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fea4b920-a492-4045-860b-30ca5c74e8b9 | look-around-for-anomalies-weakly-supervised | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cho_Look_Around_for_Anomalies_Weakly-Supervised_Anomaly_Detection_via_Context-Motion_Relational_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cho_Look_Around_for_Anomalies_Weakly-Supervised_Anomaly_Detection_via_Context-Motion_Relational_CVPR_2023_paper.pdf | Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning | Weakly-supervised Video Anomaly Detection is the task of detecting frame-level anomalies using video-level labeled training data. It is difficult to explore class representative features using minimal supervision of weak labels with a single backbone branch. Furthermore, in real-world scenarios, the boundary betwee... | ['Sangyoun Lee', 'Kyungjae Lee', 'Chaewon Park', 'Sangwon Hwang', 'Minjung Kim', 'MyeongAh Cho'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-anomaly-detection', 'supervised-anomaly-detection', 'relational-reasoning'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 2.10691556e-01 -4.10113335e-01 -3.84532005e-01 -5.81965506e-01
-1.94123819e-01 -3.43402028e-01 6.11709833e-01 3.21978748e-01
-2.48794675e-01 4.01918411e-01 1.85949251e-01 -6.90022903e-03
-1.88578546e-01 -5.73317051e-01 -6.08503044e-01 -8.90979350e-01
-3.25483590e-01 1.06589552e-02 7.38048911e-01 -2.58936018... | [7.8856682777404785, 1.5347150564193726] |
23bd5133-475f-46a1-83e4-64c6c02036bd | research-project-text-engineering-tool-for | 1601.01887 | null | http://arxiv.org/abs/1601.01887v1 | http://arxiv.org/pdf/1601.01887v1.pdf | Research Project: Text Engineering Tool for Ontological Scientometry | The number of scientific papers grows exponentially in many disciplines. The
share of online available papers grows as well. At the same time, the period of
time for a paper to loose at chance to be cited anymore shortens. The decay of
the citing rate shows similarity to ultradiffusional processes as for other
online c... | ['Rustam Tagiew'] | 2016-01-08 | null | null | null | null | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [-0.6379378 0.52262914 -0.16764157 0.19700348 -0.12048052 -0.94660056
0.72707814 0.85481495 -0.25630122 0.8282887 0.11586496 -0.68570983
-0.33545566 -1.2515776 -0.16914417 -0.430915 0.15585637 0.73128134
0.565375 -0.2084435 0.74193525 0.4532949 -1.4513339 -0.09868846
0.86449945 0.31295174 0.... | [9.620598793029785, 8.230907440185547] |
1a88501e-9387-46c5-bfb9-70cef38d2b34 | estimating-discontinuous-time-varying-risk | 2211.08991 | null | https://arxiv.org/abs/2211.08991v1 | https://arxiv.org/pdf/2211.08991v1.pdf | Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML | Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation regarding inflammation, hemostasis and vascular function in COVID-19 by performing ... | ['Rich Caruana', 'Yin Aphinyanaphongs', 'Mark E. Nunnally', 'Benjamin Lengerich'] | 2022-11-15 | null | null | null | null | ['additive-models'] | ['methodology'] | [-8.30552727e-02 -2.57077157e-01 -2.77923375e-01 1.29287168e-01
-1.67835847e-01 -7.17451572e-01 5.11317194e-01 7.13470101e-01
-3.30534250e-01 1.05577016e+00 7.03274429e-01 -6.01088941e-01
-4.22589242e-01 -8.03513944e-01 -4.22468722e-01 -2.05218613e-01
-1.13257921e+00 9.83375549e-01 -2.89550722e-01 1.25512555... | [6.085035800933838, 4.464648723602295] |
b26f0fe8-3c8d-430a-bd83-90660aa9ce6c | ceflow-a-robust-and-efficient-counterfactual | 2303.14668 | null | https://arxiv.org/abs/2303.14668v1 | https://arxiv.org/pdf/2303.14668v1.pdf | CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows | Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation... | ['Guandong Xu', 'Qian Li', 'Tri Dung Duong'] | 2023-03-26 | null | null | null | null | ['interpretable-machine-learning', 'counterfactual-explanation'] | ['methodology', 'miscellaneous'] | [-8.33068416e-02 4.31766927e-01 -3.46442163e-01 -2.87255973e-01
-3.80605221e-01 -4.33219016e-01 8.99120212e-01 -2.90305674e-01
-3.80101204e-02 1.10011303e+00 5.83175004e-01 -6.73616946e-01
-1.54026160e-02 -8.89868617e-01 -8.17154467e-01 -5.22744179e-01
-5.48101962e-03 4.75811988e-01 -4.89766806e-01 -9.59134772... | [8.678873062133789, 5.6484880447387695] |
49f7d74e-6d60-4182-9466-0336b9675ee8 | coresets-for-vector-summarization-with | 1706.05554 | null | http://arxiv.org/abs/1706.05554v1 | http://arxiv.org/pdf/1706.05554v1.pdf | Coresets for Vector Summarization with Applications to Network Graphs | We provide a deterministic data summarization algorithm that approximates the
mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in
$\REAL^d$, by a weighted mean $\tilde{p}$ of a \emph{subset} of $O(1/\eps)$
vectors, i.e., independent of both $n$ and $d$. We prove that the squared
Euclidean distance ... | ['Sedat Ozer', 'Daniela Rus', 'Dan Feldman'] | 2017-06-17 | coresets-for-vector-summarization-with-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=481 | http://proceedings.mlr.press/v70/feldman17a/feldman17a.pdf | icml-2017-8 | ['data-summarization'] | ['miscellaneous'] | [ 2.31473967e-01 3.62706542e-01 1.46889230e-02 -3.35655408e-03
-8.08954895e-01 -7.57979870e-01 7.57566914e-02 8.03918242e-01
-7.16135740e-01 7.92376697e-01 -1.36204571e-01 -2.12717235e-01
-7.27182686e-01 -1.28601873e+00 -6.23216331e-01 -7.05353379e-01
-1.01741970e+00 6.55675471e-01 2.41195321e-01 -2.68141985... | [6.593517780303955, 4.818435192108154] |
35bbdfe8-d65c-4353-b7a0-772d65d9658b | offline-versus-online-triplet-mining-based-on | 2007.02200 | null | https://arxiv.org/abs/2007.02200v3 | https://arxiv.org/pdf/2007.02200v3.pdf | Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches | We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining. While many works focus solely on selecting the triplets online (batc... | ['Milad Sikaroudi', 'Fakhri Karray', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh', 'Amir Safarpoor'] | 2020-07-04 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [-8.93762410e-02 2.80361950e-01 -3.49224359e-01 -2.81800002e-01
-9.68032420e-01 -4.66794133e-01 3.23852003e-02 9.09900069e-01
-6.90515697e-01 5.22874951e-01 -8.54975358e-02 -7.54427910e-01
-9.96572912e-01 -9.54284310e-01 -6.59389436e-01 -9.80112851e-01
-8.69252861e-01 6.31590188e-01 3.14125955e-01 -2.27127552... | [15.01358413696289, -2.721618890762329] |
a39c56fb-e3a2-4058-bccf-41b2a66fb5db | is-lip-region-of-interest-sufficient-for | 2205.14295 | null | https://arxiv.org/abs/2205.14295v2 | https://arxiv.org/pdf/2205.14295v2.pdf | Is Lip Region-of-Interest Sufficient for Lipreading? | Lip region-of-interest (ROI) is conventionally used for visual input in the lipreading task. Few works have adopted the entire face as visual input because lip-excluded parts of the face are usually considered to be redundant and irrelevant to visual speech recognition. However, faces contain much more detailed informa... | ['Jia Pan', 'Gen-Shun Wan', 'Jing-Xuan Zhang'] | 2022-05-28 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 0.08339173 0.36826316 -0.25181434 -0.464096 -1.0082207 -0.196323
0.58008564 -0.4838206 -0.5537345 0.6738204 0.5865219 -0.17328778
0.5334543 -0.1133015 -0.6425943 -0.8454833 0.741211 -0.18566561
0.06274508 0.07257358 0.38745463 0.42573592 -2.3373046 0.5362763
0.5395086 1.0868202 0.443... | [14.322608947753906, 5.004477024078369] |
f29f2d67-3bef-4976-8962-a9c9e4d8a805 | generalization-error-of-first-order-methods | 2307.04679 | null | https://arxiv.org/abs/2307.04679v2 | https://arxiv.org/pdf/2307.04679v2.pdf | Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles | In this paper, we provide a novel framework for the analysis of generalization error of first-order optimization algorithms for statistical learning when the gradient can only be accessed through partial observations given by an oracle. Our analysis relies on the regularity of the gradient w.r.t. the data samples, and ... | ['Laurent Massoulié', 'Mathieu Even', 'Kevin Scaman'] | 2023-07-10 | null | null | null | null | ['quantization', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [-5.92999300e-03 2.91938663e-01 -2.73967862e-01 -5.19510746e-01
-1.21183741e+00 -5.98372579e-01 2.02847287e-01 6.65478230e-01
-8.02906513e-01 8.70971501e-01 -1.29151225e-01 -2.62167692e-01
-2.63784945e-01 -6.99433863e-01 -1.07764423e+00 -1.04102755e+00
-2.11494759e-01 3.03785414e-01 -5.50860986e-02 1.71079174... | [6.858586311340332, 4.295633316040039] |
255d1eaa-0d3f-41a8-a6ac-a4f3418e3801 | a-structure-guided-diffusion-model-for-large | 2211.10437 | null | https://arxiv.org/abs/2211.10437v1 | https://arxiv.org/pdf/2211.10437v1.pdf | A Structure-Guided Diffusion Model for Large-Hole Diverse Image Completion | Diverse image completion, a problem of generating various ways of filling incomplete regions (i.e. holes) of an image, has made remarkable success. However, managing input images with large holes is still a challenging problem due to the corruption of semantically important structures. In this paper, we tackle this pro... | ['Kiyoharu Aizawa', 'Yuki Koyama', 'Dong Chen', 'Jiaolong Yang', 'Daichi Horita'] | 2022-11-18 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.21100885e-01 2.12643221e-01 5.54199755e-01 -2.21906930e-01
-7.51415730e-01 -2.25668475e-01 5.15578628e-01 -3.36624146e-01
-1.81568379e-03 6.38240397e-01 2.58745849e-01 1.27844319e-01
1.53143272e-01 -8.31013501e-01 -7.43581533e-01 -7.97798038e-01
4.49461937e-01 4.50986117e-01 2.96661258e-01 -2.53794882... | [11.32010555267334, -1.1555771827697754] |
571c013a-5af0-488d-bc61-51373d0fc517 | viewnerf-unsupervised-viewpoint-estimation | 2212.00436 | null | https://arxiv.org/abs/2212.00436v1 | https://arxiv.org/pdf/2212.00436v1.pdf | ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields | We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF is usually trained with ground-truth camera poses, multiple extensions have been proposed to reduce the need for this expensive supervision. ... | ['Hakan Bilen', 'Oisin Mac Aodha', 'Octave Mariotti'] | 2022-12-01 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 3.36150587e-01 3.68886511e-03 -1.17442809e-01 -7.09575653e-01
-6.99991822e-01 -6.07026339e-01 5.84545434e-01 -2.96292841e-01
-5.94918281e-02 4.54877228e-01 1.00247711e-01 2.58240551e-02
1.72686681e-01 -6.69140399e-01 -1.08584690e+00 -5.30843377e-01
4.83754247e-01 4.84331220e-01 2.96424329e-01 1.82775762... | [8.520075798034668, -2.708165168762207] |
c4fa5f3a-6d0a-4439-8253-159e6bedf606 | neural-projection-mapping-using-reflectance | 2306.06595 | null | https://arxiv.org/abs/2306.06595v1 | https://arxiv.org/pdf/2306.06595v1.pdf | Neural Projection Mapping Using Reflectance Fields | We introduce a high resolution spatially adaptive light source, or a projector, into a neural reflectance field that allows to both calibrate the projector and photo realistic light editing. The projected texture is fully differentiable with respect to all scene parameters, and can be optimized to yield a desired appea... | ['Amit H. Bermano', 'Daisuke Iwai', 'Yotam Erel'] | 2023-06-11 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.72240078e-01 -1.34323761e-01 5.33402681e-01 -4.11759466e-01
-4.53859478e-01 -8.25866520e-01 5.61624229e-01 -7.98844039e-01
-5.13907745e-02 2.54190803e-01 2.04113394e-01 -2.69123077e-01
2.10021242e-01 -7.39360571e-01 -9.09308493e-01 -5.70324183e-01
7.70033419e-01 6.54461205e-01 -6.78635016e-02 -1.20217241... | [9.61951732635498, -3.086657762527466] |
94448961-ca11-4eec-a65e-c2e4341dcaa2 | quantum-finance-a-tutorial-on-quantum | 2208.04382 | null | https://arxiv.org/abs/2208.04382v2 | https://arxiv.org/pdf/2208.04382v2.pdf | Quantum Finance: a tutorial on quantum computing applied to the financial market | Previously only considered a frontier area of Physics, nowadays quantum computing is one of the fastest growing research field, precisely because of its technological applications in optimization problems, machine learning, information security and simulations. The goal of this article is to introduce the fundamentals ... | ['Taysa M. Mendonça', 'Askery Canabarro', 'Rafael Chaves', 'Gleydson F. de Jesus', 'Anton S. Albino', 'George Moreno', 'Ranieri Nery'] | 2022-08-08 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.72049636e-01 -3.15776616e-01 1.26257434e-01 5.41180670e-02
-4.88739729e-01 -4.87413019e-01 4.01163578e-01 4.65209067e-01
-7.17226207e-01 1.16654944e+00 -2.58900911e-01 -3.23000431e-01
-2.40006149e-01 -1.32016242e+00 -5.40693760e-01 -6.72316670e-01
-1.54481664e-01 8.47188771e-01 -3.40873569e-01 -5.56399703... | [5.5733466148376465, 4.915469169616699] |
