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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b2168455-a8b2-478c-bdca-4b3c4a6b1b7e | grammatical-error-correction-as-multiclass | null | null | https://aclanthology.org/W13-3610 | https://aclanthology.org/W13-3610.pdf | Grammatical Error Correction as Multiclass Classification with Single Model | null | ['Hai Zhao', 'Zhongye Jia', 'Peilu Wang'] | 2013-08-01 | null | null | null | ws-2013-8 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.322076320648193, 3.7806546688079834] |
9dc520c1-3bbe-451c-946d-2972b2ae9bc8 | user-guided-deep-anime-line-art-colorization | 1808.03240 | null | http://arxiv.org/abs/1808.03240v2 | http://arxiv.org/pdf/1808.03240v2.pdf | User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks | Scribble colors based line art colorization is a challenging computer vision
problem since neither greyscale values nor semantic information is presented in
line arts, and the lack of authentic illustration-line art training pairs also
increases difficulty of model generalization. Recently, several Generative
Adversari... | ['Yuanzheng Ci', 'Zhongxuan Luo', 'Xinzhu Ma', 'Zhihui Wang', 'Haojie Li'] | 2018-08-09 | null | null | null | null | ['line-art-colorization'] | ['computer-vision'] | [ 4.74829823e-01 -1.03330880e-01 4.02681977e-01 -2.47426748e-01
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2.71143287e-01 -1.06910169e+00 -1.07100368e+00 -6.92042470e-01
5.77944934e-01 3.92422855e-01 -2.00306177e-02 -3.92108709... | [11.717928886413574, -0.5912800431251526] |
38ec8399-7d1f-4b25-b8cb-4817b64977df | analyzing-the-effectiveness-of-the-underlying | 2302.05963 | null | https://arxiv.org/abs/2302.05963v1 | https://arxiv.org/pdf/2302.05963v1.pdf | Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering | To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets. However, it remains an open question as to how effective UR tasks are for the QA task when training models on both tasks in an end... | ['Akiko Aizawa', 'Saku Sugawara', 'Anh-Khoa Duong Nguyen', 'Xanh Ho'] | 2023-02-12 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 9.54488665e-02 4.90716189e-01 3.36396359e-02 -2.60150313e-01
-1.34030640e+00 -8.73279691e-01 3.99765879e-01 1.40481353e-01
-2.71911174e-01 8.46420050e-01 4.83887434e-01 -8.71102989e-01
-2.31518418e-01 -1.10380268e+00 -9.55387115e-01 -1.13192815e-02
3.17463636e-01 5.48108995e-01 7.70429909e-01 -9.57928896... | [11.021955490112305, 7.946394443511963] |
f66c681d-8437-4a83-9d99-735d889ac732 | evaluation-of-fem-and-mlfem-ai-explainers-in | 2212.01222 | null | https://arxiv.org/abs/2212.01222v5 | https://arxiv.org/pdf/2212.01222v5.pdf | Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference Metrics | The most popular methods in AI-machine learning paradigm are mainly black boxes. This is why explanation of AI decisions is of emergency. Although dedicated explanation tools have been massively developed, the evaluation of their quality remains an open research question. In this paper, we generalize the methodologies ... | ['R. Giot', 'J. Benois-Pineau', 'A. Zhukov'] | 2022-12-02 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [-3.43193449e-02 5.63089252e-01 2.67372224e-02 -5.30113757e-01
9.47792605e-02 -3.67836952e-01 9.04634118e-01 4.28472131e-01
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-5.09662509e-01 -3.63838017e-01 -6.18597686e-01 -4.95867640e-01
3.81699771e-01 4.98728037e-01 3.05041641e-01 -1.77620143... | [9.981803894042969, 1.8810181617736816] |
055a2c9a-be4e-410a-b788-50d8e66ee246 | knowledge-incorporating-esim-models-for | 1907.05792 | null | https://arxiv.org/abs/1907.05792v1 | https://arxiv.org/pdf/1907.05792v1.pdf | Knowledge-incorporating ESIM models for Response Selection in Retrieval-based Dialog Systems | Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e.g. flight booking, hotel reservation, technical support, student advising etc. These dialog systems must learn to interact with external domain kno... | ['Kshitij Fadnis', 'Siva Sankalp Patel', 'Jatin Ganhotra'] | 2019-07-11 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [ 1.49106579e-02 5.15167475e-01 -1.37810141e-01 -1.16515994e+00
-7.31057703e-01 -7.20907390e-01 7.61108160e-01 2.94263750e-01
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-1.19558081e-01 -6.13255084e-01 -8.28357860e-02 -1.73084941e-02
2.47399598e-01 1.18038070e+00 5.21178484e-01 -9.63336706... | [12.862383842468262, 7.814314365386963] |
cb23df09-5ead-482f-a899-32e7ee3e8a87 | bridging-the-sim2real-gap-with-care | 2302.04832 | null | https://arxiv.org/abs/2302.04832v1 | https://arxiv.org/pdf/2302.04832v1.pdf | Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting | Sim2Real domain adaptation (DA) research focuses on the constrained setting of adapting from a labeled synthetic source domain to an unlabeled or sparsely labeled real target domain. However, for high-stakes applications (e.g. autonomous driving), it is common to have a modest amount of human-labeled real data in addit... | ['James Lucas', 'Sanja Fidler', 'Judy Hoffman', 'Marc T. Law', 'Rafid Mahmood', 'Andrew Liao', 'David Acuna', 'Viraj Prabhu'] | 2023-02-09 | null | null | null | null | ['2d-object-detection'] | ['computer-vision'] | [ 5.57682335e-01 3.66531968e-01 -4.34404492e-01 -6.25237226e-01
-1.20141995e+00 -9.05954778e-01 8.95206571e-01 -2.59471953e-01
-6.48335040e-01 9.84555006e-01 9.59385559e-02 -3.67938489e-01
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3.83853853e-01 1.04241979e+00 1.77442104e-01 -2.38065779... | [9.951866149902344, 1.7148150205612183] |
d5826f42-62a5-4722-9062-459fb17bd77b | 190503397 | 1905.03397 | null | https://arxiv.org/abs/1905.03397v3 | https://arxiv.org/pdf/1905.03397v3.pdf | A Dual-Path Model With Adaptive Attention For Vehicle Re-Identification | In recent years, attention models have been extensively used for person and vehicle re-identification. Most re-identification methods are designed to focus attention on key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper, we present a novel dual-path adap... | ['Rama Chellappa', 'Jun-Cheng Chen', 'Sai Saketh Rambhatla', 'Pirazh Khorramshahi', 'Neehar Peri', 'Amit Kumar'] | 2019-05-09 | a-dual-path-model-with-adaptive-attention-for | http://openaccess.thecvf.com/content_ICCV_2019/html/Khorramshahi_A_Dual-Path_Model_With_Adaptive_Attention_for_Vehicle_Re-Identification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Khorramshahi_A_Dual-Path_Model_With_Adaptive_Attention_for_Vehicle_Re-Identification_ICCV_2019_paper.pdf | iccv-2019-10 | ['vehicle-key-point-and-orientation-estimation'] | ['computer-vision'] | [-6.15197659e-01 -3.62321198e-01 -3.55839252e-01 -2.53991961e-01
-9.23935592e-01 -5.94794691e-01 7.98650563e-01 3.30906063e-01
-2.25672245e-01 2.11924121e-01 -8.30159932e-02 -4.25490960e-02
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2.46426463e-02 7.13642061e-01 2.40008160e-02 -2.81935818... | [8.103017807006836, -0.9945830702781677] |
e29601e0-8a9a-4f28-8d38-6cf51201bbb2 | evaluating-lenet-algorithms-in-classification | 2305.13333 | null | https://arxiv.org/abs/2305.13333v1 | https://arxiv.org/pdf/2305.13333v1.pdf | Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases | The advancement of computer-aided detection systems had a significant impact on clinical analysis and decision-making on human disease. Lung cancer requires more attention among the numerous diseases being examined because it affects both men and women, increasing the mortality rate. LeNet, a deep learning model, is us... | ['Jafar Abdollahi'] | 2023-05-19 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [-7.71587342e-02 -1.51250422e-01 -1.93838090e-01 4.23687510e-02
-5.17630160e-01 -2.52880901e-01 3.18443477e-01 3.47755611e-01
-6.74836874e-01 7.97225714e-01 8.41707140e-02 -7.25270748e-01
-1.34741131e-03 -1.02596772e+00 1.21073857e-01 -1.02567697e+00
-7.23401308e-02 6.56040013e-01 3.00897777e-01 1.96021006... | [15.284333229064941, -2.626682758331299] |
526d14a0-71cc-46cd-b83f-8152fe63446b | multiple-element-joint-detection-for-aspect | null | null | https://www.sciencedirect.com/science/article/pii/S0950705121003361 | https://www.sciencedirect.com/science/article/pii/S0950705121003361/pdfft?md5=cc3e69ea2595c62b115f6a46b7d7d2b3&pid=1-s2.0-S0950705121003361-main.pdf | Multiple-element joint detection for Aspect-Based Sentiment Analysis | Aspect-Based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task, which aims to detect target-aspect-sentiment elements in a sentence. Most of the existing research work distinguished the sentiment for aspects or targets independently, ignoring the corresponding relation between the targets and the aspe... | ['Jie Chen', 'Min Gao', 'Qiwu Zhu', 'Yang Yu', 'Hualing Yi', 'Qingyu Xiong', 'Chao Wu'] | 2020-10-22 | null | null | null | knowledge-based-systems-2020-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.50374204e-01 7.94641897e-02 -2.11701989e-01 -7.51488805e-01
-1.04071522e+00 -6.20318353e-01 5.28863966e-01 4.85331655e-01
-2.35796064e-01 8.37383792e-02 7.47460127e-01 -2.48028949e-01
3.03464442e-01 -9.50927556e-01 -7.94718862e-01 -2.66771346e-01
2.19217375e-01 2.85812974e-01 6.03108108e-02 -5.69400728... | [11.498398780822754, 6.6271562576293945] |
212b5d99-9369-42f3-8021-672c8a723ed3 | query-encoder-distillation-via-embedding | 2306.11550 | null | https://arxiv.org/abs/2306.11550v1 | https://arxiv.org/pdf/2306.11550v1.pdf | Query Encoder Distillation via Embedding Alignment is a Strong Baseline Method to Boost Dense Retriever Online Efficiency | The information retrieval community has made significant progress in improving the efficiency of Dual Encoder (DE) dense passage retrieval systems, making them suitable for latency-sensitive settings. However, many proposed procedures are often too complex or resource-intensive, which makes it difficult for practitione... | ['Hong Lyu', 'Yuxuan Wang'] | 2023-06-05 | null | null | null | null | ['passage-retrieval', 'information-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [-1.28541917e-01 -5.62215269e-01 -4.01973963e-01 -1.97443724e-01
-1.36328316e+00 -8.27001452e-01 4.26233709e-01 5.02977073e-01
-8.69802415e-01 7.01312363e-01 2.78571278e-01 -6.40497506e-01
-9.77033973e-02 -7.83927917e-01 -6.82812691e-01 -5.33620477e-01
-1.13518730e-01 4.80821431e-01 2.92256325e-01 -4.27859426... | [11.443892478942871, 7.66028356552124] |
16b407e1-a5b9-411a-81ed-182d67a644bd | improving-occlusion-and-hard-negative | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Noh_Improving_Occlusion_and_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Noh_Improving_Occlusion_and_CVPR_2018_paper.pdf | Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors | We propose methods of addressing two critical issues of pedestrian detection: (i) occlusion of target objects as false negative failure, and (ii) confusion with hard negative examples like vertical structures as false positive failure. Our solutions to these two problems are general and flexible enough to be applicable... | ['Gunhee Kim', 'Junhyug Noh', 'Beomsu Kim', 'Soochan Lee'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['occlusion-handling'] | ['computer-vision'] | [ 1.89173669e-01 3.64811271e-02 -1.20706232e-02 -4.07462150e-01
-9.72089708e-01 -4.26405251e-01 3.12070042e-01 1.64741948e-01
-5.79243600e-01 6.19157076e-01 -2.87022531e-01 -3.77456397e-01
4.84559774e-01 -6.60198927e-01 -7.51787663e-01 -5.18112719e-01
3.09096258e-02 5.04509628e-01 1.20551312e+00 1.35271922... | [8.21009635925293, -0.3441934883594513] |
dd4bc826-f4e0-4648-a672-a61eea9b4bbf | centerpoly-real-time-instance-segmentation | 2108.08923 | null | https://arxiv.org/abs/2108.08923v2 | https://arxiv.org/pdf/2108.08923v2.pdf | CenterPoly: real-time instance segmentation using bounding polygons | We present a novel method, called CenterPoly, for real-time instance segmentation using bounding polygons. We apply it to detect road users in dense urban environments, making it suitable for applications in intelligent transportation systems like automated vehicles. CenterPoly detects objects by their center keypoint ... | ['Maguelonne Héritier', 'Nicolas Saunier', 'Guillaume-Alexandre Bilodeau', 'Hughes Perreault'] | 2021-08-19 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [-1.57229602e-01 3.59906614e-01 -4.04955715e-01 -3.84896666e-01
-5.69713414e-01 -5.83985984e-01 5.30663908e-01 7.06596226e-02
-5.05560696e-01 4.03258651e-01 -4.47319478e-01 -4.80122358e-01
2.23886877e-01 -1.34857523e+00 -8.53832662e-01 -5.57394087e-01
-4.21531558e-01 1.22265697e+00 1.03220737e+00 -2.44081374... | [8.261362075805664, -2.113797426223755] |
