paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8e6acb4b-82a1-4ab5-8467-36753c280a40 | conditional-density-estimation-tools-in | 1908.11523 | null | https://arxiv.org/abs/1908.11523v2 | https://arxiv.org/pdf/1908.11523v2.pdf | Conditional Density Estimation Tools in Python and R with Applications to Photometric Redshifts and Likelihood-Free Cosmological Inference | It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference approaches, which are beginning to emerge as a tool for cosmological analysis, require a characterization... | ['Alex I. Malz', 'Rafael Izbicki', 'Niccolò Dalmasso', 'Taylor Pospisil', 'Ann B. Lee', 'Peter E. Freeman'] | 2019-08-30 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [-3.58895034e-01 -7.25516304e-02 6.45282269e-01 -7.14109123e-01
-1.03684807e+00 -6.32727206e-01 4.92915213e-01 -5.49569093e-02
-2.91056156e-01 8.51931214e-01 -3.80590260e-01 -8.14457655e-01
-5.45202971e-01 -1.09708846e+00 -7.17676222e-01 -9.71122861e-01
-1.16067313e-01 5.94548047e-01 4.47346359e-01 2.29054436... | [7.199306964874268, 3.542888879776001] |
d0bc3eed-44d2-4d70-9545-96bbbbee67e8 | literature-on-hand-gesture-recognition-using | 2207.00329 | null | https://arxiv.org/abs/2207.00329v1 | https://arxiv.org/pdf/2207.00329v1.pdf | Literature on Hand GESTURE Recognition using Graph based methods | Skeleton based recognition systems are gaining popularity and machine learning models focusing on points or joints in a skeleton have proved to be computationally effective and application in many areas like Robotics. It is easy to track points and thereby preserving spatial and temporal information, which plays an imp... | ['Varun Sharma', 'Neha Baranwal'] | 2022-07-01 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.43972382e-02 -2.02485561e-01 -1.17107652e-01 -2.71371827e-02
-1.99046999e-01 -5.68269268e-02 5.92925906e-01 3.76071960e-01
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-3.32946956e-01 5.38947940e-01 7.03099191e-01 -1.16704278... | [7.871546745300293, 0.32571202516555786] |
29607222-5992-4de6-8129-e6952dd49072 | emergence-of-hierarchical-reference-systems | 2203.13176 | null | https://arxiv.org/abs/2203.13176v2 | https://arxiv.org/pdf/2203.13176v2.pdf | Emergence of hierarchical reference systems in multi-agent communication | In natural language, referencing objects at different levels of specificity is a fundamental pragmatic mechanism for efficient communication in context. We develop a novel communication game, the hierarchical reference game, to study the emergence of such reference systems in artificial agents. We consider a simplified... | ['Elia Bruni', 'Marko Duda', 'Xenia Ohmer'] | 2022-03-24 | null | https://aclanthology.org/2022.coling-1.501 | https://aclanthology.org/2022.coling-1.501.pdf | coling-2022-10 | ['novel-concepts'] | ['reasoning'] | [ 1.27923295e-01 3.67246866e-01 3.38950008e-01 -1.15957133e-01
1.73019290e-01 -9.24174011e-01 1.16043746e+00 2.96383739e-01
-4.46698695e-01 7.09095895e-01 2.36490473e-01 1.44045427e-01
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-2.71081805e-01 4.71763581e-01 4.30776387e-01 -8.46945524... | [4.331182956695557, 1.4431917667388916] |
14cb8801-07a7-4ed6-a996-4b0302a75169 | ssp-self-supervised-post-training-for | 2307.00569 | null | https://arxiv.org/abs/2307.00569v1 | https://arxiv.org/pdf/2307.00569v1.pdf | SSP: Self-Supervised Post-training for Conversational Search | Conversational search has been regarded as the next-generation search paradigm. Constrained by data scarcity, most existing methods distill the well-trained ad-hoc retriever to the conversational retriever. However, these methods, which usually initialize parameters by query reformulation to discover contextualized dep... | ['Rui Yan', 'Ji-Rong Wen', 'Zhao Cao', 'Xiaolong Wu', 'Shen Gao', 'Quan Tu'] | 2023-07-02 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-6.90948665e-02 3.25692385e-01 -3.41475397e-01 -4.94836897e-01
-8.75585020e-01 -5.63477993e-01 9.90836918e-01 -2.47911006e-01
-3.56518984e-01 8.41042399e-01 6.88668787e-01 -3.62308919e-01
7.25375414e-02 -5.42614818e-01 -3.05176854e-01 -3.42708081e-01
4.73014832e-01 8.75663400e-01 2.70519674e-01 -8.38607728... | [12.17314338684082, 7.847255706787109] |
55b2cbc5-d201-4da6-8340-4281cb151978 | quality-question-answering-with-long-input | 2112.08608 | null | https://arxiv.org/abs/2112.08608v2 | https://arxiv.org/pdf/2112.08608v2.pdf | QuALITY: Question Answering with Long Input Texts, Yes! | To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process. Unlike in prior work with passages, our questions are written ... | ['Samuel R. Bowman', 'He He', 'Jana Thompson', 'Johnny Ma', 'Vishakh Padmakumar', 'Angelica Chen', 'Jason Phang', 'Nikita Nangia', 'Nitish Joshi', 'Alicia Parrish', 'Richard Yuanzhe Pang'] | 2021-12-16 | null | https://aclanthology.org/2022.naacl-main.391 | https://aclanthology.org/2022.naacl-main.391.pdf | naacl-2022-7 | ['multiple-choice-qa'] | ['natural-language-processing'] | [-3.97959277e-02 3.45293522e-01 3.35544348e-02 -2.55306005e-01
-1.83811164e+00 -1.20727432e+00 4.93379176e-01 6.07706547e-01
-8.39256704e-01 1.23346162e+00 7.83747673e-01 -6.37463689e-01
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3.02228987e-01 6.00150347e-01 3.54749829e-01 -4.64486957... | [11.380522727966309, 8.101594924926758] |
1d4e69b7-aa13-4668-a0b3-dec1a5e06537 | big-earth-data-and-machine-learning-for | 2211.12584 | null | https://arxiv.org/abs/2211.12584v1 | https://arxiv.org/pdf/2211.12584v1.pdf | Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture | Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation da... | ['Vasileios Sitokonstantinou'] | 2022-11-22 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.02918917e-01 3.64066124e-01 -3.69981527e-01 -1.35388579e-02
2.52747741e-02 -7.06994832e-01 5.24743319e-01 7.45033324e-01
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-5.89401722e-01 -1.36883020e+00 -7.44085670e-01 -6.00953758e-01
-5.28906286e-01 2.60288537e-01 -4.60140891e-02 -7.76516497... | [9.487431526184082, -1.4811115264892578] |
bd6a8f19-fb63-4952-8f88-0f260291217f | evaluating-the-timing-and-magnitude-of | null | null | https://openreview.net/forum?id=4Gew0VrWfkx | https://openreview.net/pdf?id=4Gew0VrWfkx | Evaluating the timing and magnitude of semantic change in diachronic word embedding models | Recent studies have suggested that diachronic word embedding models are able to track the direction of changes in public perception. Building on these works, we evaluate the ability of diachronic word embedding models to accurately capture such changes both qualitatively and quantitatively, such as their timing and mag... | ['Anonymous'] | 2022-01-20 | null | null | null | acl-arr-january-2022-1 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-4.79052067e-01 9.71257538e-02 -4.26473826e-01 -1.29371464e-01
-3.03552955e-01 -8.04852724e-01 1.15182042e+00 9.30506885e-01
-7.14392960e-01 -3.73675935e-02 1.18964434e+00 -2.78707802e-01
-2.06227899e-01 -1.09654999e+00 -5.61153829e-01 -1.55520692e-01
6.79619312e-02 -1.24686420e-01 -1.63761407e-01 -5.75425625... | [10.133910179138184, 8.936760902404785] |
faa613ca-9497-4ea9-b35f-f0fc7179ed35 | efficient-ensemble-architecture-for | 2302.13376 | null | https://arxiv.org/abs/2302.13376v1 | https://arxiv.org/pdf/2302.13376v1.pdf | Efficient Ensemble Architecture for Multimodal Acoustic and Textual Embeddings in Punctuation Restoration using Time-Delay Neural Networks | Punctuation restoration plays an essential role in the post-processing procedure of automatic speech recognition, but model efficiency is a key requirement for this task. To that end, we present EfficientPunct, an ensemble method with a multimodal time-delay neural network that outperforms the current best model by 1.0... | ['Homayoon Beigi', 'Xing Yi Liu'] | 2023-02-26 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.15240315e-01 7.62238950e-02 2.10363299e-01 -3.29781026e-01
-1.09169257e+00 -5.86646378e-01 6.42372489e-01 5.99322431e-02
-8.63811016e-01 2.60316432e-01 6.86288357e-01 -6.00472093e-01
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9.79976580e-02 5.23290813e-01 7.22653493e-02 -1.15602493... | [14.429940223693848, 6.652266979217529] |
bd8bdb1e-a5ce-44e5-8942-1bef812ac9d6 | a-comparative-study-of-pre-trained-encoders-1 | 2204.04980 | null | https://arxiv.org/abs/2204.04980v1 | https://arxiv.org/pdf/2204.04980v1.pdf | A Comparative Study of Pre-trained Encoders for Low-Resource Named Entity Recognition | Pre-trained language models (PLM) are effective components of few-shot named entity recognition (NER) approaches when augmented with continued pre-training on task-specific out-of-domain data or fine-tuning on in-domain data. However, their performance in low-resource scenarios, where such data is not available, remain... | ['Leonhard Hennig', 'Christoph Alt', 'Arne Binder', 'Jonas Mikkelsen', 'Yuxuan Chen'] | 2022-04-11 | null | https://aclanthology.org/2022.repl4nlp-1.6 | https://aclanthology.org/2022.repl4nlp-1.6.pdf | repl4nlp-acl-2022-5 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 7.45608360e-02 1.70370508e-02 -1.34229332e-01 -4.40484554e-01
-9.91578698e-01 -5.98664463e-01 8.53226483e-01 1.27932221e-01
-1.22566617e+00 8.64494383e-01 6.90788686e-01 -1.34816647e-01
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-3.42740417e-02 6.04831219e-01 2.92932183e-01 -4.57931936... | [9.68366527557373, 9.358780860900879] |
8cc662ad-f626-48b6-8857-5b1c9207cc31 | implicit-models-latent-compression-intrinsic | 2210.09186 | null | https://arxiv.org/abs/2210.09186v6 | https://arxiv.org/pdf/2210.09186v6.pdf | Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection | The task of community detection, which aims to partition a network into clusters of nodes to summarize its large-scale structure, has spawned the development of many competing algorithms with varying objectives. Some community detection methods are inferential, explicitly deriving the clustering objective through a pro... | ['Alec Kirkley', 'Tiago P. Peixoto'] | 2022-10-17 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 4.71483886e-01 3.54357362e-01 -1.76146746e-01 -1.11317396e-01
-4.20012087e-01 -9.65956688e-01 8.56741607e-01 4.69608009e-01
-2.70221025e-01 6.78390563e-01 3.28779578e-01 -5.59462726e-01
-5.89877367e-01 -9.64601219e-01 -1.75522193e-01 -9.34204280e-01
-5.13133228e-01 1.03330278e+00 3.42296898e-01 1.33664757... | [6.964204788208008, 5.3193888664245605] |
2f964756-d8be-46bb-987a-11cd0f72d2fc | safety-aware-task-composition-for-discrete | 2306.17033 | null | https://arxiv.org/abs/2306.17033v1 | https://arxiv.org/pdf/2306.17033v1.pdf | Safety-Aware Task Composition for Discrete and Continuous Reinforcement Learning | Compositionality is a critical aspect of scalable system design. Reinforcement learning (RL) has recently shown substantial success in task learning, but has only recently begun to truly leverage composition. In this paper, we focus on Boolean composition of learned tasks as opposed to functional or sequential composit... | ['Zachary Serlin', 'Makai Mann', 'Kevin Leahy'] | 2023-06-29 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [ 2.69236416e-01 2.48556405e-01 -1.37142971e-01 -1.22040763e-01
-7.12147593e-01 -7.61504591e-01 1.06448770e+00 5.25046512e-02
-5.85994661e-01 1.11204433e+00 1.65015891e-01 -5.66919744e-01
-3.38733822e-01 -8.64562511e-01 -9.77892816e-01 -8.87246668e-01
-7.20021904e-01 3.82086456e-01 3.02684993e-01 -5.23022115... | [4.412973880767822, 2.025777578353882] |
e6822731-e084-4094-8366-60549584ef73 | automatic-exposure-compensation-for-multi | 1805.11211 | null | http://arxiv.org/abs/1805.11211v1 | http://arxiv.org/pdf/1805.11211v1.pdf | Automatic Exposure Compensation for Multi-Exposure Image Fusion | This paper proposes a novel luminance adjustment method based on automatic
exposure compensation for multi-exposure image fusion. Multi-exposure image
fusion is a method to produce images without saturation regions, by using
photos with different exposures. In conventional works, it has been pointed out
that the qualit... | ['Sayaka Shiota', 'Hitoshi Kiya', 'Yuma Kinoshita'] | 2018-05-29 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 5.72718024e-01 -4.58742797e-01 3.26260567e-01 -2.76656985e-01
-2.62965590e-01 -2.26085484e-01 4.42951769e-01 1.10431783e-01
-5.98560572e-01 6.45786405e-01 1.36879802e-01 3.30774188e-02
-3.35005999e-01 -8.01781952e-01 -2.37685785e-01 -7.71649480e-01
4.32527691e-01 -4.34234381e-01 1.12939782e-01 -3.46396446... | [10.9227933883667, -2.459397792816162] |
694c8dc5-607d-4f84-ad17-b8551de5b79d | interoperability-between-service-composition | null | null | https://aclanthology.org/I13-1145 | https://aclanthology.org/I13-1145.pdf | Interoperability between Service Composition and Processing Pipeline: Case Study on the Language Grid and UIMA | null | ['Toru Ishida', 'Trang Mai Xuan', 'Donghui Lin', 'Yohei Murakami'] | 2013-10-01 | interoperability-between-service-composition-1 | https://aclanthology.org/I13-1145 | https://aclanthology.org/I13-1145.pdf | ijcnlp-2013-10 | ['service-composition'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.214504718780518, 3.786614418029785] |
