paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
da7283e1-382b-4bf7-bdba-4fe696bb6adf | active-perception-using-light-curtains-for | 2008.02191 | null | https://arxiv.org/abs/2008.02191v1 | https://arxiv.org/pdf/2008.02191v1.pdf | Active Perception using Light Curtains for Autonomous Driving | Most real-world 3D sensors such as LiDARs perform fixed scans of the entire environment, while being decoupled from the recognition system that processes the sensor data. In this work, we propose a method for 3D object recognition using light curtains, a resource-efficient controllable sensor that measures depth at use... | ['Srinivasa G. Narasimhan', 'Siddharth Ancha', 'David Held', 'Yaadhav Raaj', 'Peiyun Hu'] | 2020-08-05 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4458_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500732.pdf | eccv-2020-8 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.89959937e-01 3.25506747e-01 2.32028607e-02 -5.20267785e-01
-4.97465372e-01 -5.48252523e-01 1.24013387e-01 -9.52188820e-02
-4.57943976e-01 6.29218966e-02 -4.33324307e-01 -2.75155365e-01
-6.09022677e-02 -6.23234987e-01 -1.05150592e+00 -5.69805503e-01
2.19105244e-01 5.11054218e-01 2.52609372e-01 5.28631330... | [7.685495853424072, -2.716139316558838] |
087e96e1-1c52-4e43-aaa2-21331a2d4822 | prioritycut-occlusion-guided-regularization | 2103.11600 | null | https://arxiv.org/abs/2103.11600v1 | https://arxiv.org/pdf/2103.11600v1.pdf | PriorityCut: Occlusion-guided Regularization for Warp-based Image Animation | Image animation generates a video of a source image following the motion of a driving video. State-of-the-art self-supervised image animation approaches warp the source image according to the motion of the driving video and recover the warping artifacts by inpainting. These approaches mostly use vanilla convolution for... | ['Gyeongsu Chae', 'Wai Ting Cheung'] | 2021-03-22 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 3.54689181e-01 -9.20135304e-02 -3.00807953e-01 -1.20742777e-02
-4.22980368e-01 -3.67707551e-01 5.99002004e-01 -4.11626101e-01
-3.79329860e-01 6.76468372e-01 5.76355644e-02 5.95086589e-02
2.93726444e-01 -6.27325833e-01 -1.06674707e+00 -9.59672928e-01
2.55853441e-02 1.11415558e-01 4.62503046e-01 -2.79854447... | [10.910529136657715, -0.8914024233818054] |
10d0726a-5559-48d4-8f88-abf34394a6b6 | chemical-identification-and-indexing-in-1 | null | null | https://doi.org/10.1093/database/baac047 | https://doi.org/10.1093/database/baac047 | Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics | The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain addition... | ['Sérgio Matos', 'João R. Almeida', 'João F. Silva', 'Rui Antunes', 'Tiago Almeida'] | 2022-07-01 | null | null | null | database-the-journal-of-biological-databases | ['chemical-indexing'] | ['natural-language-processing'] | [ 2.07434237e-01 1.22262854e-02 -3.80854458e-01 -1.37814701e-01
-9.42538679e-01 -5.92782557e-01 6.13301694e-01 1.17023432e+00
-8.35090637e-01 8.76399159e-01 2.48245835e-01 -4.56822544e-01
-2.00857893e-01 -6.96330726e-01 -8.49651992e-01 -6.72008753e-01
5.99312186e-02 6.56717241e-01 -1.36635289e-01 3.19389522... | [8.483121871948242, 8.751444816589355] |
92f9f5db-e03b-4c53-bc5c-5488c0dcad6e | infinite-wide-finite-depth-neural-networks | 2112.15577 | null | https://arxiv.org/abs/2112.15577v4 | https://arxiv.org/pdf/2112.15577v4.pdf | How Infinitely Wide Neural Networks Can Benefit from Multi-task Learning -- an Exact Macroscopic Characterization | In practice, multi-task learning (through learning features shared among tasks) is an essential property of deep neural networks (NNs). While infinite-width limits of NNs can provide good intuition for their generalization behavior, the well-known infinite-width limits of NNs in the literature (e.g., neural tangent ker... | ['Hanna Wutte', 'Josef Teichmann', 'Jakob Heiss'] | 2021-12-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.32727213e-02 3.11332822e-01 -6.65206313e-02 -4.00476724e-01
-6.68296099e-01 -6.08542502e-01 5.23879409e-01 -1.35200039e-01
-6.41709328e-01 5.25782406e-01 -6.76646605e-02 -2.63709813e-01
-6.42863750e-01 -5.94552517e-01 -8.81571114e-01 -1.12307334e+00
-6.61294088e-02 2.85708427e-01 2.14247316e-01 -8.51276219... | [7.732889175415039, 3.756253719329834] |
dbef8179-ecaf-44c8-bcc7-4e4eabfe87df | eagermot-3d-multi-object-tracking-via-sensor | 2104.14682 | null | https://arxiv.org/abs/2104.14682v1 | https://arxiv.org/pdf/2104.14682v1.pdf | EagerMOT: 3D Multi-Object Tracking via Sensor Fusion | Multi-object tracking (MOT) enables mobile robots to perform well-informed motion planning and navigation by localizing surrounding objects in 3D space and time. Existing methods rely on depth sensors (e.g., LiDAR) to detect and track targets in 3D space, but only up to a limited sensing range due to the sparsity of th... | ['Laura Leal-Taixé', 'Aljoša Ošep', 'Aleksandr Kim'] | 2021-04-29 | null | null | null | null | ['3d-multi-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.13661326e-01 -4.59952474e-01 -2.35784978e-01 -8.17148909e-02
-5.67607999e-01 -9.26873922e-01 5.53623199e-01 4.37092269e-03
-5.35199046e-01 5.27178705e-01 -1.83978707e-01 1.14757665e-01
-1.38260409e-01 -8.03854585e-01 -8.06699753e-01 -7.82386482e-01
1.56352874e-02 6.79167211e-01 7.65251100e-01 -3.86894271... | [6.960919380187988, -2.1672356128692627] |
7ec8e2be-bde9-473d-8258-0e64a3a0d962 | part-aligned-bilinear-representations-for | 1804.07094 | null | http://arxiv.org/abs/1804.07094v1 | http://arxiv.org/pdf/1804.07094v1.pdf | Part-Aligned Bilinear Representations for Person Re-identification | We propose a novel network that learns a part-aligned representation for
person re-identification. It handles the body part misalignment problem, that
is, body parts are misaligned across human detections due to pose/viewpoint
change and unreliable detection. Our model consists of a two-stream network
(one stream for a... | ['Kyoung Mu Lee', 'Yumin Suh', 'Jingdong Wang', 'Tao Mei', 'Siyu Tang'] | 2018-04-19 | part-aligned-bilinear-representations-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yumin_Suh_Part-Aligned_Bilinear_Representations_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yumin_Suh_Part-Aligned_Bilinear_Representations_ECCV_2018_paper.pdf | eccv-2018-9 | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.19239658e-01 1.36387218e-02 -1.58909755e-03 -3.93804312e-01
-5.86154282e-01 -4.64603901e-01 4.15776163e-01 9.46652442e-02
-6.96599185e-01 4.19979215e-01 1.28911570e-01 5.71437061e-01
1.78830549e-01 -5.74709296e-01 -8.34885776e-01 -5.59590578e-01
-2.21166629e-02 6.45526230e-01 5.99857494e-02 -2.38230646... | [14.666483879089355, 0.9584407806396484] |
622c1d76-5743-496c-a70b-655e15fc32ce | exploiting-deep-learning-for-persian | 1808.05077 | null | http://arxiv.org/abs/1808.05077v1 | http://arxiv.org/pdf/1808.05077v1.pdf | Exploiting Deep Learning for Persian Sentiment Analysis | The rise of social media is enabling people to freely express their opinions
about products and services. The aim of sentiment analysis is to automatically
determine subject's sentiment (e.g., positive, negative, or neutral) towards a
particular aspect such as topic, product, movie, news etc. Deep learning has
recently... | ['Kia Dashtipour', 'Amir Hussain', 'Mandar Gogate', 'Hadi Larijani', 'Cosimo Ieracitano', 'Ahsan Adeel'] | 2018-08-15 | null | null | null | null | ['persian-sentiment-anlysis'] | ['natural-language-processing'] | [-3.76503706e-01 -1.03931658e-01 -3.13755065e-01 -8.75999212e-01
-5.04806265e-02 -2.27173612e-01 4.83085394e-01 4.41868663e-01
-6.04537308e-01 7.21267700e-01 2.28677884e-01 -4.20492381e-01
4.32555109e-01 -7.99230456e-01 -3.76294345e-01 -5.37648678e-01
2.79326081e-01 2.49759346e-01 -3.47082108e-01 -7.54270375... | [11.152005195617676, 7.017779350280762] |
d1dcb3df-51d6-468a-a4ec-617de1621955 | an-effective-motion-centric-paradigm-for-3d | 2303.12535 | null | https://arxiv.org/abs/2303.12535v1 | https://arxiv.org/pdf/2303.12535v1.pdf | An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds | 3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching. However, LiDAR point clouds are usually textureless and incomplete, which hinders effective appearance matching. Besides, previous meth... | ['Zhen Li', 'Shuguang Cui', 'Shenghui Cheng', 'Baoyuan Wang', 'Haiming Zhang', 'Xu Yan', 'Chaoda Zheng'] | 2023-03-21 | null | null | null | null | ['3d-single-object-tracking', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 5.69390915e-02 -3.25885624e-01 -4.45355147e-01 -3.13531309e-01
-7.68444896e-01 -6.03461087e-01 6.14862978e-01 -1.13143995e-01
-5.47572792e-01 4.39992249e-01 -4.37809885e-01 -3.30866903e-01
1.92998443e-02 -3.81847382e-01 -8.85453284e-01 -5.65364957e-01
1.27866879e-01 6.51701212e-01 7.52040207e-01 -1.86568826... | [6.603890419006348, -2.2706778049468994] |
75b3c15b-121d-4a3f-b5a2-f166e82c2ead | an-analysis-of-gpt-3-s-performance-in | 2303.14342 | null | https://arxiv.org/abs/2303.14342v2 | https://arxiv.org/pdf/2303.14342v2.pdf | Analyzing the Performance of GPT-3.5 and GPT-4 in Grammatical Error Correction | GPT-3 and GPT-4 models are powerful, achieving high performance on a variety of Natural Language Processing tasks. However, there is a relative lack of detailed published analysis of their performance on the task of grammatical error correction (GEC). To address this, we perform experiments testing the capabilities of ... | ['Kentaro Inui', 'Michael Zock', 'Diana Galvan-Sosa', 'Keisuke Sakaguchi', 'Steven Coyne'] | 2023-03-25 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 1.18694186e-01 2.52307862e-01 4.43863004e-01 -5.09520590e-01
-1.21623182e+00 -3.75911832e-01 5.10342777e-01 9.43384886e-01
-7.43732274e-01 5.27917266e-01 4.05246556e-01 -5.05522728e-01
-1.67327300e-02 -2.42804438e-01 -7.20770836e-01 2.41265401e-01
8.24205652e-02 7.23321497e-01 3.36886853e-01 -8.05095077... | [11.065794944763184, 10.727232933044434] |
3dcf48ec-77f0-4468-a880-1a256f6e454a | lqr-trees-with-sampling-based-exploration-of | 2303.00553 | null | https://arxiv.org/abs/2303.00553v1 | https://arxiv.org/pdf/2303.00553v1.pdf | LQR-trees with Sampling Based Exploration of the State Space | This paper introduces an extension of the LQR-tree algorithm, which is a feedback-motion-planning algorithm for stabilizing a system of ordinary differential equations from a bounded set of initial conditions to a goal. The constructed policies are represented by a tree of exemplary system trajectories, so called demon... | ['Stefan Ratschan', 'Jiří Fejlek'] | 2023-03-01 | null | null | null | null | ['motion-planning'] | ['robots'] | [-4.46567163e-02 5.28633833e-01 -4.31663036e-01 1.54396236e-01
-7.13917375e-01 -8.30577731e-01 7.24711537e-01 -1.15891330e-01
-2.10915625e-01 1.17658007e+00 -8.66957828e-02 -6.29517138e-01
-4.13120866e-01 -3.31847548e-01 -7.94743717e-01 -7.72349715e-01
-3.06457728e-01 3.48886669e-01 4.39919412e-01 -5.53352773... | [4.909358024597168, 2.029085397720337] |
6cf79960-756d-4082-a3b6-6ce9c3873003 | diffconv-analyzing-irregular-point-clouds | 2111.14658 | null | https://arxiv.org/abs/2111.14658v3 | https://arxiv.org/pdf/2111.14658v3.pdf | diffConv: Analyzing Irregular Point Clouds with an Irregular View | Standard spatial convolutions assume input data with a regular neighborhood structure. Existing methods typically generalize convolution to the irregular point cloud domain by fixing a regular "view" through e.g. a fixed neighborhood size, where the convolution kernel size remains the same for each point. However, sinc... | ['Aasa Feragen', 'Manxi Lin'] | 2021-11-29 | null | null | null | null | ['3d-shape-retrieval', '3d-object-classification'] | ['computer-vision', 'computer-vision'] | [-9.39245597e-02 6.53883144e-02 1.63940579e-01 -4.71721917e-01
-3.72202247e-02 -6.38620973e-01 7.26011634e-01 5.99764176e-02
-2.55369157e-01 3.29862982e-01 -3.25631686e-02 -5.64904392e-01
-1.60579812e-02 -1.38249612e+00 -1.18463421e+00 -6.50854707e-01
1.41197052e-02 3.73062372e-01 3.39905947e-01 6.68095201... | [7.937928676605225, -3.6849215030670166] |
445483b4-b09e-4e1b-8bad-3153af325c0f | sequential-ensemble-learning-for-outlier | 1609.05528 | null | http://arxiv.org/abs/1609.05528v1 | http://arxiv.org/pdf/1609.05528v1.pdf | Sequential Ensemble Learning for Outlier Detection: A Bias-Variance Perspective | Ensemble methods for classification and clustering have been effectively used
for decades, while ensemble learning for outlier detection has only been
studied recently. In this work, we design a new ensemble approach for outlier
detection in multi-dimensional point data, which provides improved accuracy by
reducing err... | ['Wen Zhong', 'Shebuti Rayana', 'Leman Akoglu'] | 2016-09-18 | null | null | null | null | ['outlier-ensembles'] | ['methodology'] | [-1.66190177e-01 -3.64255279e-01 2.96843439e-01 -3.21350694e-01
-8.68099630e-01 -3.23088169e-01 4.34809327e-01 5.47717392e-01
-3.63195926e-01 4.56633329e-01 1.27185494e-01 -1.96504354e-01
-2.68081754e-01 -4.99085724e-01 -5.14275074e-01 -7.99613655e-01
-2.67281324e-01 4.62005228e-01 7.51333609e-02 1.02859735... | [7.547979831695557, 2.7002744674682617] |
dfaaaafb-62dd-44fc-921b-c3fe1147fabd | md-gcn-a-multi-scale-temporal-dual-graph | null | null | https://kns.cnki.net/kcms2/article/abstract?v=LeQIq0pPraN7z56UFBXYmp5cqSpFXzXCFpgvv08RLM-paCwYX2_gXb2meUAoCMzJBhBlFELjQyQx1hYWJOzD5oGwuoPshYZpIjSZ02gYn8LByhWo_x9WmW2F6JdeiXmK&uniplatform=NZKPT | https://www.mdpi.com/1424-8220/23/2/841 | MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction | The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. Due to the complex spatial correlation between different roads and the dyna... | ['Huang Xiaohui;Wang Junyang;Lan Yuanchun;Jiang Chaojie;Yuan Xinhua'] | 2023-01-11 | null | null | null | iaengqi-kan-2023-1 | ['graph-sampling'] | ['graphs'] | [-1.59607098e-01 -5.99303186e-01 -1.44998759e-01 -2.40920514e-01
