paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5d4f6f61-4e26-48e5-974b-e1a73ce18d18 | feasible-actor-critic-constrained | 2105.10682 | null | https://arxiv.org/abs/2105.10682v3 | https://arxiv.org/pdf/2105.10682v3.pdf | Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety | The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks. In this paper, we introduce the feasible actor-critic (FAC) algorithm, which... | ['Jianyu Chen', 'Sifa Zheng', 'Xiangteng Zhang', 'Shegnbo Eben Li', 'Yang Guan', 'Haitong Ma'] | 2021-05-22 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.03940850e-02 6.10398650e-01 -9.60899591e-01 -1.70245823e-02
-5.65332234e-01 -3.73094410e-01 3.05858374e-01 -2.42029447e-02
-6.31482959e-01 1.27132165e+00 5.75840753e-03 -6.34610236e-01
-4.07574534e-01 -6.16330922e-01 -7.12546289e-01 -9.82250810e-01
-4.22709674e-01 8.64023641e-02 1.63242415e-01 -5.08456051... | [4.511089324951172, 2.1226017475128174] |
2764890a-7bcf-4c63-b280-d373d514a0f2 | controllable-3d-generative-adversarial-face | 2208.14263 | null | https://arxiv.org/abs/2208.14263v1 | https://arxiv.org/pdf/2208.14263v1.pdf | Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance | 3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation. Existing 3D deep learning generative models (e.g., VAE, GANs) allow generating compact face representations (both s... | ['Daeil Kim', 'Aayush Prakash', 'Steven Song', 'Fernando de la Torre', 'Xuanbai Chen', 'Shaunak Srivastava', 'Quankai Gao', 'Aashish Rai', 'Fariborz Taherkhani'] | 2022-08-30 | null | null | null | null | ['3d-face-modeling', 'face-model'] | ['computer-vision', 'computer-vision'] | [ 2.17970312e-01 5.81854999e-01 3.01061749e-01 -5.66517711e-01
-1.77163154e-01 -6.88052654e-01 9.01027262e-01 -8.44278455e-01
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1.52696297e-01 3.92109811e-01 -4.68054920e-01 -4.30449635... | [12.58619499206543, -0.31712496280670166] |
17013b56-98a6-4e72-a04b-33bb1d1a1f4f | aspect-category-based-sentiment-analysis-with | null | null | https://aclanthology.org/2020.coling-main.72 | https://aclanthology.org/2020.coling-main.72.pdf | Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network | Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection ... | ['Rui Xia', 'Jianfei Yu', 'Xiangsheng Zhou', 'Yaofeng Tu', 'Hongjie Cai'] | 2020-12-01 | null | null | null | coling-2020-8 | ['aspect-category-detection'] | ['natural-language-processing'] | [-1.09884776e-01 1.85265422e-01 -4.34571981e-01 -7.05619097e-01
-6.45674467e-02 -5.55951715e-01 7.05334127e-01 3.78766477e-01
1.55349106e-01 8.92069563e-02 6.67611539e-01 -4.07801658e-01
1.13895603e-01 -9.90039110e-01 -1.71428338e-01 -5.85800767e-01
1.89732298e-01 2.88572729e-01 1.96894735e-01 -6.17965996... | [11.472707748413086, 6.638809680938721] |
c329e07c-cfef-4297-84a7-185ffbad8836 | adaptive-reinforcement-learning-of-multi | 2307.00552 | null | https://arxiv.org/abs/2307.00552v1 | https://arxiv.org/pdf/2307.00552v1.pdf | Adaptive reinforcement learning of multi-agent ethically-aligned behaviours: the QSOM and QDSOM algorithms | The numerous deployed Artificial Intelligence systems need to be aligned with our ethical considerations. However, such ethical considerations might change as time passes: our society is not fixed, and our social mores evolve. This makes it difficult for these AI systems; in the Machine Ethics field especially, it has ... | ['Mathieu Guillermin', 'Olivier Boissier', 'Rémy Chaput'] | 2023-07-02 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-1.20773979e-01 4.95959997e-01 -8.16684291e-02 -2.88311988e-01
9.64455381e-02 -5.27053833e-01 5.81236959e-01 2.10221589e-01
-5.70417166e-01 1.28157747e+00 2.04752367e-02 4.90940511e-02
-5.89171529e-01 -8.71342003e-01 -3.36915880e-01 -8.57413352e-01
-2.88522214e-01 8.18407476e-01 2.24078353e-03 -6.23334348... | [4.052988529205322, 2.0296082496643066] |
aec21fab-28ef-40b8-ae7f-eab272cec14c | feature-control-as-intrinsic-motivation-for | 1705.06769 | null | http://arxiv.org/abs/1705.06769v2 | http://arxiv.org/pdf/1705.06769v2.pdf | Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning | The problem of sparse rewards is one of the hardest challenges in
contemporary reinforcement learning. Hierarchical reinforcement learning (HRL)
tackles this problem by using a set of temporally-extended actions, or options,
each of which has its own subgoal. These subgoals are normally handcrafted for
specific tasks. ... | ['Nat Dilokthanakul', 'Murray Shanahan', 'Nick Pawlowski', 'Christos Kaplanis'] | 2017-05-18 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-2.43651718e-02 1.32020667e-01 3.57235037e-02 1.25562266e-01
-6.45717680e-01 -6.26286805e-01 7.96472847e-01 -5.84395826e-02
-8.08033228e-01 1.10574961e+00 4.15229380e-01 -7.25939423e-02
-5.63076138e-01 -5.70163846e-01 -6.21601880e-01 -8.35172117e-01
-6.76813602e-01 5.92251062e-01 4.60596651e-01 -9.47562873... | [3.960681915283203, 1.491626501083374] |
043aa160-d9bf-402d-95dc-b7e872dd352c | 3dfpn-hs2-3d-feature-pyramid-network-based | 1906.03467 | null | https://arxiv.org/abs/1906.03467v2 | https://arxiv.org/pdf/1906.03467v2.pdf | 3DFPN-HS$^2$: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection | Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which... | ['YingLi Tian', 'Oguz Akin', 'Jingya Liu', 'Liangliang Cao'] | 2019-06-08 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 7.39674717e-02 1.23099171e-01 -1.46431729e-01 -1.16224408e-01
-8.46557736e-01 -1.20072596e-01 2.88555533e-01 -8.51274058e-02
-3.70474428e-01 2.34050170e-01 -1.64759591e-01 -2.83220887e-01
-2.20697492e-01 -9.86661196e-01 -3.14602822e-01 -7.51910985e-01
-6.62404671e-02 6.35569155e-01 1.06048477e+00 1.80379391... | [15.391792297363281, -2.157292127609253] |
5fc66979-6dd7-4ee7-8566-e33e61fb7650 | a-fault-detection-scheme-utilizing | 2210.09226 | null | https://arxiv.org/abs/2210.09226v1 | https://arxiv.org/pdf/2210.09226v1.pdf | A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy | Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the solar system, this paper proposes a trained convolutional neural network (CNN) ... | ['Amin Kazemi', 'Mary Pa'] | 2022-10-14 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.66396558e-01 -2.41775036e-01 2.32423559e-01 7.67235309e-02
7.37082288e-02 -8.17082226e-01 2.53996223e-01 1.09290317e-01
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5.99767208e-01 -2.74260134e-01 1.67816564e-01 1.62198275... | [7.2884745597839355, 1.8699862957000732] |
939d8ae4-9142-4a6a-b3fd-20a3c7522ec1 | towards-scalable-multi-view-reconstruction-of | 2306.03747 | null | https://arxiv.org/abs/2306.03747v1 | https://arxiv.org/pdf/2306.03747v1.pdf | Towards Scalable Multi-View Reconstruction of Geometry and Materials | In this paper, we propose a novel method for joint recovery of camera pose, object geometry and spatially-varying Bidirectional Reflectance Distribution Function (svBRDF) of 3D scenes that exceed object-scale and hence cannot be captured with stationary light stages. The input are high-resolution RGB-D images captured ... | ['Andreas Geiger', 'Joo Ho Lee', 'Andrei Neculai', 'Božidar Antić', 'Carolin Schmitt'] | 2023-06-06 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 2.69239217e-01 -3.05915564e-01 1.80639982e-01 -2.34272793e-01
-9.81796443e-01 -8.11264932e-01 2.58474827e-01 -2.77104646e-01
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6.32382557e-02 -3.77903789e-01 -8.22521210e-01 -4.94550407e-01
3.12666833e-01 6.55956507e-01 3.98386717e-01 2.35578045... | [9.284674644470215, -2.9167842864990234] |
c3635163-cc5a-40ee-a2a8-5b4ac090d7f8 | data-efficient-image-quality-assessment-with | 2304.04952 | null | https://arxiv.org/abs/2304.04952v1 | https://arxiv.org/pdf/2304.04952v1.pdf | Data-Efficient Image Quality Assessment with Attention-Panel Decoder | Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an ef... | ['Yan Zhang', 'Xiu Li', 'Haotian Liu', 'Xiawu Zheng', 'Yutao Liu', 'Runze Hu', 'Guanyi Qin'] | 2023-04-11 | null | null | null | null | ['blind-image-quality-assessment', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [-3.77478302e-02 -4.10016567e-01 3.24153900e-01 -3.95288974e-01
-9.17186618e-01 -3.95654649e-01 4.29197103e-01 -1.92767262e-01
-3.25130016e-01 3.54641646e-01 2.47408584e-01 -1.33477896e-01
-2.65467763e-01 -3.85858327e-01 -5.42752326e-01 -8.40035677e-01
4.49691951e-01 -9.19960067e-02 2.64418870e-01 -2.20466316... | [11.831747055053711, -1.84293532371521] |
ad1c216c-6ce5-40c5-973c-460737f747e0 | experimenting-with-additive-margins-for | 2306.03664 | null | https://arxiv.org/abs/2306.03664v1 | https://arxiv.org/pdf/2306.03664v1.pdf | Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification | Most state-of-the-art self-supervised speaker verification systems rely on a contrastive-based objective function to learn speaker representations from unlabeled speech data. We explore different ways to improve the performance of these methods by: (1) revisiting how positive and negative pairs are sampled through a "s... | ['Reda Dehak', 'Theo Lepage'] | 2023-06-06 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 2.70149976e-01 4.28501457e-01 -2.16707155e-01 -9.52844739e-01
-1.17719305e+00 -5.40965974e-01 8.35766971e-01 -9.83008966e-02
-5.36700547e-01 7.89734960e-01 2.43675888e-01 -2.52642661e-01
3.35544437e-01 -1.81965142e-01 -5.72702587e-01 -8.31762731e-01
-1.07597560e-01 1.31261468e-01 -1.16957776e-01 -8.20662677... | [14.322166442871094, 6.112907409667969] |
837e88fc-a49d-4916-a471-7bdc77cf9370 | video-and-accelerometer-based-motion-analysis | 1702.07772 | null | http://arxiv.org/abs/1702.07772v1 | http://arxiv.org/pdf/1702.07772v1.pdf | Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment | Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video ana... | ['Yachna Sharma', 'Aneeq Zia', 'Vinay Bettadapura', 'Eric L. Sarin', 'Irfan Essa'] | 2017-02-24 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [ 1.10904433e-01 -1.69865176e-01 -1.06461838e-01 2.51963418e-02
-6.70670867e-01 -3.95773917e-01 4.41350192e-01 7.33136535e-01
-7.77664542e-01 3.01280767e-01 4.15551841e-01 -3.90142828e-01
-9.97560084e-01 -5.07900059e-01 -3.11189830e-01 -7.22097397e-01
-4.83824760e-01 2.02597678e-02 2.27053583e-01 -5.14626801... | [14.04727554321289, -3.3572328090667725] |
aa387a2e-0dea-42a9-9311-ef082d4d2186 | democratizing-llms-for-low-resource-languages | 2306.11372 | null | https://arxiv.org/abs/2306.11372v1 | https://arxiv.org/pdf/2306.11372v1.pdf | Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts | Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. However, in low-resource languages, obtaining such hand-picked exemplars can still be challenging, where unsupervised techniques may be necessary. Moreover, competent generative capabilities of LLMs are observed only ... | ['Lidong Bing', 'Shafiq Joty', 'Sharifah Mahani Aljunied', 'Xuan-Phi Nguyen'] | 2023-06-20 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [ 3.66640300e-01 1.59458712e-01 -6.62600100e-01 -2.23593518e-01
-1.85712826e+00 -7.08012760e-01 9.87630904e-01 -2.65081003e-02
-5.17081380e-01 1.21852672e+00 7.66119599e-01 -4.31931823e-01
2.16418818e-01 -4.41470772e-01 -7.95366824e-01 -4.39356416e-01
3.10054570e-01 1.22376084e+00 -2.21077666e-01 -7.08245158... | [11.657715797424316, 10.081573486328125] |
cc56a7f4-bb04-40c5-aac6-d8340966900f | atrial-fibrillation-recurrence-risk | 2208.10550 | null | https://arxiv.org/abs/2208.10550v1 | https://arxiv.org/pdf/2208.10550v1.pdf | Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure | Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial fibrillation (AF) catheter ablation procedure (CAP). It is not easy to determine if CAP was successful without a long follow-up assessing for AF recurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a better management of CAP p... | ['Joachim A. Behar', 'Andrea Natale', 'Jason Lewen', 'Sanghamitra Mohanty', 'Sheina Gendelman', 'Eran Zvuloni'] | 2022-08-22 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.35118711e-01 -2.52201647e-01 -4.39986259e-01 -1.37548998e-01
-8.71842861e-01 -8.99271667e-01 -2.13004220e-02 6.54789269e-01
-2.78674036e-01 6.82634056e-01 2.26562127e-01 -1.15230727e+00
-6.08619571e-01 -5.42069554e-01 -4.92531396e-02 -4.45539325e-01
-7.89859772e-01 4.58526611e-01 -5.47318339e-01 6.06597662... | [14.28527545928955, 3.255077600479126] |
