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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6e474b2a-e422-4395-8d95-7fb581f5eca1 | low-cost-gnss-simulators-with-wireless-clock | 2306.00633 | null | https://arxiv.org/abs/2306.00633v1 | https://arxiv.org/pdf/2306.00633v1.pdf | Low-Cost GNSS Simulators with Wireless Clock Synchronization for Indoor Positioning | In regions where global navigation satellite systems (GNSS) signals are unavailable, such as underground areas and tunnels, GNSS simulators can be deployed for transmitting simulated GNSS signals. Then, a GNSS receiver in the simulator coverage outputs the position based on the received GNSS signals (e.g., Global Posit... | ['Jiwon Seo', 'Woohyun Kim'] | 2023-06-01 | null | null | null | null | ['outdoor-positioning'] | ['miscellaneous'] | [-9.11018103e-02 1.08141275e-02 1.32587567e-01 -3.80421570e-03
-4.87835795e-01 -6.21609032e-01 -6.95326552e-02 -1.44923449e-01
-3.74141991e-01 9.41496968e-01 -7.79111803e-01 -8.01910520e-01
1.26691721e-02 -1.00768805e+00 -8.81746531e-01 -9.56551373e-01
-3.23670030e-01 2.38366872e-01 5.55851161e-01 -3.06818396... | [6.310115814208984, 1.0392972230911255] |
7a77660f-2a5e-4a32-967f-94de41cc1717 | region-aware-adaptive-instance-normalization | 2106.02853 | null | https://arxiv.org/abs/2106.02853v1 | https://arxiv.org/pdf/2106.02853v1.pdf | Region-aware Adaptive Instance Normalization for Image Harmonization | Image composition plays a common but important role in photo editing. To acquire photo-realistic composite images, one must adjust the appearance and visual style of the foreground to be compatible with the background. Existing deep learning methods for harmonizing composite images directly learn an image mapping netwo... | ['Xiao Gu', 'Rong Xie', 'Li Song', 'Han Xue', 'Jun Ling'] | 2021-06-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Ling_Region-Aware_Adaptive_Instance_Normalization_for_Image_Harmonization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Ling_Region-Aware_Adaptive_Instance_Normalization_for_Image_Harmonization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-harmonization'] | ['computer-vision'] | [ 4.27598685e-01 -4.98713329e-02 2.41122693e-02 -3.84286493e-01
-2.98020154e-01 -4.99800682e-01 6.20384216e-01 -3.18831027e-01
-3.04788381e-01 5.18852651e-01 -1.41334802e-01 -1.56060353e-01
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6.98825955e-01 1.93228886e-01 3.44242245e-01 -3.58919919... | [11.28406047821045, -1.2622649669647217] |
abeab3b7-bc5b-4127-a30a-68b857e78fbf | using-active-speaker-faces-for-diarization-in | 2203.15961 | null | https://arxiv.org/abs/2203.15961v1 | https://arxiv.org/pdf/2203.15961v1.pdf | Using Active Speaker Faces for Diarization in TV shows | Speaker diarization is one of the critical components of computational media intelligence as it enables a character-level analysis of story portrayals and media content understanding. Automated audio-based speaker diarization of entertainment media poses challenges due to the diverse acoustic conditions present in medi... | ['Shrikanth Narayanan', 'Rahul Sharma'] | 2022-03-30 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 4.88168925e-01 -1.04649074e-01 2.87506551e-01 -3.54908198e-01
-1.37617874e+00 -6.79601192e-01 6.30956173e-01 4.92160320e-02
3.34797129e-02 1.49146244e-01 5.28594196e-01 4.26814735e-01
-2.02429444e-02 -4.29885209e-01 -5.65799177e-01 -9.52133536e-01
-2.32258793e-02 5.21882057e-01 8.47864673e-02 -6.82188794... | [14.470536231994629, 5.223229885101318] |
303b35ab-1227-49a3-8d68-29c6a636fbc3 | query-guided-regression-network-with-context | 1708.01676 | null | http://arxiv.org/abs/1708.01676v1 | http://arxiv.org/pdf/1708.01676v1.pdf | Query-guided Regression Network with Context Policy for Phrase Grounding | Given a textual description of an image, phrase grounding localizes objects
in the image referred by query phrases in the description. State-of-the-art
methods address the problem by ranking a set of proposals based on the
relevance to each query, which are limited by the performance of independent
proposal generation ... | ['Ram Nevatia', 'Rama Kovvuri', 'Kan Chen'] | 2017-08-04 | query-guided-regression-network-with-context-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Chen_Query-Guided_Regression_Network_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Query-Guided_Regression_Network_ICCV_2017_paper.pdf | iccv-2017-10 | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.43931821e-01 1.52984217e-01 -3.49650323e-01 -3.98889184e-01
-1.36391068e+00 -5.29739916e-01 7.99253345e-01 1.45804465e-01
-6.11146390e-01 6.40925944e-01 4.58461791e-01 1.76610122e-03
-1.17638826e-01 -6.55050576e-01 -8.84411335e-01 -5.12812972e-01
-6.32671174e-03 3.89708847e-01 6.65535212e-01 -2.08065435... | [10.441947937011719, 1.4467012882232666] |
d3ea9c36-e570-451c-b013-4f01b00b4e24 | deep-submodular-networks-for-extractive-data | 2010.08593 | null | https://arxiv.org/abs/2010.08593v1 | https://arxiv.org/pdf/2010.08593v1.pdf | Deep Submodular Networks for Extractive Data Summarization | Deep Models are increasingly becoming prevalent in summarization problems (e.g. document, video and images) due to their ability to learn complex feature interactions and representations. However, they do not model characteristics such as diversity, representation, and coverage, which are also very important for summar... | ['Rishabh Iyer', 'Chandrashekhar Lavania', 'Jiten Girdhar', 'Suraj Kothawade'] | 2020-10-16 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-1.18486350e-02 -5.09617142e-02 -4.47445273e-01 -1.59950837e-01
-9.59902525e-01 -5.13084531e-01 6.86650872e-01 3.65614861e-01
-1.73448354e-01 6.30096853e-01 7.67734587e-01 2.53433406e-01
-2.80709118e-01 -4.58347797e-01 -9.55418825e-01 -6.99042737e-01
-2.14439675e-01 7.83223689e-01 -4.14854437e-02 -2.57013798... | [12.340677261352539, 9.351949691772461] |
c20562c2-9c2f-4875-a943-162821e91b90 | parents-and-children-distinguishing | 2304.00500 | null | https://arxiv.org/abs/2304.00500v1 | https://arxiv.org/pdf/2304.00500v1.pdf | Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images | Recent advancements in diffusion models have enabled the generation of realistic deepfakes by writing textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised concerns about the potential misuse of fake images and cast new pressures on fake image detec... | ['Rita Cucchiara', 'Alberto del Bimbo', 'Lorenzo Baraldi', 'Marcella Cornia', 'Davide Morelli', 'Roberto Amoroso'] | 2023-04-02 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 2.62648225e-01 4.61613424e-02 -1.36788487e-01 -7.84005448e-02
-5.48348963e-01 -8.86064470e-01 1.36726999e+00 1.14303790e-01
-2.14319512e-01 3.57961088e-01 2.58553684e-01 -3.41184735e-01
3.00620556e-01 -5.80866098e-01 -7.53449559e-01 -4.67952132e-01
2.15183958e-01 7.14482293e-02 2.46208720e-02 -5.67434072... | [12.342619895935059, 1.1392738819122314] |
51681c95-d1dd-4c63-8a08-ea5750c17482 | bringing-the-state-of-the-art-to-customers-a | 2302.03222 | null | https://arxiv.org/abs/2302.03222v1 | https://arxiv.org/pdf/2302.03222v1.pdf | Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support | Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology. We combine expertise from academia and industry to bridge the gap and build task/domain-specific Neu... | ['Elham Dolatabadi', 'Frank Rudzicz', 'Xiaodan Zhu', 'Deval Pandya', 'Jaswinder Narain', 'Kanishk Patel', 'Kwesi P. Apponsah', 'Sean X. Zhang', 'Yu-Jia Shiah', 'Griffin Tanner', 'Michael Wang', 'Bukola Ishola', 'Erin Li', 'Waqar Muhammad', 'Iris Ren', 'Bencheng Wei', 'Karthik Raja K. Bhaskar', 'Alif Munim', 'Winnie Au'... | 2023-02-07 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.93132240e-01 3.93219531e-01 -2.59437636e-02 -6.22336268e-01
-8.58304322e-01 -7.63406634e-01 7.40586460e-01 1.29459485e-01
-1.99609548e-01 3.69211763e-01 7.34836817e-01 -5.66017985e-01
-1.26924187e-01 -4.47228611e-01 -1.97126195e-01 -2.73303270e-01
2.58564889e-01 1.13264823e+00 -4.36728835e-01 -5.02203345... | [12.680472373962402, 7.904555797576904] |
c6fa281a-3790-422d-8e33-0cc29a725f7e | visual-speech-recognition-for-multiple | 2202.13084 | null | https://arxiv.org/abs/2202.13084v2 | https://arxiv.org/pdf/2202.13084v2.pdf | Visual Speech Recognition for Multiple Languages in the Wild | Visual speech recognition (VSR) aims to recognize the content of speech based on lip movements, without relying on the audio stream. Advances in deep learning and the availability of large audio-visual datasets have led to the development of much more accurate and robust VSR models than ever before. However, these adva... | ['Maja Pantic', 'Stavros Petridis', 'Pingchuan Ma'] | 2022-02-26 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.59108454e-01 1.94281526e-03 -2.64958233e-01 -1.95423365e-01
-1.35149550e+00 -5.51681042e-01 6.36873603e-01 -1.24846004e-01
-3.74957681e-01 5.10631680e-01 6.65246606e-01 -3.78206819e-01
3.64227861e-01 1.16255134e-02 -5.73846698e-01 -4.47922140e-01
2.15810820e-01 4.52077955e-01 6.32986948e-02 -1.26915172... | [14.368806838989258, 5.11421537399292] |
b7f5974b-448a-407a-860d-107d3471e432 | collaborative-regret-minimization-in-multi | 2301.11442 | null | https://arxiv.org/abs/2301.11442v1 | https://arxiv.org/pdf/2301.11442v1.pdf | Collaborative Regret Minimization in Multi-Armed Bandits | In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent reinforcement learning. For a fundamental problem in bandit theory, regret minimization in multi-armed bandits, we present the first and almost tight tradeoffs between the ... | ['Qin Zhang', 'Nikolai Karpov'] | 2023-01-26 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-6.32102847e-01 1.69485092e-01 -6.69515669e-01 -1.55626148e-01
-8.84177029e-01 -5.73538899e-01 4.22187716e-01 3.22641969e-01
-7.58698702e-01 1.25490415e+00 6.25266358e-02 -5.71079314e-01
-6.95613980e-01 -7.36004174e-01 -6.23400509e-01 -7.60970592e-01
-5.67371726e-01 7.78548658e-01 -7.50240311e-02 -3.46433856... | [4.324129581451416, 3.0253093242645264] |
c9ccb27a-48a8-4f90-8796-240c14bee030 | shallow-optical-flow-three-stream-cnn-for | 2106.06489 | null | https://arxiv.org/abs/2106.06489v1 | https://arxiv.org/pdf/2106.06489v1.pdf | Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos | Facial expressions vary from the visible to the subtle. In recent years, the analysis of micro-expressions $-$ a natural occurrence resulting from the suppression of one's true emotions, has drawn the attention of researchers with a broad range of potential applications. However, spotting microexpressions in long video... | ['Lai-Kuan Wong', 'John See', 'Gen-Bing Liong'] | 2021-06-11 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.68783927e-01 -1.98712558e-01 -2.26812512e-01 -8.00819755e-01
-4.18318480e-01 -2.29105651e-01 4.17401493e-01 -3.70941639e-01
-5.54247499e-01 6.24156177e-01 1.49840280e-01 1.05446413e-01
2.05464080e-01 -3.09778512e-01 -5.38481534e-01 -7.01923251e-01
-4.31136012e-01 -3.90846223e-01 -3.24922413e-01 -3.19736660... | [13.620864868164062, 1.8100894689559937] |
465a30eb-6291-40f0-9b9e-fb6a0250248c | comparison-of-evolving-granular-classifiers | 2005.04156 | null | https://arxiv.org/abs/2005.04156v1 | https://arxiv.org/pdf/2005.04156v1.pdf | Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers | Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented adhoc information, is quite often given as unstructured big data. Log data processing is a time... | ['Fabio Viola', 'Daniele Bonacorsi', 'Leticia Decker', 'Daniel Leite'] | 2020-04-08 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 6.04240596e-02 -1.98063701e-01 7.73746073e-02 -5.49206853e-01
-2.96633214e-01 -1.24451354e-01 5.36551714e-01 1.20661151e+00
-2.30836600e-01 8.17924798e-01 -3.43395889e-01 -3.09955716e-01
-6.28830612e-01 -1.18945837e+00 -1.03552721e-01 -6.85815573e-01
-7.61560977e-01 9.99954820e-01 3.75068754e-01 -2.38380674... | [7.021764278411865, 2.594040632247925] |
06500746-bb21-4bd1-84fd-46a20185308a | adaptive-conformal-predictions-for-time | 2202.07282 | null | https://arxiv.org/abs/2202.07282v1 | https://arxiv.org/pdf/2202.07282v1.pdf | Adaptive Conformal Predictions for Time Series | Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{... | ['Julie Josse', 'Yannig Goude', 'Olivier Féron', 'Aymeric Dieuleveut', 'Margaux Zaffran'] | 2022-02-15 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 7.91696683e-02 2.42322251e-01 1.21813446e-01 -5.91204822e-01
-8.45677614e-01 -6.22788250e-01 8.75610411e-01 -1.39518231e-02
-1.42781681e-03 1.27841675e+00 -8.05174559e-02 -7.90337563e-01
-8.68716717e-01 -1.05858290e+00 -3.19745690e-01 -7.81093121e-01
-4.58440155e-01 8.76462698e-01 -7.55933151e-02 1.71216771... | [6.4068684577941895, 3.4103095531463623] |
896ddd6b-66ef-436c-a11c-4664bb543a01 | pens-a-dataset-and-generic-framework-for | null | null | https://aclanthology.org/2021.acl-long.7 | https://aclanthology.org/2021.acl-long.7.pdf | PENS: A Dataset and Generic Framework for Personalized News Headline Generation | In this paper, we formulate the personalized news headline generation problem whose goal is to output a user-specific title based on both a user{'}s reading interests and a candidate news body to be exposed to her. To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized New... | ['Xing Xie', 'Qing He', 'Ying Qiao', 'Ling Luo', 'Xiting Wang', 'Xiang Ao'] | 2021-08-01 | null | null | null | acl-2021-5 | ['headline-generation'] | ['natural-language-processing'] | [ 2.22034827e-01 4.68989648e-02 -4.73125964e-01 -7.82923818e-01
-9.46119010e-01 -5.56362391e-01 8.39826584e-01 3.86223383e-03
-3.23360115e-01 6.19890034e-01 6.44278109e-01 -2.30734020e-01
2.73374557e-01 -6.77547038e-01 -6.08793080e-01 -1.06769189e-01
3.34057570e-01 7.42590249e-01 1.59565762e-01 -3.96534234... | [10.406988143920898, 5.911024570465088] |
e3e45955-2cad-4e35-bae6-3cb0c68b5f3b | ra-sr-using-a-ranking-algorithm-to | null | null | https://aclanthology.org/W13-1613 | https://aclanthology.org/W13-1613.pdf | RA-SR: Using a ranking algorithm to automatically building resources for subjectivity analysis over annotated corpora | null | ['Rafael Mu{\\~n}oz', "Andr{\\'e}s Montoyo", "Antonio Fern{\\'a}ndez", "Andy Gonz{\\'a}lez", "Yoan Guti{\\'e}rrez"] | 2013-06-01 | null | null | null | ws-2013-6 | ['subjectivity-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... | [-7.276517391204834, 3.7236030101776123] |
a60643f7-8b70-4936-9722-2c80cdb18b02 | illumination-normalization-via-merging | 1905.03904 | null | https://arxiv.org/abs/1905.03904v1 | https://arxiv.org/pdf/1905.03904v1.pdf | Illumination Normalization via Merging Locally Enhanced Textures for Robust Face Recognition | In order to improve the accuracy of face recognition under varying illumination conditions, a local texture enhanced illumination normalization method based on fusion of differential filtering images (FDFI-LTEIN) is proposed to weaken the influence caused by illumination changes. Firstly, the dynamic range of the face ... | ['Chaobing Zheng', 'Wangming Xu', 'Shoulie Xie', 'Shiqian Wu'] | 2019-05-10 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.01726812e-01 -7.25632429e-01 3.38327795e-01 -6.56223655e-01
1.61891252e-01 -1.90978751e-01 1.84972838e-01 -7.61690199e-01
-5.32486916e-01 6.28814697e-01 4.60788347e-02 1.42282978e-01
-6.05941527e-02 -9.69571173e-01 -2.84003466e-01 -1.42118144e+00
3.04346651e-01 -4.14471567e-01 8.74344781e-02 -1.08930767... | [13.047440528869629, 0.4177974760532379] |
abfb1aae-97d8-4eeb-95e1-10e40e0e5dc9 | learning-based-video-motion-magnification | 1804.02684 | null | http://arxiv.org/abs/1804.02684v3 | http://arxiv.org/pdf/1804.02684v3.pdf | Learning-based Video Motion Magnification | Video motion magnification techniques allow us to see small motions
previously invisible to the naked eyes, such as those of vibrating airplane
wings, or swaying buildings under the influence of the wind. Because the motion
is small, the magnification results are prone to noise or excessive blurring.