1.79493561e-01 -4.71597426e-02 4.35259759e-01 -2.14175954e-01
-1.13861084e-01 6.20030761e-01 5.05383722e-02 -8.11820686e-01
-5.03177822e-01 -1.29845095e+00 -2.60710746e-01 -6.97138786e-01
-5.71686924e-01 6.21818379e-02 7.18424737e-01 -3.05095345... | [6.448951244354248, 2.0573604106903076] |
fc4f8011-ea8f-48bf-a75b-ea75bb097cd5 | doubly-robust-nearest-neighbors-in-factor | 2211.14297 | null | https://arxiv.org/abs/2211.14297v2 | https://arxiv.org/pdf/2211.14297v2.pdf | Doubly robust nearest neighbors in factor models | In this technical note, we introduce an improved variant of nearest neighbors for counterfactual inference in panel data settings where multiple units are assigned multiple treatments over multiple time points, each sampled with constant probabilities. We call this estimator a doubly robust nearest neighbor estimator a... | ['Devavrat Shah', 'Susan Murphy', 'Predrag Klasnja', 'Sabina Tomkins', 'Katherine Tian', 'Raaz Dwivedi'] | 2022-11-25 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-2.31353827e-02 8.15466493e-02 -8.23730588e-01 -5.64985871e-01
-1.07525373e+00 -8.84590566e-01 5.95725954e-01 2.33823314e-01
-3.96689564e-01 1.36317301e+00 6.38167500e-01 -5.38338304e-01
-5.08072317e-01 -9.73773599e-01 -1.04920363e+00 -5.95122457e-01
-3.80328476e-01 4.36240137e-01 -4.10589159e-01 3.75564426... | [7.996118068695068, 5.233266353607178] |
016f39d3-9df2-425f-bcf5-1d444fe108ec | advancing-learned-video-compression-with-in | 2211.07004 | null | https://arxiv.org/abs/2211.07004v3 | https://arxiv.org/pdf/2211.07004v3.pdf | Advancing Learned Video Compression with In-loop Frame Prediction | Recent years have witnessed an increasing interest in end-to-end learned video compression. Most previous works explore temporal redundancy by detecting and compressing a motion map to warp the reference frame towards the target frame. Yet, it failed to adequately take advantage of the historical priors in the sequenti... | ['Luc van Gool', 'Radu Timofte', 'Ren Yang'] | 2022-11-13 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 2.72214741e-01 -1.90050989e-01 -2.86505908e-01 -2.14318812e-01
-8.22974384e-01 1.52313545e-01 4.48505133e-01 -2.18422011e-01
-3.85099769e-01 5.54913044e-01 6.31900191e-01 -1.11144498e-01
-8.15839544e-02 -5.47277927e-01 -7.48998523e-01 -8.36405039e-01
-3.97323400e-01 -1.64978489e-01 5.97503901e-01 -2.70206910... | [11.303062438964844, -1.6635611057281494] |
689a8448-12ab-48cd-9b98-c797af06533e | exploiting-asymmetry-for-synthetic-training | 2303.04132 | null | https://arxiv.org/abs/2303.04132v1 | https://arxiv.org/pdf/2303.04132v1.pdf | Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction | Large language models (LLMs) show great potential for synthetic data generation. This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by the LLM: we show that, for problems with structured outputs, it is possible to prompt an LLM to perform the task in the opposi... | ['Robert West', 'Maxime Peyrard', 'Marija Sakota', 'Martin Josifoski'] | 2023-03-07 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.55686605e-01 7.61759341e-01 7.99780246e-03 -3.77665013e-01
-1.48458612e+00 -9.09883142e-01 9.89117444e-01 -1.29413143e-01
-3.86032939e-01 1.10012031e+00 5.13315797e-01 -4.23503131e-01
2.90425241e-01 -4.95580167e-01 -1.09212983e+00 -1.09646738e-01
2.47479826e-01 7.35175490e-01 1.58241577e-02 -3.19708645... | [11.419493675231934, 8.81213665008545] |
043d33b8-164e-4d53-a8d8-bee147e8f35d | few-shot-class-incremental-audio-1 | 2305.19539 | null | https://arxiv.org/abs/2305.19539v1 | https://arxiv.org/pdf/2305.19539v1.pdf | Few-shot Class-incremental Audio Classification Using Dynamically Expanded Classifier with Self-attention Modified Prototypes | Most existing methods for audio classification assume that the vocabulary of audio classes to be classified is fixed. When novel (unseen) audio classes appear, audio classification systems need to be retrained with abundant labeled samples of all audio classes for recognizing base (initial) and novel audio classes. If ... | ['Emmanouil Benetos', 'Jialong Li', 'Wei Xie', 'Wenchang Cao', 'Yanxiong Li'] | 2023-05-31 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 2.50898242e-01 -1.11566477e-01 -7.65256360e-02 -4.27151442e-01
-8.58477116e-01 -6.42792106e-01 8.09823647e-02 2.26914391e-01
-4.31246519e-01 6.27563238e-01 6.09828681e-02 2.58508831e-01
1.96827412e-01 -6.68046474e-01 -4.32885826e-01 -6.11669838e-01
-3.36672127e-01 4.59403813e-01 5.69340527e-01 -2.97372080... | [15.096755027770996, 5.18921422958374] |
4a490d47-3085-4a6f-b6e7-1b42895eb024 | semi-supervised-medical-image-classification | 2005.07377 | null | https://arxiv.org/abs/2005.07377v1 | https://arxiv.org/pdf/2005.07377v1.pdf | Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model | Training deep neural networks usually requires a large amount of labeled data to obtain good performance. However, in medical image analysis, obtaining high-quality labels for the data is laborious and expensive, as accurately annotating medical images demands expertise knowledge of the clinicians. In this paper, we pr... | ['Lequan Yu', 'Quande Liu', 'Qi Dou', 'Luyang Luo', 'Pheng Ann Heng'] | 2020-05-15 | null | null | null | null | ['multi-label-image-classification', 'semi-supervised-medical-image-classification'] | ['computer-vision', 'medical'] | [ 5.39099455e-01 4.11192566e-01 -6.22328699e-01 -9.00885940e-01
-1.00824428e+00 -3.70422244e-01 1.50114670e-01 2.86754698e-01
-1.90377414e-01 7.85083711e-01 -1.59716681e-01 -2.98337042e-01
-1.01739056e-01 -2.98344791e-01 -7.34062850e-01 -7.78477311e-01
3.12833160e-01 7.99315512e-01 -6.44936711e-02 2.62825906... | [14.870950698852539, -2.185311794281006] |
c5c55286-3277-4b50-a259-ea887653cdfa | interpreting-gnn-based-ids-detections-using | 2306.00934 | null | https://arxiv.org/abs/2306.00934v2 | https://arxiv.org/pdf/2306.00934v2.pdf | Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features | The black-box nature of complex Neural Network (NN)-based models has hindered their widespread adoption in security domains due to the lack of logical explanations and actionable follow-ups for their predictions. To enhance the transparency and accountability of Graph Neural Network (GNN) security models used in system... | ['Kangkook Jee', 'Murat Kantarcioglu', 'Feng Chen', 'Muhyun Kim', 'Tianhao Wang', 'Joshua Wiedemeier', 'Kunal Mukherjee'] | 2023-06-01 | null | null | null | null | ['explainable-models', 'malware-classification'] | ['computer-vision', 'miscellaneous'] | [ 3.99697930e-01 5.93399286e-01 -4.15228605e-01 -4.91414547e-01
2.69054055e-01 -7.00313032e-01 7.10235000e-01 2.24130258e-01
4.04263914e-01 1.70042634e-01 -2.43015066e-02 -1.56287324e+00
-2.48524591e-01 -6.99324191e-01 -7.63291895e-01 7.14575499e-02
-6.59083247e-01 -1.93836316e-01 1.17233712e-02 -1.55881509... | [6.984769821166992, 7.524519920349121] |
c7771cea-6355-445e-8ad9-414590483ff9 | a-novel-adaptive-learning-rate-scheduler-for | 1902.07399 | null | https://arxiv.org/abs/1902.07399v4 | https://arxiv.org/pdf/1902.07399v4.pdf | LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence | Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several hyper-parameters, such as learning rate, weight decay, and dropout rate. Arguably, the learning rate is the most important of these to tune, and this has gained more attention in recent works. In this paper... | ['Snehanshu Saha', 'Tejas Prashanth', 'Rahul Yedida'] | 2019-02-20 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-3.17053080e-01 -1.37742996e-01 -2.43468627e-01 -5.38206220e-01
-3.50191474e-01 -5.32821774e-01 3.36236149e-01 9.80611742e-02
-1.06463766e+00 7.94125080e-01 -1.77948445e-01 -3.65042776e-01
-1.49876894e-02 -5.72872698e-01 -8.04813385e-01 -7.06504464e-01
-9.12775472e-02 1.00074142e-01 5.01110852e-01 -1.89029723... | [7.832232475280762, 3.7470712661743164] |
9e3a31a6-1b1c-49da-a3e6-8b0d6e2d6939 | automatic-grammatical-error-detection-for | null | null | https://aclanthology.org/W16-4908 | https://aclanthology.org/W16-4908.pdf | Automatic Grammatical Error Detection for Chinese based on Conditional Random Field | In the process of learning and using Chinese, foreigners may have grammatical errors due to negative migration of their native languages. Currently, the computer-oriented automatic detection method of grammatical errors is not mature enough. Based on the evaluating task {---} CGED2016, we select and analyze the classif... | ['Hong-ying Zan', 'Liyan Zhuo', 'Yingjie Han', 'Yajun Liu'] | 2016-12-01 | null | null | null | ws-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-5.84438801e-01 -3.12904716e-01 5.30211687e-01 -6.48056149e-01
-3.36417645e-01 -5.02731279e-02 -7.95130059e-02 6.59272015e-01
-7.82248974e-01 7.96710432e-01 2.49837697e-01 -5.72332501e-01
1.60940439e-01 -8.09164345e-01 -3.58406007e-01 -2.48609319e-01
1.00038514e-01 1.91019356e-01 1.06315292e-01 -4.72495228... | [11.031722068786621, 10.782458305358887] |
b484951d-1883-43a7-95f4-6096cd7a3f1f | a-crf-based-framework-for-tracklet | 2011.14594 | null | https://arxiv.org/abs/2011.14594v2 | https://arxiv.org/pdf/2011.14594v2.pdf | A CRF-based Framework for Tracklet Inactivation in Online Multi-Object Tracking | Online multi-object tracking (MOT) is an active research topic in the domain of computer vision. Although many previously proposed algorithms have exhibited decent results, the issue of tracklet inactivation has not been sufficiently studied. Simple strategies such as using a fixed threshold on classification scores ar... | ['Huijun Gao', 'Zidong Wang', 'Huihui Pan', 'Tianze Gao'] | 2020-11-30 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [ 2.05454856e-01 -3.43204230e-01 -1.12482816e-01 -2.98824608e-01
-4.14222449e-01 -3.57120395e-01 5.53576589e-01 4.56649698e-02
-6.49308085e-01 7.50541568e-01 -3.00764292e-01 -7.11094737e-02
1.03513792e-01 -5.08890569e-01 -6.01554453e-01 -1.13030386e+00
2.01566860e-01 1.98907450e-01 8.51823270e-01 2.26318449... | [6.4819655418396, -2.029845714569092] |
3151c0f0-b28e-438e-948e-9464287ec62d | simultaneous-segmentation-and-recognition | 1909.08606 | null | https://arxiv.org/abs/1909.08606v1 | https://arxiv.org/pdf/1909.08606v1.pdf | Simultaneous Segmentation and Recognition: Towards more accurate Ego Gesture Recognition | Ego hand gestures can be used as an interface in AR and VR environments. While the context of an image is important for tasks like scene understanding, object recognition, image caption generation and activity recognition, it plays a minimal role in ego hand gesture recognition. An ego hand gesture used for AR and VR e... | ['Tejo Chalasani', 'Aljosa Smolic'] | 2019-09-18 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.51229215e-01 1.23784147e-01 -3.23720276e-02 -4.43255663e-01
-1.87104523e-01 -7.04164982e-01 9.13310409e-01 -8.14216852e-01
-6.06541574e-01 8.32101610e-03 4.12873387e-01 4.46018055e-02
1.47546172e-01 -5.21331251e-01 -5.76548934e-01 -9.28307772e-01
2.73411512e-01 6.68343902e-01 5.01838047e-03 1.27855778... | [6.6451568603515625, -0.24887032806873322] |
3bdcec06-6277-4a3b-b832-500139d424c6 | distributed-sketching-for-randomized | 2203.09755 | null | https://arxiv.org/abs/2203.09755v1 | https://arxiv.org/pdf/2203.09755v1.pdf | Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds | We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler resilience in asynchronous distribut... | ['Mert Pilanci', 'Burak Bartan'] | 2022-03-18 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.91701943e-01 -3.61206144e-01 -1.47166038e-02 -9.84852612e-02
-1.10155165e+00 -1.09415221e+00 9.82016698e-02 1.46960586e-01
-4.69388932e-01 7.95034766e-01 2.25079164e-01 -4.32112336e-01
-4.00720179e-01 -5.23710430e-01 -7.68421650e-01 -8.95917356e-01
-4.90148693e-01 1.18305482e-01 -2.75060833e-01 -1.23408481... | [6.23539400100708, 5.023632526397705] |
e7810e46-df6f-4fad-bc17-ede3d918783a | bayesian-nvh-metamodels-to-assess-interior | 2207.02120 | null | https://arxiv.org/abs/2207.02120v1 | https://arxiv.org/pdf/2207.02120v1.pdf | Bayesian NVH metamodels to assess interior cabin noise using measurement databases | In recent years, a great emphasis has been put on engineering the acoustic signature of vehicles that represents the overall comfort level for passengers. Due to highly uncertain behavior of production cars, probabilistic metamodels or surrogates can be useful to estimate the NVH dispersion and assess different NVH ris... | ['L. Gagliardini', 'J. Antoni', 'O. Sauvage', 'V. Prakash'] | 2022-06-12 | null | null | null | null | ['additive-models'] | ['methodology'] | [-4.02038634e-01 -3.34112972e-01 3.85637790e-01 -2.14568362e-01
-6.16765618e-01 -3.35233271e-01 4.99866605e-01 9.18529406e-02
-1.00142397e-01 9.61311340e-01 1.17329337e-01 -6.46981657e-01
-7.22153008e-01 -1.00119400e+00 -4.22668964e-01 -7.44663239e-01
2.27411062e-01 4.25468117e-01 4.51629519e-01 -2.19111472... | [6.260730266571045, 3.2482244968414307] |
cc9fe56a-4e3c-4b26-b8f9-ec43a2bf1dcd | why-you-should-try-the-real-data-for-the | 2107.13938 | null | https://arxiv.org/abs/2107.13938v1 | https://arxiv.org/pdf/2107.13938v1.pdf | Why You Should Try the Real Data for the Scene Text Recognition | Recent works in the text recognition area have pushed forward the recognition results to the new horizons. But for a long time a lack of large human-labeled natural text recognition datasets has been forcing researchers to use synthetic data for training text recognition models. Even though synthetic datasets are very ... | ['Vladimir Loginov'] | 2021-07-29 | why-you-should-try-the-real-data-for-the-1 | https://arxiv.org/abs/2107.13938 | https://arxiv.org/pdf/2107.13938 | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.81018329e-01 -6.71834573e-02 6.57804608e-02 -6.39055252e-01
