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194d24f4-15bf-4274-8d06-f975a325b0c3 | towards-discriminative-representation-multi | 2203.14208 | null | https://arxiv.org/abs/2203.14208v2 | https://arxiv.org/pdf/2203.14208v2.pdf | Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking | Discriminative representation is crucial for the association step in multi-object tracking. Recent work mainly utilizes features in single or neighboring frames for constructing metric loss and empowering networks to extract representation of targets. Although this strategy is effective, it fails to fully exploit the i... | ['Shoudong Han', 'Zhuoling Li', 'En Yu'] | 2022-03-27 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_Towards_Discriminative_Representation_Multi-View_Trajectory_Contrastive_Learning_for_Online_Multi-Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_Towards_Discriminative_Representation_Multi-View_Trajectory_Contrastive_Learning_for_Online_Multi-Object_CVPR_2022_paper.pdf | cvpr-2022-1 | ['online-multi-object-tracking'] | ['computer-vision'] | [-5.04200011e-02 -5.95839441e-01 -3.74546289e-01 -2.29526863e-01
-7.22447693e-01 -4.44120973e-01 5.64396858e-01 6.02187449e-03
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-1.44279927e-01 2.29853615e-02 5.56936026e-01 5.69171347... | [6.379960536956787, -2.118821144104004] |
d5c67ffa-c5af-4cb1-bc77-816b97f2d048 | noise-invariant-frame-selection-a-simple | 1805.01259 | null | http://arxiv.org/abs/1805.01259v1 | http://arxiv.org/pdf/1805.01259v1.pdf | Noise Invariant Frame Selection: A Simple Method to Address the Background Noise Problem for Text-independent Speaker Verification | The performance of speaker-related systems usually degrades heavily in
practical applications largely due to the presence of background noise. To
improve the robustness of such systems in unknown noisy environments, this
paper proposes a simple pre-processing method called Noise Invariant Frame
Selection (NIFS). Based ... | ['Björn Schuller', 'Siyang Song', 'Shuimei Zhang', 'Michel Valstar', 'Linlin Shen'] | 2018-05-03 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 6.31426200e-02 -7.74082124e-01 2.14222267e-01 -5.86002409e-01
-8.16265941e-01 -3.95218760e-01 6.65519476e-01 -2.77737707e-01
-3.74117285e-01 4.03437734e-01 3.57469887e-01 -3.38565469e-01
1.44144297e-01 -1.69772595e-01 -1.58880010e-01 -1.17379630e+00
1.49272621e-01 -2.29817867e-01 3.67928803e-01 -2.25420803... | [14.610018730163574, 6.072750091552734] |
a935687b-badb-4147-a688-87d948cd8b72 | q-learning-decision-transformer-leveraging | 2209.03993 | null | https://arxiv.org/abs/2209.03993v4 | https://arxiv.org/pdf/2209.03993v4.pdf | Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL | Recent works have shown that tackling offline reinforcement learning (RL) with a conditional policy produces promising results. The Decision Transformer (DT) combines the conditional policy approach and a transformer architecture, showing competitive performance against several benchmarks. However, DT lacks stitching a... | ['Raul Santos-Rodriguez', 'Ahmed Khalil', 'Taku Yamagata'] | 2022-09-08 | null | null | null | null | ['d4rl'] | ['robots'] | [-2.55216181e-01 1.17899561e-02 -6.46878123e-01 3.78994495e-02
-9.70464230e-01 -7.36774087e-01 6.06932640e-01 1.18133472e-02
-6.42169237e-01 1.01107860e+00 -5.90181574e-02 -6.26593590e-01
-4.12866652e-01 -6.25371456e-01 -8.04769516e-01 -9.82802033e-01
-3.86864215e-01 4.00530994e-01 3.63158584e-01 -3.74536306... | [4.049818515777588, 2.12339448928833] |
19836854-0b2f-43a2-ac0a-6ad66e89c749 | streaming-submodular-maximization-with | 2010.04412 | null | https://arxiv.org/abs/2010.04412v2 | https://arxiv.org/pdf/2010.04412v2.pdf | Fair and Representative Subset Selection from Data Streams | We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constrai... | ['Michael Mathioudakis', 'Francesco Fabbri', 'Yanhao Wang'] | 2020-10-09 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.05740958e-01 1.19734526e-01 -4.98567402e-01 -3.09252262e-01
-5.96783221e-01 -7.18195319e-01 -4.88813728e-01 6.69213235e-01
-7.34763920e-01 7.72782266e-01 -3.44840169e-01 -2.93034732e-01
-7.29567468e-01 -1.12506711e+00 -8.14377129e-01 -6.44012332e-01
-7.45572805e-01 5.68048954e-01 2.20638797e-01 -2.11347416... | [6.564200401306152, 4.8784499168396] |
b9ae51bc-3a68-477d-b9a5-cf593db4ad44 | multi-view-attention-learning-for-residual | 2306.14646 | null | https://arxiv.org/abs/2306.14646v1 | https://arxiv.org/pdf/2306.14646v1.pdf | Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer | In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (e.g., laparoscopy) or time-consuming (e.g., manual analysis). Recently, deep learning methods make many efforts in automatic analysis of med... | ['Wei Wei', 'Guoqing Hu', 'Jun Shi', 'Shulan Ruan', 'Xiangneng Gao'] | 2023-06-26 | null | null | null | null | ['disease-prediction', 'computed-tomography-ct', 'decision-making'] | ['medical', 'methodology', 'reasoning'] | [-2.19907472e-03 3.19255479e-02 -6.35410190e-01 -3.94906074e-01
-9.36846673e-01 -2.64025003e-01 1.81926236e-01 1.63865358e-01
-1.22178257e-01 5.32308936e-01 4.18467849e-01 -3.95513892e-01
-1.74472257e-01 -8.22444260e-01 -4.06978279e-01 -7.45304585e-01
9.70826522e-02 5.53196132e-01 -1.30764619e-01 9.19039920... | [14.94832992553711, -2.2734432220458984] |
2188741d-2d9b-432e-bfdf-a58e78a7fac1 | silhouette-guided-point-cloud-reconstruction | 1907.12253 | null | https://arxiv.org/abs/1907.12253v1 | https://arxiv.org/pdf/1907.12253v1.pdf | Silhouette Guided Point Cloud Reconstruction beyond Occlusion | One major challenge in 3D reconstruction is to infer the complete shape geometry from partial foreground occlusions. In this paper, we propose a method to reconstruct the complete 3D shape of an object from a single RGB image, with robustness to occlusion. Given the image and a silhouette of the visible region, our app... | ['Derek Hoiem', 'Chuhang Zou'] | 2019-07-29 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 3.92429888e-01 4.14621532e-01 4.04304653e-01 -3.07716459e-01
-7.61521041e-01 -6.96504474e-01 4.06476408e-01 -1.05009459e-01
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4.14480746e-01 -6.80416167e-01 -1.00837791e+00 -3.10656846e-01
4.64419454e-01 1.07242596e+00 6.51360095e-01 2.05257088... | [8.749171257019043, -3.0642712116241455] |
f8320fc4-8925-42cb-8720-c61e5b1db8ab | umat-uncertainty-aware-single-image-high-1 | 2305.16312 | null | https://arxiv.org/abs/2305.16312v1 | https://arxiv.org/pdf/2305.16312v1.pdf | UMat: Uncertainty-Aware Single Image High Resolution Material Capture | We propose a learning-based method to recover normals, specularity, and roughness from a single diffuse image of a material, using microgeometry appearance as our primary cue. Previous methods that work on single images tend to produce over-smooth outputs with artifacts, operate at limited resolution, or train one mode... | ['Elena Garces', 'David Pascual-Hernandez', 'Henar Dominguez-Elvira', 'Carlos Rodriguez-Pardo'] | 2023-05-25 | umat-uncertainty-aware-single-image-high | http://openaccess.thecvf.com//content/CVPR2023/html/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rodriguez-Pardo_UMat_Uncertainty-Aware_Single_Image_High_Resolution_Material_Capture_CVPR_2023_paper.pdf | cvpr-2023-1 | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 6.61083698e-01 1.14654623e-01 2.85367936e-01 -4.39343840e-01
-1.08729732e+00 -7.01318145e-01 3.05967718e-01 -2.22562402e-01
-1.53099313e-01 7.85891414e-01 -3.46108019e-01 -1.25336826e-01
-2.59438753e-01 -8.99407029e-01 -1.07053876e+00 -9.22765613e-01
2.98918009e-01 6.13220632e-01 3.20582479e-01 9.91717353... | [9.719656944274902, -2.779301166534424] |
41aa92e4-b8ba-48f5-ad6f-73a6bc77d06b | latent-multi-view-subspace-clustering | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Latent_Multi-View_Subspace_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Latent_Multi-View_Subspace_CVPR_2017_paper.pdf | Latent Multi-View Subspace Clustering | In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views. Unlike most existing single view subspace clustering methods that reconstruct data points usi... | ['QinGhua Hu', 'Huazhu Fu', 'Xiaochun Cao', 'Pengfei Zhu', 'Changqing Zhang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.82657266e-01 -3.17583561e-01 -5.35189807e-01 -6.85405284e-02
-6.87727749e-01 -8.60611856e-01 4.36435908e-01 -4.44462329e-01
1.19195759e-01 2.73897737e-01 6.96367979e-01 2.08794490e-01
-3.34327340e-01 -1.56570509e-01 -2.43730411e-01 -1.11705101e+00
2.29271069e-01 4.52619672e-01 -3.50379348e-01 4.49511111... | [8.226907730102539, 4.5981245040893555] |
8609e4e1-9133-4ee4-97ab-ccad7e82155f | multi-model-hypothesize-and-verify-approach | 1608.02052 | null | http://arxiv.org/abs/1608.02052v1 | http://arxiv.org/pdf/1608.02052v1.pdf | Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification | Loop closure detection, which is the task of identifying locations revisited
by a robot in a sequence of odometry and perceptual observations, is typically
formulated as a visual place recognition (VPR) task. However, even
state-of-the-art VPR techniques generate a considerable number of false
positives as a result of ... | ['Kanji Tanaka'] | 2016-08-06 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.08214632e-01 7.01957047e-02 -6.88651875e-02 -2.56400287e-01
-2.94699579e-01 -5.52128971e-01 7.79460669e-01 6.48510158e-01
-3.58550251e-01 6.21121347e-01 -2.91183025e-01 -3.63426059e-01
-6.34660274e-02 -5.12679636e-01 -8.76284420e-01 -2.63453960e-01
-1.28220245e-01 5.87614000e-01 5.49872994e-01 -3.64574403... | [7.349257469177246, -2.076624631881714] |
cf0b6e3c-16f9-41fe-a5bc-6a3e1643103f | predicting-visual-features-from-text-for | 1709.01362 | null | http://arxiv.org/abs/1709.01362v3 | http://arxiv.org/pdf/1709.01362v3.pdf | Predicting Visual Features from Text for Image and Video Caption Retrieval | This paper strives to find amidst a set of sentences the one best describing
the content of a given image or video. Different from existing works, which
rely on a joint subspace for their image and video caption retrieval, we
propose to do so in a visual space exclusively. Apart from this conceptual
novelty, we contrib... | ['Xirong Li', 'Jianfeng Dong', 'Cees G. M. Snoek'] | 2017-09-05 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 3.08909059e-01 -1.28284052e-01 -1.83403924e-01 -4.41739708e-01
-1.15992701e+00 -7.39077151e-01 1.01469362e+00 9.54302922e-02
-5.59742749e-01 3.57887328e-01 7.09641695e-01 -1.75162107e-01
1.65758207e-01 -1.37545094e-01 -9.22390163e-01 -4.99145925e-01
1.02501065e-01 2.95320839e-01 -2.01443836e-01 -6.88770339... | [10.44547176361084, 1.1706440448760986] |
acaa270d-2d07-420b-a378-11a66662b762 | conjugated-discrete-distributions-for | 2112.07424 | null | https://arxiv.org/abs/2112.07424v1 | https://arxiv.org/pdf/2112.07424v1.pdf | Conjugated Discrete Distributions for Distributional Reinforcement Learning | In this work we continue to build upon recent advances in reinforcement learning for finite Markov processes. A common approach among previous existing algorithms, both single-actor and distributed, is to either clip rewards or to apply a transformation method on Q-functions to handle a large variety of magnitudes in r... | ['Karl-Olof Lindahl', 'Jonas Nordqvist', 'Björn Lindenberg'] | 2021-12-14 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 5.32963574e-02 -3.23999189e-02 6.39475584e-02 -1.76493570e-01
-9.99782681e-01 -6.83406055e-01 9.53812242e-01 -2.81464905e-02
-1.10107231e+00 1.16211474e+00 -8.66267979e-02 -3.60915750e-01
-2.64451772e-01 -8.65328372e-01 -7.21444130e-01 -9.63124096e-01
-2.67370462e-01 8.41973305e-01 3.45707595e-01 -4.60136592... | [4.089419841766357, 2.4549906253814697] |
e5d15c56-705c-4851-a4c1-52e46f146be9 | langevin-dynamics-based-algorithm-e-th | 2210.13193 | null | https://arxiv.org/abs/2210.13193v1 | https://arxiv.org/pdf/2210.13193v1.pdf | Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient | We introduce a new Langevin dynamics based algorithm, called e-TH$\varepsilon$O POULA, to solve optimization problems with discontinuous stochastic gradients which naturally appear in real-world applications such as quantile estimation, vector quantization, CVaR minimization, and regularized optimization problems invol... | ['Ying Zhang', 'Sotirios Sabanis', 'Ariel Neufeld', 'Dong-Young Lim'] | 2022-10-24 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.03376013e-01 -8.46119523e-02 -2.52920482e-02 -3.75653148e-01
-1.15313017e+00 -2.38056332e-01 -7.43869022e-02 3.99815857e-01
-6.29267812e-01 9.99107599e-01 -2.62771428e-01 -4.55384642e-01
-7.04314470e-01 -9.05601740e-01 -9.61242318e-01 -8.32922578e-01
