paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9ed33008-50ec-413d-bca3-e2469d702595 | deep-learning-representation-using | 1409.7164 | null | http://arxiv.org/abs/1409.7164v1 | http://arxiv.org/pdf/1409.7164v1.pdf | Deep Learning Representation using Autoencoder for 3D Shape Retrieval | We study the problem of how to build a deep learning representation for 3D
shape. Deep learning has shown to be very effective in variety of visual
applications, such as image classification and object detection. However, it
has not been successfully applied to 3D shape recognition. This is because 3D
shape has complex... | ['Song Bai', 'Zhuotun Zhu', 'Xinggang Wang', 'Cong Yao', 'Xiang Bai'] | 2014-09-25 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition'] | ['computer-vision', 'computer-vision'] | [-4.65285420e-01 -7.22081721e-01 -1.38298824e-01 -4.00615841e-01
-5.98659515e-01 -5.19092023e-01 8.44276130e-01 7.28919953e-02
-1.24752954e-01 -9.63500664e-02 1.88638091e-01 -1.60679609e-01
-3.68946940e-01 -1.07227337e+00 -4.24140006e-01 -7.71175683e-01
1.98411699e-02 7.19120681e-01 4.85014319e-02 2.11172774... | [8.1653413772583, -3.9036552906036377] |
72ea91b6-1932-4970-88b9-87ef29ab08ed | deepmask-an-algorithm-for-cloud-and-cloud | 1911.03607 | null | https://arxiv.org/abs/1911.03607v1 | https://arxiv.org/pdf/1911.03607v1.pdf | DeepMask: an algorithm for cloud and cloud shadow detection in optical satellite remote sensing images using deep residual network | Detecting and masking cloud and cloud shadow from satellite remote sensing images is a pervasive problem in the remote sensing community. Accurate and efficient detection of cloud and cloud shadow is an essential step to harness the value of remotely sensed data for almost all downstream analysis. DeepMask, a new algor... | ['Ke Xu', 'Kaiyu Guan', 'Yunan Luo', 'Sibo Wang', 'Jian Peng'] | 2019-11-09 | null | null | null | null | ['shadow-detection', 'cloud-detection'] | ['computer-vision', 'computer-vision'] | [ 2.42018655e-01 -5.90726376e-01 2.76814289e-02 -5.84861450e-02
-5.09223342e-01 -8.48342657e-01 5.04653573e-01 -3.31192166e-01
-1.99513346e-01 6.18647397e-01 -3.65506440e-01 -7.88057864e-01
1.04954675e-01 -1.04649436e+00 -2.54916161e-01 -8.92560601e-01
-2.69739598e-01 1.99122094e-02 4.11329716e-02 -2.49942347... | [9.75967025756836, -1.7043261528015137] |
86e4b810-115e-4803-b914-356dc3f5c550 | pointclm-a-contrastive-learning-based | 2209.00219 | null | https://arxiv.org/abs/2209.00219v1 | https://arxiv.org/pdf/2209.00219v1.pdf | PointCLM: A Contrastive Learning-based Framework for Multi-instance Point Cloud Registration | Multi-instance point cloud registration is the problem of estimating multiple poses of source point cloud instances within a target point cloud. Solving this problem is challenging since inlier correspondences of one instance constitute outliers of all the other instances. Existing methods often rely on time-consuming ... | ['Manning Wang', 'Xinrong Chen', 'Qiuye Jin', 'Zhihao LI', 'Mingzhi Yuan'] | 2022-09-01 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.23023249e-01 -2.78926432e-01 1.39766455e-01 -2.63451695e-01
-1.41268873e+00 -3.49543393e-01 5.56007504e-01 4.04923886e-01
-1.04209691e-01 4.65656072e-01 -3.87789488e-01 2.28633344e-01
-2.22543448e-01 -4.86676753e-01 -1.21594715e+00 -5.27013481e-01
-1.59395292e-01 1.01122761e+00 4.15390611e-01 1.32699355... | [7.7149553298950195, -3.0300865173339844] |
93739500-759d-4fe8-88f5-fc77d05cdf38 | on-exploring-and-improving-robustness-of | 2110.057 | null | https://arxiv.org/abs/2110.05700v1 | https://arxiv.org/pdf/2110.05700v1.pdf | On Exploring and Improving Robustness of Scene Text Detection Models | It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. For systematically exploring this problem, we propose two datasets from which to evaluate scene text detection models: ICDAR2015-C (IC15-C) an... | ['Zengfu Wang', 'Kewei Wang', 'Yongrui Li', 'Wei Zhai', 'Shilian Wu'] | 2021-10-12 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.27621692e-01 -4.29031193e-01 1.86111644e-01 -3.10295284e-01
-6.53305471e-01 -4.39711362e-01 8.86469305e-01 1.01262592e-01
-2.33098850e-01 3.50368261e-01 2.38572627e-01 -2.57938296e-01
2.93932855e-01 -7.36585617e-01 -5.18734574e-01 -7.99921870e-01
3.69950056e-01 1.04871228e-01 7.83735216e-01 1.31633468... | [12.04699420928955, 2.2847037315368652] |
52cf92f3-246a-4279-b168-6a08bb629f05 | multi-modal-classifiers-for-open-vocabulary | 2306.05493 | null | https://arxiv.org/abs/2306.05493v1 | https://arxiv.org/pdf/2306.05493v1.pdf | Multi-Modal Classifiers for Open-Vocabulary Object Detection | The goal of this paper is open-vocabulary object detection (OVOD) $\unicode{x2013}$ building a model that can detect objects beyond the set of categories seen at training, thus enabling the user to specify categories of interest at inference without the need for model retraining. We adopt a standard two-stage object de... | ['Andrew Zisserman', 'Weidi Xie', 'Prannay Kaul'] | 2023-06-08 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.19329147e-01 1.28356308e-01 -2.66138762e-01 -2.77297914e-01
-9.95845854e-01 -8.26904655e-01 9.48579431e-01 2.95191497e-01
-4.47426647e-01 2.88918465e-01 2.31134370e-01 -1.25511110e-01
1.70277759e-01 -5.48456073e-01 -8.75387490e-01 -3.71942729e-01
1.09754868e-01 5.45897424e-01 5.05068541e-01 2.10281555... | [9.900038719177246, 1.6387935876846313] |
e94d31d4-e6b6-4ed7-aae5-3062f63a79bc | a-theoretical-justification-for-image | 2302.01217 | null | https://arxiv.org/abs/2302.01217v1 | https://arxiv.org/pdf/2302.01217v1.pdf | A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models | We provide a theoretical justification for sample recovery using diffusion based image inpainting in a linear model setting. While most inpainting algorithms require retraining with each new mask, we prove that diffusion based inpainting generalizes well to unseen masks without retraining. We analyze a recently propose... | ['Sanjay Shakkottai', 'Constantine Caramanis', 'Advait Parulekar', 'Litu Rout'] | 2023-02-02 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.04971427e-01 1.49602056e-01 -4.09752637e-01 9.81127936e-03
-9.95531321e-01 -5.75820923e-01 4.87048477e-01 -2.43916973e-01
-5.09936333e-01 9.38576877e-01 1.06581107e-01 -6.52956516e-02
-7.90488049e-02 -3.18027943e-01 -1.01265681e+00 -8.27635586e-01
1.19073102e-02 4.41921443e-01 -1.07905986e-02 1.60202980... | [11.71802043914795, -2.3448965549468994] |
d350a39f-35d2-46c0-8e4a-af8d508678ca | a-review-of-machine-learning-approaches | 2206.01728 | null | https://arxiv.org/abs/2206.01728v1 | https://arxiv.org/pdf/2206.01728v1.pdf | A review of machine learning approaches, challenges and prospects for computational tumor pathology | Computational pathology is part of precision oncology medicine. The integration of high-throughput data including genomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics into clinical practice improves cancer treatment plans, treatment cycles, and cure rates, and helps doctors open up innovative a... | ['Shaoliang Peng', 'Zhichao Feng', 'Liangrui Pan'] | 2022-05-31 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 4.25796568e-01 -3.12271863e-01 -8.34981441e-01 1.05045021e-01
-7.67146587e-01 -2.24814117e-01 -2.85685109e-03 1.01881289e+00
-1.34050071e-01 5.27458549e-01 1.43888354e-01 -5.49750566e-01
-3.66715938e-01 -6.89024091e-01 2.39497751e-01 -1.11583686e+00
-4.37062867e-02 7.51493573e-01 -3.20299000e-01 2.49598294... | [15.23124885559082, -3.026981830596924] |
c361c5a7-f604-40b8-9dff-d94e8579aa76 | self-relation-attention-and-temporal | 2209.07629 | null | https://arxiv.org/abs/2209.07629v2 | https://arxiv.org/pdf/2209.07629v2.pdf | Self-Relation Attention and Temporal Awareness for Emotion Recognition via Vocal Burst | The technical report presents our emotion recognition pipeline for high-dimensional emotion task (A-VB High) in The ACII Affective Vocal Bursts (A-VB) 2022 Workshop \& Competition. Our proposed method contains three stages. Firstly, we extract the latent features from the raw audio signal and its Mel-spectrogram by sel... | ['Guee-Sang Lee', 'Minh-Cong Vo', 'Dang-Linh Trinh'] | 2022-09-15 | null | null | null | null | ['a-vb-high'] | ['speech'] | [-1.12761199e-01 -7.73906931e-02 3.74384709e-02 -5.28723776e-01
-1.11192381e+00 -4.94234115e-01 1.16691709e-01 -6.64824545e-02
-2.15347052e-01 3.91077220e-01 3.86732787e-01 3.25945854e-01
1.79069713e-01 -3.80781777e-02 -1.40331432e-01 -6.37558818e-01
-3.36548865e-01 -3.27401310e-01 -2.87086189e-01 1.21982828... | [13.523615837097168, 5.691315174102783] |
54079074-e42a-42f7-81bc-953889e83d10 | utahbmi-at-semeval-2016-task-12-extracting | null | null | https://aclanthology.org/S16-1195 | https://aclanthology.org/S16-1195.pdf | UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text | null | ['Stephane Meystre', 'Abdulrahman Khalifa', 'Sumithra Velupillai'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['temporal-information-extraction'] | ['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... | [-7.417088985443115, 3.7920775413513184] |
242c7858-6b9f-4058-b390-17ef5223efbd | rgbd1k-a-large-scale-dataset-and-benchmark | 2208.09787 | null | https://arxiv.org/abs/2208.09787v3 | https://arxiv.org/pdf/2208.09787v3.pdf | RGBD1K: A Large-scale Dataset and Benchmark for RGB-D Object Tracking | RGB-D object tracking has attracted considerable attention recently, achieving promising performance thanks to the symbiosis between visual and depth channels. However, given a limited amount of annotated RGB-D tracking data, most state-of-the-art RGB-D trackers are simple extensions of high-performance RGB-only tracke... | ['Josef Kittler', 'Xiao-Jun Wu', 'Xiao Yang', 'Haodong Liu', 'Zucheng Wu', 'Zhangyong Tang', 'Tianyang Xu', 'Xue-Feng Zhu'] | 2022-08-21 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-4.27719295e-01 -2.54934877e-01 -3.35760713e-01 -1.22501276e-01
-5.35246372e-01 -7.90679693e-01 3.83592337e-01 -4.55904454e-01
-2.56090611e-01 2.40074545e-01 -8.36962238e-02 -2.43754506e-01
3.75069588e-01 -3.37717444e-01 -6.43545151e-01 -6.33212686e-01
-4.00525220e-02 3.19535136e-02 6.30887628e-01 3.07578240... | [6.5484747886657715, -2.1562163829803467] |
59991fc5-92e9-4378-80c5-cc1e44e1d929 | vectormapnet-end-to-end-vectorized-hd-map | 2206.0892 | null | https://arxiv.org/abs/2206.08920v6 | https://arxiv.org/pdf/2206.08920v6.pdf | VectorMapNet: End-to-end Vectorized HD Map Learning | Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads. Existing solutions approach the semantic mapping problem by offline manual annotation, which suffers from serious scalability issues. Recent learning-based methods produce dense rasterized segmentation predictions to c... | ['Tianyuan Yuan', 'Hang Zhao', 'Yilun Wang', 'Yue Wang', 'Yicheng Liu'] | 2022-06-17 | null | null | null | null | ['3d-lane-detection', 'hd-semantic-map-learning'] | ['computer-vision', 'computer-vision'] | [-1.11492701e-01 3.49693567e-01 -2.73655444e-01 -8.36683571e-01
-8.30527663e-01 -6.21226311e-01 7.73552001e-01 6.00212812e-02
-3.04325551e-01 6.37496769e-01 4.46966439e-02 -5.10661125e-01
8.93424004e-02 -1.28575206e+00 -1.01509571e+00 -5.06716259e-02
-1.49378389e-01 8.55709553e-01 5.37736177e-01 -7.17454195... | [7.994074821472168, -1.8607659339904785] |
dce863b5-eb24-4091-8492-954df6bbd54e | unsupervised-learning-of-full-waveform-1 | 2110.07584 | null | https://arxiv.org/abs/2110.07584v2 | https://arxiv.org/pdf/2110.07584v2.pdf | Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop | This paper investigates unsupervised learning of Full-Waveform Inversion (FWI), which has been widely used in geophysics to estimate subsurface velocity maps from seismic data. This problem is mathematically formulated by a second order partial differential equation (PDE), but is hard to solve. Moreover, acquiring velo... | ['Youzuo Lin', 'Zicheng Liu', 'Sharon Xiaolei Huang', 'Yinpeng Chen', 'Xitong Zhang', 'Peng Jin'] | 2021-10-14 | unsupervised-learning-of-full-waveform | https://openreview.net/forum?id=izvwgBic9q | https://openreview.net/pdf?id=izvwgBic9q | iclr-2022-4 | ['geophysics'] | ['miscellaneous'] | [ 1.89409107e-01 6.90238327e-02 3.97590220e-01 -2.49887198e-01
-1.08020604e+00 -4.71722424e-01 4.62502480e-01 -6.28538579e-02
-7.75819123e-01 5.47622323e-01 1.37705177e-01 -5.77802300e-01
-1.43393829e-01 -1.15400624e+00 -1.06404638e+00 -9.53966022e-01
-3.64284575e-01 6.36858225e-01 3.40369791e-01 -2.90065259... | [6.861297130584717, 2.513056755065918] |
4ff04f5e-37f2-497c-9d20-d92b03fbf056 | photo-pre-training-but-for-sketch | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.pdf | Photo Pre-Training, but for Sketch | The sketch community has faced up to its unique challenges over the years, that of data scarcity however still remains the most significant to date. This lack of sketch data has imposed on the community a few "peculiar" design choices -- the most representative of them all is perhaps the coerced utilisation of phot... | ['Yi-Zhe Song', 'Kaiyue Pang', 'Ke Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.95027405e-01 1.84651375e-01 -2.86066324e-01 -3.19764584e-01
-8.06413054e-01 -9.24010158e-01 1.00985396e+00 -3.33396643e-01
-2.13396579e-01 5.52359641e-01 3.61367851e-01 -2.32343167e-01
-3.86097670e-01 -5.99074125e-01 -8.59207809e-01 -7.57026374e-01
1.01625487e-01 5.29400587e-01 3.44165079e-02 -4.98918861... | [11.625414848327637, 0.5611799359321594] |