-5.83506525e-01 -3.58383715e-01 1.15757227e+00 -2.69568473e-01
-4.15053993e-01 7.03858852e-01 1.49674699e-01 -8.09931755e-02
2.67176211e-01 -5.63742697e-01 -6.12425089e-01 -7.11843610e-01
6.45473123e-01 1.17627084e+00 2.74300575e-01 2.25245580... | [11.84131908416748, 2.3011057376861572] |
5e3ab08b-2439-4ca2-bd64-2882da24cda1 | diffusion-models-beat-gans-on-image-synthesis | 2105.05233 | null | https://arxiv.org/abs/2105.05233v4 | https://arxiv.org/pdf/2105.05233v4.pdf | Diffusion Models Beat GANs on Image Synthesis | We show that diffusion models can achieve image sample quality superior to the current state-of-the-art generative models. We achieve this on unconditional image synthesis by finding a better architecture through a series of ablations. For conditional image synthesis, we further improve sample quality with classifier g... | ['Alex Nichol', 'Prafulla Dhariwal'] | 2021-05-11 | null | http://proceedings.neurips.cc/paper/2021/hash/49ad23d1ec9fa4bd8d77d02681df5cfa-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf | neurips-2021-12 | ['conditional-image-generation'] | ['computer-vision'] | [ 1.00938253e-01 2.59187669e-01 -2.60372400e-01 -4.35471386e-02
-1.03242517e+00 -5.62320292e-01 8.07077885e-01 -4.14744049e-01
-4.83313560e-01 6.17531836e-01 1.19433895e-01 -3.75596821e-01
1.82807580e-01 -8.90920401e-01 -7.98432648e-01 -6.63896263e-01
-3.05453151e-01 4.23265733e-02 -1.36152506e-01 -8.86683166... | [11.464679718017578, -0.3924142122268677] |
327fe971-b9e6-4c9f-9ba6-ee59fb97d0e3 | influencer-backdoor-attack-on-semantic | 2303.12054 | null | https://arxiv.org/abs/2303.12054v2 | https://arxiv.org/pdf/2303.12054v2.pdf | Influencer Backdoor Attack on Semantic Segmentation | When a small number of poisoned samples are injected into the training dataset of a deep neural network, the network can be induced to exhibit malicious behavior during inferences, which poses potential threats to real-world applications. While they have been intensively studied in classification, backdoor attacks on s... | ['Hengshuang Zhao', 'Philip Torr', 'Jindong Gu', 'Haoheng Lan'] | 2023-03-21 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 6.30631626e-01 2.20388189e-01 -2.45762855e-01 -1.44503891e-01
-5.98617494e-01 -1.20211363e+00 5.42566955e-01 -1.39482850e-02
-3.80531073e-01 4.85468686e-01 -5.77469528e-01 -6.89119279e-01
1.15634635e-01 -1.27139044e+00 -1.08242226e+00 -1.00084436e+00
2.15807542e-01 3.23337652e-02 6.84963763e-01 2.43024379... | [5.667520523071289, 7.7415852546691895] |
7add29be-3332-4d88-ab00-0d9426ff85cd | bayesian-methods-for-semi-supervised-text | 2010.14872 | null | https://arxiv.org/abs/2010.14872v1 | https://arxiv.org/pdf/2010.14872v1.pdf | Bayesian Methods for Semi-supervised Text Annotation | Human annotations are an important source of information in the development of natural language understanding approaches. As under the pressure of productivity annotators can assign different labels to a given text, the quality of produced annotations frequently varies. This is especially the case if decisions are diff... | ['Marko Robnik-Sikonja', 'Gregor Pirs', 'Kristian Miok'] | 2020-10-28 | null | https://aclanthology.org/2020.law-1.1 | https://aclanthology.org/2020.law-1.1.pdf | coling-law-2020-12 | ['text-annotation'] | ['natural-language-processing'] | [ 1.45734698e-02 2.97867268e-01 -3.44922170e-02 -4.34894115e-01
-4.56732273e-01 -5.49634695e-01 4.43035662e-01 3.46905500e-01
-4.27881122e-01 7.28772700e-01 3.70653331e-01 -1.67267993e-01
-1.18255056e-02 -3.65329832e-01 -2.01925471e-01 -5.82717240e-01
6.66333079e-01 5.51611722e-01 3.48424643e-01 -1.26462772... | [9.667580604553223, 4.656586647033691] |
5c7872fb-5a18-4370-98cd-45cfcbff1672 | unsupervised-learning-based-long-term | 1902.09596 | null | http://arxiv.org/abs/1902.09596v1 | http://arxiv.org/pdf/1902.09596v1.pdf | Unsupervised learning-based long-term superpixel tracking | Finding correspondences between structural entities decomposing images is of
high interest for computer vision applications. In particular, we analyze how
to accurately track superpixels - visual primitives generated by aggregating
adjacent pixels sharing similar characteristics - over extended time periods
relying on ... | ['Gwenolé Quellec', 'Pierre-Henri Conze', 'Florian Tilquin', 'Fabrice Heitz', 'Mathieu Lamard'] | 2019-02-25 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 4.25505757e-01 -3.81656766e-01 -1.61785200e-01 -1.47282928e-01
-1.04531276e+00 -6.38405800e-01 5.81621706e-01 1.91376105e-01
-6.53048992e-01 8.14226270e-01 -3.53388309e-01 2.41246879e-01
-7.24462867e-02 -5.48400819e-01 -8.02784681e-01 -6.59293354e-01
-2.86946446e-01 3.37565660e-01 1.06377614e+00 1.80562899... | [9.081554412841797, -0.3596699833869934] |
1af0594d-062c-4058-94e8-b2915f8a6cb1 | finding-the-needle-in-a-haystack-unsupervised | 2303.07991 | null | https://arxiv.org/abs/2303.07991v1 | https://arxiv.org/pdf/2303.07991v1.pdf | Finding the Needle in a Haystack: Unsupervised Rationale Extraction from Long Text Classifiers | Long-sequence transformers are designed to improve the representation of longer texts by language models and their performance on downstream document-level tasks. However, not much is understood about the quality of token-level predictions in long-form models. We investigate the performance of such architectures in the... | ['Marek Rei', 'Helen Yannakoudakis', 'Andrew Caines', 'Kamil Bujel'] | 2023-03-14 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 4.08862233e-01 5.81414938e-01 -2.64769107e-01 -4.57916617e-01
-1.46063566e+00 -5.81785977e-01 7.05715299e-01 3.09223384e-01
-3.39548051e-01 5.68762422e-01 1.18044555e+00 -7.12082863e-01
2.40100071e-01 -3.65205556e-01 -7.81998813e-01 -2.72670031e-01
3.07332933e-01 3.07512164e-01 -1.94486499e-01 -2.75116891... | [11.65208911895752, 8.93753719329834] |
01e1e148-c830-4339-adbf-35d6a6dd22a5 | few-shot-one-class-classification-via-meta-1 | 2007.04146 | null | https://arxiv.org/abs/2007.04146v2 | https://arxiv.org/pdf/2007.04146v2.pdf | Few-Shot One-Class Classification via Meta-Learning | Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strateg... | ['Hans-Georg Köpken', 'Denis Krompaß', 'Ahmed Frikha', 'Volker Tresp'] | 2020-07-08 | null | https://openreview.net/forum?id=B1ltfgSYwS | https://openreview.net/pdf?id=B1ltfgSYwS | null | ['one-class-classifier'] | ['methodology'] | [ 4.92499858e-01 -7.65248537e-02 -2.73883402e-01 -2.07452163e-01
-7.61397064e-01 6.55076131e-02 8.08270633e-01 4.08022165e-01
-4.06949371e-01 4.15714025e-01 -6.35286629e-01 -4.06181104e-02
-4.57296431e-01 -6.88563347e-01 -6.47250235e-01 -1.03624725e+00
-1.76963463e-01 7.85152078e-01 4.64093089e-01 -3.60164851... | [9.911348342895508, 3.124608278274536] |
06b277cb-b732-4de2-91b8-046d93527071 | video-inpainting-by-jointly-learning-temporal | 1806.08482 | null | http://arxiv.org/abs/1806.08482v2 | http://arxiv.org/pdf/1806.08482v2.pdf | Video Inpainting by Jointly Learning Temporal Structure and Spatial Details | We present a new data-driven video inpainting method for recovering missing
regions of video frames. A novel deep learning architecture is proposed which
contains two sub-networks: a temporal structure inference network and a spatial
detail recovering network. The temporal structure inference network is built
upon a 3D... | ['Xiaoguang Han', 'Haibin Huang', 'Chuan Wang', 'Jue Wang'] | 2018-06-22 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [ 1.19740620e-01 1.82737067e-01 -1.62160039e-01 -2.93678045e-01
-9.08341765e-01 -1.90743238e-01 3.82142544e-01 -6.33175015e-01
-2.27220029e-01 6.19198561e-01 5.69704175e-01 -2.47417223e-02
1.72913015e-01 -6.76211774e-01 -1.29434586e+00 -3.54699463e-01
-2.16427997e-01 6.49229065e-02 2.21471578e-01 3.53754684... | [10.797579765319824, -1.3117905855178833] |
6f5279e2-1d4e-4ddf-9863-1d6360f6f811 | acoustic-echo-cancellation-with-cross-domain | null | null | https://www.isca-speech.org/archive/pdfs/interspeech_2021/pfeifenberger21_interspeech.pdf | https://www.isca-speech.org/archive/pdfs/interspeech_2021/pfeifenberger21_interspeech.pdf | Acoustic Echo Cancellation with Cross-Domain Learning | This paper proposes the Cross-Domain Echo-Controller(CDEC), submitted to the Interspeech 2021 AEC-Challenge.The algorithm consists of three building blocks: (i) a Time-Delay Compensation (TDC) module, (ii) a frequency-domainblock-based Acoustic Echo Canceler (AEC), and (iii) a Time-Domain Neu... | ['Franz Pernkopf', 'Matthias Zoehrer', 'Lukas Pfeifenberger'] | 2021-08-30 | null | null | null | interspeech-2021-8 | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.54815698e-01 -2.44428858e-01 3.18702430e-01 -2.01392770e-01
-1.45342255e+00 -4.67807889e-01 3.76635045e-01 -6.86101764e-02
-6.62143648e-01 2.87812591e-01 3.73404205e-01 -5.71152747e-01
1.89546585e-01 7.87017420e-02 -6.09064341e-01 -4.25476521e-01
-4.86002564e-01 2.01243777e-02 4.45392460e-01 -1.03727810... | [15.064191818237305, 6.004395008087158] |
c86482a6-3310-42ad-96fe-8632c14c50b9 | arbitrary-video-style-transfer-via-multi | 2009.08003 | null | https://arxiv.org/abs/2009.08003v2 | https://arxiv.org/pdf/2009.08003v2.pdf | Arbitrary Video Style Transfer via Multi-Channel Correlation | Video style transfer is getting more attention in AI community for its numerous applications such as augmented reality and animation productions. Compared with traditional image style transfer, performing this task on video presents new challenges: how to effectively generate satisfactory stylized results for any speci... | ['Wei-Ming Dong', 'Changsheng Xu', 'Chongyang Ma', 'Haibin Huang', 'Fan Tang', 'Yingying Deng'] | 2020-09-17 | null | null | null | null | ['video-style-transfer'] | ['computer-vision'] | [ 3.83650154e-01 -3.51754248e-01 8.00408572e-02 -4.27994877e-01
-1.81368336e-01 -5.99233925e-01 6.30210161e-01 -4.57197487e-01
-1.14606053e-01 6.48445845e-01 2.65514255e-01 1.91981494e-01
2.53681898e-01 -6.85096562e-01 -8.46172154e-01 -6.45821989e-01
3.02241832e-01 -2.19557047e-01 1.42584950e-01 -2.66977131... | [11.310545921325684, -0.7805736064910889] |
3c0c96ef-02be-4063-93c2-4c5cc6aaaf80 | dont-give-me-the-details-just-the-summary | 1808.08745 | null | http://arxiv.org/abs/1808.08745v1 | http://arxiv.org/pdf/1808.08745v1.pdf | Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization | We introduce extreme summarization, a new single-document summarization task
which does not favor extractive strategies and calls for an abstractive
modeling approach. The idea is to create a short, one-sentence news summary
answering the question "What is the article about?". We collect a real-world,
large-scale datas... | ['Shay B. Cohen', 'Mirella Lapata', 'Shashi Narayan'] | 2018-08-27 | dont-give-me-the-details-just-the-summary-1 | https://aclanthology.org/D18-1206 | https://aclanthology.org/D18-1206.pdf | emnlp-2018-10 | ['extreme-summarization'] | ['natural-language-processing'] | [ 5.44915795e-01 6.74510717e-01 -2.44312361e-01 -3.77324313e-01
-1.41552389e+00 -6.82149231e-01 8.70523095e-01 5.29598534e-01
-6.27651155e-01 1.02893126e+00 1.23711574e+00 -3.80848795e-01
-4.30826247e-02 -4.97829050e-01 -1.12217605e+00 -6.05785698e-02
3.29109468e-02 8.03827643e-01 -5.89973154e-03 -4.02767897... | [12.49569320678711, 9.588583946228027] |
669f0773-c476-4e33-846b-8ee9e08bf14c | weakly-supervised-fine-grained-image | 1504.04943 | null | http://arxiv.org/abs/1504.04943v1 | http://arxiv.org/pdf/1504.04943v1.pdf | Weakly Supervised Fine-Grained Image Categorization | In this paper, we categorize fine-grained images without using any object /
part annotation neither in the training nor in the testing stage, a step
towards making it suitable for deployments. Fine-grained image categorization
aims to classify objects with subtle distinctions. Most existing works heavily
rely on object... | ['Minh N. Do', 'Viet-Anh Nguyen', 'Xiu-Shen Wei', 'Jianfei Cai', 'Jianxin Wu', 'Yu Zhang', 'Jiangbo Lu'] | 2015-04-20 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 4.29898314e-02 -1.32147625e-01 -9.23053324e-02 -4.31024760e-01
-4.33675706e-01 -7.17128217e-01 7.28891671e-01 6.16722763e-01
-5.13746738e-01 4.78082240e-01 -1.47551402e-01 8.95657986e-02
-3.70971024e-01 -7.87535787e-01 -5.00662565e-01 -8.00503671e-01
1.48229167e-01 7.13291228e-01 8.44773352e-01 -1.66511893... | [9.60189437866211, 1.9727554321289062] |
d17776cb-157b-493e-827d-c8404a0c9100 | custom-edit-text-guided-image-editing-with | 2305.15779 | null | https://arxiv.org/abs/2305.15779v1 | https://arxiv.org/pdf/2305.15779v1.pdf | Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models | Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts. Recent research has extended these models to support text-guided image editing. While text guidance is an intuitive editing interface for users, it often fails to ensure the precise concept conveyed by users. ... | ['Sungroh Yoon', 'Junho Kim', 'Yunji Kim', 'Yunjey Choi', 'Jooyoung Choi'] | 2023-05-25 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 4.74501044e-01 -3.53308022e-01 5.31193390e-02 -4.48660672e-01
-5.12387156e-01 -8.94182861e-01 9.42358315e-01 2.46272326e-01
-4.25338328e-01 3.14140916e-01 4.27912623e-01 -4.14955795e-01
3.88868339e-02 -4.76657301e-01 -2.63864636e-01 -1.64171889e-01
3.65302652e-01 1.24643765e-01 2.96035945e-01 -2.17995405... | [11.354877471923828, -0.3734437823295593] |
73087b15-e38e-44c5-a9ab-a6553ca7b172 | generation-augmented-retrieval-for-open | 2009.08553 | null | https://arxiv.org/abs/2009.08553v4 | https://arxiv.org/pdf/2009.08553v4.pdf | Generation-Augmented Retrieval for Open-domain Question Answering | We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GA... | ['Yelong Shen', 'Weizhu Chen', 'Yuning Mao', 'Xiaodong Liu', 'Jianfeng Gao', 'Jiawei Han', 'Pengcheng He'] | 2020-09-17 | null | https://aclanthology.org/2021.acl-long.316 | https://aclanthology.org/2021.acl-long.316.pdf | acl-2021-5 | ['triviaqa'] | ['miscellaneous'] | [ 1.83322892e-01 3.20045173e-01 -6.93325698e-02 9.50599555e-04
