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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3907e8b0-fa04-4a1b-b87e-5e30d00e09a2 | climax-a-foundation-model-for-weather-and | 2301.10343 | null | https://arxiv.org/abs/2301.10343v3 | https://arxiv.org/pdf/2301.10343v3.pdf | ClimaX: A foundation model for weather and climate | Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models ar... | ['Aditya Grover', 'Jayesh K. Gupta', 'Ashish Kapoor', 'Johannes Brandstetter', 'Tung Nguyen'] | 2023-01-24 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.84365141e-01 -4.15030539e-01 -1.12005752e-02 -4.24279988e-01
-4.71127808e-01 -8.29728961e-01 8.46894979e-01 1.30175591e-01
-6.15095310e-02 9.33138609e-01 1.86341062e-01 -8.97067189e-01
-5.82816601e-02 -1.27174389e+00 -7.32628822e-01 -7.13636160e-01
-4.42219466e-01 5.86526513e-01 -1.94743909e-02 -5.08432090... | [6.564457416534424, 2.9846603870391846] |
e4f2e8e3-6f7f-4246-b56d-640e32e43548 | doodle-to-search-practical-zero-shot-sketch | 1904.03451 | null | https://arxiv.org/abs/1904.03451v2 | https://arxiv.org/pdf/1904.03451v2.pdf | Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval | In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical appl... | ['Yi-Zhe Song', 'Josep Llados', 'Sounak Dey', 'Pau Riba', 'Anjan Dutta'] | 2019-04-06 | doodle-to-search-practical-zero-shot-sketch-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Dey_Doodle_to_Search_Practical_Zero-Shot_Sketch-Based_Image_Retrieval_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Dey_Doodle_to_Search_Practical_Zero-Shot_Sketch-Based_Image_Retrieval_CVPR_2019_paper.pdf | cvpr-2019-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.62211794e-01 -1.60288453e-01 -2.41136983e-01 -1.14902295e-01
-1.05026948e+00 -9.42311347e-01 9.95391250e-01 -4.59979147e-01
-2.07964897e-01 4.68186468e-01 2.46558219e-01 1.15124032e-01
-4.34952438e-01 -7.11773396e-01 -6.06330454e-01 -4.05693382e-01
3.41376781e-01 6.23174131e-01 8.35233182e-02 -4.36830848... | [11.6128568649292, 0.6520737409591675] |
cdc009f3-1717-4f15-92bd-acee905da4e9 | iqiyi-vid-a-large-dataset-for-multi-modal | 1811.07548 | null | http://arxiv.org/abs/1811.07548v2 | http://arxiv.org/pdf/1811.07548v2.pdf | iQIYI-VID: A Large Dataset for Multi-modal Person Identification | Person identification in the wild is very challenging due to great variation
in poses, face quality, clothes, makeup and so on. Traditional research, such
as face recognition, person re-identification, and speaker recognition, often
focuses on a single modal of information, which is inadequate to handle all the
situati... | ['Tingwei Gao', 'Ganwen Wang', 'Danming Xie', 'Bo Peng', 'Yuanliu Liu', 'Yong Zhou', 'Peipei Shi', 'Jianbin Jiang', 'Chao Lin', 'Yi Zheng', 'Yin Fan', 'Xiangju Lu', 'Jian Liu', 'He Yan', 'Bing Han'] | 2018-11-19 | null | null | null | null | ['person-identification', 'multi-modal-person-identification'] | ['computer-vision', 'miscellaneous'] | [-1.71344534e-01 -6.47019744e-01 -1.44485489e-01 -5.51711142e-01
-7.61009574e-01 -7.20288038e-01 5.24154305e-01 -4.42921489e-01
-3.44335377e-01 5.57933629e-01 3.69651079e-01 3.24423611e-01
1.31057516e-01 -2.07020104e-01 -3.86983901e-01 -7.07279086e-01
2.09835351e-01 3.69762361e-01 -2.18263566e-01 5.47628216... | [14.467227935791016, 1.108917236328125] |
d27b0399-f9d1-4bbd-8ea7-2054581a07db | the-knowledge-graph-track-at-oaei-gold | 2002.10283 | null | https://arxiv.org/abs/2002.10283v1 | https://arxiv.org/pdf/2002.10283v1.pdf | The Knowledge Graph Track at OAEI -- Gold Standards, Baselines, and the Golden Hammer Bias | The Ontology Alignment Evaluation Initiative (OAEI) is an annual evaluation of ontology matching tools. In 2018, we have started the Knowledge Graph track, whose goal is to evaluate the simultaneous matching of entities and schemas of large-scale knowledge graphs. In this paper, we discuss the design of the track and t... | ['Sven Hertling', 'Heiko Paulheim'] | 2020-02-24 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-1.10707290e-01 8.03176641e-01 -2.61082381e-01 -6.45102486e-02
-1.59366071e-01 -7.12797046e-01 6.22012854e-01 5.19308507e-01
-2.33968064e-01 4.28394198e-01 3.47535700e-01 -2.36251235e-01
-6.48353100e-01 -8.38520646e-01 -4.72016573e-01 4.31758553e-01
-1.34146675e-01 1.04183948e+00 6.60495698e-01 -5.17160416... | [9.21380615234375, 8.055939674377441] |
463f0a06-f2ee-4c1a-ba34-abd240ec331c | tsmixer-lightweight-mlp-mixer-model-for | 2306.09364 | null | https://arxiv.org/abs/2306.09364v3 | https://arxiv.org/pdf/2306.09364v3.pdf | TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting | Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. However, their high memory and computing requirements pose a critical bottleneck for long-term forecasting. To address this, we propose TSMixer, a lightweight neural architecture exclusively composed ... | ['Jayant Kalagnanam', 'Phanwadee Sinthong', 'Nam Nguyen', 'Arindam Jati', 'Vijay Ekambaram'] | 2023-06-14 | null | null | null | null | ['multivariate-time-series-forecasting'] | ['time-series'] | [ 2.34716937e-01 -2.87455767e-01 -3.53459090e-01 -2.97080278e-01
-1.03578758e+00 -4.73128945e-01 6.19024873e-01 1.24310799e-01
-7.16984943e-02 4.01057363e-01 2.82935470e-01 -6.28533185e-01
7.20806494e-02 -4.89064842e-01 -9.45767879e-01 -7.49522388e-01
-5.19414365e-01 1.70327108e-02 6.12988174e-02 -2.23083496... | [7.076905727386475, 2.9522225856781006] |
c40cce24-9492-46dd-925c-5757876fff92 | context-aware-text-based-binary-image | 1810.03767 | null | http://arxiv.org/abs/1810.03767v1 | http://arxiv.org/pdf/1810.03767v1.pdf | Context-Aware Text-Based Binary Image Stylization and Synthesis | In this work, we present a new framework for the stylization of text-based
binary images. First, our method stylizes the stroke-based geometric shape like
text, symbols and icons in the target binary image based on an input style
image. Second, the composition of the stylized geometric shape and a background
image is e... | ['Shuai Yang', 'Jiaying Liu', 'Zongming Guo', 'Wenhan Yang'] | 2018-10-09 | null | null | null | null | ['layout-design', 'image-stylization'] | ['computer-vision', 'computer-vision'] | [ 9.74809170e-01 -2.41427988e-01 2.55119968e-02 -4.35070023e-02
-3.55719067e-02 -7.33576238e-01 6.61630511e-01 -2.27681417e-02
-3.67784947e-02 5.06230772e-01 -3.40410247e-02 -3.03983688e-01
-2.28165295e-02 -9.00669158e-01 -7.90300608e-01 -5.79504430e-01
7.88674474e-01 3.25929701e-01 2.51570910e-01 -2.62670487... | [11.593738555908203, -0.6308208107948303] |
ed09c982-8987-428e-9491-6b701e0ddb99 | evidenceminer-textual-evidence-discovery-for | null | null | https://aclanthology.org/2020.acl-demos.8 | https://aclanthology.org/2020.acl-demos.8.pdf | EVIDENCEMINER: Textual Evidence Discovery for Life Sciences | Traditional search engines for life sciences (e.g., PubMed) are designed for document retrieval and do not allow direct retrieval of specific statements. Some of these statements may serve as textual evidence that is key to tasks such as hypothesis generation and new finding validation. We present EVIDENCEMINER, a web-... | ['John Caufield', 'Dibakar Sigdel', 'Enyi Jiang', 'David Liem', 'Aabhas Chauhan', 'Xuan Wang', 'Yingjun Guan', 'Weili Liu', 'Qi Li', 'Jiawei Han', 'Peipei Ping'] | 2020-07-01 | null | null | null | acl-2020-6 | ['open-information-extraction'] | ['natural-language-processing'] | [ 1.39314532e-01 2.02290162e-01 -1.14506519e+00 -2.63773315e-02
-1.00875247e+00 -8.02079082e-01 4.67222393e-01 1.47157919e+00
-4.55893397e-01 1.17938900e+00 3.60401332e-01 -7.99334526e-01
-2.48355702e-01 -7.99594581e-01 -6.64637029e-01 -4.00786489e-01
1.27608970e-01 4.92178440e-01 2.12498993e-01 1.92412153... | [8.737812042236328, 8.598203659057617] |
0d88b6d8-b6fa-4c4e-9a3c-84caaac05f67 | e-t-entity-transformers-coreference-augmented | 2011.05431 | null | https://arxiv.org/abs/2011.05431v1 | https://arxiv.org/pdf/2011.05431v1.pdf | E.T.: Entity-Transformers. Coreference augmented Neural Language Model for richer mention representations via Entity-Transformer blocks | In the last decade, the field of Neural Language Modelling has witnessed enormous changes, with the development of novel models through the use of Transformer architectures. However, even these models struggle to model long sequences due to memory constraints and increasing computational complexity. Coreference annotat... | ['Ioannis Vlahavas', 'Nikolaos Stylianou'] | 2020-11-10 | null | https://aclanthology.org/2020.crac-1.1 | https://aclanthology.org/2020.crac-1.1.pdf | coling-crac-2020-12 | ['lambada'] | ['natural-language-processing'] | [ 3.91965844e-02 5.46708703e-01 -1.84097379e-01 -4.52242881e-01
-5.62644839e-01 -7.03086972e-01 8.84884834e-01 -5.57571650e-03
-6.39612019e-01 7.10525811e-01 5.28450131e-01 -6.03382528e-01
3.68085206e-02 -8.03889155e-01 -6.30870044e-01 -2.16056138e-01
-1.14814624e-01 7.70157933e-01 2.60510504e-01 -4.14276034... | [10.05649471282959, 9.3326416015625] |
9862cec6-a9dd-4a57-89f9-5639e16dd104 | learning-large-scale-network-embedding-from | 2112.01442 | null | https://arxiv.org/abs/2112.01442v1 | https://arxiv.org/pdf/2112.01442v1.pdf | Learning Large-scale Network Embedding from Representative Subgraph | We study the problem of large-scale network embedding, which aims to learn low-dimensional latent representations for network mining applications. Recent research in the field of network embedding has led to significant progress such as DeepWalk, LINE, NetMF, NetSMF. However, the huge size of many real-world networks m... | ['Shengyu Zhang', 'Jinhui Zhu', 'Yi Cai', 'Hsieh', 'Chang-Yu', 'Jiezhong Qiu', 'Ben Liao', 'Weizhao Li', 'Junsheng Kong'] | 2021-12-02 | null | null | null | null | ['graph-sampling', 'network-embedding'] | ['graphs', 'methodology'] | [-1.35747612e-01 4.61243749e-01 -6.94012284e-01 -1.27993986e-01
-8.50379542e-02 -3.68351579e-01 3.95226598e-01 2.50517726e-01
3.60120609e-02 4.78445828e-01 3.41661870e-01 -3.40117484e-01
-5.54712713e-01 -1.35138297e+00 -3.04656178e-01 -6.01857185e-01
-6.11718774e-01 5.08607805e-01 1.15189046e-01 1.81446925... | [7.170361518859863, 6.20354700088501] |
e96c1378-7e59-435c-8770-1026e948f8a2 | joint-image-compression-and-denoising-via | 2205.01874 | null | https://arxiv.org/abs/2205.01874v2 | https://arxiv.org/pdf/2205.01874v2.pdf | Joint Image Compression and Denoising via Latent-Space Scalability | When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression. However, there are applications where image noise may be necessary to demonstrate the trustworthiness of the image, such as court evidence and image forensics. This means that noise itself needs to be coded... | ['Ivan V. Bajić', 'Hyomin Choi', 'Mateen Ulhaq', 'Saeed Ranjbar Alvar'] | 2022-05-04 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 6.89059675e-01 -1.95030540e-01 1.10493004e-01 -1.14178836e-01
-8.38532448e-01 -2.79125392e-01 3.94309789e-01 3.79671901e-02
-3.82404625e-01 3.69755536e-01 3.47034395e-01 -2.32237041e-01
-4.04549576e-02 -8.27921391e-01 -7.53265202e-01 -1.14450026e+00
3.49577181e-02 -1.68247133e-01 -1.16434798e-01 -3.02371681... | [11.442732810974121, -2.004211187362671] |
d5dab21c-5380-4365-bb66-632271efa13c | clues-before-answers-generation-enhanced | 2205.00274 | null | https://arxiv.org/abs/2205.00274v1 | https://arxiv.org/pdf/2205.00274v1.pdf | Clues Before Answers: Generation-Enhanced Multiple-Choice QA | A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the... | ['Gong Cheng', 'Yue Zhao', 'Yu Gu', 'Jiaying Zhou', 'Ao Wu', 'Zixian Huang'] | 2022-04-30 | null | https://aclanthology.org/2022.naacl-main.239 | https://aclanthology.org/2022.naacl-main.239.pdf | naacl-2022-7 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 5.37089407e-01 6.00421309e-01 2.14348853e-01 -4.78442222e-01
-1.63392913e+00 -7.13400245e-01 7.42960930e-01 -9.70162451e-02
-9.51254889e-02 8.70104134e-01 7.01424360e-01 -5.03637373e-01
4.04158980e-01 -9.26606894e-01 -7.21600890e-01 -2.22302914e-01
7.64936447e-01 8.28512907e-01 1.41291037e-01 -7.83533871... | [11.452658653259277, 8.284036636352539] |
1de6662d-ef65-4902-a3f1-376f9ed7ac7d | comparing-phonemes-and-visemes-with-dnn-based | 1805.02924 | null | http://arxiv.org/abs/1805.02924v1 | http://arxiv.org/pdf/1805.02924v1.pdf | Comparing phonemes and visemes with DNN-based lipreading | There is debate if phoneme or viseme units are the most effective for a
lipreading system. Some studies use phoneme units even though phonemes describe
unique short sounds; other studies tried to improve lipreading accuracy by
focusing on visemes with varying results. We compare the performance of a
lipreading system b... | ['Helen L. Bear', 'Kwanchiva Thangthai', 'Richard Harvey'] | 2018-05-08 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.75533548e-01 1.30989417e-01 -4.68182027e-01 7.23931286e-03
-9.00176644e-01 -1.14814997e-01 7.97557533e-01 -4.42230999e-01
-5.92914760e-01 4.32440519e-01 7.62221158e-01 -6.47758007e-01
8.22469115e-01 -1.34911403e-01 -4.58765090e-01 -6.00119889e-01
7.89376974e-01 1.19693272e-01 2.55980194e-01 8.42067003... | [14.204079627990723, 4.892361640930176] |
