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2797246d-55e0-4e02-828d-5ef158fc484d | a-divide-and-conquer-method-for-scalable-low | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Pan_A_Divide-and-Conquer_Method_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Pan_A_Divide-and-Conquer_Method_2013_CVPR_paper.pdf | A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit | Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a recent proposed method that fuses multiple output score lists from different models via pursuing a shared low-rank latent matrix. Despite showing promising performance, the repeated full Singular Value Decomposition operations in RLF's optimization algorithm limits its scalability in real world vision datasets which usually have large number of test examples. To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method. The proposed method divides the original low-rank latent matrix learning problem into two sizereduced subproblems, which may be solved via any base algorithm, and combines the results from the subproblems to obtain the final solution. Our theoretical analysis shows that with fixed probability, the proposed divide-and-conquer method has recovery guarantees comparable to those of its base algorithm. Moreover, we develop an efficient base algorithm for the corresponding subproblems by factorizing a large matrix into the product of two size-reduced matrices. We also provide high probability recovery guarantees of the base algorithm. The proposed method is evaluated on various fusion problems in object categorization and video event detection. Under comparable accuracy, the proposed method performs more than 180 times faster than the stateof-the-art baselines on the CCV dataset with about 4,500 test examples for video event detection. | ['Yan Pan', 'Cong Liu', 'Shuicheng Yan', 'Hanjiang Lai'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['object-categorization'] | ['computer-vision'] | [ 3.27025414e-01 -4.66452330e-01 -1.11643828e-01 -2.25149557e-01
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96c099d7-ce45-44a8-a1a4-f5f63fa0feed | emogator-a-new-open-source-vocal-burst | 2301.00508 | null | https://arxiv.org/abs/2301.00508v2 | https://arxiv.org/pdf/2301.00508v2.pdf | EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies | Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of speech emotion recognition, but an important aspect of human vocal communication. One barrier to study of these interesting vocalizations is a lack of large datasets. I am pleased to introduce the EmoGator dataset, which consists of 32,130 samples from 357 speakers, 16.9654 hours of audio; each sample classified into one of 30 distinct emotion categories by the speaker. Several different approaches to construct classifiers to identify emotion categories will be discussed, and directions for future research will be suggested. Data set is available for download from https://github.com/fredbuhl/EmoGator. | ['Fred W. Buhl'] | 2023-01-02 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-4.95642185e-01 -1.54127479e-01 -7.11073205e-02 -5.76464653e-01
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48e36b92-6b67-4b49-968c-74d840b68c12 | fairr-faithful-and-robust-deductive-reasoning-1 | 2203.10261 | null | https://arxiv.org/abs/2203.10261v1 | https://arxiv.org/pdf/2203.10261v1.pdf | FaiRR: Faithful and Robust Deductive Reasoning over Natural Language | Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model's logical reasoning process. Currently, these black-box models generate both the proof graph and intermediate inferences within the same model and thus may be unfaithful. In this work, we frame the deductive logical reasoning task by defining three modular components: rule selection, fact selection, and knowledge composition. The rule and fact selection steps select the candidate rule and facts to be used and then the knowledge composition combines them to generate new inferences. This ensures model faithfulness by assured causal relation from the proof step to the inference reasoning. To test our framework, we propose FaiRR (Faithful and Robust Reasoner) where the above three components are independently modeled by transformers. We observe that FaiRR is robust to novel language perturbations, and is faster at inference than previous works on existing reasoning datasets. Additionally, in contrast to black-box generative models, the errors made by FaiRR are more interpretable due to the modular approach. | ['Xiang Ren', 'Harman Singh', 'Soumya Sanyal'] | 2022-03-19 | null | https://aclanthology.org/2022.acl-long.77 | https://aclanthology.org/2022.acl-long.77.pdf | acl-2022-5 | ['fact-selection'] | ['natural-language-processing'] | [ 3.01948518e-01 1.01384687e+00 -2.48268303e-02 -9.24963132e-02
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9c041c80-1bf4-467f-bdc2-5f94e166f0ad | prototypical-contrastive-learning-of | 2005.04966 | null | https://arxiv.org/abs/2005.04966v5 | https://arxiv.org/pdf/2005.04966v5.pdf | Prototypical Contrastive Learning of Unsupervised Representations | This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning. PCL not only learns low-level features for the task of instance discrimination, but more importantly, it implicitly encodes semantic structures of the data into the learned embedding space. Specifically, we introduce prototypes as latent variables to help find the maximum-likelihood estimation of the network parameters in an Expectation-Maximization framework. We iteratively perform E-step as finding the distribution of prototypes via clustering and M-step as optimizing the network via contrastive learning. We propose ProtoNCE loss, a generalized version of the InfoNCE loss for contrastive learning, which encourages representations to be closer to their assigned prototypes. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks with substantial improvement in low-resource transfer learning. Code and pretrained models are available at https://github.com/salesforce/PCL. | ['Steven C. H. Hoi', 'Pan Zhou', 'Junnan Li', 'Caiming Xiong'] | 2020-05-11 | null | https://openreview.net/forum?id=KmykpuSrjcq | https://openreview.net/pdf?id=KmykpuSrjcq | iclr-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.64820209e-01 1.83400542e-01 -7.07444072e-01 -5.58878958e-01
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d4c25d4b-e56a-42f2-9df6-5edde9f55815 | one-shot-key-information-extraction-from | 2109.13967 | null | https://arxiv.org/abs/2109.13967v1 | https://arxiv.org/pdf/2109.13967v1.pdf | One-shot Key Information Extraction from Document with Deep Partial Graph Matching | Automating the Key Information Extraction (KIE) from documents improves efficiency, productivity, and security in many industrial scenarios such as rapid indexing and archiving. Many existing supervised learning methods for the KIE task need to feed a large number of labeled samples and learn separate models for different types of documents. However, collecting and labeling a large dataset is time-consuming and is not a user-friendly requirement for many cloud platforms. To overcome these challenges, we propose a deep end-to-end trainable network for one-shot KIE using partial graph matching. Contrary to previous methods that the learning of similarity and solving are optimized separately, our method enables the learning of the two processes in an end-to-end framework. Existing one-shot KIE methods are either template or simple attention-based learning approach that struggle to handle texts that are shifted beyond their desired positions caused by printers, as illustrated in Fig.1. To solve this problem, we add one-to-(at most)-one constraint such that we will find the globally optimized solution even if some texts are drifted. Further, we design a multimodal context ensemble block to boost the performance through fusing features of spatial, textual, and aspect representations. To promote research of KIE, we collected and annotated a one-shot document KIE dataset named DKIE with diverse types of images. The DKIE dataset consists of 2.5K document images captured by mobile phones in natural scenes, and it is the largest available one-shot KIE dataset up to now. The results of experiments on DKIE show that our method achieved state-of-the-art performance compared with recent one-shot and supervised learning approaches. The dataset and proposed one-shot KIE model will be released soo | ['Liansheng Zhuang', 'Houqiang Li', 'Liangwei Wang', 'Zhiguang Liu', 'Minghong Yao'] | 2021-09-26 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 3.77035998e-02 -3.00674438e-01 -2.58930713e-01 -3.28578919e-01
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79c64198-e852-4db6-bc4e-ec67c68a0d09 | scaling-laws-for-discriminative-speech | 2306.15815 | null | https://arxiv.org/abs/2306.15815v1 | https://arxiv.org/pdf/2306.15815v1.pdf | Scaling Laws for Discriminative Speech Recognition Rescoring Models | Recent studies have found that model performance has a smooth power-law relationship, or scaling laws, with training data and model size, for a wide range of problems. These scaling laws allow one to choose nearly optimal data and model sizes. We study whether this scaling property is also applicable to second-pass rescoring, which is an important component of speech recognition systems. We focus on RescoreBERT as the rescoring model, which uses a pre-trained Transformer-based architecture fined tuned with an ASR discriminative loss. Using such a rescoring model, we show that the word error rate (WER) follows a scaling law for over two orders of magnitude as training data and model size increase. In addition, it is found that a pre-trained model would require less data than a randomly initialized model of the same size, representing effective data transferred from pre-training step. This effective data transferred is found to also follow a scaling law with the data and model size. | ['Ivan Bulyko', 'Ariya Rastrow', 'Ankur Gandhe', 'Jari Kolehmainen', 'Prashanth Gurunath Shivakumar', 'Yile Gu'] | 2023-06-27 | null | null | null | null | ['speech-recognition'] | ['speech'] | [ 4.06023830e-01 1.42203584e-01 -1.84382483e-01 -5.60989439e-01
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1b4da802-4165-4ece-b6a8-f54a1b41871b | differentiable-outlier-detection-enable | 2302.05608 | null | https://arxiv.org/abs/2302.05608v1 | https://arxiv.org/pdf/2302.05608v1.pdf | Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis | Often, deep network models are purely inductive during training and while performing inference on unseen data. Thus, when such models are used for predictions, it is well known that they often fail to capture the semantic information and implicit dependencies that exist among objects (or concepts) on a population level. Moreover, it is still unclear how domain or prior modal knowledge can be specified in a backpropagation friendly manner, especially in large-scale and noisy settings. In this work, we propose an end-to-end vision and language model incorporating explicit knowledge graphs. We also introduce an interactive out-of-distribution (OOD) layer using implicit network operator. The layer is used to filter noise that is brought by external knowledge base. In practice, we apply our model on several vision and language downstream tasks including visual question answering, visual reasoning, and image-text retrieval on different datasets. Our experiments show that it is possible to design models that perform similarly to state-of-art results but with significantly fewer samples and training time. | ['Sathya N. Ravi', 'Sourav Medya', 'Zhu Wang'] | 2023-02-11 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 1.30412459e-01 4.26098526e-01 -1.48451447e-01 -5.50199151e-01
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21d84e4d-3c2a-4043-958e-c3f32317cff4 | window-size-selection-in-unsupervised-time | null | null | https://doi.org/10.1007/978-3-031-24378-3_6 | https://link.springer.com/content/pdf/10.1007/978-3-031-24378-3_6.pdf | Window Size Selection in Unsupervised Time Series Analytics: A Review and Benchmark | Time series (TS) are sequences of values ordered in time. Such TS have in common, that important insights from the data can be drawn by inspecting local substructures, and not the recordings as a whole. ECG recordings, for instance, are characterized by normal or anomalous heartbeats that repeat themselves often within a longer TS. As such, many state-of-the-art time series data mining (TSDM) methods characterize TS by inspecting local substructures. The window size for extracting such subsequences is a crucial hyper-parameter, and setting an inappropriate value results in poor TSDM results. Finding the optimal window size has remained to be one of the most challenging tasks in TSDM domains, where no domain-agnostic method is known for learning the window size. We provide, for the first time, a systematic survey and experimental study of 6 TS window size selection (WSS) algorithms on three diverse TSDM tasks, namely anomaly detection, segmentation and motif discovery, using state-of-the art TSDM algorithms and benchmarks. We found that WSS methods are competitive with or even surpass human annotations, if an interesting or anomalous pattern can be attributed to (changes in) the period. That is because current WSS methods aim at finding the period length of data sets. This assumption is mostly true for segmentation or anomaly detection, by definition. In the case of motif discovery, however, the results were mixed. Motifs can be independent of a period, but repeat themselves unusually often. In this domain, WSS fails and more research is needed. | ['Ulf Leser', 'Patrick Schäfer', 'Arik Ermshaus'] | 2023-02-04 | null | null | null | advanced-analytics-and-learning-on-temporal | ['hyperparameter-optimization', 'change-point-detection', 'time-series-anomaly-detection'] | ['methodology', 'time-series', 'time-series'] | [ 5.26426315e-01 -2.59272218e-01 -2.39473522e-01 -1.92093655e-01
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1099fa05-af22-499f-96a0-14c72a8afd82 | yolo-ret-towards-high-accuracy-real-time | 2110.13713 | null | https://arxiv.org/abs/2110.13713v1 | https://arxiv.org/pdf/2110.13713v1.pdf | YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs | Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency. However, in order to map deep neural network (DNN) based object detection models to edge devices, one typically needs to compress such models significantly, thus compromising the model accuracy. In this paper, we propose a novel edge GPU friendly module for multi-scale feature interaction by exploiting missing combinatorial connections between various feature scales in existing state-of-the-art methods. Additionally, we propose a novel transfer learning backbone adoption inspired by the changing translational information flow across various tasks, designed to complement our feature interaction module and together improve both accuracy as well as execution speed on various edge GPU devices available in the market. For instance, YOLO-ReT with MobileNetV2x0.75 backbone runs real-time on Jetson Nano, and achieves 68.75 mAP on Pascal VOC and 34.91 mAP on COCO, beating its peers by 3.05 mAP and 0.91 mAP respectively, while executing faster by 3.05 FPS. Furthermore, introducing our multi-scale feature interaction module in YOLOv4-tiny and YOLOv4-tiny (3l) improves their performance to 41.5 and 48.1 mAP respectively on COCO, outperforming the original versions by 1.3 and 0.9 mAP. | ['Marianne Winslett', 'Deming Chen', 'Yin Yang', 'Yao Chen', 'Prakhar Ganesh'] | 2021-10-26 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.65015209e-01 -4.88329262e-01 2.00576186e-01 -1.09721839e-01
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53f0b60d-d897-4d49-8a1a-dd1febb7659f | application-of-densenet-in-camera-model | 1809.00576 | null | http://arxiv.org/abs/1809.00576v2 | http://arxiv.org/pdf/1809.00576v2.pdf | Application of DenseNet in Camera Model Identification and Post-processing Detection | Camera model identification has earned paramount importance in the field of
image forensics with an upsurge of digitally altered images which are
constantly being shared through websites, media, and social applications. But,
the task of identification becomes quite challenging if metadata are absent
from the image and/or if the image has been post-processed. In this paper, we
present a DenseNet pipeline to solve the problem of identifying the source
camera-model of an image. Our approach is to extract patches of 256*256 from a
labeled image dataset and apply augmentations, i.e., Empirical Mode
Decomposition (EMD). We use this extended dataset to train a Neural Network
with the DenseNet-201 architecture. We concatenate the output features for 3
different sizes (64*64, 128*128, 256*256) and pass them to a secondary network
to make the final prediction. This strategy proves to be very robust for
identifying the source camera model, even when the original image is
post-processed. Our model has been trained and tested on the Forensic
Camera-Model Identification Dataset provided for the IEEE Signal Processing
(SP) Cup 2018. During testing we achieved an overall accuracy of 98.37%, which
is the current state-of-the-art on this dataset using a single model. We used
transfer learning and tested our model on the Dresden Database for Camera Model
Identification, with an overall test accuracy of over 99% for 19 models. In
addition, we demonstrate that the proposed pipeline is suitable for other
image-forensic classification tasks, such as, detecting the type of
post-processing applied to an image with an accuracy of 96.66% -- which
indicates the generality of our approach. | [] | 2019-05-27 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.45662642e-01 -2.97727734e-01 2.41590127e-01 -7.60108083e-02
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3a3c80b8-ba3d-463c-8304-997a22b0e574 | causal-estimation-for-text-data-with-apparent | 2210.00079 | null | https://arxiv.org/abs/2210.00079v3 | https://arxiv.org/pdf/2210.00079v3.pdf | Causal Estimation for Text Data with (Apparent) Overlap Violations | Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time? To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and outcome -- e.g., the topic or writing level of the text. These confounding aspects are unknown a priori, so it seems natural to adjust for the entirety of the text (e.g., using a transformer). However, causal identification and estimation procedures rely on the assumption of overlap: for all levels of the adjustment variables, there is randomness leftover so that every unit could have (not) received treatment. Since the treatment here is itself an attribute of the text, it is perfectly determined, and overlap is apparently violated. The purpose of this paper is to show how to handle causal identification and obtain robust causal estimation in the presence of apparent overlap violations. In brief, the idea is to use supervised representation learning to produce a data representation that preserves confounding information while eliminating information that is only predictive of the treatment. This representation then suffices for adjustment and can satisfy overlap. Adapting results on non-parametric estimation, we find that this procedure is robust to conditional outcome misestimation, yielding a low-bias estimator with valid uncertainty quantification under weak conditions. Empirical results show strong improvements in bias and uncertainty quantification relative to the natural baseline. | ['Victor Veitch', 'Lin Gui'] | 2022-09-30 | null | null | null | null | ['causal-identification'] | ['reasoning'] | [ 6.43557489e-01 3.90710086e-01 -9.95774150e-01 -3.52078617e-01
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0f1427cb-de13-4b5a-bb4c-a39f82cfa3b1 | s2snet-a-pretrained-neural-network-for | 2306.1627 | null | https://arxiv.org/abs/2306.16270v1 | https://arxiv.org/pdf/2306.16270v1.pdf | S2SNet: A Pretrained Neural Network for Superconductivity Discovery | Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution to superconductivity research. Superconductors are valuable for sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, industry, innovation and infrastructure, and so on. However, a unified physics theory explaining all superconductivity mechanism is still unknown. It is believed that superconductivity is microscopically due to not only molecular compositions but also the geometric crystal structure. Hence a new dataset, S2S, containing both crystal structures and superconducting critical temperature, is built upon SuperCon and Material Project. Based on this new dataset, we propose a novel model, S2SNet, which utilizes the attention mechanism for superconductivity prediction. To overcome the shortage of data, S2SNet is pre-trained on the whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures. This work is beneficial to superconductivity discovery and further SDGs. Code and datasets are available in https://github.com/zjuKeLiu/S2SNet | ['Renjun Xu', 'Jiahong Zhang', 'Kaifan Yang', 'Ke Liu'] | 2023-06-28 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-1.76883250e-01 -4.85821128e-01 -4.91623610e-01 -3.32880646e-01
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799afdfa-5a4c-47b7-95b0-2b7210627803 | using-virtual-edges-to-extract-keywords-from | 2205.02172 | null | https://arxiv.org/abs/2205.02172v1 | https://arxiv.org/pdf/2205.02172v1.pdf | Using virtual edges to extract keywords from texts modeled as complex networks | Detecting keywords in texts is important for many text mining applications. Graph-based methods have been commonly used to automatically find the key concepts in texts, however, relevant information provided by embeddings has not been widely used to enrich the graph structure. Here we modeled texts co-occurrence networks, where nodes are words and edges are established either by contextual or semantical similarity. We compared two embedding approaches -- Word2vec and BERT -- to check whether edges created via word embeddings can improve the quality of the keyword extraction method. We found that, in fact, the use of virtual edges can improve the discriminability of co-occurrence networks. The best performance was obtained when we considered low percentages of addition of virtual (embedding) edges. A comparative analysis of structural and dynamical network metrics revealed the degree, PageRank, and accessibility are the metrics displaying the best performance in the model enriched with virtual edges. | ['Diego R. Amancio', 'Thiago C. Silva', 'Jorge A. V. Tohalino'] | 2022-05-04 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [-3.11163306e-01 2.99679458e-01 -2.87063599e-01 3.31642121e-01
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29650cd2-01b6-4a87-8368-5a277162042f | epilepsy-seizure-detection-anatomy-and | 2305.19347 | null | https://arxiv.org/abs/2305.19347v1 | https://arxiv.org/pdf/2305.19347v1.pdf | Epilepsy Seizure Detection: Anatomy and Analysis | A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many techniques and methods are proposed for seizure detection; However, simplicity and affordability are key concepts for daily use while preserving the accuracy of the detection. In this study, we propose a versal, affordable noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine learning that can be customized and adapted to individual users in less than four (4) seconds of training time; the system was verified and validated using 500 subjects, with seizure detection data sampled at 178 Hz, the operated with a mean accuracy of (94.5%). | ['Nelly Elsayed', 'Murat Ozer', 'Zag ElSayed'] | 2023-05-30 | null | null | null | null | ['seizure-detection', 'anatomy'] | ['medical', 'miscellaneous'] | [-2.00278573e-05 -1.78884163e-01 -2.78181612e-01 -4.98227298e-01
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09f8a8be-1bc4-4fcf-9cbe-1eaa32205367 | an-efficient-supervised-dictionary-learning | 1812.04748 | null | http://arxiv.org/abs/1812.04748v1 | http://arxiv.org/pdf/1812.04748v1.pdf | An efficient supervised dictionary learning method for audio signal recognition | Machine hearing or listening represents an emerging area. Conventional
approaches rely on the design of handcrafted features specialized to a specific
audio task and that can hardly generalized to other audio fields. For example,
Mel-Frequency Cepstral Coefficients (MFCCs) and its variants were successfully
applied to computational auditory scene recognition while Chroma vectors are
good at music chord recognition. Unfortunately, these predefined features may
be of variable discrimination power while extended to other tasks or even
within the same task due to different nature of clips. Motivated by this need
of a principled framework across domain applications for machine listening, we
propose a generic and data-driven representation learning approach. For this
sake, a novel and efficient supervised dictionary learning method is presented.