462cbfe1-aca1-4284-9622-2e6fa4905fc5 | spatio-temporal-multi-task-learning | 2106.11401 | null | https://arxiv.org/abs/2106.11401v1 | https://arxiv.org/pdf/2106.11401v1.pdf | Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation | Moving objects have special importance for Autonomous Driving tasks. Detecting moving objects can be posed as Moving Object Segmentation, by segmenting the object pixels, or Moving Object Detection, by generating a bounding box for the moving targets. In this paper, we present a Multi-Task Learning architecture, based ... | ['Ahmed El-Sallab', 'Eslam Mohamed'] | 2021-06-21 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 3.94682914e-01 9.14164484e-02 2.67402898e-03 -4.10187066e-01
-1.13274205e+00 -4.78463978e-01 8.04899275e-01 -2.58809537e-01
-8.70872676e-01 4.67301399e-01 -1.95061550e-01 -1.55386984e-01
6.21833466e-03 -3.89548659e-01 -1.04788375e+00 -8.36633146e-01
-1.35051161e-01 6.50514126e-01 1.25529575e+00 8.86415392... | [8.327638626098633, -1.273671269416809] |
c10348d2-db3d-4fb2-8713-1ccbaa0a872e | doc-nad-a-hybrid-deep-one-class-classifier | 2212.07558 | null | https://arxiv.org/abs/2212.07558v1 | https://arxiv.org/pdf/2212.07558v1.pdf | DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection | Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malici... | ['Marius Portmann', 'Siamak Layeghy', 'Wai Weng Lo', 'Gayan Kulatilleke', 'Mohanad Sarhan'] | 2022-12-15 | null | null | null | null | ['one-class-classifier', 'one-class-classification', 'network-intrusion-detection'] | ['methodology', 'miscellaneous', 'miscellaneous'] | [ 2.76006103e-01 -2.58653075e-01 1.08411033e-02 -5.94232559e-01
6.81270584e-02 -2.01301455e-01 9.42930162e-01 6.81444705e-01
-4.86217946e-01 4.37703311e-01 -7.82213449e-01 -7.24134147e-01
-4.87330317e-01 -8.71320903e-01 -2.64717996e-01 -5.76094091e-01
-5.80976725e-01 8.24906468e-01 4.73888129e-01 -2.38084733... | [5.302399635314941, 7.254771709442139] |
88bcc639-0304-4c42-89ab-09a220ecd956 | harim-evaluating-summary-quality-with | 2211.12118 | null | https://arxiv.org/abs/2211.12118v2 | https://arxiv.org/pdf/2211.12118v2.pdf | HaRiM$^+$: Evaluating Summary Quality with Hallucination Risk | One of the challenges of developing a summarization model arises from the difficulty in measuring the factual inconsistency of the generated text. In this study, we reinterpret the decoder overconfidence-regularizing objective suggested in (Miao et al., 2021) as a hallucination risk measurement to better estimate the q... | ['Yeonsoo Lee', 'Hyungjong Noh', 'Junghwa Lee', 'Jeong-in Hwang', 'Junsoo Park', 'Seonil Son'] | 2022-11-22 | null | null | null | null | ['automated-writing-evaluation'] | ['natural-language-processing'] | [ 4.38372754e-02 7.07418025e-01 -7.28959143e-02 -4.65688437e-01
-1.32048321e+00 -6.28612101e-01 8.34588051e-01 7.26104975e-01
-3.26751411e-01 8.50315809e-01 8.93149495e-01 -1.15341961e-01
2.15916825e-03 -3.22810292e-01 -2.82003760e-01 7.65624642e-02
3.30288559e-01 4.55067337e-01 -5.70262969e-02 -2.22623676... | [12.112894058227539, 9.281764030456543] |
dc32c0b2-c9b9-48d7-82ab-2d116ca02a0e | learning-spatio-temporal-specifications-for | 2112.10714 | null | https://arxiv.org/abs/2112.10714v1 | https://arxiv.org/pdf/2112.10714v1.pdf | Learning Spatio-Temporal Specifications for Dynamical Systems | Learning dynamical systems properties from data provides important insights that help us understand such systems and mitigate undesired outcomes. In this work, we propose a framework for learning spatio-temporal (ST) properties as formal logic specifications from data. We introduce SVM-STL, an extension of Signal Signa... | ['Calin Belta', 'Ron Weiss', 'Erfan Aasi', 'Suhail Alsalehi'] | 2021-12-20 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 3.73596549e-01 -4.96431217e-02 -4.37835991e-01 -4.24534261e-01
-4.58618343e-01 -9.14898753e-01 8.41940105e-01 1.09129339e-01
3.30635697e-01 9.25008178e-01 -2.17679486e-01 -8.28433454e-01
-7.69867480e-01 -5.19788563e-01 -8.15575242e-01 -7.10322618e-01
-6.27740324e-01 3.75264846e-02 5.05646527e-01 -2.09950522... | [4.693812370300293, 2.196000576019287] |
9fe588fd-9770-4a05-8b92-cc1a76fe9d11 | bkd-fedgnn-a-benchmark-for-classification | 2306.10351 | null | https://arxiv.org/abs/2306.10351v1 | https://arxiv.org/pdf/2306.10351v1.pdf | Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network | Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly growing research topic, as it integrates the strengths of graph neural networks and federated learning to enable advanced machine learning applications without direct access to sensitive data. Despite its advantages, the distributed nature of Fed... | ['Hao liu', 'Yansong Ning', 'Siqi Lai', 'Fan Liu'] | 2023-06-17 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-1.84950963e-01 -2.64248252e-01 -5.42201281e-01 1.14492655e-01
-4.65876818e-01 -1.14272285e+00 5.47659576e-01 2.61467069e-01
-6.19426593e-02 3.42744529e-01 2.60018203e-02 -1.02089202e+00
-2.69148737e-01 -1.14264178e+00 -6.79939806e-01 -5.25167644e-01
-7.61529982e-01 -1.40209362e-01 1.64056808e-01 -2.96991616... | [6.053534507751465, 7.315046787261963] |
7849d8a1-de06-4a69-8326-6714b8d0b38f | comparison-of-deep-object-detectors-on-a-new | 2212.06218 | null | https://arxiv.org/abs/2212.06218v1 | https://arxiv.org/pdf/2212.06218v1.pdf | Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset | Pedestrian safety is one primary concern in autonomous driving. The under-representation of vulnerable groups in today's pedestrian datasets points to an urgent need for a dataset of vulnerable road users. In this paper, we first introduce a new vulnerable pedestrian detection dataset, BG Vulnerable Pedestrian (BGVP) d... | ['Qing Tian', 'Tihitina Hade', 'Devansh Sharma'] | 2022-12-12 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-6.22459829e-01 -4.64520305e-02 -2.54572541e-01 -1.56437829e-01
-6.45223677e-01 -3.04261416e-01 4.94163007e-01 3.02553535e-01
-7.63109505e-01 8.88773799e-01 2.35101342e-01 -3.49708289e-01
3.63074124e-01 -1.06105113e+00 -6.02089405e-01 -5.90912223e-01
2.25308150e-01 6.40888512e-02 8.29848707e-01 -3.29869598... | [7.954323768615723, -0.7451386451721191] |
4fd1245f-f70f-4246-b740-b1b9e25a6367 | adaptive-name-entity-recognition-under-highly | 2003.10296 | null | https://arxiv.org/abs/2003.10296v1 | https://arxiv.org/pdf/2003.10296v1.pdf | Adaptive Name Entity Recognition under Highly Unbalanced Data | For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizati... | ['Pramod Rao', 'Duy Nguyen', 'Thong Nguyen'] | 2020-03-10 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.33357087e-01 2.31420875e-01 -6.76556444e-03 -6.60197020e-01
-4.52216625e-01 -3.95122349e-01 7.24252045e-01 3.45483571e-01
-1.01761258e+00 1.00471675e+00 2.73924589e-01 -3.26157898e-01
2.78027743e-01 -1.01032412e+00 -5.44826448e-01 -4.83082384e-01
5.41978329e-02 4.58960861e-01 2.65129387e-01 -1.27645537... | [9.701411247253418, 9.513784408569336] |
7ca19af4-65ff-4103-a331-ee740ee7c5b4 | few-could-be-better-than-all-feature-sampling | 2203.15221 | null | https://arxiv.org/abs/2203.15221v2 | https://arxiv.org/pdf/2203.15221v2.pdf | Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection | Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. However, these methods cannot well cope with scene text due to its extreme variance of scales and aspect ratios. In this paper, we present a ... | ['Xiang Bai', 'Guanglong Hu', 'Bo Jiang', 'Mingkun Yang', 'Hongye Liu', 'Wenqing Zhang', 'Jingqun Tang'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-text-detection', 'object-detection-in-aerial-images'] | ['computer-vision', 'computer-vision'] | [ 3.42899948e-01 -6.42366529e-01 2.41340801e-01 -2.95317709e-01
-6.20110691e-01 -3.42772692e-01 7.29131937e-01 2.29392543e-01
-2.85687208e-01 -1.16675599e-02 7.81858936e-02 6.25164211e-02
1.92916766e-02 -9.34985101e-01 -4.55623478e-01 -8.27316582e-01
5.30931711e-01 2.51645744e-01 8.52215827e-01 -7.67466500... | [12.072299003601074, 2.26851224899292] |
dae4b97a-4f46-49d7-961e-a9808bebeb6b | plant-species-richness-prediction-from-desis | 2301.01918 | null | https://arxiv.org/abs/2301.01918v1 | https://arxiv.org/pdf/2301.01918v1.pdf | Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models | The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Monitoring species compositional diversity across large spatial scales is challenging and time consuming. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Ear... | ['Shaun R. Levick', 'Simon Ferrier', 'Peyman Moghadam', 'Cindy Ong', 'Karel Mokany', 'Yiqing Guo'] | 2023-01-05 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 5.34572601e-01 -5.78075767e-01 -2.09582731e-01 1.64381191e-01
-2.31046557e-01 -1.00138736e+00 2.74333060e-01 1.41041577e-01
-2.05144808e-01 9.31633472e-01 9.51215625e-02 -8.20530057e-01
-6.00169837e-01 -1.09502733e+00 -5.14817946e-02 -8.92452657e-01
-5.12691379e-01 -1.68544471e-01 2.25505419e-02 -4.90586132... | [9.476608276367188, -1.6236141920089722] |
13081e71-f730-4674-ab30-1fa2e48139e1 | temporal-scalability-of-dynamic-volume-data | 2303.00379 | null | https://arxiv.org/abs/2303.00379v1 | https://arxiv.org/pdf/2303.00379v1.pdf | Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting | Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orie... | ['André Kaup', 'Thomas Richter', 'Niklas Pallast', 'Wolfgang Schnurrer'] | 2023-03-01 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.70343274e-01 2.55932420e-01 4.80019487e-02 -1.41345151e-02
-8.06990802e-01 1.13994963e-02 2.41208181e-01 3.68698686e-01
-6.86120272e-01 6.37256742e-01 9.40938294e-02 -7.91711807e-02
-2.46267155e-01 -9.08424675e-01 -5.98757982e-01 -7.48649120e-01
-4.36706185e-01 5.83254099e-01 5.71742117e-01 -2.53469288... | [11.52981185913086, -2.3275744915008545] |
ff67e857-c3d6-47d6-a3ef-26b05c73deaf | end-to-end-joint-target-and-non-target | 2306.02273 | null | https://arxiv.org/abs/2306.02273v1 | https://arxiv.org/pdf/2306.02273v1.pdf | End-to-End Joint Target and Non-Target Speakers ASR | This paper proposes a novel automatic speech recognition (ASR) system that can transcribe individual speaker's speech while identifying whether they are target or non-target speakers from multi-talker overlapped speech. Target-speaker ASR systems are a promising way to only transcribe a target speaker's speech by enrol... | ['Atsushi Ando', 'Nobukatsu Hojo', 'Takafumi Moriya', 'Satoshi Suzuki', 'Akihiko Takashima', 'Tomohiro Tanaka', 'Hiroshi Sato', 'Keita Suzuki', 'Mihiro Uchida', 'Mana Ihori', 'Saki Mizuno', 'Yoshihiko Yamazaki', 'Taiga Yamane', 'Naoki Makishima', 'Ryo Masumura'] | 2023-06-04 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.52095151e-01 2.50379622e-01 -3.96643318e-02 -5.63433647e-01
-1.28203630e+00 -6.94383264e-01 5.74981034e-01 -8.08237344e-02
-9.22331307e-03 3.00665647e-01 4.62494075e-01 -7.05329120e-01
5.58520198e-01 -3.43415022e-01 -2.97323406e-01 -7.56383538e-01
2.55240351e-01 7.09374607e-01 5.59018254e-02 -4.32479978... | [14.611682891845703, 6.352229118347168] |
032570e1-af52-4a23-97a2-beeb1e689bee | the-power-of-reuse-a-multi-scale-transformer | 2205.08579 | null | https://arxiv.org/abs/2205.08579v2 | https://arxiv.org/pdf/2205.08579v2.pdf | The Power of Fragmentation: A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation | Symbolic Music Generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. The learning of musical context is also related to the structural elements in music, i.e. intro, verse, and chorus, which are currently overlooked by ... | ['Xiaoya Fan', 'Shipei Liu', 'Guowei Wu'] | 2022-05-17 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.71115160e-01 -3.29798937e-01 1.12899534e-01 -1.27705425e-01
-6.18446648e-01 -7.60638654e-01 4.95498389e-01 -1.61917061e-01
-7.54046813e-02 4.51627225e-01 5.69302142e-01 6.90630376e-02
-4.42051403e-02 -7.87807107e-01 -6.84656501e-01 -6.35376513e-01
2.64907449e-01 2.28992239e-01 1.54636011e-01 -3.68850499... | [15.974295616149902, 5.523183822631836] |
1af3fef5-db25-40da-957c-904daa8d2c2d | test-time-training-for-out-of-distribution-1 | 1909.13231 | null | https://arxiv.org/abs/1909.13231v3 | https://arxiv.org/pdf/1909.13231v3.pdf | Test-Time Training with Self-Supervision for Generalization under Distribution Shifts | In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a predi... | ['Zhuang Liu', 'Yu Sun', 'Xiaolong Wang', 'Alexei A. Efros', 'Moritz Hardt', 'John Miller'] | 2019-09-29 | null | null | null | null | ['supervised-video-summarization', 'building-change-detection-for-remote-sensing', 'carla-map-leaderboard'] | ['computer-vision', 'miscellaneous', 'robots'] | [ 3.29851270e-01 -1.39321581e-01 -6.26071334e-01 -6.96108758e-01