b47e7c34-0028-4fb0-abc6-df6292e83911 | metal-artifact-reduction-with-intra-oral-scan | 2202.03571 | null | https://arxiv.org/abs/2202.03571v1 | https://arxiv.org/pdf/2202.03571v1.pdf | Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling | Low-dose dental cone beam computed tomography (CBCT) has been increasingly used for maxillofacial modeling. However, the presence of metallic inserts, such as implants, crowns, and dental filling, causes severe streaking and shading artifacts in a CBCT image and loss of the morphological structures of the teeth, which ... | ['Jin Keun Seo', 'Hyoung Suk Park', 'Tae Jun Jang', 'Hye Sun Yun', 'Taigyntuya Bayaraa', 'Chang Min Hyun'] | 2022-02-08 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.56368637e-01 2.90562063e-01 1.00214928e-01 -3.87953520e-01
-7.82185078e-01 3.06487292e-01 -8.42712075e-02 1.21143281e-01
-5.02256691e-01 1.83970928e-01 -6.77310377e-02 -2.60799736e-01
1.69420131e-02 -8.27930927e-01 -5.31725883e-01 -8.62206876e-01
3.78088444e-01 6.75857425e-01 4.31036711e-01 9.97195989... | [13.733798027038574, -2.2509491443634033] |
bc2b838b-7bf7-42d7-8759-d7cc2f651700 | ssorn-self-supervised-outlier-removal-network | 2208.14093 | null | https://arxiv.org/abs/2208.14093v1 | https://arxiv.org/pdf/2208.14093v1.pdf | SSORN: Self-Supervised Outlier Removal Network for Robust Homography Estimation | The traditional homography estimation pipeline consists of four main steps: feature detection, feature matching, outlier removal and transformation estimation. Recent deep learning models intend to address the homography estimation problem using a single convolutional network. While these models are trained in an end-t... | ['Zhenyu He', 'Wenjie Pei', 'Yi Li'] | 2022-08-30 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-5.03486320e-02 -2.44260654e-02 3.80698740e-01 -2.53513545e-01
-7.20122755e-01 -1.74255967e-01 6.84142113e-01 1.04653180e-01
-3.18525583e-01 2.63777107e-01 3.50478031e-02 1.72624856e-01
1.94861323e-01 -7.35095978e-01 -1.18638945e+00 -4.97914791e-01
2.65715867e-01 4.75268483e-01 2.08360627e-01 1.20327719... | [8.6321439743042, -2.2521753311157227] |
f3a75e76-9aec-41ac-a837-ab9ef9572207 | rethinking-portrait-matting-with-privacy | 2203.16828 | null | https://arxiv.org/abs/2203.16828v2 | https://arxiv.org/pdf/2203.16828v2.pdf | Rethinking Portrait Matting with Privacy Preserving | Recently, there has been an increasing concern about the privacy issue raised by identifiable information in machine learning. However, previous portrait matting methods were all based on identifiable images. To fill the gap, we present P3M-10k, which is the first large-scale anonymized benchmark for Privacy-Preserving... | ['DaCheng Tao', 'He Zhang', 'Jing Zhang', 'Jizhizi Li', 'Sihan Ma'] | 2022-03-31 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 2.76688606e-01 8.56942236e-02 -1.43270448e-01 -5.56829989e-01
-7.71273315e-01 -6.57186687e-01 5.86801946e-01 -6.04881048e-01
-6.89598620e-02 6.76053345e-01 3.34160149e-01 -1.58329800e-01
-3.74799259e-02 -7.36567318e-01 -9.86822784e-01 -6.20415688e-01
2.28247121e-01 8.69129039e-03 -3.42040420e-01 -2.92005893... | [12.702186584472656, 0.5652596354484558] |
f15666d9-7b8a-48b8-8228-eeef4f0b7c39 | reprogramming-large-pretrained-language | 2210.07144 | null | https://arxiv.org/abs/2210.07144v2 | https://arxiv.org/pdf/2210.07144v2.pdf | Reprogramming Pretrained Language Models for Antibody Sequence Infilling | Antibodies comprise the most versatile class of binding molecules, with numerous applications in biomedicine. Computational design of antibodies involves generating novel and diverse sequences, while maintaining structural consistency. Unique to antibodies, designing the complementarity-determining region (CDR), which ... | ['Devleena Das', 'Inkit Padhi', 'Amit Dhurandhar', 'Payel Das', 'Pin-Yu Chen', 'Vijil Chenthamarakshan', 'Igor Melnyk'] | 2022-10-05 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 5.15667379e-01 -3.80345225e-01 -4.89342175e-02 -4.02500331e-01
-6.89070702e-01 -9.34620142e-01 3.16848904e-01 9.24577117e-02
-4.70897287e-01 1.25926352e+00 4.66918387e-02 -6.51929736e-01
4.12578940e-01 -4.68375832e-01 -1.18419492e+00 -7.68155277e-01
2.37800434e-01 5.76636016e-01 -4.84557264e-02 -6.25105739... | [4.742801189422607, 5.592767238616943] |
1d201ebb-7797-4e2f-aaca-f32026b37a3e | data-quality-estimation-framework-for-faster | null | null | https://aclanthology.org/2022.ecnlp-1.4 | https://aclanthology.org/2022.ecnlp-1.4.pdf | Data Quality Estimation Framework for Faster Tax Code Classification | This paper describes a novel framework to estimate the data quality of a collection of product descriptions to identify required relevant information for accurate product listing classification for tax-code assignment. Our Data Quality Estimation (DQE) framework consists of a Question Answering (QA) based attribute val... | ['Nicolas Nicolov', 'Allen Williams', 'Ravi Kondadadi'] | null | null | null | null | ecnlp-acl-2022-5 | ['code-classification', 'attribute-value-extraction'] | ['computer-code', 'natural-language-processing'] | [-1.37388885e-01 3.16677839e-02 -5.93072116e-01 -8.01905096e-01
-1.34760296e+00 -6.68873787e-01 -1.71502516e-01 1.08106446e+00
3.49392235e-01 4.56189513e-01 1.69173002e-01 -2.51681447e-01
-3.51889759e-01 -1.40949607e+00 -4.87335473e-01 1.65632516e-01
3.15016985e-01 6.54433370e-01 -1.18601725e-01 -5.42065427... | [9.942928314208984, 6.284664154052734] |
256a235c-166d-4f8f-9936-8b71a899f51c | a-simple-yet-effective-corpus-construction | null | null | https://aclanthology.org/2022.lrec-1.742 | https://aclanthology.org/2022.lrec-1.742.pdf | A Simple Yet Effective Corpus Construction Method for Chinese Sentence Compression | Deletion-based sentence compression in the English language has made significant progress over the past few decades. However, there is a lack of large-scale and high-quality parallel corpus (i.e., (sentence, compression) pairs) for the Chinese language to train an efficient compression system. To remedy this shortcomin... | ['Akiko Aizawa', 'Masayasu Muraoka', 'Issei Yoshida', 'Hiroshi Kanayama', 'Yang Zhao'] | null | null | null | null | lrec-2022-6 | ['sentence-compression'] | ['natural-language-processing'] | [ 2.62562424e-01 -3.62223014e-02 -9.91887003e-02 -5.43811917e-01
-1.17298937e+00 8.06947891e-03 4.85119790e-01 8.66369456e-02
-4.42098618e-01 8.36622298e-01 9.36503947e-01 -3.15958172e-01
4.58157957e-01 -9.55531895e-01 -7.02120543e-01 -2.95802146e-01
9.51503813e-02 3.41933995e-01 1.37084480e-02 -2.92527020... | [12.162492752075195, 9.327052116394043] |
f93dd1f8-aee7-4e0d-8915-abc4ce6e0845 | data-free-evaluation-of-user-contributions-in | 2108.10623 | null | https://arxiv.org/abs/2108.10623v1 | https://arxiv.org/pdf/2108.10623v1.pdf | Data-Free Evaluation of User Contributions in Federated Learning | Federated learning (FL) trains a machine learning model on mobile devices in a distributed manner using each device's private data and computing resources. A critical issues is to evaluate individual users' contributions so that (1) users' effort in model training can be compensated with proper incentives and (2) malic... | ['Chengfei Lv', 'Rongfei Jia', 'Lifeng Hua', 'Shaojie Tang', 'Fan Wu', 'Tie Luo', 'Zhenzhe Zheng', 'Hongtao Lv'] | 2021-08-24 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.32823795e-01 -1.13886476e-01 -5.77927291e-01 -3.80924612e-01
-1.00967062e+00 -6.10427678e-01 2.94155747e-01 -1.29365325e-01
-8.91442597e-03 8.35603476e-01 -9.82744023e-02 -1.11898385e-01
-2.76954859e-01 -7.97677040e-01 -8.28450382e-01 -7.59314716e-01
7.06266388e-02 5.20916939e-01 -2.30188176e-01 1.22986749... | [5.863729476928711, 6.324940204620361] |
58bd4ed9-1719-425b-b2f9-7c227b910556 | finki-at-semeval-2016-task-4-deep-learning | null | null | https://aclanthology.org/S16-1022 | https://aclanthology.org/S16-1022.pdf | Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis | null | ['Gjorgji Strezoski', 'Ivica Dimitrovski', 'Gjorgji Madjarov', 'Dario Stojanovski'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.425168037414551, 3.8160078525543213] |
67f80593-b9fc-4ca9-a618-e14c867d4bf9 | aerk-aligned-entropic-reproducing-kernels | 2303.03396 | null | https://arxiv.org/abs/2303.03396v1 | https://arxiv.org/pdf/2303.03396v1.pdf | AERK: Aligned Entropic Reproducing Kernels through Continuous-time Quantum Walks | In this work, we develop an Aligned Entropic Reproducing Kernel (AERK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph structure, and computing the Averaged Mixing Matrix (AMM) to describe how the CTQW visit all vertices from a starting vertex. More specifically... | ['Edwin R. Hancock', 'Lu Bai', 'Yue Wang', 'Ming Li', 'Lixin Cui'] | 2023-03-04 | null | null | null | null | ['graph-classification'] | ['graphs'] | [-3.74193825e-02 1.34858370e-01 1.24114320e-01 9.43756942e-03
-2.71205753e-01 -5.42354524e-01 6.16110206e-01 6.78154826e-01
-2.68559366e-01 2.92376727e-01 -3.20935935e-01 -4.24599111e-01
-5.75211167e-01 -1.19352174e+00 -4.78062451e-01 -1.02997112e+00
-5.65316558e-01 1.35254860e-01 2.45655715e-01 -2.79269695... | [7.052361488342285, 5.693635940551758] |
ea637100-6259-47b0-9864-3b72d4e35503 | rono-robust-discriminative-learning-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Feng_RONO_Robust_Discriminative_Learning_With_Noisy_Labels_for_2D-3D_Cross-Modal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Feng_RONO_Robust_Discriminative_Learning_With_Noisy_Labels_for_2D-3D_Cross-Modal_CVPR_2023_paper.pdf | RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal Retrieval | Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval becomes popular with a burst of 2D and 3D data. However, this problem is challenging given the heterogeneous structure and semantic discrepancies. Moreover, imperfect annotations are ubiquitous given the ambiguous 2D and 3D conte... | ['Peng Hu', 'Xi Peng', 'Dezhong Peng', 'Hongyuan Zhu', 'Yanglin Feng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['learning-with-noisy-labels', 'cross-modal-retrieval', 'learning-with-noisy-labels'] | ['computer-vision', 'miscellaneous', 'natural-language-processing'] | [-8.69554132e-02 -3.69767815e-01 -1.88366950e-01 -2.31158227e-01
-1.41639924e+00 -6.15266740e-01 4.09962088e-01 9.83038396e-02
-1.71043947e-01 2.34474853e-01 1.92238867e-01 2.82995790e-01
-5.36573231e-01 -2.47110084e-01 -3.44505876e-01 -1.04865992e+00
2.29493022e-01 2.61414915e-01 -1.67610884e-01 -1.54879261... | [11.115046501159668, 1.1651995182037354] |
3a894838-34ae-45b8-8644-ebcbbdfe70c2 | latent-class-conditional-noise-model | 2302.09595 | null | https://arxiv.org/abs/2302.09595v1 | https://arxiv.org/pdf/2302.09595v1.pdf | Latent Class-Conditional Noise Model | Learning with noisy labels has become imperative in the Big Data era, which saves expensive human labors on accurate annotations. Previous noise-transition-based methods have achieved theoretically-grounded performance under the Class-Conditional Noise model (CCN). However, these approaches builds upon an ideal but imp... | ['Ivor W. Tsang', 'Ya zhang', 'Zhihan Zhou', 'Bo Han', 'Jiangchao Yao'] | 2023-02-19 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.61928284e-01 2.94707596e-01 -4.60606441e-02 -5.30927718e-01
-1.27050579e+00 -3.34107518e-01 7.52645612e-01 -6.11841232e-02
-5.91868222e-01 6.57635689e-01 -1.81022227e-01 4.96632280e-03
-2.40943469e-02 -7.70767450e-01 -6.56587720e-01 -1.15962219e+00
3.85886133e-01 8.68265390e-01 2.37910092e-01 4.07811970... | [9.242593765258789, 4.005716323852539] |
1a9279f5-8305-4da7-88cc-a7ad5c3766a4 | exploring-exploiting-high-order-graph | 2306.17034 | null | https://arxiv.org/abs/2306.17034v1 | https://arxiv.org/pdf/2306.17034v1.pdf | Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completion | Sparse knowledge graph (KG) scenarios pose a challenge for previous Knowledge Graph Completion (KGC) methods, that is, the completion performance decreases rapidly with the increase of graph sparsity. This problem is also exacerbated because of the widespread existence of sparse KGs in practical applications. To allevi... | ['Bing Qin', 'Zheng Chu', 'Zihao Zheng', 'Zekun Wang', 'Yixin Cao', 'Ming Liu', 'Tao He'] | 2023-06-29 | null | null | null | null | ['knowledge-graph-completion', 'logical-reasoning'] | ['knowledge-base', 'reasoning'] | [-1.56809136e-01 5.37029803e-01 -2.75596976e-01 -1.86458871e-01
-3.96169275e-01 -1.01936497e-01 3.76437724e-01 2.69410938e-01
-5.82553931e-02 7.45267510e-01 5.71178794e-01 -9.62285474e-02
-5.43200850e-01 -1.17154968e+00 -7.75350153e-01 -5.81756175e-01
-1.58844925e-02 6.62594497e-01 1.34514719e-01 -1.41390786... | [8.748922348022461, 7.881193161010742] |
af59d92e-c46f-4621-8a44-ab28a6652f73 | autofocusformer-image-segmentation-off-the | 2304.12406 | null | https://arxiv.org/abs/2304.12406v1 | https://arxiv.org/pdf/2304.12406v1.pdf | AutoFocusFormer: Image Segmentation off the Grid | Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small object... | ['Li Fuxin', 'Alex Colburn', 'Alex Schwing', 'Zhile Ren', 'Alvin Wan', 'Shuangfei Zhai', 'Kaushik Patnaik', 'Chen Ziwen'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ziwen_AutoFocusFormer_Image_Segmentation_off_the_Grid_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ziwen_AutoFocusFormer_Image_Segmentation_off_the_Grid_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 1.87799275e-01 1.24348745e-01 -1.41307354e-01 -2.73560584e-01