The method learns dissimilar dictionaries, one per each class, in order to
extract heterogeneous information for classification. In other words, we are
seeking to minimize the intra-class homogeneity and maximize class
separability. This is made possible by promoting pairwise orthogonality between
class specific dictionaries and controlling the sparsity structure of the audio
clip's decomposition over these dictionaries. The resulting optimization
problem is non-convex and solved using a proximal gradient descent method.
Experiments are performed on both computational auditory scene (East Anglia and
Rouen) and synthetic music chord recognition datasets. Obtained results show
that our method is capable to reach state-of-the-art hand-crafted features for
both applications. | ['Romain Hérault', 'Gilles Gasso', 'Imad Rida'] | 2018-12-12 | null | null | null | null | ['chord-recognition', 'audio-signal-recognition'] | ['audio', 'audio'] | [ 3.49979073e-01 -3.96337897e-01 8.76463111e-03 -1.93070382e-01
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64e41451-c6ec-4a77-83ec-00e639268a31 | enhancing-transformer-backbone-for-egocentric | 2305.11365 | null | https://arxiv.org/abs/2305.11365v2 | https://arxiv.org/pdf/2305.11365v2.pdf | Enhancing Transformer Backbone for Egocentric Video Action Segmentation | Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation models. Therefore, it is necessary to improve transformers to enhance the robustness of action segmentation models. In this work, we propose two novel ideas to enhance the state-of-the-art transformer for action segmentation. First, we introduce a dual dilated attention mechanism to adaptively capture hierarchical representations in both local-to-global and global-to-local contexts. Second, we incorporate cross-connections between the encoder and decoder blocks to prevent the loss of local context by the decoder. We also utilize state-of-the-art visual-language representation learning techniques to extract richer and more compact features for our transformer. Our proposed approach outperforms other state-of-the-art methods on the Georgia Tech Egocentric Activities (GTEA) and HOI4D Office Tools datasets, and we validate our introduced components with ablation studies. The source code and supplementary materials are publicly available on https://www.sail-nu.com/dxformer. | ['Octavia Camps', 'Mohsen Moghaddam', 'Balaji Sundareshan', 'Sakib Reza'] | 2023-05-19 | null | null | null | null | ['mixed-reality', 'action-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.35783657e-01 -2.47402536e-03 -4.32599485e-01 -3.15310180e-01
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26c73b58-966e-4ef8-8ac9-c6677708e9b2 | houghlanenet-lane-detection-with-deep-hough | 2307.03494 | null | https://arxiv.org/abs/2307.03494v1 | https://arxiv.org/pdf/2307.03494v1.pdf | HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic Convolution | The task of lane detection has garnered considerable attention in the field of autonomous driving due to its complexity. Lanes can present difficulties for detection, as they can be narrow, fragmented, and often obscured by heavy traffic. However, it has been observed that the lanes have a geometrical structure that resembles a straight line, leading to improved lane detection results when utilizing this characteristic. To address this challenge, we propose a hierarchical Deep Hough Transform (DHT) approach that combines all lane features in an image into the Hough parameter space. Additionally, we refine the point selection method and incorporate a Dynamic Convolution Module to effectively differentiate between lanes in the original image. Our network architecture comprises a backbone network, either a ResNet or Pyramid Vision Transformer, a Feature Pyramid Network as the neck to extract multi-scale features, and a hierarchical DHT-based feature aggregation head to accurately segment each lane. By utilizing the lane features in the Hough parameter space, the network learns dynamic convolution kernel parameters corresponding to each lane, allowing the Dynamic Convolution Module to effectively differentiate between lane features. Subsequently, the lane features are fed into the feature decoder, which predicts the final position of the lane. Our proposed network structure demonstrates improved performance in detecting heavily occluded or worn lane images, as evidenced by our extensive experimental results, which show that our method outperforms or is on par with state-of-the-art techniques. | ['Miao Wang', 'Ariel Shamir', 'Jun-Long Chen', 'Hao-Bin Duan', 'Jia-Qi Zhang'] | 2023-07-07 | null | null | null | null | ['autonomous-driving', 'lane-detection'] | ['computer-vision', 'computer-vision'] | [ 1.56625807e-02 -1.48493096e-01 5.78365289e-03 -4.81405884e-01
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f40f2337-ac35-446c-82ec-e8159b86ab7f | robust-reconstruction-of-indoor-scenes | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Choi_Robust_Reconstruction_of_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Choi_Robust_Reconstruction_of_2015_CVPR_paper.pdf | Robust Reconstruction of Indoor Scenes | We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to disambiguate different surfaces in the scene. The presented optimization approach disables erroneous geometric alignments even when they significantly outnumber correct ones. Experimental results demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models. | ['Qian-Yi Zhou', 'Vladlen Koltun', 'Sungjoon Choi'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 5.24153888e-01 -2.33957022e-01 7.24292815e-01 -3.70395631e-01
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872700b1-78f9-4663-ae91-c82bc4de915a | exploratory-analysis-of-news-sentiment-using | null | null | https://aclanthology.org/2021.bsnlp-1.7 | https://aclanthology.org/2021.bsnlp-1.7.pdf | Exploratory Analysis of News Sentiment Using Subgroup Discovery | In this study, we present an exploratory analysis of a Slovenian news corpus, in which we investigate the association between named entities and sentiment in the news. We propose a methodology that combines Named Entity Recognition and Subgroup Discovery - a descriptive rule learning technique for identifying groups of examples that share the same class label (sentiment) and pattern (features - Named Entities). The approach is used to induce the positive and negative sentiment class rules that reveal interesting patterns related to different Slovenian and international politicians, organizations, and locations. | ['Senja Pollak', 'Elvys Linhares Pontes', 'Luis Adrián Cabrera-Diego', 'Anita Valmarska'] | null | null | null | null | eacl-bsnlp-2021-4 | ['subgroup-discovery'] | ['methodology'] | [-2.84999043e-01 3.54371458e-01 -7.93549299e-01 -7.19019771e-01
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dde2f0a4-0624-48c4-bed2-b269b439cc41 | on-interpretability-of-deep-learning-based | 2005.02 | null | https://arxiv.org/abs/2005.02000v1 | https://arxiv.org/pdf/2005.02000v1.pdf | On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors | Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods. | ['Stephan Alexander Braun', 'Sheraz Ahmed', 'Muhammad Naseer Bajwa', 'Adriano Lucieri', 'Muhammad Imran Malik', 'Andreas Dengel'] | 2020-05-05 | null | null | null | null | ['network-interpretation'] | ['computer-vision'] | [ 5.66409588e-01 4.41961318e-01 -1.16946362e-01 -4.93873090e-01
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e9c81a02-e82f-495c-b6d7-b1f4cf748705 | differentiable-parsing-and-visual-grounding | 2210.00215 | null | https://arxiv.org/abs/2210.00215v4 | https://arxiv.org/pdf/2210.00215v4.pdf | Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement | We present a new method, PARsing And visual GrOuNding (ParaGon), for grounding natural language in object placement tasks. Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding. For compositionality, ParaGon parses a language instruction into an object-centric graph representation to ground objects individually. For ambiguity, ParaGon uses a novel particle-based graph neural network to reason about object placements with uncertainty. Essentially, ParaGon integrates a parsing algorithm into a probabilistic, data-driven learning framework. It is fully differentiable and trained end-to-end from data for robustness against complex, ambiguous language input. | ['David Hsu', 'Wee Sun Lee', 'Zirui Zhao'] | 2022-10-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-1.04707114e-01 5.57270706e-01 -3.22786927e-01 -5.03658175e-01
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c1e91d10-353b-4399-8714-80da68b19644 | acquiring-frame-element-knowledge-with-deep | 2305.13944 | null | https://arxiv.org/abs/2305.13944v1 | https://arxiv.org/pdf/2305.13944v1.pdf | Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction | The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and propose a method that applies deep metric learning. In this method, a pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles through the use of frame-annotated data, and argument clustering is performed with embeddings obtained from the fine-tuned model. Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods. | ['Koichi Takeda', 'Ryohei Sasano', 'Kosuke Yamada'] | 2023-05-23 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.04460114e-01 4.53379363e-01 -5.76336324e-01 -6.63626432e-01
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e92cf467-5724-4b41-9b59-ba92ccfe8bfc | xlm-v-overcoming-the-vocabulary-bottleneck-in | 2301.10472 | null | https://arxiv.org/abs/2301.10472v1 | https://arxiv.org/pdf/2301.10472v1.pdf | XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models | Large multilingual language models typically rely on a single vocabulary shared across 100+ languages. As these models have increased in parameter count and depth, vocabulary size has remained largely unchanged. This vocabulary bottleneck limits the representational capabilities of multilingual models like XLM-R. In this paper, we introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap and assigning vocabulary capacity to achieve sufficient coverage for each individual language. Tokenizations using our vocabulary are typically more semantically meaningful and shorter compared to XLM-R. Leveraging this improved vocabulary, we train XLM-V, a multilingual language model with a one million token vocabulary. XLM-V outperforms XLM-R on every task we tested on ranging from natural language inference (XNLI), question answering (MLQA, XQuAD, TyDiQA), and named entity recognition (WikiAnn) to low-resource tasks (Americas NLI, MasakhaNER). | ['Madian Khabsa', 'Luke Zettlemoyer', 'Marjan Ghazvininejad', 'Naman Goyal', 'Rui Hou', 'Yuning Mao', 'Hila Gonen', 'Davis Liang'] | 2023-01-25 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-6.66674256e-01 -7.00960457e-02 -6.00353658e-01 -1.92069903e-01
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b8d31620-4d75-4a90-b725-b6f712186aed | embodied-question-answering-in-photorealistic | 1904.03461 | null | http://arxiv.org/abs/1904.03461v1 | http://arxiv.org/pdf/1904.03461v1.pdf | Embodied Question Answering in Photorealistic Environments with Point Cloud Perception | To help bridge the gap between internet vision-style problems and the goal of
vision for embodied perception we instantiate a large-scale navigation task --
Embodied Question Answering [1] in photo-realistic environments (Matterport
3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB
images, or their combination. Our analysis of these models reveals several key
findings. We find that two seemingly naive navigation baselines, forward-only
and random, are strong navigators and challenging to outperform, due to the
specific choice of the evaluation setting presented by [1]. We find a novel
loss-weighting scheme we call Inflection Weighting to be important when
training recurrent models for navigation with behavior cloning and are able to
out perform the baselines with this technique. We find that point clouds
provide a richer signal than RGB images for learning obstacle avoidance,
motivating the use (and continued study) of 3D deep learning models for
embodied navigation. | ['Georgia Gkioxari', 'Erik Wijmans', 'Stefan Lee', 'Samyak Datta', 'Oleksandr Maksymets', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Abhishek Das'] | 2019-04-06 | embodied-question-answering-in-photorealistic-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wijmans_Embodied_Question_Answering_in_Photorealistic_Environments_With_Point_Cloud_Perception_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wijmans_Embodied_Question_Answering_in_Photorealistic_Environments_With_Point_Cloud_Perception_CVPR_2019_paper.pdf | cvpr-2019-6 | ['embodied-question-answering'] | ['computer-vision'] | [-1.83076840e-02 1.42736912e-01 2.95712799e-01 -2.11070135e-01
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7428cf83-543d-46fb-8692-7a75e1df7925 | generative-colorization-of-structured-mobile | 2212.11541 | null | https://arxiv.org/abs/2212.11541v2 | https://arxiv.org/pdf/2212.11541v2.pdf | Generative Colorization of Structured Mobile Web Pages | Color is a critical design factor for web pages, affecting important factors such as viewer emotions and the overall trust and satisfaction of a website. Effective coloring requires design knowledge and expertise, but if this process could be automated through data-driven modeling, efficient exploration and alternative workflows would be possible. However, this direction remains underexplored due to the lack of a formalization of the web page colorization problem, datasets, and evaluation protocols. In this work, we propose a new dataset consisting of e-commerce mobile web pages in a tractable format, which are created by simplifying the pages and extracting canonical color styles with a common web browser. The web page colorization problem is then formalized as a task of estimating plausible color styles for a given web page content with a given hierarchical structure of the elements. We present several Transformer-based methods that are adapted to this task by prepending structural message passing to capture hierarchical relationships between elements. Experimental results, including a quantitative evaluation designed for this task, demonstrate the advantages of our methods over statistical and image colorization methods. The code is available at https://github.com/CyberAgentAILab/webcolor. | ['Kota Yamaguchi', 'Edgar Simo-Serra', 'Mayu Otani', 'Naoto Inoue', 'Kotaro Kikuchi'] | 2022-12-22 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.33135927e-02 -6.08979054e-02 5.09033538e-02 -3.84322673e-01
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1e688178-10cc-42a8-8949-866d10db6ffb | espnet-st-all-in-one-speech-translation | 2004.10234 | null | https://arxiv.org/abs/2004.10234v2 | https://arxiv.org/pdf/2004.10234v2.pdf | ESPnet-ST: All-in-One Speech Translation Toolkit | We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at https://github.com/espnet/espnet. | ['Shinji Watanabe', 'Kevin Duh', 'Hirofumi Inaguma', 'Tomoki Hayashi', 'Nelson Enrique Yalta Soplin', 'Shun Kiyono', 'Shigeki Karita'] | 2020-04-21 | espnet-st-all-in-one-speech-translation-1 | https://aclanthology.org/2020.acl-demos.34 | https://aclanthology.org/2020.acl-demos.34.pdf | acl-2020-6 | ['speech-to-speech-translation'] | ['speech'] | [ 8.79342258e-02 -4.07648571e-02 -1.41576394e-01 -4.43111628e-01
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0851f81d-3151-40db-8e92-9b12f9d46780 | cross-domain-aspect-extraction-for-sentiment | null | null | https://www.sciencedirect.com/science/article/pii/S0167923618301386 | https://www.sciencedirect.com/science/article/pii/S0167923618301386 | Cross-domain aspect extraction for sentiment analysis: a transductive learning approach | Aspect-Based Sentiment Analysis (ABSA) is a promising approach to analyze
consumer reviews at a high level of detail, where the opinion about each fea-
ture of the product or service is considered. ABSA usually explores supervised
inductive learning algorithms, which requires intense human effort for the la-
beling process. In this paper, we investigate Cross-Domain Transfer Learning
approaches, in which aspects already labeled in some domains can be used to
support the aspect extraction of another domain where there are no labeled
aspects. Existing cross-domain transfer learning approaches learn classifiers
from labeled aspects in the source domain and then apply these classifiers in
the target domain, i.e., two separate stages that may cause inconsistency due
to different feature spaces. To overcome this drawback, we present an inno-
vative approach called CD-ALPHN (Cross-Domain Aspect Label Propagation
through Heterogeneous Networks). First, we propose a heterogeneous network-
based representation that combines different features (labeled aspects, unlabeled
aspects, and linguistic features) from source and target domain as nodes in a
single network. Second, we propose a label propagation algorithm for aspect extraction from heterogeneous networks, where the linguistic features are used
as a bridge for this propagation. Our algorithm is based on a transductive
learning process, where we explore both labeled and unlabeled aspects during
the label propagation. Experimental results show that the CD-ALPHN out-
performs the state-of-the-art methods in scenarios where there is a high-level
of inconsistency between the source and target domains — the most common
scenario in real-world applications.