The state of the a... | ['Frédo Durand', 'Tae-Hyun Oh', 'Ronnachai Jaroensri', 'Wojciech Matusik', 'William T. Freeman', 'Mohamed Elgharib', 'Changil Kim'] | 2018-04-08 | learning-based-video-motion-magnification-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Tae-Hyun_Oh_Learning-based_Video_Motion_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Tae-Hyun_Oh_Learning-based_Video_Motion_ECCV_2018_paper.pdf | eccv-2018-9 | ['motion-magnification'] | ['computer-vision'] | [ 3.31189334e-01 -2.58088320e-01 7.62746036e-02 2.09743991e-01
-2.06736177e-01 -7.91248083e-01 5.30436993e-01 -5.49469054e-01
-3.30588996e-01 6.29938960e-01 2.22543702e-01 -1.89436421e-01
4.59575094e-02 -6.79230034e-01 -7.86732495e-01 -6.70496583e-01
-4.72346812e-01 -4.98717517e-01 6.54394627e-01 -6.31503388... | [10.82657527923584, -1.4254724979400635] |
c2dbb11f-5158-4d77-8bab-ff5b45800382 | learning-to-parse-wireframes-in-images-of-man-1 | 2007.07527 | null | https://arxiv.org/abs/2007.07527v1 | https://arxiv.org/pdf/2007.07527v1.pdf | Learning to Parse Wireframes in Images of Man-Made Environments | In this paper, we propose a learning-based approach to the task of automatically extracting a "wireframe" representation for images of cluttered man-made environments. The wireframe (see Fig. 1) contains all salient straight lines and their junctions of the scene that encode efficiently and accurately large-scale geome... | ['Shenghua Gao', 'Kun Huang', 'Tianjiao Ding', 'Zihan Zhou', 'Yi Ma', 'Yifan Wang'] | 2020-07-15 | learning-to-parse-wireframes-in-images-of-man | http://openaccess.thecvf.com/content_cvpr_2018/html/Huang_Learning_to_Parse_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Huang_Learning_to_Parse_CVPR_2018_paper.pdf | cvpr-2018-6 | ['junction-detection', 'line-segment-detection'] | ['computer-vision', 'computer-vision'] | [-9.90286320e-02 1.01409733e-01 2.61073589e-01 -3.54377240e-01
-7.09675431e-01 -5.65559566e-01 6.34849131e-01 1.96431980e-01
-4.60752517e-01 1.95090011e-01 -1.62757814e-01 -3.76744688e-01
1.64081022e-01 -8.03771973e-01 -1.18479860e+00 7.58900680e-03
-2.65438706e-01 4.20675218e-01 5.30935526e-01 -3.97634745... | [8.218770980834961, -1.8900768756866455] |
42af29f1-be81-464e-8423-b71b3693f84d | ccharmony-color-checker-based-image | 2206.00800 | null | https://arxiv.org/abs/2206.00800v1 | https://arxiv.org/pdf/2206.00800v1.pdf | CcHarmony: Color-checker based Image Harmonization Dataset | Image harmonization targets at adjusting the foreground in a composite image to make it compatible with the background, producing a more realistic and harmonious image. Training deep image harmonization network requires abundant training data, but it is extremely difficult to acquire training pairs of composite images ... | ['Li Niu', 'Haoxu Huang'] | 2022-06-01 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 4.56769079e-01 -3.76517117e-01 2.70217597e-01 -1.41126364e-01
-2.07683548e-01 -5.03304124e-01 3.93717051e-01 -4.06175077e-01
-2.04118490e-01 6.32540047e-01 9.40805208e-03 -4.18526754e-02
3.39412779e-01 -9.53540623e-01 -6.82159424e-01 -1.03137374e+00
8.87739897e-01 -1.14868261e-01 1.17599852e-01 -2.21869648... | [11.256548881530762, -1.2056082487106323] |
b68574f3-7dc9-49d7-9f3a-6424b25b3d5f | bloomberggpt-a-large-language-model-for | 2303.17564 | null | https://arxiv.org/abs/2303.17564v2 | https://arxiv.org/pdf/2303.17564v2.pdf | BloombergGPT: A Large Language Model for Finance | The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering. Large Language Models (LLMs) have been shown to be effective on a variety of tasks; however, no LLM specialized for the financial domain has bee... | ['Gideon Mann', 'David Rosenberg', 'Prabhanjan Kambadur', 'Sebastian Gehrmann', 'Mark Dredze', 'Vadim Dabravolski', 'Steven Lu', 'Ozan Irsoy', 'Shijie Wu'] | 2023-03-30 | null | null | null | null | ['multi-task-language-understanding', 'movie-recommendation', 'sports-understanding', 'multiple-choice-qa', 'sarcasm-detection', 'reading-comprehension', 'disambiguation-q', 'ruin-names', 'snarks', 'formal-fallacies-syllogisms-negation', 'hyperbaton', 'penguins-in-a-table', 'temporal-sequences', 'logical-reasoning', 'c... | ['methodology', 'miscellaneous', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'reasoning', 'reasonin... | [-6.57735825e-01 -8.88952613e-02 -5.64925849e-01 -5.38596332e-01
-1.06723726e+00 -1.01167607e+00 8.03085744e-01 3.76304328e-01
-6.13521338e-01 6.76384270e-01 5.05244553e-01 -7.78989553e-01
1.17673308e-01 -7.27637053e-01 -5.86949468e-01 7.65339509e-02
-1.87977865e-01 7.25002050e-01 -8.27194303e-02 -9.41867605... | [10.766013145446777, 8.258549690246582] |
4fac476e-cfe6-44d2-9ce1-0bcf9e365e9c | a-textless-metric-for-speech-to-speech | 2210.11835 | null | https://arxiv.org/abs/2210.11835v1 | https://arxiv.org/pdf/2210.11835v1.pdf | A Textless Metric for Speech-to-Speech Comparison | This paper proposes a textless speech-to-speech comparison metric that allows comparing a speech hypothesis with a speech reference without falling-back to their text transcripts. We leverage recently proposed speech2unit encoders (such as HuBERT) to pseudo-transcribe the speech utterances into discrete acoustic units ... | ['Ioan Calapodescu', 'Olivier Galibert', 'Swen Ribeiro', 'Laurent Besacier'] | 2022-10-21 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 4.84904408e-01 5.13085783e-01 -8.14038292e-02 -7.79112935e-01
-1.35197294e+00 -5.80319345e-01 7.96714544e-01 -5.16601354e-02
-4.77231592e-01 6.04116440e-01 3.99044305e-01 -7.94095635e-01
3.80461663e-01 -2.64810354e-01 -5.91603398e-01 -3.55134130e-01
2.62450695e-01 7.25808263e-01 -6.64092079e-02 -3.03210378... | [14.620027542114258, 7.151122570037842] |
14b3009d-c056-457a-b707-8b96306b2484 | towards-more-generalizable-one-shot-visual | 2110.13423 | null | https://arxiv.org/abs/2110.13423v2 | https://arxiv.org/pdf/2110.13423v2.pdf | Towards More Generalizable One-shot Visual Imitation Learning | A general-purpose robot should be able to master a wide range of tasks and quickly learn a novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches this goal by training an agent with (pairs of) expert demonstrations, such that at test time, it can directly execute a new task from just on... | ['Pieter Abbeel', 'Kimin Lee', 'Fangchen Liu', 'Zhao Mandi'] | 2021-10-26 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 4.56971824e-01 -1.41516209e-01 -1.56636268e-03 -1.58901848e-02
-6.91517949e-01 -6.24496698e-01 1.01750183e+00 -4.23432678e-01
-7.04418957e-01 9.12429273e-01 -2.14360893e-01 -9.30481404e-02
-3.84097576e-01 -1.36234000e-01 -8.25904667e-01 -7.25437462e-01
-2.71892339e-01 7.65066087e-01 6.10666096e-01 -3.81057769... | [4.369646072387695, 1.0891437530517578] |
b9bed89a-3882-494c-86fa-3a02fa3e8575 | metaheuristic-approach-to-solve-portfolio | 2211.17193 | null | https://arxiv.org/abs/2211.17193v1 | https://arxiv.org/pdf/2211.17193v1.pdf | Metaheuristic Approach to Solve Portfolio Selection Problem | In this paper, a heuristic method based on TabuSearch and TokenRing Search is being used in order to solve the Portfolio Optimization Problem. The seminal mean-variance model of Markowitz is being considered with the addition of cardinality and quantity constraints to better capture the dynamics of the trading procedur... | ['Taylan Kabbani'] | 2022-11-10 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.31594598e-01 1.24175347e-01 -3.03354293e-01 -1.19091146e-01
-2.38203824e-01 -9.24477994e-01 3.93419802e-01 -4.46513519e-02
-3.44627202e-01 1.22183943e+00 -1.59969360e-01 -4.03028995e-01
-7.23611534e-01 -8.52426112e-01 -2.29500517e-01 -7.05384672e-01
9.27300379e-03 9.07325149e-01 2.81021714e-01 -3.23527724... | [5.014044761657715, 3.952735662460327] |
5b17dac5-2aab-4875-970d-81f935830f72 | self-supervised-3d-keypoint-learning-for-ego | 1912.03426 | null | https://arxiv.org/abs/1912.03426v3 | https://arxiv.org/pdf/1912.03426v3.pdf | Self-Supervised 3D Keypoint Learning for Ego-motion Estimation | Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion. State-of-the-art learning-based methods generate training samples via homography adaptation to create 2D synthetic views with known keypoint matches from a single image. This approach, however, does not ge... | ['Patric Jensfelt', 'Rares Ambrus', 'Adrien Gaidon', 'Sudeep Pillai', 'Jiexiong Tang', 'Vitor Guizilini', 'Hanme Kim'] | 2019-12-07 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [-1.71535090e-02 1.17823355e-01 -3.94118458e-01 -5.90004146e-01
-6.76556587e-01 -6.46265745e-01 7.82362223e-01 -4.04243976e-01
-2.91711271e-01 5.23412585e-01 -3.11612278e-01 -6.37443960e-02
2.10058212e-01 -5.67987919e-01 -1.23997390e+00 -3.16687346e-01
1.49112806e-01 9.53347325e-01 3.73255312e-01 -1.66358531... | [8.088513374328613, -2.235980749130249] |
0a90f7c2-5018-4a64-ab2b-7ade1de2d5d9 | uvstyle-net-unsupervised-few-shot-learning-of | 2105.02961 | null | https://arxiv.org/abs/2105.02961v3 | https://arxiv.org/pdf/2105.02961v3.pdf | UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps | Boundary Representations (B-Reps) are the industry standard in 3D Computer Aided Design/Manufacturing (CAD/CAM) and industrial design due to their fidelity in representing stylistic details. However, they have been ignored in the 3D style research. Existing 3D style metrics typically operate on meshes or pointclouds, a... | ['Joseph Lambourne', 'Aditya Sanghi', 'Pradeep Kumar Jayaraman', 'Amir Khasahmadi', 'Hooman Shayani', 'Peter Meltzer'] | 2021-04-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Meltzer_UVStyle-Net_Unsupervised_Few-Shot_Learning_of_3D_Style_Similarity_Measure_for_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Meltzer_UVStyle-Net_Unsupervised_Few-Shot_Learning_of_3D_Style_Similarity_Measure_for_ICCV_2021_paper.pdf | iccv-2021-1 | ['unsupervised-few-shot-learning'] | ['computer-vision'] | [-2.40288535e-03 7.36072361e-02 -9.38867256e-02 -5.94143212e-01
-7.34757602e-01 -5.01893580e-01 7.63999462e-01 -1.95968114e-02
5.86239956e-02 3.02018434e-01 2.54878491e-01 4.86662164e-02
-4.88376953e-02 -8.34204614e-01 -4.72132444e-01 -4.21782315e-01
1.97734073e-01 6.09474361e-01 4.85566296e-02 -4.10236120... | [8.539926528930664, -3.078861713409424] |
4bd7d492-22bc-41be-aa0f-b9969d5a6d81 | malleable-2-5d-convolution-learning-receptive | 2007.09365 | null | https://arxiv.org/abs/2007.09365v1 | https://arxiv.org/pdf/2007.09365v1.pdf | Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing | Depth data provide geometric information that can bring progress in RGB-D scene parsing tasks. Several recent works propose RGB-D convolution operators that construct receptive fields along the depth-axis to handle 3D neighborhood relations between pixels. However, these methods pre-define depth receptive fields by hyp... | ['Yajie Xing', 'Jingbo Wang', 'Gang Zeng'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3295_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640545.pdf | eccv-2020-8 | ['scene-parsing'] | ['computer-vision'] | [ 6.55019656e-02 2.09191084e-01 -7.49978051e-02 -9.04930949e-01
-8.51815045e-02 -6.44823313e-01 4.17404383e-01 -6.43055961e-02
-5.83562255e-01 1.09368190e-01 7.83266872e-02 -6.04835451e-01
-4.24820371e-02 -1.24775553e+00 -7.04889774e-01 -5.44053912e-01
1.08003631e-01 2.21416190e-01 5.51241398e-01 -2.38190770... | [9.19113826751709, -1.2473522424697876] |
2835b29c-5b9b-4b2e-acdc-f47970021e9b | recontrast-domain-specific-anomaly-detection | 2306.02602 | null | https://arxiv.org/abs/2306.02602v1 | https://arxiv.org/pdf/2306.02602v1.pdf | ReContrast: Domain-Specific Anomaly Detection via Contrastive Reconstruction | Most advanced unsupervised anomaly detection (UAD) methods rely on modeling feature representations of frozen encoder networks pre-trained on large-scale datasets, e.g. ImageNet. However, the features extracted from the encoders that are borrowed from natural image domains coincide little with the features required in ... | ['Huiqi Li', 'Weihang Zhang', 'Lize Jia', 'Shuai Lu', 'Jia Guo'] | 2023-06-05 | null | null | null | null | ['defect-detection', 'unsupervised-anomaly-detection'] | ['computer-vision', 'methodology'] | [ 5.29707730e-01 3.11632067e-01 2.28716969e-01 -5.16967773e-01
-4.61800814e-01 3.00499313e-02 1.56284794e-01 1.31894618e-01
-2.40516588e-01 3.15559864e-01 -3.09415519e-01 -1.38381764e-01
2.02310272e-02 -7.66847908e-01 -1.16064823e+00 -6.71033740e-01
-1.86595201e-01 1.21925704e-01 1.33034840e-01 -8.47759172... | [7.642261981964111, 2.0537490844726562] |
54ac60f9-771e-4696-8d00-5fb0f199f3cc | working-paper-improved-stock-price | 1902.08938 | null | http://arxiv.org/abs/1902.08938v1 | http://arxiv.org/pdf/1902.08938v1.pdf | Working Paper: Improved Stock Price Forecasting Algorithm based on Feature-weighed Support Vector Regression by using Grey Correlation Degree | With the widespread engineering applications ranging from artificial
intelligence and big data decision-making, originally a lot of tedious
financial data processing, processing and analysis have become more and more
convenient and effective. This paper aims to improve the accuracy of stock
price forecasting. It improv... | [] | 2019-02-24 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-9.88902628e-01 -7.65980303e-01 -9.48967859e-02 -1.24233291e-01