45281a06-2eee-4fb8-bb49-3638940db557 | dronet-efficient-convolutional-neural-network | 1807.06789 | null | http://arxiv.org/abs/1807.06789v1 | http://arxiv.org/pdf/1807.06789v1.pdf | DroNet: Efficient convolutional neural network detector for real-time UAV applications | Unmanned Aerial Vehicles (drones) are emerging as a promising technology for
both environmental and infrastructure monitoring, with broad use in a plethora
of applications. Many such applications require the use of computer vision
algorithms in order to analyse the information captured from an on-board
camera. Such app... | ['Christos-Savvas Bouganis', 'Christos Kyrkou', 'Theocharis Theocharides', 'George Plastiras', 'Stylianos Venieris'] | 2018-07-18 | null | null | null | null | ['one-shot-object-detection', 'object-detection-in-aerial-images'] | ['computer-vision', 'computer-vision'] | [ 4.05096859e-01 -2.98992962e-01 3.84680182e-01 -9.93541032e-02
1.77929059e-01 -6.33507788e-01 3.26869845e-01 2.31868491e-01
-7.87979305e-01 4.05513644e-02 -9.02234554e-01 -4.17672873e-01
-1.09813318e-01 -8.87087286e-01 -5.10233045e-01 -6.84554279e-01
-5.02032757e-01 -2.49410182e-01 4.13328260e-01 -2.46787086... | [8.442584991455078, -1.0307365655899048] |
54636fca-015d-4923-aad1-25ab4b591c4a | the-effectiveness-of-pre-trained-code | null | null | https://openreview.net/forum?id=H1glKiCqtm | https://openreview.net/pdf?id=H1glKiCqtm | The Effectiveness of Pre-Trained Code Embeddings | Word embeddings are widely used in machine learning based natural language processing systems. It is common to use pre-trained word embeddings which provide benefits such as reduced training time and improved overall performance. There has been a recent interest in applying natural language processing techniques to pro... | ['Ben Trevett', 'N. K. Taylor', 'Donald Reay'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['extreme-summarization'] | ['natural-language-processing'] | [-5.83788678e-02 -9.36170481e-03 -2.91268557e-01 -5.20159602e-01
-5.52851677e-01 -4.70712870e-01 4.02849287e-01 9.83275354e-01
-9.16256309e-01 5.06516732e-02 3.69740754e-01 -6.33259356e-01
2.46644095e-01 -7.67464995e-01 -4.35264021e-01 -1.18345171e-01
-4.16799515e-01 -7.75611550e-02 3.67170244e-01 -1.70005083... | [7.65729284286499, 7.917394161224365] |
4e6e48fa-66a7-4d9c-8d61-b00bcbdea0b0 | improving-autoregressive-nlp-tasks-via | 2304.08453 | null | https://arxiv.org/abs/2304.08453v3 | https://arxiv.org/pdf/2304.08453v3.pdf | Improving Autoregressive NLP Tasks via Modular Linearized Attention | Various natural language processing (NLP) tasks necessitate models that are efficient and small based on their ultimate application at the edge or in other resource-constrained environments. While prior research has reduced the size of these models, increasing computational efficiency without considerable performance i... | ['Lizhong Chen', 'Victor Agostinelli'] | 2023-04-17 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 4.26431417e-01 2.47409999e-01 -1.81267411e-01 -4.06940997e-01
-1.39699638e+00 -5.25571465e-01 7.29680002e-01 -4.40693974e-01
-3.21581841e-01 6.16953433e-01 4.90305245e-01 -1.12421024e+00
1.27382681e-01 -1.95013434e-01 -7.37475693e-01 -3.91469091e-01
2.42248207e-01 8.24680507e-01 -4.88596648e-01 -1.05607929... | [14.461668968200684, 7.239919662475586] |
22cbf84c-5299-46ff-b748-acfaf933018b | generative-pre-trained-transformer-for | 2110.04071 | null | https://arxiv.org/abs/2110.04071v1 | https://arxiv.org/pdf/2110.04071v1.pdf | Generative Pre-Trained Transformer for Cardiac Abnormality Detection | ECG heartbeat classification plays a vital role in diagnosis of cardiac arrhythmia. The goal of the Physionet/CinC 2021 challenge was to accurately classify clinical diagnosis based on 12, 6, 4, 3 or 2-lead ECG recordings in order to aid doctors in the diagnoses of different heart conditions. Transformers have had grea... | ['Ricard Delgado-Gonzalo', 'Mathieu Lemay', 'Jérôme Van Zaen', 'Clémentine Aguet', 'Halla Sigurthorsdottir', 'Pierre Louis Gaudilliere'] | 2021-10-07 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 2.72279769e-01 8.39511976e-02 3.76357317e-01 -4.35725838e-01
-8.86221468e-01 -6.71258628e-01 2.40591615e-02 1.83762312e-01
-2.27349717e-02 7.82980144e-01 1.70903414e-01 -6.21217966e-01
-2.66860157e-01 -3.61815453e-01 -2.06037387e-01 -4.76662785e-01
-3.40336919e-01 4.92340893e-01 -2.63463438e-01 -1.71397198... | [14.365626335144043, 3.347059726715088] |
a3ef6a23-2691-41e7-b4dd-43a95c763dae | objects-can-move-3d-change-detection-by | 2208.0987 | null | https://arxiv.org/abs/2208.09870v1 | https://arxiv.org/pdf/2208.09870v1.pdf | Objects Can Move: 3D Change Detection by Geometric Transformation Constistency | AR/VR applications and robots need to know when the scene has changed. An example is when objects are moved, added, or removed from the scene. We propose a 3D object discovery method that is based only on scene changes. Our method does not need to encode any assumptions about what is an object, but rather discovers obj... | ['Tomas Pajdla', 'Konstantinos Karantzalos', 'Torsten Sattler', 'Aikaterini Adam'] | 2022-08-21 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 1.93409950e-01 2.36878358e-02 -7.00783283e-02 -3.51737887e-01
-4.50163186e-01 -9.00524914e-01 5.95065355e-01 2.34583929e-01
-2.21146584e-01 2.65673339e-01 -9.32704508e-02 7.19477981e-02
6.73235729e-02 -8.26273561e-01 -9.48470592e-01 -4.69543636e-01
-1.32799745e-01 7.82543600e-01 9.78475153e-01 -1.23827480... | [7.830582618713379, -2.341735601425171] |
f332dd69-2594-40ff-b173-4395e668c25d | real-time-facial-expression-recognition-using | 2202.00102 | null | https://arxiv.org/abs/2202.00102v1 | https://arxiv.org/pdf/2202.00102v1.pdf | Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks | This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In ... | ['Ahmad Kalhor', 'Mehdi Tale Masouleh', 'Ehsan Saeedizade', 'Mohammad Amin Haghpanah'] | 2022-01-31 | null | null | null | null | ['facial-expression-recognition', 'facial-landmark-detection'] | ['computer-vision', 'computer-vision'] | [ 3.61941725e-01 1.34204095e-02 -1.72698125e-01 -6.38331592e-01
-1.12126261e-01 2.86402199e-02 3.25871170e-01 1.83582440e-01
-6.07157052e-01 2.99305707e-01 -2.38257408e-01 1.50185540e-01
8.86017829e-03 -9.26222324e-01 -2.38585994e-01 -9.69595909e-01
-1.58575639e-01 1.63226768e-01 -1.75831556e-01 2.51401544... | [13.14210319519043, 0.7767835855484009] |
b5dfda52-4047-441a-8740-8db9f6a3166a | generative-question-answering-learning-to | null | null | https://openreview.net/forum?id=Bkx0RjA9tX | https://openreview.net/pdf?id=Bkx0RjA9tX | Generative Question Answering: Learning to Answer the Whole Question | Discriminative question answering models can overfit to superficial biases in datasets, because their loss function saturates when any clue makes the answer likely. We introduce generative models of the joint distribution of questions and answers, which are trained to explain the whole question, not just to ... | ['Mike Lewis', 'Angela Fan'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['generative-question-answering'] | ['natural-language-processing'] | [ 3.64978194e-01 7.96950638e-01 1.29333898e-01 -6.33599639e-01
-1.47736084e+00 -1.04339159e+00 7.84106672e-01 -1.44676894e-01
-3.62887442e-01 6.92927659e-01 6.55166268e-01 -6.51078522e-01
-2.83610448e-02 -1.13103795e+00 -9.97846901e-01 -1.50287569e-01
4.89129394e-01 1.05492532e+00 2.91985184e-01 -6.97524726... | [11.113917350769043, 8.040690422058105] |
f85773a1-d373-4ca9-80bd-db5fff518973 | learning-sparse-analytic-filters-for-piano | 2108.10382 | null | https://arxiv.org/abs/2108.10382v3 | https://arxiv.org/pdf/2108.10382v3.pdf | Learning Sparse Analytic Filters for Piano Transcription | In recent years, filterbank learning has become an increasingly popular strategy for various audio-related machine learning tasks. This is partly due to its ability to discover task-specific audio characteristics which can be leveraged in downstream processing. It is also a natural extension of the nearly ubiquitous de... | ['Zhiyao Duan', 'Mojtaba Heydari', 'Frank Cwitkowitz'] | 2021-08-23 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 3.63176495e-01 -1.28971174e-01 -1.48868397e-01 -7.82941282e-02
-1.13438094e+00 -6.92021668e-01 5.10101259e-01 -7.36501217e-02
-2.84480989e-01 5.79048216e-01 6.51378691e-01 1.22922726e-01
-3.49124074e-01 -2.81231225e-01 -4.59018171e-01 -7.91418195e-01
-2.47374475e-01 -1.88905761e-01 -1.14236720e-01 -8.56977403... | [15.568767547607422, 5.475106716156006] |
2e660c66-7c41-4da6-8cb4-530209948d83 | noise-and-edge-based-dual-branch-image | 2207.00724 | null | https://arxiv.org/abs/2207.00724v1 | https://arxiv.org/pdf/2207.00724v1.pdf | Noise and Edge Based Dual Branch Image Manipulation Detection | Unlike ordinary computer vision tasks that focus more on the semantic content of images, the image manipulation detection task pays more attention to the subtle information of image manipulation. In this paper, the noise image extracted by the improved constrained convolution is used as the input of the model instead o... | ['Jinjin Wang', 'Lin Zhu', 'Yanxiang Zhao', 'Yi Qian', 'Zhongyuan Zhang'] | 2022-07-02 | null | null | null | null | ['image-manipulation-detection', 'edge-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.17741567e-01 -3.00834119e-01 5.55125698e-02 -2.25214437e-01
9.28981155e-02 -1.13885783e-01 2.63637125e-01 2.51546223e-02
-2.96607345e-01 7.15598390e-02 1.26963422e-01 6.98696971e-02
6.60326611e-03 -8.09731066e-01 -9.08965886e-01 -6.77306175e-01
1.77641541e-01 -6.48864388e-01 5.67012250e-01 -2.48789608... | [12.140385627746582, 0.8312212228775024] |
64ae7d90-5662-446e-9fc4-cafee1f0bf43 | chatgpt-is-a-knowledgeable-but-inexperienced | 2303.16421 | null | https://arxiv.org/abs/2303.16421v1 | https://arxiv.org/pdf/2303.16421v1.pdf | ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models | Large language models (LLMs) such as ChatGPT and GPT-4 have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point for LLMs. It remains unclear that: (1) Can GPTs effectively answer commonsense questions? (2) Are GPTs knowledg... | ['Ben He', 'Yaojie Lu', 'Hongyu Lin', 'Le Sun', 'Xianpei Han', 'Ning Bian'] | 2023-03-29 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 1.66554078e-01 4.01316464e-01 3.33290137e-02 1.27829671e-01
-6.11799479e-01 -8.23773146e-01 3.03069085e-01 2.39122331e-01
-8.34807158e-02 9.38401639e-01 3.66711617e-01 -8.89566541e-01
-1.19073495e-01 -1.24422932e+00 -7.61947989e-01 -4.98413518e-02
5.50066710e-01 4.86194462e-01 5.61578035e-01 -8.87966394... | [10.117500305175781, 7.961483955383301] |
1b774326-e171-4866-be28-712b8986cd3b | place-recognition-in-gardens-by-learning | 1906.12151 | null | https://arxiv.org/abs/1906.12151v1 | https://arxiv.org/pdf/1906.12151v1.pdf | Place recognition in gardens by learning visual representations: data set and benchmark analysis | Visual place recognition is an important component of systems for camera localization and loop closure detection. It concerns the recognition of a previously visited place based on visual cues only. Although it is a widely studied problem for indoor and urban environments, the recent use of robots for automation of agr... | ['Maria Leyva-Vallina', 'Nicolai Petkov', 'Nicola Strisciuglio'] | 2019-06-28 | null | null | null | null | ['camera-localization', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [ 1.74616441e-01 -3.92658025e-01 3.28604728e-02 -3.25460404e-01
-2.73453048e-03 -7.66382992e-01 6.85744762e-01 5.43911159e-01
-6.12815320e-01 6.66278481e-01 -1.63631245e-01 -1.58680052e-01
-5.95968887e-02 -1.02917063e+00 -9.71516907e-01 -7.93532073e-01
-3.77541333e-01 1.49971426e-01 2.40737066e-01 -4.91532624... | [7.608855247497559, -1.8551018238067627] |
78e15dcf-a91a-4930-9e96-0fb633417d64 | argoverse-3d-tracking-and-forecasting-with-1 | 1911.0262 | null | https://arxiv.org/abs/1911.02620v1 | https://arxiv.org/pdf/1911.02620v1.pdf | Argoverse: 3D Tracking and Forecasting with Rich Maps | We present Argoverse -- two datasets designed to support autonomous vehicle machine learning tasks such as 3D tracking and motion forecasting. Argoverse was collected by a fleet of autonomous vehicles in Pittsburgh and Miami. The Argoverse 3D Tracking dataset includes 360 degree images from 7 cameras with overlapping f... | ['Slawomir Bak', 'Patsorn Sangkloy', 'Ming-Fang Chang', 'John Lambert', 'Jagjeet Singh', 'Deva Ramanan', 'James Hays', 'Andrew Hartnett', 'Peter Carr', 'Simon Lucey', 'De Wang'] | 2019-11-06 | argoverse-3d-tracking-and-forecasting-with | http://openaccess.thecvf.com/content_CVPR_2019/html/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-object-tracking'] | ['computer-vision'] | [-4.46070969e-01 6.31727129e-02 -4.73303139e-01 -7.54492939e-01
-4.13665354e-01 -1.04954553e+00 1.02575600e+00 -1.96393952e-01
-3.26580554e-01 3.71028066e-01 -1.47970524e-02 -6.29888594e-01
6.04010224e-02 -8.39203477e-01 -9.77363169e-01 -3.00608754e-01
-3.63753974e-01 8.65934074e-01 6.24812901e-01 -5.09009123... | [7.719656467437744, -1.9704999923706055] |