-3.75464171e-01 -2.49703169e-01 5.08288920e-01 8.97469819e-02
-3.35877389e-01 7.48706400e-01 2.87824869e-01 1.89086333e-01
1.29486918e-01 -1.10020781e+00 -1.14548004e+00 -1.08666754e+00
3.88024151e-01 3.79673392e-01 5.91621280e-01 -3.55132893... | [10.387979507446289, -0.9990102648735046] |
19fcef1d-f76a-46df-b13a-bb588ef6189d | combined-scaling-for-zero-shot-transfer | 2111.10050 | null | https://arxiv.org/abs/2111.10050v3 | https://arxiv.org/pdf/2111.10050v3.pdf | Combined Scaling for Zero-shot Transfer Learning | We present a combined scaling method - named BASIC - that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet example. This accuracy surpasses best published similar models - CLIP and ALIGN - by 9.3%. Our BASIC model also shows significant improvements in ... | ['Quoc V. Le', 'Yonghui Wu', 'Minh-Thang Luong', 'Yi-Ting Chen', 'Jiahui Yu', 'Kenji Kawaguchi', 'Mingxing Tan', 'Adams Wei Yu', 'Hanxiao Liu', 'Golnaz Ghiasi', 'Zihang Dai', 'Hieu Pham'] | 2021-11-19 | null | null | null | null | ['zero-shot-transfer-image-classification'] | ['computer-vision'] | [-1.19625047e-01 -2.61190176e-01 -1.41199097e-01 -3.71094912e-01
-8.04882646e-01 -4.79600191e-01 4.65785533e-01 -1.53631300e-01
-8.38293314e-01 4.11924988e-01 -4.33668979e-02 -5.35836697e-01
2.47906178e-01 -4.61297750e-01 -9.48443115e-01 -4.73699719e-01
-6.18942752e-02 2.13023394e-01 4.03313488e-01 -1.90854654... | [9.227837562561035, 2.2668113708496094] |
87fe3203-198c-4ef7-ad14-d542056be2c5 | style-erd-responsive-and-coherent-online | 2203.02574 | null | https://arxiv.org/abs/2203.02574v2 | https://arxiv.org/pdf/2203.02574v2.pdf | Style-ERD: Responsive and Coherent Online Motion Style Transfer | Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as realtime avatar animation from motion capture, motions need to be processed as... | ['Michiel Van de Panne', 'Zhongquan Chen', 'Xiaohang Zhan', 'Tianxin Tao'] | 2022-03-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tao_Style-ERD_Responsive_and_Coherent_Online_Motion_Style_Transfer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tao_Style-ERD_Responsive_and_Coherent_Online_Motion_Style_Transfer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['motion-style-transfer'] | ['computer-code'] | [ 1.70198455e-01 -1.95541710e-01 -3.53329927e-01 -1.17342986e-01
-6.10772908e-01 -7.92388558e-01 5.79371750e-01 -4.04741138e-01
-4.18553114e-01 5.39653599e-01 4.17876631e-01 -2.09307149e-01
5.06559134e-01 -5.69232762e-01 -6.67899311e-01 -3.70582491e-01
3.15101475e-01 3.14890355e-01 3.14424425e-01 -2.74692118... | [10.811979293823242, -0.6785629987716675] |
c3775171-4f0f-4208-ab90-b6b24082e906 | federated-neural-compression-under | 2305.16416 | null | https://arxiv.org/abs/2305.16416v1 | https://arxiv.org/pdf/2305.16416v1.pdf | Federated Neural Compression Under Heterogeneous Data | We discuss a federated learned compression problem, where the goal is to learn a compressor from real-world data which is scattered across clients and may be statistically heterogeneous, yet share a common underlying representation. We propose a distributed source model that encompasses both characteristics, and natura... | ['Shirin Saeedi Bidokhti', 'Hamed Hassani', 'Eric Lei'] | 2023-05-25 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-3.74175131e-01 2.68108044e-02 -6.11778378e-01 -5.01650155e-01
-1.24494016e+00 -4.98377472e-01 6.16037250e-01 -8.68010148e-03
2.74679303e-01 4.59082782e-01 1.09188116e+00 3.19267064e-01
-3.16547930e-01 -9.42886174e-01 -9.14275944e-01 -7.46532202e-01
-4.24292535e-01 1.03068185e+00 -2.76986927e-01 2.44254041... | [5.819883823394775, 6.281915187835693] |
0acb0280-24d6-40f3-8e87-40aa5be1d7b7 | reward-teaching-for-federated-multi-armed | 2305.02441 | null | https://arxiv.org/abs/2305.02441v1 | https://arxiv.org/pdf/2305.02441v1.pdf | Reward Teaching for Federated Multi-armed Bandits | Most of the existing federated multi-armed bandits (FMAB) designs are based on the presumption that clients will implement the specified design to collaborate with the server. In reality, however, it may not be possible to modify the client's existing protocols. To address this challenge, this work focuses on clients w... | ['Jing Yang', 'Cong Shen', 'Wei Xiong', 'Chengshuai Shi'] | 2023-05-03 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-1.80920675e-01 2.21571088e-01 -7.74985373e-01 -2.69328862e-01
-9.16542768e-01 -7.78996885e-01 2.22181261e-01 -2.04060271e-01
-2.47937083e-01 9.29744422e-01 -8.70900378e-02 -7.52745688e-01
-7.78806806e-01 -6.66097999e-01 -9.17178869e-01 -1.29433548e+00
-8.44629630e-02 7.80344307e-01 -9.49712172e-02 1.61177032... | [4.558437824249268, 3.34870982170105] |
a466f475-eb29-439e-a2a1-adc4db1d19ee | oo-dmvmt-a-deep-multi-view-multi-task | 2304.05956 | null | https://arxiv.org/abs/2304.05956v1 | https://arxiv.org/pdf/2304.05956v1.pdf | OO-dMVMT: A Deep Multi-view Multi-task Classification Framework for Real-time 3D Hand Gesture Classification and Segmentation | Continuous mid-air hand gesture recognition based on captured hand pose streams is fundamental for human-computer interaction, particularly in AR / VR. However, many of the methods proposed to recognize heterogeneous hand gestures are tested only on the classification task, and the real-time low-latency gesture segment... | ['Marco Cristani', 'Andrea Giachetti', 'Marco Emporio', 'Andrea Avogaro', 'Federico Girella', 'Federico Cunico'] | 2023-04-12 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.73543262e-01 -6.86218560e-01 -1.51374742e-01 -2.88072228e-01
-9.04557705e-01 -6.24604940e-01 4.47925478e-01 -2.29633689e-01
-4.45973337e-01 5.71452118e-02 -1.19624212e-01 -3.72459441e-02
-2.02857971e-01 -4.69818383e-01 -2.76002496e-01 -8.07581306e-01
1.04654513e-01 9.15100455e-01 6.95938170e-01 -1.29294619... | [6.653153419494629, -0.27552396059036255] |
d144db84-8990-4fbd-9732-66e4c43bde99 | the-proof-is-in-the-pudding-using-automated | 2203.02683 | null | https://arxiv.org/abs/2203.02683v1 | https://arxiv.org/pdf/2203.02683v1.pdf | The Proof is in the Pudding: Using Automated Theorem Proving to Generate Cooking Recipes | This paper presents FASTFOOD, a rule-based Natural Language Generation Program for cooking recipes. Recipes are generated by using an Automated Theorem Proving procedure to select the ingredients and instructions, with ingredients corresponding to axioms and instructions to implications. FASTFOOD also contains a tempor... | ['Carl Vogel', 'Louis Mahon'] | 2022-03-05 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 2.97200650e-01 5.99268258e-01 -2.70557106e-01 -1.26606703e-01
3.92902046e-02 -1.04182422e+00 6.98853374e-01 5.49018145e-01
1.57685101e-01 7.02169597e-01 5.04484832e-01 -6.26057923e-01
9.62940007e-02 -1.12503159e+00 -4.00366187e-01 -3.28675151e-01
-1.05582930e-01 4.14357603e-01 3.69176447e-01 -4.70835775... | [11.351414680480957, 4.613583087921143] |
587fd034-0f9b-4161-a298-7c494255d342 | refined-commonsense-knowledge-from-large | 2112.04596 | null | https://arxiv.org/abs/2112.04596v2 | https://arxiv.org/pdf/2112.04596v2.pdf | Refined Commonsense Knowledge from Large-Scale Web Contents | Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and strings for P and O. This pa... | ['Gerhard Weikum', 'Julien Romero', 'Simon Razniewski', 'Tuan-Phong Nguyen'] | 2021-11-30 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [-2.94027895e-01 3.35157514e-01 -4.74083990e-01 -2.61606574e-01
-5.73299646e-01 -7.91408122e-01 6.31062090e-01 7.13224590e-01
-2.20250919e-01 9.21580195e-01 4.38861340e-01 -7.55733550e-02
-4.63015050e-01 -9.56349134e-01 -5.76582849e-01 -7.14621022e-02
-1.52362466e-01 5.50642014e-01 6.71856582e-01 -6.33502126... | [9.671456336975098, 8.272083282470703] |
41236739-62c3-4bc9-ba70-05fc51c4ca3e | judge-the-judges-a-large-scale-evaluation | 1901.00398 | null | https://arxiv.org/abs/1901.00398v2 | https://arxiv.org/pdf/1901.00398v2.pdf | Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation | We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compare human-based evaluators with a variety of automated evaluation procedures, including discriminative evaluators that measure how well machi... | ['Shiyan Yan', 'Cristina Garbacea', 'Samuel Carton', 'Qiaozhu Mei'] | 2019-01-02 | judge-the-judges-a-large-scale-evaluation-1 | https://aclanthology.org/D19-1409 | https://aclanthology.org/D19-1409.pdf | ijcnlp-2019-11 | ['review-generation'] | ['natural-language-processing'] | [ 1.86880291e-01 3.90049964e-01 -2.76220918e-01 -3.28321487e-01
-1.23032129e+00 -1.20638108e+00 9.61073339e-01 5.39082408e-01
-4.34107035e-01 7.25045502e-01 5.49704373e-01 -2.87293702e-01
4.90361333e-01 -8.38493645e-01 -2.73014307e-01 -1.49469405e-01
6.33676648e-01 6.45515323e-01 -1.77468717e-01 -6.34176612... | [11.79723072052002, 8.955818176269531] |
54df8c9c-657c-40e1-a8e8-ea05c069789e | dynamic-adaptive-spatio-temporal-graph | 2109.12517 | null | https://arxiv.org/abs/2109.12517v1 | https://arxiv.org/pdf/2109.12517v1.pdf | Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling | The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this representation has advanced our understanding of brain function, it represents a simplified model of br... | ['Guido van Wingen', 'Rajat Mani Thomas', 'Ahmed El-Gazzar'] | 2021-09-26 | null | null | null | null | ['age-and-gender-classification', 'graph-structure-learning'] | ['computer-vision', 'graphs'] | [ 2.01123804e-01 3.17115575e-01 7.47489855e-02 -6.21820807e-01
1.39475897e-01 -5.17276943e-01 9.02945459e-01 3.90083611e-01
-4.87206221e-01 5.01782060e-01 3.17745537e-01 -3.63885909e-01
-6.78863108e-01 -7.71302581e-01 -4.61546272e-01 -3.91777605e-01
-1.02007794e+00 5.90989292e-01 1.20378107e-01 -5.64254709... | [12.4049711227417, 3.3747920989990234] |
1b5dce28-fb05-4899-9494-b3c5f393ecdb | few-shot-named-entity-recognition-with-joint | null | null | https://openreview.net/forum?id=CvGtHdgukK | https://openreview.net/pdf?id=CvGtHdgukK | Few-shot Named Entity Recognition with Joint Token and Sentence Awareness | Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Recently, few-shot models have been used for Named Entity Recognition (NER). Prototypical network shows high efficiency on few-shot NER. However, existing prototypical methods only consider the similarity of tokens ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['few-shot-ner'] | ['natural-language-processing'] | [-2.79346079e-01 -2.00259715e-01 4.23460593e-03 -3.97445142e-01
-6.05006397e-01 -1.72518581e-01 5.94503701e-01 3.34283203e-01
-8.21664333e-01 6.20862961e-01 3.79184961e-01 3.33276898e-01
-2.95829594e-01 -9.51607049e-01 -2.04559922e-01 -5.08789539e-01
2.11567342e-01 1.25210986e-01 8.18513751e-01 -4.50815707... | [9.713356971740723, 9.344440460205078] |
46e4f35c-1b15-47b3-b4ce-93495d2b7460 | dynamical-models-for-metabolomics-data | 2105.10365 | null | https://arxiv.org/abs/2105.10365v1 | https://arxiv.org/pdf/2105.10365v1.pdf | Dynamical models for metabolomics data integration | As metabolomics datasets are becoming larger and more complex, there is an increasing need for model-based data integration and analysis to optimally leverage these data. Dynamical models of metabolism allow for the integration of heterogeneous data and the analysis of dynamical phenotypes. Here, we review recent effor... | ['Daniel Weindl', 'Polina Lakrisenko'] | 2021-05-21 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 9.54531431e-02 -6.60386622e-01 -1.30171105e-01 -8.09169561e-02
5.80756217e-02 -9.10076678e-01 3.93394232e-01 6.82745576e-01
2.27568373e-02 6.93524301e-01 -1.19194351e-02 -2.25768149e-01
-4.15305197e-01 -4.70056474e-01 -2.61486858e-01 -7.65748143e-01
-5.16648471e-01 4.41238195e-01 -4.24088957e-03 -9.15102139... | [5.899750709533691, 4.470616340637207] |
a3904bdb-bdeb-4aac-bc6d-0a6ff07ecdf7 | bridging-clip-and-stylegan-through-latent | 2210.04506 | null | https://arxiv.org/abs/2210.04506v1 | https://arxiv.org/pdf/2210.04506v1.pdf | Bridging CLIP and StyleGAN through Latent Alignment for Image Editing | Text-driven image manipulation is developed since the vision-language model (CLIP) has been proposed. Previous work has adopted CLIP to design a text-image consistency-based objective to address this issue. However, these methods require either test-time optimization or image feature cluster analysis for single-mode ma... | ['Zhongyuan Wang', 'Pengfei Wan', 'Xiaoyan Guo', 'Qiang Li', 'Wanfeng Zheng'] | 2022-10-10 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.23898745e-01 -3.05532031e-02 -3.49449128e-01 -3.29729110e-01
-6.58235669e-01 -4.09995824e-01 8.27781796e-01 -7.41057158e-01
9.02052745e-02 4.63375360e-01 2.08928749e-01 2.15607602e-02
-2.35562846e-01 -6.79598570e-01 -6.51845098e-01 -8.00028622e-01
4.75402117e-01 3.33925873e-01 -1.18482850e-01 -1.02176256... | [11.452862739562988, -0.6104161739349365] |