d78708a9-81b9-4ec3-96bf-5279c766733a | recursion-aware-modeling-and-discovery-for | 1710.09323 | null | http://arxiv.org/abs/1710.09323v1 | http://arxiv.org/pdf/1710.09323v1.pdf | Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended) | This extended paper presents 1) a novel hierarchy and recursion extension to
the process tree model; and 2) the first, recursion aware process model
discovery technique that leverages hierarchical information in event logs,
typically available for software systems. This technique allows us to analyze
the operational pr... | ['Maikel Leemans', 'Mark G. J. van den Brand', 'Wil M. P. van der Aalst'] | 2017-10-17 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 4.23215538e-01 2.98135698e-01 1.11691356e-01 1.19871609e-02
-2.48705477e-01 -5.40296197e-01 9.62448001e-01 7.15016186e-01
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-7.39615083e-01 -1.03800237e+00 1.73254654e-01 -7.80074447e-02
-4.88778293e-01 7.01045871e-01 7.73519158e-01 1.19738251... | [8.581500053405762, 6.032574653625488] |
e5b4d47c-6d4c-474d-84bc-c1613740d5d3 | superb-slt-2022-challenge-on-generalization | 2210.08634 | null | https://arxiv.org/abs/2210.08634v2 | https://arxiv.org/pdf/2210.08634v2.pdf | SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning | We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency. The challenge builds upon the SUPERB benchmark and implements metrics to measure the computation requirements of self-supervised learning (SSL) representation... | ['Hung-Yi Lee', 'Shang-Wen Li', 'Abdelrahman Mohamed', 'Shinji Watanabe', 'Xuankai Chang', 'Haibin Wu', 'Zili Huang', 'Kai-Wei Chang', 'Jiatong Shi', 'Tzu-Quan Lin', 'Shu-wen Yang', 'Ching-Feng Yeh', 'Annie Dong', 'Tzu-hsun Feng'] | 2022-10-16 | null | null | null | null | ['audio-generation', 'speaker-recognition'] | ['audio', 'speech'] | [ 3.74213994e-01 4.90055650e-01 -3.58727247e-01 -8.11694801e-01
-1.25074208e+00 -4.65003282e-01 8.93953562e-01 3.44431400e-02
-2.21655935e-01 5.02011240e-01 9.16227341e-01 -3.58979166e-01
-4.68339399e-02 -6.40947521e-02 -6.39106810e-01 -2.54428536e-01
-3.08079720e-01 4.53686863e-01 -2.73876498e-03 -1.85948417... | [14.358903884887695, 6.6658196449279785] |
d0e07165-17d5-4011-b40f-8a67dd530d40 | systematic-generalization-and-emergent | 2210.00400 | null | https://arxiv.org/abs/2210.00400v2 | https://arxiv.org/pdf/2210.00400v2.pdf | Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks | Transformer networks have seen great success in natural language processing and machine vision, where task objectives such as next word prediction and image classification benefit from nuanced context sensitivity across high-dimensional inputs. However, there is an ongoing debate about how and when transformers can acq... | ['James L. McClelland', 'YuXuan Li'] | 2022-10-02 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 8.35178971e-01 -8.39966759e-02 -5.86317480e-03 -4.70482707e-01
-3.51287484e-01 -7.43636310e-01 7.77679741e-01 2.67962664e-01
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-3.10464799e-01 -6.46983445e-01 -5.74141383e-01 -7.23573208e-01
-1.49031058e-01 4.57787097e-01 1.50797874e-01 -1.00993931... | [9.68852710723877, 7.126200199127197] |
82c98b91-2661-4a20-a213-0f6234c3124c | rethinking-matching-based-few-shot-action | 2303.16084 | null | https://arxiv.org/abs/2303.16084v1 | https://arxiv.org/pdf/2303.16084v1.pdf | Rethinking matching-based few-shot action recognition | Few-shot action recognition, i.e. recognizing new action classes given only a few examples, benefits from incorporating temporal information. Prior work either encodes such information in the representation itself and learns classifiers at test time, or obtains frame-level features and performs pairwise temporal matchi... | ['Giorgos Tolias', 'Yannis Kalantidis', 'Juliette Bertrand'] | 2023-03-28 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.43640673e-01 -4.49581563e-01 -6.45402849e-01 -4.68019038e-01
-1.08700502e+00 -4.86931115e-01 9.32467401e-01 -1.87295437e-01
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-3.46529782e-01 -4.65023011e-01 -5.65036595e-01 -6.24274492e-01
-4.34654564e-01 4.51014191e-01 7.17401624e-01 -1.25164554... | [8.226473808288574, 0.6723277568817139] |
f54c043e-0c9e-4fe9-abcd-18912ed60cc7 | snapshot-of-algebraic-vision | 2210.11443 | null | https://arxiv.org/abs/2210.11443v1 | https://arxiv.org/pdf/2210.11443v1.pdf | Snapshot of Algebraic Vision | In this survey article, we present interactions between algebraic geometry and computer vision, which have recently come under the header of Algebraic Vision. The subject has given new insights in multiple view geometry and its application to 3D scene reconstruction, and carried a host of novel problems and ideas back ... | ['Kathlén Kohn', 'Joe Kileel'] | 2022-10-20 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [-1.77344769e-01 6.05963655e-02 1.09818600e-01 -3.56238484e-01
2.52749883e-02 -7.43941665e-01 5.53799450e-01 -2.13045895e-01
-3.91580015e-02 8.32540020e-02 7.89579004e-02 -4.63282406e-01
-3.75579298e-01 -5.88751256e-01 -1.37179628e-01 -1.45637602e-01
-2.59433180e-01 4.40163136e-01 -7.69188032e-02 -5.99023640... | [8.040183067321777, -2.2991855144500732] |
06350e38-9197-40dc-acaf-df9f308dd562 | mental-stress-detection-using-data-from | 2202.03033 | null | https://arxiv.org/abs/2202.03033v2 | https://arxiv.org/pdf/2202.03033v2.pdf | Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques | This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using da... | ['Jihyoung Ryu', 'Waleed Manzoor', 'Imran Fareed Nizami', 'Muhammad Majid', 'Syed Muhammad Anwar', 'Aamir Arsalan'] | 2022-02-07 | null | null | null | null | ['pupil-dilation', 'heart-rate-variability', 'mental-stress-detection'] | ['computer-vision', 'medical', 'robots'] | [ 3.32233489e-01 -3.47398937e-01 -2.88393050e-01 -4.77166831e-01
1.05296239e-01 -3.55516225e-01 -9.27063525e-02 7.07920969e-01
-6.55769885e-01 8.13841701e-01 1.47818193e-01 2.24247903e-01
-1.47069573e-01 -3.96865875e-01 3.74218300e-02 -4.45358455e-01
1.81331707e-03 -4.43396688e-01 -3.62280160e-01 -2.08534420... | [13.578036308288574, 3.090080976486206] |
8776f3d3-5cfa-4219-aa90-5f440e9f860b | towards-privacy-preserving-neural | 2204.10958 | null | https://arxiv.org/abs/2204.10958v1 | https://arxiv.org/pdf/2204.10958v1.pdf | Towards Privacy-Preserving Neural Architecture Search | Machine learning promotes the continuous development of signal processing in various fields, including network traffic monitoring, EEG classification, face identification, and many more. However, massive user data collected for training deep learning models raises privacy concerns and increases the difficulty of manual... | ['Robin Doss', 'Shengshan Hu', 'Lei Pan', 'Leo Yu Zhang', 'Fuyi Wang'] | 2022-04-22 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 1.76408067e-02 9.36762914e-02 -1.29503921e-01 -7.08806276e-01
-9.36114848e-01 -9.36411798e-01 -3.27514559e-02 -2.70080835e-01
-8.34094584e-01 7.25955546e-01 -2.46581748e-01 -5.69648445e-01
-5.53557314e-02 -7.72087812e-01 -7.94082046e-01 -1.02850425e+00
-1.78143695e-01 -8.68491381e-02 -9.82961282e-02 2.43994698... | [5.878929138183594, 6.81911039352417] |
97d1c438-9db6-4621-b3e4-2418f2bde2b6 | closing-the-loop-graph-networks-to-unify | 2209.11894 | null | https://arxiv.org/abs/2209.11894v1 | https://arxiv.org/pdf/2209.11894v1.pdf | Closing the Loop: Graph Networks to Unify Semantic Objects and Visual Features for Multi-object Scenes | In Simultaneous Localization and Mapping (SLAM), Loop Closure Detection (LCD) is essential to minimize drift when recognizing previously visited places. Visual Bag-of-Words (vBoW) has been an LCD algorithm of choice for many state-of-the-art SLAM systems. It uses a set of visual features to provide robust place recogni... | ['Jörg S. Wicker', 'Patricia J. Riddle', 'Martin Urschler', 'Jonathan J. Y. Kim'] | 2022-09-24 | null | null | null | null | ['simultaneous-localization-and-mapping', 'loop-closure-detection', 'graph-matching'] | ['computer-vision', 'computer-vision', 'graphs'] | [-2.71924078e-01 -2.48454183e-01 -3.51385415e-01 -2.89678395e-01
4.65852953e-02 -4.70945358e-01 7.89967179e-01 5.65489471e-01
-5.30221283e-01 6.54691219e-01 -1.67440653e-01 -5.42306304e-01
-4.31540191e-01 -8.02691817e-01 -5.05839288e-01 -2.76679695e-01
-5.90855956e-01 3.37823272e-01 7.32020617e-01 -5.83876014... | [7.3460493087768555, -2.066694498062134] |
701dd5fb-0ca5-4d9a-8044-efff5f867236 | dictionary-guided-scene-text-recognition | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Nguyen_Dictionary-Guided_Scene_Text_Recognition_CVPR_2021_paper.pdf | Dictionary-Guided Scene Text Recognition | Language prior plays an important role in the way humans perceive and recognize text in the wild. In this work, we present an approach to train and use scene text recognition models by exploiting multiple clues from a language reference. Current scene text recognition methods have used lexicons to improve recogniti... | ['Minh Hoai', 'Thien Huu Nguyen', 'Thanh Duc Ngo', 'Minh-Triet Tran', 'Vinh Tran', 'Thu Nguyen', 'Nguyen Nguyen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['text-spotting', 'scene-text-recognition', 'scene-text-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.79593229e-01 -5.79475045e-01 2.94496357e-01 -5.99780679e-01
-3.41345131e-01 -6.50884748e-01 1.14713192e+00 3.21647435e-01
-7.14682043e-01 7.23816380e-02 2.95381665e-01 -2.74577320e-01
2.01831266e-01 -7.06250787e-01 -3.71537775e-01 -6.10716760e-01
8.29376698e-01 6.01787508e-01 3.00963610e-01 -4.29679275... | [11.846658706665039, 2.2909538745880127] |
e31e8733-8948-412c-acf8-68352a8e48cc | seqlpd-sequence-matching-enhanced-loop | 1904.13030 | null | https://arxiv.org/abs/1904.13030v2 | https://arxiv.org/pdf/1904.13030v2.pdf | SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles | Place recognition and loop-closure detection are main challenges in the localization, mapping and navigation tasks of self-driving vehicles. In this paper, we solve the loop-closure detection problem by incorporating the deep-learning based point cloud description method and the coarse-to-fine sequence matching strateg... | ['Yun-hui Liu', 'Huanshu Wei', 'Hesheng Wang', 'Chuanzhe Suo', 'Zhe Liu', 'Yingtian Liu', 'Shunbo Zhou'] | 2019-04-30 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-2.50460654e-01 -5.94362915e-01 -4.89656106e-02 -4.47773129e-01
-6.72142029e-01 -1.92354381e-01 8.15764487e-01 1.88439831e-01
-6.04670167e-01 2.60242224e-01 -2.91612715e-01 -1.83632523e-01
-2.31892198e-01 -7.77713776e-01 -7.80528128e-01 -6.86758220e-01
5.97495735e-02 2.04687238e-01 4.77318496e-01 -3.18383723... | [7.576843738555908, -2.0880212783813477] |
5b650df6-10a0-4e00-a455-0a58288c85a9 | ea-2-e-improving-consistency-with-event | 2205.14847 | null | https://arxiv.org/abs/2205.14847v1 | https://arxiv.org/pdf/2205.14847v1.pdf | EA$^2$E: Improving Consistency with Event Awareness for Document-Level Argument Extraction | Events are inter-related in documents. Motivated by the one-sense-per-discourse theory, we hypothesize that a participant tends to play consistent roles across multiple events in the same document. However recent work on document-level event argument extraction models each individual event in isolation and therefore ca... | ['Heng Ji', 'Qiusi Zhan', 'Qi Zeng'] | 2022-05-30 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [ 2.72699356e-01 7.18097270e-01 -6.10467911e-01 -4.67546552e-01
-9.77239013e-01 -6.34338081e-01 1.08841956e+00 8.84052932e-01
-3.82163882e-01 1.25201857e+00 8.21882129e-01 -4.52549696e-01
-3.16420019e-01 -9.58571911e-01 -9.40126717e-01 7.32339472e-02
-9.36454982e-02 4.23720479e-01 6.71296418e-01 -1.37197986... | [9.06712818145752, 9.23333740234375] |
596cb855-ba21-4c90-a9af-6eda417b9b6b | coherent-fda-radar-systems-joint-design-of | 2204.07251 | null | https://arxiv.org/abs/2204.07251v2 | https://arxiv.org/pdf/2204.07251v2.pdf | Joint Design of the Transmit and Receive Weights for Coherent FDA Radar | Frequency diverse array (FDA) differs from conventional array techniques in that it imposes an additional frequency offset (FO) across the array elements. The use of FO provides the FDA with the controllable degree of freedom in range dimension, offering preferable performance in joint angle and range localization, ran... | ['Shungsheng Zhang', 'Wen-Qin Wang', 'Wenkai Jia'] | 2022-04-14 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 5.21699548e-01 -1.13747686e-01 -3.23917605e-02 -1.33807287e-01
-5.24640083e-01 -5.46404541e-01 -7.30510727e-02 -2.92613655e-01
-2.23246992e-01 5.80954492e-01 1.07838146e-01 -2.76733845e-01
-1.02457237e+00 -6.69989944e-01 -1.42995432e-01 -1.16048145e+00
-4.19283956e-01 -3.49682331e-01 -4.19154018e-01 1.31910220... | [6.429536819458008, 1.2839802503585815] |