1552f3b0-9775-41c9-9714-9dd85c2e6ebd | highway-long-short-term-memory-rnns-for | 1510.08983 | null | http://arxiv.org/abs/1510.08983v2 | http://arxiv.org/pdf/1510.08983v2.pdf | Highway Long Short-Term Memory RNNs for Distant Speech Recognition | In this paper, we extend the deep long short-term memory (DLSTM) recurrent
neural networks by introducing gated direct connections between memory cells in
adjacent layers. These direct links, called highway connections, enable
unimpeded information flow across different layers and thus alleviate the
gradient vanishing ... | ['Dong Yu', 'Sanjeev Khudanpur', 'James Glass', 'Guoguo Chen', 'Yu Zhang', 'Kaisheng Yao'] | 2015-10-30 | null | null | null | null | ['distant-speech-recognition'] | ['speech'] | [ 1.14127181e-01 -6.40480667e-02 -1.39110208e-01 -3.14100325e-01
-7.88374782e-01 -1.60891622e-01 5.78808427e-01 -2.30372459e-01
-7.95365572e-01 9.19337988e-01 2.70375550e-01 -8.05887580e-01
5.69666147e-01 -6.63890183e-01 -8.53154242e-01 -6.78117096e-01
-1.20280758e-01 -1.60192028e-02 6.25654995e-01 -2.20344797... | [10.918557167053223, 6.326347351074219] |
62887cea-4c71-4275-8389-e4a3effec481 | monodvps-a-self-supervised-monocular-depth | 2210.07577 | null | https://arxiv.org/abs/2210.07577v1 | https://arxiv.org/pdf/2210.07577v1.pdf | MonoDVPS: A Self-Supervised Monocular Depth Estimation Approach to Depth-aware Video Panoptic Segmentation | Depth-aware video panoptic segmentation tackles the inverse projection problem of restoring panoptic 3D point clouds from video sequences, where the 3D points are augmented with semantic classes and temporally consistent instance identifiers. We propose a novel solution with a multi-task network that performs monocular... | ['Sergiu Nedevschi', 'Andra Petrovai'] | 2022-10-14 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 4.44399744e-01 -1.96559504e-01 -5.50898910e-01 -5.39084256e-01
-9.24334049e-01 -8.28768253e-01 5.02876520e-01 -6.69406712e-01
-2.83495516e-01 5.09851933e-01 -3.09054609e-02 -3.05434048e-01
8.17893073e-02 -9.32952881e-01 -9.69320893e-01 -9.24711287e-01
-1.02554962e-01 6.98071837e-01 3.77757370e-01 5.29433131... | [8.5147123336792, -2.0112040042877197] |
c3dfc4a6-c769-4a34-99f9-d823bd19b492 | a-multi-task-multi-stage-transitional | 2301.11749 | null | https://arxiv.org/abs/2301.11749v1 | https://arxiv.org/pdf/2301.11749v1.pdf | A Multi-task Multi-stage Transitional Training Framework for Neural Chat Translation | Neural chat translation (NCT) aims to translate a cross-lingual chat between speakers of different languages. Existing context-aware NMT models cannot achieve satisfactory performances due to the following inherent problems: 1) limited resources of annotated bilingual dialogues; 2) the neglect of modelling conversation... | ['Jinsong Su', 'Min Zhang', 'Hongji Wang', 'Jinan Xu', 'Jie zhou', 'Fandong Meng', 'Yunlong Liang', 'Chulun Zhou'] | 2023-01-27 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.23722249e-01 -9.99685898e-02 -9.45780426e-02 -4.39408422e-01
-1.05204618e+00 -4.29868579e-01 7.54319549e-01 -3.88677061e-01
-4.58717167e-01 7.91719794e-01 3.92104864e-01 -7.45688260e-01
4.29729760e-01 -3.04573685e-01 -3.38299870e-01 -5.03787875e-01
4.61365640e-01 8.01258266e-01 2.14376599e-01 -5.13868451... | [12.838099479675293, 8.272337913513184] |
83bba57c-3615-4646-8f06-58184dc91915 | inverse-reinforcement-learning-without | 2303.14623 | null | https://arxiv.org/abs/2303.14623v2 | https://arxiv.org/pdf/2303.14623v2.pdf | Inverse Reinforcement Learning without Reinforcement Learning | Inverse Reinforcement Learning (IRL) is a powerful set of techniques for imitation learning that aims to learn a reward function that rationalizes expert demonstrations. Unfortunately, traditional IRL methods suffer from a computational weakness: they require repeatedly solving a hard reinforcement learning (RL) proble... | ['Zhiwei Steven Wu', 'J. Andrew Bagnell', 'Sanjiban Choudhury', 'Gokul Swamy'] | 2023-03-26 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 1.02086999e-01 4.63238806e-01 -3.04387450e-01 2.42459789e-01
-8.85822296e-01 -7.50668764e-01 4.85063583e-01 -8.34891647e-02
-7.61113763e-01 1.05338097e+00 -1.36200055e-01 -7.38177419e-01
-2.64521062e-01 -5.08907735e-01 -9.72190619e-01 -8.01769793e-01
-1.87955007e-01 4.69956547e-01 -4.27659824e-02 -4.27597433... | [4.156332015991211, 1.8093974590301514] |
05329ac9-1d0c-427f-9ba5-1b08b13689b8 | efficient-and-scalable-high-order-portfolios | 2206.02412 | null | https://arxiv.org/abs/2206.02412v1 | https://arxiv.org/pdf/2206.02412v1.pdf | Efficient and Scalable High-Order Portfolios Design via Parametric Skew-t Distribution | Since Markowitz's mean-variance framework, optimizing a portfolio that maximizes the profit and minimizes the risk has been ubiquitous in the financial industry. Initially, profit and risk were measured by the first two moments of the portfolio's return, a.k.a. the mean and variance, which are sufficient to characteriz... | ['Daniel P. Palomar', 'Jiaxi Ying', 'Rui Zhou', 'Xiwen Wang'] | 2022-06-06 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.53856915e-01 -2.92401642e-01 -1.60840124e-01 -1.48630828e-01
-5.93275130e-01 -9.06465352e-01 2.12672696e-01 4.77011576e-02
-2.60051310e-01 7.79752016e-01 -2.25117698e-01 -6.54223919e-01
-7.28649616e-01 -8.49657595e-01 -4.69729930e-01 -7.74304867e-01
-2.04936951e-01 5.02774954e-01 -1.12997822e-01 1.72305107... | [5.0334014892578125, 3.9650909900665283] |
2664add4-4530-44fa-ae88-09541f17e82e | activation-regression-for-continuous-domain | 2204.07030 | null | https://arxiv.org/abs/2204.07030v1 | https://arxiv.org/pdf/2204.07030v1.pdf | Activation Regression for Continuous Domain Generalization with Applications to Crop Classification | Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem, demonstrating how models generalise better with appropriate ... | ['Bharath Hariharan', 'Kavita Bala', 'Bram Wallace', 'Samar Khanna'] | 2022-04-14 | null | null | null | null | ['crop-classification'] | ['miscellaneous'] | [ 1.62088171e-01 -2.05044121e-01 -2.79214889e-01 -4.93083388e-01
-7.26614654e-01 -1.03258014e+00 6.03302896e-01 -6.67079836e-02
-2.73404807e-01 9.15440202e-01 2.44950011e-01 -7.74946153e-01
-2.35138685e-01 -1.09827268e+00 -8.12907338e-01 -7.23893702e-01
-6.36990070e-01 6.74157068e-02 -1.42666057e-01 -3.79036367... | [9.438830375671387, -1.5778048038482666] |
08e7472b-f885-4481-baea-c3c10d6866af | a-frequency-domain-constraint-for-synthetic-x | 2105.06887 | null | https://arxiv.org/abs/2105.06887v2 | https://arxiv.org/pdf/2105.06887v2.pdf | A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution | Synthetic X-ray images are simulated X-ray images projected from CT data. High-quality synthetic X-ray images can facilitate various applications such as surgical image guidance systems and VR training simulations. However, it is difficult to produce high-quality arbitrary view synthetic X-ray images in real-time due t... | ['WonSook Lee', 'Jae Chul Koh', 'Qing Ma'] | 2021-05-14 | null | null | null | null | ['reference-based-super-resolution'] | ['computer-vision'] | [ 5.20811379e-01 1.63004562e-01 -3.98999751e-02 -2.86174953e-01
-1.44837904e+00 1.28341332e-01 3.18606228e-01 -4.77726668e-01
-1.24241106e-01 1.12733495e+00 3.79075766e-01 3.38394158e-02
-3.24607104e-01 -9.47542429e-01 -6.45283878e-01 -6.69070423e-01
1.54080361e-01 2.87508637e-01 3.29313099e-01 -4.54635561... | [13.547468185424805, -2.4770026206970215] |
fd0e06f2-f79e-4baf-b6bf-7394b552e823 | a-fast-and-accurate-unconstrained-face | 1408.1656 | null | https://arxiv.org/abs/1408.1656v3 | https://arxiv.org/pdf/1408.1656v3.pdf | A Fast and Accurate Unconstrained Face Detector | We propose a method to address challenges in unconstrained face detection, such as arbitrary pose variations and occlusions. First, a new image feature called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed as the difference to sum ratio between two pixel values, inspired by the Weber Fraction in... | ['Stan Z. Li', 'Anil K. Jain', 'Shengcai Liao'] | 2014-08-06 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-7.87587613e-02 -1.14075094e-01 -1.22745223e-01 -5.52116275e-01
-3.29354048e-01 -5.20563960e-01 3.32455724e-01 -5.95252454e-01
-4.39313024e-01 3.42686921e-01 -3.70899320e-01 6.74787164e-02
1.11731075e-01 -4.16581899e-01 -6.38337135e-01 -7.50993013e-01
-1.72116935e-01 9.74372923e-02 9.35856029e-02 1.82323575... | [13.361499786376953, 0.5574584603309631] |
f8277761-9077-4994-992a-3fe5e4513d7e | loosely-synchronized-search-for-multi-agent | 2103.04516 | null | https://arxiv.org/abs/2103.04516v2 | https://arxiv.org/pdf/2103.04516v2.pdf | Loosely Synchronized Search for Multi-agent Path Finding with Asynchronous Actions | Multi-agent path finding (MAPF) determines an ensemble of collision-free paths for multiple agents between their respective start and goal locations. Among the available MAPF planners for workspace modeled as a graph, A*-based approaches have been widely investigated due to their guarantees on completeness and solution... | ['Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren'] | 2021-03-08 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.07524008e-01 2.23204255e-01 -4.09094989e-02 2.41203278e-01
-1.82543397e-01 -8.71405423e-01 7.17503786e-01 4.48129982e-01
-5.35205960e-01 1.14929974e+00 -3.08797240e-01 -1.76653892e-01
-1.05412471e+00 -8.37695062e-01 -5.01457751e-01 -8.73608112e-01
-6.89827502e-01 7.73493528e-01 6.12949252e-01 -5.25592327... | [4.942138195037842, 1.7074941396713257] |
c43cce28-ec23-48b4-872b-365b6461a5f6 | a-matter-of-annotation-an-empirical-study-on | 2305.08752 | null | https://arxiv.org/abs/2305.08752v1 | https://arxiv.org/pdf/2305.08752v1.pdf | A Matter of Annotation: An Empirical Study on In Situ and Self-Recall Activity Annotations from Wearable Sensors | Research into the detection of human activities from wearable sensors is a highly active field, benefiting numerous applications, from ambulatory monitoring of healthcare patients via fitness coaching to streamlining manual work processes. We present an empirical study that compares 4 different commonly used annotation... | ['Kristof Van Laerhoven', 'Alexander Hoelzemann'] | 2023-05-15 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 3.39219958e-01 4.18986082e-01 -2.14999616e-01 -3.64092946e-01
-3.65811110e-01 -5.20414531e-01 3.32804680e-01 6.19035423e-01
-6.97210312e-01 7.41750419e-01 3.73993963e-01 1.78444311e-01
-2.01082230e-01 -5.03542900e-01 -3.64296764e-01 -5.97722471e-01
-4.70754318e-02 2.92510390e-01 7.47345909e-02 2.86324561... | [7.486680507659912, 0.8135167956352234] |
90a0a433-5d01-4337-9a39-fc47d043676e | topic-switch-adapted-japanese-dialogue-system | 2302.11280 | null | https://arxiv.org/abs/2302.11280v1 | https://arxiv.org/pdf/2302.11280v1.pdf | Topic-switch adapted Japanese Dialogue System based on PLATO-2 | Large-scale open-domain dialogue systems such as PLATO-2 have achieved state-of-the-art scores in both English and Chinese. However, little work explores whether such dialogue systems also work well in the Japanese language. In this work, we create a large-scale Japanese dialogue dataset, Dialogue-Graph, which contains... | ['Kazushi Ikeda', 'Gen Hattori', 'Kazunori Matsumoto', 'Yanan Wang', 'Jianming Wu', 'Donghuo Zeng'] | 2023-02-22 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-4.09413993e-01 4.01831388e-01 1.86109856e-01 -2.55587906e-01
-7.71652818e-01 -7.13409841e-01 7.31853783e-01 -1.61735684e-01
-3.28689665e-01 9.94009674e-01 5.22880375e-01 -2.14073405e-01
4.23950702e-01 -6.54496670e-01 2.56635398e-01 -4.13831055e-01
1.17744938e-01 6.91488087e-01 5.73010147e-01 -8.85594070... | [12.817649841308594, 8.081599235534668] |
fe0f4b3d-aadc-4edc-9367-3a299436e0a4 | all-in-one-exploring-unified-video-language | 2203.07303 | null | https://arxiv.org/abs/2203.07303v1 | https://arxiv.org/pdf/2203.07303v1.pdf | All in One: Exploring Unified Video-Language Pre-training | Mainstream Video-Language Pre-training models \cite{actbert,clipbert,violet} consist of three parts, a video encoder, a text encoder, and a video-text fusion Transformer. They pursue better performance via utilizing heavier unimodal encoders or multimodal fusion Transformers, resulting in increased parameters with lowe... | ['Mike Zheng Shou', 'XiaoHu Qie', 'Ying Shan', 'Jianping Wu', 'Guanyu Cai', 'Xudong Lin', 'Yuying Ge', 'Rui Yan', 'Yixiao Ge', 'Alex Jinpeng Wang'] | 2022-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_All_in_One_Exploring_Unified_Video-Language_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_All_in_One_Exploring_Unified_Video-Language_Pre-Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 2.53802180e-01 -8.06209445e-02 -3.18481863e-01 -2.93981224e-01
-1.11820018e+00 -6.77962244e-01 8.40941191e-01 -3.31121624e-01