-4.09767866e-01 5.61432838e-01 -3.45505148e-01 -3.53088498... | [7.1083664894104, 4.047632694244385] |
5a98c8e5-cf19-4716-a921-9e4744f4a544 | engraf-net-multiple-granularity-branch | null | null | https://link.springer.com/chapter/10.1007/978-3-030-89128-2_38 | http://artelab.dista.uninsubria.it/res/research/papers/2021/2021_CAIP_Lagrassa_EnGraf_Net.pdf | EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task | Fine-Grained classification models can expressly focus on the relevant details useful to distinguish highly similar classes typically when the intra-class variance is high and the inter-class variance is low given a dataset. Most of these models use part annotations as bounding box, location part, text attributes to en... | ['Nicola Landro', 'Ignazio Gallo', 'Riccardo La Grassa'] | 2021-10-21 | null | null | null | caip-computer-analysis-of-images-and-patterns | ['fine-grained-image-classification'] | ['computer-vision'] | [ 7.83876032e-02 1.34248391e-01 9.39838588e-03 -5.65749288e-01
-1.94960549e-01 -5.33893883e-01 8.72892082e-01 5.27488232e-01
-4.40494776e-01 7.28044271e-01 1.49719432e-01 -5.85970767e-02
-5.84537029e-01 -8.97392690e-01 -6.50018573e-01 -4.82300907e-01
-1.54994369e-01 8.02853107e-01 4.82136995e-01 -1.52463421... | [9.5999755859375, 2.04390549659729] |
f0387190-cfc6-48f6-9fea-bd1ced17ae8b | to-what-extent-does-lexical-normalization | null | null | https://aclanthology.org/2021.wnut-1.50 | https://aclanthology.org/2021.wnut-1.50.pdf | To What Extent Does Lexical Normalization Help English-as-a-Second Language Learners to Read Noisy English Texts? | How difficult is it for English-as-a-second language (ESL) learners to read noisy English texts? Do ESL learners need lexical normalization to read noisy English texts? These questions may also affect community formation on social networking sites where differences can be attributed to ESL learners and native English s... | ['Yo Ehara'] | null | null | null | null | wnut-acl-2021-11 | ['lexical-normalization'] | ['natural-language-processing'] | [-5.23667037e-01 3.06769431e-01 1.77132607e-01 -1.74005598e-01
-1.02826965e+00 -7.50607193e-01 3.64435226e-01 7.70527780e-01
-1.13791275e+00 5.02421737e-01 6.82557702e-01 -5.53455889e-01
-2.67510086e-01 -9.68188286e-01 -2.92973310e-01 -1.05738185e-01
5.05224526e-01 1.14166394e-01 2.01640666e-01 -7.23130226... | [10.940711975097656, 10.233004570007324] |
9f865b84-4ddd-4feb-8d98-a4ab6fae0660 | taveer-an-interpretable-topic-agnostic | null | null | https://dl.acm.org/doi/10.1145/3407023.3409194 | https://dl.acm.org/doi/10.1145/3407023.3409194 | TAVeer: An Interpretable Topic-Agnostic Authorship Verification Method | A central problem that has been researched for many years in the field of digital text forensics is the question whether two documents were written by the same author. Authorship verification (AV) is a research branch in this field that deals with this question. Over the years, research activities in the context of AV ... | ['Roey Regev', 'Lukas Graner', 'Oren Halvani'] | 2020-08-01 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.43135071e-01 -6.48890957e-02 -1.95326820e-01 -1.09289207e-01
-3.64780158e-01 -5.29482067e-01 1.09699774e+00 5.81732810e-01
-4.33024973e-01 5.15130401e-01 1.10122226e-01 -3.50816518e-01
-9.54313651e-02 -6.31320953e-01 -2.70134211e-01 -6.78351641e-01
4.56535071e-01 4.57458973e-01 5.69617629e-01 9.13799703... | [9.574649810791016, 10.642407417297363] |
26d98431-55ac-4a7f-bf1a-4a1105462bc8 | test-time-adaptation-for-real-image-denoising | 2207.02066 | null | https://arxiv.org/abs/2207.02066v1 | https://arxiv.org/pdf/2207.02066v1.pdf | Test-time Adaptation for Real Image Denoising via Meta-transfer Learning | In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a better network architecture. We explore a different direction where we propose to improve real image denoising performance through a better lear... | ['Se Jin Park', 'Muhammad Adi Nugroho', 'Agus Gunawan'] | 2022-07-05 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 5.16658425e-01 -4.21307571e-02 2.76775360e-01 -4.95934695e-01
-9.50097978e-01 6.00848207e-03 3.97944361e-01 -4.12019044e-01
-6.11885130e-01 5.28547347e-01 7.25996718e-02 -5.67862540e-02
-2.61975955e-02 -7.64361382e-01 -7.29431748e-01 -1.03251386e+00
6.84267804e-02 -1.57204241e-01 2.28918016e-01 -4.36759204... | [11.514571189880371, -2.3896286487579346] |
cfbc6d1b-fcc0-43fd-807e-54427c6e6d70 | low-rank-multi-view-clustering-in-third-order | 1608.08336 | null | http://arxiv.org/abs/1608.08336v2 | http://arxiv.org/pdf/1608.08336v2.pdf | Low-rank Multi-view Clustering in Third-Order Tensor Space | The plenty information from multiple views data as well as the complementary
information among different views are usually beneficial to various tasks,
e.g., clustering, classification, de-noising. Multi-view subspace clustering is
based on the fact that the multi-view data are generated from a latent
subspace. To reco... | ['Ming Yin', 'Shengli Xie', 'Yi Guo', 'Junbin Gao'] | 2016-08-30 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.12280929e-01 -5.63794136e-01 -2.70101409e-02 -2.08870634e-01
-4.35932964e-01 -6.68032885e-01 5.04607141e-01 -4.16840374e-01
6.13669045e-02 2.46410891e-01 4.14989650e-01 2.33764514e-01
-5.12140095e-01 -1.91009134e-01 -1.54996261e-01 -1.25750339e+00
1.52456582e-01 1.49543554e-01 -2.37935036e-01 8.34013149... | [8.210369110107422, 4.616711139678955] |
a3501e53-5c40-4cdf-bc2c-d764c9e5bf02 | lmr-cbt-learning-modality-fused | 2112.01697 | null | https://arxiv.org/abs/2112.01697v1 | https://arxiv.org/pdf/2112.01697v1.pdf | LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences | Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition. Existing approaches use directional pairwise attention or a message hub to fuse language, visual, and audio modalities. However, those approaches introduce information ... | ['Aimin Zhou', 'Xiangling Fu', 'Jiayin Qi', 'Jiahao Zhang', 'Siyuan Shen', 'HanYang Wang', 'Feng Liu', 'Ziwang Fu'] | 2021-12-03 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.62707642e-01 -6.58516288e-01 -4.33638357e-02 -2.94595569e-01
-1.14289272e+00 -4.71152544e-01 3.86507392e-01 -2.65891254e-01
-5.64767659e-01 4.98040408e-01 5.41333258e-01 2.24775091e-01
4.99146245e-02 -9.98151600e-02 -5.75781345e-01 -9.66598094e-01
2.60803550e-01 -1.89008936e-01 -2.48434037e-01 -2.22360253... | [13.201809883117676, 5.077728271484375] |
1b89627e-e19b-4bf1-b1c7-576e6363da11 | divide-evaluate-and-refine-evaluating-and | 2307.04749 | null | https://arxiv.org/abs/2307.04749v1 | https://arxiv.org/pdf/2307.04749v1.pdf | Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback | The field of text-conditioned image generation has made unparalleled progress with the recent advent of latent diffusion models. While remarkable, as the complexity of given text input increases, the state-of-the-art diffusion models may still fail in generating images which accurately convey the semantics of the given... | ['Liang Zheng', 'Jaskirat Singh'] | 2023-07-10 | null | null | null | null | ['visual-question-answering', 'image-generation'] | ['computer-vision', 'computer-vision'] | [ 3.54334325e-01 1.46801576e-01 5.47725940e-03 -4.28840756e-01
-1.19805539e+00 -7.25232720e-01 9.25231934e-01 1.58817828e-01
-2.10934952e-01 4.72902030e-01 4.81811911e-01 -1.41964808e-01
3.44312824e-02 -4.41988677e-01 -5.25439799e-01 -6.20115995e-01
5.02910614e-01 5.00914931e-01 1.11001581e-01 -1.85945719... | [11.205798149108887, 0.7227482199668884] |
5d87c645-bcb3-42e4-a7d8-b9a0ca576025 | iterative-scene-graph-generation | 2207.13440 | null | https://arxiv.org/abs/2207.13440v1 | https://arxiv.org/pdf/2207.13440v1.pdf | Iterative Scene Graph Generation | The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large solution space, existing approaches to scene graph generation assume certain factorization of the joint distribution to make the estimation ... | ['Leonid Sigal', 'Siddhesh Khandelwal'] | 2022-07-27 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 7.60213673e-01 6.23453856e-01 8.81366357e-02 -3.52044165e-01
-5.49576759e-01 -4.31642890e-01 5.67895889e-01 3.29219967e-01
-2.53622323e-01 7.51224220e-01 1.35928929e-01 -2.43909955e-01
-9.98359360e-03 -1.04807150e+00 -1.31562257e+00 -5.96453249e-01
6.58974275e-02 8.52800906e-01 4.00523037e-01 1.79238930... | [10.313908576965332, 1.6323763132095337] |
23335d65-16be-4562-bb88-ac83598c4d37 | dual-node-and-edge-fairness-aware-graph | 2306.10123 | null | https://arxiv.org/abs/2306.10123v1 | https://arxiv.org/pdf/2306.10123v1.pdf | Dual Node and Edge Fairness-Aware Graph Partition | Fair graph partition of social networks is a crucial step toward ensuring fair and non-discriminatory treatments in unsupervised user analysis. Current fair partition methods typically consider node balance, a notion pursuing a proportionally balanced number of nodes from all demographic groups, but ignore the bias ind... | ['Hongfu Liu', 'Peizhao Li', 'TingWei Liu'] | 2023-06-16 | null | null | null | null | ['node-classification', 'link-prediction'] | ['graphs', 'graphs'] | [-5.53577058e-02 6.14831328e-01 -7.98397839e-01 -4.04717714e-01
3.30926597e-01 -5.82212210e-01 4.23062414e-01 5.48593462e-01
-3.92030105e-02 6.73470795e-01 3.01366955e-01 -2.39959702e-01
-3.65954399e-01 -1.35459173e+00 -2.13982865e-01 -3.30588728e-01
-4.28456485e-01 7.16789305e-01 -2.35908255e-01 -2.55606413... | [8.538326263427734, 5.458032608032227] |
38a0c40c-4978-4a14-943f-80e3e5707c42 | sentiment-and-knowledge-based-algorithmic | 2001.09403 | null | https://arxiv.org/abs/2001.09403v1 | https://arxiv.org/pdf/2001.09403v1.pdf | Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning | Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real world which make it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that di... | ['Osmar R. Zaiane', 'Anandh Perumal', 'Abhishek Nan'] | 2020-01-26 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-3.09231013e-01 -1.24945164e-01 -5.62809050e-01 -1.50260359e-01
1.64377227e-01 -7.71293640e-01 6.74803674e-01 5.75834334e-01
-4.18940991e-01 1.25309873e+00 9.81699675e-02 -4.42349166e-01
-3.31398189e-01 -8.82788599e-01 -5.10248959e-01 -3.91505390e-01
-3.51466030e-01 4.45669353e-01 4.43302393e-01 -6.67050004... | [4.4555253982543945, 4.052131175994873] |
e7ba4a7b-866a-4582-b6d3-d234d03fa475 | uncertainty-aware-multi-modal-ensembling-for | 2010.01440 | null | https://arxiv.org/abs/2010.01440v2 | https://arxiv.org/pdf/2010.01440v2.pdf | Uncertainty-Aware Multi-Modal Ensembling for Severity Prediction of Alzheimer's Dementia | Reliability in Neural Networks (NNs) is crucial in safety-critical applications like healthcare, and uncertainty estimation is a widely researched method to highlight the confidence of NNs in deployment. In this work, we propose an uncertainty-aware boosting technique for multi-modal ensembling to predict Alzheimer's D... | ['Pattie Maes', 'Rishab Khincha', 'Wazeer Zulfikar', 'Utkarsh Sarawgi'] | 2020-10-03 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-3.03040028e-01 1.15531914e-01 1.50905430e-01 -9.44743931e-01
-1.11743808e+00 -1.57411858e-01 3.28585535e-01 5.10875694e-02
-3.32632840e-01 8.08569908e-01 3.57318819e-01 -2.83248186e-01
-3.51410866e-01 -4.47357774e-01 -6.26041114e-01 -5.14692903e-01
-2.34880388e-01 2.08959967e-01 -1.03489757e-02 8.16663802... | [7.3715314865112305, 3.7782275676727295] |
3e16d35d-8099-4af0-97a2-6cd271905dc4 | normalization-of-relative-and-incomplete | 1510.04972 | null | http://arxiv.org/abs/1510.04972v1 | http://arxiv.org/pdf/1510.04972v1.pdf | Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives | We analyze the RI-TIMEXes in temporally annotated corpora and propose two
hypotheses regarding the normalization of RI-TIMEXes in the clinical narrative
domain: the anchor point hypothesis and the anchor relation hypothesis. We
annotate the RI-TIMEXes in three corpora to study the characteristics of
RI-TMEXes in differ... | ['Weiyi Sun', 'Anna Rumshisky', 'Ozlem Uzuner'] | 2015-10-16 | null | null | null | null | ['timex-normalization'] | ['natural-language-processing'] | [ 3.59847844e-01 5.23949504e-01 -5.61017334e-01 -5.84846258e-01
-1.16312790e+00 -6.56826794e-01 6.29874885e-01 6.72863841e-01
-7.22810686e-01 7.09575236e-01 4.89874184e-01 -3.16823632e-01
-7.54918456e-01 -2.33325243e-01 -2.49150544e-01 -5.60833275e-01
-1.66912124e-01 8.06618333e-01 1.61801502e-01 -4.64472532... | [8.550320625305176, 8.984469413757324] |
