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534752ec-66d5-4cd5-8e02-800b69fa6ceb | speaker-conditioned-target-speaker-extraction | 2104.04234 | null | https://arxiv.org/abs/2104.04234v1 | https://arxiv.org/pdf/2104.04234v1.pdf | Speaker-conditioned Target Speaker Extraction based on Customized LSTM Cells | Speaker-conditioned target speaker extraction systems rely on auxiliary information about the target speaker to extract the target speaker signal from a mixture of multiple speakers. Typically, a deep neural network is applied to isolate the relevant target speaker characteristics. In this paper, we focus on a single-channel target speaker extraction system based on a CNN-LSTM separator network and a speaker embedder network requiring reference speech of the target speaker. In the LSTM layer of the separator network, we propose to customize the LSTM cells in order to only remember the specific voice patterns corresponding to the target speaker by modifying the information processing in the forget gate. Experimental results for two-speaker mixtures using the Librispeech dataset show that this customization significantly improves the target speaker extraction performance compared to using standard LSTM cells. | ['Simon Doclo', 'Christian Rollwage', 'Marvin Tammen', 'Ragini Sinha'] | 2021-04-09 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 4.68668461e-01 2.42768511e-01 6.50520623e-02 -3.96646947e-01
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26f42721-12bb-42d5-a316-59c524860009 | learning-to-represent-bilingual-dictionaries | 1808.03726 | null | https://arxiv.org/abs/1808.03726v3 | https://arxiv.org/pdf/1808.03726v3.pdf | Learning to Represent Bilingual Dictionaries | Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages. However, many applications, such as cross-lingual semantic search and question answering, can be largely benefited from the cross-lingual correspondence between sentences and lexicons. To bridge this gap, we propose a neural embedding model that leverages bilingual dictionaries. The proposed model is trained to map the literal word definitions to the cross-lingual target words, for which we explore with different sentence encoding techniques. To enhance the learning process on limited resources, our model adopts several critical learning strategies, including multi-task learning on different bridges of languages, and joint learning of the dictionary model with a bilingual word embedding model. Experimental evaluation focuses on two applications. The results of the cross-lingual reverse dictionary retrieval task show our model's promising ability of comprehending bilingual concepts based on descriptions, and highlight the effectiveness of proposed learning strategies in improving performance. Meanwhile, our model effectively addresses the bilingual paraphrase identification problem and significantly outperforms previous approaches. | ['Carlo Zaniolo', 'Kai-Wei Chang', 'Haochen Chen', 'Muhao Chen', 'Steven Skiena', 'Yingtao Tian'] | 2018-08-10 | learning-to-represent-bilingual-dictionaries-1 | https://aclanthology.org/K19-1015 | https://aclanthology.org/K19-1015.pdf | conll-2019-11 | ['reverse-dictionary'] | ['natural-language-processing'] | [-2.07378760e-01 -4.02569890e-01 -7.79440999e-01 -3.21653694e-01
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dfa9cff5-a7f0-48cd-b66d-30a23e86c98d | compnet-complementary-segmentation-network | 1804.00521 | null | http://arxiv.org/abs/1804.00521v2 | http://arxiv.org/pdf/1804.00521v2.pdf | CompNet: Complementary Segmentation Network for Brain MRI Extraction | Brain extraction is a fundamental step for most brain imaging studies. In
this paper, we investigate the problem of skull stripping and propose
complementary segmentation networks (CompNets) to accurately extract the brain
from T1-weighted MRI scans, for both normal and pathological brain images. The
proposed networks are designed in the framework of encoder-decoder networks and
have two pathways to learn features from both the brain tissue and its
complementary part located outside of the brain. The complementary pathway
extracts the features in the non-brain region and leads to a robust solution to
brain extraction from MRIs with pathologies, which do not exist in our training
dataset. We demonstrate the effectiveness of our networks by evaluating them on
the OASIS dataset, resulting in the state of the art performance under the
two-fold cross-validation setting. Moreover, the robustness of our networks is
verified by testing on images with introduced pathologies and by showing its
invariance to unseen brain pathologies. In addition, our complementary network
design is general and can be extended to address other image segmentation
problems with better generalization. | ['Yi Hong', 'Raunak Dey'] | 2018-03-27 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 5.24802327e-01 4.90378141e-01 1.00864738e-01 -3.88615638e-01
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4cc51c08-4a4a-4c73-ab98-4923334af23d | deep-learning-for-distant-speech-recognition | 1712.06086 | null | http://arxiv.org/abs/1712.06086v1 | http://arxiv.org/pdf/1712.06086v1.pdf | Deep Learning for Distant Speech Recognition | Deep learning is an emerging technology that is considered one of the most
promising directions for reaching higher levels of artificial intelligence.
Among the other achievements, building computers that understand speech
represents a crucial leap towards intelligent machines. Despite the great
efforts of the past decades, however, a natural and robust human-machine speech
interaction still appears to be out of reach, especially when users interact
with a distant microphone in noisy and reverberant environments. The latter
disturbances severely hamper the intelligibility of a speech signal, making
Distant Speech Recognition (DSR) one of the major open challenges in the field.
This thesis addresses the latter scenario and proposes some novel techniques,
architectures, and algorithms to improve the robustness of distant-talking
acoustic models. We first elaborate on methodologies for realistic data
contamination, with a particular emphasis on DNN training with simulated data.
We then investigate on approaches for better exploiting speech contexts,
proposing some original methodologies for both feed-forward and recurrent
neural networks. Lastly, inspired by the idea that cooperation across different
DNNs could be the key for counteracting the harmful effects of noise and
reverberation, we propose a novel deep learning paradigm called network of deep
neural networks. The analysis of the original concepts were based on extensive
experimental validations conducted on both real and simulated data, considering
different corpora, microphone configurations, environments, noisy conditions,
and ASR tasks. | ['Mirco Ravanelli'] | 2017-12-17 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.43640107e-02 -3.30841541e-02 5.71598887e-01 -2.46794373e-01
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91cb59ff-a535-4880-bd7d-7cbf013002fc | hybridized-feature-extraction-and-acoustic | 1506.02170 | null | http://arxiv.org/abs/1506.02170v1 | http://arxiv.org/pdf/1506.02170v1.pdf | Hybridized Feature Extraction and Acoustic Modelling Approach for Dysarthric Speech Recognition | Dysarthria is malfunctioning of motor speech caused by faintness in the human
nervous system. It is characterized by the slurred speech along with physical
impairment which restricts their communication and creates the lack of
confidence and affects the lifestyle. This paper attempt to increase the
efficiency of Automatic Speech Recognition (ASR) system for unimpaired speech
signal. It describes state of art of research into improving ASR for speakers
with dysarthria by means of incorporated knowledge of their speech production.
Hybridized approach for feature extraction and acoustic modelling technique
along with evolutionary algorithm is proposed for increasing the efficiency of
the overall system. Here number of feature vectors are varied and tested the
system performance. It is observed that system performance is boosted by
genetic algorithm. System with 16 acoustic features optimized with genetic
algorithm has obtained highest recognition rate of 98.28% with training time of
5:30:17. | ['Megha Rughani', 'D. Shivakrishna'] | 2015-06-06 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.86537202e-02 6.58494309e-02 5.45960426e-01 -2.72102594e-01
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0c2f7852-95f2-4fd4-9c8f-30e44d801298 | deep-sketch-hashing-fast-free-hand-sketch | 1703.05605 | null | http://arxiv.org/abs/1703.05605v1 | http://arxiv.org/pdf/1703.05605v1.pdf | Deep Sketch Hashing: Fast Free-hand Sketch-Based Image Retrieval | Free-hand sketch-based image retrieval (SBIR) is a specific cross-view
retrieval task, in which queries are abstract and ambiguous sketches while the
retrieval database is formed with natural images. Work in this area mainly
focuses on extracting representative and shared features for sketches and
natural images. However, these can neither cope well with the geometric
distortion between sketches and images nor be feasible for large-scale SBIR due
to the heavy continuous-valued distance computation. In this paper, we speed up
SBIR by introducing a novel binary coding method, named \textbf{Deep Sketch
Hashing} (DSH), where a semi-heterogeneous deep architecture is proposed and
incorporated into an end-to-end binary coding framework. Specifically, three
convolutional neural networks are utilized to encode free-hand sketches,
natural images and, especially, the auxiliary sketch-tokens which are adopted
as bridges to mitigate the sketch-image geometric distortion. The learned DSH
codes can effectively capture the cross-view similarities as well as the
intrinsic semantic correlations between different categories. To the best of
our knowledge, DSH is the first hashing work specifically designed for
category-level SBIR with an end-to-end deep architecture. The proposed DSH is
comprehensively evaluated on two large-scale datasets of TU-Berlin Extension
and Sketchy, and the experiments consistently show DSH's superior SBIR
accuracies over several state-of-the-art methods, while achieving significantly
reduced retrieval time and memory footprint. | ['Yuming Shen', 'Fumin Shen', 'Xianglong Liu', 'Li Liu', 'Ling Shao'] | 2017-03-16 | deep-sketch-hashing-fast-free-hand-sketch-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Liu_Deep_Sketch_Hashing_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Liu_Deep_Sketch_Hashing_CVPR_2017_paper.pdf | cvpr-2017-7 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [-2.36149877e-01 -7.27570653e-01 -2.62993217e-01 -3.26638609e-01
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5365cbcf-0abb-4442-95c6-4e51c2eab629 | path-planning-for-autonomous-driving-the | 2303.09824 | null | https://arxiv.org/abs/2303.09824v4 | https://arxiv.org/pdf/2303.09824v4.pdf | Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives | Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey. | ['Zhe XuanYuan', 'Fenghua Zhu', 'Dongsheng Yang', 'Lingxi Li', 'Yunfeng Ai', 'Long Chen', 'Xuemin Hu', 'Bai Li', 'Yuchen Li', 'Peng Deng', 'Siyu Teng'] | 2023-03-17 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.35503516e-01 2.70442814e-01 -6.89864993e-01 -3.67090166e-01
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0ae21bea-7f86-487f-9034-db955ccd4b7b | centermask-real-time-anchor-free-instance-1 | 1911.06667 | null | https://arxiv.org/abs/1911.06667v6 | https://arxiv.org/pdf/1911.06667v6.pdf | CenterMask : Real-Time Anchor-Free Instance Segmentation | We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each box with the spatial attention map that helps to focus on informative pixels and suppress noise. We also present an improved backbone networks, VoVNetV2, with two effective strategies: (1) residual connection for alleviating the optimization problem of larger VoVNet \cite{lee2019energy} and (2) effective Squeeze-Excitation (eSE) dealing with the channel information loss problem of original SE. With SAG-Mask and VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large and small models, respectively. Using the same ResNet-101-FPN backbone, CenterMask achieves 38.3%, surpassing all previous state-of-the-art methods while at a much faster speed. CenterMask-Lite also outperforms the state-of-the-art by large margins at over 35fps on Titan Xp. We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively. The Code is available at https://github.com/youngwanLEE/CenterMask. | ['Jongyoul Park', 'Youngwan Lee'] | 2019-11-15 | centermask-real-time-anchor-free-instance | null | null | arxiv-2019-11 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 2.00158656e-01 5.33075273e-01 -1.91326201e-01 -2.28361219e-01
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81153999-5123-4087-a2d1-0496d36658af | learn-to-predict-sets-using-feed-forward | 2001.11845 | null | https://arxiv.org/abs/2001.11845v2 | https://arxiv.org/pdf/2001.11845v2.pdf | Learn to Predict Sets Using Feed-Forward Neural Networks | This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a set is not fixed in advance. Many real-world problems, such as image tagging and object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality. Depending on the problem under consideration, we define different training models for set prediction using deep neural networks. We demonstrate the validity of our set formulations on relevant vision problems such as: 1) multi-label image classification where we outperform the other competing methods on the PASCAL VOC and MS COCO datasets, 2) object detection, for which our formulation outperforms popular state-of-the-art detectors, and 3) a complex CAPTCHA test, where we observe that, surprisingly, our set-based network acquired the ability of mimicking arithmetics without any rules being coded. | ['Laura Leal-Taixé', 'Farbod T. Motlagh', 'Roman Kaskman', 'Tianyu Zhu', 'Hamid Rezatofighi', 'Anton Milan', 'Qinfeng Shi', 'Daniel Cremers', 'Ian Reid'] | 2020-01-30 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.30754173e-01 -1.86854541e-01 2.29342207e-01 -5.43673754e-01
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e15b42b8-1a9d-4165-a5db-bd7df807ae47 | disentangling-online-chats-with-dag | 2106.09024 | null | https://arxiv.org/abs/2106.09024v1 | https://arxiv.org/pdf/2106.09024v1.pdf | Disentangling Online Chats with DAG-Structured LSTMs | Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses a challenge for any task aiming to understand the content of the chat logs or gather information from them. The ability to disentangle these conversations is then tantamount to the success of many downstream tasks such as summarization and question answering. Structured information accompanying the text such as user turn, user mentions, timestamps, is used as a cue by the participants themselves who need to follow the conversation and has been shown to be important for disentanglement. DAG-LSTMs, a generalization of Tree-LSTMs that can handle directed acyclic dependencies, are a natural way to incorporate such information and its non-sequential nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement task. We perform our experiments on the Ubuntu IRC dataset. We show that the novel model we propose achieves state of the art status on the task of recovering reply-to relations and it is competitive on other disentanglement metrics. | ['Mohit Bansal', 'Ozan İrsoy', 'Marco Farina', 'Lisa Bauer', 'Duccio Pappadopulo'] | 2021-06-16 | null | https://aclanthology.org/2021.starsem-1.14 | https://aclanthology.org/2021.starsem-1.14.pdf | joint-conference-on-lexical-and-computational-1 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 3.05405110e-01 2.05886871e-01 -2.43180141e-01 -5.37039340e-01
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c0d308d8-8216-4783-98c9-d76b6b185490 | robust-multi-agent-reinforcement-learning | null | null | https://openreview.net/forum?id=JvPsKam58LX | https://openreview.net/pdf?id=JvPsKam58LX | Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium | In this paper we deal with robust cooperative multi-agent reinforcement learning (CMARL). While CMARL has many potential applications, only a trained policy that is robust enough can be confidently deployed in real world. Existing works on robust MARL mainly apply vanilla adversarial training in centralized training and decentralized execution paradigm. We, however, find that if a CMARL environment contains an adversarial agent, the performance of decentralized equilibrium might perform significantly poor for achieving such adversarial robustness. To tackle this issue, we suggest that when execution the non-adversarial agents must jointly make the decision to improve the robustness, therefore solving correlated equilibrium instead. We theoretically demonstrate the superiority of correlated equilibrium over the decentralized one in adversarial MARL settings. Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution. The global variables with mutual information are proposed to help agents learn robust policies with MARL algorithms. The experimental results show that our method can dramatically boost performance on the SMAC environments. | ['Zhanxing Zhu', 'Jun Wang', 'Yaodong Yang', 'Wulong Liu', 'Jianye Hao', 'Dong Li', 'Kun Shao', 'Yizheng Hu'] | 2021-01-01 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.94072759e-01 1.69109538e-01 -2.80303806e-01 4.85209338e-02
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0aca1209-5954-4f14-8954-cd7ae66ac915 | mfsnet-a-multi-focus-segmentation-network-for | 2203.14341 | null | https://arxiv.org/abs/2203.14341v2 | https://arxiv.org/pdf/2203.14341v2.pdf | MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentation | Segmentation is essential for medical image analysis to identify and localize diseases, monitor morphological changes, and extract discriminative features for further diagnosis. Skin cancer is one of the most common types of cancer globally, and its early diagnosis is pivotal for the complete elimination of malignant tumors from the body. This research develops an Artificial Intelligence (AI) framework for supervised skin lesion segmentation employing the deep learning approach. The proposed framework, called MFSNet (Multi-Focus Segmentation Network), uses differently scaled feature maps for computing the final segmentation mask using raw input RGB images of skin lesions. In doing so, initially, the images are preprocessed to remove unwanted artifacts and noises. The MFSNet employs the Res2Net backbone, a recently proposed convolutional neural network (CNN), for obtaining deep features used in a Parallel Partial Decoder (PPD) module to get a global map of the segmentation mask. In different stages of the network, convolution features and multi-scale maps are used in two boundary attention (BA) modules and two reverse attention (RA) modules to generate the final segmentation output. MFSNet, when evaluated on three publicly available datasets: $PH^2$, ISIC 2017, and HAM10000, outperforms state-of-the-art methods, justifying the reliability of the framework. The relevant codes for the proposed approach are accessible at https://github.com/Rohit-Kundu/MFSNet | ['Ram Sarkar', 'Rohit Kundu', 'Hritam Basak'] | 2022-03-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.11111879e-01 2.10778892e-01 -1.64699346e-01 -6.83178008e-02
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dbf51341-2050-4892-848a-389d143aae87 | o-medal-online-active-deep-learning-for | 1908.10508 | null | https://arxiv.org/abs/1908.10508v2 | https://arxiv.org/pdf/1908.10508v2.pdf | O-MedAL: Online Active Deep Learning for Medical Image Analysis | Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend our MedAL active learning framework to present new results in this paper. Our novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multi-class tasks. | ['Aurélio Campilho', 'Pei Zhang', 'Pedro Costa', 'Asim Smailagic', 'Alex Gaudio', 'Adrian Galdran', 'Susu Xu', 'Mostafa Mirshekari', 'Kartik Khandelwal', 'Hae Young Noh', 'Jonathon Fagert', 'Devesh Walawalkar'] | 2019-08-28 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 4.03511822e-01 9.89660382e-01 -8.38967085e-01 -7.37552702e-01
