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0917f06f-41d0-4af5-ba5a-477ec7c850a9 | discriminative-online-learning-for-fast-video | 1904.08630 | null | http://arxiv.org/abs/1904.08630v1 | http://arxiv.org/pdf/1904.08630v1.pdf | Discriminative Online Learning for Fast Video Object Segmentation | We address the highly challenging problem of video object segmentation. Given
only the initial mask, the task is to segment the target in the subsequent
frames. In order to effectively handle appearance changes and similar
background objects, a robust representation of the target is required. Previous
approaches either... | ['Michael Felsberg', 'Fahad Shahbaz Khan', 'Felix Järemo Lawin', 'Martin Danelljan', 'Andreas Robinson'] | 2019-04-18 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 3.78304303e-01 -9.90287438e-02 -2.37787887e-01 -4.28455174e-01
-7.72348702e-01 -5.25936186e-01 2.55414546e-01 -2.29221731e-01
-3.61431897e-01 3.00407410e-01 -3.61064494e-01 -1.51605889e-01
5.22472739e-01 -3.93667430e-01 -7.30998814e-01 -6.22375429e-01
1.47353441e-01 3.11608851e-01 7.77894020e-01 1.29365325... | [9.190515518188477, -0.15291374921798706] |
b7836506-c809-43a9-9578-deb01294ca8d | unsupervised-fine-tuning-for-text-clustering | null | null | https://aclanthology.org/2020.coling-main.482 | https://aclanthology.org/2020.coling-main.482.pdf | Unsupervised Fine-tuning for Text Clustering | Fine-tuning with pre-trained language models (e.g. BERT) has achieved great success in many language understanding tasks in supervised settings (e.g. text classification). However, relatively little work has been focused on applying pre-trained models in unsupervised settings, such as text clustering. In this paper, we... | ['Ming Zhou', 'Xingxing Zhang', 'Lei Cui', 'Furu Wei', 'Shaohan Huang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['text-clustering'] | ['natural-language-processing'] | [-2.46123403e-01 -1.21007599e-01 -2.96029687e-01 -8.65129113e-01
-8.20765555e-01 -4.47213352e-01 6.77078247e-01 5.94480038e-01
-6.97754562e-01 5.09210646e-01 3.28381300e-01 -2.82040477e-01
-8.74872878e-02 -5.60995817e-01 -5.98943949e-01 -6.35811508e-01
1.27596259e-01 1.14699554e+00 3.87916341e-02 -6.48107678... | [10.480081558227539, 6.929171085357666] |
9a466d6c-5b3b-4531-a294-9687d9f0b787 | ecpe-2d-emotion-cause-pair-extraction-based | null | null | https://aclanthology.org/2020.acl-main.288 | https://aclanthology.org/2020.acl-main.288.pdf | ECPE-2D: Emotion-Cause Pair Extraction based on Joint Two-Dimensional Representation, Interaction and Prediction | In recent years, a new interesting task, called emotion-cause pair extraction (ECPE), has emerged in the area of text emotion analysis. It aims at extracting the potential pairs of emotions and their corresponding causes in a document. To solve this task, the existing research employed a two-step framework, which first... | ['Zixiang Ding', 'Rui Xia', 'Jianfei Yu'] | 2020-07-01 | null | null | null | acl-2020-6 | ['emotion-cause-pair-extraction'] | ['natural-language-processing'] | [ 5.29266819e-02 -1.09347388e-01 2.15963751e-01 -5.03020346e-01
-7.86747098e-01 -3.58612210e-01 5.49401224e-01 1.11566089e-01
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-3.12780052e-01 -4.91411835e-01 -1.03053048e-01 -5.61544538e-01
-1.16027184e-01 7.47600198e-02 -1.25108078e-01 -2.85319239... | [12.624549865722656, 6.216001987457275] |
bb66435f-8440-492b-b997-73087f5e0c4b | wire-wavelet-implicit-neural-representations | 2301.05187 | null | https://arxiv.org/abs/2301.05187v1 | https://arxiv.org/pdf/2301.05187v1.pdf | WIRE: Wavelet Implicit Neural Representations | Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of the nonlinear activation function employed in its multilayer perceptron (MLP) network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs design... | ['Richard G. Baraniuk', 'Ashok Veeraraghavan', 'Guha Balakrishnan', 'Jasper Tan', 'Daniel LeJeune', 'Vishwanath Saragadam'] | 2023-01-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Saragadam_WIRE_Wavelet_Implicit_Neural_Representations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-inpainting'] | ['computer-vision'] | [ 6.26560926e-01 -2.15237498e-01 1.21315040e-01 -2.53785968e-01
-5.25202751e-01 -4.30604890e-02 5.49305856e-01 -2.37668082e-01
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-5.03910482e-01 -8.35453510e-01 -6.11528814e-01 -1.00322855e+00
1.70212373e-01 -1.25370517e-01 4.69520576e-02 -3.16905558... | [11.460348129272461, -2.167447328567505] |
d9583ca8-a250-453b-959d-07dd93914a32 | undiff-unsupervised-voice-restoration-with | 2306.00721 | null | https://arxiv.org/abs/2306.00721v1 | https://arxiv.org/pdf/2306.00721v1.pdf | UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model | This paper introduces UnDiff, a diffusion probabilistic model capable of solving various speech inverse tasks. Being once trained for speech waveform generation in an unconditional manner, it can be adapted to different tasks including degradation inversion, neural vocoding, and source separation. In this paper, we, fi... | ['Dmitry Vetrov', 'Nicholas Babaev', 'Ivan Shchekotov', 'Pavel Andreev', 'Anastasiia Iashchenko'] | 2023-06-01 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 4.18404877e-01 2.12954521e-01 2.28190020e-01 -9.78860855e-02
-1.02270865e+00 -4.76942658e-01 6.01164401e-01 -3.30128759e-01
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-6.68542162e-02 -3.34185243e-01 -6.45081162e-01 -9.15046811e-01
1.09379150e-01 2.09337831e-01 1.59966037e-01 -1.05434492... | [15.06286907196045, 6.0119194984436035] |
7d854708-a6d5-4ab8-93f5-d0e33f434353 | transfer-knowledge-from-natural-language-to | 2301.09017 | null | https://arxiv.org/abs/2301.09017v2 | https://arxiv.org/pdf/2301.09017v2.pdf | Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models? | Recent advancements in Large Language Models (LLMs) have drawn increasing attention since the learned embeddings pretrained on large-scale datasets have shown powerful ability in various downstream applications. However, whether the learned knowledge by LLMs can be transferred to clinical cardiology remains unknown. In... | ['Ding Zhao', 'Douglas Weber', 'Emerson Liu', 'Michael Rosenberg', 'Mengdi Xu', 'Jiacheng Zhu', 'William Han', 'JieLin Qiu'] | 2023-01-21 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.51972938e-01 2.93531805e-01 -1.28250554e-01 -3.06827515e-01
-1.37110555e+00 -4.24141437e-01 3.83354157e-01 5.33058226e-01
-2.37864882e-01 7.41714954e-01 4.41348940e-01 -2.97237307e-01
-6.42113984e-02 -7.10951388e-01 -2.94295341e-01 -5.37932813e-01
-2.19492882e-01 5.36342144e-01 -2.69705534e-01 1.88075438... | [7.9960503578186035, 6.708901882171631] |
5e769b5d-c4eb-4f3c-9998-1305ddddf64a | contrastive-self-supervised-learning-of | null | null | https://openreview.net/forum?id=Py4VjN6V2JX | https://openreview.net/pdf?id=Py4VjN6V2JX | Contrastive Self-Supervised Learning of Global-Local Audio-Visual Representations | Contrastive self-supervised learning has delivered impressive results in many audio-visual recognition tasks. However, existing approaches optimize for learning either global representations useful for high-level understanding tasks such as classification, or local representations useful for tasks such as audio-visual ... | ['Yale Song', 'Daniel McDuff', 'Zhaoyang Zeng', 'Shuang Ma'] | 2021-01-01 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 4.50624168e-01 -3.24609488e-01 -3.93042237e-01 -3.83247703e-01
-1.37316072e+00 -5.71438611e-01 5.34197450e-01 2.85690278e-01
-3.88890728e-02 4.62370068e-01 5.63959122e-01 1.85499936e-02
-1.03129521e-01 -2.21783891e-01 -7.59145081e-01 -7.57584035e-01
-2.43504331e-01 2.12917384e-03 1.15423776e-01 3.72218043... | [14.680299758911133, 4.953159809112549] |
b9c4548c-da68-427e-af55-88307fe24969 | neural-pose-transfer-by-spatially-adaptive | 2003.07254 | null | https://arxiv.org/abs/2003.07254v2 | https://arxiv.org/pdf/2003.07254v2.pdf | Neural Pose Transfer by Spatially Adaptive Instance Normalization | Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information. Traditional... | ['yinda zhang', 'xiangyang xue', 'Yanwei Fu', 'Chao Wen', 'Tianyun Zou', 'Jiashun Wang', 'Haitao Lin'] | 2020-03-16 | neural-pose-transfer-by-spatially-adaptive-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Neural_Pose_Transfer_by_Spatially_Adaptive_Instance_Normalization_CVPR_2020_paper.pdf | cvpr-2020-6 | ['pose-transfer'] | ['computer-vision'] | [ 1.29587024e-01 1.46289796e-01 5.13625517e-02 -3.74494106e-01
-5.09478927e-01 -5.14262378e-01 3.16763848e-01 -2.16411054e-01
-2.30642468e-01 6.13058031e-01 -1.49458930e-01 3.69075239e-01
1.83592930e-01 -9.37012970e-01 -1.00219357e+00 -5.48842967e-01
3.79624993e-01 5.55852294e-01 3.04499984e-01 -3.39349836... | [7.291581630706787, -1.4917888641357422] |
501a5ec6-4e11-4c6b-9eb8-d6b2368999d7 | multilingual-contextual-adapters-to-improve | 2307.00759 | null | https://arxiv.org/abs/2307.00759v1 | https://arxiv.org/pdf/2307.00759v1.pdf | Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages | Connectionist Temporal Classification (CTC) models are popular for their balance between speed and performance for Automatic Speech Recognition (ASR). However, these CTC models still struggle in other areas, such as personalization towards custom words. A recent approach explores Contextual Adapters, wherein an attenti... | ['Sravan Bodapati', 'Brady Houston', 'Saket Dingliwal', 'Devang Kulshreshtha'] | 2023-07-03 | null | null | null | null | ['speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 1.58365309e-01 1.47943392e-01 -1.36741951e-01 -3.30392927e-01
-1.22141647e+00 -4.64439988e-01 7.20197499e-01 1.73256025e-01
-8.34970176e-01 5.25035441e-01 5.07332385e-01 -5.09935439e-01
2.49696240e-01 -1.99648544e-01 -5.36050856e-01 -5.79296172e-01
2.51981527e-01 3.92209679e-01 2.90629447e-01 -4.24863368... | [14.281710624694824, 6.7858567237854] |
9499f29e-984d-4530-9bed-2858510bff88 | neural-network-models-for-stock-selection | 1906.05327 | null | https://arxiv.org/abs/1906.05327v1 | https://arxiv.org/pdf/1906.05327v1.pdf | Neural Network Models for Stock Selection Based on Fundamental Analysis | Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial rati... | ['Luiz Fernando Capretz', 'Danny Ho', 'Yuxuan Huang'] | 2019-06-12 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.00596011e-01 -2.33453795e-01 -7.46823922e-02 -3.42788935e-01
3.46018225e-01 -4.83553678e-01 6.17304921e-01 -1.70594007e-01
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-8.54250908e-01 -1.07224369e+00 6.01858869e-02 -3.84255648e-01
-2.33454749e-01 4.06049848e-01 2.60844260e-01 -8.03365886... | [4.5707688331604, 4.165517330169678] |
0bcc26e4-e597-463b-a4c9-8067113e1d0c | next-sentence-prediction-helps-implicit | null | null | https://aclanthology.org/D19-1586 | https://aclanthology.org/D19-1586.pdf | Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains | Implicit discourse relation classification is one of the most difficult tasks in discourse parsing. Previous studies have generally focused on extracting better representations of the relational arguments. In order to solve the task, it is however additionally necessary to capture what events are expected to cause or f... | ['Wei Shi', 'Vera Demberg'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 6.30634725e-01 1.02681565e+00 -4.64474738e-01 -3.11406970e-01
-8.84615064e-01 -4.71452177e-01 1.06217253e+00 5.16694546e-01
-3.23603243e-01 1.08369172e+00 6.54948711e-01 -7.14871407e-01
-1.35900483e-01 -8.72117937e-01 -7.77787447e-01 -2.89321333e-01
9.20835696e-03 6.78518713e-01 5.80243230e-01 -5.95092714... | [10.774229049682617, 9.272637367248535] |
dfc704a2-1054-42b9-b572-a4c1cb1f5717 | figo-enhanced-fingerprint-identification | 2208.05615 | null | https://arxiv.org/abs/2208.05615v2 | https://arxiv.org/pdf/2208.05615v2.pdf | FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques | Fingerprint evidence plays an important role in a criminal investigation for the identification of individuals. Although various techniques have been proposed for fingerprint classification and feature extraction, automated fingerprint identification of fingerprints is still in its earliest stage. The performance of tr... | ['Mahmoud Abouyoussef', 'Ibrahim Yilmaz'] | 2022-08-11 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 7.71307230e-01 -2.37269133e-01 -1.57182425e-01 -4.94702011e-01
-2.68834323e-01 -6.12043321e-01 3.74209851e-01 -2.58761585e-01
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-3.69552851e-01 -1.13288856e+00 -8.54813814e-01 -5.79238236e-01
-2.44273953e-02 1.04233541e-01 7.40925819e-02 -6.64205253... | [12.987854957580566, 0.996212363243103] |