882f4699-fd60-4816-b542-8a59c373e4a6 | guided-training-a-simple-method-for-single | 2103.14330 | null | https://arxiv.org/abs/2103.14330v1 | https://arxiv.org/pdf/2103.14330v1.pdf | Guided Training: A Simple Method for Single-channel Speaker Separation | Deep learning has shown a great potential for speech separation, especially for speech and non-speech separation. However, it encounters permutation problem for multi-speaker separation where both target and interference are speech. Permutation Invariant training (PIT) was proposed to solve this problem by permuting th... | ['Guanglai Gao', 'Xueliang Zhang', 'Hao Li'] | 2021-03-26 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 4.90628272e-01 -2.09485576e-01 -4.20082174e-02 -1.39269680e-01
-6.37467384e-01 -3.50305855e-01 4.78823423e-01 -2.94712991e-01
-3.34953845e-01 4.42466497e-01 2.15291366e-01 -3.70919555e-01
7.99229543e-04 -2.28795335e-01 -4.08627659e-01 -1.18330884e+00
6.05216715e-03 2.92245746e-01 1.00436084e-01 1.11080287... | [14.738252639770508, 5.969944477081299] |
df2be6c8-476c-446f-8331-f468181ff371 | dual-clustering-co-teaching-with-consistent | 2210.03339 | null | https://arxiv.org/abs/2210.03339v1 | https://arxiv.org/pdf/2210.03339v1.pdf | Dual Clustering Co-teaching with Consistent Sample Mining for Unsupervised Person Re-Identification | In unsupervised person Re-ID, peer-teaching strategy leveraging two networks to facilitate training has been proven to be an effective method to deal with the pseudo label noise. However, training two networks with a set of noisy pseudo labels reduces the complementarity of the two networks and results in label noise a... | ['Yuehu Liu', 'Jiahuan Zhou', 'Chi Zhang', 'Zhichao Cui', 'Zeqi Chen'] | 2022-10-07 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 1.66579604e-01 2.36339569e-02 -7.75271910e-04 -3.80485922e-01
-2.25978911e-01 -2.82478988e-01 6.33584082e-01 -2.19013318e-02
-6.45439506e-01 5.59707284e-01 3.15572843e-02 3.85859221e-01
-4.22416389e-01 -8.01889718e-01 -4.07318056e-01 -9.80709612e-01
2.38866717e-01 5.89389443e-01 2.50299960e-01 1.47113964... | [14.858081817626953, 1.1258504390716553] |
1028f3cd-c7b4-4d8b-97ba-8f65347defc0 | metatrader-an-reinforcement-learning-approach | 2210.01774 | null | https://arxiv.org/abs/2210.01774v1 | https://arxiv.org/pdf/2210.01774v1.pdf | MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization | Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequen... | ['Jian Li', 'Siyuan Li', 'Hui Niu'] | 2022-09-01 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.44741160e-01 -2.95048743e-01 -2.84707695e-01 -3.54474522e-02
-5.91069460e-01 -7.22400308e-01 5.79776943e-01 -3.12505931e-01
-4.62430745e-01 8.67813289e-01 -1.11048415e-01 -4.36021030e-01
-3.30767900e-01 -1.03009951e+00 -5.45387864e-01 -6.49660408e-01
-1.24613956e-01 7.86776245e-01 1.17178626e-01 -3.36139530... | [4.424527168273926, 3.887826442718506] |
3889041b-ef94-47f6-8f4f-fa137d39625b | let-images-give-you-more-point-cloud-cross | 2210.04208 | null | https://arxiv.org/abs/2210.04208v1 | https://arxiv.org/pdf/2210.04208v1.pdf | Let Images Give You More:Point Cloud Cross-Modal Training for Shape Analysis | Although recent point cloud analysis achieves impressive progress, the paradigm of representation learning from a single modality gradually meets its bottleneck. In this work, we take a step towards more discriminative 3D point cloud representation by fully taking advantages of images which inherently contain richer ap... | ['Zhen Li', 'Shuguang Cui', 'Ruimao Zhang', 'Jiantao Gao', 'Chaoda Zheng', 'Heshen Zhan', 'Xu Yan'] | 2022-10-09 | null | null | null | null | ['3d-point-cloud-classification'] | ['computer-vision'] | [-1.49653871e-02 -1.65184751e-01 -2.87656605e-01 -3.90295058e-01
-9.70798969e-01 -5.51119745e-01 4.77919906e-01 -8.53976235e-02
-5.14011532e-02 2.60842621e-01 -3.00542742e-01 -2.90139318e-01
-2.53485306e-03 -7.98822045e-01 -9.94046628e-01 -8.46293926e-01
4.34586108e-01 3.69930565e-01 5.48330657e-02 -1.04705065... | [8.159150123596191, -3.275864362716675] |
fc90b3e7-1b44-4e5f-9446-bd6d74c882be | in-orbit-lunar-satellite-image-super | 2110.10109 | null | https://arxiv.org/abs/2110.10109v1 | https://arxiv.org/pdf/2110.10109v1.pdf | In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission | Rapid technological advancements have tremendously increased the data acquisition capabilities of remote sensing satellites. However, the data utilization efficiency in satellite missions is very low. This growing data also escalates the cost required for data downlink transmission and post-processing. Selective data t... | ['Nitin Khanna', 'Chennuri Prateek', 'Atal Tewari'] | 2021-10-19 | null | null | null | null | ['satellite-image-super-resolution'] | ['computer-vision'] | [ 2.73757994e-01 -2.02592313e-01 -1.05620004e-01 -4.83462632e-01
-4.49408531e-01 -5.45865074e-02 2.10495025e-01 -5.54173350e-01
-4.98742223e-01 6.56917155e-01 2.22194895e-01 -1.59994081e-01
-3.33220065e-01 -1.01843917e+00 -5.52261353e-01 -8.96576524e-01
-2.36840814e-01 2.28858571e-02 3.11745375e-01 -3.77672702... | [10.712570190429688, -2.000833511352539] |
ad694259-8b03-42d8-bed8-4ec3cbc32edc | multi-goal-multi-agent-pickup-and-delivery | 2208.01223 | null | https://arxiv.org/abs/2208.01223v1 | https://arxiv.org/pdf/2208.01223v1.pdf | Multi-Goal Multi-Agent Pickup and Delivery | In this work, we consider the Multi-Agent Pickup-and-Delivery (MAPD) problem, where agents constantly engage with new tasks and need to plan collision-free paths to execute them. To execute a task, an agent needs to visit a pair of goal locations, consisting of a pickup location and a delivery location. We propose two ... | ['Hang Ma', 'Sven Koenig', 'Jiaoyang Li', 'Qinghong Xu'] | 2022-08-02 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.58572584e-01 2.11067647e-01 -3.73993218e-02 3.30478586e-02
-6.34180605e-01 -7.91704834e-01 4.17272151e-01 6.62714362e-01
-5.62838972e-01 1.02431238e+00 -1.33730367e-01 -4.19836491e-01
-1.09286129e+00 -1.14508617e+00 -6.91682637e-01 -7.13341475e-01
-7.90053904e-01 1.56351066e+00 8.46667111e-01 -5.91586113... | [4.953705310821533, 1.7301417589187622] |
9de83f6f-5296-4d55-8f7e-875269f16346 | towards-mixed-optimization-for-reinforcement | 1807.00403 | null | http://arxiv.org/abs/1807.00403v2 | http://arxiv.org/pdf/1807.00403v2.pdf | Towards Mixed Optimization for Reinforcement Learning with Program Synthesis | Deep reinforcement learning has led to several recent breakthroughs, though
the learned policies are often based on black-box neural networks. This makes
them difficult to interpret and to impose desired specification constraints
during learning. We present an iterative framework, MORL, for improving the
learned polici... | ['Surya Bhupatiraju', 'Kumar Krishna Agrawal', 'Rishabh Singh'] | 2018-07-01 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 1.39277950e-01 3.55072200e-01 -4.37391400e-01 -3.68996412e-01
-3.70915473e-01 -9.45374668e-01 4.44900244e-01 1.73672006e-01
-1.44177660e-01 8.86628330e-01 -9.26776901e-02 -7.02710211e-01
1.19612314e-01 -9.23929036e-01 -1.11369157e+00 -4.05891001e-01
-3.51441726e-02 1.28145829e-01 2.43517101e-01 -1.73767805... | [8.348824501037598, 7.287537097930908] |
aa9b55ec-16e9-4c30-b8ed-548bae90213a | a-study-of-morphological-robustness-of-neural | null | null | https://aclanthology.org/2021.sigmorphon-1.6 | https://aclanthology.org/2021.sigmorphon-1.6.pdf | A Study of Morphological Robustness of Neural Machine Translation | In this work, we analyze the robustness of neural machine translation systems towards grammatical perturbations in the source. In particular, we focus on morphological inflection related perturbations. While this has been recently studied for English→French (MORPHEUS) (Tan et al., 2020), it is unclear how this extends ... | ['Adithya Pratapa', 'Sai Muralidhar Jayanthi'] | null | null | null | null | acl-sigmorphon-2021-8 | ['morphological-inflection'] | ['natural-language-processing'] | [ 1.52220085e-01 1.95829034e-01 1.79863408e-01 -2.17488632e-01
-1.17825782e+00 -1.21891880e+00 6.93807364e-01 2.60668367e-01
-4.84519392e-01 8.56375575e-01 2.94143379e-01 -8.15554142e-01
2.77816623e-01 -4.75650966e-01 -1.26431417e+00 -2.94171602e-01
3.86512727e-01 5.28317392e-01 -3.11667323e-01 -7.33764589... | [11.51664924621582, 10.280893325805664] |
4d94365f-795c-49a7-b1e0-7d8cff9603e3 | monocular-differentiable-rendering-for-self-1 | 2009.14524 | null | https://arxiv.org/abs/2009.14524v1 | https://arxiv.org/pdf/2009.14524v1.pdf | Monocular Differentiable Rendering for Self-Supervised 3D Object Detection | 3D object detection from monocular images is an ill-posed problem due to the projective entanglement of depth and scale. To overcome this ambiguity, we present a novel self-supervised method for textured 3D shape reconstruction and pose estimation of rigid objects with the help of strong shape priors and 2D instance ma... | ['Takahiro Ando', 'Mihai Adrian Morariu', 'Deniz Beker', 'Adrien Gaidon', 'Wadim Kehl', 'Toru Matsuoka', 'Hiroharu Kato'] | 2020-09-30 | monocular-differentiable-rendering-for-self | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3794_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660511.pdf | eccv-2020-8 | ['3d-object-detection-from-monocular-images'] | ['computer-vision'] | [ 1.47384107e-01 1.17560200e-01 2.27518361e-02 -5.23995399e-01
-6.86915815e-01 -7.38050520e-01 6.74211144e-01 -4.74896491e-01
-4.22456145e-01 3.58157396e-01 -2.89873600e-01 -7.93724414e-03
1.85640708e-01 -4.77924019e-01 -7.76120543e-01 -3.38202327e-01
2.20416009e-01 1.21750700e+00 4.74440604e-01 3.52777183... | [7.8102240562438965, -2.7008981704711914] |
157b7aa9-fa54-4737-93f4-c193cc1ee2ec | large-scale-3d-shape-reconstruction-and | 1710.06104 | null | http://arxiv.org/abs/1710.06104v2 | http://arxiv.org/pdf/1710.06104v2.pdf | Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 | We introduce a large-scale 3D shape understanding benchmark using data and
annotation from ShapeNet 3D object database. The benchmark consists of two
tasks: part-level segmentation of 3D shapes and 3D reconstruction from single
view images. Ten teams have participated in the challenge and the best
performing teams have... | ['M. Ramanathan', 'Qi-Xing Huang', 'Leonidas Guibas', 'Pat Hanrahan', 'Jingyi Yu', 'Brejesh lall', 'Narasimha Murthy', 'Wei Wu', 'Victor Lempitsky', 'Haibin Huang', 'Yang Zhou', 'Thomas Funkhouser', 'Silvio Savarese', 'Rui Bu', 'Roman Klokov', 'Panpan Shui', 'Jaehoon Choi', 'Christian Haene', 'Yuan Gan', 'Yangyan Li', ... | 2017-10-17 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [-3.74704719e-01 1.70270339e-01 1.38411894e-01 -7.19789386e-01
-5.34572065e-01 -7.42444932e-01 6.41112924e-01 -4.71958891e-02
-3.71527821e-02 -7.36976191e-02 1.86918806e-02 -4.01526019e-02
2.37761095e-01 -4.90186721e-01 -8.90688777e-01 -3.91225666e-01
-2.19612673e-01 1.31701934e+00 5.46389282e-01 -2.35909317... | [8.176399230957031, -3.54510498046875] |
0d10004a-5ef2-4626-a3b4-4cf835f51c6c | gradient-estimators-for-normalising-flows | 2202.01314 | null | https://arxiv.org/abs/2202.01314v2 | https://arxiv.org/pdf/2202.01314v2.pdf | Gradient estimators for normalising flows | Recently a machine learning approach to Monte-Carlo simulations called Neural Markov Chain Monte-Carlo (NMCMC) is gaining traction. In its most popular form it uses neural networks to construct normalizing flows which are then trained to approximate the desired target distribution. In this contribution we present new g... | ['Tomasz Stebel', 'Piotr Korcyl', 'Piotr Bialas'] | 2022-02-02 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [-1.04070172e-01 -1.18071340e-01 -1.06410541e-01 -4.53435659e-01
-6.77681208e-01 -7.89524764e-02 8.43006253e-01 -2.11649612e-02
-9.39191580e-01 1.47010946e+00 6.21781535e-02 -4.27721500e-01
5.73426150e-02 -9.55306768e-01 -6.93451107e-01 -1.01041746e+00
7.59274065e-02 8.91553342e-01 4.85292912e-01 -1.50467142... | [5.688460350036621, 4.784193515777588] |
3cf8f803-e71d-4507-9d9e-e15aafcc538a | contour-proposal-networks-for-biomedical | 2104.03393 | null | https://arxiv.org/abs/2104.03393v1 | https://arxiv.org/pdf/2104.03393v1.pdf | Contour Proposal Networks for Biomedical Instance Segmentation | We present a conceptually simple framework for object instance segmentation called Contour Proposal Network (CPN), which detects possibly overlapping objects in an image while simultaneously fitting closed object contours using an interpretable, fixed-sized representation based on Fourier Descriptors. The CPN can incor... | ['Timo Dickscheid', 'Katrin Amunts', 'Stefan Harmeling', 'Eric Upschulte'] | 2021-04-07 | null | null | null | null | ['real-time-object-detection', 'blood-cell-detection'] | ['computer-vision', 'medical'] | [ 5.66083550e-01 7.30080724e-01 -1.38418466e-01 -2.58138835e-01