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ef4a7d70-ad83-47e0-8bff-fbedcb82574c | fastgeodis-fast-generalised-geodesic-distance | 2208.00001 | null | https://arxiv.org/abs/2208.00001v2 | https://arxiv.org/pdf/2208.00001v2.pdf | FastGeodis: Fast Generalised Geodesic Distance Transform | The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware. In particular, it implements the paralellisable raster scan method from Criminisi et al. (2009), where elements in a row (2D) or plane (3D) can be computed with parallel threads. This package is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup on a CPU and up to a 74x speedup on a GPU as compared to an existing open-source library (Wang, 2020) that uses a non-parallelisable single-thread CPU implementation. The performance speedups reported here were evaluated using 3D volume data on an Nvidia GeForce Titan X (12 GB) with a 6-Core Intel Xeon E5-1650 CPU. Further in-depth comparison of performance improvements are discussed in the FastGeodis documentation: https://fastgeodis.readthedocs.io | ['Tom Vercauteren', 'Reuben Dorent', 'Muhammad Asad'] | 2022-07-26 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [-3.24324220e-01 -4.10553455e-01 4.29434448e-01 2.66514393e-03
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0849408a-70ac-47e4-8017-116618c8c5ff | zero-shot-video-editing-using-off-the-shelf | 2303.17599 | null | https://arxiv.org/abs/2303.17599v2 | https://arxiv.org/pdf/2303.17599v2.pdf | Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models | Large-scale text-to-image diffusion models achieve unprecedented success in image generation and editing. However, how to extend such success to video editing is unclear. Recent initial attempts at video editing require significant text-to-video data and computation resources for training, which is often not accessible. In this work, we propose vid2vid-zero, a simple yet effective method for zero-shot video editing. Our vid2vid-zero leverages off-the-shelf image diffusion models, and doesn't require training on any video. At the core of our method is a null-text inversion module for text-to-video alignment, a cross-frame modeling module for temporal consistency, and a spatial regularization module for fidelity to the original video. Without any training, we leverage the dynamic nature of the attention mechanism to enable bi-directional temporal modeling at test time. Experiments and analyses show promising results in editing attributes, subjects, places, etc., in real-world videos. Code is made available at \url{https://github.com/baaivision/vid2vid-zero}. | ['Chunhua Shen', 'Xinlong Wang', 'Yue Cao', 'Hao Chen', 'Zide Liu', 'Kangyang Xie', 'Wen Wang'] | 2023-03-30 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 1.64081976e-01 -2.32423484e-01 -1.28560662e-01 -2.38100767e-01
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152f4c93-d60d-4085-9176-f11d91f74cf7 | improving-candidate-retrieval-with-entity | null | null | https://openreview.net/forum?id=jFOEfjXapqP | https://openreview.net/pdf?id=jFOEfjXapqP | Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking | There is little work on entity linking (EL) over Wikidata, even though it is the most extensive crowdsourced knowledge base. The scale of Wikidata can open up many new real-world applications, but its massive number of entities also makes EL challenging. To effectively narrow down the search space, we propose a novel candidate retrieval paradigm based on entity profiling. Wikidata entities and their textual fields are first indexed into a text search engine (e.g., Elasticsearch). During inference, given a mention and its context, we use a sequence-to-sequence (seq2seq) model to generate the profile of the target entity, which consists of its title and description. We use the profile to query the indexed search engine to retrieve candidate entities. Our approach complements the traditional approach of using a Wikipedia anchor-text dictionary, enabling us to further design a highly effective hybrid method for candidate retrieval. Combined with a simple cross-attention reranker, our complete EL framework achieves state-of-the-art results on three Wikidata-based datasets and strong performance on TACKBP-2010. | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['pgtask'] | ['natural-language-processing'] | [-3.02661210e-01 1.25487983e-01 -6.99964345e-01 8.45799893e-02
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be04968e-85ab-497c-bd42-ea5e1d024a28 | fine-grained-urban-flow-inference-with | null | null | https://ieeexplore.ieee.org/abstract/document/9723595 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9723595 | Fine-grained Urban Flow Inference with Incomplete Data | Fine-grained urban flow inference, which aims to infer the fine-grained urban flows of a city given the coarse-grained urban flow observations, is critically important to various smart city related applications such as urban planning and public safety. Previous works assume that the urban flow monitoring sensors are evenly distributed in space for data collection and thus the observed urban flows are complete. However, in real-world scenarios, sensors are usually unevenly deployed in space. For example, the traffic cameras are mostly deployed at the crossroads and central areas of a city, but less likely to be deployed in suburb. The data scarcity issue poses great challenges to existing methods for accurately inferring the fine-grained urban flows, because they require all urban flow observations to be available. In this paper, we make the first attempt to infer fine-grained urban flows based on the incomplete coarse-grained urban flow observations, and propose a Multi-Task urban flow Completion and Super-Resolution network (MT-CSR for short) to simultaneously complete the coarse-grained urban flows and infer the fine-grained flows. Specifically, MT-CSR consists of the data completion network (CMPNet for short) and data super-resolution network (SRNet for short). CmpNet is composed of a local spatial information based data completion module LocCmp and an auxiliary information based data completion module AuxCmp to consider both the local geographical and global semantic correlations for urban flow data completion. SRNet is designed to capture the complex associations between fine-and coarse-grained urban flows and upsample the coarse-grained data by stacking the designed super-resolution blocks. To gain an accurate inference, two parts are jointly conducted under a multi-task learning framework, and trained in an end-to-end manner using a two-stage training strategy. Extensive experiments on four large real-world datasets validate the effectiveness and efficiency of our method compared with the state-of-the-art baselines. | ['Jiyue Li; Senzhang Wang; Jiaqiang Zhang; Hao Miao; Junbo Zhang; Philip Yu'] | 2022-03-01 | null | null | null | ieee-transactions-on-knowledge-and-data-7 | ['fine-grained-urban-flow-inference'] | ['miscellaneous'] | [-1.19293004e-01 -2.98118979e-01 -3.53040457e-01 -4.35061872e-01
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4df098ec-916c-44ad-ac96-ed97ba95bbc0 | modelling-temporal-information-using-discrete | 1603.06568 | null | http://arxiv.org/abs/1603.06568v2 | http://arxiv.org/pdf/1603.06568v2.pdf | Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos | With the widespread of user-generated Internet videos, emotion recognition in
those videos attracts increasing research efforts. However, most existing works
are based on framelevel visual features and/or audio features, which might fail
to model the temporal information, e.g. characteristics accumulated along time.
In order to capture video temporal information, in this paper, we propose to
analyse features in frequency domain transformed by discrete Fourier transform
(DFT features). Frame-level features are firstly extract by a pre-trained deep
convolutional neural network (CNN). Then, time domain features are transferred
and interpolated into DFT features. CNN and DFT features are further encoded
and fused for emotion classification. By this way, static image features
extracted from a pre-trained deep CNN and temporal information represented by
DFT features are jointly considered for video emotion recognition. Experimental
results demonstrate that combining DFT features can effectively capture
temporal information and therefore improve emotion recognition performance. Our
approach has achieved a state-of-the-art performance on the largest video
emotion dataset (VideoEmotion-8 dataset), improving accuracy from 51.1% to
62.6%. | ['Haimin Zhang', 'Min Xu'] | 2016-03-20 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [ 2.30746254e-01 -4.37658697e-01 -1.27691805e-01 -4.31896299e-01
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83f16f45-eb86-48c6-ad9b-c204072e54b8 | fully-bayesian-vib-deepssm | 2305.05797 | null | https://arxiv.org/abs/2305.05797v1 | https://arxiv.org/pdf/2305.05797v1.pdf | Fully Bayesian VIB-DeepSSM | Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variational information bottleneck DeepSSM (VIB-DeepSSM) is an effective, principled framework for predicting probabilistic shapes of anatomy from images with aleatoric uncertainty quantification. However, VIB is only half-Bayesian and lacks epistemic uncertainty inference. We derive a fully Bayesian VIB formulation from both the probably approximately correct (PAC)-Bayes and variational inference perspectives. We demonstrate the efficacy of two scalable approaches for Bayesian VIB with epistemic uncertainty: concrete dropout and batch ensemble. Additionally, we introduce a novel combination of the two that further enhances uncertainty calibration via multimodal marginalization. Experiments on synthetic shapes and left atrium data demonstrate that the fully Bayesian VIB network predicts SSM from images with improved uncertainty reasoning without sacrificing accuracy. | ['Shireen Elhabian', 'Jadie Adams'] | 2023-05-09 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [-7.51357228e-02 7.68805146e-01 -1.36838615e-01 -6.83909953e-01
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4f162e89-7167-4062-bd8e-9d1f2090efd0 | multi-spectral-visual-odometry-without | 1908.08814 | null | https://arxiv.org/abs/1908.08814v1 | https://arxiv.org/pdf/1908.08814v1.pdf | Multi-Spectral Visual Odometry without Explicit Stereo Matching | Multi-spectral sensors consisting of a standard (visible-light) camera and a long-wave infrared camera can simultaneously provide both visible and thermal images. Since thermal images are independent from environmental illumination, they can help to overcome certain limitations of standard cameras under complicated illumination conditions. However, due to the difference in the information source of the two types of cameras, their images usually share very low texture similarity. Hence, traditional texture-based feature matching methods cannot be directly applied to obtain stereo correspondences. To tackle this problem, a multi-spectral visual odometry method without explicit stereo matching is proposed in this paper. Bundle adjustment of multi-view stereo is performed on the visible and the thermal images using direct image alignment. Scale drift can be avoided by additional temporal observations of map points with the fixed-baseline stereo. Experimental results indicate that the proposed method can provide accurate visual odometry results with recovered metric scale. Moreover, the proposed method can also provide a metric 3D reconstruction in semi-dense density with multi-spectral information, which is not available from existing multi-spectral methods. | ['Weichen Dai', 'Naira Hovakimyan', 'Yu Zhang', 'Ping Li', 'Donglei Sun'] | 2019-08-23 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 4.56183076e-01 -5.44335246e-01 1.39866129e-01 -2.74088860e-01
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841c85d7-4bea-40f1-876c-252d4d3f4045 | trilateral-attention-network-for-real-time | 2106.09201 | null | https://arxiv.org/abs/2106.09201v1 | https://arxiv.org/pdf/2106.09201v1.pdf | Trilateral Attention Network for Real-time Medical Image Segmentation | Accurate segmentation of medical images into anatomically meaningful regions is critical for the extraction of quantitative indices or biomarkers. The common pipeline for segmentation comprises regions of interest detection stage and segmentation stage, which are independent of each other and typically performed using separate deep learning networks. The performance of the segmentation stage highly relies on the extracted set of spatial features and the receptive fields. In this work, we propose an end-to-end network, called Trilateral Attention Network (TaNet), for real-time detection and segmentation in medical images. TaNet has a module for region localization, and three segmentation pathways: 1) handcrafted pathway with hand-designed convolutional kernels, 2) detail pathway with regular convolutional kernels, and 3) a global pathway to enlarge the receptive field. The first two pathways encode rich handcrafted and low-level features extracted by hand-designed and regular kernels while the global pathway encodes high-level context information. By jointly training the network for localization and segmentation using different sets of features, TaNet achieved superior performance, in terms of accuracy and speed, when evaluated on an echocardiography dataset for cardiac segmentation. The code and models will be made publicly available in TaNet Github page. | ['Sameer Antani', 'Vandana Sachdev', 'Ghada Zamzmi'] | 2021-06-17 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 2.25194708e-01 3.56968567e-02 -1.15463436e-01 -4.30098385e-01
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424e3685-8346-4c0f-aafe-6cb54066112d | query-efficient-imitation-learning-for-end-to | 1605.06450 | null | http://arxiv.org/abs/1605.06450v1 | http://arxiv.org/pdf/1605.06450v1.pdf | Query-Efficient Imitation Learning for End-to-End Autonomous Driving | One way to approach end-to-end autonomous driving is to learn a policy
function that maps from a sensory input, such as an image frame from a
front-facing camera, to a driving action, by imitating an expert driver, or a
reference policy. This can be done by supervised learning, where a policy
function is tuned to minimize the difference between the predicted and
ground-truth actions. A policy function trained in this way however is known to
suffer from unexpected behaviours due to the mismatch between the states
reachable by the reference policy and trained policy functions. More advanced
algorithms for imitation learning, such as DAgger, addresses this issue by
iteratively collecting training examples from both reference and trained
policies. These algorithms often requires a large number of queries to a
reference policy, which is undesirable as the reference policy is often
expensive. In this paper, we propose an extension of the DAgger, called
SafeDAgger, that is query-efficient and more suitable for end-to-end autonomous
driving. We evaluate the proposed SafeDAgger in a car racing simulator and show
that it indeed requires less queries to a reference policy. We observe a
significant speed up in convergence, which we conjecture to be due to the
effect of automated curriculum learning. | ['Kyunghyun Cho', 'Jiakai Zhang'] | 2016-05-20 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [ 1.13408379e-01 3.53708535e-01 -5.18272184e-02 -4.13245231e-01
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c5e0ce16-31cb-440e-a2f3-3e8e31a660bb | lpformer-lidar-pose-estimation-transformer | 2306.12525 | null | https://arxiv.org/abs/2306.12525v1 | https://arxiv.org/pdf/2306.12525v1.pdf | LPFormer: LiDAR Pose Estimation Transformer with Multi-Task Network | In this technical report, we present the 1st place solution for the 2023 Waymo Open Dataset Pose Estimation challenge. Due to the difficulty of acquiring large-scale 3D human keypoint annotation, previous methods have commonly relied on 2D image features and 2D sequential annotations for 3D human pose estimation. In contrast, our proposed method, named LPFormer, uses only LiDAR as its input along with its corresponding 3D annotations. LPFormer consists of two stages: the first stage detects the human bounding box and extracts multi-level feature representations, while the second stage employs a transformer-based network to regress the human keypoints using these features. Experimental results on the Waymo Open Dataset demonstrate the top performance, and improvements even compared to previous multi-modal solutions. | ['Hassan Foroosh', 'Zixiang Zhou', 'Weijia Chen', 'Yufei Xie', 'Dongqiangzi Ye'] | 2023-06-21 | null | null | null | null | ['pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.54088134e-01 7.60567710e-02 -2.74578005e-01 -2.07482561e-01
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2b481c15-5e1a-46fe-a00e-2c752f3cf460 | understanding-aesthetics-with-language-a | 2206.08614 | null | https://arxiv.org/abs/2206.08614v3 | https://arxiv.org/pdf/2206.08614v3.pdf | Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment | Computational inference of aesthetics is an ill-defined task due to its subjective nature. Many datasets have been proposed to tackle the problem by providing pairs of images and aesthetic scores based on human ratings. However, humans are better at expressing their opinion, taste, and emotions by means of language rather than summarizing them in a single number. In fact, photo critiques provide much richer information as they reveal how and why users rate the aesthetics of visual stimuli. In this regard, we propose the Reddit Photo Critique Dataset (RPCD), which contains tuples of image and photo critiques. RPCD consists of 74K images and 220K comments and is collected from a Reddit community used by hobbyists and professional photographers to improve their photography skills by leveraging constructive community feedback. The proposed dataset differs from previous aesthetics datasets mainly in three aspects, namely (i) the large scale of the dataset and the extension of the comments criticizing different aspects of the image, (ii) it contains mostly UltraHD images, and (iii) it can easily be extended to new data as it is collected through an automatic pipeline. To the best of our knowledge, in this work, we propose the first attempt to estimate the aesthetic quality of visual stimuli from the critiques. To this end, we exploit the polarity of the sentiment of criticism as an indicator of aesthetic judgment. We demonstrate how sentiment polarity correlates positively with the aesthetic judgment available for two aesthetic assessment benchmarks. Finally, we experiment with several models by using the sentiment scores as a target for ranking images. Dataset and baselines are available (https://github.com/mediatechnologycenter/aestheval). | ['Clara Fernandez-Labrador', 'Luigi Celona', 'Daniel Vera Nieto'] | 2022-06-17 | null | null | null | null | ['aesthetic-image-captioning', 'aesthetics-quality-assessment'] | ['computer-vision', 'computer-vision'] | [-6.05550073e-02 1.61459804e-01 7.88604245e-02 -5.53785622e-01
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eb179d24-c691-4e61-b725-f9296121e9d7 | a-multi-objective-memetic-algorithm-for-auto | 2208.06984 | null | https://arxiv.org/abs/2208.06984v1 | https://arxiv.org/pdf/2208.06984v1.pdf | A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design | The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, with the rapid development of defense technologies, it also tends to be more difficult to evaluate the robustness of the defensed model due to the weak performance of existing manually designed adversarial attacks. To address the challenge, given the defensed model, the efficient adversarial attack with less computational burden and lower robust accuracy is needed to be further exploited. Therefore, we propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models. Firstly, the more general mathematical model of auto adversarial attack optimization design is constructed, where the search space includes not only the attacker operations, magnitude, iteration number, and loss functions but also the connection ways of multiple adversarial attacks. In addition, we develop a multi-objective memetic algorithm combining NSGA-II and local search to solve the optimization problem. Finally, to decrease the evaluation cost during the search, we propose a representative data selection strategy based on the sorting of cross entropy loss values of each images output by models. Experiments on CIFAR10, CIFAR100, and ImageNet datasets show the effectiveness of our proposed method. | ['Xiaoqian Chen', 'Tingsong Jiang', 'Wen Yao', 'Jialiang Sun'] | 2022-08-15 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [-7.52428398e-02 -4.49188620e-01 2.54157931e-01 -1.72632188e-01