744e2a12-71f5-4b02-a44e-d5f2d8c2e4f9 | neglectable-effect-of-brain-mri-data | 2204.05278 | null | https://arxiv.org/abs/2204.05278v3 | https://arxiv.org/pdf/2204.05278v3.pdf | Negligible effect of brain MRI data preprocessing for tumor segmentation | Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply preprocessing transformations such as anatomy alignment, voxel resampling, signal intensity equalization... | ['Mikhail Belyaev', 'Boris Shirokikh', 'Andrey Golanov', 'Svetlana Zolotova', 'Anvar Kurmukov', 'Alexandra Dalechina', 'Polina Druzhinina', 'Ekaterina Kondrateva'] | 2022-04-11 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 3.34929675e-01 -1.03530481e-01 2.36593578e-02 -6.68475449e-01
-6.85154259e-01 -5.55747449e-01 4.42769289e-01 7.18150914e-01
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-2.49546066e-01 -5.77534020e-01 -8.99160504e-01 -6.04096889e-01
6.44618794e-02 4.24081296e-01 2.56615072e-01 -4.34142631... | [14.08011245727539, -2.3323686122894287] |
343c3d39-1696-4a9e-bccb-9de977472dcf | extensive-deep-temporal-point-process | 2110.09823 | null | https://arxiv.org/abs/2110.09823v4 | https://arxiv.org/pdf/2110.09823v4.pdf | An Empirical Study: Extensive Deep Temporal Point Process | Temporal point process as the stochastic process on continuous domain of time is commonly used to model the asynchronous event sequence featuring with occurrence timestamps. Thanks to the strong expressivity of deep neural networks, they are emerging as a promising choice for capturing the patterns in asynchronous sequ... | ['Stan. Z. Li', 'Zhangyang Gao', 'Lirong Wu', 'Cheng Tan', 'Haitao Lin'] | 2021-10-19 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 6.88856319e-02 5.64984195e-02 -3.40737015e-01 -8.79051760e-02
-3.13216180e-01 -5.69158316e-01 8.90798926e-01 2.99544483e-01
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-7.60488093e-01 5.40725946e-01 1.47650629e-01 1.43195108... | [6.982316970825195, 3.52599835395813] |
c293918e-64d9-4631-9734-38a20e10b857 | radam-texture-recognition-through-randomized-1 | 2303.04554 | null | https://arxiv.org/abs/2303.04554v1 | https://arxiv.org/pdf/2303.04554v1.pdf | RADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps | Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained backbone and then fine-tuning the new architecture on specific texture recognition tasks. Here we prop... | ['Odemir M. Bruno', 'Bernard De Baets', 'Wesley N. Gonçalves', 'Lucas C. Ribas', 'Kallil M. Zielinski', 'Leonardo Scabini'] | 2023-03-08 | radam-texture-recognition-through-randomized | https://arxiv.org/abs/2303.04554 | https://arxiv.org/pdf/2303.04554.pdf | null | ['texture-classification'] | ['computer-vision'] | [ 5.18145978e-01 2.84056455e-01 -9.21589136e-02 -6.22936070e-01
-4.96037424e-01 -1.54192686e-01 5.10564685e-01 -1.04830489e-01
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-3.76368165e-02 4.03333575e-01 3.88665348e-01 -3.04689944... | [10.161561965942383, -0.10434209555387497] |
322aa261-4617-473d-bba6-4155ce69dac0 | high-fidelity-audio-generation-and | 2006.00877 | null | https://arxiv.org/abs/2006.00877v2 | https://arxiv.org/pdf/2006.00877v2.pdf | High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder | Unsupervised disentangled representation learning from the unlabelled audio data, and high fidelity audio generation have become two linchpins in the machine learning research fields. However, the representation learned from an unsupervised setting does not guarantee its' usability for any downstream task at hand, whic... | ['Björn W. Schuller', 'Rajib Rana', 'Kazi Nazmul Haque'] | 2020-06-01 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.79010612e-01 4.28059429e-01 4.28838134e-02 -4.50878590e-03
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2.93323807e-02 8.70058015e-02 -1.67743832e-01 -4.58892941... | [15.213607788085938, 5.298123836517334] |
c3ba396e-23b8-4b03-9d12-8df22807f937 | selective-in-context-data-augmentation-for | 2302.05096 | null | https://arxiv.org/abs/2302.05096v1 | https://arxiv.org/pdf/2302.05096v1.pdf | Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information | This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone does not improve performance, we introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the use... | ['Dilek Hakkani-Tur', 'Yang Liu', 'Di Jin', 'Mahdi Namazifar', 'Devamanyu Hazarika', 'Sungjin Lee', 'Seokhwan Kim', 'Alexandros Papangelis', 'Yen-Ting Lin'] | 2023-02-10 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 5.63248038e-01 2.68362015e-01 -2.76461363e-01 -3.64291906e-01
-1.14083493e+00 -4.07154202e-01 9.99918282e-01 2.81924814e-01
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-1.51419476e-01 3.47939074e-01 6.80668354e-02 -4.47813511... | [11.97902774810791, 7.633860111236572] |
c2211214-667c-4a72-950f-ae353f5866e5 | facebook-aaoaac3cfacebook-activity-event | null | null | https://aclanthology.org/O16-1022 | https://aclanthology.org/O16-1022.pdf | Facebook 活動事件擷取系統(Facebook Activity Event Extraction System)[In Chinese] | null | ['Chia-Hui Chang', 'Yuan-Hao Lin'] | 2016-10-01 | facebook-facebook-activity-event-extraction | https://aclanthology.org/O16-1022 | https://aclanthology.org/O16-1022.pdf | roclingijclclp-2016-10 | ['sequential-pattern-mining'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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1a613d1d-92fa-47b7-bc06-eb7fa3816dd2 | controlled-data-generation-via-insertion | null | null | https://aclanthology.org/2022.naacl-industry.7 | https://aclanthology.org/2022.naacl-industry.7.pdf | Controlled Data Generation via Insertion Operations for NLU | Use of synthetic data is rapidly emerging as a realistic alternative to manually annotating live traffic for industry-scale model building. Manual data annotation is slow, expensive and not preferred for meeting customer privacy expectations. Further, commercial natural language applications are required to support con... | ['Wael Hamza', 'Anna Rumshisky', 'Rahul Gupta', 'Haidar Khan', 'Yuval Merhav', 'Manoj Kumar'] | null | null | null | null | naacl-acl-2022-7 | ['intent-classification'] | ['natural-language-processing'] | [ 2.47137755e-01 2.61366785e-01 -3.38460118e-01 -8.61153245e-01
-8.74240041e-01 -7.55614460e-01 5.69642663e-01 1.49323270e-01
-5.74270129e-01 8.17014337e-01 2.15885743e-01 -4.19791281e-01
3.63456875e-01 -5.01740336e-01 -3.79061550e-01 6.17528409e-02
2.34781697e-01 8.44349205e-01 3.23265880e-01 -6.97161397... | [12.510309219360352, 7.641727447509766] |
3d748919-de9d-4813-a6af-ea4182347500 | continuous-space-representations-of | null | null | https://aclanthology.info/papers/N15-1036/n15-1036 | https://www.aclweb.org/anthology/N15-1036 | Continuous Space Representations of Linguistic Typology and their Application to Phylogenetic Inference | null | ['Yugo Murawaki'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['electrical-engineering'] | ['miscellaneous'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391809940338135, 15.869182586669922] |
9b30478e-814a-46b9-bd81-7eb51c538d5c | neural-imaging-pipelines-the-scourge-or-hope | 1902.10707 | null | http://arxiv.org/abs/1902.10707v1 | http://arxiv.org/pdf/1902.10707v1.pdf | Neural Imaging Pipelines - the Scourge or Hope of Forensics? | Forensic analysis of digital photographs relies on intrinsic statistical
traces introduced at the time of their acquisition or subsequent editing. Such
traces are often removed by post-processing (e.g., down-sampling and
re-compression applied upon distribution in the Web) which inhibits reliable
provenance analysis. I... | ['Pawel Korus', 'Nasir Memon'] | 2019-02-27 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 5.76762736e-01 -1.44050509e-01 4.75411974e-02 -1.81239426e-01
-9.92156506e-01 -8.73559415e-01 4.78803545e-01 3.94565076e-01
-5.54227352e-01 8.33716393e-02 -4.41186391e-02 -5.33008397e-01
1.05844826e-01 -5.01575530e-01 -9.57699537e-01 -2.73457617e-01
-2.08991468e-01 -8.93439204e-02 1.70885339e-01 4.40184951... | [12.315629005432129, 1.0206505060195923] |
8792c599-1abf-42c9-95a9-46e6271bb7e6 | mdaesf-cine-mri-reconstruction-based-on | 2303.04968 | null | https://arxiv.org/abs/2303.04968v2 | https://arxiv.org/pdf/2303.04968v2.pdf | MDAMF: Reconstruction of Cardiac Cine MRI under Free-breathing using Motion-guided Deformable Alignment and Multi-resolution Fusion | Cardiac cine magnetic resonance imaging not only requires higher imaging speed but also needs to address motion artifacts. Especially in the case of free-breathing, more motion artifacts are inevitably introduced. This poses higher demands on the reconstruction performance of the model and its ability to capture tempor... | ['Weikun Zhang', 'Keyan Chen', 'Qiaohong Liu', 'Yuanjie Lin', 'Yiman Liu', 'Xiaoxiang Han'] | 2023-03-09 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 2.92008042e-01 -3.30572635e-01 1.69559479e-01 -6.97865412e-02
-5.96272230e-01 -1.97564080e-01 1.27028838e-01 1.65442824e-02
-3.18391532e-01 6.44063652e-01 3.02410215e-01 9.70057677e-03
-2.63770878e-01 -3.41779858e-01 -1.27070382e-01 -8.19430053e-01
-1.74322844e-01 -3.12895358e-01 3.74308079e-01 1.49184406... | [13.529254913330078, -2.448354959487915] |
818d762a-9794-4be5-9e18-3329df969b2f | accelerating-the-evolutionary-algorithms-by | 2210.06814 | null | https://arxiv.org/abs/2210.06814v1 | https://arxiv.org/pdf/2210.06814v1.pdf | Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $ε$-greedy acquisition function | In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitness landscape of original problems based on every generation of optimiza... | ['Masaharu Munetomo', 'Enzhi Zhang', 'Rui Zhong'] | 2022-10-13 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-6.42549247e-02 -1.47791013e-01 3.32974255e-01 2.42696367e-02
-2.30248049e-01 -4.96162735e-02 2.28893682e-01 2.05885351e-01
-4.43914264e-01 1.14079642e+00 -2.30212793e-01 6.59187511e-02
-4.62520897e-01 -9.77896094e-01 -5.09191215e-01 -1.10169733e+00
1.27135605e-01 4.27704602e-01 -2.57177260e-02 -1.04415342... | [5.990037441253662, 3.608774423599243] |
efbdb5e5-347f-4c6a-9da8-43c1b7dd4da9 | multijugate-dual-learning-for-low-resource | 2305.16106 | null | https://arxiv.org/abs/2305.16106v1 | https://arxiv.org/pdf/2305.16106v1.pdf | Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System | Dialogue data in real scenarios tend to be sparsely available, rendering data-starved end-to-end dialogue systems trained inadequately. We discover that data utilization efficiency in low-resource scenarios can be enhanced by mining alignment information uncertain utterance and deterministic dialogue state. Therefore, ... | ['Xipeng Qiu', 'Linyang Li', 'Yanjun Zheng', 'Xiaotian Zhang', 'ShiMin Li'] | 2023-05-25 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [-1.41435519e-01 3.47394049e-01 -4.56084698e-01 -5.39772213e-01
-7.17377484e-01 -6.75610244e-01 7.22492337e-01 -2.98899323e-01
-4.39168274e-01 9.77470517e-01 4.36596215e-01 -4.57895011e-01
-8.62939805e-02 -5.10838866e-01 -1.60195500e-01 -3.99617285e-01
-1.13060281e-01 8.08859825e-01 3.69533189e-02 -8.70181978... | [12.844890594482422, 8.00502872467041] |
60dd1afb-eb1d-4d4b-8bc1-b23b7e183f10 | domain-specific-author-attribution-based-on | 1602.07393 | null | http://arxiv.org/abs/1602.07393v1 | http://arxiv.org/pdf/1602.07393v1.pdf | Domain Specific Author Attribution Based on Feedforward Neural Network Language Models | Authorship attribution refers to the task of automatically determining the
author based on a given sample of text. It is a problem with a long history and
has a wide range of application. Building author profiles using language models
is one of the most successful methods to automate this task. New language
modeling me... | ['Yufang Sun', 'Zhenhao Ge'] | 2016-02-24 | null | null | null | null | ['author-attribution'] | ['natural-language-processing'] | [-9.84037220e-02 -1.37959599e-01 -3.67471308e-01 -4.24698859e-01
-3.92849922e-01 -6.60172343e-01 8.52185428e-01 1.60900325e-01
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9.59935933e-02 -4.29740548e-01 -2.03164801e-01 -1.94825932e-01
4.06271070e-01 8.03178668e-01 -1.73607647e-01 3.87173891... | [9.656070709228516, 10.54629135131836] |
462f0d54-d73b-4ee0-af47-abb9898daade | fracture-detection-in-wrist-x-ray-images | 2111.07355 | null | https://arxiv.org/abs/2111.07355v3 | https://arxiv.org/pdf/2111.07355v3.pdf | Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models | Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patients medical records and physical examination. The aim of this study is to p... | ['Fatih Mert', 'Boran Demirciler', 'Uğurhan Kutbay', 'Nil Tokgöz', 'Tolga Tolunay', 'Murat Çiçeklidağ', 'Ozan Peker', 'Fatih Uysal', 'Fırat Hardalaç'] | 2021-11-14 | null | null | null | null | ['medical-object-detection'] | ['computer-vision'] | [-5.32584369e-01 -2.73043811e-01 1.11996368e-01 9.55111235e-02
-1.03049672e+00 -1.58297122e-01 -1.67254090e-01 -1.56034296e-02
-4.76355314e-01 6.62722409e-01 3.50610524e-01 -3.34387422e-01