-7.70880044e-01 -6.53771818e-01 2.71943659e-01 1.34700015e-01
-5.65875828e-01 4.25959945e-01 -4.71646070e-01 -1.33471489e-01
1.46028981e-01 -7.37698138e-01 -8.57925117e-01 -5.25823057e-01
-2.00731650e-01 8.32908332e-01 7.33388007e-01 1.22325085... | [9.50735092163086, 0.2601689398288727] |
91a7a7da-fa4d-4a95-b8d8-2d60ae53f47a | not-made-for-each-other-audio-visual | 2005.14405 | null | https://arxiv.org/abs/2005.14405v3 | https://arxiv.org/pdf/2005.14405v3.pdf | Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization | We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS). We hypothesize that manipulation of either modality will lead to dis-harmony between the two modalities, eg, loss of lip-sync, unnatural facial and lip movements, et... | ['Ramanathan Subramanian', 'Parul Gupta', 'Abhinav Dhall', 'Komal Chugh'] | 2020-05-29 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 2.90964603e-01 -3.05204898e-01 -2.51265228e-01 -8.46760496e-02
-1.07769656e+00 -7.03279376e-01 4.97492135e-01 6.28870651e-02
-1.26743153e-01 2.09270462e-01 5.11170030e-01 3.08532506e-01
2.70686485e-02 -4.95778285e-02 -7.12714911e-01 -6.75553799e-01
-3.28972131e-01 -5.50360143e-01 5.19284308e-02 2.72136241... | [13.052696228027344, 1.2424888610839844] |
32b6d414-8614-41e2-bb88-f98969102e8b | leveraging-hierarchical-structures-for-few | 2107.07029 | null | https://arxiv.org/abs/2107.07029v2 | https://arxiv.org/pdf/2107.07029v2.pdf | Leveraging Hierarchical Structures for Few-Shot Musical Instrument Recognition | Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data. In this work, we exploit hierarchical relationships between instruments in a few-shot learning setup to enable classification of a wider set of musical instruments, given a few examples at i... | ['Bryan Pardo', 'Ethan Manilow', 'Aldo Aguilar', 'Hugo Flores Garcia'] | 2021-07-14 | null | null | null | null | ['instrument-recognition'] | ['audio'] | [ 4.13857341e-01 1.03729423e-02 1.66696593e-01 -2.82770991e-02
-4.47420448e-01 -8.59912813e-01 3.07128727e-01 9.64525566e-02
-4.90510792e-01 5.20511210e-01 1.42398357e-01 2.18707412e-01
-5.52501559e-01 -7.12496042e-01 -5.36218703e-01 -3.79089355e-01
-3.12087983e-02 5.39394677e-01 3.34983885e-01 -3.57015312... | [15.815591812133789, 5.248183250427246] |
278323cf-45cc-464e-97eb-f6f9ca8f8d46 | teach-task-driven-embodied-agents-that-chat | 2110.00534 | null | https://arxiv.org/abs/2110.00534v3 | https://arxiv.org/pdf/2110.00534v3.pdf | TEACh: Task-driven Embodied Agents that Chat | Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolve ambiguity and recover from mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human--human, interactive dialogues to com... | ['Robinson Piramuthu', 'Dilek Hakkani-Tur', 'Gokhan Tur', 'Spandana Gella', 'Anjali Narayan-Chen', 'Patrick Lange', 'Ayush Shrivastava', 'Jesse Thomason', 'Aishwarya Padmakumar'] | 2021-10-01 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 2.41252080e-01 7.19068706e-01 5.67793071e-01 -6.42794490e-01
-3.50019217e-01 -9.92881596e-01 5.41766167e-01 1.43348083e-01
-4.67788607e-01 7.90219784e-01 4.26356643e-01 -3.59746665e-01
2.28437986e-02 -3.33694428e-01 -4.00761813e-01 -2.06822932e-01
-3.49924445e-01 1.04241443e+00 -2.81511247e-01 -6.32090509... | [4.382754802703857, 0.7564280033111572] |
21d2aa18-01fe-42a5-9cb9-d369bc6f1be9 | 190600098 | 1906.00098 | null | https://arxiv.org/abs/1906.00098v1 | https://arxiv.org/pdf/1906.00098v1.pdf | Spectral Perturbation Meets Incomplete Multi-view Data | Beyond existing multi-view clustering, this paper studies a more realistic clustering scenario, referred to as incomplete multi-view clustering, where a number of data instances are missing in certain views. To tackle this problem, we explore spectral perturbation theory. In this work, we show a strong link between per... | ['Bing Liu', 'Linlin Zong', 'Hao Wang', 'Wei Zhou', 'Yan Yang'] | 2019-05-31 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [ 2.14078948e-02 4.04281840e-02 5.86354136e-02 -1.70332775e-01
-1.01825082e+00 -1.01082718e+00 1.59506097e-01 -5.83353499e-03
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-5.22893190e-01 -2.77299196e-01 -6.42289698e-01 -1.01913691e+00
1.04190253e-01 2.73922712e-01 -7.73731992e-02 -1.09078862... | [8.187013626098633, 4.6212358474731445] |
ab383922-0204-46ea-b863-ba9707c7057f | teaching-humans-when-to-defer-to-a-classifier | 2111.11297 | null | https://arxiv.org/abs/2111.11297v2 | https://arxiv.org/pdf/2111.11297v2.pdf | Teaching Humans When To Defer to a Classifier via Exemplars | Expert decision makers are starting to rely on data-driven automated agents to assist them with various tasks. For this collaboration to perform properly, the human decision maker must have a mental model of when and when not to rely on the agent. In this work, we aim to ensure that human decision makers learn a valid ... | ['David Sontag', 'Arvind Satyanarayan', 'Hussein Mozannar'] | 2021-11-22 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.67802195e-02 3.68079931e-01 1.73546746e-01 -4.96795356e-01
-3.99212569e-01 -4.35086071e-01 6.15859866e-01 3.93678606e-01
-7.36343086e-01 5.97511172e-01 -6.97127655e-02 -2.70132571e-01
-5.44750988e-01 -8.02138984e-01 -2.76187211e-01 -7.15692818e-01
3.55763644e-01 1.08898413e+00 6.42025471e-01 -4.89954084... | [4.2925286293029785, 1.3073965311050415] |
fc88d97a-de79-448a-9c11-e5ecce845a85 | face-recognition-using-3d-cnns | 2102.01441 | null | https://arxiv.org/abs/2102.01441v1 | https://arxiv.org/pdf/2102.01441v1.pdf | Face Recognition using 3D CNNs | The area of face recognition is one of the most widely researched areas in the domain of computer vision and biometric. This is because, the non-intrusive nature of face biometric makes it comparatively more suitable for application in area of surveillance at public places such as airports. The application of primitive... | ['Satish Kumar Singh', 'Nayaneesh Kumar Mishra'] | 2021-02-02 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-1.07565247e-01 -5.01919091e-01 1.11919209e-01 -4.61948663e-01
1.04047455e-01 -3.38567942e-01 6.13041997e-01 -3.23182464e-01
-4.53736722e-01 5.34901679e-01 -1.51744843e-01 -7.11505711e-02
-1.57080382e-01 -7.12711990e-01 -3.91789466e-01 -7.19843388e-01
-1.15730442e-01 1.14398763e-01 -6.50154725e-02 -1.87452301... | [13.3058443069458, 0.8570706248283386] |
15efaf37-fe17-4fc2-b4c1-3bce3d6e27fa | trac-1-shared-task-on-aggression | null | null | https://aclanthology.org/W18-4407 | https://aclanthology.org/W18-4407.pdf | TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18 | This paper describes the work that our team bhanodaig did at Indian Institute of Technology (ISM) towards TRAC-1 Shared Task on Aggression Identification in Social Media for COLING 2018. In this paper we label aggression identification into three categories: Overtly Aggressive, Covertly Aggressive and Non-aggressive. W... | ['Ritesh Kumar', 'Rajendra Pamula', 'Maheshwar Reddy Chennuru', 'Guggilla Bhanodai'] | 2018-08-01 | null | null | null | coling-2018-8 | ['aggression-identification'] | ['natural-language-processing'] | [-5.25379121e-01 4.26172763e-02 2.81142473e-01 -2.29268163e-01
-4.75065857e-01 -4.63823676e-01 7.72748828e-01 9.87230241e-02
-9.10214484e-01 6.51634097e-01 3.83826554e-01 -3.13726902e-01
-2.69863099e-01 -5.45412004e-01 -1.65320590e-01 -4.46974665e-01
-2.29153708e-01 5.97909272e-01 2.89431870e-01 -6.33751452... | [8.806570053100586, 10.735445022583008] |
d91a049b-7c1f-4089-9c21-0daf60f9c198 | contrastive-graph-clustering-in-curvature | 2305.03555 | null | https://arxiv.org/abs/2305.03555v1 | https://arxiv.org/pdf/2305.03555v1.pdf | Contrastive Graph Clustering in Curvature Spaces | Graph clustering is a longstanding research topic, and has achieved remarkable success with the deep learning methods in recent years. Nevertheless, we observe that several important issues largely remain open. On the one hand, graph clustering from the geometric perspective is appealing but has rarely been touched bef... | ['Philip S. Yu', 'Hao Peng', 'Junda Ye', 'Feiyang Wang', 'Li Sun'] | 2023-05-05 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.71373054e-01 2.92891383e-01 1.48582399e-01 -2.49522209e-01
-5.36623776e-01 -3.88517827e-01 4.37323630e-01 2.41172433e-01
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-2.79267609e-01 -7.35336661e-01 -6.53660119e-01 -8.14429283e-01
-4.07709539e-01 4.00498450e-01 1.01710603e-01 -2.98716307... | [7.363681793212891, 6.003580093383789] |
8172767e-35ad-4e47-aa67-0c81e333689e | passive-aggressive-sequence-labeling-with | null | null | https://aclanthology.org/E14-4014 | https://aclanthology.org/E14-4014.pdf | Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets | null | ['Leon Derczynski', 'Kalina Bontcheva'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['person-recognition'] | ['computer-vision'] | [-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... | [-7.2812180519104, 3.536957025527954] |
e10cbbcc-2dff-4902-a16c-98c091a9a7b0 | an-image-enhancing-pattern-based-sparsity-for | 2001.07710 | null | https://arxiv.org/abs/2001.07710v3 | https://arxiv.org/pdf/2001.07710v3.pdf | An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices | Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms. However, most of the pruning techniques are essentially trade-offs between model accuracy and regularity which lead to impaire... | ['Sijia Liu', 'Kaisheng Ma', 'Wei Niu', 'Sheng Lin', 'Jian Tang', 'Hongjia Li', 'Xiaolong Ma', 'Xiang Chen', 'Yanzhi Wang', 'Tianyun Zhang', 'Bin Ren'] | 2020-01-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1991_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580613.pdf | eccv-2020-8 | ['compiler-optimization'] | ['computer-code'] | [ 2.03946009e-01 -2.81889319e-01 -5.78052580e-01 -4.30913597e-01
8.97817016e-02 -1.51996478e-01 1.27128676e-01 1.46767274e-02
-5.30930102e-01 5.46129882e-01 -1.10685937e-01 -7.34834790e-01
-2.92393446e-01 -9.60950851e-01 -6.00603044e-01 -3.87676537e-01
1.23785578e-01 9.15282592e-02 3.20007533e-01 -7.60389939... | [8.542000770568848, 3.060358762741089] |
9c9eecd3-42bf-4e44-8c2b-48f44d4a94b4 | on-practical-aspects-of-aggregation-defenses | 2306.16415 | null | https://arxiv.org/abs/2306.16415v1 | https://arxiv.org/pdf/2306.16415v1.pdf | On Practical Aspects of Aggregation Defenses against Data Poisoning Attacks | The increasing access to data poses both opportunities and risks in deep learning, as one can manipulate the behaviors of deep learning models with malicious training samples. Such attacks are known as data poisoning. Recent advances in defense strategies against data poisoning have highlighted the effectiveness of agg... | ['Soheil Feizi', 'Wenxiao Wang'] | 2023-06-28 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [-1.56960756e-01 -2.76254594e-01 4.88867201e-02 2.88228720e-01
-6.97529614e-01 -8.22724044e-01 5.33712208e-01 2.60808200e-01
-7.91578352e-01 5.18592119e-01 -1.61690116e-02 -4.65965718e-01
-1.91524640e-01 -7.60373592e-01 -8.61005545e-01 -1.08574235e+00
-4.43738818e-01 1.41419634e-01 3.23950261e-01 -6.37622997... | [5.7988128662109375, 7.602414608001709] |
31cbc552-3b61-48ce-8d88-cb7a54476e1c | time-series-is-a-special-sequence-forecasting | 2106.09305 | null | https://arxiv.org/abs/2106.09305v3 | https://arxiv.org/pdf/2106.09305v3.pdf | SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction | One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences. By taking advantage of this property, we propose a novel neural network architecture that conducts sample convolution and interaction for temporal modeling and forecasting, named SCINet. Sp... | ['Qiang Xu', 'Lingna Ma', 'Qiuxia Lai', 'Zhijian Xu', 'Muxi Chen', 'Ailing Zeng', 'Minhao Liu'] | 2021-06-17 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [-1.51276395e-01 -7.34989524e-01 -2.10988775e-01 -4.32616174e-01
-1.29526839e-01 -3.99536580e-01 7.94817388e-01 -2.68001258e-01
5.36275208e-02 6.01962626e-01 5.05928397e-01 -2.87944257e-01
-1.71409562e-01 -9.73320127e-01 -5.50567567e-01 -5.70353508e-01
-5.27852774e-01 -3.38471122e-02 4.84195016e-02 -5.10747492... | [7.010348796844482, 2.9694180488586426] |
10e6a8d3-da95-45f0-ad1a-b6537c615a94 | a-simple-way-to-initialize-recurrent-networks | 1504.00941 | null | http://arxiv.org/abs/1504.00941v2 | http://arxiv.org/pdf/1504.00941v2.pdf | A Simple Way to Initialize Recurrent Networks of Rectified Linear Units | Learning long term dependencies in recurrent networks is difficult due to
vanishing and exploding gradients. To overcome this difficulty, researchers
have developed sophisticated optimization techniques and network architectures.