-8.57968569e-01 -6.61198497e-01 6.17017388e-01 3.37517560e-01
-3.45788360e-01 9.23509061e-01 -3.52594197e-01 -4.74280894e-01
-1.87840253e-01 -6.67135894e-01 -7.14178443e-01 -7.31378853e-01
-1.93494439e-01 1.05726910e+00 4.70998138e-01 1.20602503... | [9.536076545715332, 3.273986339569092] |
1e289887-1bca-4cd8-8e8d-8c9ce6fc76f1 | fits-modeling-time-series-with-10k-parameters | 2307.03756 | null | https://arxiv.org/abs/2307.03756v1 | https://arxiv.org/pdf/2307.03756v1.pdf | FITS: Modeling Time Series with $10k$ Parameters | In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain. By discarding high-frequency component... | ['Qiang Xu', 'Ailing Zeng', 'Zhijian Xu'] | 2023-07-06 | null | null | null | null | ['anomaly-detection', 'time-series-forecasting', 'time-series'] | ['methodology', 'time-series', 'time-series'] | [-2.65232146e-01 -2.78132200e-01 -5.97528517e-02 -3.15186262e-01
-4.45645839e-01 -6.64218128e-01 3.09843004e-01 3.53116900e-01
-2.62237132e-01 3.70124549e-01 -1.27009094e-01 -7.29641259e-01
-1.96462974e-01 -6.97784603e-01 -6.24639750e-01 -4.08851683e-01
-6.73377812e-01 2.49912329e-02 9.34017599e-02 -1.39554530... | [7.1448822021484375, 2.98561429977417] |
cb1ddbf1-ea5c-4101-9db9-6529c27a9ce4 | relational-generalized-few-shot-learning | 1907.09557 | null | https://arxiv.org/abs/1907.09557v2 | https://arxiv.org/pdf/1907.09557v2.pdf | Relational Generalized Few-Shot Learning | Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only. Instead, we consider the extended setup of ... | ['Leonard Salewski', 'Xiahan Shi', 'Martin Schiegg', 'Zeynep Akata', 'Max Welling'] | 2019-07-22 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 5.60017705e-01 1.71654131e-02 -1.77887827e-01 -5.87048292e-01
-5.01907647e-01 -5.03782868e-01 7.23251104e-01 3.77968609e-01
-4.11521077e-01 6.14634871e-01 1.54059045e-02 1.32020712e-01
-2.96039730e-01 -1.14329743e+00 -5.69658935e-01 -6.52705371e-01
1.28820404e-01 4.68122184e-01 4.43362087e-01 -6.01265915... | [10.016674995422363, 2.80098032951355] |
ca5379ea-0200-45af-a16f-bc5be33e3d88 | multi-goal-reinforcement-learning | 2106.13687 | null | https://arxiv.org/abs/2106.13687v1 | https://arxiv.org/pdf/2106.13687v1.pdf | Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot | This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. They all follow a Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. To foster ope... | ['Liming Chen', 'Emmanuel Dellandréa', 'Nicolas Cazin', 'Quentin Gallouédec'] | 2021-06-25 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-7.54250944e-01 1.93450630e-01 -9.40000117e-02 -8.82493183e-02
-5.48404753e-01 -6.31125093e-01 5.51716149e-01 -5.65746203e-02
-5.66969872e-01 1.25841045e+00 -1.15521520e-01 -1.84204429e-01
-3.41178089e-01 -8.99304152e-01 -8.43025744e-01 -7.54608810e-01
-5.07234454e-01 8.71170998e-01 3.32537502e-01 -8.40378582... | [4.266605854034424, 1.2514803409576416] |
bc754494-83f1-4de7-b1f5-25452d1bd58a | multi-class-probabilistic-classification | null | null | https://proceedings.mlr.press/v60/manokhin17a.html | https://proceedings.mlr.press/v60/manokhin17a.html | Multi-class probabilistic classification using inductive and cross Venn-Abers predictors | Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems. We present a new approach to multi-class probability estimation by turning IVAPs and CVAPs into multi-class probabilistic predictors. The proposed multi-class ... | ['Valery Manokhin'] | 2017-06-16 | null | null | null | proceedings-of-the-sixth-workshop-on | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 5.25515638e-02 3.13998848e-01 -4.47914362e-01 -6.98557317e-01
-9.28870797e-01 -5.88445961e-01 8.14616024e-01 4.15485531e-01
-4.13705967e-02 1.33830643e+00 -3.21705252e-01 -6.66344523e-01
-8.70315969e-01 -9.95139480e-01 -7.57717013e-01 -8.13177943e-01
-1.61323566e-02 1.01659989e+00 6.01211965e-01 3.00955266... | [8.049118041992188, 4.182581901550293] |
b71de1e8-0676-4407-b4a5-61783b3bc1eb | query-resolution-for-conversational-search | 2005.11723 | null | https://arxiv.org/abs/2005.11723v1 | https://arxiv.org/pdf/2005.11723v1.pdf | Query Resolution for Conversational Search with Limited Supervision | In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational hi... | ['Evangelos Kanoulas', 'Nikos Voskarides', 'Pengjie Ren', 'Maarten de Rijke', 'Dan Li'] | 2020-05-24 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.29582274e-01 1.55224800e-01 -5.41592598e-01 -3.68882716e-01
-1.59179878e+00 -7.98835695e-01 1.00219297e+00 2.52710640e-01
-6.18676364e-01 8.73664975e-01 6.81624293e-01 -3.40886682e-01
-2.70279169e-01 -5.54836392e-01 -6.25671029e-01 -1.84534505e-01
4.05141443e-01 1.08152175e+00 3.68971854e-01 -7.76801944... | [12.129769325256348, 7.8326287269592285] |
489d5f2d-2362-4af5-9be3-c4aea935991b | enhancing-underwater-imagery-using-generative | 1801.04011 | null | http://arxiv.org/abs/1801.04011v1 | http://arxiv.org/pdf/1801.04011v1.pdf | Enhancing Underwater Imagery using Generative Adversarial Networks | Autonomous underwater vehicles (AUVs) rely on a variety of sensors -
acoustic, inertial and visual - for intelligent decision making. Due to its
non-intrusive, passive nature, and high information content, vision is an
attractive sensing modality, particularly at shallower depths. However, factors
such as light refract... | ['Cameron Fabbri', 'Junaed Sattar', 'Md Jahidul Islam'] | 2018-01-11 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 2.51690030e-01 3.99196059e-01 8.94984901e-01 -2.47736022e-01
-3.91904831e-01 -5.77144444e-01 2.73953825e-01 -3.07999030e-02
-7.40305960e-01 7.01279938e-01 1.05666690e-01 1.55910999e-01
1.77113846e-01 -8.75368953e-01 -9.57049847e-01 -8.41185689e-01
-1.86077043e-01 -9.47338641e-02 4.36581731e-01 -5.24075091... | [10.676161766052246, -3.5360777378082275] |
3893f8f3-bc68-4cec-87c5-cd077add7b75 | a-fast-network-exploration-strategy-to | 2202.02361 | null | https://arxiv.org/abs/2202.02361v1 | https://arxiv.org/pdf/2202.02361v1.pdf | A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting | Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibility and high accuracy. However, coming up with a suitable configuration for both accuracy requirements and hardware deployment is a challen... | ['Tinoosh Mohsenin', 'Arnab Neelim Mazumder'] | 2022-02-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.25895751e-02 -2.34835654e-01 -8.06162804e-02 -3.92001688e-01
-4.51739617e-02 -5.17710865e-01 1.07229084e-01 1.54747114e-01
-7.10735381e-01 4.15062696e-01 -4.89945561e-01 -8.44874203e-01
-4.07975048e-01 -8.63006949e-01 -2.43265748e-01 -5.33098936e-01
-1.53499603e-01 1.21307801e-02 1.02183551e-01 -1.60930261... | [8.3718900680542, 2.774735689163208] |
2d06c90a-c5f2-4b0d-be8b-b1095b697b4c | towards-real-time-and-energy-efficient | 2205.10653 | null | https://arxiv.org/abs/2205.10653v1 | https://arxiv.org/pdf/2205.10653v1.pdf | Towards real-time and energy efficient Siamese tracking -- a hardware-software approach | Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPU... | ['Tomasz Kryjak', 'Dominika Przewlocka-Rus'] | 2022-05-21 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-5.22428043e-02 -2.60723203e-01 -1.47209868e-01 1.70267299e-01
-8.27437863e-02 -6.10342324e-01 4.72702622e-01 -3.40396203e-02
-5.39081633e-01 3.02749306e-01 -4.95756000e-01 -4.22892451e-01
-1.35616109e-01 -6.31596565e-01 -5.61179757e-01 -6.82490647e-01
-2.49728635e-02 5.34148812e-01 6.98103309e-01 -2.03374043... | [8.540785789489746, -1.0773286819458008] |
62595f52-c730-4497-bb05-1a6f6ce7b30e | loss-guided-activation-for-action-recognition | 1812.04194 | null | http://arxiv.org/abs/1812.04194v1 | http://arxiv.org/pdf/1812.04194v1.pdf | Loss Guided Activation for Action Recognition in Still Images | One significant problem of deep-learning based human action recognition is
that it can be easily misled by the presence of irrelevant objects or
backgrounds. Existing methods commonly address this problem by employing
bounding boxes on the target humans as part of the input, in both training and
testing stages. This re... | ['ShaoDi You', 'Robby T. Tan', 'Lu Liu'] | 2018-12-11 | null | null | null | null | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 3.97953600e-01 -1.32212758e-01 1.36868760e-01 -3.86248946e-01
-5.88035941e-01 -3.07126611e-01 4.47668403e-01 -8.62559155e-02
-8.08073223e-01 8.40366066e-01 -1.41707314e-02 1.31054334e-02
1.45767704e-01 -6.64682031e-01 -9.31154490e-01 -8.65290582e-01
6.49932772e-02 3.27467591e-01 7.10691392e-01 -5.75682335... | [7.966427326202393, 0.2835901379585266] |
badb7c6d-1f9f-4c3d-8a4a-0ed4d24c9ef1 | low-light-enhancement-in-the-frequency-domain | 2306.16782 | null | https://arxiv.org/abs/2306.16782v1 | https://arxiv.org/pdf/2306.16782v1.pdf | Low-Light Enhancement in the Frequency Domain | Decreased visibility, intensive noise, and biased color are the common problems existing in low-light images. These visual disturbances further reduce the performance of high-level vision tasks, such as object detection, and tracking. To address this issue, some image enhancement methods have been proposed to increase ... | ['Zhi Jin', 'Hao Chen'] | 2023-06-29 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 4.16937977e-01 -5.61853051e-01 2.24726751e-01 -1.57121047e-01
-4.61175114e-01 -2.79190820e-02 2.05888346e-01 -3.45743150e-01
-5.44385076e-01 5.32844365e-01 1.61618620e-01 9.05630440e-02
-5.31866774e-02 -7.91611433e-01 -6.78247452e-01 -1.07891417e+00
3.92269194e-01 -7.69387186e-01 5.55761755e-01 -3.26597720... | [10.925824165344238, -2.4493536949157715] |
db379736-9f0c-4fff-a549-599e552be620 | rediscovering-hashed-random-projections-for | 2304.02481 | null | https://arxiv.org/abs/2304.02481v2 | https://arxiv.org/pdf/2304.02481v2.pdf | Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings | Training and inference on edge devices often requires an efficient setup due to computational limitations. While pre-computing data representations and caching them on a server can mitigate extensive edge device computation, this leads to two challenges. First, the amount of storage required on the server that scales l... | ['Iryna Gurevych', 'Alexander Geyken', 'Ji-Ung Lee', 'Ulf A. Hamster'] | 2023-03-13 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'sentence-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.04568392e-01 -2.53982902e-01 -2.93691218e-01 -2.74054885e-01
-9.08094347e-01 -4.37753469e-01 8.03997740e-02 4.57091480e-01
-5.82937419e-01 5.23568034e-01 9.99163017e-02 -7.81762779e-01
2.46946409e-01 -1.13380158e+00 -6.39438808e-01 -4.45871949e-01
8.11253861e-02 3.24311644e-01 5.66623174e-02 2.52773345... | [8.627456665039062, 3.382085084915161] |
4583f14a-93f7-4c69-afc3-cebbef5414ad | global-visual-localization-in-lidar-maps | 1910.04871 | null | https://arxiv.org/abs/1910.04871v2 | https://arxiv.org/pdf/1910.04871v2.pdf | Global visual localization in LiDAR-maps through shared 2D-3D embedding space | Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonomous driving field. While most vision-based approaches match an image w.r.t. an image database, global visual localization within LiDAR-maps ... | ['Simone Fontana', 'Matteo Vaghi', 'Daniele Cattaneo', 'Domenico Giorgio Sorrenti', 'Augusto Luis Ballardini'] | 2019-10-02 | null | null | null | null | ['image-to-3d'] | ['computer-vision'] | [-1.95754647e-01 -2.97840655e-01 -1.90969124e-01 -7.07274377e-01
-5.43233573e-01 -5.41176379e-01 8.27110469e-01 3.54015380e-01
-8.63503098e-01 5.64716160e-01 -2.78006047e-01 -6.24365732e-02
-1.49014696e-01 -9.93815184e-01 -9.50557113e-01 -4.94350106e-01
-3.39833237e-02 5.70558488e-01 2.96772897e-01 -1.34483948... | [7.582420349121094, -2.051384210586548] |
