paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
691ad7d9-5018-4808-ba12-1eaabbc57309 | compounding-the-performance-improvements-of | 2001.06268 | null | https://arxiv.org/abs/2001.06268v2 | https://arxiv.org/pdf/2001.06268v2.pdf | Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network | Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs). However, attempts to combine existing techniques to create a practical model are still uncommon. In this study, we carry out extensive experiments to validate that care... | ['Jungkyu Lee', 'Hyemin Lee', 'Tae Kwan Lee', 'Kiho Hong', 'Taeryun Won', 'Geonmo Gu'] | 2020-01-17 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 3.56870703e-02 -3.03944796e-01 -7.08490163e-02 -4.28040653e-01
-5.77357173e-01 -5.63592315e-01 5.81680715e-01 -2.73795873e-01
-8.08459759e-01 1.04810679e+00 -2.96843708e-01 -3.86812836e-01
-1.70504317e-01 -8.95059228e-01 -1.02586818e+00 -5.19094408e-01
7.59491399e-02 -4.16880287e-03 2.56775111e-01 -2.59492993... | [9.289335250854492, 2.2759459018707275] |
fc390fa5-6f3c-483f-8add-debc62c4eb62 | don-t-lose-yourself-empathetic-response | 2210.03884 | null | https://arxiv.org/abs/2210.03884v2 | https://arxiv.org/pdf/2210.03884v2.pdf | Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness | As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via othe... | ['Bing Qin', 'Xin Lu', 'Yanyan Zhao', 'Weixiang Zhao'] | 2022-10-08 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.78057450e-01 2.82819778e-01 7.97674358e-02 -5.10027766e-01
-5.07105850e-02 -3.57455492e-01 6.99176133e-01 7.70456204e-03
-1.54369295e-01 7.52756059e-01 4.55571264e-01 3.73912603e-01
1.41915917e-01 -5.91325283e-01 3.44933271e-01 -4.43265557e-01
6.98768377e-01 2.71076828e-01 -2.71011829e-01 -7.23865747... | [13.165223121643066, 7.603718280792236] |
f26116c6-8f46-466d-995d-d60d6d6be295 | modular-proximal-optimization-for | 1411.0589 | null | http://arxiv.org/abs/1411.0589v3 | http://arxiv.org/pdf/1411.0589v3.pdf | Modular proximal optimization for multidimensional total-variation regularization | We study \emph{TV regularization}, a widely used technique for eliciting
structured sparsity. In particular, we propose efficient algorithms for
computing prox-operators for $\ell_p$-norm TV. The most important among these
is $\ell_1$-norm TV, for whose prox-operator we present a new geometric
analysis which unveils a ... | ['Álvaro Barbero', 'Suvrit Sra'] | 2014-11-03 | null | null | null | null | ['video-denoising', 'image-deconvolution'] | ['computer-vision', 'computer-vision'] | [ 3.41721356e-01 9.01429206e-02 3.28413963e-01 -1.16646171e-01
-1.14432740e+00 -4.35285270e-01 -5.58513999e-02 -1.87891215e-01
-1.93018749e-01 6.51029825e-01 3.27117771e-01 -4.31644261e-01
-3.43614608e-01 -4.61639374e-01 -8.99273932e-01 -1.09110236e+00
-4.54599947e-01 1.49272025e-01 -2.42192388e-01 -3.42251688... | [6.966151237487793, 4.285747528076172] |
c01a9c63-eb28-4e3c-9d41-7f4c7f37a681 | self-supervised-speech-representation-1 | 2303.04255 | null | https://arxiv.org/abs/2303.04255v1 | https://arxiv.org/pdf/2303.04255v1.pdf | Self-supervised speech representation learning for keyword-spotting with light-weight transformers | Self-supervised speech representation learning (S3RL) is revolutionizing the way we leverage the ever-growing availability of data. While S3RL related studies typically use large models, we employ light-weight networks to comply with tight memory of compute-constrained devices. We demonstrate the effectiveness of S3RL ... | ['Yuzong Liu', 'Francesco Caliva', 'Yue Gu', 'Chenyang Gao'] | 2023-03-07 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.00760680e-01 1.54228136e-01 -3.28581959e-01 -5.15687346e-01
-1.19319081e+00 -2.80650407e-01 3.41942519e-01 1.97026152e-02
-5.83812952e-01 3.67390543e-01 1.20429106e-01 -8.22037458e-01
-1.90486982e-01 -3.22085083e-01 -5.53964198e-01 -4.36686546e-01
-3.77777740e-02 2.49961048e-01 2.50602454e-01 -2.49613717... | [14.212139129638672, 6.331079006195068] |
aaac2c1a-3f44-4d99-865f-82148de63383 | unbiased-methods-for-multi-goal-reinforcement | 2106.08863 | null | https://arxiv.org/abs/2106.08863v1 | https://arxiv.org/pdf/2106.08863v1.pdf | Unbiased Methods for Multi-Goal Reinforcement Learning | In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reaching a reward vanishes and the agent receives little learning signal. Methods such as Hindsight Experience Replay (HER) tackle this issue by... | ['Yann Ollivier', 'Léonard Blier'] | 2021-06-16 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-3.22629362e-01 6.05548561e-01 -3.46249491e-01 9.01206732e-02
-1.15249372e+00 -5.51766872e-01 2.82629728e-01 -7.88200926e-03
-8.01255763e-01 1.48077869e+00 2.93506593e-01 -1.08683072e-01
-2.72572309e-01 -6.69721365e-01 -9.86808717e-01 -8.88666034e-01
-4.59463060e-01 6.12880409e-01 -3.39111328e-01 -2.14508012... | [4.091702938079834, 2.105059862136841] |
f2de0f5b-fbfe-43b4-ba3d-2ea31d4b82f0 | unifying-vision-text-and-layout-for-universal | 2212.02623 | null | https://arxiv.org/abs/2212.02623v3 | https://arxiv.org/pdf/2212.02623v3.pdf | Unifying Vision, Text, and Layout for Universal Document Processing | We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and... | ['Mohit Bansal', 'Cha Zhang', 'Michael Zeng', 'Chenguang Zhu', 'Yang Liu', 'Yuwei Fang', 'Guoxin Wang', 'ZiYi Yang', 'Zineng Tang'] | 2022-12-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_Unifying_Vision_Text_and_Layout_for_Universal_Document_Processing_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_Unifying_Vision_Text_and_Layout_for_Universal_Document_Processing_CVPR_2023_paper.pdf | cvpr-2023-1 | ['document-ai'] | ['natural-language-processing'] | [ 8.13710868e-01 -5.06617427e-02 -1.38429105e-01 -3.33604455e-01
-1.14545739e+00 -1.16421640e+00 1.24232364e+00 -8.01737309e-02
-1.04203500e-01 4.20776963e-01 4.25143898e-01 -3.82424533e-01
1.30165279e-01 -5.75075090e-01 -1.12181187e+00 -3.47467512e-01
7.24772155e-01 1.09984481e+00 -3.09553117e-01 -1.99740633... | [11.458283424377441, 2.159536838531494] |
2e9affce-96d6-4fdb-8342-ea9eea969635 | point-voxel-transformer-an-efficient-approach | 2108.06076 | null | https://arxiv.org/abs/2108.06076v4 | https://arxiv.org/pdf/2108.06076v4.pdf | PVT: Point-Voxel Transformer for Point Cloud Learning | The recently developed pure Transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks. However, existing point cloud Transformers are computationally expensive since they waste a significant amount of time on structuring the irregular data. T... | ['Xinyi Shen', 'Zizhao Wu', 'Haocheng Wan', 'Cheng Zhang'] | 2021-08-13 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [ 1.54649774e-02 2.24809851e-02 8.69396254e-02 -3.25980216e-01
-8.66717219e-01 -1.68852851e-01 3.74145836e-01 1.61040798e-01
-2.74869114e-01 3.05131495e-01 -2.90525466e-01 -2.60659009e-01
-4.63167578e-02 -1.29051471e+00 -1.19601846e+00 -5.29153764e-01
8.43195394e-02 7.39618361e-01 6.60809636e-01 -7.01394826... | [7.93692684173584, -3.501772165298462] |
dae157bc-3463-4c48-94e6-f87249c6d87f | non-generative-generalized-zero-shot-learning | 2203.05335 | null | https://arxiv.org/abs/2203.05335v4 | https://arxiv.org/pdf/2203.05335v4.pdf | Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis | Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero-Shot Learning (GZSL) problem. Most models achieve competitive performance but still suffer from two problems: (1) Feature confounding, the overall representations confound task-correlated and task-independent features, and exi... | ['Jitao Sang', 'Jian Yu', 'Pengbo Yang', 'Xiaowen Huang', 'Yaogong Feng'] | 2022-03-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Feng_Non-Generative_Generalized_Zero-Shot_Learning_via_Task-Correlated_Disentanglement_and_Controllable_Samples_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Feng_Non-Generative_Generalized_Zero-Shot_Learning_via_Task-Correlated_Disentanglement_and_Controllable_Samples_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 4.42002058e-01 5.61301857e-02 -1.21738046e-01 -2.86644161e-01
-9.21430707e-01 -1.56275839e-01 8.15288246e-01 -4.82506692e-01
-6.97534010e-02 1.07755291e+00 1.11469857e-01 3.83409053e-01
-2.31711105e-01 -8.51111591e-01 -8.25166106e-01 -1.01284420e+00
4.13851798e-01 5.70155382e-01 3.63311708e-01 -3.85236323... | [10.041853904724121, 2.7386059761047363] |
3bf29711-7c69-4318-81a1-01b76cf0e567 | cross-modal-implicit-relation-reasoning-and | 2303.12501 | null | https://arxiv.org/abs/2303.12501v1 | https://arxiv.org/pdf/2303.12501v1.pdf | Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval | Text-to-image person retrieval aims to identify the target person based on a given textual description query. The primary challenge is to learn the mapping of visual and textual modalities into a common latent space. Prior works have attempted to address this challenge by leveraging separately pre-trained unimodal mode... | ['Mang Ye', 'Ding Jiang'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_Cross-Modal_Implicit_Relation_Reasoning_and_Aligning_for_Text-to-Image_Person_Retrieval_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_Cross-Modal_Implicit_Relation_Reasoning_and_Aligning_for_Text-to-Image_Person_Retrieval_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-retrieval', 'nlp-based-person-retrival', 'text-matching'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.32607707e-01 -1.29165530e-01 -4.08311784e-01 -5.08486509e-01
-1.05193448e+00 -5.86498857e-01 1.16982496e+00 1.43223912e-01
-5.26031196e-01 1.64702401e-01 5.19338787e-01 3.29523504e-01
-6.60326779e-02 -2.75845140e-01 -4.99203235e-01 -6.01034760e-01
4.15846586e-01 5.19988894e-01 -2.03974694e-01 6.56818897... | [10.956867218017578, 1.3728755712509155] |
e7800b42-9235-4d08-8cea-c166cc765de3 | memory-efficient-cnn-accelerator-based-on | 2110.06155 | null | https://arxiv.org/abs/2110.06155v1 | https://arxiv.org/pdf/2110.06155v1.pdf | Memory-Efficient CNN Accelerator Based on Interlayer Feature Map Compression | Existing deep convolutional neural networks (CNNs) generate massive interlayer feature data during network inference. To maintain real-time processing in embedded systems, large on-chip memory is required to buffer the interlayer feature maps. In this paper, we propose an efficient hardware accelerator with an interlay... | ['Zhongfeng Wang', 'Chenjia Xie', 'Huadong Wei', 'Wei Zhuang', 'Yuan Du', 'Lei Chen', 'Li Du', 'Xiaoliang Chen', 'Zhuang Shao'] | 2021-10-12 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 2.44035274e-01 -7.62471855e-02 -4.01182950e-01 -5.10971546e-01
2.76962042e-01 3.48407812e-02 3.02992195e-01 4.73860413e-01
-9.23613310e-01 1.50622070e-01 -1.39381122e-02 -6.71015799e-01
1.05149992e-01 -1.08147216e+00 -5.79813659e-01 -5.02852380e-01
-3.06151249e-02 -4.01579410e-01 4.24958408e-01 1.79343253... | [8.370702743530273, 2.782902956008911] |
3e882648-bfa4-44c4-8982-2e9cb5e9dc6c | few-shot-image-classification-just-use-a | 2101.00562 | null | https://arxiv.org/abs/2101.00562v3 | https://arxiv.org/pdf/2101.00562v3.pdf | Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier | Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical conclusion, and suggest the use of an ensemble of high-quality, pre-trained feature extractors for few-shot image classification. We show exper... | ['Swarat Chaudhuri', 'Chris Jermaine', 'Mingchao Jiang', 'Arkabandhu Chowdhury'] | 2021-01-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chowdhury_Few-Shot_Image_Classification_Just_Use_a_Library_of_Pre-Trained_Feature_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chowdhury_Few-Shot_Image_Classification_Just_Use_a_Library_of_Pre-Trained_Feature_ICCV_2021_paper.pdf | iccv-2021-1 | ['cross-domain-few-shot'] | ['computer-vision'] | [ 2.78810233e-01 -3.33679944e-01 -6.94359481e-01 -4.90002722e-01
-1.08524489e+00 1.39812008e-01 7.51023293e-01 -2.32902803e-02
-6.87708676e-01 6.58704460e-01 2.17377782e-01 2.07927004e-01
-2.10247666e-01 -6.15048647e-01 -5.27416468e-01 -6.04744673e-01
-3.94573994e-02 2.53776670e-01 1.23969674e-01 -3.17469150... | [9.958173751831055, 2.9812281131744385] |
412722aa-3d7b-4fe1-b1d5-04888cf717d9 | fully-convolutional-asr-for-less-resourced | null | null | https://aclanthology.org/2020.sltu-1.17 | https://aclanthology.org/2020.sltu-1.17.pdf | Fully Convolutional ASR for Less-Resourced Endangered Languages | The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale. In this paper, we compare a fully convolutional approach for acoust... | ["Emily Prud{'}hommeaux", 'Robert Jimerson', 'Raymond Ptucha', 'Bao Thai'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['acoustic-modelling'] | ['speech'] | [-5.71313798e-02 -1.50243118e-01 2.22182035e-01 -4.39014435e-01
-1.37599301e+00 -4.84130234e-01 5.95704973e-01 -1.36926815e-01
-8.16653907e-01 3.08519006e-01 3.53043228e-01 -7.63288140e-01
3.65479052e-01 -3.68598938e-01 -4.36150014e-01 -4.23496753e-01
-1.92365274e-01 5.93766689e-01 -1.88801005e-01 -4.22670960... | [14.288917541503906, 6.744553565979004] |
90eafdfa-17dd-4f4a-a8de-bd74d290d7d0 | deep-gradient-learning-for-efficient | 2205.12853 | null | https://arxiv.org/abs/2205.12853v2 | https://arxiv.org/pdf/2205.12853v2.pdf | Deep Gradient Learning for Efficient Camouflaged Object Detection | This paper introduces DGNet, a novel deep framework that exploits object gradient supervision for camouflaged object detection (COD). It decouples the task into two connected branches, i.e., a context and a texture encoder. The essential connection is the gradient-induced transition, representing a soft grouping betwee... | ['Luc van Gool', 'Alexander Liniger', 'Dengxin Dai', 'Yu-Cheng Chou', 'Deng-Ping Fan', 'Ge-Peng Ji'] | 2022-05-25 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.67398804e-01 1.92022771e-01 -1.73185006e-01 -1.28594190e-01