-4.59063292e-01 3.94065917e-01 3.98328871e-01 -3.76427323e-01
5.89921251e-02 -3.14466476e-01 -9.89183247e-01 -7.13348210e-01
2.06499055e-01 1.33273318e-01 1.28990903e-01 -1.52326792... | [10.24433708190918, 0.9402483701705933] |
bf718834-7639-4f47-9abf-719a72d00483 | hybrid-life-integrating-biological-artificial | 2212.00285 | null | https://arxiv.org/abs/2212.00285v1 | https://arxiv.org/pdf/2212.00285v1.pdf | Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems | Artificial life is a research field studying what processes and properties define life, based on a multidisciplinary approach spanning the physical, natural and computational sciences. Artificial life aims to foster a comprehensive study of life beyond "life as we know it" and towards "life as it could be", with theore... | ['Keisuke Suzuki', 'Lana Sinapayen', 'Olaf Witkowski', 'Hiroyuki Iizuka', 'Manuel Baltieri'] | 2022-12-01 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-3.74325621e-03 2.48415500e-01 2.92060729e-02 5.99175453e-01
4.96038586e-01 -7.58694589e-01 1.06500375e+00 8.09238702e-02
1.75668932e-02 9.06406522e-01 9.28752944e-02 -4.50543202e-02
5.22528216e-02 -9.79165375e-01 -3.47388625e-01 -1.00527728e+00
-3.37740779e-01 2.48266816e-01 -1.66437790e-01 -8.63889456... | [5.605409145355225, 4.158647060394287] |
8bd70d5d-d335-40d1-80cc-27108c6fe5ba | iflyea-a-chinese-essay-assessment-system-with | null | null | https://aclanthology.org/2021.acl-demo.29 | https://aclanthology.org/2021.acl-demo.29.pdf | IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation | Automated Essay Assessment (AEA) aims to judge students{'} writing proficiency in an automatic way. This paper presents a Chinese AEA system IFlyEssayAssess (IFlyEA), targeting on evaluating essays written by native Chinese students from primary and junior schools. IFlyEA provides multi-level and multi-dimension analyt... | ['Ting Liu', 'Shijin Wang', 'Bo Zhu', 'Zhichao Sheng', 'Ruiji Fu', 'Wei Song', 'Xiao Hu', 'Jiefu Gong'] | 2021-08-01 | null | null | null | acl-2021-5 | ['review-generation'] | ['natural-language-processing'] | [-4.26151305e-01 8.89313295e-02 -1.95004120e-01 -1.07797449e-02
-8.21611226e-01 -9.92089093e-01 4.40939903e-01 5.50469279e-01
2.36496851e-02 5.17436206e-01 3.29683036e-01 -1.09874380e+00
-6.91682518e-01 -8.99651945e-01 1.00620359e-01 -2.53324687e-01
8.28678310e-01 4.14798796e-01 5.16551323e-02 -4.51784313... | [11.281232833862305, 9.248703002929688] |
2c3a46a4-4275-491d-a4c0-2ab3e6578bdc | information-screening-whilst-exploiting | 2305.11719 | null | https://arxiv.org/abs/2305.11719v2 | https://arxiv.org/pdf/2305.11719v2.pdf | Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling | Existing research on multimodal relation extraction (MRE) faces two co-existing challenges, internal-information over-utilization and external-information under-exploitation. To combat that, we propose a novel framework that simultaneously implements the idea of internal-information screening and external-information e... | ['Tat-Seng Chua', 'Lidong Bing', 'Yixin Cao', 'Hao Fei', 'Shengqiong Wu'] | 2023-05-19 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 3.58270764e-01 3.55654985e-01 -2.33139783e-01 -1.44481465e-01
-8.40959549e-01 -5.60881019e-01 6.48265302e-01 2.09757164e-01
-2.40910910e-02 3.05979997e-01 6.44149184e-01 -1.55809641e-01
-1.12473980e-01 -7.04741180e-01 -4.90556121e-01 -5.49562275e-01
1.36575893e-01 3.36461663e-01 1.99994147e-02 3.01373415... | [10.523110389709473, 1.3403679132461548] |
cc9a00b6-6de9-484b-b2b2-9ae0b552f52f | learning-synthetic-environments-for | 2101.09721 | null | https://arxiv.org/abs/2101.09721v3 | https://arxiv.org/pdf/2101.09721v3.pdf | Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies | This work explores learning agent-agnostic synthetic environments (SEs) for Reinforcement Learning. SEs act as a proxy for target environments and allow agents to be trained more efficiently than when directly trained on the target environment. We formulate this as a bi-level optimization problem and represent an SE as... | ['Frank Hutter', 'Thomas Nierhoff', 'Fabio Ferreira'] | 2021-01-24 | null | null | null | null | ['acrobot'] | ['playing-games'] | [ 8.39480013e-02 7.92896561e-03 3.10046375e-01 3.78581695e-02
-5.17391026e-01 -5.76322317e-01 8.23927343e-01 -9.25497040e-02
-1.24456501e+00 1.26933837e+00 -3.83884847e-01 3.53670679e-02
-1.08140029e-01 -7.65961349e-01 -1.03695464e+00 -9.06833887e-01
-4.88822877e-01 9.32151139e-01 5.42938948e-01 -7.06191063... | [4.120968818664551, 1.8131026029586792] |
992b41d9-ba1c-448a-b3c4-19796ceb46fd | gvdoc-graph-based-visual-document | 2305.17219 | null | https://arxiv.org/abs/2305.17219v1 | https://arxiv.org/pdf/2305.17219v1.pdf | GVdoc: Graph-based Visual Document Classification | The robustness of a model for real-world deployment is decided by how well it performs on unseen data and distinguishes between in-domain and out-of-domain samples. Visual document classifiers have shown impressive performance on in-distribution test sets. However, they tend to have a hard time correctly classifying an... | ['Ashish Verma', 'Catherine Finegan-Dollak', 'Mohammed J. Zaki', 'Fnu Mohbat'] | 2023-05-26 | null | null | null | null | ['graph-attention', 'document-classification'] | ['graphs', 'natural-language-processing'] | [ 2.96727661e-03 -2.46426746e-01 -3.32475275e-01 -3.34658831e-01
-7.01514781e-01 -1.11994004e+00 9.16240454e-01 3.73994827e-01
-2.66063539e-03 2.12674126e-01 -1.78057179e-01 -6.77575171e-01
-2.51748767e-02 -7.78970420e-01 -5.89577198e-01 -6.44729912e-01
-2.78886128e-02 1.08654225e+00 3.85832369e-01 1.34509085... | [11.50561237335205, 2.4737002849578857] |
c54a914f-b6bf-4a37-ae4e-72058e90fc06 | prompt-free-diffusion-taking-text-out-of-text | 2305.16223 | null | https://arxiv.org/abs/2305.16223v2 | https://arxiv.org/pdf/2305.16223v2.pdf | Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models | Text-to-image (T2I) research has grown explosively in the past year, owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet, one pain point persists: the text prompt engineering, and searching high-quality text prompts for customized results is more art than ... | ['Humphrey Shi', 'Irfan Essa', 'Gao Huang', 'Zhangyang Wang', 'Jiayi Guo', 'Xingqian Xu'] | 2023-05-25 | null | null | null | null | ['image-variation', 'conditional-text-to-image-synthesis', 'virtual-try-on', 'prompt-engineering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [ 5.71526349e-01 -9.28391470e-04 1.58230305e-01 -2.64769644e-01
-5.82511246e-01 -6.32264137e-01 8.93818438e-01 -2.18121901e-01
-3.14653426e-01 5.26394129e-01 2.73023069e-01 -4.57448065e-01
1.82566062e-01 -5.71420789e-01 -8.22924376e-01 -5.25585175e-01
4.13988978e-01 3.62907290e-01 2.51995862e-01 -2.46439293... | [11.306215286254883, -0.20923607051372528] |
0f803a30-6db0-49e6-a22e-299a25d128e4 | modar-using-motion-forecasting-for-3d-object-1 | 2306.03206 | null | https://arxiv.org/abs/2306.03206v1 | https://arxiv.org/pdf/2306.03206v1.pdf | MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences | Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded or distant object can be observed from different viewpoints or gets better visibility over time. However, the efficiency and effectiveness ... | ['Dragomir Anguelov', 'Chenxi Liu', 'Yin Zhou', 'Charles R. Qi', 'Yingwei Li'] | 2023-06-05 | modar-using-motion-forecasting-for-3d-object | http://openaccess.thecvf.com//content/CVPR2023/html/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-detection', 'motion-forecasting'] | ['computer-vision', 'computer-vision'] | [ 3.42990495e-02 -3.53397906e-01 -1.87143058e-01 -2.49898389e-01
-8.13010812e-01 -8.15395832e-01 7.64482021e-01 1.44529700e-01
-3.13795477e-01 2.17352346e-01 -2.21085161e-01 -1.35189891e-01
2.02537134e-01 -8.83232653e-01 -8.15658569e-01 -4.45170462e-01
-1.49832338e-01 8.51114392e-01 9.80090499e-01 -1.59585193... | [7.1104736328125, -2.3084030151367188] |
61ff0c7c-956e-41d3-9780-28ef313f32c8 | batman-bilateral-attention-transformer-in | 2208.01159 | null | https://arxiv.org/abs/2208.01159v4 | https://arxiv.org/pdf/2208.01159v4.pdf | BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation | Video Object Segmentation (VOS) is fundamental to video understanding. Transformer-based methods show significant performance improvement on semi-supervised VOS. However, existing work faces challenges segmenting visually similar objects in close proximity of each other. In this paper, we propose a novel Bilateral Atte... | ['Mei Chen', 'Li Fuxin', 'Gaurav Mittal', 'Jialin Yuan', 'Ye Yu'] | 2022-08-01 | null | null | null | null | ['semi-supervised-video-object-segmentation', 'visual-object-tracking'] | ['computer-vision', 'computer-vision'] | [-3.68303806e-02 -3.64294410e-01 -3.56024384e-01 -2.99196303e-01
-5.85746825e-01 -5.69069684e-01 1.42455176e-01 -4.11881864e-01
-3.05099428e-01 5.91080964e-01 1.11931518e-01 -3.24461609e-02
3.22766870e-01 -3.80984098e-01 -9.12084877e-01 -4.01843995e-01
1.97933704e-01 1.50677204e-01 7.33699143e-01 1.09091140... | [9.166528701782227, -0.17317698895931244] |
60bdd847-89b3-4add-899c-007c8ce845cc | robust-fair-clustering-a-novel-fairness | 2210.01953 | null | https://arxiv.org/abs/2210.01953v3 | https://arxiv.org/pdf/2210.01953v3.pdf | Robust Fair Clustering: A Novel Fairness Attack and Defense Framework | Clustering algorithms are widely used in many societal resource allocation applications, such as loan approvals and candidate recruitment, among others, and hence, biased or unfair model outputs can adversely impact individuals that rely on these applications. To this end, many fair clustering approaches have been rece... | ['Hongfu Liu', 'Prasant Mohapatra', 'Peizhao Li', 'Anshuman Chhabra'] | 2022-10-04 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 4.03207447e-03 6.93708062e-02 -1.59050018e-01 -3.77401441e-01
-4.34872538e-01 -9.57017720e-01 6.43024266e-01 1.42315865e-01
-1.47071674e-01 8.63800585e-01 -2.43686140e-01 -4.59088147e-01
-1.99278846e-01 -9.53757882e-01 -4.40855205e-01 -5.62299550e-01
-5.12738302e-02 3.94899011e-01 -9.28386226e-02 -4.66266498... | [7.3006720542907715, 5.2504496574401855] |
ebda8f31-9758-436f-b52d-899224ec62c4 | very-fast-approximate-counterfactual | 2303.02883 | null | https://arxiv.org/abs/2303.02883v1 | https://arxiv.org/pdf/2303.02883v1.pdf | Very fast, approximate counterfactual explanations for decision forests | We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest. This requires solving an optimization problem to find the closest input instance to a given instance for which the forest outputs a desired value. Finding an exact solution has a cost that is exponential... | ['Suryabhan Singh Hada', 'Miguel Á. Carreira-Perpiñán'] | 2023-03-06 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.92726952e-01 4.08331096e-01 -6.83973253e-01 -3.14394325e-01
-5.66380918e-01 -8.03201497e-01 5.74040949e-01 9.70282704e-02
-3.73399854e-01 1.13435912e+00 1.20750725e-01 -7.96337843e-01
-4.60590124e-01 -1.28049958e+00 -6.71723127e-01 -4.98179972e-01
-1.91685766e-01 9.06109571e-01 2.26236746e-01 1.88337252... | [8.616141319274902, 5.459455490112305] |
50aa13a2-c2fa-4de6-b74f-cf4c6ea77fd1 | clip-guided-prototype-modulating-for-few-shot | 2303.02982 | null | https://arxiv.org/abs/2303.02982v1 | https://arxiv.org/pdf/2303.02982v1.pdf | CLIP-guided Prototype Modulating for Few-shot Action Recognition | Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to ... | ['Nong Sang', 'Deli Zhao', 'Yingya Zhang', 'Changxin Gao', 'Jun Cen', 'Shiwei Zhang', 'Xiang Wang'] | 2023-03-06 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 3.51049900e-01 -4.96875972e-01 -4.49567705e-01 -2.44492859e-01
-1.13799739e+00 -3.27498496e-01 6.76650107e-01 -2.92524129e-01
-1.87602982e-01 4.42698151e-01 4.92144972e-01 2.45862827e-01
2.74465904e-02 -1.85749456e-01 -7.87429690e-01 -8.36094975e-01
2.59209871e-01 1.20343693e-01 2.50989527e-01 -2.08185732... | [9.398029327392578, 0.8979353308677673] |
3472eedb-e3d6-49c1-8c8e-dfb14f32b4a7 | sampling-theorems-for-unsupervised-learning | 2203.12513 | null | https://arxiv.org/abs/2203.12513v2 | https://arxiv.org/pdf/2203.12513v2.pdf | Sensing Theorems for Unsupervised Learning in Linear Inverse Problems | Solving an ill-posed linear inverse problem requires knowledge about the underlying signal model. In many applications, this model is a priori unknown and has to be learned from data. However, it is impossible to learn the model using observations obtained via a single incomplete measurement operator, as there is no in... | ['Mike Davies', 'Dongdong Chen', 'Julián Tachella'] | 2022-03-23 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 7.65922189e-01 2.84389496e-01 -8.64795595e-03 -1.89440906e-01
-4.07658935e-01 -6.08989120e-01 2.05411807e-01 -1.86360911e-01
-2.82019556e-01 5.88041127e-01 2.60749876e-01 -2.36709550e-01
-6.18291378e-01 -4.88741964e-01 -8.10150623e-01 -9.70484853e-01
9.91070569e-02 5.14644325e-01 -4.85235125e-01 -3.81300360... | [7.0635480880737305, 4.446740627288818] |
