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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4b5e6486-78fc-47d6-8080-3c52e5df9da5 | learning-where-to-fixate-on-foveated-images | 1811.06868 | null | https://arxiv.org/abs/1811.06868v2 | https://arxiv.org/pdf/1811.06868v2.pdf | Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations | We consider the problem of fine-grained classification on an edge camera device that has limited power. The edge device must sparingly interact with the cloud to minimize communication bits to conserve power, and the cloud upon receiving the edge inputs returns a classification label. To deal with fine-grained classifi... | ['Venkatesh Saligrama', 'Stan Sclaroff', 'Hanxiao Wang', 'Vitaly Ablavsky'] | 2018-11-16 | cost-aware-fine-grained-recognition-for-iots | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Cost-Aware_Fine-Grained_Recognition_for_IoTs_Based_on_Sequential_Fixations_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Cost-Aware_Fine-Grained_Recognition_for_IoTs_Based_on_Sequential_Fixations_ICCV_2019_paper.pdf | iccv-2019-10 | ['foveation'] | ['computer-vision'] | [ 3.18050563e-01 4.92364615e-02 -2.99606800e-01 -1.52334601e-01
-4.03748006e-01 -6.82512403e-01 2.67221540e-01 -5.43820083e-01
-5.59069157e-01 7.53428638e-01 -2.04403952e-01 -4.03582335e-01
1.07386345e-02 -6.15077317e-01 -1.03735197e+00 -8.48156631e-01
3.17013562e-02 -1.03230119e-01 -1.34167343e-01 4.68261123... | [9.015300750732422, 0.23387470841407776] |
a86be8e6-6afe-4cd4-9ca7-5822d61b04db | dual-projection-generative-adversarial | 2108.09016 | null | https://arxiv.org/abs/2108.09016v2 | https://arxiv.org/pdf/2108.09016v2.pdf | Dual Projection Generative Adversarial Networks for Conditional Image Generation | Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been established as powerful generative models capable of generating high-fidelity imagery. A challenge of training such a model lies in properly infusing... | ['Dimitris Metaxas', 'Asim Kadav', 'Ruijiang Gao', 'Yu Tian', 'Anastasis Stathopoulos', 'Martin Renqiang Min', 'Ligong Han'] | 2021-08-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Han_Dual_Projection_Generative_Adversarial_Networks_for_Conditional_Image_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Han_Dual_Projection_Generative_Adversarial_Networks_for_Conditional_Image_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 7.19596267e-01 3.82513613e-01 -6.23224750e-02 -4.22079474e-01
-1.21926856e+00 -7.56508172e-01 7.59414434e-01 -4.15147007e-01
-3.30945820e-01 1.07516897e+00 -3.53394717e-01 -2.07883894e-01
-2.10694950e-02 -1.29758418e+00 -1.00918758e+00 -1.24459374e+00
1.31577685e-01 6.14900231e-01 -3.27089965e-01 1.81123123... | [11.614266395568848, -0.2624765634536743] |
8dc30177-95ce-4999-8d1f-a5446f207e40 | contextual-object-detection-with-multimodal | 2305.18279 | null | https://arxiv.org/abs/2305.18279v1 | https://arxiv.org/pdf/2305.18279v1.pdf | Contextual Object Detection with Multimodal Large Language Models | Recent Multimodal Large Language Models (MLLMs) are remarkable in vision-language tasks, such as image captioning and question answering, but lack the essential perception ability, i.e., object detection. In this work, we address this limitation by introducing a novel research problem of contextual object detection -- ... | ['Chen Change Loy', 'Kaiyang Zhou', 'Jun Han', 'Wei Li', 'Yuhang Zang'] | 2023-05-29 | null | null | null | null | ['image-captioning', 'cloze-test'] | ['computer-vision', 'natural-language-processing'] | [ 3.11700732e-01 -1.52268503e-02 -4.50848043e-02 -3.23284686e-01
-1.04874790e+00 -6.40629768e-01 4.70536917e-01 6.85377046e-02
-4.39358830e-01 2.78871030e-01 8.33446309e-02 -6.04030192e-01
6.06005549e-01 -3.22378278e-01 -9.63041782e-01 -5.48699975e-01
3.98217589e-01 2.95260519e-01 2.23572984e-01 1.47622213... | [10.857056617736816, 1.656062126159668] |