-5.22333980e-01 -1.09758890e+00 -2.95350760e-01 -5.71800351e-01
-3.14562887e-01 7.53393531e-01 1.57478571e-01 -2.59403497... | [14.8873872756958, -2.230151891708374] |
6b10b272-73fa-4620-8e80-71f3dc3c3323 | zero-shot-robot-manipulation-from-passive | 2302.02011 | null | https://arxiv.org/abs/2302.02011v1 | https://arxiv.org/pdf/2302.02011v1.pdf | Zero-Shot Robot Manipulation from Passive Human Videos | Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of learning task-specific behaviors or direct imitation of a human video, we develop a a framework for extracting agent-agnostic action represe... | ['Vikash Kumar', 'Shubham Tulsiani', 'Abhinav Gupta', 'Homanga Bharadhwaj'] | 2023-02-03 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.94812381e-01 3.86803001e-01 -3.54180336e-01 -4.41047251e-02
-1.13136977e-01 -6.23600245e-01 7.51527190e-01 -4.96316671e-01
-3.64364564e-01 6.37217343e-01 3.06664079e-01 2.02294946e-01
-1.20878875e-01 -2.68845737e-01 -1.09129465e+00 -2.76805550e-01
-4.19081748e-01 7.56503403e-01 4.70733166e-01 -4.66293365... | [4.601211071014404, 0.740848958492279] |
0d5ffecb-3078-4cfa-83cd-c431a3fad78f | synthesized-feature-based-few-shot-class | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cheraghian_Synthesized_Feature_Based_Few-Shot_Class-Incremental_Learning_on_a_Mixture_of_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cheraghian_Synthesized_Feature_Based_Few-Shot_Class-Incremental_Learning_on_a_Mixture_of_ICCV_2021_paper.pdf | Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces | Few-shot class incremental learning (FSCIL) aims to incrementally add sets of novel classes to a well-trained base model in multiple training sessions with the restriction that only a few novel instances are available per class. While learning novel classes, FSCIL methods gradually forget base (old) class training ... | ['Mehrtash Harandi', 'Lars Petersson', 'Christian Simon', 'Pengfei Fang', 'Sameera Ramasinghe', 'Shafin Rahman', 'Ali Cheraghian'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 3.30386251e-01 -2.22329739e-02 -3.02996218e-01 -4.35403526e-01
-5.04050910e-01 -3.75395358e-01 7.07070470e-01 5.94077557e-02
-3.85144055e-01 7.98908889e-01 -6.57228695e-04 4.16819334e-01
-1.64527893e-02 -7.51621783e-01 -8.11701775e-01 -7.29565501e-01
4.53390628e-01 5.15807211e-01 4.41728890e-01 1.22715116... | [9.84778881072998, 3.2611300945281982] |
ec0f27c5-529b-422e-8a3e-83f7e4dffc4c | dagobah-table-and-graph-contexts-for | null | null | https://www.eurecom.fr/fr/publication/6842 | http://ceur-ws.org/Vol-3103/paper2.pdf | DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data | In this paper, we present the latest improvements of the DAGOBAH system that performs automatic pre-processing and semantic interpretation of tables. In particular, we report promising results obtained in the SemTab 2021 challenge thanks to optimisations in lookup mechanisms and new techniques for studying the context ... | ['Raphaël Troncy', 'Pierre Monnin', 'Thomas Labbé', 'Frédéric Deuzé', 'Yoan Chabot', 'Jixiong Liu', 'Viet-Phi Huynh'] | 2021-10-01 | null | null | null | proceedings-of-the-semantic-web-challenge-on | ['table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.18621397e-02 8.07252824e-01 -1.99374348e-01 -5.68614781e-01
-5.13275206e-01 -9.10646319e-01 5.65656960e-01 7.62561083e-01
1.32143036e-01 6.11905098e-01 5.69879189e-02 -5.85707843e-01
-4.96479958e-01 -1.26789689e+00 -5.87295353e-01 3.70266646e-01
1.90668963e-02 1.23984766e+00 6.12947106e-01 -5.39165318... | [9.393739700317383, 7.933229446411133] |
c0ef0c81-5668-4505-886d-4d7bc0149d84 | self-supervised-3d-human-pose-estimation-with | 2108.07777 | null | https://arxiv.org/abs/2108.07777v1 | https://arxiv.org/pdf/2108.07777v1.pdf | Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry | We present a self-supervised learning algorithm for 3D human pose estimation of a single person based on a multiple-view camera system and 2D body pose estimates for each view. To train our model, represented by a deep neural network, we propose a four-loss function learning algorithm, which does not require any 2D or ... | ['Vasileios Belagiannis', 'Ulrich Kressel', 'Julian Wiederer', 'Arij Bouazizi'] | 2021-08-17 | null | null | null | null | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [-2.23025933e-01 4.09159094e-01 -4.10859227e-01 -5.21167397e-01
-8.30088496e-01 -3.94850731e-01 3.39956135e-01 -2.37146810e-01
-5.81825852e-01 6.02187157e-01 3.01523596e-01 3.85069758e-01
3.44556421e-01 -3.57704937e-01 -1.05567229e+00 -3.71704638e-01
-1.50635228e-01 1.02265906e+00 -5.23570785e-03 5.82714379... | [6.981475830078125, -0.936110258102417] |
814b4cbb-f30c-43de-b86b-85599e37245e | egocentric-videoconferencing | 2107.03109 | null | https://arxiv.org/abs/2107.03109v1 | https://arxiv.org/pdf/2107.03109v1.pdf | Egocentric Videoconferencing | We introduce a method for egocentric videoconferencing that enables hands-free video calls, for instance by people wearing smart glasses or other mixed-reality devices. Videoconferencing portrays valuable non-verbal communication and face expression cues, but usually requires a front-facing camera. Using a frontal came... | ['Christian Theobalt', 'Vladislav Golyanik', 'Ayush Tewari', 'Hans-Peter Seidel', 'Matthias Nießner', 'Justus Thies', 'Mohit Mendiratta', 'Mohamed Elgharib'] | 2021-07-07 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 3.69535238e-01 1.93485841e-01 2.89466470e-01 -2.38745585e-01
-4.18770552e-01 -7.34902740e-01 5.33848464e-01 -1.16977394e+00
-9.09182802e-02 5.75557053e-01 2.43471712e-01 8.61101523e-02
3.42802763e-01 -3.97861242e-01 -8.74716759e-01 -8.35253060e-01
3.33015382e-01 9.79909003e-02 -1.52926564e-01 -2.08062395... | [13.03918743133545, -0.37275102734565735] |
121873aa-0112-465c-81e3-0ace3421e74d | gumbel-attention-for-multi-modal-machine | 2103.08862 | null | https://arxiv.org/abs/2103.08862v2 | https://arxiv.org/pdf/2103.08862v2.pdf | Gumbel-Attention for Multi-modal Machine Translation | Multi-modal machine translation (MMT) improves translation quality by introducing visual information. However, the existing MMT model ignores the problem that the image will bring information irrelevant to the text, causing much noise to the model and affecting the translation quality. This paper proposes a novel Gumbe... | ['Tiejun Zhao', 'Hailong Cao', 'Pengbo Liu'] | 2021-03-16 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 1.42821714e-01 5.96516989e-02 -4.13817704e-01 -6.15970939e-02
-8.82837772e-01 -3.68819237e-01 5.88635445e-01 -3.72524709e-01
-2.21835330e-01 6.58787847e-01 5.08075535e-01 -2.48369858e-01
1.89604878e-01 -5.28890967e-01 -1.00441909e+00 -8.25128078e-01
8.66939247e-01 3.70159388e-01 -1.09678041e-02 -4.45713788... | [11.483243942260742, 1.5029425621032715] |
dd70c42e-659b-40b8-92d0-3d382f2d1a3d | a-causal-lens-for-controllable-text-1 | 2201.09119 | null | https://arxiv.org/abs/2201.09119v1 | https://arxiv.org/pdf/2201.09119v1.pdf | A Causal Lens for Controllable Text Generation | Controllable text generation concerns two fundamental tasks of wide applications, namely generating text of given attributes (i.e., attribute-conditional generation), and minimally editing existing text to possess desired attributes (i.e., text attribute transfer). Extensive prior work has largely studied the two probl... | ['Li Erran Li', 'Zhiting Hu'] | 2022-01-22 | a-causal-lens-for-controllable-text | http://proceedings.neurips.cc/paper/2021/hash/d0f5edad9ac19abed9e235c0fe0aa59f-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d0f5edad9ac19abed9e235c0fe0aa59f-Paper.pdf | neurips-2021-12 | ['text-attribute-transfer'] | ['natural-language-processing'] | [ 8.26337039e-01 6.71231151e-01 -7.46354640e-01 -4.11528498e-01
-6.26096129e-01 -3.37675780e-01 1.01749372e+00 1.47937089e-01
4.13244627e-02 1.37961411e+00 7.06373334e-01 -3.85394275e-01
-2.13722125e-01 -1.11837590e+00 -8.76228333e-01 -4.88879681e-01
2.88751096e-01 3.46531540e-01 -5.08592010e-01 7.39080831... | [8.026649475097656, 5.427872657775879] |
c44f6f8b-3e73-4980-a312-eaefe169e9f1 | what-is-missing-in-deep-music-generation-a | 2209.00182 | null | https://arxiv.org/abs/2209.00182v1 | https://arxiv.org/pdf/2209.00182v1.pdf | What is missing in deep music generation? A study of repetition and structure in popular music | Structure is one of the most essential aspects of music, and music structure is commonly indicated through repetition. However, the nature of repetition and structure in music is still not well understood, especially in the context of music generation, and much remains to be explored with Music Information Retrieval (M... | ['Roger B. Dannenberg', 'Huiran Yu', 'Shuqi Dai'] | 2022-09-01 | null | null | null | null | ['music-generation', 'music-generation', 'music-information-retrieval'] | ['audio', 'music', 'music'] | [ 1.98531196e-01 -4.16076213e-01 -1.18658982e-01 1.33084804e-01
-3.26384276e-01 -8.97523880e-01 5.10863483e-01 9.91195664e-02
1.19285202e-02 4.29063618e-01 8.51632953e-01 2.94360459e-01
-7.65324950e-01 -5.76414347e-01 -3.92713130e-01 -7.13915467e-01
-2.95789838e-01 2.13699907e-01 -1.69573560e-01 -5.68441749... | [15.970067024230957, 5.456104278564453] |
3a810454-57d7-4286-93d9-3b37e5ac2ff3 | sture-spatial-temporal-mutual-representation | 2201.06824 | null | https://arxiv.org/abs/2201.06824v3 | https://arxiv.org/pdf/2201.06824v3.pdf | STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking | Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each... | ['Haidong Wang', 'Ming Wen', 'Ke Nai', 'Yaping Li', 'Zhiyong Li'] | 2022-01-18 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [ 1.42854095e-01 -5.52484870e-01 -3.74947757e-01 -5.50101660e-02
-4.56565261e-01 -4.90892380e-01 7.68107414e-01 1.61995143e-01
-6.24303579e-01 5.65204442e-01 -3.04992586e-01 3.52042615e-02
-2.76594311e-01 -6.97110832e-01 -8.57349873e-01 -8.88633549e-01
-1.84531108e-01 1.86245859e-01 7.64863312e-01 -1.26488969... | [6.379729747772217, -2.11128568649292] |
26bccad5-5453-4ed7-a36a-43cd64075fed | stereovae-a-lightweight-stereo-matching | 2305.11566 | null | https://arxiv.org/abs/2305.11566v2 | https://arxiv.org/pdf/2305.11566v2.pdf | StereoVAE: A lightweight stereo matching system through embedded GPUs | We present a lightweight system for stereo matching through embedded GPUs. It breaks the trade-off between accuracy and processing speed in stereo matching, enabling our embedded system to further improve the matching accuracy while ensuring real-time processing. The main idea of our method is to construct a tiny neura... | ['Miyazaki Jun', 'Yun Li', 'Xin Liu', 'Xin Xu', 'Xiang Li', 'Qiong Chang'] | 2023-05-19 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 8.14784840e-02 -2.33282939e-01 3.34675521e-01 -1.17337607e-01
-5.45529127e-01 -3.97735760e-02 3.91145259e-01 -1.06443852e-01
-5.52546561e-01 3.91675830e-01 1.79602895e-02 -3.02615017e-01
4.41378444e-01 -1.38193417e+00 -9.09765840e-01 -6.01568818e-01
2.70064265e-01 5.93262166e-02 3.68074387e-01 -2.66449839... | [8.929697036743164, -2.2585041522979736] |
9ede3bab-276e-4833-bc55-99a8f54b64a8 | representation-learning-on-unit-ball-with-3d | 1912.01454 | null | https://arxiv.org/abs/1912.01454v1 | https://arxiv.org/pdf/1912.01454v1.pdf | Representation Learning on Unit Ball with 3D Roto-Translational Equivariance | Convolution is an integral operation that defines how the shape of one function is modified by another function. This powerful concept forms the basis of hierarchical feature learning in deep neural networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topol... | ['Sameera Ramasinghe', 'Nick Barnes', 'Salman Khan', 'Stephen Gould'] | 2019-11-30 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-3.17739174e-02 5.17543964e-02 7.09383428e-01 -4.37596738e-01
-9.20528769e-02 -6.35617256e-01 2.80109286e-01 1.76030095e-03
-3.56566876e-01 5.43459117e-01 -3.63075972e-01 -6.18407011e-01
-5.21071494e-01 -1.35777342e+00 -9.16562200e-01 -8.72179687e-01
-5.54560363e-01 1.15099102e-01 7.56512806e-02 -3.87537986... | [8.030726432800293, -3.6811439990997314] |
16576f27-e6a3-4a9b-9dda-279a26758d94 | u-shape-transformer-for-underwater-image | 2111.11843 | null | https://arxiv.org/abs/2111.11843v6 | https://arxiv.org/pdf/2111.11843v6.pdf | U-shape Transformer for Underwater Image Enhancement | The light absorption and scattering of underwater impurities lead to poor underwater imaging quality. The existing data-driven based underwater image enhancement (UIE) techniques suffer from the lack of a large-scale dataset containing various underwater scenes and high-fidelity reference images. Besides, the inconsist... | ['Liheng Bian', 'Chunli Zhu', 'Lintao Peng'] | 2021-11-23 | null | null | null | null | ['underwater-image-restoration', 'uie'] | ['computer-vision', 'computer-vision'] | [ 1.67858958e-01 -4.10775274e-01 1.06235683e+00 -3.83536518e-01