95f49e66-aa2c-48dc-9dda-98cc65785ad2 | aedfact-scientific-fact-checking-made-easier | 2305.07796 | null | https://arxiv.org/abs/2305.07796v1 | https://arxiv.org/pdf/2305.07796v1.pdf | aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions | In this highly digitised world, fake news is a challenging problem that can cause serious harm to society. Considering how fast fake news can spread, automated methods, tools and services for assisting users to do fact-checking (i.e., fake news detection) become necessary and helpful, for both professionals, such as jo... | ['Shujun Li', 'Lisa Bonheme', 'Haiyue Yuan', 'Sophie Kaleba', 'Meryem Bagriacik', 'Jason R. C. Nurse', 'Enes Altuncu'] | 2023-05-12 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-5.18580198e-01 2.78907418e-01 -4.76117194e-01 -2.51774415e-02
-7.78554082e-01 -8.83768201e-01 4.23473954e-01 7.28377879e-01
-3.46538365e-01 7.43111014e-01 3.40880632e-01 -7.74630666e-01
2.59624720e-01 -8.11483502e-01 -6.32943034e-01 -5.51237762e-02
5.61160207e-01 1.76983088e-01 7.18976736e-01 -4.72059906... | [8.143113136291504, 10.218208312988281] |
33bde67b-8028-4a4f-91d4-e4b10f7d7357 | msfa-frequency-aware-transformer-for | 2303.13404 | null | https://arxiv.org/abs/2303.13404v1 | https://arxiv.org/pdf/2303.13404v1.pdf | MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing | Hyperspectral imaging systems that use multispectral filter arrays (MSFA) capture only one spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-measured components. While deep learning methods have shown promise in this area, they still suffer from several challenges, including limited... | ['Wilfried Philips', 'Hiep Luong', 'Hongyan zhang', 'Yongyong Chen', 'JieZhang Cao', 'Shaoguang Huang', 'Kai Feng', 'Haijin Zeng'] | 2023-03-23 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 5.63283265e-01 -4.66684341e-01 3.02089781e-01 -2.11269766e-01
-1.18100202e+00 -2.88410276e-01 3.28720629e-01 -4.63864267e-01
1.97201185e-02 7.07640231e-01 5.63834488e-01 8.54474679e-02
-6.18834913e-01 -7.54254043e-01 -8.10094118e-01 -1.21067595e+00
-5.35280854e-02 -3.74091893e-01 -1.62475914e-01 -8.67494494... | [10.32061767578125, -2.045870780944824] |
114d9999-2a98-4520-a055-a9f00c37050e | mining-fine-grained-opinions-on-closed | 1708.02420 | null | http://arxiv.org/abs/1708.02420v1 | http://arxiv.org/pdf/1708.02420v1.pdf | Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN | Video reviews are the natural evolution of written product reviews. In this
paper we target this phenomenon and introduce the first dataset created from
closed captions of YouTube product review videos as well as a new attention-RNN
model for aspect extraction and joint aspect extraction and sentiment
classification. O... | ['Edison Marrese-Taylor', 'Yutaka Matsuo', 'Jorge A. Balazs'] | 2017-08-08 | mining-fine-grained-opinions-on-closed-1 | https://aclanthology.org/W17-5213 | https://aclanthology.org/W17-5213.pdf | ws-2017-9 | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.31588021e-01 3.70620936e-01 -4.63285476e-01 -4.89483118e-01
-8.22564304e-01 -6.85570002e-01 9.38401639e-01 1.23266637e-01
-5.59180200e-01 3.85917127e-01 5.41936815e-01 -5.10168135e-01
4.11501586e-01 -3.94364387e-01 -7.79104650e-01 -4.25068110e-01
2.31060654e-01 1.73462853e-01 -3.66811082e-02 -3.71896774... | [11.424421310424805, 6.71360445022583] |
8479b528-11e5-4947-8333-634d81a2ae1c | estimating-reflectance-layer-from-a-single | 2211.14751 | null | https://arxiv.org/abs/2211.14751v2 | https://arxiv.org/pdf/2211.14751v2.pdf | Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning | Estimating reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often render an inaccurate estimate of the reflectance layer. Therefore, we propose a two-stage learning method, including reflectance guidance and a Sh... | ['Robby T. Tan', 'Wenhan Yang', 'Ruoteng Li', 'Yeying Jin'] | 2022-11-27 | null | null | null | null | ['shadow-removal', 'intrinsic-image-decomposition', 'highlight-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.75058115e-01 3.43008759e-03 1.86012208e-01 -6.00862503e-01
-1.69125304e-01 -1.92400232e-01 4.83605862e-01 -4.57080752e-01
-2.03083977e-01 5.91197789e-01 1.54309824e-01 -1.72392763e-02
2.19592571e-01 -7.64442801e-01 -6.60520971e-01 -9.85252976e-01
3.89402628e-01 1.51547641e-01 4.76913780e-01 -9.89575312... | [10.72258186340332, -3.657235860824585] |
f1780913-6c1f-42e1-a6dd-30751fa76f3e | weakly-supervised-temporal-article-grounding | 2210.12444 | null | https://arxiv.org/abs/2210.12444v2 | https://arxiv.org/pdf/2210.12444v2.pdf | Weakly-Supervised Temporal Article Grounding | Given a long untrimmed video and natural language queries, video grounding (VG) aims to temporally localize the semantically-aligned video segments. Almost all existing VG work holds two simple but unrealistic assumptions: 1) All query sentences can be grounded in the corresponding video. 2) All query sentences for the... | ['Shih-Fu Chang', 'Heng Ji', 'Hammad Ayyubi', 'Christopher Thomas', 'Guangxing Han', 'Xudong Lin', 'Brian Chen', 'Yulei Niu', 'Long Chen'] | 2022-10-22 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-6.41512871e-02 9.80342627e-02 -5.68204761e-01 -2.86255836e-01
-1.09125853e+00 -5.95654249e-01 3.65030140e-01 -4.96551432e-02
-1.62692681e-01 6.29981697e-01 4.18305576e-01 6.65948167e-02
5.06559871e-02 -4.02655423e-01 -1.03528416e+00 -4.68378693e-01
-1.78837329e-01 5.18281199e-02 4.23264951e-01 -2.05217510... | [9.988091468811035, 0.7167975306510925] |
5eefeefb-4e61-4b0f-9ecc-a2fd418dc55b | a-method-for-incremental-discovery-of | 2302.08205 | null | https://arxiv.org/abs/2302.08205v1 | https://arxiv.org/pdf/2302.08205v1.pdf | A method for incremental discovery of financial event types based on anomaly detection | Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios. This limitation... | ['Lan Huang', 'Rui Zhang', 'Zhenhai Guan', 'Zixu Li', 'Dianyue Gu'] | 2023-02-16 | null | null | null | null | ['deep-clustering', 'keyword-extraction', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-4.56180751e-01 -1.57099590e-01 4.39467058e-02 -5.20295382e-01
-2.03656629e-01 -5.54347456e-01 4.06770438e-01 7.06392050e-01
-1.82353333e-01 5.00227094e-01 2.51752824e-01 -2.80312985e-01
-3.42861235e-01 -1.21387601e+00 -4.07072157e-01 -4.52929258e-01
-2.24294052e-01 7.21419811e-01 4.11015362e-01 2.30200037... | [7.290308475494385, 2.7259838581085205] |
97e46c9c-3c71-4b73-aa68-90b8f009368c | a-modular-framework-for-reinforcement | 2208.06244 | null | https://arxiv.org/abs/2208.06244v1 | https://arxiv.org/pdf/2208.06244v1.pdf | A Modular Framework for Reinforcement Learning Optimal Execution | In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups. Rather than focusing on agents and optimization methods, we foc... | ['Florin Dascalu', 'Christoph Auth', 'Fernando de Meer Pardo'] | 2022-08-11 | null | null | null | null | ['algorithmic-trading', 'stock-market-prediction'] | ['time-series', 'time-series'] | [-1.25119314e-01 -3.06953024e-02 -1.93985552e-01 -2.74878144e-01
-3.38963658e-01 -8.00275445e-01 7.03708470e-01 3.32554042e-01
-7.78626382e-01 8.70362461e-01 -2.77639359e-01 -5.28162241e-01
-4.84426111e-01 -1.00203502e+00 -7.81767011e-01 -5.37484825e-01
-5.53082645e-01 1.10076702e+00 4.60077748e-02 -3.23299140... | [4.455404281616211, 2.545891761779785] |
f094564e-4183-4d9c-881e-585dc114d21a | conceptual-text-region-network-cognition | 2103.09179 | null | https://arxiv.org/abs/2103.09179v1 | https://arxiv.org/pdf/2103.09179v1.pdf | Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection | Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: 1) current label generation techniques are mostly empirical and lack theoretical support, discouraging elaborate label design; 2) as a resu... | ['Amir Hussain', 'Zhiyuan Tan', 'Liangfu Lu', 'Chenwei Cui'] | 2021-03-16 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.63615763e-01 7.26414397e-02 -2.01456413e-01 -1.77041769e-01
-5.74217677e-01 -4.77415025e-01 8.04007471e-01 2.07700998e-01
-3.21499646e-01 2.37284154e-01 5.14272042e-02 -3.74638498e-01
5.60652465e-02 -7.68778622e-01 -3.00977826e-01 -4.55394119e-01
5.08497834e-01 4.57885355e-01 6.83283150e-01 5.25644980... | [12.005745887756348, 2.2999415397644043] |
61dfaacf-f824-4cb0-a92a-51e73c10f55a | what-can-we-learn-about-a-generated-image | 2210.06257 | null | https://arxiv.org/abs/2210.06257v1 | https://arxiv.org/pdf/2210.06257v1.pdf | What can we learn about a generated image corrupting its latent representation? | Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effect... | ['Shadi Albarqouni', 'Nassir Navab', 'Slobodan Ilic', 'Aarushi Gupta', 'Agnieszka Tomczak'] | 2022-10-12 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 8.02310646e-01 4.32494432e-01 -7.03890771e-02 -3.16293210e-01
-9.53167021e-01 -7.53313363e-01 4.87420440e-01 -1.75173193e-01
-1.52846128e-01 9.04047251e-01 1.66921853e-03 -3.13926339e-01
4.86847460e-01 -7.12120831e-01 -1.07357371e+00 -1.20857954e+00
4.23804998e-01 4.11261290e-01 -9.25903916e-02 2.01913834... | [14.026626586914062, -2.0008368492126465] |
f814fa93-ec93-415d-b078-b12db2d2f6e3 | less-is-more-learning-prominent-and-diverse | 1611.09921 | null | http://arxiv.org/abs/1611.09921v2 | http://arxiv.org/pdf/1611.09921v2.pdf | Less is More: Learning Prominent and Diverse Topics for Data Summarization | Statistical topic models efficiently facilitate the exploration of
large-scale data sets. Many models have been developed and broadly used to
summarize the semantic structure in news, science, social media, and digital
humanities. However, a common and practical objective in data exploration tasks
is not to enumerate a... | ['Qiaozhu Mei', 'Ming Zhang', 'Jian Tang', 'Cheng Li'] | 2016-11-29 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-2.25968763e-01 4.81371433e-01 -4.17255700e-01 -1.28139272e-01
-8.25570285e-01 -2.74351150e-01 6.85517609e-01 6.30844593e-01
-1.98110178e-01 8.76908123e-01 6.24463618e-01 1.06513888e-01
-3.25092405e-01 -1.03501260e+00 -4.40670699e-01 -6.33941352e-01
-3.36253978e-02 6.88020229e-01 5.47809839e-01 -1.25635907... | [10.365283012390137, 6.915014266967773] |
48f5cfd6-6d07-4e5c-b872-05a54398399a | explainable-label-flipping-attacks-on-human | 2302.04109 | null | https://arxiv.org/abs/2302.04109v1 | https://arxiv.org/pdf/2302.04109v1.pdf | Explainable Label-flipping Attacks on Human Emotion Assessment System | This paper's main goal is to provide an attacker's point of view on data poisoning assaults that use label-flipping during the training phase of systems that use electroencephalogram (EEG) signals to evaluate human emotion. To attack different machine learning classifiers such as Adaptive Boosting (AdaBoost) and Random... | ['Chan Yeob Yeun', 'Ernesto Damiani', 'Ahmed Y. Al Hammadi', 'Zhibo Zhang'] | 2023-02-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.42201528e-02 -1.30382687e-01 1.83509469e-01 -6.68795884e-01
5.60785597e-03 -8.11809599e-01 4.49806154e-01 2.35053807e-01
-5.36671340e-01 1.15164709e+00 -3.40813667e-01 -6.17049158e-01
-3.06700677e-01 -4.58398551e-01 -4.96697932e-01 -7.13493288e-01
-3.42216194e-01 1.98729023e-01 -3.67573231e-01 -2.47606799... | [13.253315925598145, 3.1123692989349365] |
1a321d4b-e530-4e1a-a378-fe79cbcb94a6 | understanding-neural-architecture-search | 1904.00438 | null | https://arxiv.org/abs/1904.00438v2 | https://arxiv.org/pdf/1904.00438v2.pdf | Understanding Neural Architecture Search Techniques | Automatic methods for generating state-of-the-art neural network architectures without human experts have generated significant attention recently. This is because of the potential to remove human experts from the design loop which can reduce costs and decrease time to model deployment. Neural architecture search (NAS)... | ['Jonathan Lorraine', 'George Adam'] | 2019-03-31 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 3.59730244e-01 6.37173414e-01 -1.34791464e-01 -1.99347273e-01
7.87879247e-03 -6.16188049e-01 5.51145256e-01 -1.89723983e-01
-2.29666457e-01 4.13743585e-01 2.68127143e-01 -7.43526697e-01
-2.24107236e-01 -4.81986433e-01 -6.78916991e-01 -4.72576201e-01
1.45557031e-01 4.91256982e-01 1.80463120e-01 -2.21597522... | [8.424788475036621, 3.3884437084198] |
e7875d80-6b97-4064-bbcd-8be4e15c0e6a | synthetic-temporal-anomaly-guided-end-to-end | 2110.09768 | null | https://arxiv.org/abs/2110.09768v1 | https://arxiv.org/pdf/2110.09768v1.pdf | Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection | Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data. At test time, the AE is then expected to reconstruct the normal input well while rec... | ['Seung-Ik Lee', 'Muhammad Zaigham Zaheer', 'Marcella Astrid'] | 2021-10-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 5.52307725e-01 7.81402178e-03 1.52153715e-01 -7.91017115e-02
