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85f2804e-6632-4257-a4b4-c719fd07974e | preliminary-wildfire-detection-using-state-of | 2109.05083 | null | https://arxiv.org/abs/2109.05083v1 | https://arxiv.org/pdf/2109.05083v1.pdf | Preliminary Wildfire Detection Using State-of-the-art PTZ (Pan, Tilt, Zoom) Camera Technology and Convolutional Neural Networks | Wildfires are uncontrolled fires in the environment that can be caused by humans or nature. In 2020 alone, wildfires in California have burned 4.2 million acres, damaged 10,500 buildings or structures, and killed more than 31 people, exacerbated by climate change and a rise in average global temperatures. This also means there has been an increase in the costs of extinguishing these treacherous wildfires. The objective of the research is to detect forest fires in their earlier stages to prevent them from spreading, prevent them from causing damage to a variety of things, and most importantly, reduce or eliminate the chances of someone dying from a wildfire. A fire detection system should be efficient and accurate with respect to extinguishing wildfires in their earlier stages to prevent the spread of them along with their consequences. Computer Vision is potentially a more reliable, fast, and widespread method we need. The current research in the field of preliminary fire detection has several problems related to unrepresentative data being used to train models and their existing varied amounts of label imbalance in the classes of their dataset. We propose a more representative and evenly distributed data through better settings, lighting, atmospheres, etc., and class distribution in the entire dataset. After thoroughly examining the results of this research, it can be inferred that they supported the datasets strengths by being a viable resource when tested in the real world on unfamiliar data. This is evident since as the model trains on the dataset, it is able to generalize on it, hence confirming this is a viable Machine Learning setting that has practical impact. | ['Samarth Shah'] | 2021-09-10 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.24261123e-01 -2.09652171e-01 -8.65715090e-03 1.27743021e-01
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1d96e9dc-d160-4525-bf1a-e95f3a318faa | mask-r-cnn-with-pyramid-attention-network-for | 1811.09058 | null | http://arxiv.org/abs/1811.09058v1 | http://arxiv.org/pdf/1811.09058v1.pdf | Mask R-CNN with Pyramid Attention Network for Scene Text Detection | In this paper, we present a new Mask R-CNN based text detection approach
which can robustly detect multi-oriented and curved text from natural scene
images in a unified manner. To enhance the feature representation ability of
Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention
Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate
that PAN can suppress false alarms caused by text-like backgrounds more
effectively. Our proposed approach has achieved superior performance on both
multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text
detection benchmark tasks by only using single-scale and single-model testing. | ['Qiang Huo', 'Zhuoyao Zhong', 'Zhida Huang', 'Lei Sun'] | 2018-11-22 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 4.54877019e-01 -5.49841702e-01 1.82252675e-01 -1.78787068e-01
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c8a92b38-723d-4d79-b7ef-470373891cae | backdoor-attacks-for-remote-sensing-data-with | 2211.08044 | null | https://arxiv.org/abs/2211.08044v2 | https://arxiv.org/pdf/2211.08044v2.pdf | Backdoor Attacks for Remote Sensing Data with Wavelet Transform | Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm. Nevertheless, the security and robustness of deep learning models deserve special attention when addressing safety-critical remote sensing tasks. In this paper, we provide a systematic analysis of backdoor attacks for remote sensing data, where both scene classification and semantic segmentation tasks are considered. While most of the existing backdoor attack algorithms rely on visible triggers like squared patches with well-designed patterns, we propose a novel wavelet transform-based attack (WABA) method, which can achieve invisible attacks by injecting the trigger image into the poisoned image in the low-frequency domain. In this way, the high-frequency information in the trigger image can be filtered out in the attack, resulting in stealthy data poisoning. Despite its simplicity, the proposed method can significantly cheat the current state-of-the-art deep learning models with a high attack success rate. We further analyze how different trigger images and the hyper-parameters in the wavelet transform would influence the performance of the proposed method. Extensive experiments on four benchmark remote sensing datasets demonstrate the effectiveness of the proposed method for both scene classification and semantic segmentation tasks and thus highlight the importance of designing advanced backdoor defense algorithms to address this threat in remote sensing scenarios. The code will be available online at \url{https://github.com/ndraeger/waba}. | ['Pedram Ghamisi', 'Yonghao Xu', 'Nikolaus Dräger'] | 2022-11-15 | null | null | null | null | ['data-poisoning', 'scene-classification'] | ['adversarial', 'computer-vision'] | [ 2.57861376e-01 -3.84265751e-01 2.04580262e-01 1.66720688e-01
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a7ea859b-6a75-422a-afa1-3c8a1d6f762e | learning-from-heterogeneity-a-dynamic | 2307.03411 | null | https://arxiv.org/abs/2307.03411v1 | https://arxiv.org/pdf/2307.03411v1.pdf | Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs | Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods. | ['Jiong Jin', 'Jun Yin', 'Xiaowei Huang', 'Xin Chen', 'Xingjun Ma', 'Zhishu Shen', 'Yuze Liu', 'Tiehua Zhang'] | 2023-07-07 | null | null | null | null | ['node-classification', 'link-prediction', 'graph-learning'] | ['graphs', 'graphs', 'graphs'] | [-8.70536119e-02 4.33113128e-01 -5.17869771e-01 -9.89792645e-02
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4e7556aa-38a0-4fdd-8ab5-e6bec7b786ef | counting-and-locating-high-density-objects | 2102.04366 | null | https://arxiv.org/abs/2102.04366v1 | https://arxiv.org/pdf/2102.04366v1.pdf | Counting and Locating High-Density Objects Using Convolutional Neural Network | This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was evaluated in two counting datasets: tree and car. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R$^2$) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R$^2$ of 0.975 and 0.999, respectively. The proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects. | ['Wesley Nunes Gonçalves', 'Jonathan de Andrade Silva', 'Jonathan Li', 'Zhipeng Luo', 'Edson Takashi Matsubara', 'Ana Paula Marques Ramos', 'José Marcato Junior', 'Diogo Nunes Gonçalves', 'Plabiany Rodrigo Acosta', 'Lucas Prado Osco', 'Mauro dos Santos de Arruda'] | 2021-02-08 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-8.83221030e-02 -3.73984754e-01 1.55789316e-01 -1.90240905e-01
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251b1c8c-8a9f-4a49-bd1f-a1dc61b2c36a | label-assisted-autoencoder-for-anomaly | 2302.02896 | null | https://arxiv.org/abs/2302.02896v1 | https://arxiv.org/pdf/2302.02896v1.pdf | Label Assisted Autoencoder for Anomaly Detection in Power Generation Plants | One of the critical factors that drive the economic development of a country and guarantee the sustainability of its industries is the constant availability of electricity. This is usually provided by the national electric grid. However, in developing countries where companies are emerging on a constant basis including telecommunication industries, those are still experiencing a non-stable electricity supply. Therefore, they have to rely on generators to guarantee their full functionality. Those generators depend on fuel to function and the rate of consumption gets usually high, if not monitored properly. Monitoring operation is usually carried out by a (non-expert) human. In some cases, this could be a tedious process, as some companies have reported an exaggerated high consumption rate. This work proposes a label assisted autoencoder for anomaly detection in the fuel consumed by power generating plants. In addition to the autoencoder model, we added a labelling assistance module that checks if an observation is labelled, the label is used to check the veracity of the corresponding anomaly classification given a threshold. A consensus is then reached on whether training should stop or whether the threshold should be updated or the training should continue with the search for hyper-parameters. Results show that the proposed model is highly efficient for reading anomalies with a detection accuracy of $97.20\%$ which outperforms the existing model of $96.1\%$ accuracy trained on the same dataset. In addition, the proposed model is able to classify the anomalies according to their degree of severity. | ['Arnaud Nguembang Fadja', 'Franklin Tchakounte', 'Theophilus Ansah-Narh', 'Jecinta Mulongo', 'Sisipho Hamlomo', 'Rockefeller Rockefeller', 'Victor Osanyindoro', 'Marcellin Atemkeng'] | 2023-02-06 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [-1.25641435e-01 3.22941411e-03 8.38978291e-02 -9.42930654e-02
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7a8be63d-f61b-4370-a6b5-1c107c937ab3 | eden-a-high-performance-general-purpose | 2106.06752 | null | https://arxiv.org/abs/2106.06752v1 | https://arxiv.org/pdf/2106.06752v1.pdf | EDEN: A high-performance, general-purpose, NeuroML-based neural simulator | Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modelling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs up to 2 orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available. | ['Christos Strydis', 'Dimitrios Soudris', 'Mario Negrello', 'Harry Sidiropoulos', 'Sotirios Panagiotou'] | 2021-06-12 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-3.68369550e-01 -1.70176029e-01 5.05579293e-01 3.93111967e-02
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d74912ae-f65d-44cb-bd8e-4c6373ce3008 | learning-to-compose-dynamic-tree-structures | 1812.01880 | null | http://arxiv.org/abs/1812.01880v1 | http://arxiv.org/pdf/1812.01880v1.pdf | Learning to Compose Dynamic Tree Structures for Visual Contexts | We propose to compose dynamic tree structures that place the objects in an
image into a visual context, helping visual reasoning tasks such as scene graph
generation and visual Q&A. Our visual context tree model, dubbed VCTree, has
two key advantages over existing structured object representations including
chains and fully-connected graphs: 1) The efficient and expressive binary tree
encodes the inherent parallel/hierarchical relationships among objects, e.g.,
"clothes" and "pants" are usually co-occur and belong to "person"; 2) the
dynamic structure varies from image to image and task to task, allowing more
content-/task-specific message passing among objects. To construct a VCTree, we
design a score function that calculates the task-dependent validity between
each object pair, and the tree is the binary version of the maximum spanning
tree from the score matrix. Then, visual contexts are encoded by bidirectional
TreeLSTM and decoded by task-specific models. We develop a hybrid learning
procedure which integrates end-task supervised learning and the tree structure
reinforcement learning, where the former's evaluation result serves as a
self-critic for the latter's structure exploration. Experimental results on two
benchmarks, which require reasoning over contexts: Visual Genome for scene
graph generation and VQA2.0 for visual Q&A, show that VCTree outperforms
state-of-the-art results while discovering interpretable visual context
structures. | ['Wenhan Luo', 'Baoyuan Wu', 'Wei Liu', 'Hanwang Zhang', 'Kaihua Tang'] | 2018-12-05 | learning-to-compose-dynamic-tree-structures-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Tang_Learning_to_Compose_Dynamic_Tree_Structures_for_Visual_Contexts_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Learning_to_Compose_Dynamic_Tree_Structures_for_Visual_Contexts_CVPR_2019_paper.pdf | cvpr-2019-6 | ['panoptic-scene-graph-generation'] | ['computer-vision'] | [ 4.04970914e-01 2.02396080e-01 -3.95767123e-01 -3.92395884e-01
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2503dac9-03c0-42cc-a09c-c0474272cf0a | opencl-based-fpga-accelerator-for-disparity | 1903.03509 | null | http://arxiv.org/abs/1903.03509v1 | http://arxiv.org/pdf/1903.03509v1.pdf | OpenCL-based FPGA accelerator for disparity map generation with stereoscopic event cameras | Although event-based cameras are already commercially available. Vision
algorithms based on them are still not common. As a consequence, there are few
Hardware Accelerators for them. In this work we present some experiments to
create FPGA accelerators for a well-known vision algorithm using event-based
cameras. We present a stereo matching algorithm to create a stream of disparity
events disparity map and implement several accelerators using the Intel FPGA
OpenCL tool-chain. The results show that multiple designs can be easily tested
and that a performance speedup of more than 8x can be achieved with simple code
transformations. | ['David Castells-Rufas', 'Jordi Carrabina'] | 2019-03-08 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-4.09303699e-03 -6.38970792e-01 3.79676938e-01 -6.67513371e-01
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7f4b3bdd-7a5c-4d33-9ac5-81c33b5704f9 | learning-what-makes-a-difference-from | 2004.09034 | null | https://arxiv.org/abs/2004.09034v1 | https://arxiv.org/pdf/2004.09034v1.pdf | Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision | One of the primary challenges limiting the applicability of deep learning is its susceptibility to learning spurious correlations rather than the underlying mechanisms of the task of interest. The resulting failure to generalise cannot be addressed by simply using more data from the same distribution. We propose an auxiliary training objective that improves the generalization capabilities of neural networks by leveraging an overlooked supervisory signal found in existing datasets. We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task. We show that such pairs can be identified in a number of existing datasets in computer vision (visual question answering, multi-label image classification) and natural language processing (sentiment analysis, natural language inference). The new training objective orients the gradient of a model's decision function with pairs of counterfactual examples. Models trained with this technique demonstrate improved performance on out-of-distribution test sets. | ['Anton Van Den Hengel', 'Ehsan Abbasnedjad', 'Damien Teney'] | 2020-04-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1165_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550579.pdf | eccv-2020-8 | ['multi-label-image-classification'] | ['computer-vision'] | [ 7.28020549e-01 1.93485960e-01 -3.91451389e-01 -8.86793017e-01
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219ec872-17c6-4b8e-b95c-96baca511541 | joint-multi-person-body-detection-and | 2210.15586 | null | https://arxiv.org/abs/2210.15586v2 | https://arxiv.org/pdf/2210.15586v2.pdf | Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding | Human body orientation estimation (HBOE) is widely applied into various applications, including robotics, surveillance, pedestrian analysis and autonomous driving. Although many approaches have been addressing the HBOE problem from specific under-controlled scenes to challenging in-the-wild environments, they assume human instances are already detected and take a well cropped sub-image as the input. This setting is less efficient and prone to errors in real application, such as crowds of people. In the paper, we propose a single-stage end-to-end trainable framework for tackling the HBOE problem with multi-persons. By integrating the prediction of bounding boxes and direction angles in one embedding, our method can jointly estimate the location and orientation of all bodies in one image directly. Our key idea is to integrate the HBOE task into the multi-scale anchor channel predictions of persons for concurrently benefiting from engaged intermediate features. Therefore, our approach can naturally adapt to difficult instances involving low resolution and occlusion as in object detection. We validated the efficiency and effectiveness of our method in the recently presented benchmark MEBOW with extensive experiments. Besides, we completed ambiguous instances ignored by the MEBOW dataset, and provided corresponding weak body-orientation labels to keep the integrity and consistency of it for supporting studies toward multi-persons. Our work is available at https://github.com/hnuzhy/JointBDOE. | ['Hongtao Lu', 'Jiaxin Si', 'Fei Jiang', 'Huayi Zhou'] | 2022-10-27 | null | null | null | null | ['body-detection'] | ['computer-vision'] | [ 1.45758400e-02 1.95643932e-01 1.40247434e-01 -6.17330790e-01
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89e0aff3-ef7b-40c9-a1c4-3ac3f2f7a66c | on-the-robustness-of-average-losses-for | 2106.06152 | null | https://arxiv.org/abs/2106.06152v2 | https://arxiv.org/pdf/2106.06152v2.pdf | On the Robustness of Average Losses for Partial-Label Learning | Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to select the (most likely) TL for training; average-based strategy (ABS) treats all candidate labels equally for training and let trained models be able to predict TL. Although PLL research has focused on IBS for better performance, ABS is also worthy of study since modern IBS behaves like ABS in the beginning of training to prepare for PL purification and TL selection. In this paper, we analyze why ABS was unsatisfactory and propose how to improve it. Theoretically, we formalize five problem settings of PLL and prove that average PL losses (APLLs) with bounded multi-class losses are always robust, while APLLs with unbounded losses may be non-robust, which is the first robustness analysis for PLL. Experimentally, we have two promising findings: ABS using bounded losses can match/exceed state-of-the-art performance of IBS using unbounded losses; after using robust APLLs to warm start, IBS can further improve upon itself. Our work draws attention to ABS research, which can in turn boost IBS and push forward the whole PLL. | ['Ning Xu', 'Lei Feng', 'Biao Liu', 'Masashi Sugiyama', 'Xin Geng', 'Gang Niu', 'Bo An', 'Miao Xu', 'Jiaqi Lv'] | 2021-06-11 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.04489017e-01 2.63285041e-01 -5.14781296e-01 -2.28398040e-01
