Paper Group ANR 184
Sleep-deprived Fatigue Pattern Analysis using Large-Scale Selfies from Social Med. Late Fusion of Local Indexing and Deep Feature Scores for Fast Image-to-Video Search on Large-Scale Databases. Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation. Imperfect Segmentation Labels: How Much Do They Matter?. Organ at Risk …
Sleep-deprived Fatigue Pattern Analysis using Large-Scale Selfies from Social Med
| Title | Sleep-deprived Fatigue Pattern Analysis using Large-Scale Selfies from Social Med |
| Authors | Xuefeng Peng, Jiebo Luo, Catherine Glenn, Li-Kai Chi, Jingyao Zhan |
| Abstract | The complexities of fatigue have drawn much attention from researchers across various disciplines. Short-term fatigue may cause safety issue while driving; thus, dynamic systems were designed to track driver fatigue. Long-term fatigue could lead to chronic syndromes, and eventually affect individuals physical and psychological health. Traditional methodologies of evaluating fatigue not only require sophisticated equipment but also consume enormous time. In this paper, we attempt to develop a novel and efficient method to predict individual’s fatigue rate by scrutinizing human facial cues. Our goal is to predict fatigue rate based on a selfie. To associate the fatigue rate with user behaviors, we have collected nearly 1-million timeline posts from 10,480 users on Instagram. We first detect all the faces and identify their demographics using automatic algorithms. Next, we investigate the fatigue distribution by weekday over different age, gender, and ethnic groups. This work represents a promising way to assess sleep-deprived fatigue, and our study provides a viable and efficient computational framework for user fatigue modeling in large-scale via social media. |
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| Published | 2018-02-22 |
| URL | http://arxiv.org/abs/1802.08310v1 |
| http://arxiv.org/pdf/1802.08310v1.pdf | |
| PWC | https://paperswithcode.com/paper/sleep-deprived-fatigue-pattern-analysis-using |
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Late Fusion of Local Indexing and Deep Feature Scores for Fast Image-to-Video Search on Large-Scale Databases
| Title | Late Fusion of Local Indexing and Deep Feature Scores for Fast Image-to-Video Search on Large-Scale Databases |
| Authors | Savas Ozkan, Gozde Bozdagi Akar |
| Abstract | Low cost visual representation and fast query-by-example content search are two challenging objectives which should be supplied for web-scale visual retrieval task on moderate hardwares. In this paper, we introduce a fast yet robust method that ensures these two objectives by obtaining the state-of-the-art results for the image-to-video search scenario. For this purpose, we present critical improvements to the commonly used indexing and visual representation techniques by promoting faster, better and modest retrieval performance. Also, we boost the effectiveness of the method for visual distortions by exploiting the individual decision scores of local and global descriptors in the query time. By this way, local content descriptors effectively depict copy/duplicate scenes with large geometric deformations, while global descriptors are more practical for the near-duplicate and semantic search. Experiments are conducted on the large-scale Stanford I2V dataset. The experimental results show that the method is effective in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. Moreover, the proposed method is quite accurate and obtains state-of-the art performance based on the mAP score on the dataset. Lastly, we report additional mAP scores after updating the ground annotations obtained by the retrieval results of the proposed method which demonstrates the actual performance more clearly. |
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| Published | 2018-08-03 |
| URL | http://arxiv.org/abs/1808.01101v1 |
| http://arxiv.org/pdf/1808.01101v1.pdf | |
| PWC | https://paperswithcode.com/paper/late-fusion-of-local-indexing-and-deep |
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Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation
| Title | Deeply Self-Supervised Contour Embedded Neural Network Applied to Liver Segmentation |
| Authors | Minyoung Chung, Jingyu Lee, Minkyung Lee, Jeongjin Lee, Yeong-Gil Shin |
| Abstract | Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. Results and Conclusion: 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task. |
| Tasks | Computed Tomography (CT), Liver Segmentation, Semantic Segmentation |
| Published | 2018-08-02 |
| URL | https://arxiv.org/abs/1808.00739v5 |
| https://arxiv.org/pdf/1808.00739v5.pdf | |
| PWC | https://paperswithcode.com/paper/deeply-self-supervising-edge-to-contour |
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Imperfect Segmentation Labels: How Much Do They Matter?