-5.29179633e-01 2.18027066e-02 9.59896967e-02 -1.70259371e-01
-8.70280623e-01 6.13373458e-01 3.01903427e-01 1.96848720e-01
-3.03940028e-01 -9.80939567e-01 -6.89043641e-01 -1.24414670e+00
-2.45894521e-01 -5.98143220e-01 4.89974797e-01 -6.50377691... | [10.695923805236816, -3.5101583003997803] |
bb9f5c71-bc63-4772-9978-2eeeaecaa588 | motion-state-alignment-for-video-semantic | 2304.08820 | null | https://arxiv.org/abs/2304.08820v1 | https://arxiv.org/pdf/2304.08820v1.pdf | Motion-state Alignment for Video Semantic Segmentation | In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentat... | ['Junfeng Luo', 'Shuaibin Zhang', 'Ruihong Yin', 'Jinming Su'] | 2023-04-18 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 1.68157771e-01 -4.08076882e-01 -5.65806389e-01 -4.03079927e-01
-3.46719056e-01 -2.34524652e-01 3.48136991e-01 -2.37438023e-01
-5.04792035e-01 3.38885665e-01 -4.38137539e-02 1.22693114e-01
-1.39689624e-01 -8.41536701e-01 -4.11728293e-01 -9.27963316e-01
2.17873901e-01 1.36901215e-01 9.83051777e-01 -5.56809381... | [9.374553680419922, -0.25610044598579407] |
a4b23fc8-69e7-426c-8919-a89352dd3b23 | a-worst-case-performance-optimization-based | 1908.11470 | null | http://arxiv.org/abs/1908.11470v1 | http://arxiv.org/pdf/1908.11470v1.pdf | A Worst-Case Performance Optimization Based Design Approach to Robust Symbol-Level Precoding for Downlink MU-MIMO | This paper addresses the optimization problem of symbol-level precoding (SLP)
in the downlink of a multiuser multiple-input multiple-output (MU-MIMO)
wireless system while the precoder's output is subject to partially-known
distortions. In particular, we assume a linear distortion model with bounded
additive noise. The... | [] | 2019-08-29 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 6.18022799e-01 3.25310886e-01 -4.22810763e-01 8.16145614e-02
-8.46208811e-01 -4.13614124e-01 1.22322358e-01 -1.55178428e-01
-3.10339123e-01 8.52572441e-01 1.81403503e-01 -7.57375360e-01
-3.71626914e-01 -5.78166783e-01 -5.58223665e-01 -1.13338411e+00
-1.30086631e-01 -5.85658908e-01 -5.79280972e-01 -2.00340673... | [6.156276226043701, 1.4227195978164673] |
afca5732-8fd8-4156-b725-5ab77027157b | black-box-variational-inference-with-a | 2304.05527 | null | https://arxiv.org/abs/2304.05527v1 | https://arxiv.org/pdf/2304.05527v1.pdf | Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box | Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty est... | ['Tamara Broderick', 'Martin Ingram', 'Ryan Giordano'] | 2023-04-11 | null | null | null | null | ['probabilistic-programming', 'stochastic-optimization'] | ['methodology', 'methodology'] | [-8.99827927e-02 -3.57152969e-02 -1.30983200e-02 -4.38153863e-01
-1.64632618e+00 -7.55193055e-01 8.16993654e-01 -2.64603555e-01
-3.87286842e-01 1.11257660e+00 6.82661310e-02 -2.85567880e-01
-5.43143630e-01 -5.68653941e-01 -7.94888616e-01 -9.93267298e-01
-2.95573846e-02 9.65802252e-01 8.81493688e-02 1.99326456... | [6.934023380279541, 3.9871633052825928] |
fc25ab57-3699-4e81-b410-3a2d4ef46517 | salypath360-saliency-and-scanpath-prediction | 2201.00096 | null | https://arxiv.org/abs/2201.00096v1 | https://arxiv.org/pdf/2201.00096v1.pdf | SalyPath360: Saliency and Scanpath Prediction Framework for Omnidirectional Images | This paper introduces a new framework to predict visual attention of omnidirectional images. The key setup of our architecture is the simultaneous prediction of the saliency map and a corresponding scanpath for a given stimulus. The framework implements a fully encoder-decoder convolutional neural network augmented by ... | ['Mohamed Sayeh', 'Aladine Chetouani', 'Marouane Tliba', 'Mohamed Amine Kerkouri'] | 2022-01-01 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 3.69278103e-01 3.81854564e-01 -1.20050155e-01 -6.28957331e-01
-5.15235364e-01 6.25089258e-02 5.75466394e-01 -1.68160707e-01
-3.05249065e-01 7.20843673e-01 4.34271842e-01 -6.73400983e-02
9.40455422e-02 -6.66844964e-01 -1.11782420e+00 -7.59047985e-01
2.14231506e-01 3.02182487e-03 3.67431045e-01 -1.02123566... | [9.870128631591797, -0.3573067784309387] |
16d91683-bebc-4ca2-bb62-e29a0a2718db | joint-extraction-of-entities-and-relations | 1706.05075 | null | http://arxiv.org/abs/1706.05075v1 | http://arxiv.org/pdf/1706.05075v1.pdf | Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme | Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and th... | ['Peng Zhou', 'Feng Wang', 'Bo Xu', 'Yuexing Hao', 'Suncong Zheng', 'Hongyun Bao'] | 2017-06-07 | joint-extraction-of-entities-and-relations-1 | https://aclanthology.org/P17-1113 | https://aclanthology.org/P17-1113.pdf | acl-2017-7 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-2.68583328e-01 4.16002989e-01 -3.06712449e-01 -4.12643820e-01
-6.67191029e-01 -5.13805270e-01 5.11274517e-01 2.00199291e-01
-6.46907032e-01 9.83507276e-01 3.58378172e-01 -1.17554277e-01
-1.41317531e-01 -7.37818718e-01 -3.72952372e-01 -3.02530587e-01
-2.15745151e-01 5.51260114e-01 6.39938653e-01 -7.47371241... | [9.249361038208008, 8.651212692260742] |
08af5b99-de08-4592-842d-0303e9d57735 | a-survey-of-deep-learning-for-low-shot-object | 2112.02814 | null | https://arxiv.org/abs/2112.02814v3 | https://arxiv.org/pdf/2112.02814v3.pdf | A Survey of Deep Learning for Low-Shot Object Detection | Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been ... | ['Mengqi Xue', 'Mingli Song', 'Jie Song', 'Haofei Zhang', 'Qihan Huang'] | 2021-12-06 | null | null | null | null | ['one-shot-object-detection', 'zero-shot-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.85251170e-01 -1.53326720e-01 -3.34480375e-01 -9.06497613e-02
-6.76347196e-01 -1.01705179e-01 5.76148331e-01 1.23356320e-01
-3.17089230e-01 3.60289693e-01 -2.62126982e-01 2.50284255e-01
-2.17632622e-01 -7.15114713e-01 -2.25833982e-01 -8.23125958e-01
4.39449176e-02 1.57415271e-01 9.37945902e-01 6.48118779... | [9.365795135498047, 1.5210758447647095] |
38711df9-f2c4-4459-9f22-97f7500c5741 | generalized-expectation-maximization | 2305.13880 | null | https://arxiv.org/abs/2305.13880v1 | https://arxiv.org/pdf/2305.13880v1.pdf | Generalized Expectation Maximization Framework for Blind Image Super Resolution | Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels. However, these methods mostly require an independent step to estimate the blur kernel, l... | ['Yuan Shen', 'Zhiming Wang', 'Yuxiao Li'] | 2023-05-23 | null | null | null | null | ['image-super-resolution', 'image-restoration'] | ['computer-vision', 'computer-vision'] | [ 3.69109362e-01 -2.67488718e-01 -9.11581144e-02 -4.28689837e-01
-1.35454810e+00 -8.13149512e-02 4.54265147e-01 -7.87206113e-01
-2.21006736e-01 8.90638471e-01 4.15466934e-01 -8.39510337e-02
-4.49333906e-01 -2.98332155e-01 -5.67875087e-01 -8.48461628e-01
2.86497116e-01 7.19534531e-02 -1.54880562e-03 2.95367479... | [11.447802543640137, -2.4938530921936035] |
64af0f74-27d6-4f2c-b47d-7b37ddef7aad | hierarchical-graph-matching-networks-for-deep-1 | 2007.04395 | null | https://arxiv.org/abs/2007.04395v4 | https://arxiv.org/pdf/2007.04395v4.pdf | Multilevel Graph Matching Networks for Deep Graph Similarity Learning | While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node... | ['Chunming Wu', 'Saizhuo Wang', 'Lingfei Wu', 'Xiang Ling', 'Shouling Ji', 'Fangli Xu', 'Alex X. Liu', 'Tengfei Ma'] | 2020-07-08 | null | null | null | null | ['graph-similarity', 'graph-regression'] | ['graphs', 'graphs'] | [ 7.87806138e-03 2.93663502e-01 -1.39355257e-01 -1.89431995e-01
-1.70256376e-01 -4.20375705e-01 6.09851599e-01 7.45897412e-01
3.29718851e-02 1.53462887e-01 -1.73818931e-01 -3.00601721e-01
-2.12828502e-01 -1.19121742e+00 -7.27240086e-01 -5.88200033e-01
-5.43770790e-01 4.73609596e-01 3.44974875e-01 -1.92119196... | [7.183712482452393, 6.264597415924072] |
cee00ab8-2476-4f46-b2e1-61ecbe5743ee | uierl-internal-external-representation | 2306.08344 | null | https://arxiv.org/abs/2306.08344v1 | https://arxiv.org/pdf/2306.08344v1.pdf | UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement | Underwater image enhancement (UIE) is a meaningful but challenging task, and many learning-based UIE methods have been proposed in recent years. Although much progress has been made, these methods still exist two issues: (1) There exists a significant region-wise quality difference in a single underwater image due to t... | ['Yuan Hui', 'Yihan Yu', 'Liquan Shen', 'Zhengyong Wang'] | 2023-06-14 | null | null | null | null | ['image-enhancement', 'uie'] | ['computer-vision', 'computer-vision'] | [ 3.95000517e-01 -1.56638965e-01 4.35622990e-01 -4.68836695e-01
-5.37004769e-01 -4.16695364e-02 8.58250856e-02 1.03127457e-01
-8.52364898e-01 4.56021130e-01 2.97273874e-01 3.63448888e-01
-2.50903428e-01 -1.12169337e+00 -4.59083468e-01 -8.12159598e-01
-1.56192034e-01 -3.56590420e-01 6.18923604e-01 -5.54276228... | [10.683446884155273, -3.5111565589904785] |
dd93e307-b242-48d0-afa4-82a2e83c75ed | intrinsic-and-extrinsic-evaluation-of | null | null | https://aclanthology.org/W17-2624 | https://aclanthology.org/W17-2624.pdf | Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams | Language in social media is a dynamic system, constantly evolving and adapting, with words and concepts rapidly emerging, disappearing, and changing their meaning. These changes can be estimated using word representations in context, over time and across locations. A number of methods have been proposed to track these ... | ['Lawrence Phillips', 'Kyle Shaffer', 'Dustin Arendt', 'Svitlana Volkova', 'Nathan Hodas'] | 2017-08-01 | null | null | null | ws-2017-8 | ['type-prediction'] | ['computer-code'] | [-5.16528338e-02 -3.61124545e-01 -1.26992211e-01 -2.41563246e-01
-5.42215466e-01 -8.98922384e-01 1.40372515e+00 1.27283442e+00
-5.64931393e-01 6.00854158e-01 9.30400789e-01 -3.03982288e-01
-2.04299152e-01 -8.61515641e-01 -2.14549750e-01 -2.30296239e-01
-6.55930519e-01 6.44409331e-03 1.70897886e-01 -5.10936558... | [10.18116569519043, 8.9557466506958] |
195fd689-6aea-45d6-8d27-f3fd884a8a5f | deep-template-matching-for-offline | 1811.06347 | null | http://arxiv.org/abs/1811.06347v1 | http://arxiv.org/pdf/1811.06347v1.pdf | Deep Template Matching for Offline Handwritten Chinese Character Recognition | Just like its remarkable achievements in many computer vision tasks, the
convolutional neural networks (CNN) provide an end-to-end solution in
handwritten Chinese character recognition (HCCR) with great success. However,
the process of learning discriminative features for image recognition is
difficult in cases where l... | ['Huaxiang Lu', 'Qi Wu', 'Zhiyuan Li', 'Min Jin'] | 2018-11-15 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 1.33045018e-01 -6.57421231e-01 -4.55638394e-02 -5.19817173e-01
-3.29107225e-01 -5.08218527e-01 5.84494710e-01 -4.00083423e-01
-7.33127117e-01 7.72938609e-01 -2.10616618e-01 -9.08390880e-02
-6.25189394e-02 -4.99922186e-01 -4.72499460e-01 -9.13730383e-01
-1.30233169e-02 5.28738201e-01 1.39811754e-01 -1.86875060... | [11.848799705505371, 2.5567684173583984] |
597badac-e6c9-43a5-868f-e8d2aa2c5d5b | towards-real-time-visual-tracking-with-graded | 2206.08701 | null | https://arxiv.org/abs/2206.08701v1 | https://arxiv.org/pdf/2206.08701v1.pdf | Towards Real-Time Visual Tracking with Graded Color-names Features | MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate b... | ['Xuemei Guo', 'Guoli Wang', 'Lin Li'] | 2022-06-17 | null | null | null | null | ['visual-tracking', 'real-time-visual-tracking'] | ['computer-vision', 'computer-vision'] | [ 6.51180446e-02 -7.06619561e-01 -3.51810083e-02 4.23437208e-02
-1.15275748e-01 -5.37023604e-01 5.63341737e-01 -6.18613958e-02
-4.30632263e-01 5.39937496e-01 -4.30827409e-01 -9.75017920e-02
1.69422105e-01 -7.29324460e-01 -1.28238425e-01 -1.01796901e+00
1.97687984e-01 -2.12775506e-02 1.00411510e+00 4.33965698... | [6.6433491706848145, -1.9304852485656738] |
a36a9805-2b6b-4b4b-bf7f-c3b9789ef3a4 | generalizability-vs-robustness-adversarial | 1804.00504 | null | http://arxiv.org/abs/1804.00504v1 | http://arxiv.org/pdf/1804.00504v1.pdf | Generalizability vs. Robustness: Adversarial Examples for Medical Imaging | In this paper, for the first time, we propose an evaluation method for deep
learning models that assesses the performance of a model not only in an unseen
test scenario, but also in extreme cases of noise, outliers and ambiguous input
data. To this end, we utilize adversarial examples, images that fool machine
learning... | ['Fernando Navarro', 'Sailesh Conjeti', 'Magdalini Paschali', 'Nassir Navab'] | 2018-03-23 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.73239625e-01 4.27833855e-01 3.78530234e-01 -1.80307880e-01
-5.87979615e-01 -8.08549225e-01 6.29976094e-01 -4.99434732e-02
-3.79427433e-01 5.33894598e-01 -2.82726973e-01 -3.29266608e-01
-3.11361756e-02 -4.24515456e-01 -7.50558734e-01 -7.56179512e-01
-2.20316425e-01 7.83680379e-02 2.13455319e-01 -8.81191343... | [5.629190921783447, 7.835010051727295] |