-2.27170181e+00 -9.08945084e-01 9.81020153e-01 -6.02243431e-02
-3.81343096e-01 8.40783596e-01 8.87921393e-01 -1.72587037e-02
-2.00512245e-01 -8.16315770e-01 -8.43439579e-01 -2.71946818e-01
3.70007426e-01 1.28433180e+00 1.09204382e-01 -6.85075402... | [11.379488945007324, 7.79613733291626] |
9d3e7daf-0cd2-4d0d-8c6a-2f38149dc058 | evaluating-diverse-knowledge-sources-for | 2208.09554 | null | https://arxiv.org/abs/2208.09554v3 | https://arxiv.org/pdf/2208.09554v3.pdf | Integrating Diverse Knowledge Sources for Online One-shot Learning of Novel Tasks | Autonomous agents are able to draw on a wide variety of potential sources of task knowledge; however current approaches invariably focus on only one or two. Here we investigate the challenges and impact of exploiting diverse knowledge sources to learn online, in one-shot, new tasks for a simulated office mobile robot. ... | ['John E. Laird', 'Peter Lindes', 'Robert E. Wray', 'James R. Kirk'] | 2022-08-19 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 2.37705737e-01 2.46911664e-02 2.58049350e-02 -9.20576602e-02
-5.60100555e-01 -7.71621466e-01 7.89283872e-01 3.38202000e-01
-8.25908303e-01 8.74522626e-01 3.05325501e-02 -3.16075265e-01
-6.11499846e-01 -4.68478858e-01 -4.16856349e-01 -2.33710364e-01
-2.12969538e-03 8.16154003e-01 6.77800238e-01 -6.88245714... | [4.231500148773193, 1.163480281829834] |
ee701544-9d3b-44f7-b0ee-1ee49814f2ba | recurrent-network-models-for-human-dynamics | 1508.00271 | null | http://arxiv.org/abs/1508.00271v2 | http://arxiv.org/pdf/1508.00271v2.pdf | Recurrent Network Models for Human Dynamics | We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and
prediction of human body pose in videos and motion capture. The ERD model is a
recurrent neural network that incorporates nonlinear encoder and decoder
networks before and after recurrent layers. We test instantiations of ERD
architectures in the ... | ['Sergey Levine', 'Panna Felsen', 'Katerina Fragkiadaki', 'Jitendra Malik'] | 2015-08-02 | recurrent-network-models-for-human-dynamics-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Fragkiadaki_Recurrent_Network_Models_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Fragkiadaki_Recurrent_Network_Models_ICCV_2015_paper.pdf | iccv-2015-12 | ['human-pose-forecasting', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [ 1.13994151e-01 1.83230594e-01 -4.72976685e-01 -2.88148765e-02
-5.18369257e-01 -2.84203768e-01 6.50836408e-01 -6.46941125e-01
-2.83697367e-01 4.51627791e-01 9.08990681e-01 3.49216402e-01
2.31481388e-01 -1.91648141e-01 -8.57491255e-01 -5.67316949e-01
-4.32377905e-01 3.47084045e-01 1.62304074e-01 -2.02229053... | [7.182224750518799, -0.28494909405708313] |
3de03ea8-7929-479c-b187-72d201a40f4a | on-the-difficulty-of-intersection-checking | 2305.09901 | null | https://arxiv.org/abs/2305.09901v2 | https://arxiv.org/pdf/2305.09901v2.pdf | On the Difficulty of Intersection Checking with Polynomial Zonotopes | Polynomial zonotopes, a non-convex set representation, have a wide range of applications from real-time motion planning and control in robotics, to reachability analysis of nonlinear systems and safety shielding in reinforcement learning. Despite this widespread use, a frequently overlooked difficulty associated with p... | ['Yifan Sun', 'Stanley Bak', 'Ertai Luo', 'Yushen Huang'] | 2023-05-17 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 2.02569097e-01 5.04416108e-01 -1.77631721e-01 4.29729342e-01
-6.23775959e-01 -1.01429164e+00 2.46094659e-01 3.01678449e-01
-1.45078838e-01 1.12256205e+00 -4.65399951e-01 -8.46937358e-01
-5.11262953e-01 -9.14155781e-01 -9.55686152e-01 -7.90813506e-01
-6.20804429e-01 8.07691574e-01 6.03286445e-01 -3.38822126... | [4.872641086578369, 1.9602086544036865] |
81b47360-0a42-408d-b895-385543d7f6a8 | distilling-large-vision-language-model-with | 2307.03135 | null | https://arxiv.org/abs/2307.03135v1 | https://arxiv.org/pdf/2307.03135v1.pdf | Distilling Large Vision-Language Model with Out-of-Distribution Generalizability | Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models,... | ['Hao Su', 'Zhuowen Tu', 'Zhan Ling', 'Minghua Liu', 'Yunhao Fang', 'Xuanlin Li'] | 2023-07-06 | null | null | null | null | ['zero-shot-transfer-image-classification', 'few-shot-image-classification', 'zero-shot-learning', 'prompt-engineering', 'natural-language-understanding'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 8.39050412e-02 7.36791268e-02 -4.04534131e-01 -3.58184159e-01
-6.87245905e-01 -6.31017148e-01 5.87719142e-01 2.84113914e-01
-4.23029572e-01 2.98578650e-01 2.08430111e-01 -4.81788188e-01
5.56687228e-02 -5.43107748e-01 -5.21410286e-01 -5.94968379e-01
5.27819455e-01 4.99627590e-01 2.62529939e-01 -9.75560322... | [10.058477401733398, 2.14921236038208] |
e30408bf-6fd9-45ff-a791-67f0cc3fce02 | deep-reinforcement-learning-for-unknown | 2009.06847 | null | https://arxiv.org/abs/2009.06847v2 | https://arxiv.org/pdf/2009.06847v2.pdf | Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data | We consider the problem of anomaly detection with a small set of partially labeled anomaly examples and a large-scale unlabeled dataset. This is a common scenario in many important applications. Existing related methods either exclusively fit the limited anomaly examples that typically do not span the entire set of ano... | ['Anton Van Den Hengel', 'Chunhua Shen', 'Longbing Cao', 'Guansong Pang'] | 2020-09-15 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.27934381e-01 1.52108252e-01 2.26650074e-01 -5.40650904e-01
-9.77588832e-01 -4.34911251e-01 5.31982481e-01 4.07801151e-01
-2.28015140e-01 5.51124513e-01 -3.48105401e-01 -4.55868095e-01
2.33752709e-02 -4.40377325e-01 -7.71175683e-01 -7.32124865e-01
-5.12582600e-01 1.00371802e+00 2.56239325e-01 -8.26116651... | [7.612226963043213, 2.387421131134033] |
a1663058-deb6-4a2c-ac21-bd52ed5f46c4 | deep-networks-with-internal-selective | 1407.3068 | null | http://arxiv.org/abs/1407.3068v2 | http://arxiv.org/pdf/1407.3068v2.pdf | Deep Networks with Internal Selective Attention through Feedback Connections | Traditional convolutional neural networks (CNN) are stationary and
feedforward. They neither change their parameters during evaluation nor use
feedback from higher to lower layers. Real brains, however, do. So does our
Deep Attention Selective Network (dasNet) architecture. DasNets feedback
structure can dynamically al... | ['Marijn Stollenga', 'Juergen Schmidhuber', 'Jonathan Masci', 'Faustino Gomez'] | 2014-07-11 | deep-networks-with-internal-selective-1 | http://papers.nips.cc/paper/5276-deep-networks-with-internal-selective-attention-through-feedback-connections | http://papers.nips.cc/paper/5276-deep-networks-with-internal-selective-attention-through-feedback-connections.pdf | neurips-2014-12 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 8.96374807e-02 1.65625766e-01 -2.05824412e-02 -1.39100373e-01
1.92057505e-01 -6.19986057e-01 5.30741453e-01 -3.93086642e-01
-9.12385285e-01 7.89929032e-01 1.97618499e-01 -2.47033983e-01
-4.89199832e-02 -5.28151870e-01 -6.62924290e-01 -7.28484869e-01
-1.35400087e-01 2.94136345e-01 6.62768781e-01 -3.53899688... | [8.667489051818848, 3.1673424243927] |
176ea388-f093-4aef-aa1c-570b50011979 | woad-weakly-supervised-online-action | 2006.03732 | null | https://arxiv.org/abs/2006.03732v2 | https://arxiv.org/pdf/2006.03732v2.pdf | WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos | Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a we... | ['ran Xu', 'Richard Socher', 'Mingfei Gao', 'Yingbo Zhou', 'Caiming Xiong'] | 2020-06-05 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Gao_WOAD_Weakly_Supervised_Online_Action_Detection_in_Untrimmed_Videos_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Gao_WOAD_Weakly_Supervised_Online_Action_Detection_in_Untrimmed_Videos_CVPR_2021_paper.pdf | cvpr-2021-1 | ['online-action-detection'] | ['computer-vision'] | [ 4.81831849e-01 4.42373417e-02 -1.07091177e+00 -1.29263416e-01
-9.37813401e-01 -4.51234639e-01 5.89464724e-01 -3.21261704e-01
-3.68306577e-01 3.92231703e-01 5.54304898e-01 -8.99012461e-02
5.48215449e-01 -2.65307158e-01 -6.65254295e-01 -6.00055456e-01
-4.27600503e-01 1.18404195e-01 7.71274328e-01 2.61436552... | [8.461669921875, 0.6005592346191406] |
1e84d047-b691-440a-95e9-312809a7cf9d | fast-diffusion-sampler-for-inverse-problems | 2303.05754 | null | https://arxiv.org/abs/2303.05754v1 | https://arxiv.org/pdf/2303.05754v1.pdf | Fast Diffusion Sampler for Inverse Problems by Geometric Decomposition | Diffusion models have shown exceptional performance in solving inverse problems. However, one major limitation is the slow inference time. While faster diffusion samplers have been developed for unconditional sampling, there has been limited research on conditional sampling in the context of inverse problems. In this s... | ['Jong Chul Ye', 'Suhyeon Lee', 'Hyungjin Chung'] | 2023-03-10 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 4.70092624e-01 7.49145225e-02 7.68104121e-02 -5.84100857e-02
-1.03934932e+00 -5.84981069e-02 5.24216890e-01 -2.13158533e-01
-2.85376281e-01 7.87141502e-01 4.66995716e-01 -1.02196828e-01
-3.08522284e-01 -6.35489762e-01 -6.67713583e-01 -1.23671830e+00
8.56691226e-02 5.94650686e-01 -1.69610307e-02 1.98981091... | [11.916077613830566, -2.3972079753875732] |
96a1e929-0888-42b1-a624-71e7a23a75cc | skill-based-multi-objective-reinforcement | 2203.10033 | null | https://arxiv.org/abs/2203.10033v1 | https://arxiv.org/pdf/2203.10033v1.pdf | Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration | In modern industrial settings with small batch sizes it should be easy to set up a robot system for a new task. Strategies exist, e.g. the use of skills, but when it comes to handling forces and torques, these systems often fall short. We introduce an approach that provides a combination of task-level planning with tar... | ['Volker Krueger', 'Luigi Nardi', 'Konstantinos Chatzilygeroudis', 'Faseeh Ahmad', 'Matthias Mayr'] | 2022-03-18 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.83462971e-01 2.88813174e-01 -1.23546734e-01 -1.03542283e-01
-7.96339691e-01 -6.42804205e-01 4.08468485e-01 7.09075928e-02
-4.17408317e-01 9.19867158e-01 -1.98357552e-01 -4.18671548e-01
-7.25704730e-01 -4.60495472e-01 -8.43123913e-01 -7.09780991e-01
-3.36040229e-01 1.12738681e+00 4.32835132e-01 -3.70968461... | [4.633107662200928, 1.4901834726333618] |
985f1e64-28c6-4132-a543-6ba3f2cf73ba | collaborative-multi-object-tracking-with | 2303.14346 | null | https://arxiv.org/abs/2303.14346v1 | https://arxiv.org/pdf/2303.14346v1.pdf | Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation | Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object d... | ['Fei Miao', 'Caiwen Ding', 'Chen Feng', 'Zhili Zhang', 'Yiming Li', 'Songyang Han', 'Sanbao Su'] | 2023-03-25 | null | null | null | null | ['motion-prediction', 'multiple-object-tracking'] | ['computer-vision', 'computer-vision'] | [-1.66952327e-01 3.96757424e-02 -7.73443207e-02 -4.56042945e-01
-8.11275005e-01 -4.73249346e-01 7.92443871e-01 2.59370863e-01
-5.40374637e-01 4.34928417e-01 -6.41153678e-02 -1.64595410e-01
-8.20770636e-02 -7.38381326e-01 -7.70212650e-01 -3.23156059e-01
-1.61616877e-01 7.09859550e-01 1.07594013e+00 -1.96500301... | [6.661075592041016, -2.1271770000457764] |
a0ce96d6-38e2-419d-8852-b3fc246b4d70 | stylenat-giving-each-head-a-new-perspective | 2211.05770 | null | https://arxiv.org/abs/2211.05770v1 | https://arxiv.org/pdf/2211.05770v1.pdf | StyleNAT: Giving Each Head a New Perspective | Image generation has been a long sought-after but challenging task, and performing the generation task in an efficient manner is similarly difficult. Often researchers attempt to create a "one size fits all" generator, where there are few differences in the parameter space for drastically different datasets. Herein, we... | ['Humphrey Shi', 'Zhangyang Wang', 'Xingqian Xu', 'Ali Hassani', 'Steven Walton'] | 2022-11-10 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 1.71459526e-01 1.68602895e-02 1.10017814e-01 -6.81909025e-02
-1.01299465e+00 -6.35290504e-01 6.85800016e-01 -5.05290985e-01
-1.64777443e-01 6.43180013e-01 3.87600720e-01 -3.52669328e-01
1.07105426e-01 -9.90891397e-01 -7.45016873e-01 -6.95631623e-01
2.90932536e-01 3.39300543e-01 -9.83361006e-02 -4.66894537... | [11.511700630187988, -0.44549763202667236] |
90e99467-1e6b-4a12-986e-8e1bd6bd7227 | hand-priming-in-object-localization-for | 2002.12557 | null | https://arxiv.org/abs/2002.12557v1 | https://arxiv.org/pdf/2002.12557v1.pdf | Hand-Priming in Object Localization for Assistive Egocentric Vision | Egocentric vision holds great promises for increasing access to visual information and improving the quality of life for people with visual impairments, with object recognition being one of the daily challenges for this population. While we strive to improve recognition performance, it remains difficult to identify whi... | ['Hernisa Kacorri', 'Kyungjun Lee', 'Abhinav Shrivastava'] | 2020-02-28 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [-1.38449922e-01 -1.65398479e-01 -2.60327786e-01 -2.48840228e-01
-4.84760970e-01 -6.77928805e-01 1.03625402e-01 -8.31868649e-02
-7.00890779e-01 3.36610764e-01 4.87376451e-01 -8.00153762e-02
-1.26703784e-01 -1.84762150e-01 -6.38481855e-01 -7.44719148e-01
4.79256660e-01 1.87315717e-01 2.14988306e-01 2.32047141... | [14.070630073547363, 0.053048595786094666] |
8d6ffed0-be6b-414f-baa8-348f6d7b7d42 | unsupervised-alignment-based-iterative | 2005.01218 | null | https://arxiv.org/abs/2005.01218v1 | https://arxiv.org/pdf/2005.01218v1.pdf | Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering | Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment ap... | ['Vikas Yadav', 'Mihai Surdeanu', 'Steven Bethard'] | 2020-05-04 | unsupervised-alignment-based-iterative-1 | https://aclanthology.org/2020.acl-main.414 | https://aclanthology.org/2020.acl-main.414.pdf | acl-2020-6 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.18702906e-01 6.62724450e-02 -2.67501622e-01 -4.41935360e-01
