect this valuable data. In this paper, we break down this task using satellite imaging into two approaches: 1) Segmentation of parcel boundaries, and 2) Segmentation of parcel areas. We implemented variations of U-Nets, one of which takes into account temporal information, which achieved the best results on our dataset on farm parcels in France in 2017.

关键词： Satellites Task analysis Image segmentation Meteorology Machine learning computer vision computer science

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Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batc...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vincenzo Lomonaco Davide Maltoni Lorenzo Pellegrini University of Bologna Italy

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Robotic vision is a field where continual learning can play a significant role. An embodied agent operating in a complex environment subject to frequent and unpredictable changes is required to learn and adapt continuously. In the context of object recognition, for example, a robot should be able to learn (without forgetting) objects of never before seen classes as well as improving its recognition capabilities as new instances of already known classes are discovered. Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates. In this paper, we introduce a novel continual learning protocol based on the CORe50 benchmark and propose two rehearsal-free continual learning techniques, CWR* and AR1*, that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches. In particular, our experiments show that AR1* can outperform other state-of-the-art rehearsal-free techniques by more than 15% accuracy in some cases, with a very light and constant computational and memory overhead across training batches.

关键词： Training Robots Videos Object recognition Benchmark testing computer architecture Computational modeling

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Take the Scenic Route: Improving Generalization in vision-and-Language Navigation

Take the Scenic Route: Improving Generalization in Vision-an...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Felix Yu Zhiwei Deng Karthik Narasimhan Olga Russakovsky Princeton University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In the vision-and-Language Navigation (VLN) task, an agent with egocentric vision navigates to a destination given natural language instructions. The act of manually annotating these instructions is timely and expensive, such that many existing approaches automatically generate additional samples to improve agent performance. However, these approaches still have difficulty generalizing their performance to new environments. In this work, we investigate the popular Room-to-Room (R2R) VLN benchmark and discover that what is important is not only the amount of data you synthesize, but also how you do it. We find that shortest path sampling, which is used by both the R2R benchmark and existing augmentation methods, encode biases in the action space of the agent which we dub as action priors. We then show that these action priors offer one explanation toward the poor generalization of existing works. To mitigate such priors, we propose a path sampling method based on random walks to augment the data. By training with this augmentation strategy, our agent is able to generalize better to unknown environments compared to the baseline, significantly improving model performance in the process.

关键词： Navigation Benchmark testing Task analysis Natural languages Visualization Training data Markov processes

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Spatio-temporal action detection and localization using a hierarchical LSTM

Spatio-temporal action detection and localization using a hi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Akshaya Ramaswamy Karthik Seemakurthy Jayavardhana Gubbi Balamuralidhar Purushothaman Embedded Systems and Robotics TCS Research and Innovation Bangalore India

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Video analysis is gaining importance in the recent past due to its usefulness in a wide variety of applications. The efficiency of a video analytics engine primarily depends on its ability to extract the spatio-temporal features, which has enough discriminative. Inspired by the way the human visual system operates, we propose a hierarchical architecture to capture the spatio-temporal information from a given input video at different time scales. The proposed architecture has a 3D Inception module followed by two layers of modified Convolutional Long Short Term Memory (ConvLSTM) as the fundamental unit. At each level, we consolidate the LSTM cell and hidden states to the next level by using an visual attention-based pooling approach. The proposed network is used for video action detection and localization application that is the foundational element for video analysis. UCF101 and AVA datasets are used to show that the recognition accuracy achieved by the proposed algorithm advances the state-of-the-art in spatio-temporal action detection and localization application.

关键词： Feature extraction computer architecture Microprocessors Task analysis Visualization Proposals Three-dimensional displays

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Feedback U-net for Cell Image Segmentation

Feedback U-net for Cell Image Segmentation

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Eisuke Shibuya Kazuhiro Hotta Meijo University Tempaku-ku Nagoya Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human brain is a layered structure, and performs not only a feedforward process from a lower layer to an upper layer but also a feedback process from an upper layer to a lower layer. The layer is a collection of neurons, and neural network is a mathematical model of the function of neurons. Although neural network imitates the human brain, everyone uses only feedforward process from the lower layer to the upper layer, and feedback process from the upper layer to the lower layer is not used. Therefore, in this paper, we propose Feedback U-Net using Convolutional LSTM which is the segmentation method using Convolutional LSTM and feedback process. The output of U-net gave feedback to the input, and the second round is performed. By using Convolutional LSTM, the features in the second round are extracted based on the features acquired in the first round. On both of the Drosophila cell image and Mouse cell image datasets, our method outperformed conventional U-Net which uses only feedforward process.

关键词： Feature extraction Image segmentation Neurons Convolution Decoding Logic gates computer architecture

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Multi-Domain Learning and Identity Mining for Vehicle Re-Identification

Multi-Domain Learning and Identity Mining for Vehicle Re-Ide...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Shuting He Hao Luo Weihua Chen Miao Zhang Yuqi Zhang Fan Wang Hao Li Wei Jiang Zhejiang University Alibaba Group

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic *** solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a multi-domain learning method to joint the real-world and synthetic data to train the model. Then, we propose the Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering. The tracklet-level re-ranking strategy with weighted features is also used to post-process the results. Finally, with multiple-model ensemble, our method achieves 0.7322 in the mAP score which yields third place in the competition. The codes are available at https://***/heshuting555/AICITY2020_DMT_VehicleReID.