e231fea6-49aa-4e27-95d5-33d583499180 | brain-tumor-segmentation-from-mri-images | 2305.00257 | null | https://arxiv.org/abs/2305.00257v1 | https://arxiv.org/pdf/2305.00257v1.pdf | Brain Tumor Segmentation from MRI Images using Deep Learning Techniques | A brain tumor, whether benign or malignant, can potentially be life threatening and requires painstaking efforts in order to identify the type, origin and location, let alone cure one. Manual segmentation by medical specialists can be time-consuming, which calls out for the involvement of technology to hasten the proce... | ['Atul Dayal', 'Attulya Singh', 'Vipul Kumar Mishra', 'Mayank Dixit', 'Ayan Gupta'] | 2023-04-29 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 1.23751923e-01 4.64098573e-01 -1.09632639e-02 -1.87053159e-01
-6.10010386e-01 -1.42764479e-01 2.76741177e-01 3.87356542e-02
-7.09342778e-01 7.87462056e-01 -6.44829050e-02 -6.10207558e-01
-1.97701707e-01 -5.34470379e-01 -4.22814369e-01 -1.01907265e+00
-2.44426802e-01 9.10830140e-01 2.08441198e-01 -6.56628236... | [14.739429473876953, -2.5025086402893066] |
245f969e-c910-420e-8543-56d02a628ec6 | spact-self-supervised-privacy-preservation | 2203.15205 | null | https://arxiv.org/abs/2203.15205v1 | https://arxiv.org/pdf/2203.15205v1.pdf | SPAct: Self-supervised Privacy Preservation for Action Recognition | Visual private information leakage is an emerging key issue for the fast growing applications of video understanding like activity recognition. Existing approaches for mitigating privacy leakage in action recognition require privacy labels along with the action labels from the video dataset. However, annotating frames ... | ['Mubarak Shah', 'Chen Chen', 'Ishan Rajendrakumar Dave'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Dave_SPAct_Self-Supervised_Privacy_Preservation_for_Action_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-classification'] | ['computer-vision'] | [ 5.08843422e-01 1.94201797e-01 -4.92180735e-01 -7.73573577e-01
-1.03201747e+00 -8.60000610e-01 3.59985590e-01 1.74597055e-02
-5.73280334e-01 7.24887848e-01 3.52915287e-01 -3.66918072e-02
5.46734557e-02 -5.25148332e-01 -9.57171679e-01 -8.14303577e-01
4.81215753e-02 -1.01749405e-01 2.88687218e-02 5.23854196... | [5.860019207000732, 6.705010890960693] |
7a9cc8a1-a2cd-4a45-b531-56e5bc3094e1 | temporal-transformer-networks-joint-learning-1 | 1906.05947 | null | https://arxiv.org/abs/1906.05947v1 | https://arxiv.org/pdf/1906.05947v1.pdf | Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping | Many time-series classification problems involve developing metrics that are invariant to temporal misalignment. In human activity analysis, temporal misalignment arises due to various reasons including differing initial phase, sensor sampling rates, and elastic time-warps due to subject-specific biomechanics. Past wor... | ['Suhas Lohit', 'Pavan Turaga', 'Qiao Wang'] | 2019-06-13 | temporal-transformer-networks-joint-learning | http://openaccess.thecvf.com/content_CVPR_2019/html/Lohit_Temporal_Transformer_Networks_Joint_Learning_of_Invariant_and_Discriminative_Time_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Lohit_Temporal_Transformer_Networks_Joint_Learning_of_Invariant_and_Discriminative_Time_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 6.21501207e-01 -2.36428455e-01 -2.74416149e-01 -4.30586070e-01
-6.48190618e-01 -5.75344145e-01 6.99619174e-01 -1.14527248e-01
-3.91206115e-01 4.69469130e-01 4.98059064e-01 1.06587619e-01
-4.44135040e-01 -4.42029148e-01 -5.71094334e-01 -7.37866819e-01
-2.42164582e-01 1.59447312e-01 2.70395190e-01 -1.34466335... | [7.496930122375488, 3.1097609996795654] |
010cf2d6-3e16-43a9-bf08-afee3f10ed07 | deep-neural-networks-for-visual-reasoning | 2209.11990 | null | https://arxiv.org/abs/2209.11990v1 | https://arxiv.org/pdf/2209.11990v1.pdf | Deep Neural Networks for Visual Reasoning | Visual perception and language understanding are - fundamental components of human intelligence, enabling them to understand and reason about objects and their interactions. It is crucial for machines to have this capacity to reason using these two modalities to invent new robot-human collaborative systems. Recent adva... | ['Thao Minh Le'] | 2022-09-24 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 6.03455752e-02 1.60966456e-01 -1.72417816e-02 -6.38900399e-01
-7.42652118e-02 -6.72178030e-01 1.09423590e+00 2.03022003e-01
-4.44480509e-01 4.58805621e-01 3.73545289e-01 -3.26843351e-01
-2.03286320e-01 -6.77009881e-01 -4.78265047e-01 -3.66929233e-01
-5.07962815e-02 5.13713181e-01 1.83626026e-01 -4.64282393... | [10.609118461608887, 1.8569799661636353] |
0e877f83-5a83-46ce-aee1-a615e391e614 | intelligent-trading-systems-a-sentiment-aware | 2112.02095 | null | https://arxiv.org/abs/2112.02095v1 | https://arxiv.org/pdf/2112.02095v1.pdf | Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach | The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers. Reinforcement Learning (RL) and Natural Language Processing have gained notoriety in these single-asset trading tasks, but only a few works have explored their combination. M... | ['Anna Helena Reali Costa', 'Reinaldo Augusto da Costa Bianchi', 'Leonardo Kanashiro Felizardo', 'Francisco Caio Lima Paiva'] | 2021-11-14 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-3.50806177e-01 -1.48015723e-01 -6.26353741e-01 -3.94353062e-01
-7.21987665e-01 -9.53579783e-01 9.10979092e-01 3.44859213e-01
-3.64361823e-01 6.94879889e-01 3.16680938e-01 -3.47968936e-01
-2.31564298e-01 -9.90277231e-01 -3.55098516e-01 -3.37548494e-01
-3.38171989e-01 3.01600128e-01 -1.06776707e-01 -6.43109560... | [4.4636969566345215, 4.202647686004639] |
646844e4-58a3-4681-a120-e4cd4f244b7e | self-supervised-viewpoint-learning-from-image | 2004.01793 | null | https://arxiv.org/abs/2004.01793v1 | https://arxiv.org/pdf/2004.01793v1.pdf | Self-Supervised Viewpoint Learning From Image Collections | Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., ... | ['Shalini De Mello', 'Sifei Liu', 'Jan Kautz', 'Umar Iqbal', 'Carsten Rother', 'Varun Jampani', 'Siva Karthik Mustikovela'] | 2020-04-03 | self-supervised-viewpoint-learning-from-image-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Mustikovela_Self-Supervised_Viewpoint_Learning_From_Image_Collections_CVPR_2020_paper.pdf | cvpr-2020-6 | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.23080739e-01 3.99872124e-01 2.99376566e-02 -8.52850318e-01
-7.18627810e-01 -8.06068838e-01 4.93938148e-01 -6.00671291e-01
-7.19227642e-02 4.86485273e-01 -2.67174840e-01 -1.31251723e-01
3.96679223e-01 -7.53280699e-01 -1.16503239e+00 -7.80385673e-01
4.98050570e-01 6.88117385e-01 1.40341595e-01 -1.21150158... | [8.348320007324219, -2.7254621982574463] |
8d80a585-882d-49e2-ab35-7c2e4b2809a4 | in-search-of-a-robust-facial-expressions | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656 | https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656 | In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study | Many researchers have been seeking robust emotion recognition system for already last two decades. It would advance computer systems to a new level of interaction, providing much more natural feedback during human–computer interaction due to analysis of user affect state. However, one of the key problems in this domain... | ['Alexey Karpov', 'Denis Dresvyanskiy', 'Elena Ryumina'] | 2022-10-07 | null | null | null | neurocomputing-2022-10 | ['face-detection', 'facial-expression-recognition', 'cross-corpus'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.29752006e-02 -4.88651007e-01 8.66580680e-02 -4.18518692e-01
-2.87597865e-01 -3.38623762e-01 4.48769361e-01 3.77753153e-02
-5.48893094e-01 3.08015823e-01 9.33354944e-02 1.21631324e-01
4.13548380e-01 -1.00956544e-01 -3.71891379e-01 -4.55955893e-01
-4.02579814e-01 -1.95999518e-01 -5.84927127e-02 -2.23948270... | [13.292342185974121, 5.039684295654297] |
e5eea6f6-99f6-4ffc-9ead-161c4599594d | modular-transformers-compressing-transformers | 2306.02379 | null | https://arxiv.org/abs/2306.02379v1 | https://arxiv.org/pdf/2306.02379v1.pdf | Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference | Pre-trained Transformer models like T5 and BART have advanced the state of the art on a wide range of text generation tasks. Compressing these models into smaller ones has become critically important for practical use. Common neural network compression techniques such as knowledge distillation or quantization are limit... | ['Yejin Choi', 'Ronan Le Bras', 'Wangchunshu Zhou'] | 2023-06-04 | null | null | null | null | ['neural-network-compression', 'quantization', 'model-compression', 'neural-network-compression'] | ['methodology', 'methodology', 'methodology', 'miscellaneous'] | [ 5.65631986e-01 3.16662014e-01 -3.90105814e-01 -1.96401820e-01
-6.48712456e-01 -4.89234090e-01 5.51232517e-01 1.54347017e-01
-4.70523179e-01 7.93517113e-01 1.52743429e-01 -5.27182341e-01
1.04575947e-01 -7.15008318e-01 -9.34911788e-01 -4.06005472e-01
1.98769286e-01 6.04171336e-01 1.83891997e-01 -1.36977047... | [8.735963821411133, 3.5865471363067627] |
18c8019c-b3bf-4767-a1f0-c7a745900581 | sentence-boundary-detection-on-line-breaks-in | null | null | https://aclanthology.org/2020.wnut-1.10 | https://aclanthology.org/2020.wnut-1.10.pdf | Sentence Boundary Detection on Line Breaks in Japanese | For NLP, sentence boundary detection (SBD) is an essential task to decompose a text into sentences. Most of the previous studies have used a simple rule that uses only typical characters as sentence boundaries. However, some characters may or may not be sentence boundaries depending on the context. We focused on line b... | ['Kensuke Mitsuzawa', 'Yuta Hayashibe'] | null | null | null | null | emnlp-wnut-2020-11 | ['boundary-detection'] | ['computer-vision'] | [ 1.60449460e-01 -3.77564393e-02 -1.34388223e-01 -4.86846089e-01
-5.76509476e-01 -8.14310312e-01 2.29373679e-01 6.62194312e-01
-3.31441194e-01 9.39201295e-01 1.92093849e-01 -6.77924335e-01
4.08402801e-01 -6.44417703e-01 -2.96501458e-01 4.51604389e-02
7.41410404e-02 1.19116634e-01 9.60768700e-01 -2.35164225... | [10.215600967407227, 10.05868148803711] |
ed287a0f-1b53-46c1-8375-dcf71b5f92b4 | generating-2d-and-3d-master-faces-for | 2211.13964 | null | https://arxiv.org/abs/2211.13964v2 | https://arxiv.org/pdf/2211.13964v2.pdf | Generating 2D and 3D Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution | A master face is a face image that passes face-based identity authentication for a high percentage of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information. We optimize these faces for 2D and 3D face verification models, by us... | ['Lior Wolf', 'Ron Shmelkin', 'Tomer Friedlander'] | 2022-11-25 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.39271873e-01 2.97141165e-01 2.59115607e-01 -3.03579688e-01
-4.29597676e-01 -8.59852254e-01 6.66315734e-01 -8.19473207e-01
-8.78092274e-02 5.33590913e-01 -3.69116485e-01 -3.26659590e-01
1.22316338e-01 -8.73338878e-01 -5.95657110e-01 -8.07420909e-01
-1.65395647e-01 5.91297626e-01 -6.21863246e-01 -3.04230034... | [12.808758735656738, 0.7714151740074158] |
da8176c7-d0f3-418c-9e3f-f87489d94a4c | the-emergence-of-objectness-learning-zero | 2111.06394 | null | https://arxiv.org/abs/2111.06394v1 | https://arxiv.org/pdf/2111.06394v1.pdf | The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos | Humans can easily segment moving objects without knowing what they are. That objectness could emerge from continuous visual observations motivates us to model grouping and movement concurrently from unlabeled videos. Our premise is that a video has different views of the same scene related by moving components, and the... | ['Stephen Lin', 'Stella X. Yu', 'Zhirong Wu', 'Runtao Liu'] | 2021-11-11 | null | http://proceedings.neurips.cc/paper/2021/hash/6d9cb7de5e8ac30bd5e8734bc96a35c1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/6d9cb7de5e8ac30bd5e8734bc96a35c1-Paper.pdf | neurips-2021-12 | ['zero-shot-segmentation', 'unsupervised-object-segmentation', 'video-polyp-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.78126502e-01 1.80577382e-01 -5.20677686e-01 -3.69334459e-01
-4.48754787e-01 -9.36446905e-01 4.47493225e-01 -4.12975669e-01
-1.01482362e-01 3.40413749e-01 8.94069299e-03 -5.59445620e-02
2.18567982e-01 -5.95721185e-01 -9.51551437e-01 -5.34560740e-01
1.25403553e-02 4.54323024e-01 7.55821109e-01 -2.34530699... | [9.040366172790527, -0.2813248038291931] |
