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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3287b254-306c-48a5-9d61-ac166674cb91 | evaluation-methodologies-for-code-learning | 2108.09619 | null | https://arxiv.org/abs/2108.09619v2 | https://arxiv.org/pdf/2108.09619v2.pdf | Impact of Evaluation Methodologies on Code Summarization | There has been a growing interest in developing machine learning (ML) models for code summarization tasks, e.g., comment generation and method naming. Despite substantial increase in the effectiveness of ML models, the evaluation methodologies, i.e., the way people split datasets into training, validation, and test set... | ['Milos Gligoric', 'Raymond J. Mooney', 'Junyi Jessy Li', 'Jiyang Zhang', 'Pengyu Nie'] | 2021-08-22 | null | https://aclanthology.org/2022.acl-long.339 | https://aclanthology.org/2022.acl-long.339.pdf | acl-2022-5 | ['comment-generation'] | ['natural-language-processing'] | [ 2.92584002e-01 7.60981515e-02 -4.02609974e-01 -4.57251757e-01
-8.78984153e-01 -7.45787680e-01 6.78771257e-01 7.18304157e-01
-1.29070327e-01 4.84493226e-01 5.73278189e-01 -5.41050494e-01
1.04799166e-01 -1.40779734e-01 -2.82803625e-01 -5.31061627e-02
2.55423993e-01 -1.05171660e-02 7.35909045e-02 1.71834044... | [7.654990196228027, 7.937382221221924] |
ed3c98a1-40e7-4b8d-b034-bb79d3aec377 | amstertime-a-visual-place-recognition | 2203.16291 | null | https://arxiv.org/abs/2203.16291v2 | https://arxiv.org/pdf/2203.16291v2.pdf | AmsterTime: A Visual Place Recognition Benchmark Dataset for Severe Domain Shift | We introduce AmsterTime: a challenging dataset to benchmark visual place recognition (VPR) in presence of a severe domain shift. AmsterTime offers a collection of 2,500 well-curated images matching the same scene from a street view matched to historical archival image data from Amsterdam city. The image pairs capture t... | ['Jan van Gemert', 'Ronald Maria Siebes', 'Seyran Khademi', 'Burak Yildiz'] | 2022-03-30 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-1.94574699e-01 -2.00900018e-01 -2.18290334e-05 -8.07460606e-01
-1.16150951e+00 -8.56794596e-01 9.98855531e-01 2.68029302e-01
-6.26621366e-01 3.36437404e-01 3.04019809e-01 -2.42775511e-02
5.07172868e-02 -6.21255696e-01 -9.36233103e-01 -3.66608799e-01
8.15798640e-02 5.96701741e-01 1.33861184e-01 -2.71085322... | [7.670629024505615, -1.835938572883606] |
ef0da43c-d607-435d-b58f-7db19936e72b | larnet-lie-algebra-residual-network-for | 2103.08147 | null | https://arxiv.org/abs/2103.08147v2 | https://arxiv.org/pdf/2103.08147v2.pdf | LARNet: Lie Algebra Residual Network for Face Recognition | Face recognition is an important yet challenging problem in computer vision. A major challenge in practical face recognition applications lies in significant variations between profile and frontal faces. Traditional techniques address this challenge either by synthesizing frontal faces or by pose invariant learning. In... | ['Wei Liu', 'Zhifeng Li', 'Dong-Ming Yan', 'Dihong Gong', 'Xiaohong Jia', 'Xiaolong Yang'] | 2021-03-15 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.01816037e-01 9.99628305e-02 3.00629139e-02 -6.56663537e-01
-1.21816531e-01 -3.41510415e-01 5.49953341e-01 -1.17134333e+00
2.52148672e-03 1.73055768e-01 1.30011201e-01 -1.45643830e-01
-1.10280201e-01 -5.85653007e-01 -1.00234044e+00 -1.05228782e+00
7.66762793e-02 -9.39824656e-02 -5.82411706e-01 -3.45626563... | [13.201752662658691, 0.5036616921424866] |
0f39dcb3-4bad-4eaf-8a59-50b81e44a182 | once-is-enough-a-light-weight-cross-attention | 2210.05261 | null | https://arxiv.org/abs/2210.05261v2 | https://arxiv.org/pdf/2210.05261v2.pdf | Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling | Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI). These models generally perform cross-attention over input pairs, leading to prohibitive computational costs. Recent studies propose dual-encoder and late interaction archi... | ['Chuanyi Liu', 'Qifan Wang', 'Yulan He', 'Zenglin Xu', 'Cuiyun Gao', 'shiyi qi', 'Yuanhang Yang'] | 2022-10-11 | null | null | null | null | ['sentence-pair-modeling', 'answer-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.60324350e-02 -9.84285399e-02 -1.08077556e-01 -7.51039505e-01
-1.15240371e+00 -2.52755284e-01 5.27339935e-01 4.86166090e-01
-7.52187371e-01 5.17404318e-01 2.15771586e-01 -6.62213027e-01
2.57923335e-01 -8.22509408e-01 -8.86912704e-01 -2.36389413e-02
2.77308315e-01 8.06357741e-01 -3.30850249e-03 -4.76884067... | [11.239624977111816, 8.314621925354004] |
2e138576-666c-4d92-b985-37364106875a | sketch-less-for-more-on-the-fly-fine-grained-1 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Bhunia_Sketch_Less_for_More_On-the-Fly_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Bhunia_Sketch_Less_for_More_On-the-Fly_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.pdf | Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval | Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a particular photo instance given a user's query sketch. Its widespread applicability is however hindered by the fact that drawing a sketch takes time, and most people struggle to draw a complete and faithful sketch. In this paper, ... | [' Yi-Zhe Song', ' Tao Xiang', ' Timothy M. Hospedales', ' Yongxin Yang', 'Ayan Kumar Bhunia'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['sketch-based-image-retrieval', 'on-the-fly-sketch-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 1.38438940e-01 -6.20708466e-01 -3.64417017e-01 -1.38808802e-01
-1.37134206e+00 -7.90986478e-01 7.90322959e-01 -7.49179944e-02
-3.09913665e-01 4.70492095e-01 2.64421642e-01 2.80230314e-01
-4.59008545e-01 -7.85730064e-01 -5.36022425e-01 -5.31275272e-01
3.98496091e-01 5.83254755e-01 4.79805395e-02 -3.56492698... | [11.651995658874512, 0.5691048502922058] |
5d6fa8cf-ce35-4552-9d3b-95c68aeacff6 | generalization-bounds-for-neural-belief | 2305.1054 | null | https://arxiv.org/abs/2305.10540v1 | https://arxiv.org/pdf/2305.10540v1.pdf | Generalization Bounds for Neural Belief Propagation Decoders | Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven ma... | ['Tamal Bose', 'Bane Vasic', 'Ravi Tandon', 'Xin Xiao', 'Sudarshan Adiga'] | 2023-05-17 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [ 3.24572802e-01 2.28051528e-01 -4.84307736e-01 -1.41203299e-01
-5.49149394e-01 -8.64949003e-02 3.99106413e-01 3.81226212e-01
-1.54523045e-01 7.30135143e-01 -8.00931305e-02 -9.47329164e-01
9.23124179e-02 -7.09600806e-01 -7.07895815e-01 -8.71450007e-01
-6.06031299e-01 3.22509766e-01 3.36630106e-01 -3.56999516... | [6.505362033843994, 1.6522228717803955] |
d8d7e06b-a058-4fac-97bd-02f3134a0e9f | expression-analysis-based-on-face-regions-in | 1911.05188 | null | https://arxiv.org/abs/1911.05188v1 | https://arxiv.org/pdf/1911.05188v1.pdf | Expression Analysis Based on Face Regions in Read-world Conditions | Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory environment. However, it faces many challenges in real-world conditions, i.e., illumination changes, large pose variations and partial or full ... | ['Ming-Yue Niu', 'Jian Huang', 'Jian-Hua Tao', 'Ya Li', 'Zheng Lian'] | 2019-10-23 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 2.70082746e-02 -1.76561475e-01 1.90788269e-01 -7.12382615e-01
-1.28193889e-02 -3.54930431e-01 2.25547954e-01 -4.19467598e-01
-2.07458496e-01 6.77056849e-01 -9.91316810e-02 3.56364459e-01
1.33348480e-01 -4.58873093e-01 -2.32000470e-01 -8.59482527e-01
-1.46271372e-02 -2.46566847e-01 -3.37614745e-01 -4.33989525... | [13.524161338806152, 1.9101639986038208] |
4fc595d8-9694-470a-a93a-448c16a23d42 | sicilian-translator-a-recipe-for-low-resource | 2110.01938 | null | https://arxiv.org/abs/2110.01938v1 | https://arxiv.org/pdf/2110.01938v1.pdf | Sicilian Translator: A Recipe for Low-Resource NMT | With 17,000 pairs of Sicilian-English translated sentences, Arba Sicula developed the first neural machine translator for the Sicilian language. Using small subword vocabularies, we trained small Transformer models with high dropout parameters and achieved BLEU scores in the upper 20s. Then we supplemented our dataset ... | ['Eryk Wdowiak'] | 2021-10-05 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-7.57161900e-02 4.09650713e-01 -3.91774267e-01 -3.46910596e-01
-1.24381566e+00 -9.10202801e-01 6.05090737e-01 -1.36332273e-01
-7.40240276e-01 1.29558969e+00 5.56197524e-01 -1.04127729e+00
1.12386957e-01 -6.85098350e-01 -1.02520895e+00 -6.10786788e-02
5.39206326e-01 9.93699551e-01 -2.50454128e-01 -7.23300576... | [11.552206039428711, 10.309807777404785] |
d49ca235-b81c-438c-bf54-822cd723cf85 | an-online-algorithm-for-nonparametric | 1712.01521 | null | http://arxiv.org/abs/1712.01521v1 | http://arxiv.org/pdf/1712.01521v1.pdf | An Online Algorithm for Nonparametric Correlations | Nonparametric correlations such as Spearman's rank correlation and Kendall's
tau correlation are widely applied in scientific and engineering fields. This
paper investigates the problem of computing nonparametric correlations on the
fly for streaming data. Standard batch algorithms are generally too slow to
handle real... | ['Wei Xiao'] | 2017-12-05 | null | null | null | null | ['sequential-correlation-estimation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [-3.86751890e-01 -5.88659108e-01 -3.97320449e-01 -6.74237311e-01
-4.80700165e-01 -4.31737989e-01 1.59541249e-01 5.89382231e-01
-6.20941937e-01 8.92866254e-01 -3.78790975e-01 -4.45327193e-01
-4.57169533e-01 -1.05492508e+00 -1.98441252e-01 -6.65624380e-01
-7.60041416e-01 7.60130167e-01 7.83625126e-01 -2.92771626... | [7.165168285369873, 3.9698615074157715] |
5ac3348f-63a9-4a15-bc87-8a8fc2655cec | a-neural-attention-model-for-abstractive | 1509.00685 | null | http://arxiv.org/abs/1509.00685v2 | http://arxiv.org/pdf/1509.00685v2.pdf | A Neural Attention Model for Abstractive Sentence Summarization | Summarization based on text extraction is inherently limited, but
generation-style abstractive methods have proven challenging to build. In this
work, we propose a fully data-driven approach to abstractive sentence
summarization. Our method utilizes a local attention-based model that generates
each word of the summary ... | ['Alexander M. Rush', 'Jason Weston', 'Sumit Chopra'] | 2015-09-02 | a-neural-attention-model-for-abstractive-1 | https://aclanthology.org/D15-1044 | https://aclanthology.org/D15-1044.pdf | emnlp-2015-9 | ['abstractive-sentence-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.97609025e-01 5.70030689e-01 -2.74218529e-01 -3.75368506e-01
-1.50123632e+00 -4.60454911e-01 7.24481344e-01 3.82373720e-01
-4.04014438e-01 1.11679602e+00 1.09333038e+00 -3.22101116e-01
4.12727207e-01 -4.69897121e-01 -6.31481469e-01 -8.61407071e-02
2.81138062e-01 6.05201542e-01 -1.25637606e-01 -4.15218771... | [12.445212364196777, 9.436189651489258] |
3e2ddb7e-3032-455d-a072-ff1dc7b40a05 | semi-supervised-anomaly-detection-on | 2002.12011 | null | https://arxiv.org/abs/2002.12011v1 | https://arxiv.org/pdf/2002.12011v1.pdf | Semi-supervised Anomaly Detection on Attributed Graphs | We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anomaly detection methods it is usually assumed that instances are independent and identically distributed, in many real-world applications, inst... | ['Tomoharu Iwata', 'Yasuhiro Fujiwara', 'Atsutoshi Kumagai'] | 2020-02-27 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-1.20548457e-02 4.45364058e-01 1.46022037e-01 -3.37196738e-01
4.34651792e-01 -3.22181553e-01 3.69613945e-01 7.50048637e-01
2.28337590e-02 2.10427523e-01 -3.59788507e-01 -2.23843515e-01
-1.05039753e-01 -1.34454906e+00 -4.01777208e-01 -9.26661432e-01
-4.83873367e-01 6.89602852e-01 1.18356161e-01 -9.12495255... | [6.6659016609191895, 5.79884672164917] |
912afabe-6b5c-4e86-88ff-a7646d196f02 | show-me-how-to-revise-improving-lexically | 2109.05797 | null | https://arxiv.org/abs/2109.05797v1 | https://arxiv.org/pdf/2109.05797v1.pdf | Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet | Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine translation, and dialog response generation. Previous work usually used Markov Chain Monte Carlo (MCMC) sampling to generate lexically constrained... | ['Victor O. K. Li', 'Xingwei He'] | 2021-09-13 | null | null | null | null | ['text-infilling'] | ['natural-language-processing'] | [ 6.70403302e-01 2.74912685e-01 -2.15761051e-01 -5.75798631e-01
-1.16469932e+00 -6.27667844e-01 9.91249740e-01 1.02429770e-01
-4.77710038e-01 1.22244990e+00 4.65497553e-01 -5.58626950e-01
5.81267059e-01 -6.40943885e-01 -5.57305336e-01 -3.84996116e-01
5.85289240e-01 8.36987257e-01 2.12476924e-01 -1.88343823... | [11.879053115844727, 8.957694053649902] |
ba8dd14d-7af1-4227-b050-4f501f9c5592 | to-wake-up-or-not-to-wake-up-reducing-keyword | 2304.03416 | null | https://arxiv.org/abs/2304.03416v1 | https://arxiv.org/pdf/2304.03416v1.pdf | To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement | Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happens when the system falsely registers a keyword despite the keyword not being uttered. In this paper, we propose a simple yet elegant soluti... | ['Hongxia Jin', 'Yilin Shen', 'Chouchang Yang', 'Ching-Hua Lee', 'Rakshith Sharma Srinivasa', 'Yashas Malur Saidutta'] | 2023-04-06 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.58627206e-01 -3.45738344e-02 1.37517676e-01 -1.75795987e-01