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cec45503-725a-44b7-a519-58d0d7f25ff3 | grouped-variable-selection-with-discrete | 2104.07084 | null | https://arxiv.org/abs/2104.07084v2 | https://arxiv.org/pdf/2104.07084v2.pdf | Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives | We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristics, we focus on optimal solutions for the $\ell_0$-regularized formulation, a problem that is relatively unexplored due to computational challenges. Our methodology covers both high-dimensional linear regression and nonparametric sparse additive modeling with smooth components. Our algorithmic framework consists of approximate and exact algorithms. The approximate algorithms are based on coordinate descent and local search, with runtimes comparable to popular sparse learning algorithms. Our exact algorithm is based on a standalone branch-and-bound (BnB) framework, which can solve the associated mixed integer programming (MIP) problem to certified optimality. By exploiting the problem structure, our custom BnB algorithm can solve to optimality problem instances with $5 \times 10^6$ features and $10^3$ observations in minutes to hours -- over $1000$ times larger than what is currently possible using state-of-the-art commercial MIP solvers. We also explore statistical properties of the $\ell_0$-based estimators. We demonstrate, theoretically and empirically, that our proposed estimators have an edge over popular group-sparse estimators in terms of statistical performance in various regimes. We provide an open-source implementation of our proposed framework. | ['Peter Radchenko', 'Rahul Mazumder', 'Hussein Hazimeh'] | 2021-04-14 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.70478091e-01 1.46611556e-01 -5.58221340e-01 -4.87280309e-01
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6cdeb286-7faf-4570-92db-e32dadbc4add | alem-at-case-2021-task-1-multilingual-text | null | null | https://aclanthology.org/2021.case-1.19 | https://aclanthology.org/2021.case-1.19.pdf | ALEM at CASE 2021 Task 1: Multilingual Text Classification on News Articles | We participated CASE shared task in ACL-IJCNLP 2021. This paper is a summary of our experiments and ideas about this shared task. For each subtask we shared our approach, successful and failed methods and our thoughts about them. We submit our results once for every subtask, except for subtask3, in task submission system and present scores based on our validation set formed from given training samples in this paper. Techniques and models we mentioned includes BERT, Multilingual BERT, oversampling, undersampling, data augmentation and their implications with each other. Most of the experiments we came up with were not completed, as time did not permit, but we share them here as we plan to do them as suggested in the future work part of document. | ['Emre Emin', 'Alaeddin Gürel'] | null | null | null | null | acl-case-2021-8 | ['multilingual-text-classification'] | ['miscellaneous'] | [-6.94354177e-02 1.96153775e-01 2.60543302e-02 -7.70275652e-01
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558c2bae-9e7c-45c4-a02f-124925509ebd | explaining-agent-s-decision-making-in-a | 2212.06967 | null | https://arxiv.org/abs/2212.06967v1 | https://arxiv.org/pdf/2212.06967v1.pdf | Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario | Reinforcement learning is a machine learning approach based on behavioral psychology. It is focused on learning agents that can acquire knowledge and learn to carry out new tasks by interacting with the environment. However, a problem occurs when reinforcement learning is used in critical contexts where the users of the system need to have more information and reliability for the actions executed by an agent. In this regard, explainable reinforcement learning seeks to provide to an agent in training with methods in order to explain its behavior in such a way that users with no experience in machine learning could understand the agent's behavior. One of these is the memory-based explainable reinforcement learning method that is used to compute probabilities of success for each state-action pair using an episodic memory. In this work, we propose to make use of the memory-based explainable reinforcement learning method in a hierarchical environment composed of sub-tasks that need to be first addressed to solve a more complex task. The end goal is to verify if it is possible to provide to the agent the ability to explain its actions in the global task as well as in the sub-tasks. The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior. | ['Francisco Cruz', 'Bruno Fernandes', 'Angel Ayala', 'Ernesto Portugal', 'Hugo Muñoz'] | 2022-12-14 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 4.35540192e-02 4.27114338e-01 1.78682163e-01 -2.79232800e-01
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639bb615-ebe6-4074-98f2-2927f24bf9d2 | d-calm-a-dynamic-clustering-based-active | 2305.17013 | null | https://arxiv.org/abs/2305.17013v1 | https://arxiv.org/pdf/2305.17013v1.pdf | D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias | Despite recent advancements, NLP models continue to be vulnerable to bias. This bias often originates from the uneven distribution of real-world data and can propagate through the annotation process. Escalated integration of these models in our lives calls for methods to mitigate bias without overbearing annotation costs. While active learning (AL) has shown promise in training models with a small amount of annotated data, AL's reliance on the model's behavior for selective sampling can lead to an accumulation of unwanted bias rather than bias mitigation. However, infusing clustering with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL's annotation efficiency. In this paper, we propose a novel adaptive clustering-based active learning algorithm, D-CALM, that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. Experiments on eight datasets for a diverse set of text classification tasks, including emotion, hatespeech, dialog act, and book type detection, demonstrate that our proposed algorithm significantly outperforms baseline AL approaches with both pretrained transformers and traditional Support Vector Machines. D-CALM showcases robustness against different measures of information gain and, as evident from our analysis of label and error distribution, can significantly reduce unwanted model bias. | ['Malihe Alikhani', 'Sabit Hassan'] | 2023-05-26 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 3.56479079e-01 4.74311352e-01 -7.03147233e-01 -8.36131394e-01
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75eb922d-5165-4bd0-b654-5f06a77632cf | learning-to-move-with-affordance-maps-1 | 2001.02364 | null | https://arxiv.org/abs/2001.02364v2 | https://arxiv.org/pdf/2001.02364v2.pdf | Learning to Move with Affordance Maps | The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model dynamic objects (such as other agents) or semantic constraints (such as wet floors or doorways). Learning-based RL agents are an attractive alternative because they can incorporate both semantic and geometric information, but are notoriously sample inefficient, difficult to generalize to novel settings, and are difficult to interpret. In this paper, we combine the best of both worlds with a modular approach that learns a spatial representation of a scene that is trained to be effective when coupled with traditional geometric planners. Specifically, we design an agent that learns to predict a spatial affordance map that elucidates what parts of a scene are navigable through active self-supervised experience gathering. In contrast to most simulation environments that assume a static world, we evaluate our approach in the VizDoom simulator, using large-scale randomly-generated maps containing a variety of dynamic actors and hazards. We show that learned affordance maps can be used to augment traditional approaches for both exploration and navigation, providing significant improvements in performance. | ['William Qi', 'Deva Ramanan', 'Ravi Teja Mullapudi', 'Saurabh Gupta'] | 2020-01-08 | null | https://openreview.net/forum?id=BJgMFxrYPB | https://openreview.net/pdf?id=BJgMFxrYPB | iclr-2020-1 | ['pointgoal-navigation'] | ['robots'] | [-2.76293196e-02 4.11832064e-01 1.31326960e-03 -2.78387487e-01
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42223d67-c78f-4eb4-95f1-5cb6ab1d928e | headlinecause-a-dataset-of-news-headlines-for | 2108.12626 | null | https://arxiv.org/abs/2108.12626v2 | https://arxiv.org/pdf/2108.12626v2.pdf | HeadlineCause: A Dataset of News Headlines for Detecting Causalities | Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations. We also present a set of models and experiments that demonstrates the dataset validity, including a multilingual XLM-RoBERTa based model for causality detection and a GPT-2 based model for possible effects prediction. | ['Alexey Tikhonov', 'Ilya Gusev'] | 2021-08-28 | null | https://aclanthology.org/2022.lrec-1.662 | https://aclanthology.org/2022.lrec-1.662.pdf | lrec-2022-6 | ['commonsense-causal-reasoning'] | ['natural-language-processing'] | [-3.88151146e-02 2.78811574e-01 -6.28903091e-01 -5.94428718e-01
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652b5831-0c41-4ed4-b723-3457ff6f47fd | few-shot-object-detection-with-refined | 2211.13495 | null | https://arxiv.org/abs/2211.13495v1 | https://arxiv.org/pdf/2211.13495v1.pdf | Few-shot Object Detection with Refined Contrastive Learning | Due to the scarcity of sampling data in reality, few-shot object detection (FSOD) has drawn more and more attention because of its ability to quickly train new detection concepts with less data. However, there are still failure identifications due to the difficulty in distinguishing confusable classes. We also notice that the high standard deviation of average precisions reveals the inconsistent detection performance. To this end, we propose a novel FSOD method with Refined Contrastive Learning (FSRC). A pre-determination component is introduced to find out the Resemblance Group (GR) from novel classes which contains confusable classes. Afterwards, refined contrastive learning (RCL) is pointedly performed on this group of classes in order to increase the inter-class distances among them. In the meantime, the detection results distribute more uniformly which further improve the performance. Experimental results based on PASCAL VOC and COCO datasets demonstrate our proposed method outperforms the current state-of-the-art research. FSRC can not only decouple the relevance of confusable classes to get a better performance, but also makes predictions more consistent by reducing the standard deviation of the AP of classes to be detected. | ['Xingqun Jiang', 'Tong Liu', 'Lian Huai', 'Zeyu Shangguan'] | 2022-11-24 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.94176668e-01 -2.77737468e-01 1.30674735e-01 -2.97779888e-01
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0b1255e2-b572-40e6-9514-503062f31276 | graphmr-graph-neural-network-for-mathematical | null | null | https://aclanthology.org/2021.emnlp-main.273 | https://aclanthology.org/2021.emnlp-main.273.pdf | GraphMR: Graph Neural Network for Mathematical Reasoning | Mathematical reasoning aims to infer satisfiable solutions based on the given mathematics questions. Previous natural language processing researches have proven the effectiveness of sequence-to-sequence (Seq2Seq) or related variants on mathematics solving. However, few works have been able to explore structural or syntactic information hidden in expressions (e.g., precedence and associativity). This dissertation set out to investigate the usefulness of such untapped information for neural architectures. Firstly, mathematical questions are represented in the format of graphs within syntax analysis. The structured nature of graphs allows them to represent relations of variables or operators while preserving the semantics of the expressions. Having transformed to the new representations, we proposed a graph-to-sequence neural network GraphMR, which can effectively learn the hierarchical information of graphs inputs to solve mathematics and speculate answers. A complete experimental scenario with four classes of mathematical tasks and three Seq2Seq baselines is built to conduct a comprehensive analysis, and results show that GraphMR outperforms others in hidden information learning and mathematics resolving. | ['Yun Xu', 'Qilong Zheng', 'Dongpeng Xu', 'Binbin Liu', 'Weijie Feng'] | null | null | null | null | emnlp-2021-11 | ['graph-to-sequence', 'mathematical-reasoning'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.91726464e-01 2.55249619e-01 -2.12040022e-01 -6.48117840e-01
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bdc20196-fa89-4d12-bac3-4d87dc2edbf1 | classification-of-remote-sensing-images-using | 1806.06985 | null | http://arxiv.org/abs/1806.06985v1 | http://arxiv.org/pdf/1806.06985v1.pdf | Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study | The motivation of this paper is to conduct a comparative study on remote
sensing image classification using the morphological attribute profiles (APs)
and feature profiles (FPs) generated from different types of tree structures.
Over the past few years, APs have been among the most effective methods to
model the image's spatial and contextual information. Recently, a novel
extension of APs called FPs has been proposed by replacing pixel gray-levels
with some statistical and geometrical features when forming the output
profiles. FPs have been proved to be more efficient than the standard APs when
generated from component trees (max-tree and min-tree). In this work, we
investigate their performance on the inclusion tree (tree of shapes) and
partition trees (alpha tree and omega tree). Experimental results from both
panchromatic and hyperspectral images again confirm the efficiency of FPs
compared to APs. | ['Sébastien Lefèvre', 'Minh-Tan Pham', 'Erchan Aptoula'] | 2018-06-18 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 6.56939566e-01 -3.42210740e-01 1.40324041e-01 -3.60893279e-01
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5f6fe37e-0487-4f62-9d19-bf868ea17d08 | k-meansnet-when-k-means-meets-differentiable | 1808.07292 | null | https://arxiv.org/abs/1808.07292v3 | https://arxiv.org/pdf/1808.07292v3.pdf | XAI Beyond Classification: Interpretable Neural Clustering | In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, rather than giving post-hoc explanations of a black-box model. The second is implementing discrete $k$-means with a differentiable neural network that embraces the advantages of parallel computing, online clustering, and clustering-favorable representation learning. To address these two challenges, we design a novel neural network, which is a differentiable reformulation of the vanilla $k$-means, called inTerpretable nEuraL cLustering (TELL). Our contributions are threefold. First, to the best of our knowledge, most existing XAI works focus on supervised learning paradigms. This work is one of the few XAI studies on unsupervised learning, in particular, data clustering. Second, TELL is an interpretable, or the so-called intrinsically explainable and transparent model. In contrast, most existing XAI studies resort to various means for understanding a black-box model with post-hoc explanations. Third, from the view of data clustering, TELL possesses many properties highly desired by $k$-means, including but not limited to online clustering, plug-and-play module, parallel computing, and provable convergence. Extensive experiments show that our method achieves superior performance comparing with 14 clustering approaches on three challenging data sets. The source code could be accessed at \url{www.pengxi.me}. | ['Joey Tianyi Zhou', 'Jiancheng Lv', 'Ivor W. Tsang', 'Yunnan Li', 'Xi Peng', 'Hongyuan Zhu'] | 2018-08-22 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 1.05329074e-01 4.45415497e-01 -1.73031449e-01 -7.36285985e-01
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5ca7f82d-222d-42b1-b723-8a7d99f5b649 | bridging-resolution-making-sense-of-the-state | null | null | https://aclanthology.org/2021.naacl-main.131 | https://aclanthology.org/2021.naacl-main.131.pdf | Bridging Resolution: Making Sense of the State of the Art | While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model. | ['Vincent Ng', 'Hideo Kobayashi'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [-8.27674419e-02 4.44688678e-01 -6.20981216e-01 -1.35344371e-01
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7a146518-5c0b-4e35-8189-589370d99ec0 | efficient-text-based-reinforcement-learning | null | null | https://aclanthology.org/2021.acl-short.91 | https://aclanthology.org/2021.acl-short.91.pdf | Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations | Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents. | ['Murray Campbell', 'Mrinmaya Sachan', 'Kartik Talamadupula', 'Pavan Kapanipathi', 'Mattia Atzeni', 'Keerthiram Murugesan'] | 2021-08-01 | null | null | null | acl-2021-5 | ['text-based-games'] | ['playing-games'] | [ 1.33964822e-01 5.92555523e-01 -1.53210819e-01 7.75637403e-02
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4d869554-188b-40e4-9f87-453213b0266c | from-random-search-to-bandit-learning-in | 2305.11509 | null | https://arxiv.org/abs/2305.11509v3 | https://arxiv.org/pdf/2305.11509v3.pdf | From Random Search to Bandit Learning in Metric Measure Spaces | Random Search is one of the most widely-used method for Hyperparameter Optimization, and is critical to the success of deep learning models. Despite its astonishing performance, little non-heuristic theory has been developed to describe the underlying working mechanism. This paper gives a theoretical accounting of Random Search. We introduce the concept of \emph{scattering dimension} that describes the landscape of the underlying function, and quantifies the performance of random search. We show that, when the environment is noise-free, the output of random search converges to the optimal value in probability at rate $ \widetilde{\mathcal{O}} \left( \left( \frac{1}{T} \right)^{ \frac{1}{d_s} } \right) $, where $ d_s \ge 0 $ is the scattering dimension of the underlying function. When the observed function values are corrupted by bounded $iid$ noise, the output of random search converges to the optimal value in probability at rate $ \widetilde{\mathcal{O}} \left( \left( \frac{1}{T} \right)^{ \frac{1}{d_s + 1} } \right) $. In addition, based on the principles of random search, we introduce an algorithm, called BLiN-MOS, for Lipschitz bandits in doubling metric spaces that are also endowed with a Borel measure, and show that BLiN-MOS achieves a regret rate of order $ \widetilde{\mathcal{O}} \left( T^{ \frac{d_z}{d_z + 1} } \right) $, where $d_z$ is the zooming dimension of the problem instance. Our results show that under certain conditions, the known information-theoretical lower bounds for Lipschitz bandits $\Omega \left( T^{\frac{d_z+1}{d_z+2}} \right)$ can be improved. | ['Tianyu Wang', 'Yasong Feng', 'Chuying Han'] | 2023-05-19 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 1.18518375e-01 3.13010782e-01 -8.17216188e-02 6.63816258e-02
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4f567732-8f39-434b-8222-c50978e9eb43 | learning-variational-neighbor-labels-for-test | 2307.04033 | null | https://arxiv.org/abs/2307.04033v1 | https://arxiv.org/pdf/2307.04033v1.pdf | Learning Variational Neighbor Labels for Test-Time Domain Generalization | This paper strives for domain generalization, where models are trained exclusively on source domains before being deployed at unseen target domains. We follow the strict separation of source training and target testing but exploit the value of the unlabeled target data itself during inference. We make three contributions. First, we propose probabilistic pseudo-labeling of target samples to generalize the source-trained model to the target domain at test time. We formulate the generalization at test time as a variational inference problem by modeling pseudo labels as distributions to consider the uncertainty during generalization and alleviate the misleading signal of inaccurate pseudo labels. Second, we learn variational neighbor labels that incorporate the information of neighboring target samples to generate more robust pseudo labels. Third, to learn the ability to incorporate more representative target information and generate more precise and robust variational neighbor labels, we introduce a meta-generalization stage during training to simulate the generalization procedure. Experiments on six widely-used datasets demonstrate the benefits, abilities, and effectiveness of our proposal. | ['Cees G. M. Snoek', 'XianTong Zhen', 'Jiayi Shen', 'Zehao Xiao', 'Sameer Ambekar'] | 2023-07-08 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 3.50715280e-01 3.69942099e-01 -6.00826085e-01 -7.29037285e-01