2209ba79-73e5-43d3-939a-1673c5fbcab4 | approximate-knowledge-graph-query-answering | 2102.11389 | null | https://arxiv.org/abs/2102.11389v1 | https://arxiv.org/pdf/2102.11389v1.pdf | Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification | Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where th... | ['Michael Cochez', 'Dimitrios Alivanistos', 'Daniel Daza', 'Teodor Aleksiev', 'Ruud van Bakel'] | 2021-02-22 | null | null | null | null | ['complex-query-answering'] | ['knowledge-base'] | [ 1.61308590e-02 5.19202650e-01 -1.89771995e-01 -2.21325308e-01
-5.65245211e-01 -6.75098479e-01 7.57623672e-01 1.25030506e+00
-4.22635466e-01 9.06439126e-01 3.49337012e-01 -9.55826715e-02
-6.64664507e-01 -1.43605936e+00 -5.80779612e-01 -2.14365065e-01
-1.25515506e-01 8.66260350e-01 6.62892163e-01 -4.85470206... | [9.19325065612793, 7.972320556640625] |
af26e7c6-6aad-457f-aaa6-e3f3a849ade3 | inferencing-based-on-unsupervised-learning-of | 1803.02627 | null | http://arxiv.org/abs/1803.02627v1 | http://arxiv.org/pdf/1803.02627v1.pdf | Inferencing Based on Unsupervised Learning of Disentangled Representations | Combining Generative Adversarial Networks (GANs) with encoders that learn to
encode data points has shown promising results in learning data representations
in an unsupervised way. We propose a framework that combines an encoder and a
generator to learn disentangled representations which encode meaningful
information a... | ['Tobias Hinz', 'Stefan Wermter'] | 2018-03-07 | null | null | null | null | ['unsupervised-image-classification', 'unsupervised-mnist'] | ['computer-vision', 'methodology'] | [ 3.63742262e-01 7.22896278e-01 -3.28499794e-01 -5.98257482e-01
-8.38520348e-01 -7.60115683e-01 1.01726890e+00 -2.96540201e-01
2.26774439e-01 1.00283909e+00 4.13488746e-01 -3.20813544e-02
3.58149186e-02 -1.19999278e+00 -8.71612668e-01 -1.03584123e+00
-2.48991866e-02 9.11745191e-01 -5.66836834e-01 -1.35051429... | [11.5714750289917, -0.05522846058011055] |
30315405-a653-4df6-b44a-02d75a7c57d8 | a-literature-review-on-length-of-stay | 2201.00005 | null | https://arxiv.org/abs/2201.00005v1 | https://arxiv.org/pdf/2201.00005v1.pdf | A Literature Review on Length of Stay Prediction for Stroke Patients using Machine Learning and Statistical Approaches | Hospital length of stay (LOS) is one of the most essential healthcare metrics that reflects the hospital quality of service and helps improve hospital scheduling and management. LOS prediction helps in cost management because patients who remain in hospitals usually do so in hospital units where resources are severely ... | ['Ayman Alahmar', 'Ola Alkhatib'] | 2021-12-30 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [-7.34448314e-01 -6.03465736e-01 -8.27542007e-01 -3.33604634e-01
-4.42513645e-01 -2.07834944e-01 -2.09769607e-01 9.12950099e-01
-8.12403321e-01 8.12431157e-01 7.91123807e-01 -6.63092136e-01
-4.19614792e-01 -6.38067842e-01 9.65139419e-02 -6.48039758e-01
-1.12496108e-01 5.58296800e-01 -2.25065514e-01 1.49705485... | [8.015809059143066, 6.1444830894470215] |
6dea8412-2013-4921-ac58-6fc14bbe6a36 | poet-a-generative-model-of-protein-families | 2306.06156 | null | https://arxiv.org/abs/2306.06156v1 | https://arxiv.org/pdf/2306.06156v1.pdf | PoET: A generative model of protein families as sequences-of-sequences | Generative protein language models are a natural way to design new proteins with desired functions. However, current models are either difficult to direct to produce a protein from a specific family of interest, or must be trained on a large multiple sequence alignment (MSA) from the specific family of interest, making... | ['Tristan Bepler', 'Timothy F. Truong Jr'] | 2023-06-09 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 7.06288099e-01 1.70614153e-01 1.13997169e-01 -5.46379089e-01
-9.74979818e-01 -8.42324495e-01 2.54536897e-01 9.47548673e-02
-3.04706782e-01 1.23813832e+00 -9.11135450e-02 -6.52463734e-01
1.11682341e-02 -7.11997688e-01 -1.37309790e+00 -8.74142051e-01
-5.87140694e-02 9.27522480e-01 3.26935649e-01 -4.56717223... | [4.690733909606934, 5.611966133117676] |
8b0fbe71-7122-479e-bf71-17ef1ce765db | enhancing-user-behavior-sequence-modeling-by | 2208.10846 | null | https://arxiv.org/abs/2208.10846v1 | https://arxiv.org/pdf/2208.10846v1.pdf | Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search | Users' search tasks have become increasingly complicated, requiring multiple queries and interactions with the results. Recent studies have demonstrated that modeling the historical user behaviors in a session can help understand the current search intent. Existing context-aware ranking models primarily encode the curr... | ['Ji-Rong Wen', 'Xiaohua Cheng', 'Zhao Cao', 'Yutao Zhu', 'Zhicheng Dou', 'Haonan Chen'] | 2022-08-23 | null | null | null | null | ['session-search', 'document-ranking'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.81401122e-01 -2.60952234e-01 -5.70380569e-01 -8.65861416e-01
-1.16774070e+00 -5.10571778e-01 7.82477796e-01 -3.46605182e-01
-2.94728905e-01 4.13284808e-01 7.31171131e-01 -4.60727692e-01
-3.38014774e-02 -6.32507563e-01 -6.75662994e-01 -1.35577455e-01
-7.43244663e-02 6.15876913e-01 3.59737217e-01 -3.72189343... | [11.741608619689941, 7.641104698181152] |
bb601f03-be83-4ad8-b1b1-56af61d63894 | towards-multi-sense-cross-lingual-alignment-1 | 2103.06459 | null | https://arxiv.org/abs/2103.06459v4 | https://arxiv.org/pdf/2103.06459v4.pdf | Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings | Cross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this work, we propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lin... | ['Luo Si', 'Lidong Bing', 'Shafiq Joty', 'Thien Hai Nguyen', 'Linlin Liu'] | 2021-03-11 | towards-multi-sense-cross-lingual-alignment | https://aclanthology.org/2022.coling-1.386 | https://aclanthology.org/2022.coling-1.386.pdf | coling-2022-10 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.98488268e-02 -3.65015209e-01 -3.91940922e-01 -5.18975258e-01
-1.11589587e+00 -8.24639678e-01 4.46177185e-01 4.69127476e-01
-1.15283537e+00 6.81342125e-01 3.29445899e-01 -3.44528705e-01
1.50989264e-01 -6.26565874e-01 -4.33132023e-01 -4.67131793e-01
3.71345371e-01 1.99726075e-01 -1.79705322e-01 -6.20222807... | [10.887657165527344, 9.771105766296387] |
af055459-5ee7-4b5d-bd95-3ca2f2f5198b | explicit3d-graph-network-with-spatial | 2302.06494 | null | https://arxiv.org/abs/2302.06494v1 | https://arxiv.org/pdf/2302.06494v1.pdf | Explicit3D: Graph Network with Spatial Inference \\for Single Image 3D Object Detection | Indoor 3D object detection is an essential task in single image scene understanding, impacting spatial cognition fundamentally in visual reasoning. Existing works on 3D object detection from a single image either pursue this goal through independent predictions of each object or implicitly reason over all possible obje... | ['Wenming Yang', 'Qingmin Liao', 'Yehu Shen', 'Yanjun Liu'] | 2023-02-13 | null | null | null | null | ['scene-graph-generation', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 6.67009279e-02 2.18619421e-01 7.26143597e-04 -5.14330983e-01
-1.65225416e-01 -4.27961975e-01 6.29325211e-01 3.31299365e-01
-2.05162883e-01 1.26939967e-01 4.40851003e-02 -2.04002172e-01
-2.16400594e-01 -8.27817559e-01 -9.29073036e-01 -4.77403939e-01
3.15906629e-02 4.83963579e-01 7.16630101e-01 -4.86186184... | [8.199882507324219, -2.852332592010498] |
ac0cd072-c9af-408a-9593-f4d9a39bf9c3 | robust-end-to-end-focal-liver-lesion | 2112.01535 | null | https://arxiv.org/abs/2112.01535v2 | https://arxiv.org/pdf/2112.01535v2.pdf | Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images | The computer-aided diagnosis of focal liver lesions (FLLs) can help improve workflow and enable correct diagnoses; FLL detection is the first step in such a computer-aided diagnosis. Despite the recent success of deep-learning-based approaches in detecting FLLs, current methods are not sufficiently robust for assessing... | ['Sungroh Yoon', 'Jung Hoon Kim', 'Jae Seok Bae', 'Eunji Kim', 'Sang-gil Lee'] | 2021-12-02 | null | null | null | null | ['medical-object-detection', 'liver-segmentation', 'automatic-liver-and-tumor-segmentation'] | ['computer-vision', 'medical', 'medical'] | [-0.09696203 -0.16188528 -0.06011811 -0.3366642 -1.4846491 -0.40395322
0.41549268 0.37339112 -0.34721008 0.01722383 0.30348513 -0.49722221
-0.28141147 -0.40678284 -0.238929 -0.8376237 -0.5511697 0.7230072
0.21980192 0.29344591 -0.10559823 0.7355355 -0.55845004 0.4162025
0.5422789 0.8419171 0.53... | [14.620542526245117, -2.568955659866333] |
cfa18a19-161c-49f8-b981-c54f61100274 | unsupervised-clustering-of-time-series | 2102.09200 | null | https://arxiv.org/abs/2102.09200v1 | https://arxiv.org/pdf/2102.09200v1.pdf | Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks | Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc. These applications typically involve small, low-power devices on the edge that collect and process real-time sensory signals. State-of-the-art time-series clustering methods p... | ['John Paul Shen', 'José M. F. Moura', 'Harideep Nair', 'Shreyas Chaudhari'] | 2021-02-18 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 4.29262072e-01 -4.24693286e-01 8.67524594e-02 -2.94426292e-01
-3.04929286e-01 -4.51398492e-01 9.23337489e-02 5.76669395e-01
-7.69214988e-01 4.57993835e-01 -5.71190536e-01 -2.80366898e-01
-4.36812222e-01 -3.92870843e-01 -6.52009130e-01 -7.18593299e-01
-4.61116731e-01 3.04188430e-01 3.22332352e-01 1.79087698... | [8.203685760498047, 2.4849863052368164] |
09f7a0f9-1a88-4a36-876b-0b746a430de8 | online-signature-verification-using-deep | 1806.09986 | null | http://arxiv.org/abs/1806.09986v1 | http://arxiv.org/pdf/1806.09986v1.pdf | Online Signature Verification using Deep Representation: A new Descriptor | This paper presents an accurate method for verifying online signatures. The
main difficulty of signature verification come from: (1) Lacking enough
training samples (2) The methods must be spatial change invariant. To deal with
these difficulties and modeling the signatures efficiently, we propose a method
that a one-c... | ['Mohammad Sabokrou', 'Mahmood Fathy', 'Mohsen Fayyaz', 'Mohammad Hajizadeh Saffar'] | 2018-06-24 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 4.12409365e-01 -5.62277138e-01 -2.85050780e-01 -6.95696712e-01
-7.59149790e-01 -5.59933782e-01 8.10165167e-01 -1.76297486e-01
-8.02528113e-02 6.02631509e-01 1.63474865e-02 -8.81496370e-02
-3.38764369e-01 -8.28652978e-01 -6.89787209e-01 -7.40282595e-01
-3.62128228e-01 4.49027032e-01 2.83736169e-01 -2.60605048... | [12.62831974029541, 1.2272861003875732] |
daf8c406-33e1-4d96-b7c8-2f5c1fab6bc4 | swinrdm-integrate-swinrnn-with-diffusion | 2306.03110 | null | https://arxiv.org/abs/2306.03110v1 | https://arxiv.org/pdf/2306.03110v1.pdf | SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting | Data-driven medium-range weather forecasting has attracted much attention in recent years. However, the forecasting accuracy at high resolution is unsatisfactory currently. Pursuing high-resolution and high-quality weather forecasting, we develop a data-driven model SwinRDM which integrates an improved version of SwinR... | ['Zhibin Wang', 'Fan Wang', 'Yuan Hu', 'Fei Du', 'Lei Chen'] | 2023-06-05 | null | null | null | null | ['super-resolution', 'weather-forecasting'] | ['computer-vision', 'miscellaneous'] | [-4.10097212e-01 -2.71158427e-01 9.60748196e-02 -2.94008285e-01
-6.05902255e-01 -5.56913018e-01 8.37007999e-01 -1.51929008e-02
5.05299866e-02 1.12172759e+00 4.62608129e-01 -7.57309318e-01
-1.90738633e-01 -1.37900269e+00 -7.87652622e-04 -9.37093735e-01
-4.98922706e-01 3.37035730e-02 2.55391985e-01 -9.04976070... | [6.554004669189453, 2.9579529762268066] |
8357ef85-efc6-4c4e-beec-cd0e0c6ec74e | unsupervised-attention-based-instance | 2011.01888 | null | https://arxiv.org/abs/2011.01888v1 | https://arxiv.org/pdf/2011.01888v1.pdf | Unsupervised Attention Based Instance Discriminative Learning for Person Re-Identification | Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements---including the degree of data curations---are becoming increasingly complex and laborious, there is a critical need for unsupervised me... | ['Benjamin S. Riggan', 'Kshitij Nikhal'] | 2020-11-03 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.56969424e-02 -3.79918844e-01 1.79253325e-01 -7.67693281e-01
-6.71609581e-01 -4.99178529e-01 5.38437426e-01 1.34326756e-01
-1.09267354e+00 6.20916069e-01 2.80638784e-01 8.64427984e-02
-1.45942166e-01 -3.91581446e-01 -6.35886788e-01 -4.73267436e-01
9.10916105e-02 5.98446548e-01 -2.10261047e-01 6.18745685... | [14.683653831481934, 0.9695492386817932] |
21b390c2-ff20-4bfd-9019-f2d4aa5bbb1c | scale-selective-extended-local-binary-pattern | 1812.04174 | null | http://arxiv.org/abs/1812.04174v1 | http://arxiv.org/pdf/1812.04174v1.pdf | Scale Selective Extended Local Binary Pattern for Texture Classification | In this paper, we propose a new texture descriptor, scale selective extended
local binary pattern (SSELBP), to characterize texture images with scale
variations. We first utilize multi-scale extended local binary patterns (ELBP)
with rotation-invariant and uniform mappings to capture robust local micro- and
macro-featu... | [] | 2018-12-11 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.60710230e-01 -8.49542201e-01 -4.18464422e-01 -3.57018888e-01
-8.83673728e-01 -3.63352507e-01 5.01183987e-01 2.20441237e-01