1e1bb31d-3b3b-4235-9d7f-ebaa85c22d15 | a-probabilistic-graphical-model-based-on | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_A_Probabilistic_Graphical_Model_Based_on_Neural-Symbolic_Reasoning_for_Visual_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_A_Probabilistic_Graphical_Model_Based_on_Neural-Symbolic_Reasoning_for_Visual_CVPR_2022_paper.pdf | A Probabilistic Graphical Model Based on Neural-Symbolic Reasoning for Visual Relationship Detection | This paper aims to leverage symbolic knowledge to improve the performance and interpretability of the Visual Relationship Detection (VRD) models. Existing VRD methods based on deep learning suffer from the problems of poor performance on insufficient labeled examples and lack of interpretability. To overcome the af... | ['Shirui Pan', 'Anchen Li', 'Qianhao Wei', 'Bo Yang', 'Dongran Yu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['visual-relationship-detection'] | ['computer-vision'] | [-2.63211370e-01 6.39504433e-01 -4.81940806e-01 -3.53525609e-01
-3.39545310e-01 -2.12850422e-01 5.54849088e-01 -9.40327495e-02
3.34391028e-01 5.08967578e-01 5.04682623e-02 -7.62854576e-01
-4.40313786e-01 -1.07410717e+00 -1.02044630e+00 -2.87034661e-01
4.95100878e-02 6.79680705e-01 -1.19037153e-02 1.73547581... | [8.964925765991211, 7.432952880859375] |
e3885754-34b5-4141-bf2d-8507e8bc597c | how-do-you-do-it-fine-grained-action | 2203.12344 | null | https://arxiv.org/abs/2203.12344v2 | https://arxiv.org/pdf/2203.12344v2.pdf | How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs | We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a method which recognizes adverbs across different actions. However, such fine-grained annotations are difficult to obtain and their long-tailed nature makes it challenging to... | ['Cees G. M. Snoek', 'Hazel Doughty'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Doughty_How_Do_You_Do_It_Fine-Grained_Action_Understanding_With_Pseudo-Adverbs_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-understanding'] | ['computer-vision'] | [ 3.94550800e-01 -2.85735339e-01 -5.89146137e-01 -5.36650121e-01
-9.99534547e-01 -9.95629251e-01 6.40354514e-01 9.18802395e-02
-2.73175925e-01 5.24170160e-01 4.96469259e-01 1.13331988e-01
-7.13161305e-02 -4.32439774e-01 -1.14005411e+00 -7.45004833e-01
-4.28013019e-02 4.24605727e-01 6.84047818e-01 -3.36760163... | [8.573904037475586, 0.7257434725761414] |
39a287ad-814c-419d-a9d3-80c194d48a32 | audio-visual-deception-detection-dolos | 2303.12745 | null | https://arxiv.org/abs/2303.12745v1 | https://arxiv.org/pdf/2303.12745v1.pdf | Audio-Visual Deception Detection: DOLOS Dataset and Parameter-Efficient Crossmodal Learning | Deception detection in conversations is a challenging yet important task, having pivotal applications in many fields such as credibility assessment in business, multimedia anti-frauds, and custom security. Despite this, deception detection research is hindered by the lack of high-quality deception datasets, as well as ... | ['Alex Kot', 'Bingquan Shen', 'Adams Kong', 'Zitong Yu', 'Nithish Muthuchamy Selvaraj', 'Xiaobao Guo'] | 2023-03-09 | null | null | null | null | ['deception-detection'] | ['miscellaneous'] | [-1.17800675e-01 -4.67836261e-01 -9.83635858e-02 -3.97974104e-01
-1.23926842e+00 -6.38755262e-01 7.63794661e-01 -9.71117392e-02
-2.57295281e-01 4.94441926e-01 4.65676725e-01 -1.61003396e-01
-7.46833608e-02 -1.16591334e-01 -4.65255171e-01 -5.85717559e-01
5.67979366e-02 -6.05958654e-03 1.15581222e-01 -2.60319710... | [13.263473510742188, 1.934983491897583] |
8e88663f-0df6-4fd2-8dd3-a1a7763f2a17 | structured-attention-composition-for-temporal | 2205.09956 | null | https://arxiv.org/abs/2205.09956v2 | https://arxiv.org/pdf/2205.09956v2.pdf | Structured Attention Composition for Temporal Action Localization | Temporal action localization aims at localizing action instances from untrimmed videos. Existing works have designed various effective modules to precisely localize action instances based on appearance and motion features. However, by treating these two kinds of features with equal importance, previous works cannot tak... | ['Dingwen Zhang', 'Nian Liu', 'Tao Zhao', 'Junwei Han', 'Le Yang'] | 2022-05-20 | null | null | null | null | ['action-localization'] | ['computer-vision'] | [ 2.72376776e-01 -5.01690209e-02 -5.26300907e-01 -1.42990068e-01
-8.89065921e-01 -3.00321192e-01 7.31206894e-01 -1.18484095e-01
-4.07247841e-01 5.21044791e-01 5.22327721e-01 2.85487417e-02
-5.08570559e-02 -3.24018866e-01 -8.35752249e-01 -8.86355758e-01
1.37476042e-01 2.14993432e-01 4.51332688e-01 2.09758226... | [8.49087142944336, 0.6074482202529907] |
1ae7821a-f352-447b-ac62-2b76545b65bd | on-the-evaluation-of-user-privacy-in-deep | 2208.01113 | null | https://arxiv.org/abs/2208.01113v2 | https://arxiv.org/pdf/2208.01113v2.pdf | On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel | Recent Deep Learning (DL) advancements in solving complex real-world tasks have led to its widespread adoption in practical applications. However, this opportunity comes with significant underlying risks, as many of these models rely on privacy-sensitive data for training in a variety of applications, making them an ov... | ['Pabitra Mitra', 'Debdeep Mukhopadhyay', 'Sarani Bhattacharya', 'Manaar Alam', 'Shubhi Shukla'] | 2022-08-01 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 1.99060902e-01 -2.01749071e-01 -5.72944432e-02 -6.63642585e-01
-1.04268157e+00 -1.16293025e+00 5.44170499e-01 1.54561833e-01
-6.92740440e-01 6.87702477e-01 -4.52063829e-01 -1.16949415e+00
1.33079916e-01 -8.74981284e-01 -1.11079574e+00 -9.35852706e-01
-3.83099020e-01 1.01413019e-02 -4.15971875e-02 1.97104424... | [5.876695156097412, 7.03877592086792] |
548bb502-1dbd-4219-aa25-92529a2bee73 | openhands-making-sign-language-recognition | 2110.05877 | null | https://arxiv.org/abs/2110.05877v1 | https://arxiv.org/pdf/2110.05877v1.pdf | OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages | AI technologies for Natural Languages have made tremendous progress recently. However, commensurate progress has not been made on Sign Languages, in particular, in recognizing signs as individual words or as complete sentences. We introduce OpenHands, a library where we take four key ideas from the NLP community for lo... | ['Mitesh Khapra', 'Pratyush Kumar', 'Gokul NC', 'Prem Selvaraj'] | 2021-10-12 | null | https://aclanthology.org/2022.acl-long.150 | https://aclanthology.org/2022.acl-long.150.pdf | acl-2022-5 | ['sign-language-recognition'] | ['computer-vision'] | [ 1.36528820e-01 -2.13772044e-01 -4.71938014e-01 -5.23601830e-01
-1.27827001e+00 -8.32514584e-01 4.75212187e-01 -7.48814166e-01
-7.03475177e-01 5.85383058e-01 7.72812545e-01 -2.48052612e-01
1.28093883e-01 -7.58737698e-02 -6.09638810e-01 -3.84127587e-01
2.05666982e-02 7.21473932e-01 8.98018703e-02 -6.81274906... | [9.168389320373535, -6.497805118560791] |
81fd7d7d-e343-4046-bf26-2556a95fd103 | contact-area-detector-using-cross-view | 2008.07712 | null | https://arxiv.org/abs/2008.07712v1 | https://arxiv.org/pdf/2008.07712v1.pdf | Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects | The ability to determine what parts of objects and surfaces people touch as they go about their daily lives would be useful in understanding how the COVID-19 virus spreads. To determine whether a person has touched an object or surface using visual data, images, or videos, is a hard problem. Computer vision 3D reconstr... | ['Jacky Bibliowicz', 'Wilfredo Torres Calderon', 'Liviu Calin', 'Pan Zhang', 'Michael Lee', 'Alex Tessier', 'Bokyung Lee'] | 2020-08-18 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 4.20553565e-01 -4.00902748e-01 4.78424169e-02 -2.12519303e-01
5.42121567e-03 -5.65171957e-01 4.79384512e-01 -2.77314603e-01
-2.80333519e-01 3.04806858e-01 -3.20055634e-01 -8.14059004e-02
2.30552375e-01 -8.41040552e-01 -6.30441606e-01 -5.03443718e-01
3.23549479e-01 8.54911149e-01 2.06134692e-01 -1.04462832... | [7.256834030151367, -1.3705089092254639] |
41ce1259-dbeb-4f25-be8b-a5637e8bccde | conerf-controllable-neural-radiance-fields | 2112.01983 | null | https://arxiv.org/abs/2112.01983v2 | https://arxiv.org/pdf/2112.01983v2.pdf | CoNeRF: Controllable Neural Radiance Fields | We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attri... | ['Andrea Tagliasacchi', 'Tomasz Trzciński', 'Marek Kowalski', 'Kwang Moo Yi', 'Kacper Kania'] | 2021-12-03 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kania_CoNeRF_Controllable_Neural_Radiance_Fields_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-face-modeling'] | ['computer-vision'] | [ 4.89646137e-01 3.50145161e-01 -4.96028662e-02 -7.38064528e-01
-1.38724983e-01 -8.13745856e-01 6.79808497e-01 -1.99239209e-01
-2.47488543e-01 5.13069510e-01 1.66584358e-01 2.23860994e-01
9.56931114e-02 -6.07708991e-01 -9.25966322e-01 -5.36421776e-01
5.97466454e-02 4.59533215e-01 -1.15734212e-01 -5.54854870... | [10.06628131866455, 0.20640979707241058] |
e4ee51ad-9862-4951-867a-13e4dad19582 | lip-reading-driven-deep-learning-approach-for | 1808.00046 | null | http://arxiv.org/abs/1808.00046v1 | http://arxiv.org/pdf/1808.00046v1.pdf | Lip-Reading Driven Deep Learning Approach for Speech Enhancement | This paper proposes a novel lip-reading driven deep learning framework for
speech enhancement. The proposed approach leverages the complementary strengths
of both deep learning and analytical acoustic modelling (filtering based
approach) as compared to recently published, comparatively simpler benchmark
approaches that... | ['Mandar Gogate', 'William M. Whitmer', 'Amir Hussain', 'Ahsan Adeel'] | 2018-07-31 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.95256996e-01 -2.74473310e-01 9.38843861e-02 -1.72221400e-02
-1.49919689e+00 -8.29463676e-02 5.49257994e-01 1.66599840e-01
-3.97222310e-01 7.86487103e-01 7.08054066e-01 -2.36099020e-01
-2.68832803e-01 -3.74518633e-01 -5.58719099e-01 -9.35940623e-01
4.17697579e-02 -7.05052376e-01 -6.98491633e-02 -1.04551651... | [14.616402626037598, 5.478416442871094] |
fde48f96-69c1-4e9d-aa6e-4432dc868f76 | iiit-dwd-eacl2021-identifying-troll-meme-in | null | null | https://aclanthology.org/2021.dravidianlangtech-1.33 | https://aclanthology.org/2021.dravidianlangtech-1.33.pdf | IIIT_DWD@EACL2021: Identifying Troll Meme in Tamil using a hybrid deep learning approach | Social media are an open forum that allows people to share their knowledge, abilities, talents, ideas, or expressions. Simultaneously, it also allows people to post disrespectful, trolling, defamation, or negative content targeting users or the community based on their gender, race, religious beliefs, etc. Such posts a... | ['Sunil Saumya', 'Ankit Kumar Mishra'] | null | null | null | null | eacl-dravidianlangtech-2021-4 | ['meme-classification'] | ['natural-language-processing'] | [-3.86567831e-01 -3.41526210e-01 -2.53278166e-01 2.05084234e-01
-2.50608742e-01 -5.17713785e-01 9.02182996e-01 7.08488941e-01
-5.70136189e-01 7.76067078e-01 4.79278624e-01 6.38960376e-02
2.56883949e-01 -8.29560101e-01 -2.38423690e-01 -5.35913587e-01
4.11269665e-01 -2.31834557e-02 1.33625478e-01 -6.37646794... | [8.52037239074707, 10.706768989562988] |
aff3886d-7655-45d1-8b96-db5ac0119876 | commonsense-for-generative-multi-hop-question | 1809.06309 | null | https://arxiv.org/abs/1809.06309v3 | https://arxiv.org/pdf/1809.06309v3.pdf | Commonsense for Generative Multi-Hop Question Answering Tasks | Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context t... | ['Lisa Bauer', 'Yicheng Wang', 'Mohit Bansal'] | 2018-09-17 | commonsense-for-generative-multi-hop-question-1 | https://aclanthology.org/D18-1454 | https://aclanthology.org/D18-1454.pdf | emnlp-2018-10 | ['multi-hop-question-answering', 'implicit-relations'] | ['knowledge-base', 'natural-language-processing'] | [ 3.58480573e-01 7.77948678e-01 -1.09833628e-01 -2.45146140e-01
-1.57627916e+00 -6.92102313e-01 8.65997195e-01 3.34879845e-01
-1.69570848e-01 1.05699098e+00 9.88143086e-01 -4.76722360e-01
-1.05501615e-01 -1.18677878e+00 -8.11687350e-01 -2.12223396e-01
3.72685730e-01 1.12533450e+00 2.21428677e-01 -7.90617049... | [10.836118698120117, 8.186586380004883] |
18f79f9d-ce3c-4774-8a6a-157e97d4e949 | phase-slam-phase-based-simultaneous | 2201.09048 | null | https://arxiv.org/abs/2201.09048v1 | https://arxiv.org/pdf/2201.09048v1.pdf | Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems | Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degree 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this paper, we propose a phase based Simultaneous Localiz... | ['Qi Hao', 'Rui Gao', 'Rui Ma', 'Xi Zheng'] | 2022-01-22 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction', 'loop-closure-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.62361735e-01 -1.94289684e-01 1.35317177e-01 -3.58398557e-01
-9.52840567e-01 -4.67612326e-01 4.63994890e-01 9.85978357e-03
-3.05929989e-01 5.39119303e-01 -3.06313902e-01 -2.92279184e-01
-3.88938993e-01 -7.68391609e-01 -9.77191329e-01 -5.17457485e-01
-9.37044472e-02 9.70850050e-01 8.94075036e-02 -1.61470488... | [7.349800109863281, -2.191901445388794] |