07063ac9-e330-48d4-a0ad-95a6d040f74e | 3dfill-reference-guided-image-inpainting-by | 2211.04831 | null | https://arxiv.org/abs/2211.04831v1 | https://arxiv.org/pdf/2211.04831v1.pdf | 3DFill:Reference-guided Image Inpainting by Self-supervised 3D Image Alignment | Most existing image inpainting algorithms are based on a single view, struggling with large holes or the holes containing complicated scenes. Some reference-guided algorithms fill the hole by referring to another viewpoint image and use 2D image alignment. Due to the camera imaging process, simple 2D transformation is ... | ['Long Zeng', 'Xinyu Zhang', 'Hailong Ma', 'Xinyuan Zhao', 'Liang Zhao'] | 2022-11-09 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.99798214e-01 -2.70618312e-02 -2.50543088e-01 -2.20153153e-01
-7.38623738e-01 -2.01355278e-01 4.75871533e-01 -5.35291910e-01
-1.17266357e-01 5.35150290e-01 2.08684683e-01 -2.10033972e-02
3.47065806e-01 -6.75033271e-01 -9.39807057e-01 -6.10245347e-01
7.71765769e-01 3.64298016e-01 3.86054456e-01 -7.75780901... | [10.735343933105469, -1.4451358318328857] |
0c23448f-21bc-4e82-9ffc-f00d8c77a1ec | decoding-visemes-improving-machine-lipreading-1 | 1710.01169 | null | http://arxiv.org/abs/1710.01169v1 | http://arxiv.org/pdf/1710.01169v1.pdf | Decoding visemes: improving machine lipreading | To undertake machine lip-reading, we try to recognise speech from a visual
signal. Current work often uses viseme classification supported by language
models with varying degrees of success. A few recent works suggest phoneme
classification, in the right circumstances, can outperform viseme
classification. In this work... | ['Richard Harvey', 'Helen L. Bear'] | 2017-10-03 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 6.14897490e-01 8.89739245e-02 -4.03464675e-01 -1.48622721e-01
-8.09453547e-01 -2.27710575e-01 9.25329864e-01 -2.14341611e-01
-4.14787650e-01 9.16619539e-01 5.27099371e-01 -5.54753840e-01
4.14161980e-01 -3.23888987e-01 -3.42828780e-01 -5.32450199e-01
4.57006812e-01 3.67315002e-02 2.65105575e-01 -2.31198043... | [14.301273345947266, 4.990958213806152] |
5fcc1363-c909-4200-a653-8e220fc1a5ee | clickbait-classification-and-spoiling-using | 2306.14907 | null | https://arxiv.org/abs/2306.14907v1 | https://arxiv.org/pdf/2306.14907v1.pdf | Clickbait Classification and Spoiling Using Natural Language Processing | Clickbait is the practice of engineering titles to incentivize readers to click through to articles. Such titles with sensationalized language reveal as little information as possible. Occasionally, clickbait will be intentionally misleading, so natural language processing (NLP) can scan the article and answer the ques... | ['Elisa Ferracane', 'Adhitya Thirumala'] | 2023-06-16 | null | null | null | null | ['classification-1', 'question-answering'] | ['methodology', 'natural-language-processing'] | [ 2.90592194e-01 4.54388291e-01 -8.69120136e-02 -1.54326141e-01
-1.00675631e+00 -9.91151750e-01 7.86747694e-01 3.38909686e-01
-3.19158792e-01 4.92910802e-01 2.63491422e-01 -7.89186895e-01
4.85700630e-02 -4.70862180e-01 -9.55855370e-01 -1.03239812e-01
5.46546519e-01 5.89967012e-01 4.24702644e-01 -2.32470855... | [11.989368438720703, 8.991891860961914] |
72a6ab0e-35af-45c0-9b37-8bfacf04b594 | physics-informed-machine-learning-method-for-1 | 2108.00037 | null | https://arxiv.org/abs/2108.00037v1 | https://arxiv.org/pdf/2108.00037v1.pdf | Physics-Informed Machine Learning Method for Large-Scale Data Assimilation Problems | We develop a physics-informed machine learning approach for large-scale data assimilation and parameter estimation and apply it for estimating transmissivity and hydraulic head in the two-dimensional steady-state subsurface flow model of the Hanford Site given synthetic measurements of said variables. In our approach, ... | ['Alexandre M. Tartakovsky', 'David A. Barajas-Solano', 'Yu-Hong Yeung'] | 2021-07-30 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.41969666e-01 -2.65690003e-04 3.67198735e-01 5.35098426e-02
-7.31775284e-01 -3.78839314e-01 3.34166110e-01 2.91945159e-01
-3.88795882e-01 1.00668550e+00 -2.05563217e-01 -7.26943970e-01
-2.25071847e-01 -1.12214613e+00 -7.43258357e-01 -1.02122486e+00
-4.85686809e-01 5.47509432e-01 4.09199186e-02 -3.24305773... | [6.494843482971191, 3.3833484649658203] |
7b8256d8-7f71-45f8-b662-ba3ba2191684 | distilled-mid-fusion-transformer-networks-for | 2305.03810 | null | https://arxiv.org/abs/2305.03810v1 | https://arxiv.org/pdf/2305.03810v1.pdf | Distilled Mid-Fusion Transformer Networks for Multi-Modal Human Activity Recognition | Human Activity Recognition is an important task in many human-computer collaborative scenarios, whilst having various practical applications. Although uni-modal approaches have been extensively studied, they suffer from data quality and require modality-specific feature engineering, thus not being robust and effective ... | ['Claude Sammut', 'Binghao Li', 'Lina Yao', 'Jingcheng Li'] | 2023-05-05 | null | null | null | null | ['human-activity-recognition', 'feature-engineering', 'human-activity-recognition'] | ['computer-vision', 'methodology', 'time-series'] | [ 2.69627780e-01 -2.81778514e-01 -3.22666943e-01 -9.66576114e-02
-1.14334750e+00 -1.18669719e-01 6.09986544e-01 -1.00043342e-01
-3.25963318e-01 5.96441031e-01 4.14608449e-01 1.99840620e-01
-3.73286903e-01 -6.71987295e-01 -4.16646689e-01 -8.81928682e-01
4.29039933e-02 2.62760729e-01 3.47566187e-01 -1.56430751... | [7.949157238006592, 0.754128098487854] |
8a42a8e4-ae82-4bf9-b0a2-1d2bdd3e3474 | keep-it-consistent-topic-aware-storytelling | 1911.04192 | null | https://arxiv.org/abs/1911.04192v2 | https://arxiv.org/pdf/1911.04192v2.pdf | Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication | Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a n... | ['Ying Cheng', 'Ruize Wang', 'Piji Li', 'Haijun Shan', 'Xuanjing Huang', 'Zhongyu Wei', 'Qi Zhang', 'Ji Zhang'] | 2019-11-11 | null | https://aclanthology.org/2020.coling-main.204 | https://aclanthology.org/2020.coling-main.204.pdf | coling-2020-8 | ['visual-storytelling'] | ['natural-language-processing'] | [ 4.10405695e-01 4.93487418e-01 1.42108083e-01 -5.72030731e-02
-8.74493420e-01 -7.51858413e-01 1.29137254e+00 -3.34179923e-02
1.17298216e-01 8.00270498e-01 7.85850286e-01 2.13993877e-01
4.84740436e-01 -8.36914957e-01 -6.51133537e-01 -5.40226281e-01
3.48651886e-01 4.79218751e-01 2.59307981e-01 -2.26046532... | [11.182755470275879, 0.7346868515014648] |
0839b64d-6b12-46e6-8b9a-7d6d2b4a6543 | writing-by-memorizing-hierarchical-retrieval | 2106.06471 | null | https://arxiv.org/abs/2106.06471v1 | https://arxiv.org/pdf/2106.06471v1.pdf | Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation | Medical report generation is one of the most challenging tasks in medical image analysis. Although existing approaches have achieved promising results, they either require a predefined template database in order to retrieve sentences or ignore the hierarchical nature of medical report generation. To address these issue... | ['Fenglong Ma', 'Quanzeng You', 'Muchao Ye', 'Xingyi Yang'] | 2021-05-25 | null | https://aclanthology.org/2021.acl-long.387 | https://aclanthology.org/2021.acl-long.387.pdf | acl-2021-5 | ['medical-report-generation'] | ['medical'] | [ 4.59333271e-01 -1.17787912e-01 -8.63147825e-02 -4.08965796e-01
-1.49955404e+00 -4.73950118e-01 4.88328844e-01 3.69852483e-01
-2.49573067e-01 6.24421537e-01 4.79322612e-01 -3.00700516e-01
2.05020830e-01 -5.33282042e-01 -2.39559740e-01 -3.49878341e-01
3.00187439e-01 1.86978295e-01 2.60836542e-01 1.85776740... | [15.050836563110352, -1.3898720741271973] |
e68ec98a-1b49-4dd9-9f75-6b232d8f56f9 | heterogeneous-knowledge-transfer-in-video | 1511.04798 | null | http://arxiv.org/abs/1511.04798v2 | http://arxiv.org/pdf/1511.04798v2.pdf | Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization | Emotion is a key element in user-generated videos. However, it is difficult
to understand emotions conveyed in such videos due to the complex and
unstructured nature of user-generated content and the sparsity of video frames
expressing emotion. In this paper, for the first time, we study the problem of
transferring kno... | ['Yu-Gang Jiang', 'Yanwei Fu', 'Leonid Sigal', 'Boyang Li', 'Baohan Xu'] | 2015-11-16 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [ 6.24573708e-01 -5.96533865e-02 -4.11761224e-01 -4.42329824e-01
-6.53708756e-01 -4.74556804e-01 1.55882016e-01 -2.72109713e-02
-1.70681044e-01 7.39049375e-01 7.26689458e-01 4.15293515e-01
4.27705884e-01 -2.78831214e-01 -8.42793763e-01 -6.45611763e-01
-1.14081465e-01 -1.68779463e-01 -3.42364907e-01 1.79358870... | [13.151470184326172, 4.844359874725342] |
1ddc8e17-8278-4a6c-8d55-a9bc95211a88 | adapting-language-audio-models-as-few-shot | 2305.17719 | null | https://arxiv.org/abs/2305.17719v1 | https://arxiv.org/pdf/2305.17719v1.pdf | Adapting Language-Audio Models as Few-Shot Audio Learners | We presented the Treff adapter, a training-efficient adapter for CLAP, to boost zero-shot classification performance by making use of a small set of labelled data. Specifically, we designed CALM to retrieve the probability distribution of text-audio clips over classes using a set of audio-label pairs and combined it wi... | ['Wenwu Wang', 'Mark D. Plumbley', 'Emmanouil Benetos', 'Huy Phan', 'Haohe Liu', 'Xubo Liu', 'Jinhua Liang'] | 2023-05-28 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 2.78482974e-01 -2.55112827e-01 -1.84564367e-01 -5.31691611e-01
-1.45160890e+00 -4.70786721e-01 3.69165152e-01 3.36890399e-01
-3.78684670e-01 4.22766626e-01 4.02916700e-01 2.92905211e-01
-2.47959375e-01 -5.58091938e-01 -4.62071061e-01 -4.51772511e-01
-2.68377513e-01 6.07635081e-01 6.87645912e-01 -2.00707540... | [15.203974723815918, 5.088539123535156] |
5784cbad-9793-423d-b432-d2f235680a9e | multi-scale-efficient-graph-transformer-for | 2305.15773 | null | https://arxiv.org/abs/2305.15773v1 | https://arxiv.org/pdf/2305.15773v1.pdf | Multi-scale Efficient Graph-Transformer for Whole Slide Image Classification | The multi-scale information among the whole slide images (WSIs) is essential for cancer diagnosis. Although the existing multi-scale vision Transformer has shown its effectiveness for learning multi-scale image representation, it still cannot work well on the gigapixel WSIs due to their extremely large image sizes. To ... | ['Jun Shi', 'Shihui Ying', 'Jun Wang', 'Juncheng Li', 'Saisai Ding'] | 2023-05-25 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 1.35968313e-01 -2.08479837e-01 -4.99385372e-02 -8.38392228e-02
-9.75573242e-01 -1.25795320e-01 2.38400415e-01 3.22803378e-01
-1.86553076e-01 3.61850053e-01 1.01372227e-01 -9.12728757e-02
-3.90066743e-01 -9.91447568e-01 -5.56231499e-01 -1.10431361e+00
3.76807839e-01 1.10538332e-02 5.48081815e-01 -2.40542591... | [15.031338691711426, -2.7585055828094482] |
2022340d-37bf-4e17-9298-44820bcda990 | boosting-detection-in-crowd-analysis-via | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Boosting_Detection_in_Crowd_Analysis_via_Underutilized_Output_Features_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Boosting_Detection_in_Crowd_Analysis_via_Underutilized_Output_Features_CVPR_2023_paper.pdf | Boosting Detection in Crowd Analysis via Underutilized Output Features | Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. However, we argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. Specifically, the area size and confidence ... | ['Fengyu Yang', 'Shaokai Wu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['crowd-counting'] | ['computer-vision'] | [-2.80666292e-01 -2.79958904e-01 2.52539128e-01 -1.86701298e-01
-5.83424747e-01 -6.63688838e-01 6.37260258e-01 4.13204104e-01
-6.53977513e-01 6.73233807e-01 4.52577412e-01 -1.91667497e-01
4.58677620e-01 -6.78049386e-01 -1.89750358e-01 -5.68993747e-01
4.00977507e-02 3.26503098e-01 8.30164552e-01 -2.56986290... | [8.273000717163086, -0.40276414155960083] |
a21ded50-ed9f-47e7-b23c-5d8243f2d8cc | bldnet-a-semi-supervised-change-detection | 2201.10389 | null | https://arxiv.org/abs/2201.10389v1 | https://arxiv.org/pdf/2201.10389v1.pdf | BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge | Change detection is instrumental to localize damage and understand destruction in disaster informatics. While convolutional neural networks are at the core of recent change detection solutions, we present in this work, BLDNet, a novel graph formulation for building damage change detection and enable learning relationsh... | ['Mariette Awad', 'Ali Ismail'] | 2022-01-25 | null | null | null | null | ['semi-supervised-change-detection'] | ['computer-vision'] | [ 2.56880552e-01 1.62300691e-02 7.65538663e-02 -2.34808400e-01
-4.94161218e-01 -4.21020418e-01 7.74141431e-01 1.05918097e+00
-3.59455854e-01 5.87914288e-01 7.68977821e-01 -2.71214008e-01
-3.15197736e-01 -1.47800863e+00 -8.53116870e-01 -2.34852821e-01
-6.41985834e-01 1.74776554e-01 2.03339085e-01 -6.92422271... | [9.639846801757812, -1.2887578010559082] |