In this paper, we propose a simpler solution that use recurrent neural networks
composed o... | ['Quoc V. Le', 'Navdeep Jaitly', 'Geoffrey E. Hinton'] | 2015-04-03 | null | null | null | null | ['sequential-image-classification'] | ['computer-vision'] | [-8.06650147e-03 7.52853677e-02 -1.52070090e-01 -3.21461231e-01
-4.23653841e-01 -3.03536057e-01 4.97256368e-01 -5.53258955e-01
-6.79232955e-01 7.32185602e-01 3.87228906e-01 -5.90554535e-01
1.82743073e-01 -3.34443510e-01 -5.25356531e-01 -6.22507870e-01
-2.18102574e-01 7.37115815e-02 1.95078269e-01 -3.87886971... | [10.870087623596191, 6.326731204986572] |
9740eb62-0cf7-4ca9-af45-716d1a438b99 | co-saliency-detection-via-mask-guided-fully | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Co-Saliency_Detection_via_Mask-Guided_Fully_Convolutional_Networks_With_Multi-Scale_Label_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Co-Saliency_Detection_via_Mask-Guided_Fully_Convolutional_Networks_With_Multi-Scale_Label_CVPR_2019_paper.pdf | Co-Saliency Detection via Mask-Guided Fully Convolutional Networks With Multi-Scale Label Smoothing | In image co-saliency detection problem, one critical issue is how to model the concurrent pattern of the co-salient parts, which appears both within each image and across all the relevant images. In this paper, we propose a hierarchical image co-saliency detection framework as a coarse to fine strategy to capture this ... | [' Qingshan Liu', ' Bo Liu', ' Tengpeng Li', 'Kaihua Zhang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['co-saliency-detection'] | ['computer-vision'] | [ 6.23426795e-01 -1.29654124e-01 -1.33916348e-01 -2.27402538e-01
-6.23246312e-01 5.19437939e-02 4.15437996e-01 1.56545192e-01
-2.59596765e-01 5.57470679e-01 1.13858625e-01 7.77831823e-02
1.54782206e-01 -5.92958331e-01 -7.92265415e-01 -5.81473470e-01
2.86931694e-01 -3.35521549e-01 1.00747943e+00 -4.17400524... | [9.791387557983398, -0.35366517305374146] |
0d7b9ec5-74da-4410-af8e-1b67a048f129 | vfp290k-a-large-scale-benchmark-dataset-for | null | null | https://openreview.net/forum?id=y2AbfIXgBK3 | https://openreview.net/pdf?id=y2AbfIXgBK3 | VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection | Detection of fallen persons due to, for example, health problems, violence, or accidents, is a critical challenge. Accordingly, detection of these anomalous events is of paramount importance for a number of applications, including but not limited to CCTV surveillance, security, and health care. Given that many detectio... | ['Simon S. Woo', 'Donghee Hong', 'Saebyeol Shin', 'Minha Kim', 'Junhyung Kang', 'Jinbeom Kim', 'Hanbeen Lee', 'Jeongho Kim', 'Jaeju An'] | 2022-01-14 | null | null | null | neurips-2021-track-datasets-and-benchmarks | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.64933378e-01 -5.86093009e-01 2.15252832e-01 -2.57882625e-01
-6.31066442e-01 -2.83259273e-01 1.12117954e-01 -1.38835475e-01
-4.50823277e-01 5.91222167e-01 3.57718706e-01 -2.43091471e-02
-8.31415504e-02 -7.54536808e-01 -2.98333019e-01 -7.66949177e-01
3.03795096e-02 1.21221602e-01 4.85734910e-01 -1.23690411... | [7.780787944793701, 0.9907849431037903] |
0e21b485-e120-47d9-ae51-1ae4fac7dbcc | dealing-with-the-database-variability-problem | 1906.06666 | null | https://arxiv.org/abs/1906.06666v3 | https://arxiv.org/pdf/1906.06666v3.pdf | Addressing database variability in learning from medical data: an ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring | In this work we examine some of the problems associated with the development of machine learning models with the objective to achieve robust generalization capabilities on common-task multiple-database scenarios. Referred to as the "database variability problem", we focus on a specific medical domain (sleep staging in ... | ['Isaac Fernández-Varela', 'Diego Alvarez-Estevez'] | 2019-06-16 | null | null | null | null | ['sleep-staging'] | ['medical'] | [-2.93253176e-02 -1.19572222e-01 -5.51495254e-02 -7.68711388e-01
-8.25317740e-01 -2.02151820e-01 2.73949206e-01 5.12023389e-01
-7.54549265e-01 8.88492823e-01 -1.96041480e-01 -1.82953328e-01
-4.25884694e-01 -7.06044734e-01 -6.52353466e-01 -5.46454132e-01
-5.66648021e-02 7.76769340e-01 2.23730877e-01 -2.38841653... | [13.50866985321045, 3.487637758255005] |
e1eca3a2-2e2e-491b-aa59-93ef5949910c | med3d-transfer-learning-for-3d-medical-image | 1904.00625 | null | https://arxiv.org/abs/1904.00625v4 | https://arxiv.org/pdf/1904.00625v4.pdf | Med3D: Transfer Learning for 3D Medical Image Analysis | The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learnin... | ['Yefeng Zheng', 'Sihong Chen', 'Kai Ma'] | 2019-04-01 | null | null | null | null | ['3d-medical-imaging-segmentation', 'liver-segmentation'] | ['medical', 'medical'] | [-2.55030960e-01 2.40307882e-01 -1.65264338e-01 -2.90432006e-01
-9.53139722e-01 -4.50026453e-01 2.35567227e-01 -2.50139032e-02
-4.25585628e-01 3.57585192e-01 1.17436364e-01 -5.59494317e-01
1.47500768e-01 -5.71370721e-01 -6.52546942e-01 -5.82084835e-01
-1.44111693e-01 9.31091428e-01 4.34298515e-01 2.00015724... | [14.691300392150879, -2.4097838401794434] |
8d198884-8d70-4ff6-bc0f-fa50dac4a2bf | policy-evaluation-in-distributional-lqr | 2303.13657 | null | https://arxiv.org/abs/2303.13657v1 | https://arxiv.org/pdf/2303.13657v1.pdf | Policy Evaluation in Distributional LQR | Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard RL. At the same time, a main challenge in DRL is that policy evaluation in DRL typically r... | ['Karl H. Johansson', 'Alessandro Abate', 'Michael M. Zavlanos', 'Siyi Wang', 'Yulong Gao', 'Zifan Wang'] | 2023-03-23 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-3.31587136e-01 2.76345193e-01 -3.11741561e-01 1.10312901e-01
-9.29780185e-01 -6.35300934e-01 2.60842770e-01 1.81045830e-01
-7.71710455e-01 1.00769734e+00 1.21785052e-01 -3.62540245e-01
-3.84486437e-01 -7.76972175e-01 -9.23087358e-01 -1.00310957e+00
-3.49627703e-01 2.40385249e-01 -2.79882610e-01 -3.37938249... | [4.260954856872559, 2.558685064315796] |
c97a86f1-8054-4073-9a90-ddc4bcb0ce0a | challenges-to-open-domain-constituency | null | null | https://aclanthology.org/2022.findings-acl.11 | https://aclanthology.org/2022.findings-acl.11.pdf | Challenges to Open-Domain Constituency Parsing | Neural constituency parsers have reached practical performance on news-domain benchmarks. However, their generalization ability to other domains remains weak. Existing findings on cross-domain constituency parsing are only made on a limited number of domains. Tracking this, we manually annotate a high-quality constitue... | ['Yue Zhang', 'Di wu', 'Ruoxi Ning', 'Leyang Cui', 'Sen yang'] | null | null | null | null | findings-acl-2022-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.19912283e-04 3.86571169e-01 -7.25672901e-01 -7.36824393e-01
-1.48407388e+00 -1.33536017e+00 4.75347102e-01 3.92121077e-01
-2.43019223e-01 9.42897141e-01 7.91904151e-01 -5.91673136e-01
2.99319923e-01 -7.72503912e-01 -4.90673095e-01 -7.52330720e-02
1.26056895e-01 2.69561917e-01 3.23764354e-01 -5.58471084... | [10.4121732711792, 9.762420654296875] |
5ec5e398-182b-4dc3-b05c-486484f57ad0 | robust-graph-structure-learning-over-images | 2210.03956 | null | https://arxiv.org/abs/2210.03956v4 | https://arxiv.org/pdf/2210.03956v4.pdf | Robust Graph Structure Learning via Multiple Statistical Tests | Graph structure learning aims to learn connectivity in a graph from data. It is particularly important for many computer vision related tasks since no explicit graph structure is available for images for most cases. A natural way to construct a graph among images is to treat each image as a node and assign pairwise ima... | ['Rong Jin', 'Xiuyu Sun', 'Senzhang Wang', 'Ming Lin', 'Fangyi Zhang', 'Yaohua Wang'] | 2022-10-08 | null | null | null | null | ['face-clustering', 'graph-structure-learning'] | ['computer-vision', 'graphs'] | [ 2.11704925e-01 1.00778610e-01 -6.37933658e-03 -5.88540554e-01
-3.48918438e-01 -4.44611222e-01 2.84561157e-01 5.92157245e-01
-3.89832735e-01 4.62031245e-01 -2.33340755e-01 -3.66821170e-01
-3.85256588e-01 -1.00871122e+00 -7.58656681e-01 -8.23670626e-01
-2.76756525e-01 3.50793391e-01 1.61197968e-02 7.76275024... | [7.227056980133057, 6.1131181716918945] |
ed82677c-c977-479d-ba19-591ca7acb6d9 | hybrid-multimodal-feature-extraction-mining | 2208.03051 | null | https://arxiv.org/abs/2208.03051v2 | https://arxiv.org/pdf/2208.03051v2.pdf | Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis | In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges. The MuSe 2022 focuses on humor detection, emotional reactions and multimodal emotional stress utilizing different modalities and data sets. In our... | ['Meng Wang', 'Xiao Sun', 'Chunxiao Fan', 'Jie Lin', 'Sheng Gao', 'Yangyang Xu', 'Peng Zou', 'Liuwei An', 'Yueqi Jiang', 'Junjie Lang', 'Ziyang Zhang', 'Jia Li'] | 2022-08-05 | null | null | null | null | ['humor-detection'] | ['natural-language-processing'] | [-2.21165210e-01 -1.84329599e-01 5.47988862e-02 -2.68642455e-01
-9.93412375e-01 -3.81594449e-01 2.64645666e-01 1.09435849e-01
-4.35970128e-01 3.70254934e-01 4.64421451e-01 6.42628014e-01
4.67897385e-01 -1.04444154e-01 -1.70400128e-01 -5.38418591e-01
7.92901963e-02 -4.12680805e-01 -3.47624213e-01 -4.21362609... | [13.300309181213379, 5.083961009979248] |
75a2e890-6db2-4675-a91f-359ee79748d4 | deep-learning-assisted-co-registration-of | 2202.07755 | null | https://arxiv.org/abs/2202.07755v1 | https://arxiv.org/pdf/2202.07755v1.pdf | Deep Learning-Assisted Co-registration of Full-Spectral Autofluorescence Lifetime Microscopic Images with H&E-Stained Histology Images | Autofluorescence lifetime images reveal unique characteristics of endogenous fluorescence in biological samples. Comprehensive understanding and clinical diagnosis rely on co-registration with the gold standard, histology images, which is extremely challenging due to the difference of both images. Here, we show an unsu... | ['Marta Vallejo', 'James R. Hopgood', 'Kevin Dhaliwal', 'Ahsan R. Akram', 'Neil Finlayson', 'Gareth O. S. Williams', 'Susan Fernandes', 'Qiang Wang'] | 2022-02-15 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 7.31881618e-01 -4.84025419e-01 1.12541512e-01 -1.44544855e-01
-9.79029715e-01 -7.54578352e-01 3.93360615e-01 2.69262940e-01
-8.75724852e-01 8.14670205e-01 -2.84917563e-01 -1.13103926e-01
-1.14818327e-01 -3.41923654e-01 -7.95849785e-02 -1.54605544e+00
4.18246947e-02 4.64937180e-01 1.60065979e-01 8.07232559... | [14.650146484375, -3.109755754470825] |
7912a040-eec6-492c-a492-f634d17a1e12 | a-5-mw-standard-cell-memory-based | 2102.02758 | null | https://arxiv.org/abs/2102.02758v1 | https://arxiv.org/pdf/2102.02758v1.pdf | A 5 μW Standard Cell Memory-based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing | Hyperdimensional computing (HDC) is a brain-inspired computing paradigm based on high-dimensional holistic representations of vectors. It recently gained attention for embedded smart sensing due to its inherent error-resiliency and suitability to highly parallel hardware implementations. In this work, we propose a prog... | ['Luca Benini', 'Abbas Rahimi', 'Manuel Eggimann'] | 2021-02-04 | null | null | null | null | ['emg-gesture-recognition'] | ['medical'] | [ 6.08642757e-01 -1.06393799e-01 -1.61052402e-02 -2.98367620e-01
-3.32337350e-01 -8.87499154e-02 3.45642239e-01 3.15890312e-01
-8.29061985e-01 4.02978808e-01 -1.29328683e-01 -2.98005700e-01
-2.87647426e-01 -6.77931786e-01 -6.32355094e-01 -7.90444493e-01
-3.78215283e-01 1.79245666e-01 2.35891789e-01 -2.00384691... | [8.312994956970215, 2.543429374694824] |
1199d932-ebb6-4c1c-98b2-105346714b58 | question-answering-over-knowledge-bases-by | 2012.01707 | null | https://arxiv.org/abs/2012.01707v2 | https://arxiv.org/pdf/2012.01707v2.pdf | Leveraging Abstract Meaning Representation for Knowledge Base Question Answering | Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training datasets. In this work, we propose Neuro-Symbolic Question Answering (NSQA... | ['Mo Yu', 'G P Shrivatsa Bhargav', 'Udit Sharma', 'Gaetano Rossiello', 'Ryan Riegel', 'Revanth Reddy', 'Lucian Popa', 'Sumit Neelam', 'Tahira Naseem', 'Nandana Mihindukulasooriya', 'Ndivhuwo Makondo', 'Francois Luus', 'Yunyao Li', 'Young-suk Lee', 'Dinesh Khandelwal', 'Naweed Khan', 'Hima Karanam', 'Sairam Gurajada', '... | 2020-12-03 | null | https://aclanthology.org/2021.findings-acl.339 | https://aclanthology.org/2021.findings-acl.339.pdf | findings-acl-2021-8 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.93713710e-01 8.11940670e-01 1.46624893e-01 -5.46735883e-01
-1.02838457e+00 -6.25845194e-01 1.87967926e-01 4.00206298e-01
-2.45554999e-01 8.47578824e-01 1.59497112e-01 -6.66051924e-01
-3.35096449e-01 -1.30779743e+00 -9.69000101e-01 2.18950912e-01
1.29857823e-01 9.77679849e-01 7.95403004e-01 -9.11953747... | [10.336697578430176, 7.920892715454102] |
b76ca619-c90b-4c1c-975c-97bdf0a0877e | knowledge-graph-embedding-with-3d-compound | 2304.00378 | null | https://arxiv.org/abs/2304.00378v1 | https://arxiv.org/pdf/2304.00378v1.pdf | Knowledge Graph Embedding with 3D Compound Geometric Transformations | The cascade of 2D geometric transformations were exploited to model relations between entities in a knowledge graph (KG), leading to an effective KG embedding (KGE) model, CompoundE. Furthermore, the rotation in the 3D space was proposed as a new KGE model, Rotate3D, by leveraging its non-commutative property. Inspired... | ['C. -C. Jay Kuo', 'Bin Wang', 'Yun-Cheng Wang', 'Xiou Ge'] | 2023-04-01 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [-4.80301648e-01 3.56451243e-01 -5.91774106e-01 -8.24883059e-02
3.86062741e-01 -5.55200160e-01 7.28746414e-01 -9.01618879e-03