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6f6f8a7c-f959-400c-9fd8-c533f0bd2d5b | a-probabilistic-framework-for-imitating-human | 2001.08255 | null | https://arxiv.org/abs/2001.08255v2 | https://arxiv.org/pdf/2001.08255v2.pdf | A Probabilistic Framework for Imitating Human Race Driver Behavior | Understanding and modeling human driver behavior is crucial for advanced vehicle development. However, unique driving styles, inconsistent behavior, and complex decision processes render it a challenging task, and existing approaches often lack variability or robustness. To approach this problem, we propose Probabilistic Modeling of Driver behavior (ProMoD), a modular framework which splits the task of driver behavior modeling into multiple modules. A global target trajectory distribution is learned with Probabilistic Movement Primitives, clothoids are utilized for local path generation, and the corresponding choice of actions is performed by a neural network. Experiments in a simulated car racing setting show considerable advantages in imitation accuracy and robustness compared to other imitation learning algorithms. The modular architecture of the proposed framework facilitates straightforward extensibility in driving line adaptation and sequencing of multiple movement primitives for future research. | ['Stefan Löckel', 'Jan Peters', 'Peter van Vliet'] | 2020-01-22 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.36286193e-01 -6.98729977e-02 -5.99776506e-01 -4.21199828e-01
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d066c218-1a61-47f3-8a40-2c4306e42f4f | effects-of-lead-position-cardiac-rhythm | 1912.04672 | null | https://arxiv.org/abs/1912.04672v2 | https://arxiv.org/pdf/1912.04672v2.pdf | Effects of lead position, cardiac rhythm variation and drug-induced QT prolongation on performance of machine learning methods for ECG processing | Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effects of QT-prolongation agents on the performance of machine learning methods for ECG processing. We choose the problem of subject identification for analysis, because this problem may be solved for almost any available dataset of ECG data. In a discussion, we compare our findings with observations from other works that use machine learning for ECG processing with different problem statements. Our results show the importance of training dataset enrichment with ECG signals acquired in specific physiological conditions for obtaining good performance of ECG processing for real applications. | ['Konstantin Ushenin', 'Aygul Fabarisova', 'Marat Bogdanov', 'Salim Baigildin', 'Olga Solovyova'] | 2019-12-10 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 5.57552695e-01 -4.18844879e-01 2.24213645e-01 -3.65064144e-01
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b1d9cb65-88b3-4083-8f19-e66e8b0f0f5e | 190601054 | 1906.01054 | null | https://arxiv.org/abs/1906.01054v1 | https://arxiv.org/pdf/1906.01054v1.pdf | Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis | Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to learn highly discriminative 3 dimensional features instead of 2D features like texture or geometric shape whick need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatio-temporal statistics.The developed model is end-to-end and is able to predict malignancy of each voxel for given input scan. Simulation results demonstrate the effectiveness of proposed 3D CNN network for classification of lung nodule in-spite of limited computational capabilities. | ['Pallavi Asthana', 'Anil Kumar', 'Sumita Mishra', 'Naresh Kumar Chaudhary'] | 2019-05-04 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-2.24440739e-01 5.53125814e-02 -1.24333188e-01 -3.52673918e-01
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7ff86bd5-23d8-4f89-b8ca-caa5df6fa1cd | nnsvs-a-neural-network-based-singing-voice | 2210.15987 | null | https://arxiv.org/abs/2210.15987v2 | https://arxiv.org/pdf/2210.15987v2.pdf | NNSVS: A Neural Network-Based Singing Voice Synthesis Toolkit | This paper describes the design of NNSVS, an open-source software for neural network-based singing voice synthesis research. NNSVS is inspired by Sinsy, an open-source pioneer in singing voice synthesis research, and provides many additional features such as multi-stream models, autoregressive fundamental frequency models, and neural vocoders. Furthermore, NNSVS provides extensive documentation and numerous scripts to build complete singing voice synthesis systems. Experimental results demonstrate that our best system significantly outperforms our reproduction of Sinsy and other baseline systems. The toolkit is available at https://github.com/nnsvs/nnsvs. | ['Tomoki Toda', 'Reo Yoneyama', 'Ryuichi Yamamoto'] | 2022-10-28 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-4.80171829e-01 -3.00821096e-01 -3.50538641e-01 8.37236643e-02
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03f177c0-bb51-43f3-a624-69c1a4865804 | deep-learning-framework-with-multi-head | 2306.11137 | null | https://arxiv.org/abs/2306.11137v1 | https://arxiv.org/pdf/2306.11137v1.pdf | Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging | T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components for cervical cancer diagnosis. However, combining these channels for training deep learning models are challenging due to misalignment of images. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and feature encoding configurations. All experiments were performed using a cohort including 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI, and combined b1000 DWI and apparent diffusion coefficient (ADC) images achieved the best median Dice coefficient similarity (DSC) score, 0.823 (95% confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant (p>0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentations. However, our results showed that b1000 DWI had a minor impact on overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings can have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications. | ['Dow-Mu Koh', 'Matthew D Blackledge', 'Christina Messiou', 'Gigin Lin', 'Jessica M Winfield', 'Sebastian Curcean', 'Reza Kalantar'] | 2023-06-19 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 3.93504947e-01 -4.24369276e-02 -1.48754299e-01 -3.13563645e-01
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9bdaa40d-9023-4226-8fd3-b4f198e31dae | htlm-hyper-text-pre-training-and-prompting-of | 2107.06955 | null | https://arxiv.org/abs/2107.06955v1 | https://arxiv.org/pdf/2107.06955v1.pdf | HTLM: Hyper-Text Pre-Training and Prompting of Language Models | We introduce HTLM, a hyper-text language model trained on a large-scale web crawl. Modeling hyper-text has a number of advantages: (1) it is easily gathered at scale, (2) it provides rich document-level and end-task-adjacent supervision (e.g. class and id attributes often encode document category information), and (3) it allows for new structured prompting that follows the established semantics of HTML (e.g. to do zero-shot summarization by infilling title tags for a webpage that contains the input text). We show that pretraining with a BART-style denoising loss directly on simplified HTML provides highly effective transfer for a wide range of end tasks and supervision levels. HTLM matches or exceeds the performance of comparably sized text-only LMs for zero-shot prompting and fine-tuning for classification benchmarks, while also setting new state-of-the-art performance levels for zero-shot summarization. We also find that hyper-text prompts provide more value to HTLM, in terms of data efficiency, than plain text prompts do for existing LMs, and that HTLM is highly effective at auto-prompting itself, by simply generating the most likely hyper-text formatting for any available training data. We will release all code and models to support future HTLM research. | ['Luke Zettlemoyer', 'Gargi Ghosh', 'Hu Xu', 'Mandar Joshi', 'Mike Lewis', 'Dmytro Okhonko', 'Armen Aghajanyan'] | 2021-07-14 | htlm-hyper-text-pre-training-and-prompting-of-1 | https://openreview.net/forum?id=P-pPW1nxf1r | https://openreview.net/pdf?id=P-pPW1nxf1r | iclr-2022-4 | ['table-to-text-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.56549442e-01 3.68939042e-01 -3.56180906e-01 -3.09749007e-01
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3c4d1221-b4e9-4031-9482-f0044d8fe7c9 | haav-hierarchical-aggregation-of-augmented-1 | 2305.16295 | null | https://arxiv.org/abs/2305.16295v1 | https://arxiv.org/pdf/2305.16295v1.pdf | HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning | A great deal of progress has been made in image captioning, driven by research into how to encode the image using pre-trained models. This includes visual encodings (e.g. image grid features or detected objects) and more recently textual encodings (e.g. image tags or text descriptions of image regions). As more advanced encodings are available and incorporated, it is natural to ask: how to efficiently and effectively leverage the heterogeneous set of encodings? In this paper, we propose to regard the encodings as augmented views of the input image. The image captioning model encodes each view independently with a shared encoder efficiently, and a contrastive loss is incorporated across the encoded views in a novel way to improve their representation quality and the model's data efficiency. Our proposed hierarchical decoder then adaptively weighs the encoded views according to their effectiveness for caption generation by first aggregating within each view at the token level, and then across views at the view level. We demonstrate significant performance improvements of +5.6% CIDEr on MS-COCO and +12.9% CIDEr on Flickr30k compared to state of the arts, and conduct rigorous analyses to demonstrate the importance of each part of our design. | ['Zsolt Kira', 'Chia-Wen Kuo'] | 2023-05-25 | haav-hierarchical-aggregation-of-augmented | http://openaccess.thecvf.com//content/CVPR2023/html/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kuo_HAAV_Hierarchical_Aggregation_of_Augmented_Views_for_Image_Captioning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-captioning'] | ['computer-vision'] | [ 5.62185228e-01 2.81063616e-01 -3.11031908e-01 -4.91951942e-01
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1afc0e49-2f06-4272-8e46-446868b13184 | riemannian-low-rank-model-compression-for | 2306.02433 | null | https://arxiv.org/abs/2306.02433v1 | https://arxiv.org/pdf/2306.02433v1.pdf | Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation | Low-rank model compression is a widely used technique for reducing the computational load when training machine learning models. However, existing methods often rely on relaxing the low-rank constraint of the model weights using a regularized nuclear norm penalty, which requires an appropriate hyperparameter that can be difficult to determine in practice. Furthermore, existing compression techniques are not directly applicable to efficient over-the-air (OTA) aggregation in federated learning (FL) systems for distributed Internet-of-Things (IoT) scenarios. In this paper, we propose a novel manifold optimization formulation for low-rank model compression in FL that does not relax the low-rank constraint. Our optimization is conducted directly over the low-rank manifold, guaranteeing that the model is exactly low-rank. We also introduce a consensus penalty in the optimization formulation to support OTA aggregation. Based on our optimization formulation, we propose an alternating Riemannian optimization algorithm with a precoder that enables efficient OTA aggregation of low-rank local models without sacrificing training performance. Additionally, we provide convergence analysis in terms of key system parameters and conduct extensive experiments with real-world datasets to demonstrate the effectiveness of our proposed Riemannian low-rank model compression scheme compared to various state-of-the-art baselines. | ['Vincent Lau', 'Ye Xue'] | 2023-06-04 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 1.31436825e-01 -1.40948385e-01 -2.05951244e-01 -4.54833210e-01
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9b502e0d-8f7e-4a08-a9dd-a810549e9963 | challenging-on-car-racing-problem-from-openai | 1911.04868 | null | https://arxiv.org/abs/1911.04868v1 | https://arxiv.org/pdf/1911.04868v1.pdf | Challenging On Car Racing Problem from OpenAI gym | This project challenges the car racing problem from OpenAI gym environment. The problem is very challenging since it requires computer to finish the continuous control task by learning from pixels. To tackle this challenging problem, we explored two approaches including evolutionary algorithm based genetic multi-layer perceptron and double deep Q-learning network. The result shows that the genetic multi-layer perceptron can converge fast but when training many episodes, double deep Q-learning can get better score. We analyze the result and draw a conclusion that for limited hardware resources, using genetic multi-layer perceptron sometimes can be more efficient. | ['Changmao Li'] | 2019-11-02 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-2.20737919e-01 -2.45546445e-01 -6.07365780e-02 -7.78193250e-02
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9158745b-8020-4759-a5b5-e5ab720c6054 | leaf-cultivar-identification-via-prototype | 2305.03351 | null | https://arxiv.org/abs/2305.03351v1 | https://arxiv.org/pdf/2305.03351v1.pdf | Leaf Cultivar Identification via Prototype-enhanced Learning | Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years. Due to the high similarity among different varieties, leaf cultivar recognition is also considered to be an ultra-fine-grained visual classification (UFGVC) task, which is facing a huge challenge. In practice, an instance may be related to multiple varieties to varying degrees, especially in the UFGVC datasets. However, deep learning methods trained on one-hot labels fail to reflect patterns shared across categories and thus perform poorly on this task. To address this issue, we generate soft targets integrated with inter-class similarity information. Specifically, we continuously update the prototypical features for each category and then capture the similarity scores between instances and prototypes accordingly. Original one-hot labels and the similarity scores are incorporated to yield enhanced labels. Prototype-enhanced soft labels not only contain original one-hot label information, but also introduce rich inter-category semantic association information, thus providing more effective supervision for deep model training. Extensive experimental results on public datasets show that our method can significantly improve the performance on the UFGVC task of leaf cultivar identification. | ['Xiaogang Xu', 'Xianzhong Feng', 'Jun Wang', 'Nannan Li', 'Cuiling Wu', 'Ying Zheng', 'Zhiwen Ying', 'Yiyi Zhang'] | 2023-05-05 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 1.61110371e-01 -4.51592833e-01 -3.49461108e-01 -5.43150604e-01
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bd21bc84-75c8-40cc-9c70-7e52a874daf2 | on-frequency-wise-normalizations-for-better | 2306.11764 | null | https://arxiv.org/abs/2306.11764v1 | https://arxiv.org/pdf/2306.11764v1.pdf | On Frequency-Wise Normalizations for Better Recording Device Generalization in Audio Spectrogram Transformers | Varying conditions between the data seen at training and at application time remain a major challenge for machine learning. We study this problem in the context of Acoustic Scene Classification (ASC) with mismatching recording devices. Previous works successfully employed frequency-wise normalization of inputs and hidden layer activations in convolutional neural networks to reduce the recording device discrepancy. The main objective of this work was to adopt frequency-wise normalization for Audio Spectrogram Transformers (ASTs), which have recently become the dominant model architecture in ASC. To this end, we first investigate how recording device characteristics are encoded in the hidden layer activations of ASTs. We find that recording device information is initially encoded in the frequency dimension; however, after the first self-attention block, it is largely transformed into the token dimension. Based on this observation, we conjecture that suppressing recording device characteristics in the input spectrogram is the most effective. We propose a frequency-centering operation for spectrograms that improves the ASC performance on unseen recording devices on average by up to 18.2 percentage points. | ['Gerhard Widmer', 'Paul Primus and'] | 2023-06-20 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification'] | ['audio', 'computer-vision'] | [ 6.63569450e-01 -3.00120085e-01 2.11446524e-01 -2.58233249e-01
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d088796a-b101-4270-a1e1-c8b35182fde1 | achieving-rgb-d-level-segmentation | 2306.17636 | null | https://arxiv.org/abs/2306.17636v1 | https://arxiv.org/pdf/2306.17636v1.pdf | Achieving RGB-D level Segmentation Performance from a Single ToF Camera | Depth is a very important modality in computer vision, typically used as complementary information to RGB, provided by RGB-D cameras. In this work, we show that it is possible to obtain the same level of accuracy as RGB-D cameras on a semantic segmentation task using infrared (IR) and depth images from a single Time-of-Flight (ToF) camera. In order to fuse the IR and depth modalities of the ToF camera, we introduce a method utilizing depth-specific convolutions in a multi-task learning framework. In our evaluation on an in-car segmentation dataset, we demonstrate the competitiveness of our method against the more costly RGB-D approaches. | ['Juergen Seiler', 'Didier Stricker', 'Bruno Mirbach', 'Jason Rambach', 'Jigyasa Singh Katrolia', 'Pranav Sharma'] | 2023-06-30 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 5.47342300e-01 -9.29794535e-02 7.07725659e-02 -4.87206250e-01
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95b30ebf-17e0-40c1-b3b3-f42c45b5787f | age-and-gender-classification-from-ear-images | 1806.05742 | null | http://arxiv.org/abs/1806.05742v1 | http://arxiv.org/pdf/1806.05742v1.pdf | Age and Gender Classification From Ear Images | In this paper, we present a detailed analysis on extracting soft biometric
traits, age and gender, from ear images. Although there have been a few
previous work on gender classification using ear images, to the best of our
knowledge, this study is the first work on age classification from ear images.