b051af18-7601-4592-9999-85f714154b44 | fourier-mixed-window-attention-accelerating | 2307.00493 | null | https://arxiv.org/abs/2307.00493v1 | https://arxiv.org/pdf/2307.00493v1.pdf | Fourier-Mixed Window Attention: Accelerating Informer for Long Sequence Time-Series Forecasting | We study a fast local-global window-based attention method to accelerate Informer for long sequence time-series forecasting. While window attention is local and a considerable computational saving, it lacks the ability to capture global token information which is compensated by a subsequent Fourier transform block. Our... | ['Jack Xin', 'Nhat Thanh Tran'] | 2023-07-02 | null | null | null | null | ['time-series-forecasting'] | ['time-series'] | [ 2.16528565e-01 -1.29240662e-01 -4.28558290e-01 -4.35256571e-01
-1.13258100e+00 -3.43910486e-01 5.35005629e-01 1.81549028e-01
-5.52421868e-01 6.16421044e-01 3.92983884e-01 -4.54326510e-01
-4.76017706e-02 -5.56654811e-01 -9.93995070e-01 -4.53459084e-01
-5.07180810e-01 4.22467440e-01 6.56755362e-03 2.44308226... | [7.171378135681152, 3.0531742572784424] |
1ad3fa79-cb98-49de-8bf1-e3473c799413 | safe-exploration-for-optimizing-contextual | 2002.00467 | null | https://arxiv.org/abs/2002.00467v1 | https://arxiv.org/pdf/2002.00467v1.pdf | Safe Exploration for Optimizing Contextual Bandits | Contextual bandit problems are a natural fit for many information retrieval tasks, such as learning to rank, text classification, recommendation, etc. However, existing learning methods for contextual bandit problems have one of two drawbacks: they either do not explore the space of all possible document rankings (i.e.... | ['Rolf Jagerman', 'Ilya Markov', 'Maarten de Rijke'] | 2020-02-02 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-2.17986125e-02 1.00077532e-01 -7.48758137e-01 7.42756799e-02
-1.04629016e+00 -8.04280221e-01 6.01111650e-01 2.17482179e-01
-6.34637237e-01 1.22684586e+00 2.45178014e-01 -6.70787811e-01
-5.52781224e-01 -6.45164788e-01 -8.46528471e-01 -1.00468171e+00
5.23386709e-02 7.59358287e-01 2.66522437e-01 2.10660234... | [4.562575817108154, 3.2390694618225098] |
03c397e9-c73c-4dcc-a172-1261a03e792e | influencerrank-discovering-effective | 2304.01897 | null | https://arxiv.org/abs/2304.01897v2 | https://arxiv.org/pdf/2304.01897v2.pdf | InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks | As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propos... | ['Wei Wang', 'Jinyoung Han', 'Jyun-Yu Jiang', 'Seungbae Kim'] | 2023-04-04 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 1.23117767e-01 2.23326251e-01 -6.90166891e-01 -5.41865528e-01
-1.36380866e-01 -4.01060283e-01 9.28985476e-01 1.24194935e-01
-1.72066599e-01 4.85792726e-01 6.86461627e-01 -3.58943433e-01
-5.68406165e-01 -1.30189395e+00 -6.85734272e-01 -3.51199329e-01
-4.87056613e-01 5.81384599e-01 -3.87813188e-02 -7.34882116... | [7.306843280792236, 6.067253112792969] |
ec01a7e8-0017-4713-ba12-ff59cec94ecb | tech-report-a-fast-multiscale-spatial | 1712.01770 | null | http://arxiv.org/abs/1712.01770v3 | http://arxiv.org/pdf/1712.01770v3.pdf | Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing | Sparse hyperspectral unmixing from large spectral libraries has been
considered to circumvent limitations of endmember extraction algorithms in many
applications. This strategy often leads to ill-posed inverse problems, which
can benefit from spatial regularization strategies. While existing spatial
regularization meth... | ['Cédric Richard', 'José Carlos Moreira Bermudez', 'Tales Imbiriba', 'Ricardo Augusto Borsoi'] | 2017-12-05 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.07074738e-01 -7.16355979e-01 1.22577228e-01 2.15600263e-02
-1.06108081e+00 -4.98119354e-01 2.95505643e-01 -2.22555816e-01
-7.64832869e-02 1.06455481e+00 5.96006699e-02 -1.25474766e-01
-2.95696139e-01 -5.46644628e-01 -4.61698890e-01 -1.47949719e+00
4.31834072e-01 9.98218283e-02 -6.37651086e-02 -6.43964261... | [10.144158363342285, -2.062161445617676] |
e278d6bd-d62c-45c6-9536-d30920915bd7 | using-answer-set-programming-for-pattern | 1409.7777 | null | http://arxiv.org/abs/1409.7777v1 | http://arxiv.org/pdf/1409.7777v1.pdf | Using Answer Set Programming for pattern mining | Serial pattern mining consists in extracting the frequent sequential patterns
from a unique sequence of itemsets. This paper explores the ability of a
declarative language, such as Answer Set Programming (ASP), to solve this issue
efficiently. We propose several ASP implementations of the frequent sequential
pattern mi... | ['René Quiniou', 'Yves Moinard', 'Thomas Guyet'] | 2014-09-27 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 2.66732126e-01 3.75574142e-01 -4.55579698e-01 -6.25333846e-01
-5.87901706e-03 -4.81339186e-01 1.33460850e-01 3.99727851e-01
-1.96346909e-01 9.24753129e-01 -1.91121355e-01 -5.55969536e-01
-5.30772388e-01 -1.44943416e+00 -5.82614601e-01 -5.98592274e-02
-5.41211486e-01 1.08529639e+00 9.81120110e-01 -1.40779227... | [8.323698043823242, 6.323720932006836] |
0f275a47-99d3-492e-aef6-6526b910e1a5 | umad-universal-model-adaptation-under-domain | 2112.08553 | null | https://arxiv.org/abs/2112.08553v1 | https://arxiv.org/pdf/2112.08553v1.pdf | UMAD: Universal Model Adaptation under Domain and Category Shift | Learning to reject unknown samples (not present in the source classes) in the target domain is fairly important for unsupervised domain adaptation (UDA). There exist two typical UDA scenarios, i.e., open-set, and open-partial-set, and the latter assumes that not all source classes appear in the target domain. However, ... | ['Ran He', 'Jiashi Feng', 'Dapeng Hu', 'Jian Liang'] | 2021-12-16 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 4.34754044e-01 2.13679045e-01 -4.86870855e-01 -7.16758013e-01
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2.82719463e-01 5.75725734e-01 1.79189339e-01 1.40797850... | [10.388480186462402, 3.199429512023926] |
ad636de3-dde0-4b4a-a32a-2b68132613e4 | learning-to-predict-stereo-reliability | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Poggi_Learning_to_Predict_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Poggi_Learning_to_Predict_CVPR_2017_paper.pdf | Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps | Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network, the effectiveness of state-of-the-art confidence measures exploiting the local consistency assumpti... | ['Stefano Mattoccia', 'Matteo Poggi'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['stereo-matching'] | ['computer-vision'] | [-3.10790958e-03 8.78084227e-02 2.07392693e-01 -4.04005677e-01
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8085febe-aaed-4fc5-9620-6da5bf7d0cd2 | risk-averse-decision-making-under-uncertainty | 2109.04082 | null | https://arxiv.org/abs/2109.04082v1 | https://arxiv.org/pdf/2109.04082v1.pdf | Risk-Averse Decision Making Under Uncertainty | A large class of decision making under uncertainty problems can be described via Markov decision processes (MDPs) or partially observable MDPs (POMDPs), with application to artificial intelligence and operations research, among others. Traditionally, policy synthesis techniques are proposed such that a total expected c... | ['Aaron D. Ames', 'Richard M. Murray', 'Michel D. Ingham', 'Ugo Rosolia', 'Mohamadreza Ahmadi'] | 2021-09-09 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 1.81843862e-01 6.38494551e-01 -4.40988481e-01 -3.11359260e-02
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-2.96398789e-01 6.48321092e-01 -2.09816117e-02 -1.86606701... | [4.551140308380127, 2.4308853149414062] |
a9f4da55-a49c-4f9d-b46a-0c873cf21ea8 | simple-diffusion-end-to-end-diffusion-for | 2301.11093 | null | https://arxiv.org/abs/2301.11093v1 | https://arxiv.org/pdf/2301.11093v1.pdf | simple diffusion: End-to-end diffusion for high resolution images | Currently, applying diffusion models in pixel space of high resolution images is difficult. Instead, existing approaches focus on diffusion in lower dimensional spaces (latent diffusion), or have multiple super-resolution levels of generation referred to as cascades. The downside is that these approaches add additional... | ['Tim Salimans', 'Jonathan Heek', 'Emiel Hoogeboom'] | 2023-01-26 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 2.83036590e-01 1.40366927e-01 1.80110902e-01 -6.87410235e-02
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2.67376184e-01 1.03946917e-01 6.75845444e-01 -3.77623796... | [11.21985912322998, -1.7896032333374023] |
385cd010-6d8c-4cb4-a379-ab76b8958887 | acoustic-model-adaptation-from-raw-waveforms | 1909.13759 | null | https://arxiv.org/abs/1909.13759v1 | https://arxiv.org/pdf/1909.13759v1.pdf | Acoustic Model Adaptation from Raw Waveforms with SincNet | Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a given scenario than hand-crafted features. SincNet has been proposed to reduce the number of parameters required in raw-waveform modelling,... | ['Peter Bell', 'Ondřej Klejch', 'Joachim Fainberg', 'Erfan Loweimi', 'Steve Renals'] | 2019-09-30 | null | null | null | null | ['acoustic-modelling'] | ['speech'] | [ 3.13269377e-01 2.17150941e-01 3.62680972e-01 -5.31815886e-01
-5.39087176e-01 -4.62976754e-01 4.22631443e-01 1.74251482e-01
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-3.12415421e-01 3.20661724e-01 6.15819633e-01 -1.80580065... | [15.085411071777344, 5.6948323249816895] |
b51f2ed5-eb4c-4dba-85b2-371ca715952d | pushing-the-limits-of-chatgpt-on-nlp-tasks | 2306.09719 | null | https://arxiv.org/abs/2306.09719v1 | https://arxiv.org/pdf/2306.09719v1.pdf | Pushing the Limits of ChatGPT on NLP Tasks | Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its subpar performance was caused by the following factors: (1) token limit in the prompt does not allow for the full utilization of the supervis... | ['Guoyin Wang', 'Fei Wu', 'Lingjuan Lyu', 'Fei Cheng', 'Jiwei Li', 'Tianwei Zhang', 'Shuhe Wang', 'Zhen Wan', 'Xiaoya Li', 'Linfeng Dong', 'Xiaofei Sun'] | 2023-06-16 | null | null | null | null | ['question-answering', 'natural-language-inference', 'sentiment-analysis', 'event-extraction', 'dependency-parsing', 'semantic-role-labeling', 'part-of-speech-tagging', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 7.61038810e-02 4.16473895e-01 -1.63239077e-01 -2.89220691e-01
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9.44045708e-02 5.78671753e-01 4.93549943e-01 -3.73322845... | [10.628684043884277, 8.632710456848145] |
a797516a-d147-4025-858d-735c8c57ecef | clrnet-cross-layer-refinement-network-for | 2203.10350 | null | https://arxiv.org/abs/2203.10350v1 | https://arxiv.org/pdf/2203.10350v1.pdf | CLRNet: Cross Layer Refinement Network for Lane Detection | Lane is critical in the vision navigation system of the intelligent vehicle. Naturally, lane is a traffic sign with high-level semantics, whereas it owns the specific local pattern which needs detailed low-level features to localize accurately. Using different feature levels is of great importance for accurate lane det... | ['Xiaofei He', 'Deng Cai', 'Zheng Yang', 'Wenjian Tang', 'Yang Liu', 'Yifei HUANG', 'Tu Zheng'] | 2022-03-19 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_CLRNet_Cross_Layer_Refinement_Network_for_Lane_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['lane-detection'] | ['computer-vision'] | [-4.12913747e-02 -2.22131476e-01 -3.23893100e-01 -7.15265810e-01
-2.67967671e-01 -2.10817471e-01 5.72317719e-01 -1.12907372e-01
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-3.10189258e-02 -4.08864260e-01 1.06245720e+00 -3.60593319... | [8.054889678955078, -1.492987036705017] |
089c6322-b806-42da-bc12-447147b1d8b3 | genie-nf-ai-identifying-neurofibromatosis | 2304.13429 | null | https://arxiv.org/abs/2304.13429v1 | https://arxiv.org/pdf/2304.13429v1.pdf | GENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets | In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose an interpretable AI approach to diagnose patients with neurofibromatosis using blood tests and path... | ['Hamdan Abdellatef', 'Muhammed Nadir Yalçın', 'Amin Jafari', 'Fırat Sefaoğlu', 'Omid Hamza', 'Ali Davar', 'Elnaz Abedini', 'Ferhat Atasoy', 'Michael Bidollahkhani'] | 2023-04-26 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.24669361e-01 6.79173589e-01 -4.87204134e-01 -5.29376268e-01
-9.66361612e-02 -1.52396142e-01 1.47991002e-01 1.73835933e-01
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-1.50305256e-01 1.31905425e+00 -3.13557059e-01 -8.67673159... | [8.302999496459961, 5.863886833190918] |
9130acca-9e0d-4355-b4d0-2e1562a5a4d0 | confidence-based-ensembles-of-end-to-end | 2306.15824 | null | https://arxiv.org/abs/2306.15824v1 | https://arxiv.org/pdf/2306.15824v1.pdf | Confidence-based Ensembles of End-to-End Speech Recognition Models | The number of end-to-end speech recognition models grows every year. These models are often adapted to new domains or languages resulting in a proliferation of expert systems that achieve great results on target data, while generally showing inferior performance outside of their domain of expertise. We explore combinat... | ['Boris Ginsburg', 'Aleksandr Laptev', 'Vitaly Lavrukhin', 'Igor Gitman'] | 2023-06-27 | null | null | null | null | ['language-identification', 'model-selection', 'language-identification', 'speech-recognition'] | ['audio', 'methodology', 'natural-language-processing', 'speech'] | [ 4.19703871e-02 1.78765118e-01 1.06696654e-02 -6.51230633e-01