-7.08858192e-01 -3.41841578e-01 5.55770770e-02 -1.88269913e-01
-1.24564156e-01 3.16685706e-01 -3.75236958e-01 -4.48174506e-01
5.31154811e-01 -5.80088556e-01 -7.89517760e-01 -6.65036798e-01
6.20993301e-02 -5.69722429e-02 1.03016686e+00 1.10493928... | [9.45703125, -0.09012100845575333] |
a54e9b0b-247e-4b4f-ad40-6d755c9fb14d | robust-bayesian-inference-for-measurement | 2306.01468 | null | https://arxiv.org/abs/2306.01468v1 | https://arxiv.org/pdf/2306.01468v1.pdf | Robust Bayesian Inference for Measurement Error Models | Measurement error occurs when a set of covariates influencing a response variable are corrupted by noise. This can lead to misleading inference outcomes, particularly in problems where accurately estimating the relationship between covariates and response variables is crucial, such as causal effect estimation. Existing... | ['Theodoros Damoulas', 'Charita Dellaporta'] | 2023-06-02 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 6.58023536e-01 1.50981620e-01 -2.64208227e-01 -6.52782738e-01
-8.44458997e-01 -2.61573106e-01 5.20123065e-01 5.61678588e-01
-5.80998778e-01 1.15721321e+00 2.56512374e-01 -2.24609897e-01
-5.80899000e-01 -7.23106742e-01 -1.09187686e+00 -7.72992194e-01
-1.16412662e-01 4.61978734e-01 -1.98962521e-02 2.92164594... | [7.8248419761657715, 5.0554022789001465] |
5b886e2a-de7d-4988-bc0f-81593e131f75 | a-normal-form-characterization-for-efficient | 2104.14098 | null | https://arxiv.org/abs/2104.14098v2 | https://arxiv.org/pdf/2104.14098v2.pdf | A Normal Form Characterization for Efficient Boolean Skolem Function Synthesis | Boolean Skolem function synthesis concerns synthesizing outputs as Boolean functions of inputs such that a relational specification between inputs and outputs is satisfied. This problem, also known as Boolean functional synthesis, has several applications, including design of safe controllers for autonomous systems, ce... | ['Supratik Chakraborty', 'S. Akshay', 'Aman Bansal', 'Preey Shah'] | 2021-04-29 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 4.44314837e-01 5.58660030e-01 -2.88923740e-01 -3.79427493e-01
-5.19202530e-01 -1.12997627e+00 5.40802538e-01 1.86342970e-02
2.80273616e-01 9.83291447e-01 4.26295549e-02 -7.98557699e-01
-5.02872646e-01 -1.37538826e+00 -9.65458214e-01 -6.01209164e-01
-1.31936651e-02 3.76591831e-01 3.49341482e-01 -5.63846886... | [8.667452812194824, 6.837119102478027] |
47f5462b-0922-4f47-976d-d4a75d6cd8e7 | infinite-photorealistic-worlds-using-1 | 2306.0931 | null | https://arxiv.org/abs/2306.09310v2 | https://arxiv.org/pdf/2306.09310v2.pdf | Infinite Photorealistic Worlds using Procedural Generation | We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external source and allowing infinite variation and composition. Infinigen offers b... | ['Jia Deng', 'Kaiyu Yang', 'Ankit Goyal', 'Hei Law', 'Alejandro Newell', 'Yihan Wang', 'Beining Han', 'Hongyu Wen', 'Karhan Kayan', 'Yiming Zuo', 'Mingzhe Wang', 'Lingjie Mei', 'Zeyu Ma', 'Lahav Lipson', 'Alexander Raistrick'] | 2023-06-15 | infinite-photorealistic-worlds-using | http://openaccess.thecvf.com//content/CVPR2023/html/Raistrick_Infinite_Photorealistic_Worlds_Using_Procedural_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Raistrick_Infinite_Photorealistic_Worlds_Using_Procedural_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 1.36502132e-01 -7.91233033e-02 2.11929366e-01 1.05268009e-01
1.81602344e-01 -9.61140037e-01 6.63528681e-01 -1.89061582e-01
6.96652234e-02 5.91612995e-01 -2.13607743e-01 -4.36649323e-01
2.39952445e-01 -1.20831716e+00 -3.56822580e-01 -3.70339483e-01
-1.02232501e-01 3.53516698e-01 4.54316974e-01 -3.26726317... | [9.240809440612793, -2.9483330249786377] |
78ef3b20-a42d-4f2a-ab13-1c7c8833c276 | nl2cmd-an-updated-workflow-for-natural | 2302.07845 | null | https://arxiv.org/abs/2302.07845v3 | https://arxiv.org/pdf/2302.07845v3.pdf | NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation | Translating natural language into Bash Commands is an emerging research field that has gained attention in recent years. Most efforts have focused on producing more accurate translation models. To the best of our knowledge, only two datasets are available, with one based on the other. Both datasets involve scraping thr... | ['Douglas C. Schmidt', 'Jules White', 'Marco Georgaklis', 'Zhongwei Teng', 'Quchen Fu'] | 2023-02-15 | null | null | null | null | ['code-translation', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [ 2.01289326e-01 4.68518764e-01 -3.73111032e-02 -7.05433130e-01
-1.33187485e+00 -1.01643133e+00 6.06045902e-01 3.72091644e-02
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3.90549034e-01 -6.92198038e-01 -7.54737139e-01 1.32234856e-01
6.16390407e-01 9.99795616e-01 4.12849516e-01 -5.62837541... | [11.22420597076416, 9.004302978515625] |
41121c8b-0060-4331-b5b3-d68d3792b285 | similarity-maps-for-self-training-weakly | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shaharabany_Similarity_Maps_for_Self-Training_Weakly-Supervised_Phrase_Grounding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shaharabany_Similarity_Maps_for_Self-Training_Weakly-Supervised_Phrase_Grounding_CVPR_2023_paper.pdf | Similarity Maps for Self-Training Weakly-Supervised Phrase Grounding | A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are that these maps res... | ['Lior Wolf', 'Tal Shaharabany'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 5.03992975e-01 6.56606495e-01 -4.73344386e-01 -3.35518569e-01
-1.14509416e+00 -7.96043277e-01 9.34386790e-01 3.22146624e-01
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-1.57239567e-02 -5.80751359e-01 -1.06832242e+00 -7.12130725e-01
3.27838928e-01 6.88908339e-01 3.49122256e-01 -1.90854333... | [10.50529670715332, 1.5586795806884766] |
f3c2cbb0-0059-40dd-b83e-4a2640a685ed | adapters-for-enhanced-modeling-of | 2210.13617 | null | https://arxiv.org/abs/2210.13617v2 | https://arxiv.org/pdf/2210.13617v2.pdf | Adapters for Enhanced Modeling of Multilingual Knowledge and Text | Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned. Language models have recently been extended to multilingual language models (MLLMs), e... | ['Mrinmaya Sachan', 'Carl Allen', 'Zhaopeng Tu', 'Meizhen Liu', 'Wenxiang Jiao', 'Yifan Hou'] | 2022-10-24 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-3.92216682e-01 3.30007702e-01 -8.31236303e-01 -1.66223332e-01
-7.26378083e-01 -1.01401711e+00 7.30833054e-01 4.73692060e-01
-6.73763394e-01 9.61485982e-01 3.01165909e-01 -5.09905219e-01
8.23580250e-02 -9.65366900e-01 -1.18003011e+00 -8.38544890e-02
3.43986787e-03 6.96337938e-01 7.09070563e-02 -3.56669605... | [9.409282684326172, 8.531822204589844] |
86478e2b-bd55-40b9-a428-7f9cecd1d154 | randomrooms-unsupervised-pre-training-from | 2108.07794 | null | https://arxiv.org/abs/2108.07794v1 | https://arxiv.org/pdf/2108.07794v1.pdf | RandomRooms: Unsupervised Pre-training from Synthetic Shapes and Randomized Layouts for 3D Object Detection | 3D point cloud understanding has made great progress in recent years. However, one major bottleneck is the scarcity of annotated real datasets, especially compared to 2D object detection tasks, since a large amount of labor is involved in annotating the real scans of a scene. A promising solution to this problem is to ... | ['Jie zhou', 'Cho-Jui Hsieh', 'Jiwen Lu', 'Yi Wei', 'Benlin Liu', 'Yongming Rao'] | 2021-08-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Rao_RandomRooms_Unsupervised_Pre-Training_From_Synthetic_Shapes_and_Randomized_Layouts_for_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Rao_RandomRooms_Unsupervised_Pre-Training_From_Synthetic_Shapes_and_Randomized_Layouts_for_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-pre-training'] | ['methodology'] | [ 4.23914462e-01 5.28129227e-02 7.54387155e-02 -4.84281570e-01
-6.77585483e-01 -6.79717720e-01 7.93563068e-01 2.91882977e-02
-2.73820609e-01 2.60377616e-01 -3.41240644e-01 -2.97896713e-01
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1.33542627e-01 9.60014999e-01 7.69732177e-01 -3.99279088... | [8.13074016571045, -2.8414418697357178] |
bd8906f8-c7e8-400c-93fb-67607a2a4292 | news-clustering-approach-based-on-discourse | null | null | https://aclanthology.org/W15-4503 | https://aclanthology.org/W15-4503.pdf | News clustering approach based on discourse text structure | null | ['Tatyana Makhalova', 'Boris Galitsky', 'Dmitry Ilvovsky'] | 2015-07-01 | null | null | null | ws-2015-7 | ['text-clustering'] | ['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... | [-7.395345211029053, 3.6547932624816895] |
a39a46c5-9c60-422c-9027-8f51d67c5be5 | spcolor-semantic-prior-guided-exemplar-based | 2304.06255 | null | https://arxiv.org/abs/2304.06255v2 | https://arxiv.org/pdf/2304.06255v2.pdf | SPColor: Semantic Prior Guided Exemplar-based Image Colorization | Exemplar-based image colorization aims to colorize a target grayscale image based on a color reference image, and the key is to establish accurate pixel-level semantic correspondence between these two images. Previous methods search for correspondence across the entire reference image, and this type of global matching ... | ['Yue Zhang', 'Yu Zhang', 'Mingdao Wang', 'Xianlin Zhang', 'Xueming Li', 'Siqi Chen'] | 2023-04-13 | null | null | null | null | ['colorization', 'semantic-correspondence'] | ['computer-vision', 'computer-vision'] | [ 4.62743312e-01 -1.07098907e-01 -1.25531286e-01 -3.12102705e-01
-6.13967776e-01 -5.55119932e-01 3.20686877e-01 -1.42277882e-01
-3.49420577e-01 5.54967880e-01 -2.79665262e-01 4.60228138e-02
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7.24275172e-01 1.79998234e-01 6.30601108e-01 -2.59813637... | [11.166094779968262, -1.229921579360962] |
9f879f2c-2b2c-47a4-ab1a-e23996a63271 | multiview-based-3d-scene-understanding-on | 1812.01712 | null | http://arxiv.org/abs/1812.01712v1 | http://arxiv.org/pdf/1812.01712v1.pdf | Multiview Based 3D Scene Understanding On Partial Point Sets | Deep learning within the context of point clouds has gained much research
interest in recent years mostly due to the promising results that have been
achieved on a number of challenging benchmarks, such as 3D shape recognition
and scene semantic segmentation. In many realistic settings however, snapshots
of the environ... | ['Miklas Strøm Kristoffersen', 'Pablo Martínez-Nuevo', 'Zhuang Fu', 'Sven Ewan Shepstone', 'Fabien Moutarde', 'Ye Zhu'] | 2018-11-30 | null | null | null | null | ['3d-shape-recognition', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.07933420e-01 -2.01235209e-02 1.05648808e-01 -5.88877380e-01
-6.35611236e-01 -6.63159847e-01 5.21234453e-01 4.09639150e-01
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-3.15565728e-02 -8.36510539e-01 -1.08577859e+00 -3.97584379e-01
3.43979299e-01 9.37524140e-01 3.69273871e-01 4.03154315... | [8.070469856262207, -3.098529100418091] |
836fb890-8052-4c82-9af9-4bc3ad319e12 | aligning-step-by-step-instructional-diagrams | 2303.138 | null | https://arxiv.org/abs/2303.13800v2 | https://arxiv.org/pdf/2303.13800v2.pdf | Aligning Step-by-Step Instructional Diagrams to Video Demonstrations | Multimodal alignment facilitates the retrieval of instances from one modality when queried using another. In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-th... | ['Stephen Gould', 'Cristian Rodriguez', 'Yizhak Ben-Shabat', 'Yanbin Liu', 'Anoop Cherian', 'Jiahao Zhang'] | 2023-03-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Aligning_Step-by-Step_Instructional_Diagrams_to_Video_Demonstrations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Aligning_Step-by-Step_Instructional_Diagrams_to_Video_Demonstrations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-retrieval', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 5.09320498e-01 -1.02800645e-01 -1.32827312e-01 -4.36993986e-01
-1.45338261e+00 -9.74370062e-01 5.13436556e-01 -1.04774654e-01
-2.88369775e-01 1.90613195e-01 3.19941670e-01 1.15848035e-01
-4.03185874e-01 -1.14782624e-01 -1.40854013e+00 -5.55043697e-01
-7.34860450e-02 6.92607284e-01 -1.41632393e-01 -1.76695034... | [10.224395751953125, 0.8496160507202148] |
beef27c7-06e3-45ea-aafe-1accaa4658e8 | millie-modular-iterative-multilingual-open | null | null | https://openreview.net/forum?id=KNqKOUnl_3F | https://openreview.net/pdf?id=KNqKOUnl_3F | MILLIE: Modular & Iterative Multilingual Open Information Extraction | Open Information Extraction (OpenIE) is the task of extracting $(subject, predicate, object)$ triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we investigate the hypothesis that it may be beneficial to extract triple slots iteratively: first extract ea... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['open-information-extraction'] | ['natural-language-processing'] | [ 8.01536739e-02 8.50348592e-01 -3.52497280e-01 -2.33441040e-01
-7.99727142e-01 -7.03092456e-01 4.30408329e-01 9.48085859e-02
-4.35005456e-01 9.29582179e-01 1.08478740e-01 -7.02202559e-01
-7.82919526e-02 -1.07891595e+00 -7.31033742e-01 1.17589578e-01
-1.87393442e-01 6.79876268e-01 2.04660594e-01 -4.46843714... | [9.577545166015625, 8.619068145751953] |
