paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
874729a7-8d34-4ca9-9d80-487cc6f2f51b | mlbf-net-a-multi-lead-branch-fusion-network | 2008.07263 | null | https://arxiv.org/abs/2008.07263v1 | https://arxiv.org/pdf/2008.07263v1.pdf | MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG | Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of fe... | ['Aiping Liu', 'Min Gao', 'Jing Zhang', 'Xu Zhang', 'Xun Chen', 'Xiang Chen', 'Deng Liang'] | 2020-08-17 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 4.54159640e-02 -4.55007821e-01 1.38686061e-01 -3.89151007e-01
-1.06028724e+00 -4.22122329e-01 -5.09074569e-01 1.23484001e-01
-2.30546549e-01 5.80360949e-01 3.41058634e-02 -2.58635789e-01
-5.49197197e-01 -4.12948221e-01 -2.50345945e-01 -8.16092372e-01
-4.34493333e-01 7.73363784e-02 -5.34760714e-01 -1.58592127... | [14.272746086120605, 3.251847982406616] |
cca66376-a139-4213-bc79-e157afc4481a | unssor-unsupervised-neural-speech-separation | 2305.20054 | null | https://arxiv.org/abs/2305.20054v1 | https://arxiv.org/pdf/2305.20054v1.pdf | UNSSOR: Unsupervised Neural Speech Separation by Leveraging Over-determined Training Mixtures | In reverberant conditions with multiple concurrent speakers, each microphone acquires a mixture signal of multiple speakers at a different location. In over-determined conditions where the microphones out-number speakers, we can narrow down the solutions to speaker images and realize unsupervised speech separation by l... | ['Shinji Watanabe', 'Zhong-Qiu Wang'] | 2023-05-31 | null | null | null | null | ['speech-separation', 'speaker-separation'] | ['speech', 'speech'] | [ 1.45078003e-01 -7.72938654e-02 3.90592307e-01 -2.71932602e-01
-1.18857980e+00 -4.89987344e-01 3.49145383e-02 -4.26626146e-01
-2.72215605e-01 4.47133571e-01 2.32786462e-01 -2.14992031e-01
-2.43834555e-01 -4.90375459e-01 -8.77505779e-01 -1.09908772e+00
-1.46439284e-01 2.55401395e-02 -2.85242766e-01 1.12898536... | [15.099431037902832, 5.919931888580322] |
d93df94d-59df-4e19-abfa-32112263ec95 | jump-starting-item-parameters-for-adaptive | null | null | https://aclanthology.org/2021.emnlp-main.67 | https://aclanthology.org/2021.emnlp-main.67.pdf | Jump-Starting Item Parameters for Adaptive Language Tests | A challenge in designing high-stakes language assessments is calibrating the test item difficulties, either a priori or from limited pilot test data. While prior work has addressed ‘cold start’ estimation of item difficulties without piloting, we devise a multi-task generalized linear model with BERT features to jump-s... | ['Burr Settles', 'Manqian Liao', 'Jesse Egbert', 'Geoff T. LaFlair', 'Kevin P. Yancey', 'Arya D. McCarthy'] | null | null | null | null | emnlp-2021-11 | ['skills-assessment', 'language-acquisition'] | ['computer-vision', 'natural-language-processing'] | [-3.65842789e-01 2.59205908e-01 -4.87657726e-01 -2.35840455e-01
-1.52064514e+00 -1.00365055e+00 9.87883359e-02 5.80108821e-01
-1.05942249e+00 8.26502919e-01 5.57060957e-01 -6.67042732e-01
-6.50039196e-01 -7.08136559e-01 -4.90664214e-01 2.34531835e-01
5.21882534e-01 3.68876487e-01 1.11166947e-01 -3.84997926... | [10.953479766845703, 9.912497520446777] |
9592d7dd-eb5b-460a-96c5-dc350a2c8bf0 | ms-ranker-accumulating-evidence-from | 2010.04970 | null | https://arxiv.org/abs/2010.04970v1 | https://arxiv.org/pdf/2010.04970v1.pdf | MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection | As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the question and the candidate. To address this problem, we propose a novel reinforcement learning (RL) based multi-step ranking model, named MS-... | ['Jie zhou', 'Ping Jian', 'Peng Li', 'Fandong Meng', 'Yingxue Zhang'] | 2020-10-10 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-8.71429220e-02 -1.99041087e-02 -2.99148202e-01 -5.07082582e-01
-1.41286206e+00 -6.76349998e-01 3.71724397e-01 6.16152883e-01
-7.36581147e-01 7.86700547e-01 3.66746336e-01 -3.01723987e-01
-2.48345390e-01 -9.94085550e-01 -7.57350087e-01 9.67738777e-03
4.13510233e-01 6.70141697e-01 7.61056602e-01 -3.11856717... | [11.241872787475586, 8.017742156982422] |
2b4d7038-76f4-4971-9ade-7994116e1c86 | high-temporal-resolution-event-based-vehicle | 2212.14289 | null | https://arxiv.org/abs/2212.14289v2 | https://arxiv.org/pdf/2212.14289v2.pdf | High-temporal-resolution event-based vehicle detection and tracking | Event-based vision has been rapidly growing in recent years justified by the unique characteristics it presents such as its high temporal resolutions (~1us), high dynamic range (>120dB), and output latency of only a few microseconds. This work further explores a hybrid, multi-modal, approach for object detection and tr... | ['Samir Rawashdeh', 'Zaid El-Shair'] | 2022-12-29 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 1.84070110e-01 -4.49412853e-01 1.51090175e-01 1.83498207e-02
-9.71018016e-01 -4.61052895e-01 6.27158344e-01 7.36134499e-02
-7.48278975e-01 5.93696773e-01 -3.28714699e-01 -2.08453834e-01
-2.50480790e-02 -5.64071000e-01 -6.09651983e-01 -3.73557180e-01
-2.68473655e-01 1.90003216e-01 1.12891757e+00 1.83531061... | [6.612081527709961, -2.0080997943878174] |
7d62803d-bb77-49fb-a56c-7c8a187dd381 | time-aware-graph-structure-learning-via | 2306.07699 | null | https://arxiv.org/abs/2306.07699v1 | https://arxiv.org/pdf/2306.07699v1.pdf | Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs | Temporal Graph Learning, which aims to model the time-evolving nature of graphs, has gained increasing attention and achieved remarkable performance recently. However, in reality, graph structures are often incomplete and noisy, which hinders temporal graph networks (TGNs) from learning informative representations. Gra... | ['Jing Bai', 'Xi Xiao', 'Xueting Han', 'Haozhen Zhang'] | 2023-06-13 | null | null | null | null | ['graph-structure-learning', 'link-prediction'] | ['graphs', 'graphs'] | [ 1.97497323e-01 3.04755241e-01 -6.32033050e-01 -2.08543792e-01
-3.59662235e-01 -5.33604145e-01 6.56199872e-01 4.11016017e-01
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-5.72842002e-01 -8.36113393e-01 -6.46968782e-01 -6.69255972e-01
-7.55507648e-01 3.71501803e-01 4.11119848e-01 -1.75582290... | [7.256490230560303, 6.014803409576416] |
3f456cf4-15f3-4c33-b424-e977ca8245e0 | cspn-learning-context-and-resource-aware | 1911.05377 | null | https://arxiv.org/abs/1911.05377v2 | https://arxiv.org/pdf/1911.05377v2.pdf | CSPN++: Learning Context and Resource Aware Convolutional Spatial Propagation Networks for Depth Completion | Depth Completion deals with the problem of converting a sparse depth map to a dense one, given the corresponding color image. Convolutional spatial propagation network (CSPN) is one of the state-of-the-art (SoTA) methods of depth completion, which recovers structural details of the scene. In this paper, we propose CSPN... | ['Ruigang Yang', 'Xinjing Cheng', 'Peng Wang', 'Chenye Guan'] | 2019-11-13 | null | null | null | null | ['stereo-lidar-fusion'] | ['computer-vision'] | [ 2.24305972e-01 -1.82507306e-01 6.82268813e-02 -2.76422203e-01
-5.30897915e-01 -2.05264062e-01 3.91527414e-01 -6.26005381e-02
-6.70011222e-01 3.90682459e-01 1.54094189e-01 -9.84223336e-02
-1.12808738e-02 -1.05107737e+00 -6.21641576e-01 -7.97620416e-01
6.60056099e-02 3.81505728e-01 5.70072353e-01 8.28252211... | [8.776022911071777, -2.2460436820983887] |
d985734d-24a9-43f9-8192-f5111f3b078a | chatgpt-to-replace-crowdsourcing-of | 2305.12947 | null | https://arxiv.org/abs/2305.12947v1 | https://arxiv.org/pdf/2305.12947v1.pdf | ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness | The emergence of generative large language models (LLMs) raises the question: what will be its impact on crowdsourcing. Traditionally, crowdsourcing has been used for acquiring solutions to a wide variety of human-intelligence tasks, including ones involving text generation, manipulation or evaluation. For some of thes... | ['Peter Brusilovsky', 'Jakub Simko', 'Jan Cegin'] | 2023-05-22 | null | null | null | null | ['paraphrase-generation', 'paraphrase-generation', 'intent-classification'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 1.35484844e-01 3.37971687e-01 9.32390541e-02 -1.88052371e-01
-8.84898067e-01 -8.26012909e-01 9.77744222e-01 1.71789899e-01
-5.92363417e-01 8.82604599e-01 6.31388187e-01 -2.79107928e-01
2.53595382e-01 -5.80527723e-01 -5.16540110e-01 -4.13148165e-01
5.02398312e-01 8.16857517e-01 3.44139874e-01 -5.53410769... | [11.728463172912598, 8.445504188537598] |
4530dd4b-4d74-484e-a6ec-9d650c47a5c2 | reflection-removal-for-in-vehicle-black-box | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Simon_Reflection_Removal_for_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Simon_Reflection_Removal_for_2015_CVPR_paper.pdf | Reflection Removal for In-Vehicle Black Box Videos | In-vehicle black box camera becomes an popular equipment in many countries for security monitoring and event capturing. The readability of video contents is the most important capability, which is, however, often degraded due to the reflection on the windscreen. In this paper, we propose a novel method to remove the re... | ['In Kyu Park', 'Christian Simon'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['reflection-removal'] | ['computer-vision'] | [ 1.72724620e-01 -1.75654322e-01 1.23761944e-01 -1.64911970e-01
-2.18860209e-01 -2.94024438e-01 3.81002694e-01 -4.08890992e-01
-5.29136717e-01 4.05453652e-01 2.35020500e-02 -1.39823854e-01
-1.33927077e-01 -5.56207895e-01 -8.62590253e-01 -1.05223954e+00
1.15435772e-01 -2.05164537e-01 5.98137021e-01 9.21708867... | [9.087042808532715, -1.0335736274719238] |
2ce3b540-5f4d-465c-bdf4-cac76761da55 | sca-net-a-self-correcting-two-layer | 2102.05713 | null | https://arxiv.org/abs/2102.05713v5 | https://arxiv.org/pdf/2102.05713v5.pdf | SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing | Hyperspectral unmixing involves separating a pixel as a weighted combination of its constituent endmembers and corresponding fractional abundances, with the current state of the art results achieved by neural models on benchmark datasets. However, these networks are severely over-parameterized and consequently, the inv... | ['Clint Dawson', 'Soumyajit Gupta', 'Gurpreet Singh'] | 2021-02-10 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.78543341e-01 -5.97023427e-01 2.88448244e-01 -2.51413286e-01
-5.63578308e-01 -4.57059324e-01 5.61934888e-01 4.08281758e-02
-5.72379529e-01 9.19188142e-01 -5.27574457e-02 -1.22435167e-01
-3.27688754e-01 -7.82603860e-01 -7.81432867e-01 -1.34729564e+00
-3.20694536e-01 1.99036494e-01 -3.72981459e-01 -5.17365001... | [10.09930419921875, -2.052030563354492] |
e7fc37d2-ca1d-40ce-a659-2ae0ed2beb0f | sim-to-real-learning-for-casualty-detection | 1908.03057 | null | https://arxiv.org/abs/1908.03057v2 | https://arxiv.org/pdf/1908.03057v2.pdf | Sim-to-Real Learning for Casualty Detection from Ground Projected Point Cloud Data | This paper addresses the problem of human body detection---particularly a human body lying on the ground (a.k.a. casualty)---using point cloud data. This ability to detect a casualty is one of the most important features of mobile rescue robots, in order for them to be able to operate autonomously. We propose a deep-le... | ['Roni Permana Saputra', 'Petar Kormushev', 'Nemanja Rakicevic'] | 2019-08-08 | null | null | null | null | ['body-detection'] | ['computer-vision'] | [ 3.55035514e-01 3.45777005e-01 5.87190032e-01 -3.49349082e-01
-1.28398210e-01 2.06906945e-01 1.43255487e-01 1.83365971e-01
-6.87751293e-01 2.61790067e-01 -2.60165960e-01 -4.74312007e-02
-4.69978712e-02 -1.20355320e+00 -9.86610115e-01 -4.44699615e-01
-5.64346194e-01 8.48609805e-01 2.27292806e-01 -8.59477639... | [7.757058143615723, -1.8780211210250854] |
7a4d53e3-3fbd-4ac5-a47f-64142d00dbdf | human-activity-recognition-using-deep-1 | 2304.14499 | null | https://arxiv.org/abs/2304.14499v1 | https://arxiv.org/pdf/2304.14499v1.pdf | Human activity recognition using deep learning approaches and single frame cnn and convolutional lstm | Human activity recognition is one of the most important tasks in computer vision and has proved useful in different fields such as healthcare, sports training and security. There are a number of approaches that have been explored to solve this task, some of them involving sensor data, and some involving video data. In ... | ['Manoj Kumar Rajagopal', 'Balamurugan MS', 'Pooja', 'Annapoorani Subramanian', 'Sheryl Mathew'] | 2023-04-18 | null | null | null | null | ['action-recognition-in-videos', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 4.93662477e-01 -9.08396766e-02 -2.10910723e-01 -2.42017671e-01
-9.38222408e-02 -4.83605340e-02 7.69068182e-01 -2.01037243e-01
-8.36026967e-01 8.36131394e-01 3.07076603e-01 -1.44121721e-02
-1.42818406e-01 -7.54342079e-01 -6.72914147e-01 -6.99941993e-01
-2.76108325e-01 -3.31537500e-02 4.98754293e-01 1.03254162... | [8.015755653381348, 0.522996723651886] |
e76c94bb-2699-4a9a-ad06-b36c8cdb9623 | vicomtech-at-ehealth-kd-challenge-2020-deep | null | null | http://ceur-ws.org/Vol-2664/eHealth-KD_paper3.pdf | http://ceur-ws.org/Vol-2664/eHealth-KD_paper3.pdf | Vicomtech at eHealth-KD Challenge 2020: Deep End-to-End Model for Entity and Relation Extraction in Medical Text | This paper describes the participation of the Vicomtech NLP team in the eHealth-KD 2020 shared task about