35488610-4d23-4759-83f5-9712f1f126be | advanced-deep-convolutional-neural-network | 1904.09075 | null | http://arxiv.org/abs/1904.09075v1 | http://arxiv.org/pdf/1904.09075v1.pdf | Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases | Deep Learning (DL) approaches have been providing state-of-the-art
performance in different modalities in the field of medical imagining including
Digital Pathology Image Analysis (DPIA). Out of many different DL approaches,
Deep Convolutional Neural Network (DCNN) technique provides superior
performance for classifica... | ['Simon Arkell', 'Vijayan K. Asari', 'Theus Aspiras', 'Tarek M. Taha', 'Md Zahangir Alom', 'Dave Billiter', 'TJ Bowen'] | 2019-04-19 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.67351979e-01 -1.45690963e-01 -1.45970121e-01 -1.01977140e-02
-7.86843836e-01 -1.53600186e-01 6.13718569e-01 3.46572161e-01
-5.03885806e-01 6.59222364e-01 8.26393142e-02 -5.91702342e-01
-2.74543166e-01 -7.85025358e-01 -1.17969319e-01 -1.03137481e+00
-7.35402256e-02 4.03393954e-01 2.28967249e-01 9.18442663... | [15.236001968383789, -2.9078681468963623] |
b244ef85-869f-42fc-bd6d-fd3fdbd51940 | end-to-end-emotion-cause-pair-extraction-via | 2002.10710 | null | https://arxiv.org/abs/2002.10710v4 | https://arxiv.org/pdf/2002.10710v4.pdf | End-to-end Emotion-Cause Pair Extraction via Learning to Link | Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. Existing approaches to ... | ['Qiuchi Li', 'Haolin Song', 'Chen Zhang', 'Dawei Song'] | 2020-02-25 | null | null | null | null | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.28015468e-01 7.36696124e-02 -9.05958116e-02 -4.16722536e-01
-8.46968472e-01 -5.52342653e-01 7.20184982e-01 3.45217317e-01
-1.67038798e-01 6.48485899e-01 2.52818078e-01 -5.42143825e-03
-1.36448145e-01 -5.58319926e-01 -4.22726005e-01 -5.98363519e-01
-2.04897806e-01 5.66035695e-02 -1.59967691e-01 -1.32069707... | [12.63134765625, 6.216489791870117] |
52f410c5-ddc1-42b9-b46a-3211708027f3 | towards-lexical-chains-for-knowledge-graph | null | null | https://aclanthology.org/R17-1087 | https://aclanthology.org/R17-1087.pdf | Towards Lexical Chains for Knowledge-Graph-based Word Embeddings | Word vectors with varying dimensionalities and produced by different algorithms have been extensively used in NLP. The corpora that the algorithms are trained on can contain either natural language text (e.g. Wikipedia or newswire articles) or artificially-generated pseudo corpora due to natural data sparseness. We exp... | ['Petya Osenova', 'Svetla Boytcheva', 'Kiril Simov'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-4.10259068e-02 1.07520692e-01 -4.83826071e-01 6.03427924e-03
-3.20545822e-01 -7.65646994e-01 9.61613059e-01 4.39024895e-01
-8.70922029e-01 9.36381102e-01 6.66336536e-01 -2.31902033e-01
-1.99322104e-01 -9.01659012e-01 -3.98497015e-01 -4.59677190e-01
-1.48602217e-01 8.34102631e-01 2.75530100e-01 -4.38258857... | [10.447007179260254, 8.86188793182373] |
9f1d9c5e-7098-414f-aa40-f2d7595116e9 | speech-structured-prediction-with-energy | 2305.13617 | null | https://arxiv.org/abs/2305.13617v2 | https://arxiv.org/pdf/2305.13617v2.pdf | SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres | Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based ... | ['Bryan Hooi', 'Ningyu Zhang', 'Shengyu Mao', 'Shumin Deng'] | 2023-05-23 | null | null | null | null | ['event-relation-extraction'] | ['natural-language-processing'] | [-1.30726323e-01 2.33052269e-01 4.23797145e-02 -6.38803303e-01
4.26949635e-02 -4.66226548e-01 1.15652514e+00 7.59055674e-01
1.90825224e-01 9.54841495e-01 9.20631170e-01 -1.36093587e-01
-3.01713020e-01 -1.25579572e+00 -4.51132268e-01 -3.12027633e-01
-7.55532920e-01 6.95744634e-01 2.38361090e-01 -1.49354652... | [9.030327796936035, 9.216711044311523] |
81d9312b-40fe-4a1b-a05b-d27c8fa35a02 | denseclip-extract-free-dense-labels-from-clip | 2112.01071 | null | https://arxiv.org/abs/2112.01071v2 | https://arxiv.org/pdf/2112.01071v2.pdf | Extract Free Dense Labels from CLIP | Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition. Many recent studies leverage the pre-trained CLIP models for image-level classification and manipulation. In this paper, we wish examine the intrinsic potential of CLIP for pixel-level dense... | ['Bo Dai', 'Chen Change Loy', 'Chong Zhou'] | 2021-12-02 | null | null | null | null | ['unsupervised-semantic-segmentation-with', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 4.58614379e-01 -6.74077943e-02 -4.98178393e-01 -4.46074694e-01
-1.23522854e+00 -6.44131064e-01 4.34913069e-01 -6.44094497e-02
-5.16890228e-01 7.07463861e-01 -1.61161929e-01 4.67928238e-02
4.13160026e-01 -5.23914337e-01 -9.75057602e-01 -8.14644635e-01
4.57957149e-01 3.50314885e-01 3.83592546e-01 -7.39199594... | [9.66943359375, 0.7517912983894348] |
8b0337c2-6ec9-4869-90dd-a0df6b12c633 | iam-a-comprehensive-and-large-scale-dataset | 2203.12257 | null | https://arxiv.org/abs/2203.12257v3 | https://arxiv.org/pdf/2203.12257v3.pdf | IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks | Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedi... | ['Luo Si', 'Yan Zhang', 'Qian Yu', 'Ruidan He', 'Lidong Bing', 'Liying Cheng'] | 2022-03-23 | null | https://aclanthology.org/2022.acl-long.162 | https://aclanthology.org/2022.acl-long.162.pdf | acl-2022-5 | ['claim-extraction-with-stance-classification', 'claim-evidence-pair-extraction-cepe'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.75340098e-01 3.88288319e-01 -6.24968469e-01 -3.78609449e-01
-1.37521398e+00 -7.00831652e-01 9.49330032e-01 6.99879467e-01
-4.85466093e-01 9.19029117e-01 5.62983513e-01 -7.85325766e-01
-9.41678137e-02 -6.12779796e-01 -6.82084620e-01 -2.97524661e-01
4.89308149e-01 7.05449522e-01 3.86562198e-01 -1.99082822... | [9.41915512084961, 9.579170227050781] |
0f730fe9-facc-433d-b695-087c575c6000 | he-said-she-said-style-transfer-for-shifting | 2210.15462 | null | https://arxiv.org/abs/2210.15462v1 | https://arxiv.org/pdf/2210.15462v1.pdf | He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues | In this work, we define a new style transfer task: perspective shift, which reframes a dialogue from informal first person to a formal third person rephrasing of the text. This task requires challenging coreference resolution, emotion attribution, and interpretation of informal text. We explore several baseline approac... | ['Matthew R. Gormley', 'Graham Neubig', 'Amanda Bertsch'] | 2022-10-27 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 7.32391119e-01 9.51241851e-01 -1.60790086e-01 -5.60324728e-01
-1.26336551e+00 -8.98098350e-01 9.83839631e-01 2.46309310e-01
-4.48480070e-01 1.07452071e+00 1.35720658e+00 2.56502628e-01
4.02250230e-01 -1.66896105e-01 -1.97340816e-01 -2.39338845e-01
2.66810834e-01 8.75306189e-01 -2.27658391e-01 -6.31543517... | [12.487363815307617, 9.084352493286133] |
1484b15d-aba5-4686-aa74-dc4a5ca6e7dc | deep-feature-factorization-for-concept | 1806.10206 | null | http://arxiv.org/abs/1806.10206v5 | http://arxiv.org/pdf/1806.10206v5.pdf | Deep Feature Factorization For Concept Discovery | We propose Deep Feature Factorization (DFF), a method capable of localizing
similar semantic concepts within an image or a set of images. We use DFF to
gain insight into a deep convolutional neural network's learned features, where
we detect hierarchical cluster structures in feature space. This is visualized
as heat m... | ['Sabine Süsstrunk', 'Radhakrishna Achanta', 'Edo Collins'] | 2018-06-26 | deep-feature-factorization-for-concept-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [-1.71045601e-01 -5.72382398e-02 1.28353700e-01 -5.95255733e-01
-2.99562097e-01 -8.22787344e-01 7.21810460e-01 5.76945782e-01
-2.59213388e-01 -8.04096460e-02 4.33268905e-01 4.19665426e-02
-1.38100356e-01 -8.25058818e-01 -7.24216163e-01 -4.43900347e-01
-4.82889771e-01 7.20937401e-02 2.70278245e-01 -2.85778660... | [9.724595069885254, 1.6438273191452026] |
c6ce30d8-1dcb-4983-9a55-b0caee123b70 | decentralized-multi-robot-formation-control | 2306.14489 | null | https://arxiv.org/abs/2306.14489v1 | https://arxiv.org/pdf/2306.14489v1.pdf | Decentralized Multi-Robot Formation Control Using Reinforcement Learning | This paper presents a decentralized leader-follower multi-robot formation control based on a reinforcement learning (RL) algorithm applied to a swarm of small educational Sphero robots. Since the basic Q-learning method is known to require large memory resources for Q-tables, this work implements the Double Deep Q-Netw... | ['Stjepan Bogdan', 'Marko Krizmancic', 'Juraj Obradovic'] | 2023-06-26 | null | null | null | null | ['q-learning'] | ['methodology'] | [-6.12155437e-01 3.21552068e-01 2.14924291e-01 3.25561672e-01
1.94677055e-01 -2.54393190e-01 6.90221131e-01 6.71627745e-02
-5.30678689e-01 1.39721739e+00 -6.31448150e-01 -1.52366295e-01
-5.63929737e-01 -7.95627654e-01 -7.82222569e-01 -1.03670204e+00
-3.78921568e-01 7.28159189e-01 3.48549843e-01 -1.00203741... | [4.058236598968506, 1.984879493713379] |
2b1a83b8-5e35-4a34-bfaa-c304db25c4cf | warteam-at-semeval-2017-task-6-using-neural | null | null | https://aclanthology.org/S17-2068 | https://aclanthology.org/S17-2068.pdf | \#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets | This paper presents the participation of {\#}WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. {\#}WarTeam{'}s system uses a neural network (TensorFlow) having inputs from a Na{\"\i}ve Bayes humor ... | ['Diana ab{\\u{a}}{\\textcommabelow{t}}', 'Tr', 'ra Maria', 'S ei', 'Amar', 'Cristina S{\\^\\i}rbu', 'ra', 'Iuliana Alex Fle{\\textcommabelow{s}}can-Lovin-Arseni', 'Ramona Andreea Turcu', 'Nichita Herciu', 'Adrian Iftene', 'Larisa Alexa', 'Constantin Scutaru'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-4.32235956e-01 1.12925820e-01 9.79028195e-02 -4.98561442e-01
-3.94329801e-02 -4.12934810e-01 6.30114675e-01 3.70693237e-01
-4.67106819e-01 6.07632160e-01 6.76914871e-01 -3.83768916e-01
2.32540131e-01 -6.22366607e-01 -4.68462296e-02 2.27903314e-02
2.21050140e-02 2.71007299e-01 -4.07332741e-02 -1.14826453... | [8.857280731201172, 11.057674407958984] |
c52c2d1c-c5d1-4a4b-958b-8405d091ef1e | learning-pseudo-labels-for-semi-and-weakly | null | null | https://www.sciencedirect.com/science/article/pii/S003132032200406X | https://www.sciencedirect.com/science/article/pii/S003132032200406X | Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation | In this paper, we aim to tackle semi-and-weakly supervised semantic segmentation (SWSSS), where many image-level classification labels and a few pixel-level annotations are available. We believe the most crucial point for solving SWSSS is to produce high-quality pseudo labels, and our method deals with it from two pers... | ['Shiguang Shan', 'Meina Kan', 'Jie Zhang', 'Yude Wang'] | 2022-08-02 | null | null | null | pattern-recognition-2022-8 | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 7.86766469e-01 5.37267864e-01 -4.29312378e-01 -5.74210346e-01
-1.05924213e+00 -4.90678072e-01 5.72020173e-01 -6.35804534e-02
-7.34525502e-01 8.37756336e-01 -3.56791168e-01 -2.55027205e-01
1.22101068e-01 -5.36100090e-01 -9.05341923e-01 -6.18831873e-01
3.68571192e-01 5.04466951e-01 6.43524826e-01 1.13799967... | [9.508323669433594, 0.7692221403121948] |
4700fcaa-144e-45f5-a73e-d80e200c7599 | a-food-recommender-system-in-academic | 2306.16528 | null | https://arxiv.org/abs/2306.16528v1 | https://arxiv.org/pdf/2306.16528v1.pdf | A Food Recommender System in Academic Environments Based on Machine Learning Models | Background: People's health depends on the use of proper diet as an important factor. Today, with the increasing mechanization of people's lives, proper eating habits and behaviors are neglected. On the other hand, food recommendations in the field of health have also tried to deal with this issue. But with the introdu... | ['Babak Teimourpour', 'Abolfazl Ajami'] | 2023-06-26 | null | null | null | null | ['feature-engineering', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-4.96822983e-01 -3.51421386e-01 -7.21979201e-01 -4.28711593e-01
1.45126417e-01 -1.91513360e-01 -2.54743367e-01 1.10435164e+00
-3.70652199e-01 3.88733506e-01 5.20978987e-01 -3.10479254e-01
-6.53828442e-01 -1.05712473e+00 -9.02007744e-02 -4.64316577e-01
1.54683515e-01 2.57656813e-01 3.62876579e-02 -5.92066109... | [11.534172058105469, 4.483644485473633] |
e8f53d3b-b347-4f35-af98-dc72a3929d14 | structured-time-series-prediction-without | 2202.03539 | null | https://arxiv.org/abs/2202.03539v1 | https://arxiv.org/pdf/2202.03539v1.pdf | Structured Time Series Prediction without Structural Prior | Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.). In the case of multivariate time series, the key to good performances is to properly capture the dependencies between the variates. Often, these variates a... | ['Jean-Marc Andreoli', 'Darko Drakulic'] | 2022-02-07 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 8.17873850e-02 -5.60882688e-02 -1.47599876e-01 -9.47167426e-02