8660ef49-3e8f-4bbc-9a37-0400e35903d6 | learning-robust-agents-for-visual-navigation | 2109.10493 | null | https://arxiv.org/abs/2109.10493v2 | https://arxiv.org/pdf/2109.10493v2.pdf | Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge | Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation. However, while impressive progress has been made for teaching embodied agents to navigate static environments, much less progress has been mad... | ['Sehoon Ha', 'Dhruv Batra', 'Qian Luo', 'Naoki Yokoyama'] | 2021-09-22 | null | null | null | null | ['image-augmentation', 'pointgoal-navigation'] | ['computer-vision', 'robots'] | [ 1.35626295e-03 4.68018539e-02 1.80486575e-01 -2.29071304e-01
-5.26638687e-01 -6.49282932e-01 8.90745461e-01 -9.69596729e-02
-9.36975837e-01 9.18155015e-01 2.36783117e-01 -3.94031793e-01
3.60483944e-01 -6.31830156e-01 -9.49523509e-01 -4.42844033e-01
-4.06043470e-01 4.44744080e-01 3.62742454e-01 -7.56580889... | [4.459345817565918, 0.8035050630569458] |
347afeb9-9325-459a-99a0-bb84d054280d | transformer-based-vulnerability-detection-in | 2306.01754 | null | https://arxiv.org/abs/2306.01754v1 | https://arxiv.org/pdf/2306.01754v1.pdf | Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning? | Software vulnerabilities bear enterprises significant costs. Despite extensive efforts in research and development of software vulnerability detection methods, uncaught vulnerabilities continue to put software owners and users at risk. Many current vulnerability detection methods require that code snippets can compile ... | ['Neel Sundaresan', 'Mohamed Elkamhawy', 'Eslam Kamal', 'Alec Helyar', 'Yevhen Mohylevskyy', 'Roshanak Zilouchian Moghaddam', 'Anant Kharkar', 'Aaron Chan'] | 2023-05-23 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [ 3.41084227e-02 -9.81758349e-04 -2.45852292e-01 -8.50499719e-02
-1.20956707e+00 -1.08997440e+00 1.66167811e-01 6.51059091e-01
1.37783453e-01 -7.35442489e-02 -5.07693999e-02 -9.86844838e-01
2.13949367e-01 -9.44974184e-01 -6.88549638e-01 1.32372186e-01
-4.72093880e-01 -3.36748123e-01 5.81389785e-01 -4.55372423... | [7.112658500671387, 7.785399913787842] |
85bf0592-6b6b-44d5-8746-40f82c28321e | docbank-a-benchmark-dataset-for-document | 2006.01038 | null | https://arxiv.org/abs/2006.01038v3 | https://arxiv.org/pdf/2006.01038v3.pdf | DocBank: A Benchmark Dataset for Document Layout Analysis | Document layout analysis usually relies on computer vision models to understand documents while ignoring textual information that is vital to capture. Meanwhile, high quality labeled datasets with both visual and textual information are still insufficient. In this paper, we present \textbf{DocBank}, a benchmark dataset... | ['Furu Wei', 'Yiheng Xu', 'Zhoujun Li', 'Shaohan Huang', 'Ming Zhou', 'Minghao Li', 'Lei Cui'] | 2020-06-01 | null | https://aclanthology.org/2020.coling-main.82 | https://aclanthology.org/2020.coling-main.82.pdf | coling-2020-8 | ['document-layout-analysis'] | ['computer-vision'] | [-2.13357046e-01 -1.08633690e-01 -2.67683089e-01 -3.44394058e-01
-1.18250453e+00 -1.22313070e+00 7.59040654e-01 2.27363870e-01
2.73392219e-02 3.09793621e-01 5.35247326e-01 -3.84882867e-01
4.68727425e-02 -4.42520380e-01 -6.82181418e-01 -6.31486356e-01
2.42150024e-01 5.47777116e-01 7.91411498e-04 2.66341269... | [11.637399673461914, 2.407153606414795] |
af7ef0d8-21de-4add-90b8-a6e3bee67521 | enhanced-low-resolution-lidar-camera | 2211.03932 | null | https://arxiv.org/abs/2211.03932v1 | https://arxiv.org/pdf/2211.03932v1.pdf | Enhanced Low-resolution LiDAR-Camera Calibration Via Depth Interpolation and Supervised Contrastive Learning | Motivated by the increasing application of low-resolution LiDAR recently, we target the problem of low-resolution LiDAR-camera calibration in this work. The main challenges are two-fold: sparsity and noise in point clouds. To address the problem, we propose to apply depth interpolation to increase the point density and... | ['Fengbo Ren', 'Sanjeev Agarwal', 'Raghuveer Rao', 'Suya You', 'Zifan Yu', 'Zhikang Zhang'] | 2022-11-08 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 1.56349361e-01 -3.83123994e-01 -3.63711774e-01 -4.27385807e-01
-1.12840116e+00 -1.02511607e-01 3.42809170e-01 -2.40313441e-01
-4.78977680e-01 8.62891793e-01 -2.86993504e-01 -1.28045946e-01
-3.65667343e-02 -6.74271047e-01 -7.11470306e-01 -4.57194090e-01
-7.23091885e-02 6.94756150e-01 2.92347759e-01 1.66245908... | [7.987765789031982, -2.72666597366333] |
43a3b42b-bafc-47a0-ab19-1d48f85be669 | emergence-of-selective-invariance-in | 1701.08837 | null | http://arxiv.org/abs/1701.08837v1 | http://arxiv.org/pdf/1701.08837v1.pdf | Emergence of Selective Invariance in Hierarchical Feed Forward Networks | Many theories have emerged which investigate how in- variance is generated in
hierarchical networks through sim- ple schemes such as max and mean pooling.
The restriction to max/mean pooling in theoretical and empirical studies has
diverted attention away from a more general way of generating invariance to
nuisance tra... | ['Dipan K. Pal', 'Vishnu Boddeti', 'Marios Savvides'] | 2017-01-30 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.33056772e-01 1.24694861e-01 -6.65929243e-02 -5.92322111e-01
3.17579173e-02 -9.78521645e-01 6.72215641e-01 -2.40963653e-01
-7.42998838e-01 4.00089473e-01 4.34493959e-01 -9.98069271e-02
-6.74861133e-01 -9.88109887e-01 -6.75050378e-01 -7.35022664e-01
-3.61067802e-01 1.27390563e-01 3.38111937e-01 -1.93243921... | [9.487236022949219, 2.4299702644348145] |
a7ac2fc3-6c90-4a1e-9786-cc05118acd54 | can-your-face-detector-do-anti-spoofing-face | 2006.16836 | null | https://arxiv.org/abs/2006.16836v2 | https://arxiv.org/pdf/2006.16836v2.pdf | Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector | In a typical face recognition pipeline, the task of the face detector is to localize the face region. However, the face detector localizes regions that look like a face, irrespective of the liveliness of the face, which makes the entire system susceptible to presentation attacks. In this work, we try to reformulate the... | ['Sebastien Marcel', 'Anjith George'] | 2020-06-30 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.49230117e-01 4.07036804e-02 3.32299560e-01 -8.28303397e-02
-5.97519279e-01 -8.57211351e-01 4.45578605e-01 -1.46502331e-01
-3.50231647e-01 3.36808199e-03 -3.04731131e-01 -3.14884990e-01
4.77119505e-01 -6.17864847e-01 -7.04436123e-01 -9.68162596e-01
1.71011418e-01 -2.90567040e-01 3.80097330e-01 1.04016766... | [13.199747085571289, 0.924435555934906] |
b75c7e23-99e4-44c8-8073-64c217655712 | calculating-question-similarity-is-enough-a | 2111.07658 | null | https://arxiv.org/abs/2111.07658v4 | https://arxiv.org/pdf/2111.07658v4.pdf | Calculating Question Similarity is Enough: A New Method for KBQA Tasks | Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base. The core idea is to find the link between the internal knowledge behind questions and known triples of the knowledge base. Traditional KBQA task pipelines contain several steps, including enti... | ['Jie Tang', 'Ledell Wu', 'Guoqiang Wang', 'Xiang Pan', 'Jiahong Leng', 'Sha Yuan', 'Hanyu Zhao'] | 2021-11-15 | null | null | null | null | ['knowledge-base-question-answering', 'question-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-2.74952829e-01 5.53023636e-01 2.88519651e-01 -1.02572985e-01
-1.34800541e+00 -6.70696497e-01 5.04144251e-01 4.62129936e-02
-2.53042668e-01 1.15509355e+00 2.74492174e-01 -3.53836536e-01
-1.05524331e-01 -1.24014807e+00 -1.00294340e+00 -4.16534990e-02
5.42067945e-01 1.14547420e+00 8.59126031e-01 -8.21495831... | [10.671053886413574, 7.948182582855225] |
8484baae-f1d3-4888-9018-4cdfb24ccc12 | a-general-purpose-algorithm-for-constrained | null | null | https://aclanthology.org/K19-1045 | https://aclanthology.org/K19-1045.pdf | A General-Purpose Algorithm for Constrained Sequential Inference | Inference in structured prediction involves finding the best output structure for an input, subject to certain constraints. Many current approaches use sequential inference, which constructs the output in a left-to-right manner. However, there is no general framework to specify constraints in these approaches. We prese... | ['Dan Roth', 'Daniel Deutsch', 'Shyam Upadhyay'] | 2019-11-01 | null | null | null | conll-2019-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 8.08387220e-01 6.29566252e-01 -6.03384435e-01 -8.02278578e-01
-1.02787161e+00 -1.03780890e+00 2.50627100e-01 1.68647602e-01
-3.12770039e-01 6.89050019e-01 3.71488303e-01 -8.10645580e-01
2.81093508e-01 -1.00824118e+00 -7.86622703e-01 -1.75673977e-01
2.92623311e-01 6.06264710e-01 5.82466066e-01 -5.01997657... | [10.435199737548828, 9.454289436340332] |
91cee885-4eae-4bbf-a06c-32d1b5341ff9 | a-lip-sync-expert-is-all-you-need-for-speech | 2008.1001 | null | https://arxiv.org/abs/2008.10010v1 | https://arxiv.org/pdf/2008.10010v1.pdf | A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild | In this work, we investigate the problem of lip-syncing a talking face video of an arbitrary identity to match a target speech segment. Current works excel at producing accurate lip movements on a static image or videos of specific people seen during the training phase. However, they fail to accurately morph the lip mo... | ['C. V. Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'K R Prajwal'] | 2020-08-23 | null | null | null | null | ['talking-head-generation', 'lip-sync', 'talking-face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-4.76680361e-02 -6.82248082e-03 -4.37790543e-01 -1.43156737e-01
-1.31654882e+00 -6.53105915e-01 3.13324958e-01 -5.74463069e-01
1.93493426e-01 5.84776461e-01 3.79322350e-01 -6.06014244e-02
2.81106710e-01 -1.24645434e-01 -6.67374015e-01 -6.86771452e-01
6.35551885e-02 2.22179711e-01 9.97163579e-02 1.54419318... | [13.312664031982422, -0.3519928455352783] |
d2dce1d5-735b-4d15-812f-e288fc02cb90 | interactive-video-stylization-using-few-shot | 2004.14489 | null | https://arxiv.org/abs/2004.14489v1 | https://arxiv.org/pdf/2004.14489v1.pdf | Interactive Video Stylization Using Few-Shot Patch-Based Training | In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of the sequence. Its key advantage is that the resulting stylization is semantically meaningful, i.e., specific parts of moving objects are sty... | ['Daniel Sýkora', 'Sergey Tulyakov', 'Šárka Sochorová', 'Ondřej Jamriška', 'Michal Kučera', 'Ondřej Texler', 'Menglei Chai', 'David Futschik'] | 2020-04-29 | null | null | null | null | ['video-propagation'] | ['computer-vision'] | [ 4.78370726e-01 -2.53410488e-02 2.85928603e-03 -2.09484279e-01
-4.57826704e-01 -6.84600353e-01 7.39730120e-01 -3.07718758e-02
-4.21396643e-01 6.07365191e-01 -2.11081401e-01 -1.39152631e-01
2.75619894e-01 -6.80684328e-01 -9.51016128e-01 -5.40624261e-01
2.84521431e-01 4.70486969e-01 5.11325538e-01 -2.44570985... | [11.059039115905762, -0.707206666469574] |
664d4699-c60b-4d86-89eb-d99bca25e810 | actor-critic-approach-for-temporal-predictive | null | null | https://openreview.net/forum?id=r1ln504YvH | https://openreview.net/pdf?id=r1ln504YvH | Actor-Critic Approach for Temporal Predictive Clustering | Due to the wider availability of modern electronic health records (EHR), patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients’ prognoses by identifying “similar” patients, and designing treatment guidelines that ar... | ['Mihaela van der Schaar', 'Changhee Lee'] | 2019-09-25 | null | null | null | null | ['patient-phenotyping', 'time-series-clustering'] | ['medical', 'time-series'] | [-1.43077761e-01 5.13081811e-02 -3.80518585e-01 -6.74203992e-01
-1.07076824e+00 -1.27012253e-01 6.33723885e-02 9.58583415e-01
-1.23823218e-01 5.42093694e-01 6.08597457e-01 -3.46515238e-01
-5.38764358e-01 -5.41281939e-01 -3.45149785e-01 -8.28306198e-01
-5.80269456e-01 9.07423139e-01 -5.92060626e-01 4.79781151... | [7.878611087799072, 6.178296089172363] |
423d389b-8368-49ae-8c59-72424faceac7 | rocnet-3d-robust-registration-of-point-clouds | 2303.07963 | null | https://arxiv.org/abs/2303.07963v1 | https://arxiv.org/pdf/2303.07963v1.pdf | RoCNet: 3D Robust Registration of Point-Clouds using Deep Learning | This paper introduces a new method for 3D point cloud registration based on deep learning. The architecture is composed of three distinct blocs: (i) an encoder composed of a convolutional graph-based descriptor that encodes the immediate neighbourhood of each point and an attention mechanism that encodes the variations... | ['Catherine Achard', 'Brahim Tamadazte', 'Karim Slimani'] | 2023-03-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.33518308e-01 -1.05629072e-01 2.89405525e-01 -2.12687910e-01
-6.54026389e-01 -2.55661994e-01 8.18227291e-01 3.58950049e-01
-3.27056468e-01 1.71761448e-03 -2.09798649e-01 1.84177086e-02
-2.69842982e-01 -7.24623442e-01 -1.06126571e+00 -6.29360795e-01
-3.02162200e-01 1.02466989e+00 4.51147616e-01 -4.58355784... | [7.731423854827881, -2.994391918182373] |
ab9d34b7-e258-4eb7-9bc0-779da61a60a8 | learning-roi-transformer-for-oriented-object | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Ding_Learning_RoI_Transformer_for_Oriented_Object_Detection_in_Aerial_Images_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ding_Learning_RoI_Transformer_for_Oriented_Object_Detection_in_Aerial_Images_CVPR_2019_paper.pdf | Learning RoI Transformer for Oriented Object Detection in Aerial Images | Object detection in aerial images is an active yet challenging task in computer vision because of the bird's-eye view perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common... | [' Qikai Lu', ' Gui-Song Xia', ' Yang Long', ' Nan Xue', 'Jian Ding'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 3.14864993e-01 -1.73051998e-01 3.57167423e-01 -3.29952538e-01