-1.28090286e+00 -8.30540419e-01 7.09354341e-01 -2.29451135e-01
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1.85344040e-01 -1.30235806e-01 -4.73671228e-01 -1.86555386e-01
7.23797157e-02 8.67055893e-01 4.27434474e-01 -2.62391269... | [14.403002738952637, 6.668713569641113] |
e9caf8d9-0fd8-4186-8806-b69485b38694 | supervised-relation-classification-as-two-way | null | null | https://openreview.net/forum?id=Drjb0jGXtGe | https://openreview.net/pdf?id=Drjb0jGXtGe | Supervised Relation Classification as Two-way Span-Prediction | Most of the current supervised relation classification (RC) algorithms use a single embedding to represent the relation between a pair of entities. We argue that a better approach is to treat the RC task as a Span-Prediction (SP) problem, similar to Question Answering (QA). We present an SP-based system for RC and eval... | ['Anonymous'] | 2021-09-17 | null | null | null | acl-arr-september-2021-9 | ['relation-classification'] | ['natural-language-processing'] | [ 7.09700659e-02 7.24071085e-01 -3.90966266e-01 -4.21703905e-01
-8.85123491e-01 -3.64432037e-01 7.42071509e-01 7.81856775e-01
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-2.98590779e-01 9.90759611e-01 6.82821989e-01 -5.39861143... | [9.537646293640137, 8.475790023803711] |
e68a569d-b431-45be-8573-85fd385cd143 | a-comprehensive-survey-on-graph-anomaly | 2106.07178 | null | https://arxiv.org/abs/2106.07178v5 | https://arxiv.org/pdf/2106.07178v5.pdf | A Comprehensive Survey on Graph Anomaly Detection with Deep Learning | Anomalies represent rare observations (e.g., data records or events) that deviate significantly from others. Over several decades, research on anomaly mining has received increasing interests due to the implications of these occurrences in a wide range of disciplines. Anomaly detection, which aims to identify rare obse... | ['Leman Akoglu', 'Hui Xiong', 'Quan Z. Sheng', 'Chuan Zhou', 'Jian Yang', 'Shan Xue', 'Jia Wu', 'Xiaoxiao Ma'] | 2021-06-14 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.33787515e-02 2.08811555e-02 3.08846477e-02 -1.86357304e-01
1.14087500e-01 -2.49719232e-01 3.45185697e-01 8.88904870e-01
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-8.19721401e-01 3.72166783e-01 4.78325151e-02 -3.02050591... | [6.641246795654297, 5.80620813369751] |
92bdc894-85bf-43e8-ab98-26cecc25e76a | betrayed-by-motion-camouflaged-object | 2011.11630 | null | https://arxiv.org/abs/2011.11630v1 | https://arxiv.org/pdf/2011.11630v1.pdf | Betrayed by Motion: Camouflaged Object Discovery via Motion Segmentation | The objective of this paper is to design a computational architecture that discovers camouflaged objects in videos, specifically by exploiting motion information to perform object segmentation. We make the following three contributions: (i) We propose a novel architecture that consists of two essential components for b... | ['Andrew Zisserman', 'Weidi Xie', 'Charig Yang', 'Hala Lamdouar'] | 2020-11-23 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 3.83753628e-01 -3.34184140e-01 -1.44374207e-01 -1.45335957e-01
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-2.76484907e-01 1.19405068e-01 7.45292187e-01 -1.59107000... | [9.213602066040039, -0.17378944158554077] |
fe4505c7-bf70-411b-9069-455f5cad8f50 | progressive-motion-context-refine-network-for | 2211.06024 | null | https://arxiv.org/abs/2211.06024v1 | https://arxiv.org/pdf/2211.06024v1.pdf | Progressive Motion Context Refine Network for Efficient Video Frame Interpolation | Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation. However, above cascaded architecture can lead to large model size and inference delay, hindering them from mobile ... | ['Jie Yang', 'Jinfeng Liu', 'Lingtong Kong'] | 2022-11-11 | null | null | null | null | ['video-frame-interpolation'] | ['computer-vision'] | [ 2.33850151e-01 -2.21072659e-01 -2.10401416e-01 -3.44699144e-01
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2.26055726e-01 -2.64065564e-01 6.19985342e-01 9.55457613... | [10.72938346862793, -1.3926918506622314] |
6cd2e104-b1bb-4ab8-8d6d-52b483baf646 | towards-a-one-stop-solution-to-both-aspect | null | null | https://ieeexplore.ieee.org/document/8489042/authors#authors | https://ieeexplore.ieee.org/document/8489042/authors#authors | Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning | Previous studies usually divided aspect-based sentiment analysis into several subtasks in pipeline, i.e., first aspect term and/or opinion term extraction, then aspect-based sentiment prediction, resulting in error propagation and external resources dependency. To overcome the problems mentioned above, in this work we ... | ['Wenting Wang', 'Man Lan', 'Feixiang Wang'] | 2018-10-14 | null | null | null | ieee-2018-10 | ['term-extraction', 'aspect-extraction', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.39312908e-01 -2.29065511e-02 -3.37180257e-01 -6.33837521e-01
-1.00130343e+00 -3.94976407e-01 5.24467647e-01 1.75166100e-01
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3.80943775e-01 3.01565379e-01 -5.06816916e-02 -1.50708050... | [11.474335670471191, 6.646939754486084] |
81a2bd82-aec9-4ed5-a06d-8462367c98dd | benchmarking-robustness-of-3d-object | 2303.11040 | null | https://arxiv.org/abs/2303.11040v1 | https://arxiv.org/pdf/2303.11040v1.pdf | Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving | 3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to real-world corruptions caused by adverse weathers, sensor noises, etc., provoking concerns about the safety and reliability of autonomous drivi... | ['Jun Zhu', 'Xingxing Wei', 'Hang Su', 'Xiao Yang', 'Yikai Wang', 'Zijian Zhu', 'Jinlai Zhang', 'Caixin Kang', 'Yinpeng Dong'] | 2023-03-20 | null | null | null | null | ['robust-3d-object-detection'] | ['computer-vision'] | [-3.85650992e-01 -6.18913889e-01 -2.26453152e-02 -3.50070179e-01
-6.48956835e-01 -6.70433700e-01 7.38939285e-01 -1.34469151e-01
-3.45956296e-01 2.62529552e-01 -2.46030688e-01 -4.96816337e-01
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-8.10501948e-02 -3.69419977e-02 7.28558481e-01 -3.50576371... | [7.923798084259033, -1.9954513311386108] |
6813b39f-6913-4943-ac51-cee8f630dd46 | traffic-surveillance-using-vehicle-license | 2012.02218 | null | https://arxiv.org/abs/2012.02218v1 | https://arxiv.org/pdf/2012.02218v1.pdf | Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh | Computer vision coupled with Deep Learning (DL) techniques bring out a substantial prospect in the field of traffic control, monitoring and law enforcing activities. This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting the license plate o... | ['Raiyan Ibne Hafiz', 'Mahmudul Haque', 'Muhaiminul Islam Akash', 'Md. Saif Hassan Onim'] | 2020-12-03 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [-2.23544151e-01 -6.70928478e-01 1.08294569e-01 8.65629017e-02
-5.23616195e-01 -7.21086323e-01 5.63363910e-01 -2.88579524e-01
-6.04907393e-01 3.46412539e-01 -5.91041028e-01 -5.79240501e-01
5.56385577e-01 -9.96312976e-01 -9.36359704e-01 -5.59229553e-01
4.54820730e-02 2.95913368e-01 8.07036579e-01 -7.13383332... | [9.898487091064453, -4.867773532867432] |
e9e2f0ee-c3c2-4084-b9ec-b93394d1e252 | leaf-segmentation-and-counting-with-deep | 2012.11486 | null | https://arxiv.org/abs/2012.11486v1 | https://arxiv.org/pdf/2012.11486v1.pdf | Leaf Segmentation and Counting with Deep Learning: on Model Certainty, Test-Time Augmentation, Trade-Offs | Plant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits. Since it is well-suited for these tasks, deep supervised learning has been prevalent in recent works proposing better performing models at segmenting and counting leaves. Despite good efforts from research ... | ['Lihong Zheng', 'Douglas Pinto Sampaio Gomes'] | 2020-12-21 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.41234118e-01 2.30955258e-01 -3.12429339e-01 -4.25432026e-01
-5.05467117e-01 -7.46812284e-01 2.81660348e-01 4.81148034e-01
-2.21102178e-01 5.42209387e-01 -5.53638101e-01 -4.07676399e-01
-3.76146913e-01 -7.97038734e-01 -4.10016567e-01 -8.30191195e-01
-1.57472461e-01 1.12051654e+00 4.77705240e-01 6.10301942... | [9.126411437988281, -1.5114164352416992] |
aba770f7-d7f7-4baa-9052-86f345b84a10 | unsupervised-domain-adaptation-with-4 | 2009.00520 | null | https://arxiv.org/abs/2009.00520v1 | https://arxiv.org/pdf/2009.00520v1.pdf | Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces | Unsupervised Domain Adaptation (UDA) aims to classify unlabeled target domain by transferring knowledge from labeled source domain with domain shift. Most of the existing UDA methods try to mitigate the adverse impact induced by the shift via reducing domain discrepancy. However, such approaches easily suffer a notorio... | ['Weikai Li', 'Songcan Chen'] | 2020-09-01 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 3.25678766e-01 8.51111785e-02 -4.15417910e-01 -2.75312424e-01
-1.07766604e+00 -8.70729029e-01 5.74833632e-01 -8.29878449e-02
-2.13218346e-01 1.06242394e+00 2.68194944e-01 4.11721431e-02
-2.07028627e-01 -5.27699351e-01 -5.52893579e-01 -9.66037214e-01
3.51650387e-01 6.25029743e-01 2.05008999e-01 -2.15179563... | [10.329292297363281, 3.1257221698760986] |
80c56a7d-cbe1-4282-b478-d5f3462ff41b | low-light-image-enhancement-by-learning | 2303.13412 | null | https://arxiv.org/abs/2303.13412v1 | https://arxiv.org/pdf/2303.13412v1.pdf | Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains | Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknow low-light conditions. I... | ['Ziliang Feng', 'Ming Yang', 'Bokai Liu', 'Tingting Liu', 'Gui Fu', 'Xiaoguang Tu', 'Yi Huang'] | 2023-03-23 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 1.68705955e-01 -5.07677138e-01 -2.35018618e-02 -3.47563416e-01
-2.39002571e-01 -3.42076421e-01 4.64266300e-01 -4.02043074e-01
-1.60465926e-01 7.35715568e-01 1.51199088e-01 1.54113054e-01
-4.91112061e-02 -6.85423851e-01 -5.91345489e-01 -1.11167037e+00
1.42574906e-01 -7.26278543e-01 9.56153423e-02 -2.76077807... | [10.834203720092773, -2.5671029090881348] |
3beda96a-b795-4e29-a1b6-88a764750d74 | cross-domain-3d-equivariant-image-embeddings | 1812.02716 | null | https://arxiv.org/abs/1812.02716v2 | https://arxiv.org/pdf/1812.02716v2.pdf | Cross-Domain 3D Equivariant Image Embeddings | Spherical convolutional networks have been introduced recently as tools to learn powerful feature representations of 3D shapes. Spherical CNNs are equivariant to 3D rotations making them ideally suited to applications where 3D data may be observed in arbitrary orientations. In this paper we learn 2D image embeddings wi... | ['Ameesh Makadia', 'Avneesh Sud', 'Zhengyi Luo', 'Kostas Daniilidis', 'Carlos Esteves'] | 2018-12-06 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-3.27357836e-02 4.31060702e-01 -2.64530182e-01 -5.43817699e-01
-1.31379411e-01 -9.72243071e-01 9.46653008e-01 -2.71177381e-01
-9.24269110e-02 -2.67651957e-02 4.03359652e-01 -9.54744890e-02
1.77387983e-01 -5.68196058e-01 -1.05991793e+00 -7.22253561e-01
-5.27156815e-02 8.27907979e-01 -1.16852187e-01 -1.50197059... | [8.805188179016113, 2.3417704105377197] |
a8bce9d7-7272-4ca8-940a-9a98602cef0f | neural-temporal-relation-extraction | null | null | https://aclanthology.org/E17-2118 | https://aclanthology.org/E17-2118.pdf | Neural Temporal Relation Extraction | We experiment with neural architectures for temporal relation extraction and establish a new state-of-the-art for several scenarios. We find that neural models with only tokens as input outperform state-of-the-art hand-engineered feature-based models, that convolutional neural networks outperform LSTM models, and that ... | ['Dmitriy Dligach', 'Guergana Savova', 'Chen Lin', 'Timothy Miller', 'Steven Bethard'] | 2017-04-01 | null | null | null | eacl-2017-4 | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.86219946e-01 8.51804972e-01 -8.27157736e-01 -5.84606886e-01
-8.04108322e-01 -3.86809647e-01 8.52880180e-01 5.74213803e-01
-5.23052037e-01 6.93359792e-01 4.03654814e-01 -9.36757386e-01
-4.26827967e-01 -1.19345176e+00 -9.33723152e-01 2.54932255e-01
-8.17517936e-01 7.69177437e-01 5.89419663e-01 -5.93852520... | [9.212636947631836, 8.963118553161621] |
85e2ca24-1828-446b-b27f-92cb5af02159 | a-triplet-loss-dilated-residual-network-for | 2303.08398 | null | https://arxiv.org/abs/2303.08398v1 | https://arxiv.org/pdf/2303.08398v1.pdf | A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval | Content-based image retrieval is the process of retrieving a subset of images from an extensive image gallery based on visual contents, such as color, shape or spatial relations, and texture. In some applications, such as localization, image retrieval is employed as the initial step. In such cases, the accuracy of the ... | ['Hamidreza Mahyar', 'Hamidreza Pourreza', 'Saeideh Yousefzadeh'] | 2023-03-15 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.11946337e-01 -6.23516858e-01 -2.36737549e-01 -1.48854569e-01