| Title | Imperfect Segmentation Labels: How Much Do They Matter? |
| Authors | Nicholas Heller, Joshua Dean, Nikolaos Papanikolopoulos |
| Abstract | Labeled datasets for semantic segmentation are imperfect, especially in medical imaging where borders are often subtle or ill-defined. Little work has been done to analyze the effect that label errors have on the performance of segmentation methodologies. Here we present a large-scale study of model performance in the presence of varying types and degrees of error in training data. We trained U-Net, SegNet, and FCN32 several times for liver segmentation with 10 different modes of ground-truth perturbation. Our results show that for each architecture, performance steadily declines with boundary-localized errors, however, U-Net was significantly more robust to jagged boundary errors than the other architectures. We also found that each architecture was very robust to non-boundary-localized errors, suggesting that boundary-localized errors are fundamentally different and more challenging problem than random label errors in a classification setting. |
| Tasks | Liver Segmentation, Semantic Segmentation |
| Published | 2018-06-12 |
| URL | http://arxiv.org/abs/1806.04618v3 |
| http://arxiv.org/pdf/1806.04618v3.pdf | |
| PWC | https://paperswithcode.com/paper/imperfect-segmentation-labels-how-much-do |
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Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net
| Title | Organ at Risk Segmentation in Head and Neck CT Images by Using a Two-Stage Segmentation Framework Based on 3D U-Net |
| Authors | Yueyue Wang, Liang Zhao, Zhijian Song, Manning Wang |
| Abstract | Accurate segmentation of organ at risk (OAR) play a critical role in the treatment planning of image guided radiation treatment of head and neck cancer. This segmentation task is challenging for both human and automatic algorithms because of the relatively large number of OARs to be segmented, the large variability of the size and morphology across different OARs, and the low contrast of between some OARs and the background. In this paper, we proposed a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two sub-tasks: locating a bounding box of the OAR and segment it from a small volume within the bounding box, and each sub-tasks is fulfilled by a dedicated 3D U-Net. The decomposition makes each of the two sub-tasks much easier, so that they can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods by using the MICCAI 2015 Challenge dataset. In terms of the boundary-based metric 95HD, the proposed method ranked first in eight of all nine OARs and ranked second in the other OAR. In terms of the area-based metric DSC, the proposed method ranked first in six of the nine OARs and ranked second in the other three OARs with small difference with the first one. |
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| Published | 2018-08-25 |
| URL | https://arxiv.org/abs/1809.00960v2 |
| https://arxiv.org/pdf/1809.00960v2.pdf | |
| PWC | https://paperswithcode.com/paper/organ-at-risk-segmentation-in-head-and-neck |
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VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control
| Title | VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control |
| Authors | Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard |
| Abstract | In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, we seek to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a simple yet effective shift loss that is agnostic to the downstream task, to constrain the consistency between subsequent frames which is important for consistent policy outputs. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in indoor and outdoor robotics experiments. |
| Tasks | Domain Adaptation, Style Transfer |
| Published | 2018-02-01 |
| URL | http://arxiv.org/abs/1802.00265v4 |
| http://arxiv.org/pdf/1802.00265v4.pdf | |
| PWC | https://paperswithcode.com/paper/vr-goggles-for-robots-real-to-sim-domain |
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MPC-Inspired Neural Network Policies for Sequential Decision Making
| Title | MPC-Inspired Neural Network Policies for Sequential Decision Making |
| Authors | Marcus Pereira, David D. Fan, Gabriel Nakajima An, Evangelos Theodorou |
| Abstract | In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making. We introduce an extension to the DAgger algorithm for training such policies and show how they have improved training performance and generalization capabilities. We take advantage of this extension to show scalable and efficient training of complex planning policy architectures in continuous state and action spaces. We provide an extensive comparison of neural network policies by considering feed forward policies, recurrent policies, and recurrent policies with planning structure inspired by the Path Integral control framework. Our results suggest that MPC-type recurrent policies have better robustness to disturbances and modeling error. |
| Tasks | Decision Making |
| Published | 2018-02-15 |
| URL | http://arxiv.org/abs/1802.05803v2 |
| http://arxiv.org/pdf/1802.05803v2.pdf | |
| PWC | https://paperswithcode.com/paper/mpc-inspired-neural-network-policies-for |
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A Convergence indicator for Multi-Objective Optimisation Algorithms
| Title | A Convergence indicator for Multi-Objective Optimisation Algorithms |
| Authors | Thiago Santos, Sebastiao Xavier |