3.39432925e-01 -3.69333744e-01 4.49938267e-01 -1.19545400e-01
-3.40736479e-01 6.61001444e-01 2.11507976e-01 -3.33572000e-01
-1.92460388e-01 -1.15533650e+00 2.55445033e-01 -9.08940196e-01
-3.35485458e-01 1.20981641e-01 2.07358152e-01 -8.49035740... | [4.647156238555908, 4.122612476348877] |
af136fe9-3b6c-4cf4-873e-7bad0637e546 | continual-learning-for-abdominal-multi-organ | 2306.00988 | null | https://arxiv.org/abs/2306.00988v1 | https://arxiv.org/pdf/2306.00988v1.pdf | Continual Learning for Abdominal Multi-Organ and Tumor Segmentation | The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of... | ['Zongwei Zhou', 'Yaoyao Liu', 'Alan Yuille', 'Huimiao Chen', 'Xinyi Li', 'Yixiao Zhang'] | 2023-06-01 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 4.60709989e-01 5.42861879e-01 -5.32743812e-01 -5.00720263e-01
-7.95756698e-01 -5.57001650e-01 4.07359034e-01 7.14213908e-01
-7.99476981e-01 7.26141155e-01 2.21801654e-01 -2.60217994e-01
9.32424292e-02 -7.43599057e-01 -7.77725816e-01 -7.30835855e-01
-1.50309846e-01 4.53076541e-01 2.62814552e-01 1.84142768... | [14.73132038116455, -2.29110050201416] |
6d02b1dc-58fe-4280-a7cc-a584d353dd53 | spsg-self-supervised-photometric-scene | 2006.14660 | null | https://arxiv.org/abs/2006.14660v2 | https://arxiv.org/pdf/2006.14660v2.pdf | SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans | We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion. Our self-supervised approach learns to jointly inpaint geometry and color by correlating an incomplete RGB-D scan wi... | ['Matthias Nießner', 'Julien Valentin', 'Yawar Siddiqui', 'Justus Thies', 'Angela Dai'] | 2020-06-25 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Dai_SPSG_Self-Supervised_Photometric_Scene_Generation_From_RGB-D_Scans_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Dai_SPSG_Self-Supervised_Photometric_Scene_Generation_From_RGB-D_Scans_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-generation'] | ['computer-vision'] | [ 6.14221632e-01 3.58307570e-01 4.24115300e-01 -4.60207820e-01
-1.12331128e+00 -8.39977145e-01 4.54027861e-01 -2.62870401e-01
6.04536757e-02 6.09743118e-01 1.08109422e-01 3.95857543e-02
1.80528164e-01 -9.68271315e-01 -1.12578166e+00 -5.76017857e-01
-3.57264467e-02 5.41115105e-01 -6.27034670e-03 3.24812904... | [9.308293342590332, -3.1939940452575684] |
ddad88db-f05c-4f2d-99eb-88071707e022 | continuous-time-audiovisual-fusion-with | 2203.13285 | null | https://arxiv.org/abs/2203.13285v2 | https://arxiv.org/pdf/2203.13285v2.pdf | Continuous-Time Audiovisual Fusion with Recurrence vs. Attention for In-The-Wild Affect Recognition | In this paper, we present our submission to 3rd Affective Behavior Analysis in-the-wild (ABAW) challenge. Learningcomplex interactions among multimodal sequences is critical to recognise dimensional affect from in-the-wild audiovisual data. Recurrence and attention are the two widely used sequence modelling mechanisms ... | ['Björn W. Schuller', 'Michel Valstar', 'Adria Mallol-Ragolta', 'Mani Kumar Tellamekala', 'Vincent Karas'] | 2022-03-24 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.14454502e-01 -1.28817692e-01 2.60957152e-01 -4.46736485e-01
-8.14692557e-01 -4.85483915e-01 5.12831569e-01 1.28645990e-02
-7.17076898e-01 2.68156260e-01 4.15603638e-01 1.33613214e-01
-1.20673299e-01 1.39459958e-02 -2.60933727e-01 -7.68359601e-01
-4.77920771e-01 3.65801781e-01 -5.16246557e-01 -5.90916336... | [13.377293586730957, 5.241767406463623] |
3e81e878-3e24-47e1-9ca9-8a63068197f3 | modelling-latent-translations-for-cross | 2107.11353 | null | https://arxiv.org/abs/2107.11353v1 | https://arxiv.org/pdf/2107.11353v1.pdf | Modelling Latent Translations for Cross-Lingual Transfer | While achieving state-of-the-art results in multiple tasks and languages, translation-based cross-lingual transfer is often overlooked in favour of massively multilingual pre-trained encoders. Arguably, this is due to its main limitations: 1) translation errors percolating to the classification phase and 2) the insuffi... | ['Siva Reddy', 'Ivan Vulić', 'Julia Kreutzer', 'Edoardo Maria Ponti'] | 2021-07-23 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 4.43879485e-01 1.55177116e-01 -4.88585174e-01 -2.70182967e-01
-1.58308339e+00 -7.02798903e-01 9.56246734e-01 6.67496696e-02
-6.59330606e-01 9.75659907e-01 3.49090099e-01 -5.28638422e-01
1.74501717e-01 -6.64318025e-01 -1.00459898e+00 -3.87123197e-01
4.57497746e-01 9.10465062e-01 -1.97701320e-01 -2.19092250... | [11.605688095092773, 10.176007270812988] |
b2ed562f-255c-4d87-ad15-32e0894d3647 | self-supervised-multi-frame-monocular-scene | 2105.02216 | null | https://arxiv.org/abs/2105.02216v1 | https://arxiv.org/pdf/2105.02216v1.pdf | Self-Supervised Multi-Frame Monocular Scene Flow | Estimating 3D scene flow from a sequence of monocular images has been gaining increased attention due to the simple, economical capture setup. Owing to the severe ill-posedness of the problem, the accuracy of current methods has been limited, especially that of efficient, real-time approaches. In this paper, we introdu... | ['Stefan Roth', 'Junhwa Hur'] | 2021-05-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hur_Self-Supervised_Multi-Frame_Monocular_Scene_Flow_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hur_Self-Supervised_Multi-Frame_Monocular_Scene_Flow_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.29021466e-01 -4.32222188e-01 -1.75219715e-01 -3.23468655e-01
-4.92575854e-01 -3.83814156e-01 6.95430815e-01 -6.26028299e-01
-5.16095221e-01 8.45757365e-01 4.08786118e-01 -2.37895548e-01
2.75240451e-01 -4.69598174e-01 -7.65622139e-01 -4.92817283e-01
4.82932664e-02 8.50956216e-02 5.62041640e-01 1.35309055... | [8.684260368347168, -1.8766149282455444] |
c74ab011-3a71-4831-9c63-feb04274dadf | implementing-a-reverse-dictionary-based-on | 1606.00025 | null | http://arxiv.org/abs/1606.00025v5 | http://arxiv.org/pdf/1606.00025v5.pdf | Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture | In this paper, we outline an approach to build graph-based reverse
dictionaries using word definitions. A reverse dictionary takes a phrase as an
input and outputs a list of words semantically similar to that phrase. It is a
solution to the Tip-of-the-Tongue problem. We use a distance-based similarity
measure, computed... | ['Sushrut Thorat', 'Varad Choudhari'] | 2016-05-31 | implementing-a-reverse-dictionary-based-on-1 | https://aclanthology.org/C16-1263 | https://aclanthology.org/C16-1263.pdf | coling-2016-12 | ['reverse-dictionary'] | ['natural-language-processing'] | [-1.94531828e-01 2.44575609e-02 -6.10291779e-01 -4.26622272e-01
-5.14829040e-01 -1.17190754e+00 7.70257056e-01 5.33254623e-01
-6.34837508e-01 2.34792754e-01 6.88363671e-01 -6.11932158e-01
6.45767301e-02 -9.41620588e-01 -1.54713526e-01 -4.02746856e-01
2.77197957e-01 8.56733680e-01 2.20290646e-01 -9.14353430... | [10.478796005249023, 8.977665901184082] |
d8ab98e0-e4ae-4e5e-93cf-6a9de1c2773b | separate-what-you-describe-language-queried | 2203.15147 | null | https://arxiv.org/abs/2203.15147v1 | https://arxiv.org/pdf/2203.15147v1.pdf | Separate What You Describe: Language-Queried Audio Source Separation | In this paper, we introduce the task of language-queried audio source separation (LASS), which aims to separate a target source from an audio mixture based on a natural language query of the target source (e.g., "a man tells a joke followed by people laughing"). A unique challenge in LASS is associated with the complex... | ['Wenwu Wang', 'Mark D. Plumbley', 'Qiushi Huang', 'Jinzheng Zhao', 'Xinhao Mei', 'Qiuqiang Kong', 'Haohe Liu', 'Xubo Liu'] | 2022-03-28 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [-5.85293546e-02 -2.65960366e-01 -8.73121619e-02 -4.36707199e-01
-1.79738736e+00 -5.70435524e-01 2.72234946e-01 -5.31418622e-03
-2.42451519e-01 4.04592276e-01 5.72874010e-01 3.11779350e-01
9.70484614e-02 -1.80157721e-01 -6.81030273e-01 -5.48187792e-01
-1.63942158e-01 3.84776592e-01 -1.04573868e-01 1.10620363... | [15.258419036865234, 5.188126087188721] |
b30b9110-7779-409f-a841-dd6a301d3ad2 | completionformer-depth-completion-with | 2304.13030 | null | https://arxiv.org/abs/2304.13030v1 | https://arxiv.org/pdf/2304.13030v1.pdf | CompletionFormer: Depth Completion with Convolutions and Vision Transformers | Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth prediction. Despite the tremendous progress of deep-learning-based depth completion methods, the locality of the convolutional layer or graph model... | ['Mattoccia Stefano', 'Huang Guan', 'Zhu Zheng', 'Poggi Matteo', 'Guo Xianda', 'Zhang Youmin'] | 2023-04-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_CompletionFormer_Depth_Completion_With_Convolutions_and_Vision_Transformers_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_CompletionFormer_Depth_Completion_With_Convolutions_and_Vision_Transformers_CVPR_2023_paper.pdf | cvpr-2023-1 | ['depth-completion'] | ['computer-vision'] | [ 1.33519284e-02 2.19239578e-01 6.80740550e-02 -4.48431611e-01
-4.97625589e-01 -9.64428782e-02 4.84488577e-01 -2.27586642e-01
-1.53356493e-01 4.24307615e-01 4.62164938e-01 -2.10360885e-01
1.29742667e-01 -1.05840039e+00 -9.44126368e-01 -6.36617661e-01
1.48111910e-01 1.36129633e-01 3.06292027e-01 -7.13375807... | [8.82699203491211, -2.5334582328796387] |
d049bf0c-aeb9-403f-ba6c-7f6a960e4a4a | pp-structurev2-a-stronger-document-analysis | 2210.05391 | null | https://arxiv.org/abs/2210.05391v2 | https://arxiv.org/pdf/2210.05391v2.pdf | PP-StructureV2: A Stronger Document Analysis System | A large amount of document data exists in unstructured form such as raw images without any text information. Designing a practical document image analysis system is a meaningful but challenging task. In previous work, we proposed an intelligent document analysis system PP-Structure. In order to further upgrade the func... | ['dianhai yu', 'Xiaoguang Hu', 'Yi Liu', 'Lingfeng Zhu', 'Yuning Du', 'Mengtao An', 'Jun Zhou', 'Ruoyu Guo', 'Chenxia Li'] | 2022-10-11 | null | null | null | null | ['table-recognition', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 2.13912472e-01 -9.05184969e-02 -5.62655665e-02 -1.87816054e-01
-2.31524408e-01 -5.55983663e-01 3.68150324e-01 3.09139401e-01
-2.94106305e-01 4.58518058e-01 -5.45400456e-02 -4.36282277e-01
-2.96658218e-01 -1.11297441e+00 -6.36642277e-01 -2.69844979e-01
4.32020098e-01 5.04872501e-01 5.59213161e-01 -9.07660928... | [11.693243026733398, 2.682088613510132] |
90bb23f9-56a2-4622-9727-f3637f5b79a6 | temporal-context-aggregation-network-for | 2103.13141 | null | https://arxiv.org/abs/2103.13141v1 | https://arxiv.org/pdf/2103.13141v1.pdf | Temporal Context Aggregation Network for Temporal Action Proposal Refinement | Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing... | ['Nong Sang', 'Changxin Gao', 'Junjie Yan', 'Yu Qiao', 'Xiang Wang', 'Wei Wu', 'Dongliang Wang', 'Weihao Gan', 'Haisheng Su', 'Zhiwu Qing'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Qing_Temporal_Context_Aggregation_Network_for_Temporal_Action_Proposal_Refinement_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Qing_Temporal_Context_Aggregation_Network_for_Temporal_Action_Proposal_Refinement_CVPR_2021_paper.pdf | cvpr-2021-1 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 2.89782047e-01 -2.97226399e-01 -6.51701689e-01 -3.12522918e-01
-8.51481378e-01 -6.45891726e-02 5.41832447e-01 -1.76377907e-01
-4.44045454e-01 7.15980828e-01 5.24012029e-01 1.13541491e-01
1.65858865e-02 -3.03757548e-01 -4.19727236e-01 -5.44441521e-01
-2.44062722e-01 -5.82127050e-02 8.73802900e-01 9.61082354... | [8.414219856262207, 0.48251813650131226] |
938ee548-5f25-4a48-a771-0d2629eaebb7 | automatic-extraction-of-ranked-snp-phenotype | 2012.00902 | null | https://arxiv.org/abs/2012.00902v1 | https://arxiv.org/pdf/2012.00902v1.pdf | Automatic Extraction of Ranked SNP-Phenotype Associations from Literature through Detecting Neural Candidates, Negation and Modality Markers | Genome-wide association (GWA) constitutes a prominent portion of studies which have been conducted on personalized medicine and pharmacogenomics. Recently, very few methods have been developed for extracting mutation-diseases associations. However, there is no available method for extracting the association of SNP-phen... | ['Alberto Diaz', 'Behrouz Bokharaeian'] | 2020-12-02 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 4.04542863e-01 2.41282836e-01 -4.11067575e-01 -2.98683584e-01
-6.62457407e-01 -3.79688650e-01 4.37353700e-01 7.26960599e-01
-1.95182547e-01 1.21808982e+00 3.04485768e-01 -4.12200719e-01
-4.27372426e-01 -7.58841097e-01 -3.81053388e-01 -5.51643252e-01
-1.66499615e-02 5.42779826e-02 9.32583436e-02 -1.83401719... | [8.425073623657227, 8.66662883758545] |
