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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
45062238-81d6-42a5-b1f4-5eefcccc9f2a | an-effective-loss-function-for-generating-3d | 2103.03390 | null | https://arxiv.org/abs/2103.03390v2 | https://arxiv.org/pdf/2103.03390v2.pdf | An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering | Differentiable rendering is a very successful technique that applies to a Single-View 3D Reconstruction. Current renderers use losses based on pixels between a rendered image of some 3D reconstructed object and ground-truth images from given matched viewpoints to optimise parameters of the 3D shape. These models requir... | ['Pietro Liò', 'Nikola Zubić'] | 2021-03-05 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 1.14191838e-01 2.15578303e-01 4.13439542e-01 -3.83847266e-01
-6.98254228e-01 -3.22025567e-01 8.24758112e-01 -1.34843156e-01
-9.86910760e-02 3.65760356e-01 -3.04121822e-01 -1.14973888e-01
1.79684356e-01 -1.17579925e+00 -1.11762559e+00 -5.00282288e-01
2.02085942e-01 1.23813069e+00 4.12544668e-01 -1.23278968... | [8.894289016723633, -3.368813991546631] |
c88ec9c9-a4fc-46d6-9903-a52a5dcbda4c | exploration-of-whether-skylight-polarization | 2012.09154 | null | https://arxiv.org/abs/2012.09154v1 | https://arxiv.org/pdf/2012.09154v1.pdf | Exploration of Whether Skylight Polarization Patterns Contain Three-dimensional Attitude Information | Our previous work has demonstrated that Rayleigh model, which is widely used in polarized skylight navigation to describe skylight polarization patterns, does not contain three-dimensional (3D) attitude information [1]. However, it is still necessary to further explore whether the skylight polarization patterns contain... | ['Tong Zhou', 'Hongyang Bai', 'Huaju Liang'] | 2020-11-30 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [-2.10664704e-01 -5.80379725e-01 2.46514201e-01 -2.32308939e-01
2.48710990e-01 -5.95663667e-01 1.90116778e-01 -7.57151186e-01
-9.46203433e-03 6.94948196e-01 3.61341760e-02 -4.49083239e-01
-1.80803298e-03 -8.77302825e-01 -3.22658032e-01 -1.40914547e+00
1.88242957e-01 2.37218067e-01 3.20521295e-02 -4.45853084... | [9.965824127197266, -2.7836861610412598] |
9731b9cd-91cc-4458-95e1-31ea1c0c43bc | learning-to-detect-specular-highlights-from | null | null | https://dl.acm.org/doi/abs/10.1145/3394171.3413586 | https://dl.acm.org/doi/abs/10.1145/3394171.3413586 | Learning to Detect Specular Highlights from Real-world Images | Specular highlight detection is a challenging problem, and has many applications such as shiny object detection and light source estimation. Although various highlight detection methods have been proposed, they fail to disambiguate bright material surfaces from highlights, and cannot handle non-white-balanced images. M... | ['and Chunaxia Xiao', 'Lei Zhu', 'QiFeng Lin', 'Qing Zhang', 'Gang Fu'] | 2020-10-10 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 4.86837059e-01 -6.32480502e-01 2.85011947e-01 -1.11037098e-01
-5.01498461e-01 -6.91548645e-01 1.72083810e-01 -6.62868246e-02
1.34998098e-01 6.08361542e-01 2.65863054e-02 3.30101810e-02
3.46497446e-01 -5.24246454e-01 -4.72892135e-01 -8.61508191e-01
-2.75782328e-02 -3.92970532e-01 7.34707534e-01 -2.98806354... | [10.800372123718262, -2.7258996963500977] |
fe236193-6149-46ad-9b80-65eecd2c2ab2 | iterative-gradient-encoding-network-with | 2103.15903 | null | https://arxiv.org/abs/2103.15903v1 | https://arxiv.org/pdf/2103.15903v1.pdf | Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal | Removing undesired reflections from a photo taken in front of glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a d... | ['Prabir Kumar Biswas', 'Sutanu Bera'] | 2021-03-29 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 8.30469489e-01 -1.36101454e-01 5.64826131e-01 -2.62803733e-01
-7.26877928e-01 -1.43371165e-01 2.46916100e-01 -5.57330668e-01
-1.84834331e-01 5.90612352e-01 3.73858400e-02 -3.53077352e-01
-1.24626420e-01 -6.74864948e-01 -8.86988461e-01 -1.07278836e+00
-6.93506077e-02 -5.21917462e-01 2.71789998e-01 -2.80336738... | [10.566685676574707, -2.7855827808380127] |
a0690f56-9fb5-455e-a7a0-c4d23346252c | multi-view-vector-valued-manifold | 1904.03921 | null | http://arxiv.org/abs/1904.03921v1 | http://arxiv.org/pdf/1904.03921v1.pdf | Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification | In computer vision, image datasets used for classification are naturally
associated with multiple labels and comprised of multiple views, because each
image may contain several objects (e.g. pedestrian, bicycle and tree) and is
properly characterized by multiple visual features (e.g. color, texture and
shape). Currentl... | ['DaCheng Tao', 'Yong Luo', 'Hong Liu', 'Chao Xu', 'Chang Xu', 'Yonggang Wen'] | 2019-04-08 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 1.99836064e-02 -2.78742969e-01 -1.25348955e-01 -5.74513733e-01
-5.62334180e-01 -6.48675680e-01 4.27942395e-01 7.89804310e-02
-1.03489205e-01 4.38236564e-01 -2.58418173e-02 -9.82244387e-02
-4.66081321e-01 -4.91190404e-01 -7.63307810e-01 -8.46397996e-01
-1.16957344e-01 -2.61309624e-01 4.05493053e-03 -3.97366732... | [8.659677505493164, 4.394859790802002] |
b9f55108-f784-496d-b5cd-4894e57ed906 | prema-predictive-maintenance-of-solenoid | 2211.12326 | null | https://arxiv.org/abs/2211.12326v1 | https://arxiv.org/pdf/2211.12326v1.pdf | PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level | In industrial process automation, sensors (pressure, temperature, etc.), controllers, and actuators (solenoid valves, electro-mechanical relays, circuit breakers, motors, etc.) make sure that production lines are working under the pre-defined conditions. When these systems malfunction or sometimes completely fail, aler... | ['T. V. Prabhakar', 'Vishwanath Shastry', 'Harsha Yelchuri', 'Prajwal BN'] | 2022-11-21 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.45655885e-01 1.15128227e-01 5.30808382e-02 -1.98053140e-02
2.31933683e-01 -5.65458119e-01 3.76318634e-01 2.61692643e-01
3.37739915e-01 3.17431062e-01 -8.71439517e-01 -7.08949745e-01
-5.54374874e-01 -8.26220393e-01 -7.93483078e-01 -7.09645748e-01
-1.09745413e-01 3.64916503e-01 3.36765110e-01 -1.22857414... | [6.77626371383667, 2.3470122814178467] |
bff8bf06-0f38-419c-81f4-a98a75887d31 | basic-level-categorization-facilitates-visual | 1511.04103 | null | http://arxiv.org/abs/1511.04103v3 | http://arxiv.org/pdf/1511.04103v3.pdf | Basic Level Categorization Facilitates Visual Object Recognition | Recent advances in deep learning have led to significant progress in the
computer vision field, especially for visual object recognition tasks. The
features useful for object classification are learned by feed-forward deep
convolutional neural networks (CNNs) automatically, and they are shown to be
able to predict and ... | ['Garrison W. Cottrell', 'Panqu Wang'] | 2015-11-12 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.10769756e-01 1.41556323e-01 -1.44346535e-01 -4.94565964e-01
3.50366712e-01 -3.91072571e-01 5.29517114e-01 2.83435374e-01
-7.92111874e-01 1.61735058e-01 2.53383338e-01 -1.56515881e-01
-2.66948313e-01 -6.92350686e-01 -8.06394815e-01 -3.05122107e-01
-2.85026401e-01 5.82299046e-02 1.36255220e-01 -7.23990723... | [9.622639656066895, 2.3792426586151123] |
8fc5fb13-419a-45f2-b6e7-a311d722a855 | what-constitutes-good-contrastive-learning-in | 2306.12086 | null | https://arxiv.org/abs/2306.12086v1 | https://arxiv.org/pdf/2306.12086v1.pdf | What Constitutes Good Contrastive Learning in Time-Series Forecasting? | In recent years, the introduction of self-supervised contrastive learning (SSCL) has demonstrated remarkable improvements in representation learning across various domains, including natural language processing and computer vision. By leveraging the inherent benefits of self-supervision, SSCL enables the pre-training o... | ['Tristan Sylvain', 'Lili Meng', 'Qi Yan', 'Chiyu Zhang'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 4.58440810e-01 -3.57678473e-01 -4.01722729e-01 -5.21206558e-01
-5.55235565e-01 -5.55362940e-01 7.39759684e-01 3.35019916e-01
-1.21925071e-01 4.05971706e-01 1.97909668e-01 -5.10286033e-01
-3.84997696e-01 -5.68597496e-01 -5.40586352e-01 -8.32952023e-01
-6.32005990e-01 -3.86835188e-02 -2.72247612e-01 -4.43110466... | [7.05460786819458, 3.0108392238616943] |
6a08f2ea-73e9-4193-92e0-61f195c562a5 | a-hassle-free-machine-learning-method-for | 1808.04694 | null | http://arxiv.org/abs/1808.04694v1 | http://arxiv.org/pdf/1808.04694v1.pdf | A Hassle-Free Machine Learning Method for Cohort Selection of Clinical Trials | Traditional text classification techniques in clinical domain have heavily
relied on the manually extracted textual cues. This paper proposes a generally
supervised machine learning method that is equally hassle-free and does not use
clinical knowledge. The employed methods were simple to implement, fast to run
and yet... | ['Liu Man'] | 2018-08-10 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 2.90930510e-01 2.50511199e-01 -3.71882200e-01 -4.03049618e-01
-7.40104198e-01 -2.98095912e-01 5.79094172e-01 1.13452077e+00
-9.23621714e-01 9.53082383e-01 4.83143091e-01 -4.31612223e-01
-8.47592652e-01 -7.03213394e-01 2.39716247e-01 -6.41171157e-01
1.77815467e-01 3.05044740e-01 -3.44833732e-03 -3.84811401... | [8.462166786193848, 8.709704399108887] |
0b91cea9-35c5-4053-b180-a5b47eda4624 | hierarchical-cross-modal-transformer-for-rgb | 2302.08052 | null | https://arxiv.org/abs/2302.08052v1 | https://arxiv.org/pdf/2302.08052v1.pdf | Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection | Most of existing RGB-D salient object detection (SOD) methods follow the CNN-based paradigm, which is unable to model long-range dependencies across space and modalities due to the natural locality of CNNs. Here we propose the Hierarchical Cross-modal Transformer (HCT), a new multi-modal transformer, to tackle this pro... | ['Feihong Shen', 'Hao Chen'] | 2023-02-16 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 5.81636466e-02 -1.37564585e-01 -2.24181861e-01 -2.91660964e-01
-9.99949396e-01 -5.50356150e-01 7.33560681e-01 -1.53190196e-01
-1.24762706e-01 2.96191931e-01 6.75582409e-01 1.02715842e-01
-1.34162232e-01 -6.16219938e-01 -8.05260897e-01 -6.80225611e-01
2.07902133e-01 -2.09284306e-01 8.47971797e-01 -3.26788127... | [9.743783950805664, -0.7476024627685547] |
5ce2ebd1-2a10-483e-af11-144cb8a1ef52 | coco-gan-conditional-coordinate-generative | null | null | https://openreview.net/forum?id=r14Aas09Y7 | https://openreview.net/pdf?id=r14Aas09Y7 | COCO-GAN: Conditional Coordinate Generative Adversarial Network | Recent advancements on Generative Adversarial Network (GAN) have inspired a wide range of works that generate synthetic images. However, the current processes have to generate an entire image at once, and therefore resolutions are limited by memory or computational constraints. In this work, we propose COnditional COor... | ['Hwann-Tzong Chen', 'Da-Cheng Juan', 'Yu-Sheng Chen', 'Chia-Che Chang', 'Wei Wei', 'Chieh Hubert Lin'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['scene-generation'] | ['computer-vision'] | [ 5.37289917e-01 3.04904044e-01 2.54317164e-01 2.74042934e-02
-7.60771930e-01 -6.08668864e-01 6.94712043e-01 -5.32569587e-01
1.92555636e-01 9.32544291e-01 -8.39654654e-02 1.25111071e-02
1.85430825e-01 -1.40389037e+00 -8.92888963e-01 -1.01930666e+00
4.58393127e-01 7.20629320e-02 3.30904365e-01 -3.96711558... | [11.648832321166992, -0.6373625993728638] |
a9a81707-f3b6-466f-92f2-ea26abb6e857 | multi-agent-deep-reinforcement-learning-for-10 | 2209.02633 | null | https://arxiv.org/abs/2209.02633v3 | https://arxiv.org/pdf/2209.02633v3.pdf | Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning | The future transportation system will be a multi-agent network where connected AI agents can work together to address the grand challenges in our age, e.g., mitigation of real-world driving energy consumption. Distinguished from the existing research on vehicle energy management, which decoupled multiple inputs and mul... | ['Quan Zhou', 'Zhi Li', 'Min Hua'] | 2022-09-06 | null | null | null | null | ['total-energy', 'energy-management'] | ['miscellaneous', 'time-series'] | [-2.80750602e-01 1.76081672e-01 -5.65949321e-01 8.28672349e-02
-1.46766394e-01 -3.33849192e-01 4.68114406e-01 -1.72527030e-01
-4.78381515e-01 1.07141876e+00 -2.25509152e-01 -3.52187365e-01
-5.33562362e-01 -1.10349739e+00 -5.38318276e-01 -1.19243610e+00
-4.86279372e-03 1.85207009e-01 9.43303257e-02 -6.23226464... | [5.462203502655029, 2.180488109588623] |
c70fc396-07cd-4249-b5dc-c1f9f536bd6c | distinguishing-cause-from-effect-on | 2303.08572 | null | https://arxiv.org/abs/2303.08572v1 | https://arxiv.org/pdf/2303.08572v1.pdf | Distinguishing Cause from Effect on Categorical Data: The Uniform Channel Model | Distinguishing cause from effect using observations of a pair of random variables is a core problem in causal discovery. Most approaches proposed for this task, namely additive noise models (ANM), are only adequate for quantitative data. We propose a criterion to address the cause-effect problem with categorical variab... | ['Catarina A. Oliveira', 'Mário A. T. Figueiredo'] | 2023-03-14 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 6.21137023e-01 2.04253614e-01 -2.07806900e-01 -5.33613935e-02