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08211bf1-a56e-4856-85f1-10f26368e828 | bic-twitter-bot-detection-with-text-graph | 2208.08320 | null | https://arxiv.org/abs/2208.08320v2 | https://arxiv.org/pdf/2208.08320v2.pdf | BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency | Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify bots based on texts and networks on social media. Existing methods only leverage texts or networks alone, and while few works explored the shallow combination of the two modalities, we hypothesize that the interaction and information exchange between texts and graphs could be crucial for holistically evaluating bot activities on social media. In addition, according to a recent survey (Cresci, 2020), Twitter bots are constantly evolving while advanced bots steal genuine users' tweets and dilute their malicious content to evade detection. This results in greater inconsistency across the timeline of novel Twitter bots, which warrants more attention. In light of these challenges, we propose BIC, a Twitter Bot detection framework with text-graph Interaction and semantic Consistency. Specifically, in addition to separately modeling the two modalities on social media, BIC employs a text-graph interaction module to enable information exchange across modalities in the learning process. In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process. Extensive experiments demonstrate that BIC consistently outperforms state-of-the-art baselines on two widely adopted datasets. Further analyses reveal that text-graph interactions and modeling semantic consistency are essential improvements and help combat bot evolution. | ['Minnan Luo', 'Qinghua Zheng', 'Jundong Li', 'Zilong Chen', 'Shangbin Feng', 'Wenqian Zhang', 'Herun Wan', 'Zhenyu Lei'] | 2022-08-17 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.22387239e-02 -1.17153354e-01 -5.21521807e-01 1.41888589e-01
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87728bc1-107e-4f89-925a-16708a64fb2e | method-for-the-generation-of-depth-images-for | 2006.16500 | null | https://arxiv.org/abs/2006.16500v1 | https://arxiv.org/pdf/2006.16500v1.pdf | Method for the generation of depth images for view-based shape retrieval of 3D CAD model from partial point cloud | A laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry information but also semantic information. However, recognition process is often a bottleneck in 3D reconstruction because it requires expertise on domain knowledge and intensive labor. To address this problem, various methods have been developed to recognize objects by retrieving the corresponding model in the database from an input geometry query. In recent years, the technique of converting geometric data into an image and applying view-based 3D shape retrieval has demonstrated high accuracy. Depth image which encodes depth value as intensity of pixel is frequently used for view-based 3D shape retrieval. However, geometric data collected from objects is often incomplete due to the occlusions and the limit of line of sight. Image generated by occluded point clouds lowers the performance of view-based 3D object retrieval due to loss of information. In this paper, we propose a method of viewpoint and image resolution estimation method for view-based 3D shape retrieval from point cloud query. Automatic selection of viewpoint and image resolution by calculating the data acquisition rate and density from the sampled viewpoints and image resolutions are proposed. The retrieval performance from the images generated by the proposed method is experimented and compared for various dataset. Additionally, view-based 3D shape retrieval performance with deep convolutional neural network has been experimented with the proposed method. | ['Hyungki Kim', 'Duhwan Mun', 'Moohyun Cha'] | 2020-06-30 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [ 7.42617473e-02 -8.79645407e-01 2.77332425e-01 -5.26275873e-01
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e68ea6e7-af4c-49ff-ac1d-0af1d2324abd | rnn-based-counterfactual-time-series | 1712.03553 | null | https://arxiv.org/abs/1712.03553v7 | https://arxiv.org/pdf/1712.03553v7.pdf | RNN-based counterfactual prediction, with an application to homestead policy and public schooling | This paper proposes a method for estimating the effect of a policy intervention on an outcome over time. We train recurrent neural networks (RNNs) on the history of control unit outcomes to learn a useful representation for predicting future outcomes. The learned representation of control units is then applied to the treated units for predicting counterfactual outcomes. RNNs are specifically structured to exploit temporal dependencies in panel data, and are able to learn negative and nonlinear interactions between control unit outcomes. We apply the method to the problem of estimating the long-run impact of U.S. homestead policy on public school spending. | ['Shuxi Zeng', 'Jason Poulos'] | 2017-12-10 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.82074320e-02 3.36098969e-01 -1.07623541e+00 -5.27073264e-01
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8d54ba75-7330-453d-acc6-119f2f8acdf2 | segsort-segmentation-by-discriminative | 1910.06962 | null | https://arxiv.org/abs/1910.06962v2 | https://arxiv.org/pdf/1910.06962v2.pdf | SegSort: Segmentation by Discriminative Sorting of Segments | Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels, but by decomposing it into perceptual groups and structures that are the basic building blocks of recognition. This motivates us to propose an end-to-end pixel-wise metric learning approach that mimics this process. In our approach, the optimal visual representation determines the right segmentation within individual images and associates segments with the same semantic classes across images. The core visual learning problem is therefore to maximize the similarity within segments and minimize the similarity between segments. Given a model trained this way, inference is performed consistently by extracting pixel-wise embeddings and clustering, with the semantic label determined by the majority vote of its nearest neighbors from an annotated set. As a result, we present the SegSort, as a first attempt using deep learning for unsupervised semantic segmentation, achieving $76\%$ performance of its supervised counterpart. When supervision is available, SegSort shows consistent improvements over conventional approaches based on pixel-wise softmax training. Additionally, our approach produces more precise boundaries and consistent region predictions. The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments. | ['Liang-Chieh Chen', 'Tien-Ju Yang', 'Maxwell D. Collins', 'Jyh-Jing Hwang', 'Xiao Zhang', 'Stella X. Yu', 'Jianbo Shi'] | 2019-10-15 | segsort-segmentation-by-discriminative-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Hwang_SegSort_Segmentation_by_Discriminative_Sorting_of_Segments_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Hwang_SegSort_Segmentation_by_Discriminative_Sorting_of_Segments_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 4.25564617e-01 3.72255713e-01 -4.82272148e-01 -8.60981345e-01
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31103821-96e2-4380-b549-7fa7aeebfaa1 | glassloc-plenoptic-grasp-pose-detection-in | 1909.04269 | null | https://arxiv.org/abs/1909.04269v2 | https://arxiv.org/pdf/1909.04269v2.pdf | GlassLoc: Plenoptic Grasp Pose Detection in Transparent Clutter | Transparent objects are prevalent across many environments of interest for dexterous robotic manipulation. Such transparent material leads to considerable uncertainty for robot perception and manipulation, and remains an open challenge for robotics. This problem is exacerbated when multiple transparent objects cluster into piles of clutter. In household environments, for example, it is common to encounter piles of glassware in kitchens, dining rooms, and reception areas, which are essentially invisible to modern robots. We present the GlassLoc algorithm for grasp pose detection of transparent objects in transparent clutter using plenoptic sensing. GlassLoc classifies graspable locations in space informed by a Depth Likelihood Volume (DLV) descriptor. We extend the DLV to infer the occupancy of transparent objects over a given space from multiple plenoptic viewpoints. We demonstrate and evaluate the GlassLoc algorithm on a Michigan Progress Fetch mounted with a first-generation Lytro. The effectiveness of our algorithm is evaluated through experiments for grasp detection and execution with a variety of transparent glassware in minor clutter. | ['Odest Chadwicke Jenkins', 'Zheming Zhou', 'Haonan Chang', 'Tianyang Pan', 'Shiyu Wu'] | 2019-09-10 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 8.61398503e-02 -1.47930279e-01 4.09346491e-01 -1.46445092e-02
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fdba5474-db43-444f-9c5e-8f6db19adde6 | featurebooster-boosting-feature-descriptors | 2211.15069 | null | https://arxiv.org/abs/2211.15069v3 | https://arxiv.org/pdf/2211.15069v3.pdf | FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network | We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The boosted descriptors can be either real-valued or binary ones. We use the proposed network to boost both hand-crafted (ORB, SIFT) and the state-of-the-art learning-based descriptors (SuperPoint, ALIKE) and evaluate them on image matching, visual localization, and structure-from-motion tasks. The results show that our method significantly improves the performance of each task, particularly in challenging cases such as large illumination changes or repetitive patterns. Our method requires only 3.2ms on desktop GPU and 27ms on embedded GPU to process 2000 features, which is fast enough to be applied to a practical system. The code and trained weights are publicly available at github.com/SJTU-ViSYS/FeatureBooster. | ['Danping Zou', 'Wenxian Yu', 'Wei Xi', 'Yu Hu', 'Zeyu Liu', 'Xinjiang Wang'] | 2022-11-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_FeatureBooster_Boosting_Feature_Descriptors_With_a_Lightweight_Neural_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [-5.28296344e-02 -7.93044090e-01 -4.17706102e-01 -4.28334266e-01
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e3a77e1b-ff69-4739-baf4-a39289a73a24 | mla-bin-model-level-attention-and-batch | 2306.17008 | null | https://arxiv.org/abs/2306.17008v1 | https://arxiv.org/pdf/2306.17008v1.pdf | MLA-BIN: Model-level Attention and Batch-instance Style Normalization for Domain Generalization of Federated Learning on Medical Image Segmentation | The privacy protection mechanism of federated learning (FL) offers an effective solution for cross-center medical collaboration and data sharing. In multi-site medical image segmentation, each medical site serves as a client of FL, and its data naturally forms a domain. FL supplies the possibility to improve the performance of seen domains model. However, there is a problem of domain generalization (DG) in the actual de-ployment, that is, the performance of the model trained by FL in unseen domains will decrease. Hence, MLA-BIN is proposed to solve the DG of FL in this study. Specifically, the model-level attention module (MLA) and batch-instance style normalization (BIN) block were designed. The MLA represents the unseen domain as a linear combination of seen domain models. The atten-tion mechanism is introduced for the weighting coefficient to obtain the optimal coefficient ac-cording to the similarity of inter-domain data features. MLA enables the global model to gen-eralize to unseen domain. In the BIN block, batch normalization (BN) and instance normalization (IN) are combined to perform the shallow layers of the segmentation network for style normali-zation, solving the influence of inter-domain image style differences on DG. The extensive experimental results of two medical image seg-mentation tasks demonstrate that the proposed MLA-BIN outperforms state-of-the-art methods. | ['Weihua Zhou', 'Ni Yao', 'Jiaofen Nan', 'Yanting Li', 'Chuang Han', 'Yanhui Tian', 'Fubao Zhu'] | 2023-06-29 | null | null | null | null | ['medical-image-segmentation', 'domain-generalization'] | ['medical', 'methodology'] | [ 2.46659189e-01 9.64422598e-02 -3.30955476e-01 -6.57660306e-01
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dd105136-ebdf-498b-81b1-0fa953468e21 | 3d-object-detection-and-instance-segmentation | null | null | https://www.mdpi.com/1424-8220/21/4/1213/htm | https://www.mdpi.com/1424-8220/21/4/1213/pdf | 3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images | Instance segmentation and object detection are significant problems in the fields of computer vision and robotics. We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images. A 3D convolutional-based system, named Frustum VoxNet, is proposed. This system generates frustums from 2D detection results, proposes 3D candidate voxelized images for each frustum, and uses a 3D convolutional neural network (CNN) based on these candidates voxelized images to perform the 3D instance segmentation and object detection. Results on the SUN RGB-D dataset show that our RGB-D-based system’s 3D inference is much faster than state-of-the-art methods, without a significant loss of accuracy. At the same time, we can provide segmentation and detection results using depth only images, with accuracy comparable to RGB-D-based systems. This is important since our methods can also work well in low lighting conditions, or with sensors that do not acquire RGB images. Finally, the use of segmentation as part of our pipeline increases detection accuracy, while providing at the same time 3D instance segmentation. | ['Ioannis Stamos', 'Xiaoke Shen 1'] | 2021-02-09 | null | null | null | sensors-2021-2 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.71505466e-01 8.17045644e-02 1.44179434e-01 -1.29673123e-01
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1ad2fd3b-7056-4877-bd0e-f5aa4216db9c | end-to-end-spoken-language-understanding-with | 2210.16554 | null | https://arxiv.org/abs/2210.16554v2 | https://arxiv.org/pdf/2210.16554v2.pdf | End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator | End-to-end spoken language understanding (SLU) suffers from the long-tail word problem. This paper exploits contextual biasing, a technique to improve the speech recognition of rare words, in end-to-end SLU systems. Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model component, is studied, which leverages a slot shortlist with corresponding entities to extract biasing lists. Meanwhile, to bias the SLU model output slot distribution, a slot probability biasing (SPB) mechanism is proposed to calculate a slot distribution from TCPGen. Experiments on the SLURP dataset showed consistent SLU-F1 improvements using TCPGen and SPB, especially on unseen entities. On a new split by holding out 5 slot types for the test, TCPGen with SPB achieved zero-shot learning with an SLU-F1 score over 50% compared to baselines which can not deal with it. In addition to slot filling, the intent classification accuracy was also improved. | ['Philip C. Woodland', 'Chao Zhang', 'Guangzhi Sun'] | 2022-10-29 | null | null | null | null | ['spoken-language-understanding', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 1.71425983e-01 3.53884816e-01 -5.44515014e-01 -6.72922254e-01
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4de60527-0dab-449b-a271-74f59411f4cf | linear-dynamics-clustering-without | 1908.01039 | null | https://arxiv.org/abs/1908.01039v3 | https://arxiv.org/pdf/1908.01039v3.pdf | Linear Dynamics: Clustering without identification | Linear dynamical systems are a fundamental and powerful parametric model class. However, identifying the parameters of a linear dynamical system is a venerable task, permitting provably efficient solutions only in special cases. This work shows that the eigenspectrum of unknown linear dynamics can be identified without full system identification. We analyze a computationally efficient and provably convergent algorithm to estimate the eigenvalues of the state-transition matrix in a linear dynamical system. When applied to time series clustering, our algorithm can efficiently cluster multi-dimensional time series with temporal offsets and varying lengths, under the assumption that the time series are generated from linear dynamical systems. Evaluating our algorithm on both synthetic data and real electrocardiogram (ECG) signals, we see improvements in clustering quality over existing baselines. | ['Chloe Ching-Yun Hsu', 'Moritz Hardt', 'Michaela Hardt'] | 2019-08-02 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 5.27931228e-02 -3.49809974e-01 1.08598508e-01 1.00099698e-01
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cf9d95a7-1b13-491e-8dd6-38a3b951807e | a-graph-neural-network-approach-for-temporal | 2306.13452 | null | https://arxiv.org/abs/2306.13452v1 | https://arxiv.org/pdf/2306.13452v1.pdf | A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence | We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph neural network that processes an augmented graph to estimate the correspondence. We have designed a novel conditional refinement scheme to find the exact correspondence when certain conditions are satisfied. We further develop a graph neural network that takes the aligned meshes and the time value as input and fuses this information to process further and generate the desired result. Using motion capture datasets and human mesh designing software, we create a large-scale synthetic dataset consisting of temporal sequences of human meshes in motion. Our results demonstrate that our approach generates realistic deformation of body parts given complex inputs. | ['Shanmuganathan Raman', 'Prajwal Singh', 'Abhinav Narayan Harish', 'Aalok Gangopadhyay'] | 2023-06-23 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 1.41351044e-01 2.60862380e-01 1.12012178e-01 -2.60587752e-01
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cb481abf-125a-47d0-a03f-808abfcabc18 | cross-modality-data-augmentation-for-end-to | 2305.11096 | null | https://arxiv.org/abs/2305.11096v2 | https://arxiv.org/pdf/2305.11096v2.pdf | Cross-modality Data Augmentation for End-to-End Sign Language Translation | End-to-end sign language translation (SLT) aims to convert sign language videos into spoken language texts directly without intermediate representations. It has been a challenging task due to the modality gap between sign videos and texts and the data scarcity of labeled data. To tackle these challenges, we propose a novel Cross-modality Data Augmentation (XmDA) framework to transfer the powerful gloss-to-text translation capabilities to end-to-end sign language translation (i.e. video-to-text) by exploiting pseudo gloss-text pairs from the sign gloss translation model. Specifically, XmDA consists of two key components, namely, cross-modality mix-up and cross-modality knowledge distillation. The former explicitly encourages the alignment between sign video features and gloss embeddings to bridge the modality gap. The latter utilizes the generation knowledge from gloss-to-text teacher models to guide the spoken language text generation. Experimental results on two widely used SLT datasets, i.e., PHOENIX-2014T and CSL-Daily, demonstrate that the proposed XmDA framework significantly and consistently outperforms the baseline models. Extensive analyses confirm our claim that XmDA enhances spoken language text generation by reducing the representation distance between videos and texts, as well as improving the processing of low-frequency words and long sentences. | ['Hui Xiong', 'Zhaopeng Tu', 'Xing Wang', 'Wenxiang Jiao', 'Jinhui Ye'] | 2023-05-18 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 4.12752420e-01 -1.52602971e-01 -2.09726751e-01 -4.55783844e-01
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8d448d7d-7541-4b28-b3dd-151690179557 | deep-adversarial-decomposition-a-unified | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zou_Deep_Adversarial_Decomposition_A_Unified_Framework_for_Separating_Superimposed_Images_CVPR_2020_paper.pdf | Deep Adversarial Decomposition: A Unified Framework for Separating Superimposed Images | Separating individual image layers from a single mixed image has long been an important but challenging task. We propose a unified framework named "deep adversarial decomposition" for single superimposed image separation. Our method deals with both linear and non-linear mixtures under an adversarial training paradigm. Considering the layer separating ambiguity that given a single mixed input, there could be an infinite number of possible solutions, we introduce a "Separation-Critic" - a discriminative network which is trained to identify whether the output layers are well-separated and thus further improves the layer separation. We also introduce a "crossroad L1" loss function, which computes the distance between the unordered outputs and their references in a crossover manner so that the training can be well-instructed with pixel-wise supervision. Experimental results suggest that our method significantly outperforms other popular image separation frameworks. Without specific tuning, our method achieves the state of the art results on multiple computer vision tasks, including the image deraining, photo reflection removal, and image shadow removal.