d6715357-88cd-4d3c-a11e-697a66ac451c | covtinet-covid-text-identification-network | null | null | https://link.springer.com/article/10.1007/s00521-023-08442-y | https://link.springer.com/article/10.1007/s00521-023-08442-y | CovTiNet: Covid text identification network using attention-based positional embedding feature fusion | Covid text identification (CTI) is a crucial research concern in natural language processing (NLP). Social and electronic media are simultaneously adding a large volume of Covid-affiliated text on the World Wide Web due to the effortless access to the Internet, electronic gadgets and the Covid outbreak. Most of these t... | ['Nazmul Siddique & Iqbal H. Sarker', 'Mohammed Moshiul Hoque', 'Md. Rajib Hossain'] | 2023-03-14 | null | null | null | neural-computing-and-applications-2023-3 | ['misinformation'] | ['miscellaneous'] | [-3.19170713e-01 -3.18557441e-01 1.24772433e-02 1.15640491e-01
-3.77932876e-01 -7.83857286e-01 9.70741749e-01 5.26112914e-01
-5.97118974e-01 8.48387361e-01 6.38389766e-01 -3.45187336e-01
3.69951911e-02 -6.34017527e-01 -2.15940297e-01 -3.04700345e-01
3.49088460e-01 9.03968513e-01 -1.90974429e-01 -6.71395183... | [8.841241836547852, 10.399020195007324] |
86ba8008-cc0a-45e3-9a4d-9e5903500441 | an-overview-of-differentiable-particle | 2302.09639 | null | https://arxiv.org/abs/2302.09639v1 | https://arxiv.org/pdf/2302.09639v1.pdf | An overview of differentiable particle filters for data-adaptive sequential Bayesian inference | By approximating posterior distributions with weighted samples, particle filters (PFs) provide an efficient mechanism for solving non-linear sequential state estimation problems. While the effectiveness of particle filters has been recognised in various applications, the performance of particle filters relies on the kn... | ['Yunpeng Li', 'Xiongjie Chen'] | 2023-02-19 | null | null | null | null | ['sequential-bayesian-inference'] | ['time-series'] | [ 1.43513143e-01 -2.90658414e-01 -2.31024828e-02 -4.27739918e-02
-3.92526299e-01 -2.03099266e-01 1.14947331e+00 -9.69032291e-03
-7.50278175e-01 1.02727532e+00 8.06943700e-02 -1.89513192e-01
-4.98039991e-01 -6.85012460e-01 -7.40212083e-01 -7.73754239e-01
-4.51764792e-01 6.28355324e-01 4.26498324e-01 -1.20672673... | [6.573317050933838, 3.591362237930298] |
f8dda1a0-383d-48c1-b36a-5fcb3d6b57b3 | pars-pseudo-label-aware-robust-sample-1 | 2201.10836 | null | https://arxiv.org/abs/2201.10836v1 | https://arxiv.org/pdf/2201.10836v1.pdf | PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels | Acquiring accurate labels on large-scale datasets is both time consuming and expensive. To reduce the dependency of deep learning models on learning from clean labeled data, several recent research efforts are focused on learning with noisy labels. These methods typically fall into three design categories to learn a no... | ['Jordan Massiah', 'Yunlong Jiao', 'Arushi Goel'] | 2022-01-26 | pars-pseudo-label-aware-robust-sample | https://openreview.net/forum?id=ovRQmeVFbrC | https://openreview.net/pdf?id=ovRQmeVFbrC | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.24782048e-01 -3.27568173e-01 4.39932197e-02 -7.92291522e-01
-1.91881919e+00 -4.77021754e-01 2.76520580e-01 2.00270507e-02
-8.75779033e-01 9.54873502e-01 7.53249228e-02 2.57467121e-01
-8.15027282e-02 -5.14778018e-01 -6.17160678e-01 -8.99362981e-01
2.89573312e-01 4.55839992e-01 -7.57747218e-02 2.86431253... | [9.377847671508789, 3.877126932144165] |
86f81758-cc42-4ca6-ab21-631634669fcd | scalable-randomized-kernel-methods-for | 2304.04692 | null | https://arxiv.org/abs/2304.04692v1 | https://arxiv.org/pdf/2304.04692v1.pdf | Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction | We develop scalable randomized kernel methods for jointly associating data from multiple sources and simultaneously predicting an outcome or classifying a unit into one of two or more classes. The proposed methods model nonlinear relationships in multiview data together with predicting a clinical outcome and are capabl... | ['Han Lu', 'Sandra E. Safo'] | 2023-04-10 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-2.5306368e-01 -2.8042260e-01 -5.3392631e-01 -5.5959409e-01
-7.3315227e-01 -5.7549566e-01 1.9622612e-01 3.8677517e-01
1.3209301e-01 6.6471785e-01 3.2520479e-01 -1.7000689e-01
-4.7331974e-01 -4.2099518e-01 -5.7331836e-01 -7.3338103e-01
-5.9388220e-01 5.1503927e-01 -3.1362054e-01 3.2618362e-01
-7.3872708e-02... | [6.769935607910156, 5.376345634460449] |
238790be-9926-45cf-8da7-293869a9e025 | multi-span-acoustic-modelling-using-raw | 1906.11047 | null | https://arxiv.org/abs/1906.11047v2 | https://arxiv.org/pdf/1906.11047v2.pdf | Multi-Span Acoustic Modelling using Raw Waveform Signals | Traditional automatic speech recognition (ASR) systems often use an acoustic model (AM) built on handcrafted acoustic features, such as log Mel-filter bank (FBANK) values. Recent studies found that AMs with convolutional neural networks (CNNs) can directly use the raw waveform signal as input. Given sufficient training... | ['Philip Woodland', 'Patrick von Platen', 'Chao Zhang'] | 2019-06-21 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 2.50369072e-01 -7.87010267e-02 4.90367532e-01 -4.79343623e-01
-1.05953586e+00 -3.15146595e-01 3.14914674e-01 6.30906150e-02
-9.47542548e-01 2.94656098e-01 1.54375225e-01 -6.10674739e-01
1.92854047e-01 -5.37453771e-01 -6.36396110e-01 -4.77651119e-01
-7.62289315e-02 -1.13021761e-01 2.14022353e-01 -4.91178811... | [14.816458702087402, 6.0371599197387695] |
236f7ea2-3678-44be-b2f3-2d958303f18f | convolutional-sequence-to-sequence-learning | 1705.03122 | null | http://arxiv.org/abs/1705.03122v3 | http://arxiv.org/pdf/1705.03122v3.pdf | Convolutional Sequence to Sequence Learning | The prevalent approach to sequence to sequence learning maps an input
sequence to a variable length output sequence via recurrent neural networks. We
introduce an architecture based entirely on convolutional neural networks.
Compared to recurrent models, computations over all elements can be fully
parallelized during t... | ['Jonas Gehring', 'Michael Auli', 'David Grangier', 'Yann N. Dauphin', 'Denis Yarats'] | 2017-05-08 | convolutional-sequence-to-sequence-learning-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=806 | http://proceedings.mlr.press/v70/gehring17a/gehring17a.pdf | icml-2017-8 | ['bangla-spelling-error-correction'] | ['natural-language-processing'] | [ 5.91902494e-01 -8.05346891e-02 -1.81169763e-01 -9.14514884e-02
-9.23355341e-01 -8.81646931e-01 5.78266978e-01 -9.43939239e-02
-8.27929795e-01 9.81100559e-01 1.28575549e-01 -9.34114993e-01
6.32211506e-01 -5.92869937e-01 -9.89373386e-01 -3.42509538e-01
-4.07073274e-02 3.98319215e-01 -1.51867986e-01 -2.90574491... | [10.769718170166016, 7.249521255493164] |
af2f9ddb-6dd1-4656-9bef-76e70a01a5ea | the-food-recognition-benchmark-using | 2106.14977 | null | https://arxiv.org/abs/2106.14977v2 | https://arxiv.org/pdf/2106.14977v2.pdf | The Food Recognition Benchmark: Using DeepLearning to Recognize Food on Images | The automatic recognition of food on images has numerous interesting applications, including nutritional tracking in medical cohorts. The problem has received significant research attention, but an ongoing public benchmark to develop open and reproducible algorithms has been missing. Here, we report on the setup of suc... | ['Marcel Salathé', 'Victor Boulanger', 'Harris Héritier', 'Djilani Kebaili', 'Eric Antoine Scuccimarra', 'Gaurav Singhal', 'Sharada Prasanna Mohanty'] | 2021-06-28 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 5.16794398e-02 -1.79471802e-02 -3.24967265e-01 -4.98837113e-01
-1.11697435e+00 -7.78327167e-01 1.59695491e-01 8.47139418e-01
-5.21579206e-01 2.72040248e-01 3.79009753e-01 7.91385174e-02
1.30027622e-01 -7.10756958e-01 -1.06821287e+00 -6.07644200e-01
-5.20975888e-01 3.47957492e-01 3.38712424e-01 1.74940050... | [11.566028594970703, 4.3730387687683105] |
74cc0638-d0cc-4285-95f0-c0b64e6dff25 | relational-program-synthesis-with-numerical | 2210.00764 | null | https://arxiv.org/abs/2210.00764v2 | https://arxiv.org/pdf/2210.00764v2.pdf | Relational program synthesis with numerical reasoning | Program synthesis approaches struggle to learn programs with numerical values. An especially difficult problem is learning continuous values over multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational learning with numerical reas... | ['Andrew Cropper', 'Céline Hocquette'] | 2022-10-03 | null | null | null | null | ['program-synthesis', 'inductive-logic-programming', 'relational-reasoning'] | ['computer-code', 'methodology', 'natural-language-processing'] | [ 9.31817293e-03 2.12327167e-01 -8.69023323e-01 -3.17776799e-01
-9.07580733e-01 -8.64364803e-01 1.87406495e-01 4.01832879e-01
-3.27016674e-02 1.28062165e+00 -3.41753989e-01 -9.98680890e-01
-3.13384414e-01 -1.67657840e+00 -1.04535258e+00 -1.33575827e-01
-5.56836069e-01 6.64698482e-01 3.80548388e-01 -2.97656029... | [8.809698104858398, 7.154491424560547] |
5d414f17-6208-456d-80e3-7faeaee23dac | bsdar-beam-search-decoding-with-attention | 1909.09485 | null | https://arxiv.org/abs/1909.09485v1 | https://arxiv.org/pdf/1909.09485v1.pdf | BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation | This study mainly investigates two decoding problems in neural keyphrase generation: sequence length bias and beam diversity. We introduce an extension of beam search inference based on word-level and n-gram level attention score to adjust and constrain Seq2Seq prediction at test time. Results show that our proposed so... | ["Iftitahu Ni'mah", 'Mykola Pechenizkiy', 'Vlado Menkovski'] | 2019-09-17 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 6.13431633e-01 -2.00891539e-01 -2.73087561e-01 -7.51723498e-02
-9.79842663e-01 -8.26165974e-01 6.66219294e-01 9.88472253e-02
-8.73042107e-01 1.28619015e+00 8.31783712e-01 -7.42523015e-01
8.20031464e-02 -7.88231373e-01 -6.05453968e-01 -5.59183061e-01
5.68778664e-02 1.39551014e-01 4.91116047e-02 -6.16245449... | [12.15931224822998, 8.989936828613281] |
9413d928-30e8-4e5f-b589-4d6f8b2a2b06 | robustness-benchmark-of-road-user-trajectory | 2304.01895 | null | https://arxiv.org/abs/2304.01895v1 | https://arxiv.org/pdf/2304.01895v1.pdf | Robustness Benchmark of Road User Trajectory Prediction Models for Automated Driving | Accurate and robust trajectory predictions of road users are needed to enable safe automated driving. To do this, machine learning models are often used, which can show erratic behavior when presented with previously unseen inputs. In this work, two environment-aware models (MotionCNN and MultiPath++) and two common ba... | ['René van de Molengraft', 'Jos Elfring', 'Emilia Silvas', 'Manuel Muñoz Sánchez'] | 2023-04-04 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 3.49589109e-01 1.69729844e-01 -4.88830283e-02 -1.82966858e-01
-5.59485376e-01 -5.39435744e-01 5.49598634e-01 2.62412250e-01
-5.19121289e-01 7.49895275e-01 2.12474599e-01 -8.12539577e-01
-1.28071204e-01 -7.65655637e-01 -1.07136655e+00 -5.60487509e-01
-2.10435480e-01 2.41101339e-01 5.10675669e-01 -4.01344597... | [5.780621528625488, 1.0670394897460938] |
1aae8bb9-9b4a-49af-9c5d-c9b987d14d5a | a-novel-multimodal-music-genre-classifier | 2011.11970 | null | https://arxiv.org/abs/2011.11970v1 | https://arxiv.org/pdf/2011.11970v1.pdf | A Novel Multimodal Music Genre Classifier using Hierarchical Attention and Convolutional Neural Network | Music genre classification is one of the trending topics in regards to the current Music Information Retrieval (MIR) Research. Since, the dependency of genre is not only limited to the audio profile, we also make use of textual content provided as lyrics of the corresponding song. We implemented a CNN based feature ext... | ['Abhilash Nandy', 'Manish Agrawal'] | 2020-11-24 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 2.89723992e-01 -4.44284439e-01 -8.53919610e-02 -8.35056230e-02
-8.15225720e-01 -6.22353435e-01 5.92911363e-01 1.05602995e-01
-5.25759459e-01 3.85770649e-01 6.61518872e-01 1.30579278e-01
-4.41665590e-01 -5.82610488e-01 -2.37390772e-01 -7.38008022e-01
2.41757229e-01 -1.86563283e-02 -2.44489878e-01 -8.24049115... | [15.857216835021973, 5.1790876388549805] |
dac8c475-60f3-4988-b3bd-60fab2b08556 | hyperspectral-unmixing-with-endmember-1 | 1609.03500 | null | http://arxiv.org/abs/1609.03500v1 | http://arxiv.org/pdf/1609.03500v1.pdf | Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation | The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for
hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA
provides a model for a hyperspectral image analysis that accounts for spectral
variability and incorporates spatial information through the use of
superpixel-based ... | ['Alina Zare', 'Sheng Zou'] | 2016-09-12 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.90170985e-01 -3.16700101e-01 -3.35164219e-01 -1.81309819e-01
-3.24770182e-01 -7.60081708e-01 7.69248545e-01 -2.29012966e-01
2.58517504e-01 8.84638608e-01 4.07326818e-01 -3.42413843e-01
-1.07330002e-01 -1.00437462e+00 -1.95149347e-01 -1.30084682e+00
1.38806105e-01 6.22930825e-01 -4.35335070e-01 2.57711142... | [9.98596477508545, -1.9871857166290283] |
f580251a-9e0a-4166-81f3-b816a9c73f8a | using-scene-graph-context-to-improve-image | 1901.03762 | null | http://arxiv.org/abs/1901.03762v2 | http://arxiv.org/pdf/1901.03762v2.pdf | Using Scene Graph Context to Improve Image Generation | Generating realistic images from scene graphs asks neural networks to be able
to reason about object relationships and compositionality. As a relatively new
task, how to properly ensure the generated images comply with scene graphs or