c14bd148-95bc-4c28-afe5-207f0f2a1fdc | multi-source-transformer-architectures-for | 2210.10212 | null | https://arxiv.org/abs/2210.10212v1 | https://arxiv.org/pdf/2210.10212v1.pdf | Multi-Source Transformer Architectures for Audiovisual Scene Classification | In this technical report, the systems we submitted for subtask 1B of the DCASE 2021 challenge, regarding audiovisual scene classification, are described in detail. They are essentially multi-source transformers employing a combination of auditory and visual features to make predictions. These models are evaluated utili... | ['Hugo Van hamme', 'Wim Boes'] | 2022-10-18 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 5.21642109e-03 -1.36971667e-01 -3.05730700e-02 -1.63636193e-01
-1.36639738e+00 -5.41126549e-01 5.64703584e-01 4.23907816e-01
-3.37635726e-01 3.97603869e-01 3.11203182e-01 -6.51952475e-02
2.57362753e-01 -2.62510926e-01 -4.93603915e-01 -6.02447450e-01
-2.12008357e-01 -2.35243484e-01 2.64991581e-01 -2.35478252... | [15.0498046875, 5.091681480407715] |
60b1bc5b-347d-44d7-bfdf-1a7bf43749bc | learning-advisor-networks-for-noisy-image-1 | 2211.04177 | null | https://arxiv.org/abs/2211.04177v1 | https://arxiv.org/pdf/2211.04177v1.pdf | Learning advisor networks for noisy image classification | In this paper, we introduced the novel concept of advisor network to address the problem of noisy labels in image classification. Deep neural networks (DNN) are prone to performance reduction and overfitting problems on training data with noisy annotations. Weighting loss methods aim to mitigate the influence of noisy ... | ['Alberto del Bimbo', 'Tiberio Uricchio', 'Simone Ricci'] | 2022-11-08 | learning-advisor-networks-for-noisy-image | https://link.springer.com/chapter/10.1007/978-3-031-06430-2_37 | https://link.springer.com/chapter/10.1007/978-3-031-06430-2_37 | iciap-2022-5 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.92799413e-01 2.41298020e-01 1.29518136e-01 -6.96877122e-01
-5.44812799e-01 -4.62667078e-01 2.57809579e-01 1.79122388e-01
-9.87450063e-01 7.15500534e-01 -6.66805729e-02 -2.65216958e-02
1.94860678e-02 -6.69265270e-01 -8.08908820e-01 -8.35567176e-01
1.51435316e-01 3.29080671e-01 1.80539042e-01 8.27918127... | [9.389491081237793, 3.8142952919006348] |
7787eb27-7967-4e8a-a351-c87519b17474 | vrdformer-end-to-end-video-visual-relation | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_VRDFormer_End-to-End_Video_Visual_Relation_Detection_With_Transformers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_VRDFormer_End-to-End_Video_Visual_Relation_Detection_With_Transformers_CVPR_2022_paper.pdf | VRDFormer: End-to-End Video Visual Relation Detection With Transformers | Visual relation understanding plays an essential role for holistic video understanding. Most previous works adopt a multi-stage framework for video visual relation detection (VidVRD), which cannot capture long-term spatiotemporal contexts in different stages and also suffers from inefficiency. In this paper, we pro... | ['Qin Jin', 'ShiZhe Chen', 'Sipeng Zheng'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['video-visual-relation-detection', 'relation-classification'] | ['computer-vision', 'natural-language-processing'] | [-5.57097159e-02 -1.41808450e-01 -7.12336957e-01 -1.79153487e-01
-5.28659284e-01 -4.43801463e-01 8.16267312e-01 -9.99164432e-02
-1.17662273e-01 2.77828097e-01 2.08413959e-01 -5.35539865e-01
8.04653242e-02 -6.18132353e-01 -6.46140516e-01 -2.51252443e-01
1.89397801e-02 2.64086604e-01 8.07111144e-01 -1.14032373... | [9.435338973999023, 0.776303768157959] |
fe60c03b-e4db-470c-ae75-eb0ff8ced870 | towards-automatic-manipulation-of-intra | 2009.05859 | null | https://arxiv.org/abs/2009.05859v3 | https://arxiv.org/pdf/2009.05859v3.pdf | Towards Automatic Manipulation of Intra-cardiac Echocardiography Catheter | Intra-cardiac Echocardiography (ICE) is a powerful imaging modality for guiding electrophysiology and structural heart interventions. ICE provides real-time observation of anatomy, catheters, and emergent complications. However, this increased reliance on intraprocedural imaging creates a high cognitive demand on physi... | ['C. Huie Lin', 'Ankur Kapoor', 'Ponraj Chinnadurai', 'Zhongyu Li', 'Young-Ho Kim', 'Tommaso Mansi', 'Jarrod Collins'] | 2020-09-12 | null | null | null | null | ['non-linear-elasticity'] | ['miscellaneous'] | [-8.54112282e-02 -1.45757720e-01 2.67243564e-01 5.45246266e-02
-2.87255883e-01 -1.35229158e+00 -2.46454686e-01 1.16855644e-01
-1.99230537e-01 3.88801247e-01 -2.88219750e-01 -1.11512518e+00
-3.90114814e-01 -1.43137261e-01 -5.04594445e-01 -2.98549742e-01
-3.07580173e-01 5.78414083e-01 -8.53314027e-02 2.04163760... | [13.891419410705566, -2.7652714252471924] |
b12e69db-2e40-48aa-bfe7-8859240e3454 | mutual-information-regularization-for-weakly | 2306.0363 | null | https://arxiv.org/abs/2306.03630v1 | https://arxiv.org/pdf/2306.03630v1.pdf | Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection | In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter-modal mutual information regularization. In particular, following the principle of disentangled rep... | ['Yuchao Dai', 'Jing Zhang', 'Yuxin Mao', 'Aixuan Li'] | 2023-06-06 | null | null | null | null | ['rgb-d-salient-object-detection', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 3.93359333e-01 5.31228542e-01 -5.45467675e-01 -2.30528980e-01
-1.08036613e+00 -3.80702138e-01 6.08205497e-01 -1.04307368e-01
-1.47990733e-01 4.76029724e-01 5.39993167e-01 1.64135583e-02
-1.02114119e-01 -3.83137345e-01 -8.35814834e-01 -1.06145859e+00
2.23092332e-01 3.83791625e-01 -1.93012193e-01 -7.66336396... | [10.75829029083252, 1.1855634450912476] |
7edfa1e7-bab6-4ee6-8a5c-3cd143eaf024 | alleviating-the-sample-selection-bias-in-few | 2210.16834 | null | https://arxiv.org/abs/2210.16834v1 | https://arxiv.org/pdf/2210.16834v1.pdf | Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid | Few-shot learning (FSL) targets at generalization of vision models towards unseen tasks without sufficient annotations. Despite the emergence of a number of few-shot learning methods, the sample selection bias problem, i.e., the sensitivity to the limited amount of support data, has not been well understood. In this pa... | ['Zenglin Xu', 'Yanan Li', 'Wenjie Pei', 'Xinglin Pan', 'Xu Luo', 'Jing Xu'] | 2022-10-30 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 3.72085333e-01 -2.59777904e-01 -1.33504361e-01 -3.11756581e-01
-6.21629477e-01 -4.88813490e-01 7.30105281e-01 -1.41741022e-01
-5.28938949e-01 6.49098337e-01 1.53554454e-01 1.05863787e-01
-5.30548453e-01 -2.74033725e-01 -4.50707495e-01 -9.23786581e-01
3.42626661e-01 2.40961641e-01 5.13642907e-01 -1.13304360... | [9.924725532531738, 2.9500880241394043] |
054f5caa-4fb2-432d-a59b-025935581b04 | semantic-source-code-search-a-study-of-the | 1908.06738 | null | https://arxiv.org/abs/1908.06738v2 | https://arxiv.org/pdf/1908.06738v2.pdf | Semantic Source Code Search: A Study of the Past and a Glimpse at the Future | With the recent explosion in the size and complexity of source codebases and software projects, the need for efficient source code search engines has increased dramatically. Unfortunately, existing information retrieval-based methods fail to capture the query semantics and perform well only when the query contains synt... | ['Muhammad Khalifa'] | 2019-08-15 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-3.13547671e-01 -3.52255136e-01 -5.57965457e-01 -2.17508271e-01
-9.77268934e-01 -8.25903237e-01 3.51672769e-01 5.64390779e-01
-1.26209125e-01 2.57492840e-01 1.20119013e-01 -6.34392321e-01
-3.58318716e-01 -6.31196260e-01 -1.16547585e-01 2.67468184e-01
-2.18282882e-02 6.29534274e-02 7.54017234e-01 -3.95205855... | [7.548091888427734, 8.059141159057617] |
7537d8e4-f091-42d5-a085-acde789b6660 | integration-of-radiomics-and-tumor-biomarkers | 2303.11177 | null | https://arxiv.org/abs/2303.11177v1 | https://arxiv.org/pdf/2303.11177v1.pdf | Integration of Radiomics and Tumor Biomarkers in Interpretable Machine Learning Models | Despite the unprecedented performance of deep neural networks (DNNs) in computer vision, their practical application in the diagnosis and prognosis of cancer using medical imaging has been limited. One of the critical challenges for integrating diagnostic DNNs into radiological and oncological applications is their lac... | ['Neo Christopher Chung', 'Lennart Brocki'] | 2023-03-20 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.06084803e-01 3.38095933e-01 -5.07754803e-01 -2.85789490e-01
-4.64428902e-01 -2.76414603e-01 3.72866571e-01 2.44502902e-01
-4.46737438e-01 7.34838068e-01 -7.58580072e-03 -7.54302800e-01
-5.51429212e-01 -6.98307931e-01 -3.48045260e-01 -9.01268244e-01
-1.26118017e-02 7.37863779e-01 6.11232640e-03 1.16248079... | [15.307098388671875, -2.221406936645508] |
b71653a8-ef32-4215-8766-a299253367cb | modality-aware-negative-sampling-for-multi | 2304.11618 | null | https://arxiv.org/abs/2304.11618v1 | https://arxiv.org/pdf/2304.11618v1.pdf | Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding | Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when multi-modal information is considered in KGE models. They are also inefficient due to their complex des... | ['Wen Zhang', 'Mingyang Chen', 'Yichi Zhang'] | 2023-04-23 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding', 'multi-modal-knowledge-graph'] | ['graphs', 'graphs', 'knowledge-base'] | [-3.33085209e-01 1.85308680e-01 -6.71140671e-01 -8.78894255e-02
-3.01521361e-01 -3.02156240e-01 5.20739973e-01 3.90501954e-02
-3.79447848e-01 5.67271888e-01 5.24872780e-01 -1.56956464e-01
-1.99650884e-01 -1.17386580e+00 -5.53968191e-01 -5.85699022e-01
-1.41289234e-01 1.35923356e-01 1.02273889e-01 -4.18825209... | [8.6871976852417, 7.7601399421691895] |
b016a235-4f10-477a-9acb-a9aa675388bf | uwspeech-speech-to-speech-translation-for | 2006.07926 | null | https://arxiv.org/abs/2006.07926v2 | https://arxiv.org/pdf/2006.07926v2.pdf | UWSpeech: Speech to Speech Translation for Unwritten Languages | Existing speech to speech translation systems heavily rely on the text of target language: they usually translate source language either to target text and then synthesize target speech from text, or directly to target speech with target text for auxiliary training. However, those methods cannot be applied to unwritten... | ['Ke-jun Zhang', 'Tie-Yan Liu', 'Yi Ren', 'Chen Zhang', 'Xu Tan', 'Tao Qin'] | 2020-06-14 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 2.58030146e-01 1.51789337e-01 -2.22921386e-01 -3.07169229e-01
-1.50792575e+00 -9.01007354e-01 6.89606607e-01 -6.72771573e-01
-1.15682021e-01 9.27354217e-01 3.38435680e-01 -9.56861675e-01
7.20449865e-01 -5.90435922e-01 -1.00010133e+00 -5.48712671e-01
8.29403639e-01 8.19426596e-01 -1.04215100e-01 -5.44779718... | [14.5374755859375, 7.108080863952637] |
ce974d44-c0b2-4448-a03d-2679d7f9f3e2 | gispy-a-tool-for-measuring-gist-inference | 2205.12484 | null | https://arxiv.org/abs/2205.12484v1 | https://arxiv.org/pdf/2205.12484v1.pdf | GisPy: A Tool for Measuring Gist Inference Score in Text | Decision making theories such as Fuzzy-Trace Theory (FTT) suggest that individuals tend to rely on gist, or bottom-line meaning, in the text when making decisions. In this work, we delineate the process of developing GisPy, an open-source tool in Python for measuring the Gist Inference Score (GIS) in text. Evaluation o... | ['David A. Broniatowski', 'Mona Diab', 'Christopher R. Wolfe', 'Pedram Hosseini'] | 2022-05-25 | null | https://aclanthology.org/2022.wnu-1.5 | https://aclanthology.org/2022.wnu-1.5.pdf | naacl-wnu-2022-7 | ['coherence-evaluation'] | ['natural-language-processing'] | [-2.89942116e-01 8.25092345e-02 -2.86274523e-01 -7.17625737e-01
-2.17207730e-01 -5.99814117e-01 9.36157167e-01 6.23417735e-01
-1.80731177e-01 4.95669127e-01 5.95991075e-01 -8.44379485e-01
-6.20015502e-01 -1.15482426e+00 -1.71022505e-01 1.56536803e-01
4.12884951e-01 4.45068151e-01 -2.27095246e-01 -1.56170309... | [9.755837440490723, 7.871340751647949] |
6a2eb5cc-3b38-4968-b65b-814dde2cf01c | 6-dof-pose-estimation-of-household-objects | 2203.05701 | null | https://arxiv.org/abs/2203.05701v2 | https://arxiv.org/pdf/2203.05701v2.pdf | 6-DoF Pose Estimation of Household Objects for Robotic Manipulation: An Accessible Dataset and Benchmark | We present a new dataset for 6-DoF pose estimation of known objects, with a focus on robotic manipulation research. We propose a set of toy grocery objects, whose physical instantiations are readily available for purchase and are appropriately sized for robotic grasping and manipulation. We provide 3D scanned textured ... | ['Stan Birchfield', 'Jeffrey Smith', 'Terry Mosier', 'Jia Cheng', 'Thang To', 'Jonathan Tremblay', 'Stephen Tyree'] | 2022-03-11 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-4.92957607e-02 1.16667233e-01 1.56364255e-02 -4.81185108e-01
-7.92170644e-01 -8.68193448e-01 2.36616313e-01 -3.26876253e-01
6.66293800e-02 3.20354074e-01 -3.29365104e-01 1.38335332e-01
-3.96024019e-01 -3.81211907e-01 -1.03890479e+00 -4.92065012e-01
-1.99212059e-01 1.31382120e+00 3.28045726e-01 -1.95425421... | [5.934572219848633, -0.9830020070075989] |
7375f39a-ed04-4d73-be43-f12418a73215 | region-of-interest-detection-in-melanocytic | 2210.16457 | null | https://arxiv.org/abs/2210.16457v1 | https://arxiv.org/pdf/2210.16457v1.pdf | Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images | Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology help us to reduce costs and increase the speed and accuracy of regions of interest detection an... | ['Nancy E. Thomas', 'J. S. Marron', 'Sherif Farag', 'Jayson R. Miedema', 'Yao Li', 'Yi Cui'] | 2022-10-29 | null | null | null | null | ['medical-image-detection'] | ['computer-vision'] | [ 3.42276424e-01 -4.31163125e-02 -2.50196248e-01 -3.01898848e-02