| Abstract | The algorithms of multi-objective optimisation had a relative growth in the last years. Thereby, it’s requires some way of comparing the results of these. In this sense, performance measures play a key role. In general, it’s considered some properties of these algorithms such as capacity, convergence, diversity or convergence-diversity. There are some known measures such as generational distance (GD), inverted generational distance (IGD), hypervolume (HV), Spread($\Delta$), Averaged Hausdorff distance ($\Delta_p$), R2-indicator, among others. In this paper, we focuses on proposing a new indicator to measure convergence based on the traditional formula for Shannon entropy. The main features about this measure are: 1) It does not require tho know the true Pareto set and 2) Medium computational cost when compared with Hypervolume. |
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| Published | 2018-10-29 |
| URL | http://arxiv.org/abs/1810.12140v1 |
| http://arxiv.org/pdf/1810.12140v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-convergence-indicator-for-multi-objective |
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Neural Metaphor Detection in Context
| Title | Neural Metaphor Detection in Context |
| Authors | Ge Gao, Eunsol Choi, Yejin Choi, Luke Zettlemoyer |
| Abstract | We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text. |
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| Published | 2018-08-29 |
| URL | http://arxiv.org/abs/1808.09653v1 |
| http://arxiv.org/pdf/1808.09653v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-metaphor-detection-in-context |
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Exploring the Challenges towards Lifelong Fact Learning
| Title | Exploring the Challenges towards Lifelong Fact Learning |
| Authors | Mohamed Elhoseiny, Francesca Babiloni, Rahaf Aljundi, Marcus Rohrbach, Manohar Paluri, Tinne Tuytelaars |
| Abstract | So far life-long learning (LLL) has been studied in relatively small-scale and relatively artificial setups. Here, we introduce a new large-scale alternative. What makes the proposed setup more natural and closer to human-like visual systems is threefold: First, we focus on concepts (or facts, as we call them) of varying complexity, ranging from single objects to more complex structures such as objects performing actions, and objects interacting with other objects. Second, as in real-world settings, our setup has a long-tail distribution, an aspect which has mostly been ignored in the LLL context. Third, facts across tasks may share structure (e.g., <person, riding, wave> and <dog, riding, wave>). Facts can also be semantically related (e.g., “liger” relates to seen categories like “tiger” and “lion”). Given the large number of possible facts, a LLL setup seems a natural choice. To avoid model size growing over time and to optimally exploit the semantic relations and structure, we combine it with a visual semantic embedding instead of discrete class labels. We adapt existing datasets with the properties mentioned above into new benchmarks, by dividing them semantically or randomly into disjoint tasks. This leads to two large-scale benchmarks with 906,232 images and 165,150 unique facts, on which we evaluate and analyze state-of-the-art LLL methods. |
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| Published | 2018-12-26 |
| URL | http://arxiv.org/abs/1812.10524v1 |
| http://arxiv.org/pdf/1812.10524v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploring-the-challenges-towards-lifelong |
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More Adaptive Algorithms for Adversarial Bandits
| Title | More Adaptive Algorithms for Adversarial Bandits |
| Authors | Chen-Yu Wei, Haipeng Luo |
| Abstract | We develop a novel and generic algorithm for the adversarial multi-armed bandit problem (or more generally the combinatorial semi-bandit problem). When instantiated differently, our algorithm achieves various new data-dependent regret bounds improving previous work. Examples include: 1) a regret bound depending on the variance of only the best arm; 2) a regret bound depending on the first-order path-length of only the best arm; 3) a regret bound depending on the sum of first-order path-lengths of all arms as well as an important negative term, which together lead to faster convergence rates for some normal form games with partial feedback; 4) a regret bound that simultaneously implies small regret when the best arm has small loss and logarithmic regret when there exists an arm whose expected loss is always smaller than those of others by a fixed gap (e.g. the classic i.i.d. setting). In some cases, such as the last two results, our algorithm is completely parameter-free. The main idea of our algorithm is to apply the optimism and adaptivity techniques to the well-known Online Mirror Descent framework with a special log-barrier regularizer. The challenges are to come up with appropriate optimistic predictions and correction terms in this framework. Some of our results also crucially rely on using a sophisticated increasing learning rate schedule. |
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| Published | 2018-01-10 |
| URL | http://arxiv.org/abs/1801.03265v3 |
| http://arxiv.org/pdf/1801.03265v3.pdf | |
| PWC | https://paperswithcode.com/paper/more-adaptive-algorithms-for-adversarial |
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Algae Detection Using Computer Vision and Deep Learning
| Title | Algae Detection Using Computer Vision and Deep Learning |
| Authors | Arabinda Samantaray, Baijian Yang, J. Eric Dietz, Byung-Cheol Min |