how to measure task performance remains an open question. In this paper, we
propose t... | ['Subarna Tripathi', 'Hanlin Tang', 'Anahita Bhiwandiwalla', 'Alexei Bastidas'] | 2019-01-11 | null | null | null | null | ['image-generation-from-scene-graphs'] | ['computer-vision'] | [ 7.07305610e-01 3.48456860e-01 3.26082468e-01 -4.64830279e-01
-6.31035388e-01 -5.88539064e-01 7.99153090e-01 -5.54917306e-02
-2.24638253e-01 5.00181913e-01 1.74751073e-01 -6.50798380e-02
1.62012309e-01 -1.13689053e+00 -1.13871920e+00 -2.74109185e-01
2.95592487e-01 2.36771643e-01 3.17227840e-01 -2.60083705... | [11.395779609680176, -0.27312785387039185] |
66dd9ba1-376d-47c1-b4be-438f2e85d1dd | videotrack-learning-to-track-objects-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xie_VideoTrack_Learning_To_Track_Objects_via_Video_Transformer_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xie_VideoTrack_Learning_To_Track_Objects_via_Video_Transformer_CVPR_2023_paper.pdf | VideoTrack: Learning To Track Objects via Video Transformer | Existing Siamese tracking methods, which are built on pair-wise matching between two single frames, heavily rely on additional sophisticated mechanism to exploit temporal information among successive video frames, hindering them from high efficiency and industrial deployments. In this work, we resort to sequence-le... | ['Chao Ma', 'Yan Lu', 'Jiahao Li', 'Lei Chu', 'Fei Xie'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-tracking'] | ['computer-vision'] | [ 2.62453109e-01 -6.32385612e-01 -6.54401839e-01 -1.09506413e-01
-6.10755086e-01 -7.34400153e-01 6.88537657e-01 -4.89396065e-01
-3.05676341e-01 3.79008293e-01 3.07038188e-01 -1.19819835e-01
-7.32261464e-02 -3.19864333e-01 -8.93266201e-01 -8.04188490e-01
-9.71147511e-03 -2.35666275e-01 4.79162723e-01 -3.80131393... | [8.907095909118652, 0.31437185406684875] |
918a949c-4c16-4e25-b3b8-94728ef1b44a | learning-semantics-enriched-representation | 2007.06959 | null | https://arxiv.org/abs/2007.06959v1 | https://arxiv.org/pdf/2007.06959v1.pdf | Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration | Medical images are naturally associated with rich semantics about the human anatomy, reflected in an abundance of recurring anatomical patterns, offering unique potential to foster deep semantic representation learning and yield semantically more powerful models for different medical applications. But how exactly such ... | ['Zongwei Zhou', 'Michael B. Gotway', 'Jianming Liang', 'Fatemeh Haghighi', 'Mohammad Reza Hosseinzadeh Taher'] | 2020-07-14 | null | null | null | null | ['lung-nodule-detection', 'lung-nodule-segmentation', 'liver-segmentation'] | ['medical', 'medical', 'medical'] | [ 3.35746557e-01 6.83367848e-01 -3.83067489e-01 -6.38563335e-01
-7.26467609e-01 -3.82392853e-01 4.40127075e-01 2.94933885e-01
-1.72782183e-01 6.26737714e-01 6.16798937e-01 2.13999432e-02
-1.73248470e-01 -6.11700058e-01 -7.51159072e-01 -5.72388113e-01
-2.35795975e-01 4.52383101e-01 -1.31323477e-02 -2.43373826... | [14.674354553222656, -2.1915102005004883] |
284d28db-b7b1-4b02-ab66-ed7b54c8b927 | normalized-human-pose-features-for-human | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Normalized_Human_Pose_Features_for_Human_Action_Video_Alignment_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Normalized_Human_Pose_Features_for_Human_Action_Video_Alignment_ICCV_2021_paper.pdf | Normalized Human Pose Features for Human Action Video Alignment | We present a novel approach for extracting human pose features from human action videos. The goal is to let the pose features capture only the poses of the action while being invariant to other factors, including video backgrounds, the video subject's anthropometric characteristics and viewpoints. Such human pose f... | ['Chiew-Lan Tai', 'Hongbo Fu', 'Qifeng Chen', 'Mingyi Shi', 'Jingyuan Liu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['pose-retrieval', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 2.65595257e-01 -1.15576826e-01 -2.73997784e-01 -5.06762564e-01
-5.98560035e-01 -5.37298620e-01 6.88481510e-01 1.58634767e-01
-4.45255041e-01 2.81187356e-01 8.29166055e-01 4.52326655e-01
-1.87249318e-01 -2.89471179e-01 -4.34822232e-01 -5.67564130e-01
-2.53912985e-01 3.99205178e-01 1.43572584e-01 -9.25284401... | [7.129944324493408, -0.6922248601913452] |
6ed9e226-2ea6-45ff-b676-ad8df1e70103 | unconstrained-face-detection-and-open-set | 1708.02337 | null | http://arxiv.org/abs/1708.02337v3 | http://arxiv.org/pdf/1708.02337v3.pdf | Unconstrained Face Detection and Open-Set Face Recognition Challenge | Face detection and recognition benchmarks have shifted toward more difficult
environments. The challenge presented in this paper addresses the next step in
the direction of automatic detection and identification of people from outdoor
surveillance cameras. While face detection has shown remarkable success in
images col... | ['Cezary Stankiewicz', 'Mohammad Iqbal Nouyed', 'Jürgen Beyerer', 'Deva Ramanan', 'Manuel Günther', 'Akshay Raj Dhamija', 'Mohamad Al Jazaery', 'Josef Kittler', 'Guodong Guo', 'Chi Ho Chan', 'Shufan Yang', 'Peiyun Hu', 'Terrance E. Boult', 'Min Jiang', 'Christian Herrmann'] | 2017-08-08 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.51736510e-01 -2.89439261e-01 1.00499779e-01 -7.33828306e-01
-5.85284829e-01 -9.77986336e-01 7.28419542e-01 -5.26953459e-01
-5.27209044e-01 7.11254179e-01 -2.77388424e-01 -8.69222879e-02
1.57156140e-01 -2.32444763e-01 -3.47718000e-01 -7.17225552e-01
-7.22033605e-02 4.33324099e-01 -9.23009515e-02 2.86978367... | [13.340620994567871, 0.7946226596832275] |
f7f4fe64-17b8-41e9-b686-6fcf5b8029fb | se-md-a-single-encoder-multiple-decoder-deep | 2106.15325 | null | https://arxiv.org/abs/2106.15325v1 | https://arxiv.org/pdf/2106.15325v1.pdf | SE-MD: A Single-encoder multiple-decoder deep network for point cloud generation from 2D images | 3D model generation from single 2D RGB images is a challenging and actively researched computer vision task. Various techniques using conventional network architectures have been proposed for the same. However, the body of research work is limited and there are various issues like using inefficient 3D representation fo... | ['M. Hassaballah', 'Shabir Ahmad Parah', 'Rouf Ul Alam Bhat', 'Abdul Mueed Hafiz'] | 2021-06-17 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 1.71646494e-02 -2.27004766e-01 2.92667389e-01 -2.96747088e-01
-5.15829921e-01 -4.20729488e-01 5.19933641e-01 -3.31645042e-01
-1.31523445e-01 5.00920713e-01 -1.50122896e-01 -1.76739320e-01
1.44002825e-01 -9.10808980e-01 -8.14564943e-01 -5.85655749e-01
3.35663468e-01 4.66418952e-01 4.23208177e-01 -4.37696755... | [8.293181419372559, -3.242771625518799] |
95acce13-76b5-4033-ac06-1e7c01398656 | pruning-the-way-to-reliable-policies-a-multi | 2306.08044 | null | https://arxiv.org/abs/2306.08044v1 | https://arxiv.org/pdf/2306.08044v1.pdf | Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care | Most medical treatment decisions are sequential in nature. Hence, there is substantial hope that reinforcement learning may make it possible to formulate precise data-driven treatment plans. However, a key challenge for most applications in this field is the sparse nature of primarily mortality-based reward functions, ... | ['Ahmed Alaa', 'Alexander Schubert', 'Ali Shirali'] | 2023-06-13 | null | null | null | null | ['q-learning'] | ['methodology'] | [ 3.65499139e-01 3.54510784e-01 -5.30798852e-01 7.36440299e-03
-9.74329233e-01 -3.04350793e-01 1.35255426e-01 6.75136805e-01
-7.40504384e-01 1.22741199e+00 3.27319086e-01 -3.90304893e-01
-6.61219120e-01 -6.52962089e-01 -5.22387981e-01 -7.89817929e-01
-4.06756163e-01 7.24027991e-01 -2.44230554e-01 -3.22080478... | [4.018851280212402, 2.730570077896118] |
f8e129e6-9ec4-4976-8168-462edc86ff61 | video-super-resolution-via-deep-draft | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Liao_Video_Super-Resolution_via_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Liao_Video_Super-Resolution_via_ICCV_2015_paper.pdf | Video Super-Resolution via Deep Draft-Ensemble Learning | We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution. Our method contains two main components -- i.e., SR draft ensemble generation and its optimal reconstruction. The first componen... | ['Ziyang Ma', 'Ruiyu Li', 'Xin Tao', 'Jiaya Jia', 'Renjie Liao'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['image-deconvolution'] | ['computer-vision'] | [ 4.70558971e-01 -1.63448572e-01 1.58431321e-01 -1.20348014e-01
-8.51829886e-01 -1.54756382e-01 3.39078844e-01 -9.91636157e-01
-1.71222731e-01 1.01362419e+00 4.33590025e-01 2.10130453e-01
-8.08222219e-02 -7.21127629e-01 -9.30190623e-01 -6.84633911e-01
4.27861363e-02 -9.60082412e-02 5.05383372e-01 -3.62570792... | [11.029964447021484, -1.9616544246673584] |
5e7e3d67-bb65-4909-bdd4-23fc0babbeb1 | flexlip-a-controllable-text-to-lip-system | 2206.03206 | null | https://arxiv.org/abs/2206.03206v1 | https://arxiv.org/pdf/2206.03206v1.pdf | FlexLip: A Controllable Text-to-Lip System | The task of converting text input into video content is becoming an important topic for synthetic media generation. Several methods have been proposed with some of them reaching close-to-natural performances in constrained tasks. In this paper, we tackle a subissue of the text-to-video generation problem, by converting... | ['Horia Cucu', 'Adriana Stan', 'Beata Lorincz', 'Dan Oneata'] | 2022-06-07 | null | null | null | null | ['audio-generation', 'text-to-video-generation'] | ['audio', 'natural-language-processing'] | [ 3.03010136e-01 5.51858246e-01 3.07447854e-02 1.68723483e-02
-1.01725459e+00 -5.55357218e-01 7.92643607e-01 -1.59604147e-01
-2.72546977e-01 7.62664616e-01 2.05863535e-01 2.31712535e-01
2.25784913e-01 -5.03414571e-01 -6.94033682e-01 -8.70010793e-01
2.99987853e-01 5.56806445e-01 2.41316751e-01 -9.62087288... | [13.21837043762207, -0.3604690730571747] |
3359c645-8e10-4b95-8a27-0010a90bec84 | exploiting-knowledge-graphs-for-facilitating | 2010.05213 | null | https://arxiv.org/abs/2010.05213v1 | https://arxiv.org/pdf/2010.05213v1.pdf | Exploiting Knowledge Graphs for Facilitating Product/Service Discovery | Most of the existing techniques to product discovery rely on syntactic approaches, thus ignoring valuable and specific semantic information of the underlying standards during the process. The product data comes from different heterogeneous sources and formats giving rise to the problem of interoperability. Above all, d... | ['Sarika Jain'] | 2020-10-11 | null | null | null | null | ['product-categorization'] | ['miscellaneous'] | [-4.70995232e-02 2.19397880e-02 -5.15542626e-01 -6.31790817e-01
-1.82398647e-01 -7.89128363e-01 3.79021019e-01 7.37069547e-01
-9.08944383e-02 3.85647327e-01 1.01387672e-01 -2.00119391e-01
-8.43886018e-01 -1.14001918e+00 -3.85886803e-02 -1.82827637e-01
4.13168639e-01 7.84629226e-01 4.90101069e-01 -6.81041420... | [9.11515998840332, 7.822516918182373] |
2fb4dd9d-19eb-4c52-b5df-aea7e785918e | complementary-learning-subnetworks-for | 2306.11967 | null | https://arxiv.org/abs/2306.11967v1 | https://arxiv.org/pdf/2306.11967v1.pdf | Complementary Learning Subnetworks for Parameter-Efficient Class-Incremental Learning | In the scenario of class-incremental learning (CIL), deep neural networks have to adapt their model parameters to non-stationary data distributions, e.g., the emergence of new classes over time. However, CIL models are challenged by the well-known catastrophic forgetting phenomenon. Typical methods such as rehearsal-ba... | ['Zhigang Zeng', 'Depeng Li'] | 2023-06-21 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.17635179e-01 -9.52491770e-04 4.50725481e-02 -2.99511641e-01
-2.05443546e-01 -4.66623753e-01 3.91618043e-01 2.85577625e-01
-8.78770590e-01 9.93228257e-01 -3.84760737e-01 -2.27811590e-01
-1.83249518e-01 -8.35775316e-01 -1.12487614e+00 -9.42011893e-01
3.15251462e-02 4.02027577e-01 4.08968866e-01 1.19354866... | [9.817081451416016, 3.4233343601226807] |
c24b9ea1-1fa9-4f93-b7be-627e8482f1cc | a-novel-workflow-for-accurately-and | null | null | https://aclanthology.org/2020.findings-emnlp.38 | https://aclanthology.org/2020.findings-emnlp.38.pdf | A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels | Resources for Semantic Role Labeling (SRL) are typically annotated by experts at great expense. Prior attempts to develop crowdsourcing methods have either had low accuracy or required substantial expert annotation. We propose a new multi-stage crowd workflow that substantially reduces expert involvement without sacrif... | ['Walter Lasecki', 'Yunyao Li', 'Jonathan K. Kummerfeld', 'Huaiyu Zhu', 'Youxuan Jiang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.69558090e-01 8.36752295e-01 1.08055361e-01 -4.28099036e-01
-1.27179527e+00 -1.35925627e+00 5.96901238e-01 6.63213193e-01
-8.67858171e-01 1.15417981e+00 4.20129389e-01 -2.70269722e-01
2.64740318e-01 -3.69508892e-01 -4.70743001e-01 2.78624855e-02
6.81607425e-01 9.54937756e-01 7.02858031e-01 -2.58568645... | [9.705842018127441, 4.916252136230469] |