-1.22725272e+00 -3.03452790e-01 2.47922704e-01 4.88578796e-01
-6.55428112e-01 5.81562400e-01 -3.11645269e-01 -4.80635434e-01
2.91759539e-02 -7.90302634e-01 -1.51484430e-01 -1.43230546e+00
4.88254279e-02 4.10426944e-01 3.53448749e-01 4.16148342... | [15.172819137573242, -3.027350664138794] |
8621e8bd-0900-4c5e-9150-ff54ac7ec819 | continuous-landmark-detection-with-3d-queries | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chandran_Continuous_Landmark_Detection_With_3D_Queries_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chandran_Continuous_Landmark_Detection_With_3D_Queries_CVPR_2023_paper.pdf | Continuous Landmark Detection With 3D Queries | Neural networks for facial landmark detection are notoriously limited to a fixed set of landmarks in a dedicated layout, which must be specified at training time. Dedicated datasets must also be hand-annotated with the corresponding landmark configuration for training. We propose the first facial landmark detection... | ['Derek Bradley', 'Paulo Gotardo', 'Gaspard Zoss', 'Prashanth Chandran'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-face-reconstruction', 'facial-landmark-detection', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.24379650e-01 -3.15430947e-02 -2.78202653e-01 -4.35156107e-01
-7.77951539e-01 -7.48513997e-01 4.67438549e-01 -7.32641816e-02
-3.79506767e-01 1.65184408e-01 -5.40776432e-01 -3.29938233e-01
9.27948430e-02 -5.59172273e-01 -6.40443504e-01 -5.00051618e-01
-1.03100941e-01 1.05711079e+00 2.57410318e-01 -5.49061922... | [13.448878288269043, 0.2824179530143738] |
15587263-acc2-41e0-a569-3f468625786e | solving-the-undirected-feedback-vertex-set | 1405.0446 | null | http://arxiv.org/abs/1405.0446v1 | http://arxiv.org/pdf/1405.0446v1.pdf | Solving the undirected feedback vertex set problem by local search | An undirected graph consists of a set of vertices and a set of undirected
edges between vertices. Such a graph may contain an abundant number of cycles,
then a feedback vertex set (FVS) is a set of vertices intersecting with each of
these cycles. Constructing a FVS of cardinality approaching the global minimum
value is... | ['Hai-Jun Zhou', 'Shao-Meng Qin'] | 2014-05-01 | null | null | null | null | ['feedback-vertex-set-fvs'] | ['graphs'] | [ 4.49253976e-01 8.07814777e-01 -3.48334283e-01 -1.06950425e-01
-2.46590555e-01 -6.71565413e-01 7.52849817e-01 2.10470840e-01
-1.13979369e-01 9.18052971e-01 -2.05307961e-01 -4.75900352e-01
-3.66410464e-01 -1.25891209e+00 -7.08719671e-01 -1.03272271e+00
-4.95747745e-01 1.16369939e+00 5.58832943e-01 -2.12582707... | [6.893631935119629, 5.372917652130127] |
839d2fae-c6c9-4c38-9225-d2aff2a8a7fc | css-a-large-scale-cross-schema-chinese-text | 2305.15891 | null | https://arxiv.org/abs/2305.15891v1 | https://arxiv.org/pdf/2305.15891v1.pdf | CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset | The cross-domain text-to-SQL task aims to build a system that can parse user questions into SQL on complete unseen databases, and the single-domain text-to-SQL task evaluates the performance on identical databases. Both of these setups confront unavoidable difficulties in real-world applications. To this end, we introd... | ['Kai Yu', 'Yefeng Zheng', 'Yu Huang', 'Yunyan Zhang', 'Ruisheng Cao', 'Lu Chen', 'Jieyu Li', 'Hanchong Zhang'] | 2023-05-25 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-1.63716041e-02 -1.12266026e-01 1.60662215e-02 -8.80925596e-01
-1.46290278e+00 -7.93531597e-01 3.03812176e-01 3.29428315e-01
-4.49049383e-01 6.47875011e-01 1.87697217e-01 -4.40286398e-01
3.87109630e-02 -9.00318027e-01 -8.61803651e-01 -2.00469211e-01
4.40563291e-01 8.01033497e-01 3.45310897e-01 -5.03650844... | [9.885321617126465, 7.834498882293701] |
6edd797e-81db-43d3-87d6-93ad5bc6bebb | graph-edit-distance-computation-via-graph | 1808.05689 | null | https://arxiv.org/abs/1808.05689v4 | https://arxiv.org/pdf/1808.05689v4.pdf | SimGNN: A Neural Network Approach to Fast Graph Similarity Computation | Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other ... | ['Yunsheng Bai', 'Ting Chen', 'Hao Ding', 'Yizhou Sun', 'Wei Wang', 'Song Bian'] | 2018-08-16 | simgnn-a-neural-network-approach-to-fast | https://doi.org/10.1145/3289600.3290967 | http://web.cs.ucla.edu/~yzsun/papers/2019_WSDM_SimGNN.pdf | wsdm-19-proceedings-of-the-twelfth-acm | ['graph-similarity'] | ['graphs'] | [ 2.27837667e-01 -4.10041213e-02 -2.22343788e-01 -3.63509983e-01
-2.74227887e-01 -5.16706705e-01 3.94336611e-01 9.54741418e-01
-3.85703087e-01 3.22747439e-01 -1.13040328e-01 -4.55637753e-01
-4.49003696e-01 -1.30666900e+00 -5.80054343e-01 -5.60877681e-01
-1.80147409e-01 2.89816380e-01 1.41775131e-01 -1.86145440... | [7.168111801147461, 6.259625434875488] |
23cbd5fe-79e9-4e0e-9e94-38c2b38b774f | injecting-numerical-reasoning-skills-into-1 | 2112.06109 | null | https://arxiv.org/abs/2112.06109v2 | https://arxiv.org/pdf/2112.06109v2.pdf | Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models | Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. We prese... | ['Hong Chen', 'Cuiping Li', 'Lemao Liu', 'Xiaokang Zhang', 'Jing Zhang', 'Yu Feng'] | 2021-12-12 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.94569939e-01 4.74015713e-01 -1.49604008e-01 -4.94475991e-01
-1.38956892e+00 -5.57812572e-01 2.86508322e-01 1.81138575e-01
-6.63840353e-01 8.03345084e-01 4.66036677e-01 -7.19692647e-01
-4.50013340e-01 -1.29395413e+00 -6.54538274e-01 5.85613074e-03
1.24252550e-01 1.25205493e+00 2.01378688e-01 -9.33695912... | [10.55698299407959, 7.92397403717041] |
4b9443c6-f57b-423f-a8db-ef8f703aa169 | extending-automatic-discourse-segmentation | 1703.04718 | null | http://arxiv.org/abs/1703.04718v1 | http://arxiv.org/pdf/1703.04718v1.pdf | Extending Automatic Discourse Segmentation for Texts in Spanish to Catalan | At present, automatic discourse analysis is a relevant research topic in the
field of NLP. However, discourse is one of the phenomena most difficult to
process. Although discourse parsers have been already developed for several
languages, this tool does not exist for Catalan. In order to implement this
kind of parser, ... | ['Irene Castellón', 'Juan-Manuel Torres-Moreno', 'Iria da Cunha', 'Eric SanJuan'] | 2017-03-11 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [ 1.58923224e-01 9.03812647e-01 -1.70603782e-01 -1.07008919e-01
-4.84863341e-01 -7.06758976e-01 1.00856781e+00 6.89072371e-01
-4.08836514e-01 1.11269557e+00 6.08470976e-01 -7.33151197e-01
5.08516170e-02 -7.45412171e-01 -3.14263463e-01 -3.22440565e-01
3.39829504e-01 6.44489944e-01 7.02929735e-01 -4.82812762... | [10.777650833129883, 9.463423728942871] |
b2531109-919f-4cd2-8f6f-15e36f351072 | is-neural-language-acquisition-similar-to | 2207.0056 | null | https://arxiv.org/abs/2207.00560v1 | https://arxiv.org/pdf/2207.00560v1.pdf | Is neural language acquisition similar to natural? A chronological probing study | The probing methodology allows one to obtain a partial representation of linguistic phenomena stored in the inner layers of the neural network, using external classifiers and statistical analysis. Pre-trained transformer-based language models are widely used both for natural language understanding (NLU) and natural lan... | ['Tatiana Shavrina', 'Oleg Serikov', 'Ekaterina Voloshina'] | 2022-07-01 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [-5.56175448e-02 5.73187292e-01 -3.49677235e-01 -2.85721362e-01
-5.02060354e-01 -7.57603049e-01 1.12383556e+00 2.38934740e-01
-1.66316435e-01 5.75157404e-01 4.70159233e-01 -8.27815711e-01
4.59497944e-02 -9.62874591e-01 -7.97293246e-01 -2.53380448e-01
2.63271462e-02 7.49386489e-01 2.42418930e-01 -6.05632961... | [10.524857521057129, 9.345829963684082] |
5656f1f5-5257-4231-b2aa-76cbfcef9a76 | example-based-image-synthesis-via-randomized | 1609.0737 | null | http://arxiv.org/abs/1609.07370v1 | http://arxiv.org/pdf/1609.07370v1.pdf | Example-Based Image Synthesis via Randomized Patch-Matching | Image and texture synthesis is a challenging task that has long been drawing
attention in the fields of image processing, graphics, and machine learning.
This problem consists of modelling the desired type of images, either through
training examples or via a parametric modeling, and then generating images that
belong t... | ['Yi Ren', 'Michael Elad', 'Yaniv Romano'] | 2016-09-23 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 7.55428553e-01 2.08462819e-01 2.72845715e-01 -2.64314860e-01
-5.42989612e-01 -2.94783324e-01 9.53435183e-01 -1.23551726e-01
-1.09680198e-01 7.42346525e-01 -2.06352919e-01 2.56400406e-01
-2.93549806e-01 -8.86608005e-01 -7.65357792e-01 -1.04910541e+00
1.67251751e-01 5.79106867e-01 1.37758300e-01 -8.92150849... | [11.671029090881348, -0.7433927059173584] |
4dda640d-f906-4ed1-b9cc-37b0b83b3ad1 | modeling-of-spatio-temporal-hawkes-processes | 2003.03671 | null | https://arxiv.org/abs/2003.03671v2 | https://arxiv.org/pdf/2003.03671v2.pdf | Modeling of Spatio-Temporal Hawkes Processes with Randomized Kernels | We investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatiotemporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used d... | ['Suleyman Serdar Kozat', 'Fatih Ilhan'] | 2020-03-07 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [ 3.65018994e-02 -3.76338720e-01 1.91014066e-01 -1.55832976e-01
-3.47020566e-01 -4.62358952e-01 1.03107691e+00 5.21099627e-01
-5.74599147e-01 6.54222429e-01 3.96703184e-01 -1.42042443e-01
-5.86035252e-01 -1.00498152e+00 -7.13676214e-01 -9.36316967e-01
-3.61537129e-01 4.16581750e-01 1.75365135e-01 -9.14680064... | [6.820353031158447, 3.593109607696533] |
6f09ea46-d38d-42b5-a162-33b522953fd1 | suspicious-vehicle-detection-using-licence | 2304.14507 | null | https://arxiv.org/abs/2304.14507v1 | https://arxiv.org/pdf/2304.14507v1.pdf | Suspicious Vehicle Detection Using Licence Plate Detection And Facial Feature Recognition | With the increasing need to strengthen vehicle safety and detection, the availability of pre-existing methods of catching criminals and identifying vehicles manually through the various traffic surveillance cameras is not only time-consuming but also inefficient. With the advancement of technology in every field the us... | ['Manoj Kumar Rajagopal', 'Bala Murugan MS', 'Manideep Ramisetty', 'Aaron George Pichappa', 'Vrinda Agarwal'] | 2023-04-18 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [-3.18878181e-02 -4.49941605e-01 -1.23708867e-01 -1.89240575e-01
-1.91200338e-02 -7.10900605e-01 6.90015256e-01 -1.67157993e-01
-6.67044759e-01 6.25029266e-01 -5.42076170e-01 -5.96591055e-01
2.59364881e-02 -7.71081030e-01 -2.26298526e-01 -4.74214047e-01
1.90371871e-01 2.21308634e-01 5.56361973e-01 -5.35002649... | [13.053977966308594, 0.9787275791168213] |
5374385b-06d7-47ad-8c76-3483a87a2f96 | unsupervised-cross-spectral-stereo-matching | 1903.01078 | null | http://arxiv.org/abs/1903.01078v1 | http://arxiv.org/pdf/1903.01078v1.pdf | Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize | Unsupervised cross-spectral stereo matching aims at recovering disparity
given cross-spectral image pairs without any supervision in the form of ground
truth disparity or depth. The estimated depth provides additional information
complementary to individual semantic features, which can be helpful for other
vision tasks... | ['You Song', 'Mingyang Liang', 'Hongsheng Li', 'Xiaoyang Guo', 'Xiaogang Wang'] | 2019-03-04 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 8.30464363e-01 -3.47813874e-01 -4.72382531e-02 -4.23303187e-01
-6.80932045e-01 -4.87744182e-01 3.29532087e-01 -3.01740944e-01
-3.80389035e-01 4.48525816e-01 -4.23229672e-02 1.37909621e-01
2.15280697e-01 -9.79152501e-01 -8.77977431e-01 -8.57432961e-01
8.49450350e-01 9.74530652e-02 2.97649682e-01 -1.61574945... | [9.074911117553711, -2.337958335876465] |
b766ef30-03b4-4614-85c5-a92580a9a46f | finding-the-law-enhancing-statutory-article | 2301.12847 | null | https://arxiv.org/abs/2301.12847v1 | https://arxiv.org/pdf/2301.12847v1.pdf | Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks | Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of legal professionals and provide basic legal assistance to citizens in need at no co... | ['Gerasimos Spanakis', 'Gijs Van Dijck', 'Antoine Louis'] | 2023-01-30 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.97251037e-01 3.56574118e-01 -7.96714664e-01 -7.92191476e-02
-1.31892848e+00 -7.84984708e-01 5.02277374e-01 6.97681546e-01
-4.14174587e-01 5.41491807e-01 9.71550941e-01 -8.43957365e-01
-7.79654264e-01 -1.06773508e+00 -4.70045686e-01 -1.54373229e-01
3.41209114e-01 9.68275428e-01 1.54130086e-01 -7.23595321... | [9.82319164276123, 9.050939559936523] |
3c9cf053-b25a-48aa-aead-768c734e17d3 | representation-learning-for-person-or-entity | 2305.0564 | null | https://arxiv.org/abs/2305.05640v2 | https://arxiv.org/pdf/2305.05640v2.pdf | Representation Learning for Person or Entity-centric Knowledge Graphs: An Application in Healthcare | Knowledge graphs (KGs) are a popular way to organise information based on ontologies or schemas and have been used across a variety of scenarios from search to recommendation. Despite advances in KGs, representing knowledge remains a non-trivial task across industries and it is especially challenging in the biomedical ... | ['Joao Bettencourt-Silva', 'Thaddeus Stappenbeck', 'Natasha Mulligan', 'Christos Theodoropoulos'] | 2023-05-09 | null | null | null | null | ['person-centric-knowledge-graphs', 'readmission-prediction'] | ['graphs', 'medical'] | [ 2.66914666e-01 8.84395659e-01 -4.28163320e-01 -3.08722526e-01