-1.62550718e-01 -6.78127825e-01 9.18582380e-01 6.83206260e-01
-5.01870751e-01 6.93741381e-01 2.91599870e-01 -3.93558234e-01
-5.10101497e-01 -1.04673398e+00 -7.81373501e-01 -9.00152683e-01
-3.15083921e-01 5.22690058e-01 4.19957489e-01 -3.75243872... | [7.172552108764648, 3.709766149520874] |
deea7e3f-5ba7-4841-a8c2-4ed0c3b7c708 | an-efficient-statistical-method-for-image | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Chen_An_Efficient_Statistical_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Chen_An_Efficient_Statistical_ICCV_2015_paper.pdf | An Efficient Statistical Method for Image Noise Level Estimation | In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on the statistical relationship between the noise variance and the eigenvalues of the covariance matrix of patches within an image, which shows that m... | ['Fengyuan Zhu', 'Pheng Ann Heng', 'Guangyong Chen'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['noise-estimation'] | ['medical'] | [ 2.19419032e-01 -3.93478185e-01 5.51431954e-01 1.26035482e-01
-1.17407978e+00 -3.91185045e-01 2.08244130e-01 -3.58784020e-01
-1.74278185e-01 2.90173769e-01 4.37456630e-02 1.94691926e-01
-8.56243595e-02 -5.92226982e-01 -6.10937059e-01 -1.28536284e+00
-3.76089066e-02 -3.09652209e-01 1.00896880e-01 1.53597653... | [11.545130729675293, -2.4225432872772217] |
1998785e-6eb7-40bb-9e1a-a48ef2f0b66c | downstream-task-agnostic-speech-enhancement | 2305.14723 | null | https://arxiv.org/abs/2305.14723v1 | https://arxiv.org/pdf/2305.14723v1.pdf | Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss | Self-supervised learning (SSL) is the latest breakthrough in speech processing, especially for label-scarce downstream tasks by leveraging massive unlabeled audio data. The noise robustness of the SSL is one of the important challenges to expanding its application. We can use speech enhancement (SE) to tackle this issu... | ['Nobukatsu Hojo', 'Tomohiro Tanaka', 'Mana Ihori', 'Saki Mizuno', 'Kentaro Shinayama', 'Takanori Ashihara', 'Takafumi Moriya', 'Marc Delcroix', 'Tsubasa Ochiai', 'Ryo Masumura', 'Hiroshi Sato'] | 2023-05-24 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 4.66867477e-01 9.44804847e-02 1.16514713e-01 -4.62258458e-01
-1.17496407e+00 -5.72340608e-01 3.74398500e-01 -3.23741063e-02
-4.73044157e-01 5.40724814e-01 6.09738350e-01 -3.82001907e-01
1.72650665e-02 -1.20379873e-01 -4.09227639e-01 -6.64540708e-01
2.77138203e-01 -3.84477735e-01 4.20083731e-01 -4.00607884... | [14.607346534729004, 6.384759426116943] |
58d4a6b9-ebc0-4e3f-9df2-e0bf69c32ee3 | autoencoding-binary-classifiers-for | 1903.10709 | null | http://arxiv.org/abs/1903.10709v1 | http://arxiv.org/pdf/1903.10709v1.pdf | Autoencoding Binary Classifiers for Supervised Anomaly Detection | We propose the Autoencoding Binary Classifiers (ABC), a novel supervised
anomaly detector based on the Autoencoder (AE). There are two main approaches
in anomaly detection: supervised and unsupervised. The supervised approach
accurately detects the known anomalies included in training data, but it cannot
detect the unk... | ['Tomoharu Iwata', 'Sekitoshi Kanai', 'Masanori Yamada', 'Hiroshi Takahashi', 'Yuki Yamanaka'] | 2019-03-26 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-1.08046070e-01 -9.27226059e-03 2.65614152e-01 -4.13795531e-01
-5.05442202e-01 -3.49064797e-01 2.42137671e-01 2.51486748e-01
-5.03593013e-02 2.02013418e-01 -2.64006495e-01 -2.84090582e-02
-1.15788445e-01 -1.09210062e+00 -4.28156942e-01 -9.43613231e-01
-1.27733693e-01 5.51830411e-01 3.60821903e-01 1.77932978... | [7.5822248458862305, 2.3703835010528564] |
cf089d6f-a70f-4863-b069-00252c27e0de | right-for-the-wrong-scientific-reasons | 2001.05371 | null | https://arxiv.org/abs/2001.05371v3 | https://arxiv.org/pdf/2001.05371v3.pdf | Making deep neural networks right for the right scientific reasons by interacting with their explanations | Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show "Clever Hans"-like behavior---making use of confounding factors within datasets---to achieve high performance. In this work, we introduce the novel learning setting of "explanatory interactive learning" ... | ['Anne-Katrin Mahlein', 'Hans-Georg Luigs', 'Patrick Schramowski', 'Anna Brugger', 'Xiaoting Shao', 'Stefano Teso', 'Wolfgang Stammer', 'Kristian Kersting'] | 2020-01-15 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.33992355e-02 7.28342772e-01 -3.84969205e-01 -7.54611373e-01
8.52409452e-02 -4.46477741e-01 2.70914704e-01 9.63899866e-02
5.05795330e-02 7.16034055e-01 -2.78328776e-01 -1.17012644e+00
-3.58115464e-01 -6.57540560e-01 -1.18401396e+00 -7.21680820e-01
-2.53658831e-01 2.39932105e-01 -4.83275920e-01 6.87612295... | [8.872892379760742, 5.54940938949585] |
a8e9db5d-77a0-4112-88cb-880045371355 | deep-spectral-correspondence-for-matching | 1809.04642 | null | http://arxiv.org/abs/1809.04642v1 | http://arxiv.org/pdf/1809.04642v1.pdf | Deep Spectral Correspondence for Matching Disparate Image Pairs | A novel, non-learning-based, saliency-aware, shape-cognizant correspondence
determination technique is proposed for matching image pairs that are
significantly disparate in nature. Images in the real world often exhibit high
degrees of variation in scale, orientation, viewpoint, illumination and affine
projection param... | ['Suchendra M. Bhandarkar', 'Arun CS Kumar', 'Shefali Srivastava', 'Anirban Mukhopadhyay'] | 2018-09-12 | null | null | null | null | ['matching-disparate-images'] | ['computer-vision'] | [ 4.76483315e-01 -4.10995990e-01 -1.56764969e-01 -3.35596204e-01
-6.19636655e-01 -7.20569313e-01 7.34349549e-01 4.76994276e-01
-1.14650898e-01 3.86160314e-01 -1.60564315e-02 1.62606671e-01
-5.97703815e-01 -8.13081861e-01 -4.76525307e-01 -9.33188438e-01
3.23632538e-01 3.23228650e-02 4.14400429e-01 -2.36240074... | [8.345571517944336, -2.170257329940796] |
930f055f-a5b9-48ea-871d-fa112e4af324 | machine-learning-algorithms-for-b-jet-tagging | 1711.08811 | null | http://arxiv.org/abs/1711.08811v1 | http://arxiv.org/pdf/1711.08811v1.pdf | Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment | The separation of $b$-quark initiated jets from those coming from lighter
quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program
at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms
combine information from low-level taggers, exploiting reconstructed track and
vertex info... | ['Michela Paganini'] | 2017-11-23 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-6.35527551e-01 -1.55849680e-01 -7.59161353e-01 -6.01397276e-01
-8.57135594e-01 -5.45130610e-01 8.74667168e-01 5.30858219e-01
-4.43210393e-01 7.37436831e-01 4.17273007e-02 -7.29201555e-01
9.67165753e-02 -9.76183951e-01 -3.17088366e-01 -9.04373169e-01
-2.70889401e-01 1.12664413e+00 4.95408326e-01 1.70962475... | [15.700911521911621, 2.9187498092651367] |
35f54fec-a630-4085-841b-2080ef53060c | ldso-direct-sparse-odometry-with-loop-closure | 1808.01111 | null | http://arxiv.org/abs/1808.01111v1 | http://arxiv.org/pdf/1808.01111v1.pdf | LDSO: Direct Sparse Odometry with Loop Closure | In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains... | ['Xiang Gao', 'Rui Wang', 'Daniel Cremers', 'Nikolaus Demmel'] | 2018-08-03 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-8.09160247e-02 -1.08352304e-01 -1.29641563e-01 -1.29792839e-01
-6.31857932e-01 -6.43871725e-01 7.10697532e-01 3.15462470e-01
-5.75503767e-01 6.45243108e-01 -1.79561123e-01 4.48149294e-02
-1.06266037e-01 -5.50835609e-01 -7.10586369e-01 -4.41624433e-01
-2.65815228e-01 5.66286504e-01 4.33938026e-01 -2.75729775... | [7.342815399169922, -2.2192585468292236] |
78268844-7a63-4155-b26f-601221248960 | enhancing-generalizable-6d-pose-tracking-of | 2210.04026 | null | https://arxiv.org/abs/2210.04026v1 | https://arxiv.org/pdf/2210.04026v1.pdf | Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing | While holding and manipulating an object, humans track the object states through vision and touch so as to achieve complex tasks. However, nowadays the majority of robot research perceives object states just from visual signals, hugely limiting the robotic manipulation abilities. This work presents a tactile-enhanced g... | ['Li Yi', 'Rui Chen', 'Jing Xu', 'He Wang', 'Haocheng Yuan', 'Weihang Chen', 'Yun Liu', 'Xiaomeng Xu'] | 2022-10-08 | null | null | null | null | ['pose-tracking', 'hand-object-pose'] | ['computer-vision', 'computer-vision'] | [-2.56526284e-02 -2.84598768e-01 -2.39800692e-01 2.04999790e-01
-2.12037653e-01 -8.87299776e-01 1.78119257e-01 -2.58146971e-01
-2.95433670e-01 1.95482805e-01 -3.51108074e-01 2.04472184e-01
-2.31910758e-02 1.49737680e-02 -7.40940034e-01 -4.82786506e-01
3.79451900e-03 5.22184372e-01 6.30630195e-01 3.89865227... | [6.015322208404541, -0.9749985337257385] |
a762de1d-a244-40c5-a9cc-b65c3cdfad9a | matchzoo-a-toolkit-for-deep-text-matching | 1707.07270 | null | http://arxiv.org/abs/1707.07270v1 | http://arxiv.org/pdf/1707.07270v1.pdf | MatchZoo: A Toolkit for Deep Text Matching | In recent years, deep neural models have been widely adopted for text
matching tasks, such as question answering and information retrieval, showing
improved performance as compared with previous methods. In this paper, we
introduce the MatchZoo toolkit that aims to facilitate the designing, comparing
and sharing of dee... | ['Xue-Qi Cheng', 'Jianpeng Hou', 'Yixing Fan', 'Jiafeng Guo', 'Yanyan Lan', 'Liang Pang'] | 2017-07-23 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-8.05696994e-02 -3.58275384e-01 3.13213579e-02 -7.15856016e-01
-4.82028067e-01 -2.92682320e-01 7.41172433e-01 4.92591083e-01
-5.29592156e-01 -1.30233973e-01 3.51727158e-01 -4.50965613e-01
-2.93624401e-01 -9.48284566e-01 -8.99486318e-02 -9.22080800e-02
4.68514889e-01 1.01832700e+00 3.55919152e-02 -4.25041467... | [11.174757957458496, 8.207661628723145] |
9bfa4965-8151-4dec-845c-33fa8b6d1cdb | a-neural-network-based-convex-regularizer-for | 2211.12461 | null | https://arxiv.org/abs/2211.12461v1 | https://arxiv.org/pdf/2211.12461v1.pdf | A Neural-Network-Based Convex Regularizer for Image Reconstruction | The emergence of deep-learning-based methods for solving inverse problems has enabled a significant increase in reconstruction quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these shortcomings while retaining the performance. In this work,... | ['Michael Unser', 'Stanislas Ducotterd', 'Pakshal Bohra', 'Sebastian Neumayer', 'Alexis Goujon'] | 2022-11-22 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 2.99406052e-02 4.79420930e-01 6.87158853e-02 -4.25069869e-01
-1.01403046e+00 2.69898981e-01 2.44408786e-01 -4.01112646e-01
-2.73413658e-01 8.37854028e-01 2.88408726e-01 -1.19711637e-01
-3.41075391e-01 -5.07916689e-01 -8.92872453e-01 -1.12403345e+00
-2.16516078e-01 3.33929896e-01 -8.70765522e-02 -3.09696663... | [11.973286628723145, -2.4471211433410645] |
bc9d5060-8b20-41c0-ae4e-3e7b2fad3028 | building-neural-networks-on-matrix-manifolds | 2305.04560 | null | https://arxiv.org/abs/2305.04560v3 | https://arxiv.org/pdf/2305.04560v3.pdf | Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach | Matrix manifolds, such as manifolds of Symmetric Positive Definite (SPD) matrices and Grassmann manifolds, appear in many applications. Recently, by applying the theory of gyrogroups and gyrovector spaces that is a powerful framework for studying hyperbolic geometry, some works have attempted to build principled genera... | ['Shuo Yang', 'Xuan Son Nguyen'] | 2023-05-08 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition', 'knowledge-graph-completion'] | ['computer-vision', 'computer-vision', 'knowledge-base'] | [-1.52475163e-01 3.49328995e-01 -1.49499014e-01 2.49008019e-03
3.26696277e-01 -4.25183594e-01 6.30911350e-01 9.15851593e-02
-3.87072176e-01 5.13079166e-01 1.79859072e-01 -3.20700765e-01
-7.31267929e-01 -7.77646780e-01 -6.07691884e-01 -7.35544026e-01
-6.21911764e-01 7.13608041e-02 1.75461546e-01 -4.90525663... | [7.7495293617248535, 4.076087474822998] |