| [' Jieping Ye', ' Zhenwei Shi', ' Tianyang Shi', ' Sen Lei', 'Zhengxia Zou'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['shadow-removal', 'reflection-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.42538726e-01 2.44643524e-01 -3.24503332e-02 -2.01526657e-01
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1d8c4f2b-e215-4fc7-b7e6-b7ce2663ff0d | app-anytime-progressive-pruning | 2204.01640 | null | https://arxiv.org/abs/2204.01640v2 | https://arxiv.org/pdf/2204.01640v2.pdf | APP: Anytime Progressive Pruning | With the latest advances in deep learning, there has been a lot of focus on the online learning paradigm due to its relevance in practical settings. Although many methods have been investigated for optimal learning settings in scenarios where the data stream is continuous over time, sparse networks training in such settings have often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of $\approx 7\%$ and a reduction in the generalization gap by $\approx 22\%$, while being $\approx 1/3$ rd the size of the dense baseline model in few-shot restricted imagenet training. We further observe interesting nonmonotonic transitions in the generalization gap in the high number of megabatches-based ALMA. The code and experiment dashboards can be accessed at \url{https://github.com/landskape-ai/Progressive-Pruning} and \url{https://wandb.ai/landskape/APP}, respectively. | ['Irina Rish', 'Zhangyang Wang', 'Tianlong Chen', 'Bharat Runwal', 'Diganta Misra'] | 2022-04-04 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.68883100e-01 5.33205308e-02 -1.10317566e-01 -3.96144122e-01
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07f91a4c-34d3-4066-bb3f-c4c47c49edbc | efficient-deep-learning-based-estimation-of | 2204.08989 | null | https://arxiv.org/abs/2204.08989v2 | https://arxiv.org/pdf/2204.08989v2.pdf | Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones | Nowadays, due to the widespread use of smartphones in everyday life and the improvement of computational capabilities of these devices, many complex tasks can now be deployed on them. Concerning the need for continuous monitoring of vital signs, especially for the elderly or those with certain types of diseases, the development of algorithms that can estimate vital signs using smartphones has attracted researchers worldwide. Such algorithms estimate vital signs (heart rate and oxygen saturation level) by processing an input PPG signal. These methods often apply multiple pre-processing steps to the input signal before the prediction step. This can increase the computational complexity of these methods, meaning only a limited number of mobile devices can run them. Furthermore, multiple pre-processing steps also require the design of a couple of hand-crafted stages to obtain an optimal result. This research proposes a novel end-to-end solution to mobile-based vital sign estimation by deep learning. The proposed method does not require any pre-processing. Due to the use of fully convolutional architecture, the parameter count of our proposed model is, on average, a quarter of the ordinary architectures that use fully-connected layers as the prediction heads. As a result, the proposed model has less over-fitting chance and computational complexity. A public dataset for vital sign estimation, including 62 videos collected from 35 men and 27 women, is also provided. The experimental results demonstrate state-of-the-art estimation accuracy. | ['Aboozar Ghaffari', 'Mahdi Farvardin', 'Taha Samavati'] | 2022-04-13 | null | null | null | null | ['spo2-estimation', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 5.60712032e-02 -7.62921646e-02 -1.20602995e-01 -5.88475585e-01
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da1eaa41-702b-431c-ac61-37ed17331689 | unsupervised-segmentation-of-fire-and-smoke | 1909.12937 | null | https://arxiv.org/abs/1909.12937v1 | https://arxiv.org/pdf/1909.12937v1.pdf | Unsupervised Segmentation of Fire and Smoke from Infra-Red Videos | This paper proposes a vision-based fire and smoke segmentation system which use spatial, temporal and motion information to extract the desired regions from the video frames. The fusion of information is done using multiple features such as optical flow, divergence and intensity values. These features extracted from the images are used to segment the pixels into different classes in an unsupervised way. A comparative analysis is done by using multiple clustering algorithms for segmentation. Here the Markov Random Field performs more accurately than other segmentation algorithms since it characterizes the spatial interactions of pixels using a finite number of parameters. It builds a probabilistic image model that selects the most likely labeling using the maximum a posteriori (MAP) estimation. This unsupervised approach is tested on various images and achieves a frame-wise fire detection rate of 95.39%. Hence this method can be used for early detection of fire in real-time and it can be incorporated into an indoor or outdoor surveillance system. | ['Manel Martínez-Ramón', 'Meenu Ajith'] | 2019-09-18 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 5.27463675e-01 -6.79131091e-01 -5.71585000e-02 -3.24840993e-01
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8999bbcd-065f-4a40-b618-c738d49d6430 | tldr-at-semeval-2022-task-1-using | null | null | https://aclanthology.org/2022.semeval-1.6 | https://aclanthology.org/2022.semeval-1.6.pdf | TLDR at SemEval-2022 Task 1: Using Transformers to Learn Dictionaries and Representations | We propose a pair of deep learning models, which employ unsupervised pretraining, attention mechanisms and contrastive learning for representation learning from dictionary definitions, and definition modeling from such representations. Our systems, the Transformers for Learning Dictionaries and Representations (TLDR), were submitted to the SemEval 2022 Task 1: Comparing Dictionaries and Word Embeddings (CODWOE), where they officially ranked first on the definition modeling subtask, and achieved competitive performance on the reverse dictionary subtask. In this paper we describe our methodology and analyse our system design hypotheses. | ['Harsha Vardhan Vemulapati', 'Aditya Srivastava'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 1.81797639e-01 1.07685171e-01 -5.70058346e-01 -2.72989690e-01
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2c3a34d7-856c-4ede-913a-a596d2ef919d | an-analysis-of-vaccine-related-sentiments | 2306.13797 | null | https://arxiv.org/abs/2306.13797v1 | https://arxiv.org/pdf/2306.13797v1.pdf | An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines | Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic had fear and uncertainty about vaccines which has been well expressed on social media platforms such as Twitter. We analyse Twitter sentiments from the beginning of the COVID-19 pandemic and study the public behaviour during the planning, development and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. In this way, we provide visualisation and analysis of anti-vaccine sentiments over the course of the COVID-19 pandemic. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19 cases. We also found that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised which implies that the vaccine rollout had an impact on the nature of discussions on social media. | ['Cathy Yu', 'Janhavi Lande', 'Jayesh Sonawane', 'Rohitash Chandra'] | 2023-06-23 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.11979258e-02 2.50441551e-01 -4.12412919e-02 -1.51341811e-01
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ebcf2cfc-f2b8-4f4a-a839-00a3e8e4bb0f | towards-inter-character-relationship-driven | 2211.00676 | null | https://arxiv.org/abs/2211.00676v1 | https://arxiv.org/pdf/2211.00676v1.pdf | Towards Inter-character Relationship-driven Story Generation | In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference bring interpretability to ReLiSt. | ['Snigdha Chaturvedi', 'Faeze Brahman', 'Anvesh Rao Vijjini'] | 2022-11-01 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.14421290e-01 6.89055562e-01 -3.79107088e-01 -7.79251397e-01
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94efbf6e-f295-4145-896e-a1dbfeb0bceb | moving-object-detection-for-event-based | 2109.01879 | null | https://arxiv.org/abs/2109.01879v4 | https://arxiv.org/pdf/2109.01879v4.pdf | Moving Object Detection for Event-based Vision using k-means Clustering | Moving object detection is important in computer vision. Event-based cameras are bio-inspired cameras that work by mimicking the working of the human eye. These cameras have multiple advantages over conventional frame-based cameras, like reduced latency, HDR, reduced motion blur during high motion, low power consumption, etc. In spite of these advantages, event-based cameras are noise-sensitive and have low resolution. Moreover, the task of moving object detection in these cameras is difficult, as event-based sensors lack useful visual features like texture and color. In this paper, we investigate the application of the k-means clustering technique in detecting moving objects in event-based data. | ['Mayukhmali Das', 'Anindya Mondal'] | 2021-09-04 | null | null | null | null | ['moving-object-detection', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 1.79076836e-01 -8.15814197e-01 7.65003711e-02 -2.47281138e-03
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d358c6c7-c0e0-424c-8282-1c3619435340 | rethinking-bayesian-deep-learning-methods-for-1 | 2206.09293 | null | https://arxiv.org/abs/2206.09293v1 | https://arxiv.org/pdf/2206.09293v1.pdf | Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation | Recently, several Bayesian deep learning methods have been proposed for semi-supervised medical image segmentation. Although they have achieved promising results on medical benchmarks, some problems are still existing. Firstly, their overall architectures belong to the discriminative models, and hence, in the early stage of training, they only use labeled data for training, which might make them overfit to the labeled data. Secondly, in fact, they are only partially based on Bayesian deep learning, as their overall architectures are not designed under the Bayesian framework. However, unifying the overall architecture under the Bayesian perspective can make the architecture have a rigorous theoretical basis, so that each part of the architecture can have a clear probabilistic interpretation. Therefore, to solve the problems, we propose a new generative Bayesian deep learning (GBDL) architecture. GBDL belongs to the generative models, whose target is to estimate the joint distribution of input medical volumes and their corresponding labels. Estimating the joint distribution implicitly involves the distribution of data, so both labeled and unlabeled data can be utilized in the early stage of training, which alleviates the potential overfitting problem. Besides, GBDL is completely designed under the Bayesian framework, and thus we give its full Bayesian formulation, which lays a theoretical probabilistic foundation for our architecture. Extensive experiments show that our GBDL outperforms previous state-of-the-art methods in terms of four commonly used evaluation indicators on three public medical datasets. | ['Thomas Lukasiewicz', 'JianFeng Wang'] | 2022-06-18 | rethinking-bayesian-deep-learning-methods-for | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Rethinking_Bayesian_Deep_Learning_Methods_for_Semi-Supervised_Volumetric_Medical_Image_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-medical-image-segmentation', 'volumetric-medical-image-segmentation'] | ['computer-vision', 'medical'] | [-1.81640923e-01 2.20298842e-01 -3.65570545e-01 -7.21986592e-01
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fae095d2-51f8-4401-83de-7c8647a928e7 | learning-barycentric-representations-of-3d | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Learning_Barycentric_Representations_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Xie_Learning_Barycentric_Representations_CVPR_2017_paper.pdf | Learning Barycentric Representations of 3D Shapes for Sketch-Based 3D Shape Retrieval | Retrieving 3D shapes with sketches is a challenging problem since 2D sketches and 3D shapes are from two heterogeneous domains, which results in large discrepancy between them. In this paper, we propose to learn barycenters of 2D projections of 3D shapes for sketch-based 3D shape retrieval. Specifically, we first use two deep convolutional neural networks (CNNs) to extract deep features of sketches and 2D projections of 3D shapes. For 3D shapes, we then compute the Wasserstein barycenters of deep features of multiple projections to form a barycentric representation. Finally, by constructing a metric network, a discriminative loss is formulated on the Wasserstein barycenters of 3D shapes and sketches in the deep feature space to learn discriminative and compact 3D shape and sketch features for retrieval. The proposed method is evaluated on the SHREC'13 and SHREC'14 sketch track benchmark datasets. Compared to the state-of-the-art methods, our proposed method can significantly improve the retrieval performance.