-6.61182582e-01 -5.69146931e-01 9.27809596e-01 4.90252495e-01
-2.71559417e-01 1.21989048e+00 3.46502513e-01 -6.15201771e-01
-7.37927735e-01 -1.12899649e+00 -1.22434044e+00 -8.72235417e-01
-4.06327993e-01 2.19290391e-01 1.72865577e-02 1.93133220... | [7.806193828582764, 5.283635139465332] |
1fd05aff-9ff6-4530-9fd7-ead079e867b9 | tightly-coupled-learning-strategy-for-weakly | 2202.06470 | null | https://arxiv.org/abs/2202.06470v1 | https://arxiv.org/pdf/2202.06470v1.pdf | Tightly Coupled Learning Strategy for Weakly Supervised Hierarchical Place Recognition | Visual place recognition (VPR) is a key issue for robotics and autonomous systems. For the trade-off between time and performance, most of methods use the coarse-to-fine hierarchical architecture, which consists of retrieving top-N candidates using global features, and re-ranking top-N with local features. However, sin... | ['N. Zheng', 'W. Zuo', 'R. Wang', 'Y. Shen'] | 2022-02-14 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.44127765e-01 -6.67758584e-01 -5.13718784e-01 -1.99719593e-01
-1.22433221e+00 -3.63877952e-01 6.37022078e-01 1.80648804e-01
-4.28548783e-01 4.36362088e-01 1.66943714e-01 1.45048246e-01
-5.94702125e-01 -6.88211679e-01 -6.43958807e-01 -7.81015456e-01
-2.32289821e-01 5.50677836e-01 6.75236225e-01 -4.77135986... | [7.742311954498291, -1.9380050897598267] |
3cc47838-4e70-468c-9572-8229ecc4bbea | a-dual-encoding-system-for-dialect | null | null | https://aclanthology.org/2020.vardial-1.20 | https://aclanthology.org/2020.vardial-1.20.pdf | A dual-encoding system for dialect classification | In this paper we present the architecture, processing pipeline and results of the ensemble model developed for Romanian Dialect Identification task. The ensemble model consists of two TF-IDF encoders and a deep learning model aimed together at classifying input samples based on the writing patterns which are specific t... | ['Dan Cristea', 'Petru Rebeja'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [ 4.90419157e-02 -1.77979633e-01 -1.28542800e-02 -8.16760600e-01
-4.14623320e-01 -7.37054229e-01 8.09089780e-01 -1.13119997e-01
-4.70099419e-01 2.75249749e-01 5.68052053e-01 -3.33126664e-01
-2.55485445e-01 -7.05869794e-01 -5.07374592e-02 -3.73355627e-01
2.89872169e-01 9.34196293e-01 -1.65853754e-01 -3.23476970... | [10.235244750976562, 10.667710304260254] |
f16697ff-751a-4c87-8b63-c8f20bcbe317 | active-learning-in-physics-from-101-to | 2307.03899 | null | https://arxiv.org/abs/2307.03899v1 | https://arxiv.org/pdf/2307.03899v1.pdf | Active Learning in Physics: From 101, to Progress, and Perspective | Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informa... | ['Xi Chen', 'Yolanda Vives-Gilabert', 'José D. Martín-Guerrero', 'Yongcheng Ding'] | 2023-07-08 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 6.38883710e-01 5.23245335e-01 -7.13979006e-01 -4.75834161e-01
-1.03800499e+00 -4.76362318e-01 7.29264259e-01 4.83646125e-01
-5.89942098e-01 9.64432359e-01 -9.25357938e-02 -2.63159901e-01
-2.98926346e-02 -9.70173538e-01 -3.82438689e-01 -8.91491473e-01
-5.04052937e-02 6.18068695e-01 5.08068800e-02 5.48708029... | [9.533804893493652, 4.181944370269775] |
30b57193-6ede-43eb-8bd7-de7d00d7bec6 | unsupervised-moving-object-detection-via | 1901.03360 | null | http://arxiv.org/abs/1901.03360v2 | http://arxiv.org/pdf/1901.03360v2.pdf | Unsupervised Moving Object Detection via Contextual Information Separation | We propose an adversarial contextual model for detecting moving objects in
images. A deep neural network is trained to predict the optical flow in a
region using information from everywhere else but that region (context), while
another network attempts to make such context as uninformative as possible. The
result is a ... | ['Stefano Soatto', 'Yanchao Yang', 'Davide Scaramuzza', 'Antonio Loquercio'] | 2019-01-10 | unsupervised-moving-object-detection-via-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Unsupervised_Moving_Object_Detection_via_Contextual_Information_Separation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Unsupervised_Moving_Object_Detection_via_Contextual_Information_Separation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['moving-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.48322964e-01 5.02404928e-01 7.57059967e-03 -3.28336030e-01
-4.60193098e-01 -7.52607703e-01 6.28740907e-01 -2.82978386e-01
-6.74778581e-01 6.64379776e-01 -1.16336204e-01 -3.36434275e-01
2.76391178e-01 -9.12707210e-01 -9.09181356e-01 -9.21928227e-01
2.62454227e-02 4.99855310e-01 6.30340815e-01 -7.88189396... | [9.029215812683105, -0.34647640585899353] |
a1b9213b-0489-418a-b62a-9b154165a377 | reason-from-context-with-self-supervised | 2211.12817 | null | https://arxiv.org/abs/2211.12817v2 | https://arxiv.org/pdf/2211.12817v2.pdf | Reason from Context with Self-supervised Learning | Self-supervised learning (SSL) learns to capture discriminative visual features useful for knowledge transfers. To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects fr... | ['Mengmi Zhang', 'Zenglin Shi', 'Gabriel Kreiman', 'Ankur Sikarwar', 'Xiao Liu'] | 2022-11-23 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.18133080e-01 -4.70938273e-02 -2.00657770e-01 -3.34166884e-01
6.23519626e-03 -6.19437218e-01 8.98941755e-01 2.96616286e-01
-5.79045713e-01 6.81545675e-01 9.19652954e-02 -4.24727887e-01
-1.97721839e-01 -6.64919615e-01 -8.14192533e-01 -9.18892920e-01
-8.29263553e-02 5.66837080e-02 2.75075108e-01 -2.21958935... | [10.05467700958252, 1.7358895540237427] |
24fef516-6be1-4b16-b0e4-f61cebcbaecc | inno-at-semeval-2020-task-11-leveraging-pure-1 | null | null | https://aclanthology.org/2020.semeval-1.193 | https://aclanthology.org/2020.semeval-1.193.pdf | Inno at SemEval-2020 Task 11: Leveraging Pure Transfomer for Multi-Class Propaganda Detection | The paper presents the solution of team {''}Inno{''} to a SEMEVAL 2020 task 11 {''}Detection of propaganda techniques in news articles{''}. The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-b... | ['Vladimir Ivanov', 'Dmitry Grigorev'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [ 1.26045376e-01 1.86858207e-01 -3.83389801e-01 -1.96789905e-01
-8.15474927e-01 -7.00102985e-01 1.45798254e+00 2.56710708e-01
-5.35411298e-01 5.74553192e-01 6.08707488e-01 -7.30751634e-01
-4.25314873e-01 -5.96083224e-01 -5.52178860e-01 -3.40744436e-01
-5.60775287e-02 6.22368634e-01 3.13016862e-01 -6.37098312... | [8.465950012207031, 10.675714492797852] |
3a663ebb-b0ee-4eb6-8b7c-12f57e7d7d56 | flexivit-one-model-for-all-patch-sizes | 2212.08013 | null | https://arxiv.org/abs/2212.08013v2 | https://arxiv.org/pdf/2212.08013v2.pdf | FlexiViT: One Model for All Patch Sizes | Vision Transformers convert images to sequences by slicing them into patches. The size of these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher accuracy at greater computational cost, but changing the patch size typically requires retraining the model. In this paper, we demonstrate th... | ['Filip Pavetic', 'Ibrahim Alabdulmohsin', 'Michael Tschannen', 'Matthias Minderer', 'Xiaohua Zhai', 'Simon Kornblith', 'Mathilde Caron', 'Alexander Kolesnikov', 'Pavel Izmailov', 'Lucas Beyer'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.13736171e-01 -3.81498188e-01 -2.22435519e-01 -3.07808220e-01
-8.06677997e-01 -1.08383763e+00 6.74090505e-01 -5.87548912e-02
-4.56409514e-01 1.24161460e-01 -2.01445863e-01 -4.88416255e-01
1.59979016e-01 -6.72543764e-01 -7.71735251e-01 -6.38968766e-01
2.80355364e-01 6.20549619e-01 5.49750865e-01 -1.51142441... | [9.561599731445312, 1.3129940032958984] |
71ace4a8-ba8a-4cac-8412-ba69e988cd1e | efficientderain-learning-pixel-wise-dilation | 2009.09238 | null | https://arxiv.org/abs/2009.09238v1 | https://arxiv.org/pdf/2009.09238v1.pdf | EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining | Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse circumstances in the real world, making them have to employ complex optimization or progressive refinement. This, however, significantly aff... | ['Xiaofei Xie', 'Felix Juefei-Xu', 'Lei Ma', 'Jingyang Sun', 'Yang Liu', 'Qing Guo', 'Wei Feng'] | 2020-09-19 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.51772827e-01 -4.17434365e-01 4.02326912e-01 -5.25269449e-01
-4.98169631e-01 -2.74582624e-01 1.09558754e-01 -2.69721419e-01
-5.85706770e-01 7.76813805e-01 -2.72035003e-01 -4.74564701e-01
2.68421710e-01 -8.50115180e-01 -7.66345024e-01 -1.02139723e+00
1.34348944e-01 5.05150445e-02 2.07653135e-01 -1.67028546... | [10.907706260681152, -3.2555227279663086] |
d628b3b2-2bc5-4657-9f9a-db22879edb0f | a-comparative-study-on-speaker-attributed | 2203.16834 | null | https://arxiv.org/abs/2203.16834v3 | https://arxiv.org/pdf/2203.16834v3.pdf | A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings | In this paper, we conduct a comparative study on speaker-attributed automatic speech recognition (SA-ASR) in the multi-party meeting scenario, a topic with increasing attention in meeting rich transcription. Specifically, three approaches are evaluated in this study. The first approach, FD-SOT, consists of a frame-leve... | ['Lei Xie', 'Yuxiao Lin', 'Shiliang Zhang', 'Zhihao Du', 'Fan Yu'] | 2022-03-31 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 3.53105634e-01 -1.11192852e-01 2.56983310e-01 -5.53246856e-01
-1.48967421e+00 -2.86599308e-01 5.71603477e-01 7.89578184e-02
-2.83292621e-01 3.97497803e-01 3.20138484e-01 -2.85902560e-01
1.26953155e-01 6.41444996e-02 -3.25637519e-01 -8.17727506e-01
5.50920181e-02 4.99033362e-01 2.07939580e-01 -2.25082815... | [14.625097274780273, 6.2413153648376465] |
0a232953-3f44-48eb-9c92-32cb00e8b74d | neural-transformation-fields-for-arbitrary | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fu_Neural_Transformation_Fields_for_Arbitrary-Styled_Font_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_Neural_Transformation_Fields_for_Arbitrary-Styled_Font_Generation_CVPR_2023_paper.pdf | Neural Transformation Fields for Arbitrary-Styled Font Generation | Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-content disentanglement paradigm, which transfers the target font styles to characters by combining the ... | ['Yu Qiao', 'Jianjun Wang', 'Junjun He', 'Bin Fu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['disentanglement'] | ['methodology'] | [ 3.81216317e-01 -2.25024119e-01 9.64269936e-02 -2.38771915e-01
-5.19760549e-01 -5.98676920e-01 7.81100512e-01 -4.53543037e-01
2.41337195e-01 5.45391977e-01 3.40619802e-01 -6.79707304e-02
3.38389009e-01 -1.00750709e+00 -7.58861363e-01 -7.11976469e-01
8.61795843e-01 9.68278423e-02 4.85832877e-02 -4.72530901... | [11.635649681091309, -0.38205671310424805] |
3d25569c-c2bb-4a99-bb84-7f85e3355a37 | learning-to-recommend-frame-for-interactive | 2103.10391 | null | https://arxiv.org/abs/2103.10391v2 | https://arxiv.org/pdf/2103.10391v2.pdf | Learning to Recommend Frame for Interactive Video Object Segmentation in the Wild | This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively. Then, based on the user annotations, a segmentation algorithm refines the masks. The previous interactive VOS paradigm selects the frame with some worst evaluat... | ['Shenghua Gao', 'Hanling Zhang', 'Shenhan Qian', 'Weixin Luo', 'Jia Zheng', 'Zhaoyuan Yin'] | 2021-03-18 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yin_Learning_To_Recommend_Frame_for_Interactive_Video_Object_Segmentation_in_CVPR_2021_paper.pdf | cvpr-2021-1 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 4.99680005e-02 1.19955927e-01 -3.56500655e-01 -3.99376929e-01
-7.98370779e-01 -6.25979185e-01 1.81453675e-02 -1.44099295e-01
-5.86274922e-01 5.30117750e-01 -1.13686204e-01 -1.97506830e-01
1.13241836e-01 -6.32489085e-01 -8.00682008e-01 -9.03615713e-01
6.36637062e-02 6.20103955e-01 6.25935555e-01 1.01145640... | [9.195234298706055, -0.12504230439662933] |
ded3f26d-fe50-41ce-81ce-482ef2eaef96 | few-shot-bayesian-imitation-learning-with | 1904.06317 | null | https://arxiv.org/abs/1904.06317v2 | https://arxiv.org/pdf/1904.06317v2.pdf | Few-Shot Bayesian Imitation Learning with Logical Program Policies | Humans can learn many novel tasks from a very small number (1--5) of demonstrations, in stark contrast to the data requirements of nearly tabula rasa deep learning methods. We propose an expressive class of policies, a strong but general prior, and a learning algorithm that, together, can learn interesting policies fro... | ['Josh Tenenbaum', 'Leslie Pack Kaelbling', 'Kelsey R. Allen', 'Alex K. Lew', 'Tom Silver'] | 2019-04-12 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 8.86778533e-02 1.77232444e-01 -4.51260477e-01 -4.82613444e-01