d336d4e9-711e-4367-aa9b-6e5134857f40 | dr-wlc-dimensionality-reduction-cognition-for | 2301.06944 | null | https://arxiv.org/abs/2301.06944v1 | https://arxiv.org/pdf/2301.06944v1.pdf | DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking | Object detection and pose estimation are difficult tasks in robotics and autonomous driving. Existing object detection and pose estimation methods mostly adopt the same-dimensional data for training. For example, 2D object detection usually requires a large amount of 2D annotation data with high cost. Using high-dimens... | ['Mengyin Fu', 'Yufeng Yue', 'Yi Yang', 'Siyuan Chen', 'Tianji Jiang', 'Xi Xu', 'Yu Gao'] | 2023-01-17 | null | null | null | null | ['2d-object-detection'] | ['computer-vision'] | [-2.91153163e-01 6.19344860e-02 -3.09542622e-02 -5.63255847e-01
-2.11608648e-01 -4.90995497e-01 3.03976148e-01 -7.88148716e-02
-5.60596049e-01 2.01765552e-01 -4.29652303e-01 -2.82826066e-01
1.07868478e-01 -5.89421690e-01 -7.11812317e-01 -6.29230618e-01
1.32473946e-01 7.95274079e-01 8.44726443e-01 6.71125799... | [7.791979789733887, -2.4641411304473877] |
5f5bb4fa-f0d4-4f63-b1fe-87f17b61881a | hardware-acceleration-of-neural-graphics | 2303.05735 | null | https://arxiv.org/abs/2303.05735v6 | https://arxiv.org/pdf/2303.05735v6.pdf | Hardware Acceleration of Neural Graphics | Rendering and inverse-rendering algorithms that drive conventional computer graphics have recently been superseded by neural representations (NR). NRs have recently been used to learn the geometric and the material properties of the scenes and use the information to synthesize photorealistic imagery, thereby promising ... | ['Rakesh Kumar', 'Tobias Zirr', 'Ramakrishna Kanungo', 'Muhammad Husnain Mubarik'] | 2023-03-10 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 2.00317934e-01 -3.39984484e-02 3.20199817e-01 -1.52557954e-01
-5.06565511e-01 -3.18052977e-01 5.79751492e-01 -1.50337378e-02
-4.31033760e-01 2.58090794e-01 2.18593366e-02 -8.39777410e-01
1.03913814e-01 -1.14271677e+00 -8.94312859e-01 -6.62446737e-01
-5.19772209e-02 1.60387293e-01 3.78319561e-01 -2.25746989... | [9.922449111938477, -2.3544015884399414] |
249c6809-b9a7-4106-b9f8-cd85f7dc80b3 | non-uniform-blind-deblurring-by-reblurring | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Bahat_Non-Uniform_Blind_Deblurring_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Bahat_Non-Uniform_Blind_Deblurring_ICCV_2017_paper.pdf | Non-Uniform Blind Deblurring by Reblurring | We present an approach for blind image deblurring, which handles non-uniform blurs. Our algorithm has two main components: (i) A new method for recovering the unknown blur-field directly from the blurry image, and (ii) A method for deblurring the image given the recovered nonuniform blur-field. Our blur-field estimatio... | ['Netalee Efrat', 'Yuval Bahat', 'Michal Irani'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.13237554e-01 -4.90068495e-01 3.96563351e-01 4.85406332e-02
-6.31266832e-01 -6.15561366e-01 4.31295216e-01 -6.04278028e-01
-4.47198413e-02 8.15659821e-01 6.94901645e-01 -3.31330225e-02
-2.08649680e-01 -2.14040756e-01 -7.66956568e-01 -8.34933102e-01
8.15608278e-02 -2.98972894e-02 1.31674752e-01 8.29207897... | [11.618648529052734, -2.759296178817749] |
7e3978ad-9737-450b-90a5-018f0c52f5e8 | taskmatrix-ai-completing-tasks-by-connecting | 2303.16434 | null | https://arxiv.org/abs/2303.16434v1 | https://arxiv.org/pdf/2303.16434v1.pdf | TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs | Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific t... | ['Nan Duan', 'Ming Gong', 'Linjun Shou', 'Yun Wang', 'Shaoguang Mao', 'Lei Ji', 'Shuai Lu', 'Yang Ou', 'Yu Liu', 'Yan Xia', 'Wenshan Wu', 'Ting Song', 'Chenfei Wu', 'Yaobo Liang'] | 2023-03-29 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.87856201e-02 2.92733997e-01 -1.74569711e-02 -1.24820307e-01
-3.43990743e-01 -6.08556628e-01 5.84140778e-01 -4.91819054e-01
1.51886433e-01 7.44252205e-01 -1.40723642e-02 -5.23634911e-01
-3.95679593e-01 -8.30516875e-01 -6.45722747e-01 -1.07054748e-01
5.97074106e-02 8.63764346e-01 2.32217073e-01 -6.98978782... | [8.640336990356445, 7.288346767425537] |
7c69ad60-df37-4b21-b179-dfdf1050380b | visualization-of-decision-trees-based-on | 2205.04035 | null | https://arxiv.org/abs/2205.04035v1 | https://arxiv.org/pdf/2205.04035v1.pdf | Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models | Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates call... | ['Boris Kovalerchuk', 'Sridevi Wagle', 'Alex Worland'] | 2022-05-09 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-1.82168841e-01 4.08443600e-01 -2.03089714e-01 -3.18402588e-01
2.23903600e-02 -6.66781008e-01 4.13973033e-01 4.90993410e-01
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-5.32154024e-01 6.59457147e-01 1.90220833e-01 2.82687377... | [8.006885528564453, 4.649509429931641] |
150762de-d016-42fb-9ccf-c3b66909e019 | self-training-through-classifier-disagreement | 2302.14719 | null | https://arxiv.org/abs/2302.14719v1 | https://arxiv.org/pdf/2302.14719v1.pdf | Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction | Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributio... | ['Xudong Liu', 'Yongyi Mao', 'Nikolaos Aletras', 'Samuel Mensah', 'Richong Zhang', 'Kai Sun'] | 2023-02-28 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 6.99159801e-01 2.74493158e-01 -3.74044836e-01 -5.70947111e-01
-8.27363789e-01 -9.26107526e-01 8.05561066e-01 1.64714321e-01
-2.26194859e-01 1.08093452e+00 -1.21298656e-01 -2.52543002e-01
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5.19739568e-01 7.28862166e-01 3.02136868e-01 -2.84224302... | [10.793659210205078, 7.634707450866699] |
18c12214-ba53-4067-af43-bd5295a22598 | pose-driven-attention-guided-image-generation | 2104.13773 | null | https://arxiv.org/abs/2104.13773v1 | https://arxiv.org/pdf/2104.13773v1.pdf | Pose-driven Attention-guided Image Generation for Person Re-Identification | Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person re-ID is pose variations across the camera network, which significantly affects the appearance of a person. Existing development data lack a... | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Amena Khatun'] | 2021-04-28 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.66405731e-01 -1.13077931e-01 4.74661142e-01 -5.20322084e-01
-5.49961627e-01 -7.79830456e-01 5.35735607e-01 -4.86483395e-01
-4.61772025e-01 3.98930430e-01 1.28632277e-01 4.63532478e-01
2.30719015e-01 -3.75869125e-01 -9.60238338e-01 -5.74839473e-01
5.58642030e-01 5.05473554e-01 -1.05575904e-01 -3.20658647... | [14.53158187866211, 0.8892262578010559] |
14096e1e-b8a3-43f6-8289-1d5852a0afeb | reflash-dropout-in-image-super-resolution | 2112.12089 | null | https://arxiv.org/abs/2112.12089v3 | https://arxiv.org/pdf/2112.12089v3.pdf | Reflash Dropout in Image Super-Resolution | Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, w... | ['Chao Dong', 'Yu Qiao', 'Jinjin Gu', 'Xina Liu', 'Xiangtao Kong'] | 2021-12-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['network-interpretation'] | ['computer-vision'] | [ 5.09705603e-01 2.88340479e-01 -3.33419114e-01 -2.88844377e-01
-2.95505732e-01 -4.10092473e-02 2.25848660e-01 -3.54303449e-01
-2.59186208e-01 6.96480513e-01 2.27454528e-01 -1.70999393e-01
-3.20543319e-01 -4.69542921e-01 -8.31454813e-01 -9.50550675e-01
1.34514257e-01 -2.36903086e-01 5.90642154e-01 -2.95631349... | [11.232247352600098, -1.989359974861145] |
b8a8cc52-605b-4041-aa38-68cb3efd56fb | transformers-generalize-deepsets-and-can-be | 2110.14416 | null | https://arxiv.org/abs/2110.14416v2 | https://arxiv.org/pdf/2110.14416v2.pdf | Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs | We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs). We begin by observing that Transformers generalize DeepSets, or first-order (set-input) permutation invariant MLPs. Then, based on recently characterized higher-order invariant MLPs, we extend the concep... | ['Seunghoon Hong', 'Saeyoon Oh', 'Jinwoo Kim'] | 2021-10-27 | transformers-generalize-deepsets-and-can-be-1 | http://proceedings.neurips.cc/paper/2021/hash/ec0f40c389aeef789ce03eb814facc6c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ec0f40c389aeef789ce03eb814facc6c-Paper.pdf | neurips-2021-12 | ['hyperedge-prediction', 'set-to-graph-prediction-1', 'graph-regression'] | ['graphs', 'graphs', 'graphs'] | [ 3.76431555e-01 4.71439391e-01 -1.15219578e-01 -2.17053697e-01
-7.21651912e-01 -5.49194455e-01 2.68426239e-01 2.90425509e-01
-3.20195466e-01 4.98824209e-01 -7.27246106e-02 -7.88844407e-01
-5.64635515e-01 -1.25426054e+00 -1.39241183e+00 -7.05325425e-01
-8.53538454e-01 5.89622498e-01 9.00460333e-02 -5.27542308... | [6.93179178237915, 6.219197750091553] |
38665b15-9bf7-4ea8-bcb8-68fa76ab91b3 | videdit-zero-shot-and-spatially-aware-text | 2306.08707 | null | https://arxiv.org/abs/2306.08707v1 | https://arxiv.org/pdf/2306.08707v1.pdf | VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing | Recently, diffusion-based generative models have achieved remarkable success for image generation and edition. However, their use for video editing still faces important limitations. This paper introduces VidEdit, a novel method for zero-shot text-based video editing ensuring strong temporal and spatial consistency. Fi... | ['Nicolas Thome', 'Jean-Emmanuel Haugeard', 'Clément Rambour', 'Paul Couairon'] | 2023-06-14 | null | null | null | null | ['video-editing'] | ['computer-vision'] | [ 6.56491965e-02 -1.08723409e-01 -2.12576672e-01 -1.67986140e-01
-5.04427671e-01 -6.11700952e-01 6.76342845e-01 -1.52119651e-01
-4.11103100e-01 3.93940657e-01 9.15836766e-02 8.52031857e-02
1.23275690e-01 -5.89696825e-01 -5.99130094e-01 -3.68217736e-01
2.58334100e-01 2.51293540e-01 6.32396638e-01 -1.58052117... | [11.091538429260254, -0.6390040516853333] |
c94f7f33-cc51-4319-9927-85480b2c030e | automatic-generation-of-abstracts-for | null | null | https://aclanthology.org/2022.rocling-1.28 | https://aclanthology.org/2022.rocling-1.28.pdf | Automatic Generation of Abstracts for Research Papers | Summarizing has always been an important utility for reading long documents. Research papers are unique in this regard, as they have a compulsory summary in the form of the abstract in the beginning of the document which gives the gist of the entire study often within a set upper limit for the word count. Writing the a... | ['Nisansa de Silva', 'Dushan Kumarasinghe'] | null | null | null | null | rocling-2022-11 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 0.24080944 0.5483719 -0.42481977 -0.17746876 -0.7043371 -0.6667422
0.66701317 0.54429567 -0.34919623 0.98101074 0.7012805 -0.6704709
-0.3958388 -0.38085276 -0.3405874 -0.29300532 0.49055052 0.5030295
-0.06976752 -0.30987746 0.9435038 0.3163837 -1.1823711 0.00818935
1.1786714 0.17737786 0.530... | [12.372564315795898, 9.554471015930176] |
f031235a-d6cb-4afc-925c-64293e3050af | toolqa-a-dataset-for-llm-question-answering | 2306.13304 | null | https://arxiv.org/abs/2306.13304v1 | https://arxiv.org/pdf/2306.13304v1.pdf | ToolQA: A Dataset for LLM Question Answering with External Tools | Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, but they still suffer from challenges such as hallucination and weak numerical reasoning. To overcome these challenges, external tools can be used to enhance LLMs' question-answering abilities. However, current evaluation method... | ['Chao Zhang', 'Haotian Sun', 'Kuan Wang', 'Yue Yu', 'Yuchen Zhuang'] | 2023-06-23 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-2.26807460e-01 2.13165611e-01 -1.41856611e-01 -2.39770055e-01
-1.08921373e+00 -1.03562653e+00 5.00517666e-01 5.02157211e-01
-5.29665411e-01 4.66960907e-01 3.37077469e-01 -5.02197385e-01
-2.47069493e-01 -8.41543138e-01 -4.18579966e-01 2.18480393e-01
4.21714664e-01 6.49884582e-01 2.16151655e-01 -2.89700776... | [11.009197235107422, 7.9936299324035645] |
181d449e-1e37-4598-b909-e85490fa95d1 | pvdd-a-practical-video-denoising-dataset-with | 2207.01356 | null | https://arxiv.org/abs/2207.01356v2 | https://arxiv.org/pdf/2207.01356v2.pdf | Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network | To facilitate video denoising research, we construct a compelling dataset, namely, "Practical Video Denoising Dataset" (PVDD), containing 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information, PVDD covers dynamic scenes with varying and... | ['Jiaya Jia', 'Bei Yu', 'Jiangbo Lu', 'Nianjuan Jiang', 'Yitong Yu', 'Xiaogang Xu'] | 2022-07-04 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [-2.06948444e-02 -6.36395693e-01 3.39304835e-01 -2.40674555e-01
-7.71017373e-01 -1.40282258e-01 3.51266354e-01 -7.31456459e-01
-3.37324023e-01 3.98799986e-01 5.20923436e-01 -9.54144225e-02
5.47091328e-02 -7.70746410e-01 -9.01693046e-01 -1.11887074e+00
-6.34805188e-02 -4.62169945e-01 1.66242540e-01 -3.47697586... | [11.378090858459473, -2.2205214500427246] |