detecting and classifying entities and relations in health-related texts written in Spanish. The proposed system
consists of a single end-to-end deep neural network with pre-trained BERT models as the core for th... | ['Montse Cuadros and Elena Zotova', 'Naiara Perez', 'Aitor García-Pablos'] | 2020-09-20 | null | null | null | null | ['medical-procedure', 'multi-label-classification-of-biomedical'] | ['medical', 'medical'] | [-3.31122041e-01 1.03655887e+00 2.92199496e-02 -5.36442578e-01
-6.30631268e-01 -2.22266361e-01 7.92336702e-01 4.46393132e-01
-7.91870058e-01 7.90466785e-01 4.23609406e-01 -4.55991417e-01
-1.92442492e-01 -6.17439687e-01 -7.03608990e-01 -1.32774413e-01
-1.06786393e-01 1.46803057e+00 9.40473601e-02 -4.27699566... | [8.796296119689941, 8.794349670410156] |
b71b7123-e520-4b3d-80c3-24d443af6138 | an-iterative-similarity-based-adaptation | null | null | https://aclanthology.org/K15-1006 | https://aclanthology.org/K15-1006.pdf | An Iterative Similarity based Adaptation Technique for Cross-domain Text Classification | null | ['Shourya Roy', 'Himanshu Sharad Bhatt', 'Deepali Semwal'] | 2015-07-01 | null | null | null | conll-2015-7 | ['cross-domain-text-classification'] | ['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... | [-7.278365135192871, 3.7177627086639404] |
3a9021d6-72d7-4416-9f55-5165f00b4a60 | deeptilebars-visualizing-term-distribution | 1811.00606 | null | https://arxiv.org/abs/1811.00606v3 | https://arxiv.org/pdf/1811.00606v3.pdf | DeepTileBars: Visualizing Term Distribution for Neural Information Retrieval | Most neural Information Retrieval (Neu-IR) models derive query-to-document ranking scores based on term-level matching. Inspired by TileBars, a classical term distribution visualization method, in this paper, we propose a novel Neu-IR model that handles query-to-document matching at the subtopic and higher levels. Our ... | ['Zhiwen Tang', 'Grace Hui Yang'] | 2018-11-01 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-1.85933858e-01 -6.46590367e-02 -3.38495344e-01 -2.53605306e-01
-9.75867033e-01 -6.08181059e-01 1.09280741e+00 6.15742505e-01
-1.41795516e-01 2.79183481e-02 8.64626706e-01 -5.65719664e-01
-7.18941629e-01 -6.32937431e-01 -1.57622993e-01 -3.49627763e-01
-3.43358099e-01 6.54515266e-01 3.15842986e-01 -4.27712619... | [11.48652458190918, 7.579827308654785] |
e1ea5465-f3d7-4204-8e6f-a4c014d56bda | towards-an-efficient-iris-recognition-system | 2210.13101 | null | https://arxiv.org/abs/2210.13101v1 | https://arxiv.org/pdf/2210.13101v1.pdf | Towards an efficient Iris Recognition System on Embedded Devices | Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is requ... | ['Christoph Busch', 'Enrique Lopez Droguett', 'Leonardo Causa', 'Mauricio Vasquez', 'Juan E. Tapia', 'Daniel P. Benalcazar'] | 2022-10-24 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 4.68681306e-01 -1.71218380e-01 9.29280967e-02 -2.24794999e-01
-3.64366435e-02 -5.34296632e-01 -1.00164652e-01 -3.93567950e-01
-4.50603038e-01 1.43890545e-01 -2.86007613e-01 -7.35535383e-01
1.03219964e-01 -5.42304337e-01 -3.95276427e-01 -5.26003242e-01
7.34015882e-01 -9.53843147e-02 -2.77552277e-01 2.97502559... | [3.7464237213134766, -3.6280136108398438] |
b4b3588f-a287-45fc-b288-37d3398be2c7 | real-time-multiple-people-tracking-with | 1809.04427 | null | http://arxiv.org/abs/1809.04427v1 | http://arxiv.org/pdf/1809.04427v1.pdf | Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification | Online multi-object tracking is a fundamental problem in time-critical video
analysis applications. A major challenge in the popular tracking-by-detection
framework is how to associate unreliable detection results with existing
tracks. In this paper, we propose to handle unreliable detection by collecting
candidates fr... | ['Haizhou Ai', 'Chong Shang', 'Zijie Zhuang', 'Long Chen'] | 2018-09-12 | null | null | null | null | ['multiple-people-tracking', 'large-scale-person-re-identification', 'online-multi-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.08062524e-01 -6.96472824e-01 -9.76175666e-02 -3.07553858e-01
-7.02021599e-01 -6.19329870e-01 1.62329301e-01 -1.58756793e-01
-6.19947731e-01 6.56242907e-01 -9.37020108e-02 2.19716460e-01
2.87398219e-01 -4.16583598e-01 -8.73214483e-01 -4.26150620e-01
-1.24883093e-01 3.72394174e-01 6.18705094e-01 1.37877613... | [6.448641777038574, -1.9335602521896362] |
b0d2aa79-0a04-4cd6-ad05-6852e13380bc | instant-image-denoising-plugin-for-imagej | 2006.13801 | null | http://arxiv.org/abs/2006.13801v1 | http://arxiv.org/pdf/2006.13801v1.pdf | Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks | We present a new convolutional neural network (CNN) based ImageJ plugin for
fluorescence microscopy image denoising with an average improvement of 7.5 dB
in peak signal-to-noise ratio (PSNR) and denoising instantly within 80 msec. | [] | 2020-06-23 | null | null | null | null | ['intensity-image-denoising'] | ['computer-vision'] | [ 1.96899742e-01 -4.76455152e-01 9.53196347e-01 -4.27749395e-01
-8.20968390e-01 -2.73293912e-01 -1.02267839e-01 3.56895745e-01
-1.25577521e+00 8.83885026e-01 -4.10376936e-01 -3.16132784e-01
4.28004980e-01 -2.81881899e-01 -6.91578627e-01 -1.07616329e+00
-1.35363221e-01 -6.59264386e-01 2.54339784e-01 1.07837655... | [13.035679817199707, -2.6352291107177734] |
814e75aa-c776-43d0-8300-94a01513ed9e | program-synthesis-for-the-oeis | 2202.11908 | null | https://arxiv.org/abs/2202.11908v3 | https://arxiv.org/pdf/2202.11908v3.pdf | Learning Program Synthesis for Integer Sequences from Scratch | We present a self-learning approach for synthesizing programs from integer sequences. Our method relies on a tree search guided by a learned policy. Our system is tested on the On-Line Encyclopedia of Integer Sequences. There, it discovers, on its own, solutions for 27987 sequences starting from basic operators and wit... | ['Josef Urban', 'Thibault Gauthier'] | 2022-02-24 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.56995529e-01 1.61400050e-01 -9.87064362e-01 -2.22385019e-01
-8.62105012e-01 -7.89342523e-01 8.45116153e-02 1.26840934e-01
-3.64505559e-01 1.27100956e+00 -5.12311123e-02 -9.49413180e-01
4.86417785e-02 -8.88264716e-01 -8.40676069e-01 -3.46033096e-01
-5.88998735e-01 5.76506436e-01 3.74084115e-01 -5.94953716... | [8.230254173278809, 7.393875598907471] |
8cc71dfa-f5ba-4135-9674-fd092316784f | open-set-domain-adaptation-by-novel-class | 2203.03329 | null | https://arxiv.org/abs/2203.03329v1 | https://arxiv.org/pdf/2203.03329v1.pdf | Open Set Domain Adaptation By Novel Class Discovery | In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain. Due to the lack of their specific belonging, existing methods indiscriminately regard them as a single class unknown. We challenge this broadly-adopted practice that may a... | ['Liang Lin', 'Guanbin Li', 'Pengxu Wei', 'Ziliang Chen', 'Jingyu Zhuang'] | 2022-03-07 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 2.22072288e-01 3.89133632e-01 -5.30592084e-01 -5.79046667e-01
-6.21869862e-01 -7.37968564e-01 5.65055430e-01 4.77369828e-03
-3.02090794e-01 1.00681019e+00 2.28160582e-02 1.89095177e-03
-4.41744961e-02 -6.85154974e-01 -6.38687611e-01 -6.57473683e-01
1.97786778e-01 7.65383720e-01 4.00052071e-01 1.38164669... | [10.294285774230957, 3.151414155960083] |
0fdaa35d-08d1-4030-8f4c-521a5c0d639c | visual-place-recognition-with-low-resolution | 2305.05776 | null | https://arxiv.org/abs/2305.05776v1 | https://arxiv.org/pdf/2305.05776v1.pdf | Visual Place Recognition with Low-Resolution Images | Images incorporate a wealth of information from a robot's surroundings. With the widespread availability of compact cameras, visual information has become increasingly popular for addressing the localisation problem, which is then termed as Visual Place Recognition (VPR). While many applications use high-resolution cam... | ['Shoaib Ehsan', 'Klaus McDonald-Maier', 'Michael Milford', 'Bruno Ferrarini', 'Mihnea-Alexandru Tomita'] | 2023-05-09 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 9.26571712e-02 -3.63813490e-01 -1.67715460e-01 -2.19441056e-01
-3.89156640e-01 -6.76204860e-01 6.70993447e-01 -1.06051914e-01
-6.45762682e-01 2.68418640e-01 -8.32038298e-02 4.32605632e-02
-3.12915146e-02 -5.24827898e-01 -5.75269699e-01 -2.33060405e-01
6.45142943e-02 1.89086109e-01 6.37668312e-01 -2.50831366... | [7.531856536865234, -1.9235386848449707] |
8b92b888-0a54-413e-b17a-a7e8c25f0778 | abaw-valence-arousal-estimation-expression | 2202.10659 | null | https://arxiv.org/abs/2202.10659v2 | https://arxiv.org/pdf/2202.10659v2.pdf | ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges | This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, IEEE FG 2020 and IEEE CVPR 2017 Con... | ['Dimitrios Kollias'] | 2022-02-22 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.89953709e-01 -1.09497100e-01 -2.26357188e-02 -7.52839029e-01
-9.37880576e-01 -4.31917846e-01 5.13965428e-01 2.03243196e-01
-5.83572567e-01 8.13627481e-01 2.96777397e-01 8.40658188e-01
4.84821469e-01 1.05098329e-01 -6.15658723e-02 -6.38728738e-01
-3.85499477e-01 1.83167025e-01 -3.46201003e-01 -2.81568736... | [13.570419311523438, 2.220968246459961] |
518acb38-bd27-4f8f-a432-b3cebe43bca1 | a-new-humanlike-facial-attractiveness | 1511.02465 | null | http://arxiv.org/abs/1511.02465v1 | http://arxiv.org/pdf/1511.02465v1.pdf | A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model | This paper proposes a deep leaning method to address the challenging facial
attractiveness prediction problem. The method constructs a convolutional neural
network of facial beauty prediction using a new deep cascaded fine-turning
scheme with various face inputting channels, such as the original RGB face
image, the det... | ['Lingyu Liang', 'Lianwen Jin', 'Ziyong Feng', 'Jie Xu', 'Duorui Xie'] | 2015-11-08 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [-3.46680582e-01 3.18923593e-01 -6.20324686e-02 -8.81357133e-01
5.46373785e-01 2.21146047e-01 1.93678662e-01 -3.52045149e-01
-1.52154461e-01 2.15758830e-01 2.03022659e-01 1.04590869e-02
-1.15975380e-01 -8.03065419e-01 -4.13992226e-01 -6.92980945e-01
-6.59655333e-02 -2.21216112e-01 -4.77082044e-01 -7.01687932... | [13.402387619018555, 1.1830917596817017] |
e19bb30d-7407-43c3-9479-0dc11b535333 | perimeter-control-autonomous-vehicle-and | 2307.02156 | null | https://arxiv.org/abs/2307.02156v1 | https://arxiv.org/pdf/2307.02156v1.pdf | Perimeter control, autonomous vehicle, and urban spatial structure | This paper examines the effects of hypercongestion mitigation by perimeter control and the introduction of autonomous vehicles on the spatial structures of cities. By incorporating a bathtub model, we develop a land use model where hypercongestion occurs in the downtown area and interacts with land use. We show that hy... | ['Yuki Takayama', 'Takao Dantsuji'] | 2023-07-05 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [-7.07685351e-01 3.89840066e-01 -3.53453428e-01 3.38905066e-01
3.55251104e-01 -5.23854792e-01 4.74587321e-01 1.17239378e-01
-7.35264957e-01 1.05836892e+00 1.53973460e-01 -9.84936774e-01
-3.01627070e-01 -1.60253823e+00 -6.01498485e-01 -7.03216791e-01
-1.86289832e-01 6.00036867e-02 5.37554145e-01 -6.23137414... | [5.639978885650635, 1.569669485092163] |
519eec58-4862-4f4c-ab80-51da13d90700 | becoming-self-instruct-introducing-early | 2307.03692 | null | https://arxiv.org/abs/2307.03692v1 | https://arxiv.org/pdf/2307.03692v1.pdf | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric that detects language models' ability to follow instructions. The metric has a dual purpose. First, IFS can be used to distinguish between base and instruct models. We benchmark publicly available base and instruct models, and show that the rat... | ['Melisa Russak', 'Parikshith Kulkarni', 'Kiran Kamble', 'Brock Imel', 'Kirk Goddard', 'Manhal Daaboul', 'Waseem AlShikh'] | 2023-07-05 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.25188604e-01 1.94691122e-01 -3.69050562e-01 -8.95552516e-01
-7.03549385e-01 -8.76542568e-01 6.67919397e-01 2.71507770e-01
-4.79445666e-01 5.62209368e-01 4.64055061e-01 -6.78779542e-01
-2.26553395e-01 -7.09939718e-01 -8.34726989e-01 -2.59549528e-01
3.26488227e-01 6.16219521e-01 4.95164037e-01 -7.24193335... | [10.654903411865234, 8.221073150634766] |
79e6c126-ddce-4dad-96b1-18d435d07306 | guiding-neural-entity-alignment-with | 2211.15833 | null | https://arxiv.org/abs/2211.15833v1 | https://arxiv.org/pdf/2211.15833v1.pdf | Guiding Neural Entity Alignment with Compatibility | Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled data only. In this work, we argue that different entities within one KG should have compatible counterparts in the other KG due to the pote... | ['Xia Zhang', 'Genghong Zhao', 'Guido Zuccon', 'Wen Hua', 'Harrisen Scells', 'Bing Liu'] | 2022-11-29 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [ 1.67259112e-01 6.75945103e-01 -5.19133583e-02 -4.89088655e-01
6.73095062e-02 -3.76444429e-01 2.80716538e-01 3.36671650e-01
-6.80082619e-01 6.71935260e-01 -7.68684298e-02 -3.00933868e-01