| ['Fan Zhu', 'Yi Fang', 'Guoxian Dai', 'Jin Xie'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['3d-shape-retrieval'] | ['computer-vision'] | [-4.44035530e-01 -6.27366304e-01 -9.98952016e-02 -4.05887872e-01
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5c6d89ca-7575-4f4b-a2aa-ec49e7dd6321 | evaluating-robustness-of-support-vector | 2306.02639 | null | https://arxiv.org/abs/2306.02639v1 | https://arxiv.org/pdf/2306.02639v1.pdf | Evaluating robustness of support vector machines with the Lagrangian dual approach | Adversarial examples bring a considerable security threat to support vector machines (SVMs), especially those used in safety-critical applications. Thus, robustness verification is an essential issue for SVMs, which can provide provable robustness against various kinds of adversary attacks. The evaluation results obtained through the robustness verification can provide a safe guarantee for the use of SVMs. The existing verification method does not often perform well in verifying SVMs with nonlinear kernels. To this end, we propose a method to improve the verification performance for SVMs with nonlinear kernels. We first formalize the adversarial robustness evaluation of SVMs as an optimization problem. Then a lower bound of the original problem is obtained by solving the Lagrangian dual problem of the original problem. Finally, the adversarial robustness of SVMs is evaluated concerning the lower bound. We evaluate the adversarial robustness of SVMs with linear and nonlinear kernels on the MNIST and Fashion-MNIST datasets. The experimental results show that the percentage of provable robustness obtained by our method on the test set is better than that of the state-of-the-art. | ['Pan Qin', 'Hong Gu', 'YuTing Liu'] | 2023-06-05 | null | null | null | null | ['adversarial-robustness'] | ['adversarial'] | [-1.22749057e-04 -7.26316646e-02 -2.55738586e-01 -2.81098157e-01
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df4db926-5212-4f77-98de-8b979ac2e4e7 | discreetly-exploiting-inter-session | 2304.08894 | null | https://arxiv.org/abs/2304.08894v1 | https://arxiv.org/pdf/2304.08894v1.pdf | Discreetly Exploiting Inter-session Information for Session-based Recommendation | Limited intra-session information is the performance bottleneck of the early GNN based SBR models. Therefore, some GNN based SBR models have evolved to introduce additional inter-session information to facilitate the next-item prediction. However, we found that the introduction of inter-session information may bring interference to these models. The possible reasons are twofold. First, inter-session dependencies are not differentiated at the factor-level. Second, measuring inter-session weight by similarity is not enough. In this paper, we propose DEISI to solve the problems. For the first problem, DEISI differentiates the types of inter-session dependencies at the factor-level with the help of DRL technology. For the second problem, DEISI introduces stability as a new metric for weighting inter-session dependencies together with the similarity. Moreover, CL is used to improve the robustness of the model. Extensive experiments on three datasets show the superior performance of the DEISI model compared with the state-of-the-art models. | ['Haotong Wang', 'Gang Wu', 'Zihan Wang'] | 2023-04-18 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-2.52986699e-01 -4.80174601e-01 -6.03482068e-01 -3.41958761e-01
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2a1afbf2-f189-4574-a2d8-a75e827a5465 | single-image-reflection-separation-with | 1806.05376 | null | http://arxiv.org/abs/1806.05376v1 | http://arxiv.org/pdf/1806.05376v1.pdf | Single Image Reflection Separation with Perceptual Losses | We present an approach to separating reflection from a single image. The
approach uses a fully convolutional network trained end-to-end with losses that
exploit low-level and high-level image information. Our loss function includes
two perceptual losses: a feature loss from a visual perception network, and an
adversarial loss that encodes characteristics of images in the transmission
layers. We also propose a novel exclusion loss that enforces pixel-level layer
separation. We create a dataset of real-world images with reflection and
corresponding ground-truth transmission layers for quantitative evaluation and
model training. We validate our method through comprehensive quantitative
experiments and show that our approach outperforms state-of-the-art reflection
removal methods in PSNR, SSIM, and perceptual user study. We also extend our
method to two other image enhancement tasks to demonstrate the generality of
our approach. | ['Ren Ng', 'Xuaner Zhang', 'Qifeng Chen'] | 2018-06-14 | single-image-reflection-separation-with-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Single_Image_Reflection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single_Image_Reflection_CVPR_2018_paper.pdf | cvpr-2018-6 | ['reflection-removal'] | ['computer-vision'] | [ 9.21955645e-01 -6.78139105e-02 2.79848158e-01 -4.39075828e-01
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85e48158-79a2-48ee-9681-b61ce2b2a3ab | computational-modelling-and-data-driven | 2107.05707 | null | https://arxiv.org/abs/2107.05707v2 | https://arxiv.org/pdf/2107.05707v2.pdf | Computational modelling and data-driven homogenisation of knitted membranes | Knitting is an effective technique for producing complex three-dimensional surfaces owing to the inherent flexibility of interlooped yarns and recent advances in manufacturing providing better control of local stitch patterns. Fully yarn-level modelling of large-scale knitted membranes is not feasible. Therefore, we use a two-scale homogenisation approach and model the membrane as a Kirchhoff-Love shell on the macroscale and as Euler-Bernoulli rods on the microscale. The governing equations for both the shell and the rod are discretised with cubic B-spline basis functions. For homogenisation we consider only the in-plane response of the membrane. The solution of the nonlinear microscale problem requires a significant amount of time due to the large deformations and the enforcement of contact constraints, rendering conventional online computational homogenisation approaches infeasible. To sidestep this problem, we use a pre-trained statistical Gaussian Process Regression (GPR) model to map the macroscale deformations to macroscale stresses. During the offline learning phase, the GPR model is trained by solving the microscale problem for a sufficiently rich set of deformation states obtained by either uniform or Sobol sampling. The trained GPR model encodes the nonlinearities and anisotropies present in the microscale and serves as a material model for the membrane response of the macroscale shell. The bending response can be chosen in dependence of the mesh size to penalise the fine out-of-plane wrinkling of the membrane. After verifying and validating the different components of the proposed approach, we introduce several examples involving membranes subjected to tension and shear to demonstrate its versatility and good performance. | ['Fehmi Cirak', 'Xiao Xiao', 'Sumudu Herath'] | 2021-07-12 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.46706939e-01 1.16808504e-01 4.92421836e-01 2.98991382e-01
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4532512b-58ad-4bcc-a51e-2269afbdfe07 | large-ai-models-in-health-informatics | 2303.11568 | null | https://arxiv.org/abs/2303.11568v1 | https://arxiv.org/pdf/2303.11568v1.pdf | Large AI Models in Health Informatics: Applications, Challenges, and the Future | Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which often reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A concrete example is the recent debut of ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our life. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multimodality data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents an up-to-date comprehensive review of large AI models, from background to their applications. We identify seven key sectors that large AI models are applicable and might have substantial influence, including 1) molecular biology and drug discovery; 2) medical diagnosis and decision-making; 3) medical imaging and vision; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges in health informatics, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics. | ['Benny Lo', 'Dong Xu', 'Wu Yuan', 'Bo Xiao', 'Frank P. -W. Lo', 'Kyle Lam', 'Yinzhao Dong', 'Ruiyang Zhang', 'Peilun Shi', 'Jiachuan Peng', 'Jiankai Sun', 'Lin Li', 'Jianing Qiu'] | 2023-03-21 | null | null | null | null | ['drug-discovery', 'medical-diagnosis'] | ['medical', 'medical'] | [ 3.89475703e-01 4.05348748e-01 -2.04711735e-01 -2.77493279e-02
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48495de7-c413-44d2-b434-058f1aeb3c98 | vietnamese-transition-based-dependency | 1911.03726 | null | https://arxiv.org/abs/1911.03726v1 | https://arxiv.org/pdf/1911.03726v1.pdf | Vietnamese transition-based dependency parsing with supertag features | In recent years, dependency parsing is a fascinating research topic and has a lot of applications in natural language processing. In this paper, we present an effective approach to improve dependency parsing by utilizing supertag features. We performed experiments with the transition-based dependency parsing approach because it can take advantage of rich features. Empirical evaluation on Vietnamese Dependency Treebank showed that, we achieved an improvement of 18.92% in labeled attachment score with gold supertags and an improvement of 3.57% with automatic supertags. | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen'] | 2019-11-09 | null | null | null | null | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-4.37937707e-01 2.11208910e-01 -8.88573155e-02 -8.49559307e-01
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1922f8e8-9058-43df-b9a9-7e6259d600a8 | an-attention-based-deep-learning-approach-for | null | null | https://ieeexplore.ieee.org/document/9417097 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9417097 | An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG | Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR). The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features. The second module is the temporal context encoder (TCE) that leverages a multi-head attention mechanism to capture the temporal dependencies among the extracted features. Particularly, the multi-head attention deploys causal convolutions to model the temporal relations in the input features. We evaluate the performance of our proposed AttnSleep model using three public datasets. The results show that our AttnSleep outperforms state-of-the-art techniques in terms of different evaluation metrics. Our source codes, experimental data, and supplementary materials are available at https://github.com/emadeldeen24/AttnSleep . | ['Cuntai Guan', 'XiaoLi Li', 'Chee-Keong Kwoh', 'Min Wu', 'Chengyu Liu', 'Zhenghua Chen', 'Emadeldeen Eldele'] | 2021-04-28 | null | null | null | null | ['sleep-stage-detection', 'sleep-quality-prediction', 'automatic-sleep-stage-classification'] | ['medical', 'medical', 'medical'] | [-2.51463830e-01 -3.15958440e-01 -1.86177325e-02 -6.20127678e-01
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db5ac0fe-5521-4abb-8b56-280b8c8fab2b | pontogammarus-maeoticus-swarm-optimization-a | 1807.01844 | null | http://arxiv.org/abs/1807.01844v1 | http://arxiv.org/pdf/1807.01844v1.pdf | Pontogammarus Maeoticus Swarm Optimization: A Metaheuristic Optimization Algorithm | Nowadays, metaheuristic optimization algorithms are used to find the global
optima in difficult search spaces. Pontogammarus Maeoticus Swarm Optimization
(PMSO) is a metaheuristic algorithm imitating aquatic nature and foraging
behavior. Pontogammarus Maeoticus, also called Gammarus in short, is a tiny
creature found mostly in coast of Caspian Sea in Iran. In this algorithm,
global optima is modeled as sea edge (coast) to which Gammarus creatures are
willing to move in order to rest from sea waves and forage in sand. Sea waves
satisfy exploration and foraging models exploitation. The strength of sea wave
is determined according to distance of Gammarus from sea edge. The angles of
waves applied on several particles are set randomly helping algorithm not be
stuck in local bests. Meanwhile, the neighborhood of particles change
adaptively resulting in more efficient progress in searching. The proposed
algorithm, although is applicable on any optimization problem, is experimented
for partially shaded solar PV array. Experiments on CEC05 benchmarks, as well
as solar PV array, show the effectiveness of this optimization algorithm. | ['Saeed Sharifian', 'Benyamin Ghojogh'] | 2018-07-05 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-8.54941383e-02 -4.50140297e-01 2.53056705e-01 1.60764605e-01
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5870a6a4-61fb-4504-9b00-b89fa82deaf1 | event-based-video-reconstruction-using | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Weng_Event-Based_Video_Reconstruction_Using_Transformer_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Weng_Event-Based_Video_Reconstruction_Using_Transformer_ICCV_2021_paper.pdf | Event-Based Video Reconstruction Using Transformer | Event cameras, which output events by detecting spatio-temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency. Previous works have achieved impressive performances on event-based video reconstruction by introducing convolutional neural networks (CNNs). However, intrinsic locality of convolutional operations is not capable of modeling long-range dependency, which is crucial to many vision tasks. In this paper, we present a hybrid CNN-Transformer network for event-based video reconstruction (ET-Net), which merits the fine local information from CNN and global contexts from Transformer. In addition, we further propose a Token Pyramid Aggregation strategy to implement multi-scale token integration for relating internal and intersected semantic concepts in the token-space. Experimental results demonstrate that our proposed method achieves superior performance over state-of-the-art methods on multiple real-world event datasets. The code is available at https://github.com/WarranWeng/ET-Net | ['Zhiwei Xiong', 'Yueyi Zhang', 'Wenming Weng'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 3.85177322e-02 -7.11753666e-01 2.41080150e-02 -4.24593270e-01
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5787030b-0c36-4091-b877-0f429f3629b6 | feature-engineering-methods-on-multivariate | 2303.16117 | null | https://arxiv.org/abs/2303.16117v2 | https://arxiv.org/pdf/2303.16117v2.pdf | Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions | This paper is a work in progress. We are looking for collaborators to provide us financial datasets in Equity/Futures market to conduct more bench-marking studies. The authors have papers employing similar methods applied on the Numerai dataset, which is freely available but obfuscated. We apply different feature engineering methods for time-series to US market price data. The predictive power of models are tested against Numerai-Signals targets. | ['Mauricio Barahona', 'Thomas Wong'] | 2023-03-26 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-1.87144533e-01 -2.61910260e-01 -1.00488529e-01 -3.67130697e-01
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b5aebe1d-7b38-499f-ab4e-ddaaf8ec5fcc | de-abuse-tamilnlp-acl-2022-transliteration-as | null | null | https://aclanthology.org/2022.dravidianlangtech-1.5 | https://aclanthology.org/2022.dravidianlangtech-1.5.pdf | DE-ABUSE@TamilNLP-ACL 2022: Transliteration as Data Augmentation for Abuse Detection in Tamil | With the rise of social media and internet, thereis a necessity to provide an inclusive space andprevent the abusive topics against any gender,race or community. This paper describes thesystem submitted to the ACL-2022 shared taskon fine-grained abuse detection in Tamil. In ourapproach we transliterated code-mixed datasetas an augmentation technique to increase thesize of the data. Using this method we wereable to rank 3rd on the task with a 0.290 macroaverage F1 score and a 0.590 weighted F1score | ['Bharathi Raja Chakravarthi', 'Adeep Hande', 'Sean Benhur', 'Vasanth Palanikumar'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['transliteration', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-4.07255113e-01 3.37799862e-02 -5.63497841e-01 -3.69696796e-01
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61ffe980-b124-4647-92a3-63919df563a9 | uncertainty-aware-system-identification-with | 2202.05844 | null | https://arxiv.org/abs/2202.05844v1 | https://arxiv.org/pdf/2202.05844v1.pdf | Uncertainty Aware System Identification with Universal Policies | Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real transfer is estimating the real-world environmental parameters to ground the simulated environment to. Although existing methods such as Domain Randomisation (DR) can produce robust policies by sampling from a distribution of parameters during training, there is no established method for identifying the parameters of the corresponding distribution for a given real-world setting. In this work, we propose Uncertainty-aware policy search (UncAPS), where we use Universal Policy Network (UPN) to store simulation-trained task-specific policies across the full range of environmental parameters and then subsequently employ robust Bayesian optimisation to craft robust policies for the given environment by combining relevant UPN policies in a DR like fashion. Such policy-driven grounding is expected to be more efficient as it estimates only task-relevant sets of parameters. Further, we also account for the estimation uncertainties in the search process to produce policies that are robust against both aleatoric and epistemic uncertainties. We empirically evaluate our approach in a range of noisy, continuous control environments, and show its improved performance compared to competing baselines. | ['Svetha Venkatesh', 'Santu Rana', 'Thommen George Karimpanal', 'Buddhika Laknath Semage'] | 2022-02-11 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 2.73594439e-01 -6.48093922e-03 -3.42102721e-02 -7.13068619e-02
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d37bca4f-4a8f-4e0b-b741-d65bf37ad17c | mmd-mix-value-function-factorisation-with | 2106.11652 | null | https://arxiv.org/abs/2106.11652v1 | https://arxiv.org/pdf/2106.11652v1.pdf | MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning | In the real world, many tasks require multiple agents to cooperate with each other under the condition of local observations. To solve such problems, many multi-agent reinforcement learning methods based on Centralized Training with Decentralized Execution have been proposed. One representative class of work is value decomposition, which decomposes the global joint Q-value $Q_\text{jt}$ into individual Q-values $Q_a$ to guide individuals' behaviors, e.g. VDN (Value-Decomposition Networks) and QMIX. However, these baselines often ignore the randomness in the situation. We propose MMD-MIX, a method that combines distributional reinforcement learning and value decomposition to alleviate the above weaknesses. Besides, to improve data sampling efficiency, we were inspired by REM (Random Ensemble Mixture) which is a robust RL algorithm to explicitly introduce randomness into the MMD-MIX. The experiments demonstrate that MMD-MIX outperforms prior baselines in the StarCraft Multi-Agent Challenge (SMAC) environment. | ['Guoliang Fan', 'Yunpeng Bai', 'Dapeng Li', 'Zhiwei Xu'] | 2021-06-22 | null | null | null | null | ['distributional-reinforcement-learning', 'smac-1', 'smac'] | ['methodology', 'playing-games', 'playing-games'] | [-6.08802915e-01 -5.84759451e-02 -4.21298772e-01 -1.12874798e-01
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10bf9080-a8e5-4697-b22b-da276f17a912 | semantic-segmentation-of-surgical | 2303.10972 | null | https://arxiv.org/abs/2303.10972v1 | https://arxiv.org/pdf/2303.10972v1.pdf | Semantic segmentation of surgical hyperspectral images under geometric domain shifts | Robust semantic segmentation of intraoperative image data could pave the way for automatic surgical scene understanding and autonomous robotic surgery. Geometric domain shifts, however, although common in real-world open surgeries due to variations in surgical procedures or situs occlusions, remain a topic largely unaddressed in the field. To address this gap in the literature, we (1) present the first analysis of state-of-the-art (SOA) semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data, and (2) address generalizability with a dedicated augmentation technique termed "Organ Transplantation" that we adapted from the general computer vision community. According to a comprehensive validation on six different OOD data sets comprising 600 RGB and hyperspectral imaging (HSI) cubes from 33 pigs semantically annotated with 19 classes, we demonstrate a large performance drop of SOA organ segmentation networks applied to geometric OOD data. Surprisingly, this holds true not only for conventional RGB data (drop of Dice similarity coefficient (DSC) by 46 %) but also for HSI data (drop by 45 %), despite the latter's rich information content per pixel. Using our augmentation scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data. The simplicity and effectiveness of our augmentation scheme makes it a valuable network-independent tool for addressing geometric domain shifts in semantic scene segmentation of intraoperative data. Our code and pre-trained models will be made publicly available. | ['Lena Maier-Hein', 'Felix Nickel', 'Beat Peter Müller-Stich', 'Berkin Özdemir', 'Alessandro Motta', 'Alexander Studier-Fischer', 'Silvia Seidlitz', 'Jan Sellner'] | 2023-03-20 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.38701737e-01 6.78430140e-01 -2.01221872e-02 -1.94321856e-01
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c0b43e6e-e75f-4082-84b7-c6d00f77a140 | implementation-of-hand-detection-based | 1312.7560 | null | http://arxiv.org/abs/1312.7560v1 | http://arxiv.org/pdf/1312.7560v1.pdf | Implementation of Hand Detection based Techniques for Human Computer Interaction | The computer industry is developing at a fast pace. With this development
almost all of the fields under computers have advanced in the past couple of
decades. But the same technology is being used for human computer interaction
that was used in 1970s. Even today the same type of keyboard and mouse is used
for interacting with computer systems. With the recent boom in the mobile
segment touchscreens have become popular for interaction with cell phones. But
these touchscreens are rarely used on traditional systems. This paper tries to
introduce methods for human computer interaction using the users hand which can
be used both on traditional computer platforms as well as cell phones. The
methods explain how the users detected hand can be used as input for
applications and also explain applications that can take advantage of this type
of interaction mechanism. | ['Vipul Honrao', 'Amiraj Dhawan'] | 2013-12-29 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.69841960e-01 -1.41878352e-01 -2.39962518e-01 1.41064664e-02
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895ca35e-7f9d-4ead-b302-2a4209a44100 | graftnet-towards-domain-generalized-stereo | 2204.00179 | null | https://arxiv.org/abs/2204.00179v1 | https://arxiv.org/pdf/2204.00179v1.pdf | GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature | Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://github.com/SpadeLiu/Graft-PSMNet. | ['Guodong Qi', 'Huimin Yu', 'Biyang Liu'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_GraftNet_Towards_Domain_Generalized_Stereo_Matching_With_a_Broad-Spectrum_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 1.64007828e-01 -2.95296788e-01 -1.87248170e-01 -6.51179790e-01
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c720fb54-1aeb-417b-a707-662eb84b2e1d | dyngraph2vec-capturing-network-dynamics-using | 1809.02657 | null | https://arxiv.org/abs/1809.02657v2 | https://arxiv.org/pdf/1809.02657v2.pdf | dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning | Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need of capturing dynamics for prediction on a toy data set created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real world data sets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction. | ['Arquimedes Canedo', 'Palash Goyal', 'Sujit Rokka Chhetri'] | 2018-09-07 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-3.08344692e-01 1.34926990e-01 -4.06259418e-01 5.27227670e-02
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0bdecddf-6ec4-4bf6-8994-2c2bfe1b9d8a | quality-of-syntactic-implication-of-rl-based | 1912.05493 | null | https://arxiv.org/abs/1912.05493v1 | https://arxiv.org/pdf/1912.05493v1.pdf | Quality of syntactic implication of RL-based sentence summarization | Work on summarization has explored both reinforcement learning (RL) optimization using ROUGE as a reward and syntax-aware models, such as models those input is enriched with part-of-speech (POS)-tags and dependency information. However, it is not clear what is the respective impact of these approaches beyond the standard ROUGE evaluation metric. Especially, RL-based for summarization is becoming more and more popular. In this paper, we provide a detailed comparison of these two approaches and of their combination along several dimensions that relate to the perceived quality of the generated summaries: number of repeated words, distribution of part-of-speech tags, impact of sentence length, relevance and grammaticality. Using the standard Gigaword sentence summarization task, we compare an RL self-critical sequence training (SCST) method with syntax-aware models that leverage POS tags and Dependency information. We show that on all qualitative evaluations, the combined model gives the best results, but also that only training with RL and without any syntactic information already gives nearly as good results as syntax-aware models with less parameters and faster training convergence. | ['Hoa T. Le', 'Claire Gardent', 'Christophe Cerisara'] | 2019-12-11 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 5.07700481e-02 2.67468601e-01 -3.49209607e-01 -2.17788666e-01
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42b8fbba-677d-45fc-94ee-58ca08d63ea5 | seeded-ising-model-and-statistical-natures-of | 1802.02223 | null | https://arxiv.org/abs/1802.02223v1 | https://arxiv.org/pdf/1802.02223v1.pdf | Seeded Ising Model and Statistical Natures of Human Iris Templates | We propose a variant of Ising model, called the Seeded Ising Model, to model probabilistic nature of human iris templates. This model is an Ising model in which the values at certain lattice points are held fixed throughout Ising model evolution. Using this we show how to reconstruct the full iris template from partial information, and we show that about 1/6 of the given template is needed to recover almost all information content of the original one in the sense that the resulting Hamming distance is well within the range to assert correctly the identity of the subject. This leads us to propose the concept of effective statistical degree of freedom of iris templates and show it is about 1/6 of the total number of bits. In particular, for a template of $2048$ bits, its effective statistical degree of freedom is about $342$ bits, which coincides very well with the degree of freedom computed by the completely different method proposed by Daugman. | ['Nam-Sook Wee', 'Song-Hwa Kwon', 'Sung Jin Lee', 'Hyeong In Choi'] | 2018-01-03 | null | null | null | null | ['2048'] | ['playing-games'] | [ 5.88782489e-01 5.83260834e-01 -1.46861479e-01 8.26964453e-02
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99886770-4ad7-4792-a02d-681d9d62a722 | a-new-approach-to-learning-in-dynamic | 1812.09027 | null | http://arxiv.org/abs/1812.09027v2 | http://arxiv.org/pdf/1812.09027v2.pdf | A new approach to learning in Dynamic Bayesian Networks (DBNs) | In this paper, we revisit the parameter learning problem, namely the
estimation of model parameters for Dynamic Bayesian Networks (DBNs). DBNs are
directed graphical models of stochastic processes that encompasses and
generalize Hidden Markov models (HMMs) and Linear Dynamical Systems (LDSs).