-3.16420764e-01 -1.72532752e-01 5.57586551e-01 1.03680634e+00
-1.09881930e-01 7.22715974e-01 6.88487828e-01 -1.98315904e-01
-7.38452375e-01 -1.14431822e+00 -4.91202623e-01 -4.49793279e-01
-4.44563746e-01 7.67795980e-01 -1.51712531e-02 -2.56196678... | [8.451953887939453, 7.760760307312012] |
d546b951-576d-4784-9f79-738995485296 | semipfl-personalized-semi-supervised | 2203.08176 | null | https://arxiv.org/abs/2203.08176v2 | https://arxiv.org/pdf/2203.08176v2.pdf | SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence | Recent advances in wearable devices and Internet-of-Things (IoT) have led to massive growth in sensor data generated in edge devices. Labeling such massive data for classification tasks has proven to be challenging. In addition, data generated by different users bear various personal attributes and edge heterogeneity, ... | ['Peyman Servati', 'Z. Jane Wang', 'Wenwen Zhang', 'Arvin Tashakori'] | 2022-03-15 | null | null | null | null | ['semi-supervised-time-series-classification'] | ['time-series'] | [-2.81320084e-02 -3.01929209e-02 -3.71632248e-01 -6.51263297e-01
-3.94752949e-01 -4.40274417e-01 -2.28546396e-01 3.23013067e-01
-3.54170233e-01 7.92719126e-01 1.63867608e-01 5.37631214e-02
-1.52822003e-01 -9.15945411e-01 -6.79614842e-01 -6.29902601e-01
-1.86169177e-01 5.11758029e-01 -5.04816957e-02 5.03212631... | [6.021755695343018, 6.179160118103027] |
11857e00-8a53-4ede-83dd-e122b5dbf2bf | efficient-reward-poisoning-attacks-on-online | 2205.14842 | null | https://arxiv.org/abs/2205.14842v2 | https://arxiv.org/pdf/2205.14842v2.pdf | Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning | We study reward poisoning attacks on online deep reinforcement learning (DRL), where the attacker is oblivious to the learning algorithm used by the agent and the dynamics of the environment. We demonstrate the intrinsic vulnerability of state-of-the-art DRL algorithms by designing a general, black-box reward poisoning... | ['Gagandeep Singh', 'Qi Zeng', 'Yinglun Xu'] | 2022-05-30 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-7.15554118e-01 -6.89166784e-02 -2.20541179e-01 2.31264293e-01
-6.56395614e-01 -1.09176981e+00 7.11640894e-01 4.19164784e-02
-1.01273727e+00 1.06549358e+00 -2.78732508e-01 -5.25582075e-01
4.38115522e-02 -9.34159636e-01 -1.05525517e+00 -9.14011955e-01
-8.18658471e-01 5.15167832e-01 3.34421009e-01 -3.81994158... | [3.9621288776397705, 2.3787074089050293] |
49688cd6-4168-4572-be67-3740bfc1f781 | learning-graphs-for-knowledge-transfer-with | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ghosh_Learning_Graphs_for_Knowledge_Transfer_With_Limited_Labels_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ghosh_Learning_Graphs_for_Knowledge_Transfer_With_Limited_Labels_CVPR_2021_paper.pdf | Learning Graphs for Knowledge Transfer With Limited Labels | Fixed input graphs are a mainstay in approaches that utilize Graph Convolution Networks (GCNs) for knowledge transfer. The standard paradigm is to utilize relationships in the input graph to transfer information using GCNs from training to testing nodes in the graph; for example, the semi-supervised, zero-shot, and... | ['Abhinav Shrivastava', 'Larry S. Davis', 'Nirat Saini', 'Pallabi Ghosh'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 3.57212514e-01 5.33489645e-01 -4.87472713e-01 -4.62978333e-01
-2.64109850e-01 -5.78536570e-01 6.47971511e-01 6.01855993e-01
-4.29329365e-01 9.14774299e-01 3.20211411e-01 -2.96292245e-01
-4.19358373e-01 -1.17224061e+00 -1.03235006e+00 -5.24546325e-01
-1.93341345e-01 6.48824930e-01 2.16384560e-01 -1.29742652... | [6.947887897491455, 6.2983551025390625] |
379fe1ca-a6b3-4e83-b14c-4e4097030b3e | counting-from-sky-a-large-scale-dataset-for | 2008.1247 | null | https://arxiv.org/abs/2008.12470v1 | https://arxiv.org/pdf/2008.12470v1.pdf | Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method | Object counting, whose aim is to estimate the number of objects from a given image, is an important and challenging computation task. Significant efforts have been devoted to addressing this problem and achieved great progress, yet counting the number of ground objects from remote sensing images is barely studied. In t... | ['Qingjie Liu', 'Yunhong Wang', 'Guangshuai Gao'] | 2020-08-28 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 7.44631663e-02 -5.78807831e-01 3.80525559e-01 -2.39495143e-01
-3.07325035e-01 -2.37171784e-01 5.35870135e-01 4.26447429e-02
-8.72795641e-01 8.59783828e-01 8.05996135e-02 -1.66279316e-01
8.65007862e-02 -1.15251768e+00 -4.40630078e-01 -6.29911125e-01
5.87472878e-02 3.53349537e-01 4.75225538e-01 1.05062433... | [8.498454093933105, -0.26599931716918945] |
b87e7bec-97e9-49f2-af19-b6fdb9be2aa5 | separation-free-spectral-super-resolution-via | 2211.15361 | null | https://arxiv.org/abs/2211.15361v1 | https://arxiv.org/pdf/2211.15361v1.pdf | Separation-Free Spectral Super-Resolution via Convex Optimization | Atomic norm methods have recently been proposed for spectral super-resolution with flexibility in dealing with missing data and miscellaneous noises. A notorious drawback of these convex optimization methods however is their lower resolution in the high signal-to-noise (SNR) regime as compared to conventional methods s... | ['Zongben Xu', 'Gongguo Tang', 'Yi-Lin Mo', 'Zai Yang'] | 2022-11-28 | null | null | null | null | ['spectral-super-resolution', 'miscellaneous'] | ['computer-vision', 'miscellaneous'] | [ 5.54488957e-01 1.99433062e-02 1.90701693e-01 1.06296837e-01
-1.13926661e+00 -3.05632263e-01 1.30769596e-01 -3.38009179e-01
-3.20415825e-01 1.24213505e+00 2.66377538e-01 1.64482713e-01
-7.41936207e-01 -5.25550246e-01 -3.95629674e-01 -1.19365239e+00
8.27338025e-02 9.56433043e-02 -1.44602224e-01 -3.51621240... | [6.560561656951904, 1.5051029920578003] |
8e9680db-d287-4d64-8f5c-13b280c67eec | conscious-inference-for-object-detection | null | null | https://openreview.net/forum?id=HygYqs0qKX | https://openreview.net/pdf?id=HygYqs0qKX | Conscious Inference for Object Detection | Current Convolutional Neural Network (CNN)-based object detection models adopt strictly feedforward inference to predict the final detection results. However, the widely used one-way inference is agnostic to the global image context and the interplay between input image and task semantics. In this work, we present a ge... | ['Gang Hua', 'Ying Wu', 'Nikolaos Karianakis', 'Jiahuan Zhou'] | 2018-09-27 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 6.27825558e-02 -1.01198256e-01 3.91696334e-01 -2.62076408e-01
2.10841939e-01 -4.17598397e-01 8.42814744e-01 4.07046646e-01
-8.50291908e-01 2.98526436e-01 -2.87642568e-01 -1.32691905e-01
2.01094389e-01 -8.60912323e-01 -7.21349597e-01 -6.02054954e-01
1.22856110e-01 4.00221884e-01 1.01112139e+00 -8.45478922... | [9.306233406066895, 0.29851096868515015] |
7ab4b1af-d639-4215-a82e-1c99ca1a2227 | improving-image-captioning-descriptiveness-by | 2306.11593 | null | https://arxiv.org/abs/2306.11593v1 | https://arxiv.org/pdf/2306.11593v1.pdf | Improving Image Captioning Descriptiveness by Ranking and LLM-based Fusion | State-of-The-Art (SoTA) image captioning models often rely on the Microsoft COCO (MS-COCO) dataset for training. This dataset contains annotations provided by human annotators, who typically produce captions averaging around ten tokens. However, this constraint presents a challenge in effectively capturing complex scen... | ['Paolo Napoletano', 'Marco Donzella', 'Luigi Celona', 'Simone Bianco'] | 2023-06-20 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 5.06444216e-01 3.73023748e-01 -8.52984935e-02 -3.41596454e-01
-1.14996803e+00 -6.62612021e-01 7.78376222e-01 1.22900940e-01
-2.73972780e-01 7.44160771e-01 4.73436266e-01 -1.48156077e-01
4.30843592e-01 -5.12762070e-01 -1.00578117e+00 -4.54691648e-01
4.64709431e-01 4.98639435e-01 9.57700908e-02 -2.21308112... | [10.987144470214844, 1.0575937032699585] |
cda6d9a8-1e6a-4fcf-8328-d1085e205956 | intent-recognition-in-conversational | 2212.03721 | null | https://arxiv.org/abs/2212.03721v1 | https://arxiv.org/pdf/2212.03721v1.pdf | Intent Recognition in Conversational Recommender Systems | Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, t... | ['Sahar Moradizeyveh'] | 2022-12-06 | null | null | null | null | ['feature-engineering', 'intent-recognition'] | ['methodology', 'natural-language-processing'] | [ 2.02053905e-01 4.71348494e-01 3.00774761e-02 -8.34852099e-01
-8.99930596e-01 -8.00932646e-01 8.71003985e-01 4.63769644e-01
-1.08957648e-01 3.10915828e-01 8.21770370e-01 -4.28843886e-01
-1.30272180e-01 -4.60916728e-01 2.00637147e-01 -4.26685117e-04
3.30913067e-01 7.89258718e-01 -1.18434012e-01 -8.42272222... | [12.465458869934082, 7.894413471221924] |
d8020847-529f-4fa4-a648-56e2c4ca0d5a | your-face-mirrors-your-deepest-beliefs | 2112.12455 | null | https://arxiv.org/abs/2112.12455v1 | https://arxiv.org/pdf/2112.12455v1.pdf | Your Face Mirrors Your Deepest Beliefs-Predicting Personality and Morals through Facial Emotion Recognition | Can we really "read the mind in the eyes"? Moreover, can AI assist us in this task? This paper answers these two questions by introducing a machine learning system that predicts personality characteristics of individuals on the basis of their face. It does so by tracking the emotional response of the individual's face ... | ['T. Schaefer', 'L. Ripperger', 'M. F. Kaiser', 'C. Cetinkaya', 'E. Altuntas', 'A. Fronzetti Colladon', 'P. A. Gloor'] | 2021-12-23 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [-4.76679236e-01 2.18730614e-01 -1.65743575e-01 -8.49064529e-01
1.06590129e-02 -2.43997321e-01 1.92069858e-02 -3.64248276e-01
-3.88395309e-01 5.85614979e-01 1.76286206e-01 5.98488033e-01
-4.03080881e-02 -5.58589756e-01 -4.79283556e-02 -4.76369649e-01
-6.84317499e-02 3.27246517e-01 -4.23404187e-01 -4.76237804... | [13.364453315734863, 2.0258219242095947] |
b32d1b11-9445-499c-89b6-e2d6f469728a | core-a-knowledge-graph-entity-type-prediction | 2112.10067 | null | https://arxiv.org/abs/2112.10067v1 | https://arxiv.org/pdf/2112.10067v1.pdf | CORE: A Knowledge Graph Entity Type Prediction Method via Complex Space Regression and Embedding | Entity type prediction is an important problem in knowledge graph (KG) research. A new KG entity type prediction method, named CORE (COmplex space Regression and Embedding), is proposed in this work. The proposed CORE method leverages the expressive power of two complex space embedding models; namely, RotatE and ComplE... | ['C. -C. Jay Kuo', 'Bin Wang', 'Yun-Cheng Wang', 'Xiou Ge'] | 2021-12-19 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-3.82163912e-01 3.57007653e-01 -7.88811862e-01 -3.17800760e-01
1.07086837e-01 -5.56601584e-01 5.20939231e-01 4.75452423e-01
-3.76084089e-01 6.88783526e-01 3.32438082e-01 -2.34375596e-01
-4.88733917e-01 -1.40669572e+00 -5.92592239e-01 -3.67328227e-01
-1.01406731e-01 2.65292674e-01 9.58434716e-02 -2.39059448... | [8.741496086120605, 7.9024529457092285] |
3b295faa-5221-4210-a8ec-daeb6fce980c | fusion-for-visual-infrared-person-reid-in | 2305.0032 | null | https://arxiv.org/abs/2305.00320v1 | https://arxiv.org/pdf/2305.00320v1.pdf | Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data | Visible-infrared person re-identification (V-I ReID) seeks to match images of individuals captured over a distributed network of RGB and IR cameras. The task is challenging due to the significant differences between V and I modalities, especially under real-world conditions, where images are corrupted by, e.g, blur, no... | ['Eric Granger', 'Rafael M. O. Cruz', 'Mahdi Alehdaghi', 'Arthur Josi'] | 2023-04-29 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 1.81480907e-02 -6.21053934e-01 3.20962995e-01 -2.88408369e-01
-7.54138052e-01 -7.07472622e-01 6.41482115e-01 -1.42751843e-01
-5.76004565e-01 5.00582576e-01 2.13083863e-01 1.20366417e-01
-8.06598663e-02 -2.94643909e-01 -6.92682743e-01 -8.12502265e-01
1.10313781e-01 1.30519137e-01 -3.20360005e-01 -3.50960255... | [14.575874328613281, 0.9999595880508423] |
26fe264a-854d-4c4f-8b22-32cf07856cf2 | machine-learning-for-real-time-anomaly | 2306.10741 | null | https://arxiv.org/abs/2306.10741v1 | https://arxiv.org/pdf/2306.10741v1.pdf | Machine Learning for Real-Time Anomaly Detection in Optical Networks | This work proposes a real-time anomaly detection scheme that leverages the multi-step ahead prediction capabilities of encoder-decoder (ED) deep learning models with recurrent units. Specifically, an encoder-decoder is used to model soft-failure evolution over a long future horizon (i.e., for several days ahead) by ana... | ['Georgios Ellinas', 'Tania Panayiotou', 'Sadananda Behera'] | 2023-06-19 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 4.22553197e-02 -5.86067922e-02 1.07720010e-02 -1.04881175e-01
-5.54765761e-01 -4.44765508e-01 3.08447152e-01 7.09115565e-01
7.91375618e-03 5.39825678e-01 -2.40467414e-01 -7.16882706e-01
-1.72850400e-01 -8.90701354e-01 -6.48849666e-01 -7.02015102e-01
-5.74098170e-01 1.21113975e-02 6.34291843e-02 -1.82882130... | [7.308893203735352, 2.6909446716308594] |
1a5b7c2c-e90d-4800-8d4e-f11eadb473dc | markbert-marking-word-boundaries-improves | null | null | https://openreview.net/forum?id=7uE-SSLTgxw | https://openreview.net/pdf?id=7uE-SSLTgxw | MarkBERT: Marking Word Boundaries Improves Chinese BERT | We present a Chinese BERT model dubbed MarkBERT that uses word information in this work.