-3.32150310e-01 -1.01252270e+00 -9.82100546e-01 -3.70113015e-01
-1.75514325e-01 7.79800057e-01 3.17346931e-01 -3.45659137... | [9.069852828979492, 8.260058403015137] |
2d0373f3-b5f0-4e3a-b358-57c33c40cca5 | the-sensitivity-of-topic-coherence-evaluation | null | null | https://aclanthology.org/N16-1057 | https://aclanthology.org/N16-1057.pdf | The Sensitivity of Topic Coherence Evaluation to Topic Cardinality | null | ['Timothy Baldwin', 'Jey Han Lau'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['coherence-evaluation'] | ['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... | [-7.339041233062744, 3.7041850090026855] |
377ca261-be3f-457f-b2f7-33636867f8a6 | developing-cooperative-policies-for-multi-1 | 2205.05230 | null | https://arxiv.org/abs/2205.05230v1 | https://arxiv.org/pdf/2205.05230v1.pdf | Developing cooperative policies for multi-stage reinforcement learning tasks | Many hierarchical reinforcement learning algorithms utilise a series of independent skills as a basis to solve tasks at a higher level of reasoning. These algorithms don't consider the value of using skills that are cooperative instead of independent. This paper proposes the Cooperative Consecutive Policies (CCP) metho... | ['Chris Lehnert', 'Jordan Erskine'] | 2022-05-11 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.06819579e-01 5.92159748e-01 1.51133627e-01 -4.74622175e-02
-5.22386968e-01 -7.39322901e-01 7.05566227e-01 3.16789091e-01
-9.17377949e-01 1.53607213e+00 1.60284787e-01 -2.22220480e-01
-6.42515302e-01 -5.77358127e-01 -4.20241892e-01 -9.91161823e-01
-2.59382278e-01 9.55074430e-01 6.73405886e-01 -5.83893239... | [3.8774595260620117, 1.5840948820114136] |
1f20bfab-d704-4a9e-995a-9d9de7b3b73a | differentiable-time-frequency-scattering-in | 2204.08269 | null | https://arxiv.org/abs/2204.08269v4 | https://arxiv.org/pdf/2204.08269v4.pdf | Differentiable Time-Frequency Scattering on GPU | Joint time-frequency scattering (JTFS) is a convolutional operator in the time-frequency domain which extracts spectrotemporal modulations at various rates and scales. It offers an idealized model of spectrotemporal receptive fields (STRF) in the primary auditory cortex, and thus may serve as a biological plausible sur... | ['George Fazekas', 'Mathieu Lagrange', 'Vincent Lostanlen', 'Han Han', 'Changhong Wang', 'Cyrus Vahidi', 'John Muradeli'] | 2022-04-18 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 2.06977576e-01 -4.63153213e-01 5.10228038e-01 -6.24750704e-02
-9.74404871e-01 -7.32223809e-01 5.07547796e-01 1.01924829e-01
-4.89711553e-01 4.07375783e-01 3.78140837e-01 -2.99301893e-01
-2.21827060e-01 -4.92000520e-01 -4.28465098e-01 -6.48173988e-01
-6.61971569e-01 -3.79824519e-01 3.23493540e-01 -1.47445545... | [15.631645202636719, 5.4766106605529785] |
50ac815d-d839-487d-a9cb-a3b5d71d1b59 | superinfection-and-the-hypnozoite-reservoir | 2306.14329 | null | https://arxiv.org/abs/2306.14329v1 | https://arxiv.org/pdf/2306.14329v1.pdf | Superinfection and the hypnozoite reservoir for Plasmodium vivax: a general framework | Malaria is a parasitic disease, transmitted by mosquito vectors. Plasmodium vivax presents particular challenges for disease control, in light of an undetectable reservoir of latent parasites (hypnozoites) within the host liver. Superinfection, which is driven by temporally proximate mosquito inoculation and/or hypnozo... | ['Peter G. Taylor', 'James M. McCaw', 'Somya Mehra'] | 2023-06-25 | null | null | null | null | ['epidemiology'] | ['medical'] | [-9.63017419e-02 -2.05729291e-01 3.13561380e-01 2.27784634e-01
1.84443861e-01 -7.03331590e-01 6.50914550e-01 -8.58319700e-02
-4.62711036e-01 8.62266481e-01 -3.64725053e-01 -5.25742650e-01
-2.65118390e-01 -6.68850183e-01 -3.07631165e-01 -1.46681488e+00
-9.95491028e-01 6.94991589e-01 -1.65102154e-01 -2.82279164... | [5.931520462036133, 4.33725643157959] |
fdf83b2c-4cea-4c5f-9fd7-a1d037f5f6d7 | a-novel-feature-selection-and-extraction | 1412.7934 | null | http://arxiv.org/abs/1412.7934v1 | http://arxiv.org/pdf/1412.7934v1.pdf | A Novel Feature Selection and Extraction Technique for Classification | This paper presents a versatile technique for the purpose of feature
selection and extraction - Class Dependent Features (CDFs). We use CDFs to
improve the accuracy of classification and at the same time control
computational expense by tackling the curse of dimensionality. In order to
demonstrate the generality of thi... | ['Raunaq Vohra', 'Kratarth Goel', 'Ainesh Bakshi'] | 2014-12-26 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.71339840e-01 -7.84980059e-01 -2.62261748e-01 -7.35875487e-01
-2.22041830e-01 -5.61189711e-01 5.92696249e-01 2.27497771e-01
-3.55254710e-01 7.48967886e-01 -3.61594319e-01 -3.70577633e-01
-6.56127095e-01 -8.21435273e-01 2.20254585e-01 -8.55440795e-01
-2.01277733e-01 8.91986489e-02 7.25133345e-02 9.91155356... | [8.232643127441406, 4.205712795257568] |
a0d6d95a-9ba2-49e8-99f7-87bda66f45d6 | semi-supervised-convolutive-nmf-for-automatic | 2202.04989 | null | https://arxiv.org/abs/2202.04989v2 | https://arxiv.org/pdf/2202.04989v2.pdf | Semi-Supervised Convolutive NMF for Automatic Piano Transcription | Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difficult Music Information Retrieval task. In this work, which focuses on piano transcription, we propose a semi-supervised approach using low-rank matrix factorization techniques, ... | ['Jérémy E. Cohen', 'Axel Marmoret', 'Haoran Wu'] | 2022-02-10 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 2.61406362e-01 -2.74640381e-01 -7.11214244e-02 4.94394712e-02
-1.03106534e+00 -9.94225383e-01 2.39936709e-01 -1.47966251e-01
-3.61964703e-01 5.28833926e-01 2.71126598e-01 -1.17198616e-01
-5.94891608e-01 -3.95153821e-01 -5.80147505e-01 -7.04411149e-01
-1.53476149e-02 6.06590450e-01 -3.96828026e-01 -1.90210044... | [15.765318870544434, 5.346141338348389] |
a0264697-afef-4dbb-b499-9d52ace8680a | lipschitz-recurrent-neural-networks | 2006.12070 | null | https://arxiv.org/abs/2006.12070v3 | https://arxiv.org/pdf/2006.12070v3.pdf | Lipschitz Recurrent Neural Networks | Viewing recurrent neural networks (RNNs) as continuous-time dynamical systems, we propose a recurrent unit that describes the hidden state's evolution with two parts: a well-understood linear component plus a Lipschitz nonlinearity. This particular functional form facilitates stability analysis of the long-term behavio... | ['Liam Hodgkinson', 'Omri Azencot', 'N. Benjamin Erichson', 'Michael W. Mahoney', 'Alejandro Queiruga'] | 2020-06-22 | null | https://openreview.net/forum?id=-N7PBXqOUJZ | https://openreview.net/pdf?id=-N7PBXqOUJZ | iclr-2021-1 | ['sequential-image-classification'] | ['computer-vision'] | [-1.87805109e-02 2.69954860e-01 -1.09923579e-01 -2.04319283e-02
-2.36074761e-01 -5.47219992e-01 5.78637242e-01 -5.99145234e-01
2.25612801e-02 3.58957231e-01 3.79049152e-01 -6.28432155e-01
8.45996290e-02 -2.42560089e-01 -8.35912466e-01 -1.03860235e+00
-2.13096172e-01 -2.16258973e-01 -1.28462940e-01 -5.39828241... | [7.600526332855225, 3.375199794769287] |
a9b16487-ac22-4ece-867b-83414ccf4eb5 | self-supervised-motion-retargeting-with | 2103.06447 | null | https://arxiv.org/abs/2103.06447v1 | https://arxiv.org/pdf/2103.06447v1.pdf | Self-Supervised Motion Retargeting with Safety Guarantee | In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations... | ['Joohyung Kim', 'Hyemin Ahn', 'Min Jae Song', 'Sungjoon Choi'] | 2021-03-11 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-6.71763644e-02 3.43613684e-01 -3.55053693e-01 8.33484437e-03
-7.20876217e-01 -5.72782636e-01 6.94581389e-01 -6.77250922e-01
-4.63605762e-01 7.34716356e-01 4.34917480e-01 2.45496824e-01
-2.25521773e-02 -4.86990631e-01 -7.59182751e-01 -8.01189721e-01
-2.08257928e-01 6.84567988e-01 5.76823503e-02 -6.79283440... | [7.179248332977295, -0.5015988349914551] |
bbe06b70-f772-41a7-82d4-6f47cb54ff8f | repairing-bugs-in-python-assignments-using | 2209.14876 | null | https://arxiv.org/abs/2209.14876v1 | https://arxiv.org/pdf/2209.14876v1.pdf | Repairing Bugs in Python Assignments Using Large Language Models | Students often make mistakes on their introductory programming assignments as part of their learning process. Unfortunately, providing custom repairs for these mistakes can require a substantial amount of time and effort from class instructors. Automated program repair (APR) techniques can be used to synthesize such fi... | ['Gust Verbruggen', 'Gustavo Soares', 'Ruzica Piskac', 'Vu Le', 'Sumit Gulwani', 'José Cambronero', 'Jialu Zhang'] | 2022-09-29 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-1.61639582e-02 1.51189193e-01 2.23835576e-02 -5.72045386e-01
-9.79363620e-01 -8.03304017e-01 -2.85239905e-01 8.11492682e-01
-2.42272884e-01 1.86918348e-01 -3.62949632e-02 -8.34320128e-01
1.82306603e-01 -9.27363038e-01 -1.06205618e+00 2.20100507e-01
4.49153334e-01 1.34333655e-01 6.93674743e-01 -4.62736815... | [9.304601669311523, 7.43756103515625] |
4d366a83-bb7a-4d60-bf29-c5bbb3902bb1 | compete-to-win-enhancing-pseudo-labels-for | 2304.07519 | null | https://arxiv.org/abs/2304.07519v1 | https://arxiv.org/pdf/2304.07519v1.pdf | Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation | This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a d... | ['Kwang-Ting Cheng', 'Yiqun Lin', 'Xiaomeng Li', 'Huimin Wu'] | 2023-04-15 | null | null | null | null | ['tumor-segmentation', 'pancreas-segmentation', 'pseudo-label'] | ['computer-vision', 'medical', 'miscellaneous'] | [ 4.52696383e-01 5.86542487e-01 -5.40903807e-01 -6.73132896e-01
-1.05960321e+00 -3.11268359e-01 2.90021062e-01 1.86179146e-01
-4.81801391e-01 8.69302511e-01 -1.03955142e-01 -3.47025812e-01
-4.57795672e-02 -4.67279673e-01 -5.97158015e-01 -9.19256210e-01
4.16480422e-01 5.44870615e-01 6.64817393e-01 2.75199294... | [14.68511962890625, -2.124584674835205] |
bfffe3f9-b7aa-4ce0-bbd1-6b512dd363c2 | indoor-localization-and-multi-person-tracking | 2305.05062 | null | https://arxiv.org/abs/2305.05062v1 | https://arxiv.org/pdf/2305.05062v1.pdf | Indoor Localization and Multi-person Tracking Using Privacy Preserving Distributed Camera Network with Edge Computing | Localization of individuals in a built environment is a growing research topic. Estimating the positions, face orientation (or gaze direction) and trajectories of people through space has many uses, such as in crowd management, security, and healthcare. In this work, we present an open-source, low-cost, scalable and pr... | ['Gari D. Clifford', 'Craig M. Zimring', 'Leandro Miletto Tonetto', 'Robert Tweedy', 'ArjunSinh Nakum', 'Ratan Singh', 'Venkata Siva Krishna Madala', 'Yashar Kiarashi', 'Chaitra Hedge', 'Hyeokhyen Kwon'] | 2023-05-08 | null | null | null | null | ['multiple-object-tracking', 'indoor-localization', 'human-detection', 'edge-computing'] | ['computer-vision', 'computer-vision', 'computer-vision', 'time-series'] | [-3.46244216e-01 -4.53486621e-01 5.82275510e-01 -1.36702359e-01
-1.53936550e-01 -6.15407228e-01 -2.58749157e-01 1.09094962e-01
-4.95315820e-01 5.87090909e-01 -4.68911678e-02 -1.34443969e-01
1.46219864e-01 -7.98138559e-01 -5.25373876e-01 -8.19418013e-01
-1.48898453e-01 1.84536740e-01 2.03067750e-01 2.72577971... | [6.96907901763916, 0.30729493498802185] |
40a8cbfd-ff02-45f3-8cec-0c53675a6270 | generating-and-weighting-semantically | 2212.04097 | null | https://arxiv.org/abs/2212.04097v1 | https://arxiv.org/pdf/2212.04097v1.pdf | Generating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning | Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-level expertise. Model pre-training based on ImageNet is a common practice to gain better generalizatio... | ['Li Liu', 'Chris H. Q. Ding', 'Chunhui Zhang', 'Yixiong Chen'] | 2022-12-08 | null | null | null | null | ['tumor-segmentation', 'pneumonia-detection'] | ['computer-vision', 'medical'] | [ 5.65864265e-01 1.88182831e-01 -5.29812813e-01 -4.18021858e-01
-1.25568855e+00 -4.88350168e-02 2.24038497e-01 1.50513306e-01
-3.98134619e-01 5.07114410e-01 1.45924017e-01 -4.32256967e-01
1.14483421e-03 -9.39281285e-01 -8.38315964e-01 -7.25827813e-01
6.07764982e-02 4.21529800e-01 2.67355025e-01 -1.14921033... | [14.790709495544434, -2.242672920227051] |
68222789-4ab1-4e26-b3ec-64ac9d693054 | multi-view-inverse-rendering-for-large-scale | 2211.10206 | null | https://arxiv.org/abs/2211.10206v4 | https://arxiv.org/pdf/2211.10206v4.pdf | Multi-view Inverse Rendering for Large-scale Real-world Indoor Scenes | We present a efficient multi-view inverse rendering method for large-scale real-world indoor scenes that reconstructs global illumination and physically-reasonable SVBRDFs. Unlike previous representations, where the global illumination of large scenes is simplified as multiple environment maps, we propose a compact rep... | ['Jiaqi Yang', 'Cihui Pan', 'Mofang Cheng', 'Lingli Wang', 'Zhen Li'] | 2022-11-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Multi-View_Inverse_Rendering_for_Large-Scale_Real-World_Indoor_Scenes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Multi-View_Inverse_Rendering_for_Large-Scale_Real-World_Indoor_Scenes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['mixed-reality'] | ['computer-vision'] | [ 2.93130755e-01 -2.58389235e-01 7.00934649e-01 -3.84618640e-01