-3.11272442e-01 -6.48411870e-01 3.49415690e-01 -3.59472305e-01
-7.03640640e-01 3.99531215e-01 -3.38165402e-01 3.12507758e-03
4.67785262e-02 -6.42629683e-01 -7.45957851e-01 -8.10311258e-01
1.60994649e-01 -9.53702778e-02 9.80627775e-01 -2.18066648... | [8.705233573913574, -0.7603228688240051] |
26bf56b3-f7b4-4578-9a25-34118f726d0e | fast-video-shot-transition-localization-with | 1808.04234 | null | http://arxiv.org/abs/1808.04234v1 | http://arxiv.org/pdf/1808.04234v1.pdf | Fast Video Shot Transition Localization with Deep Structured Models | Detection of video shot transition is a crucial pre-processing step in video
analysis. Previous studies are restricted on detecting sudden content changes
between frames through similarity measurement and multi-scale operations are
widely utilized to deal with transitions of various lengths. However,
localization of gr... | ['Wei zhang', 'Zhangkui Kuang', 'Yimin Chen', 'Shitao Tang', 'Litong Feng'] | 2018-08-13 | null | null | null | null | ['camera-shot-boundary-detection'] | ['computer-vision'] | [ 2.28313386e-01 -6.25425220e-01 -1.83495760e-01 -1.26019955e-01
-4.44636613e-01 -3.16200495e-01 2.80053467e-01 8.02067295e-02
-4.78095502e-01 2.61006176e-01 1.66860834e-01 8.21933448e-02
1.12038508e-01 -6.11332715e-01 -7.36807287e-01 -5.01912415e-01
-3.29447687e-01 -1.82238027e-01 1.01655269e+00 -5.01899123... | [8.695755004882812, 0.24842388927936554] |
755d3be0-c4ec-4834-b615-ad592707db21 | smoothing-matters-momentum-transformer-for | 2203.07988 | null | https://arxiv.org/abs/2203.07988v1 | https://arxiv.org/pdf/2203.07988v1.pdf | Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation | After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation. Unfortunately, straightforwardly applying local ViTs in domain adaptive semantic segmentation does not bring in expected improvement. We find that the pitf... | ['Wenbing Huang', 'Tingyang Xu', 'Fuchun Sun', 'Jiaqi Han', 'Shangmin Guo', 'Yu Rong', 'Runfa Chen'] | 2022-03-15 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 1.79025635e-01 -5.36124595e-02 -2.32118621e-01 -5.00118852e-01
-8.90151918e-01 -4.52359736e-01 6.54278576e-01 -1.87557787e-01
-3.99903297e-01 5.83074391e-01 -9.30031613e-02 -2.52725370e-02
2.78997906e-02 -4.87044424e-01 -6.12861991e-01 -8.22420597e-01
2.78858751e-01 6.28122330e-01 7.06579506e-01 -4.15758267... | [9.59254264831543, 1.3822647333145142] |
70e3a4a5-cbae-4212-a9ba-0d988c611c30 | on-sequential-bayesian-inference-for | 2301.01828 | null | https://arxiv.org/abs/2301.01828v2 | https://arxiv.org/pdf/2301.01828v2.pdf | On Sequential Bayesian Inference for Continual Learning | Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks. We revisit sequential Bayesian inference and test whether having access to the true posterior is guaranteed to prevent catastrophic forgetting in Ba... | ['Stephen J. Roberts', 'Stefan Zohren', 'Tim G. J. Rudner', 'Adam Cobb', 'Samuel Kessler'] | 2023-01-04 | null | null | null | null | ['sequential-bayesian-inference'] | ['time-series'] | [ 3.86672407e-01 9.41928253e-02 -3.79259139e-02 -4.73199457e-01
-4.46114093e-01 -1.93816409e-01 7.92473376e-01 3.48023325e-02
-8.92501295e-01 1.18258607e+00 -5.02852462e-02 -5.21944165e-01
-5.51649868e-01 -5.28871596e-01 -1.22027946e+00 -7.06394315e-01
8.31169784e-02 6.06389344e-01 5.29299796e-01 2.55951017... | [7.187438488006592, 3.8544070720672607] |
56cd9bae-eb62-497e-b2b7-9ab53856546f | improving-mitosis-detection-via-unet-based | 2209.09193 | null | https://arxiv.org/abs/2209.09193v1 | https://arxiv.org/pdf/2209.09193v1.pdf | Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer | The effective localization of mitosis is a critical precursory task for deciding tumor prognosis and grade. Automated mitosis detection through deep learning-oriented image analysis often fails on unseen patient data due to inherent domain biases. This paper proposes a domain homogenizer for mitosis detection that atte... | ['Amit Sethi', 'Nikhil Cherian Kurian', 'Sahar Almahfouz Nasser', 'Tirupati Saketh Chandr'] | 2022-09-15 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.48816210e-01 3.05932432e-01 -1.94050491e-01 -3.99737433e-02
-1.27731729e+00 -7.02769876e-01 4.62527126e-01 4.15538400e-01
-8.31659019e-01 1.10406506e+00 1.54474691e-01 -3.31848681e-01
1.75730988e-01 -5.96562445e-01 -7.01426387e-01 -1.10162103e+00
3.34543139e-01 6.37944996e-01 1.07208073e-01 1.38390586... | [15.10588264465332, -3.139479160308838] |
2ad127c4-3cef-421f-9776-643d72231581 | self-supervised-learning-for-time-series | 2306.10125 | null | https://arxiv.org/abs/2306.10125v1 | https://arxiv.org/pdf/2306.10125v1.pdf | Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects | Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance. Compared ... | ['Shirui Pan', 'Dongjin Song', 'Guansong Pang', 'Yuxuan Liang', 'James Zhang', 'Yong liu', 'Ming Jin', 'Rongyao Cai', 'Chaoli Zhang', 'Qingsong Wen', 'Kexin Zhang'] | 2023-06-16 | null | null | null | null | ['anomaly-detection', 'time-series'] | ['methodology', 'time-series'] | [-2.90328432e-02 -5.59263706e-01 -1.80878505e-01 -5.06773949e-01
-4.12374020e-01 -8.04782212e-01 5.80089748e-01 3.68700773e-01
-9.03070346e-03 3.98643434e-01 -2.77455062e-01 -2.59778142e-01
-1.84847713e-01 -7.07874298e-01 -2.50804514e-01 -8.83601308e-01
-8.50515902e-01 2.18317926e-01 -1.43800676e-01 -2.58874953... | [7.194713592529297, 2.897273540496826] |
de1764b7-47df-4df4-bba5-d641ea183937 | using-pre-trained-language-models-for | 2204.0144 | null | https://arxiv.org/abs/2204.01440v1 | https://arxiv.org/pdf/2204.01440v1.pdf | Using Pre-Trained Language Models for Producing Counter Narratives Against Hate Speech: a Comparative Study | In this work, we present an extensive study on the use of pre-trained language models for the task of automatic Counter Narrative (CN) generation to fight online hate speech in English. We first present a comparative study to determine whether there is a particular Language Model (or class of LMs) and a particular deco... | ['Marco Guerini', 'Margherita Fanton', 'Helena Bonaldi', 'Serra Sinem Tekiroglu'] | 2022-04-04 | null | https://aclanthology.org/2022.findings-acl.245 | https://aclanthology.org/2022.findings-acl.245.pdf | findings-acl-2022-5 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 2.47576624e-01 1.48421660e-01 4.86339554e-02 -1.22938149e-01
-8.23863029e-01 -7.01379836e-01 1.16837430e+00 1.48476347e-01
-5.92581153e-01 7.17546761e-01 5.50766468e-01 -2.43806258e-01
5.79222366e-02 -3.76385957e-01 -6.11936510e-01 -3.26620758e-01
2.06008971e-01 6.08730078e-01 3.10395241e-01 -6.35943472... | [11.640914916992188, 8.864846229553223] |
d3fb28e3-db57-4b55-b127-05aaf9d2d15b | hurricane-forecasting-a-novel-multimodal | 2011.06125 | null | https://arxiv.org/abs/2011.06125v4 | https://arxiv.org/pdf/2011.06125v4.pdf | Hurricane Forecasting: A Novel Multimodal Machine Learning Framework | This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with... | ['Dimitris Bertsimas', 'Théo Guénais', 'Cynthia Zeng', 'Léonard Boussioux'] | 2020-11-11 | null | null | null | null | ['hurricane-forecasting', 'tropical-cyclone-intensity-forecasting', 'tropical-cyclone-track-forecasting'] | ['computer-vision', 'time-series', 'time-series'] | [-3.01940918e-01 -3.83263677e-01 -6.28853023e-01 -8.63330960e-01
-1.17629218e+00 -7.12506950e-01 1.08688033e+00 2.47976389e-02
-2.63453782e-01 8.44098270e-01 7.57335186e-01 -8.97216439e-01
1.29416427e-02 -6.99654520e-01 -3.70715618e-01 -6.31863952e-01
-5.18206000e-01 3.57746601e-01 -6.94284737e-01 -6.45837009... | [6.583719253540039, 2.919562816619873] |
68d5527e-0e6b-4a8a-aabd-ecb8ddc245df | sensor-fault-detection-and-isolation-in | 2304.08837 | null | https://arxiv.org/abs/2304.08837v1 | https://arxiv.org/pdf/2304.08837v1.pdf | Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers | This paper presents a new observer-based approach to detect and isolate faulty sensors in industrial systems. Two types of sensor faults are considered: complete failure and sensor deterioration. The proposed method is applicable to general autonomous nonlinear systems without making any assumptions about its triangula... | ['Karl Henrik Johansson', 'Muhammad Umar B. Niazi', 'John Cao'] | 2023-04-18 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 4.74243850e-01 5.95133185e-01 1.16449157e-02 3.43403339e-01
-3.50950271e-01 -5.69799066e-01 1.94708630e-01 3.29109222e-01
1.09165214e-01 6.14228845e-01 -5.70591569e-01 -3.53983879e-01
-3.55106205e-01 -2.43361145e-01 -1.06100285e+00 -9.28171217e-01
-4.26847488e-02 3.32839936e-02 2.01204345e-01 -1.34825855... | [5.37402868270874, 2.5767807960510254] |
1a73de4c-d28b-4b37-9bab-3c504e76ebbd | darkvision-a-benchmark-for-low-light-image | 2301.06269 | null | https://arxiv.org/abs/2301.06269v1 | https://arxiv.org/pdf/2301.06269v1.pdf | DarkVision: A Benchmark for Low-light Image/Video Perception | Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which degrades the image quality severely and poses challenges for downstream ... | ['Qionghai Dai', 'Jinli Suo', 'Jiayi Xie', 'Zhihong Zhang', 'Runzhao Yang', 'Yuchen Guo', 'Bo Zhang'] | 2023-01-16 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 6.01707816e-01 -7.73142636e-01 4.86127436e-02 -4.63757962e-01
-9.73607302e-01 -5.62669337e-01 5.94196856e-01 -1.57894388e-01
-6.71346128e-01 5.18400967e-01 -2.15161629e-02 -1.03114687e-01
1.83472529e-01 -5.74593306e-01 -7.46560633e-01 -1.13520670e+00
2.43811399e-01 -3.25275689e-01 5.57869017e-01 -3.60365883... | [10.732760429382324, -2.3827462196350098] |
ffef19df-03b6-44b9-9670-746c2ce13dbc | exploring-topic-metadata-relationships-with-1 | null | null | https://openreview.net/forum?id=5zmfwLi_mzB | https://openreview.net/pdf?id=5zmfwLi_mzB | Exploring Topic-Metadata Relationships with the STM: A Bayesian Approach | The initial purpose of topic models was to identify latent topical clusters within unstructured text. Meanwhile, the focus of advanced studies has changed primarily to estimating the relationship between the discovered topical structure and theoretically relevant metadata. Methods used to estimate such relationships m... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['topic-models'] | ['natural-language-processing'] | [ 4.36955243e-02 2.81625688e-01 -4.32374775e-01 -5.12052953e-01
-8.67840052e-01 -5.00840127e-01 1.33090758e+00 4.54995364e-01
-4.04984593e-01 9.45314050e-01 7.12641537e-01 -2.51871139e-01
-3.23243380e-01 -8.76512647e-01 -7.65496731e-01 -8.04060280e-01
1.72351629e-01 5.87688208e-01 1.65576637e-01 3.62691134... | [10.334623336791992, 6.998668670654297] |
4d08612e-4c6c-4693-b55b-a06ed097b129 | taxonomy-and-evolution-predicting-using-deep | 2206.14011 | null | https://arxiv.org/abs/2206.14011v1 | https://arxiv.org/pdf/2206.14011v1.pdf | Taxonomy and evolution predicting using deep learning in images | Molecular and morphological characters, as important parts of biological taxonomy, are contradictory but need to be integrated. Organism's image recognition and bioinformatics are emerging and hot problems nowadays but with a gap between them. In this work, a multi-branching recognition framework mediated by genetic in... | ['Yihua Yang', 'Jianxin Wang', 'Jing Wang', 'Ming Zhang', 'Wenbin Liao', 'Jiewen Xiao'] | 2022-06-28 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 6.29075706e-01 -2.66704112e-01 -9.25824940e-02 -1.97986305e-01
-2.46992037e-01 -5.10109305e-01 4.22509134e-01 3.19616884e-01
-4.39121544e-01 6.05271339e-01 -1.52761459e-01 4.25792672e-02
-3.68029356e-01 -1.16374278e+00 -6.23699546e-01 -1.33537471e+00
-5.11889486e-03 2.62351662e-01 1.00332521e-01 1.49453897... | [9.546222686767578, 2.17950701713562] |
ce408803-d54e-4f02-9f59-c56b8952b85e | label-semantics-for-few-shot-named-entity | 2203.08985 | null | https://arxiv.org/abs/2203.08985v1 | https://arxiv.org/pdf/2203.08985v1.pdf | Label Semantics for Few Shot Named Entity Recognition | We study the problem of few shot learning for named entity recognition. Specifically, we leverage the semantic information in the names of the labels as a way of giving the model additional signal and enriched priors. We propose a neural architecture that consists of two BERT encoders, one to encode the document and it... | ['Dan Roth', 'Yaser Al-Onaizan', 'Sunil Mallya', 'Rishita Anubhai', 'Srikanth Doss', 'Miguel Ballesteros', 'Jie Ma'] | 2022-03-16 | null | https://aclanthology.org/2022.findings-acl.155 | https://aclanthology.org/2022.findings-acl.155.pdf | findings-acl-2022-5 | ['few-shot-ner'] | ['natural-language-processing'] | [ 1.42371789e-01 3.35379690e-01 -3.70709211e-01 -6.57486200e-01
-9.54657555e-01 -5.59908867e-01 8.83826137e-01 2.07125321e-01
-7.39927351e-01 6.92535996e-01 8.08363676e-01 2.88491517e-01
2.64259934e-01 -9.27706957e-01 -6.81493282e-01 -3.91677052e-01
5.96975256e-03 6.99947596e-01 3.11201513e-01 -5.96627370... | [9.693373680114746, 9.329890251159668] |
56f003a3-c666-4fef-b8e0-e8ca4cc0ee99 | graph-structure-learning-from-unlabeled-data | 1701.0147 | null | http://arxiv.org/abs/1701.01470v1 | http://arxiv.org/pdf/1701.01470v1.pdf | Graph Structure Learning from Unlabeled Data for Event Detection | Processes such as disease propagation and information diffusion often spread
over some latent network structure which must be learned from observation.