-1.52257586e+00 -8.12797844e-01 8.08792830e-01 7.96072423e-01
-5.16865075e-01 6.26216173e-01 6.11771464e-01 -5.89778185e-01
-5.20386755e-01 -6.34919167e-01 -6.18682921e-01 -3.40599149e-01
4.00516033e-01 1.07293022e+00 6.08024836e-01 -5.01155198... | [11.13028335571289, 7.958475112915039] |
49b5f6a9-b7be-4f22-b946-7208d6ad5a00 | simplex-autoencoders | 2301.06489 | null | https://arxiv.org/abs/2301.06489v1 | https://arxiv.org/pdf/2301.06489v1.pdf | Simplex Autoencoders | Synthetic data generation is increasingly important due to privacy concerns. While Autoencoder-based approaches have been widely used for this purpose, sampling from their latent spaces can be challenging. Mixture models are currently the most efficient way to sample from these spaces. In this work, we propose a new ap... | ['David Naccache', 'Aymene Mohammed Bouayed'] | 2023-01-16 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-1.74643975e-02 1.32544652e-01 4.06362960e-04 -1.52917072e-01
-9.42914128e-01 -5.44787586e-01 8.05860043e-01 -1.99524850e-01
-6.81626737e-01 1.12773037e+00 -6.28923811e-03 -8.30055773e-02
1.07913718e-01 -8.78923953e-01 -8.00015688e-01 -9.78318691e-01
7.19885007e-02 5.99538743e-01 -1.53919801e-01 1.67895988... | [11.461160659790039, -0.005925709847360849] |
9c05881a-5fd0-4713-8cf5-362ee59d18e8 | leveraging-characteristics-of-the-output | 2305.17000 | null | https://arxiv.org/abs/2305.17000v1 | https://arxiv.org/pdf/2305.17000v1.pdf | Leveraging characteristics of the output probability distribution for identifying adversarial audio examples | Adversarial attacks represent a security threat to machine learning based automatic speech recognition (ASR) systems. To prevent such attacks we propose an adversarial example detection strategy applicable to any ASR system that predicts a probability distribution over output tokens in each time step. We measure a set ... | ['Asja Fischer', 'Dorothea Kolossa', 'Matías P. Pizarro B.'] | 2023-05-26 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.30040061e-01 3.40597689e-01 3.13982338e-01 -8.33457410e-02
-1.27944326e+00 -1.00459659e+00 6.38672888e-01 3.47376227e-01
-2.94532567e-01 4.24849480e-01 1.40786842e-01 -5.52709520e-01
1.52773395e-01 -4.47025359e-01 -6.21786356e-01 -6.45819724e-01
-4.67990935e-01 -5.85852452e-02 2.82061964e-01 -7.74963871... | [14.00976848602295, 5.805953502655029] |
b1fd6326-9b34-4145-b84a-7a9277c5995d | adversarially-robust-clustering-with | 2306.09977 | null | https://arxiv.org/abs/2306.09977v1 | https://arxiv.org/pdf/2306.09977v1.pdf | Adversarially robust clustering with optimality guarantees | We consider the problem of clustering data points coming from sub-Gaussian mixtures. Existing methods that provably achieve the optimal mislabeling error, such as the Lloyd algorithm, are usually vulnerable to outliers. In contrast, clustering methods seemingly robust to adversarial perturbations are not known to satis... | ['Sanjeev Kulkarni', 'Kun Yang', 'Soham Jana'] | 2023-06-16 | null | null | null | null | ['clustering'] | ['methodology'] | [-3.48160088e-01 4.46776003e-02 -1.19919732e-01 -2.66106069e-01
-1.08328247e+00 -8.95995677e-01 4.40929443e-01 2.73106277e-01
-4.18186039e-01 5.54394901e-01 -2.60867894e-01 -1.76083356e-01
-7.17871711e-02 -4.81489956e-01 -1.03067100e+00 -9.64383066e-01
-1.73990682e-01 5.83870709e-01 1.83410510e-01 2.97690392... | [5.859010219573975, 7.483976364135742] |
3d768155-ba92-4d69-ba42-a69b2cd8ce42 | next-best-view-regression-using-a-3d | 2101.09397 | null | https://arxiv.org/abs/2101.09397v1 | https://arxiv.org/pdf/2101.09397v1.pdf | Next-best-view Regression using a 3D Convolutional Neural Network | Automated three-dimensional (3D) object reconstruction is the task of building a geometric representation of a physical object by means of sensing its surface. Even though new single view reconstruction techniques can predict the surface, they lead to incomplete models, specially, for non commons objects such as antiqu... | ['Rafael Murrieta-Cid', 'Enrique Sucar', 'Israel Becerra', 'David Troncoso', 'J. Irving Vasquez-Gomez'] | 2021-01-23 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 3.59224111e-01 3.97714049e-01 2.27055520e-01 -3.91158670e-01
-6.02007627e-01 -3.43554467e-01 6.76891744e-01 5.86574301e-02
-7.15481192e-02 3.19487512e-01 -3.79113287e-01 -2.24091649e-01
-2.00145289e-01 -9.97925460e-01 -1.02728426e+00 -4.35046554e-01
1.61262870e-01 9.43532646e-01 6.65327728e-01 -3.18212174... | [8.028343200683594, -2.8297412395477295] |
b76eb1d4-262a-46a1-82cf-6e38bdb1fb38 | neighborhood-normalization-for-robust | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Neighborhood_Normalization_for_Robust_Geometric_Feature_Learning_CVPR_2021_paper.pdf | Neighborhood Normalization for Robust Geometric Feature Learning | Extracting geometric features from 3D models is a common first step in applications such as 3D registration, tracking, and scene flow estimation. Many hand-crafted and learning-based methods aim to produce consistent and distinguishable geometric features for 3D models with partial overlap. These methods work well ... | ['Mathias Unberath', 'Russell H. Taylor', 'Gregory D. Hager', 'Masaru Ishii', 'Ayushi Sinha', 'Benjamin D. Killeen', 'Xingtong Liu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['scene-flow-estimation'] | ['computer-vision'] | [ 3.87770385e-02 -2.99658179e-01 -1.05750360e-01 -1.96410596e-01
-8.96200776e-01 -4.58594292e-01 5.33240438e-01 7.18187094e-01
-3.87567252e-01 4.88349676e-01 2.05582663e-01 -8.59079808e-02
-2.09679022e-01 -5.02943635e-01 -4.80558932e-01 -6.79979861e-01
-2.55359411e-01 7.38869667e-01 3.09372157e-01 -7.65041187... | [7.725114822387695, -2.8373353481292725] |
6c5df2b1-a72b-4aa3-b7e5-f96d7098148e | a-unified-approach-to-controlling-implicit | 2306.13853 | null | https://arxiv.org/abs/2306.13853v1 | https://arxiv.org/pdf/2306.13853v1.pdf | A Unified Approach to Controlling Implicit Regularization via Mirror Descent | Inspired by the remarkable success of deep neural networks, there has been significant interest in understanding the generalization performance of overparameterized models. Substantial efforts have been invested in characterizing how optimization algorithms impact generalization through their "preferred" solutions, a p... | ['Navid Azizan', 'Kwangjun Ahn', 'Khashayar Gatmiry', 'Haoyuan Sun'] | 2023-06-24 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 8.47842470e-02 1.48775041e-01 -4.44386005e-01 -3.66679758e-01
-6.84486151e-01 -3.84669870e-01 3.81618142e-01 4.18099426e-02
-3.83617938e-01 8.89710009e-01 -2.45668623e-03 -2.01794490e-01
-4.07662779e-01 -6.71306372e-01 -6.88699722e-01 -9.93350208e-01
1.17760628e-01 9.17738602e-02 -3.25939357e-01 -4.70313281... | [7.884871006011963, 3.772325038909912] |
d30abc10-cbe3-4bd1-95c2-274e572a9d7c | ndt-transformer-large-scale-3d-point-cloud | 2103.12292 | null | https://arxiv.org/abs/2103.12292v1 | https://arxiv.org/pdf/2103.12292v1.pdf | NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation | 3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for realtime and large-scale place recognition using... | ['Li Sun', 'Tom Duckett', 'Yang Gao', 'Songzhi Su', 'Daniel Adolfsson', 'Cheng Zhao', 'Zhicheng Zhou'] | 2021-03-23 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-2.08133355e-01 -3.06723922e-01 -2.72287548e-01 -5.73839962e-01
-1.06947923e+00 -5.34817874e-01 8.26994359e-01 5.65851629e-01
-5.53928316e-01 3.22553933e-01 -1.97774410e-01 -8.55504423e-02
-2.92323321e-01 -1.12034178e+00 -1.02787507e+00 -6.45817101e-01
1.10907473e-01 9.55588639e-01 3.28408509e-01 -1.62483782... | [7.493706226348877, -2.1892752647399902] |
c74d4536-4ffe-4b5a-a074-49874c193a38 | universal-prototype-transport-for-zero-shot | 2203.03971 | null | https://arxiv.org/abs/2203.03971v1 | https://arxiv.org/pdf/2203.03971v1.pdf | Universal Prototype Transport for Zero-Shot Action Recognition and Localization | This work addresses the problem of recognizing action categories in videos for which no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a shared semantic space, either trained on large-scale seen actions or on objects. Whil... | ['Pascal Mettes'] | 2022-03-08 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.12987173e-01 2.41133258e-01 -2.61044472e-01 -1.88880503e-01
-5.02096534e-01 -3.69659454e-01 9.30681229e-01 -5.21029890e-01
-3.94129097e-01 4.91455734e-01 3.07084829e-01 3.91937256e-01
-2.45346963e-01 -7.09248066e-01 -8.98841977e-01 -1.08765578e+00
1.12209946e-01 6.91930592e-01 4.68392193e-01 -1.26474917... | [8.683539390563965, 1.0571928024291992] |
0c67c273-3ae1-417b-aeba-2f017ded23e6 | efficient-anomaly-detection-using-self | 2111.12379 | null | https://arxiv.org/abs/2111.12379v3 | https://arxiv.org/pdf/2111.12379v3.pdf | Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks | Anomaly detection is important in many real-life applications. Recently, self-supervised learning has greatly helped deep anomaly detection by recognizing several geometric transformations. However these methods lack finer features, usually highly depend on the anomaly type, and do not perform well on fine-grained prob... | ['Aymeric Histace', 'Jean Beaudet', 'Ngoc-Son Vu', 'Loic Jezequel'] | 2021-11-24 | null | null | null | null | ['self-supervised-anomaly-detection'] | ['computer-vision'] | [ 2.09097236e-01 -5.38631558e-01 1.72879562e-01 -2.49414846e-01
-4.02443707e-01 -3.91552567e-01 7.36216366e-01 1.38563305e-01
-1.00704357e-01 3.14273447e-01 -8.73950273e-02 -1.63837880e-01
-7.90832192e-02 -8.10702324e-01 -6.37922287e-01 -9.41104233e-01
-2.44158376e-02 3.29771161e-01 2.78131664e-01 -3.87022793... | [12.839604377746582, 1.0509196519851685] |
095867b4-3f63-40fc-ac35-ac3fa9376ac2 | extracting-or-guessing-improving-faithfulness | 2210.04992 | null | https://arxiv.org/abs/2210.04992v2 | https://arxiv.org/pdf/2210.04992v2.pdf | Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction | In this paper, we seek to improve the faithfulness of TempRel extraction models from two perspectives. The first perspective is to extract genuinely based on contextual description. To achieve this, we propose to conduct counterfactual analysis to attenuate the effects of two significant types of training biases: the e... | ['Dan Roth', 'Muhao Chen', 'Jacob R. Gardner', 'Yuqian Deng', 'Hongming Zhang', 'Haoyu Wang'] | 2022-10-10 | null | null | null | null | ['temporal-relation-extraction', 'temporal-relation-classification'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.51146260e-02 4.22119439e-01 -4.86148864e-01 -6.95099235e-01
-5.61620831e-01 -6.42791450e-01 9.87928569e-01 4.12984252e-01
-4.78310645e-01 1.03747630e+00 6.17800713e-01 -3.14445943e-01
3.49588841e-02 -9.76961672e-01 -7.69597054e-01 -3.39440763e-01
2.24000290e-01 2.98502922e-01 -5.85335828e-02 1.74325317... | [9.873991966247559, 8.064735412597656] |
d542f93c-4b67-4cab-9797-9ebab6bfee71 | individualized-conditioning-and-negative | 2210.06368 | null | https://arxiv.org/abs/2210.06368v1 | https://arxiv.org/pdf/2210.06368v1.pdf | Individualized Conditioning and Negative Distances for Speaker Separation | Speaker separation aims to extract multiple voices from a mixed signal. In this paper, we propose two speaker-aware designs to improve the existing speaker separation solutions. The first model is a speaker conditioning network that integrates speech samples to generate individualized speaker conditions, which then pro... | ['Jundong Liu', 'Li Xu', 'Xianhui Wang', 'Charles D. Smith', 'Zhewei Wang', 'Shuyu Gong', 'Nidal Abuhajar', 'Tao Sun'] | 2022-10-12 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 2.13375628e-01 1.90932989e-01 1.52336150e-01 -4.99205351e-01
-1.04858768e+00 -4.91906703e-01 2.78841257e-01 -4.07646745e-01
1.48910359e-01 5.53815722e-01 5.60509682e-01 -2.21679822e-01
-2.24834785e-01 -1.58759207e-01 -3.90761316e-01 -9.52586889e-01
1.61975771e-01 -9.48022455e-02 -1.57503188e-01 -2.72403926... | [14.830686569213867, 5.875107765197754] |
b0115e59-04a6-43ff-a533-acf00536a211 | a-topological-view-of-rule-learning-in-1 | null | null | https://openreview.net/forum?id=-xhk0O7iAc0 | https://openreview.net/pdf?id=-xhk0O7iAc0 | A Topological View of Rule Learning in Knowledge Graphs | Inductive relation prediction is an important learning task for knowledge graph completion. One can use the existence of rules, namely a sequence of relations, to predict the relation between two entities. Previous works view rules as paths and primarily focus on the searching of paths between entities. The space of pa... | ['Chao Chen', 'Zhi Tang', 'Liangcai Gao', 'Tengfei Ma', 'Zuoyu Yan'] | 2021-09-29 | null | null | null | null | ['inductive-relation-prediction'] | ['graphs'] | [ 1.09325282e-01 2.99386173e-01 -7.01095164e-01 -1.96527429e-02
1.68626949e-01 -8.11094344e-01 4.42500263e-01 2.92162240e-01
6.12310395e-02 4.04223621e-01 9.89527181e-02 -9.53047335e-01
-4.44768876e-01 -1.52541316e+00 -9.39662516e-01 -2.25206330e-01
-6.88115180e-01 5.87059438e-01 3.66209030e-01 -1.53765976... | [8.663936614990234, 7.688998222351074] |
520af4ab-d6fb-4d03-9cea-ddbad172af36 | saliency-weighted-convolutional-features-for | 1711.10795 | null | http://arxiv.org/abs/1711.10795v1 | http://arxiv.org/pdf/1711.10795v1.pdf | Saliency Weighted Convolutional Features for Instance Search | This work explores attention models to weight the contribution of local
convolutional representations for the instance search task. We present a
retrieval framework based on bags of local convolutional features (BLCF) that
benefits from saliency weighting to build an efficient image representation.