-4.88041192e-01 -4.46742266e-01 5.30738771e-01 -4.03424501e-01
2.47174576e-01 7.53989220e-01 3.36230636e-01 -2.02717617e-01
8.66816565e-02 -1.06356549e+00 -8.77040505e-01 -6.57373250e-01
6.62986755e-01 3.74075294e-01 3.69304121e-02 -2.53348142... | [9.638041496276855, -3.087989330291748] |
993cbd40-efbe-4bc9-8c8e-e85dafd0bb6e | cvlnet-cross-view-semantic-correspondence | 2208.03660 | null | https://arxiv.org/abs/2208.03660v1 | https://arxiv.org/pdf/2208.03660v1.pdf | CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization | This paper tackles the problem of Cross-view Video-based camera Localization (CVL). The task is to localize a query camera by leveraging information from its past observations, i.e., a continuous sequence of images observed at previous time stamps, and matching them to a large overhead-view satellite image. The critica... | ['Hongdong Li', 'Shan Wang', 'Xin Yu', 'Yujiao Shi'] | 2022-08-07 | null | null | null | null | ['image-based-localization', 'camera-localization'] | ['computer-vision', 'computer-vision'] | [ 1.46261901e-02 -5.84186435e-01 -1.98032647e-01 -2.58469880e-01
-1.07290888e+00 -1.00389624e+00 5.61749220e-01 -8.24413151e-02
-3.50197434e-01 3.03600788e-01 -1.87741175e-01 1.76469699e-01
-6.44900203e-02 -6.24996066e-01 -9.14548576e-01 -8.54796112e-01
-9.73404720e-02 2.48086780e-01 3.30239266e-01 8.31422359... | [7.670236587524414, -2.145390510559082] |
5e0278a8-5e9e-41fd-9efc-adeb0c9f50a4 | anchor-free-person-search | 2103.11617 | null | https://arxiv.org/abs/2103.11617v2 | https://arxiv.org/pdf/2103.11617v2.pdf | Anchor-Free Person Search | Person search aims to simultaneously localize and identify a query person from realistic, uncropped images, which can be regarded as the unified task of pedestrian detection and person re-identification (re-id). Most existing works employ two-stage detectors like Faster-RCNN, yielding encouraging accuracy but with high... | ['Jinpeng Li', 'Ling Shao', 'Fan Zhu', 'Li Liu', 'Shengcai Liao', 'Song Bai', 'Jie Qin', 'Yichao Yan'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yan_Anchor-Free_Person_Search_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yan_Anchor-Free_Person_Search_CVPR_2021_paper.pdf | cvpr-2021-1 | ['person-search'] | ['computer-vision'] | [-4.86575067e-01 -4.77968842e-01 -1.77556314e-02 -2.13247493e-01
-9.46780801e-01 -4.39199388e-01 6.61369860e-01 -1.22461691e-01
-9.73776639e-01 4.52039897e-01 4.91504014e-01 1.63487464e-01
3.58293205e-01 -4.70891207e-01 -3.96895528e-01 -6.28358960e-01
1.94021419e-01 3.28397423e-01 3.23577106e-01 -1.94937102... | [14.807944297790527, 0.833098292350769] |
d6a8a405-894b-4e33-a70f-83ad4fafa8c5 | learning-clause-representation-from | null | null | https://aclanthology.org/2021.textgraphs-1.6 | https://aclanthology.org/2021.textgraphs-1.6.pdf | Learning Clause Representation from Dependency-Anchor Graph for Connective Prediction | Semantic representation that supports the choice of an appropriate connective between pairs of clauses inherently addresses discourse coherence, which is important for tasks such as narrative understanding, argumentation, and discourse parsing. We propose a novel clause embedding method that applies graph learning to a... | ['Rebecca J. Passonneau', 'Ting-Hao Huang', 'Yanjun Gao'] | null | null | null | null | naacl-textgraphs-2021-6 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.30417684e-01 8.30260396e-01 -6.21250749e-01 -5.38777769e-01
-5.05670786e-01 -5.97048879e-01 9.12713885e-01 9.04311359e-01
-2.85905421e-01 5.82141638e-01 1.18831122e+00 -5.92767119e-01
-1.83277428e-01 -1.07121718e+00 -5.37884116e-01 -3.26064914e-01
1.19787380e-01 2.15415329e-01 7.98594728e-02 -5.65390110... | [10.890220642089844, 9.28274917602539] |
dc58718e-a52b-42bc-9d30-8853f3b66c1e | dl-drl-a-double-layer-deep-reinforcement | 2208.02447 | null | https://arxiv.org/abs/2208.02447v3 | https://arxiv.org/pdf/2208.02447v3.pdf | DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV | Exploiting unmanned aerial vehicles (UAVs) to execute tasks is gaining growing popularity recently. To solve the underlying task scheduling problem, the deep reinforcement learning (DRL) based methods demonstrate notable advantage over the conventional heuristics as they rely less on hand-engineered rules. However, the... | ['Witold Pedrycz', 'Guohua Wu', 'Zhiguang Cao', 'Mingfeng Fan', 'Xiao Mao'] | 2022-08-04 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 0.08418955 0.04024578 -0.20249002 -0.08527556 -0.60231835 -0.7560742
0.3043226 -0.0471041 -0.46512237 0.69785535 -0.04505197 -0.6482539
-0.3862844 -0.7116381 -0.88295496 -0.60634667 -0.2331431 0.21021211
0.02072825 -0.23858498 -0.07500894 0.25356305 -1.3763376 -0.08353584
1.1063597 1.1403662 0.73... | [4.8123884201049805, 2.548185110092163] |
581f3a7f-922e-4bd5-8360-caa092f75de0 | understanding-advertisements-with-bert | null | null | https://aclanthology.org/2020.acl-main.674 | https://aclanthology.org/2020.acl-main.674.pdf | Understanding Advertisements with BERT | We consider a task based on CVPR 2018 challenge dataset on advertisement (Ad) understanding. The task involves detecting the viewer{'}s interpretation of an Ad image captured as text. Recent results have shown that the embedded scene-text in the image holds a vital cue for this task. Motivated by this, we fine-tune the... | ['Kar', 'Silpa Vadakkeeveetil Sreelatha', 'Shirish e', 'Bhargav Kurma', 'Manasi Patwardhan', 'Kanika Kalra'] | 2020-07-01 | null | null | null | acl-2020-6 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 7.10071504e-01 3.53994220e-01 -8.00344869e-02 -7.74337590e-01
-1.18372297e+00 -6.27477229e-01 6.58416986e-01 1.86093077e-01
-5.25328517e-01 1.39414608e-01 1.50701165e-01 -4.85100538e-01
5.33680797e-01 -2.85566062e-01 -1.15046787e+00 -2.48731941e-01
9.43367407e-02 3.41356844e-01 2.04464763e-01 4.94350679... | [10.846444129943848, 1.3417291641235352] |
f3ef1a8b-a560-4a28-a070-6197ffea44f8 | weakly-supervised-class-agnostic-motion | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Weakly_Supervised_Class-Agnostic_Motion_Prediction_for_Autonomous_Driving_CVPR_2023_paper.pdf | Weakly Supervised Class-Agnostic Motion Prediction for Autonomous Driving | Understanding the motion behavior of dynamic environments is vital for autonomous driving, leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds. Outdoor scenes can often be decomposed into mobile foregrounds and static backgrounds, which enables us to associate motion understand... | ['Guosheng Lin', 'Zhe Wang', 'Ziang Fu', 'Hanyu Shi', 'Ruibo Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-prediction', 'scene-parsing'] | ['computer-vision', 'computer-vision'] | [ 3.84132415e-01 1.87755212e-01 -8.14396620e-01 -6.28277659e-01
-5.62493920e-01 -6.61063910e-01 6.11092091e-01 -8.09378177e-02
-6.23229444e-01 5.41840911e-01 -1.19409949e-01 -2.47243956e-01
1.46670491e-01 -5.34862220e-01 -9.90651309e-01 -8.14499795e-01
3.29992287e-02 4.85826939e-01 9.97795939e-01 1.56931967... | [9.101534843444824, -0.14933444559574127] |
08effa78-015f-4cf8-a4db-7111b6fe41d1 | improving-attacks-on-round-reduced-speck32-64 | null | null | https://eprint.iacr.org/2019/037.pdf | https://eprint.iacr.org/2019/037.pdf | Improving Attacks on Round-Reduced Speck32/64 Using Deep Learning | This paper has four main contributions.1 First, we calculate the predicted difference distribution of Speck32/64 with one specific input difference under the Markov assumption completely for up to eight rounds and verify that this yields a globally fairly good model of the difference distribution of Speck32/64. Secondl... | ['Aron Gohr'] | 2019-08-01 | null | null | null | conference-2019-8 | ['cryptanalysis'] | ['miscellaneous'] | [ 3.89768720e-01 -3.45370509e-02 3.79840173e-02 -8.55112746e-02
-1.49826229e+00 -1.22984231e+00 6.26932800e-01 6.64734468e-02
-6.11064017e-01 6.95377469e-01 -1.38326153e-01 -1.26445818e+00
-1.01489268e-01 -6.97367072e-01 -8.97313714e-01 -9.78865981e-01
-4.17316139e-01 5.19079208e-01 9.77749676e-02 -5.25990546... | [5.935407638549805, 7.341752529144287] |
bf0f0c28-d7f4-413a-9dc1-01777d881d5a | decoding-dynamic-brain-patterns-from-evoked | 1606.02840 | null | http://arxiv.org/abs/1606.02840v2 | http://arxiv.org/pdf/1606.02840v2.pdf | Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data | Multivariate pattern analysis (MVPA) or brain decoding methods have become
standard practice in analysing fMRI data. Although decoding methods have been
extensively applied in Brain Computing Interfaces (BCI), these methods have
only recently been applied to time-series neuroimaging data such as MEG and EEG
to address ... | [] | 2016-09-30 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 9.00085568e-01 -5.46696484e-01 6.71059549e-01 -7.92214215e-01
-3.27005118e-01 -4.85778302e-01 8.30483615e-01 1.49517104e-01
-7.90542245e-01 5.67306638e-01 2.56055355e-01 -6.78144217e-01
-5.58087707e-01 -2.71504045e-01 -2.57246554e-01 -6.21961474e-01
-6.49933279e-01 2.78912950e-02 1.45967007e-01 6.03701919... | [12.972424507141113, 3.4114291667938232] |
e457b9d9-efb5-4bf4-97bd-24ee1f9bc6a5 | arthroscopic-multi-spectral-scene | 2103.02465 | null | https://arxiv.org/abs/2103.02465v1 | https://arxiv.org/pdf/2103.02465v1.pdf | Arthroscopic Multi-Spectral Scene Segmentation Using Deep Learning | Knee arthroscopy is a minimally invasive surgical (MIS) procedure which is performed to treat knee-joint ailment. Lack of visual information of the surgical site obtained from miniaturized cameras make this surgical procedure more complex. Knee cavity is a very confined space; therefore, surgical scenes are captured at... | ['Dr. Ajay K. Pandey', 'Cameron Brown', 'Ross Crawford', 'Jonathan Roberts', 'Yu Takeda', 'Dr. Yaqub Jonmohamadi', 'Shahnewaz Ali'] | 2021-03-03 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 1.06473848e-01 -7.04589784e-02 -2.56904095e-01 1.91346914e-01
-5.68970680e-01 -5.26853383e-01 1.12410501e-01 1.32514358e-01
-4.99576449e-01 8.32313299e-01 2.22600088e-01 -2.08765164e-01
5.83221540e-02 -1.09275937e-01 -4.69358772e-01 -6.21376336e-01
1.36240408e-01 6.30859435e-02 5.36000252e-01 2.17600584... | [13.809867858886719, -3.1339075565338135] |
877db9f1-07ed-4e45-8cdb-a99238525b67 | reinforce-security-a-model-free-approach | 2106.00343 | null | https://arxiv.org/abs/2106.00343v1 | https://arxiv.org/pdf/2106.00343v1.pdf | Reinforce Security: A Model-Free Approach Towards Secure Wiretap Coding | The use of deep learning-based techniques for approximating secure encoding functions has attracted considerable interest in wireless communications due to impressive results obtained for general coding and decoding tasks for wireless communication systems. Of particular importance is the development of model-free tech... | ['Gerhard Wunder', 'Rafael F. Schaefer', 'Rick Fritschek'] | 2021-06-01 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.98011798e-01 5.47869682e-01 1.69590935e-02 -1.53047949e-01
-9.11110044e-01 -4.05034602e-01 6.06522381e-01 4.90323417e-02
-4.67137426e-01 9.31127906e-01 -1.14676408e-01 -6.71554387e-01
-2.51949906e-01 -8.33286166e-01 -7.83649266e-01 -1.03577852e+00
-6.58049822e-01 1.23450875e-01 -5.14132798e-01 -4.10535514... | [6.433241844177246, 1.5596212148666382] |
3b24f56a-a1ca-4778-a470-2ddebeda81b2 | generative-knowledge-selection-for-knowledge | 2304.04836 | null | https://arxiv.org/abs/2304.04836v1 | https://arxiv.org/pdf/2304.04836v1.pdf | Generative Knowledge Selection for Knowledge-Grounded Dialogues | Knowledge selection is the key in knowledge-grounded dialogues (KGD), which aims to select an appropriate knowledge snippet to be used in the utterance based on dialogue history. Previous studies mainly employ the classification approach to classify each candidate snippet as "relevant" or "irrelevant" independently. Ho... | ['Zhaochun Ren', 'Pengjie Ren', 'Weiwei Sun'] | 2023-04-10 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 1.80266246e-01 5.36302328e-01 -4.78928715e-01 -4.24875230e-01
-6.87628746e-01 -5.37673831e-01 8.82418394e-01 2.75566373e-02
-2.50612050e-01 1.04875839e+00 7.28084624e-01 -1.40062526e-01
-1.24126546e-01 -8.96378636e-01 -4.78235781e-01 -5.73451281e-01
2.56547630e-01 7.76818514e-01 2.56007165e-01 -6.06018722... | [12.498544692993164, 8.074708938598633] |
6723f275-08f8-435e-ae51-b6f8e272ad4e | end-to-end-pseudo-lidar-for-image-based-3d | 2004.03080 | null | https://arxiv.org/abs/2004.03080v2 | https://arxiv.org/pdf/2004.03080v2.pdf | End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection | Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the ac... | ['Wei-Lun Chao', 'Yan Wang', 'Divyansh Garg', 'Serge Belongie', 'Rui Qian', 'Kilian Q. Weinberger', 'Yurong You', 'Mark Campbell', 'Bharath Hariharan'] | 2020-04-07 | end-to-end-pseudo-lidar-for-image-based-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Qian_End-to-End_Pseudo-LiDAR_for_Image-Based_3D_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Qian_End-to-End_Pseudo-LiDAR_for_Image-Based_3D_Object_Detection_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-depth-estimation'] | ['computer-vision'] | [ 5.83423488e-03 -1.42266795e-01 -5.04603386e-02 -4.95840073e-01