Given a set of unlabeled training examples representing occurrences of an event
type of interest (e.g., a disease outbreak), our goal is to learn a graph
structure that... | ['Sriram Somanchi', 'Daniel B. Neill'] | 2017-01-05 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 4.67005312e-01 5.09270966e-01 -2.94279695e-01 -2.61922091e-01
-3.15386087e-01 -5.74639857e-01 4.12480950e-01 8.41659963e-01
-4.01750579e-02 5.81922889e-01 -9.45156533e-03 -6.19309068e-01
-4.33252692e-01 -9.72718775e-01 -5.96619844e-01 -5.94064295e-01
-1.14602470e+00 9.91100550e-01 4.05003726e-01 4.23571706... | [6.792013645172119, 5.185769081115723] |
4bb8ada2-7027-43e5-a663-7ec09a6a0a27 | safe-reinforcement-learning-for-probabilistic | 2002.10126 | null | https://arxiv.org/abs/2002.10126v1 | https://arxiv.org/pdf/2002.10126v1.pdf | Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approach | Emerging applications in robotics and autonomous systems, such as autonomous driving and robotic surgery, often involve critical safety constraints that must be satisfied even when information about system models is limited. In this regard, we propose a model-free safety specification method that learns the maximal pro... | ['Subin Huh', 'Insoon Yang'] | 2020-02-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 4.00826670e-02 4.68487680e-01 -6.43137038e-01 4.62053828e-02
-1.01714933e+00 -6.54462337e-01 4.20923740e-01 1.02612182e-01
-5.14678836e-01 8.96955729e-01 -5.62512241e-02 -6.66989326e-01
-4.50872213e-01 -5.56419015e-01 -9.06177700e-01 -9.87009943e-01
-2.05030456e-01 1.80908054e-01 1.78064689e-01 -2.61247069... | [4.631158828735352, 2.214484930038452] |
d6a27429-09ed-4793-b919-6797bd599376 | feature-normalisation-for-robust-speech | 1507.04019 | null | http://arxiv.org/abs/1507.04019v1 | http://arxiv.org/pdf/1507.04019v1.pdf | Feature Normalisation for Robust Speech Recognition | Speech recognition system performance degrades in noisy environments. If the
acoustic models are built using features of clean utterances, the features of a
noisy test utterance would be acoustically mismatched with the trained model.
This gives poor likelihoods and poor recognition accuracy. Model adaptation and
featu... | ['D. S. Pavan Kumar'] | 2015-07-14 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.99716288e-01 -3.11383784e-01 5.08940160e-01 -5.00050902e-01
-9.38945711e-01 -3.95702809e-01 5.74648917e-01 -5.77832878e-01
-4.17674452e-01 4.76630956e-01 5.18121779e-01 -3.48182738e-01
-1.34643450e-01 -3.73329282e-01 -2.70019650e-01 -1.10029840e+00
2.66575843e-01 -3.89728583e-02 7.41531104e-02 -2.61527032... | [14.879878044128418, 5.865756034851074] |
02a05199-436f-4ff1-add9-c2a550013ed3 | learn-to-combine-linguistic-and-symbolic | null | null | https://aclanthology.org/2020.coling-main.466 | https://aclanthology.org/2020.coling-main.466.pdf | Learn to Combine Linguistic and Symbolic Information for Table-based Fact Verification | Table-based fact verification is expected to perform both linguistic reasoning and symbolic reasoning. Existing methods lack attention to take advantage of the combination of linguistic information and symbolic information. In this work, we propose HeterTFV, a graph-based reasoning approach, that learns to combine ling... | ['Ting Liu', 'Qingyu Yin', 'Yu Zhang', 'Qi Shi'] | 2020-12-01 | null | null | null | coling-2020-8 | ['table-based-fact-verification'] | ['natural-language-processing'] | [-9.14796367e-02 3.38909417e-01 -7.51880586e-01 -5.89881361e-01
-5.65014839e-01 -6.82241082e-01 6.10184371e-01 4.83290404e-01
2.69355476e-01 5.04082680e-01 1.78476393e-01 -6.49548292e-01
1.59643404e-02 -1.53541756e+00 -1.19552219e+00 1.73884571e-01
-7.78997540e-02 2.07104594e-01 5.98985374e-01 -3.65630597... | [9.262487411499023, 7.573309898376465] |
1bae6445-b0cc-4bf6-9c31-6e38d1dccc71 | revisiting-random-forests-in-a-comparative | 2305.19292 | null | https://arxiv.org/abs/2305.19292v1 | https://arxiv.org/pdf/2305.19292v1.pdf | Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction | Traffic prediction is a spatiotemporal predictive task that plays an essential role in intelligent transportation systems. Today, graph convolutional neural networks (GCNNs) have become the prevailing models in the traffic prediction literature since they excel at extracting spatial correlations. In this work, we class... | ['Baher Abdulhai', 'Scott Sanner', 'Xiaocan Li', 'Ta Jiun Ting'] | 2023-05-30 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-8.35757628e-02 -3.46021466e-02 -6.06495142e-01 -4.72909838e-01
-2.33478919e-01 -1.33540764e-01 6.02322876e-01 -6.16349056e-02
-2.17179403e-01 7.87071288e-01 4.91896898e-01 -1.07379031e+00
-2.84837306e-01 -1.14845145e+00 -7.18677402e-01 -2.24992305e-01
-3.22058916e-01 3.85862857e-01 5.20540178e-01 -5.28407276... | [6.4677958488464355, 2.024811267852783] |
3345622d-b95b-47a5-a0f6-b47b204734cc | deepmeshflow-content-adaptive-mesh | 1912.05131 | null | https://arxiv.org/abs/1912.05131v1 | https://arxiv.org/pdf/1912.05131v1.pdf | DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration | Image alignment by mesh warps, such as meshflow, is a fundamental task which has been widely applied in various vision applications(e.g., multi-frame HDR/denoising, video stabilization). Traditional mesh warp methods detect and match image features, where the quality of alignment highly depends on the quality of image ... | ['Lanpeng Jia', 'Shuaicheng Liu', 'Chuan Wang', 'Yongqing Cui', 'Jue Wang', 'Nianjin Ye'] | 2019-12-11 | null | null | null | null | ['video-stabilization', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.02425358e-02 -4.20914352e-01 -8.55861008e-02 -8.77891928e-02
-2.09969252e-01 -3.12514216e-01 4.28025186e-01 -2.90964723e-01
-1.53839335e-01 5.70395470e-01 1.21854106e-02 4.47916001e-01
-2.11988732e-01 -1.00502455e+00 -8.64659607e-01 -8.99527550e-01
3.75176698e-01 4.92837667e-01 3.55908245e-01 -4.67665017... | [9.208930969238281, -2.3349547386169434] |
c0708912-c4a8-487f-96c6-33a3fa119306 | learning-fine-grained-visual-understanding | 2210.03941 | null | https://arxiv.org/abs/2210.03941v1 | https://arxiv.org/pdf/2210.03941v1.pdf | Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling | While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To ... | ['Winston H. Hsu', 'Jia-Fong Yeh', 'Tsung-Han Wu', 'Bing-Chen Tsai', 'Hung-Ting Su', 'Hsin-Ying Lee'] | 2022-10-08 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [-4.29664627e-02 -1.61730006e-01 -4.94854510e-01 -5.38308740e-01
-8.24256003e-01 -7.15711474e-01 8.25406373e-01 -1.34525821e-01
-2.76586741e-01 3.94119114e-01 5.86745024e-01 -4.32811767e-01
2.52982259e-01 -5.60980976e-01 -1.19374275e+00 -2.11719275e-01
-8.87820795e-02 1.67267606e-01 6.09843075e-01 2.08384991... | [10.023728370666504, 0.8418449759483337] |
7b8d9fb0-87bf-4b67-a004-12cf073127f0 | dummy-prototypical-networks-for-few-shot-open | 2206.13691 | null | https://arxiv.org/abs/2206.13691v1 | https://arxiv.org/pdf/2206.13691v1.pdf | Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting | Keyword spotting is the task of detecting a keyword in streaming audio. Conventional keyword spotting targets predefined keywords classification, but there is growing attention in few-shot (query-by-example) keyword spotting, e.g., N-way classification given M-shot support samples. Moreover, in real-world scenarios, th... | ['Simyung Chang', 'Inseop Chung', 'Seunghan Yang', 'Byeonggeun Kim'] | 2022-06-28 | null | null | null | null | ['open-set-learning', 'keyword-spotting'] | ['miscellaneous', 'speech'] | [ 5.65063596e-01 1.11420423e-01 -8.05531889e-02 -1.84375376e-01
-1.25883925e+00 -4.04119283e-01 4.62026924e-01 3.01821589e-01
-4.24173772e-01 4.30144995e-01 1.33381197e-02 -3.94283384e-02
-4.18336689e-01 -3.86563599e-01 -7.76640236e-01 -5.71132123e-01
-3.83387893e-01 5.74933529e-01 8.47470641e-01 -3.56399804... | [14.17634391784668, 6.199252128601074] |
bc002dbe-f286-4515-ab29-e71028c26395 | head2headfs-video-based-head-reenactment-with | 2103.16229 | null | https://arxiv.org/abs/2103.16229v1 | https://arxiv.org/pdf/2103.16229v1.pdf | Head2HeadFS: Video-based Head Reenactment with Few-shot Learning | Over the past years, a substantial amount of work has been done on the problem of facial reenactment, with the solutions coming mainly from the graphics community. Head reenactment is an even more challenging task, which aims at transferring not only the facial expression, but also the entire head pose from a source pe... | ['Stefanos Zafeiriou', 'Viktoriia Sharmanska', 'Mohammad Rami Koujan', 'Michail Christos Doukas'] | 2021-03-30 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.27610067e-01 4.39396381e-01 2.23496944e-01 -7.65000820e-01
-8.94715488e-01 -3.90214235e-01 6.99711084e-01 -6.21297419e-01
-8.48586857e-02 5.63134611e-01 3.36891681e-01 5.65036535e-01
6.70050561e-01 -4.71632928e-01 -6.14677250e-01 -6.29961967e-01
2.24518493e-01 8.57723057e-01 3.24036032e-02 -4.18131799... | [12.887380599975586, -0.3275938332080841] |
c5f45edc-ab11-4d2f-b205-c6646362db58 | mmdf-mobile-microscopy-deep-framework | 2007.13701 | null | https://arxiv.org/abs/2007.13701v3 | https://arxiv.org/pdf/2007.13701v3.pdf | Deep learning Framework for Mobile Microscopy | Mobile microscopy is a promising technology to assist and to accelerate disease diagnostics, with its widespread adoption being hindered by the mediocre quality of acquired images. Although some paired image translation and super-resolution approaches for mobile microscopy have emerged, a set of essential challenges, n... | ['Dmitry V. Dylov', 'Valeriya Pronina', 'Olga Novitskaya', 'Egor Sevriugov', 'Mikhail Salnikov', 'Kirill Shcherbakov', 'Maria Begicheva', 'Anatasiia Kornilova'] | 2020-07-27 | null | null | null | null | ['multi-focus-microscopical-images-fusion'] | ['medical'] | [ 5.30494750e-01 -4.29871321e-01 3.84225458e-01 -2.08446667e-01
-8.02725434e-01 -3.42968851e-01 3.37323546e-01 -7.19709694e-02
-6.62329376e-01 6.47005558e-01 -9.06369910e-02 -2.54350245e-01
-2.84356385e-01 -2.00025454e-01 -5.02453983e-01 -1.16563380e+00
1.54154673e-02 4.09052014e-01 3.48860294e-01 -4.27592024... | [12.940359115600586, -2.735344171524048] |
edf5112b-fcaa-4c4d-96cc-08cb8a549281 | deep-probabilistic-time-series-forecasting | 2106.05848 | null | https://arxiv.org/abs/2106.05848v2 | https://arxiv.org/pdf/2106.05848v2.pdf | Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems | The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, ... | ['Xiaofang Wang', 'Shuhua Yang', 'Xudong Chen', 'Xiaomo Jiang', 'Changjun Liu', 'Haitao Liu'] | 2021-06-03 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 2.14135274e-02 -2.85249233e-01 3.29661340e-01 -8.31986070e-02
-6.00827932e-01 -2.13208050e-01 5.23137093e-01 -3.73126417e-01
1.88022465e-01 7.07636356e-01 3.37031186e-01 -5.35254061e-01
-3.91727418e-01 -5.42146266e-01 -7.25693882e-01 -1.23486400e+00
2.70830765e-02 4.45696920e-01 -2.47010320e-01 -5.37610538... | [6.962170600891113, 3.1276347637176514] |
4ca025ab-548e-4c43-b08b-da8d2115300a | disentangling-visual-embeddings-for | 2205.08536 | null | https://arxiv.org/abs/2205.08536v1 | https://arxiv.org/pdf/2205.08536v1.pdf | Disentangling Visual Embeddings for Attributes and Objects | We study the problem of compositional zero-shot learning for object-attribute recognition. Prior works use visual features extracted with a backbone network, pre-trained for object classification and thus do not capture the subtly distinct features associated with attributes. To overcome this challenge, these studies e... | ['Abhinav Shrivastava', 'Khoi Pham', 'Nirat Saini'] | 2022-05-17 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Saini_Disentangling_Visual_Embeddings_for_Attributes_and_Objects_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Saini_Disentangling_Visual_Embeddings_for_Attributes_and_Objects_CVPR_2022_paper.pdf | cvpr-2022-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 1.63741544e-01 -1.98336497e-01 -5.09679735e-01 -5.25883853e-01
-8.48494112e-01 -5.97147465e-01 8.62808526e-01 2.00429782e-01
-3.73258114e-01 3.19081992e-01 6.81094587e-01 8.75158608e-03
6.52141273e-02 -7.75176287e-01 -3.48956853e-01 -6.49095595e-01
2.72051662e-01 5.21447957e-01 -2.01312482e-01 9.03845429... | [10.159984588623047, 2.226302146911621] |
c8cec394-75cd-449b-9fc2-6984581ac00f | privacy-preserving-in-non-intrusive-load | 2011.06205 | null | https://arxiv.org/abs/2011.06205v1 | https://arxiv.org/pdf/2011.06205v1.pdf | Privacy Preserving in Non-Intrusive Load Monitoring: A Differential Privacy Perspective | Smart meter devices enable a better understanding of the demand at the potential risk of private information leakage. One promising solution to mitigating such risk is to inject noises into the meter data to achieve a certain level of differential privacy. In this paper, we cast one-shot non-intrusive load monitoring (... | ['Chenye Wu', 'Chenbei Lu', 'Jiasheng Zhang', 'Haoxiang Wang'] | 2020-11-12 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 3.85958493e-01 -2.30141133e-02 -2.81757116e-01 -3.38025153e-01