The use of human vis... | ["Noel E. O'Connor", 'Xavier Giro-i-Nieto', 'Kevin McGuinness', 'Eva Mohedano'] | 2017-11-29 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [-1.30896568e-01 -2.36493826e-01 -3.44528824e-01 -2.21968487e-01
-1.00063503e+00 -3.41425180e-01 8.73327255e-01 4.32915658e-01
-7.54056215e-01 4.91062522e-01 3.37713271e-01 -1.08485796e-01
-3.12093496e-01 -5.98464727e-01 -7.19181299e-01 -4.18980211e-01
8.53910148e-02 3.20241421e-01 5.18090844e-01 -4.16865706... | [10.634035110473633, 0.685482382774353] |
a3638840-3350-4306-870b-9d60f6990d5d | information-extraction-of-clinical-trial | 2006.07296 | null | https://arxiv.org/abs/2006.07296v6 | https://arxiv.org/pdf/2006.07296v6.pdf | Information Extraction of Clinical Trial Eligibility Criteria | Clinical trials predicate subject eligibility on a diversity of criteria ranging from patient demographics to food allergies. Trials post their requirements as semantically complex, unstructured free-text. Formalizing trial criteria to a computer-interpretable syntax would facilitate eligibility determination. In this ... | ['Ahmed Mohamed', 'M. I. Salkola', 'Yitong Tseo', 'Freddy Abnousi', 'Anuj Kumar'] | 2020-06-12 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [ 3.54978114e-01 3.62166345e-01 -9.83618736e-01 -4.75740790e-01
-1.44787395e+00 -8.77591431e-01 1.26559854e-01 9.85876381e-01
-7.18226790e-01 8.22846115e-01 5.91634393e-01 -1.00874627e+00
-3.24712783e-01 -4.85630393e-01 -4.95325208e-01 -1.37951295e-03
2.04011127e-02 8.11347842e-01 -4.89121536e-03 8.81466120... | [8.435770988464355, 8.709287643432617] |
ab4abac0-f0fc-4a51-bd32-f31b33d4b451 | unsupervised-learning-of-object-frames-by | 1706.02932 | null | http://arxiv.org/abs/1706.02932v2 | http://arxiv.org/pdf/1706.02932v2.pdf | Unsupervised learning of object frames by dense equivariant image labelling | One of the key challenges of visual perception is to extract abstract models
of 3D objects and object categories from visual measurements, which are
affected by complex nuisance factors such as viewpoint, occlusion, motion, and
deformations. Starting from the recent idea of viewpoint factorization, we
propose a new app... | ['Hakan Bilen', 'Andrea Vedaldi', 'James Thewlis'] | 2017-06-09 | unsupervised-learning-of-object-frames-by-1 | http://papers.nips.cc/paper/6686-unsupervised-learning-of-object-frames-by-dense-equivariant-image-labelling | http://papers.nips.cc/paper/6686-unsupervised-learning-of-object-frames-by-dense-equivariant-image-labelling.pdf | neurips-2017-12 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 1.85588270e-01 9.47012380e-02 6.20818697e-02 -4.56728041e-01
-7.45688975e-02 -8.35979939e-01 7.83383667e-01 -1.88515216e-01
-4.18470860e-01 2.79228896e-01 1.18578814e-01 2.02672526e-01
1.52200729e-01 -4.88137752e-01 -1.01954377e+00 -6.09188497e-01
2.52465934e-01 5.23409009e-01 1.80645585e-01 5.87752424... | [8.519933700561523, -2.440596342086792] |
4f2331f2-6970-4b72-84a5-aeb8a52a816b | gpu-accelerated-sift-aided-source | 2207.14507 | null | https://arxiv.org/abs/2207.14507v1 | https://arxiv.org/pdf/2207.14507v1.pdf | GPU-accelerated SIFT-aided source identification of stabilized videos | Video stabilization is an in-camera processing commonly applied by modern acquisition devices. While significantly improving the visual quality of the resulting videos, it has been shown that such operation typically hinders the forensic analysis of video signals. In fact, the correct identification of the acquisition ... | ['Fernando Pérez-González', "Stefano Dell'Anna", 'Giulia Boato', 'Cecilia Pasquini', 'Andrea Montibeller'] | 2022-07-29 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 3.07534635e-01 -5.50134182e-01 2.98637562e-02 1.20118029e-01
-6.97765708e-01 -4.85712975e-01 3.51483911e-01 3.99397224e-01
-6.46595895e-01 4.35440868e-01 -3.85657638e-01 -1.40204892e-01
-5.02213053e-02 -4.62649673e-01 -6.72071993e-01 -9.94273901e-01
7.33817965e-02 2.83797574e-03 1.67658404e-01 2.20549569... | [12.179353713989258, 0.7736184000968933] |
4dca89c1-652c-47da-b66d-4b2bbc882da4 | some-theoretical-considerations-in-off-the | null | null | https://aclanthology.org/R15-2002 | https://aclanthology.org/R15-2002.pdf | Some Theoretical Considerations in Off-the-Shelf Text Analysis Software | null | ['Emma Franklin'] | 2015-09-01 | some-theoretical-considerations-in-off-the-1 | https://aclanthology.org/R15-2002 | https://aclanthology.org/R15-2002.pdf | ranlp-2015-9 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.243312358856201, 3.7369399070739746] |
3c205f2e-51a8-47d7-9cc5-d49c926beb2a | supervised-pretraining-for-molecular-force | 2211.14429 | null | https://arxiv.org/abs/2211.14429v1 | https://arxiv.org/pdf/2211.14429v1.pdf | Supervised Pretraining for Molecular Force Fields and Properties Prediction | Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods suffer from scarcity of labeled data for particular tasks, motivating the use of large-scale dataset for other relevant tasks. We propose to p... | ['Liang Xiang', 'Chong Wang', 'Zhirui Wang', 'Wenzhi Xiao', 'Weihao Gao', 'Xiang Gao'] | 2022-11-23 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 3.51630509e-01 -1.01412781e-01 -1.02671254e+00 -7.40995765e-01
-4.85493600e-01 -3.17533791e-01 2.61921376e-01 6.32440209e-01
-4.07897592e-01 1.28163838e+00 8.84424821e-02 -5.51910043e-01
5.84172979e-02 -1.00266743e+00 -1.23715949e+00 -7.65479088e-01
-4.21437979e-01 4.69842464e-01 -6.95921779e-02 9.64921992... | [5.138836860656738, 5.760371208190918] |
2486e7cf-3eac-4fe6-9d1f-52ec878657a1 | namerec-highly-accurate-and-fine-grained | 2103.11360 | null | https://arxiv.org/abs/2103.11360v2 | https://arxiv.org/pdf/2103.11360v2.pdf | NameRec*: Highly Accurate and Fine-grained Person Name Recognition | In this paper, we introduce the NameRec* task, which aims to do highly accurate and fine-grained person name recognition. Traditional Named Entity Recognition models have good performance in recognising well-formed person names from text with consistent and complete syntax, such as news articles. However, there are rap... | ['Shijie Liu', 'Yimeng Dai', 'Rui Zhang'] | 2021-03-21 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [-3.74496043e-01 -3.89606804e-01 -2.56710678e-01 -5.93836725e-01
-4.34383839e-01 -5.12130439e-01 5.95522046e-01 -2.08864599e-01
-7.21024692e-01 7.93516636e-01 6.98802710e-01 -1.22943439e-01
-2.67879248e-01 -8.27095211e-01 -2.73403466e-01 -2.42521107e-01
2.87738681e-01 5.07703424e-01 -6.04640730e-02 -1.82615981... | [9.660387992858887, 9.281572341918945] |
b91dcc7d-64d3-4b1d-9806-8f474dbbde5e | stitching-stabilizer-two-frame-stitching | 1603.06678 | null | http://arxiv.org/abs/1603.06678v1 | http://arxiv.org/pdf/1603.06678v1.pdf | Stitching Stabilizer: Two-frame-stitching Video Stabilization for Embedded Systems | In conventional electronic video stabilization, the stabilized frame is
obtained by cropping the input frame to cancel camera shake. While a small
cropping size results in strong stabilization, it does not provide us
satisfactory results from the viewpoint of image quality, because it narrows
the angle of view. By fusi... | ['Masaki Satoh'] | 2016-03-22 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.83274135e-01 -1.78778440e-01 6.04838245e-02 5.53257018e-02
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1d1ce7bc-d4f5-45a0-b472-b89abd4951db | self-supervised-dense-consistency | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ko_Self-Supervised_Dense_Consistency_Regularization_for_Image-to-Image_Translation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ko_Self-Supervised_Dense_Consistency_Regularization_for_Image-to-Image_Translation_CVPR_2022_paper.pdf | Self-Supervised Dense Consistency Regularization for Image-to-Image Translation | Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs). In this paper, we present a simple but effective regularization technique for improving GAN-based image-to-image translation. To generate images with reali... | ['Bohyung Han', 'Jinwoo Shin', 'Jae-Joon Han', 'Huijin Lee', 'Sungjoo Suh', 'Eunju Cha', 'Minsu Ko'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 7.78228462e-01 3.46628726e-01 -1.45842448e-01 -3.46407562e-01
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3.96806210e-01 3.09841931e-01 -1.80744633e-01 -2.10499763... | [11.726645469665527, -0.4690330922603607] |
de66c584-0a89-4719-b97b-0632af5455bb | a-unified-pyramid-recurrent-network-for-video | 2211.03456 | null | https://arxiv.org/abs/2211.03456v2 | https://arxiv.org/pdf/2211.03456v2.pdf | A Unified Pyramid Recurrent Network for Video Frame Interpolation | Flow-guided synthesis provides a common framework for frame interpolation, where optical flow is estimated to guide the synthesis of intermediate frames between consecutive inputs. In this paper, we present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework,... | ['Cheul-hee Hahm', 'Jayoon Koo', 'Youxin Chen', 'Jie Chen', 'Longhai Wu', 'Xin Jin'] | 2022-11-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jin_A_Unified_Pyramid_Recurrent_Network_for_Video_Frame_Interpolation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jin_A_Unified_Pyramid_Recurrent_Network_for_Video_Frame_Interpolation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-frame-interpolation'] | ['computer-vision'] | [ 2.04049516e-02 -2.03926802e-01 -3.96460116e-01 -1.42105538e-02
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2.67889779e-02 -3.35439712e-01 3.53503674e-01 -2.46726915... | [10.68064022064209, -1.378367304801941] |
cc7d8bf1-913f-4252-9090-ca218b54d755 | query-as-context-pre-training-for-dense | 2212.09598 | null | https://arxiv.org/abs/2212.09598v2 | https://arxiv.org/pdf/2212.09598v2.pdf | Query-as-context Pre-training for Dense Passage Retrieval | Recently, methods have been developed to improve the performance of dense passage retrieval by using context-supervised pre-training. These methods simply consider two passages from the same document to be relevant, without taking into account the possibility of weakly correlated pairs. Thus, this paper proposes query-... | ['Songlin Hu', 'Fuzheng Zhang', 'Zijia Lin', 'Wanhui Qian', 'Guangyuan Ma', 'Xing Wu'] | 2022-12-19 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-5.25013097e-02 -5.64991832e-01 -3.89113873e-01 -2.49634042e-01
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-6.45692050e-02 -8.87158394e-01 -5.36821008e-01 -4.30182487e-01
-1.16235398e-01 5.74536502e-01 5.57418227e-01 -4.36889589... | [11.49951457977295, 7.709858417510986] |
67e1d98b-0fe6-4c62-91bf-05525ed6cc04 | faceatlasar-atlas-of-facial-acupuncture | 2111.14755 | null | https://arxiv.org/abs/2111.14755v2 | https://arxiv.org/pdf/2111.14755v2.pdf | FaceAtlasAR: Atlas of Facial Acupuncture Points in Augmented Reality | Acupuncture is a technique in which practitioners stimulate specific points on the body. Those points, called acupuncture points (or acupoints), anatomically define areas on the skin relative to specific landmarks on the body. However, mapping the acupoints to individuals could be challenging for inexperienced acupunct... | ['Dong Zhang', 'Jurgen Schulze', 'Menghe Zhang'] | 2021-11-29 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-8.02474022e-02 2.91477982e-02 -2.35241383e-01 -1.89786434e-01
-5.96293449e-01 -5.79933167e-01 -3.10861468e-01 -6.59600943e-02
-6.24828087e-03 5.33150911e-01 9.86090153e-02 2.02410206e-01
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2.55267739e-01 4.42764759e-01 -1.59416184e-01 -3.35981011... | [14.232036590576172, -2.7626099586486816] |
4c6710b4-5eb1-489f-bed9-b459b4532f3b | push-the-boundary-boundary-aware-feature | 2212.12402 | null | https://arxiv.org/abs/2212.12402v1 | https://arxiv.org/pdf/2212.12402v1.pdf | Push-the-Boundary: Boundary-aware Feature Propagation for Semantic Segmentation of 3D Point Clouds | Feedforward fully convolutional neural networks currently dominate in semantic segmentation of 3D point clouds. Despite their great success, they suffer from the loss of local information at low-level layers, posing significant challenges to accurate scene segmentation and precise object boundary delineation. Prior wor... | ['Liangliang Nan', 'Julian Kooij', 'Jantien Stoter', 'Nail Ibrahimli', 'Shenglan Du'] | 2022-12-23 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.82564873e-01 6.68646470e-02 -6.39169523e-03 -7.72661448e-01
-6.41648352e-01 -6.50234759e-01 4.48401749e-01 1.50854826e-01
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4.27066647e-02 6.23016417e-01 5.84535837e-01 8.48305747... | [8.092015266418457, -3.040123462677002] |