-7.86754727e-01 -5.48388541e-01 6.01750851e-01 -1.88335422e-02
-7.10946977e-01 1.71922773e-01 -3.83312166e-01 -5.14526486e-01
3.15277338e-01 -8.00814688e-01 -1.14173555e+00 -1.76698357e-01
8.11283439e-02 7.58595347e-01 5.16621828e-01 -1.89920411... | [7.805871486663818, -2.5940866470336914] |
aeacc466-1eb4-4e6a-aa32-c5bc058d6588 | towards-end-to-end-optimisation-of-functional | 1610.04079 | null | http://arxiv.org/abs/1610.04079v1 | http://arxiv.org/pdf/1610.04079v1.pdf | Towards end-to-end optimisation of functional image analysis pipelines | The study of neurocognitive tasks requiring accurate localisation of activity
often rely on functional Magnetic Resonance Imaging, a widely adopted technique
that makes use of a pipeline of data processing modules, each involving a
variety of parameters. These parameters are frequently set according to the
local goal o... | ['Kristoffer Hougaard Madsen', 'Lars Kai Hansen', 'Albert Vilamala'] | 2016-10-13 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 2.34396666e-01 1.61051288e-01 4.13742900e-01 -5.55359542e-01
-3.48056823e-01 -4.66548145e-01 7.99476087e-01 1.93845704e-01
-9.08205807e-01 4.00999159e-01 3.27096373e-01 -2.76058614e-01
-3.75720203e-01 -3.91136318e-01 -5.75811386e-01 -5.14385521e-01
-2.03002706e-01 6.76563859e-01 5.26988387e-01 -1.20289363... | [14.159839630126953, -2.119623899459839] |
43d385c0-a794-4f10-b27b-1c2ee0eb6ede | nudging-the-envelope-of-direct-transfer | null | null | https://aclanthology.org/W12-1908 | https://aclanthology.org/W12-1908.pdf | Nudging the Envelope of Direct Transfer Methods for Multilingual Named Entity Recognition | null | ['Oscar T{\\"a}ckstr{\\"o}m'] | 2012-06-01 | null | null | null | ws-2012-6 | ['multilingual-named-entity-recognition'] | ['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... | [-7.425469875335693, 3.7895476818084717] |
b59d29fe-b1ed-4e6e-87ea-928fceaa4c34 | nkululeko-a-tool-for-rapid-speaker | null | null | https://aclanthology.org/2022.lrec-1.205 | https://aclanthology.org/2022.lrec-1.205.pdf | Nkululeko: A Tool For Rapid Speaker Characteristics Detection | We present advancements with a software tool called Nkululeko, that lets users perform (semi-) supervised machine learning experiments in the speaker characteristics domain. It is based on audformat, a format for speech database metadata description. Due to an interface based on configurable templates, it supports best... | ['Björn Schuller', 'Florian Eyben', 'Hagen Wierstorf', 'Johannes Wagner', 'Felix Burkhardt'] | null | null | null | null | lrec-2022-6 | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-4.79268968e-01 1.00735135e-01 2.67579615e-01 -6.77468002e-01
-4.84225452e-01 -4.71928239e-01 4.61265564e-01 2.27609351e-01
-6.59201384e-01 4.07050878e-01 3.05111051e-01 -5.61964393e-01
-4.33713943e-02 -2.27734163e-01 -1.34825319e-01 -5.13559937e-01
-3.31598103e-01 6.15988433e-01 -4.16874811e-02 -2.41804987... | [13.919981002807617, 6.038158416748047] |
ccb80b81-f43c-4df0-9396-037cd8323641 | deformable-video-transformer | 2203.16795 | null | https://arxiv.org/abs/2203.16795v1 | https://arxiv.org/pdf/2203.16795v1.pdf | Deformable Video Transformer | Video transformers have recently emerged as an effective alternative to convolutional networks for action classification. However, most prior video transformers adopt either global space-time attention or hand-defined strategies to compare patches within and across frames. These fixed attention schemes not only have hi... | ['Lorenzo Torresani', 'Jue Wang'] | 2022-03-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Deformable_Video_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Deformable_Video_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-classification'] | ['computer-vision'] | [ 9.97159258e-02 -3.79451245e-01 -4.25701171e-01 -5.32168634e-02
-5.03503203e-01 -4.70992208e-01 3.63986224e-01 8.70494023e-02
-5.77499330e-01 3.41954738e-01 1.93827331e-01 -4.35201116e-02
-8.77102464e-02 -6.82029486e-01 -7.71733880e-01 -6.36226714e-01
-1.18669324e-01 1.81646988e-01 6.18363142e-01 -3.12613249... | [8.857187271118164, 0.4478188753128052] |
37f266c2-16cb-4b3a-bfb1-c11b752f5a9b | occlusion-robust-face-recognition-based-on | 1908.06290 | null | https://arxiv.org/abs/1908.06290v1 | https://arxiv.org/pdf/1908.06290v1.pdf | Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network | Deep Convolutional Neural Networks (CNNs) have been pushing the frontier of the face recognition research in the past years. However, existing general CNN face models generalize poorly to the scenario of occlusions on variable facial areas. Inspired by the fact that a human visual system explicitly ignores occlusions a... | ['Lingxue Song', 'Wei Liu', 'Zhifeng Li', 'Dihong Gong', 'Changsong Liu'] | 2019-08-17 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 1.79938868e-01 -2.31898762e-02 1.33127317e-01 -5.43094456e-01
-5.51924482e-02 -6.22915551e-02 4.89130586e-01 -4.39188451e-01
-2.67939597e-01 4.42102998e-01 1.40194073e-01 2.92870939e-01
-2.30748266e-01 -6.49348676e-01 -6.80553794e-01 -9.00854111e-01
1.16995558e-01 2.46851027e-01 -1.86625928e-01 -1.17633611... | [13.207659721374512, 0.4264540672302246] |
6be82154-7f47-4d01-a5f2-3d6965ef23c3 | norquad-norwegian-question-answering-dataset | 2305.01957 | null | https://arxiv.org/abs/2305.01957v1 | https://arxiv.org/pdf/2305.01957v1.pdf | NorQuAD: Norwegian Question Answering Dataset | In this paper we present NorQuAD: the first Norwegian question answering dataset for machine reading comprehension. The dataset consists of 4,752 manually created question-answer pairs. We here detail the data collection procedure and present statistics of the dataset. We also benchmark several multilingual and Norwegi... | ['Lilja Øvrelid', 'Sondre Wold', 'Matias Jentoft', 'Fredrik Aas Andreassen', 'Sardana Ivanova'] | 2023-05-03 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [-1.47963852e-01 3.50757867e-01 1.77287415e-01 -3.41986090e-01
-1.38100147e+00 -9.59892929e-01 4.61341858e-01 6.27017200e-01
-1.00492895e+00 9.86197650e-01 4.51439470e-01 -8.65145802e-01
6.64828494e-02 -4.41032976e-01 -5.28819442e-01 1.49369806e-01
4.43091303e-01 9.69584644e-01 4.94119972e-01 -7.83880115... | [11.356464385986328, 8.1957426071167] |
11b80a44-7ac0-42f9-bf98-b8cdefd80045 | glavnet-global-local-audio-visual-cues-for | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Shi_GLAVNet_Global-Local_Audio-Visual_Cues_for_Fine-Grained_Material_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Shi_GLAVNet_Global-Local_Audio-Visual_Cues_for_Fine-Grained_Material_Recognition_CVPR_2021_paper.pdf | GLAVNet: Global-Local Audio-Visual Cues for Fine-Grained Material Recognition | In this paper, we aim to recognize materials with combined use of auditory and visual perception. To this end, we construct a new dataset named GLAudio that consists of both the geometry of the object being struck and the sound captured from either modal sound synthesis (for virtual objects) or real measurements (f... | ['Yanwen Guo', 'Xiying Wang', 'Shan Yang', 'Haonan Zhang', 'Jie Guo', 'Fengmin Shi'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['material-recognition'] | ['computer-vision'] | [-2.38750782e-02 -6.05884731e-01 3.61057937e-01 -6.72894418e-02
-7.96871662e-01 -7.61147380e-01 6.47129118e-01 1.95542619e-01
-1.65668130e-01 5.76747917e-02 9.83603969e-02 4.07936946e-02
-1.55831218e-01 -1.14636779e+00 -7.26018488e-01 -8.33803773e-01
1.56383753e-01 2.68121332e-01 4.78600413e-01 -5.75818084... | [10.214286804199219, -0.16316315531730652] |
209b811e-26a8-44aa-85a5-537126edaf86 | machine-reading-fast-and-slow-when-do-models-1 | null | null | https://aclanthology.org/2022.coling-1.8 | https://aclanthology.org/2022.coling-1.8.pdf | Machine Reading, Fast and Slow: When Do Models “Understand” Language? | Two of the most fundamental issues in Natural Language Understanding (NLU) at present are: (a) how it can established whether deep learning-based models score highly on NLU benchmarks for the ”right” reasons; and (b) what those reasons would even be. We investigate the behavior of reading comprehension models with resp... | ['Isabelle Augenstein', 'Anna Rogers', 'Sagnik Ray Choudhury'] | null | null | null | null | coling-2022-10 | ['coreference-resolution'] | ['natural-language-processing'] | [ 3.02387029e-01 9.54855025e-01 -1.92560583e-01 -3.53211671e-01
-4.95809525e-01 -5.11913121e-01 8.82511377e-01 7.09118545e-01
-4.76138294e-01 6.07453942e-01 6.93963110e-01 -7.02977002e-01
-4.71731722e-01 -9.00042892e-01 -1.11996508e+00 -1.19858131e-01
1.92937002e-01 9.03446376e-01 3.86945844e-01 -5.76180339... | [9.954545021057129, 7.748218059539795] |
44f79923-3624-4eaf-8e80-7c30184b5144 | learning-sentence-embeddings-using-recursive | 1805.08353 | null | http://arxiv.org/abs/1805.08353v1 | http://arxiv.org/pdf/1805.08353v1.pdf | Learning sentence embeddings using Recursive Networks | Learning sentence vectors that generalise well is a challenging task. In this
paper we compare three methods of learning phrase embeddings: 1) Using LSTMs,
2) using recursive nets, 3) A variant of the method 2 using the POS information
of the phrase. We train our models on dictionary definitions of words to obtain
a re... | ['Anson Bastos'] | 2018-05-22 | null | null | null | null | ['reverse-dictionary'] | ['natural-language-processing'] | [ 2.69116908e-01 1.82852000e-02 -8.26801583e-02 -2.21626386e-01
-2.26639047e-01 -8.29486728e-01 5.93933582e-01 4.70356941e-01
-8.94960105e-01 6.96554184e-01 3.17385942e-01 -4.45613146e-01
4.38940860e-02 -8.95687342e-01 -8.32624137e-01 -5.33778310e-01
-4.75671515e-02 4.81224209e-01 3.16764385e-01 -4.37127590... | [10.67034912109375, 8.715271949768066] |
a73b4ae8-8fa9-4cc7-8f69-5ac33ec8f68f | measuring-massive-multitask-language | 2009.03300 | null | https://arxiv.org/abs/2009.03300v3 | https://arxiv.org/pdf/2009.03300v3.pdf | Measuring Massive Multitask Language Understanding | We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent mode... | ['Andy Zou', 'Dan Hendrycks', 'Mantas Mazeika', 'Collin Burns', 'Steven Basart', 'Jacob Steinhardt', 'Dawn Song'] | 2020-09-07 | null | null | null | null | ['multi-task-language-understanding', 'elementary-mathematics'] | ['methodology', 'reasoning'] | [-2.62778848e-01 3.62349570e-01 -5.96900940e-01 -1.99164540e-01
-1.02410614e+00 -7.42681384e-01 4.23004389e-01 2.98379749e-01
-4.01255786e-01 1.04800975e+00 -4.26276959e-02 -1.03276491e+00
-5.92621624e-01 -6.80464149e-01 -6.38676107e-01 -1.58598423e-01
5.11431754e-01 5.72007835e-01 -5.26159480e-02 -1.72908664... | [9.820452690124512, 7.507683753967285] |
9b08a1ea-cad9-4b6d-980b-28309078a461 | hicu-leveraging-hierarchy-for-curriculum | 2208.02301 | null | https://arxiv.org/abs/2208.02301v1 | https://arxiv.org/pdf/2208.02301v1.pdf | HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding | There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning... | ['Rahul G. Krishnan', 'Tianshu Zhu', 'Tongzi Wu', 'Ruijing Zeng', 'Weiming Ren'] | 2022-08-03 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 2.36696571e-01 3.23991925e-01 -1.17407300e-01 -3.97051752e-01
-7.16210008e-01 -6.08800948e-01 -2.21544012e-01 6.41663015e-01
5.60436323e-02 2.37739876e-01 3.82374048e-01 -1.02132750e+00
-4.88613755e-01 -6.10827744e-01 -4.81655747e-01 -1.41227722e-01
-9.93138626e-02 6.53865516e-01 -2.50106901e-01 -2.06905268... | [7.990718841552734, 6.810083866119385] |
fa0551db-0333-41ca-b170-7ded7270aa56 | three-dimensional-deep-learning-approach-for | 1806.05824 | null | http://arxiv.org/abs/1806.05824v1 | http://arxiv.org/pdf/1806.05824v1.pdf | Three dimensional Deep Learning approach for remote sensing image classification | Recently, a variety of approaches has been enriching the field of Remote
Sensing (RS) image processing and analysis. Unfortunately, existing methods
remain limited faced to the rich spatio-spectral content of today's large
datasets. It would seem intriguing to resort to Deep Learning (DL) based
approaches at this stage... | ['A. Benoit', 'Amina Ben Hamida', 'Chokri Ben Amar', 'Patrick Lambert'] | 2018-06-15 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.94201809e-01 -2.83759266e-01 6.04027323e-02 -2.93003321e-01
-6.26475751e-01 -4.93888140e-01 8.23200941e-01 1.03281379e-01
-3.90889525e-01 7.79638708e-01 -2.43122011e-01 -3.41183454e-01
-8.31289649e-01 -9.61135626e-01 -1.10671364e-01 -1.04393065e+00
-2.05540106e-01 1.10351086e-01 -9.01099816e-02 -2.71064103... | [9.731595993041992, -1.626183032989502] |
5321d606-1c61-4eb7-8e09-86e82d5da94b | on-label-efficient-computer-vision-building | null | null | https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0402554 | https://open.library.ubc.ca/media/download/pdf/24/1.0402554/4 | On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers | Modern deep learning requires large-scale extensively labelled datasets for training. Few-shot learning aims to alleviate this issue by learning effectively from few labelled examples. In previously proposed few-shot visual classifiers, it is assumed that the feature manifold arriving at the classifier has uncorrelated... | ['Peyman Bateni'] | 2021-10-19 | null | null | null | university-of-british-columbia-theses-and | ['cross-domain-few-shot'] | ['computer-vision'] | [ 3.98183942e-01 1.61541730e-01 -3.84511292e-01 -4.24678534e-01