| Abstract | A disconcerting ramification of water pollution caused by burgeoning populations, rapid industrialization and modernization of agriculture, has been the exponential increase in the incidence of algal growth across the globe. Harmful algal blooms (HABs) have devastated fisheries, contaminated drinking water and killed livestock, resulting in economic losses to the tune of millions of dollars. Therefore, it is important to constantly monitor water bodies and identify any algae build-up so that prompt action against its accumulation can be taken and the harmful consequences can be avoided. In this paper, we propose a computer vision system based on deep learning for algae monitoring. The proposed system is fast, accurate and cheap, and it can be installed on any robotic platforms such as USVs and UAVs for autonomous algae monitoring. The experimental results demonstrate that the proposed system can detect algae in distinct environments regardless of the underlying hardware with high accuracy and in real time. |
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| Published | 2018-11-27 |
| URL | http://arxiv.org/abs/1811.10847v1 |
| http://arxiv.org/pdf/1811.10847v1.pdf | |
| PWC | https://paperswithcode.com/paper/algae-detection-using-computer-vision-and |
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A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control
| Title | A Survey on Artificial Intelligence Trends in Spacecraft Guidance Dynamics and Control |
| Authors | Dario Izzo, Marcus Märtens, Binfeng Pan |
| Abstract | The rapid developments of Artificial Intelligence in the last decade are influencing Aerospace Engineering to a great extent and research in this context is proliferating. We share our observations on the recent developments in the area of Spacecraft Guidance Dynamics and Control, giving selected examples on success stories that have been motivated by mission designs. Our focus is on evolutionary optimisation, tree searches and machine learning, including deep learning and reinforcement learning as the key technologies and drivers for current and future research in the field. From a high-level perspective, we survey various scenarios for which these approaches have been successfully applied or are under strong scientific investigation. Whenever possible, we highlight the relations and synergies that can be obtained by combining different techniques and projects towards future domains for which newly emerging artificial intelligence techniques are expected to become game changers. |
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| Published | 2018-12-07 |
| URL | http://arxiv.org/abs/1812.02948v1 |
| http://arxiv.org/pdf/1812.02948v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-survey-on-artificial-intelligence-trends-in |
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Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
| Title | Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization |
| Authors | Aditya Grover, Stefano Ermon |
| Abstract | Compressed sensing techniques enable efficient acquisition and recovery of sparse, high-dimensional data signals via low-dimensional projections. In this work, we propose Uncertainty Autoencoders, a learning framework for unsupervised representation learning inspired by compressed sensing. We treat the low-dimensional projections as noisy latent representations of an autoencoder and directly learn both the acquisition (i.e., encoding) and amortized recovery (i.e., decoding) procedures. Our learning objective optimizes for a tractable variational lower bound to the mutual information between the datapoints and the latent representations. We show how our framework provides a unified treatment to several lines of research in dimensionality reduction, compressed sensing, and generative modeling. Empirically, we demonstrate a 32% improvement on average over competing approaches for the task of statistical compressed sensing of high-dimensional datasets. |
| Tasks | Dimensionality Reduction, Representation Learning, Unsupervised Representation Learning |
| Published | 2018-12-26 |
| URL | http://arxiv.org/abs/1812.10539v3 |
| http://arxiv.org/pdf/1812.10539v3.pdf | |
| PWC | https://paperswithcode.com/paper/uncertainty-autoencoders-learning-compressed |
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Map Memorization and Forgetting in the IARA Autonomous Car
| Title | Map Memorization and Forgetting in the IARA Autonomous Car |
| Authors | Thomas Teixeira, Filipe Mutz, Vinicius B. Cardoso, Lucas Veronese, Claudine Badue, Thiago Oliveira-Santos, Alberto F. De Souza |
| Abstract | In this work, we present a novel strategy for correcting imperfections in occupancy grid maps called map decay. The objective of map decay is to correct invalid occupancy probabilities of map cells that are unobservable by sensors. The strategy was inspired by an analogy between the memory architecture believed to exist in the human brain and the maps maintained by an autonomous vehicle. It consists in merging sensory information obtained during runtime (online) with a priori data from a high-precision map constructed offline. In map decay, cells observed by sensors are updated using traditional occupancy grid mapping techniques and unobserved cells are adjusted so that their occupancy probabilities tend to the values found in the offline map. This strategy is grounded in the idea that the most precise information available about an unobservable cell is the value found in the high-precision offline map. Map decay was successfully tested and is still in use in the IARA autonomous vehicle from Universidade Federal do Esp'irito Santo. |
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| Published | 2018-10-04 |
| URL | http://arxiv.org/abs/1810.02355v1 |
| http://arxiv.org/pdf/1810.02355v1.pdf | |
| PWC | https://paperswithcode.com/paper/map-memorization-and-forgetting-in-the-iara |
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