-1.09962308e+00 -7.34559298e-01 2.92039186e-01 4.66477066e-01
-5.68658650e-01 4.54630166e-01 -1.25617862e-01 -6.72145963e-01
-1.51535928e-01 -4.14320946e-01 -7.65430689e-01 -5.32550395e-01
-1.26074642e-01 3.74548405e-01 7.02192783e-01 2.12209076... | [14.433695793151855, 6.075394153594971] |
fbc5c14c-92ab-4b34-9559-fdd59d78fd97 | deconvolutional-time-series-regression-a | null | null | https://aclanthology.org/D18-1288 | https://aclanthology.org/D18-1288.pdf | Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects | Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects. However, time series may violate the assumptions of these models through temporal diffusion, where stimulus presentation has a lingering influence on the response as the rest of the experim... | ['William Schuler', 'Cory Shain'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['time-series-regression'] | ['time-series'] | [ 2.01456577e-01 -4.18975800e-01 7.98387453e-02 -4.45279360e-01
-2.81559706e-01 -7.53923178e-01 8.68038774e-01 -1.47951429e-03
-4.77992922e-01 4.76800263e-01 5.55459499e-01 -5.88538826e-01
-5.24472445e-02 -4.34424907e-01 -6.77746415e-01 -4.10287887e-01
-5.73596537e-01 2.65479535e-02 -5.45694083e-02 -1.84790164... | [7.066016674041748, 3.2098586559295654] |
09d3a796-b357-4ae9-a683-33476322ca1a | an-effortless-way-to-create-large-scale | null | null | https://aclanthology.org/L14-1283 | https://aclanthology.org/L14-1283.pdf | An Effortless Way To Create Large-Scale Datasets For Famous Speakers | The creation of large-scale multimedia datasets has become a scientific matter in itself. Indeed, the fully-manual annotation of hundreds or thousands of hours of video and/or audio turns out to be practically infeasible. In this paper, we propose an extremly handy approach to automatically construct a database of famo... | ["F{\\'e}licien Vallet", 'Fran{\\c{c}}ois Salmon'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['person-identification'] | ['computer-vision'] | [ 3.56037728e-02 2.67863482e-01 3.26568604e-01 -4.22382414e-01
-9.96885240e-01 -6.74800634e-01 7.11136520e-01 2.68807143e-01
-4.55574304e-01 7.26645172e-01 -9.16267708e-02 -9.15736333e-02
-9.31139886e-02 -6.15715325e-01 -6.20118082e-01 -5.07603645e-01
-1.59746394e-01 6.52666986e-01 6.32741153e-01 -2.67793477... | [8.749320983886719, 0.04438069090247154] |
c5cf1c02-42b0-4483-b469-a9299d61bd5a | wssl-weighted-self-supervised-learning | 2211.13856 | null | https://arxiv.org/abs/2211.13856v1 | https://arxiv.org/pdf/2211.13856v1.pdf | WSSL: Weighted Self-supervised Learning Framework For Image-inpainting | Image inpainting is the process of regenerating lost parts of the image. Supervised algorithm-based methods have shown excellent results but have two significant drawbacks. They do not perform well when tested with unseen data. They fail to capture the global context of the image, resulting in a visually unappealing re... | ['Natarajan Subramanyam', 'Poojasree Dwarkanath', 'Madhoolika Gangaraju', 'Rahul Kunigal Ravishankar', 'Shubham Gupta'] | 2022-11-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.72933024e-01 1.00806709e-02 -2.75117099e-01 -3.67156327e-01
-8.17866087e-01 -9.39813480e-02 3.64006490e-01 -4.38845940e-02
-3.21703970e-01 8.58405113e-01 3.62286270e-01 9.79131088e-02
1.21247500e-01 -6.85108542e-01 -7.74049044e-01 -7.01441228e-01
1.55193627e-01 -9.83542427e-02 1.79934338e-01 -2.79265200... | [11.366942405700684, -1.1124476194381714] |
075f5276-75ca-4635-9ae0-fd307ff8a98f | simrod-a-simple-adaptation-method-for-robust | 2107.13389 | null | https://arxiv.org/abs/2107.13389v1 | https://arxiv.org/pdf/2107.13389v1.pdf | SimROD: A Simple Adaptation Method for Robust Object Detection | This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD). To overcome the challenging issues of domain shift and pseudo-label noise, our method integrates a novel domain-centric augmentation method, a gradual self-labeling adaptation procedure, and a teacher-guided ... | ['Yong Zhang', 'Xiaolong Bai', 'Xinyu Kang', 'Amin Banitalebi-Dehkordi', 'Rindra Ramamonjison'] | 2021-07-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ramamonjison_SimROD_A_Simple_Adaptation_Method_for_Robust_Object_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ramamonjison_SimROD_A_Simple_Adaptation_Method_for_Robust_Object_Detection_ICCV_2021_paper.pdf | iccv-2021-1 | ['robust-object-detection'] | ['computer-vision'] | [ 3.13897699e-01 -2.33721837e-01 -7.99357668e-02 -3.54353160e-01
-1.35737717e+00 -7.87848711e-01 7.85498857e-01 -1.12685822e-01
-7.48137593e-01 6.32856727e-01 -7.59728029e-02 1.65817022e-01
5.69464922e-01 -2.41495311e-01 -8.88739705e-01 -5.03354967e-01
3.44689637e-01 4.89021778e-01 8.94485772e-01 -2.16593951... | [9.72154712677002, 1.7019108533859253] |
efa0e3cc-36f4-4584-887d-f1ad223fe90e | automatic-cattle-identification-using-yolov5 | 2210.11939 | null | https://arxiv.org/abs/2210.11939v1 | https://arxiv.org/pdf/2210.11939v1.pdf | Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis | You Only Look Once (YOLO) is a single-stage object detection model popular for real-time object detection, accuracy, and speed. This paper investigates the YOLOv5 model to identify cattle in the yards. The current solution to cattle identification includes radio-frequency identification (RFID) tags. The problem occurs ... | ['Will Swain', 'Dave Swain', 'Jonathan Medway', 'Shawn McGrath', 'Muhammad Ashad Kabir', 'Lihong Zheng', 'Rabin Dulal'] | 2022-10-21 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.31036593e-02 -1.67553842e-01 -3.15388739e-02 -2.04203531e-01
2.78675072e-02 -4.95779037e-01 1.94966316e-01 6.37144819e-02
-2.40206122e-01 3.17709148e-01 -5.23225069e-01 -1.04357518e-01
-3.78188640e-01 -9.21546102e-01 -9.19338405e-01 -6.42999411e-01
-4.26375002e-01 5.84193468e-01 4.14171576e-01 -8.71049911... | [12.911779403686523, 0.938870370388031] |
d9a01af5-752e-4a90-a815-bd1e32bdff10 | nuclear-instance-segmentation-using-a | 1908.10356 | null | https://arxiv.org/abs/1908.10356v1 | https://arxiv.org/pdf/1908.10356v1.pdf | Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework | Nuclear segmentation in histology images is a challenging task due to significant variations in the shape and appearance of nuclei. One of the main hurdles in nuclear instance segmentation is overlapping nuclei where a smart algorithm is needed to separate each nucleus. In this paper, we introduce a proposal-free deep ... | ['Ali Gooya', 'Navid Alemi Koohbanani', 'Mostafa Jahanifar', 'Nasir Rajpoot'] | 2019-08-27 | null | null | null | null | ['nuclear-segmentation'] | ['medical'] | [ 1.06912851e-01 -4.32534851e-02 2.32371435e-01 -5.09780407e-01
-9.27640736e-01 -7.18079805e-01 3.20118725e-01 4.16617721e-01
-6.77202344e-01 4.16685313e-01 -1.98812410e-01 1.28649399e-01
-5.55911809e-02 -7.10458577e-01 -5.71592331e-01 -1.13721287e+00
1.56740054e-01 6.57682240e-01 5.88247836e-01 2.67764300... | [14.90174674987793, -3.0397047996520996] |
1573ab7d-1bb6-4fa0-bf3d-bd824eb2bd6e | c-textgen-conditional-text-generation-for | 1909.03409 | null | https://arxiv.org/abs/1909.03409v2 | https://arxiv.org/pdf/1909.03409v2.pdf | Conditional Text Generation for Harmonious Human-Machine Interaction | In recent years, with the development of deep learning, text generation technology has undergone great changes and provided many kinds of services for human beings, such as restaurant reservation and daily communication. The automatically generated text is becoming more and more fluent so researchers begin to consider ... | ['Wei Wu', 'Zhiwen Yu', 'Shaoyang Hao', 'Yueqi Sun', 'Bin Guo', 'Hao Wang', 'Yasan Ding'] | 2019-09-08 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 6.00161143e-02 3.84864390e-01 -1.16670832e-01 -2.59091765e-01
-3.18618178e-01 7.68687055e-02 7.15284526e-01 -1.10021114e-01
6.13541491e-02 1.06137550e+00 5.06178737e-01 -3.85317281e-02
3.57760310e-01 -1.11508930e+00 -2.11884633e-01 -6.82393551e-01
1.44434988e-01 3.28271687e-01 -3.63744050e-01 -6.02572680... | [11.992610931396484, 9.118602752685547] |
8c490cba-3f64-4c1b-b9e3-7f61973974ca | blind-image-deblurring-using-dark-channel | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Pan_Blind_Image_Deblurring_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Pan_Blind_Image_Deblurring_CVPR_2016_paper.pdf | Blind Image Deblurring Using Dark Channel Prior | We present a simple and effective blind image deblurring method based on the dark channel prior. Our work is inspired by the interesting observation that the dark channel of blurred images is less sparse. While most image patches in the clean image contain some dark pixels, these pixels are not dark when averaged with ... | ['Ming-Hsuan Yang', 'Deqing Sun', 'Jinshan Pan', 'Hanspeter Pfister'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.31706434e-01 -5.66740930e-01 8.33974257e-02 -7.75236934e-02
-2.41729572e-01 -5.73732257e-01 4.42016542e-01 -8.45940351e-01
9.12799849e-04 7.60928750e-01 6.27025366e-01 -9.47117358e-02
-3.81690543e-03 -1.88466892e-01 -6.76442444e-01 -1.04622495e+00
-1.99971534e-02 -3.61886680e-01 -1.16704263e-01 2.07157061... | [11.635109901428223, -2.7722268104553223] |
84772c99-31d1-47e7-8db4-7497abdb05c7 | building-and-modelling-multilingual | null | null | https://aclanthology.org/L14-1340 | https://aclanthology.org/L14-1340.pdf | Building and Modelling Multilingual Subjective Corpora | Building multilingual opinionated models requires multilingual corpora annotated with opinion labels. Unfortunately, such kind of corpora are rare. We consider opinions in this work as subjective or objective. In this paper, we introduce an annotation method that can be reliably transferred across topic domains and acr... | ['Kamel Sma{\\"\\i}li', 'David Langlois', 'Motaz Saad'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-1.34852052e-01 2.93597102e-01 -3.08912843e-01 -9.27465200e-01
-9.51039433e-01 -9.47268546e-01 6.20726645e-01 5.21192193e-01
-6.86963141e-01 1.22211564e+00 2.19678938e-01 -4.68647242e-01
3.70473176e-01 -5.64670324e-01 -3.86860669e-01 -5.28254688e-01
2.44609237e-01 6.10761821e-01 2.54311621e-01 -4.76152807... | [11.29684066772461, 6.927677631378174] |
1d0a1a43-e1ef-487d-9635-8fc4105f2be1 | biometric-recognition-why-not-massively | 2203.03719 | null | https://arxiv.org/abs/2203.03719v2 | https://arxiv.org/pdf/2203.03719v2.pdf | Biometric recognition: why not massively adopted yet? | Although there has been a dramatically reduction on the prices of capturing devices and an increase on computing power in the last decade, it seems that biometric systems are still far from massive adoption for civilian applications. This paper deals with the causes of this phenomenon, as well as some misconceptions re... | ['Marcos Faundez-Zanuy'] | 2022-02-23 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.99268341e-01 -2.30724737e-01 -2.77933050e-02 -6.37729168e-01
8.60931128e-02 -5.12055159e-01 5.86415172e-01 1.44160688e-01
-5.83879113e-01 7.83096611e-01 -8.21593106e-02 -6.25932455e-01
-5.12680411e-02 -6.79702878e-01 8.49392042e-02 -5.33431768e-01
5.04490316e-01 1.73322693e-01 -1.70889914e-01 -1.16597384... | [13.048223495483398, 1.075966477394104] |
0ba809b1-11b7-4319-b326-71439f2dc296 | robust-homography-estimation-via-dual | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Ding_Robust_Homography_Estimation_via_Dual_Principal_Component_Pursuit_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Ding_Robust_Homography_Estimation_via_Dual_Principal_Component_Pursuit_CVPR_2020_paper.pdf | Robust Homography Estimation via Dual Principal Component Pursuit | We revisit robust estimation of homographies over point correspondences between two or three views, a fundamental problem in geometric vision. The analysis serves as a platform to support a rigorous investigation of Dual Principal Component Pursuit (DPCP) as a valid and powerful alternative to RANSAC for robust model f... | [' Manolis C. Tsakiris', ' Laurent Kneip', ' Rene Vidal', ' Daniel P. Robinson', ' Zhihui Zhu', ' Yunchen Yang', 'Tianjiao Ding'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['homography-estimation'] | ['computer-vision'] | [-8.29591528e-02 -2.41090983e-01 -1.63838919e-02 5.37742786e-02
-7.38126934e-01 -9.24724638e-01 8.67296100e-01 -2.93142885e-01
-1.93855852e-01 1.71764150e-01 2.22592160e-01 -2.36449882e-01
-3.17219824e-01 -4.29493904e-01 -9.21903551e-01 -7.25765646e-01
3.46118689e-01 7.69019246e-01 -4.25981909e-01 -1.49144664... | [8.061165809631348, -2.2950212955474854] |
14da53e7-6f4c-47dc-89df-3782978f3fa1 | multi-sensor-prognostics-using-an | 1608.06154 | null | http://arxiv.org/abs/1608.06154v1 | http://arxiv.org/pdf/1608.06154v1.pdf | Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder | Many approaches for estimation of Remaining Useful Life (RUL) of a machine,
using its operational sensor data, make assumptions about how a system degrades
or a fault evolves, e.g., exponential degradation. However, in many domains
degradation may not follow a pattern. We propose a Long Short Term Memory based
Encoder-... | ['Anusha Ramakrishnan', 'Vishnu Tv', 'Lovekesh Vig', 'Gaurangi Anand', 'Pankaj Malhotra', 'Gautam Shroff', 'Puneet Agarwal'] | 2016-08-22 | null | null | null | null | ['exponential-degradation'] | ['time-series'] | [ 1.76034465e-01 -1.14718474e-01 -1.91125162e-02 -2.31336534e-01