-4.68448788e-01 -2.62926340e-01 5.48220754e-01 -3.33856829e-02
-3.88206467e-02 3.77767026e-01 -1.53216884e-01 -2.05015406e-01
4.29878145e-01 -7.88372874e-01 -1.02695477e+00 -7.18234777e-01
-3.42709363e-01 1.05943680e-01 2.01287791e-01 -1.28728047... | [7.713048458099365, 2.011918067932129] |
69b867c0-0eeb-416d-8394-8ad1d9b34d9d | rxfood-plug-in-rgb-x-fusion-for-object-of | 2306.12621 | null | https://arxiv.org/abs/2306.12621v1 | https://arxiv.org/pdf/2306.12621v1.pdf | RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection | The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB input and another type of data input to resolve specific problems, have become a popular research topic in multimedia. A crucial part in two-bra... | ['Hongkai Yu', 'Yuewei Lin', 'Tianyun Zhang', 'Qing Guo', 'Jinlong Li', 'Jin Ma'] | 2023-06-22 | null | null | null | null | ['image-manipulation-detection', 'rgb-d-salient-object-detection', 'image-manipulation', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.61890173e-01 -1.98429003e-01 -1.09960474e-01 -2.76917428e-01
-7.23664939e-01 -7.97173232e-02 1.01216957e-01 -3.56683172e-02
-3.73698294e-01 3.69499147e-01 2.05026478e-01 -6.04700251e-03
-1.56070158e-01 -6.36023462e-01 -7.86436439e-01 -1.08787310e+00
5.06726384e-01 -6.63299620e-01 2.85764903e-01 -5.68929732... | [9.683828353881836, -0.8863015174865723] |
86f6bae0-075d-4248-9f86-c29adeb75ea4 | detect-any-shadow-segment-anything-for-video | 2305.16698 | null | https://arxiv.org/abs/2305.16698v1 | https://arxiv.org/pdf/2305.16698v1.pdf | Detect Any Shadow: Segment Anything for Video Shadow Detection | Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to classify shadows as background, resulting in poor segmentation performance for shadow detection task. In this paper, we propose an simple but effective approach for fine tuning SAM to detect sh... | ['Houqiang Li', 'Yunyao Mao', 'Wengang Zhou', 'Yonghui Wang'] | 2023-05-26 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.22425240e-01 -1.15139253e-01 -1.49094522e-01 -3.69704247e-01
-3.66272837e-01 -2.84965008e-01 1.24350242e-01 -3.67629021e-01
-3.52444589e-01 7.35374629e-01 5.06535061e-02 -4.27986264e-01
4.28378135e-01 -6.01090550e-01 -6.94090307e-01 -8.54503870e-01
-6.06530011e-02 -4.08401527e-02 1.02620995e+00 -8.83191265... | [10.836583137512207, -4.1027116775512695] |
2bcdc4bb-bb63-41e0-88ac-b7f069900614 | meteornet-deep-learning-on-dynamic-3d-point | 1910.09165 | null | https://arxiv.org/abs/1910.09165v2 | https://arxiv.org/pdf/1910.09165v2.pdf | MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences | Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called $MeteorNet$ for learning representations for dynamic 3D point cloud sequences. Different from previous work that adopts a grid-based representation and applies 3D or 4D c... | ['Xingyu Liu', 'Mengyuan Yan', 'Jeannette Bohg'] | 2019-10-21 | meteornet-deep-learning-on-dynamic-3d-point-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_MeteorNet_Deep_Learning_on_Dynamic_3D_Point_Cloud_Sequences_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_MeteorNet_Deep_Learning_on_Dynamic_3D_Point_Cloud_Sequences_ICCV_2019_paper.pdf | iccv-2019-10 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.36461475e-01 -4.68741417e-01 -1.01535739e-02 -2.51193464e-01
-2.65383929e-01 -6.79637849e-01 8.94513845e-01 4.43523712e-02
-6.55176878e-01 2.21606433e-01 -2.10676178e-01 -5.08623600e-01
2.55766809e-01 -9.84926879e-01 -9.97174799e-01 -4.33527172e-01
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e8aa969a-dd1e-496a-9f41-2eb53438315f | mirrorflow-exploiting-symmetries-in-joint | 1708.05355 | null | http://arxiv.org/abs/1708.05355v1 | http://arxiv.org/pdf/1708.05355v1.pdf | MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation | Optical flow estimation is one of the most studied problems in computer
vision, yet recent benchmark datasets continue to reveal problem areas of
today's approaches. Occlusions have remained one of the key challenges. In this
paper, we propose a symmetric optical flow method to address the well-known
chicken-and-egg re... | ['Junhwa Hur', 'Stefan Roth'] | 2017-08-17 | mirrorflow-exploiting-symmetries-in-joint-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_paper.pdf | iccv-2017-10 | ['occlusion-estimation'] | ['computer-vision'] | [-2.27355495e-01 -5.59454322e-01 -2.45783821e-01 -2.74148971e-01
-1.29702121e-01 -5.73066473e-01 3.89822900e-01 -3.25385183e-01
-2.02445313e-01 7.02399611e-01 4.52932030e-01 9.91209447e-02
-4.88890819e-02 -2.85440445e-01 -5.84981263e-01 -6.50466263e-01
1.02575812e-02 -3.58798131e-02 1.63072318e-01 -3.12024597... | [8.848747253417969, -1.7995994091033936] |
c5528606-fc8e-47ed-b4b9-eefaedf1c285 | light-field-distortion-feature-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Maeno_Light_Field_Distortion_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Maeno_Light_Field_Distortion_2013_CVPR_paper.pdf | Light Field Distortion Feature for Transparent Object Recognition | Current object-recognition algorithms use local features, such as scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), for visually learning to recognize objects. These approaches though cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features ... | ['Rin-ichiro Taniguchi', 'Atsushi Shimada', 'Hajime Nagahara', 'Kazuki Maeno'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['transparent-objects'] | ['computer-vision'] | [ 0.36359677 -0.6056713 0.3099673 -0.48933047 -0.08691908 -0.596984
0.5740817 -0.03556548 -0.21201707 0.37173414 -0.1189862 0.13417025
-0.05595486 -0.9250634 -0.64569175 -0.90581983 -0.10226993 0.10485233
0.6906654 0.03194567 0.60292584 1.1384614 -1.9207026 0.386445
0.37774593 1.0578722 0.3105... | [9.910962104797363, -2.7299954891204834] |
51441821-df4b-4f3c-adf8-c35622dc5ceb | vowel-based-meeteilon-dialect-identification | 2107.13419 | null | https://arxiv.org/abs/2107.13419v1 | https://arxiv.org/pdf/2107.13419v1.pdf | Vowel-based Meeteilon dialect identification using a Random Forest classifier | This paper presents a vowel-based dialect identification system for Meeteilon. For this work, a vowel dataset is created by using Meeteilon Speech Corpora available at Linguistic Data Consortium for Indian Languages (LDC-IL). Spectral features such as formant frequencies (F1, F1 and F3) and prosodic features such as pi... | ['Kabita Thaoroijam', 'Thangjam Clarinda Devi'] | 2021-07-26 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-5.01434326e-01 -3.55219841e-01 -6.28456399e-02 -4.78069246e-01
-5.34432113e-01 -9.66378868e-01 3.88427556e-01 2.33696297e-01
-1.23547129e-01 6.72523499e-01 6.51760876e-01 -2.69424260e-01
-9.40987468e-02 -5.79931021e-01 4.13538665e-01 -6.83786631e-01
1.04667433e-02 2.48571083e-01 -1.75956815e-01 -5.52458525... | [14.52737808227539, 6.700427055358887] |
8fc66c65-9c15-4c9f-953d-d61f6c126855 | clustering-based-contrastive-learning-for | 2004.02195 | null | https://arxiv.org/abs/2004.02195v1 | https://arxiv.org/pdf/2004.02195v1.pdf | Clustering based Contrastive Learning for Improving Face Representations | A good clustering algorithm can discover natural groupings in data. These groupings, if used wisely, provide a form of weak supervision for learning representations. In this work, we present Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that uses labels obtained fr... | ['Rainer Stiefelhagen', 'Vivek Sharma', 'M. Saquib Sarfraz', 'Makarand Tapaswi'] | 2020-04-05 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.14648327e-01 -2.02131182e-01 -3.96070510e-01 -8.85400236e-01
-7.37318993e-01 -4.38809693e-01 7.84813881e-01 -2.25519195e-01
-9.05632004e-02 1.74110293e-01 1.97535932e-01 9.44624171e-02
-1.86145395e-01 -5.21333404e-02 -5.82040489e-01 -7.99313188e-01
-6.60220444e-01 4.36715275e-01 -2.37214595e-01 2.14842886... | [13.458189964294434, 1.1110275983810425] |
af3505b3-4f89-4f4d-8fbb-4106d4f0d444 | bimal-bijective-maximum-likelihood-approach | 2108.03267 | null | https://arxiv.org/abs/2108.03267v1 | https://arxiv.org/pdf/2108.03267v1.pdf | BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation | Semantic segmentation aims to predict pixel-level labels. It has become a popular task in various computer vision applications. While fully supervised segmentation methods have achieved high accuracy on large-scale vision datasets, they are unable to generalize on a new test environment or a new domain well. In this wo... | ['Khoa Luu', 'Chase Rainwater', 'Son Lam Phung', 'Ngan Le', 'Chi Nhan Duong', 'Thanh-Dat Truong'] | 2021-08-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Truong_BiMaL_Bijective_Maximum_Likelihood_Approach_to_Domain_Adaptation_in_Semantic_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Truong_BiMaL_Bijective_Maximum_Likelihood_Approach_to_Domain_Adaptation_in_Semantic_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-segmentation'] | ['computer-vision'] | [ 4.55469340e-01 1.04767151e-01 3.03799156e-02 -5.54510355e-01
-9.82703269e-01 -5.05235434e-01 7.06192970e-01 -1.24051891e-01
-6.27239764e-01 9.42442298e-01 -3.16070735e-01 6.52126744e-02
8.36712271e-02 -6.90550804e-01 -8.18610966e-01 -7.92726696e-01
3.25705022e-01 5.85340142e-01 5.86951256e-01 1.79502532... | [9.760839462280273, 1.3258408308029175] |
e52272a7-83fa-4564-9dd5-cc1edf0eadca | spotting-rumors-via-novelty-detection | 1611.06322 | null | http://arxiv.org/abs/1611.06322v1 | http://arxiv.org/pdf/1611.06322v1.pdf | Spotting Rumors via Novelty Detection | Rumour detection is hard because the most accurate systems operate
retrospectively, only recognizing rumours once they have collected repeated
signals. By then the rumours might have already spread and caused harm. We
introduce a new category of features based on novelty, tailored to detect
rumours early on. To compens... | ['Yumeng Qin', 'Dominik Wurzer', 'Cunchen Tang', 'Victor Lavrenko'] | 2016-11-19 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-1.86528698e-01 1.05327845e-01 -3.77807915e-01 -1.29244551e-01
-2.01218367e-01 -3.86537671e-01 1.28662908e+00 1.00039101e+00
-2.53515482e-01 1.03033996e+00 6.03132188e-01 -2.97318399e-02
7.87945390e-02 -9.01309013e-01 -7.91059673e-01 -2.53214896e-01
-4.82991844e-01 3.45993340e-01 4.51287866e-01 -5.34931660... | [8.22209358215332, 10.123135566711426] |
0ca41a58-74e6-4660-a8d2-fbbbe71ef7dd | attention-is-all-you-need-for-videos-self | 1906.02792 | null | https://arxiv.org/abs/1906.02792v1 | https://arxiv.org/pdf/1906.02792v1.pdf | Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers | Video Captioning and Summarization have become very popular in the recent years due to advancements in Sequence Modelling, with the resurgence of Long-Short Term Memory networks (LSTMs) and introduction of Gated Recurrent Units (GRUs). Existing architectures extract spatio-temporal features using CNNs and utilize eithe... | ['Tushar Dobhal', 'Siyang Wang', 'Manjot Bilkhu'] | 2019-06-06 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 2.58496046e-01 6.30032271e-02 -8.57613906e-02 -2.19836310e-01
-4.26419675e-01 -2.67011613e-01 9.26991880e-01 -2.40387172e-01
-2.59047449e-01 6.53583586e-01 9.70497549e-01 6.67874562e-03
2.02639490e-01 -4.26025271e-01 -7.52156556e-01 -4.63715822e-01
-2.59021610e-01 1.42518789e-01 2.65987158e-01 -1.09501705... | [10.47210693359375, 0.6509080529212952] |
8d063305-33c3-4ff2-a480-f494700cc93a | deploying-machine-learning-models-to-ahead-of | 2304.04842 | null | https://arxiv.org/abs/2304.04842v2 | https://arxiv.org/pdf/2304.04842v2.pdf | Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM | In the past few years, more and more AI applications have been applied to edge devices. However, models trained by data scientists with machine learning frameworks, such as PyTorch or TensorFlow, can not be seamlessly executed on edge. In this paper, we develop an end-to-end code generator parsing a pre-trained model t... | ['Christian Mayr', 'Johannes Partzsch', 'Xinyue Shi', 'Liyuan Guo', 'Matthias Jobst', 'Chen Liu'] | 2023-04-10 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.37620613e-02 -1.15928859e-01 -4.46523935e-01 -4.87985909e-01
-3.29176560e-02 -3.69586557e-01 2.66035110e-01 -2.56011099e-01
-1.92898527e-01 2.17744187e-01 -3.18990760e-02 -1.12359130e+00
5.99702537e-01 -8.87278795e-01 -7.33447611e-01 -2.06049010e-01