-6.43760264e-01 -8.41742754e-01 9.09856021e-01 1.99405774e-02
-7.59990394e-01 1.25272834e+00 -2.82495748e-02 -6.59195900e-01
-1.32265136e-01 -6.40207589e-01 -1.26814449e+00 -4.83053476e-01
-3.09880793e-01 8.96443486e-01 3.61847550e-01 -1.66979432... | [4.141976833343506, 1.5532859563827515] |
d0219c4c-fb31-499d-974f-ccb2f40a309e | structure-alignment | 2307.02170 | null | https://arxiv.org/abs/2307.02170v2 | https://arxiv.org/pdf/2307.02170v2.pdf | Structure Alignment | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['Sanne Abeln', 'K. Anton Feenstra', 'Robbin Bouwmeester', 'Isabel Houtkamp', 'Dea Gogishvili', 'Jose Gavaldá-Garciá', 'Olga Ivanova'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 3.45271170e-01 -1.13632925e-01 -1.36302188e-01 -2.63135374e-01
-2.85244405e-01 -5.23010492e-01 6.71913996e-02 4.98019129e-01
-2.81369239e-01 1.22770357e+00 -2.96828926e-01 -7.10291207e-01
-1.32240459e-01 -3.07857305e-01 -4.61693794e-01 -1.26117849e+00
-5.25597855e-02 5.17355204e-01 4.37318951e-01 -6.49237156... | [4.741698265075684, 5.2831854820251465] |
9f4220a3-ab5d-49f2-ab73-90feecaaf775 | a-paraboost-stereoscopic-image-quality | 1603.09469 | null | http://arxiv.org/abs/1603.09469v1 | http://arxiv.org/pdf/1603.09469v1.pdf | A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System | The problem of stereoscopic image quality assessment, which finds
applications in 3D visual content delivery such as 3DTV, is investigated in
this work. Specifically, we propose a new ParaBoost (parallel-boosting)
stereoscopic image quality assessment (PBSIQA) system. The system consists of
two stages. In the first sta... | ['C. -C. Jay Kuo', 'Rui Song', 'Hyunsuk Ko'] | 2016-03-31 | null | null | null | null | ['stereoscopic-image-quality-assessment'] | ['computer-vision'] | [ 6.36782348e-02 -6.10833108e-01 -6.42125495e-03 -3.75290811e-01
-1.21970248e+00 -2.45689586e-01 5.80881476e-01 5.47651425e-02
-1.91250116e-01 5.35792291e-01 4.49603230e-01 -9.12202150e-02
-2.89307445e-01 -6.21733963e-01 -3.36287290e-01 -8.94041657e-01
2.67331004e-01 3.23459208e-01 4.34683055e-01 -3.96685660... | [11.844151496887207, -1.9748085737228394] |
8225f791-2d40-46fe-aca9-9bd55e088c93 | a-frugal-approach-to-music-source-separation | null | null | https://hal.archives-ouvertes.fr/hal-02986241/ | https://hal.archives-ouvertes.fr/hal-02986241/document | A frugal approach to music source separation | During the past years, deep learning brought a big step in performance of music source separation algorithms. A lot has been done on the architecture optimisation, but training data remains an important bias for model comparison. In this work, we choose to work with the frugal and well-known original TasNet neural netw... | ['Nathan Souviraà-Labastie', 'Emery Pierson Lancaster'] | 2020-10-02 | null | null | null | null | ['music-source-separation'] | ['music'] | [-9.94953327e-03 -2.77547747e-01 -9.44148675e-02 -9.53358039e-02
-5.89739978e-01 -6.52234912e-01 5.24623215e-01 -9.19374749e-02
-4.68848139e-01 5.51407158e-01 3.96352500e-01 -2.01260932e-02
-5.02900541e-01 -3.14302266e-01 -2.16448784e-01 -6.97087765e-01
-2.92486399e-01 6.90647423e-01 1.73760742e-01 -7.70880222... | [15.789474487304688, 5.3004984855651855] |
d44cb183-647b-4afe-9608-5c052115c6c6 | temporally-anchored-relation-extraction | null | null | https://aclanthology.org/P12-1012 | https://aclanthology.org/P12-1012.pdf | Temporally Anchored Relation Extraction | null | ["{\\'A}lvaro Rodrigo", 'Bernardo Cabaleiro', 'Anselmo Pe{\\~n}as', 'Guillermo Garrido'] | 2012-07-01 | null | null | null | acl-2012-7 | ['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... | [-7.331573963165283, 3.6997148990631104] |
8bb3c6d4-8930-4bce-8352-2f54572f6166 | review-of-deep-learning-based-malware | 2307.01494 | null | https://arxiv.org/abs/2307.01494v1 | https://arxiv.org/pdf/2307.01494v1.pdf | Review of Deep Learning-based Malware Detection for Android and Windows System | Differentiating malware is important to determine their behaviors and level of threat; as well as to devise defensive strategy against them. In response, various anti-malware systems have been developed to distinguish between different malwares. However, most of the recent malware families are Artificial Intelligence (... | ['Seokjoo Shin', 'Nazmul Islam'] | 2023-07-04 | null | null | null | null | ['malware-detection'] | ['miscellaneous'] | [ 2.24177599e-01 -4.92980421e-01 -3.94230217e-01 1.22341290e-01
1.58725902e-01 -1.14349306e+00 8.38227391e-01 1.25762984e-01
-7.56557006e-03 5.92982948e-01 -4.50809598e-01 -8.19493890e-01
8.17131177e-02 -7.11539328e-01 -8.22255164e-02 -7.99349129e-01
-4.43359971e-01 1.70238227e-01 2.67469198e-01 -3.34379345... | [14.412065505981445, 9.676471710205078] |
9ea5b67f-fafd-4338-9dc0-cb031c76d7b0 | cdistnet-perceiving-multi-domain-character | 2111.11011 | null | https://arxiv.org/abs/2111.11011v3 | https://arxiv.org/pdf/2111.11011v3.pdf | CDistNet: Perceiving Multi-Domain Character Distance for Robust Text Recognition | The Transformer-based encoder-decoder framework is becoming popular in scene text recognition, largely because it naturally integrates recognition clues from both visual and semantic domains. However, recent studies show that the two kinds of clues are not always well registered and therefore, feature and character mig... | ['Yu-Gang Jiang', 'Hongtao Xie', 'Shancheng Fang', 'Zhineng Chen', 'Tianlun Zheng'] | 2021-11-22 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 1.56764269e-01 -6.67764008e-01 -1.77228957e-01 -5.03344059e-01
-6.25121355e-01 -5.50003767e-01 7.06008255e-01 3.52991112e-02
-3.75196159e-01 2.85020709e-01 4.49918061e-01 9.90907997e-02
-3.98338512e-02 -7.32438505e-01 -5.66604972e-01 -6.92937553e-01
6.78908110e-01 3.80349070e-01 3.74866575e-01 -2.24653140... | [11.69140625, 2.0540895462036133] |
66488eb0-a16f-4d12-9aea-c98c2f890ae9 | daa-a-delta-age-adain-operation-for-age | 2303.07929 | null | https://arxiv.org/abs/2303.07929v1 | https://arxiv.org/pdf/2303.07929v1.pdf | DAA: A Delta Age AdaIN operation for age estimation via binary code transformer | Naked eye recognition of age is usually based on comparison with the age of others. However, this idea is ignored by computer tasks because it is difficult to obtain representative contrast images of each age. Inspired by the transfer learning, we designed the Delta Age AdaIN (DAA) operation to obtain the feature diffe... | ['Zongjie Jiang', 'Bing Wang', 'Bin Xiao', 'Ju Tao', 'Ye Li', 'Xingpeng Zhang', 'Ping Chen'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_DAA_A_Delta_Age_AdaIN_Operation_for_Age_Estimation_via_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_DAA_A_Delta_Age_AdaIN_Operation_for_Age_Estimation_via_CVPR_2023_paper.pdf | cvpr-2023-1 | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-9.01319385e-02 -1.86764210e-01 1.19934678e-01 -9.15876687e-01
-1.75217658e-01 -2.31277823e-01 4.66797680e-01 -2.60053873e-01
-6.01560116e-01 6.87147915e-01 -6.66011199e-02 2.05221429e-01
2.59791434e-01 -9.29539680e-01 -4.92692262e-01 -9.63357508e-01
6.21284172e-03 5.61890192e-02 -4.50325459e-02 1.40437841... | [13.50946044921875, 0.7438858151435852] |
ef37a7bd-d520-4a0e-b167-5c6ebef10fa2 | 3d-aware-adversarial-makeup-generation-for | 2306.14640 | null | https://arxiv.org/abs/2306.14640v1 | https://arxiv.org/pdf/2306.14640v1.pdf | 3D-Aware Adversarial Makeup Generation for Facial Privacy Protection | The privacy and security of face data on social media are facing unprecedented challenges as it is vulnerable to unauthorized access and identification. A common practice for solving this problem is to modify the original data so that it could be protected from being recognized by malicious face recognition (FR) system... | ['Jing Dong', 'Yunfan Liu', 'Bo Peng', 'Ziwen He', 'Yue Jiang', 'Yueming Lyu'] | 2023-06-26 | null | null | null | null | ['face-recognition', 'face-verification'] | ['computer-vision', 'computer-vision'] | [ 3.15935791e-01 8.49135071e-02 2.36325219e-01 -3.55887830e-01
-5.45421302e-01 -8.08507085e-01 5.85927308e-01 -6.99898124e-01
1.72185779e-01 6.22538388e-01 -2.48250321e-01 -2.70187557e-01
3.72671604e-01 -1.01066375e+00 -8.70621383e-01 -9.35189843e-01
2.08381400e-01 -1.90105274e-01 -3.24950069e-01 -2.65712261... | [12.783671379089355, 0.8111989498138428] |
f087f966-5ac5-4588-a2aa-472369c80c5d | mx2m-masked-cross-modality-modeling-in-domain | 2307.04231 | null | https://arxiv.org/abs/2307.04231v1 | https://arxiv.org/pdf/2307.04231v1.pdf | Mx2M: Masked Cross-Modality Modeling in Domain Adaptation for 3D Semantic Segmentation | Existing methods of cross-modal domain adaptation for 3D semantic segmentation predict results only via 2D-3D complementarity that is obtained by cross-modal feature matching. However, as lacking supervision in the target domain, the complementarity is not always reliable. The results are not ideal when the domain gap ... | ['Wenhui Li', 'Shenghao Zhang', 'Yuanyuan Guan', 'Yonggen Ling', 'Zunran Wang', 'Boxiang Zhang'] | 2023-07-09 | null | null | null | null | ['semantic-segmentation', '3d-semantic-segmentation', 'domain-adaptation'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.70970466e-02 -4.59370902e-03 -3.96513999e-01 -4.39845979e-01
-9.74690318e-01 -5.38879931e-01 6.12748742e-01 -3.98753852e-01
-3.79692502e-02 3.92315179e-01 1.08776733e-01 6.82875961e-02
-2.93598115e-01 -8.15458715e-01 -4.67350036e-01 -7.66568661e-01
2.96823800e-01 7.91812837e-01 7.62969971e-01 -4.95236218... | [9.679058074951172, 1.360481858253479] |
c7da2153-7e56-4d59-b182-7ccd0b51dc9e | siammot-siamese-multi-object-tracking | 2105.11595 | null | https://arxiv.org/abs/2105.11595v1 | https://arxiv.org/pdf/2105.11595v1.pdf | SiamMOT: Siamese Multi-Object Tracking | In this paper, we focus on improving online multi-object tracking (MOT). In particular, we introduce a region-based Siamese Multi-Object Tracking network, which we name SiamMOT. SiamMOT includes a motion model that estimates the instance's movement between two frames such that detected instances are associated. To expl... | ['Joseph Tighe', 'Davide Modolo', 'Xinyu Li', 'Andrew Berneshawi', 'Bing Shuai'] | 2021-05-25 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Shuai_SiamMOT_Siamese_Multi-Object_Tracking_CVPR_2021_paper.pdf | cvpr-2021-1 | ['online-multi-object-tracking'] | ['computer-vision'] | [-4.75825340e-01 -4.64816242e-01 -2.89622605e-01 4.53946888e-02
-5.73767781e-01 -4.79568958e-01 4.66460973e-01 -9.95678529e-02
-6.28937542e-01 5.67881227e-01 -1.31252438e-01 8.75815451e-02
1.70920193e-01 -4.48528081e-01 -9.35160398e-01 -4.71184462e-01
-3.97908896e-01 7.05261886e-01 1.09678352e+00 -1.12800915... | [6.329741954803467, -2.0412309169769287] |
3343f38d-8304-40f6-8a65-522b1cbefaea | teravr-empowers-precise-reconstruction-of | null | null | https://doi.org/10.1038/s41467-019-11443-y | https://www.nature.com/articles/s41467-019-11443-y.pdf | TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain | Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as ... | ['Li-Juan Liu', 'Michael Hawrylycz', 'Zhi Zhou', 'Zongcai Ruan', 'Yike Guo', 'Hongkui Zeng', 'Zhongze Gu', 'Yun Wang', 'Yimin Wang', 'Wei Xie', 'Qingming Luo', 'Lingsheng Kong', 'Renjie Chai', 'Qi Li', 'Yaoyao Li', 'Xiangfeng Luo', 'Ning Zhong', 'Hanchuan Peng'] | 2019-08-02 | null | null | null | nature-communicationsvolume-10-article-number | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [-2.87969291e-01 -4.51516002e-01 7.90879607e-01 -3.67298275e-01
-5.70058048e-01 -9.24259961e-01 3.33004445e-01 3.41160685e-01
-8.47541094e-01 4.64505345e-01 -9.86327156e-02 -3.86993200e-01
1.67728350e-01 -4.49945271e-01 -6.68648064e-01 -6.15782678e-01
3.66154476e-03 6.39031053e-01 5.52345455e-01 -1.43885612... | [14.152273178100586, -3.119887113571167] |
5adb05c3-3d0b-4420-bbaa-e12723aa582b | nlip-noise-robust-language-image-pre-training | 2212.07086 | null | https://arxiv.org/abs/2212.07086v2 | https://arxiv.org/pdf/2212.07086v2.pdf | NLIP: Noise-robust Language-Image Pre-training | Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image captioning. However, their successes highly rely on the scale and quality of web-crawled data that naturally contain incomplete and noisy informa... | ['Xiaodan Liang', 'Chunjing Xu', 'Xiwen Liang', 'Hang Xu', 'Jianhua Han', 'Yanxin Long', 'Runhui Huang'] | 2022-12-14 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 5.91482580e-01 -2.85598993e-01 5.77478809e-03 -2.08494619e-01
-1.36970842e+00 -4.55372840e-01 6.30186319e-01 -9.12600011e-02
-4.05861229e-01 5.42302370e-01 4.17503208e-01 -1.37935653e-01
3.60654965e-02 -6.19511366e-01 -1.00265360e+00 -6.76924884e-01
7.24722981e-01 3.24144244e-01 1.25113025e-01 -3.12813133... | [11.082064628601074, 0.8100055456161499] |