54d8323f-2449-4962-b84e-95d672fb2b38 | multi-view-graph-representation-learning-for | 2305.03458 | null | https://arxiv.org/abs/2305.03458v1 | https://arxiv.org/pdf/2305.03458v1.pdf | Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question | Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task. Existing methods based on encoder-decoder framework employ a expression tree-based decoder to solve numerical reasoning probl... | ['Kang Liu', 'Jun Zhao', 'Yuanzhe Zhang', 'Fangyu Lei', 'Yifan Wei'] | 2023-05-05 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [-1.76287144e-01 3.11299801e-01 -1.59200788e-01 -3.68253469e-01
-1.13068044e+00 -7.44671106e-01 4.17904019e-01 4.47624445e-01
-1.21058807e-01 6.31737530e-01 4.81850564e-01 -6.30026877e-01
-9.92041305e-02 -1.44646585e+00 -9.18046713e-01 2.59797312e-02
5.30044079e-01 8.36767375e-01 3.10907811e-01 -6.68829441... | [10.614324569702148, 7.863439083099365] |
8b83bc11-dcee-4a2e-b452-8f19874eb0ee | robustness-testing-for-multi-agent | 2306.06136 | null | https://arxiv.org/abs/2306.06136v1 | https://arxiv.org/pdf/2306.06136v1.pdf | Robustness Testing for Multi-Agent Reinforcement Learning: State Perturbations on Critical Agents | Multi-Agent Reinforcement Learning (MARL) has been widely applied in many fields such as smart traffic and unmanned aerial vehicles. However, most MARL algorithms are vulnerable to adversarial perturbations on agent states. Robustness testing for a trained model is an essential step for confirming the trustworthiness o... | ['Guanjun Liu', 'Ziyuan Zhou'] | 2023-06-09 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [ 3.70848514e-02 2.57778652e-02 1.84457183e-01 3.27672064e-01
-4.31455016e-01 -6.75308943e-01 8.76590014e-01 2.06050664e-01
-3.41131449e-01 1.04261494e+00 -3.32509547e-01 -2.98731297e-01
-3.92452329e-01 -9.01833475e-01 -5.46512723e-01 -1.18251228e+00
-5.15131474e-01 4.72539753e-01 6.14016533e-01 -6.39475346... | [3.842705488204956, 2.299281597137451] |
0416f346-2610-4111-9f7a-cf7d4a067cf7 | single-multi-source-cross-lingual-ner-via | 2004.12440 | null | https://arxiv.org/abs/2004.12440v2 | https://arxiv.org/pdf/2004.12440v2.pdf | Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language | To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data. Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model t... | ['Jian-Guang Lou', 'Börje F. Karlsson', 'Qianhui Wu', 'Zijia Lin', 'Biqing Huang'] | 2020-04-26 | single-multi-source-cross-lingual-ner-via-1 | https://aclanthology.org/2020.acl-main.581 | https://aclanthology.org/2020.acl-main.581.pdf | acl-2020-6 | ['cross-lingual-ner'] | ['natural-language-processing'] | [-2.5322151e-01 -1.5716591e-01 -6.5975314e-01 -6.3391769e-01
-1.2213655e+00 -9.0067279e-01 5.9822559e-01 7.5434588e-02
-8.2970852e-01 8.2002097e-01 2.7284211e-01 -2.7425820e-01
2.9369015e-01 -4.9048555e-01 -5.8204728e-01 -4.5754239e-01
5.3929734e-01 4.6376088e-01 2.2835745e-01 -2.6647097e-01
-1.8309678e-01... | [10.031351089477539, 9.619291305541992] |
2dff84a4-ab59-47fc-99da-5c1873fe1045 | stden-towards-physics-guided-neural-networks | 2209.00225 | null | https://arxiv.org/abs/2209.00225v1 | https://arxiv.org/pdf/2209.00225v1.pdf | STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction | High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the deve... | ['Hu Zhang', 'Jiawei Jiang', 'Zhe Jiang', 'Jingyuan Wang', 'Jiahao Ji'] | 2022-09-01 | null | null | null | null | ['physics-informed-machine-learning', 'spatio-temporal-forecasting'] | ['graphs', 'time-series'] | [-2.71217525e-01 -3.28316242e-01 -4.65468407e-01 -3.62705618e-01
4.74615060e-02 2.74522360e-02 7.07071185e-01 -3.84253830e-01
1.97787866e-01 8.02300990e-01 1.97698951e-01 -9.28490043e-01
-5.54974496e-01 -1.41162145e+00 -8.07964265e-01 -8.73730063e-01
1.91168472e-01 4.87957805e-01 3.76754373e-01 -6.98480070... | [6.396213054656982, 1.9749839305877686] |
dab54f0c-6ff2-4358-a46d-b5cdb16c34b9 | automatic-fado-music-classification | 1406.4447 | null | http://arxiv.org/abs/1406.4447v1 | http://arxiv.org/pdf/1406.4447v1.pdf | Automatic Fado Music Classification | In late 2011, Fado was elevated to the oral and intangible heritage of
humanity by UNESCO. This study aims to develop a tool for automatic detection
of Fado music based on the audio signal. To do this, frequency spectrum-related
characteristics were captured form the audio signal: in addition to the Mel
Frequency Cepst... | ['Pedro Girão Antunes', 'David Martins de Matos', 'Isabel Trancoso', 'Ricardo Ribeiro'] | 2014-06-17 | null | null | null | null | ['music-classification'] | ['music'] | [ 2.24445269e-01 -1.96139425e-01 1.22992843e-01 2.39157304e-01
-6.48607612e-01 -8.89291525e-01 2.94159293e-01 3.66113156e-01
-4.19271469e-01 6.29649222e-01 3.86868179e-01 1.58934042e-01
-4.64052856e-01 -5.89382648e-01 -5.08886315e-02 -5.10066748e-01
-3.08365315e-01 -2.04273015e-01 9.18515697e-02 -1.52229220... | [15.751646995544434, 5.3428473472595215] |
97b111e7-4195-4661-96aa-5b7ced36b007 | joint-perceptual-learning-for-enhancement-and | 2307.03536 | null | https://arxiv.org/abs/2307.03536v1 | https://arxiv.org/pdf/2307.03536v1.pdf | Joint Perceptual Learning for Enhancement and Object Detection in Underwater Scenarios | Underwater degraded images greatly challenge existing algorithms to detect objects of interest. Recently, researchers attempt to adopt attention mechanisms or composite connections for improving the feature representation of detectors. However, this solution does \textit{not} eliminate the impact of degradation on imag... | ['Xin Fan', 'Risheng Liu', 'Jiewen Xiao', 'Wanqi Yuan', 'Chenping Fu'] | 2023-07-07 | null | null | null | null | ['image-enhancement', 'object-detection', 'bilevel-optimization', 'multi-task-learning'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 4.10767108e-01 1.40731081e-01 5.97412407e-01 -3.88854474e-01
-6.39866114e-01 -1.76433608e-01 5.80031872e-02 -7.59378821e-02
-9.41291749e-01 5.04958093e-01 3.27978767e-02 1.58828214e-01
-1.89408496e-01 -8.80244553e-01 -9.25259054e-01 -1.14368463e+00
-1.23675160e-01 -4.89321768e-01 4.86710995e-01 -3.98137838... | [10.674208641052246, -3.5138609409332275] |
5efcf9c1-852f-47cb-ae3f-304bc8cdb39f | boosting-adversarial-robustness-from-the | 2210.05118 | null | https://arxiv.org/abs/2210.05118v1 | https://arxiv.org/pdf/2210.05118v1.pdf | Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization | The adversarial vulnerability of deep neural networks (DNNs) has been actively investigated in the past several years. This paper investigates the scale-variant property of cross-entropy loss, which is the most commonly used loss function in classification tasks, and its impact on the effective margin and adversarial r... | ['Antoni B. Chan', 'Ziquan Liu'] | 2022-10-11 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.52021542e-01 2.58664429e-01 -2.74361670e-02 -3.39253783e-01
-6.62389576e-01 -9.43078518e-01 5.26284754e-01 -2.34979272e-01
-6.83002174e-01 7.68554211e-01 -1.24628358e-01 -5.24930418e-01
-3.51750903e-04 -8.86584103e-01 -1.07052362e+00 -8.47416937e-01
-1.46062389e-01 -2.09920928e-01 3.14535469e-01 -3.43830258... | [5.661258697509766, 7.8669915199279785] |
8e117d1e-f389-4f64-94cb-cb66ab498937 | abmdrnet-adaptive-weighted-bi-directional | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_ABMDRNet_Adaptive-Weighted_Bi-Directional_Modality_Difference_Reduction_Network_for_RGB-T_Semantic_CVPR_2021_paper.pdf | ABMDRNet: Adaptive-Weighted Bi-Directional Modality Difference Reduction Network for RGB-T Semantic Segmentation | Semantic segmentation models gain robustness against poor lighting conditions by virtue of complementary information from visible (RGB) and thermal images. Despite its importance, most existing RGB-T semantic segmentation models perform primitive fusion strategies, such as concatenation, element-wise summation and ... | ['Jungong Han', 'Nianchang Huang', 'Dingwen Zhang', 'Yongjiang Luo', 'Shenlu Zhao', 'Qiang Zhang'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 7.22345293e-01 -2.50878155e-01 -1.99831072e-02 -4.74400431e-01
-8.17377329e-01 -3.50220889e-01 6.42557025e-01 7.89698493e-03
-5.32544374e-01 4.41542953e-01 1.46311708e-02 2.70624757e-02
-4.08694983e-01 -7.56365955e-01 -3.64829391e-01 -1.15294528e+00
3.56010228e-01 -2.11257756e-01 4.72169727e-01 -4.06141311... | [9.493329048156738, -1.0513101816177368] |
09f3cd63-6b51-40ad-b666-a1bc0f96aca4 | a-closer-look-at-few-shot-out-of-distribution | null | null | https://aclanthology.org/2022.coling-1.36 | https://aclanthology.org/2022.coling-1.36.pdf | A Closer Look at Few-Shot Out-of-Distribution Intent Detection | We consider few-shot out-of-distribution (OOD) intent detection, a practical and important problem for the development of task-oriented dialogue systems. Despite its importance, this problem is seldom studied in the literature, let alone examined in a systematic way. In this work, we take a closer look at this problem ... | ['Albert Y.S. Lam', 'Xiao-Ming Wu', 'Lu Fan', 'Haowen Liang', 'Li-Ming Zhan'] | null | null | null | null | coling-2022-10 | ['intent-detection', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.00167799e-01 2.24177524e-01 -3.31956774e-01 -2.67797858e-01
-5.19335926e-01 -2.28701994e-01 1.08616996e+00 1.94033206e-01
-2.12154061e-01 4.11119223e-01 6.34446919e-01 -1.58962429e-01
2.83597596e-02 -3.82804245e-01 2.55906194e-01 -4.36209559e-01
4.48433198e-02 4.36083347e-01 2.80619591e-01 -4.98203397... | [12.417348861694336, 7.549142360687256] |
54722b42-7832-4bc5-afb0-08530ab9e423 | computationally-efficient-heart-rate | null | null | https://doi.org/10.23919/EUSIPCO.2017.8081656 | https://www.researchgate.net/publication/319176582_Computationally_Efficient_Heart_Rate_Estimation_During_Physical_Exercise_Using_Photoplethysmographic_Signals | Computationally efficient heart rate estimation during physical exercise using photoplethysmographic signals | Wearable devices that acquire photoplethysmographic (PPG) signals are becoming increasingly popular to monitor the heart rate during physical exercise. However, high accuracy and low computational complexity are conflicting requirements. We propose a method that provides highly accurate heart rate estimates at a very l... | ['Abdelhak M. Zoubir', 'Tim Schäck', 'Michael Muma'] | 2017-08-28 | null | null | null | 2017-25th-european-signal-processing | ['photoplethysmography-ppg', 'heart-rate-estimation'] | ['medical', 'medical'] | [ 3.51258636e-01 -3.39962751e-01 -8.46719146e-02 -2.04709545e-01
-6.39071822e-01 -3.61063600e-01 -8.72500092e-02 1.44022346e-01
-4.36518133e-01 7.91833997e-01 -2.70336628e-01 -9.61484835e-02
-2.20617969e-02 -3.85644585e-01 9.97706503e-02 -7.40441382e-01
4.89026494e-03 -2.07821041e-01 4.51815827e-03 3.93636674... | [13.924986839294434, 2.9539175033569336] |
7eab7df8-a444-45a0-8e45-07d4c47f3d5a | landmark-tracking-in-liver-us-images-using | 2209.06952 | null | https://arxiv.org/abs/2209.06952v1 | https://arxiv.org/pdf/2209.06952v1.pdf | Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory | This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network le... | ['Xiaofeng Yang', 'Tian Liu', 'Yue Chen', 'Pretesh Patel', 'Jacob F. Wynne', 'Yang Lei', 'Zhen Tian', 'Xianjin Dai', 'Yupei Zhang'] | 2022-09-14 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [-5.94811179e-02 2.06857890e-01 -2.75226295e-01 -9.49518606e-02
-1.02794969e+00 -4.31957185e-01 2.08842829e-01 9.29297954e-02
-4.65194434e-01 2.49272734e-01 2.80026555e-01 -3.30700904e-01
-1.39986202e-01 -5.16146183e-01 -6.69431806e-01 -1.09004533e+00
-5.20686269e-01 3.02208513e-01 3.38311553e-01 2.79674619... | [14.42657470703125, -2.6494498252868652] |
69753a6a-b140-4ad5-a49a-117776fb5de9 | direction-aware-spatial-context-features-for-1 | 1805.04635 | null | https://arxiv.org/abs/1805.04635v2 | https://arxiv.org/pdf/1805.04635v2.pdf | Direction-aware Spatial Context Features for Shadow Detection and Removal | Shadow detection and shadow removal are fundamental and challenging tasks, requiring an understanding of the global image semantics. This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner. To achieve this, we first formu... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Lei Zhu', 'Jing Qin'] | 2018-05-12 | null | null | null | null | ['shadow-removal', 'shadow-detection-and-removal', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.37583613e-01 -2.20912531e-01 3.42642128e-01 -7.21635342e-01
-1.45384178e-01 -1.62697256e-01 4.07608807e-01 -4.74074572e-01
-3.84085000e-01 6.86132729e-01 3.35789144e-01 -4.47274178e-01
2.57722437e-01 -6.33317649e-01 -6.96078897e-01 -1.11230111e+00
8.55326727e-02 -3.43520820e-01 7.17536509e-01 -1.91992417... | [10.856163024902344, -4.115509986877441] |
f11ab35c-bb67-4bdc-8b25-354aae0bd88d | multi-variate-probabilistic-time-series | 2002.06103 | null | https://arxiv.org/abs/2002.06103v3 | https://arxiv.org/pdf/2002.06103v3.pdf | Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | Time series forecasting is often fundamental to scientific and engineering problems and enables decision making. With ever increasing data set sizes, a trivial solution to scale up predictions is to assume independence between interacting time series. However, modeling statistical dependencies can improve accuracy and ... | ['Abdul-Saboor Sheikh', 'Kashif Rasul', 'Urs Bergmann', 'Roland Vollgraf', 'Ingmar Schuster'] | 2020-02-14 | multivariate-probabilistic-time-series | https://openreview.net/forum?id=WiGQBFuVRv | https://openreview.net/pdf?id=WiGQBFuVRv | iclr-2021-1 | ['probabilistic-deep-learning', 'probabilistic-time-series-forecasting'] | ['computer-vision', 'time-series'] | [-1.77152202e-01 -5.07683337e-01 -2.23631430e-02 -3.57692689e-01