d4a47f68-d391-4941-8f91-4f3894e59efa | a-real-time-fusion-framework-for-long-term | 2210.09757 | null | https://arxiv.org/abs/2210.09757v1 | https://arxiv.org/pdf/2210.09757v1.pdf | A Real-Time Fusion Framework for Long-term Visual Localization | Visual localization is a fundamental task that regresses the 6 Degree Of Freedom (6DoF) poses with image features in order to serve the high precision localization requests in many robotics applications. Degenerate conditions like motion blur, illumination changes and environment variations place great challenges in th... | ['Yandong Guo', 'Jijunnan Li', 'Yuyue Liu', 'Yishan Ping', 'Jixiang Wan', 'Shuang Gao', 'Xudong Zhang', 'Yuchen Yang'] | 2022-10-18 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-6.95378542e-01 -6.02928936e-01 -1.82252869e-01 -4.33433861e-01
-5.98200381e-01 -6.56397164e-01 6.44659758e-01 -2.90050805e-01
-6.29667759e-01 5.82008362e-01 4.01502289e-03 -1.95568770e-01
2.20304921e-01 -2.65548915e-01 -9.20310795e-01 -5.42687953e-01
-6.10692166e-02 2.01909825e-01 3.84281546e-01 -2.29717597... | [7.518321990966797, -2.2049126625061035] |
5b64d269-cf64-4c6a-bd99-71e036033db0 | exploiting-web-images-for-fine-grained-visual | 2101.09412 | null | https://arxiv.org/abs/2101.09412v1 | https://arxiv.org/pdf/2101.09412v1.pdf | Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones | Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obs... | ['Zhenmin Tang', 'Jian Zhang', 'Fumin Shen', 'Xiushen Wei', 'Yazhou Yao', 'Chuanyi Zhang', 'Huafeng Liu'] | 2021-01-23 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 3.13314259e-01 -7.47157335e-02 -3.90077859e-01 -5.69465756e-01
-9.38721061e-01 -8.48592460e-01 4.38938975e-01 5.95814474e-02
-4.23552603e-01 1.02651477e+00 -1.58601612e-01 1.94936514e-01
-2.42759272e-01 -6.64313138e-01 -8.49614978e-01 -5.54494321e-01
4.65097457e-01 3.93123537e-01 3.79589230e-01 2.60120451... | [9.586315155029297, 2.6919972896575928] |
161e5856-8e81-4044-861a-de0698c80518 | are-explainability-tools-gender-biased-a-case | 2304.13419 | null | https://arxiv.org/abs/2304.13419v2 | https://arxiv.org/pdf/2304.13419v2.pdf | Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection | Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet bee... | ['Naser Damer', 'Fadi Boutros', 'Meiling Fang', 'Marco Huber'] | 2023-04-26 | null | null | null | null | ['face-presentation-attack-detection', 'face-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.62025031e-02 6.40208185e-01 -1.60684273e-01 -8.59602451e-01
1.31899774e-01 -2.45016351e-01 7.21780479e-01 -1.31393611e-01
9.50359032e-02 5.40658057e-01 4.23030257e-01 -6.41661465e-01
-4.09091353e-01 -5.65675557e-01 -5.80779314e-01 -2.84332603e-01
1.10310659e-01 3.23847950e-01 -5.53380251e-01 -2.19124593... | [13.029621124267578, 1.2338227033615112] |
1e158a48-8342-4bee-b033-0ef6ded02ec4 | second-order-anisotropic-gaussian-directional | 2305.00435 | null | https://arxiv.org/abs/2305.00435v1 | https://arxiv.org/pdf/2305.00435v1.pdf | Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection | Interest point detection methods have received increasing attention and are widely used in computer vision tasks such as image retrieval and 3D reconstruction. In this work, second-order anisotropic Gaussian directional derivative filters with multiple scales are used to smooth the input image and a novel blob detectio... | ['Changming Sun', 'Weichuan Zhang', 'Jiapan Guo', 'Wenya Yu', 'Jie Ren'] | 2023-04-30 | null | null | null | null | ['interest-point-detection', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.38556222e-02 -5.97226858e-01 -1.58924654e-01 -1.49307862e-01
-3.76631916e-01 -2.92993754e-01 5.84333658e-01 2.53303975e-01
-7.79156089e-01 3.48416984e-01 -2.64799446e-01 -1.35411277e-01
1.80560067e-01 -6.30023837e-01 -2.84373581e-01 -7.03920603e-01
-9.09861848e-02 -3.66907567e-02 1.09651196e+00 -1.03899866... | [8.91270923614502, -1.393290400505066] |
5ee09161-e470-4a10-a50d-8fd15d0f9f77 | cost-sensitive-gnn-based-imbalanced-learning | 2303.17486 | null | https://arxiv.org/abs/2303.17486v1 | https://arxiv.org/pdf/2303.17486v1.pdf | Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection | With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To... | ['xiangyang xue', 'Yahui Wang', 'Shibo Zhang', 'Xing Li', 'Shuxin Liu', 'Hongchang Chen', 'Haotian Chen', 'Xinxin Hu'] | 2023-03-28 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-8.03623423e-02 -5.11771999e-02 -5.65608144e-01 -1.87324181e-01
3.36128399e-02 1.07640415e-01 -2.99059283e-02 3.22360396e-01
-1.33310020e-01 8.54247570e-01 -1.96928978e-01 -4.20118213e-01
-3.19794983e-01 -1.29841375e+00 -3.83005649e-01 -1.88600332e-01
-1.80711299e-01 3.06969464e-01 2.55551696e-01 -4.90080059... | [7.166060924530029, 6.024120330810547] |
a2f691c5-261f-44b8-b7b0-641569d8cbfd | adaptive-spikenet-event-based-optical-flow | 2209.11741 | null | https://arxiv.org/abs/2209.11741v2 | https://arxiv.org/pdf/2209.11741v2.pdf | Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics | Event-based cameras have recently shown great potential for high-speed motion estimation owing to their ability to capture temporally rich information asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, while neuron models such... | ['Kaushik Roy', 'Adarsh Kumar Kosta'] | 2022-09-21 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 2.37092435e-01 -4.58817810e-01 2.86560923e-01 1.17253780e-01
-2.72013843e-01 -3.82477462e-01 5.09834111e-01 -9.43923071e-02
-9.50235486e-01 8.44384432e-01 -6.59596473e-02 3.36891264e-02
-7.29213282e-02 -7.65916586e-01 -1.02533209e+00 -7.89367080e-01
-1.36533603e-01 -1.10801579e-02 6.06290400e-01 5.77486493... | [8.659826278686523, -1.111173391342163] |
69cb9999-787d-4af7-a76c-1d57fca62fb3 | is-an-affine-constraint-needed-for-affine-1 | 2005.03888 | null | https://arxiv.org/abs/2005.03888v1 | https://arxiv.org/pdf/2005.03888v1.pdf | Is an Affine Constraint Needed for Affine Subspace Clustering? | Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as motion segmentation, face and digit clustering. In face clustering, the subspaces are linear and subspace clustering methods can be applied dir... | ['Rene Vidal', 'Chun-Guang Li', 'Chong You', 'Daniel P. Robinson'] | 2020-05-08 | is-an-affine-constraint-needed-for-affine | http://openaccess.thecvf.com/content_ICCV_2019/html/You_Is_an_Affine_Constraint_Needed_for_Affine_Subspace_Clustering_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/You_Is_an_Affine_Constraint_Needed_for_Affine_Subspace_Clustering_ICCV_2019_paper.pdf | iccv-2019-10 | ['motion-segmentation', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [-8.52854364e-03 -6.97145537e-02 -2.41468266e-01 -1.39551461e-01
-2.62933731e-01 -1.15877104e+00 4.62799817e-01 -3.45323712e-01
-4.03400697e-02 1.39212817e-01 3.47977370e-01 -3.10741156e-01
-3.41156214e-01 -2.25344121e-01 -5.04478574e-01 -1.18869638e+00
-9.80803892e-02 5.39893746e-01 8.49261209e-02 9.50328857... | [7.698473930358887, 4.428025722503662] |
054e08df-83ac-4874-9f44-d71c3d0d93e9 | a-domain-generalization-approach-for-out-of | 2208.09656 | null | https://arxiv.org/abs/2208.09656v2 | https://arxiv.org/pdf/2208.09656v2.pdf | A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks | Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases. As of late, they have also established themselves in the biomedical and healthcare domains, where they have shown a lot of promise, but have not yet achieved widespread adoption. This is in part... | ['Christos Diou', 'Aristotelis Ballas'] | 2022-08-20 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.30881685e-01 -1.55086949e-01 1.46372437e-01 -6.26636744e-01
-7.09540546e-01 -5.89153767e-01 2.89421022e-01 3.12087327e-01
-4.79244232e-01 7.62698591e-01 2.64539510e-01 -3.81941289e-01
-2.89603949e-01 -6.34570360e-01 -6.52597725e-01 -5.43973446e-01
-2.59270042e-01 5.58520138e-01 -2.18547275e-03 -1.38273790... | [14.140724182128906, 3.3064939975738525] |
8e9610d6-b5db-4871-aa38-867ae584c03f | accurate-3d-body-shape-regression-using-1 | 2206.07036 | null | https://arxiv.org/abs/2206.07036v1 | https://arxiv.org/pdf/2206.07036v1.pdf | Accurate 3D Body Shape Regression using Metric and Semantic Attributes | While methods that regress 3D human meshes from images have progressed rapidly, the estimated body shapes often do not capture the true human shape. This is problematic since, for many applications, accurate body shape is as important as pose. The key reason that body shape accuracy lags pose accuracy is the lack of da... | ['Michael J. Black', 'Dimitrios Tzionas', 'Siyu Tang', 'Chun-Hao P. Huang', 'Lea Muller', 'Vasileios Choutas'] | 2022-06-14 | accurate-3d-body-shape-regression-using | http://openaccess.thecvf.com//content/CVPR2022/html/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [-1.98941022e-01 3.82100716e-02 -2.66760916e-01 -6.10875785e-01
-5.74037135e-01 -5.41615427e-01 1.90135449e-01 3.09373848e-02
-2.41566300e-01 5.06195962e-01 3.66735011e-01 2.39249691e-01
3.80513519e-01 -8.86515081e-01 -1.00949907e+00 -2.58428395e-01
1.23475224e-01 1.11263394e+00 -2.47890875e-01 -4.29644704... | [7.034684181213379, -1.1295650005340576] |
47bc1094-7cbf-4afb-a806-eb73827eab5d | beyond-a-video-frame-interpolator-a-space | 2203.09771 | null | https://arxiv.org/abs/2203.09771v1 | https://arxiv.org/pdf/2203.09771v1.pdf | Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image Transition | Video frame interpolation (VFI) aims to improve the temporal resolution of a video sequence. Most of the existing deep learning based VFI methods adopt off-the-shelf optical flow algorithms to estimate the bidirectional flows and interpolate the missing frames accordingly. Though having achieved a great success, these ... | ['Lei Zhang', 'Xiansheng Hua', 'Xuansong Xie', 'Peiran Ren', 'Tao Yang'] | 2022-03-18 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 2.1206641e-01 -3.1658822e-01 -2.7041703e-01 -2.1281865e-01
-4.2040938e-01 -5.4278755e-01 6.6486669e-01 -4.6651021e-01
-2.1300414e-01 9.6559668e-01 -1.5577232e-02 -3.5550061e-01
2.7634520e-02 -6.3150179e-01 -8.9635628e-01 -7.4231678e-01
1.5182588e-01 8.8818334e-02 9.9578813e-02 -5.7287909e-02
2.0438276e-01... | [10.81678581237793, -1.2436861991882324] |
263fe3c2-e7f3-461d-a29a-cb394840f45e | causal-feature-engineering-of-price | 2306.08157 | null | https://arxiv.org/abs/2306.08157v1 | https://arxiv.org/pdf/2306.08157v1.pdf | Causal Feature Engineering of Price Directions of Cryptocurrencies using Dynamic Bayesian Networks | Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. The popularity is partly due to their unique specifications originating from blockchain-related characteristics such as privacy, decentralisation, and untraceability. Despite their growing popularity, cryptocurrencies ... | ['Mong Shan Ee', 'Dhananjay Thiruvady', 'Asef Nazari', 'Rasoul Amirzadeh'] | 2023-06-13 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-6.83996081e-01 -3.36091936e-01 -4.81873631e-01 -2.55243410e-03
-3.34625155e-01 -8.10353160e-01 1.00601375e+00 5.15079796e-02
-1.85581848e-01 6.18088484e-01 1.71517387e-01 -6.77951753e-01
-3.85874540e-01 -7.69930840e-01 -3.68417263e-01 -9.75979030e-01
-2.76618302e-01 3.00884604e-01 -1.23221822e-01 -1.26968890... | [4.702696800231934, 4.1601881980896] |
855883a1-8abe-4d27-ba2d-1b317203fb8b | multi-label-learning-to-rank-through-multi | 2207.03060 | null | https://arxiv.org/abs/2207.03060v2 | https://arxiv.org/pdf/2207.03060v2.pdf | Multi-Label Learning to Rank through Multi-Objective Optimization | Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are often noisy measurements of human behavior, e.g., product rating for product search. The coarse measure... | ['Michinari Momma', 'Deqiang Meng', 'Yetian Chen', 'Chaosheng Dong', 'Debabrata Mahapatra'] | 2022-07-07 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.49109641e-01 -4.03359503e-01 -8.02272797e-01 -6.93423927e-01
-1.05549884e+00 -6.85936511e-01 3.02518904e-01 3.63776714e-01
-2.59926289e-01 4.10650283e-01 2.02872366e-01 -1.95836887e-01
-8.01678598e-01 -4.79160696e-01 -4.38160509e-01 -5.68062246e-01
3.08617562e-01 5.54741144e-01 -3.91402662e-01 -2.43718579... | [9.992096900939941, 5.429073810577393] |
8cefe614-d18a-41f1-9455-c8372a7d07fe | lingyi-medical-conversational-question | 2204.09220 | null | https://arxiv.org/abs/2204.09220v1 | https://arxiv.org/pdf/2204.09220v1.pdf | LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs | The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", which is designed as a pipeline framework ... | ['Jun Zhao', 'Shutao Li', 'Bin Sun', 'Kang Liu', 'Shizhu He', 'Yixuan Weng', 'Bin Li', 'Fei Xia'] | 2022-04-20 | null | null | null | null | ['multi-modal-knowledge-graph', 'entity-disambiguation'] | ['knowledge-base', 'natural-language-processing'] | [-3.88928175e-01 5.42428970e-01 -1.29898533e-01 -1.22501120e-01