6e5daadf-5e91-4a44-a75c-ce284bf1a505 | 83-imagenet-accuracy-in-one-hour | 2011.00071 | null | https://arxiv.org/abs/2011.00071v2 | https://arxiv.org/pdf/2011.00071v2.pdf | Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour | EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks. Currently, EfficientNets can take on the order of days to train; for example, training an EfficientNet-B0 model takes 23 hours on a Cloud TPU v2-8 node. In this paper, we explore techniq... | ['Sameer Kumar', 'Yang You', 'Quoc Le', 'Mingxing Tan', 'James Demmel', 'Hieu Pham', 'Arissa Wongpanich'] | 2020-10-30 | null | null | null | null | ['2048'] | ['playing-games'] | [-3.76407802e-01 -4.92247902e-02 -1.84933722e-01 -7.23112226e-01
-1.40979856e-01 -4.38838482e-01 6.06578924e-02 8.75493810e-02
-1.00688148e+00 4.06683087e-01 -4.32916522e-01 -6.47206783e-01
-1.69364020e-01 -8.61026645e-01 -9.40810919e-01 -2.64124185e-01
-2.55207777e-01 5.54851353e-01 2.79166341e-01 3.20966281... | [8.506765365600586, 3.0427117347717285] |
1ec96127-1856-4f8e-8c30-98a8a4cd8f3a | a-realistic-study-of-auto-regressive-language | 2108.11857 | null | https://arxiv.org/abs/2108.11857v2 | https://arxiv.org/pdf/2108.11857v2.pdf | Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition | Despite impressive results of language models for named entity recognition (NER), their generalization to varied textual genres, a growing entity type set, and new entities remains a challenge. Collecting thousands of annotations in each new case for training or fine-tuning is expensive and time-consuming. In contrast,... | ['Romain Hennequin', 'Elena V. Epure'] | 2021-08-26 | null | https://aclanthology.org/2022.lrec-1.151 | https://aclanthology.org/2022.lrec-1.151.pdf | lrec-2022-6 | ['few-shot-ner'] | ['natural-language-processing'] | [-2.61259884e-01 -1.13342516e-02 -2.12886587e-01 -2.70800799e-01
-7.07164943e-01 -6.82824790e-01 7.54138649e-01 2.59856820e-01
-1.29220140e+00 9.85841036e-01 3.45787793e-01 -1.57727405e-01
1.28978297e-01 -9.27546442e-01 -6.88840747e-01 -3.20400894e-01
-1.45403564e-01 7.01647282e-01 2.13418946e-01 -3.79879892... | [9.674999237060547, 9.3273286819458] |
8eb324db-d02e-47f7-b3f4-421ff96e8656 | g1020-a-benchmark-retinal-fundus-image | 2006.09158 | null | https://arxiv.org/abs/2006.09158v1 | https://arxiv.org/pdf/2006.09158v1.pdf | G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection | Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD). A few small datasets that are available for research community usually suffer from impractic... | ['Sheraz Ahmed', 'Wolfgang Neumeier', 'Muhammad Naseer Bajwa', 'Muhammad Imran Malik', 'Gur Amrit Pal Singh', 'Andreas Dengel'] | 2020-05-28 | null | null | null | null | ['optic-cup-segmentation'] | ['medical'] | [ 2.73248196e-01 9.95984674e-02 2.65610933e-01 -2.38261923e-01
-5.07588446e-01 -3.69830400e-01 1.61121234e-01 -2.09008873e-01
-4.93183553e-01 7.18881249e-01 2.24304408e-01 -5.53604662e-01
-3.32623720e-01 -4.06785607e-01 9.53930765e-02 -6.91545784e-01
-2.78387249e-01 5.14747977e-01 1.94252402e-01 2.11737886... | [15.816308975219727, -4.006500244140625] |
9d84bc73-92f0-4ac7-bee2-93d26dd9f6d0 | relightable-neural-human-assets-from-multi | 2212.07648 | null | https://arxiv.org/abs/2212.07648v3 | https://arxiv.org/pdf/2212.07648v3.pdf | Relightable Neural Human Assets from Multi-view Gradient Illuminations | Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under the same illumination. Although valuable for modeling tasks, they are... | ['Jingyi Yu', 'Lan Xu', 'Wenzheng Chen', 'Kuixiang Shao', 'Qixuan Zhang', 'Teng Xu', 'Di wu', 'Kai He', 'Taotao Zhou'] | 2022-12-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Relightable_Neural_Human_Assets_From_Multi-View_Gradient_Illuminations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Relightable_Neural_Human_Assets_From_Multi-View_Gradient_Illuminations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-relighting'] | ['computer-vision'] | [ 2.25166470e-01 -1.08394511e-01 1.91984624e-01 -2.33664095e-01
-2.61227190e-01 -3.72969389e-01 5.11004984e-01 -6.45135283e-01
2.30130762e-01 5.98696947e-01 2.24161863e-01 1.34524807e-01
4.60072398e-01 -9.54617083e-01 -8.76204491e-01 -5.42632639e-01
3.82787049e-01 2.66087502e-01 -1.86769024e-01 -4.37929809... | [12.34485912322998, -0.6283774375915527] |
0b421155-75e9-4f19-b970-5f084dfdc49f | ssh-a-self-supervised-framework-for-image | 2108.06805 | null | https://arxiv.org/abs/2108.06805v2 | https://arxiv.org/pdf/2108.06805v2.pdf | SSH: A Self-Supervised Framework for Image Harmonization | Image harmonization aims to improve the quality of image compositing by matching the "appearance" (\eg, color tone, brightness and contrast) between foreground and background images. However, collecting large-scale annotated datasets for this task requires complex professional retouching. Instead, we propose a novel Se... | ['Zhangyang Wang', 'Sarah Kong', 'Sohrab Amirghodsi', 'Simon Chen', 'Kalyan Sunkavalli', 'Zhe Lin', 'Yilin Wang', 'Jianming Zhang', 'He Zhang', 'Yifan Jiang'] | 2021-08-15 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jiang_SSH_A_Self-Supervised_Framework_for_Image_Harmonization_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jiang_SSH_A_Self-Supervised_Framework_for_Image_Harmonization_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-harmonization'] | ['computer-vision'] | [ 3.77088338e-01 -1.55270398e-01 8.54207352e-02 -2.74887234e-01
-9.23062503e-01 -5.80780327e-01 4.89703089e-01 -3.69699299e-02
-4.29149009e-02 5.89979529e-01 -3.04686222e-02 -2.51392107e-02
3.58951896e-01 -6.08429432e-01 -7.29305625e-01 -8.08807611e-01
5.08672237e-01 6.60425127e-02 8.31299126e-02 -3.25303912... | [11.262516975402832, -1.1942129135131836] |
a4d0e9ae-8293-4664-a820-c93cd01de780 | pali-x-on-scaling-up-a-multilingual-vision | 2305.18565 | null | https://arxiv.org/abs/2305.18565v1 | https://arxiv.org/pdf/2305.18565v1.pdf | PaLI-X: On Scaling up a Multilingual Vision and Language Model | We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captio... | ['Radu Soricut', 'Neil Houlsby', 'Xiaohua Zhai', 'Anelia Angelova', 'Mojtaba Seyedhosseini', 'Alexander Kolesnikov', 'Keran Rong', 'Yuanzhong Xu', 'Anurag Arnab', 'Daniel Keysers', 'Yang Li', 'Andreas Peter Steiner', 'Kenton Lee', 'Julien Amelot', 'Lucas Beyer', 'Ibrahim Alabdulmohsin', 'Gang Li', 'Austin Waters', 'Fil... | 2023-05-29 | null | null | null | null | ['video-captioning', 'video-question-answering'] | ['computer-vision', 'computer-vision'] | [ 2.43888453e-01 -4.12787884e-01 -2.35381886e-01 -2.17595860e-01
-1.34690177e+00 -6.88897789e-01 1.05675983e+00 1.71878040e-01
-7.45948553e-01 4.77419853e-01 3.36187214e-01 -3.75652373e-01
4.00505483e-01 -3.41629088e-01 -1.26076138e+00 -3.21764857e-01
1.50837943e-01 7.31782734e-01 4.04294640e-01 2.80340631... | [10.855392456054688, 1.5712324380874634] |
35a465dd-4f4b-48cc-9a13-fff32d0ec9d4 | evaluation-of-deep-learning-based-pose | 1602.09065 | null | http://arxiv.org/abs/1602.09065v3 | http://arxiv.org/pdf/1602.09065v3.pdf | Evaluation of Deep Learning based Pose Estimation for Sign Language Recognition | Human body pose estimation and hand detection are two important tasks for
systems that perform computer vision-based sign language recognition(SLR).
However, both tasks are challenging, especially when the input is color videos,
with no depth information. Many algorithms have been proposed in the literature
for these t... | ['Amir Ghaderi', 'Vassilis Athitsos', 'Srujana Gattupalli'] | 2016-02-29 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-2.89994568e-01 -5.41314185e-01 -3.76491934e-01 -3.09306234e-01
-7.16355681e-01 -3.76363367e-01 2.92637050e-01 -8.66229236e-01
-8.35280895e-01 5.53534329e-01 4.79190230e-01 1.78365111e-02
2.91100860e-01 -1.95924118e-01 -5.26584923e-01 -4.07354414e-01
-1.37224317e-01 7.53283799e-01 6.87025964e-01 -3.45277667... | [9.131863594055176, -6.44445276260376] |
48eb0e8b-816e-4f8d-80a3-2b80f6b51769 | realistic-simulation-of-users-for-it-systems | 2111.11785 | null | https://arxiv.org/abs/2111.11785v1 | https://arxiv.org/pdf/2111.11785v1.pdf | Realistic simulation of users for IT systems in cyber ranges | Generating user activity is a key capability for both evaluating security monitoring tools as well as improving the credibility of attacker analysis platforms (e.g., honeynets). In this paper, to generate this activity, we instrument each machine by means of an external agent. This agent combines both deterministic and... | ['Adrien Bécue', 'Éric Totel', 'Benjamin Costé', 'Alexandre Dey'] | 2021-11-23 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [-1.58180907e-01 -3.21061462e-01 -1.91901252e-01 -1.54442161e-01
-3.59938651e-01 -9.06476915e-01 9.68692422e-01 2.24220723e-01
-2.49534220e-01 5.84091544e-01 4.31697369e-02 -7.82159567e-01
2.29186609e-01 -1.07076049e+00 -2.60941774e-01 -3.78505677e-01
-1.86803937e-02 3.77863288e-01 3.66597682e-01 -5.60871325... | [5.474369525909424, 7.3453569412231445] |
53a07257-1040-4f36-8a06-d3e2dd59039b | the-mixing-method-low-rank-coordinate-descent | 1706.00476 | null | http://arxiv.org/abs/1706.00476v3 | http://arxiv.org/pdf/1706.00476v3.pdf | The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints | In this paper, we propose a low-rank coordinate descent approach to
structured semidefinite programming with diagonal constraints. The approach,
which we call the Mixing method, is extremely simple to implement, has no free
parameters, and typically attains an order of magnitude or better improvement
in optimization pe... | ['Wei-Cheng Chang', 'Po-Wei Wang', 'J. Zico Kolter'] | 2017-06-01 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [ 6.81096164e-04 2.37561926e-01 -4.87855226e-01 -1.48912311e-01
-1.17188466e+00 -8.99794579e-01 1.59406275e-01 8.53606611e-02
-3.06713909e-01 5.58265507e-01 2.82698154e-01 -4.82253581e-01
-5.27479470e-01 -2.94800907e-01 -7.47872293e-01 -8.79511952e-01
-2.48779118e-01 1.05773270e+00 -3.22270155e-01 -3.17535937... | [7.046410083770752, 4.560855388641357] |
5225ee29-bad1-4541-a930-4a5ad27195bd | predicting-target-language-ccg-supertags | 1702.01147 | null | http://arxiv.org/abs/1702.01147v2 | http://arxiv.org/pdf/1702.01147v2.pdf | Predicting Target Language CCG Supertags Improves Neural Machine Translation | Neural machine translation (NMT) models are able to partially learn syntactic
information from sequential lexical information. Still, some complex syntactic
phenomena such as prepositional phrase attachment are poorly modeled. This work
aims to answer two questions: 1) Does explicitly modeling target language
syntax he... | ['Marcin Junczys-Dowmunt', 'Rico Sennrich', 'Alexandra Birch', 'Maria Nadejde', 'Tomasz Dwojak', 'Siva Reddy', 'Philipp Koehn'] | 2017-02-03 | predicting-target-language-ccg-supertags-1 | https://aclanthology.org/W17-4707 | https://aclanthology.org/W17-4707.pdf | ws-2017-9 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [-2.02945635e-01 2.21245992e-03 -6.51929021e-01 -5.41157484e-01
-1.07150209e+00 -6.58986568e-01 4.72631514e-01 1.55555353e-01
-7.78748989e-01 9.93856370e-01 5.35018623e-01 -7.67358184e-01
2.52251416e-01 -5.69718063e-01 -9.12827313e-01 -4.54296768e-01
3.84401344e-02 8.86332333e-01 -9.61755216e-02 -5.30565739... | [11.49057674407959, 10.240463256835938] |
9c43a316-79e9-427d-a2b4-eb4dbf6de84a | inpars-toolkit-a-unified-and-reproducible | 2307.04601 | null | https://arxiv.org/abs/2307.04601v1 | https://arxiv.org/pdf/2307.04601v1.pdf | InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval | Recent work has explored Large Language Models (LLMs) to overcome the lack of training data for Information Retrieval (IR) tasks. The generalization abilities of these models have enabled the creation of synthetic in-domain data by providing instructions and a few examples on a prompt. InPars and Promptagator have pion... | ['Rodrigo Nogueira', 'Jakub Zavrel', 'Roberto Lotufo', 'Vitor Jeronymo', 'Luiz Bonifacio', 'Hugo Abonizio'] | 2023-07-10 | null | null | null | null | ['synthetic-data-generation', 'retrieval', 'synthetic-data-generation', 'information-retrieval'] | ['medical', 'methodology', 'miscellaneous', 'natural-language-processing'] | [-7.03626648e-02 -1.01089939e-01 -1.62246808e-01 -3.16681713e-01
-1.45972896e+00 -8.15343678e-01 8.78704071e-01 6.85577467e-02
-4.60876912e-01 8.02040756e-01 3.19319040e-01 -2.60679156e-01
-1.26086641e-02 -7.08619416e-01 -5.73175550e-01 -4.25997466e-01
1.38343066e-01 9.89445627e-01 6.47980496e-02 -6.26170099... | [11.288629531860352, 8.736373901367188] |
f37d92b0-90dd-41d9-a82e-32309a12ec42 | cluster-based-sampling-in-hindsight | 2208.14741 | null | https://arxiv.org/abs/2208.14741v1 | https://arxiv.org/pdf/2208.14741v1.pdf | Cluster-based Sampling in Hindsight Experience Replay for Robot Control | In multi-goal reinforcement learning in an environment, agents learn policies to achieve multiple goals by using experiences gained from interactions with the environment. With a sparse binary reward, training agents is particularly challenging, due to a lack of successful experiences. To solve this problem, hindsight ... | ['Dongsoo Har', 'TaeYoung Kim'] | 2022-08-31 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [ 5.02736233e-02 2.48132318e-01 -4.41909884e-04 -7.59055540e-02