In the study, we have utilized both geometric features and appearance-based
features for ear representation. The utilized geometric features are based on
eight anthropometric landmarks and consist of 14 distance measurements and two
area calculations. The appearance-based methods employ deep convolutional
neural networks for representation and classification. The well-known
convolutional neural network models, namely, AlexNet, VGG-16, GoogLeNet, and
SqueezeNet have been adopted for the study. They have been fine-tuned on a
large-scale ear dataset that has been built from the profile and
close-to-profile face images in the Multi-PIE face dataset. This way, we have
performed a domain adaptation. The updated models have been fine-tuned once
more time on the small-scale target ear dataset, which contains only around 270
ear images for training. According to the experimental results,
appearance-based methods have been found to be superior to the methods based on
geometric features. We have achieved 94\% accuracy for gender classification,
whereas 52\% accuracy has been obtained for age classification. These results
indicate that ear images provide useful cues for age and gender classification,
however, further work is required for age estimation. | ['Hazim Kemal Ekenel', 'Nurdan Sezgin', 'Fevziye Irem Eyiokur', 'Dogucan Yaman'] | 2018-06-14 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-3.45466882e-01 9.95874628e-02 1.25350982e-01 -4.67079580e-01
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d8eb255f-f7d0-4d15-8b3c-95f3dd0660c5 | bayesian-optimistic-optimisation-with | 2105.04332 | null | https://arxiv.org/abs/2105.04332v1 | https://arxiv.org/pdf/2105.04332v1.pdf | Bayesian Optimistic Optimisation with Exponentially Decaying Regret | Bayesian optimisation (BO) is a well-known efficient algorithm for finding the global optimum of expensive, black-box functions. The current practical BO algorithms have regret bounds ranging from $\mathcal{O}(\frac{logN}{\sqrt{N}})$ to $\mathcal O(e^{-\sqrt{N}})$, where $N$ is the number of evaluations. This paper explores the possibility of improving the regret bound in the noiseless setting by intertwining concepts from BO and tree-based optimistic optimisation which are based on partitioning the search space. We propose the BOO algorithm, a first practical approach which can achieve an exponential regret bound with order $\mathcal O(N^{-\sqrt{N}})$ under the assumption that the objective function is sampled from a Gaussian process with a Mat\'ern kernel with smoothness parameter $\nu > 4 +\frac{D}{2}$, where $D$ is the number of dimensions. We perform experiments on optimisation of various synthetic functions and machine learning hyperparameter tuning tasks and show that our algorithm outperforms baselines. | ['Svetha Venkatesh', 'Santu Rana', 'Sunil Gupta', 'Hung Tran-The'] | 2021-05-10 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.10445836e-01 2.58219630e-01 7.81044513e-02 -3.32332820e-01
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63fb2cc7-37a5-4be5-8be2-67f7c934934c | lighten-learning-interactions-with-graph-and | 2012.09402 | null | https://arxiv.org/abs/2012.09402v1 | https://arxiv.org/pdf/2012.09402v1.pdf | LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos | Analyzing the interactions between humans and objects from a video includes identification of the relationships between humans and the objects present in the video. It can be thought of as a specialized version of Visual Relationship Detection, wherein one of the objects must be a human. While traditional methods formulate the problem as inference on a sequence of video segments, we present a hierarchical approach, LIGHTEN, to learn visual features to effectively capture spatio-temporal cues at multiple granularities in a video. Unlike current approaches, LIGHTEN avoids using ground truth data like depth maps or 3D human pose, thus increasing generalization across non-RGBD datasets as well. Furthermore, we achieve the same using only the visual features, instead of the commonly used hand-crafted spatial features. We achieve state-of-the-art results in human-object interaction detection (88.9% and 92.6%) and anticipation tasks of CAD-120 and competitive results on image based HOI detection in V-COCO dataset, setting a new benchmark for visual features based approaches. Code for LIGHTEN is available at https://github.com/praneeth11009/LIGHTEN-Learning-Interactions-with-Graphs-and-Hierarchical-TEmporal-Networks-for-HOI | ['Ganesh Ramakrishnan', 'Rishabh Dabral', 'Sai Praneeth Reddy Sunkesula'] | 2020-12-17 | null | null | null | null | ['video-visual-relation-detection', 'visual-relationship-detection'] | ['computer-vision', 'computer-vision'] | [-3.18999812e-02 -2.40244582e-01 -1.18168741e-01 -3.18534225e-01
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66ff8a21-e2bd-4058-b8bd-30bbe7a512f6 | multi-layer-trajectory-clustering-a-network | 2005.14472 | null | https://arxiv.org/abs/2005.14472v2 | https://arxiv.org/pdf/2005.14472v2.pdf | Multi-layer Trajectory Clustering: A Network Algorithm for Disease Subtyping | Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work present a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson's subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine. | ['Sanjukta Krishnagopal'] | 2020-05-29 | null | null | null | null | ['trajectory-modeling'] | ['time-series'] | [-1.57510057e-01 -2.59292901e-01 -6.44561470e-01 -1.95798814e-01
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ab6c0eb0-2d23-4bc9-8fb7-417e516c8ceb | unitedqa-a-hybrid-approach-for-open-domain | 2101.00178 | null | https://arxiv.org/abs/2101.00178v2 | https://arxiv.org/pdf/2101.00178v2.pdf | UnitedQA: A Hybrid Approach for Open Domain Question Answering | To date, most of recent work under the retrieval-reader framework for open-domain QA focuses on either extractive or generative reader exclusively. In this paper, we study a hybrid approach for leveraging the strengths of both models. We apply novel techniques to enhance both extractive and generative readers built upon recent pretrained neural language models, and find that proper training methods can provide large improvement over previous state-of-the-art models. We demonstrate that a simple hybrid approach by combining answers from both readers can efficiently take advantages of extractive and generative answer inference strategies and outperforms single models as well as homogeneous ensembles. Our approach outperforms previous state-of-the-art models by 3.3 and 2.7 points in exact match on NaturalQuestions and TriviaQA respectively. | ['Jianfeng Gao', 'Weizhu Chen', 'Pengcheng He', 'Xiaodong Liu', 'Yelong Shen', 'Hao Cheng'] | 2021-01-01 | null | https://aclanthology.org/2021.acl-long.240 | https://aclanthology.org/2021.acl-long.240.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [ 1.10978253e-01 4.16751742e-01 -4.12601940e-02 -2.17293993e-01
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44b8d4dd-ac07-456f-8ec5-e23e3b99d5f8 | generating-soap-notes-from-doctor-patient | 2005.01795 | null | https://arxiv.org/abs/2005.01795v3 | https://arxiv.org/pdf/2005.01795v3.pdf | Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques | Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora. | ['Jeffrey P. Bigham', 'Kundan Krishna', 'Zachary C. Lipton', 'Sopan Khosla'] | 2020-05-04 | null | https://aclanthology.org/2021.acl-long.384 | https://aclanthology.org/2021.acl-long.384.pdf | acl-2021-5 | ['meeting-summarization'] | ['natural-language-processing'] | [ 5.51416337e-01 8.30062151e-01 -1.22840859e-01 -4.22410399e-01
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3b9c58cb-4aba-465f-8d94-a555c32e94c7 | on-the-soundness-of-xai-in-prognostics-and | 2303.05517 | null | https://arxiv.org/abs/2303.05517v1 | https://arxiv.org/pdf/2303.05517v1.pdf | On the Soundness of XAI in Prognostics and Health Management (PHM) | The aim of Predictive Maintenance, within the field of Prognostics and Health Management (PHM), is to identify and anticipate potential issues in the equipment before these become critical. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as Remaining Useful Life (RUL). Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that this kind of black box models are opaque decision systems, and it may be hard to explain its outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This work presents a critical and comparative revision on a number of XAI methods applied on time series regression model for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. The model used during the experimentation is a DCNN trained to predict the RUL of an aircraft engine. The methods are reviewed and compared using a set of metrics that quantifies a number of desirable properties that any XAI method should fulfill. The results show that GRAD-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of Image Processing. | ['Joaquín Borrego-Díaz', 'Juan Galán-Páez', 'David Solís-Martín'] | 2023-03-09 | null | null | null | null | ['time-series-classification', 'time-series-regression'] | ['time-series', 'time-series'] | [ 2.93205798e-01 -2.44945660e-01 -4.98790815e-02 -1.09763578e-01
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477405a5-9c6d-4ac5-8e5b-e19a9c78e94b | optimal-estimation-and-computational-limit-of | 2201.09040 | null | https://arxiv.org/abs/2201.09040v1 | https://arxiv.org/pdf/2201.09040v1.pdf | Optimal Estimation and Computational Limit of Low-rank Gaussian Mixtures | Structural matrix-variate observations routinely arise in diverse fields such as multi-layer network analysis and brain image clustering. While data of this type have been extensively investigated with fruitful outcomes being delivered, the fundamental questions like its statistical optimality and computational limit are largely under-explored. In this paper, we propose a low-rank Gaussian mixture model (LrMM) assuming each matrix-valued observation has a planted low-rank structure. Minimax lower bounds for estimating the underlying low-rank matrix are established allowing a whole range of sample sizes and signal strength. Under a minimal condition on signal strength, referred to as the information-theoretical limit or statistical limit, we prove the minimax optimality of a maximum likelihood estimator which, in general, is computationally infeasible. If the signal is stronger than a certain threshold, called the computational limit, we design a computationally fast estimator based on spectral aggregation and demonstrate its minimax optimality. Moreover, when the signal strength is smaller than the computational limit, we provide evidences based on the low-degree likelihood ratio framework to claim that no polynomial-time algorithm can consistently recover the underlying low-rank matrix. Our results reveal multiple phase transitions in the minimax error rates and the statistical-to-computational gap. Numerical experiments confirm our theoretical findings. We further showcase the merit of our spectral aggregation method on the worldwide food trading dataset. | ['Dong Xia', 'Zhongyuan Lyu'] | 2022-01-22 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.85374755e-01 1.36890218e-01 -1.79626927e-01 6.39450876e-03
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bc77466f-7d3d-4e4b-a565-eba1b7010f5d | hyperspectral-unmixing-based-on-nonnegative | 2205.09933 | null | https://arxiv.org/abs/2205.09933v1 | https://arxiv.org/pdf/2205.09933v1.pdf | Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review | Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing. | ['Antonio Plaza', 'Xiuping Jia', 'Qian Du', 'Rui Wang', 'Heng-Chao Li', 'Xin-Ru Feng'] | 2022-05-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 8.28781962e-01 -7.69734442e-01 -3.08455557e-01 -6.75172210e-02
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d94d4cbf-19a3-4b51-aee2-b1aa1fd54168 | nero-neural-geometry-and-brdf-reconstruction | 2305.17398 | null | https://arxiv.org/abs/2305.17398v1 | https://arxiv.org/pdf/2305.17398v1.pdf | NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Images | We present a neural rendering-based method called NeRO for reconstructing the geometry and the BRDF of reflective objects from multiview images captured in an unknown environment. Multiview reconstruction of reflective objects is extremely challenging because specular reflections are view-dependent and thus violate the multiview consistency, which is the cornerstone for most multiview reconstruction methods. Recent neural rendering techniques can model the interaction between environment lights and the object surfaces to fit the view-dependent reflections, thus making it possible to reconstruct reflective objects from multiview images. However, accurately modeling environment lights in the neural rendering is intractable, especially when the geometry is unknown. Most existing neural rendering methods, which can model environment lights, only consider direct lights and rely on object masks to reconstruct objects with weak specular reflections. Therefore, these methods fail to reconstruct reflective objects, especially when the object mask is not available and the object is illuminated by indirect lights. We propose a two-step approach to tackle this problem. First, by applying the split-sum approximation and the integrated directional encoding to approximate the shading effects of both direct and indirect lights, we are able to accurately reconstruct the geometry of reflective objects without any object masks. Then, with the object geometry fixed, we use more accurate sampling to recover the environment lights and the BRDF of the object. Extensive experiments demonstrate that our method is capable of accurately reconstructing the geometry and the BRDF of reflective objects from only posed RGB images without knowing the environment lights and the object masks. Codes and datasets are available at https://github.com/liuyuan-pal/NeRO. | ['Wenping Wang', 'Taku Komura', 'Lingjie Liu', 'Jiepeng Wang', 'Xiaoxiao Long', 'Cheng Lin', 'Peng Wang', 'YuAn Liu'] | 2023-05-27 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 3.13866466e-01 -2.36414075e-01 6.18105948e-01 -2.95224309e-01
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e0ad5570-5366-4adf-a4c3-d7b3a20c5311 | bounding-boxes-segmentations-and-object | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Behl_Bounding_Boxes_Segmentations_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Behl_Bounding_Boxes_Segmentations_ICCV_2017_paper.pdf | Bounding Boxes, Segmentations and Object Coordinates: How Important Is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios? | Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.