-9.42919374e-01 -2.77482092e-01 2.61779666e-01 4.58374619e-01
-1.22198246e-01 7.91431725e-01 8.92639816e-01 -7.26553738e-01
-5.49181938e-01 -9.68336642e-01 5.64741008e-02 -4.48100865e-01
1.70400739e-02 1.01674259e+00 -3.72482948e-02 -5.82210600... | [8.72169017791748, 8.588464736938477] |
328f5c26-28db-4f5f-9370-d0560bd0e279 | dssl-deep-surroundings-person-separation | 2109.05534 | null | https://arxiv.org/abs/2109.05534v1 | https://arxiv.org/pdf/2109.05534v1.pdf | DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval | Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality. Nevertheless, due to the complexity of high-dimensional data, the unconstrained mapping paradigms are not ab... | ['Gang Hua', 'Fangqiang Hu', 'Tian Wang', 'Jing Jin', 'Xili Wan', 'Yifeng Li', 'Zijie Wang', 'Aichun Zhu'] | 2021-09-12 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [-1.92113072e-01 -7.61568666e-01 -8.44381079e-02 -3.67574960e-01
-9.38083768e-01 -3.50230545e-01 8.88203681e-01 1.86511263e-01
-6.42745376e-01 5.73729694e-01 4.03190106e-01 3.00114512e-01
-5.00561178e-01 -5.94286323e-01 -1.43749312e-01 -9.33724701e-01
2.67124683e-01 5.77315748e-01 -1.42712981e-01 -1.73420236... | [14.5577392578125, 0.7993079423904419] |
d76d4da8-2ca2-41fb-977a-c48f45139007 | a-bayesian-traction-force-microscopy-method | 2005.01377 | null | https://arxiv.org/abs/2005.01377v1 | https://arxiv.org/pdf/2005.01377v1.pdf | A Bayesian traction force microscopy method with automated denoising in a user-friendly software package | Adherent biological cells generate traction forces on a substrate that play a central role for migration, mechanosensing, differentiation, and collective behavior. The established method for quantifying this cell-substrate interaction is traction force microscopy (TFM). In spite of recent advancements, inference of the... | ['Benedikt Sabass', 'Gerhard Gompper', 'Yunfei Huang'] | 2020-05-04 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 4.87081140e-01 -5.17260730e-01 6.75621778e-02 -1.10212630e-02
-7.23118126e-01 -3.57117236e-01 2.95739353e-01 1.90272704e-01
-6.64625943e-01 1.20221531e+00 -1.28528759e-01 -6.33339956e-02
-2.64621317e-01 -7.10459411e-01 -5.83789945e-01 -1.17697203e+00
2.69717067e-01 4.73929375e-01 6.24114037e-01 2.13505194... | [13.58382797241211, -3.0546648502349854] |
6dddd182-fa7d-4c47-b788-542d603a1e4e | meg-decoding-across-subjects | 1404.4175 | null | http://arxiv.org/abs/1404.4175v1 | http://arxiv.org/pdf/1404.4175v1.pdf | MEG Decoding Across Subjects | Brain decoding is a data analysis paradigm for neuroimaging experiments that
is based on predicting the stimulus presented to the subject from the
concurrent brain activity. In order to make inference at the group level, a
straightforward but sometimes unsuccessful approach is to train a classifier on
the trials of a g... | ['Emanuele Olivetti', 'Seyed Mostafa Kia', 'Paolo Avesani'] | 2014-04-16 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.35508478e-01 -1.57945037e-01 5.43246746e-01 -6.15877628e-01
-6.31089866e-01 -2.97880977e-01 7.19063461e-01 -1.83342788e-02
-5.37257552e-01 9.67171371e-01 -2.65315585e-02 -2.39856392e-01
-4.40506846e-01 -3.01715642e-01 -6.95608199e-01 -9.52859163e-01
-1.69897318e-01 2.95992583e-01 3.63800488e-02 1.32104417... | [12.859176635742188, 3.4131217002868652] |
e3b15347-fae1-4742-bdf8-57bee902fde5 | an-algorithm-for-the-visualization-of | 1903.03254 | null | http://arxiv.org/abs/1903.03254v1 | http://arxiv.org/pdf/1903.03254v1.pdf | An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves | Within the last years, the classification of variable stars with Machine
Learning has become a mainstream area of research. Recently, visualization of
time series is attracting more attention in data science as a tool to visually
help scientists to recognize significant patterns in complex dynamics. Within
the Machine ... | ['Márcio Catelán', 'Christian Pieringer', 'Karim Pichara', 'Pavlos Protopapas'] | 2019-03-08 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [-3.12890679e-01 -4.75758702e-01 -3.54244933e-02 -7.55994692e-02
2.94727325e-01 -7.88194180e-01 8.53113711e-01 6.70546889e-01
-4.41772789e-02 1.63273141e-01 2.02894900e-02 -3.28758419e-01
-3.02475363e-01 -7.43922830e-01 -3.68148625e-01 -8.15665364e-01
-3.90453428e-01 1.80908933e-01 2.95540661e-01 -5.29792249... | [7.8597412109375, 4.464378833770752] |
71f87e17-f25d-4eb7-9abd-dcd63bae82e6 | the-devil-is-in-the-wrongly-classified | 2302.04002 | null | https://arxiv.org/abs/2302.04002v1 | https://arxiv.org/pdf/2302.04002v1.pdf | The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition | Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. ... | ['Qifeng Chen', 'Shaojie Shen', 'Deli Zhao', 'Yingya Zhang', 'Yixuan Pei', 'Shiwei Zhang', 'Di Luan', 'Jun Cen'] | 2023-02-08 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.90523759e-02 -2.11598426e-01 -3.68709147e-01 -4.46476132e-01
-9.07130659e-01 -6.23870432e-01 2.38520816e-01 1.47125907e-02
-1.28352985e-01 7.36405194e-01 -3.17950338e-01 -2.48176008e-01
-2.30894938e-01 -7.00323999e-01 -7.36869633e-01 -6.13349915e-01
1.30252391e-01 6.50840044e-01 3.00620735e-01 -1.96444422... | [9.615994453430176, 2.964062213897705] |
291c1466-2330-42ed-8b99-f83c1f383692 | sequence-to-sequence-singing-voice-synthesis | 2010.12024 | null | https://arxiv.org/abs/2010.12024v2 | https://arxiv.org/pdf/2010.12024v2.pdf | Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Loss | The neural network (NN) based singing voice synthesis (SVS) systems require sufficient data to train well and are prone to over-fitting due to data scarcity. However, we often encounter data limitation problem in building SVS systems because of high data acquisition and annotation costs. In this work, we propose a Perc... | ['Qin Jin', 'Yuekai Zhang', 'Nan Huo', 'Shuai Guo', 'Jiatong Shi'] | 2020-10-22 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.67699873e-01 -2.86179394e-01 1.89947486e-01 -1.57127514e-01
-8.45240653e-01 -4.00052547e-01 -2.34931260e-01 -4.79471594e-01
-1.52604580e-01 4.31957871e-01 3.81635487e-01 -1.98228002e-01
1.60701126e-01 -2.40792498e-01 -4.07168239e-01 -7.34995544e-01
1.82871297e-01 -2.24032298e-01 4.42988835e-02 -2.54597336... | [15.529327392578125, 6.168840408325195] |
c9a584a1-cf64-4d0e-9e9a-f3255d998050 | towards-flexible-blind-jpeg-artifacts-removal | 2109.14573 | null | https://arxiv.org/abs/2109.14573v1 | https://arxiv.org/pdf/2109.14573v1.pdf | Towards Flexible Blind JPEG Artifacts Removal | Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage. However, existing deep blind methods usually directly reconstruct the image without predicting the quality factor, thus lacking t... | ['Radu Timofte', 'Kai Zhang', 'Jiaxi Jiang'] | 2021-09-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-deblocking', 'jpeg-artifact-correction', 'image-compression-artifact-reduction', 'image-forensics', 'jpeg-artifact-removal'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.42857531e-01 -4.09872621e-01 -7.19987275e-03 -3.58508080e-01
-5.80244899e-01 -4.61014628e-01 2.83689409e-01 -4.14715827e-01
-2.57426709e-01 4.32626665e-01 4.00176823e-01 -2.20266119e-01
8.61919150e-02 -4.70882803e-01 -7.38234103e-01 -7.21077442e-01
3.52226198e-01 -2.12501392e-01 1.19024612e-01 -1.95348918... | [11.31329345703125, -1.9816181659698486] |
cb7f9780-083c-4b74-9bb2-bb287a1cd031 | efficient-failure-pattern-identification-of | 2306.00760 | null | https://arxiv.org/abs/2306.00760v1 | https://arxiv.org/pdf/2306.00760v1.pdf | Efficient Failure Pattern Identification of Predictive Algorithms | Given a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this problem, we propose a human-machine collaborative framework that consists of a team of human annotators and a sequential recommendation algorith... | ['Viet Anh Nguyen', 'Bao Nguyen'] | 2023-06-01 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 4.17985082e-01 3.46164793e-01 -4.35636699e-01 -5.46678901e-01
-8.60068798e-01 -6.41507208e-01 4.30620015e-01 1.46447301e-01
-4.83851343e-01 7.04524040e-01 -1.37230143e-01 -3.45799297e-01
-2.10387334e-01 -6.83307528e-01 -4.77017969e-01 -8.51065993e-01
7.81188831e-02 8.39246094e-01 9.09207091e-02 6.28368258... | [9.162514686584473, 4.143935680389404] |
3074d8bf-8a15-415b-8350-70e9e74864a6 | actions-speak-louder-than-listening | 2110.12855 | null | https://arxiv.org/abs/2110.12855v1 | https://arxiv.org/pdf/2110.12855v1.pdf | Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience | The subjective evaluation of music generation techniques has been mostly done with questionnaire-based listening tests while ignoring the perspectives from music composition, arrangement, and soundtrack editing. In this paper, we propose an editing test to evaluate users' editing experience of music generation models i... | ['Li Su', 'Yuh-Ming Chiu', 'Meng-Hsuan Wu', 'Wei-Tsung Lu'] | 2021-10-25 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.47118646e-01 -7.58338422e-02 1.67290136e-01 -3.16609405e-02
-4.19497341e-01 -7.00692952e-01 4.33800101e-01 -3.70981872e-01
-2.75142521e-01 4.28574443e-01 3.71407568e-01 4.12538126e-02
-4.06425714e-01 -8.14204097e-01 -3.78254354e-01 -4.11075652e-01
3.78870338e-01 2.37800092e-01 -5.83089478e-02 -4.13918644... | [15.97943115234375, 5.5085649490356445] |
22f32bba-ea72-48bb-9cb8-72d6649ffbc0 | rendezvous-in-time-an-attention-based | 2211.16963 | null | https://arxiv.org/abs/2211.16963v2 | https://arxiv.org/pdf/2211.16963v2.pdf | Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition | One of the recent advances in surgical AI is the recognition of surgical activities as triplets of (instrument, verb, target). Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single frame features. Exploiting the temporal cues from earlier fr... | ['Nicolas Padoy', 'Didier Mutter', 'Chinedu Innocent Nwoye', 'Saurav Sharma'] | 2022-11-30 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 3.42761904e-01 1.55242622e-01 -6.35095596e-01 -1.02231748e-01
-6.95647001e-01 -3.27273279e-01 7.05021262e-01 9.36821327e-02
-3.45765680e-01 3.15530658e-01 8.97011161e-01 -2.25676283e-01
-5.03339648e-01 -1.14305340e-01 -6.06823027e-01 -7.35995054e-01
-4.43729639e-01 1.55212671e-01 -9.77480486e-02 -3.36183876... | [14.093084335327148, -3.364804983139038] |
084ac661-477c-45f7-9f5e-5b5527f67214 | aging-with-grace-lifelong-model-editing-with | 2211.11031 | null | https://arxiv.org/abs/2211.11031v4 | https://arxiv.org/pdf/2211.11031v4.pdf | Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adapters | Deployed models decay over time due to shifting inputs, changing user needs, or emergent knowledge gaps. When harmful behaviors are identified, targeted edits are required. However, current model editors, which adjust specific behaviors of pre-trained models, degrade model performance over multiple edits. We propose GR... | ['Marzyeh Ghassemi', 'Yoon Kim', 'Hamid Palangi', 'Swami Sankaranarayanan', 'Thomas Hartvigsen'] | 2022-11-20 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 1.87846825e-01 2.60284364e-01 -6.91711083e-02 -3.32034588e-01
-4.88778442e-01 -8.01191211e-01 4.46624339e-01 1.81362525e-01
-1.38044342e-01 4.96764690e-01 3.92039195e-02 -2.93742716e-01
-8.28789026e-02 -4.91748780e-01 -9.14844036e-01 -2.07409635e-01
1.19362459e-01 6.69066966e-01 3.50329489e-01 -1.97620079... | [8.327953338623047, 7.800725936889648] |
47ad007d-a11e-45cc-b51a-64c243438483 | automatic-procurement-fraud-detection-with | 2304.10105 | null | https://arxiv.org/abs/2304.10105v1 | https://arxiv.org/pdf/2304.10105v1.pdf | Automatic Procurement Fraud Detection with Machine Learning | Although procurement fraud is always a critical problem in almost every free market, audit departments still have a strong reliance on reporting from informed sources when detecting them. With our generous cooperator, SF Express, sharing the access to the database related with procurements took place from 2015 to 2017 ... | ['Tong Qiu', 'Jin Bai'] | 2023-04-20 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-4.32352781e-01 -1.61719859e-01 -2.66799033e-01 -4.22051221e-01
-8.51282120e-01 -6.53041780e-01 1.19305238e-01 2.98922658e-01
-4.05866414e-01 5.85995853e-01 2.62340784e-01 -8.07512283e-01
5.25719970e-02 -1.00603414e+00 -4.83509213e-01 -3.76510888e-01
3.99613902e-02 4.42890555e-01 -4.30069268e-01 1.06519209... | [7.369262218475342, 5.785840034484863] |