-4.91954535e-01 6.24258891e-02 1.92771465e-01 2.44416088e-01
2.14094579e-01 7.39385843e-01 -1.63991258e-01 -3.45793754e-01
-1.83223143e-01 -8.01094413e-01 -9.35938120e-01 -8.21716130e-01
-2.13837400e-01 3.61797363e-01 -2.46861428e-02 4.01261374... | [6.803615093231201, 2.5282604694366455] |
11782c4a-2a13-49d2-9799-370821e61030 | the-environmental-discontinuity-hypothesis | 2205.15931 | null | https://arxiv.org/abs/2205.15931v1 | https://arxiv.org/pdf/2205.15931v1.pdf | The Environmental Discontinuity Hypothesis for Down-Sampled Lexicase Selection | Down-sampling training data has long been shown to improve the generalization performance of a wide range of machine learning systems. Recently, down-sampling has proved effective in genetic programming (GP) runs that utilize the lexicase parent selection technique. Although this down-sampling procedure has been shown ... | ['Lee Spector', 'Thomas Helmuth', 'Ryan Boldi'] | 2022-05-31 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 7.96236575e-01 8.31519365e-02 -1.79388955e-01 -2.72932470e-01
-1.07909657e-01 -4.74366218e-01 4.79484022e-01 2.82801598e-01
-4.85932320e-01 9.85593438e-01 1.71011224e-01 -6.72394514e-01
-8.40657502e-02 -1.14364183e+00 -7.80146420e-01 -6.82475984e-01
1.20192751e-01 1.86415300e-01 2.16195017e-01 -3.63283068... | [8.038142204284668, 7.168479919433594] |
c001806d-59b6-4f05-b4a6-ece480731182 | icon-implicit-clothed-humans-obtained-from | 2112.09127 | null | https://arxiv.org/abs/2112.09127v2 | https://arxiv.org/pdf/2112.09127v2.pdf | ICON: Implicit Clothed humans Obtained from Normals | Current methods for learning realistic and animatable 3D clothed avatars need either posed 3D scans or 2D images with carefully controlled user poses. In contrast, our goal is to learn an avatar from only 2D images of people in unconstrained poses. Given a set of images, our method estimates a detailed 3D surface from ... | ['Michael J. Black', 'Dimitrios Tzionas', 'Jinlong Yang', 'Yuliang Xiu'] | 2021-12-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xiu_ICON_Implicit_Clothed_Humans_Obtained_From_Normals_CVPR_2022_paper.pdf | cvpr-2022-1 | ['monocular-3d-human-pose-estimation', '3d-human-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.22010121e-01 1.15891322e-01 3.78483087e-01 -2.93250769e-01
-6.68364406e-01 -5.42454302e-01 4.36858892e-01 -5.30703068e-01
-1.62233174e-01 5.80717564e-01 1.39280707e-01 3.22708249e-01
3.62691790e-01 -7.27789223e-01 -9.64801192e-01 -5.68907738e-01
7.38038123e-02 8.78341079e-01 3.12713832e-01 -4.28450465... | [7.15957498550415, -1.2473492622375488] |
007a2603-3b42-4d8b-9e44-cfeaa1becfa6 | quality-aware-network-for-human-parsing | 2103.05997 | null | https://arxiv.org/abs/2103.05997v1 | https://arxiv.org/pdf/2103.05997v1.pdf | Quality-Aware Network for Human Parsing | How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing. In order to solve this problem, this work proposes a statistical method based on the output probability map to calculate the pixel quality information, which is called pix... | ['Zhihao LI', 'Songcen Xu', 'Zhiwei Liu', 'Zhihui Wang', 'Qing Song', 'Lu Yang'] | 2021-03-10 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 2.01951891e-01 2.89196312e-01 -1.10150844e-01 -6.43779337e-01
-1.22656357e+00 -2.43371993e-01 -1.34539872e-01 -5.95572814e-02
-5.65908313e-01 5.53515851e-01 3.53798009e-02 -1.02599315e-01
2.64451295e-01 -7.65668094e-01 -7.65828013e-01 -4.55340385e-01
5.50315976e-01 2.23708138e-01 4.50028539e-01 1.76596809... | [8.904526710510254, 0.15616299211978912] |
8d47af82-d6cc-4003-8333-799a9d5e0400 | data-mining-using-unguided-symbolic | 1309.5931 | null | http://arxiv.org/abs/1309.5931v1 | http://arxiv.org/pdf/1309.5931v1.pdf | Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset | In this paper a data mining approach for variable selection and knowledge
extraction from datasets is presented. The approach is based on unguided
symbolic regression (every variable present in the dataset is treated as the
target variable in multiple regression runs) and a novel variable relevance
metric for genetic p... | ['Michael Kommenda', 'Gabriel Kronberger', 'Michael Affenzeller', 'Christoph Feilmayr'] | 2013-09-23 | null | null | null | null | ['implicit-relations'] | ['natural-language-processing'] | [ 5.39644539e-01 3.40505332e-01 -1.63011119e-01 -3.36638033e-01
1.29186347e-01 -9.15416852e-02 4.01154220e-01 6.21845305e-01
-1.59717470e-01 1.03982937e+00 -2.84667671e-01 -2.87426174e-01
-9.16549861e-01 -1.05510259e+00 -2.03661501e-01 -7.86290050e-01
-1.16541356e-01 1.01129377e+00 -8.49502236e-02 -3.67513686... | [7.699939727783203, 4.668495178222656] |
74c6b326-886a-4e58-92de-7e6b79979b1e | memory-augmented-recursive-neural-networks | 1911.01545 | null | https://arxiv.org/abs/1911.01545v5 | https://arxiv.org/pdf/1911.01545v5.pdf | Compositional Generalization with Tree Stack Memory Units | We study compositional generalization, viz., the problem of zero-shot generalization to novel compositions of concepts in a domain. Standard neural networks fail to a large extent on compositional learning. We propose Tree Stack Memory Units (Tree-SMU) to enable strong compositional generalization. Tree-SMU is a recurs... | ['Pranay Mundra', 'Animashree Anandkumar', 'Zhichu Lu', 'Sameer Singh', 'Forough Arabshahi'] | 2019-11-05 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 6.86467767e-01 4.87735659e-01 -2.40808681e-01 -4.94046986e-01
-2.73561031e-01 -5.94764948e-01 6.15417182e-01 1.58945486e-01
-2.38102451e-01 7.12033093e-01 5.96545279e-01 -6.97777510e-01
1.48879051e-01 -1.44371402e+00 -1.13351285e+00 -5.70749879e-01
-4.38039601e-01 5.83874524e-01 5.20140350e-01 -3.58037025... | [9.476664543151855, 7.379059791564941] |
d0423a91-9408-4eea-88fb-50f76c4ef541 | on-the-importance-of-building-high-quality | 2202.06649 | null | https://arxiv.org/abs/2202.06649v1 | https://arxiv.org/pdf/2202.06649v1.pdf | On the Importance of Building High-quality Training Datasets for Neural Code Search | The performance of neural code search is significantly influenced by the quality of the training data from which the neural models are derived. A large corpus of high-quality query and code pairs is demanded to establish a precise mapping from the natural language to the programming language. Due to the limited availab... | ['Xiaoning Du', 'Yan Liu', 'Li Li', 'Zhensu Sun'] | 2022-02-14 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.68407832e-02 -4.13737774e-01 -1.97102159e-01 -3.87204707e-01
-1.01851559e+00 -5.78357279e-01 3.21817130e-01 2.29019478e-01
-6.29800975e-01 2.20539793e-01 -1.31210431e-01 -3.21462005e-01
-3.77660133e-02 -7.17604935e-01 -9.23902154e-01 -1.91438228e-01
3.60611141e-01 2.18938306e-01 5.27875423e-01 -3.49340767... | [7.510554313659668, 8.042950630187988] |
01d64a40-665f-4dc3-ad43-600ffe161a94 | pointflowhop-green-and-interpretable-scene | 2302.14193 | null | https://arxiv.org/abs/2302.14193v1 | https://arxiv.org/pdf/2302.14193v1.pdf | PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds | An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work. PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for every point in the first point cloud. PointFlowHop decomposes the scene flow estimation task into a set of subtasks, including ego-motion comp... | ['C. -C. Jay Kuo', 'Shan Liu', 'Jiahao Gu', 'Pranav Kadam'] | 2023-02-27 | null | null | null | null | ['motion-compensation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-3.68087530e-01 -3.31603885e-01 -4.24157292e-01 -4.37887907e-01
-4.40787137e-01 -3.29727173e-01 4.12363201e-01 -1.31435648e-01
-4.53963101e-01 3.60308856e-01 -1.64126962e-01 -4.17991430e-01
-8.25262815e-02 -8.11686456e-01 -8.52487504e-01 -5.31908929e-01
-3.14204752e-01 5.51642179e-01 3.97649556e-01 2.81972110... | [8.516879081726074, -2.0730879306793213] |
85aed4d0-52f5-4518-b83c-3fdc2e3364b4 | unitor-combining-syntactic-and-semantic | null | null | https://aclanthology.org/S13-2060 | https://aclanthology.org/S13-2060.pdf | UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis | null | ['Roberto Basili', 'Simone Filice', 'Giuseppe Castellucci', 'Danilo Croce'] | 2013-06-01 | null | null | null | semeval-2013-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.172413349151611, 3.6024231910705566] |
89bb0ea1-4ea2-4bbd-9113-09505ba4d6c5 | mars3d-a-plug-and-play-motion-aware-model-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_MarS3D_A_Plug-and-Play_Motion-Aware_Model_for_Semantic_Segmentation_on_Multi-Scan_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_MarS3D_A_Plug-and-Play_Motion-Aware_Model_for_Semantic_Segmentation_on_Multi-Scan_CVPR_2023_paper.pdf | MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds | 3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task requires distinguishing the motion states of points in addition to their semantic categories. However, methods designed for single-scan-ba... | ['Xiaojuan Qi', 'Lan Ma', 'Xiaoyang Wu', 'Jianhui Liu', 'Chirui Chang', 'Jiahui Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-semantic-segmentation'] | ['computer-vision'] | [-8.87439176e-02 -1.10630184e-01 -3.89719784e-01 -7.16078699e-01
-7.82381713e-01 -5.63326359e-01 6.00969076e-01 -1.63940012e-01
-3.66532505e-01 -1.25438705e-01 -1.78906798e-01 -2.70292044e-01
-9.41474107e-04 -8.01080108e-01 -6.55322552e-01 -5.42007446e-01
-6.13070428e-02 6.08225584e-01 9.06979024e-01 -3.26665908... | [8.037915229797363, -2.476175546646118] |
b7987367-bd49-4fad-a498-fe86de2fcd46 | check-it-again-progressive-visual-question-1 | null | null | https://aclanthology.org/2021.acl-long.317 | https://aclanthology.org/2021.acl-long.317.pdf | Check It Again:Progressive Visual Question Answering via Visual Entailment | While sophisticated neural-based models have achieved remarkable success in Visual Question Answering (VQA), these models tend to answer questions only according to superficial correlations between question and answer. Several recent approaches have been developed to address this language priors problem. However, most ... | ['Weiping Wang', 'Peng Fu', 'Ming yu Zheng', 'Zheng Lin', 'Qingyi Si'] | 2021-08-01 | null | null | null | acl-2021-5 | ['visual-entailment'] | ['reasoning'] | [ 1.77128151e-01 7.85788894e-03 -1.48763759e-02 -4.12767619e-01
-1.08856416e+00 -6.47402287e-01 6.52973771e-01 3.36554796e-01
-4.54853445e-01 3.23707193e-01 3.96711677e-01 -4.63239312e-01
2.63749007e-02 -7.38509178e-01 -7.06739962e-01 -2.27821857e-01
5.94277143e-01 4.80220556e-01 7.20898509e-01 -1.57722712... | [10.843645095825195, 1.621757984161377] |
071a5880-82a5-487e-92e5-680869a01519 | contrastive-object-detection-using-knowledge | 2112.11366 | null | https://arxiv.org/abs/2112.11366v1 | https://arxiv.org/pdf/2112.11366v1.pdf | Contrastive Object Detection Using Knowledge Graph Embeddings | Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated. Each image region has to be assigned to one member of a set of objects, including a background class, disregarding any similarities in the object types. In this work, we compare the error stat... | ['Abhinav Valada', 'Alexander Braun', 'Christopher Lang'] | 2021-12-21 | null | null | null | null | ['open-world-object-detection', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['computer-vision', 'graphs', 'methodology'] | [ 1.87975653e-02 2.03292251e-01 -2.69634277e-01 -4.68140632e-01
-6.39224648e-01 -7.37553835e-01 7.68426061e-01 6.81017697e-01
-4.60494995e-01 1.29307866e-01 1.92931555e-02 4.35543582e-02
-4.33620214e-01 -1.14636803e+00 -5.85747898e-01 -6.38746202e-01
-2.74186283e-01 7.74865627e-01 6.77465558e-01 -4.74636704... | [9.507607460021973, 1.5354729890823364] |
8d7bf8df-e73b-459d-a22d-70ddf08158d2 | a-simple-decentralized-cross-entropy-method | 2212.08235 | null | https://arxiv.org/abs/2212.08235v1 | https://arxiv.org/pdf/2212.08235v1.pdf | A Simple Decentralized Cross-Entropy Method | Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling distribution based on only the top-$k$ operation's results on samples. In this paper, we show that such a centralized approach makes CEM vulnera... | ['Dale Schuurmans', 'Jun Luo', 'Martin Jagersand', 'Jun Jin', 'Zichen Zhang'] | 2022-12-16 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-1.89719483e-01 1.60200283e-01 -1.74068272e-01 7.39791170e-02
-1.29677749e+00 -5.95790863e-01 7.17894077e-01 2.39771456e-01
-6.90512776e-01 1.27142358e+00 -1.69527799e-01 -3.56910229e-01
-3.32967252e-01 -8.70993137e-01 -9.27379906e-01 -9.82064724e-01
-2.08718866e-01 7.70403981e-01 1.32297292e-01 -2.45161623... | [4.11870002746582, 2.10054349899292] |
a29dfa9f-884c-4ca8-97b8-85e5681cc5e3 | vision-based-food-analysis-for-automatic | 2108.02947 | null | https://arxiv.org/abs/2108.02947v2 | https://arxiv.org/pdf/2108.02947v2.pdf | A review on vision-based analysis for automatic dietary assessment | Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are not only burdensome but time-consuming, and contain substantial biase... | ['Shuqiang Jiang', 'Haisheng Li', 'Xiaoxiao Dong', 'TianHao Li', 'Weiqing Min', 'Wei Wang'] | 2021-08-06 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [-1.32621422e-01 -5.11248350e-01 -5.30059755e-01 -4.43637669e-01
-3.43452126e-01 -4.21915710e-01 -1.07154757e-01 8.45955670e-01
-2.62234688e-01 3.01775753e-01 3.57466310e-01 -2.49142852e-03