Keywords: Cross Domain, Opinion Mining, Aspect Extraction | ['Solange Oliveira Rezende', 'Ricardo Marcondes Marcacini', 'Rafael Geraldeli Rossi', 'Ivone Penque Matsuno'] | 2018-10-21 | null | null | null | decision-support-system-2018-10 | ['aspect-extraction'] | ['natural-language-processing'] | [ 7.64253139e-02 3.93102527e-01 -6.48862898e-01 -6.71111882e-01
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50304da9-2405-4f3e-9503-f663ae9fbefb | jnd-based-perceptual-optimization-for-learned | 2302.13092 | null | https://arxiv.org/abs/2302.13092v2 | https://arxiv.org/pdf/2302.13092v2.pdf | JND-Based Perceptual Optimization For Learned Image Compression | Recently, learned image compression schemes have achieved remarkable improvements in image fidelity (e.g., PSNR and MS-SSIM) compared to conventional hybrid image coding ones due to their high-efficiency non-linear transform, end-to-end optimization frameworks, etc. However, few of them take the Just Noticeable Difference (JND) characteristic of the Human Visual System (HVS) into account and optimize learned image compression towards perceptual quality. To address this issue, a JND-based perceptual quality loss is proposed. Considering that the amounts of distortion in the compressed image at different training epochs under different Quantization Parameters (QPs) are different, we develop a distortion-aware adjustor. After combining them together, we can better assign the distortion in the compressed image with the guidance of JND to preserve the high perceptual quality. All these designs enable the proposed method to be flexibly applied to various learned image compression schemes with high scalability and plug-and-play advantages. Experimental results on the Kodak dataset demonstrate that the proposed method has led to better perceptual quality than the baseline model under the same bit rate. | ['Weisi Lin', 'Lili Meng', 'Jian Jin', 'Feng Ding'] | 2023-02-25 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 4.36912209e-01 -4.38927025e-01 -3.07106018e-01 -4.10296112e-01
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f9faca90-1100-48b7-af5f-e157ec6c6b6e | rain-rate-estimation-with-sar-using-nexrad | 2207.07333 | null | https://arxiv.org/abs/2207.07333v2 | https://arxiv.org/pdf/2207.07333v2.pdf | Rainfall Estimation with SAR using NEXRAD collocations with Convolutional Neural Networks | Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA's Next-Generation Radar (NEXRAD), provide reflectivity and precipitation estimates of rainfall events. However, their observation range is limited to a few hundred kilometers, prompting the exploration of other remote sensing methods, particularly over the open ocean, that represents large areas not covered by land-based radars. Here we propose a deep learning approach to extract rainfall information from SAR imagery. SAR has the advantage of providing global coverage and a very high resolution. We demonstrate that a convolutional neural network trained on a collocated Sentinel-1/NEXRAD dataset clearly outperforms state-of-the-art filtering schemes such as the Koch's filters, which has been implemented here as a neural network in a machine learning framework. Our results indicate high performance in segmenting precipitation regimes, delineated by thresholds at 24.7, 31.5, and 38.8 dBZ. Compared to current methods that rely on Koch's filters to draw binary rainfall maps, these multi-threshold learning-based models can provide rainfall estimation. They may be of great interest for improving the qualification of SAR-derived wind field data. | ['Nicolas Longépé', 'Ronan Fablet', 'Romain Husson', 'Charles Peureux', 'Pierre Tandeo', 'Aurélien Colin'] | 2022-07-15 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [ 3.85156460e-02 -2.55323172e-01 1.28315091e-01 -6.47162378e-01
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91b63831-845a-40e1-afb5-01f3789fde20 | generalization-bounds-for-inductive-matrix | 2212.08339 | null | https://arxiv.org/abs/2212.08339v1 | https://arxiv.org/pdf/2212.08339v1.pdf | Generalization Bounds for Inductive Matrix Completion in Low-noise Settings | We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently from many works in approximate recovery, we present results both for bounded Lipschitz losses and for the absolute loss, with the latter relying on Talagrand-type inequalities. The proofs create a bridge between two approaches to the theoretical analysis of matrix completion, since they consist in a combination of techniques from both the exact recovery literature and the approximate recovery literature. | ['Marius Kloft', 'Yann Guermeur', 'Yunwen Lei', 'Rodrigo Alves', 'Antoine Ledent'] | 2022-12-16 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.14603269e-01 1.96856081e-01 -2.27492556e-01 3.66423994e-01
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185e8fb2-64e0-4bb6-af5e-2023cb5d44be | knowledge-enhanced-personalized-review | 2010.0148 | null | https://arxiv.org/abs/2010.01480v1 | https://arxiv.org/pdf/2010.01480v1.pdf | Knowledge-Enhanced Personalized Review Generation with Capsule Graph Neural Network | Personalized review generation (PRG) aims to automatically produce review text reflecting user preference, which is a challenging natural language generation task. Most of previous studies do not explicitly model factual description of products, tending to generate uninformative content. Moreover, they mainly focus on word-level generation, but cannot accurately reflect more abstractive user preference in multiple aspects. To address the above issues, we propose a novel knowledge-enhanced PRG model based on capsule graph neural network~(Caps-GNN). We first construct a heterogeneous knowledge graph (HKG) for utilizing rich item attributes. We adopt Caps-GNN to learn graph capsules for encoding underlying characteristics from the HKG. Our generation process contains two major steps, namely aspect sequence generation and sentence generation. First, based on graph capsules, we adaptively learn aspect capsules for inferring the aspect sequence. Then, conditioned on the inferred aspect label, we design a graph-based copy mechanism to generate sentences by incorporating related entities or words from HKG. To our knowledge, we are the first to utilize knowledge graph for the PRG task. The incorporated KG information is able to enhance user preference at both aspect and word levels. Extensive experiments on three real-world datasets have demonstrated the effectiveness of our model on the PRG task. | ['Ji-Rong Wen', 'Nicholas Jing Yuan', 'Zhicheng Wei', 'Gaole He', 'Wayne Xin Zhao', 'Siqing Li', 'Junyi Li'] | 2020-10-04 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 2.53837079e-01 4.31903869e-01 -4.80879188e-01 -3.23709875e-01
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6438c897-4ebd-4d71-94d4-90b89edf0de4 | object-centric-voxelization-of-dynamic-scenes | 2305.00393 | null | https://arxiv.org/abs/2305.00393v3 | https://arxiv.org/pdf/2305.00393v3.pdf | Unsupervised Object-Centric Voxelization for Dynamic Scene Understanding | Understanding the compositional dynamics of multiple objects in unsupervised visual environments is challenging, and existing object-centric representation learning methods often ignore 3D consistency in scene decomposition. We propose DynaVol, an inverse graphics approach that learns object-centric volumetric representations in a neural rendering framework. DynaVol maintains time-varying 3D voxel grids that explicitly represent the probability of each spatial location belonging to different objects, and decouple temporal dynamics and spatial information by learning a canonical-space deformation field. To optimize the volumetric features, we embed them into a fully differentiable neural network, binding them to object-centric global features and then driving a compositional NeRF for scene reconstruction. DynaVol outperforms existing methods in novel view synthesis and unsupervised scene decomposition and allows for the editing of dynamic scenes, such as adding, deleting, replacing objects, and modifying their trajectories. | ['Xiaokang Yang', 'Yunbo Wang', 'Yanpeng Zhao', 'Siyu Gao'] | 2023-04-30 | null | null | null | null | ['neural-rendering', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision'] | [-1.18856144e-03 -1.44409403e-01 5.56449108e-02 -3.61584425e-01
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a2b178ca-e3af-4229-8a08-24afe1d6887f | semiretro-semi-template-framework-boosts-deep-1 | 2202.08205 | null | https://arxiv.org/abs/2202.08205v1 | https://arxiv.org/pdf/2202.08205v1.pdf | SemiRetro: Semi-template framework boosts deep retrosynthesis prediction | Recently, template-based (TB) and template-free (TF) molecule graph learning methods have shown promising results to retrosynthesis. TB methods are more accurate using pre-encoded reaction templates, and TF methods are more scalable by decomposing retrosynthesis into subproblems, i.e., center identification and synthon completion. To combine both advantages of TB and TF, we suggest breaking a full-template into several semi-templates and embedding them into the two-step TF framework. Since many semi-templates are reduplicative, the template redundancy can be reduced while the essential chemical knowledge is still preserved to facilitate synthon completion. We call our method SemiRetro, introduce a new GNN layer (DRGAT) to enhance center identification, and propose a novel self-correcting module to improve semi-template classification. Experimental results show that SemiRetro significantly outperforms both existing TB and TF methods. In scalability, SemiRetro covers 98.9\% data using 150 semi-templates, while previous template-based GLN requires 11,647 templates to cover 93.3\% data. In top-1 accuracy, SemiRetro exceeds template-free G2G 4.8\% (class known) and 6.0\% (class unknown). Besides, SemiRetro has better training efficiency than existing methods. | ['Stan Z. Li', 'Lirong Wu', 'Cheng Tan', 'Zhangyang Gao'] | 2022-02-12 | semiretro-semi-template-framework-boosts-deep | https://openreview.net/forum?id=rMbLORc8oS | https://openreview.net/pdf?id=rMbLORc8oS | null | ['retrosynthesis'] | ['medical'] | [ 4.55112517e-01 -5.87825067e-02 -8.05626750e-01 9.00809690e-02
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c7feeecd-5bb5-47bd-8ff4-413ecdc94f07 | low-rank-compression-of-neural-nets-learning | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Idelbayev_Low-Rank_Compression_of_Neural_Nets_Learning_the_Rank_of_Each_CVPR_2020_paper.pdf | Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer | Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix. The real difficulty in doing this is not in training the resulting neural net (made up of one low-rank matrix per layer), but in determining what the optimal rank of each layer is--effectively, an architecture search problem with one hyperparameter per layer. We show that, with a suitable formulation, this problem is amenable to a mixed discrete-continuous optimization jointly over the ranks and over the matrix elements, and give a corresponding algorithm. We show that this indeed can select ranks much better than existing approaches, making low-rank compression much more attractive than previously thought. For example, we can make a VGG network faster than a ResNet and with nearly the same classification error.