ce582067-a720-49f1-9286-553ae90f312c | a-language-model-for-grammatical-error | 2307.01609 | null | https://arxiv.org/abs/2307.01609v1 | https://arxiv.org/pdf/2307.01609v1.pdf | A Language Model for Grammatical Error Correction in L2 Russian | Grammatical error correction is one of the fundamental tasks in Natural Language Processing. For the Russian language, most of the spellcheckers available correct typos and other simple errors with high accuracy, but often fail when faced with non-native (L2) writing, since the latter contains errors that are not typic... | ['Anastasia Vyrenkova', 'Ivan Smirnov', 'Ekaterina Rakhilina', 'Sergei Obiedkov', 'Nikita Remnev'] | 2023-07-04 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-1.11890018e-01 2.52728373e-01 8.91944468e-02 -3.29673916e-01
-7.89117277e-01 -5.31388223e-01 1.26015708e-01 6.81833982e-01
-7.88914502e-01 1.04942954e+00 1.97340041e-01 -7.06145287e-01
2.43802950e-01 -5.82089603e-01 -6.72861874e-01 1.70854285e-01
8.02266061e-01 6.35659039e-01 4.43906188e-02 -5.99339902... | [11.047262191772461, 10.684351921081543] |
5be1eb5a-ec8d-469f-8937-956cf2d31a43 | recurrent-memory-decision-transformer | 2306.09459 | null | https://arxiv.org/abs/2306.09459v2 | https://arxiv.org/pdf/2306.09459v2.pdf | Recurrent Memory Decision Transformer | Originally developed for natural language problems, transformer models have recently been widely used in offline reinforcement learning tasks. This is because the agent's history can be represented as a sequence, and the whole task can be reduced to the sequence modeling task. However, the quadratic complexity of the t... | ['Aleksandr I. Panov', 'Dmitry Yudin', 'Alexey K. Kovalev', 'Huzhenyu Zhang', 'Alexey Staroverov', 'Arkadii Bessonov'] | 2023-06-15 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-1.72677003e-02 -9.55036730e-02 -3.23756963e-01 3.17517808e-03
-4.74895000e-01 -5.77220023e-01 6.98255599e-01 -9.90181491e-02
-6.95985734e-01 7.86180437e-01 -8.77214074e-02 -5.61460316e-01
-3.61730270e-02 -9.73947644e-01 -6.52735054e-01 -8.59938800e-01
-1.74879432e-01 3.68202597e-01 5.13531983e-01 -4.79535490... | [4.06476354598999, 1.6651772260665894] |
96074683-474d-4243-9883-4e9b1f232ce0 | towards-explainable-evaluation-metrics-for | 2203.11131 | null | https://arxiv.org/abs/2203.11131v1 | https://arxiv.org/pdf/2203.11131v1.pdf | Towards Explainable Evaluation Metrics for Natural Language Generation | Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics (such as BERTScore or MoverScore) are based on black-box language models such as BERT or XLM-R. They often achieve strong correlations with human judgments, but recent research indicates that the lower-quality classical metrics remai... | ['Steffen Eger', 'Yang Gao', 'Wei Zhao', 'Marina Fomicheva', 'Piyawat Lertvittayakumjorn', 'Christoph Leiter'] | 2022-03-21 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [ 3.40889305e-01 6.68130755e-01 -5.76216102e-01 -5.16402841e-01
-1.25751543e+00 -8.57329071e-01 8.59400392e-01 3.26441944e-01
-1.49236068e-01 1.17948008e+00 5.07889032e-01 -7.54939795e-01
-3.17576408e-01 -4.99846101e-01 -4.15381342e-01 -5.13508692e-02
2.50286072e-01 7.92308450e-01 -3.04288924e-01 -4.50265706... | [11.517572402954102, 9.599793434143066] |
b69d1f1b-cf0e-4f9d-b46e-26514ed3718d | unsupervised-learning-of-depth-optical-flow-1 | 2003.00766 | null | https://arxiv.org/abs/2003.00766v3 | https://arxiv.org/pdf/2003.00766v3.pdf | Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry | In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames are high-profile concerns recently. By analyzing tasks above, pixels in the middle frame are modeled into three parts: the rigid region, the ... | ['Xinlei Wang', 'Hesheng Wang', 'Yong Wang', 'Jingchuan Wang', 'Guangming Wang', 'Chi Zhang'] | 2020-03-02 | unsupervised-learning-of-depth-optical-flow | null | null | arxiv-2020-3 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 6.18650094e-02 -4.04329523e-02 -3.30870569e-01 -4.69353884e-01
-7.23471567e-02 -3.66753161e-01 2.43344292e-01 -7.86858261e-01
-4.55834895e-01 8.59199762e-01 2.42295563e-01 -6.33993372e-03
4.30303514e-01 -6.98132038e-01 -7.86096275e-01 -1.01068199e+00
3.32341164e-01 -9.98120233e-02 5.23493707e-01 2.48525247... | [8.597942352294922, -2.098019599914551] |
07e3ff0f-3f88-44f7-9502-9e27431d9c95 | a-novel-framework-based-on-unknown-estimation | 2209.09616 | null | https://arxiv.org/abs/2209.09616v7 | https://arxiv.org/pdf/2209.09616v7.pdf | Provably Uncertainty-Guided Universal Domain Adaptation | Universal domain adaptation (UniDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain without any assumptions of the label sets, which requires distinguishing the unknown samples from the known ones in the target domain. A main challenge of UniDA is that the nonidentical label se... | ['Wei zhang', 'Yibin Li', 'Paul L. Rosin', 'Ran Song', 'Lin Zhang', 'Yifan Wang'] | 2022-09-19 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 2.82474924e-02 9.48070288e-02 -4.48603600e-01 -4.71300006e-01
-8.77176046e-01 -5.40217936e-01 3.58831108e-01 -2.62502372e-01
-1.59567073e-02 9.49426532e-01 -1.47803515e-01 2.72540033e-01
-1.26685306e-01 -6.11252666e-01 -6.60805166e-01 -1.04461706e+00
4.63582844e-01 7.70711958e-01 2.15164334e-01 3.32581580... | [10.356827735900879, 3.1776435375213623] |
ee246fd3-0e39-4c38-9366-6642c8b467c0 | coupling-global-and-local-context-for | null | null | https://aclanthology.org/D19-1465 | https://aclanthology.org/D19-1465.pdf | Coupling Global and Local Context for Unsupervised Aspect Extraction | Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions. To identify aspects, most previous efforts focus on using sequence tagging models trained on human-annotated data. This work studies unsupervised aspect extraction and explores how words appear in global context (on ... | ['Kam-Fai Wong', 'Xixin Wu', 'Lingzhi Wang', 'Jing Li', 'Ming Liao', 'Haisong Zhang'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['aspect-extraction'] | ['natural-language-processing'] | [ 3.49374682e-01 4.71890420e-02 -5.77572048e-01 -6.05354071e-01
-6.50674820e-01 -8.04507971e-01 8.36554527e-01 6.18153393e-01
-3.71501625e-01 8.40413630e-01 9.11249578e-01 -4.14578378e-01
3.40359181e-01 -8.25921059e-01 -2.65714347e-01 -4.75784183e-01
-6.09470308e-02 2.74301827e-01 -3.92745584e-02 -5.04900098... | [11.447754859924316, 6.758535861968994] |
5fe9885d-409b-4814-8ed7-bfce1f534b37 | multi-view-mera-subspace-clustering | 2305.09095 | null | https://arxiv.org/abs/2305.09095v1 | https://arxiv.org/pdf/2305.09095v1.pdf | Multi-view MERA Subspace Clustering | Tensor-based multi-view subspace clustering (MSC) can capture high-order correlation in the self-representation tensor. Current tensor decompositions for MSC suffer from highly unbalanced unfolding matrices or rotation sensitivity, failing to fully explore inter/intra-view information. Using the advanced tensor network... | ['Yipeng Liu', 'Yazhou Ren', 'Zihan Li', 'Jie Chen', 'Ce Zhu', 'Zhen Long'] | 2023-05-16 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.47182924e-01 -4.05015022e-01 -8.19542408e-02 7.57187158e-02
-6.13183975e-01 -6.99975789e-01 4.95598108e-01 -4.20606256e-01
2.20012292e-01 -1.91914719e-02 6.27732456e-01 -6.15025461e-02
-6.36877656e-01 -4.24315929e-01 -2.55202532e-01 -1.07954407e+00
-3.64679843e-01 4.18539077e-01 -6.55241832e-02 -4.56386507... | [8.217118263244629, 4.62626314163208] |
9a530e17-777e-44ab-a7b3-34e50ed56d99 | exploiting-points-and-lines-in-regression | 1710.10519 | null | http://arxiv.org/abs/1710.10519v3 | http://arxiv.org/pdf/1710.10519v3.pdf | Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization | Camera relocalization plays a vital role in many robotics and computer vision
tasks, such as global localization, recovery from tracking failure and loop
closure detection. Recent random forests based methods exploit randomly sampled
pixel comparison features to predict 3D world locations for 2D image locations
to guid... | ['Clarence de Silva', 'Julien Valentin', 'James J. Little', 'Lili Meng', 'Frederick Tung'] | 2017-10-28 | null | null | null | null | ['camera-relocalization', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 1.31735608e-01 -4.01043773e-01 -3.19461882e-01 -1.53148264e-01
-7.48423934e-01 -6.67266130e-01 9.08853710e-01 -4.19933982e-02
-7.24904716e-01 8.90875459e-01 -7.61360526e-02 -1.80610828e-02
-1.81536585e-01 -3.60536516e-01 -1.00095570e+00 -7.44164586e-01
-4.79818135e-02 4.11708295e-01 4.51830924e-01 1.96807861... | [7.5651936531066895, -2.323021411895752] |
d35dc249-b968-4910-93af-0d73fcee7d29 | solving-geometry-problems-combining-text-and | null | null | https://aclanthology.org/D15-1171 | https://aclanthology.org/D15-1171.pdf | Solving Geometry Problems: Combining Text and Diagram Interpretation | null | ['Hannaneh Hajishirzi', 'Minjoon Seo', 'Ali Farhadi', 'Clint Malcolm', 'Oren Etzioni'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['mathematical-question-answering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.270893573760986, 3.539719343185425] |
50ec9f93-24d4-42c3-91af-ad27d6eb9e86 | dual-stream-shallow-networks-for-facial-micro | null | null | https://ieeexplore.ieee.org/abstract/document/8802965 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8802965 | Dual-stream shallow networks for facial micro-expression recognition | Micro-expressions are spontaneous, brief and subtle facial muscle movements that exposes underlying emotions. Motivated by recent exploits into deep learning for micro-expression analysis, we propose a lightweight dual-stream shallow network in the form of a pair of truncated CNNs with heterogeneous input features. The... | ['Raphael C. -W. Phan', 'Sze-Teng Liong', 'Huai-Qian Khor', 'John See', 'Weiyao Lin'] | 2019-08-26 | null | null | null | 2019-ieee-international-conference-on-image | ['micro-expression-recognition'] | ['computer-vision'] | [ 1.78044155e-01 2.71873981e-01 -3.93204600e-01 -6.96419775e-01
-5.57350576e-01 -3.04308385e-01 6.33974075e-01 -1.93147600e-01
-3.71252060e-01 3.39883715e-01 3.20863783e-01 4.22663152e-01
3.16350460e-01 -2.85400033e-01 -6.41005218e-01 -8.97205412e-01
-4.82990056e-01 -2.69013196e-01 -5.81288218e-01 -5.20408988... | [13.589929580688477, 1.8101000785827637] |
b4b8fe18-1640-4939-b223-5461bcfc99b5 | query-embedding-pruning-for-dense-retrieval | 2108.10341 | null | https://arxiv.org/abs/2108.10341v1 | https://arxiv.org/pdf/2108.10341v1.pdf | Query Embedding Pruning for Dense Retrieval | Recent advances in dense retrieval techniques have offered the promise of being able not just to re-rank documents using contextualised language models such as BERT, but also to use such models to identify documents from the collection in the first place. However, when using dense retrieval approaches that use multiple... | ['Craig Macdonald', 'Nicola Tonellotto'] | 2021-08-23 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-4.88391099e-03 -2.65037835e-01 2.88480334e-02 1.29788488e-01
-1.32322991e+00 -6.14496946e-01 7.64078379e-01 8.97110105e-01
-9.37288523e-01 4.71893758e-01 3.49193752e-01 -2.18949959e-01
-6.28504992e-01 -8.77158225e-01 -2.90504754e-01 -5.05194485e-01
-3.01695347e-01 8.66830945e-01 5.51771939e-01 -2.63541698... | [11.465134620666504, 7.587547779083252] |
a3376d9c-c68c-45cc-b8f9-0cc21e5ed245 | outlier-detection-on-network-flow-analysis | 1808.02024 | null | http://arxiv.org/abs/1808.02024v1 | http://arxiv.org/pdf/1808.02024v1.pdf | Outlier detection on network flow analysis | It is important to be able to detect and classify malicious network traffic
flows such as DDoS attacks from benign flows. Normally the task is performed by
using supervised classification algorithms. In this paper we analyze the usage
of outlier detection algorithms for the network traffic classification problem. | ['Quang-Vinh Dang'] | 2018-08-06 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-1.56044349e-01 -3.24653089e-01 -1.14206634e-01 -5.63693464e-01
3.10015529e-01 -3.80243808e-01 3.62759948e-01 4.20139462e-01
-3.22480530e-01 7.17991889e-01 -5.56867540e-01 -7.57879913e-01
2.14528795e-02 -7.85902441e-01 9.23275873e-02 -4.76737618e-01
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637116da-53dd-45c8-80d6-6297d58b0405 | the-ntnu-yzu-system-in-the-aesw-shared-task | null | null | https://aclanthology.org/W16-0513 | https://aclanthology.org/W16-0513.pdf | The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network | null | ['Yuen-Hsien Tseng', 'Liang-Chih Yu', 'Bo-Lin Lin', 'Lung-Hao Lee'] | 2016-06-01 | null | null | null | ws-2016-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.404189109802246, 3.694606065750122] |
e3848d65-950f-4936-8093-f5c82df99324 | a-re-balancing-strategy-for-class-imbalanced | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_A_Re-Balancing_Strategy_for_Class-Imbalanced_Classification_Based_on_Instance_Difficulty_CVPR_2022_paper.pdf | A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty | Real-world data often exhibits class-imbalanced distributions, where a few classes (a.k.a. majority classes) occupy most instances and lots of classes (a.k.a. minority classes) have few instances. Neural classification models usually perform poorly on minority classes when training on such imbalanced datasets. To i... | ['Xueqi Cheng', 'Zizhen Wang', 'Yixing Fan', 'Ruqing Zhang', 'Jiafeng Guo', 'Sihao Yu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.41888991e-01 7.09574297e-02 -5.61783016e-01 -6.23468101e-01