-1.06729531e+00 -2.73149580e-01 8.93484831e-01 5.48461415e-02
-5.24435222e-01 6.56236112e-01 -4.48381854e-03 1.32437482e-01
-3.29479814e-01 -6.14411712e-01 -6.52548492e-01 -9.88173902e-01
5.35332151e-02 4.51942503e-01 3.90860856e-01 -2.25144535... | [9.946184158325195, 2.8480257987976074] |
de550431-7813-4c06-ae1b-01a14e41b5e4 | learning-single-multi-attribute-of-object | 2110.04603 | null | https://arxiv.org/abs/2110.04603v1 | https://arxiv.org/pdf/2110.04603v1.pdf | Learning Single/Multi-Attribute of Object with Symmetry and Group | Attributes and objects can compose diverse compositions. To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e.g., coupling and decoupling. However, complex transformations need to satisfy specific principles to guarantee rationality. Here, we first propose a previ... | ['Cewu Lu', 'Xiaohan Mao', 'Xinyu Xu', 'Yue Xu', 'Yong-Lu Li'] | 2021-10-09 | null | null | null | null | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 1.33775249e-01 -4.82413210e-02 -9.94384363e-02 -6.52100265e-01
-1.36441916e-01 -4.40775424e-01 6.62572443e-01 -1.77261472e-01
-1.89739704e-01 2.88723260e-01 2.10534975e-01 1.27053693e-01
-2.48089150e-01 -1.10675025e+00 -6.78837717e-01 -8.04409862e-01
3.70373130e-01 6.14377379e-01 2.30122045e-01 -3.24152857... | [10.118444442749023, 2.3518729209899902] |
7cf294b8-14fe-40ab-b1e7-39f7b6786391 | wise-whitebox-image-stylization-by-example | 2207.14606 | null | https://arxiv.org/abs/2207.14606v1 | https://arxiv.org/pdf/2207.14606v1.pdf | WISE: Whitebox Image Stylization by Example-based Learning | Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering. In contrast to deep learning-based methods, these heuristics-based filtering techniques can operate on high-resolution images, are interpretable, and can be parameterized according to various design aspects. ... | ['Matthias Trapp', 'Jürgen Döllner', 'Amir Semmo', 'Martin Büssemeyer', 'Max Reimann', 'Winfried Lötzsch'] | 2022-07-29 | null | null | null | null | ['image-stylization', 'parameter-prediction'] | ['computer-vision', 'miscellaneous'] | [ 6.58888459e-01 -1.56517997e-02 1.04108177e-01 -4.87167209e-01
-5.45267642e-01 -8.72816801e-01 7.14737177e-01 -4.64558929e-01
-1.71824411e-01 5.83567441e-01 -4.78834771e-02 -1.55495510e-01
2.11959139e-01 -9.80655134e-01 -9.49268222e-01 -5.25917590e-01
5.06460428e-01 3.52097660e-01 -5.07660173e-02 -5.22032559... | [11.662017822265625, -0.4645424485206604] |
4d9770ca-cb35-4c4f-b8b1-d7c00e26a742 | v-nas-neural-architecture-search-for | 1906.02817 | null | https://arxiv.org/abs/1906.02817v2 | https://arxiv.org/pdf/1906.02817v2.pdf | V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation | Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks (FCNs), have rapidly become the mainstream methodology for volumetric medical image segmentation. However, 2D convolutions cannot fully leverage the rich spatial information along the third axis, while 3D convolutions suffer from the ... | ['Dong Yang', 'Zhuotun Zhu', 'Daguang Xu', 'Alan Yuille', 'Chenxi Liu'] | 2019-06-06 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [-4.84508984e-02 2.35079765e-01 -3.69461536e-01 -3.41447890e-01
-5.81972182e-01 -6.38510644e-01 2.76556402e-01 -4.75305915e-02
-4.11006808e-01 3.45298141e-01 -1.42356288e-02 -7.72781193e-01
1.80091634e-02 -6.60376430e-01 -6.75110698e-01 -8.31763625e-01
-2.42235333e-01 6.48338437e-01 1.04081370e-01 2.60841638... | [14.579432487487793, -2.6307849884033203] |
aa7dadf2-9e97-4d99-88f1-85193ef31f17 | universal-dependency-treebank-for-odia | 2205.11976 | null | https://arxiv.org/abs/2205.11976v1 | https://arxiv.org/pdf/2205.11976v1.pdf | Universal Dependency Treebank for Odia Language | This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia selected from "Samantar", the largest available parallel corpora collection for Indic languages. All the selected sentences are manual... | ['Bijayalaxmi Dash', 'Satya Ranjan Dash', 'Saraswati Sahoo', 'Atul Kr. Ojha', 'Kalyanamalini Sahoo', 'Shantipriya Parida'] | 2022-05-24 | null | https://aclanthology.org/2022.wildre-1.15 | https://aclanthology.org/2022.wildre-1.15.pdf | wildre-lrec-2022-6 | ['morphological-analysis'] | ['natural-language-processing'] | [-6.68536186e-01 1.87306553e-01 -3.01579177e-01 -1.46889463e-01
-8.74083519e-01 -6.85532808e-01 1.84318379e-01 5.73576987e-01
-6.44158483e-01 1.07243919e+00 2.66382158e-01 -4.68878746e-01
3.74169886e-01 -8.25469255e-01 -2.23735482e-01 -5.50232708e-01
-5.03834616e-03 7.62648523e-01 9.50898230e-02 -2.94264615... | [10.364304542541504, 10.113279342651367] |
51e96b02-a4c3-4ac3-b6af-66a08ac1503d | kafk-at-semeval-2020-task-8-extracting | null | null | https://aclanthology.org/2020.semeval-1.152 | https://aclanthology.org/2020.semeval-1.152.pdf | KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes | This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec). The first approach uses both text and image features, while the second approach uses only the images. Error analysis of the two approaches shows that using only the images is more robust to the... | ['Kuntal Dey', 'Ferdous Ahmed Barbhuiya', 'Arup Baruah', 'Kaushik Amar Das'] | 2020-12-01 | null | null | null | semeval-2020 | ['meme-classification'] | ['natural-language-processing'] | [-2.93903649e-01 -2.95469344e-01 2.63149470e-01 -7.12551475e-02
-4.95451808e-01 -6.83606923e-01 1.07165909e+00 1.12893984e-01
-9.73106325e-01 8.42712045e-01 -3.67819145e-03 -9.77820605e-02
3.00635099e-01 -6.61135256e-01 -7.71268308e-01 -4.01754618e-01
1.04861744e-01 2.43124679e-01 4.66308624e-01 -1.49501711... | [8.419997215270996, 10.696388244628906] |
f937fb6c-2084-4457-a0d2-20cc6eae0b3b | dynamic-graph-based-label-propagation-for | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0957417418304998 | https://www.sciencedirect.com/science/article/abs/pii/S0957417418304998 | Dynamic Graph-Based Label Propagation for Density Peaks Clustering | Clustering is a major approach in data mining and machine learning and has been successful in many real-world applications. Density peaks clustering (DPC) is a recently published method that uses an intuitive to cluster data objects efficiently and effectively. However, DPC and most of its improvements suffer from some... | ['Abdulrahman Lotfi', 'Nooruldeen Nasih Qader', 'Seyed Amjad Seyedi', 'Parham Moradi'] | 2019-01-01 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-5.44806533e-02 -3.06166321e-01 -1.46459863e-01 -1.08436719e-01
-1.76130667e-01 -2.94389188e-01 2.40899265e-01 6.04116619e-01
-3.15546125e-01 5.47532260e-01 -7.47349411e-02 3.95282805e-02
-5.69617450e-01 -9.55298007e-01 -1.88560128e-01 -1.12692797e+00
-1.29201338e-01 6.95139885e-01 6.17891371e-01 1.99624643... | [7.643976211547852, 4.569610595703125] |
146c0e05-3b5c-462b-9e1d-213fb9b4e2be | level-generation-and-style-enhancement-deep | 2107.07397 | null | https://arxiv.org/abs/2107.07397v1 | https://arxiv.org/pdf/2107.07397v1.pdf | Level generation and style enhancement -- deep learning for game development overview | We present practical approaches of using deep learning to create and enhance level maps and textures for video games -- desktop, mobile, and web. We aim to present new possibilities for game developers and level artists. The task of designing levels and filling them with details is challenging. It is both time-consumin... | ['Błażej Podgórski', 'Bartłomiej Olechno', 'Piotr Migdał'] | 2021-07-15 | null | null | null | null | ['texture-synthesis', 'unsupervised-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.64171565e-01 3.52706671e-01 3.65880460e-01 -8.35156515e-02
-7.15952575e-01 -5.91533482e-01 5.07889271e-01 -5.40325522e-01
1.83376651e-02 6.71483338e-01 3.00548553e-01 -2.21118212e-01
2.47654870e-01 -1.30320489e+00 -9.43514884e-01 -4.85836864e-01
1.00363247e-01 1.95896626e-01 3.11919630e-01 -7.14502275... | [11.641351699829102, -0.4539114534854889] |
58548c42-3746-4703-be6b-26c322ebca6b | fisheye8k-a-benchmark-and-dataset-for-fisheye | 2305.17449 | null | https://arxiv.org/abs/2305.17449v2 | https://arxiv.org/pdf/2305.17449v2.pdf | FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection | With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset pre... | ['Fang-Pang Lin', 'Mohammed Abduljabbar', 'Fady Alnajjar', 'Ganzorig Batnasan', 'Hamad Al Jassmi', 'Byambaa Dorj', 'Ping-Yang Chen', 'Ming-Ching Chang', 'Jun-Wei Hsieh', 'Erkhembayar Ganbold', 'Munkh-Erdene Otgonbold', 'Munkhjargal Gochoo'] | 2023-05-27 | null | null | null | null | ['2d-object-detection'] | ['computer-vision'] | [-1.48174375e-01 -3.42239201e-01 -1.77329361e-01 -2.01191440e-01
-6.58245385e-01 -5.42524397e-01 3.50187182e-01 -6.80030704e-01
-5.97248197e-01 6.15748286e-01 -2.75029093e-01 -4.45467383e-01
2.74650007e-01 -8.75437975e-01 -8.31484258e-01 -8.82544339e-01
1.09352924e-01 1.11930802e-01 8.78539443e-01 -1.74747631... | [8.08397102355957, -1.0126968622207642] |
a1950272-71c9-469b-acbe-f7f919ba637a | does-character-level-information-always | 2306.02302 | null | https://arxiv.org/abs/2306.02302v1 | https://arxiv.org/pdf/2306.02302v1.pdf | Does Character-level Information Always Improve DRS-based Semantic Parsing? | Even in the era of massive language models, it has been suggested that character-level representations improve the performance of neural models. The state-of-the-art neural semantic parser for Discourse Representation Structures uses character-level representations, improving performance in the four languages (i.e., En... | ['Hitomi Yanaka', 'Tomoya Kurosawa'] | 2023-06-04 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 2.19958887e-01 1.59350738e-01 -2.28158608e-01 -2.13747233e-01
-5.78579426e-01 -7.88980067e-01 5.74278593e-01 6.22745216e-01
-9.64721501e-01 4.57853377e-01 8.13170135e-01 -5.88782132e-01
1.66636407e-01 -9.08303320e-01 -5.61168313e-01 -3.06119442e-01
2.03080535e-01 3.80569071e-01 4.13141549e-01 -3.34555268... | [10.66201114654541, 9.35583782196045] |
826b1a55-5243-43d1-96ae-a98e6ffd220f | byzantine-robust-loopless-stochastic-variance | 2303.04560 | null | https://arxiv.org/abs/2303.04560v1 | https://arxiv.org/pdf/2303.04560v1.pdf | Byzantine-Robust Loopless Stochastic Variance-Reduced Gradient | Distributed optimization with open collaboration is a popular field since it provides an opportunity for small groups/companies/universities, and individuals to jointly solve huge-scale problems. However, standard optimization algorithms are fragile in such settings due to the possible presence of so-called Byzantine w... | ['Eduard Gorbunov', 'Nikita Fedin'] | 2023-03-08 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.33605140e-01 -5.35001792e-02 1.42623544e-01 -1.56615451e-01
-1.21012735e+00 -6.48749530e-01 1.97775319e-01 2.49634370e-01
-4.54303116e-01 1.15930057e+00 2.54677702e-02 -6.31654114e-02
-4.75674540e-01 -5.60093939e-01 -8.08503389e-01 -1.05856502e+00
-2.03562587e-01 3.94487321e-01 -1.96546931e-02 -1.43935144... | [6.293889045715332, 4.888726711273193] |
0352dd1f-b338-4277-9aa0-968a2f7f61e5 | joint-communication-and-computation-design-in | 2210.15399 | null | https://arxiv.org/abs/2210.15399v1 | https://arxiv.org/pdf/2210.15399v1.pdf | Joint Communication and Computation Design in Transmissive RMS Transceiver Enabled Multi-Tier Computing Networks | In this paper, a novel transmissive reconfigurable meta-surface (RMS) transceiver enabled multi-tier computing network architecture is proposed for improving computing capability, decreasing computing delay and reducing base station (BS) deployment cost, in which transmissive RMS equipped with a feed antenna can be reg... | ['Jianmin Lu', 'Hongying Tang', 'Ziwei Liu', 'Wen Chen', 'Zhendong Li'] | 2022-10-27 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 3.64072561e-01 -2.00259343e-01 -1.14683837e-01 1.25220031e-01
-4.75978911e-01 -5.24001002e-01 -3.24383788e-02 -4.35500741e-01
-3.78271848e-01 1.02322185e+00 -2.75213957e-01 -4.58539903e-01
-6.20411396e-01 -7.18351960e-01 -3.29891950e-01 -1.29359984e+00
-6.12405241e-02 -1.01258412e-01 -1.95356756e-01 -6.84107393... | [6.015433311462402, 1.520037055015564] |
a20e6cf4-3ff3-4641-a96a-7679328814de | pose-agnostic-cross-spectral-hallucination | 1909.04365 | null | https://arxiv.org/abs/1909.04365v2 | https://arxiv.org/pdf/1909.04365v2.pdf | Cross-Spectral Face Hallucination via Disentangling Independent Factors | The cross-sensor gap is one of the challenges that have aroused much research interests in Heterogeneous Face Recognition (HFR). Although recent methods have attempted to fill the gap with deep generative networks, most of them suffer from the inevitable misalignment between different face modalities. Instead of imagin... | ['Boyan Duan', 'Yi Li', 'Xingguang Song', 'Ran He', 'Chaoyou Fu'] | 2019-09-10 | cross-spectral-face-hallucination-via | http://openaccess.thecvf.com/content_CVPR_2020/html/Duan_Cross-Spectral_Face_Hallucination_via_Disentangling_Independent_Factors_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Duan_Cross-Spectral_Face_Hallucination_via_Disentangling_Independent_Factors_CVPR_2020_paper.pdf | cvpr-2020-6 | ['heterogeneous-face-recognition', 'face-hallucination'] | ['computer-vision', 'computer-vision'] | [ 6.58705592e-01 -3.49362604e-02 3.91753703e-01 -4.95973200e-01