| ['Aseem Behl', 'Omid Hosseini Jafari', 'Carsten Rother', 'Siva Karthik Mustikovela', 'Hassan Abu Alhaija', 'Andreas Geiger'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['scene-flow-estimation'] | ['computer-vision'] | [ 4.05088782e-01 -1.28723100e-01 -1.68970913e-01 -9.11889076e-02
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7080203c-8a96-4f85-9067-323bf471e10a | appearance-harmonization-for-single-image | 1603.06398 | null | http://arxiv.org/abs/1603.06398v1 | http://arxiv.org/pdf/1603.06398v1.pdf | Appearance Harmonization for Single Image Shadow Removal | Shadows often create unwanted artifacts in photographs, and removing them can
be very challenging. Previous shadow removal methods often produce de-shadowed
regions that are visually inconsistent with the rest of the image. In this work
we propose a fully automatic shadow region harmonization approach that improves
the appearance compatibility of the de-shadowed region as typically produced by
previous methods. It is based on a shadow-guided patch-based image synthesis
approach that reconstructs the shadow region using patches sampled from
non-shadowed regions. The result is then refined based on the reconstruction
confidence to handle unique image patterns. Many shadow removal results and
comparisons are show the effectiveness of our improvement. Quantitative
evaluation on a benchmark dataset suggests that our automatic shadow
harmonization approach effectively improves upon the state-of-the-art. | ['Shi-Min Hu', 'Kalyan Sunkavalli', 'Liqian Ma', 'Jue Wang', 'Eli Shechtman'] | 2016-03-21 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 8.38459730e-01 1.52075976e-01 4.92896020e-01 -2.22911865e-01
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0290bc7e-c1c0-46c0-a6df-cbc02ae97231 | aweu-net-an-attention-aware-weight-excitation | 2110.05144 | null | https://arxiv.org/abs/2110.05144v1 | https://arxiv.org/pdf/2110.05144v1.pdf | AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation | Lung cancer is deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage. Most of the existing systems are semi-automated and need to manually select the lung and nodules regions to perform the segmentation task. To address these challenges, we proposed a fully automated end-to-end lung nodule detection and segmentation system based on a deep learning approach. In this paper, we used Optimized Faster R-CNN; a state-of-the-art detection model to detect the lung nodule regions in the CT scans. Furthermore, we proposed an attention-aware weight excitation U-Net, called AWEU-Net, for lung nodule segmentation and boundaries detection. To achieve more accurate nodule segmentation, in AWEU-Net, we proposed position attention-aware weight excitation (PAWE), and channel attention-aware weight excitation (CAWE) blocks to highlight the best aligned spatial and channel features in the input feature maps. The experimental results demonstrate that our proposed model yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly LUNA16 and LIDC-IDRI datasets, respectively. | ['Hatem A. Raswan', 'Domenec Puig', 'Mohamed Abdel-Nasser', 'Md. Mostafa Kamal Sarker', 'Syeda Furruka Banu'] | 2021-10-11 | null | null | null | null | ['lung-nodule-detection', 'lung-nodule-segmentation'] | ['medical', 'medical'] | [-2.14584712e-02 1.12589933e-01 -3.92223120e-01 -8.12988281e-02
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3be9b6eb-90ec-4f10-bf59-7820f76d5e27 | graph2vid-flow-graph-to-video-grounding | 2210.04996 | null | https://arxiv.org/abs/2210.04996v2 | https://arxiv.org/pdf/2210.04996v2.pdf | Graph2Vid: Flow graph to Video Grounding for Weakly-supervised Multi-Step Localization | In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute a procedure successfully, by following the set of steps in slightly varying orders. Thus, for successful localization in a given video, recent works require the actual order of procedure steps in the video, to be provided by human annotators at both training and test times. Instead, here, we only rely on generic procedural text that is not tied to a specific video. We represent the various ways to complete the procedure by transforming the list of instructions into a procedure flow graph which captures the partial order of steps. Using the flow graphs reduces both training and test time annotation requirements. To this end, we introduce the new problem of flow graph to video grounding. In this setup, we seek the optimal step ordering consistent with the procedure flow graph and a given video. To solve this problem, we propose a new algorithm - Graph2Vid - that infers the actual ordering of steps in the video and simultaneously localizes them. To show the advantage of our proposed formulation, we extend the CrossTask dataset with procedure flow graph information. Our experiments show that Graph2Vid is both more efficient than the baselines and yields strong step localization results, without the need for step order annotation. | ['Allan D. Jepson', 'Afsaneh Fazly', 'Brais Martinez', 'Dhaivat Bhatt', 'Hai Pham', 'Isma Hadji', 'Nikita Dvornik'] | 2022-10-10 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 2.56027281e-01 -3.41346145e-01 -3.41257364e-01 -3.46265316e-01
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b862fd6f-fc19-4e0d-9668-f7ef57ba46c7 | investigation-of-japanese-png-bert-language | 2212.08321 | null | https://arxiv.org/abs/2212.08321v1 | https://arxiv.org/pdf/2212.08321v1.pdf | Investigation of Japanese PnG BERT language model in text-to-speech synthesis for pitch accent language | End-to-end text-to-speech synthesis (TTS) can generate highly natural synthetic speech from raw text. However, rendering the correct pitch accents is still a challenging problem for end-to-end TTS. To tackle the challenge of rendering correct pitch accent in Japanese end-to-end TTS, we adopt PnG~BERT, a self-supervised pretrained model in the character and phoneme domain for TTS. We investigate the effects of features captured by PnG~BERT on Japanese TTS by modifying the fine-tuning condition to determine the conditions helpful inferring pitch accents. We manipulate content of PnG~BERT features from being text-oriented to speech-oriented by changing the number of fine-tuned layers during TTS. In addition, we teach PnG~BERT pitch accent information by fine-tuning with tone prediction as an additional downstream task. Our experimental results show that the features of PnG~BERT captured by pretraining contain information helpful inferring pitch accent, and PnG~BERT outperforms baseline Tacotron on accent correctness in a listening test. | ['Tomoki Toda', 'Yusuke Yasuda'] | 2022-12-16 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 1.06064074e-01 3.06580275e-01 -5.43765612e-02 -5.62498748e-01
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f295170c-9456-46a0-a738-6cdcc6d94ce9 | 3d-object-aided-self-supervised-monocular | 2212.01768 | null | https://arxiv.org/abs/2212.01768v1 | https://arxiv.org/pdf/2212.01768v1.pdf | 3D Object Aided Self-Supervised Monocular Depth Estimation | Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised learning methods based on the framework of Structure-From-Motion (SfM) simultaneously predict depth and camera relative pose. However, dynamically moving objects in the scene violate the static world assumption, resulting in inaccurate depths of dynamic objects. In this work, we propose a new method to address such dynamic object movements through monocular 3D object detection. Specifically, we first detect 3D objects in the images and build the per-pixel correspondence of the dynamic pixels with the detected object pose while leaving the static pixels corresponding to the rigid background to be modeled with camera motion. In this way, the depth of every pixel can be learned via a meaningful geometry model. Besides, objects are detected as cuboids with absolute scale, which is used to eliminate the scale ambiguity problem inherent in monocular vision. Experiments on the KITTI depth dataset show that our method achieves State-of-The-Art performance for depth estimation. Furthermore, joint training of depth, camera motion and object pose also improves monocular 3D object detection performance. To the best of our knowledge, this is the first work that allows a monocular 3D object detection network to be fine-tuned in a self-supervised manner. | ['Lining Sun', 'Zhenhua Wang', 'Wenzheng Chi', 'Guodong Chen', 'Songlin Wei'] | 2022-12-04 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.17531791e-01 -1.64870307e-01 -1.83440551e-01 -3.08100224e-01
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7f01a71c-a26f-4bc2-9c84-5d0df84a4a9e | octnet-learning-deep-3d-representations-at | 1611.05009 | null | http://arxiv.org/abs/1611.05009v4 | http://arxiv.org/pdf/1611.05009v4.pdf | OctNet: Learning Deep 3D Representations at High Resolutions | We present OctNet, a representation for deep learning with sparse 3D data. In
contrast to existing models, our representation enables 3D convolutional
networks which are both deep and high resolution. Towards this goal, we exploit
the sparsity in the input data to hierarchically partition the space using a
set of unbalanced octrees where each leaf node stores a pooled feature
representation. This allows to focus memory allocation and computation to the
relevant dense regions and enables deeper networks without compromising
resolution. We demonstrate the utility of our OctNet representation by
analyzing the impact of resolution on several 3D tasks including 3D object
classification, orientation estimation and point cloud labeling. | ['Ali Osman Ulusoy', 'Gernot Riegler', 'Andreas Geiger'] | 2016-11-15 | octnet-learning-deep-3d-representations-at-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Riegler_OctNet_Learning_Deep_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Riegler_OctNet_Learning_Deep_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-object-classification'] | ['computer-vision'] | [-4.16502059e-01 3.09559375e-01 -1.48821309e-01 -3.92032683e-01
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dde17d8c-66b6-4d56-a2da-081e6e886278 | prototex-explaining-model-decisions-with | 2204.05426 | null | https://arxiv.org/abs/2204.05426v2 | https://arxiv.org/pdf/2204.05426v2.pdf | ProtoTEx: Explaining Model Decisions with Prototype Tensors | We present ProtoTEx, a novel white-box NLP classification architecture based on prototype networks. ProtoTEx faithfully explains model decisions based on prototype tensors that encode latent clusters of training examples. At inference time, classification decisions are based on the distances between the input text and the prototype tensors, explained via the training examples most similar to the most influential prototypes. We also describe a novel interleaved training algorithm that effectively handles classes characterized by the absence of indicative features. On a propaganda detection task, ProtoTEx accuracy matches BART-large and exceeds BERT-large with the added benefit of providing faithful explanations. A user study also shows that prototype-based explanations help non-experts to better recognize propaganda in online news. | ['Junyi Jessy Li', 'Matthew Lease', 'Venelin Kovatchev', 'Chitrank Gupta', 'Anubrata Das'] | 2022-04-11 | null | https://aclanthology.org/2022.acl-long.213 | https://aclanthology.org/2022.acl-long.213.pdf | acl-2022-5 | ['propaganda-detection'] | ['natural-language-processing'] | [-1.59388974e-01 5.78230023e-01 -7.35489547e-01 -4.44919437e-01
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2b7d5786-c00d-443e-a98e-61bda59fe78b | attacut-a-fast-and-accurate-neural-thai-word | 1911.07056 | null | https://arxiv.org/abs/1911.07056v1 | https://arxiv.org/pdf/1911.07056v1.pdf | AttaCut: A Fast and Accurate Neural Thai Word Segmenter | Word segmentation is a fundamental pre-processing step for Thai Natural Language Processing. The current off-the-shelf solutions are not benchmarked consistently, so it is difficult to compare their trade-offs. We conducted a speed and accuracy comparison of the popular systems on three different domains and found that the state-of-the-art deep learning system is slow and moreover does not use sub-word structures to guide the model. Here, we propose a fast and accurate neural Thai Word Segmenter that uses dilated CNN filters to capture the environment of each character and uses syllable embeddings as features. Our system runs at least 5.6x faster and outperforms the previous state-of-the-art system on some domains. In addition, we develop the first ML-based Thai orthographical syllable segmenter, which yields syllable embeddings to be used as features by the word segmenter. | ['Pattarawat Chormai', 'Attapol Rutherford', 'Ponrawee Prasertsom'] | 2019-11-16 | null | null | null | null | ['thai-word-tokenization'] | ['natural-language-processing'] | [-1.31607249e-01 -1.76094338e-01 -2.34310940e-01 -3.51283789e-01
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956eef06-ec31-4cf6-a2e5-7c31db6e78e8 | differentially-private-video-activity | 2306.15742 | null | https://arxiv.org/abs/2306.15742v1 | https://arxiv.org/pdf/2306.15742v1.pdf | Differentially Private Video Activity Recognition | In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored. This paper addresses the challenges of applying differential privacy to video activity recognition, which primarily stem from: (1) a discrepancy between the desired privacy level for entire videos and the nature of input data processed by contemporary video architectures, which are typically short, segmented clips; and (2) the complexity and sheer size of video datasets relative to those in image classification, which render traditional differential privacy methods inadequate. To tackle these issues, we propose Multi-Clip DP-SGD, a novel framework for enforcing video-level differential privacy through clip-based classification models. This method samples multiple clips from each video, averages their gradients, and applies gradient clipping in DP-SGD without incurring additional privacy loss. Moreover, we incorporate a parameter-efficient transfer learning strategy to make the model scalable for large-scale video datasets. Through extensive evaluations on the UCF-101 and HMDB-51 datasets, our approach exhibits impressive performance, achieving 81% accuracy with a privacy budget of epsilon=5 on UCF-101, marking a 76% improvement compared to a direct application of DP-SGD. Furthermore, we demonstrate that our transfer learning strategy is versatile and can enhance differentially private image classification across an array of datasets including CheXpert, ImageNet, CIFAR-10, and CIFAR-100. | ['Animashree Anandkumar', 'Li Fei-Fei', 'Chaowei Xiao', 'Zhiding Yu', 'De-An Huang', 'Zane Durante', 'Yijin Yang', 'Yuliang Zou', 'Zelun Luo'] | 2023-06-27 | null | null | null | null | ['activity-recognition', 'classification-1', 'transfer-learning'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 4.19890314e-01 -4.83393997e-01 -4.49185133e-01 -2.89081961e-01
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07ec02cf-f804-430a-a000-8b78cca2f765 | robust-superpixel-guided-attentional | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Dong_Robust_Superpixel-Guided_Attentional_Adversarial_Attack_CVPR_2020_paper.pdf | Robust Superpixel-Guided Attentional Adversarial Attack | Deep Neural Networks are vulnerable to adversarial samples, which can fool classifiers by adding small perturbations onto the original image. Since the pioneering optimization-based adversarial attack method, many following methods have been proposed in the past several years. However most of these methods add perturbations in a "pixel-wise" and "global" way. Firstly, because of the contradiction between the local smoothness of natural images and the noisy property of these adversarial perturbations, this "pixel-wise" way makes these methods not robust to image processing based defense methods and steganalysis based detection methods. Secondly, we find adding perturbations to the background is less useful than to the salient object, thus the "global" way is also not optimal. Based on these two considerations, we propose the first robust superpixel-guided attentional adversarial attack method. Specifically, the adversarial perturbations are only added to the salient regions and guaranteed to be same within each superpixel. Through extensive experiments, we demonstrate our method can preserve the attack ability even in this highly constrained modification space. More importantly, compared to existing methods, it is significantly more robust to image processing based defense and steganalysis based detection.