-5.68479121e-01 -2.92009562e-01 4.88271803e-01 3.31770658e-01
-7.90991783e-01 1.33461118e+00 -1.48687214e-01 3.79109085e-01
-7.71491766e-01 -5.63434899e-01 -4.36599791e-01 -1.17909992e+00
-1.98245600e-01 5.31654179e-01 2.60856207e-02 -1.86049685... | [3.973743200302124, 1.763352870941162] |
4a679d1c-b5bb-472a-b289-ae8d1d4c428c | end-to-end-video-text-spotting-with | 2203.10539 | null | https://arxiv.org/abs/2203.10539v3 | https://arxiv.org/pdf/2203.10539v3.pdf | End-to-End Video Text Spotting with Transformer | Recent video text spotting methods usually require the three-staged pipeline, i.e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results. These methods typically follow the tracking-by-match paradigm and develop sophisticated pipelines. In... | ['Ping Luo', 'Yuanqiang Cai', 'Hong Zhou', 'Chunhua Shen', 'Ying Fu', 'Debing Zhang', 'Weijia Wu'] | 2022-03-20 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.75828481e-01 -1.00414646e+00 -2.11748034e-01 -1.13909096e-01
-7.66151786e-01 -5.30651629e-01 6.89740300e-01 -2.37745151e-01
-4.39783782e-01 6.83164150e-02 1.79748937e-01 -2.96558380e-01
4.15411621e-01 -3.69760513e-01 -7.11441457e-01 -4.06808913e-01
5.46768129e-01 2.99013317e-01 5.85726738e-01 2.28406429... | [11.961345672607422, 2.17992901802063] |
6da3b8c5-c0d7-4239-a48f-8ceef50388d4 | hd-maps-fine-grained-road-segmentation-by | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Mattyus_HD_Maps_Fine-Grained_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Mattyus_HD_Maps_Fine-Grained_CVPR_2016_paper.pdf | HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images | In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inferenc... | ['Sanja Fidler', 'Raquel Urtasun', 'Shenlong Wang', 'Gellert Mattyus'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['road-segementation'] | ['computer-vision'] | [ 1.87540248e-01 6.15300909e-02 2.09242180e-01 -4.32516366e-01
-6.44349039e-01 -8.01333368e-01 8.70440423e-01 -9.29126740e-02
-5.41594982e-01 7.58378327e-01 -5.72848320e-02 -5.11070132e-01
-1.69942066e-01 -1.32979977e+00 -7.53226578e-01 -3.88045251e-01
1.78349158e-03 7.20664322e-01 4.43119228e-01 -2.85524547... | [8.414305686950684, -2.0275676250457764] |
5c9a1332-e0ea-47df-9281-2178519c07ec | maximal-clique-based-non-autoregressive-open | null | null | https://aclanthology.org/2021.emnlp-main.764 | https://aclanthology.org/2021.emnlp-main.764.pdf | Maximal Clique Based Non-Autoregressive Open Information Extraction | Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. In essence, the facts contained in plain text are unordered. However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an ... | ['Bin Wang', 'Limin Sun', 'Hongsong Zhu', 'Tingwen Liu', 'Yucheng Wang', 'Bowen Yu'] | null | null | null | null | emnlp-2021-11 | ['open-information-extraction'] | ['natural-language-processing'] | [-3.69823864e-03 7.69692183e-01 -8.35943520e-02 -2.12211996e-01
-9.31976974e-01 -4.72889960e-01 2.47274756e-01 5.01876354e-01
-1.20304422e-02 9.95210052e-01 4.15679157e-01 -2.20828518e-01
-2.78790414e-01 -1.15885973e+00 -9.50301707e-01 -5.72595954e-01
-1.38176516e-01 7.12019026e-01 1.55935794e-01 1.02444887... | [9.450746536254883, 8.561880111694336] |
a57bb8a2-cdbe-47a7-8680-9db71a152b52 | privacy-preserving-structure-from-motion | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1273_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460324.pdf | Privacy Preserving Structure-from-Motion | Over the last years, visual localization and mapping solutions have been adopted by an increasing number of mixed reality and robotics systems. The recent trend towards cloud-based localization and mapping systems has raised significant privacy concerns. These are mainly grounded by the fact that these services require... | ['Johannes L. Schönberger', 'Viktor Larsson', 'Marc Pollefeys', 'Pablo Speciale', 'Marcel Geppert'] | null | null | null | null | eccv-2020-8 | ['image-based-localization'] | ['computer-vision'] | [-1.47192135e-01 -2.21274067e-02 4.80725104e-03 -5.85031509e-01
-7.22691357e-01 -9.97403145e-01 6.90345824e-01 6.44575655e-02
-6.23266339e-01 7.62874126e-01 -6.90502077e-02 -1.29593536e-01
2.64111906e-02 -6.85266554e-01 -8.21380258e-01 -4.58507389e-01
-1.29213324e-02 2.83660412e-01 4.07568038e-01 2.62167808... | [7.591151237487793, -2.1216816902160645] |
3579445a-c3a8-4fd4-af81-332fc6d4bbf4 | isa-net-improved-spatial-attention-network | 2211.02256 | null | https://arxiv.org/abs/2211.02256v1 | https://arxiv.org/pdf/2211.02256v1.pdf | ISA-Net: Improved spatial attention network for PET-CT tumor segmentation | Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of the... | ['Zhanli Hua', 'Xiaohua Zhuc', 'Tianye Niu', 'Dong Liang', 'Haining Wangg', 'Fan Yang', 'Lu Zhang', 'Na Zhang', 'HaiYan Wang', 'Hao Shen', 'Zixiang Chen', 'Guoshuai Wang', 'Sijuan Zou', 'Zhengyong Huang'] | 2022-11-04 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 1.65350273e-01 -1.25879869e-01 -4.28739041e-01 -2.64049798e-01
-7.77347744e-01 -3.27646852e-01 9.00551379e-02 -1.25560053e-02
-7.24742293e-01 5.51509500e-01 1.09587699e-01 -4.37927753e-01
8.84172022e-02 -6.64379358e-01 -1.26551718e-01 -1.04651988e+00
3.92683297e-01 5.05335271e-01 6.00658357e-01 -8.37630555... | [14.576802253723145, -2.4167816638946533] |
c0fdb65f-ea59-4e03-9ea6-6ebce9d542fb | an-empirical-survey-of-unsupervised-text | 2012.03468 | null | https://arxiv.org/abs/2012.03468v1 | https://arxiv.org/pdf/2012.03468v1.pdf | An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data | The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, there has been a noticeable improvement in text representation. It is, however, unclear whether these improvements translate to noisy user-gener... | ['Soroush Vosoughi', 'Ruibo Liu', 'Weicheng Ma', 'Jason Wei', 'Chongyang Gao', 'Lili Wang'] | 2020-12-07 | null | https://aclanthology.org/2020.wnut-1.27 | https://aclanthology.org/2020.wnut-1.27.pdf | emnlp-wnut-2020-11 | ['text-clustering'] | ['natural-language-processing'] | [ 9.73567292e-02 5.09732068e-02 8.94529149e-02 -4.53433096e-01
-1.03331530e+00 -6.40901864e-01 9.46902692e-01 6.64186180e-01
-4.51857090e-01 5.40803075e-01 8.93570662e-01 -5.02633035e-01
-6.33898079e-02 -5.73629260e-01 -2.36741692e-01 -4.40297991e-01
-6.61778729e-03 8.50316107e-01 9.69530195e-02 -5.59976876... | [10.482566833496094, 8.354775428771973] |
db72afc6-4505-4807-9308-cb4121a5d438 | on-merging-feature-engineering-and-deep | 2207.06096 | null | https://arxiv.org/abs/2207.06096v2 | https://arxiv.org/pdf/2207.06096v2.pdf | On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG | Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an open question. Moreover, it remains unclear whether combining DL with... | ['Joachim A. Behar', 'Antonio Luiz P. Ribeiro', 'Antônio H. Ribeiro', 'Jesse Read', 'Eran Zvuloni'] | 2022-07-13 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 2.35397622e-01 2.83973217e-02 -2.71875202e-03 -4.09157962e-01
-9.72714365e-01 -4.27274436e-01 2.21905008e-01 4.98495996e-01
-5.41166008e-01 9.03259099e-01 -1.20426200e-01 -7.74633050e-01
-4.67358261e-01 -4.29480731e-01 -3.01057011e-01 -6.74685955e-01
-2.83536911e-01 7.51523197e-01 -3.18657368e-01 3.14463109... | [14.307344436645508, 3.3087663650512695] |
52a97910-5db0-45dc-a192-4a443ba09158 | temporal-scale-estimation-for-oversampled | 2109.10937 | null | https://arxiv.org/abs/2109.10937v1 | https://arxiv.org/pdf/2109.10937v1.pdf | Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment | Spreading processes on graphs arise in a host of application domains, from the study of online social networks to viral marketing to epidemiology. Various discrete-time probabilistic models for spreading processes have been proposed. These are used for downstream statistical estimation and prediction problems, often in... | ['Petko Bogdanov', 'Carolyn Kaminski', 'Abram Magner'] | 2021-09-22 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 4.51707602e-01 1.16335824e-01 -5.13191335e-02 -5.39996177e-02
-4.13474858e-01 -6.57189667e-01 1.02597618e+00 3.62735301e-01
-3.73051554e-01 4.36593533e-01 4.06909585e-02 -3.89371693e-01
-2.16853857e-01 -9.05840278e-01 -6.48904920e-01 -6.75037801e-01
-5.40900946e-01 1.04784358e+00 3.52769703e-01 8.65122229... | [6.769892692565918, 5.033936500549316] |
0d41ff85-2f52-4ff6-aafa-a35518d08c97 | data-efficient-autoregressive-document | 2211.09388 | null | https://arxiv.org/abs/2211.09388v1 | https://arxiv.org/pdf/2211.09388v1.pdf | Data-Efficient Autoregressive Document Retrieval for Fact Verification | Document retrieval is a core component of many knowledge-intensive natural language processing task formulations such as fact verification and question answering. Sources of textual knowledge, such as Wikipedia articles, condition the generation of answers from the models. Recent advances in retrieval use sequence-to-s... | ['James Thorne'] | 2022-11-17 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 1.71410337e-01 1.47710219e-01 -4.24840331e-01 -4.23456252e-01
-1.51906097e+00 -7.02831209e-01 8.23808134e-01 4.78823394e-01
-6.43110096e-01 6.60468459e-01 4.18537945e-01 -3.35029304e-01
-1.78524435e-01 -6.72381401e-01 -8.52908790e-01 -2.11586788e-01
3.20626944e-02 7.48159230e-01 4.93303746e-01 -4.30352420... | [11.4216947555542, 7.877123832702637] |
dac7ec4a-ae68-4460-a0c5-021458a4e84d | learning-to-automate-cryo-electron-microscopy | 2112.01534 | null | https://arxiv.org/abs/2112.01534v2 | https://arxiv.org/pdf/2112.01534v2.pdf | Learning to automate cryo-electron microscopy data collection with Ptolemy | Over the past decade, cryogenic electron microscopy (cryo-EM) has emerged as a primary method for determining near-native, near-atomic resolution 3D structures of biological macromolecules. In order to meet increasing demand for cryo-EM, automated methods to improve throughput and efficiency while lowering costs are ne... | ['Tristan Bepler', 'Anchi Cheng', 'Alex J. Noble', 'Paul T. Kim'] | 2021-12-01 | null | null | null | null | ['cryogenic-electron-microscopy-cryo-em'] | ['computer-vision'] | [ 1.00096002e-01 -5.41684747e-01 6.38826489e-01 -5.89699388e-01
-9.83487487e-01 -7.64662743e-01 2.19467863e-01 3.93807292e-01
-1.01182902e+00 6.72286093e-01 -5.66686511e-01 -4.87416416e-01
2.42822409e-01 -3.34214896e-01 -8.56838226e-01 -7.12316096e-01
-1.37448236e-01 9.72764909e-01 4.17782307e-01 1.47264421... | [13.464141845703125, -3.0876641273498535] |
ac5430dd-5227-4233-866b-cbba4649da9e | procst-boosting-semantic-segmentation-using | 2204.11891 | null | https://arxiv.org/abs/2204.11891v2 | https://arxiv.org/pdf/2204.11891v2.pdf | ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer | Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, synthetic and real world data have a domain gap. Reducing this gap, also known as domain adaptation, has been widely studied in recent years.... | ['Raja Giryes', 'Shady Abu-Hussein', 'Shahaf Ettedgui'] | 2022-04-25 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.23113275e-01 2.55147964e-01 -8.80831331e-02 -4.20441359e-01
-1.12068117e+00 -6.89774215e-01 7.26453662e-01 -2.33097941e-01
-5.51720262e-01 8.45858693e-01 -2.37702489e-01 -3.47156942e-01
3.02304000e-01 -9.27585006e-01 -1.08320963e+00 -5.42830229e-01
8.02173436e-01 8.04712534e-01 4.94399607e-01 -3.26007843... | [9.795074462890625, 1.4056127071380615] |
39bcb9e4-da00-45a2-99c1-b73d632be13f | improving-multimodal-named-entity-recognition | null | null | https://aclanthology.org/2020.acl-main.306 | https://aclanthology.org/2020.acl-main.306.pdf | Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer | In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought b... | ['Rui Xia', 'Jing Jiang', 'Jianfei Yu', 'Li Yang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['multi-modal-named-entity-recognition'] | ['natural-language-processing'] | [ 6.82794675e-02 -1.06501184e-01 -2.49303490e-01 -2.34220341e-01
-9.55666304e-01 -4.92570609e-01 5.19396067e-01 4.16696519e-02
-4.38137263e-01 4.02268618e-01 4.54981565e-01 -3.45726311e-01
3.78908575e-01 -5.54048657e-01 -6.48997009e-01 -4.22759175e-01
3.94113153e-01 -3.30538899e-02 5.09781539e-01 -2.27543727... | [10.789027214050293, 1.5515800714492798] |
46ec63d0-3049-47d7-a763-3b1af6f0cc48 | neural-collaborative-ranking | 1808.04957 | null | http://arxiv.org/abs/1808.04957v1 | http://arxiv.org/pdf/1808.04957v1.pdf | Neural Collaborative Ranking | Recommender systems are aimed at generating a personalized ranked list of
items that an end user might be interested in. With the unprecedented success
of deep learning in computer vision and speech recognition, recently it has
been a hot topic to bridge the gap between recommender systems and deep neural