Whenever we apply these models to economics and finance, we are forced to make
some modeling assumptions about the state dynamics and the graph topology (the
DBN structure). These assumptions may be incorrectly specified and contain some
additional noise compared to reality. Trying to use a best fit approach through
maximum likelihood estimation may miss this point and try to fit at any price
these models on data. We present here a new methodology that takes a radical
point of view and instead focus on the final efficiency of our model.
Parameters are hence estimated in terms of their efficiency rather than their
distributional fit to the data. The resulting optimization problem that
consists in finding the optimal parameters is a hard problem. We rely on
Covariance Matrix Adaptation Evolution Strategy (CMA-ES) method to tackle this
issue. We apply this method to the seminal problem of trend detection in
financial markets. We see on numerical results that the resulting parameters
seem less error prone to over fitting than traditional moving average cross
over trend detection and perform better. The method developed here for
algorithmic trading is general. It can be applied to other real case
applications whenever there is no physical law underlying our DBNs. | ['J. Atif', 'R. Laraki', 'E. Benhamou'] | 2018-12-21 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-1.38685822e-01 -3.80716519e-03 1.07238114e-01 2.82581355e-02
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3cef7cec-5255-4b9f-8961-98d42ea8711c | fast-and-flexible-human-program-induction-in | 2103.05823 | null | https://arxiv.org/abs/2103.05823v1 | https://arxiv.org/pdf/2103.05823v1.pdf | Fast and flexible: Human program induction in abstract reasoning tasks | The Abstraction and Reasoning Corpus (ARC) is a challenging program induction dataset that was recently proposed by Chollet (2019). Here, we report the first set of results collected from a behavioral study of humans solving a subset of tasks from ARC (40 out of 1000). Although this subset of tasks contains considerable variation, our results showed that humans were able to infer the underlying program and generate the correct test output for a novel test input example, with an average of 80% of tasks solved per participant, and with 65% of tasks being solved by more than 80% of participants. Additionally, we find interesting patterns of behavioral consistency and variability within the action sequences during the generation process, the natural language descriptions to describe the transformations for each task, and the errors people made. Our findings suggest that people can quickly and reliably determine the relevant features and properties of a task to compose a correct solution. Future modeling work could incorporate these findings, potentially by connecting the natural language descriptions we collected here to the underlying semantics of ARC. | ['Todd M. Gureckis', 'Brenden M. Lake', 'Wai Keen Vong', 'Aysja Johnson'] | 2021-03-10 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 1.76063925e-01 3.52015316e-01 5.79355955e-02 -4.67035472e-01
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5ccbaf59-dea4-4f04-b999-75f36edcfcde | combining-global-and-local-merges-in-logic | 2305.16926 | null | https://arxiv.org/abs/2305.16926v2 | https://arxiv.org/pdf/2305.16926v2.pdf | Combining Global and Local Merges in Logic-based Entity Resolution | In the recently proposed Lace framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: all occurrences of those entity references (possibly across multiple database tuples) are deemed equal and can be merged. By contrast, a local form of merge is often more natural when identifying pairs of data values, e.g. some occurrences of 'J. Smith' may be equated with 'Joe Smith', while others should merge with 'Jane Smith'. This motivates us to extend Lace with local merges of values and explore the computational properties of the resulting formalism. | ['Yazmín Ibáñez-García', 'Víctor Gutiérrez-Basulto', 'Gianluca Cima', 'Meghyn Bienvenu'] | 2023-05-26 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-5.12233153e-02 3.68518203e-01 -2.45087013e-01 -2.73233712e-01
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211adcf2-9b1c-4da6-841a-fcb482d73050 | road-segmentation-for-remote-sensing-images | 2008.04021 | null | https://arxiv.org/abs/2008.04021v1 | https://arxiv.org/pdf/2008.04021v1.pdf | Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks | Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid network into generative adversarial networks to minimize the difference between the source and target domains. A generator is learned to produce quality synthetic images, and the discriminator attempts to distinguish them. We also propose a feature pyramid network that improves the performance of the proposed model by extracting effective features from all the layers of the network for describing different scales objects. Indeed, a novel scale-wise architecture is introduced to learn from the multi-level feature maps and improve the semantics of the features. For optimization, the model is trained by a joint reconstruction loss function, which minimizes the difference between the fake images and the real ones. A wide range of experiments on three datasets prove the superior performance of the proposed approach in terms of accuracy and efficiency. In particular, our model achieves state-of-the-art 78.86 IOU on the Massachusetts dataset with 14.89M parameters and 86.78B FLOPs, with 4x fewer FLOPs but higher accuracy (+3.47% IOU) than the top performer among state-of-the-art approaches used in the evaluation. | ['Ruili Wang', 'Huiyu Zhou', 'Pourya Shamsolmoali', 'Jie Yang', 'Masoumeh Zareapoor'] | 2020-08-10 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 3.34792554e-01 1.40752243e-02 1.34871051e-01 -2.99127966e-01
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b06c7317-119c-4f2d-a060-a305203b0b2b | enhancing-low-density-eeg-based-brain | 2212.03329 | null | https://arxiv.org/abs/2212.03329v1 | https://arxiv.org/pdf/2212.03329v1.pdf | Enhancing Low-Density EEG-Based Brain-Computer Interfaces with Similarity-Keeping Knowledge Distillation | Electroencephalogram (EEG) has been one of the common neuromonitoring modalities for real-world brain-computer interfaces (BCIs) because of its non-invasiveness, low cost, and high temporal resolution. Recently, light-weight and portable EEG wearable devices based on low-density montages have increased the convenience and usability of BCI applications. However, loss of EEG decoding performance is often inevitable due to reduced number of electrodes and coverage of scalp regions of a low-density EEG montage. To address this issue, we introduce knowledge distillation (KD), a learning mechanism developed for transferring knowledge/information between neural network models, to enhance the performance of low-density EEG decoding. Our framework includes a newly proposed similarity-keeping (SK) teacher-student KD scheme that encourages a low-density EEG student model to acquire the inter-sample similarity as in a pre-trained teacher model trained on high-density EEG data. The experimental results validate that our SK-KD framework consistently improves motor-imagery EEG decoding accuracy when number of electrodes deceases for the input EEG data. For both common low-density headphone-like and headband-like montages, our method outperforms state-of-the-art KD methods across various EEG decoding model architectures. As the first KD scheme developed for enhancing EEG decoding, we foresee the proposed SK-KD framework to facilitate the practicality of low-density EEG-based BCI in real-world applications. | ['Chun-Shu Wei', 'Sung-Yu Chen', 'Xin-Yao Huang'] | 2022-12-06 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.24354342e-01 -3.29045981e-01 2.70832628e-01 -1.70357481e-01
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de8416cd-6e0c-49c9-a079-9fac77879799 | robust-face-recognition-using-local | 1212.02415 | null | http://arxiv.org/abs/1212.2415v1 | http://arxiv.org/pdf/1212.2415v1.pdf | Robust Face Recognition using Local Illumination Normalization and Discriminant Feature Point Selection | Face recognition systems must be robust to the variation of various factors
such as facial expression, illumination, head pose and aging. Especially, the
robustness against illumination variation is one of the most important problems
to be solved for the practical use of face recognition systems. Gabor wavelet
is widely used in face detection and recognition because it gives the
possibility to simulate the function of human visual system. In this paper, we
propose a method for extracting Gabor wavelet features which is stable under
the variation of local illumination and show experiment results demonstrating
its effectiveness. | ['Jongchol Jo', 'Cholhun Kim', 'Song Han', 'Jinsong Kim', 'Sunam Han'] | 2012-12-11 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.49327928e-01 -8.38377178e-01 -5.42934798e-02 -3.34198713e-01
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3902937f-0b95-47ce-9ac8-be7c706ef6bd | egocentric-object-manipulation-graphs | 2006.03201 | null | https://arxiv.org/abs/2006.03201v1 | https://arxiv.org/pdf/2006.03201v1.pdf | Egocentric Object Manipulation Graphs | We introduce Egocentric Object Manipulation Graphs (Ego-OMG) - a novel representation for activity modeling and anticipation of near future actions integrating three components: 1) semantic temporal structure of activities, 2) short-term dynamics, and 3) representations for appearance. Semantic temporal structure is modeled through a graph, embedded through a Graph Convolutional Network, whose states model characteristics of and relations between hands and objects. These state representations derive from all three levels of abstraction, and span segments delimited by the making and breaking of hand-object contact. Short-term dynamics are modeled in two ways: A) through 3D convolutions, and B) through anticipating the spatiotemporal end points of hand trajectories, where hands come into contact with objects. Appearance is modeled through deep spatiotemporal features produced through existing methods. We note that in Ego-OMG it is simple to swap these appearance features, and thus Ego-OMG is complementary to most existing action anticipation methods. We evaluate Ego-OMG on the EPIC Kitchens Action Anticipation Challenge. The consistency of the egocentric perspective of EPIC Kitchens allows for the utilization of the hand-centric cues upon which Ego-OMG relies. We demonstrate state-of-the-art performance, outranking all other previous published methods by large margins and ranking first on the unseen test set and second on the seen test set of the EPIC Kitchens Action Anticipation Challenge. We attribute the success of Ego-OMG to the modeling of semantic structure captured over long timespans. We evaluate the design choices made through several ablation studies. Code will be released upon acceptance | ['Michael Maynord', 'Eadom Dessalene', 'Cornelia Fermuller', 'Chinmaya Devaraj', 'Yiannis Aloimonos'] | 2020-06-05 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 1.23795748e-01 1.06611900e-01 -1.93845183e-01 -2.98868641e-02
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478339e8-2402-4efe-b9ac-d4a95277684e | siod-single-instance-annotated-per-category | 2203.15353 | null | https://arxiv.org/abs/2203.15353v2 | https://arxiv.org/pdf/2203.15353v2.pdf | SIOD: Single Instance Annotated Per Category Per Image for Object Detection | Object detection under imperfect data receives great attention recently. Weakly supervised object detection (WSOD) suffers from severe localization issues due to the lack of instance-level annotation, while semi-supervised object detection (SSOD) remains challenging led by the inter-image discrepancy between labeled and unlabeled data. In this study, we propose the Single Instance annotated Object Detection (SIOD), requiring only one instance annotation for each existing category in an image. Degraded from inter-task (WSOD) or inter-image (SSOD) discrepancies to the intra-image discrepancy, SIOD provides more reliable and rich prior knowledge for mining the rest of unlabeled instances and trades off the annotation cost and performance. Under the SIOD setting, we propose a simple yet effective framework, termed Dual-Mining (DMiner), which consists of a Similarity-based Pseudo Label Generating module (SPLG) and a Pixel-level Group Contrastive Learning module (PGCL). SPLG firstly mines latent instances from feature representation space to alleviate the annotation missing problem. To avoid being misled by inaccurate pseudo labels, we propose PGCL to boost the tolerance to false pseudo labels. Extensive experiments on MS COCO verify the feasibility of the SIOD setting and the superiority of the proposed method, which obtains consistent and significant improvements compared to baseline methods and achieves comparable results with fully supervised object detection (FSOD) methods with only 40% instances annotated. | ['Wei-Shi Zheng', 'Fan Tang', 'Ke Yan', 'Xingjia Pan', 'Hanjun Li'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_SIOD_Single_Instance_Annotated_per_Category_per_Image_for_Object_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.56611782e-01 2.85199493e-01 -3.68126333e-01 -4.75991309e-01
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d660ee0a-46a6-4ff7-a8cb-1dd53546ae01 | zero-shot-information-extraction-via-chatting | 2302.10205 | null | https://arxiv.org/abs/2302.10205v1 | https://arxiv.org/pdf/2302.10205v1.pdf | Zero-Shot Information Extraction via Chatting with ChatGPT | Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile, zero-shot IE reduces the time and effort that data labeling takes. Recent efforts on large language models (LLMs, e.g., GPT-3, ChatGPT) show promising performance on zero-shot settings, thus inspiring us to explore prompt-based methods. In this work, we ask whether strong IE models can be constructed by directly prompting LLMs. Specifically, we transform the zero-shot IE task into a multi-turn question-answering problem with a two-stage framework (ChatIE). With the power of ChatGPT, we extensively evaluate our framework on three IE tasks: entity-relation triple extract, named entity recognition, and event extraction. Empirical results on six datasets across two languages show that ChatIE achieves impressive performance and even surpasses some full-shot models on several datasets (e.g., NYT11-HRL). We believe that our work could shed light on building IE models with limited resources. | ['Wenjuan Han', 'Yong Jiang', 'Meishan Zhang', 'Yufeng Chen', 'Jinan Xu', 'Pengjun Xie', 'Shen Huang', 'Xin Zhang', 'Xiaobin Wang', 'Ning Cheng', 'Xingyu Cui', 'Xiang Wei'] | 2023-02-20 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 1.66216437e-02 5.56135654e-01 -4.87916619e-01 -3.28761458e-01
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b9af430f-f042-42d2-88ab-206e30b4f99a | spatio-temporal-deep-learning-assisted | 2306.01570 | null | https://arxiv.org/abs/2306.01570v1 | https://arxiv.org/pdf/2306.01570v1.pdf | Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment | Security-constrained unit commitment (SCUC) is a computationally complex process utilized in power system day-ahead scheduling and market clearing. SCUC is run daily and requires state-of-the-art algorithms to speed up the process. The constraints and data associated with SCUC are both geographically and temporally correlated to ensure the reliability of the solution, which further increases the complexity. In this paper, an advanced machine learning (ML) model is used to study the patterns in power system historical data, which inherently considers both spatial and temporal (ST) correlations in constraints. The ST-correlated ML model is trained to understand spatial correlation by considering graph neural networks (GNN) whereas temporal sequences are studied using long short-term memory (LSTM) networks. The proposed approach is validated on several test systems namely, IEEE 24-Bus system, IEEE-73 Bus system, IEEE 118-Bus system, and synthetic South-Carolina (SC) 500-Bus system. Moreover, B-{\theta} and power transfer distribution factor (PTDF) based SCUC formulations were considered in this research. Simulation results demonstrate that the ST approach can effectively predict generator commitment schedule and classify critical and non-critical lines in the system which are utilized for model reduction of SCUC to obtain computational enhancement without loss in solution quality | ['Xingpeng Li', 'Arun Venkatesh Ramesh'] | 2023-06-02 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [-2.48291820e-01 -6.46582007e-01 -1.49291039e-01 1.45756260e-01
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543b1746-e822-4cb7-9f13-5ff8307e0918 | reconstructing-video-from-interferometric | 1711.01357 | null | http://arxiv.org/abs/1711.01357v2 | http://arxiv.org/pdf/1711.01357v2.pdf | Reconstructing Video from Interferometric Measurements of Time-Varying Sources | Very long baseline interferometry (VLBI) makes it possible to recover images
of astronomical sources with extremely high angular resolution. Most recently,
the Event Horizon Telescope (EHT) has extended VLBI to short millimeter
wavelengths with a goal of achieving angular resolution sufficient for imaging
the event horizons of nearby supermassive black holes. VLBI provides
measurements related to the underlying source image through a sparse set
spatial frequencies. An image can then be recovered from these measurements by
making assumptions about the underlying image. One of the most important
assumptions made by conventional imaging methods is that over the course of a
night's observation the image is static. However, for quickly evolving sources,
such as the galactic center's supermassive black hole (Sgr A*) targeted by the
EHT, this assumption is violated and these conventional imaging approaches