77ece2a3-3a82-46a6-b188-e52b74393549 | cas-net-conditional-atlas-generation-and | 2205.08239 | null | https://arxiv.org/abs/2205.08239v1 | https://arxiv.org/pdf/2205.08239v1.pdf | CAS-Net: Conditional Atlas Generation and Brain Segmentation for Fetal MRI | Fetal Magnetic Resonance Imaging (MRI) is used in prenatal diagnosis and to assess early brain development. Accurate segmentation of the different brain tissues is a vital step in several brain analysis tasks, such as cortical surface reconstruction and tissue thickness measurements. Fetal MRI scans, however, are prone... | ['Amir Alansary', 'Daniel Rueckert', 'Bernhard Kainz', 'A. David Edwards', 'Joseph Hajnal', 'Antonios Makropoulos', 'Matthew Sinclair', 'Qiang Ma', 'Liu Li'] | 2022-05-17 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [ 1.97566211e-01 2.98343390e-01 1.26177743e-01 -8.41306806e-01
-2.35543177e-01 -4.78851467e-01 3.81201580e-02 1.71358176e-02
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-3.76054317e-01 7.76217520e-01 2.68098086e-01 5.52787662... | [14.065080642700195, -2.414581775665283] |
9c008d97-67a9-4f64-b590-8cf819938580 | sheng-cheng-mo-xing-zai-ceng-ci-jie-gou-ji | null | null | https://aclanthology.org/2022.ccl-1.60 | https://aclanthology.org/2022.ccl-1.60.pdf | 生成模型在层次结构极限多标签文本分类中的应用(Generation Model for Hierarchical Extreme Multi-label Text Classification) | “层次结构极限多标签文本分类是自然语言处理研究领域中一个重要而又具有挑战性的课题。该任务类别标签数量巨大且自成体系,标签与标签之间还具有不同层级间的依赖关系或同层次间的相关性,这些特性进一步增加了任务难度。该文提出将层次结构极限多标签文本分类任务视为序列转换问题,将输出标签视为序列,从而可以直接从数十万标签中生成与文本相关的类别标签。通过软约束机制和词表复合映射在解码过程中利用标签之间的层次结构与相关信息。实验结果表明,该文提出的方法与基线模型相比取得了有意义的性能提升。进一步分析表明,该方法不仅可以捕获利用不同层级标签之间的上下位关系,还对极限多标签体系自身携带的噪声具有一定容错能力。” | ['Weilei Wang', 'Jianping Lu', 'Yilin Liu', 'Yansi Xiao', 'Dawang He', 'Linqing Chen'] | null | null | null | null | ccl-2022-10 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [-7.11502135e-01 -9.31421399e-01 7.62112200e-01 3.85518700e-01
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-1.06743835e-01 1.54813337e+00 4.56896454e-01 -2.12962538... | [-3.31616473197937, 6.907747268676758] |
2f916d60-2031-49b5-9975-2c1980353a57 | uncertainty-in-minimum-cost-multicuts-for | 2105.07469 | null | https://arxiv.org/abs/2105.07469v1 | https://arxiv.org/pdf/2105.07469v1.pdf | Uncertainty in Minimum Cost Multicuts for Image and Motion Segmentation | The minimum cost lifted multicut approach has proven practically good performance in a wide range of applications such as image decomposition, mesh segmentation, multiple object tracking, and motion segmentation. It addresses such problems in a graph-based model, where real-valued costs are assigned to the edges betwee... | ['Margret Keuper', 'Amirhossein Kardoost'] | 2021-05-16 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 3.82228106e-01 4.12165135e-01 -4.53218251e-01 -2.65846550e-01
-7.61837602e-01 -5.09060502e-01 5.45595229e-01 3.68215412e-01
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-8.33494425e-01 -7.59523273e-01 -8.62158179e-01 -5.86845636e-01
-1.38021141e-01 9.93511558e-01 4.96474415e-01 1.76784098... | [9.304021835327148, -0.2701524794101715] |
8f44c88d-d986-447b-9554-1d58924ec8b4 | punctuation-as-native-language-interference | null | null | https://aclanthology.org/C18-1293 | https://aclanthology.org/C18-1293.pdf | Punctuation as Native Language Interference | In this paper, we describe experiments designed to explore and evaluate the impact of punctuation marks on the task of native language identification. Punctuation is specific to each language, and is part of the indicators that overtly represent the manner in which each language organizes and conveys information. Our e... | ['Carlo Strapparava', 'Vivi Nastase', 'Ilia Markov'] | 2018-08-01 | punctuation-as-native-language-interference-1 | https://aclanthology.org/C18-1293 | https://aclanthology.org/C18-1293.pdf | coling-2018-8 | ['cross-corpus', 'native-language-identification'] | ['computer-vision', 'natural-language-processing'] | [-4.65631634e-01 -3.83637935e-01 -5.18987834e-01 -1.76439017e-01
-5.55626214e-01 -1.01196659e+00 1.16957700e+00 7.40595341e-01
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-8.76670331e-02 -4.63606507e-01 -3.49844307e-01 -3.11458915e-01
1.27668709e-01 1.85723022e-01 2.17192441e-01 -3.13099772... | [10.444252014160156, 10.379717826843262] |
4a2a6e09-638f-496a-91cb-67f191d64d80 | deep-perceptual-enhancement-for-medical-image | null | null | https://ieeexplore.ieee.org/document/9759833 | https://ieeexplore.ieee.org/document/9759833 | Deep Perceptual Enhancement for Medical Image Analysis | Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practiti... | ['S. M. A. Sharif; Rizwan Ali Naqvi; Mithun Biswas; Woong-Kee Loh'] | 2022-10-01 | null | null | null | ieee-journal-of-biomedical-and-health-4 | ['medical-image-enhancement', 'image-enhancement', 'decision-making'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 7.25633264e-01 -2.47787312e-02 3.71830702e-01 -2.46207803e-01
-7.77893424e-01 -1.13308541e-02 2.76422143e-01 1.21724129e-01
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-3.08346480e-01 -4.98224467e-01 -4.14788902e-01 -1.04251516e+00
-2.21343696e-01 -6.57519221e-01 -6.95503205e-02 -1.94167554... | [13.318652153015137, -2.4281959533691406] |
6dd4f001-66f4-4c99-9c36-2d1d9e2ea728 | world-knowledge-in-multiple-choice-reading | 2211.07040 | null | https://arxiv.org/abs/2211.07040v2 | https://arxiv.org/pdf/2211.07040v2.pdf | World Knowledge in Multiple Choice Reading Comprehension | Recently it has been shown that without any access to the contextual passage, multiple choice reading comprehension (MCRC) systems are able to answer questions significantly better than random on average. These systems use their accumulated "world knowledge" to directly answer questions, rather than using information f... | ['Mark Gales', 'Vatsal Raina', 'Adian Liusie'] | 2022-11-13 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 2.18919009e-01 6.46883965e-01 9.38935354e-02 -3.38967234e-01
-1.06642151e+00 -1.04206681e+00 4.14447993e-01 9.28862810e-01
-5.29525638e-01 8.56008828e-01 3.01378131e-01 -7.91248918e-01
-7.52606690e-01 -1.08099616e+00 -4.58654553e-01 1.48493260e-01
4.89065021e-01 3.41705412e-01 7.37512469e-01 -5.52510977... | [11.467100143432617, 8.147520065307617] |
5e2554c2-d3f4-464f-ad10-e88bb1ba5f68 | new-benchmarks-for-learning-on-non | 2104.01404 | null | https://arxiv.org/abs/2104.01404v2 | https://arxiv.org/pdf/2104.01404v2.pdf | New Benchmarks for Learning on Non-Homophilous Graphs | Much data with graph structures satisfy the principle of homophily, meaning that connected nodes tend to be similar with respect to a specific attribute. As such, ubiquitous datasets for graph machine learning tasks have generally been highly homophilous, rewarding methods that leverage homophily as an inductive bias. ... | ['Ser-Nam Lim', 'Felix Hohne', 'Xiuyu Li', 'Derek Lim'] | 2021-04-03 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-1.74132697e-02 3.62004578e-01 -6.29463315e-01 -3.81318748e-01
1.54516399e-01 -6.01148605e-01 9.54355299e-01 5.83099902e-01
8.09574798e-02 7.92621315e-01 2.06138149e-01 -4.03179884e-01
-3.92344177e-01 -1.27042294e+00 -7.25790083e-01 -6.48774028e-01
-5.78778505e-01 7.29713023e-01 -1.65618099e-02 -1.69782534... | [7.009494304656982, 6.1558613777160645] |
a8be7f4b-c51e-494f-b172-b997939708ac | semiparametric-language-models-are-scalable | 2303.01421 | null | https://arxiv.org/abs/2303.01421v1 | https://arxiv.org/pdf/2303.01421v1.pdf | Semiparametric Language Models Are Scalable Continual Learners | Semiparametric language models (LMs) have shown promise in continuously learning from new text data by combining a parameterized neural LM with a growable non-parametric memory for memorizing new content. However, conventional semiparametric LMs will finally become prohibitive for computing and storing if they are appl... | ['Houfeng Wang', 'Furu Wei', 'Si-Qing Chen', 'Tao Ge', 'Guangyue Peng'] | 2023-03-02 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-6.06511980e-02 1.28618870e-02 -1.35692507e-01 -2.92178154e-01
-9.63236034e-01 -6.82344019e-01 2.46234775e-01 4.02203441e-01
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-7.31346011e-02 -8.34954739e-01 -1.01247847e+00 -3.95554185e-01
-3.95984441e-01 7.28298903e-01 3.84983689e-01 1.46495551... | [9.73644733428955, 3.5431783199310303] |
7db11e03-0901-42b2-90a2-80738a942fe6 | panovpr-towards-unified-perspective-to | 2303.14095 | null | https://arxiv.org/abs/2303.14095v1 | https://arxiv.org/pdf/2303.14095v1.pdf | PanoVPR: Towards Unified Perspective-to-Equirectangular Visual Place Recognition via Sliding Windows across the Panoramic View | Visual place recognition has received increasing attention in recent years as a key technology in autonomous driving and robotics. The current mainstream approaches use either the perspective view retrieval perspective view (P2P) paradigm or the equirectangular image retrieval equirectangular image (E2E) paradigm. Howe... | ['Kaiwei Wang', 'Yining Lin', 'Zhe Yin', 'Kailun Yang', 'Hao Shi', 'Ze Shi'] | 2023-03-24 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.46618986e-01 -5.64774930e-01 -3.00025135e-01 -3.50119323e-01
-4.53280360e-01 -7.16970682e-01 7.00934470e-01 -4.33811694e-01
-6.50565803e-01 2.43113399e-01 -3.75647038e-01 -2.07343698e-01
-2.59385377e-01 -9.00714874e-01 -8.50752175e-01 -6.29526258e-01
2.57208049e-01 6.91100955e-02 6.03722394e-01 -3.13565105... | [7.673891067504883, -2.04423189163208] |
e604345d-b012-4445-90ec-22e8d0ce9c3c | unicom-universal-and-compact-representation | 2304.05884 | null | https://arxiv.org/abs/2304.05884v1 | https://arxiv.org/pdf/2304.05884v1.pdf | Unicom: Universal and Compact Representation Learning for Image Retrieval | Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors. However, the most widely used models are pre-trained on ImageNet-1K with limited classes. The pre-trained feature representation is therefore not universal enough to generalize well to the diverse open-... | ['Tongliang Liu', 'Jing Yang', 'Jia Guo', 'Ziyong Feng', 'Jaiwei Li', 'Kaicheng Yang', 'Jiankang Deng', 'Xiang An'] | 2023-04-12 | null | null | null | null | ['self-supervised-image-classification', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [-1.39142096e-01 -5.58387518e-01 -6.18034065e-01 -6.26696765e-01
-9.08128858e-01 -5.53678513e-01 6.23560548e-01 6.95273876e-02
-6.54992640e-01 3.78400266e-01 1.51796937e-01 4.21308726e-02
-1.98894486e-01 -7.13577986e-01 -5.78278959e-01 -6.90671265e-01
-1.60812140e-01 1.65325642e-01 6.12610094e-02 2.02964604... | [10.757006645202637, 0.9007059335708618] |
db5d7dbc-cde2-4418-9669-5322c8963544 | icicle-interpretable-class-incremental | 2303.07811 | null | https://arxiv.org/abs/2303.07811v1 | https://arxiv.org/pdf/2303.07811v1.pdf | ICICLE: Interpretable Class Incremental Continual Learning | Continual learning enables incremental learning of new tasks without forgetting those previously learned, resulting in positive knowledge transfer that can enhance performance on both new and old tasks. However, continual learning poses new challenges for interpretability, as the rationale behind model predictions may ... | ['Bartłomiej Twardowski', 'Bartosz Zieliński', 'Joost Van de Weijer', 'Dawid Rymarczyk'] | 2023-03-14 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 5.10450721e-01 3.11889797e-01 -3.42184603e-01 -3.77461821e-01
-3.60122204e-01 -6.44855201e-01 7.39208996e-01 3.87969643e-01
-6.11028910e-01 9.42911863e-01 4.08184119e-02 -2.94471264e-01
-7.05345452e-01 -2.88751930e-01 -9.12323713e-01 -4.83241767e-01
-6.76753595e-02 1.03487015e+00 2.47030839e-01 -2.99780816... | [9.793540000915527, 3.4179952144622803] |
0b67809a-4b31-45c5-8e01-c2d495933a37 | trajectory-poisson-multi-bernoulli-mixture | 2306.16890 | null | https://arxiv.org/abs/2306.16890v1 | https://arxiv.org/pdf/2306.16890v1.pdf | Trajectory Poisson multi-Bernoulli mixture filter for traffic monitoring using a drone | This paper proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras. Object detections on the images are obtained using a neural network for each type of camera. The cameras are modelled as direction-of-arrival (DOA) sensors. Each DOA detection foll... | ['Jimin Xiao', 'Ángel F. García-Fernández'] | 2023-06-29 | null | null | null | null | ['object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-6.51737228e-02 -5.04910827e-01 1.01135120e-01 -2.56633520e-01
-4.41704780e-01 -4.23616886e-01 6.88205183e-01 -4.44065750e-01
-7.65492618e-01 5.30519843e-01 -6.16937101e-01 -1.03369892e-01
-5.17469011e-02 -7.23926902e-01 -9.48256373e-01 -9.00861681e-01
6.71525300e-02 8.63829374e-01 8.04698110e-01 3.74798596... | [6.578239917755127, -2.0402681827545166] |
223e0d36-30cf-42fd-96ab-400906c57d85 | prior-networks-for-detection-of-adversarial | 1812.02575 | null | http://arxiv.org/abs/1812.02575v1 | http://arxiv.org/pdf/1812.02575v1.pdf | Prior Networks for Detection of Adversarial Attacks | Adversarial examples are considered a serious issue for safety critical
applications of AI, such as finance, autonomous vehicle control and medicinal
applications. Though significant work has resulted in increased robustness of
systems to these attacks, systems are still vulnerable to well-crafted attacks.