| [' Nenghai Yu', ' Weiming Zhang', ' Xiaogang Wang', ' Hongsheng Li', ' Zehua Ma', ' Huanyu Bian', ' Jiayang Liu', ' Dongdong Chen', ' Jiangfan Han', 'Xiaoyi Dong'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['steganalysis'] | ['computer-vision'] | [ 6.22748971e-01 1.13130165e-02 1.66334748e-01 6.71691746e-02
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a148de49-6ea5-4d4d-ab61-79165acdadad | aria-digital-twin-a-new-benchmark-dataset-for | 2306.06362 | null | https://arxiv.org/abs/2306.06362v2 | https://arxiv.org/pdf/2306.06362v2.pdf | Aria Digital Twin: A New Benchmark Dataset for Egocentric 3D Machine Perception | We introduce the Aria Digital Twin (ADT) - an egocentric dataset captured using Aria glasses with extensive object, environment, and human level ground truth. This ADT release contains 200 sequences of real-world activities conducted by Aria wearers in two real indoor scenes with 398 object instances (324 stationary and 74 dynamic). Each sequence consists of: a) raw data of two monochrome camera streams, one RGB camera stream, two IMU streams; b) complete sensor calibration; c) ground truth data including continuous 6-degree-of-freedom (6DoF) poses of the Aria devices, object 6DoF poses, 3D eye gaze vectors, 3D human poses, 2D image segmentations, image depth maps; and d) photo-realistic synthetic renderings. To the best of our knowledge, there is no existing egocentric dataset with a level of accuracy, photo-realism and comprehensiveness comparable to ADT. By contributing ADT to the research community, our mission is to set a new standard for evaluation in the egocentric machine perception domain, which includes very challenging research problems such as 3D object detection and tracking, scene reconstruction and understanding, sim-to-real learning, human pose prediction - while also inspiring new machine perception tasks for augmented reality (AR) applications. To kick start exploration of the ADT research use cases, we evaluated several existing state-of-the-art methods for object detection, segmentation and image translation tasks that demonstrate the usefulness of ADT as a benchmarking dataset. | ['Carl Yuheng Ren', 'Richard Newcombe', 'Omkar Parkhi', 'Chen Kong', 'Thomas Whelan', 'Scott Peters', 'Yongqian Yang', 'Nicholas Charron', 'Xiaqing Pan'] | 2023-06-10 | null | null | null | null | ['pose-prediction', '3d-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.86651194e-01 -2.84122285e-02 1.95305631e-01 -2.75213510e-01
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af3193d9-9809-4783-911f-080fd89b362c | learning-personalized-end-to-end-goal | 1811.04604 | null | http://arxiv.org/abs/1811.04604v1 | http://arxiv.org/pdf/1811.04604v1.pdf | Learning Personalized End-to-End Goal-Oriented Dialog | Most existing works on dialog systems only consider conversation content
while neglecting the personality of the user the bot is interacting with, which
begets several unsolved issues. In this paper, we present a personalized
end-to-end model in an attempt to leverage personalization in goal-oriented
dialogs. We first introduce a Profile Model which encodes user profiles into
distributed embeddings and refers to conversation history from other similar
users. Then a Preference Model captures user preferences over knowledge base
entities to handle the ambiguity in user requests. The two models are combined
into the Personalized MemN2N. Experiments show that the proposed model achieves
qualitative performance improvements over state-of-the-art methods. As for
human evaluation, it also outperforms other approaches in terms of task
completion rate and user satisfaction. | ['Xu sun', 'Qi Zeng', 'Liangchen Luo', 'Zaiqing Nie', 'Wenhao Huang'] | 2018-11-12 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-3.49701911e-01 3.00894529e-01 -2.05459088e-01 -6.02100670e-01
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f545df17-5fe6-4bbc-87f6-e97b1a816d29 | unsupervised-representation-learning-in | 2303.07437 | null | https://arxiv.org/abs/2303.07437v1 | https://arxiv.org/pdf/2303.07437v1.pdf | Unsupervised Representation Learning in Partially Observable Atari Games | State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation learning research. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused on using masks as an augmentation technique to represent the latent generative factors better. Partially observable environments in reinforcement learning have not yet been carefully studied using unsupervised state representation learning methods. In this article, we create an unsupervised state representation learning scheme for partially observable states. We conducted our experiment on a previous Atari 2600 framework designed to evaluate representation learning models. A contrastive method called Spatiotemporal DeepInfomax (ST-DIM) has shown state-of-the-art performance on this benchmark but remains inferior to its supervised counterpart. Our approach improves ST-DIM when the environment is not fully observable and achieves higher F1 scores and accuracy scores than the supervised learning counterpart. The mean accuracy score averaged over categories of our approach is ~66%, compared to ~38% of supervised learning. The mean F1 score is ~64% to ~33%. | ['Paal Engelstad', 'Anis Yazidi', 'Morten Goodwin', 'Li Meng'] | 2023-03-13 | null | null | null | null | ['atari-games'] | ['playing-games'] | [ 3.03811491e-01 -7.65034929e-03 -4.85366851e-01 -2.45516986e-01
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49b25997-c432-4d6f-a8a0-373ab1dc5bef | text-to-audio-generation-using-instruction | 2304.13731 | null | https://arxiv.org/abs/2304.13731v2 | https://arxiv.org/pdf/2304.13731v2.pdf | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model | The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction- and chain-of-thought-based fine-tuning, that has significantly improved zero- and few-shot performance in many natural language processing (NLP) tasks. Inspired by such successes, we adopt such an instruction-tuned LLM Flan-T5 as the text encoder for text-to-audio (TTA) generation -- a task where the goal is to generate an audio from its textual description. The prior works on TTA either pre-trained a joint text-audio encoder or used a non-instruction-tuned model, such as, T5. Consequently, our latent diffusion model (LDM)-based approach TANGO outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63 times smaller dataset and keeping the text encoder frozen. This improvement might also be attributed to the adoption of audio pressure level-based sound mixing for training set augmentation, whereas the prior methods take a random mix. | ['Soujanya Poria', 'Ambuj Mehrish', 'Navonil Majumder', 'Deepanway Ghosal'] | 2023-04-24 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.12520434e-01 2.58913606e-01 -2.67962217e-01 -1.14726290e-01
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bae6d03b-76c2-4cff-a807-e8f7e9e2bdb1 | improved-inference-via-deep-input-transfer | 1904.02307 | null | https://arxiv.org/abs/1904.02307v4 | https://arxiv.org/pdf/1904.02307v4.pdf | Improved Inference via Deep Input Transfer | Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers. In this paper, we propose a new segmentation performance boosting paradigm that relies on optimally modifying the network's input instead of the network itself. In particular, we leverage the gradients of a trained segmentation network with respect to the input to transfer it to a space where the segmentation accuracy improves. We test the proposed method on three publicly available medical image segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores, respectively. | ['Saied Asgari Taghanaki', 'Kumar Abhishek', 'Ghassan Hamarneh'] | 2019-04-04 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 5.60881853e-01 7.93268085e-02 -3.01447153e-01 -4.83882129e-01
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4411e911-9691-425e-b69c-46476a61cb7d | dunhuang-murals-contour-generation-network | 2212.00935 | null | https://arxiv.org/abs/2212.00935v2 | https://arxiv.org/pdf/2212.00935v2.pdf | Dunhuang murals contour generation network based on convolution and self-attention fusion | Dunhuang murals are a collection of Chinese style and national style, forming a self-contained Chinese-style Buddhist art. It has very high historical and cultural value and research significance. Among them, the lines of Dunhuang murals are highly general and expressive. It reflects the character's distinctive character and complex inner emotions. Therefore, the outline drawing of murals is of great significance to the research of Dunhuang Culture. The contour generation of Dunhuang murals belongs to image edge detection, which is an important branch of computer vision, aims to extract salient contour information in images. Although convolution-based deep learning networks have achieved good results in image edge extraction by exploring the contextual and semantic features of images. However, with the enlargement of the receptive field, some local detail information is lost. This makes it impossible for them to generate reasonable outline drawings of murals. In this paper, we propose a novel edge detector based on self-attention combined with convolution to generate line drawings of Dunhuang murals. Compared with existing edge detection methods, firstly, a new residual self-attention and convolution mixed module (Ramix) is proposed to fuse local and global features in feature maps. Secondly, a novel densely connected backbone extraction network is designed to efficiently propagate rich edge feature information from shallow layers into deep layers. Compared with existing methods, it is shown on different public datasets that our method is able to generate sharper and richer edge maps. In addition, testing on the Dunhuang mural dataset shows that our method can achieve very competitive performance. | ['Jianhua Wang', 'Kaiwu Zhang', 'Shiqiang Du', 'Fengjie He', 'Baokai Liu'] | 2022-12-02 | null | null | null | null | ['edge-detection', 'culture'] | ['computer-vision', 'speech'] | [-1.10517658e-01 -1.99505106e-01 2.80855179e-01 3.83690465e-03
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1775ebe3-56a9-4aeb-9a2d-6733bb4d9672 | reweighted-low-rank-tensor-decomposition | 1611.05963 | null | http://arxiv.org/abs/1611.05963v4 | http://arxiv.org/pdf/1611.05963v4.pdf | Reweighted Low-Rank Tensor Decomposition based on t-SVD and its Applications in Video Denoising | The t-SVD based Tensor Robust Principal Component Analysis (TRPCA) decomposes
low rank multi-linear signal corrupted by gross errors into low multi-rank and
sparse component by simultaneously minimizing tensor nuclear norm and l 1 norm.
But if the multi-rank of the signal is considerably large and/or large amount
of noise is present, the performance of TRPCA deteriorates. To overcome this
problem, this paper proposes a new efficient iterative reweighted tensor
decomposition scheme based on t-SVD which significantly improves tensor
multi-rank in TRPCA. Further, the sparse component of the tensor is also
recovered by reweighted l 1 norm which enhances the accuracy of decomposition.
The effectiveness of the proposed method is established by applying it to the
video denoising problem and the experimental results reveal that the proposed
algorithm outperforms its counterparts. | ['Sudhish N. George', 'M. Baburaj'] | 2016-11-18 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [-1.30798981e-01 -6.22863591e-01 1.13110080e-01 2.64513433e-01
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a47e34aa-630b-4b85-aab8-3b76dc917ca8 | bayesian-calibration-of-mems-accelerometers | 2306.06144 | null | https://arxiv.org/abs/2306.06144v1 | https://arxiv.org/pdf/2306.06144v1.pdf | Bayesian Calibration of MEMS Accelerometers | This study aims to investigate the utilization of Bayesian techniques for the calibration of micro-electro-mechanical systems (MEMS) accelerometers. These devices have garnered substantial interest in various practical applications and typically require calibration through error-correcting functions. The parameters of these error-correcting functions are determined during a calibration process. However, due to various sources of noise, these parameters cannot be determined with precision, making it desirable to incorporate uncertainty in the calibration models. Bayesian modeling offers a natural and complete way of reflecting uncertainty by treating the model parameters as variables rather than fixed values. Additionally, Bayesian modeling enables the incorporation of prior knowledge, making it an ideal choice for calibration. Nevertheless, it is infrequently used in sensor calibration. This study introduces Bayesian methods for the calibration of MEMS accelerometer data in a straightforward manner using recent advances in probabilistic programming. | ['Zong-Xian Yin', 'Po-Yu Fan', 'Oliver Dürr'] | 2023-06-09 | null | null | null | null | ['probabilistic-programming'] | ['methodology'] | [ 2.11105168e-01 -3.49320531e-01 -2.21671656e-01 -8.08149695e-01
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9c5f5767-a41e-4075-bc72-11432e2edce3 | large-language-models-are-versatile | 2301.13808 | null | https://arxiv.org/abs/2301.13808v3 | https://arxiv.org/pdf/2301.13808v3.pdf | Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning | Table-based reasoning has shown remarkable progress in combining deep models with discrete reasoning, which requires reasoning over both free-form natural language (NL) questions and structured tabular data. However, previous table-based reasoning solutions usually suffer from significant performance degradation on huge evidence (tables). In addition, most existing methods struggle to reason over complex questions since the required information is scattered in different places. To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning. Specifically, we first use the LLMs to break down the evidence (tables) involved in the current question, retaining the relevant evidence and excluding the remaining irrelevant evidence from the huge table. In addition, we propose a "parsing-execution-filling" strategy to alleviate the hallucination dilemma of the chain of thought by decoupling logic and numerical computation in each step. Extensive experiments show that our method can effectively leverage decomposed evidence and questions and outperforms the strong baselines on TabFact, WikiTableQuestion, and FetaQA datasets. Notably, our model outperforms human performance for the first time on the TabFact dataset. | ['Yongbin Li', 'Fei Huang', 'Binhua Li', 'Min Yang', 'Binyuan Hui', 'Yunhu Ye'] | 2023-01-31 | null | null | null | null | ['semantic-parsing', 'table-based-fact-verification'] | ['natural-language-processing', 'natural-language-processing'] | [-2.20868275e-01 6.09471440e-01 -1.71685487e-01 -2.41096035e-01
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fb49c258-4635-4126-a393-676e27c059aa | resource-evaluation-for-usable-speech | null | null | https://aclanthology.org/L12-1583 | https://aclanthology.org/L12-1583.pdf | Resource Evaluation for Usable Speech Interfaces: Utilizing Human-Human Dialogue | Human-human spoken dialogues are considered an important tool for effective speech interface design and are often used for stochastic model training in speech based applications. However, the less restricted nature of human-human interaction compared to human-system interaction may undermine the usefulness of such corpora for creating effective and usable interfaces. In this respect, this work examines the differences between corpora collected from human-human interaction and corpora collected from actual system use, in order to formally assess the appropriateness of the former for both the design and implementation of spoken dialogue systems. Comparison results show that there are significant differences with respect to vocabulary, sentence structure and speech recognition success rate among others. Nevertheless, compared to other available tools and techniques, human-human dialogues may still be used as a temporary at least solution for building more effective working systems. Accordingly, ways to better utilize such resources are presented. | ['Dimitris Spiliotopoulos', 'Georgios Kouroupetroglou', 'Pepi Stavropoulou'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['dialogue-management'] | ['natural-language-processing'] | [ 6.92909881e-02 3.94374073e-01 1.72223687e-01 -5.48696935e-01
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9b95959c-65af-42e4-89b8-2a549b3c2282 | homonym-normalisation-by-word-sense | null | null | https://aclanthology.org/2020.coling-main.295 | https://aclanthology.org/2020.coling-main.295.pdf | Homonym normalisation by word sense clustering: a case in Japanese | This work presents a method of word sense clustering that differentiates homonyms and merge homophones, taking Japanese as an example, where orthographical variation causes problem for language processing. It uses contextualised embeddings (BERT) to cluster tokens into distinct sense groups, and we use these groups to normalise synonymous instances to a single representative form. We see the benefit of this normalisation in language model, as well as in transliteration. | ['Kevin Heffernan', 'Yo Sato'] | 2020-12-01 | null | null | null | coling-2020-8 | ['transliteration'] | ['natural-language-processing'] | [ 9.28027555e-03 -5.46193160e-02 -1.33679971e-01 -3.51678103e-01
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3669ca63-6327-4ebc-9e25-24a4fcb3140e | sound-demixing-challenge-2023-music-demixing | 2306.09382 | null | https://arxiv.org/abs/2306.09382v2 | https://arxiv.org/pdf/2306.09382v2.pdf | Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3 | In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023. First, we propose TFC-TDF-UNet v3, a time-efficient music source separation model that achieves state-of-the-art results on the MUSDB benchmark. We then give full details regarding our solutions for each Leaderboard, including a loss masking approach for noise-robust training. Code for reproducing model training and final submissions is available at github.com/kuielab/sdx23. | ['Soonyoung Jung', 'Jun Hyung Lee', 'Minseok Kim'] | 2023-06-15 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 5.15518673e-02 -6.77085876e-01 -1.07697986e-01 7.99297094e-02
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c48c4058-41d0-459d-ae50-51a88da74caa | powarematch-a-quality-aware-deep-learning | 2109.07321 | null | https://arxiv.org/abs/2109.07321v1 | https://arxiv.org/pdf/2109.07321v1.pdf | PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching | Schema matching is a core task of any data integration process. Being investigated in the fields of databases, AI, Semantic Web and data mining for many years, the main challenge remains the ability to generate quality matches among data concepts (e.g., database attributes). In this work, we examine a novel angle on the behavior of humans as matchers, studying match creation as a process. We analyze the dynamics of common evaluation measures (precision, recall, and f-measure), with respect to this angle and highlight the need for unbiased matching to support this analysis. Unbiased matching, a newly defined concept that describes the common assumption that human decisions represent reliable assessments of schemata correspondences, is, however, not an inherent property of human matchers. In what follows, we design PoWareMatch that makes use of a deep learning mechanism to calibrate and filter human matching decisions adhering the quality of a match, which are then combined with algorithmic matching to generate better match results. We provide an empirical evidence, established based on an experiment with more than 200 human matchers over common benchmarks, that PoWareMatch predicts well the benefit of extending the match with an additional correspondence and generates high quality matches. In addition, PoWareMatch outperforms state-of-the-art matching algorithms. | ['Avigdor Gal', 'Roee Shraga'] | 2021-09-15 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 7.66232833e-02 1.57671839e-01 -2.65660465e-01 -6.49432182e-01
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aaa534ee-2e7d-46b8-bdeb-d1facf353407 | semi-supervised-models-via-data | 2004.10972 | null | https://arxiv.org/abs/2004.10972v1 | https://arxiv.org/pdf/2004.10972v1.pdf | Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses | We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses. SMDA utilizes recent transformer-based models to encode each sentence and employs back translation techniques to paraphrase given sentences as augmented data. For labeled sentences, we performed data augmentations to uniform the label distributions and computed supervised loss during training process. For unlabeled sentences, we explored self-training by regarding low-entropy predictions over unlabeled sentences as pseudo labels, assuming high-confidence predictions as labeled data for training. We further introduced consistency regularization as unsupervised loss after data augmentations on unlabeled data, based on the assumption that the model should predict similar class distributions with original unlabeled sentences as input and augmented sentences as input. Via a set of experiments, we demonstrated that our system outperformed baseline models in terms of F1-score and accuracy. | ['Yuwei Wu', 'Jiaao Chen', 'Diyi Yang'] | 2020-04-23 | null | null | null | null | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 7.81562567e-01 9.02268827e-01 -3.52087766e-01 -1.06565523e+00
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fd25141e-35b1-4f4b-868e-68454c70663d | combining-deep-and-depth-deep-learning-and | 1812.05831 | null | http://arxiv.org/abs/1812.05831v1 | http://arxiv.org/pdf/1812.05831v1.pdf | Combining Deep and Depth: Deep Learning and Face Depth Maps for Driver Attention Monitoring | Recently, deep learning approaches have achieved promising results in various
fields of computer vision. In this paper, we investigate the combination of
deep learning based methods and depth maps as input images to tackle the
problem of driver attention monitoring. Moreover, we assume the concept of
attention as Head Pose Estimation and Facial Landmark Detection tasks.