a59ebb50-f8ad-4c01-bcfd-d7235ae47347 | natural-language-descriptions-for-human | null | null | https://aclanthology.org/W17-3512 | https://aclanthology.org/W17-3512.pdf | Natural Language Descriptions for Human Activities in Video Streams | There has been continuous growth in the volume and ubiquity of video material. It has become essential to define video semantics in order to aid the searchability and retrieval of this data. We present a framework that produces textual descriptions of video, based on the visual semantic content. Detected action classes... | ['Nouf Alharbi', 'Yoshihiko Gotoh'] | 2017-09-01 | null | null | null | ws-2017-9 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.86889964e-01 -1.54647514e-01 3.52288485e-02 -3.77062172e-01
-4.48143393e-01 -9.22983170e-01 9.20337141e-01 5.56840658e-01
-3.77261251e-01 5.71829140e-01 6.02299094e-01 7.81423226e-02
-2.74076909e-01 -5.82068801e-01 -3.98543596e-01 -3.44922006e-01
-2.42846191e-01 1.52294502e-01 4.61618990e-01 -2.59328797... | [10.53429889678955, 0.6772084832191467] |
1b261519-cd7f-4a1e-9715-c67799507c2b | different-games-in-dialogue-combining | 2307.02087 | null | https://arxiv.org/abs/2307.02087v1 | https://arxiv.org/pdf/2307.02087v1.pdf | Different Games in Dialogue: Combining character and conversational types in strategic choice | In this paper, we show that investigating the interaction of conversational type (often known as language game or speech genre) with the character types of the interlocutors is worthwhile. We present a method of calculating the decision making process for selecting dialogue moves that combines character type and conver... | ['Alafate Abulimiti'] | 2023-07-05 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-3.44774812e-01 2.61309355e-01 -1.08851627e-01 -1.80173755e-01
-4.77313809e-02 -8.72543514e-01 8.94647717e-01 8.52649659e-03
-3.82618368e-01 8.44923735e-01 6.45698786e-01 -5.49739420e-01
-1.93573415e-01 -7.72337377e-01 1.38460696e-01 -5.54653823e-01
-1.13009550e-01 5.89669526e-01 2.85842508e-01 -7.92823851... | [12.957453727722168, 7.888294696807861] |
df0956bd-1bb7-4da1-bee9-ec3bb5bf793b | learning-to-detect-and-segment-for-open | 2212.12130 | null | https://arxiv.org/abs/2212.12130v5 | https://arxiv.org/pdf/2212.12130v5.pdf | Learning to Detect and Segment for Open Vocabulary Object Detection | Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask... | ['Nan Li', 'Tao Wang'] | 2022-12-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.37549162e-01 3.56685340e-01 -1.32292256e-01 -3.61545324e-01
-4.00792956e-01 -5.16129971e-01 4.96582359e-01 2.92033833e-02
-5.98016918e-01 2.89791167e-01 -2.07648680e-01 -1.30954981e-01
2.24190861e-01 -9.07114208e-01 -8.01036537e-01 -6.74138069e-01
6.98411539e-02 5.95442951e-01 9.10956085e-01 -1.75224811... | [9.504388809204102, 1.384193778038025] |
74ce0d9e-6872-4335-854d-271b4fb44ae8 | hierarchical-multi-grained-generative-model | 2009.08474 | null | https://arxiv.org/abs/2009.08474v2 | https://arxiv.org/pdf/2009.08474v2.pdf | Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis | This paper proposes a hierarchical generative model with a multi-grained latent variable to synthesize expressive speech. In recent years, fine-grained latent variables are introduced into the text-to-speech synthesis that enable the fine control of the prosody and speaking styles of synthesized speech. However, the na... | ['Yoshihiko Nankaku', 'Keiichiro Oura', 'Yukiya Hono', 'Kei Hashimoto', 'Kei Sawada', 'Keiichi Tokuda', 'Kazuna Tsuboi'] | 2020-09-17 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-7.33371601e-02 6.59571439e-02 -2.55000710e-01 -4.23971951e-01
-5.63033462e-01 -3.23568761e-01 7.63775170e-01 -6.00525856e-01
-1.80572886e-02 7.37976074e-01 6.25831425e-01 2.14143693e-01
3.91240194e-02 -9.50709164e-01 -5.35226285e-01 -1.06954038e+00
7.00351059e-01 5.35368025e-01 5.07658161e-02 -2.13380039... | [14.979374885559082, 6.538337707519531] |
6c7892dc-e24b-4d93-8f3e-c0e930182172 | edinburgh-at-semeval-2022-task-1-jointly | null | null | https://aclanthology.org/2022.semeval-1.8 | https://aclanthology.org/2022.semeval-1.8.pdf | Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions | This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs... | ['Zheng Zhao', 'Pinzhen Chen'] | null | null | null | null | semeval-naacl-2022-7 | ['definition-extraction', 'definition-modelling', 'reverse-dictionary'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 8.60837027e-02 1.30778048e-02 -5.70014536e-01 -1.55938491e-01
-1.09005868e+00 -9.53198373e-01 1.05400836e+00 1.10726796e-01
-1.01514888e+00 1.10535884e+00 4.84207839e-01 -4.25279260e-01
-1.62592292e-01 -2.64729857e-01 -4.27420855e-01 -3.99078876e-01
1.28788486e-01 9.79738712e-01 -1.77354351e-01 -6.09219491... | [10.629664421081543, 10.14803695678711] |
f6ba2ddb-5bf0-40c8-bdc6-b139c4be801f | localized-sparse-incomplete-multi-view | 2208.02998 | null | https://arxiv.org/abs/2208.02998v3 | https://arxiv.org/pdf/2208.02998v3.pdf | Localized Sparse Incomplete Multi-view Clustering | Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data ... | ['Yong Xu', 'Chao Huang', 'Jie Wen', 'Zhihao Wu', 'Chengliang Liu'] | 2022-08-05 | null | null | null | null | ['incomplete-multi-view-clustering', 'multi-view-learning'] | ['computer-vision', 'computer-vision'] | [-2.68596917e-01 -2.55739033e-01 -3.49258393e-01 -2.25716129e-01
-5.93728542e-01 -3.39920074e-01 1.87489286e-01 -1.59029528e-01
1.12708166e-01 2.94627517e-01 4.45312858e-01 2.25505933e-01
-4.17116582e-01 -5.12151182e-01 -4.54739302e-01 -9.67732608e-01
4.10706282e-01 3.21927100e-01 -4.41831537e-02 -5.04239351... | [8.268892288208008, 4.628434658050537] |
0558c59d-4cb2-4b7d-83a8-ca6359e4efb0 | zooming-slowmo-an-efficient-one-stage | 2104.07473 | null | https://arxiv.org/abs/2104.07473v1 | https://arxiv.org/pdf/2104.07473v1.pdf | Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution | In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LFR) video sequence. A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR).... | ['Chenliang Xu', 'Jan P. Allebach', 'Yun Fu', 'Yulun Zhang', 'Yapeng Tian', 'Xiaoyu Xiang'] | 2021-04-15 | null | null | null | null | ['space-time-video-super-resolution'] | ['computer-vision'] | [ 3.07917476e-01 -2.93398827e-01 -2.38463998e-01 -3.11016589e-01
-1.08336043e+00 -8.01963732e-02 4.29018706e-01 -8.17473114e-01
-3.34017992e-01 9.24395919e-01 2.30011746e-01 -5.12914322e-02
2.53881156e-01 -7.25112081e-01 -1.02270758e+00 -6.01887703e-01
1.12012066e-01 -1.02758266e-01 4.39934254e-01 -1.37005910... | [11.024508476257324, -1.9010776281356812] |
2c0a2ed7-1601-4ebb-bfd3-fdf60a943d4b | target-aware-dual-adversarial-learning-and-a | 2203.16220 | null | https://arxiv.org/abs/2203.16220v1 | https://arxiv.org/pdf/2203.16220v1.pdf | Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection | This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. Th... | ['Zhongxuan Luo', 'Wei Zhong', 'Risheng Liu', 'Guanyao Wu', 'Zhanbo Huang', 'Xin Fan', 'JinYuan Liu'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Target-Aware_Dual_Adversarial_Learning_and_a_Multi-Scenario_Multi-Modality_Benchmark_To_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Target-Aware_Dual_Adversarial_Learning_and_a_Multi-Scenario_Multi-Modality_Benchmark_To_CVPR_2022_paper.pdf | cvpr-2022-1 | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 6.77923977e-01 -3.07897598e-01 7.83783570e-03 2.34300286e-01
-1.00819635e+00 -7.66776681e-01 8.41268778e-01 -4.21571165e-01
-1.86535954e-01 7.17659593e-01 9.48880017e-02 -3.10972393e-01
3.64050083e-02 -8.22071910e-01 -8.97839904e-01 -1.09494543e+00
2.95196265e-01 -1.62251070e-01 2.22753710e-03 -3.96711349... | [10.553218841552734, -1.8063297271728516] |
49eb9566-a8dd-4b47-9d8e-b51a1050a745 | a-comprehensive-review-of-state-of-the-art | 2306.06371 | null | https://arxiv.org/abs/2306.06371v1 | https://arxiv.org/pdf/2306.06371v1.pdf | A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text | Java Code Generation consists in generating automatically Java code from a Natural Language Text. This NLP task helps in increasing programmers' productivity by providing them with immediate solutions to the simplest and most repetitive tasks. Code generation is a challenging task because of the hard syntactic rules an... | ['El Hassane Ettifouri', 'Walid Dahhane', 'El Mehdi Chouham', 'Mahaman Sanoussi Yahaya Alassan', 'Jessica López Espejel'] | 2023-06-10 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 0.27807578 0.23901276 -0.19062023 -0.28764376 -0.6810898 -0.4714248
0.5293673 -0.06078082 -0.08536384 0.643237 0.19738136 -0.5916145
0.19249372 -0.8819507 -0.7370718 -0.28542742 0.07921038 0.23899995
-0.06258906 -0.3602984 0.57489365 -0.04204089 -1.9577035 0.6041265
1.0458692 0.7002428 0.643... | [7.7344207763671875, 7.802042484283447] |
b3a8a164-26f8-40d9-a952-c4b2ff08779f | the-second-cross-lingual-challenge-on | null | null | https://aclanthology.org/W19-3709 | https://aclanthology.org/W19-3709.pdf | The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages | We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2... | ['Josef Steinberger', "Pavel P{\\v{r}}ib{\\'a}{\\v{n}}", "Micha{\\l} Marci{\\'n}czuk", 'Lidia Pivovarova', 'Laska Laskova', 'Jakub Piskorski', 'Roman Yangarber'] | 2019-08-01 | null | null | null | ws-2019-8 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-4.23244327e-01 1.38738111e-01 -4.55849946e-01 -4.83268321e-01
-1.25154710e+00 -1.15291405e+00 7.06305861e-01 5.25595188e-01
-1.05524063e+00 1.11420834e+00 5.33943594e-01 -8.90854001e-02
4.02612269e-01 -3.12762618e-01 -4.91957217e-01 3.27115133e-02
-7.90071711e-02 7.46499896e-01 2.61414677e-01 -1.02831930... | [9.807068824768066, 9.69946575164795] |
c18f0034-10f0-43cf-ab02-add24b1bcf89 | sqa3d-situated-question-answering-in-3d | 2210.07474 | null | https://arxiv.org/abs/2210.07474v5 | https://arxiv.org/pdf/2210.07474v5.pdf | SQA3D: Situated Question Answering in 3D Scenes | We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its ... | ['Siyuan Huang', 'Song-Chun Zhu', 'Yitao Liang', 'Qing Li', 'Zilong Zheng', 'Silong Yong', 'Xiaojian Ma'] | 2022-10-14 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 4.02654111e-02 2.74145603e-01 4.46961612e-01 -5.12497663e-01
-6.36874020e-01 -9.52985644e-01 7.54535735e-01 2.17769772e-01
-4.06455457e-01 4.41978604e-01 5.94663739e-01 -5.74041903e-01
-3.92678201e-01 -9.26266730e-01 -7.12454617e-01 -3.89636904e-01
-2.20415778e-02 6.53318346e-01 2.26014152e-01 -7.60907710... | [4.39261531829834, 0.5965470671653748] |
a5f5f3a0-5365-4413-9f07-7d00f38f5d10 | safety-enhanced-uav-path-planning-with | 2104.10033 | null | https://arxiv.org/abs/2104.10033v1 | https://arxiv.org/pdf/2104.10033v1.pdf | Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization | This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimizati... | ['Quang Phuc Ha', 'Manh Duong Phung'] | 2021-04-13 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-2.66219825e-02 -4.30357248e-01 3.10729057e-01 3.66102487e-01
3.81644607e-01 -7.56244361e-01 3.74127150e-01 3.00817400e-01
-4.95412260e-01 9.84766066e-01 -5.41367650e-01 -3.73994142e-01
-7.89974511e-01 -1.06774485e+00 -1.09027185e-01 -8.38245153e-01
-3.62823248e-01 2.85161763e-01 3.89181346e-01 -8.44768226... | [5.566152095794678, 3.2583911418914795] |
78a2b98b-f024-4afc-855a-9b42553e3c41 | ppmf-a-patient-based-predictive-modeling | 1704.07499 | null | http://arxiv.org/abs/1704.07499v1 | http://arxiv.org/pdf/1704.07499v1.pdf | PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction | To date, developing a good model for early intensive care unit (ICU)
mortality prediction is still challenging. This paper presents a patient based
predictive modeling framework (PPMF) to improve the performance of ICU
mortality prediction using data collected during the first 48 hours of ICU
admission. PPMF consists o... | ['Samir AbdelRahman', 'Olivia R. Liu Sheng', 'Mohammad Amin Morid'] | 2017-04-25 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-2.84137636e-01 -7.22597718e-01 -8.64415988e-02 -3.72691900e-01
-4.31195647e-01 -4.28406931e-02 6.50293902e-02 8.22781682e-01
-1.48625478e-01 8.24075937e-01 4.98527974e-01 -4.40429688e-01
-6.49753869e-01 -5.99584818e-01 1.50670901e-01 -6.47942245e-01
-3.75126988e-01 6.42159939e-01 7.11493939e-02 -1.61754098... | [8.016798973083496, 6.138937473297119] |