-1.81772962e-01 5.08662403e-01 -1.07654594e-01 8.76738355e-02
-5.12691677e-01 -1.04179454e+00 -1.51844025e-01 -1.01924431e+00
-5.96671849e-02 9.46314558e-02 9.97273982e-01 -2.54227698... | [10.431018829345703, -0.3597431480884552] |
7bdd3c69-b5bc-419c-b05a-00b4cd4b815e | vision-transformer-for-contrastive-clustering | 2206.12925 | null | https://arxiv.org/abs/2206.12925v2 | https://arxiv.org/pdf/2206.12925v2.pdf | Vision Transformer for Contrastive Clustering | Vision Transformer (ViT) has shown its advantages over the convolutional neural network (CNN) with its ability to capture global long-range dependencies for visual representation learning. Besides ViT, contrastive learning is another popular research topic recently. While previous contrastive learning works are mostly ... | ['Jian-Huang Lai', 'Chang-Dong Wang', 'Ding-Hua Chen', 'Dong Huang', 'Bowen Zhu', 'Hua-Bao Ling'] | 2022-06-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 1.50522545e-01 -3.00600469e-01 9.87974033e-02 -2.67992139e-01
-4.54591304e-01 -2.86858141e-01 7.73732662e-01 -1.63140178e-01
-2.73916692e-01 2.86835432e-01 -9.81645957e-02 -4.13965657e-02
-2.07907706e-01 -6.64272726e-01 -9.30481315e-01 -1.20656872e+00
1.46446586e-01 3.17970425e-01 2.13623866e-01 5.80223557... | [9.188694953918457, 2.9387574195861816] |
d92cc409-d444-4419-979c-1bb5c1b8b3e2 | a-deep-analysis-of-transfer-learning-based | 2304.05022 | null | https://arxiv.org/abs/2304.05022v1 | https://arxiv.org/pdf/2304.05022v1.pdf | A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images | Breast cancer is one of the most common and dangerous cancers in women, while it can also afflict men. Breast cancer treatment and detection are greatly aided by the use of histopathological images since they contain sufficient phenotypic data. A Deep Neural Network (DNN) is commonly employed to improve accuracy and br... | ['Ahmed Abdelgawad', 'Muntasir Mamun', 'Md Ishtyaq Mahmud'] | 2023-04-11 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 4.31049941e-03 2.20803738e-01 -4.95781064e-01 -2.05540717e-01
-7.30653524e-01 -2.25950569e-01 3.77504498e-01 5.87179840e-01
-6.50154948e-01 7.58296490e-01 -7.45142773e-02 -7.72128701e-01
1.24942929e-01 -9.53481495e-01 -3.18429023e-01 -8.52618575e-01
-1.43277019e-01 3.59385043e-01 2.13742226e-01 -1.39886355... | [15.179503440856934, -2.852385997772217] |
fdb9e8b7-2ca7-42fe-bea1-8be87bf3d1d1 | probing-for-predicate-argument-structures-in | null | null | https://aclanthology.org/2022.acl-long.316 | https://aclanthology.org/2022.acl-long.316.pdf | Probing for Predicate Argument Structures in Pretrained Language Models | Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workin... | ['Roberto Navigli', 'Simone Conia'] | null | null | null | null | acl-2022-5 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 3.81580770e-01 4.09804881e-01 -6.58199549e-01 -4.12481815e-01
-4.50753421e-01 -9.78732228e-01 7.55303741e-01 7.64902353e-01
-4.38603967e-01 5.50384521e-01 1.02517438e+00 -3.94298017e-01
-3.09543937e-01 -7.01577544e-01 -6.03142500e-01 -2.71456897e-01
-1.80450585e-02 3.91001374e-01 2.80339837e-01 -5.48798859... | [10.36921501159668, 9.3471040725708] |
5cd1c53e-c10e-415f-bafa-7f05fdfbbf0c | handcrafted-localized-phase-features-for | null | null | https://doi.org/10.1016/j.imavis.2022.104465 | https://www.sciencedirect.com/science/article/pii/S0262885622000944/pdfft?md5=513fcc71e4d80888039a419003552cb4&pid=1-s2.0-S0262885622000944-main.pdf | Handcrafted localized phase features for human action recognition | Human action recognition is one of the most important topics in computer vision. Monitoring elderly people and children, smart surveillance systems and human-computer interaction are a few examples of its applications. The aim of this study is to recognize human activities by utilizing the phase information extracted f... | ['Charith Abhayaratne', 'Seyed Mostafa Hejazi'] | 2022-05-05 | null | null | null | image-and-vision-computing-2022-5 | ['action-classification'] | ['computer-vision'] | [ 2.98860580e-01 -4.40672249e-01 -3.60057265e-01 -1.50999382e-01
-1.05047442e-01 -3.15514266e-01 7.42696166e-01 -1.65382653e-01
-7.31079340e-01 7.56111085e-01 4.85782385e-01 4.19835597e-01
-2.69861948e-02 -4.80880529e-01 -2.20246837e-01 -9.46065962e-01
-3.48186910e-01 3.17866430e-02 6.27379477e-01 1.38368130... | [7.99215841293335, 0.36951756477355957] |
42735d33-a6ea-4176-a2c6-dc3b23f20377 | homogeneous-learning-self-attention-1 | 2110.05290 | null | https://arxiv.org/abs/2110.05290v1 | https://arxiv.org/pdf/2110.05290v1.pdf | Homogeneous Learning: Self-Attention Decentralized Deep Learning | Federated learning (FL) has been facilitating privacy-preserving deep learning in many walks of life such as medical image classification, network intrusion detection, and so forth. Whereas it necessitates a central parameter server for model aggregation, which brings about delayed model communication and vulnerability... | ['Hideya Ochiai', 'Yuwei Sun'] | 2021-10-11 | homogeneous-learning-self-attention | https://openreview.net/forum?id=BvowzJp_Yl6 | https://openreview.net/pdf?id=BvowzJp_Yl6 | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-4.63420093e-01 3.29294086e-01 -3.06583256e-01 -2.86749661e-01
-6.37627125e-01 -5.00639677e-01 2.03725040e-01 3.35096329e-01
-6.63007021e-01 8.12370479e-01 -1.60276026e-01 -2.65944570e-01
2.42577735e-02 -7.61675715e-01 -7.03574300e-01 -1.16276050e+00
-3.92052919e-01 4.02884513e-01 -2.57645044e-02 6.55072033... | [5.934664726257324, 6.381450653076172] |
6255b9dd-ad70-4fb0-943a-640a71e08e7b | constrained-r-cnn-a-general-image | 1911.08217 | null | https://arxiv.org/abs/1911.08217v3 | https://arxiv.org/pdf/1911.08217v3.pdf | Constrained R-CNN: A general image manipulation detection model | Recently, deep learning-based models have exhibited remarkable performance for image manipulation detection. However, most of them suffer from poor universality of handcrafted or predetermined features. Meanwhile, they only focus on manipulation localization and overlook manipulation classification. To address these is... | ['Hao Zhao', 'Fangting Lin', 'Chao Yang', 'Bin Jiang', 'Huizhou Li'] | 2019-11-19 | null | null | null | null | ['image-manipulation-detection', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 8.94577429e-02 -6.38401985e-01 -5.79372227e-01 -1.45044535e-01
-9.47256982e-01 -3.39552402e-01 2.99795985e-01 -1.28021389e-01
-2.38591611e-01 1.19508728e-01 1.39707327e-01 6.98061362e-02
-1.07174240e-01 -7.12378204e-01 -6.98337793e-01 -8.38432908e-01
-1.37153305e-02 -1.99081928e-01 2.65016228e-01 -1.54158756... | [12.256134033203125, 0.8996975421905518] |
86f24687-b19d-4973-8db8-a065f25320f9 | computing-nonlinear-eigenfunctions-via | 1902.10414 | null | http://arxiv.org/abs/1902.10414v1 | http://arxiv.org/pdf/1902.10414v1.pdf | Computing Nonlinear Eigenfunctions via Gradient Flow Extinction | In this work we investigate the computation of nonlinear eigenfunctions via
the extinction profiles of gradient flows. We analyze a scheme that recursively
subtracts such eigenfunctions from given data and show that this procedure
yields a decomposition of the data into eigenfunctions in some cases as the
1-dimensional... | ['Daniel Tenbrinck', 'Martin Burger', 'Leon Bungert'] | 2019-02-27 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [-1.41254365e-01 -1.47807762e-01 3.57381195e-01 -9.28210001e-03
-1.98783781e-02 -7.94025898e-01 2.18777403e-01 -1.86823100e-01
-3.52173805e-01 6.61793768e-01 -1.75324958e-02 -5.35365641e-01
-3.54362100e-01 -6.19173169e-01 -4.00459826e-01 -9.50114548e-01
-3.96996617e-01 4.75159168e-01 1.16354339e-01 -2.90420324... | [7.040802955627441, 5.171727180480957] |
3209af84-c797-4b3e-9e9c-8060ce69e486 | search-based-repair-of-deep-neural-networks | 1912.12463 | null | https://arxiv.org/abs/1912.12463v2 | https://arxiv.org/pdf/1912.12463v2.pdf | Arachne: Search Based Repair of Deep Neural Networks | The rapid and widespread adoption of Deep Neural Networks (DNNs) has called for ways to test their behaviour, and many testing approaches have successfully revealed misbehaviour of DNNs. However, it is relatively unclear what one can do to correct such behaviour after revelation, as retraining involves costly data coll... | ['Shin Yoo', 'Sungmin Kang', 'Jeongju Sohn'] | 2019-12-28 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.38914958e-01 4.65697616e-01 -5.58646545e-02 -4.60611165e-01
-1.55168874e-02 -8.40840638e-01 4.26378012e-01 3.48712923e-03
-5.13509810e-01 9.16172087e-01 -3.67736936e-01 -6.68462873e-01
-7.98155442e-02 -1.01437712e+00 -1.27252853e+00 -8.87573957e-01
1.24616116e-01 1.07378893e-01 3.84107530e-01 -2.30035782... | [6.4157185554504395, 7.702718734741211] |
979915ae-2e40-4199-aaa4-6b07fa901722 | disir-deep-image-segmentation-with | 2003.14200 | null | https://arxiv.org/abs/2003.14200v2 | https://arxiv.org/pdf/2003.14200v2.pdf | DISIR: Deep Image Segmentation with Interactive Refinement | This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concate... | ['Adrien Chan Hon Tong', 'Nicola Luminari', 'Bertrand Le Saux', 'Gaston Lenczner', 'Guy Le Besnerais'] | 2020-03-31 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.92609364e-01 2.44969204e-01 1.68786421e-01 -5.20106316e-01
-3.51394594e-01 -1.12558806e+00 1.35275558e-01 -5.76662133e-03
-7.87441730e-01 4.89331692e-01 -2.09441736e-01 -2.80524194e-01
1.86387971e-01 -7.70397723e-01 -8.89308751e-01 -4.72348690e-01
4.02670987e-02 1.97523728e-01 6.22640729e-01 -1.89487904... | [9.455677032470703, 0.07851049304008484] |
6504c2b4-55e7-4f36-87ea-6fb29b4617af | graph-partitioning-and-graph-neural-network | 2005.08008 | null | https://arxiv.org/abs/2005.08008v3 | https://arxiv.org/pdf/2005.08008v3.pdf | Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation | Graph similarity computation aims to predict a similarity score between one pair of graphs to facilitate downstream applications, such as finding the most similar chemical compounds similar to a query compound or Fewshot 3D Action Recognition. Recently, some graph similarity computation models based on neural networks ... | ['Zhongbin Xu', 'Ziheng Duan', 'Qianru Zhang', 'Haoyan Xu', 'Jie Feng', 'Yueyang Wang', 'Runjian Chen'] | 2020-05-16 | null | null | null | null | ['3d-human-action-recognition', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 3.66359323e-01 1.05959186e-02 -3.54155123e-01 -3.91084135e-01
-3.10202271e-01 -4.67771441e-01 4.21435207e-01 9.79878724e-01
-1.00184390e-02 4.21252161e-01 -1.57851377e-03 -3.43797088e-01
-2.92278349e-01 -1.30245447e+00 -4.77610826e-01 -6.04600668e-01
-1.44542366e-01 1.82096660e-01 2.51426429e-01 -1.18567824... | [7.165014266967773, 6.234344482421875] |
04fefac7-9db3-4cb9-bc40-dd1695acd4c9 | knowledge-based-multimodal-music-similarity | 2306.12249 | null | https://arxiv.org/abs/2306.12249v1 | https://arxiv.org/pdf/2306.12249v1.pdf | Knowledge-based Multimodal Music Similarity | Music similarity is an essential aspect of music retrieval, recommendation systems, and music analysis. Moreover, similarity is of vital interest for music experts, as it allows studying analogies and influences among composers and historical periods. Current approaches to musical similarity rely mainly on symbolic con... | ['Andrea Poltronieri'] | 2023-06-21 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 2.96327509e-02 -4.23284501e-01 -2.17885762e-01 -5.82064539e-02
-2.01668933e-01 -7.64067650e-01 5.25215149e-01 6.32949531e-01
6.37904108e-02 2.00470388e-01 2.31897458e-01 -2.89653987e-02
-8.31074834e-01 -7.30444372e-01 -1.45362958e-01 -1.94741070e-01
-7.13806525e-02 1.48610488e-01 -3.72649217e-03 -3.68579507... | [15.92845344543457, 5.2680840492248535] |
8aa5d24a-b67f-4264-bf59-0951b421a600 | pifu-pixel-aligned-implicit-function-for-high | 1905.05172 | null | https://arxiv.org/abs/1905.05172v3 | https://arxiv.org/pdf/1905.05172v3.pdf | PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization | We introduce Pixel-aligned Implicit Function (PIFu), a highly effective implicit representation that locally aligns pixels of 2D images with the global context of their corresponding 3D object. Using PIFu, we propose an end-to-end deep learning method for digitizing highly detailed clothed humans that can infer both 3D... | ['Hao Li', 'Angjoo Kanazawa', 'Zeng Huang', 'Shigeo Morishima', 'Shunsuke Saito', 'Ryota Natsume'] | 2019-05-13 | pifu-pixel-aligned-implicit-function-for-high-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Saito_PIFu_Pixel-Aligned_Implicit_Function_for_High-Resolution_Clothed_Human_Digitization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Saito_PIFu_Pixel-Aligned_Implicit_Function_for_High-Resolution_Clothed_Human_Digitization_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-shape-reconstruction-from-a-single-2d', '3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.39927763e-01 -2.52305210e-01 3.84707689e-01 -3.64357293e-01
-7.01407254e-01 -7.44013488e-01 2.17950180e-01 -3.93904269e-01
1.33769400e-03 4.75504875e-01 1.77917659e-01 4.18431342e-01
2.83560485e-01 -1.04830015e+00 -1.16284251e+00 -5.62679589e-01
2.64896035e-01 7.12922454e-01 2.33944416e-01 -1.87001064... | [7.157039165496826, -1.2865161895751953] |