1.05784588e-01 -1.10446060e+00 -6.42266750e-01 -6.64117038e-01
-3.38701874e-01 3.43889922e-01 -2.22565636e-01 -3.41160148... | [11.563934326171875, 4.40177583694458] |
ddb9ef95-ca21-4e07-a5a2-39272fc0cb70 | polarized-color-image-denoising | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Polarized_Color_Image_Denoising_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Polarized_Color_Image_Denoising_CVPR_2023_paper.pdf | Polarized Color Image Denoising | Single-chip polarized color photography provides both visual textures and object surface information in one snapshot. However, the use of an additional directional polarizing filter array tends to lower photon count and SNR, when compared to conventional color imaging. As a result, such a bilayer structure usually ... | ['Yinqiang Zheng', 'Mingdeng Cao', 'Haiyang Jiang', 'Zhuoxiao Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['color-image-denoising'] | ['computer-vision'] | [ 6.95209861e-01 -2.19460621e-01 5.88526964e-01 -1.35729432e-01
-6.00794077e-01 -5.65076530e-01 2.95065612e-01 -3.13803613e-01
-3.07118088e-01 7.60485470e-01 -1.42354339e-01 -7.13755414e-02
1.40070260e-01 -6.41630173e-01 -6.46317005e-01 -1.50978386e+00
3.63264561e-01 -1.79586858e-01 3.46925445e-02 1.40028194... | [10.459046363830566, -2.630034923553467] |
0f7c6b5a-d95c-48dc-b3cc-eb5a8913aff7 | open-challenges-in-synthetic-speech-detection | 2209.0718 | null | https://arxiv.org/abs/2209.07180v3 | https://arxiv.org/pdf/2209.07180v3.pdf | Open Challenges in Synthetic Speech Detection | In this paper the current status and open challenges of synthetic speech detection are addressed. The work comprises an initial analysis of available open datasets and of existing detection methods, a description of the requirements for new research datasets compliant with regulations and better representing real-case ... | ['Dimitrios Tzovaras', 'Konstantinos Votis', 'Patrick Aichroth', 'Artem Yaroshchuk', 'Anastasios Vafeiadis', 'Christoforos Papastergiopoulos', 'Luca Cuccovillo'] | 2022-09-15 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 5.44034719e-01 6.46056533e-01 -6.12415560e-02 -2.57130891e-01
-6.98445082e-01 -7.84979522e-01 7.95751214e-01 -6.29800558e-02
8.40513632e-02 5.26856899e-01 5.25777817e-01 -3.29066902e-01
-2.95689911e-01 -4.57980305e-01 -2.11097509e-01 -3.61208841e-02
1.77328333e-01 4.03395951e-01 4.40639973e-01 -6.12620115... | [14.38897705078125, 6.90024471282959] |
44a459cd-ac4d-47d5-9869-0271a83fb2aa | unsupervised-pre-traing-for-sequence-to | 1910.12418 | null | https://arxiv.org/abs/1910.12418v2 | https://arxiv.org/pdf/1910.12418v2.pdf | Unsupervised pre-training for sequence to sequence speech recognition | This paper proposes a novel approach to pre-train encoder-decoder sequence-to-sequence (seq2seq) model with unpaired speech and transcripts respectively. Our pre-training method is divided into two stages, named acoustic pre-trianing and linguistic pre-training. In the acoustic pre-training stage, we use a large amount... | ['Zhiyun Fan', 'Bo Xu', 'Shiyu Zhou'] | 2019-10-28 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 6.97141171e-01 3.08591902e-01 3.04710388e-01 -6.64375901e-01
-1.54028177e+00 -4.74929422e-01 2.11603865e-01 -2.90406376e-01
-6.10177517e-01 6.92329109e-01 5.25210261e-01 -5.82150042e-01
7.46039987e-01 -4.34221417e-01 -8.67970049e-01 -4.86330301e-01
1.91806197e-01 4.16401058e-01 1.40949279e-01 -2.00145304... | [14.481453895568848, 6.948907852172852] |
6a6bf3fa-8819-4094-ba94-774277e3e926 | multi-view-semantic-labeling-of-3d-point | 1805.03994 | null | http://arxiv.org/abs/1805.03994v2 | http://arxiv.org/pdf/1805.03994v2.pdf | Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping | Semantic labeling of 3D point clouds is important for the derivation of 3D
models from real world scenarios in several economic fields such as building
industry, facility management, town planning or heritage conservation. In
contrast to these most common applications, we describe in this study the
semantic labeling of... | ['Volker Steinhage', 'Reinhard Töpfer', 'Katja Herzog', 'Jennifer Mack', 'Bernhard Japes', 'Florian Rist'] | 2018-05-10 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 2.02218562e-01 7.32492730e-02 -4.90357913e-02 -3.72016937e-01
-1.92927465e-01 -7.56664634e-01 4.96954739e-01 5.45285225e-01
3.14351246e-02 3.32629979e-01 -6.61011279e-01 -6.34313047e-01
-4.27460402e-01 -1.13425875e+00 -5.82151294e-01 -3.00466120e-01
-1.43871516e-01 1.11071825e+00 3.25119972e-01 -1.31600544... | [8.75876522064209, -2.0179758071899414] |
9dd25689-c240-497f-8e5b-583c7b656ccd | neurall-towards-a-unified-model-for-visual | 1902.03589 | null | https://arxiv.org/abs/1902.03589v2 | https://arxiv.org/pdf/1902.03589v2.pdf | NeurAll: Towards a Unified Model for Visual Perception in Automated Driving | Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and modeled. In this paper, we propose a joint multi-task network design ... | ['Samir Rawashdeh', 'Ciaran Hughes', 'Senthil Yogamani', 'Sumanth Chennupati', 'Stefan Milz', 'Ganesh Sistu', 'Isabelle Leang'] | 2019-02-10 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.72831059e-01 -2.22331043e-02 -4.17971462e-01 -5.54502010e-01
-7.20307887e-01 -2.22635344e-01 5.16442180e-01 -2.10765690e-01
-7.04789639e-01 4.10809755e-01 -2.26731986e-01 -3.06251526e-01
9.48203281e-02 -3.34870398e-01 -8.38695168e-01 -6.60552979e-01
9.18466300e-02 3.81915092e-01 5.78126907e-01 -2.88377523... | [8.129928588867188, -1.3603373765945435] |
c8299c9a-822b-4541-80f9-8071c0510789 | evidential-deep-learning-for-class | 2212.02863 | null | https://arxiv.org/abs/2212.02863v1 | https://arxiv.org/pdf/2212.02863v1.pdf | Evidential Deep Learning for Class-Incremental Semantic Segmentation | Class-Incremental Learning is a challenging problem in machine learning that aims to extend previously trained neural networks with new classes. This is especially useful if the system is able to classify new objects despite the original training data being unavailable. While the semantic segmentation problem has recei... | ['Michael Felsberg', 'Lena Klasén', 'Karl Holmquist'] | 2022-12-06 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 7.60105789e-01 1.96841821e-01 -2.08107859e-01 -5.63893557e-01
-5.53990543e-01 -4.81393516e-01 6.42924428e-01 2.43476070e-02
-4.75053102e-01 1.10941780e+00 -5.13511181e-01 -2.01257870e-01
1.21078737e-01 -8.27762842e-01 -9.96020675e-01 -1.04553378e+00
2.32779846e-01 6.79229856e-01 7.41036415e-01 5.42634010... | [9.383620262145996, 1.560594081878662] |
615dfda9-1a91-4896-b0ea-f1d989d9de9e | ultra-high-resolution-detector-simulation | 2303.08046 | null | https://arxiv.org/abs/2303.08046v1 | https://arxiv.org/pdf/2303.08046v1.pdf | Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning | Simulating high-resolution detector responses is a storage-costly and computationally intensive process that has long been challenging in particle physics. Despite the ability of deep generative models to make this process more cost-efficient, ultra-high-resolution detector simulation still proves to be difficult as it... | ['Thomas Kuhr', 'James Kahn', 'Sahand Sharifzadeh', 'Nikolai Hartmann', 'Hosein Hashemi'] | 2023-03-07 | null | null | null | null | ['metric-learning', 'conditional-image-generation', 'metric-learning', 'relational-reasoning'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing'] | [ 7.07094148e-02 7.28936270e-02 3.71836990e-01 -5.39139152e-01
-1.51128447e+00 -2.57033736e-01 9.25563693e-01 1.59924090e-01
-2.54950970e-01 9.91379917e-01 5.52953705e-02 -2.84677386e-01
-7.26555139e-02 -1.38859582e+00 -1.09737003e+00 -8.87122333e-01
9.48803574e-02 1.39002180e+00 2.52108544e-01 -1.44406147... | [9.258605003356934, -3.5985426902770996] |
f65991bb-ade2-4b4a-a3a5-0c05405804fb | stanford-mlab-at-semeval-2021-task-1-tree | null | null | https://aclanthology.org/2021.semeval-1.89 | https://aclanthology.org/2021.semeval-1.89.pdf | Stanford MLab at SemEval-2021 Task 1: Tree-Based Modelling of Lexical Complexity using Word Embeddings | This paper presents our system for the single- and multi-word lexical complexity prediction tasks of SemEval Task 1: Lexical Complexity Prediction. Text comprehension depends on the reader{'}s ability to understand the words present in it; evaluating the lexical complexity of such texts can enable readers to find an ap... | ['Ethan A. Chi', 'Jillian Tang', 'Zander Lack', 'Patrick Liu', 'Kevin Liu', 'Kathy J. Lee', 'Enok Choe', 'Gordon Chi', 'Niveditha Iyer', 'Erik Rozi'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-9.07423869e-02 -1.02565589e-03 -2.79245913e-01 -2.48654783e-01
-7.47713029e-01 -6.79906130e-01 2.15371132e-01 1.14913070e+00
-9.46125448e-01 -4.63197306e-02 8.44957590e-01 -9.22281742e-01
-1.44390136e-01 -6.10898197e-01 -5.17624244e-02 4.69819516e-01
2.79889375e-01 5.48729777e-01 1.22794226e-01 -5.75402558... | [10.718132972717285, 10.425771713256836] |
51c76c94-30e2-404e-9153-572dd3ec96cf | a-language-model-based-generative-classifier | null | null | https://aclanthology.org/2021.emnlp-main.188 | https://aclanthology.org/2021.emnlp-main.188.pdf | A Language Model-based Generative Classifier for Sentence-level Discourse Parsing | Discourse segmentation and sentence-level discourse parsing play important roles for various NLP tasks to consider textual coherence. Despite recent achievements in both tasks, there is still room for improvement due to the scarcity of labeled data. To solve the problem, we propose a language model-based generative cla... | ['Manabu Okumura', 'Hidetaka Kamigaito', 'Ying Zhang'] | null | null | null | null | emnlp-2021-11 | ['discourse-segmentation', 'discourse-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.23347837e-01 9.80565250e-01 -3.11666757e-01 -4.78698492e-01
-1.24211049e+00 -5.23527384e-01 8.19863856e-01 1.77061617e-01
-3.39796811e-01 9.98833418e-01 5.07861376e-01 -5.11432707e-01
4.30868179e-01 -8.34422708e-01 -5.64792454e-01 -6.46956623e-01
1.30210429e-01 6.59034669e-01 2.93050885e-01 -9.79059413... | [10.79276180267334, 9.330761909484863] |
88dd2647-e76a-45d7-8c65-4e379884b358 | non-rigid-3d-shape-retrieval-based-on-multi | 1904.00765 | null | http://arxiv.org/abs/1904.00765v1 | http://arxiv.org/pdf/1904.00765v1.pdf | Non-rigid 3D shape retrieval based on multi-view metric learning | This study presents a novel multi-view metric learning algorithm, which aims
to improve 3D non-rigid shape retrieval. With the development of non-rigid 3D
shape analysis, there exist many shape descriptors. The intrinsic descriptors
can be explored to construct various intrinsic representations for non-rigid 3D
shape r... | ['Zhixun Su', 'Ximin Liu', 'Nannan Li', 'Haohao Li', 'Shengfa Wang'] | 2019-03-20 | null | null | null | null | ['3d-shape-retrieval'] | ['computer-vision'] | [-3.78443569e-01 -8.55317116e-01 -1.38894111e-01 -3.66814196e-01
-9.83817101e-01 -6.87113464e-01 5.66873014e-01 -1.81031749e-01
7.48693049e-02 5.26284277e-02 5.16471744e-01 3.20338070e-01
-6.72245860e-01 -7.11326241e-01 -7.27099553e-02 -1.12812614e+00
3.05784345e-01 6.08834028e-01 1.54823214e-01 -1.41079322... | [8.17963981628418, -3.917731523513794] |
b5de2482-9bc5-43dd-8a7f-1770c06511fd | normalization-and-back-transliteration-for | null | null | https://aclanthology.org/2021.calcs-1.15 | https://aclanthology.org/2021.calcs-1.15.pdf | Normalization and Back-Transliteration for Code-Switched Data | Code-switching is an omnipresent phenomenon in multilingual communities all around the world but remains a challenge for NLP systems due to the lack of proper data and processing techniques. Hindi-English code-switched text on social media is often transliterated to the Roman script which prevents from utilizing monoli... | ['Thamar Solorio', 'Dwija Parikh'] | null | null | null | null | naacl-calcs-2021-6 | ['transliteration'] | ['natural-language-processing'] | [-3.55116501e-02 -2.54857004e-01 -4.81954776e-02 -6.54369831e-01
-1.02949774e+00 -1.06474113e+00 2.79981017e-01 1.29433975e-01
-4.49239165e-01 1.05938625e+00 2.28624806e-01 -6.74843490e-01
3.04620087e-01 -4.22405750e-01 -5.23863554e-01 -2.77552366e-01
4.15370017e-01 5.72955251e-01 6.86751725e-03 -3.71169090... | [10.224018096923828, 10.088836669921875] |
1e89d14c-5116-41c7-97e4-884f1ea5cd0b | gaitmpl-gait-recognition-with-memory | 2306.0465 | null | https://arxiv.org/abs/2306.04650v1 | https://arxiv.org/pdf/2306.04650v1.pdf | GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning | Gait recognition aims at identifying the pedestrians at a long distance by their biometric gait patterns. It is inherently challenging due to the various covariates and the properties of silhouettes (textureless and colorless), which result in two kinds of pair-wise hard samples: the same pedestrian could have distinct... | ['Xi Li', 'Zequn Qin', 'Lin Dong', 'Yuhan Zhao', 'Pengyi Zhang', 'Huanzhang Dou'] | 2023-06-06 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-1.33464977e-01 -6.74766958e-01 -6.09867908e-02 -1.49000823e-01
-4.39290464e-01 -6.93648914e-03 2.61253834e-01 -5.47477938e-02
-4.27649021e-01 8.41653347e-01 -1.44274577e-01 2.03078136e-01
5.80133498e-03 -7.26766407e-01 -6.27727628e-01 -1.00819647e+00
-3.30218196e-01 3.44608992e-01 5.57004094e-01 -1.23822158... | [14.376871109008789, 1.3328334093093872] |