1.66040614e-01 2.03105971e-01 2.49136761e-02 6.89247176... | [8.439223289489746, 2.967254638671875] |
a86547c3-bea8-425f-a6fc-dc43ff27861c | building-extraction-from-remote-sensing | null | null | https://www.mdpi.com/2072-4292/13/21/4441 | https://www.mdpi.com/2072-4292/13/21/4441 | Building Extraction from Remote Sensing Images with Sparse Token Transformers | Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from hi... | ['Zhenwei Shi', 'Zhengxia Zou', 'Keyan Chen'] | 2021-11-05 | null | null | null | remote-sens-2021-11-1 | ['building-change-detection-for-remote-sensing', 'extracting-buildings-in-remote-sensing-images'] | ['miscellaneous', 'miscellaneous'] | [ 2.80356020e-01 -4.33186501e-01 1.54529646e-01 -2.50675261e-01
-7.92324066e-01 -3.86747599e-01 4.86380368e-01 6.53359368e-02
-3.90938908e-01 4.74407107e-01 -5.32229841e-02 -2.07777351e-01
-2.32870474e-01 -1.61340797e+00 -8.73202562e-01 -8.68407905e-01
-2.70959616e-01 -1.08061515e-01 3.58130664e-01 9.78181511... | [9.331137657165527, -1.1854742765426636] |
c7430bab-2bd5-4e16-8672-e18277ab9a1b | visual-realism-assessment-for-face-swap | 2302.00918 | null | https://arxiv.org/abs/2302.00918v1 | https://arxiv.org/pdf/2302.00918v1.pdf | Visual Realism Assessment for Face-swap Videos | Deep-learning based face-swap videos, also known as deep fakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the as sessment of their visual realis... | ['Jing Dong', 'Bo Peng', 'Caiyong Wang', 'Beibei Dong', 'Xianyun Sun'] | 2023-02-02 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-2.99709469e-01 -1.09217681e-01 -7.67059550e-02 -4.12329167e-01
-4.65314627e-01 -3.97954345e-01 6.89260900e-01 -5.33587098e-01
2.05650572e-02 5.23255527e-01 1.60289153e-01 -1.67878434e-01
1.28627256e-01 -5.63351452e-01 -5.26620448e-01 -6.21149838e-01
-2.68672556e-01 6.75487285e-03 -1.70364574e-01 -4.42588806... | [12.67154598236084, 1.1488299369812012] |
16f13a35-bad2-4320-993c-3903b46821c4 | bayesian-sparsification-methods-for-deep | 2003.11413 | null | https://arxiv.org/abs/2003.11413v2 | https://arxiv.org/pdf/2003.11413v2.pdf | Bayesian Sparsification Methods for Deep Complex-valued Networks | With continual miniaturization ever more applications of deep learning can be found in embedded systems, where it is common to encounter data with natural complex domain representation. To this end we extend Sparse Variational Dropout to complex-valued neural networks and verify the proposed Bayesian technique by condu... | ['Ivan Nazarov', 'Evgeny Burnaev'] | 2020-03-25 | null | null | null | null | ['music-transcription'] | ['music'] | [ 1.01587050e-01 2.76290417e-01 -2.52193481e-01 -4.84439164e-01
-7.21100748e-01 -3.85019541e-01 4.16227847e-01 -2.79099613e-01
-7.64984190e-01 8.59938979e-01 -1.42657369e-01 -1.22443900e-01
-5.88764727e-01 -3.51339877e-01 -9.72658932e-01 -6.63330734e-01
-3.28345031e-01 5.94771028e-01 -1.30150735e-01 3.17163169... | [8.571181297302246, 3.2220776081085205] |
34be37ca-59d3-4806-8cf7-be78e390e2d3 | the-xpress-challenge-xray-projectomic | 2302.03819 | null | https://arxiv.org/abs/2302.03819v2 | https://arxiv.org/pdf/2302.03819v2.pdf | The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons | The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is n... | ['Aaron T. Kuan', 'Wei-Chung Lee', 'Alexandra Pacureanu', 'Lu Mi', 'Nir Shavit', 'Donglai Wei', 'Hanspeter Pfister', 'Shuhan Xie', 'Yicong Li', 'Mark Larson', 'Mukul Narwani', 'Tri Nguyen'] | 2023-02-08 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.65972799e-01 5.26551344e-02 2.26445958e-01 -3.50064278e-01
-4.40616012e-01 -6.45100236e-01 2.64510900e-01 3.26844573e-01
-6.78470552e-01 7.92641163e-01 -1.02607496e-01 -3.70013297e-01
9.35446769e-02 -8.32992017e-01 -6.83520734e-01 -5.40452182e-01
9.50132161e-02 8.96014869e-01 3.92359018e-01 1.67656332... | [14.242840766906738, -3.0978610515594482] |
b33bead6-f4c3-49bf-85f6-184e29cd21d7 | sentence-level-feedback-generation-for | 2212.08999 | null | https://arxiv.org/abs/2212.08999v1 | https://arxiv.org/pdf/2212.08999v1.pdf | Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help? | In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo d... | ['Nathan Schneider', 'Amir Zeldes', 'Shabnam Behzad'] | 2022-12-18 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 3.94648939e-01 5.40408790e-01 -5.66362366e-02 -5.00417709e-01
-1.04682350e+00 -5.24685264e-01 8.17612946e-01 5.90083420e-01
-6.76596344e-01 1.08386087e+00 8.80619168e-01 -8.79751682e-01
7.51961470e-01 -3.43324393e-01 -6.16141140e-01 -1.62448157e-02
2.91809022e-01 1.66430458e-01 9.84487459e-02 -3.34844649... | [11.804484367370605, 8.98233413696289] |
2f9dd818-20f4-4055-afb0-d547735c0185 | twibot-22-towards-graph-based-twitter-bot | 2206.04564 | null | https://arxiv.org/abs/2206.04564v6 | https://arxiv.org/pdf/2206.04564v6.pdf | TwiBot-22: Towards Graph-Based Twitter Bot Detection | Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance w... | ['Minnan Luo', 'Jundong Li', 'Zihan Ma', 'Lijing Zheng', 'Yanbo Wang', 'Zijian Cai', 'Heng Wang', 'Yuyang Bai', 'YuHan Liu', 'Hongrui Wang', 'Qingyue Zhang', 'Xinshun Feng', 'Shujie Yang', 'Zhenyu Lei', 'Wenqian Zhang', 'Qinghua Zheng', 'Binchi Zhang', 'Zilong Chen', 'Ningnan Wang', 'Herun Wan', 'Zhaoxuan Tan', 'Shangb... | 2022-06-09 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.72700727e-01 -2.07399607e-01 -7.07113743e-01 4.81859982e-01
-7.54379034e-02 -9.00740504e-01 7.90821314e-01 4.16010797e-01
-3.82187873e-01 5.48624575e-01 2.28834823e-01 -5.75747788e-01
1.96497783e-01 -9.86119151e-01 -2.87854411e-02 -1.78483039e-01
1.97280198e-02 5.16910374e-01 8.36578250e-01 -4.24946129... | [8.09969711303711, 10.127676010131836] |
88d46d58-d1bb-4a38-87a2-43f58ff0bce4 | mcgkt-net-multi-level-context-gating | 2010.09241 | null | https://arxiv.org/abs/2010.09241v1 | https://arxiv.org/pdf/2010.09241v1.pdf | MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining | Rain streak removal in a single image is a very challenging task due to its ill-posed nature in essence. Recently, the end-to-end learning techniques with deep convolutional neural networks (DCNN) have made great progress in this task. However, the conventional DCNN-based deraining methods have struggled to exploit dee... | ['Xian-Hua Han', 'Kohei Yamamichi'] | 2020-10-19 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.63157541e-02 -4.14998680e-01 5.33174455e-01 -7.23712742e-01
-5.76521456e-01 -1.54560789e-01 2.44613960e-01 -3.47475380e-01
-2.88809240e-01 9.24278021e-01 9.89218131e-02 -1.68779895e-01
-7.63224140e-02 -7.72605658e-01 -7.11859763e-01 -1.13066995e+00
-1.26107112e-01 -9.75043923e-02 4.34562862e-01 -3.98312360... | [10.926527976989746, -3.2346606254577637] |
cba51c41-dd42-4dc2-be3c-74c41f5b57a5 | anyteleop-a-general-vision-based-dexterous | 2307.04577 | null | https://arxiv.org/abs/2307.04577v1 | https://arxiv.org/pdf/2307.04577v1.pdf | AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System | Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environme... | ['Dietor Fox', 'Yu-Wei Chao', 'Xiaolong Wang', 'Hao Su', 'Karl Van Wyk', 'Binghao Huang', 'Wei Yang', 'Yuzhe Qin'] | 2023-07-10 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-4.07428026e-01 1.75044119e-01 1.49706230e-01 5.35906330e-02
-1.33360744e-01 -6.20052457e-01 3.80475014e-01 -7.32291877e-01
-4.70489234e-01 3.76389116e-01 -5.41423500e-01 -3.68618846e-01
-2.32018400e-02 -1.96833864e-01 -5.40724099e-01 -3.90358299e-01
-1.62402406e-01 7.85458386e-01 4.41586643e-01 -5.81805885... | [4.783452987670898, 0.8264957070350647] |
16a2a97f-c058-41e4-af14-4f4892a585ee | qampari-an-open-domain-question-answering | 2205.12665 | null | https://arxiv.org/abs/2205.12665v4 | https://arxiv.org/pdf/2205.12665v4.pdf | QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs | Existing benchmarks for open-domain question answering (ODQA) typically focus on questions whose answers can be extracted from a single paragraph. By contrast, many natural questions, such as "What players were drafted by the Brooklyn Nets?" have a list of answers. Answering such questions requires retrieving and readi... | ['Ohad Rubin', 'Jonathan Berant', 'Jonathan Herzig', 'Samuel Joseph Amouyal', 'Tomer Wolfson', 'Ori Yoran'] | 2022-05-25 | null | null | null | null | ['natural-questions', 'passage-retrieval'] | ['miscellaneous', 'natural-language-processing'] | [-3.15956622e-01 5.11138976e-01 1.92975998e-01 -3.52686718e-02
-1.76649082e+00 -1.32368696e+00 5.43097079e-01 7.01872408e-01
-2.99424887e-01 1.07582414e+00 6.36029363e-01 -6.31734192e-01
-3.64939719e-01 -1.28799927e+00 -9.06638861e-01 2.95805305e-01
2.31262967e-01 1.09315884e+00 7.85698652e-01 -8.45904410... | [11.133235931396484, 8.0398530960083] |
a26c7162-2ce1-4f00-83c5-daaf42c0ffac | reliableswap-boosting-general-face-swapping | 2306.05356 | null | https://arxiv.org/abs/2306.05356v1 | https://arxiv.org/pdf/2306.05356v1.pdf | ReliableSwap: Boosting General Face Swapping Via Reliable Supervision | Almost all advanced face swapping approaches use reconstruction as the proxy task, i.e., supervision only exists when the target and source belong to the same person. Otherwise, lacking pixel-level supervision, these methods struggle for source identity preservation. This paper proposes to construct reliable supervisio... | ['Huicheng Zheng', 'Yong Zhang', 'Maomao Li', 'Ge Yuan'] | 2023-06-08 | null | null | null | null | ['face-swapping', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [ 1.93921953e-01 2.52610922e-01 -1.84516102e-01 -5.32284558e-01
-4.38780665e-01 -5.23743749e-01 4.06018943e-01 -8.14739227e-01
-6.06130101e-02 7.79466331e-01 9.31244865e-02 1.66350335e-01
4.43470627e-01 -5.98716438e-01 -8.38225245e-01 -9.32547927e-01
3.94060731e-01 8.00361037e-02 -5.90688437e-02 -1.55229777... | [12.716166496276855, 0.012328946962952614] |
105202df-55c5-4659-a917-00934e84bfa8 | survshap-t-time-dependent-explanations-of | 2208.11080 | null | https://arxiv.org/abs/2208.11080v2 | https://arxiv.org/pdf/2208.11080v2.pdf | SurvSHAP(t): Time-dependent explanations of machine learning survival models | Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanations are available to overcome this issue; however, none directly explai... | ['Przemysław Biecek', 'Hubert Baniecki', 'Mikołaj Spytek', 'Mateusz Krzyziński'] | 2022-08-23 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-2.08871454e-01 3.25661123e-01 -5.74505031e-01 -7.15480566e-01
-5.43419540e-01 -2.06634358e-01 6.06911302e-01 6.25431061e-01
-2.10789684e-02 9.36251163e-01 3.49051297e-01 -9.76340652e-01
-5.53173125e-01 -6.22916043e-01 -3.83569837e-01 -9.23035324e-01
-3.24779004e-01 6.96009934e-01 -5.10748364e-02 -7.90674835... | [8.041390419006348, 5.716795921325684] |
341a3b62-81ad-4cc9-9364-54b9b21a9bb9 | future-sight-dynamic-story-generation-with | 2212.09947 | null | https://arxiv.org/abs/2212.09947v1 | https://arxiv.org/pdf/2212.09947v1.pdf | Future Sight: Dynamic Story Generation with Large Pretrained Language Models | Recent advances in deep learning research, such as transformers, have bolstered the ability for automated agents to generate creative texts similar to those that a human would write. By default, transformer decoders can only generate new text with respect to previously generated text. The output distribution of candida... | ['Olga Vechtomova', 'Gaurav Sahu', 'Brian D. Zimmerman'] | 2022-12-20 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 6.63070440e-01 6.19894087e-01 6.00752458e-02 -4.96022701e-01
-4.56501007e-01 -6.95351779e-01 1.40229154e+00 2.49589577e-01
-2.81970799e-01 8.21715951e-01 8.56515646e-01 -2.63633490e-01
1.87502265e-01 -1.29949737e+00 -1.00178957e+00 -3.69105369e-01
3.50552082e-01 7.65900433e-01 -4.44176309e-02 -2.25209922... | [11.579377174377441, 8.89402961730957] |
8c09f967-ddba-43a1-b9ef-5864de930fd5 | addressing-optimism-bias-in-sequence-modeling | 2207.10295 | null | https://arxiv.org/abs/2207.10295v1 | https://arxiv.org/pdf/2207.10295v1.pdf | Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning | Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem. Recent works based on this paradigm have achieved state-of-the-art results in several of t... | ['Jeff Schneider', 'John Dolan', 'Swapnil Pande', 'Zhe Huang', 'Adam Villaflor'] | 2022-07-21 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.61934361e-01 3.78563767e-03 -3.90078455e-01 -2.91755944e-01