01cedb45-8ba0-454b-ba98-521525380821 | audio-source-separation-via-multi-scale | 1904.04161 | null | http://arxiv.org/abs/1904.04161v1 | http://arxiv.org/pdf/1904.04161v1.pdf | Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets | Modern audio source separation techniques rely on optimizing sequence model
architectures such as, 1D-CNNs, on mixture recordings to generalize well to
unseen mixtures. Specifically, recent focus is on time-domain based
architectures such as Wave-U-Net which exploit temporal context by extracting
multi-scale features. ... | ['Andreas Spanias', 'Huan Song', 'Sameeksha Katoch', 'Vivek Sivaraman Narayanaswamy', 'Jayaraman J. Thiagarajan'] | 2019-04-08 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 1.42588988e-01 -3.75646114e-01 6.94573596e-02 -3.25842708e-01
-6.95928693e-01 -4.98051524e-01 4.07784522e-01 -1.36123776e-01
-3.91862959e-01 5.16182125e-01 5.67434072e-01 -4.74754423e-02
-4.19141799e-01 -4.56368297e-01 -5.28510332e-01 -6.15654528e-01
-5.61200440e-01 -3.77388656e-01 1.25745058e-01 -1.51946992... | [15.350550651550293, 5.572744846343994] |
a0b687a5-a800-4aab-9b9f-df14339a70ee | explainable-artificial-intelligence-on | 2305.07511 | null | https://arxiv.org/abs/2305.07511v1 | https://arxiv.org/pdf/2305.07511v1.pdf | eXplainable Artificial Intelligence on Medical Images: A Survey | Over the last few years, the number of works about deep learning applied to the medical field has increased enormously. The necessity of a rigorous assessment of these models is required to explain these results to all people involved in medical exams. A recent field in the machine learning area is explainable artifici... | ['Claudio Filipi Gonçalves dos Santos', 'Vitor Yukio Kondo', 'Danilo Xavier Silva', 'Francisco Alves de Souza Neto', 'Fabiana Cristina Queiroz de Oliveira Marucci', 'Jose Victor Nogueira Alves da Silva', 'Ana Claudia Akemi Matsuki de Faria', 'Nayara Rossi Brito da Silva', 'Vitor Lopes Fabris', 'Rodrigo Dória Villaça', ... | 2023-05-12 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 2.47449845e-01 8.12308431e-01 -4.54907298e-01 -6.36362970e-01
-3.54936980e-02 1.03082672e-01 3.30649495e-01 1.18327901e-01
1.93626195e-01 9.53517497e-01 -5.04761562e-02 -7.54188061e-01
-7.92640090e-01 -7.34986961e-01 -7.22651958e-01 -6.07332349e-01
-1.36117697e-01 7.02551782e-01 -6.42900407e-01 -1.80165425... | [8.71788215637207, 5.740085601806641] |
ad73491f-3254-473d-adaa-738a88c0eb0b | a-comprehensive-survey-on-image-dehazing | 2106.03323 | null | https://arxiv.org/abs/2106.03323v5 | https://arxiv.org/pdf/2106.03323v5.pdf | A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning | With the development of convolutional neural networks, hundreds of deep learning based dehazing methods have been proposed. In this paper, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functi... | ['DaCheng Tao', 'Jiuxin Cao', 'Jing Zhang', 'Jun Zhang', 'Wenqi Ren', 'Yuan Cao', 'Xiaofeng Cong', 'Jie Gui'] | 2021-06-07 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [-1.38735317e-03 -2.44761735e-01 -1.37640685e-01 -1.22541197e-01
-4.45186645e-01 -1.52374074e-01 5.03541231e-01 9.01144743e-02
-1.96211815e-01 7.47644544e-01 8.13803002e-02 -5.94223849e-02
-1.69900656e-01 -9.71619844e-01 -4.60726976e-01 -1.20266902e+00
-1.85580209e-01 -4.51621801e-01 3.22113752e-01 -2.67947823... | [10.939800262451172, -3.0624380111694336] |
60a67bdf-bdba-484e-bd82-07f633a1a94a | mplm-sim-unveiling-better-cross-lingual | 2305.13684 | null | https://arxiv.org/abs/2305.13684v1 | https://arxiv.org/pdf/2305.13684v1.pdf | mPLM-Sim: Unveiling Better Cross-Lingual Similarity and Transfer in Multilingual Pretrained Language Models | Recent multilingual pretrained language models (mPLMs) have been shown to encode strong language-specific signals, which are not explicitly provided during pretraining. It remains an open question whether it is feasible to employ mPLMs to measure language similarity, and subsequently use the similarity results to selec... | ['Hinrich Schütze', 'André F. T. Martins', 'Zheyu Zhang', 'Chengzhi Hu', 'Peiqin Lin'] | 2023-05-23 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'open-question', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.82847917e-01 -3.94536167e-01 -4.91405308e-01 -6.55945063e-01
-1.25226927e+00 -6.71464264e-01 8.70206356e-01 3.82418007e-01
-9.89024758e-01 6.71342969e-01 4.76526648e-01 -1.91245958e-01
1.61291882e-01 -7.18555033e-01 -8.94843757e-01 -3.34885836e-01
-7.26501718e-02 5.15680671e-01 2.75412709e-01 -5.22232115... | [10.905125617980957, 9.911439895629883] |
e6a56e09-5def-4eff-842e-4b5494d5e00a | combining-referring-expression-generation-and | null | null | https://aclanthology.org/P13-1152 | https://aclanthology.org/P13-1152.pdf | Combining Referring Expression Generation and Surface Realization: A Corpus-Based Investigation of Architectures | null | ['Sina Zarrie{\\ss}', 'Jonas Kuhn'] | 2013-08-01 | null | null | null | acl-2013-8 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.347914695739746, 3.742560625076294] |
70c1d6f9-557a-4cdb-b145-146d69c7600d | an-exploration-of-encoder-decoder-approaches | 2305.05627 | null | https://arxiv.org/abs/2305.05627v1 | https://arxiv.org/pdf/2305.05627v1.pdf | An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text | Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we compare four methods for multi-label classification, two based on an encoder only, and two based on an... | ['Ilias Chalkidis', 'Yova Kementchedjhieva'] | 2023-05-09 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 6.07527375e-01 2.91748077e-01 -2.96398550e-01 -5.53908765e-01
-1.39844024e+00 -5.12371957e-01 7.23120034e-01 3.70258301e-01
-5.56872427e-01 8.25404644e-01 3.36465955e-01 -3.91702145e-01
2.05073416e-01 -2.83822715e-01 -3.02067846e-01 -4.91885662e-01
2.76927710e-01 6.26137018e-01 -6.80310503e-02 2.47939043... | [9.518298149108887, 4.541554927825928] |
1ebdcecc-3527-4772-9ce5-83e66457a7b4 | multilingual-pre-training-with-language-and-1 | 2203.08552 | null | https://arxiv.org/abs/2203.08552v1 | https://arxiv.org/pdf/2203.08552v1.pdf | Multilingual Pre-training with Language and Task Adaptation for Multilingual Text Style Transfer | We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach... | ['Malvina Nissim', 'Antonio Toral', 'Huiyuan Lai'] | 2022-03-16 | null | https://aclanthology.org/2022.acl-short.29 | https://aclanthology.org/2022.acl-short.29.pdf | acl-2022-5 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 2.48333126e-01 -7.95747191e-02 -2.57800490e-01 -4.11593288e-01
-1.34222960e+00 -9.37429547e-01 7.62126625e-01 -1.77071273e-01
-9.44620252e-01 1.27224588e+00 2.96245903e-01 -5.87242365e-01
4.14360464e-01 -3.89798224e-01 -8.35866332e-01 -1.77637115e-01
3.07075292e-01 9.81968045e-01 7.27866590e-02 -6.97417676... | [11.392507553100586, 10.10200023651123] |
4eac61b3-06a9-43c6-9867-ee97eac3f2a0 | joint-2d-3d-breast-cancer-classification | 2002.12392 | null | https://arxiv.org/abs/2002.12392v1 | https://arxiv.org/pdf/2002.12392v1.pdf | Joint 2D-3D Breast Cancer Classification | Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery that are used in clinical practice for breast cancer detection and diagnosis. Radiologi... | ['Xiaoqin Wang', 'Hunter Blanton', 'Nathan Jacobs', 'Yu Zhang', 'Xin Xing', 'Tawfiq Salem', 'Gongbo Liang'] | 2020-02-27 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 3.99424046e-01 3.18751037e-01 -6.89523935e-01 -4.14953440e-01
-4.84283268e-01 4.44845669e-02 4.60905790e-01 3.01955342e-01
-4.03573692e-01 4.19446826e-01 -1.04353197e-01 -9.71915841e-01
8.77136663e-02 -1.16467452e+00 -5.12222350e-01 -5.23836255e-01
-3.18833962e-02 4.47625101e-01 2.14512721e-01 9.95535683... | [15.24258041381836, -2.5528173446655273] |
c14cc15b-9f84-47bb-aec7-7cf4d037917a | clinical-concept-extraction-with-contextual | 1810.10566 | null | http://arxiv.org/abs/1810.10566v2 | http://arxiv.org/pdf/1810.10566v2.pdf | Clinical Concept Extraction with Contextual Word Embedding | Automatic extraction of clinical concepts is an essential step for turning
the unstructured data within a clinical note into structured and actionable
information. In this work, we propose a clinical concept extraction model for
automatic annotation of clinical problems, treatments, and tests in clinical
notes utilizin... | ['Ioannis Ch. Paschalidis', 'Henghui Zhu', 'Amir Tahmasebi'] | 2018-10-24 | null | null | null | null | ['clinical-concept-extraction'] | ['medical'] | [ 3.77257138e-01 5.60598314e-01 -4.23349380e-01 -3.73027921e-01
-1.17340088e+00 -4.88917939e-02 3.61913800e-01 1.04153168e+00
-8.42863381e-01 7.01224566e-01 7.85028756e-01 -4.67315972e-01
-1.51476279e-01 -4.36305404e-01 -2.65388191e-02 -5.78703880e-01
-8.08309540e-02 5.83165467e-01 -2.00055674e-01 2.69973457... | [8.452741622924805, 8.711801528930664] |
cd975421-ca40-452b-8537-bad868af2861 | correlation-driven-multi-level-multimodal | 2305.02323 | null | https://arxiv.org/abs/2305.02323v1 | https://arxiv.org/pdf/2305.02323v1.pdf | Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources | Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source that can be collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently... | ['Hyuk-Yoon Kwon', 'Taehee Kim'] | 2023-05-01 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-1.11650437e-01 -5.63571274e-01 -1.83710381e-01 -1.04635425e-01
-8.01803946e-01 -6.10202610e-01 6.34051561e-01 8.53120923e-01
-4.80539128e-02 5.53377330e-01 2.16829658e-01 -2.18484905e-02
-2.36005321e-01 -9.28378344e-01 -2.93248326e-01 -9.94348586e-01
-1.69740185e-01 1.62305772e-01 -8.91640186e-02 -9.33931619... | [6.276374340057373, 2.632408857345581] |
2fcc8b9a-4012-4a30-a622-8e0cf1f6003f | deepface-emd-re-ranking-using-patch-wise | 2112.04016 | null | https://arxiv.org/abs/2112.04016v2 | https://arxiv.org/pdf/2112.04016v2.pdf | DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification | Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement. State-of-the-art FI approaches compare two images by taking the cosine similarity between their image embeddings. Yet, such an approach suffers from poor out-of-distribution (OOD) generalization to new types of images (... | ['Anh Nguyen', 'Hai Phan'] | 2021-12-07 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Phan_DeepFace-EMD_Re-Ranking_Using_Patch-Wise_Earth_Movers_Distance_Improves_Out-of-Distribution_Face_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Phan_DeepFace-EMD_Re-Ranking_Using_Patch-Wise_Earth_Movers_Distance_Improves_Out-of-Distribution_Face_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-identification'] | ['computer-vision'] | [ 1.81647167e-01 -3.65931779e-01 -5.85712604e-02 -4.56440121e-01
-5.38743734e-01 -1.01949275e+00 8.53139341e-01 -1.79267198e-01
-4.52614546e-01 2.54069090e-01 -1.80815235e-02 -6.17200322e-02
-1.08385265e-01 -5.66720963e-01 -6.52046978e-01 -6.27074778e-01
-1.22755393e-01 1.71783060e-01 8.94384366e-03 -3.39906663... | [13.11854362487793, 0.9180476069450378] |
de41bfe3-8261-4661-a755-a71d2e034c41 | imposition-implicit-backdoor-attack-through | 2306.15755 | null | https://arxiv.org/abs/2306.15755v1 | https://arxiv.org/pdf/2306.15755v1.pdf | IMPOSITION: Implicit Backdoor Attack through Scenario Injection | This paper presents a novel backdoor attack called IMPlicit BackdOor Attack through Scenario InjecTION (IMPOSITION) that does not require direct poisoning of the training data. Instead, the attack leverages a realistic scenario from the training data as a trigger to manipulate the model's output during inference. This ... | ['Amir Rasouli', 'Mohammad Sabokrou', 'Mozhgan PourKeshavarz'] | 2023-06-27 | null | null | null | null | ['backdoor-attack', 'trajectory-prediction'] | ['adversarial', 'computer-vision'] | [ 5.28732724e-02 4.10780668e-01 -1.52317852e-01 -1.36794463e-01
-2.82622606e-01 -1.08751619e+00 8.69550645e-01 -4.01788741e-01
-2.62891591e-01 4.11581844e-01 -4.54199970e-01 -8.86862934e-01
1.64553463e-01 -8.92561078e-01 -1.22459459e+00 -7.91603744e-01
-2.75552720e-01 -2.35619262e-01 4.31722909e-01 -2.40685865... | [5.541797161102295, 7.640066623687744] |
7fe8d794-b370-4d34-9a9b-6701de8a5a7f | a-multi-stage-model-based-on-yolov3-for | 2111.11709 | null | https://arxiv.org/abs/2111.11709v2 | https://arxiv.org/pdf/2111.11709v2.pdf | A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle | As solar capacity installed worldwide continues to grow, there is an increasing awareness that advanced inspection systems are becoming of utmost importance to schedule smart interventions and minimize downtime likelihood. In this work we propose a novel automatic multi-stage model to detect panel defects on aerial ima... | ['Benedetto Michelozzi', 'Giacomo Fontanelli', 'Alessandro Betti', 'Antonio Di Tommaso'] | 2021-11-23 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.38860279e-01 5.64853251e-02 5.39783120e-01 2.61412472e-01
-1.36442766e-01 -1.09098864e+00 4.07679468e-01 4.42227662e-01
3.74018312e-01 6.02136314e-01 -5.66276550e-01 -4.22096550e-01
-4.30190772e-01 -9.71374393e-01 -4.08604771e-01 -8.78627777e-01
-1.57689929e-01 1.30250677e-01 4.96831924e-01 -3.03821057... | [7.170417785644531, 1.9297723770141602] |