2cd61c65-f073-49ae-a8ca-5d27f5f95fa4 | conditioned-and-composed-image-retrieval | null | null | https://openaccess.thecvf.com/content/CVPR2022W/ODRUM/html/Baldrati_Conditioned_and_Composed_Image_Retrieval_Combining_and_Partially_Fine-Tuning_CLIP-Based_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/ODRUM/papers/Baldrati_Conditioned_and_Composed_Image_Retrieval_Combining_and_Partially_Fine-Tuning_CLIP-Based_CVPRW_2022_paper.pdf | Conditioned and Composed Image Retrieval Combining and Partially Fine-Tuning CLIP-Based Features | In this paper, we present an approach for conditioned and composed image retrieval based on CLIP features. In this extension of content-based image retrieval (CBIR), an image is combined with a text that provides information regarding user intentions and is relevant for application domains like e-commerce. The proposed... | ['Alberto del Bimbo', 'Tiberio Uricchio', 'Marco Bertini', 'Alberto Baldrati'] | 2022-06-19 | null | null | null | cvprw-2022-6 | ['composed-image-retrieval', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 5.11196971e-01 -5.69268823e-01 -3.63080919e-01 -4.39591914e-01
-1.22308755e+00 -4.62525487e-01 9.21020508e-01 3.48297566e-01
-6.85961485e-01 3.27559412e-01 2.57346153e-01 3.14954445e-02
-3.55556875e-01 -5.65908551e-01 -6.09734476e-01 -5.60365677e-01
2.41015658e-01 3.51617783e-01 1.79614633e-01 -3.44213516... | [10.814242362976074, 1.1254209280014038] |
673f00a9-42d8-44f7-bd6d-2100cd499caf | multi-video-moment-ranking-with-multimodal | 2301.13606 | null | https://arxiv.org/abs/2301.13606v1 | https://arxiv.org/pdf/2301.13606v1.pdf | Multi-video Moment Ranking with Multimodal Clue | Video corpus moment retrieval~(VCMR) is the task of retrieving a relevant video moment from a large corpus of untrimmed videos via a natural language query. State-of-the-art work for VCMR is based on two-stage method. In this paper, we focus on improving two problems of two-stage method: (1) Moment prediction bias: The... | ['Xueqi Cheng', 'HuaWei Shen', 'Yanyan Lan', 'Liang Pang', 'Danyang Hou'] | 2023-01-29 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-2.15616520e-03 -4.73256290e-01 -8.39449406e-01 -2.14783043e-01
-1.10154748e+00 -6.19137466e-01 5.35013795e-01 -3.13790321e-01
-4.79694635e-01 4.21242893e-01 4.74909842e-01 7.03524724e-02
-1.75731957e-01 -2.17100844e-01 -9.91962850e-01 -6.71771646e-01
-3.30409378e-01 3.11481148e-01 2.01913387e-01 1.76167518... | [10.13213062286377, 0.7608920931816101] |
7a1920c7-4185-4c86-90ed-12106de0c5b5 | deep-event-stereo-leveraged-by-event-to-image | null | null | https://www.researchgate.net/publication/346617073_Deep_Event_Stereo_Leveraged_by_Event-to-Image_Translation | https://www.researchgate.net/publication/346617073_Deep_Event_Stereo_Leveraged_by_Event-to-Image_Translation | Deep Event Stereo Leveraged by Event-to-Image Translation | Depth estimation in real-world applications requires precise responses to fast motion and challenging lighting conditions. Event cameras use bio-inspired event-driven sensors that provide instantaneous and asynchronous information of pixel-level log intensity changes, which makes them suitable for depth estimation in s... | ['Yong Ju Jung', 'S. M. Nadim Uddin', 'Hae Woong Jang', 'Soikat Hasan Ahmed'] | 2021-02-02 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 6.23124242e-01 -4.75795507e-01 1.22852206e-01 -5.78579426e-01
-7.05491066e-01 -1.96069822e-01 4.60927725e-01 1.56180263e-01
-5.48834264e-01 7.30169058e-01 4.33819771e-01 4.71805215e-01
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2.15536267e-01 -1.57723367e-01 4.52395141e-01 1.75989175... | [9.096512794494629, -1.7228761911392212] |
abcfee95-4cd6-469d-a4bc-dd8cb0857ddc | structural-segmentation-and-labeling-of-tabla | 2211.0879 | null | https://arxiv.org/abs/2211.08790v1 | https://arxiv.org/pdf/2211.08790v1.pdf | Structural Segmentation and Labeling of Tabla Solo Performances | Tabla is a North Indian percussion instrument used as an accompaniment and an exclusive instrument for solo performances. Tabla solo is intricate and elaborate, exhibiting rhythmic evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. Each section has a specific structure and n... | ['Hema A Murthy', 'R Aravind', 'Gowriprasad R'] | 2022-11-16 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 4.85459507e-01 -3.57955337e-01 -1.80203885e-01 4.80250418e-02
-9.78646576e-01 -1.23148727e+00 9.64431912e-02 9.49517265e-02
-5.32087013e-02 5.19384623e-01 5.07500708e-01 1.74254730e-01
-7.26144910e-01 -4.38965708e-01 -2.42079496e-01 -7.49341786e-01
-4.61712480e-01 7.22950220e-01 -9.47202891e-02 -5.09282589... | [15.900684356689453, 5.316169261932373] |
62dd321a-0df8-4c1f-80c0-597a5cfbca9b | adapool-exponential-adaptive-pooling-for | 2111.00772 | null | https://arxiv.org/abs/2111.00772v3 | https://arxiv.org/pdf/2111.00772v3.pdf | AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling | Pooling layers are essential building blocks of convolutional neural networks (CNNs), to reduce computational overhead and increase the receptive fields of proceeding convolutional operations. Their goal is to produce downsampled volumes that closely resemble the input volume while, ideally, also being computationally ... | ['Ronald Poppe', 'Alexandros Stergiou'] | 2021-11-01 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 1.06189236e-01 -1.81868151e-01 -8.41756389e-02 -4.25931573e-01
-5.91159225e-01 -5.01165688e-01 6.45976126e-01 -9.54857841e-02
-7.90205956e-01 6.86998427e-01 2.08546937e-01 1.04132645e-01
2.48667374e-01 -7.66918063e-01 -9.40027893e-01 -6.22309625e-01
-1.61033317e-01 -3.42911273e-01 7.17487872e-01 3.05415988... | [10.882368087768555, -1.2733569145202637] |
e76dffd9-8a0d-4a27-a3c7-ae2eee5b6eb2 | abstract-visual-reasoning-an-algebraic | 2303.1173 | null | https://arxiv.org/abs/2303.11730v1 | https://arxiv.org/pdf/2303.11730v1.pdf | Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices | We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably ... | ['Kai Fong Ernest Chong', 'Zhangsheng Lai', 'Saket Chandra', 'Yufei Wu', 'Tushar Vaidya', 'Jingyi Xu'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Abstract_Visual_Reasoning_An_Algebraic_Approach_for_Solving_Ravens_Progressive_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Abstract_Visual_Reasoning_An_Algebraic_Approach_for_Solving_Ravens_Progressive_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 5.44424318e-02 4.23009455e-01 6.58904091e-02 -1.62314877e-01
-5.00393271e-01 -7.55855381e-01 2.84981430e-01 4.72896397e-01
-1.27008945e-01 4.45304096e-01 -3.61784369e-01 -9.79569554e-01
-5.94498217e-01 -1.31696403e+00 -5.65050840e-01 -2.48576537e-01
-4.90994036e-01 8.61849010e-01 2.95583683e-04 -7.59104133... | [8.977677345275879, 7.134103298187256] |
4dd89e32-9671-4947-80f3-d2eede7d63ed | segmentation-of-skeletal-muscle-in-thigh | 1904.04747 | null | http://arxiv.org/abs/1904.04747v1 | http://arxiv.org/pdf/1904.04747v1.pdf | Segmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analysis | Segmentation of skeletal muscles in Magnetic Resonance Images (MRI) is
essential for the study of muscle physiology and diagnosis of muscular
pathologies. However, manual segmentation of large MRI volumes is a
time-consuming task. The state-of-the-art on algorithms for muscle segmentation
in MRI is still not very exten... | ['Antonio M. G. Pinheiro', 'Rafael Rodrigues'] | 2019-04-09 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.79809535e-01 -2.07128674e-01 -1.47141322e-01 -4.00263995e-01
-8.43402386e-01 -3.92003536e-01 8.54959339e-02 2.73450226e-01
-6.50449991e-01 3.54406774e-01 -2.41625637e-01 1.04274444e-01
-1.59132197e-01 -6.43399894e-01 -1.94289416e-01 -1.04992867e+00
-2.00462475e-01 7.32838988e-01 6.06178761e-01 5.67140132... | [14.151461601257324, -2.5697243213653564] |
c7a3f3cc-f7f0-48b1-ad80-36d682ea9f4e | proactive-multi-camera-collaboration-for-3d | 2303.03767 | null | https://arxiv.org/abs/2303.03767v1 | https://arxiv.org/pdf/2303.03767v1.pdf | Proactive Multi-Camera Collaboration For 3D Human Pose Estimation | This paper presents a multi-agent reinforcement learning (MARL) scheme for proactive Multi-Camera Collaboration in 3D Human Pose Estimation in dynamic human crowds. Traditional fixed-viewpoint multi-camera solutions for human motion capture (MoCap) are limited in capture space and susceptible to dynamic occlusions. Act... | ['Yizhou Wang', 'Fangwei Zhong', 'Xuehai Pan', 'Mickel Liu', 'Hai Ci'] | 2023-03-07 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [-3.2424489e-01 -1.6554534e-01 3.0368559e-02 -3.5488039e-02
-9.4114351e-01 -6.3305795e-01 3.3654845e-01 -1.0940472e-01
-7.5027549e-01 7.7696735e-01 3.6973462e-01 3.2619712e-01
-2.0328736e-02 -3.1315190e-01 -6.1435401e-01 -6.5459371e-01
-1.4662501e-01 7.3818612e-01 5.8328205e-01 -2.0672891e-01
1.0184950e-01... | [7.129496097564697, -0.960759162902832] |
72307e60-4401-42b6-9540-a07fb94ff0d2 | fast-quasi-optimal-power-flow-of-flexible-dc | 2211.02852 | null | https://arxiv.org/abs/2211.02852v1 | https://arxiv.org/pdf/2211.02852v1.pdf | Fast Quasi-Optimal Power Flow of Flexible DC Traction Power Systems | This paper proposes a quasi-optimal power flow (OPF) algorithm for flexible DC traction power systems (TPSs). Near-optimal solutions can be solved with high computational efficiency by the proposed quasi-OPF. Unlike conventional OPF utilizing mathematical optimization algorithms, the proposed quasi-OPF adopts analytica... | ['Xuelian Bai', 'Chao Lu', 'Yingdong Wei', 'Xiaoqian Li', 'Zhanhe Li'] | 2022-11-05 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-2.47572273e-01 1.29456908e-01 -5.35269320e-01 8.82935151e-02
2.28819996e-02 -5.37662804e-01 -4.92539853e-02 -1.42139941e-01
1.04183212e-01 1.17217910e+00 -3.48555744e-01 -7.92036295e-01
-1.12783611e+00 -9.57455695e-01 -5.45330942e-01 -9.26350951e-01
-3.55064780e-01 5.83600700e-01 8.27786922e-02 -4.49586481... | [5.5834431648254395, 2.411595344543457] |
d06faca4-b05e-4f7f-8139-18d4a67dd656 | visual-compositional-learning-for-human | 2007.12407 | null | https://arxiv.org/abs/2007.12407v2 | https://arxiv.org/pdf/2007.12407v2.pdf | Visual Compositional Learning for Human-Object Interaction Detection | Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible combinations of objects and verbs types forms a long-tail distribution. We devise a deep Visual Compositional Learning (VCL) framework, which ... | ['DaCheng Tao', 'Yu Qiao', 'Xiaojiang Peng', 'Zhi Hou'] | 2020-07-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2400_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600579.pdf | eccv-2020-8 | ['affordance-recognition'] | ['computer-vision'] | [-1.31076440e-01 -5.45176864e-01 -1.76595390e-01 1.54001107e-02
-4.10732538e-01 -3.32664490e-01 6.79058254e-01 -2.16417372e-01
-9.72452983e-02 2.52663136e-01 2.58316398e-01 1.62100911e-01
1.15963496e-01 -5.91522753e-01 -5.17942727e-01 -6.78514779e-01
-1.04521886e-01 2.97136277e-01 5.43857515e-01 4.36676331... | [9.568751335144043, 1.3989900350570679] |
22d9ff68-1e7d-4060-a465-dc41b9aaf9d2 | self-adjusting-weighted-expected-improvement | 2306.04262 | null | https://arxiv.org/abs/2306.04262v3 | https://arxiv.org/pdf/2306.04262v3.pdf | Self-Adjusting Weighted Expected Improvement for Bayesian Optimization | Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is highly configurable with many different design choices regarding the initial design, surrogate model, and acquisition function (AF). Unfortunat... | ['Marius Lindauer', 'Carola Doerr', 'Anja Jankovic', 'Elena Raponi', 'Carolin Benjamins'] | 2023-06-07 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [-1.55081786e-02 -2.68737078e-01 -2.25936696e-01 -2.98880219e-01
-1.14770174e+00 -7.31751204e-01 4.77731228e-01 3.07890214e-02
-4.94201481e-01 7.00443983e-01 1.71509460e-01 -5.31927109e-01
-6.92747235e-01 -5.72627127e-01 -5.33290505e-01 -8.12448919e-01
-9.56269354e-02 6.79216862e-01 5.58533520e-02 -3.81671071... | [6.198384761810303, 3.8136327266693115] |
f034de99-5f8f-4e8c-af54-e003a0a8f227 | recognizing-emotion-cause-in-conversations-1 | 2012.1182 | null | https://arxiv.org/abs/2012.11820v4 | https://arxiv.org/pdf/2012.11820v4.pdf | Recognizing Emotion Cause in Conversations | We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong Transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Introduction: Recognizing the cause behi... | ['Rada Mihalcea', 'Alexander Gelbukh', 'Niyati Chhaya', 'Abhinaba Roy', 'Romila Ghosh', 'Pengfei Hong', 'Samson Yu Bai Jian', 'Rishabh Bhardwaj', 'Deepanway Ghosal', 'Devamanyu Hazarika', 'Navonil Majumder', 'Soujanya Poria'] | 2020-12-22 | recognizing-emotion-cause-in-conversations | https://arxiv.org/abs/2012.11820 | https://arxiv.org/pdf/2012.11820.pdf | null | ['causal-emotion-entailment', 'emotion-cause-extraction', 'recognizing-emotion-cause-in-conversations'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.28475481e-01 4.03064042e-01 -2.01584384e-01 -6.77204847e-01
-8.53437662e-01 -9.23517168e-01 9.92259681e-01 7.87689239e-02
2.01256797e-01 7.97213376e-01 1.25126100e+00 -9.07086805e-02
-4.61610453e-03 -3.34997296e-01 -4.94685918e-01 -3.56626928e-01
-2.03731164e-01 5.14439881e-01 -7.95026898e-01 -6.57760799... | [12.80037784576416, 6.335583209991455] |