-3.45430344e-01 -7.14650989e-01 1.02400219e+00 1.47621468e-01
-2.13255003e-01 6.95421696e-01 3.38311315e-01 -6.00911975e-01
-4.28834081e-01 -7.87473798e-01 -8.13735783e-01 -9.85451758e-01
-8.32366347e-01 4.94723082e-01 -7.67756477e-02 -2.97916323... | [7.000034332275391, 3.1934292316436768] |
0a14b92b-4db8-48f8-b5e2-4037a87a779b | quantum-artificial-life-in-an-ibm-quantum | 1711.09442 | null | http://arxiv.org/abs/1711.09442v2 | http://arxiv.org/pdf/1711.09442v2.pdf | Quantum Artificial Life in an IBM Quantum Computer | We present the first experimental realization of a quantum artificial life
algorithm in a quantum computer. The quantum biomimetic protocol encodes
tailored quantum behaviors belonging to living systems, namely,
self-replication, mutation, interaction between individuals, and death, into
the cloud quantum computer IBM ... | ['U. Alvarez-Rodriguez', 'L. Lamata', 'E. Solano', 'M. Sanz'] | 2017-11-26 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.07954824e-01 1.12377413e-01 3.25771719e-01 1.41615614e-01
2.23466188e-01 -5.19961178e-01 8.89621317e-01 -3.12419310e-02
-2.11471722e-01 1.05730939e+00 -4.42633152e-01 -2.70457864e-01
-5.97891770e-02 -1.40420973e+00 -3.91762614e-01 -1.01395249e+00
-5.81281722e-01 9.16894734e-01 5.81968240e-02 -7.35529542... | [5.604348182678223, 4.8661417961120605] |
4ab93f2b-4826-4909-be36-267e9ef8c7a4 | contrastive-boundary-learning-for-point-cloud | 2203.05272 | null | https://arxiv.org/abs/2203.05272v2 | https://arxiv.org/pdf/2203.05272v2.pdf | Contrastive Boundary Learning for Point Cloud Segmentation | Point cloud segmentation is fundamental in understanding 3D environments. However, current 3D point cloud segmentation methods usually perform poorly on scene boundaries, which degenerates the overall segmentation performance. In this paper, we focus on the segmentation of scene boundaries. Accordingly, we first explor... | ['DaCheng Tao', 'Baosheng Yu', 'Zhe Chen', 'Yibing Zhan', 'Liyao Tang'] | 2022-03-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_Contrastive_Boundary_Learning_for_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 6.49597570e-02 -2.92329788e-01 -1.80413142e-01 -2.80638367e-01
-8.17840993e-01 -7.99278140e-01 4.98340577e-01 1.27267420e-01
1.32548204e-02 -8.16276018e-03 -1.77826539e-01 -3.84856552e-01
2.40482152e-01 -6.18787348e-01 -5.95892012e-01 -2.79248506e-01
1.06106684e-01 2.98118085e-01 5.92566848e-01 -3.21903871... | [7.969854831695557, -3.203348159790039] |
f0cbbe97-a0db-4704-b64d-da70634e2c83 | hpi-dhc-at-trec-2018-precision-medicine-track | null | null | https://trec.nist.gov/pubs/trec27/papers/hpi-dhc-PM.pdf | https://trec.nist.gov/pubs/trec27/papers/hpi-dhc-PM.pdf | HPI-DHC at TREC 2018 Precision Medicine Track | The TREC-PM challenge aims for advances in the field of information retrieval applied to precision medicine. Here we describe our experimental setup and the achieved results in its 2018 edition. We explored the use of unsupervised topic models, supervised document classification, and rule-based query-time search term b... | ['Jan-Philipp Sachs', 'Harry Freitas da Cruz', 'Ariane Morassi Sasso', 'Suparno Datta', 'Michel Oleynik', 'Erwin Bottinger', 'Benjamin Bergner', 'Erik Faessler', 'Arpita Kappattanavar'] | 2018-11-14 | null | null | null | notebook-papers-of-the-trec-2018-conference | ['negation-detection'] | ['natural-language-processing'] | [ 1.21025033e-01 2.87095219e-01 -3.22397858e-01 -2.23991722e-01
-1.06769598e+00 -3.90385032e-01 7.14032471e-01 7.76698411e-01
-9.02512908e-01 7.58934617e-01 4.31127489e-01 -4.85068381e-01
-6.37991071e-01 -5.18739700e-01 -4.38594133e-01 -3.45771670e-01
-2.01088414e-01 9.11098897e-01 3.99565995e-01 -3.34087461... | [8.770298957824707, 8.658711433410645] |
0f4e018c-8a01-4911-85ff-124f87c416a0 | deep-iterative-and-adaptive-learning-for | 1912.07832 | null | https://arxiv.org/abs/1912.07832v1 | https://arxiv.org/pdf/1912.07832v1.pdf | Deep Iterative and Adaptive Learning for Graph Neural Networks | In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and... | ['Mohammed J. Zaki', 'Lingfei Wu', 'Yu Chen'] | 2019-12-17 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-4.21599112e-02 4.30778056e-01 -2.78044164e-01 -4.40204769e-01
-6.15432858e-01 -5.42621672e-01 6.39749825e-01 3.36686075e-01
-2.76159853e-01 3.00623477e-01 2.08637401e-01 -4.91444200e-01
-1.44059762e-01 -7.84393132e-01 -8.43574822e-01 -4.36016142e-01
-3.29709500e-01 4.92733479e-01 3.25149037e-02 3.43435034... | [7.1616668701171875, 6.248135089874268] |
3d63b2a7-54bb-4563-9cfe-740c91a00d02 | neural-networks-with-manifold-learning-for | 1612.03961 | null | http://arxiv.org/abs/1612.03961v1 | http://arxiv.org/pdf/1612.03961v1.pdf | Neural Networks with Manifold Learning for Diabetic Retinopathy Detection | Widespread outreach programs using remote retinal imaging have proven to
decrease the risk from diabetic retinopathy, the leading cause of blindness in
the US. However, this process still requires manual verification of image
quality and grading of images for level of disease by a trained human grader
and will continue... | ['Christye Sisson', 'Rajeev Ramchandran', 'Arjun Raj Rajanna', 'Ali Shokoufandeh', 'Raymond Ptucha', 'Kamelia Aryafar'] | 2016-12-12 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 1.56149283e-01 -3.71657722e-02 -1.01896068e-02 -6.79294884e-01
-3.99338514e-01 -1.58561975e-01 4.25393656e-02 7.42546245e-02
-5.61386526e-01 5.97154677e-01 6.58870786e-02 -6.33863032e-01
-2.89766610e-01 -8.47397327e-01 -2.63407350e-01 -6.65442288e-01
1.59622625e-01 1.07324481e-01 2.63357133e-01 1.02176517... | [15.83666706085205, -4.002407550811768] |
0fc11582-9296-4036-9bf8-f7d258be7c2b | transfer-learning-from-partial-annotations | 1908.10851 | null | https://arxiv.org/abs/1908.10851v1 | https://arxiv.org/pdf/1908.10851v1.pdf | Transfer Learning from Partial Annotations for Whole Brain Segmentation | Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, which are often registration-based and suffer from expensive computation cost. Recently, there is an increased interest using deep neural networ... | ['Yike Guo', 'Wenjia Bai', 'Chengliang Dai', 'Elsa Angelini', 'Yuanhan Mo'] | 2019-08-28 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 3.32737982e-01 1.13578066e-01 3.29246610e-01 -5.78651845e-01
-7.93006778e-01 -3.55950981e-01 2.99320757e-01 2.37061217e-01
-1.01494801e+00 6.18489802e-01 -2.41112202e-01 -2.71666169e-01
-6.98444024e-02 -4.20791000e-01 -4.96737033e-01 -6.98795021e-01
-6.60651699e-02 9.91145968e-01 5.45437098e-01 -4.02938910... | [14.345670700073242, -2.418712615966797] |
ff4ce528-97c2-45ec-9b5f-152056e2e78d | contrastive-multiview-coding | 1906.05849 | null | https://arxiv.org/abs/1906.05849v5 | https://arxiv.org/pdf/1906.05849v5.pdf | Contrastive Multiview Coding | Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is noisy and incomplete, but important factors, such as physics, geometry, and semantics, tend to be shared between all views ... | ['Yonglong Tian', 'Phillip Isola', 'Dilip Krishnan'] | 2019-06-13 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1453_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560749.pdf | eccv-2020-8 | ['self-supervised-image-classification', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.12905884e-02 -1.33666605e-01 -1.13544121e-01 -3.91586006e-01
-6.68723464e-01 -7.86657929e-01 6.22297645e-01 -6.92587346e-02
-6.79330602e-02 3.35202903e-01 5.91890275e-01 2.89755553e-01
-4.06243280e-02 -6.99046731e-01 -1.02980614e+00 -7.42851138e-01
-3.87295820e-02 1.07125960e-01 5.70591912e-02 -3.75688337... | [9.826175689697266, 0.23607824742794037] |
c23dc3cf-539e-4625-a6e3-fdd5efc296c9 | multimathbf3net-segmenting-flooded-buildings | 1812.01756 | null | http://arxiv.org/abs/1812.01756v1 | http://arxiv.org/pdf/1812.01756v1.pdf | Multi$^{\mathbf{3}}$Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery | We propose a novel approach for rapid segmentation of flooded buildings by
fusing multiresolution, multisensor, and multitemporal satellite imagery in a
convolutional neural network. Our model significantly expedites the generation
of satellite imagery-based flood maps, crucial for first responders and local
authoritie... | ['Marc Rußwurm', 'Tim G. J. Rudner', 'Piotr Bilinski', 'Veronika Kopackova', 'Benjamin Bischke', 'Ramona Pelich', 'Jakub Fil'] | 2018-12-05 | null | null | null | null | ['segmenting-flooded-buildings'] | ['computer-vision'] | [ 4.10780728e-01 -1.99250236e-01 2.31439576e-01 -4.99889433e-01
-1.48851740e+00 -5.66751838e-01 3.90726238e-01 4.87267613e-01
-7.78970778e-01 6.05712235e-01 6.42602265e-01 -5.88858128e-01
-1.20826162e-01 -1.66798472e+00 -6.72499597e-01 -5.46384692e-01
-5.73179483e-01 4.57811147e-01 2.13809475e-01 -7.80642092... | [9.444518089294434, -1.3576301336288452] |
dc5b39c1-dd72-4827-ae38-d632373ea453 | dropgnn-random-dropouts-increase-the | 2111.06283 | null | https://arxiv.org/abs/2111.06283v1 | https://arxiv.org/pdf/2111.06283v1.pdf | DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks | This paper studies Dropout Graph Neural Networks (DropGNNs), a new approach that aims to overcome the limitations of standard GNN frameworks. In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs. Then, we combine the results o... | ['Roger Wattenhofer', 'Lukas Faber', 'Karolis Martinkus', 'Pál András Papp'] | 2021-11-11 | null | http://proceedings.neurips.cc/paper/2021/hash/b8b2926bd27d4307569ad119b6025f94-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/b8b2926bd27d4307569ad119b6025f94-Paper.pdf | neurips-2021-12 | ['graph-regression'] | ['graphs'] | [ 1.32617399e-01 2.63696134e-01 -1.91255003e-01 -1.06087990e-01
-3.68689686e-01 -6.21864557e-01 1.59057766e-01 1.44073874e-01
-6.18250608e-01 8.80615413e-01 -2.21759543e-01 -5.04879355e-01
-4.38605547e-01 -1.19058728e+00 -1.02886152e+00 -6.38257146e-01
-4.72291470e-01 2.65058309e-01 3.94354403e-01 -3.35305780... | [6.89182710647583, 6.195714473724365] |
4c6c275f-adf4-4327-af88-ada2bbd56ae5 | soloist-few-shot-task-oriented-dialog-with-a | 2005.05298 | null | https://arxiv.org/abs/2005.05298v4 | https://arxiv.org/pdf/2005.05298v4.pdf | SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching | We present a new method SOLOIST that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a Transformer-based auto-regressive language model, which subsumes different dialog modules into a single neural model. We pre-train, on hete... | ['Chunyuan Li', 'Shahin Shayandeh', 'Lars Liden', 'Jianfeng Gao', 'Jinchao Li', 'Baolin Peng'] | 2020-05-11 | null | null | null | null | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-1.98270883e-02 3.78165454e-01 7.11951032e-02 -5.39485574e-01
-8.22614133e-01 -1.00287914e+00 9.72981751e-01 -3.13840777e-01
-3.77351791e-01 7.83344209e-01 4.30663943e-01 -3.14183563e-01
1.03179090e-01 -7.38964796e-01 -3.25738817e-01 -2.04309300e-01
4.03903693e-01 1.40166020e+00 4.86932158e-01 -1.07978940... | [12.821136474609375, 8.061004638671875] |
9ac72554-b1a8-48ed-8e0a-9d1831ec8ba2 | augmented-balanced-image-dataset-generator | null | null | https://www.ijrar.org/viewfull.php?&p_id=IJRAR22B1901 | https://www.ijrar.org/papers/IJRAR22B1901.pdf | Augmented Balanced Image Dataset Generator Using AugStatic Library | The mixed data consists of various structured and unstructured data. The exponential boom of the amount of data has made the datasets of varying samples. This paper focuses on the image dataset generator that balances an imbalanced dataset using the AugStatic augmentation library. The datasets, including various classe... | ['Dr. Venkateswara Rao Gurrala', 'Allena Venkata Sai Abhishek'] | 2022-05-01 | null | null | null | international-journal-of-research-and-1 | ['image-manipulation-detection', 'image-smoothing', 'image-matting', 'image-variation', 'image-stitching', 'image-augmentation', 'image-manipulation', 'image-morphing', 'roi-based-image-generation', 'image-cropping', 'detecting-image-manipulation', 'data-visualization', 'data-visualization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 5.80710471e-01 2.57548600e-01 -2.51657695e-01 -5.88736892e-01
-4.72858548e-01 -3.92161667e-01 4.93640035e-01 3.63905847e-01
-5.26248962e-02 7.08662927e-01 1.72112063e-01 1.98820815e-03
7.92793930e-02 -1.05363429e+00 -4.90774214e-01 -7.28943169e-01
2.03802273e-01 1.01215518e+00 2.34886542e-01 -1.97344959... | [8.7741117477417, 4.262900352478027] |
18bfd1a9-bcff-4288-8b47-20fc5af4dbff | deepgestalt-identifying-rare-genetic | 1801.07637 | null | http://arxiv.org/abs/1801.07637v1 | http://arxiv.org/pdf/1801.07637v1.pdf | DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning | Facial analysis technologies have recently measured up to the capabilities of
expert clinicians in syndrome identification. To date, these technologies could
only identify phenotypes of a few diseases, limiting their role in clinical
settings where hundreds of diagnoses must be considered.