-1.04801607e+00 -7.79813111e-01 1.13731317e-01 9.48302522e-02
9.60367173e-03 7.07996666e-01 5.28135896e-01 -3.46263885e-01
-3.21447581e-01 -9.89687681e-01 -4.72844392e-01 -1.00165021e+00
-1.53682679e-01 -1.81018993e-01 -3.90992492e-01 -4.16564941... | [5.906966209411621, 6.53985071182251] |
df491114-b13d-4c9d-b165-7191318f136f | kernel-metric-learning-for-clustering-mixed | 2306.0189 | null | https://arxiv.org/abs/2306.01890v1 | https://arxiv.org/pdf/2306.01890v1.pdf | Kernel Metric Learning for Clustering Mixed-type Data | Distance-based clustering and classification are widely used in various fields to group mixed numeric and categorical data. A predefined distance measurement is used to cluster data points based on their dissimilarity. While there exist numerous distance-based measures for data with pure numerical attributes and severa... | ['John R. J. Thompson', 'Jesse S. Ghashti'] | 2023-06-02 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-2.89238602e-01 -6.38994217e-01 -3.18096101e-01 -8.72570574e-01
-6.30665243e-01 -7.96962678e-01 3.19235772e-01 1.07122958e+00
-6.49285018e-01 4.92679864e-01 1.07777759e-01 -4.08232093e-01
-9.52613175e-01 -1.28110230e+00 1.49254575e-01 -7.24950790e-01
-6.61526501e-01 8.49130690e-01 1.82060122e-01 1.88126251... | [7.583483695983887, 4.5744099617004395] |
ee5ffc2a-5198-4129-8b97-2f2cf680eff8 | unobtrusive-pain-monitoring-in-older-adults | 2101.03251 | null | https://arxiv.org/abs/2101.03251v1 | https://arxiv.org/pdf/2101.03251v1.pdf | Unobtrusive Pain Monitoring in Older Adults with Dementia using Pairwise and Contrastive Training | Although pain is frequent in old age, older adults are often undertreated for pain. This is especially the case for long-term care residents with moderate to severe dementia who cannot report their pain because of cognitive impairments that accompany dementia. Nursing staff acknowledge the challenges of effectively rec... | ['Babak Taati', 'Thomas Hadjistavropoulos', 'Kenneth M. Prkachin', 'Shun Zhao', 'Abhishek Moturu', 'Siavash Rezaei'] | 2021-01-08 | null | null | null | null | ['pain-intensity-regression'] | ['medical'] | [ 6.98926523e-02 -2.45973185e-01 -3.35747272e-01 -4.74571079e-01
-1.22249663e+00 -1.83235839e-01 -2.12758467e-01 -6.53477237e-02
-1.16840053e+00 1.03690827e+00 6.43255353e-01 2.57905573e-01
2.20615417e-01 -4.30878282e-01 -1.92001089e-01 -1.60961673e-01
-3.95833224e-01 4.63935494e-01 -4.91674423e-01 -8.46485943... | [13.588364601135254, 2.1585919857025146] |
f0a2dee7-dde3-44c6-bec9-a884f0ef7841 | scene-aware-egocentric-3d-human-pose | 2212.11684 | null | https://arxiv.org/abs/2212.11684v2 | https://arxiv.org/pdf/2212.11684v2.pdf | Scene-aware Egocentric 3D Human Pose Estimation | Egocentric 3D human pose estimation with a single head-mounted fisheye camera has recently attracted attention due to its numerous applications in virtual and augmented reality. Existing methods still struggle in challenging poses where the human body is highly occluded or is closely interacting with the scene. To addr... | ['Christian Theobalt', 'Diogo Luvizon', 'Kripasindhu Sarkar', 'Weipeng Xu', 'Lingjie Liu', 'Jian Wang'] | 2022-12-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Scene-Aware_Egocentric_3D_Human_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Scene-Aware_Egocentric_3D_Human_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', 'egocentric-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.71728864e-01 2.29099303e-01 2.45664507e-01 -4.94750887e-01
-1.78475335e-01 -7.97202587e-02 1.61181718e-01 -6.43931985e-01
-2.50582963e-01 3.75515103e-01 4.30423856e-01 3.72527540e-01
1.58929408e-01 -6.25835180e-01 -8.16222548e-01 -2.48106092e-01
1.30639583e-01 4.76618290e-01 1.48567721e-01 -2.49251187... | [7.056766033172607, -0.9641082882881165] |
c735b8ac-d476-45b7-b351-27461487eecd | topic-aware-encoding-for-extractive | 2112.09572 | null | https://arxiv.org/abs/2112.09572v3 | https://arxiv.org/pdf/2112.09572v3.pdf | Topic-Aware Encoding for Extractive Summarization | Document summarization provides an instrument for faster understanding the collection of text documents and has several real-life applications. With the growth of online text data, numerous summarization models have been proposed recently. The Sequence-to-Sequence (Seq2Seq) based neural summarization model is the most ... | ['Liping Jing', 'Mingyang Song'] | 2021-12-17 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 3.74205381e-01 -3.54285575e-02 -3.95997941e-01 -2.78663427e-01
-9.11895335e-01 -1.65349007e-01 3.51726294e-01 5.81968546e-01
-2.50469595e-01 9.28044915e-01 1.09403396e+00 1.16631456e-01
5.33343889e-02 -8.08509350e-01 -4.85096961e-01 -5.38153112e-01
3.15602213e-01 1.89944535e-01 3.62577230e-01 -3.02136302... | [12.560626029968262, 9.465726852416992] |
6bb8277e-1bdf-43bc-908b-c3b1b8aa4432 | optimal-energy-management-in-autonomous-power | 2208.08953 | null | https://arxiv.org/abs/2208.08953v1 | https://arxiv.org/pdf/2208.08953v1.pdf | Optimal Energy Management in Autonomous Power Systems with Probabilistic Security Constraints and Adaptive Frequency Control | The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly v... | ['Elisabetta Tedeschi', 'Vincenzo Trovato', 'Erick Alves', 'Spyridon Chapaloglou'] | 2022-08-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.14647228e-01 3.20336908e-01 -2.43466720e-01 3.42659056e-01
-2.79659927e-01 -8.57169330e-01 4.15036052e-01 3.82667810e-01
-5.39316460e-02 1.34953415e+00 -2.79595435e-01 -8.35164413e-02
-9.28090036e-01 -6.23697996e-01 -2.15484500e-01 -1.26427662e+00
-3.03590983e-01 4.08902019e-01 -3.95801157e-01 -1.92282081... | [5.63739013671875, 2.5111825466156006] |
da500857-6e29-4d2a-8560-c1d88930f310 | complex-program-induction-for-querying | null | null | https://aclanthology.org/Q19-1012 | https://aclanthology.org/Q19-1012.pdf | Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs | Recent years have seen increasingly complex question-answering on knowledge bases (KBQA) involving logical, quantitative, and comparative reasoning over KB subgraphs. Neural Program Induction (NPI) is a pragmatic approach toward modularizing the reasoning process by translating a complex natural language query into a m... | ['Abhishek Laddha', 'Ghulam Ahmed Ansari', 'Karthik Sankaranarayanan', 'Soumen Chakrabarti', 'Amrita Saha'] | 2019-03-01 | null | null | null | tacl-2019-3 | ['program-induction'] | ['computer-code'] | [ 1.34623244e-01 5.98595440e-01 -5.33984601e-01 -4.30926502e-01
-1.29708207e+00 -5.80835044e-01 -1.03149628e-02 3.29609245e-01
-2.69396156e-01 7.77007043e-01 -7.47098261e-03 -1.19351375e+00
-1.68773696e-01 -1.27497554e+00 -1.21816134e+00 1.23975866e-01
-1.31052300e-01 7.61878073e-01 4.41192418e-01 -3.91508669... | [9.396053314208984, 7.495462894439697] |
e11bbaf2-e7fe-406c-8333-284006997903 | tenet-triple-excitation-network-for-video | 2007.09943 | null | https://arxiv.org/abs/2007.09943v2 | https://arxiv.org/pdf/2007.09943v2.pdf | TENet: Triple Excitation Network for Video Salient Object Detection | In this paper, we propose a simple yet effective approach, named Triple Excitation Network, to reinforce the training of video salient object detection (VSOD) from three aspects, spatial, temporal, and online excitations. These excitation mechanisms are designed following the spirit of curriculum learning and aim to re... | ['Xin Yang', 'Guoqiang Han', 'Chu Han', 'Sucheng Ren', 'Shengfeng He'] | 2020-07-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3089_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500205.pdf | eccv-2020-8 | ['video-salient-object-detection'] | ['computer-vision'] | [ 2.34117314e-01 7.57909715e-02 -2.14063957e-01 -4.85119708e-02
-3.36747795e-01 -1.65253624e-01 3.94604683e-01 -5.91408163e-02
-3.75350773e-01 6.62887156e-01 3.72873425e-01 8.24500695e-02
-5.71751408e-02 -5.89920402e-01 -8.74949515e-01 -7.62629509e-01
1.85853288e-01 -9.92488638e-02 1.00002253e+00 -2.88756192... | [9.68899917602539, -0.33926454186439514] |
324782fa-0de2-4606-87eb-d63fd6ec1824 | talking-face-generation-with-multilingual-tts | 2205.06421 | null | https://arxiv.org/abs/2205.06421v1 | https://arxiv.org/pdf/2205.06421v1.pdf | Talking Face Generation with Multilingual TTS | In this work, we propose a joint system combining a talking face generation system with a text-to-speech system that can generate multilingual talking face videos from only the text input. Our system can synthesize natural multilingual speeches while maintaining the vocal identity of the speaker, as well as lip movemen... | ['Kang-wook Kim', 'Dongho Choi', 'Youseong Lee', 'Hyunjae Cho', 'Seungmin Yang', 'Junhyeok Lee', 'Sang Hoon Woo', 'Hyoung-Kyu Song'] | 2022-05-13 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Song_Talking_Face_Generation_With_Multilingual_TTS_CVPR_2022_paper.pdf | cvpr-2022-1 | ['talking-face-generation'] | ['computer-vision'] | [-1.60715193e-01 4.15191829e-01 3.36853378e-02 -4.28223759e-01
-9.76963639e-01 -8.89635086e-01 7.29120255e-01 -8.40002894e-01
3.06684338e-02 6.89272106e-01 4.38743532e-01 -4.42210048e-01
7.88227379e-01 -3.73920262e-01 -6.45161510e-01 -2.85909355e-01
5.41834116e-01 3.85189384e-01 -1.10070512e-01 -3.66149306... | [13.262125968933105, -0.3623529076576233] |
83019ec3-8a70-4317-bff9-63497edda49f | lipformer-high-fidelity-and-generalizable | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_LipFormer_High-Fidelity_and_Generalizable_Talking_Face_Generation_With_a_Pre-Learned_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_LipFormer_High-Fidelity_and_Generalizable_Talking_Face_Generation_With_a_Pre-Learned_CVPR_2023_paper.pdf | LipFormer: High-Fidelity and Generalizable Talking Face Generation With a Pre-Learned Facial Codebook | Generating a talking face video from the input audio sequence is a practical yet challenging task. Most existing methods either fail to capture fine facial details or need to train a specific model for each identity. We argue that a codebook pre-learned on high-quality face images can serve as a useful prior that f... | ['Jingren Zhou', 'Deli Zhao', 'Yujun Shen', 'Yingya Zhang', 'Shiwei Zhang', 'Kang Zhao', 'Jiayu Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 1.68115422e-01 -1.75830930e-01 -1.46946654e-01 -5.44610679e-01
-7.86926150e-01 -4.88447756e-01 4.11270052e-01 -1.00254130e+00
4.30839390e-01 4.37913030e-01 7.23436773e-01 4.48094666e-01
1.63349092e-01 -3.53812605e-01 -6.91653967e-01 -9.62458611e-01
4.35945392e-01 1.80621706e-02 -1.70426697e-01 -9.20400694... | [13.231817245483398, -0.3607860803604126] |
7731df50-09d7-4789-9d9f-2bb2a97c2b36 | covid-19-misinformation-on-twitter | null | null | https://openreview.net/forum?id=aDCizGE1HR2 | https://openreview.net/pdf?id=aDCizGE1HR2 | COVID-19 Misinformation on Twitter: Multilingual Analysis | In the current scenario of the coronavirus disease pandemic (COVID-19), the Internet has become an important source of health information for users worldwide. During pandemic situations, myths, sensationalism, rumours and misinformation, generated intentionally or unintentionally, spread rapidly through social networks... | ['Genoveva Vargas-Solar', 'Ambesh Shekhar', 'Mehrdad Farokhenajd', 'Raj Ratn Pranesh'] | 2021-01-06 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-2.66763479e-01 1.94657207e-01 -2.26345018e-01 8.62420276e-02
-5.33584356e-01 -6.72188938e-01 1.11784852e+00 1.12955689e+00
-4.46775854e-01 7.70030499e-01 7.94168353e-01 -4.68040794e-01
5.54540336e-01 -9.30246711e-01 -5.34747541e-01 -3.66071343e-01
-2.11125612e-01 8.41651499e-01 -3.34262729e-01 -6.75532937... | [8.42109489440918, 9.80932331085205] |
0e39f87f-a9ea-4d2d-b127-9f7fc546ea35 | enhancing-low-light-videos-by-exploring-high | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Enhancing_Low_Light_Videos_by_Exploring_High_Sensitivity_Camera_Noise_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Enhancing_Low_Light_Videos_by_Exploring_High_Sensitivity_Camera_Noise_ICCV_2019_paper.pdf | Enhancing Low Light Videos by Exploring High Sensitivity Camera Noise | Enhancing low light videos, which consists of denoising and brightness adjustment, is an intriguing but knotty problem. Under low light condition, due to high sensitivity camera setting, commonly negligible noises become obvious and severely deteriorate the captured videos. To recover high quality videos, a mass of ima... | [' Tao Yue', ' Xuemei Hu', ' Xiang Li', ' Cheng Yang', ' Xin Chen', 'Wei Wang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['video-denoising'] | ['computer-vision'] | [ 4.26052898e-01 -9.16882455e-01 4.43353146e-01 -5.52930161e-02