关键词： Data models Task analysis Testing Feature extraction Urban areas Data mining computer vision

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Inferring Temporal Compositions of Actions Using Probabilistic Automata

Inferring Temporal Compositions of Actions Using Probabilist...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rodrigo Santa Cruz Anoop Cherian Basura Fernando Dylan Campbell Stephen Gould The Australian e-Health Research Centre CSIRO Brisbane Australia Mitsubishi Electric Research Labs (MERL) Cambridge MA A*AI A*STAR Singapore Australian Centre for Robotic Vision (ACRV) Australian National University Canberra Australia

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper presents a framework to recognize temporal compositions of atomic actions in videos. Specifically, we propose to express temporal compositions of actions as semantic regular expressions and derive an inference framework using probabilistic automata to recognize complex actions as satisfying these expressions on the input video features. Our approach is different from existing works that either predict long-range complex activities as unordered sets of atomic actions, or retrieve videos using natural language sentences. Instead, the proposed approach allows recognizing complex fine-grained activities using only pretrained action classifiers, without requiring any additional data, annotations or neural network training. To evaluate the potential of our approach, we provide experiments on synthetic datasets and challenging real action recognition datasets, such as MultiTHUMOS and Charades. We conclude that the proposed approach can extend state-of-the-art primitive action classifiers to vastly more complex activities without large performance degradation.

关键词： Videos Probabilistic logic Task analysis Automata pattern recognition Natural languages Training data

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The "Vertigo Effect" on Your Smartphone: Dolly Zoom via Single Shot View Synthesis

The "Vertigo Effect" on Your Smartphone: Dolly Zoom via Sing...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yangwen Liang Rohit Ranade Shuangquan Wang Dongwoon Bai Jungwon Lee Samsung Semiconductor Inc

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Dolly zoom is a technique where the camera is moved either forwards or backwards from the subject under focus while simultaneously adjusting the field of view in order to maintain the size of the subject in the frame. This results in perspective effect so that the subject in focus appears stationary while the background field of view changes. The effect is frequently used in films and requires skill, practice and equipment. This paper presents a novel technique to model the effect given a single shot capture from a single camera. The proposed synthesis pipeline based on camera geometry simulates the effect by producing a sequence of synthesized views. The technique is also extended to allow simultaneous captures from multiple cameras as inputs and can be easily extended to video sequence captures. Our pipeline consists of efficient image warping along with depth-aware image inpainting making it suitable for smart-phone applications. The proposed method opens up new avenues for view synthesis applications in modern smart-phones.

关键词： Cameras Geometry Pipelines Three-dimensional displays Interpolation Image generation computer vision

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SR-CL-DMC: P-frame coding with Super-Resolution, Color Learning, and Deep Motion Compensation

SR-CL-DMC: P-frame coding with Super-Resolution, Color Learn...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Man M. Ho Jinjia Zhou Gang He Muchen Li Lei Li Hosei University Tokyo Japan Hosei University and JST PRESTO Tokyo Japan Xi’dian University Xi’an China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper proposes a deep learning based video coding framework to greatly increase the compression ratio and keep the video quality by efficiently leveraging the information from a reference. In the encoder, the input frame is compressed by down-sampling to a lower resolution, eliminating color information, and then encoding residual between the current frame and the reference frame using Versatile Video Coding (VVC). The decoder consists of two main parts: Super-Resolution with Color Learning (SR-CL), and Deep Motion Compensation (DMC). For the SR-CL part, we adopt Restoration-Reconstruction Deep Neural Network to firstly restore the missing information from compression at low resolution and compression without color. And then, the sampling degradation at high-resolution is compensated. For the DMC part, we adopt recursive-feedback architectures to propose an optical flow estimation and refinement using Dilated Inception Blocks. As a result, the work achieves 64:1 compression ratio with 41.81/41.34 dB PSNR and 0.9959/0.9962 MS-SSIM on the validation/test set provided by the CLIC P-frame track challenge.

关键词： Image color analysis Image resolution Estimation computer vision pattern recognition conferences Monte Carlo methods

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A Geometric ConvNet on 3D Shape Manifold for Gait recognition

A Geometric ConvNet on 3D Shape Manifold for Gait Recognitio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Nadia Hosni Boulbaba Ben Amor CRISTAL University of Manouba Tunisia Inception Institute of Artificial Intelligence UAE

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this work we propose a geometric deep convolutional auto-encoder (DCAE) for the purpose of gait recognition by analyzing time-varying 3D skeletal data. Sequences are viewed as time-parameterized trajectories on the Kendall shape space S, results of modding out shape-preserving transformations (scaling, translations and rotations). The accommodation of ConvNet architectures to properly approximate manifold-valued trajectories on the underlying non-linear space S is a must. Thus, we make use of geometric steps prior to the encoding-decoding scheme. That is, shape trajectories are first log-mapped to tangent spaces attached to the shape space at a time-varying average trajectory μ, then, obtained vectors are transported to a common tangent space T μ(0) (S) at the starting point of μ. Without applying any prior temporal alignment (e.g. Dynamic Time Warping) or modeling (e.g. HMM, RNN), the transported trajectories are then fed to a convolutional auto-encoder to build subject-specific latent spaces. The proposed approach was tested on two publicly available datasets. Our approach outperforms existing approaches on CMU gait dataset, while performances on UPCV K2 are comparable to existing approaches. We demonstrate that combining geometric invariance (i.e. Kendall's representation) with our data-driven ConvNet model is suitable to alleviate spatial and temporal variability, respectively.

关键词： conferences computer vision pattern recognition

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