99acc428-4b9b-41c7-b99a-bf8ea34371a2 | investigating-tradeoffs-in-real-world-video | 2111.12704 | null | https://arxiv.org/abs/2111.12704v1 | https://arxiv.org/pdf/2111.12704v1.pdf | Investigating Tradeoffs in Real-World Video Super-Resolution | The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training. First, while long-term propagation leads to improved performance in cases of mild degradations, severe in-the-wild degradations could be exaggerated through propagation, impairi... | ['Chen Change Loy', 'Xiangyu Xu', 'Shangchen Zhou', 'Kelvin C. K. Chan'] | 2021-11-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chan_Investigating_Tradeoffs_in_Real-World_Video_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution'] | ['computer-vision'] | [ 3.30089450e-01 -4.33468550e-01 -9.97810215e-02 -3.23015571e-01
-7.91003942e-01 -4.57893074e-01 2.99602121e-01 -3.32694978e-01
-2.54376471e-01 8.13316345e-01 2.80045420e-01 -1.16431981e-01
1.24301150e-01 -5.50530076e-01 -7.65966237e-01 -6.82027817e-01
-1.42000794e-01 -2.41251722e-01 4.21733379e-01 -2.26216719... | [11.234482765197754, -1.9218331575393677] |
a9b0514f-5df5-44b9-aa76-eb85fee06a87 | a-sparse-learning-approach-to-the-design-of | 2004.13164 | null | http://arxiv.org/abs/2004.13164v1 | http://arxiv.org/pdf/2004.13164v1.pdf | A Sparse Learning Approach to the Design of Radar Tunable Architectures with Enhanced Selectivity Properties | This paper considers the design of tunable decision schemes capable of
rejecting with high probability mismatched signals embedded in Gaussian
interference with unknown covariance matrix. To this end, a sparse recovery
technique is exploited to enhance the resolution at which the target angle of
arrival is estimated wi... | [] | 2020-04-27 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 5.71782887e-01 -8.68111253e-02 -9.29408986e-03 -1.04014568e-01
-7.61283994e-01 -5.90446949e-01 6.87282979e-01 1.16698973e-01
-3.32369596e-01 6.89133167e-01 -1.55212879e-01 -2.23508269e-01
-7.16962516e-01 -4.75496024e-01 -9.65237096e-02 -1.06681681e+00
-2.49849260e-02 2.10894290e-02 9.06972289e-02 3.75685468... | [6.5420966148376465, 1.32700514793396] |
33045bb8-9e99-448e-af3f-58a2a19ff22f | a-rule-based-bpso-approach-to-produce-low | 2111.12802 | null | https://arxiv.org/abs/2111.12802v1 | https://arxiv.org/pdf/2111.12802v1.pdf | A Rule-based/BPSO Approach to Produce Low-dimensional Semantic Basis Vectors Set | We intend to generate low-dimensional explicit distributional semantic vectors. In explicit semantic vectors, each dimension corresponds to a word, so word vectors are interpretable. In this research, we propose a new approach to obtain low-dimensional explicit semantic vectors. First, the proposed approach considers t... | ['Morteza Analoui', 'Atefe Pakzad'] | 2021-11-24 | null | null | null | null | ['word-similarity'] | ['natural-language-processing'] | [ 2.45566927e-02 -3.73478979e-01 -1.08512618e-01 -3.03814560e-01
-2.80106187e-01 -4.73339558e-01 6.31177008e-01 3.35872084e-01
-1.01554322e+00 6.90791667e-01 4.78267372e-01 -1.42360568e-01
-3.51289481e-01 -1.06449485e+00 -1.17688127e-01 -7.01689959e-01
9.09234881e-02 3.23988706e-01 1.83128923e-01 -5.04401088... | [10.252334594726562, 8.908693313598633] |
cc3216c2-a102-4167-b2b5-c1fc65224458 | urban-sound-tagging-using-convolutional | 1909.12699 | null | https://arxiv.org/abs/1909.12699v1 | https://arxiv.org/pdf/1909.12699v1.pdf | Urban Sound Tagging using Convolutional Neural Networks | In this paper, we propose a framework for environmental sound classification in a low-data context (less than 100 labeled examples per class). We show that using pre-trained image classification models along with the usage of data augmentation techniques results in higher performance over alternative approaches. We app... | ['Sainath Adapa'] | 2019-09-27 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 2.67237037e-01 -1.30603611e-01 3.88698757e-01 -3.97329122e-01
-1.10325301e+00 -5.66717863e-01 5.32590747e-01 3.01500529e-01
-9.12381172e-01 5.12429833e-01 2.91457653e-01 -8.80369842e-02
1.69579059e-01 -7.72025764e-01 -8.76057386e-01 -5.10022163e-01
-1.43520564e-01 4.51235026e-02 4.87452567e-01 -7.28194043... | [15.202110290527344, 5.15608549118042] |
13a4c3a5-63c7-4d8d-9221-436bdf18c28e | cora-adapting-clip-for-open-vocabulary | 2303.13076 | null | https://arxiv.org/abs/2303.13076v1 | https://arxiv.org/pdf/2303.13076v1.pdf | CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching | Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obst... | ['Hongsheng Li', 'Rui Zhao', 'Feng Zhu', 'Xiaoshi Wu'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_CORA_Adapting_CLIP_for_Open-Vocabulary_Detection_With_Region_Prompting_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 9.00782347e-02 -3.98277491e-02 -2.81933337e-01 -1.66798845e-01
-1.32673740e+00 -8.68717611e-01 6.61701739e-01 3.62368554e-01
-6.11830294e-01 1.30821407e-01 -2.35873327e-01 -2.66749799e-01
5.51366031e-01 -4.92006332e-01 -1.01648903e+00 -5.10020077e-01
-8.34186822e-02 3.16127002e-01 8.92103314e-01 8.08769390... | [9.618412971496582, 1.4255744218826294] |
a4356c23-9f2e-4b2c-99ca-92daabb48777 | rasa-relation-and-sensitivity-aware | 2305.13653 | null | https://arxiv.org/abs/2305.13653v1 | https://arxiv.org/pdf/2305.13653v1.pdf | RaSa: Relation and Sensitivity Aware Representation Learning for Text-based Person Search | Text-based person search aims to retrieve the specified person images given a textual description. The key to tackling such a challenging task is to learn powerful multi-modal representations. Towards this, we propose a Relation and Sensitivity aware representation learning method (RaSa), including two novel tasks: Rel... | ['Min Zhang', 'Liqiang Nie', 'Zhenfeng Fan', 'Chen Chen', 'Ziqiang Cao', 'Daming Gao', 'Min Cao', 'Yang Bai'] | 2023-05-23 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 2.88059503e-01 -5.23523353e-02 -2.00902238e-01 -2.51430780e-01
-9.79768276e-01 -4.43818092e-01 8.93077016e-01 5.79303838e-02
-4.15966302e-01 5.58661759e-01 4.78498250e-01 2.49841958e-01
-2.02807292e-01 -7.32119858e-01 -4.93745923e-01 -7.77213454e-01
3.23577970e-01 5.26325941e-01 -5.97838014e-02 -3.21909457... | [14.62649154663086, 0.9442287087440491] |
765ee641-7b2d-4bb9-a166-7292883c584a | s-2sql-injecting-syntax-to-question-schema | null | null | https://aclanthology.org/2022.findings-acl.99 | https://aclanthology.org/2022.findings-acl.99.pdf | S^2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers | The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based encoder has been successfully used in this task but does not model the question syntax well. In this paper, we propose S^2SQL, injecting Syn... | ['Yongbin Li', 'Jian Sun', 'Bowen Li', 'Yanyang Li', 'Bowen Qin', 'Lihan Wang', 'Ruiying Geng', 'Binyuan Hui'] | null | null | null | null | findings-acl-2022-5 | ['text-to-sql'] | ['computer-code'] | [ 4.36222628e-02 5.09166598e-01 -3.19061697e-01 -7.25416064e-01
-7.94605434e-01 -7.07358181e-01 3.48286122e-01 2.92376339e-01
-9.37874541e-02 5.36906496e-02 3.13646019e-01 -9.27342832e-01
6.20479472e-02 -1.24232709e+00 -1.17032158e+00 3.38431180e-01
1.14073120e-01 4.99641538e-01 6.58243060e-01 -5.74377120... | [9.945257186889648, 7.872202396392822] |
c3e02ba1-5cdb-40ab-a94c-4742aa1fb226 | graph-and-temporal-convolutional-networks-for | 2012.11806 | null | https://arxiv.org/abs/2012.11806v3 | https://arxiv.org/pdf/2012.11806v3.pdf | Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos | Despite the recent progress, 3D multi-person pose estimation from monocular videos is still challenging due to the commonly encountered problem of missing information caused by occlusion, partially out-of-frame target persons, and inaccurate person detection. To tackle this problem, we propose a novel framework integra... | ['Robby T. Tan', 'Bo Yang', 'Bo wang', 'Yu Cheng'] | 2020-12-22 | null | null | null | null | ['3d-multi-person-pose-estimation-absolute', '3d-multi-person-pose-estimation-root-relative', 'monocular-3d-human-pose-estimation', '3d-multi-person-pose-estimation', '3d-absolute-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-4.35081929e-01 -1.26656651e-01 3.90292183e-02 -8.63630697e-02
-2.92920530e-01 -1.96153790e-01 3.41884643e-01 -4.75436985e-01
-4.67103451e-01 5.71429372e-01 3.55431706e-01 4.67132479e-01
1.49426581e-02 -5.95653772e-01 -6.02910876e-01 -4.07880306e-01
-3.97091322e-02 5.64573824e-01 4.76585329e-01 -1.00929514... | [7.0864973068237305, -0.8932809829711914] |
72c819b0-cd37-4e50-b490-f29338a3c3bb | representation-based-meta-learning-for-few | 2106.15238 | null | https://arxiv.org/abs/2106.15238v1 | https://arxiv.org/pdf/2106.15238v1.pdf | Representation based meta-learning for few-shot spoken intent recognition | Spoken intent detection has become a popular approach to interface with various smart devices with ease. However, such systems are limited to the preset list of intents-terms or commands, which restricts the quick customization of personal devices to new intents. This paper presents a few-shot spoken intent classificat... | ['Brian Kingsbury', 'Karthik Sankaranarayanan', 'Saneem Chemmengath', 'Shreya Khare', 'Samarth Bharadwaj', 'Ashish Mittal'] | 2021-06-29 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 3.58801782e-01 -2.59621650e-01 -3.25574607e-01 -8.67333114e-01
-7.53707290e-01 -3.46515298e-01 7.89548874e-01 1.14545643e-01
-3.96692336e-01 3.77998471e-01 4.90927130e-01 -1.22128583e-01
2.40642473e-01 -4.20715988e-01 2.84844544e-03 -2.94336259e-01
-1.40556945e-02 4.09930348e-01 -9.49159339e-02 -3.93026620... | [12.344090461730957, 7.540412902832031] |
6018b00c-42fe-40b3-b0dc-7bcb2844112a | multimodal-self-supervised-learning-for | 1912.05396 | null | https://arxiv.org/abs/1912.05396v2 | https://arxiv.org/pdf/1912.05396v2.pdf | Multimodal Self-Supervised Learning for Medical Image Analysis | Self-supervised learning approaches leverage unlabeled samples to acquire generic knowledge about different concepts, hence allowing for annotation-efficient downstream task learning. In this paper, we propose a novel self-supervised method that leverages multiple imaging modalities. We introduce the multimodal puzzle ... | ['Moin Nabi', 'Aiham Taleb', 'Christoph Lippert', 'Tassilo Klein'] | 2019-12-11 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 6.20802224e-01 3.80834460e-01 -3.08823556e-01 -3.12096804e-01
-1.35571015e+00 -7.59201229e-01 5.41790247e-01 1.07651576e-02
-4.08017337e-01 6.20970607e-01 3.84759426e-01 -8.03559721e-02
-4.06559885e-01 -6.67535841e-01 -8.88792217e-01 -8.22706699e-01
-8.65328535e-02 7.12629378e-01 -1.64773598e-01 9.64550748... | [14.614691734313965, -2.149055242538452] |
d19a9d79-f253-4c3e-ad98-22e0833321db | ontology-based-technical-text-annotation | null | null | https://aclanthology.org/W14-6003 | https://aclanthology.org/W14-6003.pdf | Ontology-based Technical Text Annotation | null | ["Fran{\\c{c}}ois L{\\'e}vy", 'Yue Ma', 'Nadi Tomeh'] | 2014-08-01 | null | null | null | ws-2014-8 | ['text-annotation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.430415630340576, 3.8097002506256104] |
bc2eaec3-065e-4805-9059-d7aa64df1210 | tree-based-subgroup-discovery-in-electronic | 2208.14329 | null | https://arxiv.org/abs/2208.14329v1 | https://arxiv.org/pdf/2208.14329v1.pdf | Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies | The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covari... | ['Jon A. Steingrimsson', 'Joseph W. Hogan', 'Allison Delong', 'Monicah Nyambura', 'Issa J. Dahabreh', 'Rami Kantor', 'Ann W. Mwangi', 'Jiabei Yang'] | 2022-08-30 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 3.22227508e-01 -3.80063891e-01 -1.15408087e+00 -6.30908191e-01
-6.54926300e-01 -3.81850094e-01 2.38352105e-01 6.70119882e-01
-2.02127278e-01 9.51504052e-01 9.33396161e-01 -7.12453663e-01
-3.62890333e-01 -6.76941991e-01 -2.96392769e-01 -2.30705723e-01
-7.93327689e-01 6.84401989e-01 -5.52112103e-01 3.92352998... | [7.972390174865723, 5.492047309875488] |
8375773a-ea4b-40cd-978a-5163394098cf | acute-ischemic-stroke-lesion-segmentation-in | 2301.06793 | null | https://arxiv.org/abs/2301.06793v1 | https://arxiv.org/pdf/2301.06793v1.pdf | Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks | In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-exci... | ['V. B. Berikov', 'A. A. Tulupov', 'Yu. N. Sinyavskiy', 'K. M. Sherman', 'I. A. Pestunov', 'S. K. Verbitskiy', 'A. V. Dobshik'] | 2023-01-17 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [-6.32956177e-02 1.84108704e-01 9.74874124e-02 -4.40251261e-01
-6.47633076e-01 -1.62423372e-01 8.22350755e-02 3.47274721e-01
-8.06885242e-01 9.38959479e-01 -1.08041756e-01 -2.96052456e-01
-4.63495731e-01 -8.50397110e-01 -4.79185313e-01 -6.57722890e-01
-5.18178403e-01 6.07802331e-01 2.54142761e-01 3.24333757... | [14.291232109069824, -2.3118209838867188] |