-2.64045566e-01 -3.80646557e-01 2.50866890e-01 -7.93305457e-01
-5.00466824e-01 7.56949365e-01 -1.88238584e-02 -2.64456570e-02
-3.83895934e-02 -6.37569845e-01 -9.42125559e-01 -1.29693425e+00
3.72515470e-01 -5.97620845e-01 9.26013365e-02 -1.21155172... | [11.148478507995605, -2.3816940784454346] |
45679c5d-0d32-449c-a623-9f4c2abead7e | moments-in-time-dataset-one-million-videos | 1801.0315 | null | http://arxiv.org/abs/1801.03150v3 | http://arxiv.org/pdf/1801.03150v3.pdf | Moments in Time Dataset: one million videos for event understanding | We present the Moments in Time Dataset, a large-scale human-annotated
collection of one million short videos corresponding to dynamic events
unfolding within three seconds. Modeling the spatial-audio-temporal dynamics
even for actions occurring in 3 second videos poses many challenges: meaningful
events do not include ... | ['Dan Gutfruend', 'Sarah Adel Bargal', 'Lisa Brown', 'Kandan Ramakrishnan', 'Aude Oliva', 'Tom Yan', 'Quanfu Fan', 'Carl Vondrick', 'Bolei Zhou', 'Mathew Monfort', 'Alex Andonian'] | 2018-01-09 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 2.28952363e-01 -4.12548453e-01 -9.98173207e-02 -1.79605797e-01
-5.71611345e-01 -9.30930674e-01 9.25927222e-01 2.15093404e-01
-4.90924329e-01 5.11118054e-01 9.06936407e-01 2.49999523e-01
-4.71048942e-03 -4.51273054e-01 -5.57284713e-01 -4.79137510e-01
-4.17331040e-01 2.65135914e-02 4.88892525e-01 6.07610792... | [8.33129596710205, 0.5621653199195862] |
9849d113-f419-47bb-bcba-33490c0ea89e | vidimu-multimodal-video-and-imu-kinematic | 2303.1615 | null | https://arxiv.org/abs/2303.16150v1 | https://arxiv.org/pdf/2303.16150v1.pdf | VIDIMU. Multimodal video and IMU kinematic dataset on daily life activities using affordable devices | Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave... | ['Cristina Simón-Martínez', 'Henning Müller', 'Francisco J. Díaz-Pernas', 'Míriam Antón-Rodríguez', 'Javier González-Alonso', 'Mario Martínez-Zarzuela'] | 2023-03-27 | null | null | null | null | ['pose-tracking', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 1.78737998e-01 -1.22379668e-01 -2.33357579e-01 1.58488408e-01
-5.56174994e-01 -8.81437287e-02 1.88467860e-01 -1.13827832e-01
-7.81822801e-01 6.67523980e-01 4.07773167e-01 4.31641228e-02
-4.20944929e-01 -1.84191838e-01 -4.99737740e-01 -4.44253087e-01
-4.17287797e-01 8.54277194e-01 1.41992152e-01 -3.63317400... | [7.0790839195251465, 0.10524610430002213] |
2de5b422-e4cb-44e6-a6b3-add20b97836c | contextual-augmentation-data-augmentation-by | 1805.06201 | null | http://arxiv.org/abs/1805.06201v1 | http://arxiv.org/pdf/1805.06201v1.pdf | Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations | We propose a novel data augmentation for labeled sentences called contextual
augmentation. We assume an invariance that sentences are natural even if the
words in the sentences are replaced with other words with paradigmatic
relations. We stochastically replace words with other words that are predicted
by a bi-directio... | ['Sosuke Kobayashi'] | 2018-05-16 | contextual-augmentation-data-augmentation-by-1 | https://aclanthology.org/N18-2072 | https://aclanthology.org/N18-2072.pdf | naacl-2018-6 | ['text-augmentation'] | ['natural-language-processing'] | [ 6.53708100e-01 4.54833269e-01 -2.04385936e-01 -8.21934938e-01
-1.68258220e-01 -3.59709024e-01 7.78535903e-01 -1.24432512e-01
-7.03604639e-01 8.46769154e-01 7.46716797e-01 -4.74269688e-01
5.41506767e-01 -6.59596026e-01 -7.57863045e-01 -5.09337723e-01
4.31666046e-01 3.19582164e-01 -2.24880740e-01 -5.84619820... | [11.257574081420898, 8.895630836486816] |
ae57b0b0-bee9-4304-a201-d9c07f18c1f6 | using-search-queries-to-understand-health | 1806.0574 | null | http://arxiv.org/abs/1806.05740v2 | http://arxiv.org/pdf/1806.05740v2.pdf | Using Search Queries to Understand Health Information Needs in Africa | The lack of comprehensive, high-quality health data in developing nations
creates a roadblock for combating the impacts of disease. One key challenge is
understanding the health information needs of people in these nations. Without
understanding people's everyday needs, concerns, and misconceptions, health
organization... | ['H. Andrew Schwartz', 'Jennifer Wortman Vaughan', 'Rediet Abebe', 'Shawndra Hill', 'Peter M. Small'] | 2018-06-14 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-7.19975978e-02 6.83865622e-02 -8.28636587e-01 6.03568107e-02
-7.09520042e-01 -7.47039020e-01 4.64565784e-01 1.24207580e+00
-5.06521940e-01 4.16407198e-01 1.29785180e+00 -9.97022331e-01
-3.64138454e-01 -8.24316502e-01 -2.98764467e-01 -3.40220690e-01
1.16024971e-01 5.31720579e-01 -2.33036295e-01 -4.78267610... | [8.460418701171875, 9.515969276428223] |
19522628-b863-4e6a-a13d-6b3cf912b5fe | deep-residual-3d-u-net-for-joint-segmentation | 2006.14215 | null | https://arxiv.org/abs/2006.14215v2 | https://arxiv.org/pdf/2006.14215v2.pdf | Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung | In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular U-Net architecture family but modified for the joint nodule segmentation and its texture classifica... | ['Alexandr G. Rassadin'] | 2020-06-25 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 1.32317409e-01 6.34526193e-01 -7.23895311e-01 -6.01426184e-01
-8.49951088e-01 -2.27468222e-01 2.85831958e-01 -1.51746944e-01
4.36341390e-02 4.81224507e-01 6.85057521e-01 -9.46704209e-01
-6.35939479e-01 -1.00372326e+00 -2.67871320e-01 -6.12489760e-01
1.92470830e-02 1.22735870e+00 8.66820157e-01 4.32116678... | [15.383230209350586, -2.145216703414917] |
9bc79ded-bfc0-4727-87ef-8ce1011f2470 | a-reference-less-quality-metric-for-automatic | 2306.13114 | null | https://arxiv.org/abs/2306.13114v1 | https://arxiv.org/pdf/2306.13114v1.pdf | A Reference-less Quality Metric for Automatic Speech Recognition via Contrastive-Learning of a Multi-Language Model with Self-Supervision | The common standard for quality evaluation of automatic speech recognition (ASR) systems is reference-based metrics such as the Word Error Rate (WER), computed using manual ground-truth transcriptions that are time-consuming and expensive to obtain. This work proposes a multi-language referenceless quality metric, whic... | ['Golara Javadi', 'Mohamed Al-Badrashiny', 'Ahmet Gunduz', 'Thiago Ferreira', 'Kamer Ali Yuksel'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'learning-to-rank', 'contrastive-learning', 'learning-to-rank', 'automatic-speech-recognition'] | ['computer-vision', 'computer-vision', 'graphs', 'methodology', 'miscellaneous', 'speech'] | [-7.10207671e-02 -9.43816975e-02 8.45807837e-04 -4.84339863e-01
-1.77667952e+00 -5.12138903e-01 4.33927387e-01 4.42182198e-02
-5.80226302e-01 7.05607474e-01 3.71839166e-01 -4.66965675e-01
2.44503036e-01 -2.30425775e-01 -5.49559712e-01 -4.74157721e-01
3.43088508e-01 5.90123832e-01 5.84057830e-02 -3.07807237... | [14.412086486816406, 6.84608793258667] |
19dd5b7e-2666-43d8-b3be-d5f173c7035c | covir-a-virtual-rendering-of-a-novel-nn | null | null | https://www.sciencedirect.com/science/article/pii/S0097849322000358 | https://www.sciencedirect.com/science/article/pii/S0097849322000358/pdfft?md5=f5810213e6df1df1a6258b3d72776484&pid=1-s2.0-S0097849322000358-main.pdf | COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation | With the Coronavirus disease 2019 (COVID-19) spread, causing a world pandemic, and recently, the virus new variants continue to appear, making the situation more challenging and threatening, the visual assessment and quantification by expert radiologists have become costly and error-prone. Hence, there is a need to pro... | ['Hoceine Kennouche', 'Kahina Amara', 'Ali Aouf'] | 2022-05-15 | null | null | null | computers-and-graphics-2022-2022-5 | ['2d-semantic-segmentation', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 1.47127017e-01 7.83575028e-02 3.49322677e-01 -3.54628544e-03
1.63508996e-01 -4.47138280e-01 1.45986691e-01 7.82407448e-02
-5.32573938e-01 4.94370162e-01 -1.00660972e-01 -7.32009828e-01
-3.16923380e-01 -6.88037157e-01 -2.34263435e-01 -5.39396644e-01
-2.93746144e-01 7.54194260e-01 2.91551560e-01 1.54245481... | [15.564326286315918, -1.713329792022705] |
51cd3843-c7d1-49d9-aeab-8c616dbf84d0 | iql-td-mpc-implicit-q-learning-for | 2306.00867 | null | https://arxiv.org/abs/2306.00867v1 | https://arxiv.org/pdf/2306.00867v1.pdf | IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control | Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed dataset. We hypothesize that model-based RL agents struggle in these environments due to a lack of l... | ['Olivier Delalleau', 'Zheqing Zhu', 'Urun Dogan', 'Lucas Lehnert', 'Bobak Hashemi', 'Yingchen Xu', 'Rohan Chitnis'] | 2023-06-01 | null | null | null | null | ['q-learning', 'offline-rl', 'model-based-reinforcement-learning', 'd4rl'] | ['methodology', 'playing-games', 'reasoning', 'robots'] | [-2.79763281e-01 3.01488549e-01 -4.39415097e-01 5.44618517e-02
-1.03788066e+00 -5.71205437e-01 8.74994874e-01 1.02843270e-02
-6.75659359e-01 8.66098166e-01 4.67798412e-01 -2.94523656e-01
-2.31997147e-01 -5.89155555e-01 -8.43666732e-01 -6.32243812e-01
-5.86472690e-01 1.00071502e+00 1.52706265e-01 -4.74220455... | [4.118249416351318, 1.6126997470855713] |
7542cad9-a9e3-43a6-b766-10089370f33d | convolutional-neural-network-models-for | 1906.07794 | null | https://arxiv.org/abs/1906.07794v1 | https://arxiv.org/pdf/1906.07794v1.pdf | Convolutional neural network models for cancer type prediction based on gene expression | Background Precise prediction of cancer types is vital for cancer diagnosis and therapy. Important cancer marker genes can be inferred through predictive model. Several studies have attempted to build machine learning models for this task however none has taken into consideration the effects of tissue of origin that ca... | ['Yu-Chiao Chiu', 'Yidong Chen', 'Yufei Huang', 'Milad Mostavi'] | 2019-06-18 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 1.32662645e-02 3.31736475e-01 -7.52468348e-01 -3.09599340e-01
-9.46429133e-01 -8.85308981e-02 4.71393675e-01 4.42313492e-01
-2.84298539e-01 7.56627440e-01 2.78153330e-01 -6.77938342e-01
-2.80702021e-02 -9.00171757e-01 -5.03915966e-01 -1.04553866e+00
-2.04652444e-01 5.09419441e-01 -1.14410594e-01 -1.32169873... | [15.136741638183594, -2.9698455333709717] |
b4f5fb1a-f41d-4caa-86a0-48e74ccb1133 | priorlane-a-prior-knowledge-enhanced-lane | 2209.06994 | null | https://arxiv.org/abs/2209.06994v3 | https://arxiv.org/pdf/2209.06994v3.pdf | PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer | Lane detection is one of the fundamental modules in self-driving. In this paper we employ a transformer-only method for lane detection, thus it could benefit from the blooming development of fully vision transformer and achieve the state-of-the-art (SOTA) performance on both CULane and TuSimple benchmarks, by fine-tuni... | ['Xiaofei He', 'Gang Huang', 'Wei Hua', 'Haiming Gao', 'Qibo Qiu'] | 2022-09-15 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-1.91436484e-01 -1.13128863e-01 -2.18026340e-01 -3.20360243e-01
-7.69735157e-01 -2.73950845e-01 5.27308643e-01 -3.67744386e-01
-4.97648925e-01 3.31695586e-01 1.67902514e-01 -3.20528448e-01
1.51734129e-01 -7.95888424e-01 -8.06846261e-01 -6.82766676e-01
3.79251450e-01 -6.46572933e-02 8.28258157e-01 -3.49906921... | [8.082282066345215, -1.4608144760131836] |
4d80eee8-e797-4599-8e45-540065c8cc71 | temporal-spatial-feature-pyramid-for-video | 2105.04213 | null | https://arxiv.org/abs/2105.04213v2 | https://arxiv.org/pdf/2105.04213v2.pdf | Temporal-Spatial Feature Pyramid for Video Saliency Detection | Multi-level features are important for saliency detection. Better combination and use of multi-level features with time information can greatly improve the accuracy of the video saliency model. In order to fully combine multi-level features and make it serve the video saliency model, we propose a 3D fully convolutional... | ['Shiping Zhu', 'Qinyao Chang'] | 2021-05-10 | null | null | null | null | ['video-saliency-detection'] | ['computer-vision'] | [ 2.45320588e-01 -6.05274379e-01 -5.38782299e-01 -1.71694696e-01
-6.52127206e-01 5.40192202e-02 1.50862515e-01 -1.01195388e-01
-2.93915808e-01 3.40203613e-01 5.64142883e-01 9.29100737e-02
3.70821357e-01 -4.03443605e-01 -8.31549704e-01 -3.84455949e-01
-2.98237622e-01 -5.84852397e-01 1.28277647e+00 -2.47540265... | [9.72631549835205, -0.3171845078468323] |
49eb060f-2813-4ec7-9dd5-b5e4a6f7735b | perspective-transformer-nets-learning-single | 1612.00814 | null | http://arxiv.org/abs/1612.00814v3 | http://arxiv.org/pdf/1612.00814v3.pdf | Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision | Understanding the 3D world is a fundamental problem in computer vision.