Existing word-based BERT models regard words as basic units, however,
due to the vocabulary limit of BERT, they only cover high-frequency words and fall back to character level when encountering out-of-vocabulary (OOV) words.
Diffe... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-2.86226809e-01 -2.94113308e-01 -6.02996171e-01 -1.93676963e-01
-8.63611877e-01 -6.50458336e-01 2.23916814e-01 3.70935142e-01
-9.37187433e-01 6.75669909e-01 3.51106703e-01 -5.63525379e-01
2.45066479e-01 -8.39055657e-01 -3.93002421e-01 -3.28356206e-01
1.90246925e-01 4.15341914e-01 4.67378289e-01 -3.97167563... | [10.112523078918457, 10.033121109008789] |
223327ef-c20a-4d85-b467-78b2f030dccf | improving-audio-language-learning-with-mixgen | 2210.17143 | null | https://arxiv.org/abs/2210.17143v2 | https://arxiv.org/pdf/2210.17143v2.pdf | Exploring Train and Test-Time Augmentations for Audio-Language Learning | In this paper, we aim to unveil the impact of data augmentation in audio-language multi-modal learning, which has not been explored despite its importance. We explore various augmentation methods at not only train-time but also test-time and find out that proper data augmentation can lead to substantial improvements. S... | ['Kyogu Lee', 'Jinwoo Lee', 'Jaeheon Sim', 'Minju Park', 'KyungSu Kim', 'Yoori Oh', 'Jinhee Kim', 'Eungbeom Kim'] | 2022-10-31 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.22624648e-01 -1.77204445e-01 -5.21056466e-02 -2.43629470e-01
-2.05800533e+00 -7.40599632e-01 6.44136727e-01 2.83056408e-01
-4.62724894e-01 4.60633785e-01 5.39003909e-01 -2.13534027e-01
1.28817618e-01 -2.83554912e-01 -7.35152006e-01 -5.14062047e-01
-8.13688189e-02 5.99794745e-01 3.99810150e-02 -2.38499552... | [15.19153881072998, 5.086999893188477] |
6b5bb9c7-89f7-4f98-934d-14023039d135 | augmenting-message-passing-by-retrieving | 2206.00362 | null | https://arxiv.org/abs/2206.00362v3 | https://arxiv.org/pdf/2206.00362v3.pdf | Retrieval-enhanced Graph Neural Networks for Graph Property Prediction | Graph Neural Networks~(GNNs) are effective tools for graph representation learning. Most GNNs rely on a recursive neighborhood aggregation scheme, named message passing, thereby their theoretical expressive power is limited to the first order Weisfeiler-Lehman test (1-WL). Motivated by the success of retrieval-based mo... | ['Bernardo Cuenca Grau', 'Qi Liu', 'Song Le', 'Jian Tang', 'Linfeng Song', 'Hanchen Wang', 'Shengchao Liu', 'Dingmin Wang'] | 2022-06-01 | null | null | null | null | ['graph-property-prediction'] | ['graphs'] | [ 1.35876238e-01 3.12156200e-01 -4.20611322e-01 -2.30393037e-01
-5.35535216e-01 -3.01708430e-01 6.50398374e-01 6.54132426e-01
-2.75869429e-01 6.02043211e-01 1.07550502e-01 -3.48218352e-01
-5.73052108e-01 -1.26844656e+00 -8.73175561e-01 -3.93938780e-01
-5.26541591e-01 7.57023275e-01 2.01027408e-01 -4.44443434... | [7.081363677978516, 6.2723164558410645] |
5da809b2-d9c7-47af-b10b-017509e6d523 | ambient-search-a-document-retrieval-system | null | null | https://aclanthology.org/C16-1196 | https://aclanthology.org/C16-1196.pdf | Ambient Search: A Document Retrieval System for Speech Streams | We present Ambient Search, an open source system for displaying and retrieving relevant documents in real time for speech input. The system works ambiently, that is, it unobstructively listens to speech streams in the background, identifies keywords and keyphrases for query construction and continuously serves relevant... | ['Max M{\\"u}hlh{\\"a}user', 'Chris Biemann', 'Benjamin Milde', 'Stefan Radomski', 'Jonas Wacker'] | 2016-12-01 | ambient-search-a-document-retrieval-system-1 | https://aclanthology.org/C16-1196 | https://aclanthology.org/C16-1196.pdf | coling-2016-12 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.94381171e-01 4.94712405e-02 -4.00808193e-02 1.74242586e-01
-1.44094586e+00 -9.78311896e-01 8.31530988e-01 5.32683909e-01
-7.81254828e-01 3.88793111e-01 7.73605168e-01 -4.35046911e-01
-2.02781603e-01 -6.44434869e-01 -3.28932762e-01 -4.58543658e-01
-7.59631619e-02 4.01948273e-01 8.22108865e-01 -5.48434794... | [14.151311874389648, 6.420164585113525] |
2a88eec0-86f4-4bed-a2b6-700948fdc5d3 | developing-quantum-annealer-driven-data | 1603.0798 | null | http://arxiv.org/abs/1603.07980v1 | http://arxiv.org/pdf/1603.07980v1.pdf | Developing Quantum Annealer Driven Data Discovery | Machine learning applications are limited by computational power. In this
paper, we gain novel insights into the application of quantum annealing (QA) to
machine learning (ML) through experiments in natural language processing (NLP),
seizure prediction, and linear separability testing. These experiments are
performed o... | ['Michael Kim', 'Joseph Dulny III'] | 2016-03-25 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 2.87977904e-01 -7.79755320e-03 -1.64776593e-01 -6.20600581e-01
-1.62534654e+00 -4.28640246e-01 2.89736032e-01 4.54946935e-01
-6.19819880e-01 8.99802089e-01 -8.59571099e-02 -7.08442032e-01
-1.75298601e-01 -6.89110875e-01 -5.64224899e-01 -6.47310495e-01
-3.22095513e-01 4.92636561e-01 -7.52206966e-02 -4.66615260... | [5.546063423156738, 4.942598342895508] |
5aba3db7-854d-4071-aeb1-941dfd43ffcf | towards-generalizable-person-re | 2206.09362 | null | https://arxiv.org/abs/2206.09362v2 | https://arxiv.org/pdf/2206.09362v2.pdf | Towards Generalizable Person Re-identification with a Bi-stream Generative Model | Generalizable person re-identification (re-ID) has attracted growing attention due to its powerful adaptation capability in the unseen data domain. However, existing solutions often neglect either crossing cameras (e.g., illumination and resolution differences) or pedestrian misalignments (e.g., viewpoint and pose disc... | ['Qi Tian', 'Ruiming Hu', 'Zheng Wang', 'Wei Liu', 'Xin Xu'] | 2022-06-19 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 1.54537857e-01 -5.75108945e-01 1.09871253e-01 -3.97364408e-01
-8.17700624e-01 -4.43202466e-01 5.74813724e-01 -3.24474752e-01
-3.37304711e-01 7.07320452e-01 4.21771675e-01 3.78029436e-01
2.17907965e-01 -5.64106464e-01 -8.53005171e-01 -8.88567686e-01
4.42468196e-01 3.99442762e-01 3.10818627e-02 -1.36250824... | [14.668105125427246, 0.9311332106590271] |
1f6528de-77a8-46d9-b6f3-0399c54fe253 | tace-time-aware-convolutional-embedding | null | null | https://openreview.net/forum?id=hopfHdHZGYe | https://openreview.net/pdf?id=hopfHdHZGYe | TaCE: Time-aware Convolutional Embedding Learning for Temporal Knowledge Graph Completion | Temporal knowledge graph completion (TKGC) is a challenging task to infer the missing component for quadruples. The key challenge lies at how to integrate time information into the embeddings of entities and relations. Recent TKGC methods tend to capture temporal patterns via linear or multilinear models, which are fas... | ['Chen Peng', 'YanFeng Hu', 'Hong Shen', 'Jin Luo'] | 2021-09-29 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-6.71680748e-01 -6.49200976e-02 -5.96442342e-01 -2.34385863e-01
-4.17363159e-02 -7.94662714e-01 7.30953693e-01 2.50036687e-01
-3.91549885e-01 6.28745556e-01 4.14965689e-01 -2.97237426e-01
-6.25679493e-01 -1.02061033e+00 -8.01791847e-01 -3.27116013e-01
-9.24929798e-01 5.17787158e-01 2.85298884e-01 -4.47272241... | [8.550195693969727, 7.9062724113464355] |
b3720b7a-b238-4097-9324-388304636ec5 | playing-20-question-game-with-policy-based | 1808.07645 | null | https://arxiv.org/abs/1808.07645v3 | https://arxiv.org/pdf/1808.07645v3.pdf | Playing 20 Question Game with Policy-Based Reinforcement Learning | The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Then the questioner tries to guess the object by asking 20 questions. In a Q20 game system, the user is considered as the... | ['Xianchao Wu', 'Huang Hu', 'Chongyang Tao', 'Wei Wu', 'Zhan Chen', 'Can Xu', 'Bingfeng Luo'] | 2018-08-23 | playing-20-question-game-with-policy-based-1 | https://aclanthology.org/D18-1361 | https://aclanthology.org/D18-1361.pdf | emnlp-2018-10 | ['question-selection'] | ['natural-language-processing'] | [-2.47449771e-01 7.47581571e-02 1.15363784e-01 1.51619986e-01
-4.35398877e-01 -5.67689419e-01 1.48284033e-01 2.14266125e-02
-6.65332079e-01 7.49469161e-01 -3.98399979e-01 -4.40353870e-01
-2.54659444e-01 -1.19414103e+00 -3.20271254e-01 -3.62544030e-01
3.48716885e-01 5.74950397e-01 6.30105495e-01 -6.03945374... | [3.894944667816162, 1.5167460441589355] |
80a7698a-34b6-4051-801e-e2c6963a9533 | movingfashion-a-benchmark-for-the-video-to | 2110.02627 | null | https://arxiv.org/abs/2110.02627v4 | https://arxiv.org/pdf/2110.02627v4.pdf | MovingFashion: a Benchmark for the Video-to-Shop Challenge | Retrieving clothes which are worn in social media videos (Instagram, TikTok) is the latest frontier of e-fashion, referred to as "video-to-shop" in the computer vision literature. In this paper we present MovingFashion, the first publicly available dataset to cope with this challenge. MovingFashion is composed of 14855... | ['Marco Cristani', 'Geri Skenderi', 'Christian Joppi', 'Marco Godi'] | 2021-10-06 | null | null | null | null | ['video-to-shop'] | ['computer-vision'] | [ 1.02650754e-01 -3.13899010e-01 -2.17203572e-01 -2.72855937e-01
-7.93265343e-01 -7.32512832e-01 4.17478532e-01 -7.96544328e-02
-4.72524285e-01 4.12147373e-01 3.54176849e-01 5.05262613e-01
-1.23359829e-01 -6.04844570e-01 -1.26590204e+00 -5.88732898e-01
-8.59889314e-02 1.91159457e-01 1.55356526e-01 -2.57600456... | [10.317032814025879, 0.837133526802063] |
cd4ad536-cedd-4464-b0a9-faed5401fb33 | positional-spectral-temporal-attention-in-3d | 2110.09955 | null | https://arxiv.org/abs/2110.09955v2 | https://arxiv.org/pdf/2110.09955v2.pdf | Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition | Recognizing the feelings of human beings plays a critical role in our daily communication. Neuroscience has demonstrated that different emotion states present different degrees of activation in different brain regions, EEG frequency bands and temporal stamps. In this paper, we propose a novel structure to explore the i... | ['Dongmei Jiang', 'Hao Wu', 'Yanxi Zhao', 'Jiyao Liu'] | 2021-10-13 | null | null | null | null | ['eeg-emotion-recognition'] | ['miscellaneous'] | [-3.00287545e-01 -4.62303966e-01 3.26418996e-01 -5.09820700e-01
-1.50454134e-01 -1.55199587e-01 2.40282729e-01 -4.37676087e-02
-3.04017037e-01 6.11684263e-01 3.63099068e-01 3.18666607e-01
-2.05091134e-01 -5.58930635e-01 -1.84257627e-01 -6.70374990e-01
-4.97315019e-01 -2.91486472e-01 -3.99949811e-02 -4.97724935... | [13.096226692199707, 3.4180030822753906] |
b1cc3d97-8efd-432c-b61c-7537f090410c | nonnegative-tucker-decomposition-with-beta | 2110.14434 | null | https://arxiv.org/abs/2110.14434v4 | https://arxiv.org/pdf/2110.14434v4.pdf | Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals | Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information Retrieval. Nevertheless, existing algorithms to compute NTD are mostly designed for the Euclidean lo... | ['Frédéric Bimbot', 'Jérémy E. Cohen', 'Valentin Leplat', 'Florian Voorwinden', 'Axel Marmoret'] | 2021-10-27 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 1.90622613e-01 -4.42500204e-01 3.89845110e-02 -1.30662143e-01
-6.88472092e-01 -7.01295853e-01 3.09584498e-01 3.05979755e-02
-4.07551497e-01 1.76306844e-01 6.77285016e-01 -1.08716935e-01
-6.28102124e-01 -2.71257550e-01 -2.54108906e-01 -7.08746910e-01
-6.15282178e-01 2.56569624e-01 -1.92687571e-01 -1.08063288... | [15.529335021972656, 5.482382774353027] |
ec088b3f-c47e-459b-a399-ba7bc112f6f3 | improving-fairness-and-robustness-in-end-to | 2306.06083 | null | https://arxiv.org/abs/2306.06083v1 | https://arxiv.org/pdf/2306.06083v1.pdf | Improving Fairness and Robustness in End-to-End Speech Recognition through unsupervised clustering | The challenge of fairness arises when Automatic Speech Recognition (ASR) systems do not perform equally well for all sub-groups of the population. In the past few years there have been many improvements in overall speech recognition quality, but without any particular focus on advancing Equality and Equity for all user... | ['Pascale Fung', 'Irina-Elena Veliche'] | 2023-06-06 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-3.16524170e-02 2.82824934e-01 -8.94857571e-02 -7.99414039e-01