980b31d6-2856-486c-8cb0-5c53ff1d5a77 | itsa-an-information-theoretic-approach-to | 2201.02263 | null | https://arxiv.org/abs/2201.02263v2 | https://arxiv.org/pdf/2201.02263v2.pdf | ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks | State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an important factor that hinders the networks from generalizing across domains: through the lens of shortcut learning. We demonstrate that the lear... | ['David Suter', 'Alireza Bab-Hadiashar', 'Reza Hoseinnezhad', 'Ruwan Tennakoon', 'WeiQin Chuah'] | 2022-01-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chuah_ITSA_An_Information-Theoretic_Approach_to_Automatic_Shortcut_Avoidance_and_Domain_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chuah_ITSA_An_Information-Theoretic_Approach_to_Automatic_Shortcut_Avoidance_and_Domain_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 5.13079822e-01 3.02807778e-01 7.33088627e-02 -6.04830384e-01
-5.61538875e-01 -6.23547137e-01 7.94197202e-01 -4.40562725e-01
-4.15513933e-01 7.18998373e-01 9.01470259e-02 1.40674517e-01
-4.22256857e-01 -7.96157837e-01 -1.09681737e+00 -5.67392170e-01
2.08774939e-01 3.24538559e-01 3.76088411e-01 -2.53535211... | [8.71228313446045, -2.3448166847229004] |
d0002de2-9b09-4e18-b3d0-3cdf01c96676 | weakly-supervised-multi-face-3d | 2101.02 | null | https://arxiv.org/abs/2101.02000v1 | https://arxiv.org/pdf/2101.02000v1.pdf | Weakly-Supervised Multi-Face 3D Reconstruction | 3D face reconstruction plays a very important role in many real-world multimedia applications, including digital entertainment, social media, affection analysis, and person identification. The de-facto pipeline for estimating the parametric face model from an image requires to firstly detect the facial regions with lan... | ['Steven C. H. Hoi', 'Jianke Zhu', 'Lixiang Lin', 'Jialiang Zhang'] | 2021-01-06 | null | null | null | null | ['person-identification', 'face-alignment', 'face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-2.03775495e-01 3.42919938e-02 9.40930918e-02 -6.94177389e-01
-4.97262597e-01 -2.31527999e-01 4.28711623e-01 -3.58268768e-01
-4.00904983e-01 1.68031529e-01 2.36947555e-02 1.00249484e-01
1.20839439e-01 -4.53462839e-01 -7.61493504e-01 -7.04153597e-01
1.19113490e-01 5.53008378e-01 -2.55457312e-01 1.14615761... | [13.406183242797852, 0.3070332109928131] |
086e9e5f-843e-44ee-9dca-a6c4e75b2949 | 190807899 | 1908.07899 | null | https://arxiv.org/abs/1908.07899v1 | https://arxiv.org/pdf/1908.07899v1.pdf | Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples | Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially as research shows that many adversarial examples are transferable between diffe... | ['Tobias Hinz', 'Marcus Soll', 'Sven Magg', 'Stefan Wermter'] | 2019-08-21 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 7.43964314e-01 2.76297212e-01 3.83798569e-01 -2.98232913e-01
-2.44794011e-01 -1.27297711e+00 1.02232778e+00 1.97346523e-01
-5.63808858e-01 7.09330022e-01 -2.67094433e-01 -5.44098675e-01
1.36909217e-01 -8.64167035e-01 -1.10424840e+00 -5.92951655e-01
-1.10356836e-02 1.48413941e-01 2.02618644e-01 -4.45599675... | [5.8174052238464355, 8.012333869934082] |
070d98c1-3465-4c4e-85e2-4e95f383f963 | perusil-a-framework-to-build-a-continuous | null | null | https://aclanthology.org/2022.signlang-1.1 | https://aclanthology.org/2022.signlang-1.1.pdf | PeruSIL: A Framework to Build a Continuous Peruvian Sign Language Interpretation Dataset | Video-based datasets for Continuous Sign Language are scarce due to the challenging task of recording videos from native signers and the reduced number of people who can annotate sign language. COVID-19 has evidenced the key role of sign language interpreters in delivering nationwide health messages to deaf communities... | ['Pablo Rivas', 'Fernando Alva-Manchego', 'Francisco Cerna-Herrera', 'Joe Huamani-Malca', 'Gissella Bejarano'] | null | null | null | null | signlang-lrec-2022-6 | ['sign-language-recognition'] | ['computer-vision'] | [ 2.98400998e-01 2.57356673e-01 -2.30228975e-02 -4.60984617e-01
-1.24997818e+00 -7.72042811e-01 4.20808494e-01 -5.44720948e-01
-7.75325000e-01 5.18566370e-01 9.90391910e-01 -2.20204398e-01
-7.87630603e-02 -1.53221458e-01 -5.84555686e-01 -5.18940806e-01
3.26562702e-04 5.88677108e-01 5.71853936e-01 -1.33227438... | [9.134204864501953, -6.443332195281982] |
83750966-f6b0-47ad-90f4-7f4219f60ab9 | unsupervised-kinematic-motion-detection-for | 2206.08497 | null | https://arxiv.org/abs/2206.08497v1 | https://arxiv.org/pdf/2206.08497v1.pdf | Unsupervised Kinematic Motion Detection for Part-segmented 3D Shape Collections | 3D models of manufactured objects are important for populating virtual worlds and for synthetic data generation for vision and robotics. To be most useful, such objects should be articulated: their parts should move when interacted with. While articulated object datasets exist, creating them is labor-intensive. Learnin... | ['Daniel Ritchie', 'Srinath Sridhar', 'Yifan Ruan', 'Xianghao Xu'] | 2022-06-17 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.42426148e-01 6.03634179e-01 -2.16258317e-01 -1.92972973e-01
-2.56601244e-01 -7.40037322e-01 7.25102425e-01 1.50958942e-02
1.41327992e-01 3.01989645e-01 2.44306490e-01 -4.09466960e-02
-1.19846612e-01 -6.93964779e-01 -9.34164166e-01 -4.32371944e-01
3.77989113e-02 1.21931016e+00 8.22003722e-01 -2.17004627... | [7.05206298828125, -1.644737958908081] |
85844b81-fcd6-4555-896a-32e54229bda2 | slue-new-benchmark-tasks-for-spoken-language | 2111.10367 | null | https://arxiv.org/abs/2111.10367v3 | https://arxiv.org/pdf/2111.10367v3.pdf | SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speech | Progress in speech processing has been facilitated by shared datasets and benchmarks. Historically these have focused on automatic speech recognition (ASR), speaker identification, or other lower-level tasks. Interest has been growing in higher-level spoken language understanding tasks, including using end-to-end model... | ['Kyu J. Han', 'Karen Livescu', 'Yoav Artzi', 'Pablo Brusco', 'Felix Wu', 'Ankita Pasad', 'Suwon Shon'] | 2021-11-19 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.77204061e-01 2.46778414e-01 3.07132676e-02 -9.79731798e-01
-1.54909956e+00 -6.40436411e-01 7.78197050e-01 5.98123334e-02
-6.89065635e-01 4.39496934e-01 8.78614485e-01 -3.53263050e-01
3.74939591e-01 -1.13548942e-01 -3.59286606e-01 -2.81201035e-01
1.05502352e-01 8.05707574e-01 1.65133942e-02 -3.16154897... | [14.165833473205566, 6.872860908508301] |
b6bdf0ac-a549-45d9-8fce-919ec675ee66 | dgsac-density-guided-sampling-and-consensus | 2006.02413 | null | https://arxiv.org/abs/2006.02413v1 | https://arxiv.org/pdf/2006.02413v1.pdf | DGSAC: Density Guided Sampling and Consensus | Robust multiple model fitting plays a crucial role in many computer vision applications. Unlike single model fitting problems, the multi-model fitting has additional challenges. The unknown number of models and the inlier noise scale are the two most important of them, which are in general provided by the user using gr... | ['Saket Anand', 'Lokender Tiwari'] | 2020-06-03 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 4.19512689e-02 -2.30715424e-01 -2.00506449e-01 -1.41524836e-01
-1.16366160e+00 -5.51679075e-01 2.92446524e-01 2.70892203e-01
-2.27921575e-01 2.15407208e-01 -4.79689211e-01 -4.22715768e-02
-3.96798432e-01 -2.81066477e-01 -6.38493419e-01 -8.01535606e-01
4.05771196e-01 9.30974603e-01 5.78274786e-01 8.81925523... | [7.845843315124512, -2.6791834831237793] |
0a8c0292-95a0-40dd-bf9f-b6020b03bf88 | one-shot-learning-from-a-demonstration-with | null | null | https://openreview.net/forum?id=uecnUP0-PjZ | https://openreview.net/pdf?id=uecnUP0-PjZ | One-Shot Learning from a Demonstration with Hierarchical Latent Language | Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedures and generalize their execution to other contexts. In this work, we introduce DescribeWorld, an environment designed to test this sort of ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['one-shot-learning'] | ['methodology'] | [ 2.58084297e-01 4.06700373e-01 2.68730849e-01 -3.72865170e-01
-4.89596725e-01 -1.04835916e+00 1.28715682e+00 8.28318596e-02
-2.64153183e-01 7.07945287e-01 4.06313926e-01 -3.33198071e-01
9.01004225e-02 -7.09089696e-01 -6.76069558e-01 -3.63716513e-01
-2.88757861e-01 9.94467020e-01 3.75707373e-02 -3.64603817... | [4.320830345153809, 0.9621452689170837] |
440baac7-dac3-4d4d-a2c7-5e30cadc6d1f | privacy-preserving-and-uncertainty-aware | 2303.0434 | null | https://arxiv.org/abs/2303.04340v1 | https://arxiv.org/pdf/2303.04340v1.pdf | Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles | Deep learning is the method of choice for trajectory prediction for autonomous vehicles. Unfortunately, its data-hungry nature implicitly requires the availability of sufficiently rich and high-quality centralized datasets, which easily leads to privacy leakage. Besides, uncertainty-awareness becomes increasingly impor... | ['Lili Su', 'Fei Miao', 'Dongjin Song', 'Jiangwei Wang', 'Muzi Peng'] | 2023-03-08 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-5.75733125e-01 4.46294665e-01 -8.28518748e-01 -8.06696951e-01
-1.33234596e+00 -6.08690500e-01 6.09860301e-01 1.93199962e-01
-5.45482159e-01 1.04454505e+00 6.68258369e-02 -5.72264552e-01
-2.71824986e-01 -8.39617074e-01 -1.02622306e+00 -8.02695096e-01
-1.21542469e-01 7.05174625e-01 2.30541199e-01 1.85173973... | [5.86344051361084, 6.33668851852417] |
cc7caff8-7b5f-496c-91b4-c733d28e6c48 | exploring-the-use-of-foundation-models-for | 2304.05336 | null | https://arxiv.org/abs/2304.05336v1 | https://arxiv.org/pdf/2304.05336v1.pdf | Exploring the Use of Foundation Models for Named Entity Recognition and Lemmatization Tasks in Slavic Languages | This paper describes Adam Mickiewicz University's (AMU) solution for the 4th Shared Task on SlavNER. The task involves the identification, categorization, and lemmatization of named entities in Slavic languages. Our approach involved exploring the use of foundation models for these tasks. In particular, we used models ... | ['Artur Nowakowski', 'Gabriela Pałka'] | 2023-04-11 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-6.65443301e-01 9.41122770e-02 -1.79363176e-01 -3.50993812e-01
-9.30644214e-01 -9.77621138e-01 8.82208407e-01 2.02171840e-02
-6.56624794e-01 4.13410127e-01 6.29543722e-01 -5.80986738e-01
1.65513694e-01 -4.61892337e-01 -2.49644086e-01 7.57297203e-02
2.73400873e-01 8.32074881e-01 7.64412060e-02 -4.42079991... | [9.875004768371582, 9.767131805419922] |
017d076b-3b8a-4fc0-a687-98c53fbf10d0 | investigating-cross-domain-behaviors-of-bert | 2306.15123 | null | https://arxiv.org/abs/2306.15123v2 | https://arxiv.org/pdf/2306.15123v2.pdf | Investigating Cross-Domain Behaviors of BERT in Review Understanding | Review score prediction requires review text understanding, a critical real-world application of natural language processing. Due to dissimilar text domains in product reviews, a common practice is fine-tuning BERT models upon reviews of differing domains. However, there has not yet been an empirical study of cross-dom... | ['Meng Jiang', 'Albert Lu'] | 2023-06-27 | null | null | null | null | ['text-classification'] | ['natural-language-processing'] | [-1.32266104e-01 1.65908292e-01 -5.64798355e-01 -6.92957878e-01
-8.70149851e-01 -8.10035825e-01 5.40886104e-01 3.73627633e-01
-4.64230597e-01 6.37221336e-01 -5.05362265e-02 -6.63022161e-01
-2.93750077e-01 -6.33594513e-01 -4.61191922e-01 2.06110656e-01
5.80379009e-01 7.46732295e-01 9.37592313e-02 -2.90160090... | [11.207175254821777, 6.873745441436768] |
2ee6f724-f46f-4227-b6dc-a1b7ef17ab5f | morphological-word-embeddings | null | null | https://aclanthology.info/papers/N15-1140/n15-1140 | https://www.aclweb.org/anthology/N15-1140 | Morphological Word-Embeddings | null | ['Hinrich Schütze', 'Ryan Cotterell'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391836166381836, 15.869185447692871] |
7d8f341e-9724-4c31-8e17-569f4a123a44 | improving-speech-representation-learning-via | 2210.13805 | null | https://arxiv.org/abs/2210.13805v1 | https://arxiv.org/pdf/2210.13805v1.pdf | Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach | Recovering the masked speech frames is widely applied in speech representation learning. However, most of these models use random masking in the pre-training. In this work, we proposed two kinds of masking approaches: (1) speech-level masking, making the model to mask more speech segments than silence segments, (2) pho... | ['Jing Xiao', 'Kexin Zhu', 'Ning Cheng', 'Jianzong Wang', 'xulong Zhang'] | 2022-10-25 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 7.11329520e-01 1.82967961e-01 -2.17383653e-01 -4.41949666e-01
-8.22437346e-01 -4.90971386e-01 6.08146727e-01 -4.49437946e-01
-2.92189389e-01 5.10869265e-01 6.68602347e-01 -6.87632978e-01
5.36151469e-01 -3.21021855e-01 -5.73040307e-01 -9.11190271e-01
7.18766227e-02 -1.78837270e-01 2.64715374e-01 -3.33207138... | [14.582365036010742, 6.263854503631592] |
b3617fea-03a1-4521-a66c-c93d8fc2a8f4 | dfki-dkt-at-semeval-2017-task-8-rumour | null | null | https://aclanthology.org/S17-2085 | https://aclanthology.org/S17-2085.pdf | DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics | We describe our submissions for SemEval-2017 Task 8, Determining Rumour Veracity and Support for Rumours. The Digital Curation Technologies (DKT) team at the German Research Center for Artificial Intelligence (DFKI) participated in two subtasks: Subtask A (determining the stance of a message) and Subtask B (determining... | ['Georg Rehm', 'Julian Moreno Schneider', 'Ankit Srivastava'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-5.79866096e-02 3.55804861e-01 1.83392204e-02 -2.21175313e-01