388f6281-2182-44aa-a816-39d4656246b9 | habitat-synthetic-scenes-dataset-hssd-200-an | 2306.11290 | null | https://arxiv.org/abs/2306.11290v2 | https://arxiv.org/pdf/2306.11290v2.pdf | Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation | We contribute the Habitat Synthetic Scene Dataset, a dataset of 211 high-quality 3D scenes, and use it to test navigation agent generalization to realistic 3D environments. Our dataset represents real interiors and contains a diverse set of 18,656 models of real-world objects. We investigate the impact of synthetic 3D ... | ['Brennan Shacklett', 'Manolis Savva', 'Angel X. Chang', 'Eric Undersander', 'Alexander Clegg', 'Dhruv Batra', 'Sanjay Haresh', 'Hanxiao Jiang', 'Yongsen Mao', 'Mukul Khanna'] | 2023-06-20 | null | null | null | null | ['navigate'] | ['reasoning'] | [-5.37751205e-02 -2.32634485e-01 3.50060016e-01 -3.93156886e-01
-3.49962175e-01 -8.90238166e-01 8.96455765e-01 -1.22695707e-01
-8.73371422e-01 4.99008268e-01 2.84893513e-01 -2.74843901e-01
-1.28610246e-03 -7.39476264e-01 -1.04354846e+00 -3.49135816e-01
-7.49110281e-01 7.87510514e-01 3.90563607e-01 -5.27756751... | [4.569311618804932, 0.6762605309486389] |
a47c3723-2627-4222-8ee4-481c548f9a08 | constraining-latent-space-to-improve-deep | null | null | https://openreview.net/forum?id=PcBVjfeLODY | https://openreview.net/pdf?id=PcBVjfeLODY | Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks | The representation of products in a e-commerce marketplace is a key aspect to be exploited when trying to improve the user experience on the site. A well known example of the importance of a good product representation are tasks such as product search or product recommendation. There is however a multitude of lesser k... | ['Rafael Carrascosa', 'Cristian Cardellino'] | 2021-01-01 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.51858419e-01 -2.65483633e-02 -2.44818494e-01 -6.46970749e-01
-2.54141331e-01 -5.91487229e-01 7.46740520e-01 5.65502286e-01
-3.12222779e-01 5.84745035e-02 5.80779731e-01 -2.83469230e-01
-3.71576518e-01 -9.79742348e-01 -6.40036166e-01 -5.69637060e-01
-1.94210127e-01 2.90401518e-01 1.07639313e-01 -4.98478830... | [10.038718223571777, 5.963131427764893] |
40d0917b-ee23-429d-898f-03695f5ef942 | deep-fitting-degree-scoring-network-for | 1904.12681 | null | https://arxiv.org/abs/1904.12681v2 | https://arxiv.org/pdf/1904.12681v2.pdf | Deep Fitting Degree Scoring Network for Monocular 3D Object Detection | In this paper, we propose to learn a deep fitting degree scoring network for monocular 3D object detection, which aims to score fitting degree between proposals and object conclusively. Different from most existing monocular frameworks which use tight constraint to get 3D location, our approach achieves high-precision ... | ['Jie zhou', 'Lijie Liu', 'Jiwen Lu', 'Qi Tian', 'Chunjing Xu'] | 2019-04-26 | deep-fitting-degree-scoring-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Deep_Fitting_Degree_Scoring_Network_for_Monocular_3D_Object_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Deep_Fitting_Degree_Scoring_Network_for_Monocular_3D_Object_Detection_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-4.26120609e-01 -4.33328003e-02 -4.59893048e-02 -3.42886358e-01
-4.29114312e-01 -6.30601943e-01 3.66572171e-01 -2.92631060e-01
-3.97178680e-01 -4.40683262e-03 -1.35279208e-01 -2.27377862e-01
4.56161425e-02 -4.04168576e-01 -7.99039304e-01 -3.74416053e-01
1.42776713e-01 6.81073248e-01 5.38122773e-01 2.81173229... | [7.702159881591797, -2.562589645385742] |
08b0bd93-69a9-4ba9-be7e-983775bf327c | using-integer-linear-programming-in-concept | null | null | https://aclanthology.org/P13-2100 | https://aclanthology.org/P13-2100.pdf | Using Integer Linear Programming in Concept-to-Text Generation to Produce More Compact Texts | null | ['Ion Androutsopoulos', 'Gerasimos Lampouras'] | 2013-08-01 | null | null | null | acl-2013-8 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.21423864364624, 3.6275627613067627] |
01947d6e-33e0-4950-8b66-95f67f4b77bd | evaluation-of-post-processing-algorithms-for | 1906.06909 | null | https://arxiv.org/abs/1906.06909v2 | https://arxiv.org/pdf/1906.06909v2.pdf | Evaluation of post-processing algorithms for polyphonic sound event detection | Sound event detection (SED) aims at identifying audio events (audio tagging task) in recordings and then locating them temporally (localization task). This last task ends with the segmentation of the frame-level class predictions, that determines the onsets and offsets of the audio events. Yet, this step is often overl... | ['Thomas Pellegrini', 'Leo Cances', 'Patrice Guyot'] | 2019-06-17 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.94576585e-01 -1.97536200e-01 2.01548189e-01 -1.08842343e-01
-1.38224185e+00 -6.30735993e-01 4.78479564e-01 6.12573385e-01
-6.93263888e-01 2.52883673e-01 1.76802486e-01 -5.10326698e-02
-2.18997598e-01 -2.43341431e-01 -5.41550100e-01 -6.95650160e-01
-3.68602663e-01 6.41920604e-04 6.33257985e-01 5.05507052... | [15.205720901489258, 5.129100322723389] |
13c45be3-b08e-4f8b-920e-3ac9689a9870 | audio-driven-talking-face-video-generation | 2002.10137 | null | https://arxiv.org/abs/2002.10137v2 | https://arxiv.org/pdf/2002.10137v2.pdf | Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose | Real-world talking faces often accompany with natural head movement. However, most existing talking face video generation methods only consider facial animation with fixed head pose. In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a ver... | ['Yong-Jin Liu', 'Hujun Bao', 'Ran Yi', 'Juyong Zhang', 'Zipeng Ye'] | 2020-02-24 | null | null | null | null | ['3d-face-animation'] | ['computer-vision'] | [ 3.47104226e-03 1.49182513e-01 1.18521973e-01 -4.97891307e-01
-7.98770845e-01 -3.32056940e-01 4.22255278e-01 -9.74506199e-01
1.13649480e-01 6.94190621e-01 3.15912962e-01 3.44428658e-01
5.26286840e-01 -5.39403737e-01 -9.80830014e-01 -8.56823087e-01
1.91212118e-01 2.64755934e-01 -2.29994711e-02 -2.23571464... | [13.194454193115234, -0.41204938292503357] |
70f7d2dd-df6c-45db-9728-b8f400b23b06 | city-scale-scene-change-detection-using-point | 2103.14314 | null | https://arxiv.org/abs/2103.14314v1 | https://arxiv.org/pdf/2103.14314v1.pdf | City-scale Scene Change Detection using Point Clouds | We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times. We first generate 3D point clouds for each traversal from the images and approximate GNSS/INS readings using Structure-from-Motion (SfM). A direct comparison of the... | ['Gim Hee Lee', 'Zi Jian Yew'] | 2021-03-26 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [-1.04359994e-02 -5.07642031e-01 3.97488654e-01 -5.12394369e-01
-5.50857723e-01 -6.96724772e-01 9.43286121e-01 1.53963670e-01
-4.47991937e-01 2.85829633e-01 -3.08680683e-01 -2.75114249e-03
1.98809087e-01 -1.08374786e+00 -9.76478517e-01 -4.74028140e-01
-9.97964945e-03 7.56207943e-01 6.78150773e-01 -3.94453138... | [7.885989189147949, -2.4055893421173096] |
8e6952b2-93cb-45e6-91fa-587b9faaf84b | at-the-crossroads-of-epidemiology-and-biology | 2301.12975 | null | https://arxiv.org/abs/2301.12975v1 | https://arxiv.org/pdf/2301.12975v1.pdf | At the crossroads of epidemiology and biology: bridging the gap between SARS-CoV-2 viral strain properties and epidemic wave characteristics | The COVID-19 pandemic has given rise to numerous articles from different scientific fields (epidemiology, virology, immunology, airflow physics...) without any effort to link these different insights. In this review, we aim to establish relationships between epidemiological data and the characteristics of the virus str... | ['Bruno Andreotti', 'Jacques Haiech', 'Alice Lebreton', 'Florian Poydenot'] | 2023-01-30 | null | null | null | null | ['epidemiology', 'virology'] | ['medical', 'miscellaneous'] | [ 2.37873301e-01 -4.21697557e-01 3.86268735e-01 3.95707339e-01
3.20602626e-01 -6.37453556e-01 4.60971147e-01 5.40245056e-01
-6.81433856e-01 9.66438890e-01 -1.26121014e-01 -4.86524522e-01
-3.14527094e-01 -8.43809724e-01 -5.81535280e-01 -8.99787009e-01
-4.66753304e-01 8.11360180e-01 7.04429671e-02 -3.23300213... | [5.861335754394531, 4.3941240310668945] |
385193be-db67-48b0-8a99-f2801fe78d16 | retrieving-signals-with-deep-complex | null | null | https://openreview.net/forum?id=H1x22Xn5Ur | https://openreview.net/pdf?id=H1x22Xn5Ur | Retrieving Signals with Deep Complex Extractors | Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations. Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequ... | ['Christopher J Pal', 'Negar Rostamzadeh', 'Jonathan Binas', 'Mirco Ravanelli', 'Ying Zhang', 'Ousmane Dia', 'Olexa Bilaniuk', 'Chiheb Trabelsi'] | 2019-09-14 | null | null | null | neurips-workshop-deep-invers-2019-12 | ['audio-source-separation'] | ['audio'] | [ 6.09352231e-01 -2.79826134e-01 2.82114625e-01 -2.64160693e-01
-8.51685703e-01 -5.73004901e-01 6.48344696e-01 1.37208357e-01
-5.04332602e-01 6.32323742e-01 9.45589468e-02 -1.39086898e-02
-6.32111907e-01 -6.14923239e-01 -5.65198243e-01 -6.97938144e-01
-6.24567151e-01 -4.38156754e-01 -2.62423642e-02 -3.30797076... | [15.354494094848633, 5.5365753173828125] |
0b9f6197-168b-4ee6-9c0f-1e0f779dc5b3 | a-unified-multimodal-de-and-re-coupling | 2211.09146 | null | https://arxiv.org/abs/2211.09146v2 | https://arxiv.org/pdf/2211.09146v2.pdf | A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition | Motion recognition is a promising direction in computer vision, but the training of video classification models is much harder than images due to insufficient data and considerable parameters. To get around this, some works strive to explore multimodal cues from RGB-D data. Although improving motion recognition to some... | ['Fan Wang', 'Yanyan Liang', 'Jun Wan', 'Pichao Wang', 'Benjia Zhou'] | 2022-11-16 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 9.46251526e-02 -6.04318082e-01 -5.61092734e-01 -3.25202420e-02
-6.94728851e-01 -2.81740218e-01 4.99383688e-01 -4.90673274e-01
-4.08199161e-01 4.51028913e-01 3.55770409e-01 -2.58712866e-03
-1.44555330e-01 -4.49429542e-01 -5.41379094e-01 -1.06705022e+00
2.18384430e-01 -2.77747720e-01 1.41646624e-01 -2.29289368... | [8.677762985229492, 0.6292085647583008] |
9c427187-eda9-4bb4-8d43-57bf1a594233 | steex-steering-counterfactual-explanations | 2111.09094 | null | https://arxiv.org/abs/2111.09094v3 | https://arxiv.org/pdf/2111.09094v3.pdf | STEEX: Steering Counterfactual Explanations with Semantics | As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decision mechanisms of a t... | ['Matthieu Cord', 'Patrick Pérez', 'Mickaël Chen', 'Hédi Ben-Younes', 'Éloi Zablocki', 'Paul Jacob'] | 2021-11-17 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.24602240e-01 6.54116392e-01 -3.87349755e-01 -5.97812772e-01
-5.50192475e-01 -3.83434325e-01 8.37951779e-01 -3.46873701e-01
1.73719928e-01 9.64875400e-01 4.97011960e-01 -3.97684366e-01
-2.79307049e-02 -6.63990676e-01 -1.07375574e+00 -4.38212335e-01
2.08087236e-01 -2.09149416e-03 -4.38746840e-01 1.34672085... | [8.93741226196289, 5.367528915405273] |
b688fc4f-9f7a-4cf4-ab0b-c398561b6016 | heuristic-search-for-structural-constraints | 1711.02823 | null | http://arxiv.org/abs/1711.02823v1 | http://arxiv.org/pdf/1711.02823v1.pdf | Heuristic Search for Structural Constraints in Data Association | The research on multi-object tracking (MOT) is essentially to solve for the
data association assignment, the core of which is to design the association
cost as discriminative as possible. Generally speaking, the match ambiguities
caused by similar appearances of objects and the moving cameras make the data
association ... | ['Xiao Zhou', 'Fei Wang', 'Peilin Jiang'] | 2017-11-08 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-5.78136109e-02 -5.79025269e-01 -8.50367546e-02 -1.44231677e-01
-4.58230257e-01 -6.43095434e-01 2.87643731e-01 -1.40831172e-01
-3.28744233e-01 5.85186422e-01 -2.45751292e-01 2.49250188e-01
-3.62326354e-01 -4.18414742e-01 -7.06564963e-01 -8.62751901e-01
7.44480938e-02 6.31348789e-01 7.64463782e-01 1.51687428... | [6.490484714508057, -1.998177409172058] |
2e9b23ab-bb89-45cf-9563-bb4c8931b89e | when-does-aggregating-multiple-skills-with | 2305.14007 | null | https://arxiv.org/abs/2305.14007v1 | https://arxiv.org/pdf/2305.14007v1.pdf | When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP | Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work -- sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show th... | ['Markus Leippold', 'Mrinmaya Sachan', 'Qian Wang', 'Zhijing Jin', 'Jingwei Ni'] | 2023-05-23 | null | null | null | null | ['sentiment-analysis', 'open-question'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.81994495e-02 -2.53056102e-02 -1.38905019e-01 -2.01216325e-01
-7.00013697e-01 -6.93379700e-01 2.31832623e-01 3.29166234e-01
-5.66106617e-01 8.25589538e-01 5.26603699e-01 -3.20116878e-01
-6.73985600e-01 -3.26122642e-01 -6.08254731e-01 -4.01404589e-01
3.60475719e-01 5.27527988e-01 -1.38961807e-01 -2.88394541... | [9.919711112976074, 7.477308750152588] |
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