2.42265657e-01 4.38787013e-01 3.91958266e-01 -2.60684103e-01
-6.81418598e-01 -1.15287685e+00 -6.68771803e-01 -3.96779895e-01
-2.89799035e-01 1.09873742e-01 2.15481222e-01 -5.16198814... | [8.672425270080566, 7.809031009674072] |
c9006361-1fbf-432b-9dbc-892bcd509a84 | graph-based-time-series-clustering-for-end-to | 2305.19183 | null | https://arxiv.org/abs/2305.19183v1 | https://arxiv.org/pdf/2305.19183v1.pdf | Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting | Existing relationships among time series can be exploited as inductive biases in learning effective forecasting models. In hierarchical time series, relationships among subsets of sequences induce hard constraints (hierarchical inductive biases) on the predicted values. In this paper, we propose a graph-based methodolo... | ['Cesare Alippi', 'Danilo Mandic', 'Andrea Cini'] | 2023-05-30 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 3.59688282e-01 4.34824437e-01 -5.91839030e-02 -8.12764883e-01
1.01890780e-01 -5.31486452e-01 5.97511113e-01 3.72760653e-01
-1.17099702e-01 4.59976047e-01 3.11602801e-01 -3.83243680e-01
-3.37771654e-01 -9.21686232e-01 -7.94516623e-01 -8.61875713e-01
-6.53578699e-01 5.41597843e-01 8.82440358e-02 -3.80690902... | [6.8774495124816895, 3.008944272994995] |
1c878da0-734c-4674-88e1-cb703f7bde4b | median-pixel-difference-convolutional-network | 2205.15867 | null | https://arxiv.org/abs/2205.15867v1 | https://arxiv.org/pdf/2205.15867v1.pdf | Median Pixel Difference Convolutional Network for Robust Face Recognition | Face recognition is one of the most active tasks in computer vision and has been widely used in the real world. With great advances made in convolutional neural networks (CNN), lots of face recognition algorithms have achieved high accuracy on various face datasets. However, existing face recognition algorithms based o... | ['Li Liu', 'Zhuo Su', 'Jiehua Zhang'] | 2022-05-30 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 1.69861570e-01 -5.79924226e-01 3.70515138e-01 -4.59591776e-01
2.75071487e-02 -1.37573421e-01 4.80496824e-01 -6.21830702e-01
-4.34988648e-01 4.90610212e-01 7.51960501e-02 -1.21062016e-02
-1.58736363e-01 -9.14281547e-01 -5.29601991e-01 -7.76656926e-01
1.49583697e-01 -6.48398638e-01 1.42812237e-01 -1.03328079... | [11.382596015930176, -2.2996182441711426] |
a00c2a55-017c-41ad-b7cb-c6939c6868be | shadow-aware-dynamic-convolution-for-shadow | 2205.04908 | null | https://arxiv.org/abs/2205.04908v3 | https://arxiv.org/pdf/2205.04908v3.pdf | Shadow-Aware Dynamic Convolution for Shadow Removal | With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap be... | ['Fei Chao', 'Rongrong Ji', 'Hong Yang', 'Mingbao Lin', 'Yimin Xu'] | 2022-05-10 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 3.16186428e-01 -1.66118577e-01 1.69220135e-01 -3.02822202e-01
-1.01962350e-01 -4.39330965e-01 3.10596079e-01 -4.30100173e-01
-3.38483036e-01 6.07634664e-01 8.92221704e-02 -5.94111860e-01
4.30874288e-01 -8.11377168e-01 -7.16725528e-01 -1.07265306e+00
2.08255008e-01 -2.39810288e-01 7.98225641e-01 -2.41335243... | [10.844017028808594, -4.08007287979126] |
f828c32f-890d-43fe-a5cc-87468c1983e0 | optimization-based-ramping-reserve-allocation | 2204.11270 | null | https://arxiv.org/abs/2204.11270v3 | https://arxiv.org/pdf/2204.11270v3.pdf | Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement | This paper presents a novel scheme termed Optimization-based Ramping Reserve Allocation (ORRA) for addressing an ongoing challenge in Automatic Generation Control (AGC) enhancement, i.e., the optimal coordination of multiple Battery Energy Storage Systems (BESSs). While exploiting further the synergy between BESSs and ... | ['Zhengtao Ding', 'Zhen Dong', 'Zhongguo Li', 'Alessandra Parisio', 'Yiqiao Xu'] | 2022-04-24 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.77984405e-01 7.42103457e-02 -1.34836674e-01 1.98094565e-02
-5.96946001e-01 -8.12405109e-01 1.78423882e-01 3.45837593e-01
1.09447040e-01 1.21539438e+00 -2.37029314e-01 -3.71803075e-01
-5.42583585e-01 -8.79043698e-01 -5.44171035e-01 -1.20753372e+00
-4.40476805e-01 2.90571123e-01 -4.77910936e-01 -4.14349437... | [5.644495964050293, 2.5533523559570312] |
ace133b4-84c1-4331-8ce2-47cc6c9046cf | semi-supervised-recognition-under-a-noisy-and | 2006.10702 | null | https://arxiv.org/abs/2006.10702v1 | https://arxiv.org/pdf/2006.10702v1.pdf | Semi-Supervised Recognition under a Noisy and Fine-grained Dataset | Simi-Supervised Recognition Challenge-FGVC7 is a challenging fine-grained recognition competition. One of the difficulties of this competition is how to use unlabeled data. We adopted pseudo-tag data mining to increase the amount of training data. The other one is how to identify similar birds with a very small differe... | ['Zhongji Liu', 'Xinjian Li', 'Rong Pang', 'Min Yang', 'Cheng Cui', 'Bing Dai', 'Zhi Ye', 'Yanmei Zhao', 'Yangxi Li', 'Kai Wei'] | 2020-06-18 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [-9.89673883e-02 -2.94436336e-01 -1.81824202e-04 -6.66892052e-01
-2.53493458e-01 -6.73300147e-01 5.84282219e-01 -3.31703573e-01
-6.48057759e-01 7.33327925e-01 8.40842873e-02 2.14279413e-01
-1.42677054e-01 -6.89123452e-01 -6.83687747e-01 -5.15640140e-01
1.79850712e-01 6.76394939e-01 2.93482631e-01 -2.23585635... | [9.651506423950195, 2.084829807281494] |
7c6bc5a5-e734-4674-95a2-21a3789b1092 | polybot-training-one-policy-across-robots | 2307.03719 | null | https://arxiv.org/abs/2307.03719v1 | https://arxiv.org/pdf/2307.03719v1.pdf | Polybot: Training One Policy Across Robots While Embracing Variability | Reusing large datasets is crucial to scale vision-based robotic manipulators to everyday scenarios due to the high cost of collecting robotic datasets. However, robotic platforms possess varying control schemes, camera viewpoints, kinematic configurations, and end-effector morphologies, posing significant challenges wh... | ['Chelsea Finn', 'Dorsa Sadigh', 'Jonathan Yang'] | 2023-07-07 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [-3.05168182e-01 -2.17878863e-01 -3.67734194e-01 1.18042015e-01
-4.33359236e-01 -1.16304994e+00 3.13991398e-01 -4.52824444e-01
-3.32141101e-01 6.39770627e-01 -1.05032928e-01 -3.40619624e-01
-5.14289916e-01 9.63258557e-03 -8.47410202e-01 -4.71387476e-01
-1.30481109e-01 5.86416900e-01 1.62725106e-01 -3.58165115... | [4.724366188049316, 0.6269116401672363] |
bee04f8f-5278-4a16-bcdb-769dc9b850a1 | self-supervised-learning-for-real-world-super | 2203.01325 | null | https://arxiv.org/abs/2203.01325v2 | https://arxiv.org/pdf/2203.01325v2.pdf | Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations | In this paper, we consider two challenging issues in reference-based super-resolution (RefSR), (i) how to choose a proper reference image, and (ii) how to learn real-world RefSR in a self-supervised manner. Particularly, we present a novel self-supervised learning approach for real-world image SR from observations at d... | ['WangMeng Zuo', 'Yunjin Chen', 'Hongzhi Zhang', 'Ruohao Wang', 'Zhilu Zhang'] | 2022-03-02 | null | null | null | null | ['reference-based-super-resolution'] | ['computer-vision'] | [ 3.06011587e-01 -1.11872442e-01 -9.02563035e-02 -4.17772770e-01
-1.04659665e+00 -3.63746673e-01 3.46406698e-01 -8.37836623e-01
-3.07355493e-01 7.86765039e-01 4.20913920e-02 2.97745061e-03
-2.00891539e-01 -9.50542688e-01 -8.88084888e-01 -9.30225670e-01
6.31043017e-01 1.23449795e-01 3.42190266e-01 -2.99815834... | [10.798530578613281, -2.1783242225646973] |
f0ca8db1-4c32-4f77-8b6c-bb315fdf997d | relational-sentence-embedding-for-flexible | 2212.08802 | null | https://arxiv.org/abs/2212.08802v2 | https://arxiv.org/pdf/2212.08802v2.pdf | Relational Sentence Embedding for Flexible Semantic Matching | We present Relational Sentence Embedding (RSE), a new paradigm to further discover the potential of sentence embeddings. Prior work mainly models the similarity between sentences based on their embedding distance. Because of the complex semantic meanings conveyed, sentence pairs can have various relation types, includi... | ['Haizhou Li', 'Bin Wang'] | 2022-12-17 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 2.48628318e-01 6.47477731e-02 -3.03122938e-01 -6.21305287e-01
-8.71899188e-01 -4.19498742e-01 7.02336252e-01 6.42858624e-01
-5.21317482e-01 4.13495243e-01 8.61707628e-01 -3.83036226e-01
-1.76791653e-01 -7.56605387e-01 -4.73644793e-01 -2.92311192e-01
1.78586598e-02 4.04046804e-01 1.47894607e-03 -6.43184185... | [11.003284454345703, 8.854246139526367] |
dcecf661-28c1-46f5-bfd8-e2dab4cfbc67 | disasymnet-disentanglement-of-asymmetrical | 2307.02935 | null | https://arxiv.org/abs/2307.02935v1 | https://arxiv.org/pdf/2307.02935v1.pdf | DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning | Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the ... | ['Ritse Mann', 'Ruisheng Su', 'Regina Beets-Tan', 'Chunyao Lu', 'Tianyu Zhang', 'Luyi Han', 'Yuan Gao', 'Tao Tan', 'Xin Wang'] | 2023-07-06 | null | null | null | null | ['disentanglement', 'anatomy'] | ['methodology', 'miscellaneous'] | [ 7.00031757e-01 5.83662510e-01 -2.08737612e-01 -6.04785800e-01
-9.12455738e-01 -6.69292271e-01 2.57352322e-01 2.79588133e-01
7.31381995e-04 4.67869371e-01 1.80414036e-01 -8.47986996e-01
1.14174962e-01 -7.33167827e-01 -8.06863248e-01 -7.68001080e-01
-2.06400886e-01 5.21496952e-01 3.96908581e-01 -1.01857623... | [14.691365242004395, -2.1200954914093018] |
cc318a16-0b63-4be8-a516-bb2129be5810 | mobilizing-personalized-federated-learning | 2304.12534 | null | https://arxiv.org/abs/2304.12534v1 | https://arxiv.org/pdf/2304.12534v1.pdf | Mobilizing Personalized Federated Learning via Random Walk Stochastic ADMM | In this research, we investigate the barriers associated with implementing Federated Learning (FL) in real-world scenarios, where a consistent connection between the central server and all clients cannot be maintained, and data distribution is heterogeneous. To address these challenges, we focus on mobilizing the feder... | ['Jin Lu', 'Houyi Du', 'Fei Dou', 'Ziba Parsons'] | 2023-04-25 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-4.09534156e-01 4.65347152e-03 -5.70015550e-01 -2.89630294e-01
-6.53041840e-01 -5.86722672e-01 2.99424559e-01 -1.99849874e-01
-3.23044717e-01 9.46612418e-01 2.33833324e-02 -5.20917714e-01
-5.33422291e-01 -8.48395884e-01 -4.98638242e-01 -9.58477855e-01
-2.66252667e-01 9.70124662e-01 4.71064560e-02 3.18079740... | [5.853288650512695, 6.19437837600708] |
d69fbf3e-b2df-4f51-a7c9-0745fc99f816 | aligning-415-519-proteins-in-less-than-two | 1603.06958 | null | http://arxiv.org/abs/1603.06958v1 | http://arxiv.org/pdf/1603.06958v1.pdf | Aligning 415 519 proteins in less than two hours on PC | Rapid development of modern sequencing platforms enabled an unprecedented
growth of protein families databases. The abundance of sets composed of
hundreds of thousands sequences is a great challenge for multiple sequence
alignment algorithms. In the article we introduce FAMSA, a new progressive
algorithm designed for f... | [] | 2016-03-22 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 5.10710359e-01 -5.16137183e-01 -7.97542464e-03 -2.89309174e-01
-5.63114285e-01 -8.27669919e-01 2.80078173e-01 5.41203082e-01
-6.20700061e-01 1.30347824e+00 -3.48371685e-01 -4.23716992e-01
-3.84833425e-01 -3.92361969e-01 -3.14066648e-01 -1.09968412e+00
-1.07635446e-01 9.44059193e-01 6.25229359e-01 -3.47351789... | [4.853138446807861, 5.228019714355469] |
5e86bb2b-30e5-45f7-87fa-6212033b5867 | learning-and-or-models-to-represent-context | 1501.07359 | null | http://arxiv.org/abs/1501.07359v2 | http://arxiv.org/pdf/1501.07359v2.pdf | Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation | This paper presents a method for learning And-Or models to represent context
and occlusion for car detection and viewpoint estimation. The learned And-Or
model represents car-to-car context and occlusion configurations at three
levels: (i) spatially-aligned cars, (ii) single car under different occlusion
configurations... | ['Song-Chun Zhu', 'Bo Li', 'Tianfu Wu'] | 2015-01-29 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 8.25510100e-02 -4.54124361e-02 -3.07046980e-01 -5.18628836e-01
-9.95318234e-01 -6.64869606e-01 5.25896788e-01 -3.08972925e-01
5.67561090e-02 2.36230358e-01 -5.02639413e-01 -6.20588183e-01
4.04940993e-01 -7.96527445e-01 -9.80472863e-01 -6.88607395e-01
-3.26749869e-02 9.21776772e-01 1.10039032e+00 -2.42309451... | [7.892038822174072, -2.5229029655456543] |
d53ccb4c-6061-4280-a76a-7b474b6b2129 | self-organization-and-artificial-life | 1903.07456 | null | https://arxiv.org/abs/1903.07456v2 | https://arxiv.org/pdf/1903.07456v2.pdf | Self-Organization and Artificial Life | Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several... | ['Justin Werfel', 'Carlos Gershenson', 'Vito Trianni', 'Hiroki Sayama'] | 2019-03-14 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-8.77908692e-02 2.36707851e-01 -4.75092083e-02 2.35205069e-01
8.30692768e-01 -8.57700408e-01 8.25414598e-01 4.58908737e-01
3.62107381e-02 9.09227729e-01 1.48855045e-01 -1.88964084e-01
-4.35312629e-01 -8.92420650e-01 -5.37962079e-01 -1.16684842e+00
-2.44972169e-01 1.55990303e-01 1.66448846e-01 -7.21563220... | [5.591435432434082, 4.20720100402832] |
3063c1b9-e0aa-4667-b03c-a78afd0df42a | word-embeddings-for-the-armenian-language | 1906.03134 | null | https://arxiv.org/abs/1906.03134v1 | https://arxiv.org/pdf/1906.03134v1.pdf | Word Embeddings for the Armenian Language: Intrinsic and Extrinsic Evaluation | In this work, we intrinsically and extrinsically evaluate and compare existing word embedding models for the Armenian language. Alongside, new embeddings are presented, trained using GloVe, fastText, CBOW, SkipGram algorithms. We adapt and use the word analogy task in intrinsic evaluation of embeddings. For extrinsic e... | ['Tsolak Ghukasyan', 'Karen Avetisyan'] | 2019-06-07 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [-1.23186834e-01 6.07127696e-02 -5.76162815e-01 -4.10967082e-01