-4.00682390e-01 -4.35114056e-01 1.08827829e+00 5.22893846e-01
-4.73704994e-01 7.00664997e-01 4.12801772e-01 -7.57539392e-01
7.13193715e-02 -5.39869010e-01 -3.52125019e-01 -1.63609788e-01
-2.95786828e-01 4.57017243e-01 2.93848485e-01 -3.79780114... | [8.24385929107666, 10.124431610107422] |
c5562fd6-5c55-4f2f-ae95-e617f2fe38e2 | eco-evolutionary-tradeoffs-in-the-dynamics-of | 2207.02014 | null | https://arxiv.org/abs/2207.02014v1 | https://arxiv.org/pdf/2207.02014v1.pdf | Eco-evolutionary tradeoffs in the dynamics of prion strain competition | Prion and prion-like molecules are a type of self replicating aggregate protein that have been implicated in a variety of neurodegenerative diseases. Over recent decades the molecular dynamics of prions have been characterized both empirically and through mathematical models, providing insights into the epidemiology of... | ['Alexander J. Stewart', 'Saul Acevedo'] | 2022-07-05 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 4.39162970e-01 -1.09705485e-01 4.77602668e-02 1.54799938e-01
3.02156955e-01 -5.61447978e-01 5.79867423e-01 3.00906032e-01
-6.97891653e-01 1.19426787e+00 -1.43625259e-01 -1.73536241e-01
5.57480343e-02 -7.40924418e-01 -6.69322133e-01 -1.09876275e+00
-6.88070059e-01 6.00592017e-01 5.87128460e-01 -3.86625022... | [5.631960391998291, 4.299342632293701] |
2c1bc851-f452-4c18-8a61-d2f9a947595c | seeing-dynamic-scene-in-the-dark-a-high | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Seeing_Dynamic_Scene_in_the_Dark_A_High-Quality_Video_Dataset_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Seeing_Dynamic_Scene_in_the_Dark_A_High-Quality_Video_Dataset_ICCV_2021_paper.pdf | Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment | Low-light video enhancement is an important task. Previous work is mostly trained on paired static images or videos. We compile a new dataset formed by our new strategy that contains high-quality spatially-aligned video pairs from dynamic scenes in low- and normal-light conditions. We built it using a mechatronic s... | ['Jiaya Jia', 'Bei Yu', 'Jiangbo Lu', 'Chi-Wing Fu', 'Xiaogang Xu', 'RuiXing Wang'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-enhancement'] | ['computer-vision'] | [ 1.65046439e-01 -7.28834510e-01 -1.07157156e-01 -3.68971527e-01
-4.66564208e-01 -6.58665776e-01 2.53852129e-01 -7.84780860e-01
-1.45205006e-01 4.50849950e-01 1.67579129e-01 5.91851994e-02
8.64980817e-02 -3.27429116e-01 -7.09679008e-01 -7.29315102e-01
3.26694809e-02 -5.28455734e-01 3.62315655e-01 3.24445143... | [10.691097259521484, -1.6686804294586182] |
87babc91-9ad1-424d-9a53-bd8acd6fe388 | bi-box-regression-for-pedestrian-detection | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/CHUNLUAN_ZHOU_Bi-box_Regression_for_ECCV_2018_paper.pdf | Bi-box Regression for Pedestrian Detection and Occlusion Estimation | Occlusions present a great challenge for pedestrian detection in practical applications. In this paper, we propose a novel approach to simultaneous pedestrian detection and occlusion estimation by regressing two bounding boxes to localize the full body as well as the visible part of a pedestrian respectively. For this ... | ['Chunluan Zhou', 'Junsong Yuan'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['occlusion-estimation'] | ['computer-vision'] | [-1.11871436e-01 4.12659496e-02 -1.03961572e-01 -2.89406508e-01
-4.27569836e-01 -9.03159529e-02 3.64893347e-01 1.37866050e-01
-6.15375876e-01 8.75416696e-01 -1.30777761e-01 -1.49442121e-01
1.00617254e+00 -7.89068699e-01 -8.04082930e-01 -9.30633903e-01
4.26443294e-02 2.06507564e-01 9.42773819e-01 1.56427193... | [8.030909538269043, -0.5871753692626953] |
15e2e7d5-35a6-44c0-93b7-3f9efc43187d | color-face-recognition-using-high-dimension | 1712.01642 | null | http://arxiv.org/abs/1712.01642v1 | http://arxiv.org/pdf/1712.01642v1.pdf | Color Face Recognition using High-Dimension Quaternion-based Adaptive Representation | Recently, quaternion collaborative representation-based classification (QCRC)
and quaternion sparse representation-based classification (QSRC) have been
proposed for color face recognition. They can obtain correlation information
among different color channels. However, their performance is unstable in
different condit... | ['Qingxiang Feng', 'Yicong Zhou'] | 2017-11-19 | null | null | null | null | ['sparse-representation-based-classification'] | ['computer-vision'] | [-1.30799010e-01 -8.21260691e-01 7.66132772e-02 -3.20995688e-01
-5.68564117e-01 1.31006524e-01 1.59484863e-01 -4.45167035e-01
-4.31571782e-01 6.45579159e-01 4.23772121e-03 4.71011549e-02
-3.26491892e-01 -7.94509888e-01 -2.38715187e-01 -7.93842137e-01
-4.33206648e-01 -1.09641336e-01 -3.00382197e-01 -6.12271070... | [10.850279808044434, -1.6529357433319092] |
0279606f-099f-493c-b593-dbf57b78069f | combining-hybrid-attention-networks-and-lstm | null | null | https://aclanthology.org/2020.rocling-1.28 | https://aclanthology.org/2020.rocling-1.28.pdf | Combining Hybrid Attention Networks and LSTM for Stock Trend Prediction | null | ['Jenq-Haur Wang', 'Hsin-Wen Liu'] | null | null | null | null | rocling-2020-9 | ['stock-trend-prediction'] | ['time-series'] | [-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... | [-7.201622009277344, 3.7338497638702393] |
68089783-56f7-41d0-8cc4-e8f762513f2a | a-real-time-and-unsupervised-face-re | 1804.03547 | null | https://arxiv.org/abs/1804.03547v3 | https://arxiv.org/pdf/1804.03547v3.pdf | A real-time and unsupervised face Re-Identification system for Human-Robot Interaction | In the context of Human-Robot Interaction (HRI), face Re-Identification (face Re-ID) aims to verify if certain detected faces have already been observed by robots. The ability of distinguishing between different users is crucial in social robots as it will enable the robot to tailor the interaction strategy toward the ... | ['Yujiang Wang', 'Jie Shen', 'Maja Pantic', 'Stavros Petridis'] | 2018-04-10 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [-7.14427307e-02 2.57150590e-01 2.74504930e-01 -6.29579484e-01
-7.82651082e-02 -1.46335810e-01 4.63040501e-01 -4.72907364e-01
-3.89354765e-01 2.59316385e-01 -3.06613952e-01 2.34702840e-01
-1.89194158e-01 -4.47086751e-01 -3.63862187e-01 -4.38083649e-01
-3.79158109e-01 7.34378517e-01 -1.73562810e-01 -6.52741194... | [13.360966682434082, 0.620349645614624] |
5f6549ff-7791-4d6a-bde1-8ffd455bdaeb | hybridgazenet-geometric-model-guided | 2111.11691 | null | https://arxiv.org/abs/2111.11691v1 | https://arxiv.org/pdf/2111.11691v1.pdf | HybridGazeNet: Geometric model guided Convolutional Neural Networks for gaze estimation | As a critical cue for understanding human intention, human gaze provides a key signal for Human-Computer Interaction(HCI) applications. Appearance-based gaze estimation, which directly regresses the gaze vector from eye images, has made great progress recently based on Convolutional Neural Networks(ConvNets) architectu... | ['Xin Wang', 'Rui Wu', 'Zhizhong Su', 'Xiao Jiang', 'Shaobo Guo'] | 2021-11-23 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-6.08892962e-02 6.17030412e-02 -1.64589971e-01 -5.37850738e-01
-5.56740314e-02 4.07965407e-02 2.10771337e-01 -7.05311954e-01
-2.04168275e-01 4.26943004e-01 -3.00015688e-01 -2.74815112e-01
1.10961042e-01 -1.66728824e-01 -7.45728135e-01 -8.37894320e-01
3.90928656e-01 -1.70291230e-01 9.69193801e-02 -2.04931840... | [14.122788429260254, 0.06974201649427414] |
c09ee9f4-a007-49a4-926a-a593e00067c0 | communication-computation-efficient-device | 2108.13009 | null | https://arxiv.org/abs/2108.13009v2 | https://arxiv.org/pdf/2108.13009v2.pdf | Communication-Computation Efficient Device-Edge Co-Inference via AutoML | Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile applications. To accelerate the inference process, on-device model sparsification and intermediate feature compression ... | ['Jun Zhang', 'Yuyi Mao', 'Jiawei Shao', 'Xinjie Zhang'] | 2021-08-30 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 3.19814861e-01 -3.23991925e-01 -6.62902296e-01 -3.22591454e-01
-6.78118765e-01 -7.25018159e-02 2.88066685e-01 -1.38834774e-01
-3.45883489e-01 6.31084263e-01 -1.47643059e-01 -5.86203635e-01
-2.70614654e-01 -6.70725226e-01 -8.15490186e-01 -7.95376003e-01
2.46841058e-01 3.59551370e-01 -2.24555694e-02 2.85705745... | [8.494843482971191, 2.9385597705841064] |
4cfb36e0-3412-4061-847f-f8531a2db30f | the-acii-2022-affective-vocal-bursts-workshop | 2207.03572 | null | https://arxiv.org/abs/2207.03572v2 | https://arxiv.org/pdf/2207.03572v2.pdf | The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression | The ACII Affective Vocal Bursts Workshop & Competition is focused on understanding multiple affective dimensions of vocal bursts: laughs, gasps, cries, screams, and many other non-linguistic vocalizations central to the expression of emotion and to human communication more generally. This year's competition comprises f... | ['Alan Cowen', 'Dacher Keltner', 'Anton Batliner', 'Björn Schuller', 'Christopher B. Gregory', 'Jeffrey A. Brooks', 'Panagiotis Tzirakis', 'Alice Baird'] | 2022-07-07 | null | null | null | null | ['a-vb-culture', 'a-vb-high', 'a-vb-two'] | ['speech', 'speech', 'speech'] | [-4.02461320e-01 -2.44499251e-01 1.08615562e-01 -4.93345469e-01
-9.11977708e-01 -7.17359662e-01 2.93284029e-01 -1.13655552e-01
-3.72712493e-01 3.38414818e-01 2.07766145e-01 2.51687288e-01
2.36346349e-01 -8.81675705e-02 -2.01123789e-01 -5.88453412e-01
-2.56158084e-01 2.53890842e-01 -5.35269201e-01 -4.21040654... | [13.528290748596191, 5.661630153656006] |
75f755a0-3eab-4218-9cfc-85cb65a15522 | a-comparison-of-architectures-and-pretraining | 1912.10169 | null | https://arxiv.org/abs/1912.10169v1 | https://arxiv.org/pdf/1912.10169v1.pdf | A Comparison of Architectures and Pretraining Methods for Contextualized Multilingual Word Embeddings | The lack of annotated data in many languages is a well-known challenge within the field of multilingual natural language processing (NLP). Therefore, many recent studies focus on zero-shot transfer learning and joint training across languages to overcome data scarcity for low-resource languages. In this work we (i) per... | ['Ekaterina Shutova', 'Samira Abnar', 'Niels van der Heijden'] | 2019-12-15 | null | null | null | null | ['multilingual-word-embeddings'] | ['methodology'] | [-2.64542878e-01 -3.48900035e-02 -4.84844595e-01 -3.54302108e-01
-1.35112357e+00 -7.32841253e-01 6.29092932e-01 4.10673946e-01
-1.20345175e+00 9.90518808e-01 6.23945177e-01 -4.78626519e-01
4.37635303e-01 -5.39159656e-01 -7.29333580e-01 -2.28991792e-01
-5.33615164e-02 4.39429909e-01 1.04553938e-01 -2.89357692... | [10.660219192504883, 9.789510726928711] |
9e6eda32-349e-4b70-ab34-c3718e799321 | daformer-improving-network-architectures-and | 2111.14887 | null | https://arxiv.org/abs/2111.14887v2 | https://arxiv.org/pdf/2111.14887v2.pdf | DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation | As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large ... | ['Luc van Gool', 'Dengxin Dai', 'Lukas Hoyer'] | 2021-11-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hoyer_DAFormer_Improving_Network_Architectures_and_Training_Strategies_for_Domain-Adaptive_Semantic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hoyer_DAFormer_Improving_Network_Architectures_and_Training_Strategies_for_Domain-Adaptive_Semantic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.00949657e-01 4.20444608e-01 -4.59288396e-02 -5.59685826e-01
-7.20063448e-01 -5.46421707e-01 7.36174583e-01 -1.24807425e-01
-6.41796887e-01 6.77727163e-01 -2.29922652e-01 -2.87886471e-01
8.18341896e-02 -8.79376054e-01 -1.00063550e+00 -6.41828120e-01
3.46780539e-01 8.14810395e-01 5.94371319e-01 -2.39367008... | [9.747227668762207, 1.305688500404358] |
3c581156-0267-4c4a-96ff-d456933516c2 | searching-for-effective-neural-extractive | 1907.03491 | null | https://arxiv.org/abs/1907.03491v1 | https://arxiv.org/pdf/1907.03491v1.pdf | Searching for Effective Neural Extractive Summarization: What Works and What's Next | The recent years have seen remarkable success in the use of deep neural networks on text summarization. However, there is no clear understanding of \textit{why} they perform so well, or \textit{how} they might be improved. In this paper, we seek to better understand how neural extractive summarization systems could ben... | ['PengFei Liu', 'Xuanjing Huang', 'Xipeng Qiu', 'Danqing Wang', 'Ming Zhong'] | 2019-07-08 | searching-for-effective-neural-extractive-1 | https://aclanthology.org/P19-1100 | https://aclanthology.org/P19-1100.pdf | acl-2019-7 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 2.28673011e-01 4.06086087e-01 -3.15501750e-01 -3.52856219e-01
-6.19235873e-01 -5.06878674e-01 5.46940148e-01 4.65253025e-01
-4.27692145e-01 6.93275332e-01 1.00087404e+00 -4.98509437e-01
-1.97779685e-01 -8.12119961e-01 -7.23733187e-01 -1.80487409e-01
2.92935461e-01 3.70333105e-01 2.13389727e-03 -6.19748175... | [12.371536254882812, 9.403623580932617] |