-2.28339911e-01 -3.15318286e-01 1.83908075e-01 5.06884694e-01
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-2.77965367e-01 -1.32894838e+00 -6.35079086e-01 -9.50877368e-01
3.42720360e-01 7.70878375e-01 2.80376345e-01 -1.32780224... | [9.152624130249023, 3.882044553756714] |
530099c1-d838-4690-b9ce-fe50ac98bd93 | asynchronous-multi-view-slam | 2101.06562 | null | https://arxiv.org/abs/2101.06562v3 | https://arxiv.org/pdf/2101.06562v3.pdf | Asynchronous Multi-View SLAM | Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate informatio... | ['Shenlong Wang', 'Raquel Urtasun', 'Ioan Andrei Bârsan', 'Can Cui', 'Anqi Joyce Yang'] | 2021-01-17 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [-9.77816209e-02 -5.49374342e-01 -9.33475122e-02 -4.36047196e-01
-8.60829234e-01 -9.43675816e-01 5.65883100e-01 6.58816993e-02
-4.95878607e-01 6.52255118e-01 -2.92282533e-02 -1.97077319e-01
2.97433380e-02 -3.20292115e-01 -9.05245125e-01 -3.10488194e-01
1.16204917e-01 4.80603576e-01 4.81975406e-01 -3.41020405... | [7.312077045440674, -2.1549508571624756] |
df5eaffa-4f2d-41a7-9a45-b2a75076b0be | morphological-disambiguation-and-text | null | null | https://aclanthology.org/W14-5305 | https://aclanthology.org/W14-5305.pdf | Morphological Disambiguation and Text Normalization for Southern Quechua Varieties | null | ['er', 'Annette Rios Gonzales', 'Richard Alex Castro Mamani'] | 2014-08-01 | null | null | null | ws-2014-8 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.420291900634766, 3.7392120361328125] |
ca508f42-8ea5-4667-9bdb-f5a6a656a73a | gpo-global-plane-optimization-for-fast-and | 2004.12051 | null | https://arxiv.org/abs/2004.12051v2 | https://arxiv.org/pdf/2004.12051v2.pdf | GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization | Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obt... | ['Fei-Yue Wang', 'Sicong Du', 'Linfu Wen', 'Yao Chen', 'Hengkai Guo', 'Yilun Lin', 'Xiangbing Meng'] | 2020-04-25 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.72313284e-02 -4.71585244e-01 -1.59294829e-01 -3.69767994e-01
-7.44768798e-01 -7.62479365e-01 7.46910751e-01 -2.79789895e-01
-4.99811679e-01 8.09040189e-01 -2.28376299e-01 -9.93068367e-02
-4.13717888e-02 -5.71092725e-01 -7.76396930e-01 -4.76498783e-01
-6.29226342e-02 6.50635421e-01 2.95186073e-01 -1.20934188... | [7.417242050170898, -2.201425790786743] |
0612f44c-dedd-4907-8fa6-589411cb33c3 | cone-an-efficient-coarse-to-fine-alignment | 2209.10918 | null | https://arxiv.org/abs/2209.10918v2 | https://arxiv.org/pdf/2209.10918v2.pdf | CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Grounding | This paper tackles an emerging and challenging problem of long video temporal grounding~(VTG) that localizes video moments related to a natural language (NL) query. Compared with short videos, long videos are also highly demanded but less explored, which brings new challenges in higher inference computation cost and we... | ['Nan Duan', 'Zheng Shou', 'Chong-Wah Ngo', 'Wing-Kwong Chan', 'Kun Yan', 'Difei Gao', 'Lei Ji', 'Wanjun Zhong', 'Zhijian Hou'] | 2022-09-22 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-1.54001191e-01 -4.19645876e-01 -8.45607042e-01 -2.16139659e-01
-1.18321860e+00 -5.12211323e-01 5.06503999e-01 -1.95988387e-01
-5.01004219e-01 4.50767994e-01 3.87710989e-01 -1.07140891e-01
-1.23713478e-01 -5.89327395e-01 -8.69430542e-01 -5.31657398e-01
-2.91252822e-01 1.88047647e-01 5.01631796e-01 -5.19441292... | [9.736674308776855, 0.5152696967124939] |
c89dd6a0-deb6-48c6-8dae-503183395f55 | on-sentence-representations-for-propaganda | null | null | https://aclanthology.org/D19-5015 | https://aclanthology.org/D19-5015.pdf | On Sentence Representations for Propaganda Detection: From Handcrafted Features to Word Embeddings | Bias is ubiquitous in most online sources of natural language, from news media to social networks. Given the steady shift in news consumption behavior from traditional outlets to online sources, the automatic detection of propaganda, in which information is shaped to purposefully foster a predetermined agenda, is an in... | ["Andr{\\'e} Ferreira Cruz", 'Gil Rocha', 'Henrique Lopes Cardoso'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 3.59018654e-01 1.65516019e-01 -3.65976185e-01 -1.54435694e-01
-8.19423318e-01 -4.92468148e-01 1.19943786e+00 6.36200964e-01
-9.27583933e-01 5.28495967e-01 1.08190417e+00 -5.84267080e-01
4.23213273e-01 -7.25364447e-01 -5.93379200e-01 -5.24760664e-01
9.36535597e-02 2.15103492e-01 9.70780943e-03 -4.26658124... | [8.503266334533691, 10.618865966796875] |
86c52558-2253-4c90-a877-43580da7ad48 | mvp-seg-multi-view-prompt-learning-for-open | 2304.06957 | null | https://arxiv.org/abs/2304.06957v1 | https://arxiv.org/pdf/2304.06957v1.pdf | MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation | CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investigated, where most efforts directly adopt CLIP features without deliberative adaptations. In this work, we first demonstrate the necessity of ... | ['Baochang Zhang', 'Yao Hu', 'Xu Tang', 'XiaoLong Jiang', 'Yan Gao', 'Qimeng Wang', 'Jie Guo'] | 2023-04-14 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 5.60465753e-01 1.24160059e-01 -4.22995627e-01 -3.61321509e-01
-1.12379587e+00 -3.62431437e-01 5.12849629e-01 -1.18491679e-01
-5.69821119e-01 3.95516425e-01 2.08329082e-01 1.71015665e-01
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4.56245422e-01 2.54482538e-01 5.86920500e-01 -2.78576881... | [9.667485237121582, 0.7046632170677185] |
4c59552d-3cda-4a29-ad1d-b09dfbf8cca1 | meta-learning-curiosity-algorithms-1 | 2003.05325 | null | https://arxiv.org/abs/2003.05325v1 | https://arxiv.org/pdf/2003.05325v1.pdf | Meta-learning curiosity algorithms | We hypothesize that curiosity is a mechanism found by evolution that encourages meaningful exploration early in an agent's life in order to expose it to experiences that enable it to obtain high rewards over the course of its lifetime. We formulate the problem of generating curious behavior as one of meta-learning: an ... | ['Tomas Lozano-Perez', 'Leslie Pack Kaelbling', 'Martin F. Schneider', 'Ferran Alet'] | 2020-03-11 | null | https://openreview.net/forum?id=BygdyxHFDS | https://openreview.net/pdf?id=BygdyxHFDS | iclr-2020-1 | ['acrobot'] | ['playing-games'] | [-1.21914841e-01 3.15633953e-01 -1.58064365e-01 -2.62642950e-01
-3.84969026e-01 -4.54697460e-01 7.76119351e-01 1.40882701e-01
-7.58896649e-01 1.09948683e+00 4.28874679e-02 -2.65643179e-01
-1.75931841e-01 -9.38053846e-01 -9.78265762e-01 -7.58646667e-01
-4.11014438e-01 5.17465353e-01 2.38963693e-01 -8.80701244... | [4.002699851989746, 1.6133406162261963] |
4f190da4-18db-4c75-b433-22490a79c6ea | affect-analysis-in-the-wild-valence-arousal | 2103.15792 | null | https://arxiv.org/abs/2103.15792v1 | https://arxiv.org/pdf/2103.15792v1.pdf | Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework | Affect recognition based on subjects' facial expressions has been a topic of major research in the attempt to generate machines that can understand the way subjects feel, act and react. In the past, due to the unavailability of large amounts of data captured in real-life situations, research has mainly focused on contr... | ['Stefanos Zafeiriou', 'Dimitrios Kollias'] | 2021-03-29 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 6.33101016e-02 -1.11714043e-01 2.00725451e-01 -7.79247105e-01
-3.03376019e-01 -2.10015148e-01 6.91868186e-01 -4.66737524e-02
-2.95652479e-01 6.11612201e-01 -9.58768558e-03 3.79095107e-01
1.53994197e-02 -6.69161081e-01 -1.33405939e-01 -8.18543017e-01
-2.86033362e-01 3.17461371e-01 -6.37334645e-01 -4.88342941... | [13.542917251586914, 2.026261329650879] |
4ff0226e-eb59-4878-bc5f-7adc74243e89 | deep-metric-learning-for-unsupervised-remote | 2303.09536 | null | https://arxiv.org/abs/2303.09536v1 | https://arxiv.org/pdf/2303.09536v1.pdf | Deep Metric Learning for Unsupervised Remote Sensing Change Detection | Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cover, land use, human development analysis, and disaster response. The performance of existing RS-CD methods is attributed to training on large... | ['Vishal M. Patel', 'Wele Gedara Chaminda Bandara'] | 2023-03-16 | null | null | null | null | ['change-detection', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.30207011e-01 -3.19057345e-01 1.63704157e-01 -6.23338580e-01
-7.16075361e-01 -2.10769132e-01 6.28771663e-01 9.04451236e-02
-5.09146512e-01 6.02183044e-01 4.82968092e-02 -1.10722102e-01
-2.51041114e-01 -1.35278893e+00 -5.77718496e-01 -8.53215039e-01
-3.07389379e-01 1.11777700e-01 2.98241287e-01 -3.04704815... | [9.642294883728027, -1.2726634740829468] |
7db5c9e1-380b-45c3-ae37-5d980f33c7cf | real-rawvsr-real-world-raw-video-super | 2209.12475 | null | https://arxiv.org/abs/2209.12475v1 | https://arxiv.org/pdf/2209.12475v1.pdf | Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset | In recent years, real image super-resolution (SR) has achieved promising results due to the development of SR datasets and corresponding real SR methods. In contrast, the field of real video SR is lagging behind, especially for real raw videos. Considering the superiority of raw image SR over sRGB image SR, we construc... | ['Jingyu Yang', 'Zhiming Zhang', 'Huanjing Yue'] | 2022-09-26 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 7.76021361e-01 -3.68182778e-01 -1.05581731e-02 -1.30269289e-01
-7.26257741e-01 -1.77387342e-01 2.72422165e-01 -9.35115099e-01
-1.24839693e-01 7.39207804e-01 1.94925308e-01 -1.01848863e-01
4.65603694e-02 -7.12452114e-01 -8.84096920e-01 -8.41945410e-01
9.17733535e-02 -9.73619372e-02 6.26839817e-01 -3.96897972... | [10.988832473754883, -2.0566396713256836] |
b1b9fa5f-f805-4a08-9bb2-6ec7fbc64715 | sienet-siamese-expansion-network-for-image | 2007.03851 | null | https://arxiv.org/abs/2007.03851v1 | https://arxiv.org/pdf/2007.03851v1.pdf | SiENet: Siamese Expansion Network for Image Extrapolation | Different from image inpainting, image outpainting has relative less context in the image center to capture and more content at the image border to predict. Therefore, classical encoder-decoder pipeline of existing methods may not predict the outstretched unknown content perfectly. In this paper, a novel two-stage siam... | ['Xiaofeng Zhang', 'Songsong Wu', 'Ming Tao', 'Guoping Jiang', 'Feng Chen', 'Cailing Wang'] | 2020-07-08 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 3.29779953e-01 4.44351196e-01 -2.60188609e-01 -2.16097206e-01
-7.16338634e-01 -2.90452003e-01 2.02329963e-01 -4.59841162e-01
-1.01554103e-01 9.35878396e-01 4.60368365e-01 -3.62354331e-03
4.43804204e-01 -8.00378144e-01 -1.33148670e+00 -3.76625717e-01
1.60148084e-01 -5.64024337e-02 2.58927733e-01 -2.72966266... | [11.39574909210205, -1.0918046236038208] |
965766e5-cc84-41c1-ab23-f442849191d6 | application-of-machine-learning-regression | 2212.04279 | null | https://arxiv.org/abs/2212.04279v1 | https://arxiv.org/pdf/2212.04279v1.pdf | Application of machine learning regression models to inverse eigenvalue problems | In this work, we study the numerical solution of inverse eigenvalue problems from a machine learning perspective. Two different problems are considered: the inverse Strum-Liouville eigenvalue problem for symmetric potentials and the inverse transmission eigenvalue problem for spherically symmetric refractive indices. F... | ['Andreas Ntargaras', 'Nikolaos Pallikarakis'] | 2022-12-08 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [ 5.40905893e-01 6.32560700e-02 1.08693436e-01 -1.95849035e-02
-4.78458017e-01 -3.31933647e-01 3.98194909e-01 -2.48453721e-01
-7.48686016e-01 1.11836755e+00 -3.72102350e-01 -4.74504262e-01
-8.73402655e-01 -6.83230698e-01 -3.90795588e-01 -1.06284165e+00
-2.34066054e-01 1.03051424e+00 -1.00572526e-01 -2.60314554... | [6.916453838348389, 4.048468589782715] |
7d58c651-8eb8-4c79-9546-c35d3c07f989 | meta-cotgan-a-meta-cooperative-training | 2003.11530 | null | https://arxiv.org/abs/2003.11530v1 | https://arxiv.org/pdf/2003.11530v1.pdf | Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation | Training generative models that can generate high-quality text with sufficient diversity is an important open problem for Natural Language Generation (NLG) community. Recently, generative adversarial models have been applied extensively on text generation tasks, where the adversarially trained generators alleviate the ... | ['Haiyan Yin', 'Xu Li', 'Dingcheng Li', 'Ping Li'] | 2020-03-12 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.00624108e-01 4.55556244e-01 4.27400954e-02 1.88868478e-01