However, learning a good representation of 3D objects is still an open problem
due to the high dimensionality of the data and many factors of variation
involved. In this work, we investigate the task of single-view 3D object
reconstruction from a l... | ['Ersin Yumer', 'Xinchen Yan', 'Yijie Guo', 'Honglak Lee', 'Jimei Yang'] | 2016-12-01 | perspective-transformer-nets-learning-single-1 | http://papers.nips.cc/paper/6206-perspective-transformer-nets-learning-single-view-3d-object-reconstruction-without-3d-supervision | http://papers.nips.cc/paper/6206-perspective-transformer-nets-learning-single-view-3d-object-reconstruction-without-3d-supervision.pdf | neurips-2016-12 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.87032813e-01 3.03125829e-01 -8.59875306e-02 -7.01416492e-01
-6.09374166e-01 -2.88535923e-01 8.20262969e-01 -4.27678555e-01
-1.33579522e-01 3.62941891e-01 1.26715049e-01 -3.21379327e-03
-1.36530166e-02 -7.02009916e-01 -1.01837695e+00 -6.99888587e-01
3.97517197e-02 7.62068391e-01 1.65723264e-01 2.62947410... | [8.363031387329102, -3.2367196083068848] |
807106e8-6d55-4e5e-86d8-1b81f0a0c36a | align-yourself-self-supervised-pre-training | 2106.15788 | null | https://arxiv.org/abs/2106.15788v4 | https://arxiv.org/pdf/2106.15788v4.pdf | Exploring Localization for Self-supervised Fine-grained Contrastive Learning | Self-supervised contrastive learning has demonstrated great potential in learning visual representations. Despite their success in various downstream tasks such as image classification and object detection, self-supervised pre-training for fine-grained scenarios is not fully explored. We point out that current contrast... | ['Stan Z. Li', 'Zelin Zang', 'Siyuan Li', 'Di wu'] | 2021-06-30 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 8.60950530e-01 1.41971046e-03 -3.95143241e-01 -4.89823103e-01
-6.83162391e-01 -5.56328595e-01 8.47252846e-01 1.50517836e-01
4.96032238e-02 7.48389065e-01 2.37342015e-01 -8.91848356e-02
8.36236775e-02 -6.10515773e-01 -1.07281971e+00 -7.40917802e-01
1.52520508e-01 1.52842268e-01 7.69575596e-01 1.60099007... | [9.725367546081543, 1.4944113492965698] |
261b49f0-f34c-45b4-be87-527875cb3da8 | gandiffface-controllable-generation-of | 2305.19962 | null | https://arxiv.org/abs/2305.19962v1 | https://arxiv.org/pdf/2305.19962v1.pdf | GANDiffFace: Controllable Generation of Synthetic Datasets for Face Recognition with Realistic Variations | Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, eve... | ['Maxim Schaubert', 'Florian Domin', 'Dominik Lawatsch', 'Ruben Vera-Rodriguez', 'Ruben Tolosana', 'Christian Rathgeb', 'Pietro Melzi'] | 2023-05-31 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 2.81233221e-01 6.73895003e-03 3.09928566e-01 -5.62900722e-01
-5.08337975e-01 -6.76426113e-01 8.06664050e-01 -7.37825871e-01
-6.79812729e-02 9.11886036e-01 1.86562553e-01 2.35873073e-01
1.23810261e-01 -8.92332673e-01 -4.82508779e-01 -6.30838275e-01
3.27090293e-01 4.90434468e-01 -5.57792187e-01 -2.82867551... | [12.808968544006348, 0.5515887141227722] |
0e77c9db-30ea-49a1-b832-73dbbbcdac27 | semanticstylegan-learning-compositional | 2112.02236 | null | https://arxiv.org/abs/2112.02236v3 | https://arxiv.org/pdf/2112.02236v3.pdf | SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing | Recent studies have shown that StyleGANs provide promising prior models for downstream tasks on image synthesis and editing. However, since the latent codes of StyleGANs are designed to control global styles, it is hard to achieve a fine-grained control over synthesized images. We present SemanticStyleGAN, where a gene... | ['Xiaohui Shen', 'Yangyue Wan', 'Xiao Yang', 'Yichun Shi'] | 2021-12-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_SemanticStyleGAN_Learning_Compositional_Generative_Priors_for_Controllable_Image_Synthesis_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_SemanticStyleGAN_Learning_Compositional_Generative_Priors_for_Controllable_Image_Synthesis_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['facial-editing'] | ['computer-vision'] | [ 5.67536354e-01 3.89245898e-01 -1.99204117e-01 -4.36895102e-01
-4.19330806e-01 -8.94024074e-01 9.36171055e-01 -6.99139297e-01
2.92049557e-01 6.31690621e-01 5.13757885e-01 -4.25041728e-02
5.59199691e-01 -1.16431439e+00 -8.56122136e-01 -8.01088512e-01
4.88768131e-01 2.52877444e-01 4.07681018e-02 -4.44111824... | [11.655485153198242, -0.4387718439102173] |
49b21da2-1727-4f30-ac5c-7d1c1f90f6b3 | instance-segmentation-in-3d-scenes-using | 2108.07478 | null | https://arxiv.org/abs/2108.07478v1 | https://arxiv.org/pdf/2108.07478v1.pdf | Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks | Instance segmentation in 3D scenes is fundamental in many applications of scene understanding. It is yet challenging due to the compound factors of data irregularity and uncertainty in the numbers of instances. State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminat... | ['Kui Jia', 'Mingkui Tan', 'Songcen Xu', 'Zhihao LI', 'Zhihao Liang'] | 2021-08-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liang_Instance_Segmentation_in_3D_Scenes_Using_Semantic_Superpoint_Tree_Networks_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liang_Instance_Segmentation_in_3D_Scenes_Using_Semantic_Superpoint_Tree_Networks_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.73848465e-01 4.17217284e-01 -9.69371423e-02 -5.73579729e-01
-7.18625247e-01 -4.71528769e-01 4.73315507e-01 2.87517428e-01
-1.46782130e-01 3.68799865e-01 -2.24565327e-01 -9.58850160e-02
-2.24175498e-01 -7.56173670e-01 -8.66447628e-01 -4.32593346e-01
-1.22208469e-01 8.62042189e-01 9.53707635e-01 -1.18876761... | [7.999940395355225, -3.0723824501037598] |
f75e2900-4a2b-4f19-bd78-ec7913f3676e | hedging-against-complexity-distributionally | 2212.01518 | null | https://arxiv.org/abs/2212.01518v1 | https://arxiv.org/pdf/2212.01518v1.pdf | Hedging against Complexity: Distributionally Robust Optimization with Parametric Approximation | Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving stochastic optimization problems that appear in operations management and machine learning. Existing generalization error bounds for these methods depend on either the complexity of the cost function or d... | ['Tianyu Wang', 'Henry Lam', 'Garud Iyengar'] | 2022-12-03 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 1.79105103e-02 -1.58574298e-01 -1.34990335e-01 -4.17706847e-01
-1.05389988e+00 -6.20669007e-01 3.23574603e-01 3.74610513e-01
-3.67588639e-01 1.04355001e+00 -2.15595976e-01 -3.59690309e-01
-7.96472907e-01 -6.07488751e-01 -6.53353751e-01 -8.79662037e-01
-1.11854590e-01 4.68223244e-01 -1.47361085e-01 -1.53595269... | [5.34758186340332, 3.784514904022217] |
6941b6a6-e82b-4b07-aae2-24527b141fd1 | sigtyp-2021-shared-task-robust-spoken | 2106.03895 | null | https://arxiv.org/abs/2106.03895v1 | https://arxiv.org/pdf/2106.03895v1.pdf | SIGTYP 2021 Shared Task: Robust Spoken Language Identification | While language identification is a fundamental speech and language processing task, for many languages and language families it remains a challenging task. For many low-resource and endangered languages this is in part due to resource availability: where larger datasets exist, they may be single-speaker or have differe... | ['Ekaterina Vylomova', 'Ryan Cotterell', 'Ritesh Kumar', 'Edoardo Ponti', 'Oleg Serikov', 'Elena Klyachko', 'Sabrina J. Mielke', 'Badr M. Abdullah', 'Elizabeth Salesky'] | 2021-06-07 | null | https://aclanthology.org/2021.sigtyp-1.11 | https://aclanthology.org/2021.sigtyp-1.11.pdf | naacl-sigtyp-2021-6 | ['spoken-language-identification'] | ['speech'] | [ 3.96038145e-01 -1.93886802e-01 -1.10120848e-01 -6.56222641e-01
-1.17841041e+00 -9.71102297e-01 6.59758985e-01 -2.40559459e-01
-5.65983117e-01 7.53204823e-01 3.20651084e-01 -6.13858163e-01
4.20054607e-02 8.98799151e-02 -9.83146057e-02 -5.66305041e-01
1.02256626e-01 8.67618978e-01 1.58062562e-01 -2.62193054... | [14.207488059997559, 6.6061930656433105] |
d3670137-74be-49db-820c-d57a4ea91dcf | dtw-at-qur-an-qa-2022-utilising-transfer | 2205.06025 | null | https://arxiv.org/abs/2205.06025v1 | https://arxiv.org/pdf/2205.06025v1.pdf | DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain | The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understu... | ['Ruslan Mitkov', 'Wajdi Zaghouani', 'Tharindu Ranasinghe', 'Damith Premasiri'] | 2022-05-12 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 4.48334426e-01 4.02284920e-01 1.57501310e-01 -3.46688747e-01
-1.30928922e+00 -6.55555189e-01 7.32410192e-01 3.91438663e-01
-4.67572957e-01 9.62022305e-01 6.51187539e-01 -6.35913908e-01
-1.04958758e-01 -8.61895740e-01 -5.50621748e-01 -4.44011480e-01
1.10486433e-01 7.27837801e-01 2.65803516e-01 -9.29619849... | [11.370680809020996, 8.233490943908691] |
efc2f144-0dad-4862-aa97-b06047b60a54 | learning-to-attend-on-essential-terms-an | 1808.09492 | null | https://arxiv.org/abs/1808.09492v5 | https://arxiv.org/pdf/1808.09492v5.pdf | Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering | Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from... | ['Weizhu Chen', 'Jianmo Ni', 'Chenguang Zhu', 'Julian McAuley'] | 2018-08-28 | learning-to-attend-on-essential-terms-an-1 | https://aclanthology.org/N19-1030 | https://aclanthology.org/N19-1030.pdf | naacl-2019-6 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 3.80529761e-01 6.26918912e-01 -1.15355834e-01 -3.39179546e-01
-1.61596966e+00 -9.07144666e-01 6.90735161e-01 8.19698274e-01
-6.12532020e-01 6.38170421e-01 6.03641093e-01 -7.32337236e-01
-5.58167815e-01 -8.07474017e-01 -7.98335671e-01 8.77785385e-02
3.50098282e-01 1.12005365e+00 7.19995618e-01 -7.06330538... | [11.194342613220215, 7.987008571624756] |
ae23c1c9-3476-499c-85b4-5daadd5c7549 | zen-pre-training-chinese-text-encoder | 1911.0072 | null | https://arxiv.org/abs/1911.00720v1 | https://arxiv.org/pdf/1911.00720v1.pdf | ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations | The pre-training of text encoders normally processes text as a sequence of tokens corresponding to small text units, such as word pieces in English and characters in Chinese. It omits information carried by larger text granularity, and thus the encoders cannot easily adapt to certain combinations of characters. This le... | ['Jiaxin Bai', 'Yan Song', 'Shizhe Diao', 'Yonggang Wang', 'Tong Zhang'] | 2019-11-02 | null | https://aclanthology.org/2020.findings-emnlp.425 | https://aclanthology.org/2020.findings-emnlp.425.pdf | findings-of-the-association-for-computational | ['chinese-named-entity-recognition', 'sentence-pair-modeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.38645688e-01 4.60601598e-02 -3.05805326e-01 -2.63561040e-01
-6.91941381e-01 -4.31576490e-01 3.65094513e-01 1.63567126e-01
-6.22325599e-01 8.97243142e-01 2.58260071e-01 -5.22655666e-01
2.90522456e-01 -9.72778201e-01 -7.01349139e-01 -5.34683287e-01
4.70893830e-02 4.57937092e-01 3.45281631e-01 -1.02361485... | [10.189912796020508, 10.127735137939453] |
c81947e1-3ed8-4fd7-a38f-7df27ac11922 | adaptive-rotated-convolution-for-rotated | 2303.0782 | null | https://arxiv.org/abs/2303.07820v1 | https://arxiv.org/pdf/2303.07820v1.pdf | Adaptive Rotated Convolution for Rotated Object Detection | Rotated object detection aims to identify and locate objects in images with arbitrary orientation. In this scenario, the oriented directions of objects vary considerably across different images, while multiple orientations of objects exist within an image. This intrinsic characteristic makes it challenging for standard... | ['Gao Huang', 'Shiji Song', 'Zidong Wang', 'Weihao Gan', 'Yulin Wang', 'Yizeng Han', 'Zhuofan Xia', 'Yiru Wang', 'Yifan Pu'] | 2023-03-14 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.50164932e-01 -3.36795956e-01 -3.50303482e-04 -3.67989153e-01
-1.80569082e-01 -4.80618089e-01 2.74454087e-01 -4.96408612e-01
-6.80792332e-01 7.12725148e-02 -3.72869998e-01 -1.92814171e-01
-1.38342187e-01 -6.76777303e-01 -8.00208569e-01 -8.42857540e-01
-1.29267350e-01 -7.19137192e-02 6.86293602e-01 -2.29457989... | [8.781402587890625, -0.6965448260307312] |
fa377334-4a93-4f0e-bad6-a2dcc1d6d08c | st-hoi-a-spatial-temporal-baseline-for-human | 2105.11731 | null | https://arxiv.org/abs/2105.11731v2 | https://arxiv.org/pdf/2105.11731v2.pdf | ST-HOI: A Spatial-Temporal Baseline for Human-Object Interaction Detection in Videos | Detecting human-object interactions (HOI) is an important step toward a comprehensive visual understanding of machines. While detecting non-temporal HOIs (e.g., sitting on a chair) from static images is feasible, it is unlikely even for humans to guess temporal-related HOIs (e.g., opening/closing a door) from a single ... | ['Jiashi Feng', 'Roger Zimmermann', 'Li-Wei Wang', 'Chun-Yu Liao', 'Meng-Jiun Chiou'] | 2021-05-25 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 3.12119067e-01 -2.98647135e-01 -6.51997179e-02 -1.54951721e-01
-3.88768941e-01 -5.16270280e-01 7.58789361e-01 1.42613053e-01
-2.70871997e-01 3.66621882e-01 5.69413044e-02 -2.88349450e-01
-6.09412454e-02 -2.86323875e-01 -8.07450235e-01 -5.63736379e-01
-3.41235220e-01 3.23157459e-02 8.03624451e-01 5.71190529... | [8.315956115722656, 0.46128374338150024] |
a3b206f6-defd-4fde-ab49-e0ab54dd0441 | playing-go-without-game-tree-search-using | 1907.04658 | null | https://arxiv.org/abs/1907.04658v1 | https://arxiv.org/pdf/1907.04658v1.pdf | Playing Go without Game Tree Search Using Convolutional Neural Networks | The game of Go has a long history in East Asian countries, but the field of Computer Go has yet to catch up to humans until the past couple of years. While the rules of Go are simple, the strategy and combinatorics of the game are immensely complex. Even within the past couple of years, new programs that rely on neural... | ['Jeffrey Barratt', 'Chuanbo Pan'] | 2019-07-02 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-1.35862932e-01 1.18822441e-01 4.20960099e-01 -1.79059997e-01
-3.13512951e-01 -7.81346381e-01 3.49334508e-01 -1.87900990e-01
-6.95592225e-01 5.54237843e-01 -1.34443611e-01 -1.04062176e+00
7.21933469e-02 -1.30250800e+00 -6.28196955e-01 -2.50029981e-01
-3.44266772e-01 4.94106442e-01 5.01804054e-01 -9.41522717... | [3.4598724842071533, 1.437034010887146] |
e8096404-9dbe-47f2-b8ac-e9f8acfe79e5 | revisiting-end-to-end-speech-to-text | 2206.04571 | null | https://arxiv.org/abs/2206.04571v1 | https://arxiv.org/pdf/2206.04571v1.pdf | Revisiting End-to-End Speech-to-Text Translation From Scratch | End-to-end (E2E) speech-to-text translation (ST) often depends on pretraining its encoder and/or decoder using source transcripts via speech recognition or text translation tasks, without which translation performance drops substantially. However, transcripts are not always available, and how significant such pretraini... | ['Rico Sennrich', 'Barry Haddow', 'Biao Zhang'] | 2022-06-09 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 5.81387043e-01 3.72235090e-01 -1.35112286e-01 -5.16205728e-01
-1.35572112e+00 -6.19775593e-01 6.19044363e-01 -5.44268906e-01
-3.51033926e-01 7.39683986e-01 5.55792928e-01 -7.77018785e-01
4.91987675e-01 -1.89112484e-01 -1.11098731e+00 -5.74789047e-01
3.16240817e-01 4.80038702e-01 -1.67369276e-01 -2.85508811... | [14.461381912231445, 7.140631675720215] |
f6e21503-e4bf-4f03-80d3-5e86c5ba400c | mopo-lsi-a-user-guide | 2307.01719 | null | https://arxiv.org/abs/2307.01719v1 | https://arxiv.org/pdf/2307.01719v1.pdf | MOPO-LSI: A User Guide | MOPO-LSI is an open-source Multi-Objective Portfolio Optimization Library for Sustainable Investments. This document provides a user guide for MOPO-LSI version 1.0, including problem setup, workflow and the hyper-parameters in configurations. | ["Michael O'Leary", 'Wang', 'David', 'Jasmine Xu', 'Kumar Neelotpal Shukla', 'Yong Zheng'] | 2023-07-04 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.56487000e-01 -4.62545037e-01 -3.66112769e-01 -1.81688011e-01
-1.08933799e-01 -6.57409668e-01 -1.85745716e-01 -3.15565109e-01
1.17771104e-01 1.11281610e+00 -1.18230343e-01 -5.68838537e-01
-1.19553375e+00 -8.84570003e-01 -1.26347482e-01 -8.34708214e-01
-2.52836674e-01 9.98131752e-01 -3.61058831e-01 -4.47005332... | [5.818556785583496, 3.6512136459350586] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.