77d2786b-d0b0-44af-99f4-5b3fc3e3f7e5 | dimensionapp-android-app-to-estimate-object | 1609.07597 | null | http://arxiv.org/abs/1609.07597v1 | http://arxiv.org/pdf/1609.07597v1.pdf | DimensionApp : android app to estimate object dimensions | In this project, we develop an android app that uses on computer vision
techniques to estimate an object dimension present in field of view. The app
while having compact size, is accurate upto +/- 5 mm and robust towards touch
inputs. We use single-view metrology to compute accurate measurement. Unlike
previous approac... | ['Vijay Kumar', 'Suriya Singh'] | 2016-09-24 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 5.61164394e-02 -6.99125603e-02 -5.57045732e-03 -2.00809300e-01
-2.48218670e-01 -9.00554538e-01 3.30599695e-01 -4.62970883e-01
-4.42926288e-02 1.41616434e-01 -4.76389647e-01 -5.51345646e-01
7.03168288e-02 -5.62066495e-01 -7.36324668e-01 4.74560410e-02
6.16403759e-01 3.54926825e-01 5.00361621e-01 7.05518341... | [7.197399139404297, -1.853243112564087] |
9e8c17d6-4295-446c-9412-94dd421acb27 | box-adapt-domain-adaptive-medical-image | 2108.08432 | null | https://arxiv.org/abs/2108.08432v2 | https://arxiv.org/pdf/2108.08432v2.pdf | Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision | Deep learning has achieved remarkable success in medicalimage segmentation, but it usually requires a large numberof images labeled with fine-grained segmentation masks, andthe annotation of these masks can be very expensive andtime-consuming. Therefore, recent methods try to use un-supervised domain adaptation (UDA) m... | ['Shaoan Xie', 'Kayhan Batmanghelich', 'Mingming Gong', 'Yanwu Xu'] | 2021-08-19 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 6.94248825e-02 2.77795762e-01 -7.43962884e-01 -4.64359343e-01
-9.94218826e-01 -5.78198016e-01 4.32509094e-01 1.49962187e-01
-5.30058682e-01 8.78993452e-01 5.20933904e-02 -1.95907161e-01
2.89228767e-01 -7.91233659e-01 -6.51002049e-01 -9.55905318e-01
3.68231595e-01 7.56886840e-01 4.73157823e-01 1.22252606... | [14.579072952270508, -2.014826536178589] |
8ddb9a75-635b-4e6f-bc95-e46c4e9e6ddd | precise-wifi-indoor-positioning-using-deep | 2307.02011 | null | https://arxiv.org/abs/2307.02011v1 | https://arxiv.org/pdf/2307.02011v1.pdf | Precise WiFi Indoor Positioning using Deep Learning Algorithms | This study demonstrates a WiFi indoor positioning system using Deep Learning algorithms. A new method using fitting function in MATLAB will be utilized to compute the path loss coefficient and log-normal fading variance. To reduce the error, a new hybrid localization approach utilizing Received Signal Strength Indicato... | ['Zihuai Lin', 'Minxue Cai'] | 2023-07-05 | null | null | null | null | ['hybrid-positioning'] | ['computer-vision'] | [-4.29914027e-01 -2.91608483e-01 4.18885767e-01 -4.54781562e-01
-4.31278110e-01 -3.70887697e-01 7.30519369e-02 2.17082173e-01
-3.58429074e-01 1.00457561e+00 -1.00678012e-01 -8.18606019e-01
-6.78308725e-01 -1.23143065e+00 -8.94196510e-01 -7.88933873e-01
-2.83550262e-01 -4.38835680e-01 2.41108462e-01 1.73678342... | [6.415289878845215, 0.9167177677154541] |
886611f8-e26d-4b87-b6bf-09e4a9d3fa74 | weakly-supervised-one-stage-vision-and | 2007.15778 | null | https://arxiv.org/abs/2007.15778v1 | https://arxiv.org/pdf/2007.15778v1.pdf | Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies | Detecting clinically relevant objects in medical images is a challenge despite large datasets due to the lack of detailed labels. To address the label issue, we utilize the scene-level labels with a detection architecture that incorporates natural language information. We present a challenging new set of radiologist pa... | ['Yuhong Wen', 'Leo K. Tam', 'Xiaosong Wang', 'Kevin Lu', 'Daguang Xu', 'Evrim Turkbey'] | 2020-07-31 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 4.06985819e-01 4.31991935e-01 -2.26059496e-01 -4.40996647e-01
-1.61399877e+00 -6.29034936e-01 5.95071733e-01 2.66713679e-01
-5.62427163e-01 4.10317004e-01 4.81453955e-01 -5.90416312e-01
2.99097866e-01 -1.72134161e-01 -4.98657107e-01 -5.03331482e-01
1.47077098e-01 6.10270083e-01 3.03176254e-01 1.53319702... | [15.026883125305176, -1.8292726278305054] |
06482b8b-adaa-448f-b7f4-73bd5f4a9b57 | advancing-full-text-search-lemmatization | 2305.10848 | null | https://arxiv.org/abs/2305.10848v1 | https://arxiv.org/pdf/2305.10848v1.pdf | Advancing Full-Text Search Lemmatization Techniques with Paradigm Retrieval from OpenCorpora | In this paper, we unveil a groundbreaking method to amplify full-text search lemmatization, utilizing the OpenCorpora dataset and a bespoke paradigm retrieval algorithm. Our primary aim is to streamline the extraction of a word's primary form or lemma - a crucial factor in full-text search. Additionally, we propose a c... | ['Dmitriy Kalugin-Balashov'] | 2023-05-18 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [ 2.17943013e-01 -2.14404359e-01 -7.39534855e-01 4.66124564e-01
-8.57154787e-01 -1.14715683e+00 7.30132818e-01 4.99550313e-01
-7.62161791e-01 7.39429593e-01 4.82578635e-01 -1.14721262e+00
-1.38528079e-01 -7.87369490e-01 -3.86521757e-01 -2.56873310e-01
5.32220781e-01 3.37380856e-01 4.77325059e-02 -4.98232961... | [11.578847885131836, 9.078139305114746] |
9af7fe5a-9f7d-45ea-a8c2-1d24868dbb2d | structcoder-structure-aware-transformer-for | 2206.05239 | null | https://arxiv.org/abs/2206.05239v2 | https://arxiv.org/pdf/2206.05239v2.pdf | StructCoder: Structure-Aware Transformer for Code Generation | There has been a recent surge of interest in automating software engineering tasks using deep learning. This paper addresses the problem of code generation where the goal is to generate target code given source code in a different language or a natural language description. Most of the state-of-the-art deep learning mo... | ['Chandan K. Reddy', 'Ming Zhu', 'Sindhu Tipirneni'] | 2022-06-10 | null | null | null | null | ['code-translation', 'text-to-code-generation'] | ['computer-code', 'computer-code'] | [ 2.78058559e-01 2.21262738e-01 -4.11461174e-01 -3.21794122e-01
-7.65232563e-01 -7.73000598e-01 3.86405140e-01 1.84894934e-01
4.15198177e-01 1.98821098e-01 3.05441618e-01 -9.76207018e-01
6.35921061e-01 -8.90672863e-01 -1.07740903e+00 1.96363628e-01
2.26631269e-01 -5.99373020e-02 1.35964394e-01 -2.55461335... | [7.738667964935303, 7.882440090179443] |
fc7d8c9b-aa51-414a-89cb-da7d0bc2ca7b | a-simple-yet-effective-baseline-for-robust-1 | null | null | https://openreview.net/forum?id=S1xnKi5BOV | https://openreview.net/pdf?id=S1xnKi5BOV | A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels | Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance. To mitigate this issue, we propose a simple but effective method that is robust to noisy labels, even with severe noise. Our objective involves a variance regularization te... | ['Anonymous'] | 2019-03-25 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.48655155e-01 -7.21063092e-02 -2.92660668e-02 -6.49753213e-01
-7.83334613e-01 -4.91852790e-01 2.92189330e-01 -1.09229796e-01
-8.17164838e-01 8.74759674e-01 -1.45584896e-01 -1.23057969e-01
-7.41762593e-02 -7.37035394e-01 -8.20151329e-01 -8.64084005e-01
3.80355455e-02 8.33433941e-02 -3.16996649e-02 3.69667038... | [9.264730453491211, 3.812629222869873] |
f449ec8e-42f6-4e41-8a63-c44d8e99e0de | a-study-of-latent-monotonic-attention | 2103.16710 | null | https://arxiv.org/abs/2103.16710v1 | https://arxiv.org/pdf/2103.16710v1.pdf | A study of latent monotonic attention variants | End-to-end models reach state-of-the-art performance for speech recognition, but global soft attention is not monotonic, which might lead to convergence problems, to instability, to bad generalisation, cannot be used for online streaming, and is also inefficient in calculation. Monotonicity can potentially fix all of t... | ['Hermann Ney', 'Ralf Schlüter', 'Albert Zeyer'] | 2021-03-30 | null | null | null | null | ['hard-attention'] | ['methodology'] | [ 3.78587574e-01 2.14009374e-01 -2.42942110e-01 -3.11331749e-01
-1.24239206e+00 -3.96866769e-01 5.80183744e-01 -1.85729533e-01
-1.78530589e-01 7.58081079e-01 5.86102903e-01 -5.57909846e-01
-3.37768942e-01 -1.30713910e-01 -6.86180353e-01 -7.55131364e-01
-1.72276512e-01 5.36968172e-01 5.29954195e-01 -1.57990158... | [14.538613319396973, 6.33204984664917] |
2d52b2bc-e3d5-4c02-8768-eb7154c67ab0 | improving-pairwise-ranking-for-multi-label | 1704.03135 | null | http://arxiv.org/abs/1704.03135v3 | http://arxiv.org/pdf/1704.03135v3.pdf | Improving Pairwise Ranking for Multi-label Image Classification | Learning to rank has recently emerged as an attractive technique to train
deep convolutional neural networks for various computer vision tasks. Pairwise
ranking, in particular, has been successful in multi-label image
classification, achieving state-of-the-art results on various benchmarks.
However, most existing appro... | ['Yuncheng Li', 'Jiebo Luo', 'Yale Song'] | 2017-04-11 | improving-pairwise-ranking-for-multi-label-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Improving_Pairwise_Ranking_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Improving_Pairwise_Ranking_CVPR_2017_paper.pdf | cvpr-2017-7 | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.69741625e-01 -3.41341585e-01 -2.51666069e-01 -7.42509723e-01
-1.17614007e+00 -5.41150808e-01 3.91753018e-01 5.84314883e-01
-7.12913334e-01 5.39429843e-01 -2.21030340e-01 -7.34687299e-02
-3.22379053e-01 -4.26848948e-01 -6.75183892e-01 -8.77104521e-01
5.40161133e-02 4.40182477e-01 1.77254379e-01 3.06113273... | [9.514890670776367, 4.022329330444336] |
7e9669a8-ec64-4552-8470-787bdad51ce6 | see-plan-predict-language-guided-cognitive | 2210.03825 | null | https://arxiv.org/abs/2210.03825v1 | https://arxiv.org/pdf/2210.03825v1.pdf | See, Plan, Predict: Language-guided Cognitive Planning with Video Prediction | Cognitive planning is the structural decomposition of complex tasks into a sequence of future behaviors. In the computational setting, performing cognitive planning entails grounding plans and concepts in one or more modalities in order to leverage them for low level control. Since real-world tasks are often described ... | ['Animesh Garg', 'Igor Gilitschenski', 'Wei Yu', 'Ziyi Zhou', 'Advaya Gupta', 'Maria Attarian'] | 2022-10-07 | null | null | null | null | ['video-generation', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [ 6.10796392e-01 7.04238772e-01 -1.52782932e-01 -9.58848447e-02
-2.58862764e-01 -5.65749466e-01 1.28786922e+00 -4.68486287e-02
-2.83647120e-01 4.37567383e-01 1.14505255e+00 -2.95431256e-01
1.22042000e-01 -7.97546804e-01 -1.05410767e+00 -3.58714670e-01
-3.44211251e-01 3.73755962e-01 3.73352729e-02 -2.45266899... | [4.452603340148926, 0.832120954990387] |
311ee02c-04a9-4db5-9db6-61031b9d7fc5 | bagging-bert-models-for-robust-aggression | null | null | https://aclanthology.org/2020.trac-1.9 | https://aclanthology.org/2020.trac-1.9.pdf | Bagging BERT Models for Robust Aggression Identification | Modern transformer-based models with hundreds of millions of parameters, such as BERT, achieve impressive results at text classification tasks. This also holds for aggression identification and offensive language detection, where deep learning approaches consistently outperform less complex models, such as decision tre... | ['Julian Risch', 'Ralf Krestel'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['aggression-identification'] | ['natural-language-processing'] | [-3.75581086e-01 -1.44935817e-01 4.31985967e-02 -4.12523419e-01
-9.29966867e-01 -4.81493741e-01 4.97708052e-01 1.82505280e-01
-6.61438823e-01 7.27030158e-01 2.51440048e-01 -1.39922038e-01
-2.03271404e-01 -4.76351202e-01 -2.08977267e-01 -3.86217207e-01
-8.65431726e-02 9.36624527e-01 2.61764020e-01 -6.44134045... | [8.849098205566406, 10.588687896728516] |
ea914403-f7fa-47d7-adad-84f4da4f23e5 | proton-probing-schema-linking-information | 2206.14017 | null | https://arxiv.org/abs/2206.14017v2 | https://arxiv.org/pdf/2206.14017v2.pdf | Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing | The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., properly recognizing mentions of unseen columns or tables when generating SQLs. In this work, we propose a novel framework to elicit relation... | ['Yongbin Li', 'Luo Si', 'Fei Huang', 'Binhua Li', 'Bailin Wang', 'Min Yang', 'Bowen Li', 'Binyuan Hui', 'Bowen Qin', 'Lihan Wang'] | 2022-06-28 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.48990542e-01 6.22822940e-01 -4.60323304e-01 -5.28181016e-01
-1.06936693e+00 -9.25064802e-01 6.22195780e-01 6.74878120e-01
-2.40530837e-02 4.81193572e-01 2.14479178e-01 -5.48372567e-01
-9.00878981e-02 -1.37880075e+00 -1.07561135e+00 1.07407443e-01
6.52294084e-02 8.35131943e-01 4.35046405e-01 -3.54187250... | [9.489997863769531, 8.137606620788574] |
1b0d74fc-402b-4401-b689-75a6104ec7d9 | mptv-matching-pursuit-based-total-variation | 1810.05438 | null | http://arxiv.org/abs/1810.05438v1 | http://arxiv.org/pdf/1810.05438v1.pdf | MPTV: Matching Pursuit Based Total Variation Minimization for Image Deconvolution | Total variation (TV) regularization has proven effective for a range of
computer vision tasks through its preferential weighting of sharp image edges.