-1.25602579e+00 -2.77282566e-01 4.49954242e-01 6.10350408e-02
-5.46713412e-01 5.07902384e-01 -3.10710251e-01 6.41115084e-02
-6.44837379e-01 -7.15689242e-01 -6.63989484e-01 -1.05237067e+00
-1.31363928e-01 1.07290946e-01 1.95241690e-01 -2.62977511... | [10.723499298095703, 0.6844955682754517] |
b35b9cad-8211-47cf-a618-9d652e0efbeb | probabilistic-semantic-retrieval-for | 1712.06204 | null | http://arxiv.org/abs/1712.06204v2 | http://arxiv.org/pdf/1712.06204v2.pdf | Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs | We present a novel framework for finding complex activities matching
user-described queries in cluttered surveillance videos. The wide diversity of
queries coupled with unavailability of annotated activity data limits our
ability to train activity models. To bridge the semantic gap we propose to let
users describe an a... | ['Yu-Ting Chen', 'Gregory Castañón', 'Venkatesh Saligrama', 'Yannan Bai', 'Joseph Wang'] | 2017-12-17 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 4.62449372e-01 2.54221782e-02 -5.94926775e-01 -3.24607074e-01
-9.86164927e-01 -8.82325768e-01 5.04362166e-01 4.48743612e-01
-3.48812640e-01 5.04392028e-01 4.72508758e-01 7.78138787e-02
-3.54638010e-01 -6.25780880e-01 -9.23405111e-01 -2.49076381e-01
-5.50362051e-01 4.80930299e-01 8.09698761e-01 5.92673242... | [9.687668800354004, 0.7021060585975647] |
c17872d0-e1e0-48a8-9f32-9cb847bce11c | aanet-adaptive-aggregation-network-for | 2004.09548 | null | https://arxiv.org/abs/2004.09548v1 | https://arxiv.org/pdf/2004.09548v1.pdf | AANet: Adaptive Aggregation Network for Efficient Stereo Matching | Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved. Current state-of-the-art stereo models are mostly based on costly 3D convolutions, the cubic computational complexity and high memory consumption make it quite expensive to deploy in real-world applica... | ['Juyong Zhang', 'Haofei Xu'] | 2020-04-20 | aanet-adaptive-aggregation-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_AANet_Adaptive_Aggregation_Network_for_Efficient_Stereo_Matching_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_AANet_Adaptive_Aggregation_Network_for_Efficient_Stereo_Matching_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-flow-estimation'] | ['computer-vision'] | [-1.47149965e-01 -5.21046281e-01 -5.28980307e-02 -3.71280253e-01
-4.25904423e-01 -1.30349293e-01 1.78684667e-01 -1.75108284e-01
-6.02796495e-01 5.48354626e-01 -6.35925904e-02 -4.59864199e-01
1.42907172e-01 -1.15294695e+00 -7.91916251e-01 -4.16783810e-01
2.79369392e-02 2.03560635e-01 4.66244608e-01 -1.11276805... | [8.843924522399902, -2.2401058673858643] |
7692a43a-86a1-4fa7-8110-bc41b093a28b | attention-based-dynamic-subspace-learners-for | 2206.09068 | null | https://arxiv.org/abs/2206.09068v1 | https://arxiv.org/pdf/2206.09068v1.pdf | Attention-based Dynamic Subspace Learners for Medical Image Analysis | Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image sets using a single metric learner. Images, however, have a variety of o... | ['Herve Lombaert', 'Jose Dolz', 'Sukesh Adiga V'] | 2022-06-18 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.55272532e-01 1.14663221e-01 -3.56072873e-01 -3.85038137e-01
-7.87123799e-01 -7.38448620e-01 4.05673385e-01 4.20289963e-01
-5.41780651e-01 2.03599244e-01 1.72381520e-01 -1.20967224e-01
-5.84066093e-01 -3.32166910e-01 -7.50092328e-01 -9.65618551e-01
-1.03650495e-01 4.89513993e-01 1.43131077e-01 1.12148382... | [14.710687637329102, -2.158618211746216] |
8165008c-5af9-4286-995b-8be3eafbe104 | semantic2graph-graph-based-multi-modal | 2209.05653 | null | https://arxiv.org/abs/2209.05653v4 | https://arxiv.org/pdf/2209.05653v4.pdf | Semantic2Graph: Graph-based Multi-modal Feature Fusion for Action Segmentation in Videos | Video action segmentation and recognition tasks have been widely applied in many fields. Most previous studies employ large-scale, high computational visual models to understand videos comprehensively. However, few studies directly employ the graph model to reason about the video. The graph model provides the benefits ... | ['Meng-Hsun Tsai', 'Pei-Hsuan Tsai', 'Junbin Zhang'] | 2022-09-13 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 1.60160393e-01 -1.12142928e-01 -5.90536714e-01 -3.75670940e-01
-1.55614242e-01 -2.73223579e-01 4.56003398e-01 -2.33938396e-02
-3.06978405e-01 2.44780988e-01 6.52923882e-01 1.19879417e-01
-1.38108104e-01 -5.78727663e-01 -6.21721625e-01 -6.32171750e-01
-2.02448741e-01 -2.16854289e-02 6.91736221e-01 2.40983292... | [9.739890098571777, 0.8117493391036987] |
9e3601d7-4b3d-4e5c-adbb-623be18460aa | frame-subtitle-self-supervision-for-multi | 2209.03609 | null | https://arxiv.org/abs/2209.03609v1 | https://arxiv.org/pdf/2209.03609v1.pdf | Frame-Subtitle Self-Supervision for Multi-Modal Video Question Answering | Multi-modal video question answering aims to predict correct answer and localize the temporal boundary relevant to the question. The temporal annotations of questions improve QA performance and interpretability of recent works, but they are usually empirical and costly. To avoid the temporal annotations, we devise a we... | ['Weike Jin', 'Zhou Zhao', 'Jiong Wang'] | 2022-09-08 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.31540313e-01 1.03671677e-01 -7.84968361e-02 -6.38090670e-01
-1.25109828e+00 -6.24850035e-01 4.45643097e-01 -1.52362540e-01
-3.07852805e-01 5.48437178e-01 4.61659193e-01 -2.34537765e-01
-1.37519268e-02 -5.02393961e-01 -8.59807789e-01 -4.82396036e-01
2.11851999e-01 3.36962134e-01 8.66160393e-01 -2.22055450... | [10.411940574645996, 1.039218783378601] |
59418cbd-6cb2-438c-b018-2803a8b0e7e0 | emotalk-speech-driven-emotional | 2303.11089 | null | https://arxiv.org/abs/2303.11089v1 | https://arxiv.org/pdf/2303.11089v1.pdf | EmoTalk: Speech-driven emotional disentanglement for 3D face animation | Speech-driven 3D face animation aims to generate realistic facial expressions that match the speech content and emotion. However, existing methods often neglect emotional facial expressions or fail to disentangle them from speech content. To address this issue, this paper proposes an end-to-end neural network to disent... | ['Zhaoxin Fan', 'Jun He', 'Hongyan Liu', 'Xiangyu Zhu', 'Hao Xu', 'Zhenbo Song', 'HaoYu Wu', 'Ziqiao Peng'] | 2023-03-20 | null | null | null | null | ['3d-face-animation'] | ['computer-vision'] | [-1.83532998e-01 3.38186949e-01 1.18413754e-01 -6.93698049e-01
-5.24936318e-01 -3.10705632e-01 5.66503823e-01 -1.05885446e+00
1.25946879e-01 3.35120201e-01 5.84537804e-01 1.65557146e-01
4.14068222e-01 -2.98680961e-01 -4.50433910e-01 -6.94181859e-01
1.80057794e-01 1.34829178e-01 -9.20079589e-01 -3.61322075... | [13.141063690185547, -0.3889904022216797] |
4e264709-ec72-49b5-b03c-b1c0bc776ba4 | digital-twin-framework-for-time-to-failure | 2205.03513 | null | https://arxiv.org/abs/2205.03513v1 | https://arxiv.org/pdf/2205.03513v1.pdf | Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept | Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O\&M costs which ultimately reduces effective ... | ['Harsh S. Dhiman', 'Sakshi Deshmukh', 'Mili Wadhwani'] | 2022-04-28 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-4.63130713e-01 -6.06357276e-01 4.17628914e-01 2.24061191e-01
4.81206059e-01 -8.09388399e-01 5.26698604e-02 -5.34516387e-02
3.60089272e-01 8.56115103e-01 -4.37897354e-01 -3.88797253e-01
-6.16157770e-01 -8.06003928e-01 8.03733394e-02 -6.68317676e-01
-2.74635911e-01 9.52954441e-02 2.06318125e-01 -4.39730167... | [6.5703864097595215, 2.429513931274414] |
1a4279ba-fd25-44b2-b253-2cbb5557e51b | multi-fidelity-hierarchical-neural-processes | 2206.04872 | null | https://arxiv.org/abs/2206.04872v1 | https://arxiv.org/pdf/2206.04872v1.pdf | Multi-fidelity Hierarchical Neural Processes | Science and engineering fields use computer simulation extensively. These simulations are often run at multiple levels of sophistication to balance accuracy and efficiency. Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. Cheap data generated from low-fidelity sim... | ['Rose Yu', 'Yi-An Ma', 'Alessandro Vespignani', 'Matteo Chinazzi', 'Dongxia Wu'] | 2022-06-10 | null | null | null | null | ['epidemiology'] | ['medical'] | [-3.30052763e-01 -2.80710608e-01 1.97787017e-01 -2.49281868e-01
-9.24763083e-01 -3.40256065e-01 8.14702809e-01 5.41883260e-02
-3.06967854e-01 8.40710521e-01 2.98326761e-01 -5.62931061e-01
-3.16003293e-01 -1.03692257e+00 -7.55674005e-01 -8.38572562e-01
-3.80100489e-01 9.21177745e-01 -1.08289711e-01 3.04187417... | [6.944258689880371, 3.833388090133667] |
32be9120-d7b5-436b-82e9-97cd40df8766 | question-context-alignment-and-answer-context | 2306.02196 | null | https://arxiv.org/abs/2306.02196v1 | https://arxiv.org/pdf/2306.02196v1.pdf | Question-Context Alignment and Answer-Context Dependencies for Effective Answer Sentence Selection | Answer sentence selection (AS2) in open-domain question answering finds answer for a question by ranking candidate sentences extracted from web documents. Recent work exploits answer context, i.e., sentences around a candidate, by incorporating them as additional input string to the Transformer models to improve the co... | ['Thuy Vu', 'Ankit Chadha', 'Thien Huu Nguyen', 'Toan Nguyen', 'Kishan Kc', 'Minh Van Nguyen'] | 2023-06-03 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 2.10091516e-01 4.17482018e-01 6.71093836e-02 -5.54911315e-01
-1.20880961e+00 -7.44556904e-01 2.54392922e-01 6.97738171e-01
-1.88577965e-01 4.97994632e-01 5.78728855e-01 -4.82212275e-01
-1.08365603e-01 -1.16918480e+00 -7.80459166e-01 1.68750659e-01
3.17109376e-01 4.94432062e-01 1.03569055e+00 -5.44247150... | [11.148985862731934, 8.009674072265625] |
2b0b572e-d5b2-4869-a5cd-96a4cb67c69c | modeling-the-sequence-of-brain-volumes-by | 1603.01067 | null | http://arxiv.org/abs/1603.01067v1 | http://arxiv.org/pdf/1603.01067v1.pdf | Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding | We represent the sequence of fMRI (Functional Magnetic Resonance Imaging)
brain volumes recorded during a cognitive stimulus by a graph which consists of
a set of local meshes. The corresponding cognitive process, encoded in the
brain, is then represented by these meshes each of which is estimated assuming
a linear rel... | ['Itir Onal', 'Mete Ozay', 'Fatos T. Yarman Vural', 'Ilke Oztekin', 'Eda Mizrak'] | 2016-03-03 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [-3.85010727e-02 6.28959984e-02 2.31809244e-02 -3.97269398e-01
2.00559422e-01 -1.90743655e-01 4.33377177e-01 6.36369467e-01
-3.76916856e-01 3.39299232e-01 2.32046604e-01 3.21120471e-01
-3.28663051e-01 -1.10357726e+00 -7.54641891e-01 -6.45114064e-01
-8.09351563e-01 4.63722557e-01 9.72513855e-02 1.42386481... | [12.523965835571289, 3.3765509128570557] |
36871990-dee8-4d0a-aaad-2cf91a8f59b6 | reliable-amortized-variational-inference-with | 2207.11640 | null | https://arxiv.org/abs/2207.11640v3 | https://arxiv.org/pdf/2207.11640v3.pdf | Reliable amortized variational inference with physics-based latent distribution correction | Bayesian inference for high-dimensional inverse problems is computationally costly and requires selecting a suitable prior distribution. Amortized variational inference addresses these challenges via a neural network that approximates the posterior distribution not only for one instance of data, but a distribution of d... | ['Felix J. Herrmann', 'Rafael Orozco', 'Gabrio Rizzuti', 'Ali Siahkoohi'] | 2022-07-24 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 2.22181916e-01 -2.79664733e-02 3.88541549e-01 -1.65354192e-01
-9.97608900e-01 -4.45172966e-01 5.99316955e-01 -5.75835109e-02
-6.54267371e-01 7.95462012e-01 1.64536029e-01 -2.45104134e-01
-5.90786457e-01 -9.53191876e-01 -9.24437702e-01 -1.00596595e+00
1.08799534e-02 8.71945441e-01 1.39279887e-01 3.26460078... | [6.824497699737549, 3.6165761947631836] |
c6f94d1f-3a70-4d48-ae7a-da5f6bcaf1e6 | the-recent-advances-in-automatic-term | 2301.06767 | null | https://arxiv.org/abs/2301.06767v1 | https://arxiv.org/pdf/2301.06767v1.pdf | The Recent Advances in Automatic Term Extraction: A survey | Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. As units of knowledge in a specific field of expertise, extracted terms are not only beneficial for several terminographica... | ['Senja Pollak', 'Antoine Doucet', 'Jaya Caporusso', 'Matej Martinc', 'Hanh Thi Hong Tran'] | 2023-01-17 | null | null | null | null | ['feature-engineering', 'term-extraction'] | ['methodology', 'natural-language-processing'] | [ 4.44005996e-01 1.20251682e-02 -5.37165582e-01 -3.49235654e-01
-9.85526562e-01 -6.75065815e-01 7.07571208e-01 5.76068401e-01
-4.57879514e-01 6.08588934e-01 7.30515271e-02 -5.23615479e-01