To address t... | ['Andrey Malinin', 'Mark Gales'] | 2018-12-06 | null | https://openreview.net/forum?id=H1gh_sC9tm | https://openreview.net/pdf?id=H1gh_sC9tm | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 2.77186960e-01 1.68573007e-01 8.87178350e-03 -3.17889363e-01
-1.09095442e+00 -7.70396531e-01 8.67930472e-01 4.90331016e-02
-4.44813371e-01 1.09338999e+00 -1.81498408e-01 -2.32986420e-01
2.33952910e-01 -9.06908214e-01 -1.17483628e+00 -6.94540441e-01
-3.68175516e-03 5.34848392e-01 3.20365191e-01 -1.90597847... | [5.647232532501221, 7.856144905090332] |
6b99c2d5-9af4-4ef6-81ff-e27cd069dbe2 | automatic-skin-lesion-segmentation-using-deep | 1807.06466 | null | http://arxiv.org/abs/1807.06466v1 | http://arxiv.org/pdf/1807.06466v1.pdf | Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks | This paper summarizes our method and validation results for the ISIC
Challenge 2018 - Skin Lesion Analysis Towards Melanoma Detection - Task 1:
Lesion Segmentation | ['Hongming Xu', 'Tae Hyun Hwang'] | 2018-07-17 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 1.02169394e+00 1.29649537e-02 -6.90934122e-01 1.33312494e-01
-1.35453868e+00 -5.32573164e-01 6.50741339e-01 7.89962932e-02
-6.05744958e-01 4.21840668e-01 -1.34663165e-01 -6.66431487e-01
3.31978768e-01 -2.84893792e-02 1.95076335e-02 -9.20407057e-01
1.51909301e-02 -1.60157621e-01 5.39524138e-01 1.41423447... | [15.802346229553223, -3.0565314292907715] |
80bf8305-ebc8-4525-9204-d8f4f2bb924d | eegeyenet-a-simultaneous | 2111.05100 | null | https://arxiv.org/abs/2111.05100v2 | https://arxiv.org/pdf/2111.05100v2.pdf | EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction | We present a new dataset and benchmark with the goal of advancing research in the intersection of brain activities and eye movements. Our dataset, EEGEyeNet, consists of simultaneous Electroencephalography (EEG) and Eye-tracking (ET) recordings from 356 different subjects collected from three different experimental par... | ['Martyna Beata Płomecka', 'Nicolas Langer', 'Roger Wattenhofer', 'Victor Gillioz', 'Lukas Wolf', 'Damián Pascual', 'Ard Kastrati'] | 2021-11-06 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 7.38711283e-02 -4.15003270e-01 1.33820429e-01 -6.08403444e-01
-1.37642965e-01 -2.85837322e-01 4.05945718e-01 -2.26284504e-01
-6.44208729e-01 9.48589623e-01 -1.45873457e-01 -9.85670462e-02
-7.68498629e-02 1.34579331e-01 -5.88575184e-01 -5.99263966e-01
-3.90556425e-01 -2.81883657e-01 2.04842299e-01 -5.47089847... | [14.113465309143066, 0.17096258699893951] |
67a6952f-ad03-4f96-874b-18fdc63d165b | on-conditioning-the-input-noise-for | 2205.03859 | null | https://arxiv.org/abs/2205.03859v1 | https://arxiv.org/pdf/2205.03859v1.pdf | On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models | Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation. This continues to be a significant area of interest with the rise of new state-of-the-art methods that are based on diffusion models. However, diffusion models provide very littl... | ['Vineeth N. Balasubramanian', 'Balaji Krishnamurthy', 'Siddharth Ramesh', 'Ayush Chopra', 'Surgan Jandial', 'Vedant Singh'] | 2022-05-08 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.94259560e-01 3.82974207e-01 3.12209390e-02 -3.51554483e-01
-5.58095336e-01 -4.69354063e-01 1.18536532e+00 1.37620391e-02
-3.42413872e-01 6.32030964e-01 3.10058743e-01 -3.17688167e-01
-7.72199258e-02 -1.01456106e+00 -4.61250007e-01 -8.46143365e-01
1.20250858e-01 4.89626229e-01 3.55051458e-01 -1.31573245... | [11.324429512023926, -0.21012060344219208] |
a498ba85-4ae0-4e9f-b8d2-11a0a3db240f | learning-an-effective-context-response | 2009.06265 | null | https://arxiv.org/abs/2009.06265v1 | https://arxiv.org/pdf/2009.06265v1.pdf | Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues | Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on building a context-response matching model with various neural architectures or PLMs and typically learning with a single response prediction task... | ['Rui Yan', 'Chongyang Tao', 'Dongyan Zhao', 'Daxin Jiang', 'Xueliang Zhao', 'Ruijian Xu'] | 2020-09-14 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 4.55646634e-01 -1.67419598e-01 -3.14045131e-01 -8.86611938e-01
-1.10169697e+00 -1.98374912e-01 6.87561929e-01 1.38925925e-01
-2.66812593e-01 6.71657324e-01 6.40636981e-01 -7.47611523e-02
-9.58851948e-02 -3.52725416e-01 2.84040179e-02 -6.63601816e-01
3.26812059e-01 7.59840012e-01 2.17363179e-01 -8.86285484... | [12.628416061401367, 7.741860866546631] |
c5292166-b874-4161-92fc-4ca0b9f8e002 | ancient-chinese-word-segmentation-and-part-of-1 | 2303.01912 | null | https://arxiv.org/abs/2303.01912v2 | https://arxiv.org/pdf/2303.01912v2.pdf | Ancient Chinese Word Segmentation and Part-of-Speech Tagging Using Distant Supervision | Ancient Chinese word segmentation (WSG) and part-of-speech tagging (POS) are important to study ancient Chinese, but the amount of ancient Chinese WSG and POS tagging data is still rare. In this paper, we propose a novel augmentation method of ancient Chinese WSG and POS tagging data using distant supervision over para... | ['Piji Li', 'Shuo Feng'] | 2023-03-03 | null | null | null | null | ['memorization', 'part-of-speech-tagging', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.28312290e-01 2.48697456e-02 -8.42682272e-02 -6.04730964e-01
-6.69176102e-01 -5.30991495e-01 2.00417086e-01 -1.05108313e-01
-9.85219121e-01 9.75110292e-01 4.64487284e-01 -4.30104673e-01
7.20898509e-01 -6.40760243e-01 -6.13748491e-01 -5.07487774e-01
1.00503370e-01 4.44650501e-01 5.38720667e-01 -2.94965059... | [9.960715293884277, 10.088488578796387] |
da38cdc6-9221-424c-9289-ee181cd7999a | analysis-and-tuning-of-a-voice-assistant | 2106.11759 | null | https://arxiv.org/abs/2106.11759v1 | https://arxiv.org/pdf/2106.11759v1.pdf | Analysis and Tuning of a Voice Assistant System for Dysfluent Speech | Dysfluencies and variations in speech pronunciation can severely degrade speech recognition performance, and for many individuals with moderate-to-severe speech disorders, voice operated systems do not work. Current speech recognition systems are trained primarily with data from fluent speakers and as a consequence do ... | ['Jefferey Bigham', 'Sachin Kajarekar', 'Panayiotis Georgiou', 'Shrinath Thelapurath', 'Ashwini Palekar', 'Darren Botten', 'Sarah Wu', 'Lauren Tooley', 'Colin Lea', 'Zifang Huang', 'Vikramjit Mitra'] | 2021-06-18 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.07670197e-01 2.14192450e-01 -8.02587792e-02 -2.98345327e-01
-1.05686080e+00 -4.25733298e-01 2.19524086e-01 -2.99268931e-01
-4.85094279e-01 5.66389084e-01 7.57624149e-01 -7.09238708e-01
1.83031693e-01 1.28280120e-02 3.60463886e-03 -1.39875770e-01
2.68289924e-01 5.36251009e-01 -6.59544617e-02 -3.45283300... | [14.466484069824219, 6.458902835845947] |
46031044-9086-4e2e-966f-1c5db8d3f75d | context-aware-configuration-and-management-of | 2307.03126 | null | https://arxiv.org/abs/2307.03126v1 | https://arxiv.org/pdf/2307.03126v1.pdf | Context-Aware Configuration and Management of WiFi Direct Groups for Real Opportunistic Networks | Wi-Fi Direct is a promising technology for the support of device-to-device communications (D2D) on commercial mobile devices. However, the standard as-it-is is not sufficient to support the real deployment of networking solutions entirely based on D2D such as opportunistic networks. In fact, WiFi Direct presents some c... | ['Franca Delmastro', 'Mattia Giovanni Campana', 'Valerio Arnaboldi'] | 2023-07-06 | null | null | null | null | ['management'] | ['miscellaneous'] | [-4.09171171e-02 3.55284750e-01 -3.11946809e-01 -6.04780875e-02
1.30368829e-01 -5.51413774e-01 7.06172526e-01 7.29298145e-02
-2.94076353e-01 1.02101541e+00 -3.34854454e-01 -8.34623992e-01
-7.13982046e-01 -1.14855456e+00 -1.81108907e-01 -6.95809543e-01
-4.47097063e-01 5.84414423e-01 8.27222884e-01 -1.03738405... | [6.201981544494629, 1.3398306369781494] |
b92751af-4b1c-460d-b4e9-a9642644b0be | deep-structured-learning-for-mass | 1410.7454 | null | http://arxiv.org/abs/1410.7454v2 | http://arxiv.org/pdf/1410.7454v2.pdf | Deep Structured learning for mass segmentation from Mammograms | In this paper, we present a novel method for the segmentation of breast
masses from mammograms exploring structured and deep learning. Specifically,
using structured support vector machine (SSVM), we formulate a model that
combines different types of potential functions, including one that classifies
image regions usin... | ['Gustavo Carneiro', 'Andrew P. Bradley', 'Neeraj Dhungel'] | 2014-10-27 | null | null | null | null | ['mass-segmentation-from-mammograms'] | ['medical'] | [ 3.23712349e-01 3.39339435e-01 -1.66540816e-01 -8.41133773e-01
-9.16545510e-01 -2.16664582e-01 4.93613362e-01 6.53818190e-01
-5.26414037e-01 4.57004905e-01 -2.99816012e-01 -5.84088922e-01
-3.27784151e-01 -9.46600020e-01 -6.33244932e-01 -7.47927606e-01
-3.24410945e-01 8.51784825e-01 2.65720814e-01 1.00879580... | [14.982802391052246, -2.4754631519317627] |
d5e58f7d-5bb9-4d5d-a601-df2c36b9f613 | early-diagnosis-of-lung-cancer-using-computer | 2107.12205 | null | https://arxiv.org/abs/2107.12205v1 | https://arxiv.org/pdf/2107.12205v1.pdf | Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach | Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have... | ['Chaitra K M', 'Mustafa Basthikodi', 'Ananth Prabhu G', 'Abhir Bhandary'] | 2021-07-23 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-3.31098102e-02 1.49288222e-01 -2.48518437e-01 2.10798398e-01
-2.54311651e-01 -1.19270287e-01 4.65255469e-01 3.96634877e-01
-5.64380169e-01 7.22277820e-01 6.12905696e-02 -2.39304110e-01
8.48447625e-03 -9.22713280e-01 2.11028770e-01 -8.10658455e-01
3.33963066e-01 8.85527611e-01 6.02604151e-01 1.86639443... | [15.313376426696777, -2.390078067779541] |
54656492-b9e1-496f-b200-06dce3d105af | zero-shot-cross-lingual-abstractive-sentence | null | null | https://aclanthology.org/P19-1305 | https://aclanthology.org/P19-1305.pdf | Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention | Abstractive Sentence Summarization (ASSUM) targets at grasping the core idea of the source sentence and presenting it as the summary. It is extensively studied using statistical models or neural models based on the large-scale monolingual source-summary parallel corpus. But there is no cross-lingual parallel corpus, wh... | ['Xiangyu Duan', 'Mingming Yin', 'Weihua Luo', 'Min Zhang', 'Boxing Chen'] | 2019-07-01 | null | null | null | acl-2019-7 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 1.32252350e-01 2.33549058e-01 -2.55910575e-01 -3.73682559e-01
-1.57438588e+00 -5.81460536e-01 8.25621545e-01 6.09399714e-02
-3.79708230e-01 8.55488539e-01 8.45346272e-01 -4.41673070e-01
6.94843888e-01 -3.74595761e-01 -9.53662872e-01 -2.55602479e-01
4.46486056e-01 5.05915523e-01 1.07430339e-01 -6.14760518... | [12.273290634155273, 9.509532928466797] |
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