-5.46119690e-01 -7.77698994e-01 9.37729061e-01 4.35862914e-02
-6.46427155e-01 8.80063772e-01 8.15884545e-02 -7.89696336e-01
-1.00322962e-02 -7.49245048e-01 -8.53411973e-01 -6.31690502e-01
-3.15608382e-01 5.07710934e-01 2.26265550e-01 -6.04899645... | [4.513063907623291, 2.0536725521087646] |
808ffd83-2884-4de6-b68f-c862a17f93a1 | 3d-sic-3d-semantic-instance-completion-for | 1904.12012 | null | https://arxiv.org/abs/1904.12012v3 | https://arxiv.org/pdf/1904.12012v3.pdf | RevealNet: Seeing Behind Objects in RGB-D Scans | During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geometry can be fundamentally limiting for many applications, e.g., a robot needs to know the unseen geometry to perform a precise grasp on an ob... | ['Matthias Nießner', 'Angela Dai', 'Ji Hou'] | 2019-04-26 | revealnet-seeing-behind-objects-in-rgb-d | http://openaccess.thecvf.com/content_CVPR_2020/html/Hou_RevealNet_Seeing_Behind_Objects_in_RGB-D_Scans_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hou_RevealNet_Seeing_Behind_Objects_in_RGB-D_Scans_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-semantic-instance-segmentation'] | ['computer-vision'] | [ 2.28603989e-01 3.76052618e-01 2.74534911e-01 -5.78104973e-01
-8.22112739e-01 -8.74272227e-01 3.14387023e-01 1.87037200e-01
-2.54333466e-01 -9.14878473e-02 -3.04229409e-01 -2.28207320e-01
9.14515778e-02 -8.54360402e-01 -1.26029813e+00 -3.21877927e-01
-4.43768129e-02 1.35415471e+00 6.60119236e-01 5.29737957... | [8.134236335754395, -3.038555860519409] |
ddbb4969-be78-4ba2-9b7c-1ed99479ee72 | 3dinvnet-a-deep-learning-based-3d-ground | 2305.05425 | null | https://arxiv.org/abs/2305.05425v1 | https://arxiv.org/pdf/2305.05425v1.pdf | 3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion | The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity. Traditional iterative 3D reconstruction algorithms suffer from strong non-linearity, ill-posedness, and high computational co... | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Hai-Han Sun', 'Yee Hui Lee', 'Qiqi Dai'] | 2023-05-09 | null | null | null | null | ['3d-reconstruction', 'gpr', 'gpr'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.22779775e-01 9.89405885e-02 7.33585417e-01 -7.10934460e-01
-1.04325449e+00 9.33971405e-02 -4.59445007e-02 -1.43951163e-01
-8.96580070e-02 2.86171019e-01 2.42356613e-01 -5.15059590e-01
-6.29045427e-01 -1.26518691e+00 -7.57140636e-01 -9.30962324e-01
-6.05425835e-01 2.68226594e-01 -2.14840367e-01 -4.41479355... | [6.878546714782715, 1.6951985359191895] |
602a9eae-5112-4ab4-9a58-a207f6345946 | gap-a-graph-aware-language-model-framework | 2204.06674 | null | https://arxiv.org/abs/2204.06674v4 | https://arxiv.org/pdf/2204.06674v4.pdf | GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation | Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. These tasks require extensive computational resources while only suggesting marginal improvements. Here, we demonstrate that by fusing graph-aware elements into exi... | ['Daisy Zhe Wang', 'Mehrdad Alvandipour', 'Anthony Colas'] | 2022-04-13 | null | https://aclanthology.org/2022.coling-1.506 | https://aclanthology.org/2022.coling-1.506.pdf | coling-2022-10 | ['data-to-text-generation', 'kg-to-text'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.66903359e-01 7.39902079e-01 -1.27366871e-01 -3.96170884e-01
-6.15035951e-01 -5.22260725e-01 1.18268180e+00 2.97126532e-01
-1.63239941e-01 6.10577464e-01 4.69191611e-01 -5.53008139e-01
1.17862485e-01 -1.17602623e+00 -9.74251986e-01 -4.52963233e-01
1.01637626e-02 8.76498938e-01 2.46229962e-01 -5.46155512... | [10.261255264282227, 8.27689266204834] |
9c4b73e6-8f2a-431f-b927-5217ccfb977a | point-cloud-generation-with-continuous | 2202.08526 | null | https://arxiv.org/abs/2202.08526v1 | https://arxiv.org/pdf/2202.08526v1.pdf | Point Cloud Generation with Continuous Conditioning | Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper proposes a novel generative adversarial network (GAN) setup that generates 3D point cloud shapes conditioned on a continuous parameter. In a... | ['J. Marius Zöllner', 'Fabian B. Flohr', 'David Peter', 'Andre Bühler', 'Larissa T. Triess'] | 2022-02-17 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.58529121e-01 4.28151786e-01 2.26568133e-01 -6.78247353e-03
-1.04970300e+00 -8.89121473e-01 9.81668711e-01 -3.04753423e-01
1.44923506e-02 7.62948215e-01 -1.86549220e-02 6.96439296e-03
3.00017595e-01 -1.01377797e+00 -9.13943112e-01 -8.62435818e-01
4.13327903e-01 7.51689851e-01 -1.75534263e-01 9.34642777... | [9.100947380065918, -3.531834840774536] |
442c8021-0308-4014-95b7-61aa6662ddef | convolutional-set-matching-for-graph | 1810.10866 | null | http://arxiv.org/abs/1810.10866v3 | http://arxiv.org/pdf/1810.10866v3.pdf | Convolutional Set Matching for Graph Similarity | We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural
Networks) for predicting the similarity score between two graphs. As the core
operation of graph similarity search, pairwise graph similarity computation is
a challenging problem due to the NP-hard nature of computing many graph
distance/simila... | ['Yunsheng Bai', 'Hao Ding', 'Yizhou Sun', 'Wei Wang'] | 2018-10-23 | null | null | null | null | ['set-matching', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 8.42827559e-02 7.49439225e-02 -1.63567662e-01 -4.47986484e-01
-3.07788372e-01 -6.02709174e-01 2.69752353e-01 8.64625156e-01
-3.00008208e-01 1.31680712e-01 -9.32508484e-02 -5.32023191e-01
-5.29933155e-01 -1.26001644e+00 -4.49718207e-01 -1.14532180e-01
-7.67840683e-01 5.51031649e-01 2.61917561e-01 -3.97882640... | [7.164955139160156, 6.187484264373779] |
ffc2e971-c4c1-482e-b9a9-f30bd7cccd4d | feed-forward-and-noise-tolerant-detection-of | 1806.03881 | null | https://arxiv.org/abs/1806.03881v4 | https://arxiv.org/pdf/1806.03881v4.pdf | Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code | In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this informati... | ['Marc-Oliver Gewaltig', 'Thomas Wachtler', 'Ad Aertsen', 'Rüdiger Kupper', 'Michael Schmuker'] | 2018-06-11 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 4.16106492e-01 -6.73639536e-01 4.89283711e-01 -1.19587176e-01
1.42161727e-01 -6.57746911e-01 3.63239437e-01 6.15633965e-01
-8.46807420e-01 5.39199948e-01 -3.50849301e-01 -1.23426288e-01
-1.87051967e-02 -7.93109655e-01 -8.06760252e-01 -1.01990354e+00
-1.23614743e-01 -4.28346634e-01 1.01448846e+00 -1.64518446... | [8.197469711303711, 2.576270818710327] |
47676f65-c68a-4a64-9982-08a6b46f3b13 | diversity-induced-multi-view-subspace | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.pdf | Diversity-Induced Multi-View Subspace Clustering | In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering... | ['Hua Zhang', 'Huazhu Fu', 'Xiaochun Cao', 'Si Liu', 'Changqing Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['multi-view-subspace-clustering', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [-3.23289812e-01 -6.34995997e-01 -8.36557820e-02 -2.18551457e-01
-7.13875175e-01 -6.93535447e-01 4.83970731e-01 -5.84239781e-01
5.94783612e-02 9.00580660e-02 5.39719582e-01 2.82850683e-01
-3.84259969e-01 -2.55470455e-01 1.49032492e-02 -1.21137118e+00
3.64742160e-01 1.99817732e-01 -1.37066664e-02 8.93706232... | [8.238842964172363, 4.586676120758057] |
a98f0626-6a9c-4af1-9004-1237aef5214b | few-shot-subgoal-planning-with-language | 2205.14288 | null | https://arxiv.org/abs/2205.14288v1 | https://arxiv.org/pdf/2205.14288v1.pdf | Few-shot Subgoal Planning with Language Models | Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable plans in real-world environments. Given a text instruction, we show that language priors encoded in pre-trained language models allow us to ... | ['Honglak Lee', 'Moontae Lee', 'Yao Fu', 'Lajanugen Logeswaran'] | 2022-05-28 | null | https://aclanthology.org/2022.naacl-main.402 | https://aclanthology.org/2022.naacl-main.402.pdf | naacl-2022-7 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 3.25388491e-01 7.67759323e-01 -3.77861053e-01 -6.87575519e-01
-8.38110149e-01 -4.86945301e-01 8.93133163e-01 4.71120507e-01
-5.52780271e-01 5.96963525e-01 8.67169261e-01 -6.72161698e-01
2.23284990e-01 -6.99478805e-01 -1.12300348e+00 8.27151164e-02
-1.38378993e-01 9.71133947e-01 4.79031622e-01 -6.50998771... | [4.342350959777832, 0.9304488897323608] |
335f5d4d-7771-4dd7-9109-d45714a818e2 | learning-topology-specific-experts-for | 2302.13693 | null | https://arxiv.org/abs/2302.13693v3 | https://arxiv.org/pdf/2302.13693v3.pdf | Learning Topology-Specific Experts for Molecular Property Prediction | Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structura... | ['Hwanjo Yu', 'Seonghyeon Lee', 'SeongKu Kang', 'Dongha Lee', 'Su Kim'] | 2023-02-27 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.91473776e-01 -5.59412688e-03 -6.78201675e-01 -4.33285713e-01
-2.11601779e-01 -7.27966905e-01 3.74375284e-01 7.75286317e-01
2.02338509e-02 9.54603016e-01 1.38281127e-02 -5.46745181e-01
-2.48024046e-01 -9.63474989e-01 -1.10338485e+00 -8.39863122e-01
-3.01243931e-01 5.86685896e-01 2.73459017e-01 7.11477846... | [5.148089408874512, 5.903665542602539] |
668dee0a-ce37-47c1-bfc3-20479c2030dd | vl-bert-detecting-protected-groups-in-hateful | null | null | https://aclanthology.org/2021.woah-1.22 | https://aclanthology.org/2021.woah-1.22.pdf | VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes | This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as... | ['Torsten Zesch', 'Darina Gold', 'Michelle Espranita Liman', 'Piush Aggarwal'] | null | null | null | null | acl-woah-2021-8 | ['meme-classification'] | ['natural-language-processing'] | [-3.23059857e-01 5.87155893e-02 -2.81065136e-01 -2.24520922e-01
-1.69266433e-01 -4.31134254e-01 7.86557019e-01 4.68622416e-01
-6.13760412e-01 7.02023983e-01 6.20811522e-01 3.05519819e-01
5.91271996e-01 -6.74272537e-01 -1.69768259e-01 -1.70738950e-01
7.19064847e-02 1.93085149e-01 -7.07086921e-02 -4.30287391... | [8.62825870513916, 10.602901458740234] |
6aed6682-dba8-4d1d-a458-8e461975c3ac | variational-information-pursuit-for | 2302.02876 | null | https://arxiv.org/abs/2302.02876v2 | https://arxiv.org/pdf/2302.02876v2.pdf | Variational Information Pursuit for Interpretable Predictions | There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make interpretable decisions by sequentially asking interpretable queries about data until a prediction can be made with high confidence based o... | ['René Vidal', 'Donald Geman', 'Benjamin D. Haeffele', 'Kwan Ho Ryan Chan', 'Aditya Chattopadhyay'] | 2023-02-06 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 4.05979753e-01 4.56457645e-01 -3.16702574e-01 -4.84864891e-01
-1.29672492e+00 -5.77559114e-01 4.36841518e-01 6.76412806e-02
-3.57773632e-01 5.00051796e-01 2.87181139e-03 -4.20691878e-01
-3.33680898e-01 -6.73554420e-01 -7.95275509e-01 -6.90033197e-01
1.32414266e-01 1.08772886e+00 -8.91832337e-02 2.30844781... | [7.926423072814941, 4.264987945556641] |
f0b6d220-d3d4-4670-89bd-757cbe32db09 | learning-visibility-field-for-detailed-3d | 2304.11900 | null | https://arxiv.org/abs/2304.11900v1 | https://arxiv.org/pdf/2304.11900v1.pdf | Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting | Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field--it not only resolves occlu... | ['Tao Yu', 'Haoqian Wang', 'Peng Li', 'Ruichen Zheng'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-reconstruction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.14394748e-01 -1.14042833e-01 2.10273549e-01 -5.24297178e-01
-5.78912079e-01 -1.36673793e-01 4.80987132e-01 -5.57047017e-02
-8.05717334e-02 6.16744041e-01 2.77690649e-01 1.84428226e-02
2.04956010e-01 -1.05856609e+00 -7.21882105e-01 -5.02525806e-01
2.05256566e-01 5.17929971e-01 2.31854886e-01 -3.09592456... | [9.092124938964844, -2.854485511779785] |
a54eeab2-566c-4ae4-99eb-815a94698a71 | new-benchmarks-for-accountable-text-based | 2303.05983 | null | https://arxiv.org/abs/2303.05983v2 | https://arxiv.org/pdf/2303.05983v2.pdf | Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation | The recent success of ChatGPT and GPT-4 has drawn widespread attention to multimodal dialogue systems. However, the academia community lacks a dataset that can validate the multimodal generation capabilities of Visual Language Models (VLMs) in textual-visual chat tasks. In this paper, we construct two new multimodal da... | ['Yuliang Liu', 'Zhiwei Zhang'] | 2023-03-10 | null | null | null | null | ['visual-reasoning', 'multimodal-generation', 'visual-reasoning'] | ['computer-vision', 'natural-language-processing', 'reasoning'] | [ 1.59570038e-01 4.47284102e-01 1.80610061e-01 -4.22827750e-01