Differently from other proposals in the literature, the proposed systems are
able to work directly and based only on raw depth data. All presented methods
are trained and tested on two new public datasets, namely Pandora and
MotorMark, achieving state-of-art results and running with real time
performance. | ['Guido Borghi'] | 2018-12-14 | null | null | null | null | ['head-pose-estimation', 'driver-attention-monitoring'] | ['computer-vision', 'computer-vision'] | [-1.83609858e-01 3.07486296e-01 -3.01717043e-01 -4.48978126e-01
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4b2b2a3b-1c65-4df3-aab4-c571ecdc8141 | action-tubelet-detector-for-spatio-temporal | 1705.01861 | null | http://arxiv.org/abs/1705.01861v3 | http://arxiv.org/pdf/1705.01861v3.pdf | Action Tubelet Detector for Spatio-Temporal Action Localization | Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level that are then linked or tracked across
time. In this paper, we leverage the temporal continuity of videos instead of
operating at the frame level. We propose the ACtion Tubelet detector
(ACT-detector) that takes as input a sequence of frames and outputs tubelets,
i.e., sequences of bounding boxes with associated scores. The same way
state-of-the-art object detectors rely on anchor boxes, our ACT-detector is
based on anchor cuboids. We build upon the SSD framework. Convolutional
features are extracted for each frame, while scores and regressions are based
on the temporal stacking of these features, thus exploiting information from a
sequence. Our experimental results show that leveraging sequences of frames
significantly improves detection performance over using individual frames. The
gain of our tubelet detector can be explained by both more accurate scores and
more precise localization. Our ACT-detector outperforms the state-of-the-art
methods for frame-mAP and video-mAP on the J-HMDB and UCF-101 datasets, in
particular at high overlap thresholds. | ['Vittorio Ferrari', 'Cordelia Schmid', 'Vicky Kalogeiton', 'Philippe Weinzaepfel'] | 2017-05-04 | action-tubelet-detector-for-spatio-temporal-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Kalogeiton_Action_Tubelet_Detector_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Kalogeiton_Action_Tubelet_Detector_ICCV_2017_paper.pdf | iccv-2017-10 | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 6.26028553e-02 -4.01668727e-01 -3.08435231e-01 -1.59791604e-01
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1be77f2c-2718-4ffc-aef3-da2a1e6535e8 | happy-or-grumpy-a-machine-learning-approach | 2209.14363 | null | https://arxiv.org/abs/2209.14363v1 | https://arxiv.org/pdf/2209.14363v1.pdf | Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets | As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. This study aims to measure customer satisfaction by analyzing sentiments of Tweets that mention airlines using a machine learning approach. Relevant Tweets are retrieved from Twitter's API and processed through tokenization and vectorization. After that, these processed vectors are passed into a pre-trained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also perform lexical analysis on the collected Tweets to model keywords' frequencies, which provide meaningful contexts to facilitate the interpretation of sentiments. We then apply time series methods such as Bollinger Bands to detect abnormalities in sentiment data. Using historical records from January to July 2022, our approach is proven to be capable of capturing sudden and significant changes in passengers' sentiment. This study has the potential to be developed into an application that can help airlines, along with several other customer-facing businesses, efficiently detect abrupt changes in customers' sentiments and take adequate measures to counteract them. | ['Yi Gao', 'Shengyang Wu'] | 2022-09-28 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [-3.63481976e-02 -4.02426898e-01 -5.09005964e-01 -5.44891000e-01
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e8339194-e886-439d-ae7f-a1e136c9c866 | active-fire-detection-in-landsat-8-imagery-a | 2101.03409 | null | https://arxiv.org/abs/2101.03409v2 | https://arxiv.org/pdf/2101.03409v2.pdf | Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study | Active fire detection in satellite imagery is of critical importance to the management of environmental conservation policies, supporting decision-making and law enforcement. This is a well established field, with many techniques being proposed over the years, usually based on pixel or region-level comparisons involving sensor-specific thresholds and neighborhood statistics. In this paper, we address the problem of active fire detection using deep learning techniques. In recent years, deep learning techniques have been enjoying an enormous success in many fields, but their use for active fire detection is relatively new, with open questions and demand for datasets and architectures for evaluation. This paper addresses these issues by introducing a new large-scale dataset for active fire detection, with over 150,000 image patches (more than 200 GB of data) extracted from Landsat-8 images captured around the world in August and September 2020, containing wildfires in several locations. The dataset was split in two parts, and contains 10-band spectral images with associated outputs, produced by three well known handcrafted algorithms for active fire detection in the first part, and manually annotated masks in the second part. We also present a study on how different convolutional neural network architectures can be used to approximate these handcrafted algorithms, and how models trained on automatically segmented patches can be combined to achieve better performance than the original algorithms - with the best combination having 87.2% precision and 92.4% recall on our manually annotated dataset. The proposed dataset, source codes and trained models are available on Github (https://github.com/pereira-gha/activefire), creating opportunities for further advances in the field | ['Rodrigo Minetto', 'Bogdan Tomoyuki Nassu', 'André Minoro Fusioka', 'Gabriel Henrique de Almeida Pereira'] | 2021-01-09 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 5.83723545e-01 -2.02536210e-01 -2.68140972e-01 -2.38166451e-01
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99d57b9e-793b-4003-9694-ce27c012d0f9 | medical-image-retrieval-via-nearest-neighbor | 2210.02401 | null | https://arxiv.org/abs/2210.02401v1 | https://arxiv.org/pdf/2210.02401v1.pdf | Medical Image Retrieval via Nearest Neighbor Search on Pre-trained Image Features | Nearest neighbor search (NNS) aims to locate the points in high-dimensional space that is closest to the query point. The brute-force approach for finding the nearest neighbor becomes computationally infeasible when the number of points is large. The NNS has multiple applications in medicine, such as searching large medical imaging databases, disease classification, diagnosis, etc. With a focus on medical imaging, this paper proposes DenseLinkSearch an effective and efficient algorithm that searches and retrieves the relevant images from heterogeneous sources of medical images. Towards this, given a medical database, the proposed algorithm builds the index that consists of pre-computed links of each point in the database. The search algorithm utilizes the index to efficiently traverse the database in search of the nearest neighbor. We extensively tested the proposed NNS approach and compared the performance with state-of-the-art NNS approaches on benchmark datasets and our created medical image datasets. The proposed approach outperformed the existing approach in terms of retrieving accurate neighbors and retrieval speed. We also explore the role of medical image feature representation in content-based medical image retrieval tasks. We propose a Transformer-based feature representation technique that outperformed the existing pre-trained Transformer approach on CLEF 2011 medical image retrieval task. The source code of our experiments are available at https://github.com/deepaknlp/DLS. | ['Dina Demner-Fushman', 'Soumya Gayen', 'Russell Loane', 'Deepak Gupta'] | 2022-10-05 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 2.22296566e-01 -3.76760900e-01 -4.20670182e-01 -2.93033600e-01
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645c23f7-f21c-42c9-b59c-fd96c526f264 | explanatory-machine-learning-for-sequential | 2205.10250 | null | https://arxiv.org/abs/2205.10250v2 | https://arxiv.org/pdf/2205.10250v2.pdf | Explanatory machine learning for sequential human teaching | The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance. | ['Ute Schmid', 'Stephen H. Muggleton', 'Johannes Langer', 'Lun Ai'] | 2022-05-20 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 4.22459632e-01 4.77209419e-01 -1.39645755e-01 -4.10413146e-01
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7a2e25c5-4335-442b-9630-def60a8566c1 | learning-latent-graph-dynamics-for-deformable | 2104.12149 | null | https://arxiv.org/abs/2104.12149v2 | https://arxiv.org/pdf/2104.12149v2.pdf | Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects | Manipulating deformable objects, such as ropes and clothing, is a long-standing challenge in robotics, because of their large degrees of freedom, complex non-linear dynamics, and self-occlusion in visual perception. The key difficulty is a suitable representation, rich enough to capture the object shape, dynamics for manipulation and yet simple enough to be estimated reliably from visual observations. This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (G-DOOM). G-DOOM approximates a deformable object as a sparse set of interacting keypoints, which are extracted automatically from images via unsupervised learning. It learns a graph neural network that captures abstractly the geometry and the interaction dynamics of the keypoints. To handle object self-occlusion, G-DOOM uses a recurrent neural network to track the keypoints over time and condition their interactions on the history. We then train the resulting recurrent graph dynamics model through contrastive learning in a high-fidelity simulator. For manipulation planning, G-DOOM reasons explicitly about the learned dynamics model through model-predictive control applied at each keypoint. Preliminary experiments of G-DOOM on a set of challenging rope and cloth manipulation tasks indicate strong performance, compared with state-of-the-art methods. Although trained in a simulator, G-DOOM transfers directly to a real robot for both rope and cloth manipulation. | ['Wee Sun Lee', 'David Hsu', 'Xiao Ma'] | 2021-04-25 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-9.47762206e-02 2.05855310e-01 -1.54402167e-01 1.15432441e-01
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6e59c653-d3e6-4df4-8301-10f8a7b4d744 | multi-view-harmonized-bilinear-network-for-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yu_Multi-View_Harmonized_Bilinear_CVPR_2018_paper.pdf | Multi-View Harmonized Bilinear Network for 3D Object Recognition | View-based methods have achieved considerable success in $3$D object recognition tasks. Different from existing view-based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilinear pooling, we obtain an effective $3$D object representation by aggregating local convolutional features through bilinear pooling. Meanwhile, we harmonize different components inherited in the pooled bilinear feature to obtain a more discriminative representation for a $3$D object. To achieve an end-to-end trainable framework, we incorporate the harmonized bilinear pooling operation as a layer of a network, constituting the proposed Multi-view Harmonized Bilinear Network (MHBN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed methods in $3$D object recognition. | ['Tan Yu', 'Jingjing Meng', 'Junsong Yuan'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-object-recognition'] | ['computer-vision'] | [-8.51140171e-02 -4.97046679e-01 1.36764133e-02 -7.09732771e-01
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5fbd55ac-34f8-485a-bb5a-1177ca626142 | native-language-identification-a-simple-n | null | null | https://aclanthology.org/W13-1729 | https://aclanthology.org/W13-1729.pdf | Native Language Identification: a Simple n-gram Based Approach | null | ['Binod Gyawali', 'Thamar Solorio', 'Gabriela Ramirez'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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05d5baca-fbca-4849-a063-790a074834b8 | deep-hyperspectral-unmixing-using-transformer | 2203.17076 | null | https://arxiv.org/abs/2203.17076v1 | https://arxiv.org/pdf/2203.17076v1.pdf | Deep Hyperspectral Unmixing using Transformer Network | Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep unmixing model with transformers. We aim to utilize the ability of transformers to better capture the global feature dependencies in order to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, i.e., Samson, Apex, and Washington DC mall and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at \url{https://github.com/preetam22n/DeepTrans-HSU}. | ['Paul Scheunders', 'Behnood Rasti', 'Bikram Koirala', 'Swalpa Kumar Roy', 'Preetam Ghosh'] | 2022-03-31 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.64996010e-01 -4.64496434e-01 2.10804403e-01 -2.01039404e-01
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cb914df9-6dad-4a35-a976-9946c99bc6db | relational-symmetry-based-knowledge-graph | 2211.10738 | null | https://arxiv.org/abs/2211.10738v4 | https://arxiv.org/pdf/2211.10738v4.pdf | Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure | Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive learning has been widely leveraged in graph learning as an effective mechanism to enhance the discriminative capacity of the learned representations. However, the complex structures of KG make it hard to construct appropriate contrastive pairs. Only a few attempts have integrated contrastive learning strategies with KGE. But, most of them rely on language models ( e.g., Bert) for contrastive pair construction instead of fully mining information underlying the graph structure, hindering expressive ability. Surprisingly, we find that the entities within a relational symmetrical structure are usually similar and correlated. To this end, we propose a knowledge graph contrastive learning framework based on relation-symmetrical structure, KGE-SymCL, which mines symmetrical structure information in KGs to enhance the discriminative ability of KGE models. Concretely, a plug-and-play approach is proposed by taking entities in the relation-symmetrical positions as positive pairs. Besides, a self-supervised alignment loss is designed to pull together positive pairs. Experimental results on link prediction and entity classification datasets demonstrate that our KGE-SymCL can be easily adopted to various KGE models for performance improvements. Moreover, extensive experiments show that our model could outperform other state-of-the-art baselines. | ['Xiangjun Dong', 'Xihong Yang', 'Yi Wen', 'Wenxuan Tu', 'Xinwang Liu', 'Sihang Zhou', 'Yue Liu', 'Ke Liang'] | 2022-11-19 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.00983912e-01 3.32961082e-01 -6.21382296e-01 -2.41907910e-01
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c76f7182-f8b4-4aa4-95d5-d8ef082dd35b | automatic-portrait-video-matting-via-context | 2109.04598 | null | https://arxiv.org/abs/2109.04598v2 | https://arxiv.org/pdf/2109.04598v2.pdf | Automatic Portrait Video Matting via Context Motion Network | Automatic portrait video matting is an under-constrained problem. Most state-of-the-art methods only exploit the semantic information and process each frame individually. Their performance is compromised due to the lack of temporal information between the frames. To solve this problem, we propose the context motion network to leverage semantic information and motion information. To capture the motion information, we estimate the optical flow and design a context-motion updating operator to integrate features between frames recurrently. Our experiments show that our network outperforms state-of-the-art matting methods significantly on the Video240K SD dataset. | ['Charlie Wang', 'Qiqi Hou'] | 2021-09-10 | automatic-portrait-video-matting-via-context-1 | https://openreview.net/forum?id=zNlkpFBT9aD | https://openreview.net/pdf?id=zNlkpFBT9aD | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 2.70411402e-01 -4.74707633e-01 -5.78920543e-01 -3.52862656e-01
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69929153-9e4b-43e0-b117-509f5f8bcc5d | nerd-neural-field-based-demosaicking | 2304.06566 | null | https://arxiv.org/abs/2304.06566v1 | https://arxiv.org/pdf/2304.06566v1.pdf | NeRD: Neural field-based Demosaicking | We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates and a low-resolution Bayer pattern, while the outputs are the corresponding RGB values. An encoder network, which is a blend of ResNet and U-net, enhances the implicit neural representation of the image to improve its quality and ensure spatial consistency through prior learning. Our experimental results demonstrate that NeRD outperforms traditional and state-of-the-art CNN-based methods and significantly closes the gap to transformer-based methods. | ['Jan Flusser', 'Adam Novozamsky', 'Filip Sroubek', 'Tomas Kerepecky'] | 2023-04-13 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 1.56751692e-01 -1.28747001e-01 3.01589876e-01 -2.45683149e-01
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94941563-eca6-4cf7-91ff-231e98f3eb43 | benchmarking-graph-neural-networks | 2003.00982 | null | https://arxiv.org/abs/2003.00982v5 | https://arxiv.org/pdf/2003.00982v5.pdf | Benchmarking Graph Neural Networks | In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting. | ['Xavier Bresson', 'Yoshua Bengio', 'Thomas Laurent', 'Anh Tuan Luu', 'Vijay Prakash Dwivedi', 'Chaitanya K. Joshi'] | 2020-03-02 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.33684853e-01 -3.64792608e-02 -2.98604310e-01 1.15997583e-01
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105765d8-85f2-4aff-b511-baad03789126 | self-supervised-learning-via-multi | 2102.10378 | null | https://arxiv.org/abs/2102.10378v1 | https://arxiv.org/pdf/2102.10378v1.pdf | Self-Supervised Learning via multi-Transformation Classification for Action Recognition | Self-supervised tasks have been utilized to build useful representations that can be used in downstream tasks when the annotation is unavailable. In this paper, we introduce a self-supervised video representation learning method based on the multi-transformation classification to efficiently classify human actions. Self-supervised learning on various transformations not only provides richer contextual information but also enables the visual representation more robust to the transforms. The spatio-temporal representation of the video is learned in a self-supervised manner by classifying seven different transformations i.e. rotation, clip inversion, permutation, split, join transformation, color switch, frame replacement, noise addition. First, seven different video transformations are applied to video clips. Then the 3D convolutional neural networks are utilized to extract features for clips and these features are processed to classify the pseudo-labels. We use the learned models in pretext tasks as the pre-trained models and fine-tune them to recognize human actions in the downstream task. We have conducted the experiments on UCF101 and HMDB51 datasets together with C3D and 3D Resnet-18 as backbone networks. The experimental results have shown that our proposed framework is outperformed other SOTA self-supervised action recognition approaches. The code will be made publicly available. | ['Jia-Ching Wang', 'Ngan T. H. Le', 'Duc Quang Vu'] | 2021-02-20 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.93348897e-01 -2.86669672e-01 -3.36007416e-01 -5.34041822e-01