-1.80402294e-01 -7.17086196e-01 -4.67350662e-01 -5.75753987e-01
-4.16207165e-02 5.31937957e-01 -3.34856242e-01 -4.40852731... | [10.271455764770508, 8.608400344848633] |
32935de6-2b80-46ee-ade5-40e43cb358f0 | machine-learning-based-forward-solver-an | 2111.12148 | null | https://arxiv.org/abs/2111.12148v1 | https://arxiv.org/pdf/2111.12148v1.pdf | Machine Learning Based Forward Solver: An Automatic Framework in gprMax | General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems. We explore the performance of a near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture... | ['Antonios Giannopoulos', 'Craig Warren', 'Iraklis Giannakis', 'Utsav Akhaury'] | 2021-11-23 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 3.05885002e-02 -2.33326122e-01 1.00768781e+00 -3.60273421e-01
-1.13331640e+00 -3.25551420e-03 3.02511781e-01 -2.91713178e-01
4.43494171e-02 6.81709111e-01 -6.35106191e-02 -8.88196945e-01
-4.94581938e-01 -1.12243700e+00 -2.20433250e-01 -5.26031494e-01
-4.07958657e-01 8.61981511e-01 -1.28281683e-01 -4.54680920... | [6.826801300048828, 2.000822067260742] |
6b97a35c-6a5f-472f-bedf-ec4e26bd6d46 | a-monte-carlo-language-model-pipeline-for | 2305.15051 | null | https://arxiv.org/abs/2305.15051v1 | https://arxiv.org/pdf/2305.15051v1.pdf | A Monte Carlo Language Model Pipeline for Zero-Shot Sociopolitical Event Extraction | We consider dyadic zero-shot event extraction (EE) to identify actions between pairs of actors. The \emph{zero-shot} setting allows social scientists or other non-computational researchers to extract any customized, user-specified set of events without training, resulting in a \emph{dyadic} event database, allowing ins... | ["Brendan O'Connor", 'Erica Cai'] | 2023-05-24 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 2.10951865e-01 4.86335754e-01 -2.36672953e-01 -5.34385554e-02
-1.46002066e+00 -9.80115712e-01 1.21025717e+00 4.63202447e-01
-5.72956800e-01 6.69198215e-01 1.00960147e+00 -5.07509589e-01
-1.05372965e-01 -9.80081141e-01 -4.47279751e-01 -3.57951880e-01
1.84236199e-01 8.50329459e-01 1.14763692e-01 -2.97514200... | [9.059900283813477, 9.367782592773438] |
3c6ebab9-f34b-4fe2-a629-90e7ba132745 | object-detection-in-aerial-images-with | 2208.10781 | null | https://arxiv.org/abs/2208.10781v2 | https://arxiv.org/pdf/2208.10781v2.pdf | Object Detection in Aerial Images with Uncertainty-Aware Graph Network | In this work, we propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects and their spatial-semantic similarities, respectively. Specifically, we aim to consider relationships among objects for effectively contextualizing them. To achieve this, w... | ['Sung Ju Hwang', 'Jinheon Baek', 'Jongha Kim'] | 2022-08-23 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 7.73310140e-02 3.35820854e-01 2.03209013e-01 -5.74595332e-01
-3.24976236e-01 -5.24345696e-01 4.34022695e-01 3.48893285e-01
-1.09607860e-01 4.17333007e-01 -1.98628843e-01 3.80897447e-02
-5.25721312e-01 -9.92697060e-01 -8.86429965e-01 -5.29019535e-01
-3.44930112e-01 3.51437062e-01 6.55128658e-01 4.45819236... | [10.013474464416504, 1.62588632106781] |
0b0d9423-d5a1-4b93-a9f7-d68e031e8942 | h-gan-the-power-of-gans-in-your-hands | 2103.15017 | null | https://arxiv.org/abs/2103.15017v2 | https://arxiv.org/pdf/2103.15017v2.pdf | H-GAN: the power of GANs in your Hands | We present HandGAN (H-GAN), a cycle-consistent adversarial learning approach implementing multi-scale perceptual discriminators. It is designed to translate synthetic images of hands to the real domain. Synthetic hands provide complete ground-truth annotations, yet they are not representative of the target distribution... | ['Antonis Argyros', 'Jose Garcia-Rodriguez', 'Aggeliki Tsoli', 'Alberto Garcia-Garcia', 'Iason Oikonomidis', 'Sergio Orts-Escolano', 'Nikolaos Kyriazis', 'Pablo Martinez-Gonzalez', 'Giorgos Karvounas', 'Sergiu Oprea'] | 2021-03-27 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.07450366e-01 1.99810177e-01 1.92096472e-01 -3.10119893e-02
-1.04156172e+00 -1.03998137e+00 6.28015339e-01 -8.51064622e-01
3.57668996e-02 9.28014457e-01 4.85631898e-02 2.77847588e-01
4.30848867e-01 -7.69750118e-01 -1.04688418e+00 -7.47177362e-01
3.04957747e-01 6.10108614e-01 9.73008722e-02 -3.01582694... | [11.770124435424805, -0.5404829382896423] |
654687c1-7f82-4d05-ae78-89d8de429b5c | xwikigen-cross-lingual-summarization-for | 2303.12308 | null | https://arxiv.org/abs/2303.12308v2 | https://arxiv.org/pdf/2303.12308v2.pdf | XWikiGen: Cross-lingual Summarization for Encyclopedic Text Generation in Low Resource Languages | Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on English only where English reference articles are summarized to generate English Wikipedia pages. But, for low-re... | ['Vasudeva Varma', 'Manish Gupta', 'Shivansh Subramanian', 'Anupam Patil', 'Shivprasad Sagare', 'Dhaval Taunk'] | 2023-03-22 | null | null | null | null | ['unsupervised-extractive-summarization', 'multi-document-summarization', 'extractive-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.83763430e-01 4.08713609e-01 -4.73893851e-01 1.42702863e-01
-1.47871315e+00 -5.66931546e-01 9.26116824e-01 1.54779613e-01
-3.71519059e-01 1.48364317e+00 8.81880641e-01 -2.48289511e-01
2.92333275e-01 -7.59328187e-01 -9.47485626e-01 -1.50055960e-01
5.05614698e-01 5.46791792e-01 -7.35667720e-02 -5.14901578... | [12.342965126037598, 9.517804145812988] |
6ddb1fda-8b6b-4db3-9232-e703f2834909 | yolo3d-end-to-end-real-time-3d-oriented | 1808.02350 | null | http://arxiv.org/abs/1808.02350v1 | http://arxiv.org/pdf/1808.02350v1.pdf | YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud | Object detection and classification in 3D is a key task in Automated Driving
(AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction
of the surrounding environment, while the task of 3D object bounding box
detection in real time remains a strong algorithmic challenge. In this paper,
we build on th... | ['Mahmoud Zidan', 'Sherif Abdelkarim', 'Mohamed Zahran', 'Waleed Ali', 'Ahmad El Sallab'] | 2018-08-07 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.05099723e-01 -3.12628210e-01 1.17177129e-01 -4.33734596e-01
-6.60787761e-01 -4.19448078e-01 4.13656771e-01 -8.54594111e-02
-7.44090796e-01 3.22725147e-01 -6.25875592e-01 -5.64575911e-01
2.43621081e-01 -7.77781308e-01 -8.84729624e-01 -5.37077665e-01
-9.41216722e-02 7.90493369e-01 5.64348161e-01 -4.76500422... | [7.768596172332764, -2.6304521560668945] |
6b53273c-a9ec-49ac-bce9-ab2d0986e49a | two-hand-global-3d-pose-estimation-using | 2006.01320 | null | https://arxiv.org/abs/2006.01320v4 | https://arxiv.org/pdf/2006.01320v4.pdf | Two-hand Global 3D Pose Estimation Using Monocular RGB | We tackle the challenging task of estimating global 3D joint locations for both hands via only monocular RGB input images. We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despite occlusion between two hands and complex background noise and estima... | ['Fanqing Lin', 'Tony Martinez', 'Connor Wilhelm'] | 2020-06-01 | null | null | null | null | ['3d-canonical-hand-pose-estimation'] | ['computer-vision'] | [-3.98273200e-01 -3.35621625e-01 -7.84466341e-02 1.44378990e-02
-7.28725791e-01 -8.22143316e-01 2.66758949e-01 -6.40625358e-01
-8.44865501e-01 4.21730161e-01 1.28227681e-01 6.75219148e-02
2.69451499e-01 -2.09274501e-01 -6.42842412e-01 -3.41097593e-01
3.00993204e-01 1.08852410e+00 1.36899814e-01 1.29313841... | [6.579189777374268, -0.7757436633110046] |
6dffe18d-773a-42e2-bdcc-a5562e644eb9 | sportscap-monocular-3d-human-motion-capture | 2104.11452 | null | https://arxiv.org/abs/2104.11452v4 | https://arxiv.org/pdf/2104.11452v4.pdf | SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos | Markerless motion capture and understanding of professional non-daily human movements is an important yet unsolved task, which suffers from complex motion patterns and severe self-occlusion, especially for the monocular setting. In this paper, we propose SportsCap -- the first approach for simultaneously capturing 3D h... | ['Jingyi Yu', 'Lan Xu', 'Yuexin Ma', 'Wei Yang', 'Anqi Pang', 'Xin Chen'] | 2021-04-23 | null | null | null | null | ['markerless-motion-capture', 'action-assessment'] | ['computer-vision', 'computer-vision'] | [ 5.58403581e-02 -5.63761830e-01 -5.67539692e-01 -1.44989923e-01
-7.27414489e-01 -5.46306729e-01 2.78166294e-01 -4.82949972e-01
-3.20115060e-01 3.20693105e-01 8.59729409e-01 2.49358878e-01
-1.00141749e-01 -4.84707087e-01 -6.93481505e-01 -6.13098681e-01
8.51841867e-02 3.19789827e-01 4.85156685e-01 -1.20454662... | [7.295968055725098, -0.4924871325492859] |
c04d3266-0bd1-4e7f-8fd2-5e9481324f91 | adviser-networks-learning-what-question-to | 1802.01666 | null | http://arxiv.org/abs/1802.01666v3 | http://arxiv.org/pdf/1802.01666v3.pdf | Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation | Humans have an unparalleled visual intelligence and can overcome visual
ambiguities that machines currently cannot. Recent works have shown that
incorporating guidance from humans during inference for monocular
viewpoint-estimation can help overcome difficult cases in which the
computer-alone would have otherwise faile... | ['Mohamed El Banani', 'Jason J. Corso'] | 2018-02-05 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 1.01373784e-01 7.23256111e-01 1.80859968e-01 -5.80798626e-01
-3.76871943e-01 -6.31807327e-01 5.57841063e-01 -3.04136515e-01
-5.67101300e-01 6.17159128e-01 8.17524865e-02 -4.64617789e-01
-2.66786128e-01 -4.60321367e-01 -6.71190858e-01 -3.55062395e-01
6.13617063e-01 1.06454182e+00 1.50023788e-01 -1.71683326... | [10.739184379577637, 1.9580763578414917] |
3e9eb429-f297-4ad8-811f-dc0090225252 | cylin-painting-seamless-360deg-panoramic | 2204.08563 | null | https://arxiv.org/abs/2204.08563v1 | https://arxiv.org/pdf/2204.08563v1.pdf | Cylin-Painting: Seamless 360° Panoramic Image Outpainting and Beyond with Cylinder-Style Convolutions | Image outpainting gains increasing attention since it can generate the complete scene from a partial view, providing a valuable solution to construct 360{\deg} panoramic images. As image outpainting suffers from the intrinsic issue of unidirectional completion flow, previous methods convert the original problem into in... | ['Yao Zhao', 'Yunchao Wei', 'Wenqi Ren', 'Chunyu Lin', 'Xiangyu Xu', 'Kang Liao'] | 2022-04-18 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 6.47050858e-01 1.36191308e-01 -1.01788223e-01 -8.67802948e-02
-4.37977344e-01 -5.54144204e-01 4.27392423e-01 -2.94074625e-01
-7.70915300e-02 7.60406911e-01 2.06447244e-01 -1.05753578e-01
-1.36482671e-01 -9.04215276e-01 -7.61434197e-01 -8.22538972e-01
4.70444798e-01 -1.20042548e-01 1.14608943e-01 -2.06956148... | [11.250763893127441, -1.1583794355392456] |
609e22f8-6c0d-41b3-a323-2cd646fff14e | aligning-to-social-norms-and-values-in | 2205.01975 | null | https://arxiv.org/abs/2205.01975v2 | https://arxiv.org/pdf/2205.01975v2.pdf | Aligning to Social Norms and Values in Interactive Narratives | We focus on creating agents that act in alignment with socially beneficial norms and values in interactive narratives or text-based games -- environments wherein an agent perceives and interacts with a world through natural language. Such interactive agents are often trained via reinforcement learning to optimize task ... | ['Yejin Choi', 'Hannaneh Hajishirzi', 'Maarten Sap', 'Liwei Jiang', 'Prithviraj Ammanabrolu'] | 2022-05-04 | null | https://aclanthology.org/2022.naacl-main.439 | https://aclanthology.org/2022.naacl-main.439.pdf | naacl-2022-7 | ['text-based-games'] | ['playing-games'] | [ 3.85492653e-01 9.64081466e-01 3.30884196e-02 -2.21766129e-01
-8.09409991e-02 -4.22882587e-01 1.10147250e+00 2.37254664e-01
-8.01123977e-01 1.20145988e+00 8.83746564e-01 2.43804350e-01
-1.58837020e-01 -9.65080619e-01 -2.73491442e-01 -6.21051908e-01
-1.46129340e-01 8.30425143e-01 -8.69957581e-02 -9.38598871... | [11.641067504882812, 8.019150733947754] |
3cc4cb15-e3e6-4c9c-a937-37238ab069d3 | culturebert-fine-tuning-transformer-based | 2212.00509 | null | https://arxiv.org/abs/2212.00509v2 | https://arxiv.org/pdf/2212.00509v2.pdf | CultureBERT: Fine-Tuning Transformer-Based Language Models for Corporate Culture | This paper introduces supervised machine learning to the literature measuring corporate culture from text documents. We compile a unique data set of employee reviews that were labeled by human evaluators with respect to the information the reviews reveal about the firms' corporate culture. Using this data set, we fine-... | ['Stefan Pasch', 'Sebastian Koch'] | 2022-12-01 | null | null | null | null | ['culture'] | ['speech'] | [-1.52161986e-01 3.53535831e-01 -9.08506989e-01 -7.22907424e-01
-1.13778222e+00 -7.30599642e-01 8.26180398e-01 4.99539942e-01
-4.85386431e-01 5.84039092e-01 5.53032219e-01 -7.20842481e-01
4.02590007e-01 -6.71393514e-01 -3.29741210e-01 7.92191625e-02
7.08567321e-01 7.11901128e-01 -4.88752276e-01 -3.49354625... | [11.062344551086426, 7.194669246673584] |
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