| [' Miguel A. Carreira-Perpinan', 'Yerlan Idelbayev'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['low-rank-compression'] | ['computer-code'] | [ 2.90467203e-01 4.88749146e-01 -1.76371232e-01 -1.45549133e-01
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21171961-85d4-4c89-937f-d94d09ab5f2b | forecasting-wireless-demand-with-extreme | 1905.06744 | null | https://arxiv.org/abs/1905.06744v2 | https://arxiv.org/pdf/1905.06744v2.pdf | Forecasting Wireless Demand with Extreme Values using Feature Embedding in Gaussian Processes | Wireless traffic prediction is a fundamental enabler to proactive network optimisation in beyond 5G. Forecasting extreme demand spikes and troughs due to traffic mobility is essential to avoiding outages and improving energy efficiency. Current state-of-the-art deep learning forecasting methods predominantly focus on overall forecast performance and do not offer probabilistic uncertainty quantification (UQ). Whilst Gaussian Process (GP) models have UQ capability, it is not able to predict extreme values very well. Here, we design a feature embedding (FE) kernel for a GP model to forecast traffic demand with extreme values. Using real 4G base station data, we compare our FE-GP performance against both conventional naive GPs, ARIMA models, as well as demonstrate the UQ output. For short-term extreme value prediction, we demonstrated a 32\% reduction vs. S-ARIMA and 17\% reduction vs. Naive-GP. For long-term average value prediction, we demonstrated a 21\% reduction vs. S-ARIMA and 12\% reduction vs. Naive-GP. The FE kernel also enabled us to create a flexible trade-off between overall forecast accuracy against peak-trough accuracy. The advantage over neural network (e.g. CNN, LSTM) is that the probabilistic forecast uncertainty can inform us of the risk of predictions, as well as the full posterior distribution of the forecast. | ['Weisi Guo', 'Chengyao Sun'] | 2019-05-15 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-3.64230782e-01 3.77032697e-01 -2.04498842e-01 -2.01687366e-01
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a84cb372-4b26-4c9d-8fec-572792f92e23 | graph-reinforcement-learning-for-operator | 2302.14678 | null | https://arxiv.org/abs/2302.14678v1 | https://arxiv.org/pdf/2302.14678v1.pdf | Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic | ALNS is a popular metaheuristic with renowned efficiency in solving combinatorial optimisation problems. However, despite 16 years of intensive research into ALNS, whether the embedded adaptive layer can efficiently select operators to improve the incumbent remains an open question. In this work, we formulate the choice of operators as a Markov Decision Process, and propose a practical approach based on Deep Reinforcement Learning and Graph Neural Networks. The results show that our proposed method achieves better performance than the classic ALNS adaptive layer due to the choice of operator being conditioned on the current solution. We also discuss important considerations such as the size of the operator portfolio and the impact of the choice of operator scales. Notably, our approach can also save significant time and labour costs for handcrafting problem-specific operator portfolios. | ['Joerg Kalcsics', 'Julia Handl', 'Victor-Alexandru Darvariu', 'Syu-Ning Johnn'] | 2023-02-28 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 4.37244743e-01 2.79802307e-02 -3.15474600e-01 2.31972694e-01
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8d3ef10c-8e52-44a1-95e8-c93f3f548cba | shadow-removal-from-single-rgb-d-images | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Xiao_Shadow_Removal_from_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Xiao_Shadow_Removal_from_2014_CVPR_paper.pdf | Shadow Removal from Single RGB-D Images | We present the first automatic method to remove shadows from single RGB-D images. Using normal cues directly derived from depth, we can remove hard and soft shadows while preserving surface texture and shading. Our key assumption is: pixels with similar normals, spatial locations and chromaticity should have similar colors. A modified nonlocal matching is used to compute a shadow confidence map that localizes well hard shadow boundary, thus handling hard and soft shadows within the same framework. We compare our results produced using state-of-the-art shadow removal on single RGB images, and intrinsic image decomposition on standard RGB-D datasets. | ['Chi-Keung Tang', 'Efstratios Tsougenis', 'Yao Xiao'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['shadow-removal', 'intrinsic-image-decomposition'] | ['computer-vision', 'computer-vision'] | [ 8.53562236e-01 2.32664213e-01 5.07410824e-01 -4.27427888e-01
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0058cee4-f76d-40a7-b9a6-f30c892d9814 | codeexp-explanatory-code-document-generation | 2211.15395 | null | https://arxiv.org/abs/2211.15395v1 | https://arxiv.org/pdf/2211.15395v1.pdf | CodeExp: Explanatory Code Document Generation | Developing models that can automatically generate detailed code explanation can greatly benefit software maintenance and programming education. However, existing code-to-text generation models often produce only high-level summaries of code that do not capture implementation-level choices essential for these scenarios. To fill in this gap, we propose the code explanation generation task. We first conducted a human study to identify the criteria for high-quality explanatory docstring for code. Based on that, we collected and refined a large-scale code docstring corpus and formulated automatic evaluation metrics that best match human assessments. Finally, we present a multi-stage fine-tuning strategy and baseline models for the task. Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones. We envision our training dataset, human-evaluation protocol, recommended metrics, and fine-tuning strategy can boost future code explanation research. The code and annotated data are available at https://github.com/subercui/CodeExp. | ['Nan Duan', 'Jianfeng Gao', 'Bo wang', 'Todd Mytkowicz', 'Jeevana Priya Inala', 'JunJie Huang', 'Chenglong Wang', 'Haotian Cui'] | 2022-11-25 | null | null | null | null | ['explanation-generation'] | ['natural-language-processing'] | [-2.23119017e-02 5.64135790e-01 -2.71154851e-01 -5.28205693e-01
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e73fd0e7-b446-423c-bf2d-9fb5b59221c7 | non-stationary-contextual-bandits-and | 2302.07186 | null | https://arxiv.org/abs/2302.07186v2 | https://arxiv.org/pdf/2302.07186v2.pdf | Adversarial Rewards in Universal Learning for Contextual Bandits | We study the fundamental limits of learning in contextual bandits, where a learner's rewards depend on their actions and a known context, which extends the canonical multi-armed bandit to the case where side-information is available. We are interested in universally consistent algorithms, which achieve sublinear regret compared to any measurable fixed policy, without any function class restriction. For stationary contextual bandits, when the underlying reward mechanism is time-invariant, Blanchard et. al (2022) characterized learnable context processes for which universal consistency is achievable; and further gave algorithms ensuring universal consistency whenever this is achievable, a property known as optimistic universal consistency. It is well understood, however, that reward mechanisms can evolve over time, possibly adversarially, and depending on the learner's actions. We show that optimistic universal learning for contextual bandits with adversarial rewards is impossible in general, contrary to all previously studied settings in online learning -- including standard supervised learning. We also give necessary and sufficient conditions for universal learning under various adversarial reward models, and an exact characterization for online rewards. In particular, the set of learnable processes for these reward models is still extremely general -- larger than i.i.d., stationary or ergodic -- but in general strictly smaller than that for supervised learning or stationary contextual bandits, shedding light on new adversarial phenomena. | ['Patrick Jaillet', 'Steve Hanneke', 'Moise Blanchard'] | 2023-02-14 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.19454482e-01 1.79914042e-01 -8.02248418e-01 -6.26887679e-02
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9ff17d27-a22c-4a4c-b03e-277ac61e598e | completedt-point-cloud-completion-with-dense | 2205.14999 | null | https://arxiv.org/abs/2205.14999v2 | https://arxiv.org/pdf/2205.14999v2.pdf | CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers | Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, namely CompleteDT. Specifically, features are learned from point clouds with different resolutions, which is sampled from the incomplete input, and are converted to a series of \textit{spots} based on the geometrical structure. Then, the Dense Relation Augment Module (DRA) based on the transformer is proposed to learn features within \textit{spots} and consider the correlation among these \textit{spots}. The DRA consists of Point Local-Attention Module (PLA) and Point Dense Multi-Scale Attention Module (PDMA), where the PLA captures the local information within the local \textit{spots} by adaptively measuring weights of neighbors and the PDMA exploits the global relationship between these \textit{spots} in a multi-scale densely connected manner. Lastly, the complete shape is predicted from \textit{spots} by the Multi-resolution Point Fusion Module (MPF), which gradually generates complete point clouds from \textit{spots}, and updates \textit{spots} based on these generated point clouds. Experimental results show that, because the DRA based on the transformer can learn the expressive features from the incomplete input and the MPF can fully explore these feature to predict the complete input, our method largely outperforms the state-of-the-art methods. | ['Shaokun Han', 'Shangwei Guo', 'Jun Li'] | 2022-05-30 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-2.97443364e-02 6.27102330e-02 2.32634023e-01 -1.93163559e-01
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5f7b438d-9445-403f-95eb-0dedac809e3b | boosting-differentiable-causal-discovery-via | 2303.03187 | null | https://arxiv.org/abs/2303.03187v1 | https://arxiv.org/pdf/2303.03187v1.pdf | Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting | Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges. Worse still, inherent mostly in these methods the common homogeneity assumption can be easily violated, due to the widespread existence of heterogeneous data in the real world, resulting in performance vulnerability when noise distributions vary. We propose a simple yet effective model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore for short, where the weights tailor quantitatively to the importance degree of each sample. Intuitively, we leverage the bilevel optimization scheme to \wx{alternately train a standard DAG learner and reweight samples -- that is, upweight the samples the learner fails to fit and downweight the samples that the learner easily extracts the spurious information from. Extensive experiments on both synthetic and real-world datasets are carried out to validate the effectiveness of ReScore. We observe consistent and significant boosts in structure learning performance. Furthermore, we visualize that ReScore concurrently mitigates the influence of spurious edges and generalizes to heterogeneous data. Finally, we perform the theoretical analysis to guarantee the structure identifiability and the weight adaptive properties of ReScore in linear systems. Our codes are available at https://github.com/anzhang314/ReScore. | ['Tat-Seng Chua', 'Xiang Wang', 'Zhibo Cai', 'Wenchang Ma', 'Fangfu Liu', 'An Zhang'] | 2023-03-06 | null | null | null | null | ['causal-discovery', 'bilevel-optimization'] | ['knowledge-base', 'methodology'] | [ 2.03706831e-01 2.27833465e-01 -5.00924528e-01 -3.94713014e-01
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0a3f9e98-24f6-4af8-9dab-150a635800b4 | equivariant-single-view-pose-prediction-via | 2307.03704 | null | https://arxiv.org/abs/2307.03704v1 | https://arxiv.org/pdf/2307.03704v1.pdf | Equivariant Single View Pose Prediction Via Induced and Restricted Representations | Learning about the three-dimensional world from two-dimensional images is a fundamental problem in computer vision. An ideal neural network architecture for such tasks would leverage the fact that objects can be rotated and translated in three dimensions to make predictions about novel images. However, imposing SO(3)-equivariance on two-dimensional inputs is difficult because the group of three-dimensional rotations does not have a natural action on the two-dimensional plane. Specifically, it is possible that an element of SO(3) will rotate an image out of plane. We show that an algorithm that learns a three-dimensional representation of the world from two dimensional images must satisfy certain geometric consistency properties which we formulate as SO(2)-equivariance constraints. We use the induced and restricted representations of SO(2) on SO(3) to construct and classify architectures which satisfy these geometric consistency constraints. We prove that any architecture which respects said consistency constraints can be realized as an instance of our construction. We show that three previously proposed neural architectures for 3D pose prediction are special cases of our construction. We propose a new algorithm that is a learnable generalization of previously considered methods. We test our architecture on three pose predictions task and achieve SOTA results on both the PASCAL3D+ and SYMSOL pose estimation tasks. | ['Robin Walters', 'Linfeng Zhao', 'Ondrej Biza', 'David Klee', 'Owen Howell'] | 2023-07-07 | null | null | null | null | ['pose-prediction', 'pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.02818616e-01 4.15469021e-01 -2.18835816e-01 -6.03582144e-01
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493fd0a7-2082-4307-918d-8c00444f8ac3 | exploiting-global-and-local-hierarchies-for | 2205.02613 | null | https://arxiv.org/abs/2205.02613v3 | https://arxiv.org/pdf/2205.02613v3.pdf | Exploiting Global and Local Hierarchies for Hierarchical Text Classification | Hierarchical text classification aims to leverage label hierarchy in multi-label text classification. Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels. Since global hierarchy is static and irrelevant to text samples, it makes these methods hard to exploit hierarchical information. Contrary to global hierarchy, local hierarchy as a structured labels hierarchy corresponding to each text sample. It is dynamic and relevant to text samples, which is ignored in previous methods. To exploit global and local hierarchies,we propose Hierarchy-guided BERT with Global and Local hierarchies (HBGL), which utilizes the large-scale parameters and prior language knowledge of BERT to model both global and local hierarchies.Moreover,HBGL avoids the intentional fusion of semantic and hierarchical modules by directly modeling semantic and hierarchical information with BERT.Compared with the state-of-the-art method HGCLR,our method achieves significant improvement on three benchmark datasets. | ['Qinghong Yang', 'Fuzhen Zhuang', 'Zhongzhi Chen', 'Leilei Sun', 'Deqing Wang', 'Ting Jiang'] | 2022-05-05 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 2.86519099e-02 2.66799122e-01 -5.72594345e-01 -4.37580526e-01
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9d75b6c0-e1c8-4f31-adb8-056eafbda268 | variability-matters-evaluating-inter-rater | 2210.05175 | null | https://arxiv.org/abs/2210.05175v1 | https://arxiv.org/pdf/2210.05175v1.pdf | Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection | Large annotated datasets have been a key component in the success of deep learning. However, annotating medical images is challenging as it requires expertise and a large budget. In particular, annotating different types of cells in histopathology suffer from high inter- and intra-rater variability due to the ambiguity of the task. Under this setting, the relation between annotators' variability and model performance has received little attention. We present a large-scale study on the variability of cell annotations among 120 board-certified pathologists and how it affects the performance of a deep learning model. We propose a method to measure such variability, and by excluding those annotators with low variability, we verify the trade-off between the amount of data and its quality. We found that naively increasing the data size at the expense of inter-rater variability does not necessarily lead to better-performing models in cell detection. Instead, decreasing the inter-rater variability with the expense of decreasing dataset size increased the model performance. Furthermore, models trained from data annotated with lower inter-labeler variability outperform those from higher inter-labeler variability. These findings suggest that the evaluation of the annotators may help tackle the fundamental budget issues in the histopathology domain | ['S ergio Pereira', 'Minuk Ma', 'Heon Song', 'Chunggi Lee', 'Cholmin Kang'] | 2022-10-11 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [-6.17859177e-02 1.94785297e-01 -1.24524631e-01 -4.27764565e-01
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fedcc5f4-520b-4012-a2ae-c3087840dd55 | an-xai-approach-to-deep-learning-models-in | 2106.14186 | null | https://arxiv.org/abs/2106.14186v2 | https://arxiv.org/pdf/2106.14186v2.pdf | An XAI Approach to Deep Learning Models in the Detection of DCIS | The results showed that XAI could indeed be used as a proof of concept to begin discussions on the implementation of assistive AI systems within the clinical community. | ['Michele La Ferla'] | 2021-06-27 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 6.16032369e-02 1.02076638e+00 -2.52825350e-01 -4.74100173e-01
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48693a0b-bdd7-44e6-aff5-cbaf3ce66d52 | thermal-image-processing-via-physics-inspired | 2108.07973 | null | https://arxiv.org/abs/2108.07973v2 | https://arxiv.org/pdf/2108.07973v2.pdf | Thermal Image Processing via Physics-Inspired Deep Networks | We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (that can be accurately modeled using physics) and a scene-specific radiance flux (that is well-represented using a deep network-based regularizer). DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target--making it well suited for practical computer vision tasks. We demonstrate the power of going DeepIR by developing new denoising and super-resolution algorithms that exploit multiple images of the scene captured with camera jitter. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images, achieving a 10dB PSNR improvement over competing approaches. | ['Richard Baraniuk', 'Ashok Veeraraghavan', 'Akshat Dave', 'Vishwanath Saragadam'] | 2021-08-18 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [ 6.55234039e-01 -4.88834172e-01 3.85273695e-01 -3.41861814e-01
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ac67c9b3-354b-4c2c-8792-3aac139e2d49 | a-survey-of-detection-methods-for-die | 2206.07481 | null | https://arxiv.org/abs/2206.07481v2 | https://arxiv.org/pdf/2206.07481v2.pdf | A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing | Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs). Die attachment and wire bonding are two steps of the manufacturing process that determine the power and signal transmission quality and dependability in an IC. This paper presents a survey or literature review of the methods used for detecting these defects based on different sensing modalities used including optical, radiological, acoustical, and infrared thermography. A discussion of the detection methods used is provided in this survey. Both conventional and deep learning approaches for detecting die attachment and wire bonding defects are considered along with challenges and future research directions. | ['Nasser Kehtarnavaz', 'Lamia Alam'] | 2022-06-02 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.14358000e-01 -1.92334771e-01 -2.11820707e-01 -2.88003057e-01
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0d10354b-a839-4baf-8db1-7fdcbc2c5693 | human-robot-skill-transfer-with-enhanced | 2304.05703 | null | https://arxiv.org/abs/2304.05703v1 | https://arxiv.org/pdf/2304.05703v1.pdf | Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives | Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like skills to robots by Learning from Demonstrations (LfD). The human demonstration is considered the target motion to mimic. However, human motion is typically optimal for human embodiment but not for robots because of the differences between human biomechanics and robot dynamics. The Dynamic Movement Primitives (DMP) framework is a viable solution for this limitation of LfD, but it requires tuning the second-order dynamics in the formulation. Our contribution is introducing a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the DMP framework. In addition to its use with LfD, another utility of the proposed method is that it can readily be used in conjunction with Reinforcement Learning (RL) for robot training. In this way, the extracted features facilitate the transfer of human skills by allowing the robot to explore the possible trajectories more efficiently and increasing robot compliance significantly. We introduced a methodology to extract the dynamic features from multiple trajectories based on the optimization of human-likeness and similarity in the parametric space. Our method was implemented into an actual human-robot setup to extract human dynamic features and used to regenerate the robot trajectories following both LfD and RL with DMP. It resulted in a stable performance of the robot, maintaining a high degree of human-likeness based on accumulated distance error as good as the best heuristic tuning. | ['Homayoun Najjaran', 'Amir M. Soufi Enayati', 'Zengjie Zhang', 'Jayden Hong'] | 2023-04-12 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-2.54930049e-01 2.81719357e-01 -1.94734558e-01 1.06456771e-01
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db11c5a8-022c-48c3-902b-185b6f6f1a17 | deep-multiple-instance-learning-with-distance | 2305.10552 | null | https://arxiv.org/abs/2305.10552v2 | https://arxiv.org/pdf/2305.10552v2.pdf | Deep Multiple Instance Learning with Distance-Aware Self-Attention | Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags) of instances. This problem setting, known as multiple instance learning (MIL), is particularly relevant in the medical domain, where high-resolution images are split into smaller patches, but labels apply to the image as a whole. Recent MIL models are able to capture correspondences between patches by employing self-attention, allowing them to weigh each patch differently based on all other patches in the bag. However, these approaches still do not consider the relative spatial relationships between patches within the larger image, which is especially important in computational pathology. To this end, we introduce a novel MIL model with distance-aware self-attention (DAS-MIL), which explicitly takes into account relative spatial information when modelling the interactions between patches. Unlike existing relative position representations for self-attention which are discrete, our approach introduces continuous distance-dependent terms into the computation of the attention weights, and is the first to apply relative position representations in the context of MIL. We evaluate our model on a custom MNIST-based MIL dataset that requires the consideration of relative spatial information, as well as on CAMELYON16, a publicly available cancer metastasis detection dataset, where we achieve a test AUROC score of 0.91. On both datasets, our model outperforms existing MIL approaches that employ absolute positional encodings, as well as existing relative position representation schemes applied to MIL. Our code is available at https://anonymous.4open.science/r/das-mil. | ['Ognjen Arandjelović', 'David J. Harrison', 'Pietro Liò', 'Lucie Charlotte Magister', 'Georg Wölflein'] | 2023-05-17 | null | null | null | null | ['cancer-metastasis-detection', 'multiple-instance-learning'] | ['medical', 'methodology'] | [ 3.49066526e-01 2.30669901e-01 -3.08090895e-01 -2.96471566e-01