-4.47406560e-01 -8.26171041e-01 1.15230834e+00 7.55914629e-01
-1.22910678e+00 4.35627222e-01 7.87143111e-01 -7.76871860e-01
3.51295114e-01 -8.42994332e-01 -1.45296127e-01 -1.17596492e-01
7.33961165e-02 5.65921664e-01 -7.51399100e-02 -2.85066664... | [10.509270668029785, 8.664886474609375] |
26120661-550a-4c3d-97e3-61439eba1c64 | symmetric-spaces-for-graph-embeddings-a | 2106.04941 | null | https://arxiv.org/abs/2106.04941v1 | https://arxiv.org/pdf/2106.04941v1.pdf | Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach | Learning faithful graph representations as sets of vertex embeddings has become a fundamental intermediary step in a wide range of machine learning applications. We propose the systematic use of symmetric spaces in representation learning, a class encompassing many of the previously used embedding targets. This enables... | ['Anna Wienhard', 'Michael Strube', 'Steve Trettel', 'Beatrice Pozzetti', 'Federico López'] | 2021-06-09 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 4.32174280e-03 3.01418871e-01 1.83680505e-02 -2.33278945e-01
-3.55774879e-01 -7.80097067e-01 9.06253994e-01 2.89945602e-01
-2.35173523e-01 5.00681400e-01 4.49093163e-01 -3.87205839e-01
-3.91896665e-01 -8.04364502e-01 -3.63410830e-01 -7.40829468e-01
-3.54930788e-01 4.86552387e-01 1.47551984e-01 -6.05838060... | [7.1264495849609375, 5.904357433319092] |
a9b90113-775a-483e-a47e-3671c00327ce | three-things-everyone-should-know-about | 2203.09795 | null | https://arxiv.org/abs/2203.09795v1 | https://arxiv.org/pdf/2203.09795v1.pdf | Three things everyone should know about Vision Transformers | After their initial success in natural language processing, transformer architectures have rapidly gained traction in computer vision, providing state-of-the-art results for tasks such as image classification, detection, segmentation, and video analysis. We offer three insights based on simple and easy to implement var... | ['Hervé Jégou', 'Jakob Verbeek', 'Alaaeldin El-Nouby', 'Matthieu Cord', 'Hugo Touvron'] | 2022-03-18 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 4.10084069e-01 -1.04494512e-01 2.70037502e-02 -3.47570300e-01
-4.85005438e-01 -4.92094070e-01 5.85837126e-01 1.49514318e-01
-8.17921996e-01 1.87088698e-02 -6.35975525e-02 -3.31454515e-01
3.16354364e-01 -6.84324861e-01 -7.34991968e-01 -4.71637875e-01
2.80090645e-02 2.19282731e-01 9.46763337e-01 5.39054163... | [9.431139945983887, 1.3403648138046265] |
7fbc6174-f855-45d4-8e2d-e0a92cf62004 | the-impact-of-lexical-and-grammatical | null | null | https://openreview.net/forum?id=1aP4JMLtay | https://openreview.net/pdf?id=1aP4JMLtay | The impact of lexical and grammatical processing on generating code from natural language | Considering the seq2seq architecture of Yin and Neubig (2018) for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architectu... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-translation'] | ['computer-code'] | [ 2.50913680e-01 5.26936889e-01 -3.59416991e-01 -1.24713834e-02
-7.05099881e-01 -6.81133449e-01 6.50545955e-01 1.35955572e-01
-2.40379378e-01 5.43800771e-01 4.72330630e-01 -9.27447081e-01
3.06895554e-01 -5.39157689e-01 -1.17396510e+00 1.80248305e-01
-9.32560191e-02 9.98280570e-02 8.69302452e-02 -4.53717142... | [7.759944915771484, 7.893052101135254] |
9fa64c53-4b8d-40b6-9387-7b59209732dd | class-incremental-learning-of-plant-and | 2304.06619 | null | https://arxiv.org/abs/2304.06619v1 | https://arxiv.org/pdf/2304.06619v1.pdf | Class-Incremental Learning of Plant and Disease Detection: Growing Branches with Knowledge Distillation | This paper investigates the problem of class-incremental object detection for agricultural applications where a model needs to learn new plant species and diseases incrementally without forgetting the previously learned ones. We adapt two public datasets to include new categories over time, simulating a more realistic ... | ['Mathieu Pagé Fortin'] | 2023-04-13 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 4.23640847e-01 2.67471150e-02 -1.58478588e-01 -5.55658825e-02
-1.14874840e-01 -1.06477904e+00 4.65177327e-01 4.35264826e-01
-2.86082059e-01 9.67838764e-01 -4.14978653e-01 -4.20836002e-01
-2.06410468e-01 -1.04894829e+00 -1.01678860e+00 -7.67343998e-01
-4.04026330e-01 5.81427574e-01 6.97708428e-01 -8.14745873... | [9.828019142150879, 3.2693986892700195] |
50b4e07a-58ba-47f2-817a-4d2a65143786 | ensemble-modeling-for-time-series-forecasting | 2304.04308 | null | https://arxiv.org/abs/2304.04308v1 | https://arxiv.org/pdf/2304.04308v1.pdf | Ensemble Modeling for Time Series Forecasting: an Adaptive Robust Optimization Approach | Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustness, as the performance of a single predictor can be highly variable due to shifts in the underlying d... | ['Leonard Boussioux', 'Dimitris Bertsimas'] | 2023-04-09 | null | null | null | null | ['tropical-cyclone-intensity-forecasting'] | ['time-series'] | [ 1.16635680e-01 -6.49303734e-01 8.72415006e-02 -5.64573228e-01
-7.79661298e-01 -6.72239661e-01 6.56158507e-01 -1.21798694e-01
-8.34707320e-02 9.55865204e-01 2.05215305e-01 -4.74428594e-01
-3.43786091e-01 -6.30359948e-01 -4.18555409e-01 -9.98274446e-01
-4.49755490e-01 8.18831623e-02 -2.46389508e-01 -4.46458638... | [6.915230751037598, 3.1199026107788086] |
ef34ecb6-68e7-403d-9e5d-a481e2f5c3d7 | parallel-attention-forcing-for-machine | 2211.03237 | null | https://arxiv.org/abs/2211.03237v1 | https://arxiv.org/pdf/2211.03237v1.pdf | Parallel Attention Forcing for Machine Translation | Attention-based autoregressive models have achieved state-of-the-art performance in various sequence-to-sequence tasks, including Text-To-Speech (TTS) and Neural Machine Translation (NMT), but can be difficult to train. The standard training approach, teacher forcing, guides a model with the reference back-history. Dur... | ['Mark Gales', 'Qingyun Dou'] | 2022-11-06 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 5.15919507e-01 1.50625527e-01 -1.22324966e-01 -3.07516932e-01
-9.13738132e-01 -3.99243087e-01 8.70895624e-01 -4.74997878e-01
-2.85058409e-01 5.50800979e-01 3.07157338e-01 -7.64202595e-01
3.17567289e-01 -3.78193825e-01 -7.09645331e-01 -5.58475971e-01
3.56953233e-01 8.40999126e-01 1.64844587e-01 -3.46128881... | [14.436429023742676, 7.137701988220215] |
e1fc9f1b-3ba0-421f-a0a7-120d80a0f68a | enhancing-content-planning-for-table-to-text | null | null | https://aclanthology.org/2020.findings-emnlp.262 | https://aclanthology.org/2020.findings-emnlp.262.pdf | Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification | Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress. Based on results from previous work, the performance bottleneck of current models lies in the stage of content planing (selecting and ordering salient content... | ['Ting Liu', 'Xiaojiang Liu', 'Bing Qin', 'Xiaocheng Feng', 'Wei Bi', 'Heng Gong'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['table-to-text-generation'] | ['natural-language-processing'] | [ 4.40463901e-01 6.97283506e-01 -6.12892866e-01 -5.09497941e-01
-1.19378448e+00 -5.17321765e-01 7.55698323e-01 6.73450112e-01
-6.75216138e-01 8.44887853e-01 1.17648757e+00 -3.18451613e-01
-1.27820790e-01 -8.08504641e-01 -8.16214681e-01 -1.73056766e-01
-5.79998493e-02 1.18148923e+00 1.46413654e-01 -3.00112814... | [11.559636116027832, 8.796542167663574] |
7dc6f948-2b92-446d-9e52-745ec7f576ed | face-recognition-via-locality-constrained-low | 1912.03145 | null | https://arxiv.org/abs/1912.03145v1 | https://arxiv.org/pdf/1912.03145v1.pdf | Face Recognition via Locality Constrained Low Rank Representation and Dictionary Learning | Face recognition has been widely studied due to its importance in smart cities applications. However, the case when both training and test images are corrupted is not well solved. To address such a problem, this paper proposes a locality constrained low rank representation and dictionary learning (LCLRRDL) algorithm fo... | ['Xiao-Jun Wu', 'He-Feng Yin', 'Josef Kittler'] | 2019-12-06 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-1.03562633e-02 -3.92159343e-01 -1.65875554e-01 -2.46831447e-01
-7.63846636e-01 -7.45418221e-02 5.47367454e-01 -2.98078775e-01
4.64148335e-02 6.26336575e-01 2.40530357e-01 9.31722820e-02
-1.64136589e-01 -6.34647846e-01 -4.48597133e-01 -9.83673751e-01
2.32819408e-01 5.01820408e-02 -2.20766857e-01 1.25307128... | [12.510514259338379, 0.396503210067749] |
24bce7eb-0a5a-4918-a2d7-6290d6598a5b | adaptive-distillation-aggregating-knowledge | 2110.09674 | null | https://arxiv.org/abs/2110.09674v2 | https://arxiv.org/pdf/2110.09674v2.pdf | Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation | Knowledge Distillation is becoming one of the primary trends among neural network compression algorithms to improve the generalization performance of a smaller student model with guidance from a larger teacher model. This momentous rise in applications of knowledge distillation is accompanied by the introduction of num... | ['Lin Chen', 'Zhongwei Cheng', 'Mohammad Mahdi Kamani', 'Sumanth Chennupati'] | 2021-10-19 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 4.40089554e-01 2.15429917e-01 -2.69253463e-01 -3.55862111e-01
-3.61320466e-01 -4.88753617e-01 4.94068056e-01 4.89245027e-01
-8.03803623e-01 7.39128292e-01 3.07451725e-01 -3.60277861e-01
-5.44411063e-01 -7.06088483e-01 -7.67731249e-01 -8.56189907e-01
1.80170089e-01 6.49478614e-01 7.24033177e-01 -6.41477555... | [9.3345365524292, 3.367687940597534] |
cd717208-f704-4786-bbd7-fbd9f8fb763b | intelligent-bidirectional-rapidly-exploring | 1703.08944 | null | http://arxiv.org/abs/1703.08944v1 | http://arxiv.org/pdf/1703.08944v1.pdf | Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments | The sampling based motion planning algorithm known as Rapidly-exploring
Random Trees (RRT) has gained the attention of many researchers due to their
computational efficiency and effectiveness. Recently, a variant of RRT called
RRT* has been proposed that ensures asymptotic optimality. Subsequently its
bidirectional ver... | ['Ahmed Hussain Qureshi', 'Yasar Ayaz'] | 2017-03-27 | null | null | null | null | ['optimal-motion-planning'] | ['robots'] | [ 6.69674873e-01 1.22664124e-01 -2.40327910e-01 -1.97236259e-02
-5.51713765e-01 -3.38406920e-01 4.91237015e-01 -3.53241637e-02
-3.63440007e-01 1.26200938e+00 2.47402340e-01 -6.01156950e-01
-8.59359384e-01 -9.68503833e-01 -4.66496050e-01 -6.10855162e-01
-7.33461678e-01 6.30157173e-01 7.67013490e-01 -1.44266635... | [4.9818115234375, 1.6362404823303223] |
f89626b9-55a0-4631-b4c7-9b4c3caf06f0 | self-supervised-audio-visual-representation | 2111.05329 | null | https://arxiv.org/abs/2111.05329v5 | https://arxiv.org/pdf/2111.05329v5.pdf | Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity | We present CrissCross, a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework whereby in addition to learning the intra-modal and standard 'synchronous' cross-modal relations, CrissCross also learns 'asynchronous' cross-modal relationships. We perform in-dep... | ['Ali Etemad', 'Pritam Sarkar'] | 2021-11-09 | null | null | null | null | ['sound-classification', 'self-supervised-sound-classification', 'self-supervised-action-recognition'] | ['audio', 'audio', 'computer-vision'] | [ 3.64354521e-01 -1.15764767e-01 -4.28044766e-01 -3.53713006e-01
-1.03482020e+00 -5.03957212e-01 9.51197207e-01 -2.47776546e-02
-3.97095054e-01 3.48810852e-01 6.23752832e-01 2.70571053e-01
-2.44607493e-01 -3.47054720e-01 -6.80558980e-01 -7.09190428e-01
-3.39795768e-01 3.58246058e-01 3.32018673e-01 -8.31178278... | [9.749685287475586, 1.0516899824142456] |
f31bbbd0-b677-484a-b061-66ca40da3695 | a-machine-generated-catalogue-of-charon-s | 2206.08277 | null | https://arxiv.org/abs/2206.08277v1 | https://arxiv.org/pdf/2206.08277v1.pdf | A machine-generated catalogue of Charon's craters and implications for the Kuiper belt | In this paper we investigate Charon's craters size distribution using a deep learning model. This is motivated by the recent results of Singer et al. (2019) who, using manual cataloging, found a change in the size distribution slope of craters smaller than 12 km in diameter, translating into a paucity of small Kuiper B... | ['Mohamad Ali-Dib'] | 2022-06-16 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-2.28932679e-01 -1.09322416e-02 8.75816494e-02 -2.25458592e-01
-7.78823256e-01 -7.84513652e-01 1.00711477e+00 -1.82991475e-01
-6.06972933e-01 8.48487079e-01 1.72065407e-01 -5.68261802e-01
-2.66332239e-01 -8.82598341e-01 -7.16974676e-01 -6.34121537e-01
-9.69779715e-02 7.75626540e-01 1.72367468e-01 -3.22410852... | [7.052212238311768, 2.8010334968566895] |
db98d030-6283-414a-940e-682eecf08abc | to-augment-or-not-to-augment-a-comparative | 2111.09618 | null | https://arxiv.org/abs/2111.09618v1 | https://arxiv.org/pdf/2111.09618v1.pdf | To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP | Data-hungry deep neural networks have established themselves as the standard for many NLP tasks including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they still fall behind of their statistical counter-parts in low-resource scenarios. One methodology to ... | ['Gözde Gül Şahin'] | 2021-11-18 | null | https://aclanthology.org/2022.cl-1.2 | https://aclanthology.org/2022.cl-1.2.pdf | cl-acl-2022-3 | ['text-augmentation', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.61429846e-01 1.61324292e-01 -4.48699892e-01 -4.86543119e-01