-8.04065228e-01 -8.44903052e-01 9.15413737e-01 -1.73986509e-01
-9.75846499e-03 7.04046130e-01 2.42911458e-01 -2.94481784e-01
6.98998198e-02 -1.18479073e+00 -7.93435872e-01 -8.80273223e-01
2.60527462e-01 4.01652604e-01 -1.61589429e-01 -5.77424109... | [11.813525199890137, 9.211320877075195] |
28f20d2d-babc-4c6b-9323-afefa5ae2efb | acoustics-based-intent-recognition-using | 2011.03646 | null | https://arxiv.org/abs/2011.03646v2 | https://arxiv.org/pdf/2011.03646v2.pdf | Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages | With recent advancements in language technologies, humans are now speaking to devices. Increasing the reach of spoken language technologies requires building systems in local languages. A major bottleneck here are the underlying data-intensive parts that make up such systems, including automatic speech recognition (ASR... | ['Alan W Black', 'Sai Krishna Rallabandi', 'Xinjian Li', 'Akshat Gupta'] | 2020-11-07 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 1.64432853e-01 2.11374424e-02 1.61827266e-01 -8.59964013e-01
-1.08717656e+00 -5.28607488e-01 4.67326492e-01 -3.43488485e-01
-5.14737308e-01 2.44000763e-01 5.73922753e-01 -4.69536752e-01
5.89261651e-01 -3.36416811e-01 -3.86776656e-01 -3.98410857e-01
-9.13188085e-02 7.53793716e-01 6.93569705e-02 -4.52372164... | [14.121780395507812, 6.738956451416016] |
7976f961-85e9-4125-a73b-3c2de59e5a7a | on-the-robustness-of-deep-learning-based-mri | 2211.04930 | null | https://arxiv.org/abs/2211.04930v2 | https://arxiv.org/pdf/2211.04930v2.pdf | On the Robustness of deep learning-based MRI Reconstruction to image transformations | Although deep learning (DL) has received much attention in accelerated magnetic resonance imaging (MRI), recent studies show that tiny input perturbations may lead to instabilities of DL-based MRI reconstruction models. However, the approaches of robustifying these models are underdeveloped. Compared to image classific... | ['Sijia Liu', 'Mehmet Akçakaya', 'Yimeng Zhang', 'Mingyi Hong', 'Jinghan Jia'] | 2022-11-09 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 5.59800923e-01 7.63152093e-02 3.58741768e-02 -2.17863202e-01
-7.67740905e-01 -4.59699214e-01 3.43121439e-01 -2.84570664e-01
-3.41728300e-01 6.96506560e-01 1.83931470e-01 -3.57473701e-01
-2.21061617e-01 -5.05369902e-01 -9.94267642e-01 -1.04408336e+00
-1.21068656e-01 -3.24640840e-01 1.95405841e-01 -2.49051705... | [13.564188003540039, -2.3520665168762207] |
1dbc81b3-5236-4b79-8370-74b51761467c | safe-reinforcement-learning-with-dead-ends | 2306.13944 | null | https://arxiv.org/abs/2306.13944v1 | https://arxiv.org/pdf/2306.13944v1.pdf | Safe Reinforcement Learning with Dead-Ends Avoidance and Recovery | Safety is one of the main challenges in applying reinforcement learning to realistic environmental tasks. To ensure safety during and after training process, existing methods tend to adopt overly conservative policy to avoid unsafe situations. However, overly conservative policy severely hinders the exploration, and ma... | ['Junqiao Zhao', 'Chen Ye', 'Di Zhang', 'Chang Huang', 'Hongtu Zhou', 'Hai Zhang', 'Xiao Zhang'] | 2023-06-24 | null | null | null | null | ['continuous-control', 'safe-exploration'] | ['playing-games', 'robots'] | [ 2.14930952e-01 3.83324623e-01 -4.11402196e-01 -1.24515750e-01
-4.26085383e-01 -5.59596717e-01 4.09747303e-01 1.47058770e-01
-7.79296041e-01 1.04230785e+00 -1.07142583e-01 -5.02806008e-01
-1.81935638e-01 -8.49709570e-01 -7.66862571e-01 -9.78959262e-01
-1.64729059e-01 -4.08957414e-02 3.63722116e-01 -1.96567327... | [4.450503349304199, 2.0968198776245117] |
307789a0-71e0-456d-94f8-de2379915f75 | understanding-graph-embedding-methods-and | 2012.08019 | null | https://arxiv.org/abs/2012.08019v1 | https://arxiv.org/pdf/2012.08019v1.pdf | Understanding graph embedding methods and their applications | Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques c... | ['Mengjia Xu'] | 2020-12-15 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-3.08029413e-01 2.88356870e-01 -2.45863467e-01 7.35616013e-02
1.30223215e-01 -4.60255295e-01 3.24312001e-01 6.15583062e-01
3.28943700e-01 4.26733971e-01 -5.91321709e-03 -3.20428729e-01
-6.18953586e-01 -1.17690754e+00 -1.52316049e-01 -8.89176071e-01
-7.10729539e-01 4.57518637e-01 3.55211794e-02 -1.43112734... | [7.1005377769470215, 6.075245380401611] |
304382ea-806e-4290-b5a9-010f4566e40a | telesto-a-graph-neural-network-model-for | 2102.12877 | null | https://arxiv.org/abs/2102.12877v2 | https://arxiv.org/pdf/2102.12877v2.pdf | TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services | Deployment, operation and maintenance of large IT systems becomes increasingly complex and puts human experts under extreme stress when problems occur. Therefore, utilization of machine learning (ML) and artificial intelligence (AI) is applied on IT system operation and maintenance - summarized in the term AIOps. One s... | ['Alexander Acker', 'Dominik Scheinert'] | 2021-02-25 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [-1.89852864e-01 -5.14526367e-01 1.00783594e-01 -7.57482275e-02
2.79303789e-01 -3.77072513e-01 5.63107967e-01 8.50888252e-01
-1.19731739e-01 4.70379382e-01 -5.65916598e-01 -5.86433232e-01
-5.35017729e-01 -8.04009020e-01 -2.46601716e-01 -6.47774458e-01
-1.05423963e+00 6.63806319e-01 7.61499554e-02 -9.81369913... | [7.246299743652344, 2.8279638290405273] |
493bf001-d352-48f4-b65e-6a24e975094d | beyond-fair-pay-ethical-implications-of-nlp | 2104.10097 | null | https://arxiv.org/abs/2104.10097v1 | https://arxiv.org/pdf/2104.10097v1.pdf | Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing | The use of crowdworkers in NLP research is growing rapidly, in tandem with the exponential increase in research production in machine learning and AI. Ethical discussion regarding the use of crowdworkers within the NLP research community is typically confined in scope to issues related to labor conditions such as fair ... | ['Lun-Wei Ku', 'Soumya Ray', 'Jan Fell', 'Boaz Shmueli'] | 2021-04-20 | null | https://aclanthology.org/2021.naacl-main.295 | https://aclanthology.org/2021.naacl-main.295.pdf | naacl-2021-4 | ['misconceptions'] | ['miscellaneous'] | [ 3.76437344e-02 4.47040826e-01 -6.17699437e-02 -4.70866203e-01
-5.69213986e-01 -8.93482089e-01 4.14804727e-01 5.98080695e-01
-1.03872514e+00 9.54075396e-01 6.04998291e-01 -6.08060181e-01
1.64003506e-01 -1.97195694e-01 -4.84115332e-01 -3.69955212e-01
7.56028235e-01 1.29454866e-01 -1.24411650e-01 1.07401147... | [9.297706604003906, 6.572579860687256] |
910c35e3-299b-44fb-8bbf-1d76418bf242 | event-based-timestamp-image-encoding-network | 2104.05145 | null | https://arxiv.org/abs/2104.05145v2 | https://arxiv.org/pdf/2104.05145v2.pdf | Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation | Event camera is an asynchronous, high frequency vision sensor with low power consumption, which is suitable for human action understanding task. It is vital to encode the spatial-temporal information of event data properly and use standard computer vision tool to learn from the data. In this work, we propose a timestam... | ['Chaoxing Huang'] | 2021-04-12 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 7.64763117e-01 -3.00284296e-01 -3.17495376e-01 -5.65999210e-01
-1.50922522e-01 -1.89057097e-01 8.19384754e-01 -3.24165374e-01
-5.16790748e-01 3.89840305e-01 3.57084721e-01 -7.22976401e-02
1.86255753e-01 -5.63654184e-01 -6.28736496e-01 -6.81756616e-01
-1.49902150e-01 1.22750662e-01 3.99726570e-01 1.96302488... | [8.141190528869629, 0.43895459175109863] |
519dea7f-f8c7-457e-8c8e-4e8cea6df9fa | few-shot-table-to-text-generation-with | 2108.12516 | null | https://arxiv.org/abs/2108.12516v2 | https://arxiv.org/pdf/2108.12516v2.pdf | Few-Shot Table-to-Text Generation with Prototype Memory | Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly rely on large-scale training examples, limiting their applicability in real-world applications. To address this, we propose a new framework:... | ['Nigel Collier', 'Simon Baker', 'Zaiqiao Meng', 'Yixuan Su'] | 2021-08-27 | null | https://aclanthology.org/2021.findings-emnlp.77 | https://aclanthology.org/2021.findings-emnlp.77.pdf | findings-emnlp-2021-11 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.41099471e-01 -3.42638940e-02 -3.65948468e-01 -2.94507116e-01
-1.10146558e+00 -2.80459076e-01 1.03780031e+00 1.33198202e-01
-1.94614064e-02 9.81311023e-01 2.74710268e-01 -2.77756974e-02
9.16621611e-02 -9.20208931e-01 -4.66448396e-01 -5.01776099e-01
3.88833106e-01 7.56242692e-01 2.17058271e-01 -5.64538598... | [11.713212013244629, 8.799840927124023] |
ce8b7724-476f-4cd7-b4a9-1315744560ce | submodlib-a-submodular-optimization-library | 2202.10680 | null | https://arxiv.org/abs/2202.10680v2 | https://arxiv.org/pdf/2202.10680v2.pdf | Submodlib: A Submodular Optimization Library | Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include d... | ['Rishabh Iyer', 'Ganesh Ramakrishnan', 'Vishal Kaushal'] | 2022-02-22 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-2.48877227e-01 1.39188647e-01 -7.33602583e-01 -4.90204811e-01
-7.47630417e-01 -8.15923572e-01 1.64860025e-01 3.68335128e-01
5.54374270e-02 9.74948943e-01 5.04230261e-01 1.94820940e-01
-4.67619807e-01 -7.53020108e-01 -5.13952255e-01 -6.79882109e-01
3.62155624e-02 9.79958951e-01 -3.04882918e-02 -3.67749214... | [6.590023517608643, 4.8942718505859375] |
b016dc1a-6cea-44b7-870c-295809deb534 | minimax-optimal-reward-agnostic-exploration | 2304.07278 | null | https://arxiv.org/abs/2304.07278v1 | https://arxiv.org/pdf/2304.07278v1.pdf | Minimax-Optimal Reward-Agnostic Exploration in Reinforcement Learning | This paper studies reward-agnostic exploration in reinforcement learning (RL) -- a scenario where the learner is unware of the reward functions during the exploration stage -- and designs an algorithm that improves over the state of the art. More precisely, consider a finite-horizon non-stationary Markov decision proce... | ['Jianqing Fan', 'Yuxin Chen', 'Yuling Yan', 'Gen Li'] | 2023-04-14 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 9.31369737e-02 4.86224413e-01 -6.34558201e-01 1.08334832e-01
-1.22869122e+00 -9.77865994e-01 2.10625172e-01 -8.41370597e-03
-8.97000909e-01 1.18551290e+00 -2.94540554e-01 -7.39039898e-01
-5.05135417e-01 -8.04578602e-01 -8.89376581e-01 -8.77033412e-01
-7.71998703e-01 4.20014143e-01 -1.97039500e-01 -2.78485715... | [4.317723274230957, 2.84759783744812] |
ba7c9b05-0d04-4710-851b-d3c4f4979ce3 | a-graph-framework-for-multimodal-medical | 1608.00134 | null | http://arxiv.org/abs/1608.00134v2 | http://arxiv.org/pdf/1608.00134v2.pdf | A Graph Framework for Multimodal Medical Information Processing | Multimodal medical information processing is currently the epicenter of
intense interdisciplinary research, as proper data fusion may lead to more
accurate diagnoses. Moreover, multimodality may disambiguate cases of
co-morbidity. This paper presents a framework for retrieving, analyzing, and
storing medical informatio... | ['Megalooikonomou Vasileios', 'Drakopoulos Georgios'] | 2017-02-22 | null | null | null | null | ['graph-ranking'] | ['graphs'] | [-1.31967634e-01 1.63244709e-01 -1.15878530e-01 -3.37559164e-01
-5.36684811e-01 -1.62674367e-01 4.38201785e-01 1.30845797e+00
-1.44540191e-01 6.24959528e-01 6.77693188e-01 -3.10893923e-01
-7.69379139e-01 -1.00181758e+00 1.17015824e-01 -4.48591620e-01
-2.43420959e-01 3.97520691e-01 -3.73322725e-01 -2.43930355... | [8.448174476623535, 8.566871643066406] |
42760d2d-cf7e-4e43-848b-d3c9117f8abd | ls-iq-implicit-reward-regularization-for | 2303.00599 | null | https://arxiv.org/abs/2303.00599v1 | https://arxiv.org/pdf/2303.00599v1.pdf | LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning | Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function. However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states. Previous works show that a squared norm regu... | ['Jan Peters', 'Guoping Zhao', 'Oleg Arenz', 'Davide Tateo', 'Firas Al-Hafez'] | 2023-03-01 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 1.69024877e-02 2.82933474e-01 -4.09645140e-01 8.79992098e-02
-8.65821660e-01 -5.49870253e-01 4.41541344e-01 -3.42055917e-01
-7.16514766e-01 1.11929667e+00 -2.08273709e-01 -2.70425439e-01
-2.23636523e-01 -4.03620243e-01 -9.35967028e-01 -1.11364591e+00
-7.95503408e-02 3.94280225e-01 -9.78904814e-02 -3.67225260... | [4.16714334487915, 2.368971586227417] |