fail. In this work we propose a new way to model VLBI measurements that allows
us to recover both the appearance and dynamics of an evolving source by
reconstructing a video rather than a static image. By modeling VLBI
measurements using a Gaussian Markov Model, we are able to propagate
information across observations in time to reconstruct a video, while
simultaneously learning about the dynamics of the source's emission region. We
demonstrate our proposed Expectation-Maximization (EM) algorithm, StarWarps, on
realistic synthetic observations of black holes, and show how it substantially
improves results compared to conventional imaging algorithms. Additionally, we
demonstrate StarWarps on real VLBI data of the M87 Jet from the VLBA. | ['Adrian V. Dalca', 'Katherine L. Bouman', 'Sheperd S. Doeleman', 'Freek Roelofs', 'Andrew A. Chael', 'William T. Freeman', 'Michael D. Johnson'] | 2017-11-03 | null | null | null | null | ['image-imputation', 'radio-interferometry'] | ['computer-vision', 'miscellaneous'] | [ 1.07831605e-01 -1.15173250e-01 2.26412833e-01 2.28003666e-01
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a25cbf79-5973-4a85-9c2b-25d0c5b58145 | automatic-extraction-of-nested-entities-in | null | null | https://dl.acm.org/doi/10.1145/3498324 | https://www.researchgate.net/publication/359803027_Automatic_Extraction_of_Nested_Entities_in_Clinical_Referrals_in_Spanish | Automatic Extraction of Nested Entities in Clinical Referrals in Spanish | Here we describe a new clinical corpus rich in nested entities and a series of neural models to identify them. The corpus comprises de-identified referrals from the waiting list in Chilean public hospitals. A subset of 5,000 referrals (58.6% medical and 41.4% dental) was manually annotated with 10 types of entities, six attributes, and pairs of relations with clinical relevance. In total, there are 110,771 annotated tokens. A trained medical doctor or dentist annotated these referrals, and then, together with three other researchers, consolidated each of the annotations. The annotated corpus has 48.17% of entities embedded in other entities or containing another one. We use this corpus to build models for Named Entity Recognition (NER). The best results were achieved using a Multiple Single-entity architecture with clinical word embeddings stacked with character and Flair contextual embeddings. The entity with the best performance is abbreviation, and the hardest to recognize is finding. NER models applied to this corpus can leverage statistics of diseases and pending procedures. This work constitutes the first annotated corpus using clinical narratives from Chile and one of the few in Spanish. The annotated corpus, clinical word embeddings, annotation guidelines, and neural models are freely released to the community. | ['Fabián Villena', 'Matías Rojas', 'Jocelyn Dunstan', 'Felipe Bravo-Marquez', 'Pablo Báez'] | 2022-04-07 | null | null | null | acm-transactions-on-computing-for-healthcare-1 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.76951349e-01 8.43816936e-01 -3.21094096e-01 -1.87251806e-01
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f57b0fab-0a95-47ba-aeea-8deab42cec64 | az-whiteness-test-a-test-for-uncorrelated | 2204.11135 | null | https://arxiv.org/abs/2204.11135v1 | https://arxiv.org/pdf/2204.11135v1.pdf | AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs | We present the first whiteness test for graphs, i.e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph. The statistical test aims at finding serial dependencies among close-in-time observations, as well as spatial dependencies among neighboring observations given the underlying graph. The proposed test is a spatio-temporal extension of traditional tests from the system identification literature and finds applications in similar, yet more general, application scenarios involving graph signals. The AZ-test is versatile, allowing the underlying graph to be dynamic, changing in topology and set of nodes, and weighted, thus accounting for connections of different strength, as is the case in many application scenarios like transportation networks and sensor grids. The asymptotic distribution -- as the number of graph edges or temporal observations increases -- is known, and does not assume identically distributed data. We validate the practical value of the test on both synthetic and real-world problems, and show how the test can be employed to assess the quality of spatio-temporal forecasting models by analyzing the prediction residuals appended to the graphs stream. | ['Cesare Alippi', 'Daniele Zambon'] | 2022-04-23 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [ 2.34689221e-01 1.01312116e-01 1.52108550e-01 1.05978668e-01
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eaea8f85-4060-4641-9914-7ede885ab209 | probabilistic-uncertainty-aware-risk-spot | 2303.07181 | null | https://arxiv.org/abs/2303.07181v1 | https://arxiv.org/pdf/2303.07181v1.pdf | Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving | Risk assessment is a central element for the development and validation of Autonomous Vehicles (AV). It comprises a combination of occurrence probability and severity of future critical events. Time Headway (TH) as well as Time-To-Contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g. following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal and behavioral uncertainties as they arise in real-world scenarios. The resulting Risk Spot Detector (RSD) is applied and tested on naturalistic driving data of a multi-lane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur. | ['Julian Eggert', 'Malte Probst', 'Tim Puphal'] | 2023-03-13 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-5.84311560e-02 4.97640893e-02 3.40904295e-02 -3.71987164e-01
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401d60e5-bde6-4c36-adcd-4c18e1adb852 | quadratic-programming-for-continuous-control | 2211.16720 | null | https://arxiv.org/abs/2211.16720v1 | https://arxiv.org/pdf/2211.16720v1.pdf | Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty | This paper studies the control problem for safety-critical multi-agent systems based on quadratic programming (QP). Each controlled agent is modeled as a cascade connection of an integrator and an uncertain nonlinear actuation system. In particular, the integrator represents the position-velocity relation, and the actuation system describes the dynamic response of the actual velocity to the velocity reference signal. The notion of input-to-output stability (IOS) is employed to characterize the essential velocity-tracking capability of the actuation system. The uncertain actuation dynamics may cause infeasibility or discontinuous solutions of QP algorithms for collision avoidance. Also, the interaction between the controlled integrator and the uncertain actuation dynamics may lead to significant robustness issues. By using nonlinear control methods and numerical optimization methods, this paper first contributes a new feasible-set reshaping technique and a refined QP algorithm for feasibility, robustness, and local Lipschitz continuity. Then, we present a nonlinear small-gain analysis to handle the inherent interaction for guaranteed safety of the closed-loop multi-agent system. The proposed methods are illustrated by numerical simulations and a physical experiment. | ['Zhong-Ping Jiang', 'Magnus Egerstedt', 'Tengfei Liu', 'Si Wu'] | 2022-11-30 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 4.44847830e-02 4.44190502e-01 -4.02384907e-01 5.68293929e-01
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ad923d04-15f6-44ac-ba8a-7102834dea04 | fast-event-based-optical-flow-estimation-by | 2212.12218 | null | https://arxiv.org/abs/2212.12218v1 | https://arxiv.org/pdf/2212.12218v1.pdf | Fast Event-based Optical Flow Estimation by Triplet Matching | Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios. | ['Guillermo Gallego', 'Yoshimitsu Aoki', 'Shintaro Shiba'] | 2022-12-23 | null | null | null | null | ['event-based-optical-flow', 'event-based-vision'] | ['computer-vision', 'computer-vision'] | [ 2.59329051e-01 -7.06342518e-01 -1.86997846e-01 -2.94204712e-01
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eca1c269-0de8-49e6-99f0-eefc1f2559c5 | domain-randomization-enhanced-depth | 2208.03792 | null | https://arxiv.org/abs/2208.03792v2 | https://arxiv.org/pdf/2208.03792v2.pdf | Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects | Commercial depth sensors usually generate noisy and missing depths, especially on specular and transparent objects, which poses critical issues to downstream depth or point cloud-based tasks. To mitigate this problem, we propose a powerful RGBD fusion network, SwinDRNet, for depth restoration. We further propose Domain Randomization-Enhanced Depth Simulation (DREDS) approach to simulate an active stereo depth system using physically based rendering and generate a large-scale synthetic dataset that contains 130K photorealistic RGB images along with their simulated depths carrying realistic sensor noises. To evaluate depth restoration methods, we also curate a real-world dataset, namely STD, that captures 30 cluttered scenes composed of 50 objects with different materials from specular, transparent, to diffuse. Experiments demonstrate that the proposed DREDS dataset bridges the sim-to-real domain gap such that, trained on DREDS, our SwinDRNet can seamlessly generalize to other real depth datasets, e.g. ClearGrasp, and outperform the competing methods on depth restoration with a real-time speed. We further show that our depth restoration effectively boosts the performance of downstream tasks, including category-level pose estimation and grasping tasks. Our data and code are available at https://github.com/PKU-EPIC/DREDS | ['He Wang', 'Ping Tan', 'Ziyuan Liu', 'Hao Dong', 'Tianhao Wu', 'Qiwei Li', 'Jiyao Zhang', 'Qiyu Dai'] | 2022-08-07 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 3.80468100e-01 -1.11786142e-01 6.62754893e-01 -4.43557560e-01
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0283193e-62f7-4034-9c91-5d411811240b | unsupervised-domain-adaptation-for-question | null | null | https://aclanthology.org/2022.findings-naacl.183 | https://aclanthology.org/2022.findings-naacl.183.pdf | Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training | Question generation (QG) approaches based on large neural models require (i) large-scale and (ii) high-quality training data. These two requirements pose difficulties for specific application domains where training data is expensive and difficult to obtain. The trained QG models’ effectiveness can degrade significantly when they are applied on a different domain due to domain shift. In this paper, we explore an unsupervised domain adaptation approach to combat the lack of training data and domain shift issue with domain data selection and self-training. We first present a novel answer-aware strategy for domain data selection to select data with the most similarity to a new domain. The selected data are then used as pseudo-in-domain data to retrain the QG model. We then present generation confidence guided self-training with two generation confidence modeling methods (i) generated questions’ perplexity and (ii) the fluency score. We test our approaches on three large public datasets with different domain similarities, using a transformer-based pre-trained QG model. The results show that our proposed approaches outperform the baselines, and show the viability of unsupervised domain adaptation with answer-aware data selection and self-training on the QG task. | ['Claudia Hauff', 'Peide Zhu'] | null | null | null | null | findings-naacl-2022-7 | ['question-generation'] | ['natural-language-processing'] | [ 4.59079117e-01 3.38947177e-01 -2.86448836e-01 -5.43039382e-01
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46e7bb69-3255-4998-99cf-00221e320cd5 | deep-learning-for-segmentation-based-hepatic | 2210.15149 | null | https://arxiv.org/abs/2210.15149v3 | https://arxiv.org/pdf/2210.15149v3.pdf | Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study | Despite high global prevalence of hepatic steatosis, no automated diagnostics demonstrated generalizability in detecting steatosis on multiple international datasets. Traditionally, hepatic steatosis detection relies on clinicians selecting the region of interest (ROI) on computed tomography (CT) to measure liver attenuation. ROI selection demands time and expertise, and therefore is not routinely performed in populations. To automate the process, we validated an existing artificial intelligence (AI) system for 3D liver segmentation and used it to purpose a novel method: AI-ROI, which could automatically select the ROI for attenuation measurements. AI segmentation and AI-ROI method were evaluated on 1,014 non-contrast enhanced chest CT images from eight international datasets: LIDC-IDRI, NSCLC-Lung1, RIDER, VESSEL12, RICORD-1A, RICORD-1B, COVID-19-Italy, and COVID-19-China. AI segmentation achieved a mean dice coefficient of 0.957. Attenuations measured by AI-ROI showed no significant differences (p = 0.545) and a reduction of 71% time compared to expert measurements. The area under the curve (AUC) of the steatosis classification of AI-ROI is 0.921 (95% CI: 0.883 - 0.959). If performed as a routine screening method, our AI protocol could potentially allow early non-invasive, non-pharmacological preventative interventions for hepatic steatosis. 1,014 expert-annotated liver segmentations of patients with hepatic steatosis annotations can be downloaded here: https://drive.google.com/drive/folders/1-g_zJeAaZXYXGqL1OeF6pUjr6KB0igJX. | ['Xiangchun Liu', 'Yiyi Hui', 'Joshua Lin', 'Zezhong Ye', 'Huibin Nie', 'Ning Zhao', 'Feng Xia', 'Ziqiang Wang', 'Guixia Li', 'Zhongyi Zhang'] | 2022-10-27 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-5.03392637e-01 -2.09268227e-01 -2.66871065e-01 -1.18754141e-01
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a81aff78-cf01-477d-b3d5-48f1a56b4a75 | new-insights-on-relieving-task-recency-bias | 2302.08243 | null | https://arxiv.org/abs/2302.08243v1 | https://arxiv.org/pdf/2302.08243v1.pdf | New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning | To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (CIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, the CIL faces a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose a Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. By utilizing a new weight function, the revised focal loss can pay more attention to current ambiguous samples, which can provide more information of the classification boundary. To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for CIL have shown the effectiveness of AFS. The code will be available at \url{https://github.com/czjghost/AFS}. | ['Yanning Zhang', 'Shiyu Ji', 'Zhaoqiang Chen', 'Zhaojie Chen', 'Guoqiang Liang'] | 2023-02-16 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-7.11588860e-02 -9.57594439e-02 -5.58853030e-01 -4.13180292e-01
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996e8679-ba2f-46be-8c6c-d9056b823397 | emef-ensemble-multi-exposure-image-fusion | 2305.12734 | null | https://arxiv.org/abs/2305.12734v1 | https://arxiv.org/pdf/2305.12734v1.pdf | EMEF: Ensemble Multi-Exposure Image Fusion | Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don't utilize any synthesized ground truth, design any loss function, or develop any fusion strategy. Our proposed method EMEF takes advantage of the wisdom of multiple imperfect MEF contributors including both conventional and deep learning-based methods. Specifically, EMEF consists of two main stages: pre-train an imitator network and tune the imitator in the runtime. In the first stage, we make a unified network imitate different MEF targets in a style modulation way. In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair. In the experiment, we construct EMEF from four state-of-the-art MEF methods and then make comparisons with the individuals and several other competitive methods on the latest released MEF benchmark dataset. The promising experimental results demonstrate that our ensemble framework can "get the best of all worlds". The code is available at https://github.com/medalwill/EMEF. | ['Xuan Cheng', 'Ming Zeng', 'Yinglin Zheng', 'Haitao Cao', 'Chengyang Li', 'Renshuai Liu'] | 2023-05-22 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 1.75439671e-01 -4.50728178e-01 2.37247914e-01 -2.61471689e-01
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d722d1a5-f97a-47b1-bc98-95c2efec41bc | spoken-language-change-detection-inspired-by | 2302.05265 | null | https://arxiv.org/abs/2302.05265v1 | https://arxiv.org/pdf/2302.05265v1.pdf | Spoken language change detection inspired by speaker change detection | Spoken language change detection (LCD) refers to identifying the language transitions in a code-switched utterance. Similarly, identifying the speaker transitions in a multispeaker utterance is known as speaker change detection (SCD). Since tasks-wise both are similar, the architecture/framework developed for the SCD task may be suitable for the LCD task. Hence, the aim of the present work is to develop LCD systems inspired by SCD. Initially, both LCD and SCD are performed by humans. The study suggests humans require (a) a larger duration around the change point and (b) language-specific prior exposure, for performing LCD as compared to SCD. The larger duration requirement is incorporated by increasing the analysis window length of the unsupervised distance-based approach. This leads to a relative performance improvement of 29.1% and 2.4%, and a priori language knowledge provides a relative improvement of 31.63% and 14.27% on the synthetic and practical codeswitched datasets, respectively. The performance difference between the practical and synthetic datasets is mostly due to differences in the distribution of the monolingual segment duration. | ['S. R. Mahadeva Prasanna', 'Jagabandhu Mishra'] | 2023-02-10 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.87765235e-01 9.31217894e-03 2.85659909e-01 -4.16262984e-01