-6.86153233e-01 -6.38377726e-01 7.36385524e-01 -2.62553215e-01
-2.08411247e-01 6.11234128e-01 -3.50638814e-02 -6.74204350e-01
4.50657725e-01 -5.82025826e-01 -9.37327445e-01 -4.94435310e-01
7.08357871e-01 6.04837179e-01 -3.75297405e-02 -4.36856858... | [10.965755462646484, 1.3448216915130615] |
9210cda8-ae3d-4cc3-86f4-7caed2651c26 | scalable-neural-contextual-bandit-for | 2306.14834 | null | https://arxiv.org/abs/2306.14834v1 | https://arxiv.org/pdf/2306.14834v1.pdf | Scalable Neural Contextual Bandit for Recommender Systems | High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems, only leverage recognized user interests, falling short when it c... | ['Benjamin Van Roy', 'Zheqing Zhu'] | 2023-06-26 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.29980680e-02 2.18654543e-01 -8.67884874e-01 -3.90007138e-01
-1.08700311e+00 -3.79048616e-01 4.65968758e-01 -2.80486524e-01
-2.52786100e-01 7.23026752e-01 7.09976971e-01 -7.32738018e-01
-6.40381992e-01 -6.38482988e-01 -9.12655771e-01 -4.23491478e-01
-4.47164960e-02 5.50371051e-01 -1.87950015e-01 -2.99909264... | [10.027115821838379, 5.631283283233643] |
286d725b-0a09-4b1d-834e-4df96fa20db2 | 3dmodt-attention-guided-affinities-for-joint | 2211.00746 | null | https://arxiv.org/abs/2211.00746v1 | https://arxiv.org/pdf/2211.00746v1.pdf | 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds | We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followed by data association. Our method embeds both steps into a single end-to-end trainable network eliminating the dependency on external object... | ['Mubarak Shah', 'Ajmal Mian', 'Jyoti Kini'] | 2022-11-01 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [ 7.42392689e-02 -2.13626295e-01 -3.82192247e-03 -3.30484688e-01
-5.59413970e-01 -4.92704570e-01 5.83030820e-01 1.84282735e-01
-5.56777656e-01 1.53351963e-01 -2.09418178e-01 -9.38362554e-02
-1.41897038e-01 -5.66156864e-01 -1.01100671e+00 -3.97550225e-01
-5.97739890e-02 7.10430861e-01 8.71058166e-01 9.87284482... | [6.562129497528076, -2.2363717555999756] |
26995fc9-a6cf-4161-aba3-da0c80aa1236 | multi-channel-vision-transformer-for | null | null | https://doi.org/10.3390/biomedicines10071551 | https://doi.org/10.3390/biomedicines10071551 | Multi-Channel Vision Transformer for Epileptic Seizure Prediction | Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable i... | ['Rabab Ward', 'Soojin Lee', 'Ramy Hussein'] | 2022-06-29 | null | null | null | biomedicines-2022-6 | ['seizure-prediction'] | ['medical'] | [ 2.57090658e-01 -5.44972777e-01 4.97283965e-01 -2.17551768e-01
-9.41894054e-01 -5.32516778e-01 1.30412519e-01 2.29366243e-01
-3.20307910e-01 8.50706458e-01 -3.92919453e-03 -2.62025744e-01
-3.55005473e-01 -4.61730182e-01 -3.26140314e-01 -9.98027802e-01
-6.33694112e-01 -6.31321222e-02 1.42677769e-01 2.03363940... | [13.210772514343262, 3.4989638328552246] |
180f3a47-f3fa-463b-a481-86c4f73f5338 | same-author-or-just-same-topic-towards | 2204.04907 | null | https://arxiv.org/abs/2204.04907v1 | https://arxiv.org/pdf/2204.04907v1.pdf | Same Author or Just Same Topic? Towards Content-Independent Style Representations | Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style)... | ['Dong Nguyen', 'Marijn Schraagen', 'Anna Wegmann'] | 2022-04-11 | null | https://aclanthology.org/2022.repl4nlp-1.26 | https://aclanthology.org/2022.repl4nlp-1.26.pdf | repl4nlp-acl-2022-5 | ['authorship-verification'] | ['natural-language-processing'] | [ 1.31755188e-01 1.53053403e-01 -1.91399246e-01 -8.07137012e-01
-2.57966042e-01 -8.50300491e-01 1.12568569e+00 1.17569178e-01
-3.43980968e-01 6.02006555e-01 7.16117263e-01 -3.32214862e-01
2.53136545e-01 -5.95897615e-01 -3.23151350e-01 -1.68979689e-01
4.83482003e-01 7.26825356e-01 -4.56632406e-01 -2.37978965... | [11.158514022827148, 9.921745300292969] |
4ea49bb0-eeac-4ef7-a5f2-de9e4ced0fca | satellite-image-classification-and | 1401.2416 | null | http://arxiv.org/abs/1401.2416v1 | http://arxiv.org/pdf/1401.2416v1.pdf | Satellite image classification and segmentation using non-additive entropy | Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis
entropy on the pattern recognition and segmentation of coloured images obtained
by satellites, via "Google Earth". By segmentation we mean split an image to
locate regions of interest. Here, we discriminate and define an image partition
classes acc... | ['Odemir Martinez Bruno', 'Lucas Assirati', 'Alexandre Souto Martinez'] | 2014-01-10 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 3.17344934e-01 -1.84582680e-01 5.76865636e-02 -1.05763339e-01
-2.61769872e-02 -6.11260355e-01 6.70357168e-01 4.03600074e-02
-6.70081794e-01 8.46356392e-01 -3.79207283e-01 -4.85945433e-01
-2.74149597e-01 -1.05258286e+00 -2.91604578e-01 -1.08263373e+00
-4.78656679e-01 4.12586242e-01 2.07246274e-01 -1.10381708... | [9.620743751525879, -1.735055685043335] |
91cbff5e-e81a-4b1b-ba85-dc0a3fc44fcd | intrinsic-autoencoders-for-joint-neural | 2006.16011 | null | https://arxiv.org/abs/2006.16011v3 | https://arxiv.org/pdf/2006.16011v3.pdf | Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition | Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been proposed for this task, acquiring a dataset of images with accurately aligned 3D ... | ['Matthias Nießner', 'Varun Jampani', 'Siva Karthik Mustikovela', 'Justus Thies', 'Carsten Rother', 'Hassan Abu Alhaija', 'Andreas Geiger'] | 2020-06-29 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.08654535e-01 2.46327028e-01 3.02998275e-01 -2.87869483e-01
-8.54603589e-01 -6.76870704e-01 9.04228628e-01 -3.82379979e-01
-1.74012065e-01 6.49455249e-01 -1.19547797e-02 -1.99740916e-01
3.00635397e-01 -8.32640171e-01 -1.06416821e+00 -7.87484288e-01
4.91824120e-01 7.68923998e-01 -2.10043192e-01 -1.34494871... | [9.300358772277832, -3.0640316009521484] |
f4a66c05-ea2c-4a9c-b234-89fd2d873648 | securing-the-classification-of-covid-19-in | 2203.07728 | null | https://arxiv.org/abs/2203.07728v1 | https://arxiv.org/pdf/2203.07728v1.pdf | Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach | Deep learning (DL) is being increasingly utilized in healthcare-related fields due to its outstanding efficiency. However, we have to keep the individual health data used by DL models private and secure. Protecting data and preserving the privacy of individuals has become an increasingly prevalent issue. The gap betwee... | ['Anis Koubaa', 'Bilel Benjdira', 'Adel Ammar', 'Wadii Boulila'] | 2022-03-15 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 6.89980090e-02 -8.41348059e-03 -1.93466797e-01 -5.39344072e-01
-7.47834027e-01 -4.17878926e-01 3.57522182e-02 3.52468401e-01
-8.29115391e-01 8.20335329e-01 8.99030939e-02 -4.79394078e-01
-1.80486023e-01 -1.05609238e+00 -5.30094564e-01 -9.61726367e-01
-2.53511280e-01 1.89380899e-01 -2.35372707e-01 3.07791203... | [6.094343662261963, 6.657516002655029] |
ddec9ff9-71df-4417-a980-c3c1b843bab4 | dart-a-lightweight-quality-suggestive-data-to | 2010.04141 | null | https://arxiv.org/abs/2010.04141v2 | https://arxiv.org/pdf/2010.04141v2.pdf | DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool | We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree ... | ['Vera Demberg', 'Xiaoyu Shen', 'Alex Marin', 'Jeriah Caplinger', 'Ernie Chang'] | 2020-10-08 | null | https://aclanthology.org/2020.coling-demos.3 | https://aclanthology.org/2020.coling-demos.3.pdf | coling-2020-8 | ['text-annotation'] | ['natural-language-processing'] | [ 4.48945701e-01 9.26854551e-01 -2.96706736e-01 -6.59077644e-01
-1.06046820e+00 -9.84903812e-01 8.68423060e-02 7.99066961e-01
-4.41771328e-01 9.18399453e-01 2.00523973e-01 -3.41982543e-01
6.37285635e-02 -2.45185584e-01 -3.40329528e-01 -7.78578967e-02
-1.32378981e-01 1.16879880e+00 2.99445152e-01 1.13536641... | [9.309349060058594, 8.69770622253418] |
bf4891a4-5561-4fbf-ac49-07f52f06527b | tough-tables-carefully-evaluating-entity | null | null | https://doi.org/10.1007/978-3-030-62466-8_21 | https://openaccess.city.ac.uk/id/eprint/24776/1/Tough_Tables_Carefully_Evaluating_Entity_Linking_for_Tabular_Data.pdf | Tough Tables: Carefully Evaluating Entity Linking for Tabular Data | Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate table annotation algorithms by providing a common interface and several general-purpose datasets as a gr... | ['Matteo Palmonari', 'Ernesto Jimenez-Ruiz', 'Federico Bianchi', 'Vincenzo Cutrona'] | 2020-11-01 | null | null | null | international-semantic-web-conference-iswc | ['table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.82098466e-01 3.43269020e-01 -4.05611068e-01 -1.43153235e-01
-8.05480063e-01 -1.21379447e+00 3.94646317e-01 6.42619789e-01
-1.55277699e-01 1.22168994e+00 3.30504268e-01 -6.19758517e-02
-3.58031303e-01 -1.26628280e+00 -8.38022530e-01 -2.49775603e-01
1.49851188e-01 1.03745675e+00 6.55124724e-01 -5.99501789... | [9.320585250854492, 8.01657485961914] |
91b3bd22-bc3d-405d-8515-0adcd50d2033 | modeling-diagnostic-label-correlation-for-1 | 2106.12800 | null | https://arxiv.org/abs/2106.12800v1 | https://arxiv.org/pdf/2106.12800v1.pdf | Modeling Diagnostic Label Correlation for Automatic ICD Coding | Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced data make this prediction task extremely hard. Most existing work bui... | ['Yun-Nung Chen', 'Chao-Wei Huang', 'Shang-Chi Tsai'] | 2021-06-24 | modeling-diagnostic-label-correlation-for | https://aclanthology.org/2021.naacl-main.318 | https://aclanthology.org/2021.naacl-main.318.pdf | naacl-2021-4 | ['medical-code-prediction'] | ['medical'] | [ 3.92750382e-01 1.71253964e-01 -5.57643473e-01 -7.70177543e-01
-9.08900976e-01 -4.19392914e-01 8.57396722e-02 7.74609685e-01
5.52935563e-02 5.86705983e-01 2.70471513e-01 -3.47834021e-01
-3.22173804e-01 -4.64769483e-01 -3.50757927e-01 -4.95255977e-01
1.12137645e-01 9.26360607e-01 3.50308754e-02 3.97975266... | [8.027194023132324, 6.789395332336426] |
5ed969c4-d0f1-4215-a8f2-bf6bc334c6ab | analysis-of-social-robotic-navigation | 2008.07965 | null | https://arxiv.org/abs/2008.07965v2 | https://arxiv.org/pdf/2008.07965v2.pdf | Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning | Dealing with social tasks in robotic scenarios is difficult, as having humans in the learning loop is incompatible with most of the state-of-the-art machine learning algorithms. This is the case when exploring Incremental learning models, in particular the ones involving reinforcement learning. In this work, we discuss... | ['Yves M. Galvão', 'Letícia Castro', 'Agostinho A. F. Júnior', 'Pablo Barros', 'Bruno J. T. Fernandes', 'Janderson Ferreira'] | 2020-08-18 | null | null | null | null | ['social-navigation'] | ['robots'] | [ 2.58894026e-01 9.70526040e-01 1.13607913e-01 -2.38591582e-01
2.71994382e-01 -4.57509786e-01 7.05547214e-01 5.54719046e-02
-9.81457353e-01 1.04214489e+00 1.83387220e-01 -4.43607330e-01
-6.25185609e-01 -6.78525865e-01 -7.48694122e-01 -4.48420018e-01
-3.59899014e-01 6.33345127e-01 7.20053792e-01 -9.04268980... | [4.108173370361328, 1.3664240837097168] |
6aaa23dd-f06a-4d86-9280-f24e43fee2b0 | a-dataset-for-benchmarking-image-based | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Sun_A_Dataset_for_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Sun_A_Dataset_for_CVPR_2017_paper.pdf | A Dataset for Benchmarking Image-Based Localization | A novel dataset for benchmarking image-based localization is presented. With increasing research interests in visual place recognition and localization, several datasets have been published in the past few years. One of the evident limitations of existing datasets is that precise ground truth camera poses of query imag... | ['Yuanfan Xie', 'Xun Sun', 'Pei Luo', 'Liang Wang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['image-based-localization'] | ['computer-vision'] | [-1.14870630e-01 -6.54859364e-01 -1.77913070e-01 -5.56818008e-01
-8.22352409e-01 -7.98525989e-01 7.98639834e-01 9.48883966e-02
-8.47612441e-01 8.07002187e-01 -1.24241866e-01 1.30817685e-02
-1.73792005e-01 -4.31411445e-01 -7.00663805e-01 -4.13336486e-01
9.49911401e-02 5.94506919e-01 2.54022568e-01 5.25392927... | [7.548708438873291, -2.0956573486328125] |
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