c0f24d49-b71a-41aa-bfc2-4d6aae39ab89 | you-only-look-at-one-category-level-object | 2305.12626 | null | https://arxiv.org/abs/2305.12626v1 | https://arxiv.org/pdf/2305.12626v1.pdf | You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example | In order to meaningfully interact with the world, robot manipulators must be able to interpret objects they encounter. A critical aspect of this interpretation is pose estimation: inferring quantities that describe the position and orientation of an object in 3D space. Most existing approaches to pose estimation make l... | ['Ingmar Posner', 'Ioannis Havoutis', 'Walter Goodwin'] | 2023-05-22 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 5.37897646e-01 4.32294846e-01 -5.15937805e-04 -2.73999244e-01
-8.21267307e-01 -1.00046682e+00 5.33943057e-01 2.70892650e-01
-2.77507573e-01 3.63419712e-01 -3.58335942e-01 -4.31609116e-02
-3.41906637e-01 -4.11454946e-01 -9.81491387e-01 -5.84782898e-01
-1.30224764e-01 1.21355069e+00 4.14206713e-01 -3.29955277... | [5.853408336639404, -0.8487458229064941] |
5ac64b1d-26de-4a4c-ace6-2147ad466827 | smart-roi-detection-for-alzheimer-s-disease | 2303.10401 | null | https://arxiv.org/abs/2303.10401v1 | https://arxiv.org/pdf/2303.10401v1.pdf | Smart ROI Detection for Alzheimer's disease prediction using explainable AI | Purpose Predicting the progression of MCI to Alzheimer's disease is an important step in reducing the progression of the disease. Therefore, many methods have been introduced for this task based on deep learning. Among these approaches, the methods based on ROIs are in a good position in terms of accuracy and complexit... | ['Mohsen Ebrahimi Moghaddam', 'Atefe Aghaei'] | 2023-03-18 | null | null | null | null | ['disease-prediction'] | ['medical'] | [-1.34640962e-01 8.89398381e-02 -1.75100416e-02 -5.95514596e-01
-7.76277781e-01 1.96909860e-01 1.27074599e-01 -3.39173734e-01
-6.39483511e-01 9.17850673e-01 2.42852852e-01 2.12152392e-01
-1.40658543e-01 -7.68878639e-01 -2.00367421e-01 -5.72102189e-01
-1.65905952e-01 6.49264395e-01 6.36204660e-01 -2.01597493... | [14.173338890075684, -1.7696174383163452] |
2ec3b5bd-af1f-48b4-9303-8ab0468278f6 | exit-extrapolation-and-interpolation-based | 2204.08771 | null | https://arxiv.org/abs/2204.08771v2 | https://arxiv.org/pdf/2204.08771v2.pdf | EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting | Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only p... | ['Noseong Park', 'Jayoung Kim', 'Jihyeon Hyeong', 'Jinsung Jeon', 'Seungji Kook', 'Minju Jo', 'Jaehoon Lee', 'Sheo Yon Jhin'] | 2022-04-19 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.67539306e-02 -2.03297108e-01 -1.67420641e-01 -1.54459476e-01
-4.57440287e-01 -2.90832877e-01 5.83297908e-01 -1.89266890e-01
-2.55068213e-01 5.70640326e-01 3.10423940e-01 -5.39904833e-01
2.45721981e-01 -6.77147388e-01 -8.49670708e-01 -6.02494240e-01
1.06063537e-01 -7.03296065e-02 -3.45851555e-02 -2.03354433... | [6.966161251068115, 3.194711208343506] |
7d140bf6-1d29-444e-824c-184fbe434f33 | deceptive-decision-making-under-uncertainty | 2109.06740 | null | https://arxiv.org/abs/2109.06740v1 | https://arxiv.org/pdf/2109.06740v1.pdf | Deceptive Decision-Making Under Uncertainty | We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals... | ['Ufuk Topcu', 'Christos K. Verginis', 'Yagiz Savas'] | 2021-09-14 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-2.87793819e-02 7.15197802e-01 1.09943196e-01 -4.28593457e-01
-4.03140128e-01 -4.34102267e-01 5.94971955e-01 -1.11758515e-01
-6.63951576e-01 6.99693084e-01 -1.96269657e-02 -3.53208125e-01
-2.43338477e-03 -3.83514076e-01 -2.69299150e-01 -5.62515080e-01
-4.08795923e-02 6.42305017e-01 -5.87731935e-02 -9.06958953... | [4.349917888641357, 2.1168711185455322] |
e11d5722-aa91-4845-b161-6d75021bebc6 | collage-diffusion | 2303.00262 | null | https://arxiv.org/abs/2303.00262v1 | https://arxiv.org/pdf/2303.00262v1.pdf | Collage Diffusion | Text-conditional diffusion models generate high-quality, diverse images. However, text is often an ambiguous specification for a desired target image, creating the need for additional user-friendly controls for diffusion-based image generation. We focus on having precise control over image output for scenes with severa... | ['Kayvon Fatahalian', 'Christopher Ré', 'Arden Ma', 'Linden Li', 'Vishnu Sarukkai'] | 2023-03-01 | null | null | null | null | ['image-harmonization', 'conditional-image-generation'] | ['computer-vision', 'computer-vision'] | [ 4.14851755e-01 -1.59161761e-01 8.87913108e-02 -2.81242698e-01
-4.07715201e-01 -8.46369207e-01 5.60400844e-01 1.27559388e-03
-4.24708843e-01 2.88039386e-01 1.46399915e-01 1.30414233e-01
1.01744719e-01 -7.73884058e-01 -7.88052201e-01 -8.43912721e-01
4.56467539e-01 4.01404470e-01 4.91197348e-01 -1.26907527... | [11.462137222290039, -0.33939146995544434] |
f6b294b8-1ddb-40ac-8212-3bb50acf2633 | context-aware-hypergraph-construction-for | 1401.0764 | null | http://arxiv.org/abs/1401.0764v1 | http://arxiv.org/pdf/1401.0764v1.pdf | Context-Aware Hypergraph Construction for Robust Spectral Clustering | Spectral clustering is a powerful tool for unsupervised data analysis. In
this paper, we propose a context-aware hypergraph similarity measure (CAHSM),
which leads to robust spectral clustering in the case of noisy data. We
construct three types of hypergraph---the pairwise hypergraph, the
k-nearest-neighbor (kNN) hype... | ['Zhongfei Zhang', 'Xi Li', 'Chunhua Shen', 'Anthony Dick', 'Weiming Hu'] | 2014-01-04 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [-2.81928983e-02 -1.31717533e-01 -1.75435811e-01 -1.47288501e-01
-4.16686147e-01 -6.63311541e-01 3.20176035e-01 3.95240784e-01
4.42998931e-02 1.36854857e-01 3.19343656e-01 2.20958944e-02
-9.89518642e-01 -8.45995247e-01 -2.32563823e-01 -1.09149742e+00
-2.94881552e-01 4.47670728e-01 4.18141156e-01 2.72671729... | [7.567524433135986, 4.744561195373535] |
4d128179-3651-4dd1-9a69-f3ffba8d46e0 | a-framework-for-the-identification-and | null | null | https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0162-8?ref=https://githubhelp.com | https://ij-healthgeographics.biomedcentral.com/counter/pdf/10.1186/s12942-018-0162-8.pdf | A framework for the identification and classification of homogeneous socioeconomic areas in the analysis of health care variation | Background
Detecting the variation of health indicators across similar areas or peer geographies is often useful if the spatial units are socially and economically meaningful, so that there is a degree of homogeneity in each unit. Indices are frequently constructed to generate summaries of socioeconomic status or othe... | ['Soumya Mazumdar & Federico Girosi', 'Ludovico Pinzari'] | 2018-12-04 | null | null | null | international-journal-of-health-geographics | ['unsupervised-spatial-clustering'] | ['time-series'] | [ 3.00831371e-03 1.05013169e-01 -2.05359414e-01 -2.71467894e-01
-5.43636024e-01 -1.48414999e-01 3.18678111e-01 1.03668690e+00
-4.74863172e-01 6.59500659e-01 8.81656528e-01 -7.76359975e-01
-7.65939295e-01 -1.17534387e+00 -3.38430285e-01 -7.84583747e-01
-1.34366497e-01 1.43333599e-01 -6.48274599e-03 -1.07861064... | [6.704135417938232, 2.0458133220672607] |
b3a10213-ce85-4213-bf52-84244280c901 | the-role-of-computational-stylometry-in | null | null | https://aclanthology.org/2020.trac-1.11 | https://aclanthology.org/2020.trac-1.11.pdf | The Role of Computational Stylometry in Identifying (Misogynistic) Aggression in English Social Media Texts | In this paper, we describe UniOr{\_}ExpSys team participation in TRAC-2 (Trolling, Aggression and Cyberbullying) shared task, a workshop organized as part of LREC 2020. TRAC-2 shared task is organized in two sub-tasks: Aggression Identification (a 3-way classification between {``}Overtly Aggressive{''}, {``}Covertly Ag... | ['Vincenzo Masucci', 'Johanna Monti', 'Antonio Pascucci', 'Raffaele Manna'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['misogynistic-aggression-identification', 'aggression-identification'] | ['natural-language-processing', 'natural-language-processing'] | [-3.55053782e-01 8.88571441e-02 9.13090706e-02 -4.17166531e-01
-6.42365515e-01 -4.48760986e-01 4.34662282e-01 2.25853831e-01
-8.45008612e-01 8.52853298e-01 1.41405076e-01 -3.55737507e-01
-6.23140812e-01 -3.22034955e-01 1.70106411e-01 -4.92225081e-01
2.34448630e-03 9.72252131e-01 5.15664220e-02 -5.02714455... | [8.793442726135254, 10.755603790283203] |
1eefff13-590d-4790-8208-51164213b51a | enrichment-score-a-better-quantitative-metric | 2210.10905 | null | https://arxiv.org/abs/2210.10905v4 | https://arxiv.org/pdf/2210.10905v4.pdf | Enrichment Score: a better quantitative metric for evaluating the enrichment capacity of molecular docking models | The standard quantitative metric for evaluating enrichment capacity known as $\textit{LogAUC}$ depends on a cutoff parameter that controls what the minimum value of the log-scaled x-axis is. Unless this parameter is chosen carefully for a given ROC curve, one of the two following problems occurs: either (1) some fracti... | ['John J. Irwin', 'Slava Naprienko', 'Ian Scott Knight'] | 2022-10-19 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.74272120e-01 -2.36595601e-01 -2.15966582e-01 -2.85699517e-01
-7.53664672e-01 -7.39611924e-01 1.72854245e-01 6.47275031e-01
-8.17367017e-01 1.06986344e+00 -2.84938440e-02 -5.28511941e-01
-3.09475690e-01 -7.08035052e-01 -5.27659178e-01 -9.95558262e-01
-3.61949623e-01 3.62537533e-01 4.59606856e-01 -2.15569168... | [5.1991753578186035, 5.335231304168701] |
599f0fdf-1395-4e2d-bcfb-08164c8a2f3c | spherical-image-inpainting-with-frame | 2209.14604 | null | https://arxiv.org/abs/2209.14604v1 | https://arxiv.org/pdf/2209.14604v1.pdf | Spherical Image Inpainting with Frame Transformation and Data-driven Prior Deep Networks | Spherical image processing has been widely applied in many important fields, such as omnidirectional vision for autonomous cars, global climate modelling, and medical imaging. It is non-trivial to extend an algorithm developed for flat images to the spherical ones. In this work, we focus on the challenging task of sphe... | ['Tieyong Zeng', 'Micheal Ng', 'Han Feng', 'Raymond Chan', 'Chaoyan Huang', 'Jianfei Li'] | 2022-09-29 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 2.45916918e-01 3.17233771e-01 4.52351570e-01 -2.78514326e-01
-5.75514138e-01 -3.98758128e-02 5.40824413e-01 -4.45801646e-01
-4.04245287e-01 6.53745294e-01 2.65053362e-01 -1.03638798e-01
8.19910765e-02 -9.45903301e-01 -1.06497860e+00 -9.24811840e-01
5.23424745e-01 1.97602361e-01 1.97237670e-01 -3.06195885... | [11.484163284301758, -2.268308639526367] |
42dedb4b-fcf8-4c50-a579-d057fa754ada | prediction-intervals-and-confidence-regions | 2209.06454 | null | https://arxiv.org/abs/2209.06454v1 | https://arxiv.org/pdf/2209.06454v1.pdf | Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles | Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and for decision making. The linear approximation and so-called likelihood ... | ['Gabriel Kronberger', 'Fabricio Olivetti de Franca'] | 2022-09-14 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 1.74844131e-01 1.96904898e-01 -3.85282822e-02 -7.05034673e-01
-3.33368510e-01 -2.24114969e-01 3.49285364e-01 2.01453641e-01
-1.70768142e-01 1.12205839e+00 -4.33005005e-01 -4.57557052e-01
-7.76738882e-01 -7.99935162e-01 -5.19693196e-01 -5.49275517e-01
-2.27139473e-01 7.51721799e-01 6.42089099e-02 -1.82272106... | [6.966026782989502, 4.138115406036377] |
52e72b66-92a8-4a2a-912d-01b7e565c382 | data-driven-and-physics-informed-modelling-of | 2305.03257 | null | https://arxiv.org/abs/2305.03257v1 | https://arxiv.org/pdf/2305.03257v1.pdf | Data-driven and Physics Informed Modelling of Chinese Hamster Ovary Cell Bioreactors | Fed-batch culture is an established operation mode for the production of biologics using mammalian cell cultures. Quantitative modeling integrates both kinetics for some key reaction steps and optimization-driven metabolic flux allocation, using flux balance analysis; this is known to lead to certain mathematical incon... | ['Ioannis G. Kevrekidis', 'Costas Maranas', 'Michael Betenbaugh', 'Pratik Khare', 'Nelson Ndahiro', 'Tom S. Bertalan', 'Tianqi Cui'] | 2023-05-05 | null | null | null | null | ['culture'] | ['speech'] | [ 3.38460766e-02 -1.13158017e-01 -4.05014344e-02 5.21902042e-03
-1.45143345e-01 -5.60269415e-01 5.42377353e-01 2.87896961e-01
-1.89715669e-01 1.20495701e+00 -1.12350628e-01 -4.95563835e-01
-2.94006556e-01 -4.51790005e-01 -8.98315847e-01 -1.14027739e+00
-1.28171861e-01 5.07222772e-01 -4.07938838e-01 -1.39970303... | [5.886455059051514, 4.314204692840576] |