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c7fbb96e-077f-4bfd-a7cc-7592548dd81a | learning-efficient-representations-for-3 | 2101.04792 | null | https://arxiv.org/abs/2101.04792v4 | https://arxiv.org/pdf/2101.04792v4.pdf | Learning Efficient Representations for Keyword Spotting with Triplet Loss | In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classification problems. We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves the classification accuracy for convolutional networks on a LibriSpeech-derived LibriWords datasets. To do so, we propose a novel phonetic similarity based triplet mining approach. We also improve the current best published SOTA for Google Speech Commands dataset V1 10+2 -class classification by about 34%, achieving 98.55% accuracy, V2 10+2-class classification by about 20%, achieving 98.37% accuracy, and V2 35-class classification by over 50%, achieving 97.0% accuracy. | ['Nikolay Mikhaylovskiy', 'Roman Vygon'] | 2021-01-12 | learning-efficient-representations-for-1 | https://arxiv.org/abs/2101.04792 | https://arxiv.org/ftp/arxiv/papers/2101/2101.04792.pdf | specom-2021 | ['keyword-spotting'] | ['speech'] | [-1.85667202e-01 1.92154869e-01 -3.06898415e-01 -6.79977417e-01
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c30e9b81-e6b7-4acb-a5c8-ae203083f2f4 | multi-graph-fusion-networks-for-urban-region | 2201.0976 | null | https://arxiv.org/abs/2201.09760v2 | https://arxiv.org/pdf/2201.09760v2.pdf | Multi-Graph Fusion Networks for Urban Region Embedding | Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. | ['Cheng Wang', 'Ming Cheng', 'Chuanpan Zheng', 'Shichao Zhu', 'Shirui Pan', 'Xiaoliang Fan', 'Xu Yan', 'Shangbin Wu'] | 2022-01-24 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-3.23386967e-01 -1.42631188e-01 -4.77001727e-01 -2.43451580e-01
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098c47e8-fa10-4a5a-9789-0d7a5cddddf0 | kast-knowledge-aware-adaptive-session-multi | 2210.03624 | null | https://arxiv.org/abs/2210.03624v1 | https://arxiv.org/pdf/2210.03624v1.pdf | KAST: Knowledge Aware Adaptive Session Multi-Topic Network for Click-Through Rate Prediction | Capturing the evolving trends of user interest is important for both recommendation systems and advertising systems, and user behavior sequences have been successfully used in Click-Through-Rate(CTR) prediction problems. However, if the user interest is learned on the basis of item-level behaviors, the performance may be affected by the following two issues. Firstly, some casual outliers might be included in the behavior sequences as user behaviors are likely to be diverse. Secondly, the span of time intervals between user behaviors is random and irregular, for which a RNN-based module employed from NLP is not perfectly adaptive. To handle these two issues, we propose the Knowledge aware Adaptive Session multi-Topic network(KAST). It can adaptively segment user sessions from the whole user behavior sequence, and maintain similar intents in the same session. Furthermore, in order to improve the quality of session segmentation and representation, a knowledge-aware module is introduced so that the structural information from the user-item interaction can be extracted in an end-to-end manner, and a marginal based loss with these information is merged into the major loss. Through extensive experiments on public benchmarks, we demonstrate that KAST can achieve superior performance than state-of-the-art methods for CTR prediction, and key modules and hyper-parameters are also evaluated. | ['ShengKai Yang', 'Kai Liu', 'Dike Sun'] | 2022-10-07 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [ 1.33174375e-01 -4.18553531e-01 -6.78872645e-01 -7.13864505e-01
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2c0fb5bf-21aa-48d1-a2cf-55cc228eaa70 | action-recognition-using-supervised-spiking | 1911.0363 | null | https://arxiv.org/abs/1911.03630v2 | https://arxiv.org/pdf/1911.03630v2.pdf | Action Recognition Using Supervised Spiking Neural Networks | Biological neurons use spikes to process and learn temporally dynamic inputs in an energy and computationally efficient way. However, applying the state-of-the-art gradient-based supervised algorithms to spiking neural networks (SNN) is a challenge due to the non-differentiability of the activation function of spiking neurons. Employing surrogate gradients is one of the main solutions to overcome this challenge. Although SNNs naturally work in the temporal domain, recent studies have focused on developing SNNs to solve static image categorization tasks. In this paper, we employ a surrogate gradient descent learning algorithm to recognize twelve human hand gestures recorded by dynamic vision sensor (DVS) cameras. The proposed SNN could reach 97.2% recognition accuracy on test data. | ['Saeed Reza Kheradpisheh', 'Hadi Farahani', 'Aref Moqadam Mehr'] | 2019-11-09 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 4.15157229e-01 -6.51865661e-01 3.67349945e-02 -1.97437465e-01
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6d79be5d-f8df-437d-84ba-00eae3c19770 | triangle-net-towards-robustness-in-point | 2003.00856 | null | https://arxiv.org/abs/2003.00856v2 | https://arxiv.org/pdf/2003.00856v2.pdf | Triangle-Net: Towards Robustness in Point Cloud Learning | Three dimensional (3D) object recognition is becoming a key desired capability for many computer vision systems such as autonomous vehicles, service robots and surveillance drones to operate more effectively in unstructured environments. These real-time systems require effective classification methods that are robust to various sampling resolutions, noisy measurements, and unconstrained pose configurations. Previous research has shown that points' sparsity, rotation and positional inherent variance can lead to a significant drop in the performance of point cloud based classification techniques. However, neither of them is sufficiently robust to multifactorial variance and significant sparsity. In this regard, we propose a novel approach for 3D classification that can simultaneously achieve invariance towards rotation, positional shift, scaling, and is robust to point sparsity. To this end, we introduce a new feature that utilizes graph structure of point clouds, which can be learned end-to-end with our proposed neural network to acquire a robust latent representation of the 3D object. We show that such latent representations can significantly improve the performance of object classification and retrieval tasks when points are sparse. Further, we show that our approach outperforms PointNet and 3DmFV by 35.0% and 28.1% respectively in ModelNet 40 classification tasks using sparse point clouds of only 16 points under arbitrary SO(3) rotation. | ['Juan Wachs', 'Chenxi Xiao'] | 2020-02-27 | null | null | null | null | ['3d-object-recognition', '3d-classification'] | ['computer-vision', 'computer-vision'] | [-5.97483804e-03 -4.08665150e-01 -2.81600475e-01 -3.71547580e-01
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058c1be1-2565-48fc-b261-323358bcdadd | motion-guided-attention-for-video-salient | 1909.07061 | null | https://arxiv.org/abs/1909.07061v2 | https://arxiv.org/pdf/1909.07061v2.pdf | Motion Guided Attention for Video Salient Object Detection | Video salient object detection aims at discovering the most visually distinctive objects in a video. How to effectively take object motion into consideration during video salient object detection is a critical issue. Existing state-of-the-art methods either do not explicitly model and harvest motion cues or ignore spatial contexts within optical flow images. In this paper, we develop a multi-task motion guided video salient object detection network, which learns to accomplish two sub-tasks using two sub-networks, one sub-network for salient object detection in still images and the other for motion saliency detection in optical flow images. We further introduce a series of novel motion guided attention modules, which utilize the motion saliency sub-network to attend and enhance the sub-network for still images. These two sub-networks learn to adapt to each other by end-to-end training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on a wide range of benchmarks. We hope our simple and effective approach will serve as a solid baseline and help ease future research in video salient object detection. Code and models will be made available. | ['Guanqi Chen', 'Haofeng Li', 'Yizhou Yu', 'Guanbin Li'] | 2019-09-16 | motion-guided-attention-for-video-salient-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Motion_Guided_Attention_for_Video_Salient_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Motion_Guided_Attention_for_Video_Salient_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.36445177e-01 -3.76928449e-01 -5.63267231e-01 -1.62958637e-01
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547344e1-fa24-4405-97d7-de0cdb8b4539 | using-anomaly-feature-vectors-for-detecting | 2107.00561 | null | https://arxiv.org/abs/2107.00561v1 | https://arxiv.org/pdf/2107.00561v1.pdf | Using Anomaly Feature Vectors for Detecting, Classifying and Warning of Outlier Adversarial Examples | We present DeClaW, a system for detecting, classifying, and warning of adversarial inputs presented to a classification neural network. In contrast to current state-of-the-art methods that, given an input, detect whether an input is clean or adversarial, we aim to also identify the types of adversarial attack (e.g., PGD, Carlini-Wagner or clean). To achieve this, we extract statistical profiles, which we term as anomaly feature vectors, from a set of latent features. Preliminary findings suggest that AFVs can help distinguish among several types of adversarial attacks (e.g., PGD versus Carlini-Wagner) with close to 93% accuracy on the CIFAR-10 dataset. The results open the door to using AFV-based methods for exploring not only adversarial attack detection but also classification of the attack type and then design of attack-specific mitigation strategies. | ['Atul Prakash', 'Jiguo Song', 'Sahib Singh', 'Ryan Feng', 'Nelson Manohar-Alers'] | 2021-07-01 | null | https://openreview.net/forum?id=XDo0go2IJgT | https://openreview.net/pdf?id=XDo0go2IJgT | icml-workshop-aml-2021-7 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 1.16280392e-01 -3.47987831e-01 1.29453033e-01 -3.78676385e-01
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7eccd6bf-0699-4536-8fdb-02a6dd4a13bb | listwise-view-ranking-for-image-cropping | 1905.05352 | null | https://arxiv.org/abs/1905.05352v1 | https://arxiv.org/pdf/1905.05352v1.pdf | Listwise View Ranking for Image Cropping | Rank-based Learning with deep neural network has been widely used for image cropping. However, the performance of ranking-based methods is often poor and this is mainly due to two reasons: 1) image cropping is a listwise ranking task rather than pairwise comparison; 2) the rescaling caused by pooling layer and the deformation in view generation damage the performance of composition learning. In this paper, we develop a novel model to overcome these problems. To address the first problem, we formulate the image cropping as a listwise ranking problem to find the best view composition. For the second problem, a refined view sampling (called RoIRefine) is proposed to extract refined feature maps for candidate view generation. Given a series of candidate views, the proposed model learns the Top-1 probability distribution of views and picks up the best one. By integrating refined sampling and listwise ranking, the proposed network called LVRN achieves the state-of-the-art performance both in accuracy and speed. | ['Xiaofen Xing', 'Xiangmin Xu', 'Bolun Cai', 'Weirui Lu'] | 2019-05-14 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 4.14740413e-01 -3.00781190e-01 -2.27620110e-01 -4.44953352e-01
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0aba42ef-e765-4124-ba62-42094671847e | locality-aware-inter-and-intra-video | 2203.14333 | null | https://arxiv.org/abs/2203.14333v2 | https://arxiv.org/pdf/2203.14333v2.pdf | Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning | Our target is to learn visual correspondence from unlabeled videos. We develop LIIR, a locality-aware inter-and intra-video reconstruction framework that fills in three missing pieces, i.e., instance discrimination, location awareness, and spatial compactness, of self-supervised correspondence learning puzzle. First, instead of most existing efforts focusing on intra-video self-supervision only, we exploit cross video affinities as extra negative samples within a unified, inter-and intra-video reconstruction scheme. This enables instance discriminative representation learning by contrasting desired intra-video pixel association against negative inter-video correspondence. Second, we merge position information into correspondence matching, and design a position shifting strategy to remove the side-effect of position encoding during inter-video affinity computation, making our LIIR location-sensitive. Third, to make full use of the spatial continuity nature of video data, we impose a compactness-based constraint on correspondence matching, yielding more sparse and reliable solutions. The learned representation surpasses self-supervised state-of-the-arts on label propagation tasks including objects, semantic parts, and keypoints. | ['Yi Yang', 'Jianwu Li', 'Lu Yang', 'Wenguan Wang', 'Tianfei Zhou', 'Liulei Li'] | 2022-03-27 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 1.29321203e-01 -6.26003221e-02 -6.40461981e-01 -5.06106794e-01
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57853d4a-83eb-4efb-a7e6-0f4ab824f493 | physics-informed-machine-learning-of-redox | 2306.0101 | null | https://arxiv.org/abs/2306.01010v1 | https://arxiv.org/pdf/2306.01010v1.pdf | Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model | In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available. | ['Panos Stinis', 'Yucheng Fu', 'Wenqian Chen'] | 2023-05-31 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.49121553e-01 -2.62378246e-01 -3.65386546e-01 1.02438219e-01
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06961d70-a9bb-455f-9db7-a13f48b28eeb | when-does-maml-work-the-best-an-empirical | 2005.117 | null | https://arxiv.org/abs/2005.11700v1 | https://arxiv.org/pdf/2005.11700v1.pdf | When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications | Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation. Many impacting factors, including data quantity, similarity among tasks, and the balance between general language model and task-specific adaptation, can affect the performance of MAML in NLP, but few works have thoroughly studied them. In this paper, we conduct an empirical study to investigate these impacting factors and conclude when MAML works the best based on the experimental results. | ['Yiping Song', 'Ming Zhang', 'Zequn Liu', 'Ruiyi Zhang'] | 2020-05-24 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [-1.02203735e-03 -5.70029497e-01 -6.35591924e-01 -1.97618276e-01
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7e639348-bce1-47e1-b799-425a6471e370 | real-time-emotion-classification-using-eeg | null | null | https://www.mdpi.com/1424-8220/21/5/1589 | https://www.mdpi.com/1424-8220/21/5/1589 | Real-Time Emotion Classification Using EEG Data Stream in E-Learning Contexts | In face-to-face and online learning, emotions and emotional intelligence have an influence and play an essential role. Learners’ emotions are crucial for e-learning system because they promote or restrain the learning. Many researchers have investigated the impacts of emotions in enhancing and maximizing e-learning outcomes. Several machine learning and deep learning approaches have also been proposed to achieve this goal. All such approaches are suitable for an offline mode, where the data for emotion classification are stored and can be accessed infinitely. However, these offline mode approaches are inappropriate for real-time emotion classification when the data are coming in a continuous stream and data can be seen to the model at once only. We also need real-time responses according to the emotional state. For this, we propose a real-time emotion classification system (RECS)-based Logistic Regression (LR) trained in an online fashion using the Stochastic Gradient Descent (SGD) algorithm. The proposed RECS is capable of classifying emotions in real-time by training the model in an online fashion using an EEG signal stream. To validate the performance of RECS, we have used the DEAP data set, which is the most widely used benchmark data set for emotion classification. The results show that the proposed approach can effectively classify emotions in real-time from the EEG data stream, which achieved a better accuracy and F1-score than other offline and online approaches. The developed real-time emotion classification system is analyzed in an e-learning context scenario. | ['Santi Fort', 'Laia Subirats', 'Fatos Xhafa', 'Arijit Nandi'] | 2021-02-25 | null | null | null | mdpi-sensors-2021-2 | ['emotional-intelligence'] | ['natural-language-processing'] | [-3.53406996e-01 -2.39423171e-01 1.46761327e-03 -6.81227624e-01
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4acf570a-7a59-432f-a022-1dd689c35b1c | frame-fast-and-robust-autonomous-3d-point | 2301.09213 | null | https://arxiv.org/abs/2301.09213v2 | https://arxiv.org/pdf/2301.09213v2.pdf | FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration | This article presents a 3D point cloud map-merging framework for egocentric heterogeneous multi-robot exploration, based on overlap detection and alignment, that is independent of a manual initial guess or prior knowledge of the robots' poses. The novel proposed solution utilizes state-of-the-art place recognition learned descriptors, that through the framework's main pipeline, offer a fast and robust region overlap estimation, hence eliminating the need for the time-consuming global feature extraction and feature matching process that is typically used in 3D map integration. The region overlap estimation provides a homogeneous rigid transform that is applied as an initial condition in the point cloud registration algorithm Fast-GICP, which provides the final and refined alignment. The efficacy of the proposed framework is experimentally evaluated based on multiple field multi-robot exploration missions in underground environments, where both ground and aerial robots are deployed, with different sensor configurations. | ['George Nikolakopoulos', 'Ali-akbar Agha-mohammadi', 'Anton Koval', 'Nikolaos Stathoulopoulos'] | 2023-01-22 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [ 1.10744767e-01 -5.39571308e-02 3.29903275e-01 -2.52111375e-01
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e6a1873a-ac26-43ba-a5de-cd46863571d8 | analysis-of-tomographic-reconstruction-of-2d | 2304.06376 | null | https://arxiv.org/abs/2304.06376v1 | https://arxiv.org/pdf/2304.06376v1.pdf | Analysis of Tomographic Reconstruction of 2D Images using the Distribution of Unknown Projection Angles | It is well known that a band-limited signal can be reconstructed from its uniformly spaced samples if the sampling rate is sufficiently high. More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given just the distribution of the sample locations and their ordering in 1D. In this work, we extend the analytical bounds on the reconstruction error in such scenarios for quasi-bandlimited signals. We also prove that the method for such a reconstruction is resilient to a certain proportion of errors in the specification of the sample location ordering. We then express the problem of tomographic reconstruction of 2D images from 1D Radon projections under unknown angles with known angle distribution, as a special case for reconstruction of quasi-bandlimited signals from samples at unknown locations with known distribution. Building upon our theoretical background, we present asymptotic bounds for 2D quasi-bandlimited image reconstruction from 1D Radon projections in the unknown angles setting, which commonly occurs in cryo-electron microscopy (cryo-EM). To the best of our knowledge, this is the first piece of work to perform such an analysis for 2D cryo-EM, even though the associated reconstruction algorithms have been known for a long time. | ['Ajit Rajwade', 'Karthik S. Gurumoorthy', 'Sheel Shah'] | 2023-04-13 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 5.78742683e-01 -1.32702306e-01 1.42746940e-01 -2.56110936e-01
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b3b32c79-95b7-41f4-a168-be3e3bee17a6 | grounding-of-textual-phrases-in-images-by | 1511.03745 | null | http://arxiv.org/abs/1511.03745v4 | http://arxiv.org/pdf/1511.03745v4.pdf | Grounding of Textual Phrases in Images by Reconstruction | Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual
content is a challenging problem with many applications for human-computer
interaction and image-text reference resolution. Few datasets provide the
ground truth spatial localization of phrases, thus it is desirable to learn
from data with no or little grounding supervision. We propose a novel approach
which learns grounding by reconstructing a given phrase using an attention
mechanism, which can be either latent or optimized directly. During training
our approach encodes the phrase using a recurrent network language model and
then learns to attend to the relevant image region in order to reconstruct the
input phrase. At test time, the correct attention, i.e., the grounding, is
evaluated. If grounding supervision is available it can be directly applied via
a loss over the attention mechanism. We demonstrate the effectiveness of our
approach on the Flickr 30k Entities and ReferItGame datasets with different
levels of supervision, ranging from no supervision over partial supervision to
full supervision. Our supervised variant improves by a large margin over the
state-of-the-art on both datasets. | ['Marcus Rohrbach', 'Anna Rohrbach', 'Trevor Darrell', 'Ronghang Hu', 'Bernt Schiele'] | 2015-11-12 | null | null | null | null | ['phrase-grounding', 'natural-language-visual-grounding'] | ['natural-language-processing', 'reasoning'] | [ 3.92012596e-01 3.15859050e-01 -4.08248156e-01 -3.56296092e-01
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b12e7aea-d3a2-40b8-b27d-390f43d75140 | compass-a-creative-support-system-that-alerts | 2202.13151 | null | https://arxiv.org/abs/2202.13151v1 | https://arxiv.org/pdf/2202.13151v1.pdf | COMPASS: a Creative Support System that Alerts Novelists to the Unnoticed Missing Contents | When humans write, they may unintentionally omit some information. Complementing the omitted information using a computer is helpful in providing writing support. Recently, in the field of story understanding and generation, story completion (SC) was proposed to generate the missing parts of an incomplete story. Although its applicability is limited because it requires that the user have prior knowledge of the missing part of a story, missing position prediction (MPP) can be used to compensate for this problem. MPP aims to predict the position of the missing part, but the prerequisite knowledge that "one sentence is missing" is still required. In this study, we propose Variable Number MPP (VN-MPP), a new MPP task that removes this restriction; that is, the task to predict multiple missing sentences or to judge whether there are no missing sentences in the first place. We also propose two methods for this new MPP task. Furthermore, based on the novel task and methods, we developed a creative writing support system, COMPASS. The results of a user experiment involving professional creators who write texts in Japanese confirm the efficacy and utility of the developed system. | ['Tatsuya Harada', 'Yusuke Mukuta', 'Ryohei Shimizu', 'Hiroaki Yamane', 'Yusuke Mori'] | 2022-02-26 | null | null | null | null | ['story-completion'] | ['natural-language-processing'] | [ 5.15973866e-01 3.22170258e-01 -1.04576372e-01 -2.08162174e-01
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c05d0231-33f8-4541-8746-a94168aad2be | dictionary-learning-based-reconstruction | 1311.583 | null | http://arxiv.org/abs/1311.5830v1 | http://arxiv.org/pdf/1311.5830v1.pdf | Dictionary-Learning-Based Reconstruction Method for Electron Tomography | Electron tomography usually suffers from so called missing wedge artifacts
caused by limited tilt angle range. An equally sloped tomography (EST)
acquisition scheme (which should be called the linogram sampling scheme) was
recently applied to achieve 2.4-angstrom resolution. On the other hand, a
compressive sensing-inspired reconstruction algorithm, known as adaptive
dictionary based statistical iterative reconstruction (ADSIR), has been
reported for x-ray computed tomography. In this paper, we evaluate the EST,
ADSIR and an ordered-subset simultaneous algebraic reconstruction technique
(OS-SART), and compare the ES and equally angled (EA) data acquisition modes.