network. And ... | ['Xu Congfu', 'Cao Yi', 'Yang Xin', 'Song Bo'] | 2018-08-15 | null | null | null | null | ['collaborative-ranking'] | ['graphs'] | [-2.44724508e-02 -3.91544342e-01 -2.73544461e-01 -6.97892964e-01
-3.88942510e-01 -3.12016934e-01 6.12056911e-01 -4.42689627e-01
-2.33189359e-01 4.36770231e-01 7.55373418e-01 -1.88543290e-01
-6.64947033e-01 -9.20064330e-01 -5.36899745e-01 -6.14535332e-01
-8.26315954e-02 4.94131744e-01 -3.15619886e-01 -5.23179352... | [10.159652709960938, 5.629669189453125] |
5d169344-c9f3-4d89-b6f3-7be8dab11908 | deepflash-turning-a-flash-selfie-into-a | 1901.04252 | null | https://arxiv.org/abs/1901.04252v2 | https://arxiv.org/pdf/1901.04252v2.pdf | DeepFlash: Turning a Flash Selfie into a Studio Portrait | We present a method for turning a flash selfie taken with a smartphone into a photograph as if it was taken in a studio setting with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in an ad-hoc acquisition campaign. Each pair consists of one photograph ... | ['Ugo Erra', 'Fabio Ganovelli', 'Francesco Banterle', 'Roberto Scopigno', 'Paolo Cignoni', 'Nicola Capece'] | 2019-01-14 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [ 7.93108046e-01 1.05902977e-01 6.54172719e-01 -4.99516577e-01
-3.23206604e-01 -5.18589914e-01 2.51061082e-01 -6.25546038e-01
-1.61723673e-01 6.74925685e-01 6.48875535e-02 -3.00646693e-01
4.92121667e-01 -5.75934350e-01 -1.07993519e+00 -5.17812669e-01
4.62021410e-01 -3.89704943e-01 3.35894823e-02 1.24955691... | [10.328438758850098, -2.6937763690948486] |
b52bfe2e-48ec-4257-bc42-37df44448b08 | uncertainty-guided-depth-fusion-for-spike | 2208.12653 | null | https://arxiv.org/abs/2208.12653v2 | https://arxiv.org/pdf/2208.12653v2.pdf | Uncertainty Guided Depth Fusion for Spike Camera | Depth estimation is essential for various important real-world applications such as autonomous driving. However, it suffers from severe performance degradation in high-velocity scenario since traditional cameras can only capture blurred images. To deal with this problem, the spike camera is designed to capture the pixe... | ['Shanghang Zhang', 'Tiejun Huang', 'Li Du', 'Lei Ma', 'Ming Lu', 'Jiyuan Zhang', 'Xiaobao Wei', 'Jiaming Liu', 'Jianing Li'] | 2022-08-26 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-7.86092356e-02 -4.86496657e-01 2.08119705e-01 -5.30871153e-01
-1.05285072e+00 -3.94882202e-01 4.72036839e-01 -5.51278949e-01
-6.06092811e-01 9.92192984e-01 1.43773183e-01 1.97379902e-01
2.01871768e-01 -4.93574440e-01 -8.76908302e-01 -1.02903521e+00
4.49428648e-01 2.20060453e-01 3.83293539e-01 2.96143115... | [8.892863273620605, -2.3481199741363525] |
dda57cc9-a326-4531-9482-c46b5614dc18 | rule-based-vs-neural-net-approaches-to | null | null | https://aclanthology.org/W18-3803 | https://aclanthology.org/W18-3803.pdf | Rule-based vs. Neural Net Approaches to Semantic Textual Similarity | This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper... | ['Linrui Zhang', 'Dan Moldovan'] | 2018-08-01 | null | null | null | coling-2018-8 | ['sentence-pair-modeling'] | ['natural-language-processing'] | [ 1.30540967e-01 -7.73230754e-03 -9.95792672e-02 -4.84377533e-01
-3.26826990e-01 -1.57630026e-01 6.81244075e-01 4.12095517e-01
-6.95168078e-01 4.49497104e-01 3.26306939e-01 -7.24236906e-01
-3.39650571e-01 -9.55066144e-01 -4.88414109e-01 2.26815224e-01
2.42813900e-01 7.06764936e-01 1.95027992e-01 -9.52831089... | [10.759201049804688, 9.557857513427734] |
110f952d-c222-4f15-951e-60e0252338a6 | ove6d-object-viewpoint-encoding-for-depth | 2203.01072 | null | https://arxiv.org/abs/2203.01072v3 | https://arxiv.org/pdf/2203.01072v3.pdf | OVE6D: Object Viewpoint Encoding for Depth-based 6D Object Pose Estimation | This paper proposes a universal framework, called OVE6D, for model-based 6D object pose estimation from a single depth image and a target object mask. Our model is trained using purely synthetic data rendered from ShapeNet, and, unlike most of the existing methods, it generalizes well on new real-world objects without ... | ['Esa Rahtu', 'Janne Heikkilä', 'Dingding Cai'] | 2022-03-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cai_OVE6D_Object_Viewpoint_Encoding_for_Depth-Based_6D_Object_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cai_OVE6D_Object_Viewpoint_Encoding_for_Depth-Based_6D_Object_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['6d-pose-estimation'] | ['computer-vision'] | [ 2.45050471e-02 1.79991528e-01 -1.36339888e-01 -5.71073592e-01
-6.60303354e-01 -7.19885170e-01 6.27207935e-01 -3.49557221e-01
-3.38866770e-01 1.93729475e-01 -1.09538294e-01 -2.03545336e-02
1.30412683e-01 -3.32398027e-01 -1.00905442e+00 -2.21415639e-01
-2.02641543e-02 1.20235837e+00 5.34231842e-01 2.18637332... | [7.619314193725586, -2.6631405353546143] |
0b27344e-5854-4bdb-9cc1-9e8529f6da59 | hierarchical-multi-instance-multi-label | 2305.19419 | null | https://arxiv.org/abs/2305.19419v1 | https://arxiv.org/pdf/2305.19419v1.pdf | Hierarchical Multi-Instance Multi-Label Learning for Detecting Propaganda Techniques | Since the introduction of the SemEval 2020 Task 11 (Martino et al., 2020a), several approaches have been proposed in the literature for classifying propaganda based on the rhetorical techniques used to influence readers. These methods, however, classify one span at a time, ignoring dependencies from the labels of other... | ['Bhuwan Dhingra', 'Anni Chen'] | 2023-05-30 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 1.24330632e-01 2.58868545e-01 -4.86915320e-01 -1.37304798e-01
-8.65452647e-01 -7.48467982e-01 1.09001172e+00 3.96318108e-01
-3.53882194e-01 8.46081138e-01 5.21896183e-01 -5.41377187e-01
-3.48197259e-02 -5.12146115e-01 -6.68859243e-01 -6.08004212e-01
2.49129832e-01 3.07426065e-01 1.08069576e-01 -1.88632831... | [8.505366325378418, 10.670791625976562] |
d4375ac3-d072-4dc0-b1c6-968c7926d294 | its-about-time-turn-entry-timing-for-situated | null | null | https://aclanthology.org/2020.sigdial-1.12 | https://aclanthology.org/2020.sigdial-1.12.pdf | It’s About Time: Turn-Entry Timing For Situated Human-Robot Dialogue | Turn-entry timing is an important requirement for conversation, and one that spoken dialogue systems largely fail at. In this paper, we introduce a computational framework based on work from Psycholinguistics, which is aimed at achieving proper turn-taking timing for situated agents. The approach involves incremental p... | ['Matthias Scheutz', 'Antonio Roque', 'Ravenna Thielstrom', 'Felix Gervits'] | null | null | null | null | sigdial-acl-2020-7 | ['spoken-dialogue-systems'] | ['speech'] | [ 8.74703899e-02 7.05775201e-01 2.65012562e-01 -6.30011976e-01
-6.30498171e-01 -9.07159984e-01 9.67643499e-01 1.44179061e-01
-4.14914817e-01 7.21124709e-01 6.55409455e-01 -6.26431942e-01
3.33407633e-02 -4.67138737e-01 -9.27257389e-02 -2.36985922e-01
9.29185599e-02 7.87116587e-01 2.39102542e-01 -9.16012287... | [12.903738021850586, 7.953948020935059] |
f21d37b1-9888-4685-acb4-2916c73ee777 | hierarchical-dense-correlation-distillation-1 | 2306.15278 | null | https://arxiv.org/abs/2306.15278v1 | https://arxiv.org/pdf/2306.15278v1.pdf | Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract | Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations. Previous methods limited to the semantic feature and prototype representation suffer from coarse segmentation granularity and train-set overfitting. In this work, we design Hierarchicall... | ['Jiaya Jia', 'Jingyong Su', 'Shu Liu', 'Chengyao Wang', 'Xiaoyang Wu', 'Zhuotao Tian', 'Bohao Peng'] | 2023-06-27 | null | null | null | null | ['few-shot-image-segmentation', 'semantic-correspondence'] | ['computer-vision', 'computer-vision'] | [ 3.85127008e-01 2.42037684e-01 -3.64867300e-01 -8.08536351e-01
-1.00465453e+00 -5.12246609e-01 1.30519152e-01 -9.61361974e-02
-2.46606112e-01 3.08141649e-01 -2.29936764e-01 1.72905594e-01
3.79117355e-02 -8.07609081e-01 -7.16155469e-01 -4.05432999e-01
3.29595506e-01 4.32835639e-01 8.61036360e-01 -2.28457704... | [9.600048065185547, 1.0830641984939575] |
51e4fed9-986f-465b-9b12-63ba13d89567 | p-meta-towards-on-device-deep-model | 2206.12705 | null | https://arxiv.org/abs/2206.12705v1 | https://arxiv.org/pdf/2206.12705v1.pdf | p-Meta: Towards On-device Deep Model Adaptation | Data collected by IoT devices are often private and have a large diversity across users. Therefore, learning requires pre-training a model with available representative data samples, deploying the pre-trained model on IoT devices, and adapting the deployed model on the device with local data. Such an on-device adaption... | ['Lothar Thiele', 'Yongxin Tong', 'Zimu Zhou', 'Zhongnan Qu'] | 2022-06-25 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 2.84834020e-02 -1.75437316e-01 -4.84009475e-01 -4.78428066e-01
-7.46043622e-01 -6.72556832e-02 2.65055865e-01 8.88142064e-02
-4.94219661e-01 6.32075429e-01 5.89151494e-02 1.02602072e-01
-3.45056579e-02 -8.75673711e-01 -6.75725043e-01 -6.08107507e-01
2.99672902e-01 5.44898510e-01 3.32462758e-01 5.42345271... | [9.91006088256836, 3.234574794769287] |
c3311eac-8bc3-4055-8f3f-48856b970bdc | agad-adversarial-generative-anomaly-detection | 2304.04211 | null | https://arxiv.org/abs/2304.04211v1 | https://arxiv.org/pdf/2304.04211v1.pdf | AGAD: Adversarial Generative Anomaly Detection | Anomaly detection suffered from the lack of anomalies due to the diversity of abnormalities and the difficulties of obtaining large-scale anomaly data. Semi-supervised anomaly detection methods are often used to solely leverage normal data to detect abnormalities that deviated from the learnt normality distributions. M... | ['Ni Zhang', 'Jian Shi'] | 2023-04-09 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.99900466e-01 -8.96335989e-02 4.21669662e-01 -3.53506565e-01
-8.48284721e-01 -4.73363072e-01 6.19056344e-01 3.32877964e-01
1.07276723e-01 4.53487515e-01 -1.15993097e-01 -2.63797373e-01
-4.70462069e-02 -8.07794988e-01 -5.80600798e-01 -7.27350354e-01
-2.40020201e-01 3.86208266e-01 1.99857429e-01 -2.25357711... | [7.5938873291015625, 2.322298765182495] |
f48514b8-86a5-43e3-ba82-fb5bc2d56d31 | federated-deep-transfer-learning-for-eeg | 2211.10976 | null | https://arxiv.org/abs/2211.10976v3 | https://arxiv.org/pdf/2211.10976v3.pdf | Federated deep transfer learning for EEG decoding using multiple BCI tasks | Deep learning has been successful in BCI decoding. However, it is very data-hungry and requires pooling data from multiple sources. EEG data from various sources decrease the decoding performance due to negative transfer. Recently, transfer learning for EEG decoding has been suggested as a remedy and become subject to ... | ['A. Aldo Faisal', 'Xiaoxi Wei'] | 2022-11-20 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 2.17514366e-01 3.34385335e-02 1.93998754e-01 -4.30227309e-01
-9.12005067e-01 -6.33746862e-01 4.14503098e-01 -2.97541618e-01
-8.00806880e-01 1.52668178e+00 1.98440135e-01 -7.13613182e-02
-2.62547165e-01 -4.05027747e-01 -9.91220832e-01 -8.84308219e-01
-1.71352223e-01 2.08851308e-01 -1.33371532e-01 -9.43153873... | [13.100032806396484, 3.449007749557495] |
cc77fd7f-47f6-452c-9a7d-5dc3b8ca0517 | cueing-a-pioneer-work-of-encoding-human-gaze | 2305.15710 | null | https://arxiv.org/abs/2305.15710v1 | https://arxiv.org/pdf/2305.15710v1.pdf | CUEING: A pioneer work of encoding human gaze for autonomous driving | Recent analysis of incidents involving Autonomous Driving Systems (ADS) has shown that the decision-making process of ADS can be significantly different from that of human drivers. To improve the performance of ADS, it may be helpful to incorporate the human decision-making process, particularly the signals provided by... | ['Xi Zheng', 'Chen Wang', 'Jianchao Lu', 'Yao Deng', 'Yiran Wang', 'Linfeng Liang'] | 2023-05-25 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 5.93755580e-02 3.26079309e-01 5.11829853e-02 -6.66366160e-01
-2.46359810e-01 -1.19521983e-01 2.68145472e-01 -2.62184709e-01
-2.84979880e-01 2.12113291e-01 1.67432815e-01 -3.01270276e-01
1.25527531e-01 -3.82710844e-01 -6.54964983e-01 -6.37393415e-01
3.81767571e-01 -9.47539657e-02 2.05857649e-01 -5.37092030... | [14.130745887756348, 0.077862448990345] |
883e380b-f610-44fb-9c6e-458e61bb1208 | global-and-local-semantic-completion-learning | 2306.07096 | null | https://arxiv.org/abs/2306.07096v1 | https://arxiv.org/pdf/2306.07096v1.pdf | Global and Local Semantic Completion Learning for Vision-Language Pre-training | Cross-modal alignment plays a crucial role in vision-language pre-training (VLP) models, enabling them to capture meaningful associations across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been ... | ['Wei Liu', 'Yujiu Yang', 'Hongfa Wang', 'Wenzhe Zhao', 'Chengfei Cai', 'Weijie Kong', 'Jie Jiang', 'Yatai Ji', 'Rong-Cheng Tu'] | 2023-06-12 | null | null | null | null | ['visual-question-answering-1', 'video-text-retrieval'] | ['computer-vision', 'computer-vision'] | [ 3.03402394e-01 5.39064422e-05 -3.50991100e-01 -4.64185566e-01
-1.06989598e+00 -3.22946936e-01 9.77561474e-01 1.17805436e-01