-4.37505960e-01 -2.15810239e-01 6.79496169e-01 -7.41545379e-01
1.13429032e-01 5.12618840e-01 2.50199527e-01 7.13529289e-02
-7.87870660e-02 -8.21962774e-01 -7.69469082e-01 -1.08722198e+00
6.70554936e-01 6.98617622e-02 -3.42687041e-01 -4.02913749... | [12.884637832641602, -0.0009344199788756669] |
a3e9c69f-56fd-4e9b-9430-d9ab5c7926f9 | multi-agent-path-finding-with-capacity | 1907.12648 | null | https://arxiv.org/abs/1907.12648v1 | https://arxiv.org/pdf/1907.12648v1.pdf | Multi-Agent Path Finding with Capacity Constraints | In multi-agent path finding (MAPF) the task is to navigate agents from their starting positions to given individual goals. The problem takes place in an undirected graph whose vertices represent positions and edges define the topology. Agents can move to neighbor vertices across edges. In the standard MAPF, space occup... | ['Sven Koenig', 'Pavel Surynek', 'T. K. Satish Kumar'] | 2019-07-21 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.38331681e-01 5.99333584e-01 -2.10576519e-01 -1.07366689e-01
-1.32777199e-01 -1.01294923e+00 7.05788314e-01 4.66450870e-01
-3.70413452e-01 1.23303246e+00 -2.63484925e-01 -5.13543248e-01
-9.49061036e-01 -1.45519459e+00 -5.32671809e-01 -4.25083578e-01
-6.23525441e-01 1.41883636e+00 8.48647237e-01 -4.99620646... | [4.988661289215088, 1.8399194478988647] |
502eb50f-a3b3-4ab5-9870-ce3b7f391228 | prediction-intervals-for-economic-fixed-event | 2210.13562 | null | https://arxiv.org/abs/2210.13562v1 | https://arxiv.org/pdf/2210.13562v1.pdf | Prediction intervals for economic fixed-event forecasts | The fixed-event forecasting setup is common in economic policy. It involves a sequence of forecasts of the same ('fixed') predictand, so that the difficulty of the forecasting problem decreases over time. Fixed-event point forecasts are typically published without a quantitative measure of uncertainty. To construct suc... | ['Hendrik Plett', 'Fabian Krüger'] | 2022-10-24 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-3.14127237e-01 5.26872911e-02 -3.18375915e-01 -6.55785978e-01
-9.04138029e-01 -5.92682123e-01 9.77351844e-01 2.69408047e-01
-5.73788695e-02 1.07098413e+00 5.02901435e-01 -9.07547534e-01
-4.01695758e-01 -9.00369585e-01 -4.74990249e-01 -6.93770468e-01
-7.92677477e-02 3.91121805e-01 -2.57736087e-01 9.48552936... | [6.002164840698242, 3.9471030235290527] |
82723c2f-1956-41ec-a9f8-2bb0af7eb821 | limit-distribution-theory-for-the-smooth-1 | 2107.13494 | null | https://arxiv.org/abs/2107.13494v5 | https://arxiv.org/pdf/2107.13494v5.pdf | Limit Distribution Theory for the Smooth 1-Wasserstein Distance with Applications | The smooth 1-Wasserstein distance (SWD) $W_1^\sigma$ was recently proposed as a means to mitigate the curse of dimensionality in empirical approximation while preserving the Wasserstein structure. Indeed, SWD exhibits parametric convergence rates and inherits the metric and topological structure of the classic Wasserst... | ['Kengo Kato', 'Ziv Goldfeld', 'Ritwik Sadhu'] | 2021-07-28 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [-1.93858035e-02 1.05479792e-01 -7.10166618e-02 -2.70547092e-01
-8.35319221e-01 -3.26199293e-01 1.33767068e-01 1.69029206e-01
-7.64360666e-01 9.81839120e-01 -3.18822861e-01 -3.30207229e-01
-8.75005007e-01 -8.18698108e-01 -6.90023601e-01 -1.18170202e+00
-5.04000247e-01 1.55559301e-01 1.17589533e-01 -4.93356772... | [7.157359600067139, 4.1748433113098145] |
c9eed412-58bc-462a-ad64-1ed897db8b27 | evaluating-gaussian-grasp-maps-for-generative | 2206.00432 | null | https://arxiv.org/abs/2206.00432v1 | https://arxiv.org/pdf/2206.00432v1.pdf | Evaluating Gaussian Grasp Maps for Generative Grasping Models | Generalising robotic grasping to previously unseen objects is a key task in general robotic manipulation. The current method for training many antipodal generative grasping models rely on a binary ground truth grasp map generated from the centre thirds of correctly labelled grasp rectangles. However, these binary maps ... | ['Ulrik Beierholm', 'Magnus Bordewich', 'Toby P. Breckon', 'William Prew'] | 2022-06-01 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [ 0.3776438 0.40174103 0.1992215 -0.29342386 -0.5596069 -0.76174814
0.40082398 -0.27362096 -0.16188543 0.60506135 -0.4277556 0.06327792
-0.5036291 -0.8862594 -1.3637611 -1.0589532 -0.25414765 1.1064075
0.10781762 -0.04136741 0.36289203 0.78832376 -1.679607 0.38867986
0.4802865 1.0507485 0.7... | [5.738926410675049, -0.8216999173164368] |
d6c7dbaf-6982-4559-b56f-e19623e1a36c | automated-segmentation-of-microvessels-in | 2210.00166 | null | https://arxiv.org/abs/2210.00166v2 | https://arxiv.org/pdf/2210.00166v2.pdf | Automated segmentation of microvessels in intravascular OCT images using deep learning | To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated ... | ['David L. Wilson', 'Hiram G. Bezerra', 'Giulio Guagliumi', 'Sadeer Al-Kindi', 'Ammar Hoori', 'Luis A. P. Dallan', 'Vladislav N. Zimin', 'Ga-briel T. R. Pereira', 'Issam Motairek', 'Yazan Gharaibeh', 'Lia Gomez-Perez', 'Justin N. Kim', 'Juhwan Lee'] | 2022-10-01 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 9.18796882e-02 1.03727661e-01 -9.41211060e-02 -3.18046182e-01
-7.22904086e-01 -5.34378707e-01 -2.23569125e-02 3.48047078e-01
-5.83240509e-01 7.28404403e-01 -1.45143857e-02 -6.46535456e-01
4.03597683e-01 -6.87047064e-01 -2.65002459e-01 -6.80801272e-01
-3.67153168e-01 2.69376665e-01 3.44013005e-01 4.19790655... | [14.269331932067871, -2.444283962249756] |
e00c0d0c-2ca2-406b-ac4c-82f7367e736c | datnet-dual-adversarial-transfer-for-low | null | null | https://openreview.net/forum?id=HkGzUjR5tQ | https://openreview.net/pdf?id=HkGzUjR5tQ | DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition | We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanc... | ['Kenneth Kwok', 'Hao Zhang', 'Joey Tianyi Zhou', 'Hongyuan Zhu', 'Rick Siow Mong Goh', 'Di Jin'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-3.00415128e-01 -2.11242139e-01 -2.62474045e-02 -4.53462929e-01
-1.16802800e+00 -7.95897424e-01 6.80700719e-01 -5.15360758e-02
-9.97678757e-01 1.07535410e+00 1.19570114e-01 -2.02781230e-01
2.75191456e-01 -8.30745339e-01 -6.39268100e-01 -3.46371949e-01
1.88681185e-01 2.68992931e-01 -1.38631195e-01 -4.95720059... | [9.793269157409668, 9.572829246520996] |
cf0a8189-d683-4389-84f9-304c4af82ede | benchmarking-single-image-reflection-removal | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Wan_Benchmarking_Single-Image_Reflection_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Wan_Benchmarking_Single-Image_Reflection_ICCV_2017_paper.pdf | Benchmarking Single-Image Reflection Removal Algorithms | Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of existing approaches u... | ['Ah-Hwee Tan', 'Ling-Yu Duan', 'Alex C. Kot', 'Renjie Wan', 'Boxin Shi'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['reflection-removal'] | ['computer-vision'] | [ 9.13348734e-01 -7.96856955e-02 9.54436302e-01 -1.77951068e-01
-9.64775860e-01 -3.44888479e-01 5.06049216e-01 -1.85224429e-01
-6.17238097e-02 4.56819981e-01 2.77471572e-01 -1.07590474e-01
4.40113395e-02 -4.60424304e-01 -5.85227251e-01 -8.80061805e-01
1.28236637e-01 1.70023013e-02 6.18912697e-01 -1.59832567... | [10.50070858001709, -2.8453896045684814] |
54c07edc-7d93-4b00-838d-d2cd205b3370 | translation-transformers-rediscover-inherent | 2109.07864 | null | https://arxiv.org/abs/2109.07864v1 | https://arxiv.org/pdf/2109.07864v1.pdf | Translation Transformers Rediscover Inherent Data Domains | Many works proposed methods to improve the performance of Neural Machine Translation (NMT) models in a domain/multi-domain adaptation scenario. However, an understanding of how NMT baselines represent text domain information internally is still lacking. Here we analyze the sentence representations learned by NMT Transf... | ['Mark Fishel', 'Elizaveta Korotkova', 'Maksym Del'] | 2021-09-16 | null | https://aclanthology.org/2021.wmt-1.65 | https://aclanthology.org/2021.wmt-1.65.pdf | wmt-emnlp-2021-11 | ['text-clustering'] | ['natural-language-processing'] | [ 4.81826156e-01 1.09490275e-01 -4.26133156e-01 -5.94546497e-01
-1.19457138e+00 -9.94618535e-01 9.55516160e-01 3.40056904e-02
-3.89475346e-01 9.25288618e-01 4.32140827e-01 -3.45259994e-01
1.12701073e-01 -2.97874063e-01 -8.30265880e-01 -5.49187243e-01
4.32808369e-01 1.31051755e+00 -4.91433442e-02 -2.68548578... | [11.520003318786621, 10.182589530944824] |
af876539-c342-4736-9870-e2d10bc22ee1 | evaluating-and-predicting-the-efficiency | 2201.07718 | null | https://arxiv.org/abs/2201.07718v1 | https://arxiv.org/pdf/2201.07718v1.pdf | Evaluating and predicting the Efficiency Index for Stereotactic Radiosurgery Plans using RapidMiner GO(JAVA) Based Artificial Intelligence Algorithms | Evaluation the prediction of Efficiency index by DVH parameter for SRS treatment plans using Supervised Machine learning and the performance of predictive model algorithms of RapidMiner GO in the parameter prediction are investigated. Dose volume histogram (DVH) based Efficiency index was calculated for 100 clinical SR... | ['Alexis Dimitriadis', 'Sheikh Othman', 'Hossam Donya'] | 2022-01-19 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [ 5.86010069e-02 1.04219645e-01 -6.20373905e-01 -3.11725587e-01
-7.59832561e-01 -4.41098846e-02 2.37560764e-01 4.77146596e-01
-3.17083806e-01 1.11038303e+00 2.60820359e-01 -7.56976306e-01
-8.46539736e-01 -8.34543824e-01 8.20700377e-02 -1.05742037e+00
8.22879747e-02 8.14852118e-01 2.59254277e-01 -1.56304732... | [15.157181739807129, -2.4612159729003906] |
54093628-6275-42b4-9e26-25942aa3ad46 | constant-approximation-for-normalized | 2212.14334 | null | https://arxiv.org/abs/2212.14334v1 | https://arxiv.org/pdf/2212.14334v1.pdf | Constant Approximation for Normalized Modularity and Associations Clustering | We study the problem of graph clustering under a broad class of objectives in which the quality of a cluster is defined based on the ratio between the number of edges in the cluster, and the total weight of vertices in the cluster. We show that our definition is closely related to popular clustering measures, namely no... | ['Christian Sohler', 'Vahab Mirrokni', 'Jakub Łącki'] | 2022-12-29 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.35285869e-01 3.39213997e-01 -3.31597030e-01 -2.66040377e-02
3.63721848e-02 -7.10379422e-01 4.98702936e-02 6.74533784e-01
-3.79715890e-01 2.27588177e-01 -1.07990243e-02 -1.05086520e-01
-8.02248061e-01 -9.96433735e-01 -3.99398655e-01 -6.91562951e-01
-7.26366818e-01 6.76881731e-01 3.82982850e-01 5.13493493... | [6.962836265563965, 5.25604248046875] |
11f51e6c-2f77-407a-81a5-9a41755757f4 | nonblind-image-deconvolution-via-leveraging | null | null | https://link.springer.com/article/10.1007/s11263-022-01621-9 | https://link.springer.com/article/10.1007/s11263-022-01621-9 | Nonblind image deconvolution via leveraging model uncertainty in an untrained deep neural network | Nonblind image deconvolution (NID) is about restoring the latent image with sharp details from
a noisy blurred one using a known blur kernel. This paper presents a dataset-free deep learning
approach for NID using untrained deep neural networks (DNNs), which does not require any external
training data with ground-tr... | ['Mingqin Chen; Yuhui Quan; Tongyao Pang; Hui Ji'] | 2022-05-18 | null | null | null | international-journal-of-computer-vision-2022 | ['image-deconvolution'] | ['computer-vision'] | [ 7.00189620e-02 9.28552896e-02 1.18042901e-01 -7.43419647e-01
-9.50505495e-01 -1.99495882e-01 3.76281261e-01 -8.72599781e-01
-4.24952507e-01 1.16912019e+00 4.33769107e-01 6.26167804e-02
-3.36889058e-01 -1.86603814e-01 -1.05947673e+00 -1.06281102e+00
3.58581245e-01 3.00922960e-01 -1.05216034e-01 5.52024126... | [11.62326431274414, -2.7044129371643066] |
b8d66374-3078-4559-b954-afd0d0766d8d | mesh-guided-one-shot-face-reenactment-using | 2008.07783 | null | https://arxiv.org/abs/2008.07783v2 | https://arxiv.org/pdf/2008.07783v2.pdf | Mesh Guided One-shot Face Reenactment using Graph Convolutional Networks | Face reenactment aims to animate a source face image to a different pose and expression provided by a driving image. Existing approaches are either designed for a specific identity, or suffer from the identity preservation problem in the one-shot or few-shot scenarios. In this paper, we introduce a method for one-shot ... | ['Tianjia Shao', 'Yi Yuan', 'Kun Zhou', 'Guangming Yao'] | 2020-08-18 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [-1.60703734e-01 1.46195456e-01 -5.81274144e-02 -2.27611437e-01
-1.19676977e-01 -3.06733817e-01 4.15881813e-01 -9.84459519e-01
2.40429550e-01 4.29402560e-01 3.69225055e-01 4.36588705e-01
3.20757061e-01 -9.80613112e-01 -7.48399675e-01 -8.18432570e-01
3.56630236e-01 3.68823528e-01 -3.69152606e-01 -4.02093261... | [12.796710968017578, -0.2652316093444824] |