Our results show that OS-SART is comparable to EST, and the ADSIR outperforms
EST and OS-SART. Furthermore, the equally sloped projection data acquisition
mode has no advantage over the conventional equally angled mode in the context. | ['Scott S. Verbridge', 'Hengyong Yu', 'Ge Wang', 'Baodong Liu', 'Lizhi Sun'] | 2013-11-22 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 5.16379178e-01 -2.98303932e-01 3.34431022e-01 -2.63930947e-01
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10c33b9a-42bd-450f-a73c-06dc05170637 | bifnet-bidirectional-fusion-network-for-road | 2004.08582 | null | https://arxiv.org/abs/2004.08582v1 | https://arxiv.org/pdf/2004.08582v1.pdf | BiFNet: Bidirectional Fusion Network for Road Segmentation | Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) Dense space transformation module, which solves the mutual conversion between camera image space and BEV space. 2) Context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features.This method has achieved competitive results on KITTI dataset. | ['Yaran Chen', 'Haoran Li', 'Qichao Zhang', 'Dongbin Zhao'] | 2020-04-18 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 6.09835312e-02 -4.18488920e-01 1.80425391e-01 -4.86638963e-01
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815be30b-db20-4ee9-813d-b3ef3c7d1892 | leveraging-speech-separation-for | 2204.02306 | null | https://arxiv.org/abs/2204.02306v2 | https://arxiv.org/pdf/2204.02306v2.pdf | Low-Latency Speech Separation Guided Diarization for Telephone Conversations | In this paper, we carry out an analysis on the use of speech separation guided diarization (SSGD) in telephone conversations. SSGD performs diarization by separating the speakers signals and then applying voice activity detection on each estimated speaker signal. In particular, we compare two low-latency speech separation models. Moreover, we show a post-processing algorithm that significantly reduces the false alarm errors of a SSGD pipeline. We perform our experiments on two datasets: Fisher Corpus Part 1 and CALLHOME, evaluating both separation and diarization metrics. Notably, our SSGD DPRNN-based online model achieves 11.1% DER on CALLHOME, comparable with most state-of-the-art end-to-end neural diarization models despite being trained on an order of magnitude less data and having considerably lower latency, i.e., 0.1 vs. 10 seconds. We also show that the separated signals can be readily fed to a speech recognition back-end with performance close to the oracle source signals. | ['Enrico Zovato', 'Luca Serafini', 'Stefano Squartini', 'Alessio Brutti', 'Desh Raj', 'Samuele Cornell', 'Giovanni Morrone'] | 2022-04-05 | null | null | null | null | ['activity-detection', 'speech-separation'] | ['computer-vision', 'speech'] | [ 1.86209559e-01 4.62555736e-01 2.00032890e-01 -4.43336636e-01
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fd093d0b-b7a9-4660-b5b8-3c799ffd6925 | low-cost-lidar-based-vehicle-pose-estimation | 1910.01701 | null | https://arxiv.org/abs/1910.01701v1 | https://arxiv.org/pdf/1910.01701v1.pdf | Low-cost LIDAR based Vehicle Pose Estimation and Tracking | Detecting surrounding vehicles by low-cost LIDAR has been drawing enormous attention. In low-cost LIDAR, vehicles present a multi-layer L-Shape. Based on our previous optimization/criteria-based L-Shape fitting algorithm, we here propose a data-driven and model-based method for robust vehicle segmentation and tracking. The new method uses T-linkage RANSAC to take a limited amount of noisy data and performs a robust segmentation for a moving car against noise. Compared with our previous method, T-Linkage RANSAC is more tolerant of observation uncertainties, i.e., the number of sides of the target being observed, and gets rid of the L-Shape assumption. In addition, a vehicle tracking system with Multi-Model Association (MMA) is built upon the segmentation result, which provides smooth trajectories of tracked objects. A manually labeled dataset from low-cost multi-layer LIDARs for validation will also be released with the paper. Experiments on the dataset show that the new approach outperforms previous ones based on multiple criteria. The new algorithm can also run in real-time. | ['John M. Dolan', 'Xiao Zhang', 'Chiyu Dong', 'Chen Fu'] | 2019-10-03 | null | null | null | null | ['vehicle-pose-estimation'] | ['computer-vision'] | [-2.59924054e-01 -3.78991365e-01 -9.88346264e-02 -5.05851090e-01
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979d6690-1e1f-440d-bf9a-fb4a9aa82d48 | zero-shot-learning-with-complementary | 1804.06505 | null | https://arxiv.org/abs/1804.06505v2 | https://arxiv.org/pdf/1804.06505v2.pdf | Complementary Attributes: A New Clue to Zero-Shot Learning | Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from seen classes to unseen classes. To take full advantage of the knowledge transferred by attributes, in this paper, we introduce the notion of complementary attributes (CA), as a supplement to the original attributes, to enhance the semantic representation ability. Theoretical analyses demonstrate that complementary attributes can improve the PAC-style generalization bound of original ZSL model. Since the proposed CA focuses on enhancing the semantic representation, CA can be easily applied to any existing attribute-based ZSL methods, including the label-embedding strategy based ZSL (LEZSL) and the probability-prediction strategy based ZSL (PPZSL). In PPZSL, there is a strong assumption that all the attributes are independent of each other, which is arguably unrealistic in practice. To solve this problem, a novel rank aggregation framework is proposed to circumvent the assumption. Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and achieve the state-of-the-art performance. | ['Chuancai Liu', 'Ivor W. Tsang', 'Xiaofeng Xu'] | 2018-04-17 | null | null | null | null | ['style-generalization'] | ['computer-vision'] | [ 2.02638581e-01 2.17987373e-01 -2.83687830e-01 -6.29460096e-01
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2c8062ff-57d1-47b8-81c8-96cb2d98852b | customers-churn-prediction-in-financial | 1912.11346 | null | https://arxiv.org/abs/1912.11346v1 | https://arxiv.org/pdf/1912.11346v1.pdf | Customers Churn Prediction in Financial Institution Using Artificial Neural Network | In this study, a predictive model using Multi-layer Perceptron of Artificial Neural Network architecture was developed to predict customer churn in a financial institution. Previous researches have used supervised machine learning classifiers such as Logistic Regression, Decision Tree, Support Vector Machine, K-Nearest Neighbors, and Random Forest. These classifiers require human effort to perform feature engineering which leads to over-specified and incomplete feature selection. Therefore, this research developed a model to eliminate manual feature engineering in data preprocessing stage. Fifty thousand customers? data were extracted from the database of one of the leading financial institution in Nigeria for the study. The multi-layer perceptron model was built with python programming language and used two overfitting techniques (Dropout and L2 regularization). The implementation done in python was compared with another model in Neuro solution infinity software. The results showed that the Artificial Neural Network software development (Python) had comparable performance with that obtained from the Neuro Solution Infinity software. The accuracy rates are 97.53% and 97.4% while ROC (Receiver Operating Characteristic) curve graphs are 0.89 and 0.85 respectively. | ['Kamorudeen A. Amuda', 'Adesesan B. Adeyemo'] | 2019-12-23 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-4.56470549e-01 -1.87147483e-01 -1.70285434e-01 -7.74081230e-01
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c4986370-f344-42e1-b08f-6bf3e229e4dd | squeezing-nnu-nets-with-knowledge | 2306.09886 | null | https://arxiv.org/abs/2306.09886v1 | https://arxiv.org/pdf/2306.09886v1.pdf | Squeezing nnU-Nets with Knowledge Distillation for On-Board Cloud Detection | Cloud detection is a pivotal satellite image pre-processing step that can be performed both on the ground and on board a satellite to tag useful images. In the latter case, it can reduce the amount of data to downlink by pruning the cloudy areas, or to make a satellite more autonomous through data-driven acquisition re-scheduling. We approach this task with nnU-Nets, a self-reconfigurable framework able to perform meta-learning of a segmentation network over various datasets. Unfortunately, such models are commonly memory-inefficient due to their (very) large architectures. To benefit from them in on-board processing, we compress nnU-Nets with knowledge distillation into much smaller and compact U-Nets. Our experiments, performed over Sentinel-2 and Landsat-8 images revealed that nnU-Nets deliver state-of-the-art performance without any manual design. Our approach was ranked within the top 7% best solutions (across 847 teams) in the On Cloud N: Cloud Cover Detection Challenge, where we reached the Jaccard index of 0.882 over more than 10k unseen Sentinel-2 images (the winners obtained 0.897, the baseline U-Net with the ResNet-34 backbone: 0.817, and the classic Sentinel-2 image thresholding: 0.652). Finally, we showed that knowledge distillation enables to elaborate dramatically smaller (almost 280x) U-Nets when compared to nnU-Nets while still maintaining their segmentation capabilities. | ['Jakub Nalepa', 'Bertrand Le Saux', 'Nicolas Longépé', 'Piotr Bosowski', 'Michal Kawulok', 'Maciej Ziaja', 'Bartosz Grabowski'] | 2023-06-16 | null | null | null | null | ['cloud-detection', 'meta-learning'] | ['computer-vision', 'methodology'] | [ 2.51548022e-01 2.99442075e-02 -8.95601213e-02 -1.15139708e-01
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fb472b42-d19b-4711-8ae5-5dcae404d1f6 | knowner-incremental-multilingual-knowledge-in | 1709.03544 | null | http://arxiv.org/abs/1709.03544v1 | http://arxiv.org/pdf/1709.03544v1.pdf | KnowNER: Incremental Multilingual Knowledge in Named Entity Recognition | KnowNER is a multilingual Named Entity Recognition (NER) system that
leverages different degrees of external knowledge. A novel modular framework
divides the knowledge into four categories according to the depth of knowledge
they convey. Each category consists of a set of features automatically
generated from different information sources (such as a knowledge-base, a list
of names or document-specific semantic annotations) and is used to train a
conditional random field (CRF). Since those information sources are usually
multilingual, KnowNER can be easily trained for a wide range of languages. In
this paper, we show that the incorporation of deeper knowledge systematically
boosts accuracy and compare KnowNER with state-of-the-art NER approaches across
three languages (i.e., English, German and Spanish) performing amongst
state-of-the art systems in all of them. | ['Luciano del Corro', 'Johannes Hoffart', 'Gerhard Weikum', 'Dominic Seyler', 'Tatiana Dembelova'] | 2017-09-11 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.72878397e-01 3.46793002e-03 -6.61472499e-01 -3.34078342e-01
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493e57b1-9e2f-488b-8dad-b3ea9677ad8a | retrieval-based-layer-wise-adaptive | null | null | https://openreview.net/forum?id=zikTfLBMXAg | https://openreview.net/pdf?id=zikTfLBMXAg | Retrieval-based Layer-wise Adaptive Transformer for Source Code Summarization | We propose a model that learns both the sequential and the structural features of code for source code summarization. We adopt the Abstract Syntax Tree (AST) and graph convolution to model the structural information and the Transformer to model the sequential information. We convert code snippets into ASTs and apply graph convolution to obtain structurally-encoded node representations. Then, the sequences of the graph-convolutioned AST nodes are processed by the Transformer layers. Since structurally-neighboring nodes will have similar representations in graph-convolutioned trees, the Transformer layers can effectively capture not only the sequential information but also the structural information such as sentences or blocks of source code. We show that our model outperforms the state-of-the-art for source code summarization by experiments and human evaluations. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-summarization'] | ['computer-code'] | [ 2.49814972e-01 3.35286796e-01 -2.72356182e-01 -5.47632694e-01
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529be77a-c40d-4f91-b83e-d17957546089 | self-remixing-unsupervised-speech-separation | 2211.10194 | null | https://arxiv.org/abs/2211.10194v1 | https://arxiv.org/pdf/2211.10194v1.pdf | Self-Remixing: Unsupervised Speech Separation via Separation and Remixing | We present Self-Remixing, a novel self-supervised speech separation method, which refines a pre-trained separation model in an unsupervised manner. The proposed method consists of a shuffler module and a solver module, and they grow together through separation and remixing processes. Specifically, the shuffler first separates observed mixtures and makes pseudo-mixtures by shuffling and remixing the separated signals. The solver then separates the pseudo-mixtures and remixes the separated signals back to the observed mixtures. The solver is trained using the observed mixtures as supervision, while the shuffler's weights are updated by taking the moving average with the solver's, generating the pseudo-mixtures with fewer distortions. Our experiments demonstrate that Self-Remixing gives better performance over existing remixing-based self-supervised methods with the same or less training costs under unsupervised setup. Self-Remixing also outperforms baselines in semi-supervised domain adaptation, showing effectiveness in multiple setups. | ['Tetsuji Ogawa', 'Kohei Saijo'] | 2022-11-18 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.74669492e-01 5.62531985e-02 -3.05833906e-01 -5.39799929e-01
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4d0297f4-7cbd-445f-8389-12bd31ddfca9 | deep-learning-for-network-traffic | 2106.12693 | null | https://arxiv.org/abs/2106.12693v1 | https://arxiv.org/pdf/2106.12693v1.pdf | Deep Learning for Network Traffic Classification | Monitoring network traffic to identify content, services, and applications is an active research topic in network traffic control systems. While modern firewalls provide the capability to decrypt packets, this is not appealing for privacy advocates. Hence, identifying any information from encrypted traffic is a challenging task. Nonetheless, previous work has identified machine learning methods that may enable application and service identification. The process involves high level feature extraction from network packet data then training a robust machine learning classifier for traffic identification. We propose a classification technique using an ensemble of deep learning architectures on packet, payload, and inter-arrival time sequences. To our knowledge, this is the first time such deep learning architectures have been applied to the Server Name Indication (SNI) classification problem. Our ensemble model beats the state of the art machine learning methods and our up-to-date model can be found on github: \url{https://github.com/niloofarbayat/NetworkClassification} | ['Derrick Liu', 'Weston Jackson', 'Niloofar Bayat'] | 2021-06-02 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 1.69939533e-01 -5.07376492e-01 -6.74535692e-01 -5.66255629e-01
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d34b00d0-24b0-4144-b467-bf65d6fce932 | rethinking-rotation-invariance-with-point | 2301.00149 | null | https://arxiv.org/abs/2301.00149v1 | https://arxiv.org/pdf/2301.00149v1.pdf | Rethinking Rotation Invariance with Point Cloud Registration | Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance in terms of point cloud registration and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. We first encode shape descriptors constructed with respect to reference frames defined over different scales, e.g., local patches and global topology, to generate rotation-invariant latent shape codes. Within the integration stage, we propose Aligned Integration Transformer to produce a discriminative feature representation by integrating point-wise self- and cross-relations established within the shape codes. Meanwhile, we adopt rigid transformations between reference frames to align the shape codes for feature consistency across different scales. Finally, the deep integrated feature is registered to both rotation-invariant shape codes to maximize feature similarities, such that rotation invariance of the integrated feature is preserved and shared semantic information is implicitly extracted from shape codes. Experimental results on 3D shape classification, part segmentation, and retrieval tasks prove the feasibility of our work. Our project page is released at: https://rotation3d.github.io/. | ['Weidong Cai', 'Chaoyi Zhang', 'Jianhui Yu'] | 2022-12-31 | null | null | null | null | ['3d-shape-retrieval', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [-3.65181416e-02 -4.73951787e-01 -3.54002148e-01 -5.69597840e-01
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d24cffe6-1f07-4a72-91f8-36a35b715bec | spectral-toolkit-of-algorithms-for-graphs | 2304.0317 | null | https://arxiv.org/abs/2304.03170v1 | https://arxiv.org/pdf/2304.03170v1.pdf | Spectral Toolkit of Algorithms for Graphs: Technical Report (1) | Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clustering, and this technical report presents a user's guide to STAG, showcase studies, and several technical considerations behind our development. | ['He Sun', 'Peter Macgregor'] | 2023-04-05 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.37555152e-01 1.46701247e-01 -1.89083084e-01 -1.30388662e-01