a8845fa5-278d-490a-a058-ca58020f4cbc | adversarial-instance-augmentation-for | null | null | https://ieeexplore.ieee.org/document/9386248 | https://ieeexplore.ieee.org/document/9386248 | Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images | Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change, due to both its rarity and sparsity. Contemporary methods to tackle the data insufficiency mainly ... | ['Zhenwei Shi', 'Wenyuan Li', 'Hao Chen'] | 2021-03-25 | null | null | null | ieee-transactions-on-geoscience-and-remote-7 | ['image-augmentation', 'building-change-detection-for-remote-sensing'] | ['computer-vision', 'miscellaneous'] | [ 4.40726250e-01 -2.93285578e-01 7.72846863e-02 -2.20943257e-01
-9.35305893e-01 -4.80472326e-01 5.40814459e-01 -3.27646017e-01
-3.00295209e-03 6.29469216e-01 4.47195396e-02 1.34335682e-01
1.62954509e-01 -1.28310812e+00 -1.05871892e+00 -8.35331976e-01
2.72987902e-01 3.36045146e-01 1.72634616e-01 -4.21724975... | [11.37870979309082, -0.7758793234825134] |
106f866d-48d4-4f3f-997d-b165cd4f4c13 | an-empirical-etudy-of-non-lexical-extensions | null | null | https://aclanthology.org/C12-2115 | https://aclanthology.org/C12-2115.pdf | An Empirical Etudy of Non-Lexical Extensions to Delexicalized Transfer | null | ['Anders S{\\o}gaard', 'Julie Wulff'] | 2012-12-01 | an-empirical-etudy-of-non-lexical-extensions-1 | https://aclanthology.org/C12-2115 | https://aclanthology.org/C12-2115.pdf | coling-2012-12 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.500656604766846, 3.5861518383026123] |
b3c52af2-d3c7-4e51-983c-96f0950ae6b4 | learning-based-automatic-synthesis-of | 2305.15642 | null | https://arxiv.org/abs/2305.15642v2 | https://arxiv.org/pdf/2305.15642v2.pdf | Learning-Based Automatic Synthesis of Software Code and Configuration | Increasing demands in software industry and scarcity of software engineers motivates researchers and practitioners to automate the process of software generation and configuration. Large scale automatic software generation and configuration is a very complex and challenging task. In this proposal, we set out to investi... | ['Shantanu Mandal'] | 2023-05-25 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 5.35587370e-01 -1.77738026e-01 4.02186453e-01 -3.37834358e-01
-5.75747252e-01 -6.97871745e-01 1.32106125e-01 -2.73152278e-03
-1.02515578e-01 6.36373818e-01 -3.48721743e-01 -5.80918968e-01
-6.89539313e-02 -9.19439912e-01 -9.32578802e-01 -3.35298240e-01
2.78592914e-01 4.31715310e-01 -1.66213494e-02 -4.69171226... | [8.011943817138672, 7.428036212921143] |
39a4f24b-4c31-437f-81ef-5040a58ec2d7 | orgmining-2-0-a-novel-framework-for | 2011.12445 | null | https://arxiv.org/abs/2011.12445v2 | https://arxiv.org/pdf/2011.12445v2.pdf | OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs | Providing appropriate structures around human resources can streamline operations and thus facilitate the competitiveness of an organization. To achieve this goal, modern organizations need to acquire an accurate and timely understanding of human resource grouping while faced with an ever-changing environment. The use ... | ['Yang Yu', 'Arthur H. M. ter Hofstede', 'Wil M. P. van der Aalst', 'Chun Ouyang', 'Jing Yang'] | 2020-11-24 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 3.01126093e-01 8.21862295e-02 -1.32611707e-01 -1.94084067e-02
1.26999050e-01 -2.88269699e-01 7.40371346e-01 9.94952738e-01
-3.08151931e-01 2.29717314e-01 3.95184420e-02 -3.46748173e-01
-6.01484239e-01 -1.21945393e+00 -5.06111048e-02 -2.12837547e-01
-2.89521009e-01 6.51233137e-01 3.94493759e-01 1.44690990... | [8.572782516479492, 6.020284652709961] |
0b4143bd-225a-4af1-a098-18cfb38fb14d | the-mapkurator-system-a-complete-pipeline-for | 2306.17059 | null | https://arxiv.org/abs/2306.17059v2 | https://arxiv.org/pdf/2306.17059v2.pdf | The mapKurator System: A Complete Pipeline for Extracting and Linking Text from Historical Maps | Scanned historical maps in libraries and archives are valuable repositories of geographic data that often do not exist elsewhere. Despite the potential of machine learning tools like the Google Vision APIs for automatically transcribing text from these maps into machine-readable formats, they do not work well with larg... | ['Yao-Yi Chiang', 'Leeje Jang', 'Min Namgung', 'Yijun Lin', 'Zekun Li', 'Jina Kim'] | 2023-06-29 | null | null | null | null | ['zero-shot-learning'] | ['methodology'] | [-2.15746075e-01 8.69212486e-03 1.88172892e-01 -4.32328314e-01
-1.06302297e+00 -1.13810527e+00 8.56664181e-01 6.55274272e-01
-4.45640951e-01 5.41617572e-01 4.20853227e-01 -5.61508298e-01
-3.56877416e-01 -1.34759820e+00 -6.90810442e-01 -2.54820675e-01
-5.90251498e-02 7.89976120e-01 3.23978812e-01 -4.00851667... | [9.409822463989258, 9.113073348999023] |
d047c142-cf1d-4579-b3ee-a4cd6090bfec | aggressive-language-identification-using-word | null | null | https://aclanthology.org/W18-4414 | https://aclanthology.org/W18-4414.pdf | Aggressive Language Identification Using Word Embeddings and Sentiment Features | This paper describes our participation in the First Shared Task on Aggression Identification. The method proposed relies on machine learning to identify social media texts which contain aggression. The main features employed by our method are information extracted from word embeddings and the output of a sentiment anal... | ['Constantin Or{\\u{a}}san'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-2.44813144e-01 -5.80976121e-02 -1.86817452e-01 -2.45898083e-01
-1.38274029e-01 -1.83228850e-01 9.23156619e-01 4.86170650e-01
-1.15217745e+00 7.06402302e-01 5.43197334e-01 -3.34502868e-02
-2.82056630e-01 -7.33001590e-01 2.94811159e-01 -7.01209545e-01
1.67251565e-02 7.21849740e-01 3.54423404e-01 -7.91182518... | [8.800606727600098, 10.708648681640625] |
90690175-27ae-431b-b132-44f5c5638162 | contrastive-learning-of-sentence | null | null | https://aclanthology.org/2021.icon-main.33 | https://aclanthology.org/2021.icon-main.33.pdf | Contrastive Learning of Sentence Representations | Learning sentence representations which capture rich semantic meanings has been crucial for many NLP tasks. Pre-trained language models such as BERT have achieved great success in NLP, but sentence embeddings extracted directly from these models do not perform well without fine-tuning. We propose Contrastive Learning o... | ['Ping Chen', 'Wei Ding', 'Hefei Qiu'] | null | null | null | null | icon-2021-12 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 4.04823124e-01 5.74289002e-02 -2.52801478e-01 -6.21215641e-01
-7.22890556e-01 -2.34151229e-01 9.22661126e-01 4.54082072e-01
-7.50049591e-01 5.79098463e-01 7.63068557e-01 -1.58205509e-01
2.66824335e-01 -7.82429755e-01 -6.05728149e-01 -2.98058659e-01
-1.36708803e-02 2.81038702e-01 3.15693647e-01 -5.79555929... | [10.806519508361816, 8.735346794128418] |
c6eb98cf-9676-4ac6-be5e-96f175183979 | a-cnn-based-approach-to-classify-cricket | 1909.01228 | null | https://arxiv.org/abs/1909.01228v1 | https://arxiv.org/pdf/1909.01228v1.pdf | A CNN-based approach to classify cricket bowlers based on their bowling actions | With the advances in hardware technologies and deep learning techniques, it has become feasible to apply these techniques in diverse fields. Convolutional Neural Network (CNN), an architecture from the field of deep learning, has revolutionized Computer Vision. Sports is one of the avenues in which the use of computer ... | ['Siamul Karim Khan', 'Tanzil Bin Hassan', 'Md Nafee Al Islam'] | 2019-09-03 | null | null | null | null | ['game-of-cricket'] | ['playing-games'] | [-7.81057701e-02 -2.77712792e-01 1.07838828e-02 -8.58049691e-02
-1.97084323e-01 -2.37790793e-01 4.32425946e-01 -2.85916571e-02
-8.16063941e-01 4.39483374e-01 9.41666141e-02 -2.17585228e-02
-4.39878628e-02 -1.06023872e+00 -8.33869040e-01 -5.37701786e-01
-2.13979796e-01 1.35934040e-01 6.40201747e-01 -6.51101708... | [7.71830415725708, 0.5598528385162354] |
3c96e654-6902-4093-879f-0464660bd459 | learning-the-trading-algorithm-in-simulated | 2208.02901 | null | https://arxiv.org/abs/2208.02901v3 | https://arxiv.org/pdf/2208.02901v3.pdf | Nonstationary Continuum-Armed Bandit Strategies for Automated Trading in a Simulated Financial Market | We approach the problem of designing an automated trading strategy that can consistently profit by adapting to changing market conditions. This challenge can be framed as a Nonstationary Continuum-Armed Bandit (NCAB) problem. To solve the NCAB problem, we propose PRBO, a novel trading algorithm that uses Bayesian optim... | ['John Cartlidge', 'Bingde Liu'] | 2022-08-04 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [-5.12417376e-01 -3.37212831e-01 -3.83412868e-01 -3.49735022e-02
-1.09450924e+00 -7.81277359e-01 6.11610591e-01 -4.65333313e-01
-4.01789546e-01 1.21460497e+00 4.84082885e-02 -3.63076448e-01
-5.78029931e-01 -8.00806046e-01 -7.23412335e-01 -8.99435043e-01
8.82020295e-02 1.32565320e+00 -3.42898513e-03 -1.37976408... | [4.505102157592773, 3.320913553237915] |
900f0c19-cd27-420a-861f-cb9c648ef7c6 | offline-congestion-games-how-feedback-type | 2210.13396 | null | https://arxiv.org/abs/2210.13396v1 | https://arxiv.org/pdf/2210.13396v1.pdf | Offline congestion games: How feedback type affects data coverage requirement | This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games.The existing dataset coverage assumption in offline general-sum games inevitably incurs a dependency on the number of actions, which can be exponentially large in congestion games. We consider three... | ['Simon S. Du', 'Maryam Fazel', 'Zhihan Xiong', 'Qiwen Cui', 'Haozhe Jiang'] | 2022-10-24 | null | null | null | null | ['type'] | ['speech'] | [-0.23758298 0.56508285 -0.6715462 0.32803577 -0.6382433 -1.0691041
-0.288558 0.2610016 -0.4846608 1.1700413 -0.0207483 -0.5816445
-0.8939021 -1.1371706 -1.1194836 -0.6486151 -0.22520812 0.5242937
0.25250986 -0.30793506 -0.01054925 0.2934672 -1.037284 -0.08495475
0.9184129 0.94597274 0.141... | [4.486528396606445, 3.249706983566284] |
08040463-3539-408a-85f7-4c04993f2ba9 | automatic-analysis-of-the-emotional-content | 2106.09539 | null | https://arxiv.org/abs/2106.09539v1 | https://arxiv.org/pdf/2106.09539v1.pdf | Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit | Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes. As a part of this research, hundreds of hours of daylong recordings from preterm infants' audio environments were collected from two hospitals in Finland and Estonia in the context of so-c... | ['Okko Räsänen', 'Konstantinos Drossos', 'Sari Ahlqvist-Björkroth', 'Einari Vaaras'] | 2021-06-14 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 1.33089453e-01 3.47437978e-01 3.92789431e-02 -6.50559902e-01
-9.79236186e-01 -3.30547035e-01 8.62300172e-02 5.02497792e-01
-5.79430461e-01 5.49694121e-01 1.81632668e-01 2.27885380e-01
-2.31135160e-01 -3.60061437e-01 -5.30699611e-01 -6.41688287e-01
-1.94057807e-01 4.21281964e-01 2.33082429e-01 -7.61078149... | [13.62338924407959, 5.830435752868652] |
ebceb77c-1e9b-4106-b818-e997ac0c51c9 | scene-completenesss-aware-lidar-depth | 2003.06945 | null | https://arxiv.org/abs/2003.06945v3 | https://arxiv.org/pdf/2003.06945v3.pdf | Scene Completeness-Aware Lidar Depth Completion for Driving Scenario | This paper introduces Scene Completeness-Aware Depth Completion (SCADC) to complete raw lidar scans into dense depth maps with fine and complete scene structures. Recent sparse depth completion for lidars only focuses on the lower scenes and produces irregular estimations on the upper because existing datasets, such as... | ['Cho-Ying Wu', 'Ulrich Neumann'] | 2020-03-15 | null | null | null | null | ['stereo-lidar-fusion'] | ['computer-vision'] | [ 2.53188401e-01 1.73062101e-01 -4.04138863e-03 -6.47791028e-01
-5.36273718e-01 -4.82327640e-01 2.66973287e-01 2.35243991e-01
-2.92913795e-01 7.02685177e-01 -3.05381119e-01 -2.69501925e-01
1.53121175e-02 -1.23911881e+00 -6.83737218e-01 -1.97133020e-01
1.27314180e-01 1.13492227e+00 8.67379844e-01 -2.13983923... | [8.108040809631348, -2.526770830154419] |
e5df3604-d880-49ea-8620-f1b1768a0e37 | using-randomness-to-improve-robustness-of | 1808.03601 | null | http://arxiv.org/abs/1808.03601v1 | http://arxiv.org/pdf/1808.03601v1.pdf | Using Randomness to Improve Robustness of Machine-Learning Models Against Evasion Attacks | Machine learning models have been widely used in security applications such
as intrusion detection, spam filtering, and virus or malware detection.
However, it is well-known that adversaries are always trying to adapt their
attacks to evade detection. For example, an email spammer may guess what
features spam detection... | ['ZhiYuan Chen', 'Fan Yang'] | 2018-08-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 4.11316425e-01 -2.60798454e-01 -1.20626211e-01 -2.62067795e-01
-7.42665827e-02 -8.87426019e-01 9.38328624e-01 1.36441197e-02
-5.67128181e-01 6.55073762e-01 -3.99569005e-01 -9.84582245e-01
6.33746162e-02 -1.26090658e+00 -4.66145664e-01 -5.58892846e-01
-8.16505626e-02 6.19605243e-01 6.47420108e-01 -2.92770892... | [5.6118388175964355, 7.620060443878174] |
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