-3.54463637e-01 3.25966388e-01 4.18492228e-01 -1.56333789e-01
1.68956146e-01 -3.90311927e-01 -1.03631985e+00 -6.94776595e-01
5.83769977e-01 1.91673636e-01 9.53900516e-02 -5.91343194... | [10.819766998291016, 1.4585576057434082] |
b2199e96-b2f8-4a95-9af3-f82be33ebc59 | how-to-train-your-deep-neural-network-with | 1612.07454 | null | http://arxiv.org/abs/1612.07454v1 | http://arxiv.org/pdf/1612.07454v1.pdf | How to Train Your Deep Neural Network with Dictionary Learning | Currently there are two predominant ways to train deep neural networks. The
first one uses restricted Boltzmann machine (RBM) and the second one
autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the
final representation layer is attached to the target to complete the deep
neural network. Autoe... | ['Vanika Singhal', 'Shikha Singh', 'Angshul Majumdar'] | 2016-12-22 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-1.14540130e-01 3.65684360e-01 -1.82641238e-01 -4.09763277e-01
1.79019302e-01 -1.99362323e-01 4.93608505e-01 2.37024814e-01
-7.19267845e-01 8.56127620e-01 2.11248711e-01 -2.01622039e-01
1.60958976e-01 -1.15630877e+00 -7.20789611e-01 -1.04060435e+00
9.46027935e-02 8.10374439e-01 2.42489427e-01 -1.85043678... | [9.207630157470703, 2.937370538711548] |
b6f9cab4-a3ad-4ed3-9d8f-88daf89eff9b | recommending-complementary-products-in-e | 1707.08113 | null | https://arxiv.org/abs/1707.08113v1 | https://arxiv.org/pdf/1707.08113v1.pdf | Recommending Complementary Products in E-Commerce Push Notifications with a Mixture Model Approach | Push notification is a key component for E-commerce mobile applications, which has been extensively used for user growth and engagement. The effectiveness of the push notification is generally measured by message open rate. A push message can contain a recommended product, a shopping news and etc., but often only one o... | ['Qiong Zhang', 'Luo Si', 'Xiaogang Li', 'Huasha Zhao'] | 2017-07-25 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 2.71491885e-01 -1.20866656e-01 -8.75251472e-01 -3.33326340e-01
-5.73530018e-01 -1.84376866e-01 3.65971804e-01 3.43580276e-01
-2.45820358e-01 3.05111229e-01 4.09780592e-01 -4.10368294e-01
-4.32088137e-01 -7.68062055e-01 -2.47574046e-01 -6.52778625e-01
1.33395866e-01 4.15112436e-01 3.52547407e-01 -6.12675175... | [10.068685531616211, 5.815301418304443] |
1f415d26-a47d-4409-a4b6-86b532f02a49 | instance-based-model-adaptation-for-direct | 1910.10663 | null | https://arxiv.org/abs/1910.10663v1 | https://arxiv.org/pdf/1910.10663v1.pdf | Instance-Based Model Adaptation For Direct Speech Translation | Despite recent technology advancements, the effectiveness of neural approaches to end-to-end speech-to-text translation is still limited by the paucity of publicly available training corpora. We tackle this limitation with a method to improve data exploitation and boost the system's performance at inference time. Our a... | ['Viet-Nhat Nguyen', 'Mattia Antonino Di Gangi', 'Marco Turchi', 'Matteo Negri'] | 2019-10-23 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.61268073e-01 -8.06282908e-02 -2.98412800e-01 -5.12286901e-01
-1.52001762e+00 -7.32670426e-01 7.00190961e-01 -1.57260839e-02
-3.66896689e-01 7.24255979e-01 3.72642994e-01 -4.69026029e-01
2.33600423e-01 -3.07437271e-01 -8.01431298e-01 -4.77504551e-01
4.01391476e-01 8.14057052e-01 1.19776025e-01 -3.02766562... | [14.519807815551758, 6.9855756759643555] |
0c94194f-6f14-43c0-a1ee-5a7eef28bef1 | enhancing-robustness-in-aspect-based | null | null | https://openreview.net/forum?id=Isf6O2t_99 | https://openreview.net/pdf?id=Isf6O2t_99 | Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation | In this paper, we propose to leverage data augmentation to improve the robustness of aspect-based sentiment analysis models. Our method not only exploits augmented data but also makes models focus more on predictive features. We show in experiments that our method compares favorably against strong baselines on both rob... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.18967846e-01 1.15551546e-01 -2.62445539e-01 -3.53677720e-01
-7.47755349e-01 -8.26546848e-01 1.05976558e+00 2.32956097e-01
-4.00037438e-01 3.98159027e-01 4.98673171e-01 -3.63599092e-01
2.70748287e-01 -8.04747641e-01 -5.78382611e-01 -5.72575927e-01
4.43925649e-01 2.47398049e-01 2.34890748e-02 -7.51488805... | [11.27287483215332, 6.902848720550537] |
4971d73d-4d3a-44d5-a26f-687cd1abd169 | eight-years-of-face-recognition-research | 2208.04040 | null | https://arxiv.org/abs/2208.04040v2 | https://arxiv.org/pdf/2208.04040v2.pdf | Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues | Automatic face recognition is a research area with high popularity. Many different face recognition algorithms have been proposed in the last thirty years of intensive research in the field. With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researche... | ['Dominic Schmidli', 'Manuel Günther', 'Sébastien Marcel', 'Xinyi Zhang', 'Yu Linghu', 'Tiago de Freitas Pereira'] | 2022-08-08 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [-1.51989330e-02 -3.53905648e-01 -6.43280074e-02 -8.65548372e-01
-6.06569275e-02 -1.55125588e-01 4.84357804e-01 -7.42528856e-01
-2.24727914e-01 5.43817699e-01 -1.26602247e-01 6.94093853e-02
-1.16451979e-01 -6.68415844e-01 -6.02774084e-01 -7.70331860e-01
-1.06642172e-01 3.46023142e-01 -2.78120607e-01 -4.05243367... | [13.238923072814941, 0.933622419834137] |
76d9e354-8a1f-4615-9559-c7477c424747 | interpretable-explainability-in-facial | 2211.04769 | null | https://arxiv.org/abs/2211.04769v1 | https://arxiv.org/pdf/2211.04769v1.pdf | Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection | Training facial emotion recognition models requires large sets of data and costly annotation processes. To alleviate this problem, we developed a gamified method of acquiring annotated facial emotion data without an explicit labeling effort by humans. The game, which we named Facegame, challenges the players to imitate... | ['Roland Klemke', 'Corrie Urlings', 'Felix Bottger', 'Deniz Iren', 'Krist Shingjergji'] | 2022-11-09 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 6.39464483e-02 6.20639563e-01 3.06453347e-01 -7.69934714e-01
-2.86198586e-01 -5.00952184e-01 4.21493679e-01 -3.22916865e-01
-2.22964272e-01 3.50768983e-01 -3.28486040e-02 4.51399654e-01
3.18266034e-01 -7.00228333e-01 -5.13900757e-01 -5.94337225e-01
-1.85462311e-01 4.73701954e-01 -5.76983452e-01 -5.86514652... | [13.518876075744629, 1.7856042385101318] |
d6f09529-375e-457d-85cb-920f5a729aa0 | few-nerd-a-few-shot-named-entity-recognition | 2105.07464 | null | https://arxiv.org/abs/2105.07464v6 | https://arxiv.org/pdf/2105.07464v6.pdf | Few-NERD: A Few-Shot Named Entity Recognition Dataset | Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and re-organize them to the few-shot setting for empiric... | ['Zhiyuan Liu', 'Hai-Tao Zheng', 'Pengjun Xie', 'Xu Han', 'Xiaobin Wang', 'Yulin Chen', 'Guangwei Xu', 'Ning Ding'] | 2021-05-16 | null | https://aclanthology.org/2021.acl-long.248 | https://aclanthology.org/2021.acl-long.248.pdf | acl-2021-5 | ['few-shot-ner'] | ['natural-language-processing'] | [-5.88939011e-01 -1.68222293e-01 -2.26520166e-01 -3.47004414e-01
-7.24221528e-01 -6.66939080e-01 4.34151620e-01 3.81747037e-01
-9.97222900e-01 8.83055806e-01 5.24556756e-01 3.86121683e-02
1.60294667e-01 -9.61398840e-01 -4.07379717e-01 -3.22507948e-01
4.33868878e-02 2.80012012e-01 2.23125577e-01 -3.90934438... | [9.66561222076416, 9.386706352233887] |
00e0a591-9972-4360-b8d9-07318c86b4d6 | modeling-feature-representations-for | 1911.00030 | null | https://arxiv.org/abs/1911.00030v1 | https://arxiv.org/pdf/1911.00030v1.pdf | Modeling Feature Representations for Affective Speech using Generative Adversarial Networks | Emotion recognition is a classic field of research with a typical setup extracting features and feeding them through a classifier for prediction. On the other hand, generative models jointly capture the distributional relationship between emotions and the feature profiles. Relatively recently, Generative Adversarial Ne... | ['Carol Espy-Wilson', 'Rahul Gupta', 'Saurabh Sahu'] | 2019-10-31 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 4.88685459e-01 3.61749709e-01 1.59819484e-01 -5.13141215e-01
-7.59218752e-01 -5.16480684e-01 1.17229068e+00 -5.95910586e-02
-1.64286926e-01 8.10989678e-01 2.96260417e-01 3.44612986e-01
3.86364937e-01 -9.99717176e-01 -8.30849767e-01 -8.44926357e-01
2.08158717e-01 7.58202612e-01 -5.25339723e-01 -1.20639838... | [11.7438325881958, -0.012814158573746681] |
903a6c02-427c-4371-84a5-34c587a8d0c1 | c2msnet-a-novel-approach-for-single-image | 1801.08406 | null | http://arxiv.org/abs/1801.08406v1 | http://arxiv.org/pdf/1801.08406v1.pdf | C2MSNet: A Novel approach for single image haze removal | Degradation of image quality due to the presence of haze is a very common
phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand
crafted haze relevant features. However, these methods have the problem of
color distortion in gloomy (poor illumination) environment. In this paper, a
cardinal (red, gr... | ['Subrahmanyam Murala', 'Akshay Dudhane'] | 2018-01-25 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 2.45927706e-01 -6.18274093e-01 7.98263669e-01 -4.09834087e-02
-3.60214978e-01 -2.04778194e-01 2.69604594e-01 -4.11754996e-02
-5.18362880e-01 8.31398964e-01 1.27117038e-01 -4.55538556e-02
-1.36772528e-01 -8.27973247e-01 -4.91705835e-01 -1.04082334e+00
6.14013821e-02 -3.68311077e-01 5.37884951e-01 -5.90097070... | [10.893089294433594, -3.1173858642578125] |
00272b50-eaa1-47f5-b8a5-9292c9020e63 | cuda-gr-controllable-unsupervised-domain | 2106.10852 | null | https://arxiv.org/abs/2106.10852v4 | https://arxiv.org/pdf/2106.10852v4.pdf | CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection | The robustness of gaze and head pose estimation models is highly dependent on the amount of labeled data. Recently, generative modeling has shown excellent results in generating photo-realistic images, which can alleviate the need for annotations. However, adopting such generative models to new domains while maintainin... | ['Xin Eric Wang', 'Swati Jindal'] | 2021-06-21 | null | null | null | null | ['gaze-redirection', 'head-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.24408729e-01 9.21242088e-02 2.31121909e-02 -8.20728719e-01
-7.62219608e-01 -3.71913016e-01 4.01866794e-01 -6.13077402e-01
-2.31642932e-01 6.92683697e-01 2.61981398e-01 2.89325058e-01
3.63487065e-01 -2.77037919e-01 -7.47851551e-01 -8.68297517e-01
3.81190419e-01 5.07756174e-01 -4.97268736e-02 -1.79081652... | [14.112809181213379, 0.0375446155667305] |
bb3fddbe-d93a-4e6e-b884-ca31de3f0b72 | revisiting-consistency-regularization-for-1 | 2204.08454 | null | https://arxiv.org/abs/2204.08454v3 | https://arxiv.org/pdf/2204.08454v3.pdf | Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images | Remote-sensing (RS) Change Detection (CD) aims to detect "changes of interest" from co-registered bi-temporal images. The performance of existing deep supervised CD methods is attributed to the large amounts of annotated data used to train the networks. However, annotating large amounts of remote sensing images is labo... | ['Vishal M. Patel', 'Wele Gedara Chaminda Bandara'] | 2022-04-18 | null | null | null | null | ['semi-supervised-change-detection'] | ['computer-vision'] | [ 3.82899314e-01 -2.58796930e-01 1.31869152e-01 -5.16295910e-01
-8.60701323e-01 -6.27969384e-01 6.64059877e-01 2.29664817e-02
-5.02893686e-01 6.79763317e-01 -1.37373675e-02 -1.35412171e-01
-5.94769679e-02 -8.51944923e-01 -6.21529460e-01 -8.96071672e-01
-1.35095194e-01 -1.61418337e-02 3.64471488e-02 -7.37168863... | [9.741862297058105, -1.3024202585220337] |
653a0181-d0ff-4600-a87c-2aefd1f0882c | transfer-learning-for-conflict-and-duplicate | 2301.03709 | null | https://arxiv.org/abs/2301.03709v1 | https://arxiv.org/pdf/2301.03709v1.pdf | Transfer learning for conflict and duplicate detection in software requirement pairs | Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and d... | ['Devang Parikh', 'Ayse Bener', 'Mucahit Cevik', 'Savas Yildirim', 'Garima Malik'] | 2023-01-09 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 4.30460811e-01 -8.76857117e-02 2.66075075e-01 -1.02374709e+00
-7.70560503e-01 -5.12316704e-01 3.21492463e-01 2.82502085e-01
-9.00079682e-02 1.80983722e-01 2.84441203e-01 -5.00120044e-01
-4.62066442e-01 -5.24174035e-01 -3.73101771e-01 2.93399721e-01
2.06418514e-01 4.51526910e-01 1.02932103e-01 -6.31690502... | [7.734042167663574, 7.873556137084961] |
336935a1-f8ab-4e73-bd49-84b2428dbf3f | depth-relative-self-attention-for-monocular | 2304.12849 | null | https://arxiv.org/abs/2304.12849v1 | https://arxiv.org/pdf/2304.12849v1.pdf | Depth-Relative Self Attention for Monocular Depth Estimation | Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image. To overcome the limitation, deep neural networks rely on various visual hints such as size, shade, and texture extracted from RGB information. However, we observe that if such hints are overly exploited... | ['Byonghyo Shim', 'Gusang Lee', 'Jiyoung Kim', 'Kyuhong Shim'] | 2023-04-25 | null | null | null | null | ['monocular-depth-estimation'] | ['computer-vision'] | [ 7.06992149e-02 3.13119471e-01 -1.66436970e-01 -4.96240795e-01