167969aa-a8ba-45a5-a036-50e574e9d2b4 | volumetric-and-multi-view-cnns-for-object | 1604.03265 | null | http://arxiv.org/abs/1604.03265v2 | http://arxiv.org/pdf/1604.03265v2.pdf | Volumetric and Multi-View CNNs for Object Classification on 3D Data | 3D shape models are becoming widely available and easier to capture, making
available 3D information crucial for progress in object classification. Current
state-of-the-art methods rely on CNNs to address this problem. Recently, we
witness two types of CNNs being developed: CNNs based upon volumetric
representations ve... | ['Leonidas J. Guibas', 'Mengyuan Yan', 'Matthias Niessner', 'Hao Su', 'Angela Dai', 'Charles R. Qi'] | 2016-04-12 | volumetric-and-multi-view-cnns-for-object-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Qi_Volumetric_and_Multi-View_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Qi_Volumetric_and_Multi-View_CVPR_2016_paper.pdf | cvpr-2016-6 | ['3d-object-recognition'] | ['computer-vision'] | [-0.21142255 -0.08810913 -0.24320441 -0.3570324 -0.49209148 -0.6327943
0.73459584 0.20711856 -0.14142933 0.25213274 0.19162375 -0.20867431
-0.05932335 -1.1671256 -0.6558174 -0.19594818 -0.1649012 0.42538887
0.28459382 -0.21965572 0.18313166 1.1681058 -1.6775457 0.6792851
0.08140536 1.6572212 -0.14... | [8.173046112060547, -3.6383273601531982] |
d5fd3fef-9716-4587-9a9e-ef60116190f6 | scale-recovery-for-monocular-visual-odometry | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Yin_Scale_Recovery_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Yin_Scale_Recovery_for_ICCV_2017_paper.pdf | Scale Recovery for Monocular Visual Odometry Using Depth Estimated With Deep Convolutional Neural Fields | Scale recovery is one of the central problems for monocular visual odometry. Normally, road plane and camera height are specified as reference to recover the scale. The performances of these methods depend on the plane recognition and height measurement of camera. In this work, we propose a novel method to recover the ... | ['Xiangwei Wang', 'Qijun Chen', 'Xiaochuan Yin', 'Xiaoguo Du'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['monocular-visual-odometry'] | ['robots'] | [ 5.12919538e-02 -1.99735105e-01 -1.26090169e-01 -4.68980908e-01
-1.70436874e-01 -4.36625808e-01 4.23844814e-01 -4.12961870e-01
-6.12788796e-01 5.60894608e-01 -1.21127758e-02 1.68176919e-01
2.16909409e-01 -1.02932048e+00 -8.18265676e-01 -6.04743123e-01
6.39833212e-01 2.74058849e-01 7.14528441e-01 -8.79440382... | [8.066484451293945, -2.2246460914611816] |
50b44127-8010-44e7-bc73-f14a0e02a770 | deep-neural-networks-based-invisible | 2102.09173 | null | https://arxiv.org/abs/2102.09173v1 | https://arxiv.org/pdf/2102.09173v1.pdf | Deep Neural Networks based Invisible Steganography for Audio-into-Image Algorithm | In the last few years, steganography has attracted increasing attention from a large number of researchers since its applications are expanding further than just the field of information security. The most traditional method is based on digital signal processing, such as least significant bit encoding. Recently, there ... | ['Thanh Ta Minh', 'Toan Pham Van', 'Ngoc N. Tran', 'Thoi Hoang Dinh', 'Quang Pham Huu'] | 2021-02-18 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 6.94131017e-01 9.57227424e-02 1.95779633e-02 3.14355530e-02
-5.36339879e-01 -7.61316270e-02 2.83941120e-01 -2.80027986e-01
-4.50894684e-01 3.52687567e-01 6.36394620e-02 -3.61634612e-01
2.96626985e-01 -7.28146076e-01 -6.46705210e-01 -1.12471366e+00
-3.07564139e-01 -2.57884085e-01 4.79324877e-01 -2.32122153... | [4.297200679779053, 8.060617446899414] |
853d05d5-fbb7-41cc-b086-8ed5d1395753 | cptam-constituency-parse-tree-aggregation | 2201.07905 | null | https://arxiv.org/abs/2201.07905v2 | https://arxiv.org/pdf/2201.07905v2.pdf | CPTAM: Constituency Parse Tree Aggregation Method | Diverse Natural Language Processing tasks employ constituency parsing to understand the syntactic structure of a sentence according to a phrase structure grammar. Many state-of-the-art constituency parsers are proposed, but they may provide different results for the same sentences, especially for corpora outside their ... | ['Qi Li', 'Oliver Eulenstein', 'Alexey Markin', 'Nasim Sabetpour', 'Adithya Kulkarni'] | 2022-01-19 | null | null | null | null | ['constituency-parsing'] | ['natural-language-processing'] | [ 1.92861184e-01 6.51081204e-01 -2.71187257e-02 -8.29396129e-01
-1.70232022e+00 -1.04451680e+00 3.06936920e-01 5.08942246e-01
-8.51860195e-02 8.69974017e-01 5.49932182e-01 -4.75305051e-01
3.79539460e-01 -8.81654263e-01 -6.72327101e-01 -3.21860045e-01
2.45879158e-01 4.36046332e-01 3.22993547e-01 -1.66315049... | [10.354726791381836, 9.679966926574707] |
093d869a-0009-4f32-95c6-d22b328627e0 | approximal-operator-with-application-to-audio | 2005.01437 | null | https://arxiv.org/abs/2005.01437v3 | https://arxiv.org/pdf/2005.01437v3.pdf | Approximal operator with application to audio inpainting | In their recent evaluation of time-frequency representations and structured sparsity approaches to audio inpainting, Lieb and Stark (2018) have used a particular mapping as a proximal operator. This operator serves as the fundamental part of an iterative numerical solver. However, their mapping is improperly justified.... | ['Pavel Rajmic', 'Ondřej Mokrý'] | 2020-05-04 | null | null | null | null | ['audio-inpainting'] | ['audio'] | [ 1.64421976e-01 3.61535490e-01 -2.12093949e-01 2.84777641e-01
-8.01798344e-01 -4.49281156e-01 3.77971560e-01 -6.37234449e-02
-6.94682449e-02 7.78963327e-01 4.55728829e-01 -1.65118888e-01
-2.58017927e-01 -5.69296300e-01 -7.98801422e-01 -6.03345215e-01
3.51440683e-02 -3.01263221e-02 -3.02190185e-01 -5.25527656... | [15.491037368774414, 5.563834190368652] |
a333097d-6384-496c-b53d-77cfc26f686e | multimodal-speech-emotion-recognition-and | 1904.06022 | null | http://arxiv.org/abs/1904.06022v1 | http://arxiv.org/pdf/1904.06022v1.pdf | Multimodal Speech Emotion Recognition and Ambiguity Resolution | Identifying emotion from speech is a non-trivial task pertaining to the
ambiguous definition of emotion itself. In this work, we adopt a
feature-engineering based approach to tackle the task of speech emotion
recognition. Formalizing our problem as a multi-class classification problem,
we compare the performance of two... | ['Gaurav Sahu'] | 2019-04-12 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.38622475e-01 1.18888542e-01 3.70361894e-01 -7.23325491e-01
-1.15945315e+00 -4.32635367e-01 5.79251528e-01 2.24313498e-01
-5.67058086e-01 4.20741290e-01 1.44834459e-01 -1.90050781e-01
-1.25508875e-01 -3.42356026e-01 -4.21377271e-01 -6.32464886e-01
9.78438109e-02 1.69671819e-01 -2.15696856e-01 -3.01424503... | [13.50604248046875, 5.691328525543213] |
eeb2ac89-a724-4b71-8147-0ec81d6244ca | more-photos-are-all-you-need-semi-supervised | 2103.1399 | null | https://arxiv.org/abs/2103.13990v1 | https://arxiv.org/pdf/2103.13990v1.pdf | More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrieval | A fundamental challenge faced by existing Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) models is the data scarcity -- model performances are largely bottlenecked by the lack of sketch-photo pairs. Whilst the number of photos can be easily scaled, each corresponding sketch still needs to be individually produced.... | ['Yi-Zhe Song', 'Tao Xiang', 'Yongxin Yang', 'Aneeshan Sain', 'Pinaki Nath Chowdhury', 'Ayan Kumar Bhunia'] | 2021-03-25 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Bhunia_More_Photos_Are_All_You_Need_Semi-Supervised_Learning_for_Fine-Grained_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Bhunia_More_Photos_Are_All_You_Need_Semi-Supervised_Learning_for_Fine-Grained_CVPR_2021_paper.pdf | cvpr-2021-1 | ['sketch-based-image-retrieval', 'semi-supervised-sketch-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 6.09765530e-01 2.31055412e-02 -2.79843837e-01 -2.55844802e-01
-1.32494664e+00 -7.43762314e-01 9.92345393e-01 -3.47786754e-01
-8.71039703e-02 5.63977420e-01 2.79240578e-01 5.99523038e-02
-1.83143899e-01 -6.49688065e-01 -7.22376347e-01 -7.13201761e-01
4.32878196e-01 5.05293012e-01 -1.69686913e-01 -1.84638336... | [11.609463691711426, 0.6404974460601807] |
6528f49b-9d41-4528-a5af-c5f17233f7e6 | coarse-to-fine-a-hierarchical-diffusion-model | 2305.13266 | null | https://arxiv.org/abs/2305.13266v2 | https://arxiv.org/pdf/2305.13266v2.pdf | Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D | Generating desirable molecular structures in 3D is a fundamental problem for drug discovery. Despite the considerable progress we have achieved, existing methods usually generate molecules in atom resolution and ignore intrinsic local structures such as rings, which leads to poor quality in generated structures, especi... | ['Yanyan Lan', 'WeiYing Ma', 'Hao Zhou', 'Bowen Gao', 'Jingjing Gong', 'Minkai Xu', 'Yuxuan Song', 'Bo Qiang'] | 2023-05-05 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.70054811e-01 -1.30253240e-01 -1.23610504e-01 -3.32665294e-02
-7.55444109e-01 -3.58239859e-01 4.06463951e-01 1.73685014e-01
-4.69440371e-02 1.31864476e+00 1.09884471e-01 -2.18361184e-01
-1.83756296e-02 -1.23598659e+00 -7.49602616e-01 -1.06450284e+00
4.19949517e-02 6.12422109e-01 2.35162094e-01 -2.65639663... | [4.994266033172607, 5.704833507537842] |
6b524437-4d70-4fc9-b943-7907b207279c | predicting-human-scanpaths-in-visual-question | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Predicting_Human_Scanpaths_in_Visual_Question_Answering_CVPR_2021_paper.pdf | Predicting Human Scanpaths in Visual Question Answering | Attention has been an important mechanism for both humans and computer vision systems. While state-of-the-art models to predict attention focus on estimating a static probabilistic saliency map with free-viewing behavior, real-life scenarios are filled with tasks of varying types and complexities, and visual explor... | ['Qi Zhao', 'Ming Jiang', 'Xianyu Chen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['scanpath-prediction'] | ['computer-vision'] | [ 3.97553444e-01 -1.55534491e-01 6.58924878e-02 -4.26285386e-01
-2.04266682e-01 -4.55695093e-01 4.30219352e-01 1.25361502e-01
-3.43696207e-01 2.98963666e-01 -5.47128078e-03 -4.88722235e-01
-2.01438546e-01 -3.24700028e-01 -6.96913719e-01 -2.74906665e-01
1.96989119e-01 3.88777852e-01 8.05768430e-01 -3.31960678... | [10.1679105758667, 1.232889175415039] |
17bf4c3a-58de-4598-b85c-2ee8e7c042f0 | nested-named-entity-recognition-revisited | null | null | https://aclanthology.org/N18-1079 | https://aclanthology.org/N18-1079.pdf | Nested Named Entity Recognition Revisited | We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, ... | ['Arzoo Katiyar', 'Claire Cardie'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.54681608e-01 6.43495321e-01 -3.91120195e-01 -3.37889701e-01
-8.06257486e-01 -5.08031368e-01 4.25806314e-01 5.29474854e-01
-5.03134906e-01 6.09107137e-01 5.19251525e-01 -3.73161733e-01
1.64747119e-01 -1.12113762e+00 -7.52383053e-01 -1.18125565e-01
-8.13415766e-01 6.47004008e-01 2.86105424e-01 -1.80571586... | [9.536746978759766, 9.376058578491211] |
1233cc9f-fcf7-4dff-8778-1aff13e22dd1 | document-structure-measure-for-hypernym | 1811.12728 | null | http://arxiv.org/abs/1811.12728v1 | http://arxiv.org/pdf/1811.12728v1.pdf | Document Structure Measure for Hypernym discovery | Hypernym discovery is the problem of finding terms that have is-a
relationship with a given term. We introduce a new context type, and a
relatedness measure to differentiate hypernyms from other types of semantic
relationships. Our Document Structure measure is based on hierarchical position
of terms in a document, and... | ['Aswin Kannan', 'Shanmukha C Guttula', 'Hima P Karanam', 'Balaji Ganesan', 'Arun Kumar'] | 2018-11-30 | null | null | null | null | ['hypernym-discovery'] | ['natural-language-processing'] | [ 2.56829053e-01 3.01349163e-02 -5.50479949e-01 -5.50947905e-01
3.54478389e-01 -8.45425010e-01 7.56621897e-01 8.39569628e-01
-4.87449050e-01 6.73592985e-01 4.30813581e-01 -4.10035908e-01
-9.01516676e-01 -1.32402420e+00 2.30723992e-01 -4.17041034e-01
-3.41397256e-01 7.23825037e-01 1.48111150e-01 -5.79760909... | [9.87111759185791, 8.749289512634277] |
b01e3dce-2107-4639-801b-d0ad94460d8e | synthesizing-a-progression-of-subtasks-for | 2305.17518 | null | https://arxiv.org/abs/2305.17518v1 | https://arxiv.org/pdf/2305.17518v1.pdf | Synthesizing a Progression of Subtasks for Block-Based Visual Programming Tasks | Block-based visual programming environments play an increasingly important role in introducing computing concepts to K-12 students. In recent years, they have also gained popularity in neuro-symbolic AI, serving as a benchmark to evaluate general problem-solving and logical reasoning skills. The open-ended and conceptu... | ['Adish Singla', 'Maria Christakis', 'Hasan Ferit Eniser', 'Ahana Ghosh', 'Alperen Tercan'] | 2023-05-27 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.47326551e-02 1.06811523e-02 9.55139548e-02 -1.26412839e-01
-2.82697052e-01 -9.36912477e-01 3.92459184e-01 3.83897483e-01
-1.17453165e-01 2.55619854e-01 -1.63857594e-01 -8.87052536e-01
-9.47141871e-02 -8.78861725e-01 -7.92298257e-01 -2.57422894e-01
-1.22596994e-01 4.99518186e-01 2.07602903e-01 -5.52415967... | [9.087738990783691, 7.211365699768066] |
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