-1.00786185e+00 -4.81549054e-01 6.99980497e-01 1.64429575e-01
-8.38722646e-01 5.62171221e-01 6.74720585e-01 -5.65860808e-01
1.27220199e-01 -3.70140672e-01 -2.28590101e-01 -4.00081426e-01
2.31186926e-01 2.96522409e-01 -5.48340440e-01 -2.33565599... | [14.00651741027832, 5.917810916900635] |
bee909fc-7a66-41c2-9123-9a590a6511ff | cuda-optimized-real-time-rendering-of-a | 2012.08655 | null | https://arxiv.org/abs/2012.08655v1 | https://arxiv.org/pdf/2012.08655v1.pdf | CUDA-Optimized real-time rendering of a Foveated Visual System | The spatially-varying field of the human visual system has recently received a resurgence of interest with the development of virtual reality (VR) and neural networks. The computational demands of high resolution rendering desired for VR can be offset by savings in the periphery, while neural networks trained with fove... | ['Tomaso Poggio', 'Arturo Deza', 'Elian Malkin'] | 2020-12-15 | null | https://openreview.net/forum?id=ZMsqkUadtZ7 | https://openreview.net/pdf?id=ZMsqkUadtZ7 | neurips-workshop-svrhm-2020-12 | ['foveation'] | ['computer-vision'] | [ 3.83584291e-01 -1.04806952e-01 8.59854877e-01 -8.73059705e-02
-9.24102515e-02 -6.27597332e-01 6.30194902e-01 -1.93292841e-01
-6.97120667e-01 6.36032343e-01 -2.22658291e-01 -4.66473877e-01
-1.03020459e-01 -7.08004653e-01 -4.95546162e-01 -6.57703400e-01
-2.57450104e-01 -2.78835952e-01 6.61121726e-01 -3.87429565... | [11.019867897033691, -2.1465799808502197] |
e34e83d4-d151-4bb3-9283-36cf9bf1f968 | structure-clip-enhance-multi-modal-language | 2305.06152 | null | https://arxiv.org/abs/2305.06152v1 | https://arxiv.org/pdf/2305.06152v1.pdf | Structure-CLIP: Enhance Multi-modal Language Representations with Structure Knowledge | Large-scale vision-language pre-training has shown promising advances on various downstream tasks and achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on image-text matching tasks that require a detailed semantics understanding of the tex... | ['Wen Zhang', 'Zhipeng Hu', 'Tangjie Lv', 'Zeng Zhao', 'WeiJie Chen', 'Xinfeng Zhang', 'Rongsheng Zhang', 'Zhuo Chen', 'Jiji Tang', 'Yufeng Huang'] | 2023-05-06 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 3.11876297e-01 2.96669826e-02 -3.55254799e-01 -5.06813228e-01
-6.88995600e-01 -2.53426552e-01 8.90351593e-01 -2.14725249e-02
-2.28342697e-01 4.86283749e-01 6.54017568e-01 -8.44363570e-02
-2.54016761e-02 -8.21963847e-01 -8.48178089e-01 -4.69122708e-01
5.79407513e-01 3.14779490e-01 2.99317598e-01 -3.14793348... | [10.665877342224121, 1.4490554332733154] |
26a06edc-b01e-42c5-a774-22c62bfe6975 | building-korean-sign-language-augmentation | 2207.05261 | null | https://arxiv.org/abs/2207.05261v1 | https://arxiv.org/pdf/2207.05261v1.pdf | Building Korean Sign Language Augmentation (KoSLA) Corpus with Data Augmentation Technique | We present an efficient framework of corpus for sign language translation. Aided with a simple but dramatic data augmentation technique, our method converts text into annotated forms with minimum information loss. Sign languages are composed of manual signals, non-manual signals, and iconic features. According to profe... | ['Hyunshim Han', 'Sumi Lee', 'Ohkyoon Kwon', 'Dongmyeong Noh', 'Eunkyung Han', 'Changnam An'] | 2022-07-12 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 5.08621097e-01 3.15367669e-01 -2.91443974e-01 -5.10353625e-01
-6.84308112e-01 -5.29323280e-01 6.93501115e-01 -5.15774608e-01
-7.87134290e-01 8.34161758e-01 7.65275717e-01 -1.19416714e-01
1.40536129e-01 -2.96285361e-01 -3.97258312e-01 -4.47287381e-01
1.21312946e-01 3.81967366e-01 -2.42887780e-01 -5.16616046... | [9.141382217407227, -6.446753978729248] |
560ecd73-66f3-48f2-b825-972f1d135543 | multimodal-machine-translation-with | 1805.02356 | null | http://arxiv.org/abs/1805.02356v1 | http://arxiv.org/pdf/1805.02356v1.pdf | Multimodal Machine Translation with Reinforcement Learning | Multimodal machine translation is one of the applications that integrates
computer vision and language processing. It is a unique task given that in the
field of machine translation, many state-of-the-arts algorithms still only
employ textual information. In this work, we explore the effectiveness of
reinforcement lear... | ['Xin Qian', 'Jieli Zhou', 'Ziyi Zhong'] | 2018-05-07 | null | null | null | null | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 4.37858254e-01 -8.01472664e-02 -6.09707952e-01 -3.04092854e-01
-1.63677645e+00 -6.59992099e-01 9.41074252e-01 -2.20185339e-01
-6.46666229e-01 9.81715620e-01 2.45009929e-01 -6.67971671e-01
4.37669665e-01 -2.12558225e-01 -1.06994331e+00 -5.16451836e-01
6.46924794e-01 8.43705058e-01 -5.59305131e-01 -3.18915337... | [11.488062858581543, 1.5275534391403198] |
8bfbdad0-c8e1-4ad3-9f1c-0204df28fece | a-convolutional-approach-to-reflection | 1609.05257 | null | http://arxiv.org/abs/1609.05257v1 | http://arxiv.org/pdf/1609.05257v1.pdf | A convolutional approach to reflection symmetry | We present a convolutional approach to reflection symmetry detection in 2D.
Our model, built on the products of complex-valued wavelet convolutions,
simplifies previous edge-based pairwise methods. Being parameter-centered, as
opposed to feature-centered, it has certain computational advantages when the
object sizes ar... | ['Davi Geiger', 'Vighnesh Birodkar', 'Marcelo Cicconet', 'Michael Werman', 'Mads Lund'] | 2016-09-17 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [-1.97706204e-02 2.11787131e-02 1.05332062e-01 -2.53260523e-01
-6.43331170e-01 -6.26998007e-01 6.01587832e-01 -1.43593289e-02
-3.41472507e-01 2.18846947e-01 4.47608471e-01 -2.83816367e-01
-5.18195271e-01 -6.52963459e-01 -4.29168820e-01 -3.38819742e-01
-7.29436457e-01 4.50435966e-01 1.95890307e-01 -1.51826233... | [8.607162475585938, -2.1008033752441406] |
f9e7aff5-2721-4bf8-8923-dc411f49d746 | text-driven-visual-synthesis-with-latent | 2302.0851 | null | https://arxiv.org/abs/2302.08510v2 | https://arxiv.org/pdf/2302.08510v2.pdf | Text-driven Visual Synthesis with Latent Diffusion Prior | There has been tremendous progress in large-scale text-to-image synthesis driven by diffusion models enabling versatile downstream applications such as 3D object synthesis from texts, image editing, and customized generation. We present a generic approach using latent diffusion models as powerful image priors for vario... | ['Jia-Bin Huang', 'Badour AlBahar', 'Yao-Chih Lee', 'Yiran Xu', 'Songwei Ge', 'Ting-Hsuan Liao'] | 2023-02-16 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 5.67430377e-01 3.75688225e-01 -2.75038570e-01 -3.15421700e-01
-8.20570469e-01 -5.07790685e-01 1.19392574e+00 -6.46137059e-01
9.00576562e-02 5.68853021e-01 6.75308526e-01 -1.53210506e-01
5.65653980e-01 -4.38793659e-01 -7.87258267e-01 -6.40080333e-01
4.12041843e-01 2.89050132e-01 1.77758127e-01 -1.69125259... | [11.3384370803833, -0.24147245287895203] |
f0203051-8780-4f4f-b96b-7858fc149fc8 | sparse-and-low-bias-estimation-of-high | null | null | https://openreview.net/forum?id=vK4ta9RgKMg | https://openreview.net/pdf?id=vK4ta9RgKMg | Sparse and Low-bias Estimation of High Dimensional Vector Autoregressive Models | Vector autoregressive ($VAR$) models are widely used for causal discovery and forecasting in multivariate time series analysis. In the high-dimensional setting, which is increasingly common in fields such as neuroscience and econometrics, model parameters are inferred by $L_1$-regularized maximum likelihood (RML). A we... | ['Kristofer Bouchard', 'Mahesh Balasubramanian', 'Sharmodeep Bhattacharyya', 'Trevor Ruiz'] | 2020-06-08 | null | null | null | l4dc-2020-6 | ['econometrics'] | ['miscellaneous'] | [ 1.48666203e-01 1.01710698e-02 -5.49317062e-01 -4.60199058e-01
-9.51532066e-01 -3.02038074e-01 5.68652570e-01 -1.44735742e-02
-1.42826110e-01 1.02388632e+00 5.47048151e-01 -4.72831219e-01
-6.10430479e-01 -6.41522050e-01 -8.10455322e-01 -8.42063129e-01
-6.12262368e-01 2.47413769e-01 -3.98150146e-01 7.31310472... | [7.734426975250244, 5.118396759033203] |
ac636c3c-14ea-462b-aec3-ea378eb73c8c | autonomous-aerial-cinematography-in | 1910.06988 | null | https://arxiv.org/abs/1910.06988v1 | https://arxiv.org/pdf/1910.06988v1.pdf | Autonomous Aerial Cinematography In Unstructured Environments With Learned Artistic Decision-Making | Aerial cinematography is revolutionizing industries that require live and dynamic camera viewpoints such as entertainment, sports, and security. However, safely piloting a drone while filming a moving target in the presence of obstacles is immensely taxing, often requiring multiple expert human operators. Hence, there ... | ['Sebastian Scherer', 'Erdal Kayacan', 'Cherie Ho', 'Wenshan Wang', 'Sanjiban Choudhury', 'Mirko Gschwindt', 'Aayush Ahuja', 'Rogerio Bonatti', 'Efe Camci'] | 2019-10-15 | null | null | null | null | ['occlusion-estimation'] | ['computer-vision'] | [ 3.42705220e-01 -2.57817864e-01 -6.22555837e-02 6.97748214e-02
-4.90325153e-01 -1.05555511e+00 3.62670064e-01 -1.65974889e-02
-1.83051199e-01 5.25359988e-01 -2.85835624e-01 -3.84934813e-01
-2.12537169e-01 -6.11179054e-01 -4.62321609e-01 -4.43901300e-01
-3.23337048e-01 2.95186162e-01 6.73551917e-01 -3.82167369... | [7.376079082489014, -1.450825572013855] |
6d656383-f8ab-4012-9ec1-0f9b691a2853 | learning-to-generate-realistic-lidar-point | 2209.03954 | null | https://arxiv.org/abs/2209.03954v2 | https://arxiv.org/pdf/2209.03954v2.pdf | Learning to Generate Realistic LiDAR Point Clouds | We present LiDARGen, a novel, effective, and controllable generative model that produces realistic LiDAR point cloud sensory readings. Our method leverages the powerful score-matching energy-based model and formulates the point cloud generation process as a stochastic denoising process in the equirectangular view. This... | ['Shenlong Wang', 'Xiyue Zhu', 'Vlas Zyrianov'] | 2022-09-08 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [-1.43179491e-01 -3.09409499e-01 6.23394325e-02 -2.10619211e-01
-1.06595337e+00 -9.11470175e-01 6.85886323e-01 -3.86669546e-01
2.31024995e-01 6.79350734e-01 -2.94254124e-01 -2.47473251e-02
-9.93682370e-02 -1.27244020e+00 -9.74934220e-01 -6.36956155e-01
2.07390264e-01 9.11794722e-01 7.45219761e-04 -4.21265373... | [8.879133224487305, -3.656184434890747] |
697f82ec-5e2e-4c96-84b4-12ef254856f5 | mobilesal-extremely-efficient-rgb-d-salient | 2012.13095 | null | https://arxiv.org/abs/2012.13095v3 | https://arxiv.org/pdf/2012.13095v3.pdf | MobileSal: Extremely Efficient RGB-D Salient Object Detection | The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this paper introduces a novel network, MobileSal, which focuses on efficient RGB-D SOD using mobile networks for deep feature extraction. However, mobile ... | ['Yu-Chao Gu', 'Ming-Ming Cheng', 'Jia-Wang Bian', 'Jun Xu', 'Yun Liu', 'Yu-Huan Wu'] | 2020-12-24 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.75092071e-01 3.17982845e-02 -7.63993636e-02 -1.34746283e-01
-2.89683670e-01 -1.48102149e-01 1.32972524e-01 -1.40051916e-01
-5.86057544e-01 4.25335318e-01 7.20111579e-02 -3.22606683e-01
-6.27471209e-02 -8.91624928e-01 -7.19898641e-01 -7.42263258e-01
-9.50407311e-02 -4.40832525e-01 7.41100788e-01 -4.12010640... | [9.605786323547363, -0.813919186592102] |
1b13073a-50a0-4688-bcdf-b1e5c7caa047 | skin-lesion-analysis-toward-melanoma-2 | 1605.01397 | null | http://arxiv.org/abs/1605.01397v1 | http://arxiv.org/pdf/1605.01397v1.pdf | Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC) | In this article, we describe the design and implementation of a publicly
accessible dermatology image analysis benchmark challenge. The goal of the
challenge is to sup- port research and development of algorithms for automated
diagnosis of melanoma, a lethal form of skin cancer, from dermoscopic images.