-3.71954799e-01 -3.98613483e-01 4.88078624e-01 2.58896612e-02
-4.13973123e-01 6.56313241e-01 1.79715797e-01 -6.59748539e-02
3.45478445e-01 -1.04356372e+00 -7.91962981e-01 -7.38635778e-01
3.65323961e-01 7.02652633e-02 5.36792576e-01 -1.37687204... | [8.849135398864746, -2.3470170497894287] |
5034ca6c-d8b3-4f45-bb0c-9f151d69a14a | the-use-of-data-augmentation-as-a-technique | 2205.00452 | null | https://arxiv.org/abs/2205.00452v1 | https://arxiv.org/pdf/2205.00452v1.pdf | The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19 | This paper aims to present how the application of Natural Language Processing (NLP) and data augmentation techniques can improve the performance of a neural network for better detection of fake news in the Portuguese language. Fake news is one of the main controversies during the growth of the internet in the last deca... | ['Arnaldo Bispo de Jesus', 'Andre Brasil Vieira Wyzykowski', 'Mauricio S. da Cruz', 'Wilton O. Júnior'] | 2022-05-01 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-5.70968576e-02 3.27745706e-01 -3.88547145e-02 -2.05945849e-01
1.68530777e-01 -4.16055590e-01 1.09375191e+00 6.66916013e-01
-5.72621465e-01 9.83201683e-01 2.84274995e-01 -5.33822060e-01
1.47010893e-01 -8.91111076e-01 -7.75250137e-01 -1.68318138e-01
2.28422001e-01 4.69318211e-01 2.40152448e-01 -5.96764147... | [8.19033145904541, 10.237860679626465] |
de2c1026-2be8-4c7a-8ad5-7e9aae3cdb1c | neural-free-viewpoint-performance-rendering | 2108.00362 | null | https://arxiv.org/abs/2108.00362v2 | https://arxiv.org/pdf/2108.00362v2.pdf | Neural Free-Viewpoint Performance Rendering under Complex Human-object Interactions | 4D reconstruction of human-object interaction is critical for immersive VR/AR experience and human activity understanding. Recent advances still fail to recover fine geometry and texture results from sparse RGB inputs, especially under challenging human-object interactions scenarios. In this paper, we propose a neural ... | ['Jingyi Yu', 'Jingya Wang', 'Lan Xu', 'Yuheng Jiang', 'Pei Lin', 'Anqi Pang', 'Yizhang Chen', 'Xin Chen', 'Guoxing Sun'] | 2021-08-01 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 2.25951552e-01 -2.42954656e-01 3.95427406e-01 -3.49979997e-01
-6.05473936e-01 -2.18347579e-01 2.26011068e-01 -2.66709208e-01
3.60518843e-02 4.69968140e-01 1.51303550e-02 2.20058918e-01
-5.23971431e-02 -8.37342024e-01 -7.20205843e-01 -5.98800778e-01
2.30768293e-01 7.87191629e-01 3.94093335e-01 -1.22462742... | [7.216442584991455, -1.3651942014694214] |
4f4f4499-9860-48a8-9594-e3063e78d99e | adapting-self-supervised-models-to-multi | 2211.00482 | null | https://arxiv.org/abs/2211.00482v1 | https://arxiv.org/pdf/2211.00482v1.pdf | Adapting self-supervised models to multi-talker speech recognition using speaker embeddings | Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-talker applications. However, these models often have degraded performance for multi-talker scenarios -- possibly due to the domain mismatch ... | ['Sanjeev Khudanpur', 'Paola García', 'Desh Raj', 'Zili Huang'] | 2022-11-01 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 2.95419782e-01 3.77624393e-01 -1.36631787e-01 -7.95427024e-01
-1.52806938e+00 -2.97753155e-01 6.11849606e-01 -6.03783913e-02
-4.22548831e-01 4.28245097e-01 7.83359826e-01 -3.49139750e-01
2.74156451e-01 1.00386404e-01 -4.70890850e-01 -7.46279597e-01
1.31031021e-01 5.27915835e-01 -1.26636773e-01 -1.25788063... | [14.58682918548584, 6.37366247177124] |
6363e596-c3f7-4355-a230-76a12db50dde | statistical-nlg-for-generating-the-content | null | null | https://aclanthology.org/W18-6561 | https://aclanthology.org/W18-6561.pdf | Statistical NLG for Generating the Content and Form of Referring Expressions | This paper argues that a new generic approach to statistical NLG can be made to perform Referring Expression Generation (REG) successfully. The model does not only select attributes and values for referring to a target referent, but also performs Linguistic Realisation, generating an actual Noun Phrase. Our evaluations... | ['Kees Van Deemter', 'Xiao Li', 'Chenghua Lin'] | 2018-11-01 | null | null | null | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [ 3.10345948e-01 9.15496826e-01 -2.18871728e-01 -7.88282335e-01
-1.30841911e+00 -8.24763834e-01 1.13695145e+00 1.90649271e-01
-4.36726928e-01 1.36496997e+00 6.95552230e-01 -1.81618035e-01
7.94534981e-02 -1.00255966e+00 -4.28202778e-01 -4.55619663e-01
1.83989421e-01 1.09230411e+00 6.10244237e-02 -5.37227392... | [10.484550476074219, 9.150739669799805] |
212604d9-0d9f-49b4-9f2e-785a9c5679bb | multimodal-audio-textual-architecture-for-1 | 2306.06819 | null | https://arxiv.org/abs/2306.06819v2 | https://arxiv.org/pdf/2306.06819v2.pdf | Multimodal Audio-textual Architecture for Robust Spoken Language Understanding | Recent voice assistants are usually based on the cascade spoken language understanding (SLU) solution, which consists of an automatic speech recognition (ASR) engine and a natural language understanding (NLU) system. Because such approach relies on the ASR output, it often suffers from the so-called ASR error propagati... | ['Chao Xing', 'Mehdi Rezagholizadeh', 'Anderson R. Avila'] | 2023-06-12 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding', 'automatic-speech-recognition'] | ['natural-language-processing', 'speech', 'speech'] | [ 2.57178783e-01 3.08630407e-01 3.33084822e-01 -3.81613553e-01
-1.26613569e+00 -3.98969114e-01 8.16106379e-01 -2.35236064e-02
-2.92061329e-01 4.18291092e-01 6.88645005e-01 -4.40636545e-01
1.58889309e-01 -3.67765665e-01 -8.33976388e-01 -2.27916166e-01
3.64942998e-01 3.70045513e-01 -1.94625273e-01 -4.21018541... | [14.196942329406738, 6.777410984039307] |
29a85c3f-7594-41d7-841b-665817c4ba97 | lstm-ccg-parsing | null | null | https://aclanthology.org/N16-1026 | https://aclanthology.org/N16-1026.pdf | LSTM CCG Parsing | null | ['Mike Lewis', 'Luke Zettlemoyer', 'Kenton Lee'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['ccg-supertagging'] | ['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... | [-7.263617992401123, 3.790346384048462] |
71737789-6da0-4cd3-938b-4f98bd27b059 | when-to-use-what-an-in-depth-comparative | 2211.08228 | null | https://arxiv.org/abs/2211.08228v1 | https://arxiv.org/pdf/2211.08228v1.pdf | When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications | Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying things further is the lack of comparisons that take differing training sets into account. In this paper, we present an application-focused ... | ['Yunyao Li', 'ChengXiang Zhai', 'Kevin Chen-Chuan Chang', 'Ishan Jindal', 'Kevin Pei'] | 2022-11-15 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 7.09626228e-02 1.17683530e-01 -2.36300766e-01 -5.66173673e-01
-9.75863874e-01 -8.72705817e-01 4.40396547e-01 2.72332460e-01
-5.21093726e-01 6.99712098e-01 5.14486790e-01 -9.06842470e-01
-5.13140738e-01 -3.98106575e-01 -5.12743294e-01 -2.39324406e-01
3.01259160e-01 6.13624096e-01 2.07684740e-01 -3.04007381... | [10.478492736816406, 9.202512741088867] |
d21b02b8-1eba-4cec-83d0-29b62595954a | knowledge-augmented-frame-semantic-parsing | 2303.14375 | null | https://arxiv.org/abs/2303.14375v1 | https://arxiv.org/pdf/2303.14375v1.pdf | Knowledge-augmented Frame Semantic Parsing with Hybrid Prompt-tuning | Frame semantics-based approaches have been widely used in semantic parsing tasks and have become mainstream. It remains challenging to disambiguate frame representations evoked by target lexical units under different contexts. Pre-trained Language Models (PLMs) have been used in semantic parsing and significantly impro... | ['Wei Peng', 'Jingyuan Yang', 'Yajing Sun', 'Rui Zhang'] | 2023-03-25 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.78178382e-01 2.84845054e-01 -3.80752206e-01 -6.25905097e-01
-8.53671312e-01 -5.55240214e-01 6.92961693e-01 -4.51251827e-02
-5.77995121e-01 6.18681610e-01 5.31570971e-01 -1.91662505e-01
1.12097509e-01 -9.71857131e-01 -8.04078639e-01 -3.96808326e-01
4.77369666e-01 3.29492420e-01 5.67554832e-01 -2.84121335... | [10.295258522033691, 9.254343032836914] |
c90561ad-7a35-4c48-8d1d-999a40b97921 | fademl-understanding-the-impact-of-pre | 1811.01444 | null | http://arxiv.org/abs/1811.01444v1 | http://arxiv.org/pdf/1811.01444v1.pdf | FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning | Deep neural networks (DNN)-based machine learning (ML) algorithms have
recently emerged as the leading ML paradigm particularly for the task of
classification due to their superior capability of learning efficiently from
large datasets. The discovery of a number of well-known attacks such as dataset
poisoning, adversar... | ['Semeen Rehman', 'Muhammmad Abdullah Hanif', 'Faiq Khalid', 'Muhammad Shafique', 'Junaid Qadir'] | 2018-11-04 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.55114514e-01 8.57195035e-02 3.66047740e-01 -1.58804934e-02
-4.16601837e-01 -9.26943600e-01 9.55785453e-01 1.22185320e-01
-5.47220826e-01 7.54063606e-01 -4.96805131e-01 -6.31944299e-01
-1.68304145e-01 -9.70044792e-01 -1.05806160e+00 -8.52851331e-01
-1.18123755e-01 2.32781127e-01 4.20242220e-01 -3.10059451... | [5.535895824432373, 7.800463676452637] |
a8aabf9d-9b30-4b0c-b3a3-050882f87e79 | cmuq-hybrid-sentiment-classification-by | null | null | https://aclanthology.org/S14-2028 | https://aclanthology.org/S14-2028.pdf | CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning | null | ['Kamla Al-Mannai', 'Sabih Bin Wasi', 'Behrang Mohit', 'Houda Bouamor', 'Rukhsar Neyaz', 'Hanan Alshikhabobakr'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.273940563201904, 3.725492238998413] |
ea9ba6b1-3f3e-4c98-8a82-6dc3ed4d29d4 | feature-forwarding-for-efficient-single-image | 1904.09059 | null | https://arxiv.org/abs/1904.09059v2 | https://arxiv.org/pdf/1904.09059v2.pdf | Feature Forwarding for Efficient Single Image Dehazing | Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three varia... | ['Seung Jae Lee', 'Peter Morales', 'Tzofi Klinghoffer'] | 2019-04-19 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 4.47688133e-01 -2.16178358e-01 5.60193956e-01 -1.97420686e-01
-2.70635873e-01 -8.54876786e-02 2.61291236e-01 -2.72652894e-01
-6.44747317e-01 2.66927898e-01 -9.25828889e-02 -5.09041667e-01
3.05094123e-01 -9.84971941e-01 -9.94048715e-01 -9.55639243e-01
-8.30171779e-02 -4.55213308e-01 6.96652651e-01 -4.37681198... | [10.944223403930664, -3.0784668922424316] |
f478ea61-6621-4613-a95c-22ab23d00925 | disentangled-representation-learning-for-1 | 2211.00437 | null | https://arxiv.org/abs/2211.00437v3 | https://arxiv.org/pdf/2211.00437v3.pdf | Disentangled representation learning for multilingual speaker recognition | The goal of this paper is to learn robust speaker representation for bilingual speaking scenario. The majority of the world's population speak at least two languages; however, most speaker recognition systems fail to recognise the same speaker when speaking in different languages. Popular speaker recognition evaluation... | ['Jee-weon Jung', 'Hee Soo Heo', 'Jaesung Huh', 'Joon Son Chung', 'Youkyum Kim', 'Kihyun Nam'] | 2022-11-01 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [-2.97048688e-02 -2.16575954e-02 -2.96909481e-01 -5.40053964e-01
-1.20172715e+00 -8.19831192e-01 9.51033294e-01 -4.78495598e-01
-4.51633602e-01 8.40522945e-01 4.73462641e-01 -4.05157566e-01
1.72439918e-01 -4.27846313e-01 -4.13877964e-01 -8.30931604e-01
4.07989621e-02 5.15506864e-01 -4.33132678e-01 -4.53169286... | [14.309356689453125, 6.19891357421875] |
dd6d31d0-03b3-4fb5-9ba3-8c2f1864d1a3 | artificial-neuronal-ensembles-with-learned | 2301.07187 | null | https://arxiv.org/abs/2301.07187v2 | https://arxiv.org/pdf/2301.07187v2.pdf | Artificial Neuronal Ensembles with Learned Context Dependent Gating | Biological neural networks are capable of recruiting different sets of neurons to encode different memories. However, when training artificial neural networks on a set of tasks, typically, no mechanism is employed for selectively producing anything analogous to these neuronal ensembles. Further, artificial neural netwo... | ['David J. Freedman', 'Matthew J. Tilley', 'Michelle Miller'] | 2023-01-17 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 6.98803544e-01 2.01259796e-02 2.90628016e-01 -3.66329439e-02
2.01822653e-01 -4.66260642e-01 6.12475574e-01 1.27650082e-01
-8.21836472e-01 1.19505143e+00 1.34629449e-02 1.61468722e-02
-2.43037730e-01 -1.01527274e+00 -1.09587920e+00 -1.26088476e+00
-2.07196057e-01 3.39947671e-01 4.83735472e-01 -3.29962969... | [8.730279922485352, 3.211463451385498] |
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