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f661ed10-a7b4-492c-ba37-452913b5a5f4 | h3wb-human3-6m-3d-wholebody-dataset-and | 2211.15692 | null | https://arxiv.org/abs/2211.15692v1 | https://arxiv.org/pdf/2211.15692v1.pdf | H3WB: Human3.6M 3D WholeBody Dataset and Benchmark | 3D human whole-body pose estimation aims to localize precise 3D keypoints on the entire human body, including the face, hands, body, and feet. Due to the lack of a large-scale fully annotated 3D whole-body dataset, a common approach has been to train several deep networks separately on datasets dedicated to specific body parts, and combine them during inference. This approach suffers from complex training and inference pipelines because of the different biases in each dataset used. It also lacks a common benchmark which makes it difficult to compare different methods. To address these issues, we introduce Human3.6M 3D WholeBody (H3WB) which provides whole-body annotations for the Human3.6M dataset using the COCO Wholebody layout. H3WB is a large scale dataset with 133 whole-body keypoint annotations on 100K images, made possible by our new multi-view pipeline. Along with H3WB, we propose 3 tasks: i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, iii) 3D whole-body pose estimation from a single RGB image. We also report several baselines from popular methods for these tasks. The dataset is publicly available at \url{https://github.com/wholebody3d/wholebody3d}. | ['David Picard', 'Nermin Samet', 'Yue Zhu'] | 2022-11-28 | null | null | null | null | ['3d-hand-pose-estimation', '3d-human-pose-estimation', '3d-facial-landmark-localization', '3d-hand-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'graphs'] | [-4.04216886e-01 1.20263584e-01 -1.23691231e-01 -2.84344912e-01
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27dcf798-4548-423a-8c5f-668fa56322a8 | image-color-correction-enhancement-and | 2107.13117 | null | https://arxiv.org/abs/2107.13117v1 | https://arxiv.org/pdf/2107.13117v1.pdf | Image color correction, enhancement, and editing | This thesis presents methods and approaches to image color correction, color enhancement, and color editing. To begin, we study the color correction problem from the standpoint of the camera's image signal processor (ISP). A camera's ISP is hardware that applies a series of in-camera image processing and color manipulation steps, many of which are nonlinear in nature, to render the initial sensor image to its final photo-finished representation saved in the 8-bit standard RGB (sRGB) color space. As white balance (WB) is one of the major procedures applied by the ISP for color correction, this thesis presents two different methods for ISP white balancing. Afterward, we discuss another scenario of correcting and editing image colors, where we present a set of methods to correct and edit WB settings for images that have been improperly white-balanced by the ISP. Then, we explore another factor that has a significant impact on the quality of camera-rendered colors, in which we outline two different methods to correct exposure errors in camera-rendered images. Lastly, we discuss post-capture auto color editing and manipulation. In particular, we propose auto image recoloring methods to generate different realistic versions of the same camera-rendered image with new colors. Through extensive evaluations, we demonstrate that our methods provide superior solutions compared to existing alternatives targeting color correction, color enhancement, and color editing. | ['Mahmoud Afifi'] | 2021-07-28 | null | null | null | null | ['color-manipulation'] | ['computer-vision'] | [ 7.72606909e-01 -6.67720199e-01 5.16556561e-01 -1.45718306e-01
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6e5619d4-5b56-4d2c-975e-8c611c8241dd | multi-scale-patch-aggregation-mpa-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Liu_Multi-Scale_Patch_Aggregation_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Liu_Multi-Scale_Patch_Aggregation_CVPR_2016_paper.pdf | Multi-Scale Patch Aggregation (MPA) for Simultaneous Detection and Segmentation | Aiming at simultaneous detection and segmentation (SDS), we propose a proposal-free framework, which detect and segment object instances via mid-level patches. We design a unified trainable network on patches, which is followed by a fast and effective patch aggregation algorithm to infer object instances. Our method benefits from end-to-end training. Without object proposal generation, computation time can also be reduced. In experiments, our method yields results 62.1% and 61.8% in terms of mAPr on VOC2012 segmentation val and VOC2012 SDS val, which are state-of-the-art at the time of submission. We also report results on Microsoft COCO test-std/test-dev dataset in this paper. | ['Xiaojuan Qi', 'Shu Liu', 'Jiaya Jia', 'Hong Zhang', 'Jianping Shi'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['object-proposal-generation'] | ['computer-vision'] | [ 2.83313274e-01 1.91489339e-01 -6.24981001e-02 -3.45237672e-01
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7b97e850-a5f4-4eeb-9f58-fe35e34d60ab | forecasting-significant-stock-price-changes | 1912.08791 | null | https://arxiv.org/abs/1912.08791v1 | https://arxiv.org/pdf/1912.08791v1.pdf | Forecasting significant stock price changes using neural networks | Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multi-layer perceptron, convolutional net, and long short term memory net. As benchmark models we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change. | ['Firuz Kamalov'] | 2019-11-21 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-1.93845078e-01 -4.24326599e-01 -4.73889589e-01 -4.37717170e-01
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8038a9cb-ab41-4f85-969f-ef0bb1b537a9 | investigating-the-effect-of-hard-negative | 2305.10563 | null | https://arxiv.org/abs/2305.10563v1 | https://arxiv.org/pdf/2305.10563v1.pdf | Investigating the Effect of Hard Negative Sample Distribution on Contrastive Knowledge Graph Embedding | The success of the knowledge graph completion task heavily depends on the quality of the knowledge graph embeddings (KGEs), which relies on self-supervised learning and augmenting the dataset with negative triples. There is a gap in literature between the theoretical analysis of negative samples on contrastive loss and heuristic generation of quality (i.e., hard) negative triples. In this paper, we modify the InfoNCE loss to explicitly account for the negative sample distribution. We show minimizing InfoNCE loss with hard negatives maximizes the KL-divergence between the given and negative triple embedding. However, we also show that hard negatives can lead to false negatives (i.e., accidentally factual triples) and reduce downstream task performance. To address this issue, we propose a novel negative sample distribution that uses the graph structure of the knowledge graph to remove the false negative triples. We call our algorithm Hardness and Structure-aware (\textbf{HaSa}) contrastive KGE. Experiments show that our method outperforms state-of-the-art KGE methods in several metrics for WN18RR and FB15k-237 datasets. | ['June Zhang', 'Honggen Zhang'] | 2023-05-17 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-completion', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'knowledge-base', 'methodology'] | [ 1.22512132e-02 6.91037595e-01 -4.83148158e-01 -4.79545206e-01
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9e61e98b-e007-4949-8b3a-a33ae52aaae0 | deep-steiner-learning-to-solve-the-euclidean | 2209.09983 | null | https://arxiv.org/abs/2209.09983v1 | https://arxiv.org/pdf/2209.09983v1.pdf | Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem | The Euclidean Steiner tree problem seeks the min-cost network to connect a collection of target locations, and it underlies many applications of wireless networks. In this paper, we present a study on solving the Euclidean Steiner tree problem using reinforcement learning enhanced by graph representation learning. Different from the commonly studied connectivity problems like travelling salesman problem or vehicle routing problem where the search space is finite, the Euclidean Steiner tree problem requires to search over the entire Euclidean space, thereby making the existing methods not applicable. In this paper, we design discretization methods by leveraging the unique characteristics of the Steiner tree, and propose new training schemes for handling the dynamic Steiner points emerging during the incremental construction. Our design is examined through a sanity check using experiments on a collection of datasets, with encouraging results demonstrating the utility of our method as an alternative to classic combinatorial methods. | ['Guangmo Tong', 'Yifan Wang', 'Siqi Wang'] | 2022-09-20 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 1.97072327e-01 4.81573880e-01 -8.45734835e-01 -2.83583820e-01
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f6b36da1-7773-4ce3-9a13-c990fe1b23f5 | tractable-fully-bayesian-inference-via-convex | 1509.08582 | null | http://arxiv.org/abs/1509.08582v1 | http://arxiv.org/pdf/1509.08582v1.pdf | Tractable Fully Bayesian Inference via Convex Optimization and Optimal Transport Theory | We consider the problem of transforming samples from one continuous source
distribution into samples from another target distribution. We demonstrate with
optimal transport theory that when the source distribution can be easily
sampled from and the target distribution is log-concave, this can be tractably
solved with convex optimization. We show that a special case of this, when the
source is the prior and the target is the posterior, is Bayesian inference.
Here, we can tractably calculate the normalization constant and draw posterior
i.i.d. samples. Remarkably, our Bayesian tractability criterion is simply log
concavity of the prior and likelihood: the same criterion for tractable
calculation of the maximum a posteriori point estimate. With simulated data, we
demonstrate how we can attain the Bayes risk in simulations. With physiologic
data, we demonstrate improvements over point estimation in intensive care unit
outcome prediction and electroencephalography-based sleep staging. | ['Sanggyun Kim', 'Todd P. Coleman', 'Rui Ma', 'Diego Mesa'] | 2015-09-29 | null | null | null | null | ['sleep-staging'] | ['medical'] | [ 2.97830492e-01 3.80477220e-01 -2.08872303e-01 -3.52230877e-01
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910ce506-65c3-46bf-8778-4c7763e3321e | universal-mini-batch-consistency-for-set | 2208.12401 | null | https://arxiv.org/abs/2208.12401v5 | https://arxiv.org/pdf/2208.12401v5.pdf | Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient Approximation | Recent work on mini-batch consistency (MBC) for set functions has brought attention to the need for sequentially processing and aggregating chunks of a partitioned set while guaranteeing the same output for all partitions. However, existing constraints on MBC architectures lead to models with limited expressive power. Additionally, prior work has not addressed how to deal with large sets during training when the full set gradient is required. To address these issues, we propose a Universally MBC (UMBC) class of set functions which can be used in conjunction with arbitrary non-MBC components while still satisfying MBC, enabling a wider range of function classes to be used in MBC settings. Furthermore, we propose an efficient MBC training algorithm which gives an unbiased approximation of the full set gradient and has a constant memory overhead for any set size for both train- and test-time. We conduct extensive experiments including image completion, text classification, unsupervised clustering, and cancer detection on high-resolution images to verify the efficiency and efficacy of our scalable set encoding framework. Our code is available at github.com/jeffwillette/umbc | ['Sung Ju Hwang', 'Juho Lee', 'Kenji Kawaguchi', 'Bruno Andreis', 'Seanie Lee', 'Jeffrey Willette'] | 2022-08-26 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.63204834e-01 -2.87661195e-01 -1.00711226e-01 -7.89439261e-01
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71ca4d53-3999-4c83-8a3d-1974b9a1c0aa | mmnet-a-model-based-multimodal-network-for | null | null | https://ieeexplore.ieee.org/abstract/document/9782511 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9782511 | MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos | Human action recognition (HAR) in RGB-D videos has been widely investigated since the release of affordable depth
sensors. Currently, unimodal approaches (e.g., skeleton-based and RGB video-based) have realized substantial improvements with
increasingly larger datasets. However, multimodal methods specifically with model-level fusion have seldom been investigated. In this
paper, we propose a model-based multimodal network (MMNet) that fuses skeleton and RGB modalities via a model-based approach.
The objective of our method is to improve ensemble recognition accuracy by effectively applying mutually complementary information
from different data modalities. For the model-based fusion scheme, we use a spatiotemporal graph convolution network for the skeleton modality to learn attention weights that will be transferred to the network of the RGB modality. Extensive experiments are conducted on five benchmark datasets: NTU RGB+D 60, NTU RGB+D 120, PKU-MMD, Northwestern-UCLA Multiview, and Toyota Smarthome. Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR. We also tested our MMNet on an RGB video dataset Kinetics 400 that contains more outdoor actions, which shows consistent results with those of RGB-D video datasets. | ['Keith C.C. Chan', 'Sheng-hua Zhong', 'Xiang Zhang', 'Yan Liu', 'Bruce X.B. Yu'] | 2022-05-26 | null | null | null | ieee-transactions-on-pattern-analysis-and-21 | ['action-classification', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision'] | [ 1.75368816e-01 -5.37485540e-01 -1.16943784e-01 -2.39971891e-01
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acaebb9a-7663-4096-8df1-a85872616cf4 | qdwi-morph-motion-compensated-quantitative | 2208.09836 | null | https://arxiv.org/abs/2208.09836v1 | https://arxiv.org/pdf/2208.09836v1.pdf | qDWI-Morph: Motion-compensated quantitative Diffusion-Weighted MRI analysis for fetal lung maturity assessment | Quantitative analysis of fetal lung Diffusion-Weighted MRI (DWI) data shows potential in providing quantitative imaging biomarkers that indirectly reflect fetal lung maturation. However, fetal motion during the acquisition hampered quantitative analysis of the acquired DWI data and, consequently, reliable clinical utilization. We introduce qDWI-morph, an unsupervised deep-neural-network architecture for motion compensated quantitative DWI (qDWI) analysis. Our approach couples a registration sub-network with a quantitative DWI model fitting sub-network. We simultaneously estimate the qDWI parameters and the motion model by minimizing a bio-physically-informed loss function integrating a registration loss and a model fitting quality loss. We demonstrated the added-value of qDWI-morph over: 1) a baseline qDWI analysis without motion compensation and 2) a baseline deep-learning model incorporating registration loss solely. The qDWI-morph achieved a substantially improved correlation with the gestational age through in-vivo qDWI analysis of fetal lung DWI data (R-squared=0.32 vs. 0.13, 0.28). Our qDWI-morph has the potential to enable motion-compensated quantitative analysis of DWI data and to provide clinically feasible bio-markers for non-invasive fetal lung maturity assessment. Our code is available at: https://github.com/TechnionComputationalMRILab/qDWI-Morph. | ['Moti Freiman', 'Simon Warfield', 'Sila Kurugol', 'Onur Afacan', 'Yael Zaffrani-Reznikov'] | 2022-08-21 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [-2.41458900e-02 1.18045941e-01 -1.96082696e-01 -4.19608712e-01
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1e086924-930b-47c4-8926-9eedd4a7094a | review-on-6d-object-pose-estimation-with-the | 2212.01920 | null | https://arxiv.org/abs/2212.01920v1 | https://arxiv.org/pdf/2212.01920v1.pdf | Review on 6D Object Pose Estimation with the focus on Indoor Scene Understanding | 6D object pose estimation problem has been extensively studied in the field of Computer Vision and Robotics. It has wide range of applications such as robot manipulation, augmented reality, and 3D scene understanding. With the advent of Deep Learning, many breakthroughs have been made; however, approaches continue to struggle when they encounter unseen instances, new categories, or real-world challenges such as cluttered backgrounds and occlusions. In this study, we will explore the available methods based on input modality, problem formulation, and whether it is a category-level or instance-level approach. As a part of our discussion, we will focus on how 6D object pose estimation can be used for understanding 3D scenes. | ['Pooya Fayyazsanavi', 'Negar Nejatishahidin'] | 2022-12-04 | null | null | null | null | ['6d-pose-estimation', 'robot-manipulation'] | ['computer-vision', 'robots'] | [ 2.47869909e-01 -1.84493616e-01 -1.97634235e-01 -4.38152283e-01
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c6f527f9-e691-4a1a-83a8-1241c69ebd28 | characterizing-the-load-profile-in-power | 2304.07832 | null | https://arxiv.org/abs/2304.07832v1 | https://arxiv.org/pdf/2304.07832v1.pdf | Characterizing the load profile in power grids by Koopman mode decomposition of interconnected dynamics | Electricity load forecasting is crucial for effectively managing and optimizing power grids. Over the past few decades, various statistical and deep learning approaches have been used to develop load forecasting models. This paper presents an interpretable machine learning approach that identifies load dynamics using data-driven methods within an operator-theoretic framework. We represent the load data using the Koopman operator, which is inherent to the underlying dynamics. By computing the corresponding eigenfunctions, we decompose the load dynamics into coherent spatiotemporal patterns that are the most robust features of the dynamics. Each pattern evolves independently according to its single frequency, making its predictability based on linear dynamics. We emphasize that the load dynamics are constructed based on coherent spatiotemporal patterns that are intrinsic to the dynamics and are capable of encoding rich dynamical features at multiple time scales. These features are related to complex interactions over interconnected power grids and different exogenous effects. To implement the Koopman operator approach more efficiently, we cluster the load data using a modern kernel-based clustering approach and identify power stations with similar load patterns, particularly those with synchronized dynamics. We evaluate our approach using a large-scale dataset from a renewable electric power system within the continental European electricity system and show that the Koopman-based approach outperforms a deep learning (LSTM) architecture in terms of accuracy and computational efficiency. The code for this paper has been deposited in a GitHub repository, which can be accessed at the following address github.com/Shakeri-Lab/Power-Grids. | ['Heman Shakeri', 'Behnaz MoradiJamei', 'Ali Tavasoli'] | 2023-04-16 | null | null | null | null | ['interpretable-machine-learning', 'load-forecasting'] | ['methodology', 'miscellaneous'] | [-5.62939584e-01 -5.81471384e-01 2.68707369e-02 -1.06093191e-01
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4.20198828e-01 -4.07328129e-01 -6.12236440e-01 2.03179643e-01] | [6.133538246154785, 2.766176462173462] |
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