We developed a facial analy... | ['Lina Basel-Salmon', 'Omri Bar', 'Nicole Fleischer', 'Martin Zenker', 'Dekel Gelbman', 'Yaron Gurovich', 'Peter Krawitz', 'Susanne B Kamphausen', 'Yair Hanani', 'Lynne M. Bird', 'Karen W. Gripp'] | 2018-01-23 | null | null | null | null | ['medical-genetics'] | ['miscellaneous'] | [ 2.77736276e-01 2.63110191e-01 4.28729616e-02 -6.65074468e-01
-9.42954421e-01 -4.77565855e-01 3.28600071e-02 3.40964645e-01
-1.17830865e-01 4.68493372e-01 5.11921234e-02 -1.02111325e-01
-3.38227749e-01 -5.40086985e-01 -1.90946385e-01 -4.47820216e-01
-3.91629249e-01 1.16861296e+00 -5.06299794e-01 7.28656426... | [6.213565826416016, 5.7072601318359375] |
c8fc343e-f169-439b-b75b-a7da7510cf62 | partialfed-cross-domain-personalized | null | null | http://proceedings.neurips.cc/paper/2021/hash/c429429bf1f2af051f2021dc92a8ebea-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c429429bf1f2af051f2021dc92a8ebea-Paper.pdf | PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization | The burst of applications empowered by massive data have aroused unprecedented privacy concerns in AI society. Currently, data confidentiality protection has been one core issue during deep model training. Federated Learning (FL), which enables privacy-preserving training across multiple silos, gained rising popularity... | ['Bo Bai', 'Yi Yang', 'Hongxing Huo', 'Benyuan Sun'] | 2021-12-01 | null | https://openreview.net/forum?id=lgDP84byd5 | https://openreview.net/pdf?id=lgDP84byd5 | neurips-2021-12 | ['medical-image-detection'] | ['computer-vision'] | [-5.33009470e-02 -1.03332350e-04 -2.52303332e-01 -5.55515587e-01
-7.75685430e-01 -7.50131249e-01 4.16379064e-01 -1.91945303e-02
-4.02773261e-01 9.81733739e-01 -1.36933446e-01 -4.04437631e-01
-3.61981243e-01 -6.88148022e-01 -8.20337236e-01 -9.34432387e-01
-2.11078212e-01 2.30641395e-01 1.00713588e-01 9.92037803... | [5.905886650085449, 6.408730983734131] |
68178e0b-da72-474c-b8bd-cfc011f866f5 | chatgpt-or-academic-scientist-distinguishing | 2303.16352 | null | https://arxiv.org/abs/2303.16352v1 | https://arxiv.org/pdf/2303.16352v1.pdf | ChatGPT or academic scientist? Distinguishing authorship with over 99% accuracy using off-the-shelf machine learning tools | ChatGPT has enabled access to AI-generated writing for the masses, and within just a few months, this product has disrupted the knowledge economy, initiating a culture shift in the way people work, learn, and write. The need to discriminate human writing from AI is now both critical and urgent, particularly in domains ... | ['David Hua', 'Romana Jarosova', 'Madeline Isom', 'Aleesa E. Chua', 'Heather Desaire'] | 2023-03-28 | null | null | null | null | ['culture'] | ['speech'] | [ 8.31430256e-02 3.55154902e-01 -2.53946751e-01 2.91102022e-01
-3.00599009e-01 -8.70231390e-01 1.17299676e+00 4.35489267e-01
-3.91117096e-01 8.32350194e-01 2.77194142e-01 -4.77272481e-01
-3.90291452e-01 -8.42014492e-01 -1.33788854e-01 -4.17290539e-01
7.60017633e-01 8.79455864e-01 -1.38500422e-01 -2.00928852... | [9.605244636535645, 10.538883209228516] |
b9512126-047e-4313-a255-a6ff73b78da2 | an-embarrassingly-simple-model-for-dialogue | 2012.13873 | null | https://arxiv.org/abs/2012.13873v2 | https://arxiv.org/pdf/2012.13873v2.pdf | An Embarrassingly Simple Model for Dialogue Relation Extraction | Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue. In this paper, we propose a simple yet effective model named SimpleRE for the RE task. SimpleRE captures the interrelations among multiple relations in a dialogue through a novel input format named BERT Relation T... | ['Eng Siong Chng', 'Jinjie Ni', 'Hao Zhang', 'Aixin Sun', 'Fuzhao Xue'] | 2020-12-27 | null | null | null | null | ['dialog-relation-extraction'] | ['natural-language-processing'] | [ 7.79437125e-02 9.69424665e-01 -2.70172179e-01 -5.61112761e-01
-6.13669336e-01 -6.08552039e-01 9.03434277e-01 4.04192865e-01
-4.81598854e-01 9.68043923e-01 7.35127866e-01 -4.33842391e-01
1.99946642e-01 -7.63827622e-01 -1.34052172e-01 2.09427282e-01
1.39852121e-01 8.37834954e-01 5.31656861e-01 -1.00353324... | [12.43798542022705, 8.127409934997559] |
1b4581de-7c46-4777-ab7e-c0908750c49c | 190600734 | 1906.00734 | null | https://arxiv.org/abs/1906.00734v3 | https://arxiv.org/pdf/1906.00734v3.pdf | Separate In Latent Space: Unsupervised Single Image Layer Separation | Many real world vision tasks, such as reflection removal from a transparent surface and intrinsic image decomposition, can be modeled as single image layer separation. However, this problem is highly ill-posed, requiring accurately aligned and hard to collect triplet data to train the CNN models. To address this proble... | ['Yunfei Liu', 'Feng Lu'] | 2019-06-03 | null | null | null | null | ['intrinsic-image-decomposition', 'reflection-removal'] | ['computer-vision', 'computer-vision'] | [ 5.18053472e-01 1.73764750e-01 -1.80701986e-02 -3.65772009e-01
-4.77896839e-01 -2.27034122e-01 4.71723288e-01 -8.15763116e-01
-1.18119538e-01 7.40546644e-01 -1.91351265e-01 8.07776526e-02
-4.15593497e-02 -4.54586178e-01 -7.19801068e-01 -1.19683278e+00
7.04136014e-01 2.21363366e-01 6.62730709e-02 2.42831483... | [10.030004501342773, -2.6978225708007812] |
1fea16f7-4f88-473e-b8af-6ac42f194540 | zoom-in-net-deep-mining-lesions-for-diabetic | 1706.04372 | null | http://arxiv.org/abs/1706.04372v1 | http://arxiv.org/pdf/1706.04372v1.pdf | Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection | We propose a convolution neural network based algorithm for simultaneously
diagnosing diabetic retinopathy and highlighting suspicious regions. Our
contributions are two folds: 1) a network termed Zoom-in-Net which mimics the
zoom-in process of a clinician to examine the retinal images. Trained with only
image-level su... | ['Hongsheng Li', 'Jianping Shi', 'Yanxin Yin', 'Wei Fang', 'Zhe Wang', 'Xiaogang Wang'] | 2017-06-14 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.66501844e-01 3.66453201e-01 -6.38528243e-02 -4.89540607e-01
-5.01550078e-01 -1.68580078e-02 7.25935213e-03 9.14488435e-02
-1.29393294e-01 3.93099546e-01 2.49428824e-01 -3.07293952e-01
-1.89319357e-01 -6.73333466e-01 -6.06899440e-01 -6.24928534e-01
-1.32556662e-01 2.81112254e-01 5.59752703e-01 1.48750812... | [15.789183616638184, -3.955566883087158] |
e6522313-8057-4e02-9663-c622a356d9f1 | traffic-surveillance-camera-calibration-by-3d | 1702.06451 | null | http://arxiv.org/abs/1702.06451v2 | http://arxiv.org/pdf/1702.06451v2.pdf | Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement | In this paper, we focus on fully automatic traffic surveillance camera
calibration, which we use for speed measurement of passing vehicles. We improve
over a recent state-of-the-art camera calibration method for traffic
surveillance based on two detected vanishing points. More importantly, we
propose a novel automatic ... | ['Roman Juránek', 'Adam Herout', 'Jakub Sochor'] | 2017-02-21 | null | null | null | null | ['vehicle-speed-estimation'] | ['computer-vision'] | [ 1.82927903e-02 -2.66038626e-01 4.17995788e-02 -2.59778380e-01
-5.75679302e-01 -6.74129844e-01 6.16472363e-01 -5.52352779e-02
-6.99361801e-01 3.64709377e-01 -3.25954944e-01 -5.87310910e-01
2.45679468e-01 -7.29102075e-01 -8.25190604e-01 -6.00479603e-01
3.16905409e-01 4.66547966e-01 1.02075815e+00 -2.32144892... | [8.025541305541992, -1.2039401531219482] |
eafc5604-894d-4e5e-b30f-4790db3fabce | injecting-domain-knowledge-in-language-models | 2212.08120 | null | https://arxiv.org/abs/2212.08120v1 | https://arxiv.org/pdf/2212.08120v1.pdf | Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems | Pre-trained language models (PLM) have advanced the state-of-the-art across NLP applications, but lack domain-specific knowledge that does not naturally occur in pre-training data. Previous studies augmented PLMs with symbolic knowledge for different downstream NLP tasks. However, knowledge bases (KBs) utilized in thes... | ['Saab Mansour', 'Yi Zhang', 'Sawsan Alqahtani', 'Daniele Bonadiman', 'Denis Emelin'] | 2022-12-15 | null | null | null | null | ['response-generation', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [-1.37851700e-01 5.94401658e-01 -4.41783696e-01 -3.55116129e-01
-1.00644779e+00 -9.66796160e-01 7.41769075e-01 1.28649563e-01
-5.53813040e-01 1.20282376e+00 5.63838363e-01 -3.22593838e-01
5.09439744e-02 -7.57817984e-01 -7.88831651e-01 -1.19879082e-01
2.32905328e-01 9.85692620e-01 6.53842568e-01 -6.29904926... | [11.823530197143555, 8.167834281921387] |
09c3657d-1f6f-4492-a58f-542612156710 | multi-modal-multi-class-parkinson-disease | 2307.02978 | null | https://arxiv.org/abs/2307.02978v1 | https://arxiv.org/pdf/2307.02978v1.pdf | Multi-modal multi-class Parkinson disease classification using CNN and decision level fusion | Parkinson disease is the second most common neurodegenerative disorder, as reported by the World Health Organization. In this paper, we propose a direct three-Class PD classification using two different modalities, namely, MRI and DTI. The three classes used for classification are PD, Scans Without Evidence of Dopamine... | ['Ananda S. Chowdhury', 'Sushanta Kumar Sahu'] | 2023-07-06 | null | null | null | null | ['classification-1'] | ['methodology'] | [-2.09751842e-03 4.55404036e-02 -2.93166012e-01 -3.89826775e-01
-6.26474857e-01 -1.91375360e-01 5.54374337e-01 -2.41945893e-01
-6.53936327e-01 1.14018047e+00 2.91036397e-01 -7.27005079e-02
4.89373840e-02 -6.21058583e-01 -1.44297495e-01 -9.39105332e-01
-3.61908644e-01 6.58767700e-01 3.67758870e-01 2.16164693... | [14.140833854675293, -1.7807226181030273] |
9c1ef9f3-75c9-4ed2-ae47-d1132dc48baf | self-supervised-learning-of-a-facial | 1808.06882 | null | http://arxiv.org/abs/1808.06882v1 | http://arxiv.org/pdf/1808.06882v1.pdf | Self-supervised learning of a facial attribute embedding from video | We propose a self-supervised framework for learning facial attributes by
simply watching videos of a human face speaking, laughing, and moving over
time. To perform this task, we introduce a network, Facial Attributes-Net
(FAb-Net), that is trained to embed multiple frames from the same video
face-track into a common l... | ['Olivia Wiles', 'A. Sophia Koepke', 'Andrew Zisserman'] | 2018-08-21 | null | null | null | null | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 2.52894044e-01 6.25609398e-01 -2.78016239e-01 -1.03472769e+00
-7.63981700e-01 -2.85694569e-01 6.97253644e-01 -3.37654680e-01
-2.49629602e-01 4.74032342e-01 5.34886777e-01 5.51069319e-01
1.74861729e-01 -2.99662411e-01 -9.39431071e-01 -6.45933807e-01
-4.06445742e-01 3.87686431e-01 -3.79355550e-01 3.10751855... | [13.462626457214355, 1.2177560329437256] |
4d684b6f-4406-4574-b29f-e1a8702bd88a | assisted-sound-sample-generation-with-musical | 1904.06215 | null | https://arxiv.org/abs/1904.06215v2 | https://arxiv.org/pdf/1904.06215v2.pdf | Assisted Sound Sample Generation with Musical Conditioning in Adversarial Auto-Encoders | Generative models have thrived in computer vision, enabling unprecedented image processes. Yet the results in audio remain less advanced. Our project targets real-time sound synthesis from a reduced set of high-level parameters, including semantic controls that can be adapted to different sound libraries and specific t... | ['Adrien Bitton', 'Antoine Caillon', 'Martin Fouilleul', 'Philippe Esling'] | 2019-04-12 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.28700089e-01 1.12845793e-01 3.37492734e-01 -1.48368359e-01
-8.84719431e-01 -1.22489929e+00 6.52479351e-01 -4.76643980e-01
-6.88332692e-03 3.96397829e-01 4.05860730e-02 5.00780419e-02
-1.17985778e-01 -9.25483704e-01 -6.64218307e-01 -8.02664638e-01
-1.87958777e-01 6.62808657e-01 2.03125700e-01 -2.88652003... | [15.759958267211914, 5.804262638092041] |
2c4c370c-b0bf-4d36-bf63-00b5effee826 | harnessing-multilinguality-in-unsupervised | 2009.11201 | null | https://arxiv.org/abs/2009.11201v2 | https://arxiv.org/pdf/2009.11201v2.pdf | Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages | Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this... | ['Aditya Siddhant', 'Xavier Garcia', 'Orhan Firat', 'Ankur P. Parikh'] | 2020-09-23 | null | https://aclanthology.org/2021.naacl-main.89 | https://aclanthology.org/2021.naacl-main.89.pdf | naacl-2021-4 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.83665708e-01 -2.76025832e-01 -4.23719496e-01 -3.50534558e-01
-1.60872841e+00 -8.95543873e-01 8.62968326e-01 -9.53826308e-02
-7.60691941e-01 1.08622885e+00 5.11715412e-01 -8.09554875e-01
3.13049883e-01 -2.68809319e-01 -6.83179438e-01 -2.91686237e-01
1.20813221e-01 7.07123876e-01 -1.64724976e-01 -4.78115588... | [11.559585571289062, 10.341249465942383] |
6f778f7b-1a1a-4952-8abf-5cbe325fbd7c | analysis-of-adversarial-attacks-against-cnn | 1808.08426 | null | http://arxiv.org/abs/1808.08426v1 | http://arxiv.org/pdf/1808.08426v1.pdf | Analysis of adversarial attacks against CNN-based image forgery detectors | With the ubiquitous diffusion of social networks, images are becoming a
dominant and powerful communication channel. Not surprisingly, they are also
increasingly subject to manipulations aimed at distorting information and
spreading fake news. In recent years, the scientific community has devoted
major efforts to contr... | ['Luisa Verdoliva', 'Diego Gragnaniello', 'Giovanni Poggi', 'Francesco Marra'] | 2018-08-25 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 2.06660405e-01 -6.97788596e-02 6.08245470e-02 1.50710434e-01
-2.56404966e-01 -8.03195238e-01 9.04576242e-01 1.76454440e-01
-5.16638875e-01 5.63148558e-01 -1.22235872e-01 -3.48478705e-01
3.56750399e-01 -9.58123744e-01 -7.54012883e-01 -6.07631743e-01
-2.57574469e-01 -9.02093500e-02 5.48475146e-01 -4.56944436... | [12.500328063964844, 1.1254485845565796] |
25b3d753-2cb9-4181-82b0-68d2e0910bf9 | thor-net-end-to-end-graformer-based-realistic | 2210.13853 | null | https://arxiv.org/abs/2210.13853v1 | https://arxiv.org/pdf/2210.13853v1.pdf | THOR-Net: End-to-end Graformer-based Realistic Two Hands and Object Reconstruction with Self-supervision | Realistic reconstruction of two hands interacting with objects is a new and challenging problem that is essential for building personalized Virtual and Augmented Reality environments. Graph Convolutional networks (GCNs) allow for the preservation of the topologies of hands poses and shapes by modeling them as a graph. ... | ['Didier Stricker', 'Nadia Robertini', 'Ahmed Elhayek', 'Jameel Malik', 'Ahmed Tawfik Aboukhadra'] | 2022-10-25 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [-1.30167171e-01 2.48086601e-01 3.63851815e-01 -2.06101477e-01
-4.67667818e-01 -3.75049978e-01 4.25758868e-01 -2.21520007e-01
-2.60864586e-01 5.25040030e-01 -1.40821800e-01 2.12527573e-01
-1.28489425e-02 -8.23743701e-01 -1.04714084e+00 -5.14981568e-01
2.62139648e-01 1.26430547e+00 4.22457188e-01 -3.07756096... | [6.903442859649658, -1.212636947631836] |
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