The challenge w... | ['Emre Celebi', 'David Gutman', 'Brian Helba', 'Michael Marchetti', 'Allan Halpern', 'Nabin Mishra', 'Noel C. F. Codella'] | 2016-05-04 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 7.34442174e-01 1.10294223e-01 -1.90327600e-01 -4.48342055e-01
-8.85910928e-01 -7.73247302e-01 5.20724595e-01 4.15570199e-01
-6.25993431e-01 3.18735063e-01 -2.95404047e-01 -3.49207222e-01
9.89548191e-02 -4.83647257e-01 -2.11056903e-01 -6.70672059e-01
1.75446853e-01 3.00563335e-01 2.53554016e-01 1.25498578... | [15.703741073608398, -3.001753330230713] |
141e0dc3-8a02-4487-8db0-01219663bee5 | divisive-language-and-propaganda-detection | null | null | https://aclanthology.org/D19-5014 | https://aclanthology.org/D19-5014.pdf | Divisive Language and Propaganda Detection using Multi-head Attention Transformers with Deep Learning BERT-based Language Models for Binary Classification | On the NLP4IF 2019 sentence level propaganda classification task, we used a BERT language model that was pre-trained on Wikipedia and BookCorpus as team ltuorp ranking {\#}1 of 26. It uses deep learning in the form of an attention transformer. We substituted the final layer of the neural network to a linear real valued... | ['Norman Mapes', 'Sumeet Dua', 'Radhika Medury', 'Anna White'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [-8.12832937e-02 7.23691761e-01 -3.92653137e-01 -1.77099735e-01
-6.64107800e-01 -4.95472431e-01 1.11064827e+00 6.38235658e-02
-7.79968798e-01 1.03381503e+00 9.62020338e-01 -1.18993819e+00
-9.07745212e-02 -8.51584017e-01 -8.17116737e-01 -5.65652668e-01
7.99899846e-02 6.46391213e-01 -3.24423254e-01 -8.39498341... | [8.488813400268555, 10.630599021911621] |
f87ef4f4-2551-4c28-b5e8-4f1fbf52c418 | pixel2mesh-generating-3d-mesh-models-from | 1804.01654 | null | http://arxiv.org/abs/1804.01654v2 | http://arxiv.org/pdf/1804.01654v2.pdf | Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images | We propose an end-to-end deep learning architecture that produces a 3D shape
in triangular mesh from a single color image. Limited by the nature of deep
neural network, previous methods usually represent a 3D shape in volume or
point cloud, and it is non-trivial to convert them to the more ready-to-use
mesh model. Unli... | ['Yu-Gang Jiang', 'yinda zhang', 'Zhuwen Li', 'Yanwei Fu', 'Wei Liu', 'Nanyang Wang'] | 2018-04-05 | pixel2mesh-generating-3d-mesh-models-from-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Nanyang_Wang_Pixel2Mesh_Generating_3D_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Nanyang_Wang_Pixel2Mesh_Generating_3D_ECCV_2018_paper.pdf | eccv-2018-9 | ['3d-object-reconstruction'] | ['computer-vision'] | [-2.25699827e-01 6.69297501e-02 3.56798053e-01 -8.41220841e-02
-5.15734196e-01 -7.32256532e-01 2.87215143e-01 -8.02638233e-02
1.27881542e-01 3.39686722e-01 -2.74386406e-01 -3.24903041e-01
2.28127450e-01 -1.26120114e+00 -1.14826965e+00 -2.27745190e-01
-1.19065687e-01 7.30283678e-01 1.37693137e-01 -1.77168086... | [8.709382057189941, -3.600011110305786] |
492ae2d3-c4ce-4cab-96cc-31978c24d00f | sfcnext-a-simple-fully-convolutional-network | 2305.18771 | null | https://arxiv.org/abs/2305.18771v1 | https://arxiv.org/pdf/2305.18771v1.pdf | SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size | Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection of development-related or aging-related disorders. Recent DNN models for brain a... | ['Cheng Zhuo', 'Meng Niu', 'Tianbai Yu', 'Yalin Wang', 'Shunjie Dong', 'Yanyan Huang', 'Yu Fu'] | 2023-05-30 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-8.70529935e-02 3.34906913e-02 7.29825720e-02 -8.34722102e-01
-3.19918752e-01 1.89196080e-01 4.05033857e-01 2.85329372e-01
-9.44630325e-01 7.05480337e-01 5.49307913e-02 -1.63601711e-02
-2.07975596e-01 -7.47539878e-01 -5.44268847e-01 -5.93685389e-01
-6.50223434e-01 7.26408720e-01 8.84458870e-02 1.50986761... | [14.099427223205566, -1.5465842485427856] |
a76b8f1a-5d96-4053-982e-be9ddfb5dd8b | large-scale-pedestrian-retrieval-competition | 1903.02137 | null | http://arxiv.org/abs/1903.02137v1 | http://arxiv.org/pdf/1903.02137v1.pdf | Large-Scale Pedestrian Retrieval Competition | The Large-Scale Pedestrian Retrieval Competition (LSPRC) mainly focuses on
person retrieval which is an important end application in intelligent vision
system of surveillance. Person retrieval aims at searching the interested
target with specific visual attributes or images. The low image quality,
various camera viewpo... | ['Zhang Zhang', 'Da Li'] | 2019-03-06 | null | null | null | null | ['person-retrieval'] | ['computer-vision'] | [-1.28791153e-01 -9.49553549e-01 1.80337340e-01 -4.02008921e-01
-9.80306268e-01 -5.25988042e-01 8.05422187e-01 1.35375530e-01
-8.61964107e-01 4.66287494e-01 4.77984659e-02 2.71329165e-01
-2.17432063e-02 -8.00004721e-01 -5.36540866e-01 -7.54793406e-01
9.16904807e-02 4.29478824e-01 8.23136449e-01 -1.88943759... | [14.703261375427246, 0.8472479581832886] |
41aa639a-656d-4113-80be-17af6dc1873d | capturing-pragmatic-knowledge-in-article | null | null | https://aclanthology.org/C16-1247 | https://aclanthology.org/C16-1247.pdf | Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs | We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction. We train and compare several variants of Long Short-Term Memory (LSTM) networks with an attention mechanism. Our model outperforms a previous state-of-the-a... | ['Jad Kabbara', 'Jackie Chi Kit Cheung', 'Yulan Feng'] | 2016-12-01 | capturing-pragmatic-knowledge-in-article-1 | https://aclanthology.org/C16-1247 | https://aclanthology.org/C16-1247.pdf | coling-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 2.60480344e-01 4.88432407e-01 -4.19578642e-01 -4.21094328e-01
-1.06343830e+00 -2.52363205e-01 5.78966737e-01 7.79565424e-02
-6.38418138e-01 6.88304424e-01 9.41939235e-01 -7.63103724e-01
-4.69871551e-01 -4.93010223e-01 -6.17942989e-01 -3.10507119e-01
1.85856193e-01 4.61625308e-01 -1.35410473e-01 -5.20992756... | [10.754324913024902, 9.063986778259277] |
8738270d-cccb-47e3-8b69-c4d7561ada96 | deep-learning-methods-for-sar-image | 2012.05508 | null | https://arxiv.org/abs/2012.05508v2 | https://arxiv.org/pdf/2012.05508v2.pdf | Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives | Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The f... | ['Luisa Verdoliva', 'Diego Valsesia', 'Giuseppe Scarpa', 'Giovanni Poggi', 'Enrico Magli', 'Giulia Fracastoro'] | 2020-12-10 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 5.67904890e-01 -4.32051659e-01 3.46108973e-01 -4.96419638e-01
-7.49801993e-01 -3.12788039e-01 6.82082593e-01 -3.46954346e-01
-4.86939818e-01 3.59505713e-01 3.92881185e-01 -1.81025222e-01
-5.64735353e-01 -5.60284674e-01 -1.29265323e-01 -1.05541730e+00
-1.53726846e-01 2.13937119e-01 -2.31154710e-01 -3.73900533... | [10.412603378295898, -2.218968152999878] |
400737f9-1ddb-4526-a4e5-af14df52fbe8 | visual-policy-learning-through-multi-camera | 2303.07026 | null | https://arxiv.org/abs/2303.07026v1 | https://arxiv.org/pdf/2303.07026v1.pdf | Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks | The use of multi-camera views simultaneously has been shown to improve the generalization capabilities and performance of visual policies. However, the hardware cost and design constraints in real-world scenarios can potentially make it challenging to use multiple cameras. In this study, we present a novel approach to ... | ['Wu Ya', 'Alp Tekirdağ', 'Kuluhan Binici', 'Cihan Acar'] | 2023-03-13 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-3.59140001e-02 -2.68197179e-01 -2.34679341e-01 5.35460822e-02
-3.24452788e-01 -9.01160717e-01 3.92938495e-01 -1.84906334e-01
-4.37168360e-01 8.37996781e-01 -5.82558513e-01 -1.23206891e-01
-4.83397773e-04 -3.56653422e-01 -1.17409265e+00 -1.07026732e+00
1.54042035e-01 1.51851237e-01 4.15524244e-01 -6.67053312... | [4.6678266525268555, 0.6970402002334595] |
977504e7-b87c-4791-b8df-6583fcd921b7 | zet-speech-zero-shot-adaptive-emotion | 2305.13831 | null | https://arxiv.org/abs/2305.13831v1 | https://arxiv.org/pdf/2305.13831v1.pdf | ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models | Emotional Text-To-Speech (TTS) is an important task in the development of systems (e.g., human-like dialogue agents) that require natural and emotional speech. Existing approaches, however, only aim to produce emotional TTS for seen speakers during training, without consideration of the generalization to unseen speaker... | ['Eunho Yang', 'Sung Ju Hwang', 'Wooseok Han', 'Minki Kang'] | 2023-05-23 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [-3.85384001e-02 6.72535419e-01 2.26267755e-01 -6.17401600e-01
-9.42478776e-01 -6.37113750e-01 5.97833633e-01 -5.35970986e-01
4.76617999e-02 5.36806464e-01 2.60493279e-01 -3.68123323e-01
7.50567555e-01 -4.49476510e-01 -3.87769789e-01 -6.12191141e-01
3.01348448e-01 5.01324117e-01 -3.02601218e-01 -4.74702448... | [14.769095420837402, 6.4517717361450195] |
e7c3d9fa-9b24-4ba4-8737-46836cc885e2 | a-survey-of-toxic-comment-classification | 2112.06412 | null | https://arxiv.org/abs/2112.06412v1 | https://arxiv.org/pdf/2112.06412v1.pdf | A Survey of Toxic Comment Classification Methods | While in real life everyone behaves themselves at least to some extent, it is much more difficult to expect people to behave themselves on the internet, because there are few checks or consequences for posting something toxic to others. Yet, for people on the other side, toxic texts often lead to serious psychological ... | ['Hongjun Wu', 'Jiaxi Yang', 'Kehan Wang'] | 2021-12-13 | null | null | null | null | ['toxic-comment-classification'] | ['natural-language-processing'] | [-4.88431659e-03 -7.11795017e-02 1.08594373e-01 -3.63970101e-01
-2.53932804e-01 -4.08293784e-01 7.25889087e-01 6.05754852e-01
-6.54674470e-01 8.59063983e-01 3.04298967e-01 -4.46648538e-01
-4.67887484e-02 -1.16266000e+00 -2.77622283e-01 -2.94365019e-01
1.38196483e-01 2.91271776e-01 8.34000707e-02 -3.45823556... | [8.882262229919434, 10.51397705078125] |
e1a0e296-680f-4d74-8eb1-d6f149dbcfa6 | synergistic-graph-fusion-via-encoder | 2303.18051 | null | https://arxiv.org/abs/2303.18051v1 | https://arxiv.org/pdf/2303.18051v1.pdf | Synergistic Graph Fusion via Encoder Embedding | In this paper, we introduce a novel approach to multi-graph embedding called graph fusion encoder embedding. The method is designed to work with multiple graphs that share a common vertex set. Under the supervised learning setting, we show that the resulting embedding exhibits a surprising yet highly desirable "synergi... | ['Ha Trinh', 'Jonathan Larson', 'Carey E. Priebe', 'Cencheng Shen'] | 2023-03-31 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.59053260e-01 2.96306103e-01 -6.99002981e-01 -1.38865307e-01
-6.15942836e-01 -3.08713853e-01 4.49877888e-01 4.73591328e-01
1.42464206e-01 7.59948075e-01 1.34372711e-01 -3.76811147e-01
-4.15163428e-01 -7.79290795e-01 -7.41645694e-01 -9.34207201e-01
-2.01585561e-01 2.29497463e-01 -1.77019760e-01 -3.53326052... | [7.155426502227783, 6.058928489685059] |
41b9217b-914a-4fce-95e6-f575856c62ab | ontology-matching-with-knowledge-rules | 1507.03097 | null | http://arxiv.org/abs/1507.03097v1 | http://arxiv.org/pdf/1507.03097v1.pdf | Ontology Matching with Knowledge Rules | Ontology matching is the process of automatically determining the semantic
equivalences between the concepts of two ontologies. Most ontology matching
algorithms are based on two types of strategies: terminology-based strategies,
which align concepts based on their names or descriptions, and structure-based
strategies,... | ['Shangpu Jiang', 'Dejing Dou', 'Daniel Lowd'] | 2015-07-11 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 1.49589509e-01 -1.17615145e-02 -3.32987100e-01 -5.82235932e-01
-2.77497590e-01 -5.19059658e-01 5.97692907e-01 6.03153586e-01
-2.82835871e-01 4.70351726e-01 2.72919565e-01 -2.01904118e-01
-7.40272343e-01 -1.10907364e+00 -1.33118555e-01 -1.36594966e-01
-1.77610647e-02 9.70896363e-01 7.01481938e-01 -3.23033839... | [9.238590240478516, 8.20124626159668] |
8e9936db-9422-40d5-9511-4ec7b1f36fcc | a-case-study-on-record-matching-of | 2302.07784 | null | https://arxiv.org/abs/2302.07784v1 | https://arxiv.org/pdf/2302.07784v1.pdf | A Case Study on Record Matching of Individuals in Historical Archives of Indigenous Databases | Digitization of historical records has produced a significant amount of data for analysis and interpretation. A critical challenge is the ability to relate historical information across different archives to allow for the data to be framed in the appropriate historical context. This paper presents a real-world case stu... | ['Ramon Lawrence', 'Matthew Currie'] | 2023-02-15 | null | null | null | null | ['record-linking'] | ['natural-language-processing'] | [ 2.97194924e-02 -1.98309958e-01 -2.52369255e-01 -4.39310819e-01
-7.87581027e-01 -8.75204742e-01 1.00080562e+00 7.49689221e-01
-5.77728748e-01 9.67234731e-01 7.99657404e-01 -4.34340954e-01
-7.57194221e-01 -1.03138328e+00 -4.32108521e-01 -1.40171930e-01
-6.55197024e-01 6.02283537e-01 2.79656768e-01 -4.26530659... | [9.903570175170898, 9.716473579406738] |
ae01ff2f-f7e1-47ef-8f2b-3f5e426c3a84 | distilling-self-supervised-vision-1 | 2307.03407 | null | https://arxiv.org/abs/2307.03407v1 | https://arxiv.org/pdf/2307.03407v1.pdf | Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation | We address the task of weakly-supervised few-shot image classification and segmentation, by leveraging a Vision Transformer (ViT) pretrained with self-supervision. Our proposed method takes token representations from the self-supervised ViT and leverages their correlations, via self-attention, to produce classification... | ['Naila Murray', 'Minsu Cho', 'Piotr Koniusz', 'Dahyun Kang'] | 2023-07-07 | distilling-self-supervised-vision | http://openaccess.thecvf.com//content/CVPR2023/html/Kang_Distilling_Self-Supervised_Vision_Transformers_for_Weakly-Supervised_Few-Shot_Classification__Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kang_Distilling_Self-Supervised_Vision_Transformers_for_Weakly-Supervised_Few-Shot_Classification__Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-image-classification', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 7.16965497e-01 4.38855261e-01 -5.28742433e-01 -7.21346676e-01
-1.00354624e+00 -4.83248353e-01 5.22039056e-01 -1.35326162e-01
-5.60298979e-01 6.18693292e-01 -1.35940805e-01 6.12434894e-02
6.15394652e-01 -6.89290702e-01 -1.20336699e+00 -7.08194196e-01
3.42790753e-01 6.08154178e-01 2.93512851e-01 5.41665964... | [9.67390251159668, 0.8227362632751465] |
cdbf3d6f-8cc6-4442-8557-aa39dce23d97 | pretrained-language-encoders-are-natural | 2208.09617 | null | https://arxiv.org/abs/2208.09617v1 | https://arxiv.org/pdf/2208.09617v1.pdf | Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims to extract the spans of aspect, opinion, and their sentiment relations as sentiment triplets. Existing works usually formulate the span detection as a 1D token tagging problem, and model the sentiment recognition with a 2D tagging matrix of token pairs. Moreover, by lever... | ['Yongqi Tong', 'Chunxu Shen', 'Yong Dai', 'Lingqiao Liu', 'Yinjie Lei', 'Yanjie Gou'] | 2022-08-20 | null | null | null | null | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [-3.40959817e-01 1.98336765e-01 -4.49464470e-01 -4.57599014e-01
-4.77880955e-01 -6.49709344e-01 4.38766271e-01 2.69640654e-01
-2.10895240e-01 2.29654595e-01 5.60935915e-01 -1.80544570e-01
3.31149042e-01 -9.44061697e-01 -5.43031991e-01 -4.62103635e-01
1.69030949e-01 1.40947863e-01 -7.15791853e-03 -4.89070326... | [11.476585388183594, 6.617456436157227] |
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