93ecf85e-646b-4021-aff4-07b1e9d8ae5a | machine-learning-repurposing-of-drugbank | 2303.00240 | null | https://arxiv.org/abs/2303.00240v1 | https://arxiv.org/pdf/2303.00240v1.pdf | Machine-learning Repurposing of DrugBank Compounds for Opioid Use Disorder | Opioid use disorder (OUD) is a chronic and relapsing condition that involves the continued and compulsive use of opioids despite harmful consequences. The development of medications with improved efficacy and safety profiles for OUD treatment is urgently needed. Drug repurposing is a promising option for drug discovery... | ['Guo-Wei Wei', 'Jian Jiang', 'Hongsong Feng'] | 2023-03-01 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 1.63291946e-01 -4.57448483e-01 -1.08634591e+00 -1.73291370e-01
-8.13896477e-01 -8.11193168e-01 6.52059838e-02 9.78759348e-01
-6.03293598e-01 1.09128845e+00 -4.43100519e-02 -7.44277775e-01
-1.97495058e-01 -4.43134218e-01 -3.33629519e-01 -5.86296201e-01
-3.84265661e-01 6.14481390e-01 -2.45448530e-01 -4.81569916... | [4.998068809509277, 5.587808609008789] |
fdfc4742-1f26-4088-a3e4-83b0ba037e38 | viteraser-harnessing-the-power-of-vision | 2306.12106 | null | https://arxiv.org/abs/2306.12106v1 | https://arxiv.org/pdf/2306.12106v1.pdf | ViTEraser: Harnessing the Power of Vision Transformers for Scene Text Removal with SegMIM Pretraining | Scene text removal (STR) aims at replacing text strokes in natural scenes with visually coherent backgrounds. Recent STR approaches rely on iterative refinements or explicit text masks, resulting in higher complexity and sensitivity to the accuracy of text localization. Moreover, most existing STR methods utilize convo... | ['Lianwen Jin', 'Yuliang Liu', 'Chongyu Liu', 'Dezhi Peng'] | 2023-06-21 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 5.36963701e-01 -1.09369561e-01 -4.26753536e-02 -2.37348750e-01
-7.09042728e-01 -2.52608329e-01 3.67563754e-01 -5.41828871e-01
-2.09267497e-01 3.33953053e-01 -3.09949629e-02 -2.56867439e-01
5.89763939e-01 -7.14156866e-01 -9.43125010e-01 -7.34189153e-01
7.16923118e-01 -9.14792344e-02 4.15240169e-01 -3.92381251... | [11.888226509094238, 2.04945969581604] |
e3575919-acd9-4514-be0a-a79e62c9633d | to-risk-or-not-to-risk-learning-with-risk | 2302.07399 | null | https://arxiv.org/abs/2302.07399v1 | https://arxiv.org/pdf/2302.07399v1.pdf | To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs | A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT) environment. The task offloading technique uses financial concepts such as cost functions an... | ['Melike Erol-Kantarci', 'W. Sean Kennedy', 'Aisha Syed', 'Turgay Pamuklu', 'Anne Catherine Nguyen'] | 2023-02-14 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 1.67303592e-01 3.53440583e-01 8.12361166e-02 2.10210323e-01
3.12232703e-01 -3.17493826e-01 7.96278790e-02 3.24405164e-01
-4.27420557e-01 1.05434024e+00 -4.65750724e-01 -6.61179900e-01
-8.22070301e-01 -1.18882477e+00 -6.53125823e-01 -8.37821603e-01
-3.42953891e-01 2.62866408e-01 -3.76220159e-02 -1.57640427... | [5.024773120880127, 2.0297305583953857] |
c5e6e638-cf9d-4fa0-a8ec-45308195ce68 | scalable-safe-exploration-for-global | 2201.09562 | null | https://arxiv.org/abs/2201.09562v5 | https://arxiv.org/pdf/2201.09562v5.pdf | GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems | Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning methods that guarantee safety, i.e., no failures, during exploration are limited to local optima. A notable exception is the GoSafe algorithm, w... | ['Dominik Baumann', 'Sebastian Trimpe', 'Andreas Krause', 'David Lindner', 'Matteo Turchetta', 'Bhavya Sukhija'] | 2022-01-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-2.94409513e-01 3.26412827e-01 -6.75729334e-01 3.60278815e-01
-5.23909152e-01 -5.30077159e-01 4.32845920e-01 -2.36524809e-02
-1.27817735e-01 1.39877713e+00 -6.11494958e-01 -7.78006017e-01
-4.90096718e-01 -2.73570657e-01 -9.54143286e-01 -9.07587051e-01
-7.08611846e-01 3.96281570e-01 2.80932546e-01 -1.44041061... | [4.624392032623291, 2.1039297580718994] |
29a6595a-05e4-42b6-9c51-17383fa5ccc3 | improved-logical-reasoning-of-language-models | 2305.03742 | null | https://arxiv.org/abs/2305.03742v1 | https://arxiv.org/pdf/2305.03742v1.pdf | Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming | Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the percep... | ['Eric Xing', 'Mayur Naik', 'Ziyang Li', 'Jiani Huang', 'HANLIN ZHANG'] | 2023-05-05 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.70348191e-01 6.16691828e-01 -5.01414120e-01 -2.99840301e-01
-8.41908216e-01 -6.73388004e-01 7.97162473e-01 1.05608709e-01
-1.84449047e-01 6.38873458e-01 6.83980659e-02 -8.11262488e-01
-2.48625688e-02 -9.50062513e-01 -1.26420522e+00 1.49943501e-01
8.20468087e-03 5.43392420e-01 3.59012783e-01 -4.83957410... | [9.27967643737793, 7.233613967895508] |
37d26282-dcec-4892-a2c3-6d69ff11b6c9 | simplifyur-unsupervised-lexical-text | null | null | https://aclanthology.org/2020.lrec-1.428 | https://aclanthology.org/2020.lrec-1.428.pdf | SimplifyUR: Unsupervised Lexical Text Simplification for Urdu | This paper presents the first attempt at Automatic Text Simplification (ATS) for Urdu, the language of 170 million people worldwide. Being a low-resource language in terms of standard linguistic resources, recent text simplification approaches that rely on manually crafted simplified corpora or lexicons such as WordNet... | ['Agha Ali Raza', 'Awais Athar', 'Namoos Hayat Qasmi', 'Haris Bin Zia'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['lexical-simplification'] | ['natural-language-processing'] | [-1.25475436e-01 1.74226403e-01 1.75678849e-01 -2.61332989e-01
-7.43834674e-01 -7.15409636e-01 6.86341882e-01 5.62650740e-01
-6.63724482e-01 9.44610298e-01 5.30880928e-01 -5.85791886e-01
2.88654685e-01 -9.34309900e-01 -2.18021393e-01 -3.11522968e-02
3.72466862e-01 5.52893996e-01 -6.29333779e-02 -6.87713027... | [10.852919578552246, 10.40399169921875] |
4578e67e-ae44-40df-a636-e369634e8712 | lot-a-benchmark-for-evaluating-chinese-long | 2108.12960 | null | https://arxiv.org/abs/2108.12960v2 | https://arxiv.org/pdf/2108.12960v2.pdf | LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation | Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing (NLP) usually focus only on understanding or generating short texts. However, long text modeling requires many distinct abilities in contras... | ['Minlie Huang', 'Changjie Fan', 'Xiaoxi Mao', 'Ruilin He', 'Yamei Chen', 'Zhuoer Feng', 'Jian Guan'] | 2021-08-30 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 3.06180358e-01 4.95967299e-01 2.56656017e-02 -5.72596073e-01
-1.19500625e+00 -5.34853458e-01 1.24076152e+00 -1.05175100e-01
-3.60488385e-01 1.09836364e+00 1.10409260e+00 -4.12016124e-01
4.54467714e-01 -8.83760870e-01 -9.71415579e-01 -2.41390705e-01
3.01547408e-01 9.59644139e-01 -1.75110847e-01 -4.62602645... | [11.717031478881836, 9.058362007141113] |
ddbf6a1c-fff0-49cc-86a3-39f98bd008f5 | leco-lightweight-compression-via-learning | 2306.15374 | null | https://arxiv.org/abs/2306.15374v1 | https://arxiv.org/pdf/2306.15374v1.pdf | LeCo: Lightweight Compression via Learning Serial Correlations | Lightweight data compression is a key technique that allows column stores to exhibit superior performance for analytical queries. Despite a comprehensive study on dictionary-based encodings to approach Shannon's entropy, few prior works have systematically exploited the serial correlation in a column for compression. I... | ['Huanchen Zhang', 'Xinyu Zeng', 'Yihao Liu'] | 2023-06-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 1.98766083e-01 -4.22675431e-01 -6.02319837e-01 -1.23794265e-01
-8.54456067e-01 -3.21251661e-01 3.44912738e-01 6.92223489e-01
-3.74032915e-01 6.08591080e-01 3.41617078e-01 -6.25330210e-01
-2.32553199e-01 -1.01732671e+00 -7.76742458e-01 -5.10692120e-01
-6.60130799e-01 5.30465186e-01 4.27503437e-01 -4.23785120... | [8.467611312866211, 3.413577079772949] |
ca196c03-d89b-4479-b801-a0eb9f71580f | trip-triangular-document-level-pre-training | 2212.07752 | null | https://arxiv.org/abs/2212.07752v2 | https://arxiv.org/pdf/2212.07752v2.pdf | Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models | Despite the success of multilingual sequence-to-sequence pre-training, most existing approaches rely on document-level monolingual corpora in many different languages, sentence-level bilingual corpora,\footnote{In this paper, we use `bilingual corpora' to denote parallel corpora with `bilingual translation pairs' in ma... | ['Furu Wei', 'Wai Lam', 'Dongdong Zhang', 'Shuming Ma', 'Haoyang Huang', 'Hongyuan Lu'] | 2022-12-15 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 3.60357434e-01 -5.44857502e-01 -6.68842971e-01 -2.40054995e-01
-1.67923784e+00 -1.15005183e+00 1.00586164e+00 8.06885734e-02
-4.05039281e-01 1.29661393e+00 5.79753518e-01 -9.28811669e-01
2.66436547e-01 -3.19486707e-01 -9.08762217e-01 -4.50633854e-01
2.23546445e-01 8.23289812e-01 -3.02170247e-01 -8.22720647... | [11.571969032287598, 10.300389289855957] |
73af7867-de37-440e-b7bd-048e399d5196 | divide-and-adapt-active-domain-adaptation-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Divide_and_Adapt_Active_Domain_Adaptation_via_Customized_Learning_CVPR_2023_paper.pdf | Divide and Adapt: Active Domain Adaptation via Customized Learning | Active domain adaptation (ADA) aims to improve the model adaptation performance by incorporating the active learning (AL) techniques to label a maximally-informative subset of target samples. Conventional AL methods do not consider the existence of domain shift, and hence, fail to identify the truly valuable sample... | ['Guanbin Li', 'Zhenhua Chai', 'Junshi Huang', 'Weikai Chen', 'Jichang Li', 'Duojun Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['source-free-domain-adaptation', 'unsupervised-domain-adaptation'] | ['computer-vision', 'methodology'] | [ 2.78161943e-01 4.12338376e-01 -6.44747734e-01 -4.41309690e-01
-9.30005074e-01 -8.00202310e-01 7.30947495e-01 2.19662666e-01
-4.24185693e-01 9.34414983e-01 -6.34826049e-02 -5.64097166e-02
-5.28566778e-01 -7.73371279e-01 -3.27682167e-01 -9.03768599e-01
3.34543109e-01 1.06215036e+00 5.54223239e-01 1.03689097... | [10.191494941711426, 3.261847734451294] |
552af5f5-ca05-4e17-aede-a8c2f75ee8af | approximate-stein-classes-for-truncated | 2306.00602 | null | https://arxiv.org/abs/2306.00602v1 | https://arxiv.org/pdf/2306.00602v1.pdf | Approximate Stein Classes for Truncated Density Estimation | Estimating truncated density models is difficult, as these models have intractable normalising constants and hard to satisfy boundary conditions. Score matching can be adapted to solve the truncated density estimation problem, but requires a continuous weighting function which takes zero at the boundary and is positive... | ['Song Liu', 'Daniel J. Williams'] | 2023-06-01 | null | null | null | null | ['density-estimation'] | ['methodology'] | [ 8.45383629e-02 2.60034084e-01 -1.15362406e-01 -3.40520650e-01
-1.09347200e+00 -4.01818573e-01 2.12126672e-01 -4.66139428e-02
-4.08542335e-01 9.78380024e-01 -8.78634229e-02 -1.71776652e-01
-1.85990736e-01 -6.06802166e-01 -6.60485566e-01 -7.38906622e-01
1.02881506e-01 7.54071355e-01 3.03256571e-01 2.12582529... | [7.108097076416016, 4.015805244445801] |
45f4feb2-1d9e-4376-bdbc-6f6bb17b4ae3 | l3cube-mahasbert-and-hindsbert-sentence-bert | 2211.11187 | null | https://arxiv.org/abs/2211.11187v2 | https://arxiv.org/pdf/2211.11187v2.pdf | L3Cube-MahaSBERT and HindSBERT: Sentence BERT Models and Benchmarking BERT Sentence Representations for Hindi and Marathi | Sentence representation from vanilla BERT models does not work well on sentence similarity tasks. Sentence-BERT models specifically trained on STS or NLI datasets are shown to provide state-of-the-art performance. However, building these models for low-resource languages is not straightforward due to the lack of these ... | ['Raviraj Joshi', 'Samruddhi Deode', 'Janhavi Gadre', 'Aditi Kajale', 'Ananya Joshi'] | 2022-11-21 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 5.09133190e-02 2.44745798e-02 1.08575255e-01 -6.37861490e-01
-1.17406464e+00 -5.74026585e-01 9.90884125e-01 4.80575413e-01
-8.79847467e-01 9.04579282e-01 5.08501649e-01 -5.84945261e-01
9.89162084e-03 -6.10518217e-01 -4.96097118e-01 -1.59244403e-01
8.22204202e-02 1.06688821e+00 8.28345418e-02 -9.53380764... | [11.164957046508789, 9.969478607177734] |
472b8d4b-d917-4127-a754-8e7a8731b8fc | multi-task-lung-nodule-detection-in-chest | 2207.03050 | null | https://arxiv.org/abs/2207.03050v1 | https://arxiv.org/pdf/2207.03050v1.pdf | Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network | Lung nodules can be an alarming precursor to potential lung cancer. Missed nodule detections during chest radiograph analysis remains a common challenge among thoracic radiologists. In this work, we present a multi-task lung nodule detection algorithm for chest radiograph analysis. Unlike past approaches, our algorithm... | ['Yu-Shao Peng', 'Chen-Han Tsai'] | 2022-07-07 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 5.88255584e-01 4.24807549e-01 -5.21816194e-01 -2.25590318e-01