Existing TV-based methods, however, often suffer from the over-smoothing issue
and solution bias caused by the homogeneous penalization. In this paper, we
consider addre... | ['Anton Van Den Hengel', 'Yanning Zhang', 'Dong Gong', 'Qinfeng Shi', 'Mingkui Tan'] | 2018-10-12 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 3.23690236e-01 -2.39937618e-01 1.39125541e-01 -1.88018650e-01
-5.87192118e-01 -1.82877392e-01 3.27579409e-01 -3.07717294e-01
-4.05397326e-01 5.08937418e-01 1.05776675e-01 8.85172561e-02
-1.77798718e-01 -2.88177073e-01 -4.80689853e-01 -1.06931674e+00
4.56282377e-01 -1.53518543e-01 3.46022069e-01 1.32863462... | [11.509711265563965, -2.574632167816162] |
b44b7f6a-ffa4-409c-a2c0-8f7e2c8c55d0 | multivariate-time-series-early-classification | 2306.14606 | null | https://arxiv.org/abs/2306.14606v1 | https://arxiv.org/pdf/2306.14606v1.pdf | Multivariate Time Series Early Classification Across Channel and Time Dimensions | Nowadays, the deployment of deep learning models on edge devices for addressing real-world classification problems is becoming more prevalent. Moreover, there is a growing popularity in the approach of early classification, a technique that involves classifying the input data after observing only an early portion of it... | ['Henri Bal', 'Mark Hoogendoorn', 'Kees Verstoep', 'Leonardos Pantiskas'] | 2023-06-26 | null | null | null | null | ['classification-1', 'time-series'] | ['methodology', 'time-series'] | [ 1.21681772e-01 -5.09432316e-01 -4.76807892e-01 -5.99478893e-02
-2.91730970e-01 -3.84067953e-01 3.58623683e-01 3.20795834e-01
-3.67521167e-01 4.38580424e-01 -2.52522737e-01 -6.16910219e-01
-4.80807602e-01 -6.78339124e-01 -3.51141214e-01 -5.67162335e-01
-2.82362044e-01 -9.35686901e-02 7.73828477e-02 9.48735923... | [7.171858787536621, 2.6752352714538574] |
87763037-7821-4ed5-a218-b7a596957776 | occlusion-robust-face-recognition-based-on-1 | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Song_Occlusion_Robust_Face_Recognition_Based_on_Mask_Learning_With_Pairwise_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Song_Occlusion_Robust_Face_Recognition_Based_on_Mask_Learning_With_Pairwise_ICCV_2019_paper.pdf | Occlusion Robust Face Recognition Based on Mask Learning With Pairwise Differential Siamese Network | Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of face recognition over past years. However, existing CNN models are far less accurate when handling partially occluded faces. These general face models generalize poorly for occlusions on variable facial areas. Inspired by the fact that human v... | [' Wei Liu', ' Changsong Liu', ' Zhifeng Li', ' Dihong Gong', 'Lingxue Song'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['robust-face-recognition'] | ['computer-vision'] | [ 1.25969857e-01 -7.72060603e-02 4.04843651e-02 -6.19617164e-01
-4.29014638e-02 -5.74362874e-02 3.72888923e-01 -5.49005389e-01
-1.88159898e-01 5.17911553e-01 1.45938277e-01 3.25567603e-01
-1.27975687e-01 -6.14355028e-01 -6.33290887e-01 -9.01242077e-01
2.17620991e-02 2.30683908e-01 -2.22616389e-01 -9.84800011... | [13.201336860656738, 0.4604114294052124] |
565f51a1-a0f6-4cba-b78b-7134be42b340 | spatio-temporal-lstm-with-trust-gates-for-3d | 1607.07043 | null | http://arxiv.org/abs/1607.07043v1 | http://arxiv.org/pdf/1607.07043v1.pdf | Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition | 3D action recognition - analysis of human actions based on 3D skeleton data -
becomes popular recently due to its succinctness, robustness, and
view-invariant representation. Recent attempts on this problem suggested to
develop RNN-based learning methods to model the contextual dependency in the
temporal domain. In thi... | ['Gang Wang', 'Amir Shahroudy', 'Jun Liu', 'Dong Xu'] | 2016-07-24 | null | null | null | null | ['action-analysis', '3d-human-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.38381767e-01 -3.36723775e-01 -3.09278518e-01 -4.68930632e-01
-3.27524215e-01 2.86877751e-02 4.20271069e-01 -1.48967439e-02
-5.25322020e-01 4.95448649e-01 7.61946917e-01 -4.32960950e-02
-8.52761716e-02 -6.18290782e-01 -5.11688948e-01 -6.18015468e-01
-3.08636248e-01 9.03290287e-02 7.03752160e-01 -5.11008017... | [7.879940032958984, 0.4545818865299225] |
0dbc529f-b183-4f1b-913f-f52406a84afe | 190909986 | 1909.09986 | null | https://arxiv.org/abs/1909.09986v1 | https://arxiv.org/pdf/1909.09986v1.pdf | Improving Quality and Efficiency in Plan-based Neural Data-to-Text Generation | We follow the step-by-step approach to neural data-to-text generation we proposed in Moryossef et al (2019), in which the generation process is divided into a text-planning stage followed by a plan-realization stage. We suggest four extensions to that framework: (1) we introduce a trainable neural planning component th... | ['Yoav Goldberg', 'Ido Dagan', 'Amit Moryossef'] | 2019-09-22 | improving-quality-and-efficiency-in-plan | https://aclanthology.org/W19-8645 | https://aclanthology.org/W19-8645.pdf | ws-2019-10 | ['referring-expression-generation'] | ['computer-vision'] | [ 3.96195263e-01 1.12913752e+00 -2.02522725e-01 -3.90560180e-01
-1.10667717e+00 -7.21985459e-01 1.18264389e+00 3.12638223e-01
-2.21569419e-01 1.05189705e+00 1.07854533e+00 -4.74674731e-01
8.84374902e-02 -1.19628167e+00 -5.69530249e-01 2.23054960e-01
1.02968067e-01 9.13793445e-01 3.22312176e-01 -5.95682859... | [11.458874702453613, 8.988122940063477] |
1618f024-7216-4bbd-9ce7-4bfb71ddd81a | rms-net-regression-and-masking-for-soccer | 2102.07624 | null | https://arxiv.org/abs/2102.07624v1 | https://arxiv.org/pdf/2102.07624v1.pdf | RMS-Net: Regression and Masking for Soccer Event Spotting | The recently proposed action spotting task consists in finding the exact timestamp in which an event occurs. This task fits particularly well for soccer videos, where events correspond to salient actions strictly defined by soccer rules (a goal occurs when the ball crosses the goal line). In this paper, we devise a lig... | ['Rita Cucchiara', 'Simone Bronzin', 'Simone Calderara', 'Lorenzo Baraldi', 'Matteo Tomei'] | 2021-02-15 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 3.03762436e-01 -8.91715139e-02 -3.34483862e-01 -6.79411665e-02
-5.69321513e-01 -4.25512105e-01 6.30995870e-01 3.75274211e-01
-8.36828470e-01 6.61094666e-01 2.12286860e-01 2.77513593e-01
-1.79701239e-01 -4.22006696e-01 -6.19172454e-01 -6.29064977e-01
-2.82049090e-01 3.98870558e-01 8.45502913e-01 -1.60258606... | [8.125190734863281, 0.2779960632324219] |
92871e90-c3e2-42e1-9a51-ebbd63414e7f | modelling-fixated-discourse-in-chats-with | null | null | https://aclanthology.org/W12-0413 | https://aclanthology.org/W12-0413.pdf | Modelling Fixated Discourse in Chats with Cyberpedophiles | null | ['Thamar Solorio', 'Paolo Rosso', 'Dasha Bogdanova'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.355007648468018, 3.684802532196045] |
43b06b31-df0f-4c61-947e-041bbce2422d | vita-visual-linguistic-translation-by | 2106.00250 | null | https://arxiv.org/abs/2106.00250v3 | https://arxiv.org/pdf/2106.00250v3.pdf | ViTA: Visual-Linguistic Translation by Aligning Object Tags | Multimodal Machine Translation (MMT) enriches the source text with visual information for translation. It has gained popularity in recent years, and several pipelines have been proposed in the same direction. Yet, the task lacks quality datasets to illustrate the contribution of visual modality in the translation syste... | ['Radhika Mamidi', 'Devansh Gautam', 'Kshitij Gupta'] | 2021-06-01 | null | https://aclanthology.org/2021.wat-1.19/ | https://aclanthology.org/2021.wat-1.19.pdf | workshop-on-asian-translation-2021-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.73361737e-01 -1.73669711e-01 6.99975193e-02 -2.76321918e-01
-1.31899047e+00 -9.41353381e-01 1.05238807e+00 -4.64718997e-01
-4.52169627e-01 6.69440925e-01 2.73968339e-01 -4.06225890e-01
6.60391390e-01 -1.41655028e-01 -7.91476786e-01 -5.67621946e-01
6.16316140e-01 6.31331146e-01 -1.62365630e-01 -2.67633796... | [11.47209358215332, 1.5214899778366089] |
3b9f74f8-04ff-4535-ac5f-656b88db235a | classification-and-understanding-of-cloud | 2009.12931 | null | https://arxiv.org/abs/2009.12931v4 | https://arxiv.org/pdf/2009.12931v4.pdf | Classification and understanding of cloud structures via satellite images with EfficientUNet | Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years. Shallow clouds play a significant role in understanding the Earth's climate, but they are challenging to interpret and represent in a climate model. By classifying these cloud structures, ther... | ['Noor Hossain Nuri Sabab', 'Tashin Ahmed'] | 2020-09-27 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [-6.69986382e-02 -2.12229624e-01 3.08110237e-01 -5.12609482e-01
-1.53163001e-01 -9.31039453e-01 7.57504940e-01 3.85161728e-01
-3.05916786e-01 7.98245549e-01 4.89980541e-02 -6.41583323e-01
-1.35490426e-03 -1.09186566e+00 -7.40787625e-01 -8.39514256e-01
-1.06024541e-01 3.92512977e-01 1.54446617e-01 -2.26528227... | [9.740118980407715, -1.5892373323440552] |
0ecff8f4-757c-43bb-b52a-82b245db366a | reference-production-in-human-computer | null | null | https://aclanthology.org/L18-1474 | https://aclanthology.org/L18-1474.pdf | Reference production in human-computer interaction: Issues for Corpus-based Referring Expression Generation | null | ["r{\\'e}", 'Iv Paraboni', 'Danillo Rocha'] | 2018-05-01 | reference-production-in-human-computer-1 | https://aclanthology.org/L18-1474 | https://aclanthology.org/L18-1474.pdf | lrec-2018-5 | ['referring-expression-generation'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3108696937561035, 3.785184144973755] |
084668c5-7dca-4e15-b936-23e76fee4572 | model-agnostic-meta-learning-for-multilingual | 2303.02513 | null | https://arxiv.org/abs/2303.02513v1 | https://arxiv.org/pdf/2303.02513v1.pdf | Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection | Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, ... | ['Tanmoy Chakraborty', 'Tanmay Garg', 'Eshaan Tanwar', 'Roy Ka-Wei Lee', 'Md Rabiul Awal'] | 2023-03-04 | null | null | null | null | ['hate-speech-detection', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-2.49348715e-01 -3.38783592e-01 -2.65638530e-01 5.12791574e-02
-7.78753281e-01 -6.14814699e-01 4.83809531e-01 -1.52054220e-01
-4.40665394e-01 5.38750887e-01 3.89659464e-01 -1.88079387e-01
6.53972626e-01 -2.34117821e-01 -3.85363847e-01 -5.51023245e-01
2.79037356e-01 2.24444922e-02 4.29303087e-02 -2.06921101... | [8.82630729675293, 10.558805465698242] |
d3dff89c-58e0-40af-99c6-cbb5185f1b6d | robust-and-explainable-contextual-anomaly | 2302.11239 | null | https://arxiv.org/abs/2302.11239v2 | https://arxiv.org/pdf/2302.11239v2.pdf | Explainable Contextual Anomaly Detection using Quantile Regression Forests | Traditional anomaly detection methods aim to identify objects that deviate from most other objects by treating all features equally. In contrast, contextual anomaly detection methods aim to detect objects that deviate from other objects within a context of similar objects by dividing the features into contextual featur... | ['Matthijs van Leeuwen', 'Zhong Li'] | 2023-02-22 | null | null | null | null | ['contextual-anomaly-detection'] | ['miscellaneous'] | [ 3.02222759e-01 -3.69704694e-01 7.20013604e-02 -6.77698195e-01
-4.06609416e-01 -3.32659572e-01 7.67329991e-01 8.44981968e-01
7.15566501e-02 4.35164541e-01 -2.09658012e-01 -4.28474188e-01
-4.39590931e-01 -7.79399633e-01 -4.74327534e-01 -5.18073440e-01
-5.09406149e-01 2.44330198e-01 5.32390535e-01 6.58554137... | [7.580821514129639, 2.5488498210906982] |
2f9fa8f7-0784-4382-b088-1c5aaef033ec | global-local-stepwise-generative-network-for | 2207.08808 | null | https://arxiv.org/abs/2207.08808v2 | https://arxiv.org/pdf/2207.08808v2.pdf | Global-Local Stepwise Generative Network for Ultra High-Resolution Image Restoration | While the research on image background restoration from regular size of degraded images has achieved remarkable progress, restoring ultra high-resolution (e.g., 4K) images remains an extremely challenging task due to the explosion of computational complexity and memory usage, as well as the deficiency of annotated data... | ['Guangming Lu', 'Fanglin Chen', 'Wenjie Pei', 'Haobo Ji', 'Xin Feng'] | 2022-07-16 | null | null | null | null | ['image-dehazing', 'reflection-removal'] | ['computer-vision', 'computer-vision'] | [ 6.23767376e-01 -5.52143931e-01 4.52884197e-01 -1.64718196e-01
-1.06776965e+00 -7.46955127e-02 3.59224021e-01 -2.39012837e-01
1.15564108e-01 8.29802096e-01 4.44183409e-01 -2.09437922e-01
-1.94826588e-01 -1.02880621e+00 -6.57871366e-01 -1.18103778e+00
2.69170642e-01 -3.81864071e-01 2.88319409e-01 -7.17093885... | [11.029095649719238, -2.3310329914093018] |
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