-7.52295315e-01 -9.87409472e-01 6.94326460e-01 4.24539953e-01
-9.01626229e-01 1.01872325e+00 4.42842126e-01 -6.73516989e-01
4.36760724e-01 -7.02716529e-01 -6.23802662e-01 -5.69644034e-01
1.53394476e-01 7.01345325e-01 1.53582975e-01 -5.27970493... | [10.421000480651855, 9.809067726135254] |
1b3f1d35-1524-49c9-bd20-26427712c41f | skipflow-incorporating-neural-coherence | 1711.04981 | null | http://arxiv.org/abs/1711.04981v1 | http://arxiv.org/pdf/1711.04981v1.pdf | SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring | Deep learning has demonstrated tremendous potential for Automatic Text
Scoring (ATS) tasks. In this paper, we describe a new neural architecture that
enhances vanilla neural network models with auxiliary neural coherence
features. Our new method proposes a new \textsc{SkipFlow} mechanism that models
relationships betwe... | ['Minh C. Phan', 'Luu Anh Tuan', 'Yi Tay', 'Siu Cheung Hui'] | 2017-11-14 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 1.89206563e-02 1.13656916e-01 -2.94191241e-01 -4.68960524e-01
-8.26965690e-01 -2.31465876e-01 8.74229550e-01 1.95002347e-01
-4.10373211e-01 5.41649282e-01 6.86833501e-01 -1.03466749e-01
6.45136461e-02 -6.65169418e-01 -8.85462701e-01 -4.62727249e-01
1.33381322e-01 3.75188410e-01 1.91270053e-01 -1.45710081... | [11.916120529174805, 9.180667877197266] |
dbe9389f-8c9a-4b88-b475-529158f4af60 | exploring-spoken-named-entity-recognition-a | 2307.01310 | null | https://arxiv.org/abs/2307.01310v1 | https://arxiv.org/pdf/2307.01310v1.pdf | Exploring Spoken Named Entity Recognition: A Cross-Lingual Perspective | Recent advancements in Named Entity Recognition (NER) have significantly improved the identification of entities in textual data. However, spoken NER, a specialized field of spoken document retrieval, lags behind due to its limited research and scarce datasets. Moreover, cross-lingual transfer learning in spoken NER ha... | ['M. A. Tuğtekin Turan', 'David Thulke', 'Moncef Benaicha'] | 2023-07-03 | null | null | null | null | ['retrieval', 'transfer-learning', 'named-entity-recognition-ner', 'cross-lingual-transfer', 'cg'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.24169540e-01 -3.27075571e-02 2.18811557e-01 -6.80662096e-01
-1.28598559e+00 -8.51570308e-01 7.17874944e-01 1.30195066e-01
-1.33454871e+00 8.16020250e-01 8.16812932e-01 -1.99428111e-01
2.09798709e-01 -3.68237674e-01 -4.20367450e-01 -1.80435866e-01
-6.84368163e-02 6.04114115e-01 6.73864484e-02 -4.35598075... | [9.979701042175293, 9.73009967803955] |
293f29bf-8fcf-49a6-a825-728a5546e069 | transforming-fake-news-robust-generalisable | 2109.09796 | null | https://arxiv.org/abs/2109.09796v2 | https://arxiv.org/pdf/2109.09796v2.pdf | Transforming Fake News: Robust Generalisable News Classification Using Transformers | As online news has become increasingly popular and fake news increasingly prevalent, the ability to audit the veracity of online news content has become more important than ever. Such a task represents a binary classification challenge, for which transformers have achieved state-of-the-art results. Using the publicly a... | ['Amir Atapour-Abarghouei', 'Ciara Blackledge'] | 2021-09-20 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 8.40208009e-02 3.17009896e-01 -2.80353397e-01 -2.85902232e-01
-9.18926418e-01 -8.77612650e-01 1.12464237e+00 4.05437201e-01
-4.59725738e-01 6.96713328e-01 1.70108631e-01 -4.76234883e-01
2.72582591e-01 -4.72340971e-01 -8.00777376e-01 -1.37089744e-01
2.24099189e-01 4.89719033e-01 2.88549840e-01 -3.42340946... | [8.256409645080566, 10.25097370147705] |
be14f482-12a8-4896-81c5-07c6609cd9d6 | target-oriented-opinion-words-extraction-with | null | null | https://aclanthology.org/N19-1259 | https://aclanthology.org/N19-1259.pdf | Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling | Opinion target extraction and opinion words extraction are two fundamental subtasks in Aspect Based Sentiment Analysis (ABSA). Recently, many methods have made progress on these two tasks. However, few works aim at extracting opinion targets and opinion words as pairs. In this paper, we propose a novel sequence labelin... | ['Shu-Jian Huang', 'Jia-Jun Chen', 'Xin-yu Dai', 'Zhifang Fan', 'Zhen Wu'] | 2019-06-01 | null | null | null | naacl-2019-6 | ['aspect-oriented-opinion-extraction', 'target-oriented-opinion-words-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.19824433e-01 -1.36564866e-01 -2.20664158e-01 -7.45279014e-01
-1.09388971e+00 -6.48871481e-01 4.88167375e-01 5.25981188e-01
-3.52674901e-01 7.82702684e-01 6.84011519e-01 -3.69230330e-01
3.36750358e-01 -6.73369348e-01 -3.57849240e-01 -8.21298897e-01
2.32012108e-01 -5.62828826e-03 2.58378088e-02 -5.15202224... | [11.422094345092773, 6.699160099029541] |
a847fc29-9a12-4c30-b765-820b7207bdec | shapo-implicit-representations-for-multi | 2207.13691 | null | https://arxiv.org/abs/2207.13691v1 | https://arxiv.org/pdf/2207.13691v1.pdf | ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization | Our method studies the complex task of object-centric 3D understanding from a single RGB-D observation. As it is an ill-posed problem, existing methods suffer from low performance for both 3D shape and 6D pose and size estimation in complex multi-object scenarios with occlusions. We present ShAPO, a method for joint mu... | ['Adrien Gaidon', 'Zsolt Kira', 'Thomas Kollar', 'Rares Ambrus', 'Sergey Zakharov', 'Muhammad Zubair Irshad'] | 2022-07-27 | null | null | null | null | ['3d-shape-reconstruction-from-a-single-2d', '6d-pose-estimation-using-rgbd', '6d-pose-estimation-1'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.77757695e-01 -1.52003132e-02 2.49212965e-01 -3.39146733e-01
-1.02190125e+00 -6.58250570e-01 4.95006353e-01 -1.72998086e-01
-1.00841366e-01 2.66082287e-01 2.57397834e-02 2.86604732e-01
1.19557433e-01 -4.46539819e-01 -1.16385555e+00 -5.52463353e-01
1.02684774e-01 1.16173148e+00 2.92384803e-01 1.93746224... | [7.6671223640441895, -2.6890909671783447] |
9906052e-ff00-4572-a8bd-1b35fa198673 | video-panoptic-segmentation-1 | 2006.11339 | null | https://arxiv.org/abs/2006.11339v1 | https://arxiv.org/pdf/2006.11339v1.pdf | Video Panoptic Segmentation | Panoptic segmentation has become a new standard of visual recognition task by unifying previous semantic segmentation and instance segmentation tasks in concert. In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation. The task requires generating consistent panoptic... | ['Joon-Young Lee', 'Dahun Kim', 'Sanghyun Woo', 'In So Kweon'] | 2020-06-19 | video-panoptic-segmentation | http://openaccess.thecvf.com/content_CVPR_2020/html/Kim_Video_Panoptic_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kim_Video_Panoptic_Segmentation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.24027866e-01 -2.15808675e-01 -2.60662347e-01 -2.91765809e-01
-8.87374520e-01 -5.96439958e-01 7.26700604e-01 -2.13174686e-01
-4.39982563e-01 5.69794238e-01 -5.63692711e-02 1.63925529e-01
-5.65495938e-02 -6.85390234e-01 -9.25939441e-01 -7.65229583e-01
-1.02070346e-01 4.73651171e-01 9.29984987e-01 6.67443275... | [9.080183029174805, -0.13224683701992035] |
1d70e08e-65f6-4d95-9e8e-2d3365f71861 | an-embarrassingly-easy-but-strong-baseline | 2208.04534 | null | https://arxiv.org/abs/2208.04534v3 | https://arxiv.org/pdf/2208.04534v3.pdf | An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition | Named entity recognition (NER) is the task to detect and classify the entity spans in the text. When entity spans overlap between each other, this problem is named as nested NER. Span-based methods have been widely used to tackle the nested NER. Most of these methods will get a score $n \times n$ matrix, where $n$ mean... | ['Xipeng Qiu', 'Xiaonan Li', 'Yu Sun', 'Hang Yan'] | 2022-08-09 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-4.38034058e-01 -2.43067294e-01 7.96890259e-02 -4.47017550e-01
-3.58974040e-01 -7.60994673e-01 5.26940003e-02 4.80536252e-01
-8.93047214e-01 7.22964644e-01 5.65552771e-01 -2.43960455e-01
-2.24251021e-02 -1.11216211e+00 -6.00342333e-01 -5.87339960e-02
-3.20800424e-01 -1.03194565e-01 1.84710175e-01 -3.61905873... | [9.623433113098145, 9.457525253295898] |
b142da41-cde0-4b5d-b97b-e996c7396fb7 | optimal-transport-posterior-alignment-for | 2307.04096 | null | https://arxiv.org/abs/2307.04096v1 | https://arxiv.org/pdf/2307.04096v1.pdf | Optimal Transport Posterior Alignment for Cross-lingual Semantic Parsing | Cross-lingual semantic parsing transfers parsing capability from a high-resource language (e.g., English) to low-resource languages with scarce training data. Previous work has primarily considered silver-standard data augmentation or zero-shot methods, however, exploiting few-shot gold data is comparatively unexplored... | ['Mirella Lapata', 'Tom Hosking', 'Tom Sherborne'] | 2023-07-09 | null | null | null | null | ['data-augmentation', 'semantic-parsing'] | ['methodology', 'natural-language-processing'] | [ 3.09547037e-01 3.30499440e-01 -4.91177022e-01 -6.47106171e-01
-1.71445024e+00 -8.57479393e-01 4.16178852e-01 6.61074230e-03
-5.58085978e-01 5.53963780e-01 4.82278764e-01 -3.79685193e-01
3.07410270e-01 -7.09457338e-01 -8.66608500e-01 -3.00619900e-01
3.35742682e-01 6.83867991e-01 1.09274656e-01 -1.77825704... | [10.691057205200195, 9.446306228637695] |
ec95df8d-d77f-46e1-a18e-86dc888b5d88 | fda-gan-flow-based-dual-attention-gan-for | 2112.00281 | null | https://arxiv.org/abs/2112.00281v1 | https://arxiv.org/pdf/2112.00281v1.pdf | FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer | Human pose transfer aims at transferring the appearance of the source person to the target pose. Existing methods utilizing flow-based warping for non-rigid human image generation have achieved great success. However, they fail to preserve the appearance details in synthesized images since the spatial correlation betwe... | ['Haibin Shen', 'Zhaoyan Ming', 'Dongxu Wei', 'Kejie Huang', 'Liyuan Ma'] | 2021-12-01 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.95318775e-02 -1.01146787e-01 5.42690232e-02 -4.69838262e-01
-5.85617483e-01 -5.34620881e-01 6.32220447e-01 -7.40260959e-01
-1.11032367e-01 7.89197862e-01 5.26832879e-01 6.06026411e-01
1.74671799e-01 -7.24409997e-01 -7.81133294e-01 -7.50097692e-01
5.46608031e-01 2.89375931e-01 -1.63196046e-02 -4.28181440... | [11.948832511901855, -0.9008359313011169] |
253f6d4f-5134-44f6-87cc-d5392fce5a13 | investigating-the-role-of-centering-theory-in | 2210.14678 | null | https://arxiv.org/abs/2210.14678v1 | https://arxiv.org/pdf/2210.14678v1.pdf | Investigating the Role of Centering Theory in the Context of Neural Coreference Resolution Systems | Centering theory (CT; Grosz et al., 1995) provides a linguistic analysis of the structure of discourse. According to the theory, local coherence of discourse arises from the manner and extent to which successive utterances make reference to the same entities. In this paper, we investigate the connection between centeri... | ['Mrinmaya Sachan', 'Ryan Cotterell', 'Yuchen Eleanor Jiang'] | 2022-10-26 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [-2.56807525e-02 6.21647537e-01 -4.81414795e-01 -2.90649444e-01
-4.48825687e-01 -7.77879894e-01 1.03832650e+00 4.87372130e-01
-6.52559102e-01 3.54003221e-01 1.29394710e+00 -4.66499746e-01
-3.29664588e-01 -8.91718566e-01 -5.83205342e-01 -3.26023161e-01
-1.40535697e-01 7.81255424e-01 9.48326811e-02 -8.97967458... | [9.5321683883667, 9.440016746520996] |
e52ad3f0-ae10-43dd-ba5f-71485659d596 | using-active-learning-methods-to | 2301.00628 | null | https://arxiv.org/abs/2301.00628v2 | https://arxiv.org/pdf/2301.00628v2.pdf | Using Active Learning Methods to Strategically Select Essays for Automated Scoring | Research on automated essay scoring has become increasing important because it serves as a method for evaluating students' written-responses at scale. Scalable methods for scoring written responses are needed as students migrate to online learning environments resulting in the need to evaluate large numbers of written-... | ['Mark J. Gierl', 'Hamid Mohammadi', 'Tahereh Firoozi'] | 2023-01-02 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-1.71281114e-01 -3.42042334e-02 -4.56254482e-01 -6.91848636e-01
-1.42568636e+00 -8.39251935e-01 1.75997257e-01 7.25591421e-01
-7.07224369e-01 9.64854360e-01 1.96954802e-01 -3.28162134e-01
-5.81117451e-01 -8.25115204e-01 -7.64536485e-02 -2.88828433e-01
4.40009922e-01 7.29479015e-01 3.26301336e-01 -1.95669428... | [11.316102027893066, 9.318811416625977] |
5a74821d-29da-408a-8504-ac0dca55fa04 | algorithm-design-for-online-meta-learning | 2302.00857 | null | https://arxiv.org/abs/2302.00857v1 | https://arxiv.org/pdf/2302.00857v1.pdf | Algorithm Design for Online Meta-Learning with Task Boundary Detection | Online meta-learning has recently emerged as a marriage between batch meta-learning and online learning, for achieving the capability of quick adaptation on new tasks in a lifelong manner. However, most existing approaches focus on the restrictive setting where the distribution of the online tasks remains fixed with kn... | ['Junshan Zhang', 'Yingbin Liang', 'Sen Lin', 'Daouda Sow'] | 2023-02-02 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [-1.32648349e-01 -3.60446483e-01 -5.65895855e-01 -1.21615261e-01
-8.35319757e-01 -6.14252210e-01 2.88324565e-01 2.90275156e-01
-6.56074405e-01 8.79413903e-01 -1.38325825e-01 -3.36846441e-01
-4.07362550e-01 -1.99281797e-01 -9.50095356e-01 -9.47731435e-01
-1.83145389e-01 4.28882420e-01 2.41536304e-01 4.53930162... | [8.987776756286621, 3.6342620849609375] |
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