-1.45612562e+00 -3.29950601e-01 2.98773229e-01 7.66015723e-02
-1.46490827e-01 5.49856544e-01 3.31517547e-01 -8.16473663e-01
-1.82619259e-01 -5.37344337e-01 -5.22174299e-01 -7.28629708e-01
6.96077719e-02 6.87589109e-01 7.01237202e-01 4.72925246... | [15.460201263427734, -2.081580638885498] |
01fb1142-42f7-426e-97e2-eeac06077750 | color-cerberus | 1907.06483 | null | https://arxiv.org/abs/1907.06483v1 | https://arxiv.org/pdf/1907.06483v1.pdf | Color Cerberus | Simple convolutional neural network was able to win ISISPA color constancy competition. Partial reimplementation of (Bianco, 2017) neural architecture would have shown even better results in this setup. | ['S. ~Karpenko', 'E. ~Ershov', 'A. ~Savchik'] | 2019-07-15 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-7.12800920e-02 1.15720443e-01 3.82936388e-01 -4.57285583e-01
-4.37833257e-02 -4.59255487e-01 7.15626419e-01 -5.96066058e-01
-6.81839406e-01 7.54985273e-01 -2.26561517e-01 -4.48391140e-01
4.11646396e-01 -5.06774724e-01 -5.81384003e-01 -5.32386005e-01
-3.93576890e-01 -4.50868458e-02 2.91678280e-01 -4.95149463... | [10.375167846679688, -2.451232671737671] |
3ece3d53-d2ef-4ee0-be5c-097ab30894ff | embodiedgpt-vision-language-pre-training-via | 2305.15021 | null | https://arxiv.org/abs/2305.15021v1 | https://arxiv.org/pdf/2305.15021v1.pdf | EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought | Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understa... | ['Ping Luo', 'Yu Qiao', 'Jifeng Dai', 'Bin Wang', 'Jun Jin', 'Mingyu Ding', 'Wenhai Wang', 'Mengkang Hu', 'Qinglong Zhang', 'Yao Mu'] | 2023-05-24 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 7.56140575e-02 4.19946969e-01 4.23691086e-02 -5.52336499e-02
-8.33320439e-01 -5.59327006e-01 9.24293935e-01 -3.63796026e-01
-3.17760110e-01 4.40910965e-01 7.91241407e-01 -2.38554969e-01
-2.53517162e-02 -7.24036634e-01 -1.08548629e+00 -3.94062907e-01
-2.81960994e-01 5.00474036e-01 -2.17218682e-01 -7.59457588... | [4.43734073638916, 0.6836325526237488] |
a986e26f-44a7-4e91-8c94-b6cea9d99cf1 | a-minimal-solution-to-the-generalized-pose | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Ventura_A_Minimal_Solution_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Ventura_A_Minimal_Solution_2014_CVPR_paper.pdf | A Minimal Solution to the Generalized Pose-and-Scale Problem | We propose a novel solution to the generalized camera pose problem which includes the internal scale of the generalized camera as an unknown parameter. This further generalization of the well-known absolute camera pose problem has applications in multi-frame loop closure. While a well-calibrated camera rig has a fixe... | ['Dieter Schmalstieg', 'Jonathan Ventura', 'Gerhard Reitmayr', 'Clemens Arth'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['monocular-visual-odometry'] | ['robots'] | [-9.10351984e-03 -4.44423296e-02 -1.99446633e-01 -1.56260699e-01
-7.53697872e-01 -8.80518258e-01 6.26084447e-01 -3.52531850e-01
-3.37801427e-01 5.84588051e-01 -2.27902204e-01 -2.07673028e-01
2.34011747e-02 -2.80209452e-01 -9.32943225e-01 -4.26941484e-01
3.65120649e-01 1.07203376e+00 2.10606962e-01 4.27664369... | [7.853124618530273, -2.3112409114837646] |
09412613-192a-47b2-b06c-47ee6c4e94b9 | few-shots-portrait-generation-with-style | 2303.00377 | null | https://arxiv.org/abs/2303.00377v1 | https://arxiv.org/pdf/2303.00377v1.pdf | Few-shots Portrait Generation with Style Enhancement and Identity Preservation | Nowadays, the wide application of virtual digital human promotes the comprehensive prosperity and development of digital culture supported by digital economy. The personalized portrait automatically generated by AI technology needs both the natural artistic style and human sentiment. In this paper, we propose a novel S... | ['Fan Zhang', 'Youbing Zhao', 'Naye Ji', 'Runchuan Zhu'] | 2023-03-01 | null | null | null | null | ['culture'] | ['speech'] | [ 1.60128504e-01 1.12871870e-01 -1.75636448e-02 -1.75064772e-01
6.93285689e-02 -6.47572219e-01 8.95828009e-01 -7.78250515e-01
1.24599583e-01 6.65755808e-01 3.47780287e-01 4.24889505e-01
2.49668270e-01 -8.01466465e-01 -2.64529258e-01 -7.64702022e-01
4.24826145e-01 1.60161313e-02 -2.09169880e-01 -6.59363210... | [12.417842864990234, -0.2647217810153961] |
68c9c50a-bf0f-4c68-92fc-ee822a32e0e8 | camo-mot-combined-appearance-motion | 2209.02540 | null | https://arxiv.org/abs/2209.02540v3 | https://arxiv.org/pdf/2209.02540v3.pdf | CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion | 3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to accurately track the irregular motion of objects for LiD... | ['Huaping Liu', 'Jun Li', 'Hong Wang', 'Lei Zhu', 'Zhiwei Li', 'Lei Yang', 'Xiaoyu Li', 'Wenyuan Qin', 'Xinyu Zhang', 'Li Wang'] | 2022-09-06 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.84876138e-01 -6.87010825e-01 -1.68032140e-01 -1.53952092e-01
-6.92458987e-01 -4.08738881e-01 4.07639802e-01 -1.51456177e-01
-4.72619683e-01 4.38342363e-01 -5.16876340e-01 -1.92417502e-01
-1.11450836e-01 -6.03920102e-01 -8.13760221e-01 -7.49510288e-01
3.01054567e-01 6.10958695e-01 1.14845288e+00 9.00264606... | [6.645321369171143, -2.1404762268066406] |
ca6cd9f9-7914-4de1-8669-c6f460f52e4b | fusion-of-inverse-synthetic-aperture-radar | 2209.13512 | null | https://arxiv.org/abs/2209.13512v1 | https://arxiv.org/pdf/2209.13512v1.pdf | Fusion of Inverse Synthetic Aperture Radar and Camera Images for Automotive Target Tracking | Automotive targets undergoing turns in road junctions offer large synthetic apertures over short dwell times to automotive radars that can be exploited for obtaining fine cross-range resolution. Likewise, the wide bandwidths of the automotive radar signal yield high-range resolution profiles. Together, they are exploit... | ['Shobha Sundar Ram'] | 2022-09-27 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.83467764e-01 -4.71007288e-01 -8.52439739e-03 -4.42054719e-01
-9.03811991e-01 -5.19873619e-01 7.78931677e-01 -5.90637445e-01
-4.44159418e-01 5.97425997e-01 -4.17236507e-01 -3.60515207e-01
-4.70648676e-01 -6.44768178e-01 -3.16780865e-01 -8.61089349e-01
1.94049045e-01 5.24928153e-01 2.76818454e-01 -2.07089067... | [6.752710342407227, 0.9419375658035278] |
4f12970d-359e-4653-805b-39673d0776be | selective-hearing-through-lip-reading | 2106.07150 | null | https://arxiv.org/abs/2106.07150v2 | https://arxiv.org/pdf/2106.07150v2.pdf | Selective Listening by Synchronizing Speech with Lips | A speaker extraction algorithm seeks to extract the speech of a target speaker from a multi-talker speech mixture when given a cue that represents the target speaker, such as a pre-enrolled speech utterance, or an accompanying video track. Visual cues are particularly useful when a pre-enrolled speech is not available.... | ['Haizhou Li', 'Chenglin Xu', 'Ruijie Tao', 'Zexu Pan'] | 2021-06-14 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 2.35163078e-01 1.73226252e-01 -4.34092849e-01 -1.07306391e-01
-1.23629677e+00 -5.67897320e-01 4.37574595e-01 -1.41952142e-01
-1.79882094e-01 2.20747292e-01 4.83823985e-01 -1.38950711e-02
3.34066212e-01 -4.43192199e-02 -6.07403874e-01 -9.87203717e-01
1.91780761e-01 -1.57455713e-01 -6.15913458e-02 1.12524569... | [14.536534309387207, 5.266360759735107] |
86f9f664-a043-46fd-949d-956e4996dcb6 | rebotnet-fast-real-time-video-enhancement | 2303.13504 | null | https://arxiv.org/abs/2303.13504v1 | https://arxiv.org/pdf/2303.13504v1.pdf | ReBotNet: Fast Real-time Video Enhancement | Most video restoration networks are slow, have high computational load, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework to perform real-time video enhancement for practical use-cases like live video calls and video streams. Our proposed method, called Recurrent... | ['Anne Menini', 'Vishal M. Patel', 'Andreas Lugmayr', 'Weijuan Xi', 'Xin Tong', 'Andeep Toor', 'Rahul Garg', 'Jeya Maria Jose Valanarasu'] | 2023-03-23 | null | null | null | null | ['video-enhancement', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.12921111e-01 -3.85853797e-01 -2.50984967e-01 -2.35273346e-01
-5.98526239e-01 -5.87765388e-02 3.28551143e-01 -1.47128910e-01
-5.83144963e-01 5.41236579e-01 5.00617445e-01 -1.89106241e-01
6.37084171e-02 -6.83898270e-01 -6.37542725e-01 -6.76039755e-01
-2.34765232e-01 -2.59681314e-01 7.53103316e-01 1.61551312... | [11.155815124511719, -1.6638693809509277] |
a5f4fff7-fdaa-4c55-8e06-9d36c968e424 | perception-aware-multi-sensor-fusion-for-3d | 2106.15277 | null | https://arxiv.org/abs/2106.15277v2 | https://arxiv.org/pdf/2106.15277v2.pdf | Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation | 3D LiDAR (light detection and ranging) semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate ... | ['Yuanqing Li', 'Qicheng Wang', 'Mingkui Tan', 'Kui Jia', 'Rong Li', 'Zhuangwei Zhuang'] | 2021-06-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhuang_Perception-Aware_Multi-Sensor_Fusion_for_3D_LiDAR_Semantic_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhuang_Perception-Aware_Multi-Sensor_Fusion_for_3D_LiDAR_Semantic_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 4.15124625e-01 -4.72643167e-01 3.16465609e-02 -6.03975594e-01
-8.32248628e-01 -3.23449731e-01 3.10586900e-01 1.38827696e-01
-4.73071426e-01 4.53938633e-01 -3.20026696e-01 -8.73558670e-02
-1.03299990e-01 -9.55477655e-01 -8.13411832e-01 -7.74026215e-01
6.75375640e-01 1.07767656e-01 6.94094419e-01 -2.62410313... | [8.25981330871582, -2.520550489425659] |
8080be2a-4edf-4087-95fe-b5b94eb9197c | accurate-facial-parts-localization-and-deep | 1803.05846 | null | http://arxiv.org/abs/1803.05846v1 | http://arxiv.org/pdf/1803.05846v1.pdf | Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition | Meaningful facial parts can convey key cues for both facial action unit
detection and expression prediction. Textured 3D face scan can provide both
detailed 3D geometric shape and 2D texture appearance cues of the face which
are beneficial for Facial Expression Recognition (FER). However, accurate
facial parts extracti... | ['Hongy-ing Meng', 'Huaxiong Ding', 'Liming Chen', 'Huibin Li', 'Asim Jan'] | 2018-03-04 | null | null | null | null | ['3d-facial-expression-recognition', 'action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.46794915e-01 -3.09234988e-02 -1.19607225e-01 -7.67681420e-01
-5.10348558e-01 -7.12019205e-02 3.30287933e-01 -2.92078912e-01
-1.32982031e-01 2.58293778e-01 -2.05424316e-02 3.69383842e-01
1.44389614e-01 -5.39945662e-01 -3.94410253e-01 -1.07860923e+00
-7.66512677e-02 3.13075274e-01 -1.37329727e-01 -3.73648286... | [13.493324279785156, 1.1421078443527222] |
e3010962-657a-45a4-bd00-4ea332172190 | evaluation-of-deep-convolutional-nets-for | 1502.07058 | null | http://arxiv.org/abs/1502.07058v1 | http://arxiv.org/pdf/1502.07058v1.pdf | Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval | This paper presents a new state-of-the-art for document image classification
and retrieval, using features learned by deep convolutional neural networks
(CNNs). In object and scene analysis, deep neural nets are capable of learning
a hierarchical chain of abstraction from pixel inputs to concise and
descriptive represe... | ['Konstantinos G. Derpanis', 'Alex Ufkes', 'Adam W. Harley'] | 2015-02-25 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [ 4.27249938e-01 -3.75276804e-01 -2.91014671e-01 -5.53246140e-01
-5.68141758e-01 -5.93266785e-01 1.07864738e+00 4.72887784e-01
-4.09325302e-01 3.35901439e-01 8.07215199e-02 -2.27327943e-01
-3.88350934e-01 -8.77391040e-01 -6.53349340e-01 -5.41838109e-01
-2.25705788e-01 3.99563193e-01 5.27510084e-02 -1.12093590... | [11.397265434265137, 2.5741829872131348] |
a080c904-1670-4046-9369-5dfc30d81367 | cardiac-arrhythmia-detection-using-artificial | 2304.08162 | null | https://arxiv.org/abs/2304.08162v1 | https://arxiv.org/pdf/2304.08162v1.pdf | Cardiac Arrhythmia Detection using Artificial Neural Network | The prime purpose of this project is to develop a portable cardiac abnormality monitoring device which can drastically improvise the quality of the monitoring and the overall safety of the device. While a generic, low cost, wearable battery powered device for such applications may not yield sufficient performance, such... | ['Vishal Kumar A', 'Sreevatsan B', 'Kishore Anand K', 'Prof Sangeetha R G'] | 2023-04-17 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 1.87600628e-01 7.11281151e-02 2.23683298e-01 -2.48677611e-01
-2.18128022e-02 -4.32246417e-01 -7.43411481e-02 1.44624189e-01
-1.71335250e-01 7.29837000e-01 -6.43384397e-01 -4.75891888e-01
-4.67039049e-01 -5.50168455e-01 -3.37328792e-01 -6.98271632e-01
9.52970237e-03 5.81396520e-01 -2.14760840e-01 3.42124216... | [13.8527250289917, 3.081646203994751] |
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