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2383348d-dc53-4171-a9f3-2ac80d78898a | intent-mining-from-past-conversations-for | 2005.11014 | null | https://arxiv.org/abs/2005.11014v4 | https://arxiv.org/pdf/2005.11014v4.pdf | Intent Mining from past conversations for conversational agent | Conversational systems are of primary interest in the AI community. Chatbots are increasingly being deployed to provide round-the-clock support and to increase customer engagement. Many of the commercial bot building frameworks follow a standard approach that requires one to build and train an intent model to recognize a user input. Intent models are trained in a supervised setting with a collection of textual utterance and intent label pairs. Gathering a substantial and wide coverage of training data for different intent is a bottleneck in the bot building process. Moreover, the cost of labeling a hundred to thousands of conversations with intent is a time consuming and laborious job. In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations. We have introduced a novel density-based clustering algorithm ITER-DBSCAN for unbalanced data clustering. Subject Matter Expert (Annotators with domain expertise) manually looks into the clustered user utterances and provides an intent label for discovery. We conducted user studies to validate the effectiveness of the trained intent model generated in terms of coverage of intents, accuracy and time saving concerning manual annotation. Although the system is developed for building an intent model for the conversational system, this framework can also be used for a short text clustering or as a labeling framework. | ['Ajay Chatterjee', 'Shubhashis Sengupta'] | 2020-05-22 | null | https://aclanthology.org/2020.coling-main.366 | https://aclanthology.org/2020.coling-main.366.pdf | coling-2020-8 | ['intent-discovery', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.58952251e-01 3.96989703e-01 7.51241855e-03 -7.66660452e-01
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c14499fb-cfc5-43c9-af4c-349a043dc9f7 | audio-visual-contrastive-learning-for-self | 2204.13386 | null | https://arxiv.org/abs/2204.13386v2 | https://arxiv.org/pdf/2204.13386v2.pdf | Self-supervised Contrastive Learning for Audio-Visual Action Recognition | The underlying correlation between audio and visual modalities can be utilized to learn supervised information for unlabeled videos. In this paper, we propose an end-to-end self-supervised framework named Audio-Visual Contrastive Learning (AVCL), to learn discriminative audio-visual representations for action recognition. Specifically, we design an attention based multi-modal fusion module (AMFM) to fuse audio and visual modalities. To align heterogeneous audio-visual modalities, we construct a novel co-correlation guided representation alignment module (CGRA). To learn supervised information from unlabeled videos, we propose a novel self-supervised contrastive learning module (SelfCL). Furthermore, we build a new audio-visual action recognition dataset named Kinetics-Sounds100. Experimental results on Kinetics-Sounds32 and Kinetics-Sounds100 datasets demonstrate the superiority of our AVCL over the state-of-the-art methods on large-scale action recognition benchmark. | ['Haoyuan Lan', 'Ying Tan', 'Yang Liu'] | 2022-04-28 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 3.26022178e-01 -5.08937418e-01 -3.36824507e-01 -3.80835384e-01
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c0efcdce-3717-44e4-92a0-c09c90c51082 | l2cs-net-fine-grained-gaze-estimation-in | 2203.03339 | null | https://arxiv.org/abs/2203.03339v1 | https://arxiv.org/pdf/2203.03339v1.pdf | L2CS-Net: Fine-Grained Gaze Estimation in Unconstrained Environments | Human gaze is a crucial cue used in various applications such as human-robot interaction and virtual reality. Recently, convolution neural network (CNN) approaches have made notable progress in predicting gaze direction. However, estimating gaze in-the-wild is still a challenging problem due to the uniqueness of eye appearance, lightning conditions, and the diversity of head pose and gaze directions. In this paper, we propose a robust CNN-based model for predicting gaze in unconstrained settings. We propose to regress each gaze angle separately to improve the per-angel prediction accuracy, which will enhance the overall gaze performance. In addition, we use two identical losses, one for each angle, to improve network learning and increase its generalization. We evaluate our model with two popular datasets collected with unconstrained settings. Our proposed model achieves state-of-the-art accuracy of 3.92{\deg} and 10.41{\deg} on MPIIGaze and Gaze360 datasets, respectively. We make our code open source at https://github.com/Ahmednull/L2CS-Net. | ['Ayoub Al-Hamadi', 'Aly Khalifa', 'Thorsten Hempel', 'Ahmed A. Abdelrahman'] | 2022-03-07 | null | null | null | null | ['gaze-estimation', 'eye-tracking'] | ['computer-vision', 'computer-vision'] | [-2.28003249e-01 -1.85495213e-01 -3.54853570e-02 -6.49713695e-01
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849ce658-70c2-492f-b9f6-b22b0b7fea77 | high-impedance-non-linear-fault-detection-via | 2301.04123 | null | https://arxiv.org/abs/2301.04123v1 | https://arxiv.org/pdf/2301.04123v1.pdf | High-Impedance Non-Linear Fault Detection via Eigenvalue Analysis with low PMU Sampling Rates | This technique holds several advantages over contemporary techniques: It utilizes technology that is already deployed in the field, it offers a significant degree of generality, and so far it has displayed a very high-level of sensitivity without sacrificing accuracy. Validation is performed in the form of simulations based in the IEEE 13 Node System and non-linear fault models. Test results are encouraging, indicating potential for real-life applications. | ['Sean Meyn', 'Arturo Bretas', 'Gian Paramo'] | 2023-01-10 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.20175026e-01 -1.79730073e-01 -3.05731177e-01 -2.08078250e-01
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a082249a-ed7a-40f9-842c-08b41e571009 | joint-level-generation-and-translation-using | 2306.16662 | null | https://arxiv.org/abs/2306.16662v1 | https://arxiv.org/pdf/2306.16662v1.pdf | Joint Level Generation and Translation Using Gameplay Videos | Procedural Content Generation via Machine Learning (PCGML) faces a significant hurdle that sets it apart from other fields, such as image or text generation, which is limited annotated data. Many existing methods for procedural level generation via machine learning require a secondary representation besides level images. However, the current methods for obtaining such representations are laborious and time-consuming, which contributes to this problem. In this work, we aim to address this problem by utilizing gameplay videos of two human-annotated games to develop a novel multi-tail framework that learns to perform simultaneous level translation and generation. The translation tail of our framework can convert gameplay video frames to an equivalent secondary representation, while its generation tail can produce novel level segments. Evaluation results and comparisons between our framework and baselines suggest that combining the level generation and translation tasks can lead to an overall improved performance regarding both tasks. This represents a possible solution to limited annotated level data, and we demonstrate the potential for future versions to generalize to unseen games. | ['Matthew Guzdial', 'Negar Mirgati'] | 2023-06-29 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 6.80450797e-01 3.90971631e-01 1.09442314e-02 -6.34177821e-03
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f4749c6a-2283-40da-9475-b480636661c6 | can-current-task-oriented-dialogue-models | 2212.10504 | null | https://arxiv.org/abs/2212.10504v2 | https://arxiv.org/pdf/2212.10504v2.pdf | Can Current Task-oriented Dialogue Models Automate Real-world Scenarios in the Wild? | Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. However, we argue that the current TOD benchmarks are limited to surrogate real-world scenarios and that the current TOD models are still a long way to cover the scenarios. In this position paper, we first identify current status and limitations of SF-TOD systems. After that, we explore the WebTOD framework, the alternative direction for building a scalable TOD system when a web/mobile interface is available. In WebTOD, the dialogue system learns how to understand the web/mobile interface that the human agent interacts with, powered by a large-scale language model. | ['WooMyoung Park', 'Hyungsuk Noh', 'Donghyun Kwak', 'Kyunghyun Cho', 'Wangkyo Jung', 'Hyunhoon Jung', 'Shin Ah Oh', 'Youngki Hong', 'Donghoon Ham', 'Donghyeon Ko', 'Sungdong Kim', 'Sang-Woo Lee'] | 2022-12-20 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-1.79502711e-01 7.34192252e-01 -2.20778927e-01 -4.06075656e-01
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af5cda29-2fd0-435e-a15a-6e11f73cfa15 | joint-turn-and-dialogue-level-user | 2010.02495 | null | https://arxiv.org/abs/2010.02495v2 | https://arxiv.org/pdf/2010.02495v2.pdf | Joint Turn and Dialogue level User Satisfaction Estimation on Multi-Domain Conversations | Dialogue level quality estimation is vital for optimizing data driven dialogue management. Current automated methods to estimate turn and dialogue level user satisfaction employ hand-crafted features and rely on complex annotation schemes, which reduce the generalizability of the trained models. We propose a novel user satisfaction estimation approach which minimizes an adaptive multi-task loss function in order to jointly predict turn-level Response Quality labels provided by experts and explicit dialogue-level ratings provided by end users. The proposed BiLSTM based deep neural net model automatically weighs each turn's contribution towards the estimated dialogue-level rating, implicitly encodes temporal dependencies, and removes the need to hand-craft features. On dialogues sampled from 28 Alexa domains, two dialogue systems and three user groups, the joint dialogue-level satisfaction estimation model achieved up to an absolute 27% (0.43->0.70) and 7% (0.63->0.70) improvement in linear correlation performance over baseline deep neural net and benchmark Gradient boosting regression models, respectively. | ['Josep Valls Vargas', 'Spyros Matsoukas', 'Lazaros Polymenakos', 'Aditya Tiwari', 'Praveen Kumar Bodigutla'] | 2020-10-06 | null | https://aclanthology.org/2020.findings-emnlp.347 | https://aclanthology.org/2020.findings-emnlp.347.pdf | findings-of-the-association-for-computational | ['dialogue-management'] | ['natural-language-processing'] | [-1.11689210e-01 4.75776881e-01 -5.38474545e-02 -1.15783238e+00
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65e667d6-a042-4110-a643-eb0e38b31931 | 3dsgrasp-3d-shape-completion-for-robotic | 2301.00866 | null | https://arxiv.org/abs/2301.00866v1 | https://arxiv.org/pdf/2301.00866v1.pdf | 3DSGrasp: 3D Shape-Completion for Robotic Grasp | Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance. | ['Jose Santos-Victor', 'Alessio Del Bue', 'Alexandre Bernardino', 'Plinio Moreno', 'Atabak Dehban', 'Pietro Morerio', 'Matteo Taiana', 'Yiming Wang', 'Dimitris Dimou', 'Nuno F. Duarte', 'Seyed S. Mohammadi'] | 2023-01-02 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-8.95582289e-02 -1.24906793e-01 -8.93069655e-02 -4.29570109e-01
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4d4fa507-5c62-4231-8434-756a32750ee3 | contrastive-multi-view-framework-for-customer | 2306.144 | null | https://arxiv.org/abs/2306.14400v1 | https://arxiv.org/pdf/2306.14400v1.pdf | Contrastive Multi-view Framework for Customer Lifetime Value Prediction | Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications. However, the heavy sparsity of consumption events and the interference of data variance and noise obstruct LTV estimation. Many existing LTV prediction methods directly train a single-view LTV predictor on consumption samples, which may yield inaccurate and even biased knowledge extraction. In this paper, we propose a contrastive multi-view framework for LTV prediction, which is a plug-and-play solution compatible with various backbone models. It synthesizes multiple heterogeneous LTV regressors with complementary knowledge to improve model robustness and captures sample relatedness via contrastive learning to mitigate the dependency on data abundance. Concretely, we use a decomposed scheme that converts the LTV prediction problem into a combination of estimating consumption probability and payment amount. To alleviate the impact of noisy data on model learning, we propose a multi-view framework that jointly optimizes multiple types of regressors with diverse characteristics and advantages to encode and fuse comprehensive knowledge. To fully exploit the potential of limited training samples, we propose a hybrid contrastive learning method to help capture the relatedness between samples in both classification and regression tasks. We conduct extensive experiments on a real-world game LTV prediction dataset and the results validate the effectiveness of our method. We have deployed our solution online in Huawei's mobile game center and achieved 32.26% of total payment amount gains. | ['Ruiming Tang', 'Yuan Fang', 'Hong Zhu', 'Qinglin Jia', 'Jingjie Li', 'Chuhan Wu'] | 2023-06-26 | null | null | null | null | ['value-prediction', 'contrastive-learning', 'contrastive-learning', 'marketing'] | ['computer-code', 'computer-vision', 'methodology', 'miscellaneous'] | [-3.06241602e-01 -3.16521376e-01 -9.20825839e-01 -7.08832145e-01
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9b940173-0532-41d4-aa85-03aa1adca5e5 | safe-exploration-of-nonlinear-dynamical | 1812.05506 | null | https://arxiv.org/abs/1812.05506v4 | https://arxiv.org/pdf/1812.05506v4.pdf | A predictive safety filter for learning-based control of constrained nonlinear dynamical systems | The transfer of reinforcement learning (RL) techniques into real-world applications is challenged by safety requirements in the presence of physical limitations. Most RL methods, in particular the most popular algorithms, do not support explicit consideration of state and input constraints. In this paper, we address this problem for nonlinear systems with continuous state and input spaces by introducing a predictive safety filter, which is able to turn a constrained dynamical system into an unconstrained safe system and to which any RL algorithm can be applied `out-of-the-box'. The predictive safety filter receives the proposed control input and decides, based on the current system state, if it can be safely applied to the real system, or if it has to be modified otherwise. Safety is thereby established by a continuously updated safety policy, which is based on a model predictive control formulation using a data-driven system model and considering state and input dependent uncertainties. | ['Melanie N. Zeilinger', 'Kim P. Wabersich'] | 2018-12-13 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.99149364e-01 5.28332889e-01 -2.10136145e-01 2.36462384e-01
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42dc4310-a1a1-4edb-bbaf-c2dab081d20d | a-deep-learning-system-for-domain-specific | 2303.1051 | null | https://arxiv.org/abs/2303.10510v1 | https://arxiv.org/pdf/2303.10510v1.pdf | A Deep Learning System for Domain-specific speech Recognition | As human-machine voice interfaces provide easy access to increasingly intelligent machines, many state-of-the-art automatic speech recognition (ASR) systems are proposed. However, commercial ASR systems usually have poor performance on domain-specific speech especially under low-resource settings. The author works with pre-trained DeepSpeech2 and Wav2Vec2 acoustic models to develop benefit-specific ASR systems. The domain-specific data are collected using proposed semi-supervised learning annotation with little human intervention. The best performance comes from a fine-tuned Wav2Vec2-Large-LV60 acoustic model with an external KenLM, which surpasses the Google and AWS ASR systems on benefit-specific speech. The viability of using error prone ASR transcriptions as part of spoken language understanding (SLU) is also investigated. Results of a benefit-specific natural language understanding (NLU) task show that the domain-specific fine-tuned ASR system can outperform the commercial ASR systems even when its transcriptions have higher word error rate (WER), and the results between fine-tuned ASR and human transcriptions are similar. | ['Yanan Jia'] | 2023-03-18 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [-4.08089459e-02 1.30805299e-01 2.03810990e-01 -6.06167674e-01
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1fcd7aad-6066-49af-a8c0-330e0ef8fc16 | pay-attention-to-your-tone-introducing-a-new | 2212.1019 | null | https://arxiv.org/abs/2212.10190v1 | https://arxiv.org/pdf/2212.10190v1.pdf | Pay Attention to Your Tone: Introducing a New Dataset for Polite Language Rewrite | We introduce \textsc{PoliteRewrite} -- a dataset for polite language rewrite which is a novel sentence rewrite task. Compared with previous text style transfer tasks that can be mostly addressed by slight token- or phrase-level edits, polite language rewrite requires deep understanding and extensive sentence-level edits over an offensive and impolite sentence to deliver the same message euphemistically and politely, which is more challenging -- not only for NLP models but also for human annotators to rewrite with effort. To alleviate the human effort for efficient annotation, we first propose a novel annotation paradigm by a collaboration of human annotators and GPT-3.5 to annotate \textsc{PoliteRewrite}. The released dataset has 10K polite sentence rewrites annotated collaboratively by GPT-3.5 and human, which can be used as gold standard for training, validation and test; and 100K high-quality polite sentence rewrites by GPT-3.5 without human review. We wish this work (The dataset (10K+100K) will be released soon) could contribute to the research on more challenging sentence rewrite, and provoke more thought in future on resource annotation paradigm with the help of the large-scaled pretrained models. | ['Si-Qing Chen', 'Furu Wei', 'Yuki Li', 'Allen Mao', 'Tao Ge', 'Xun Wang'] | 2022-12-20 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.96718627e-01 6.56691313e-01 4.58790101e-02 -4.45792973e-01
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9cbc7695-db84-41e8-8325-dfcd0cba32bb | orthographic-transliteration-for-kabyle | null | null | https://aclanthology.org/2021.icnlsp-1.3 | https://aclanthology.org/2021.icnlsp-1.3.pdf | Orthographic Transliteration for Kabyle Speech Recognition | null | ['Ni Lao', 'Christopher Haberland'] | null | null | null | null | icnlsp-2021-11 | ['transliteration'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1a910c4e-9e36-4ba2-bb3c-95761ad1b7c5 | lightweight-pyramid-networks-for-image | 1805.06173 | null | http://arxiv.org/abs/1805.06173v1 | http://arxiv.org/pdf/1805.06173v1.pdf | Lightweight Pyramid Networks for Image Deraining | Existing deep convolutional neural networks have found major success in image
deraining, but at the expense of an enormous number of parameters. This limits
their potential application, for example in mobile devices. In this paper, we
propose a lightweight pyramid of networks (LPNet) for single image deraining.