b6d96eb8-24ad-4180-a0eb-956c5e37ec79 | metaregnet-metamorphic-image-registration | 2303.09088 | null | https://arxiv.org/abs/2303.09088v1 | https://arxiv.org/pdf/2303.09088v1.pdf | MetaRegNet: Metamorphic Image Registration Using Flow-Driven Residual Networks | Deep learning based methods provide efficient solutions to medical image registration, including the challenging problem of diffeomorphic image registration. However, most methods register normal image pairs, facing difficulty handling those with missing correspondences, e.g., in the presence of pathology like tumors. ... | ['Yi Hong', 'Ankita Joshi'] | 2023-03-16 | null | null | null | null | ['medical-image-registration'] | ['medical'] | [-1.94041416e-01 2.56687663e-02 -6.35840073e-02 -3.13594729e-01
-8.45088601e-01 -3.15472364e-01 3.49328548e-01 -8.93562809e-02
-4.56560999e-01 4.25658494e-01 3.09731603e-01 7.56234303e-02
-5.27080372e-02 -5.66724718e-01 -3.82345021e-01 -8.18183124e-01
-6.82294294e-02 6.01892412e-01 1.09280415e-01 -3.18572700... | [13.983650207519531, -2.5773894786834717] |
59ee690d-657b-40f5-a231-ca46fae2cb86 | onion-peel-networks-for-deep-video-completion | 1908.08718 | null | https://arxiv.org/abs/1908.08718v1 | https://arxiv.org/pdf/1908.08718v1.pdf | Onion-Peel Networks for Deep Video Completion | We propose the onion-peel networks for video completion. Given a set of reference images and a target image with holes, our network fills the hole by referring the contents in the reference images. Our onion-peel network progressively fills the hole from the hole boundary enabling it to exploit richer contextual inform... | ['Joon-Young Lee', 'Seoung Wug Oh', 'Seon Joo Kim', 'Sungho Lee'] | 2019-08-23 | onion-peel-networks-for-deep-video-completion-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Oh_Onion-Peel_Networks_for_Deep_Video_Completion_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Oh_Onion-Peel_Networks_for_Deep_Video_Completion_ICCV_2019_paper.pdf | iccv-2019-10 | ['video-inpainting'] | ['computer-vision'] | [ 4.88966286e-01 3.12843651e-01 -6.47380427e-02 2.02914521e-01
-5.43078959e-01 -3.25054318e-01 -5.12833856e-02 -1.54144794e-01
-1.66405827e-01 8.59333694e-01 1.96395516e-01 5.25039174e-02
6.25749454e-02 -7.61984706e-01 -9.11345601e-01 -4.24085498e-01
1.44836083e-01 -5.21747656e-02 6.27767026e-01 -1.13574982... | [10.803101539611816, -1.3335800170898438] |
aa91eda6-5ddb-4613-91de-8f5d783b49a0 | covid-widenet-a-capsule-network-for-covid-19 | null | null | https://www.sciencedirect.com/science/article/pii/S1568494622002046 | https://www.sciencedirect.com/science/article/pii/S1568494622002046 | COVID-WideNet—A capsule network for COVID-19 detection | Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people. A number of deep learning classifiers have been p... | ['Harsh Panwar'] | 2022-03-22 | null | null | null | applied-soft-computing-2022-3 | ['covid-19-detection'] | ['medical'] | [-2.35756695e-01 -5.02688766e-01 -4.80523258e-02 1.08861074e-01
-3.61563981e-01 -4.85318869e-01 2.70489514e-01 2.11802408e-01
-5.10919750e-01 7.48016775e-01 -1.95364296e-01 -5.26994169e-01
-2.13617697e-01 -6.20119154e-01 -2.16617107e-01 -8.94219041e-01
-4.27079529e-01 8.99110317e-01 1.18154883e-01 3.15354526... | [15.564651489257812, -1.70636785030365] |
87f4d953-e61e-48a7-a609-4a3c8cbcdcf6 | efficient-cavity-searching-for-gene-network | 2211.02935 | null | https://arxiv.org/abs/2211.02935v1 | https://arxiv.org/pdf/2211.02935v1.pdf | Efficient Cavity Searching for Gene Network of Influenza A Virus | High order structures (cavities and cliques) of the gene network of influenza A virus reveal tight associations among viruses during evolution and are key signals that indicate viral cross-species infection and cause pandemics. As indicators for sensing the dynamic changes of viral genes, these higher order structures ... | ['Zheng Kou', 'Xinyue Fan', 'Yaohua Liu', 'Jiahao Shen', 'Yanqing Su', 'Jietong Zhao', 'Junjie Li'] | 2022-11-05 | null | null | null | null | ['virology'] | ['miscellaneous'] | [ 1.78959835e-02 -2.28471488e-01 -1.95853531e-01 -1.16927788e-01
3.48822951e-01 -6.46710813e-01 1.06381148e-01 5.59884571e-02
-2.04075441e-01 7.02651083e-01 -3.40337574e-01 -5.69528341e-01
-2.62903184e-01 -9.76396680e-01 -7.56348729e-01 -8.67602170e-01
-6.71525776e-01 8.78992379e-01 1.89912133e-02 -5.70745647... | [4.997255325317383, 5.560227870941162] |
b30db9c7-a355-490f-bb33-2381f5b7057e | reinforced-path-reasoning-for-counterfactual | 2207.06674 | null | https://arxiv.org/abs/2207.06674v1 | https://arxiv.org/pdf/2207.06674v1.pdf | Reinforced Path Reasoning for Counterfactual Explainable Recommendation | Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfactual explainable approaches face huge search space and their explanations are either action-based (e.g., user click) or aspect-based (i.e., i... | ['Guandong Xu', 'Dianer Yu', 'Qian Li', 'Xiangmeng Wang'] | 2022-07-14 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 2.20864505e-01 5.82434893e-01 -9.20149624e-01 -4.64756966e-01
-2.81336635e-01 -5.68118155e-01 4.70189542e-01 -6.03360124e-02
-3.26863155e-02 1.14262724e+00 8.56565058e-01 -8.71179581e-01
-6.64894283e-01 -9.90267813e-01 -9.29338336e-01 -6.42049164e-02
1.20852981e-02 4.25867081e-01 -2.71344066e-01 -3.00563723... | [9.448583602905273, 5.687960624694824] |
db29d918-6244-4689-955f-4c1b200ab916 | link-and-code-fast-indexing-with-graphs-and | 1804.09996 | null | http://arxiv.org/abs/1804.09996v2 | http://arxiv.org/pdf/1804.09996v2.pdf | Link and code: Fast indexing with graphs and compact regression codes | Similarity search approaches based on graph walks have recently attained
outstanding speed-accuracy trade-offs, taking aside the memory requirements. In
this paper, we revisit these approaches by considering, additionally, the
memory constraint required to index billions of images on a single server. This
leads us to p... | ['Hervé Jégou', 'Matthijs Douze', 'Alexandre Sablayrolles'] | 2018-04-26 | link-and-code-fast-indexing-with-graphs-and-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Douze_Link_and_Code_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Douze_Link_and_Code_CVPR_2018_paper.pdf | cvpr-2018-6 | ['image-similarity-search'] | ['computer-vision'] | [ 1.96089178e-01 -3.00593704e-01 -3.79114032e-01 -6.77746162e-02
-8.15611899e-01 -5.38663030e-01 6.40441179e-01 8.39031398e-01
-4.84117389e-01 3.33568007e-01 8.10549185e-02 -3.89748961e-01
-2.69741625e-01 -1.14863575e+00 -4.44536537e-01 -4.54096645e-01
-1.77396744e-01 4.14598793e-01 7.52321005e-01 -3.15389007... | [8.495582580566406, 3.5244452953338623] |
cfff2d1a-5c6e-41d9-80f3-8bf5bc3b5cd4 | hierarchical-multi-label-classification | null | null | https://icml.cc/Conferences/2018/Schedule?showEvent=2306 | http://proceedings.mlr.press/v80/wehrmann18a/wehrmann18a.pdf | Hierarchical Multi-Label Classification Networks |
One of the most challenging machine learning problems is a particular case of data classification in which classes are hierarchically structured and objects can be assigned to multiple paths of the class hierarchy at the same time. This task is known as hierarchical multi-label classification (HMC), with applicati... | ['Rodrigo Barros', 'Jonatas Wehrmann', 'Ricardo Cerri'] | 2018-07-01 | null | null | null | icml-2018-7 | ['protein-function-prediction'] | ['medical'] | [ 4.90614504e-01 8.62754360e-02 -3.51915747e-01 -6.69120729e-01
-9.42999125e-01 -4.09690320e-01 2.77825624e-01 7.42412984e-01
-4.22353208e-01 7.92659760e-01 3.68685126e-02 -2.12327287e-01
-4.59147364e-01 -3.81201714e-01 -5.93952894e-01 -6.96260691e-01
-1.71708003e-01 1.00813639e+00 2.62799799e-01 2.80736834... | [9.580009460449219, 4.347387790679932] |
c85e8079-2e54-4f12-b9ba-75c38fa6a9b9 | a-language-guided-benchmark-for-weakly | 2302.14163 | null | https://arxiv.org/abs/2302.14163v1 | https://arxiv.org/pdf/2302.14163v1.pdf | A Language-Guided Benchmark for Weakly Supervised Open Vocabulary Semantic Segmentation | Increasing attention is being diverted to data-efficient problem settings like Open Vocabulary Semantic Segmentation (OVSS) which deals with segmenting an arbitrary object that may or may not be seen during training. The closest standard problems related to OVSS are Zero-Shot and Few-Shot Segmentation (ZSS, FSS) and th... | ['Brejesh lall', 'Monish Natarajan', 'Mustafa Chasmai', 'Prashant Pandey'] | 2023-02-27 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 6.12911105e-01 2.91645259e-01 -3.27561170e-01 -6.19524002e-01
-1.27742851e+00 -8.02150726e-01 4.76610512e-01 1.17116734e-01
-6.85544550e-01 4.94873196e-01 -4.67489451e-01 -2.20580980e-01
3.14838231e-01 -7.90883541e-01 -1.11778605e+00 -6.24642313e-01
4.30768788e-01 6.54702365e-01 1.03385282e+00 -2.30251655... | [9.672394752502441, 0.8729109168052673] |
9c46b83b-8402-466b-87cf-14679e094db1 | cross-modal-cross-domain-moment-alignment | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Jing_Cross-Modal_Cross-Domain_Moment_Alignment_Network_for_Person_Search_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Jing_Cross-Modal_Cross-Domain_Moment_Alignment_Network_for_Person_Search_CVPR_2020_paper.pdf | Cross-Modal Cross-Domain Moment Alignment Network for Person Search | Text-based person search has drawn increasing attention due to its wide applications in video surveillance. However, most of the existing models depend heavily on paired image-text data, which is very expensive to acquire. Moreover, they always face huge performance drop when directly exploiting them to new domains. To... | [' Tieniu Tan', ' Liang Wang', ' Wei Wang', 'Ya Jing'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['person-search'] | ['computer-vision'] | [ 3.91687714e-02 -5.49372017e-01 -1.41695306e-01 -4.46695715e-01
-9.47335422e-01 -3.04423571e-01 8.37106228e-01 -3.17225873e-01
-7.25401700e-01 6.86018288e-01 3.34313780e-01 2.75424868e-01
-1.20331064e-01 -4.15576011e-01 -5.45180798e-01 -6.71641111e-01
2.98768073e-01 7.65261948e-01 2.86988705e-01 -2.12130502... | [14.682085990905762, 0.8722730278968811] |
adca26b0-66a1-4574-90f0-1547d73114bd | active-implicit-object-reconstruction-using | 2303.16739 | null | https://arxiv.org/abs/2303.16739v2 | https://arxiv.org/pdf/2303.16739v2.pdf | Active Implicit Object Reconstruction using Uncertainty-guided Next-Best-View Optimziation | Actively planning sensor views during object reconstruction is crucial for autonomous mobile robots. An effective method should be able to strike a balance between accuracy and efficiency. In this paper, we propose a seamless integration of the emerging implicit representation with the active reconstruction task. We bu... | ['Mengmeng Fu', 'Haoyao Chen', 'Fengyu Quan', 'Jianheng Liu', 'Dongyu Yan'] | 2023-03-29 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 8.46752375e-02 4.26944822e-01 -2.30546787e-01 -4.86907542e-01
-1.07409644e+00 -5.32059312e-01 5.99590182e-01 -1.99001543e-02
-4.16364312e-01 7.25331426e-01 3.05310309e-01 6.64762512e-04
-2.14237198e-01 -1.06095099e+00 -9.47658122e-01 -7.88183749e-01
2.61064380e-01 5.83026707e-01 2.22612932e-01 -6.52912036... | [8.464186668395996, -2.658402681350708] |
d613cf96-bb89-4e4b-b00d-0e1feb1b7d9b | 3d-face-reconstruction-with-dense-landmarks | 2204.02776 | null | https://arxiv.org/abs/2204.02776v2 | https://arxiv.org/pdf/2204.02776v2.pdf | 3D face reconstruction with dense landmarks | Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces more accurately, landmarks are often combined with additional signals like depth images or techniques like differentiable rendering. Can we ke... | ['Julien Valentin', 'Tom Cashman', 'Ivan Stojiljkovic', 'Toby Sharp', 'Jamie Shotton', 'Chirag Raman', 'Stephan Garbin', 'Daniel Wilde', 'Nikola Milosavljevic', 'Jingjing Shen', 'Matthew Johnson', 'Charlie Hewitt', 'Tadas Baltrusaitis', 'Erroll Wood'] | 2022-04-06 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-2.45728105e-01 2.34295934e-01 2.10402682e-02 -6.23990297e-01
-8.69812965e-01 -4.82746422e-01 5.20325005e-01 -4.95182097e-01
-8.45386237e-02 4.96746540e-01 3.70962650e-01 2.62509823e-01
5.09645522e-01 -5.09609759e-01 -7.95333803e-01 -4.11038429e-01
6.90108836e-02 5.84251344e-01 -3.30558628e-01 8.31159763... | [13.17725944519043, -0.04004315659403801] |
17eb255d-b459-48d0-960c-775aa2ba2500 | shadow-removal-via-shadow-image-decomposition | 1908.08628 | null | https://arxiv.org/abs/1908.08628v1 | https://arxiv.org/pdf/1908.08628v1.pdf | Shadow Removal via Shadow Image Decomposition | We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte lay... | ['Hieu Le', 'Dimitris Samaras'] | 2019-08-23 | shadow-removal-via-shadow-image-decomposition-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Le_Shadow_Removal_via_Shadow_Image_Decomposition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Le_Shadow_Removal_via_Shadow_Image_Decomposition_ICCV_2019_paper.pdf | iccv-2019-10 | ['shadow-removal'] | ['computer-vision'] | [ 5.57853460e-01 1.80880532e-01 7.05530047e-01 -3.27406585e-01
-2.83443570e-01 -2.23745674e-01 4.13071096e-01 -6.85718775e-01
-2.65132755e-01 6.53971732e-01 2.65143476e-02 -4.56009507e-01
5.13120115e-01 -7.20836997e-01 -8.25588107e-01 -1.04023266e+00
3.61345172e-01 9.59579349e-02 3.99780005e-01 -1.59090877... | [10.846614837646484, -4.107207298278809] |
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