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740eb24a-5503-4ffd-a957-f6dfeb26ad3f | bayesian-models-of-functional-connectomics | 2301.06182 | null | https://arxiv.org/abs/2301.06182v1 | https://arxiv.org/pdf/2301.06182v1.pdf | Bayesian Models of Functional Connectomics and Behavior | The problem of jointly analysing functional connectomics and behavioral data is extremely challenging owing to the complex interactions between the two domains. In addition, clinical rs-fMRI studies often have to contend with limited samples, especially in the case of rare disorders. This data-starved regimen can severely restrict the reliability of classical machine learning or deep learning designed to predict behavior from connectivity data. In this work, we approach this problem from the lens of representation learning and bayesian modeling. To model the distributional characteristics of the domains, we first examine the ability of approaches such as Bayesian Linear Regression, Stochastic Search Variable Selection after performing a classical covariance decomposition. Finally, we present a fully bayesian formulation for joint representation learning and prediction. We present preliminary results on a subset of a publicly available clinical rs-fMRI study on patients with Autism Spectrum Disorder. | ["Niharika Shimona D'Souza"] | 2023-01-15 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 4.71123010e-01 6.35565892e-02 -1.52631536e-01 -4.73196089e-01
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5e3a842b-e5f0-4723-a185-58bb89d1d073 | propel-probabilistic-parametric-regression | 1807.10937 | null | https://arxiv.org/abs/1807.10937v2 | https://arxiv.org/pdf/1807.10937v2.pdf | PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks | In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic methodologies are not widely applicable for addressing regression problems using CNNs, as regression involves learning unconstrained continuous and, in many cases, multi-variate target variables. We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems. PROPEL is fully differentiable and, hence, can be easily incorporated for end-to-end training of existing CNN regression architectures using existing optimization algorithms. The proposed method is flexible as it enables learning complex unconstrained probabilities while being generalizable to higher dimensional multi-variate regression problems. We utilize a PROPEL-based CNN to address the problem of learning hand and head orientation from uncalibrated color images. Our experimental validation and comparison with existing CNN regression loss functions show that PROPEL improves the accuracy of a CNN by enabling probabilistic regression, while significantly reducing required model parameters by $10 \times$, resulting in improved generalization as compared to the existing state-of-the-art. | ['Greg Slabaugh', 'S M Masudur Rahman Al Arif', 'Rilwan Basaru', 'Muhammad Asad'] | 2018-07-28 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-1.44322768e-01 -2.73787789e-02 -8.01015943e-02 -6.96776986e-01
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17406282-a490-42d5-8b6f-bbeb2fd14895 | anomaly-detection-by-recombining-gated | 2008.13763 | null | https://arxiv.org/abs/2008.13763v5 | https://arxiv.org/pdf/2008.13763v5.pdf | Anomaly Detection by Recombining Gated Unsupervised Experts | Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel data-driven anomaly detection method called ARGUE. Our method is not only applicable to unsupervised and semi-supervised environments, but also profits from prior knowledge of self-supervised settings. We designed ARGUE as a combination of dedicated expert networks, which specialise on parts of the input data. For its final decision, ARGUE fuses the distributed knowledge across the expert systems using a gated mixture-of-experts architecture. Our evaluation motivates that prior knowledge about the normal data distribution may be as valuable as known anomalies. | ['K. Böttinger', 'J. -P. Schulze', 'P. Sperl'] | 2020-08-31 | null | null | null | null | ['semi-supervised-anomaly-detection'] | ['computer-vision'] | [-3.33064012e-02 3.13885897e-01 1.91683263e-01 -6.68129861e-01
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9d99a8ab-6287-4ddc-850d-b8144ce40029 | investigating-neural-architectures-for-short | null | null | https://aclanthology.org/W17-5017 | https://aclanthology.org/W17-5017.pdf | Investigating neural architectures for short answer scoring | Neural approaches to automated essay scoring have recently shown state-of-the-art performance. The automated essay scoring task typically involves a broad notion of writing quality that encompasses content, grammar, organization, and conventions. This differs from the short answer content scoring task, which focuses on content accuracy. The inputs to neural essay scoring models {--} ngrams and embeddings {--} are arguably well-suited to evaluate content in short answer scoring tasks. We investigate how several basic neural approaches similar to those used for automated essay scoring perform on short answer scoring. We show that neural architectures can outperform a strong non-neural baseline, but performance and optimal parameter settings vary across the more diverse types of prompts typical of short answer scoring. | ['Chong MIn Lee', 'Brian Riordan', 'Andrea Horbach', 'Torsten Zesch', 'Aoife Cahill'] | 2017-09-01 | null | null | null | ws-2017-9 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-8.32214430e-02 4.10510935e-02 -3.95423800e-01 -5.62460959e-01
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25ccd9d9-8adb-4c3a-8286-c7442bddd58b | spirit-diffusion-spirit-driven-score-based | 2212.11274 | null | https://arxiv.org/abs/2212.11274v1 | https://arxiv.org/pdf/2212.11274v1.pdf | SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging | Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction. However, the existing methods do not consider the characteristics of multi-coil acquisition of MRI data. Therefore, we give a new diffusion model, called SPIRiT-Diffusion, based on the SPIRiT iterative reconstruction algorithm. Specifically, SPIRiT-Diffusion characterizes the prior distribution of coil-by-coil images by score matching and characterizes the k-space redundant prior between coils based on self-consistency. With sufficient prior constraint utilized, we achieve superior reconstruction results on the joint Intracranial and Carotid Vessel Wall imaging dataset. | ['Yanjie Zhu', 'Dong Liang', 'Hairong Zheng', 'Sen Jia', 'Jing Cheng', 'Zhuo-Xu Cui', 'Chentao Cao'] | 2022-12-14 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [-1.38524342e-02 -1.68824211e-01 -4.83751185e-02 -4.52967346e-01
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8bb64d56-3a94-481a-aac8-479cd4a67266 | speed-up-object-detection-on-gigapixel-level | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Fan_Speed_Up_Object_Detection_on_Gigapixel-Level_Images_With_Patch_Arrangement_CVPR_2022_paper.pdf | Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement | With the appearance of super high-resolution (e.g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue. Most existing works for efficient object detection on high-resolution images focus on generating local patches where objects may exist, and then every patch is detected independently. However, when the image resolution reaches gigapixel-level, they will suffer from a huge time cost for detecting numerous patches. Different from them, we devise a novel patch arrangement framework for fast object detection on gigapixel-level images. Under this framework, a Patch Arrangement Network (PAN) is proposed to accelerate the detection by determining which patches could be packed together into a compact canvas. Specifically, PAN consists of (1) a Patch Filter Module (PFM) (2) a Patch Packing Module (PPM). PFM filters patch candidates by learning to select patches between two granularities. Subsequently, from the remaining patches, PPM determines how to pack these patches together into a smaller number of canvases. Meanwhile, it generates an ideal layout of patches on canvas. These canvases are fed to the detector to get final results. Experiments show that our method could improve the inference speed on gigapixel-level images by 5 times while maintaining great performance. | ['Weiyao Lin', 'Aixin Zhang', 'John See', 'Wenjie Yang', 'Huabin Liu', 'Jiahao Fan'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['real-time-object-detection'] | ['computer-vision'] | [ 2.78157115e-01 -2.43151531e-01 -1.15701549e-01 1.41317174e-01
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f586b995-5fb8-481c-bf95-792c2f928ee7 | turn-segmentation-into-utterances-for-arabic | 1505.03081 | null | http://arxiv.org/abs/1505.03081v1 | http://arxiv.org/pdf/1505.03081v1.pdf | Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages | Text segmentation task is an essential processing task for many of Natural
Language Processing (NLP) such as text summarization, text translation,
dialogue language understanding, among others. Turns segmentation considered
the key player in dialogue understanding task for building automatic
Human-Computer systems. In this paper, we introduce a novel approach to turn
segmentation into utterances for Egyptian spontaneous dialogues and Instance
Messages (IM) using Machine Learning (ML) approach as a part of automatic
understanding Egyptian spontaneous dialogues and IM task. Due to the lack of
Egyptian dialect dialogue corpus the system evaluated by our corpus includes
3001 turns, which are collected, segmented, and annotated manually from
Egyptian call-centers. The system achieves F1 scores of 90.74% and accuracy of
95.98%. | ['AbdelRahim A. Elmadany', 'Sherif M. Abdou', 'Mervat Gheith'] | 2015-05-12 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 3.91748786e-01 8.59453499e-01 1.98101312e-01 -5.30403554e-01
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52910d8b-94fe-458b-8425-306fd95ab760 | towards-democratizing-joint-embedding-self | 2303.01986 | null | https://arxiv.org/abs/2303.01986v1 | https://arxiv.org/pdf/2303.01986v1.pdf | Towards Democratizing Joint-Embedding Self-Supervised Learning | Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid developments in recent years, due to its promise to effectively leverage large unlabeled data. The development of JE-SSL methods was driven primarily by the search for ever increasing downstream classification accuracies, using huge computational resources, and typically built upon insights and intuitions inherited from a close parent JE-SSL method. This has led unwittingly to numerous pre-conceived ideas that carried over across methods e.g. that SimCLR requires very large mini batches to yield competitive accuracies; that strong and computationally slow data augmentations are required. In this work, we debunk several such ill-formed a priori ideas in the hope to unleash the full potential of JE-SSL free of unnecessary limitations. In fact, when carefully evaluating performances across different downstream tasks and properly optimizing hyper-parameters of the methods, we most often -- if not always -- see that these widespread misconceptions do not hold. For example we show that it is possible to train SimCLR to learn useful representations, while using a single image patch as negative example, and simple Gaussian noise as the only data augmentation for the positive pair. Along these lines, in the hope to democratize JE-SSL and to allow researchers to easily make more extensive evaluations of their methods, we introduce an optimized PyTorch library for SSL. | ['Pascal Vincent', 'Randall Balestriero', 'Florian Bordes'] | 2023-03-03 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 2.22591445e-01 3.69293571e-01 -1.66745484e-01 -4.30431187e-01
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7640ecc6-0420-45f8-a79b-f2b0b309f7fe | multimodal-meta-learning-for-time-series | 2108.02842 | null | https://arxiv.org/abs/2108.02842v2 | https://arxiv.org/pdf/2108.02842v2.pdf | Multimodal Meta-Learning for Time Series Regression | Recent work has shown the efficiency of deep learning models such as Fully Convolutional Networks (FCN) or Recurrent Neural Networks (RNN) to deal with Time Series Regression (TSR) problems. These models sometimes need a lot of data to be able to generalize, yet the time series are sometimes not long enough to be able to learn patterns. Therefore, it is important to make use of information across time series to improve learning. In this paper, we will explore the idea of using meta-learning for quickly adapting model parameters to new short-history time series by modifying the original idea of Model Agnostic Meta-Learning (MAML) \cite{finn2017model}. Moreover, based on prior work on multimodal MAML \cite{vuorio2019multimodal}, we propose a method for conditioning parameters of the model through an auxiliary network that encodes global information of the time series to extract meta-features. Finally, we apply the data to time series of different domains, such as pollution measurements, heart-rate sensors, and electrical battery data. We show empirically that our proposed meta-learning method learns TSR with few data fast and outperforms the baselines in 9 of 12 experiments. | ['Lars Schmidt-Thieme', 'Kiran Madhusudhanan', 'Felix Heinrich', 'Sebastian Pineda Arango'] | 2021-08-05 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.05596155e-01 -4.07993913e-01 -2.25930497e-01 -5.83434820e-01
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b4d11af8-13b4-4f14-820e-99797086a3a8 | advanced-baseline-for-3d-human-pose | 2212.11344 | null | https://arxiv.org/abs/2212.11344v1 | https://arxiv.org/pdf/2212.11344v1.pdf | Advanced Baseline for 3D Human Pose Estimation: A Two-Stage Approach | Human pose estimation has been widely applied in various industries. While recent decades have witnessed the introduction of many advanced two-dimensional (2D) human pose estimation solutions, three-dimensional (3D) human pose estimation is still an active research field in computer vision. Generally speaking, 3D human pose estimation methods can be divided into two categories: single-stage and two-stage. In this paper, we focused on the 2D-to-3D lifting process in the two-stage methods and proposed a more advanced baseline model for 3D human pose estimation, based on the existing solutions. Our improvements include optimization of machine learning models and multiple parameters, as well as introduction of a weighted loss to the training model. Finally, we used the Human3.6M benchmark to test the final performance and it did produce satisfactory results. | ['Jungang Luo', 'Zichen Gui'] | 2022-12-21 | null | null | null | null | ['3d-human-pose-estimation', '2d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.58228844e-01 -1.04263499e-01 -9.46940780e-02 -3.18110794e-01
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d555a808-c670-43c9-8d9a-bac712e857d0 | benchenas-a-benchmarking-platform-for-1 | null | null | https://ieeexplore.ieee.org/document/9697075 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9697075 | BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search | Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS methods, evolutionary computation-based NAS (ENAS) methods have recently gained much attention. Unfortunately, the development of ENAS is hindered by unfair comparison between different ENAS algorithms due to different training conditions and high computational cost caused by expensive performance evaluation. This article develops a platform named BenchENAS, in short for benchmarking evolutionary NAS, to address these issues. BenchENAS makes it easy to achieve fair comparisons between different algorithms by keeping them under the same settings. To accelerate the performance evaluation in a common lab environment, BenchENAS designs a novel and generic efficient evaluation method for the population characteristics of evolutionary computation. This method has greatly improved the efficiency of the evaluation. Furthermore, BenchENAS is easy to install and highly configurable and modular, which brings benefits in good usability and easy extensibility. This article conducts efficient comparison experiments on eight ENAS algorithms with high GPU utilization on this platform. The experiments validate that the fair comparison issue does exist in the current ENAS algorithms, and BenchENAS can alleviate this issue. A Website has been built to promote BenchENAS at https://benchenas.com , where interested researchers can obtain the source code and document of BenchENAS for free. | ['Xiangning Xie; Yuqiao Liu; Yanan Sun; Gary G. Yen; Bing Xue; Mengjie Zhang'] | 2022-12-01 | null | null | null | ieee-transactions-on-evolutionary-computation-3 | ['architecture-search'] | ['methodology'] | [-6.09715343e-01 -8.90652537e-01 2.88319230e-01 -2.09906891e-01
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efd42929-5229-4dae-880f-362305049d47 | traffic-sign-detection-and-recognition-using | 2212.08387 | null | https://arxiv.org/abs/2212.08387v1 | https://arxiv.org/pdf/2212.08387v1.pdf | Traffic sign detection and recognition using event camera image reconstruction | This paper presents a method for detection and recognition of traffic signs based on information extracted from an event camera. The solution used a FireNet deep convolutional neural network to reconstruct events into greyscale frames. Two YOLOv4 network models were trained, one based on greyscale images and the other on colour images. The best result was achieved for the model trained on the basis of greyscale images, achieving an efficiency of 87.03%. | ['Tomasz Kryjak', 'Kamil Jeziorek'] | 2022-12-16 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.11116582e-01 -3.36496532e-01 2.67652273e-01 -2.59966254e-01
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