Instead of designing a complex network structures, we use domain-specific
knowledge to simplify the learning process. Specifically, we find that by
introducing the mature Gaussian-Laplacian image pyramid decomposition
technology to the neural network, the learning problem at each pyramid level is
greatly simplified and can be handled by a relatively shallow network with few
parameters. We adopt recursive and residual network structures to build the
proposed LPNet, which has less than 8K parameters while still achieving
state-of-the-art performance on rain removal. We also discuss the potential
value of LPNet for other low- and high-level vision tasks. | ['Yue Huang', 'Xueyang Fu', 'Borong Liang', 'Xinghao Ding', 'John Paisley'] | 2018-05-16 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.28871098e-01 1.25934586e-01 3.82070214e-01 -2.40939885e-01
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ef07540c-c4c9-4a54-bdab-e4d44a891c25 | a-machine-learning-model-of-the-combination | 2102.0941 | null | https://arxiv.org/abs/2102.09410v1 | https://arxiv.org/pdf/2102.09410v1.pdf | A Machine Learning model of the combination of normalized SD1 and SD2 indexes from 24h-Heart Rate Variability as a predictor of myocardial infarction | Aim: to evaluate the ability of the nonlinear 24-HRV as a predictor of MI using Machine Learning Methods: The sample was composed of 218 patients divided into two groups (Healthy, n=128; MI n=90). The sample dataset is part of the Telemetric and Holter Electrocardiogram Warehouse (THEW) database, from the University of Rochester Medical Center. We used the most common ML algorithms for accuracy comparison with a setting of 10-fold cross-validation (briefly, Linear Regression, Linear Discriminant Analysis, k-Nearest Neighbour, Random Forest, Supporting Vector Machine, Na\"ive Bayes, C 5.0 and Stochastic Gradient Boosting). Results: The main findings of this study show that the combination of SD1nu + SD2nu has greater predictive power for MI in comparison to other HRV indexes. Conclusion: The ML model using nonlinear HRV indexes showed to be more effective than the linear domain, evidenced through the application of ML, represented by a good precision of the Stochastic Gradient Boosting model. Keywords: heart rate variability, machine learning, nonlinear domain, cardiovascular disease | ['Cristiano Mostarda', 'Adeilson Serra Mendes Vieira', 'Sara Raquel Dutra-Macedo', 'Antonio Carlos Silva-Filho'] | 2021-02-18 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-7.31606036e-02 -4.03149307e-01 -4.34552908e-01 -3.95459205e-01
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9b9d074b-d6b4-45bc-92ac-b3bfff664cca | seeing-through-noise-visually-driven-speaker | 1708.06767 | null | http://arxiv.org/abs/1708.06767v3 | http://arxiv.org/pdf/1708.06767v3.pdf | Seeing Through Noise: Visually Driven Speaker Separation and Enhancement | Isolating the voice of a specific person while filtering out other voices or
background noises is challenging when video is shot in noisy environments. We
propose audio-visual methods to isolate the voice of a single speaker and
eliminate unrelated sounds. First, face motions captured in the video are used
to estimate the speaker's voice, by passing the silent video frames through a
video-to-speech neural network-based model. Then the speech predictions are
applied as a filter on the noisy input audio. This approach avoids using
mixtures of sounds in the learning process, as the number of such possible
mixtures is huge, and would inevitably bias the trained model. We evaluate our
method on two audio-visual datasets, GRID and TCD-TIMIT, and show that our
method attains significant SDR and PESQ improvements over the raw
video-to-speech predictions, and a well-known audio-only method. | ['Shmuel Peleg', 'Ariel Ephrat', 'Tavi Halperin', 'Aviv Gabbay'] | 2017-08-22 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 3.03137839e-01 -2.18961135e-01 9.92944166e-02 -2.59376550e-03
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a832b919-710b-483c-be8d-193821e0ad6f | treegcn-ed-encoding-point-cloud-using-a-tree | 2110.0317 | null | https://arxiv.org/abs/2110.03170v3 | https://arxiv.org/pdf/2110.03170v3.pdf | TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network | Point cloud is one of the widely used techniques for representing and storing 3D geometric data. In the past several methods have been proposed for processing point clouds. Methods such as PointNet and FoldingNet have shown promising results for tasks like 3D shape classification and segmentation. This work proposes a tree-structured autoencoder framework to generate robust embeddings of point clouds by utilizing hierarchical information using graph convolution. We perform multiple experiments to assess the quality of embeddings generated by the proposed encoder architecture and visualize the t-SNE map to highlight its ability to distinguish between different object classes. We further demonstrate the applicability of the proposed framework in applications like: 3D point cloud completion and Single image-based 3D reconstruction. | ['Shanmuganathan Raman', 'Kaustubh Sadekar', 'Prajwal Singh'] | 2021-10-07 | null | null | null | null | ['3d-shape-retrieval', 'point-cloud-completion'] | ['computer-vision', 'computer-vision'] | [-2.92019546e-01 8.64583161e-03 3.20299864e-01 -5.97691476e-01
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d4c30aae-334d-418d-af80-53168e3b6eea | rong-he-ti-shi-xue-xi-de-gu-shi-sheng-cheng | null | null | https://aclanthology.org/2022.ccl-1.16 | https://aclanthology.org/2022.ccl-1.16.pdf | 融合提示学习的故事生成方法(A Story Generation Method Incorporating Prompt Learning) | “开放式自动故事生成通过输入故事的开头、大纲、主线等,得到具有一致性、连贯性和逻辑性的故事。现有的方法想要提升生成故事的质量,往往需要大量训练数据和更多参数的模型。针对以上问题,该文利用提示学习在零样本与少样本场景下的优势,同时使用外部常识推理知识,提出了一种故事生成方法。该方法将故事生成分为三个阶段:输入故事的开头,常识推理模型生成可能的事件;根据类型不同,将事件填入问题模板中,构建引导模型生成合理回答的问题;问答模型产生对应问题的答案,并选择困惑度最小的作为故事下文。重复上述过程,最终生成完整的故事。自动评测与人工评测指标表明,与基线模型相比,该文提出的方法能够生成更连贯、具体和合乎逻辑的故事。” | ['Piji Li', 'Xuanfan Ni'] | null | null | null | null | ccl-2022-10 | ['story-generation'] | ['natural-language-processing'] | [-0.8836528 -0.90223825 0.7115538 0.41826648 0.43298185 -1.4358604
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e7477347-e09d-4ca5-9ef7-82f675cfb987 | lung-cancer-screening-using-adaptive-memory | 1710.05719 | null | http://arxiv.org/abs/1710.05719v2 | http://arxiv.org/pdf/1710.05719v2.pdf | Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks | In this paper, we investigate the effectiveness of deep learning techniques
for lung nodule classification in computed tomography scans. Using less than
10,000 training examples, our deep networks perform two times better than a
standard radiology software. Visualization of the networks' neurons reveals
semantically meaningful features that are consistent with the clinical
knowledge and radiologists' perception. Our paper also proposes a novel
framework for rapidly adapting deep networks to the radiologists' feedback, or
change in the data due to the shift in sensor's resolution or patient
population. The classification accuracy of our approach remains above 80% while
popular deep networks' accuracy is around chance. Finally, we provide in-depth
analysis of our framework by asking a radiologist to examine important
networks' features and perform blind re-labeling of networks' mistakes. | ['Supratik Moulik', 'Aryan Mobiny', 'Hien Van Nguyen'] | 2017-10-11 | null | null | null | null | ['lung-nodule-classification', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [ 1.83426023e-01 6.52044415e-01 6.52829185e-02 -5.28080106e-01
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ed636c6f-8f0a-46f4-875f-da93b56317ce | pms-net-robust-haze-removal-based-on-patch | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_PMS-Net_Robust_Haze_Removal_Based_on_Patch_Map_for_Single_CVPR_2019_paper.pdf | PMS-Net: Robust Haze Removal Based on Patch Map for Single Images | In this paper, we proposed a novel haze removal algorithm based on a new feature called the patch map. Conventional patch-based haze removal algorithms (e.g. the Dark Channel prior) usually performs dehazing with a fixed patch size. However, it may produce several problems in recovered results such as oversaturation and color distortion. Therefore, in this paper, we designed an adaptive and automatic patch size selection model called the Patch Map Selection Network (PMS-Net) to select the patch size corresponding to each pixel. This network is designed based on the convolutional neural network (CNN), which can generate the patch map from the image to image. Experimental results on both synthesized and real-world hazy images show that, with the combination of the proposed PMS-Net, the performance in haze removal is much better than that of other state-of-the-art algorithms and we can address the problems caused by the fixed patch size.
| [' Sy-Yen Kuo', ' Jian-Jiun Ding', 'Wei-Ting Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['single-image-haze-removal', 'single-image-deraining', 'computational-phenotyping'] | ['computer-vision', 'computer-vision', 'medical'] | [ 1.31364450e-01 -4.90443677e-01 5.93610466e-01 -1.88733917e-02
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7ae42fe1-dfb6-4934-b0fe-83ff7a0374df | global-trajectory-helps-person-retrieval-in-a | 2204.129 | null | https://arxiv.org/abs/2204.12900v3 | https://arxiv.org/pdf/2204.12900v3.pdf | Cross-Camera Trajectories Help Person Retrieval in a Camera Network | We are concerned with retrieving a query person from multiple videos captured by a non-overlapping camera network. Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. To address this issue, we propose a pedestrian retrieval framework based on cross-camera trajectory generation, which integrates both temporal and spatial information. To obtain pedestrian trajectories, we propose a novel cross-camera spatio-temporal model that integrates pedestrians' walking habits and the path layout between cameras to form a joint probability distribution. Such a spatio-temporal model among a camera network can be specified using sparsely sampled pedestrian data. Based on the spatio-temporal model, cross-camera trajectories can be extracted by the conditional random field model and further optimized by restricted non-negative matrix factorization. Finally, a trajectory re-ranking technique is proposed to improve the pedestrian retrieval results. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset, the Person Trajectory Dataset, in real surveillance scenarios. Extensive experiments verify the effectiveness and robustness of the proposed method. | ['Wei-Shi Zheng', 'JianHuang Lai', 'Xiaohua Xie', 'Xin Zhang'] | 2022-04-27 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-2.53441185e-01 -9.94612873e-01 -2.78961331e-01 -3.57117832e-01
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5df14826-16e2-4cc2-85a0-f7323a6c83fe | void-distributions-reveal-structural-link | 1811.00077 | null | http://arxiv.org/abs/1811.00077v1 | http://arxiv.org/pdf/1811.00077v1.pdf | Void distributions reveal structural link between jammed packings and protein cores | Dense packing of hydrophobic residues in the cores of globular proteins
determines their stability. Recently, we have shown that protein cores possess
packing fraction $\phi \approx 0.56$, which is the same as dense, random
packing of amino acid-shaped particles. In this article, we compare the
structural properties of protein cores and jammed packings of amino acid-shaped
particles in much greater depth by measuring their local and connected void
regions. We find that the distributions of surface Voronoi cell volumes and
local porosities obey similar statistics in both systems. We also measure the
probability that accessible, connected void regions percolate as a function of
the size of a spherical probe particle and show that both systems possess the
same critical probe size. By measuring the critical exponent $\tau$ that
characterizes the size distribution of connected void clusters at the onset of
percolation, we show that void percolation in packings of amino acid-shaped
particles and protein cores belong to the same universality class, which is
different from that for void percolation in jammed sphere packings. We propose
that the connected void regions of proteins are a defining feature of proteins
and can be used to differentiate experimentally observed proteins from decoy
structures that are generated using computational protein design software. This
work emphasizes that jammed packings of amino acid-shaped particles can serve
as structural and mechanical analogs of protein cores, and could therefore be
useful in modeling the response of protein cores to cavity-expanding and
-reducing mutations. | [] | 2018-10-31 | null | null | null | null | ['protein-design'] | ['medical'] | [ 3.18903923e-02 1.56632125e-01 1.15061626e-01 -1.49809420e-01
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1.56574577e-01 -8.11722159e-01 -6.41840756e-01 -2.09613383e-01] | [4.783280372619629, 5.245797634124756] |
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