3c1384c8-53d9-4ef7-b716-beb9f732835f | fairness-aware-counterfactuals-for-subgroups | 2306.14978 | null | https://arxiv.org/abs/2306.14978v1 | https://arxiv.org/pdf/2306.14978v1.pdf | Fairness Aware Counterfactuals for Subgroups | In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisiting (and generalizing) existing notions and introducing new, more refined notions of subgroup fairness. We aim to (a) formulate different as... | ['Ioannis Emiris', 'Dimitris Fotakis', 'Dimitrios Rontogiannis', 'Nikolaos Theologitis', 'Eleni Psaroudaki', 'Dimitris Sacharidis', 'Giorgos Giannopoulos', 'Konstantinos Tsopelas', 'Loukas Kavouras'] | 2023-06-26 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [ 4.22489047e-02 4.57963228e-01 -5.41732609e-01 -5.59064627e-01
-3.64043087e-01 -4.27906036e-01 7.88612902e-01 3.33355337e-01
-3.93665165e-01 9.93429184e-01 7.51833856e-01 -6.21372998e-01
-4.85761076e-01 -7.82536209e-01 -2.73771852e-01 -4.86713290e-01
-9.10005197e-02 2.42513344e-01 -3.91458124e-01 -5.68302236... | [8.723923683166504, 5.470590114593506] |
b3413201-9ee2-4c88-8268-e3f1bc393bbb | simple-baseline-for-weather-forecasting-using | 2212.02952 | null | https://arxiv.org/abs/2212.02952v2 | https://arxiv.org/pdf/2212.02952v2.pdf | Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network | Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, fr... | ['Yeji Choi', 'Sewoong Ahn', 'Eunbin Kim', 'Seungheon Shin', 'Doyi Kim', 'Minseok Seo'] | 2022-12-06 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.47984731e-01 -3.87014180e-01 1.43975452e-01 -7.42594719e-01
-1.64229020e-01 -5.38487017e-01 1.04296255e+00 2.26899739e-02
-4.09833610e-01 7.37643123e-01 4.67170119e-01 -7.22761750e-01
1.86328609e-02 -9.92113888e-01 -4.07262176e-01 -9.83046174e-01
-5.60539484e-01 2.98801333e-01 1.08314931e-01 -8.40182185... | [6.607025623321533, 2.86710524559021] |
9d161216-8c1b-45b9-8cdb-429f49f61d2f | multi-frame-quality-enhancement-for | 1803.04680 | null | http://arxiv.org/abs/1803.04680v4 | http://arxiv.org/pdf/1803.04680v4.pdf | Multi-Frame Quality Enhancement for Compressed Video | The past few years have witnessed great success in applying deep learning to
enhance the quality of compressed image/video. The existing approaches mainly
focus on enhancing the quality of a single frame, ignoring the similarity
between consecutive frames. In this paper, we investigate that heavy quality
fluctuation ex... | ['Mai Xu', 'Zulin Wang', 'Ren Yang', 'Tianyi Li'] | 2018-03-13 | multi-frame-quality-enhancement-for-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_Multi-Frame_Quality_Enhancement_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_Multi-Frame_Quality_Enhancement_CVPR_2018_paper.pdf | cvpr-2018-6 | ['video-enhancement'] | ['computer-vision'] | [ 2.37314209e-01 -2.73462355e-01 -1.77833959e-01 -2.57504522e-03
-5.26328564e-01 2.81629432e-02 1.74590588e-01 -3.02277580e-02
-3.07801306e-01 5.02395034e-01 3.31982672e-01 -1.48707420e-01
-1.18505180e-01 -8.56240273e-01 -7.65816212e-01 -6.77668154e-01
-1.42375022e-01 -5.75425386e-01 5.11559784e-01 -2.73944467... | [11.294705390930176, -1.7673527002334595] |
a41f351a-0f21-4f49-a9bb-41f87467036e | few-shot-conversational-dense-retrieval | 2105.04166 | null | https://arxiv.org/abs/2105.04166v3 | https://arxiv.org/pdf/2105.04166v3.pdf | Few-Shot Conversational Dense Retrieval | Dense retrieval (DR) has the potential to resolve the query understanding challenge in conversational search by matching in the learned embedding space. However, this adaptation is challenging due to DR models' extra needs for supervision signals and the long-tail nature of conversational search. In this paper, we pres... | ['Zhiyuan Liu', 'Tao Feng', 'Chenyan Xiong', 'Zhenghao Liu', 'Shi Yu'] | 2021-05-10 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.10855661e-01 -2.15271395e-02 -3.74224722e-01 -5.17361462e-01
-1.43723047e+00 -7.06885993e-01 9.74485576e-01 -2.44007826e-01
-5.67599654e-01 5.61946213e-01 8.81563783e-01 -2.83007860e-01
-3.43214780e-01 -5.23737490e-01 -5.53506136e-01 -5.04206836e-01
1.35747209e-01 9.11855161e-01 -1.37961973e-02 -6.17339313... | [11.755138397216797, 7.730643272399902] |
bca0e5d2-a578-448a-b605-f2c7ab3209ec | spectrally-consistent-unet-for-high-fidelity | 2004.10696 | null | https://arxiv.org/abs/2004.10696v2 | https://arxiv.org/pdf/2004.10696v2.pdf | Spectrally Consistent UNet for High Fidelity Image Transformations | Convolutional Neural Networks (CNNs) are the current de-facto models used for many imaging tasks due to their high learning capacity as well as their architectural qualities. The ubiquitous UNet architecture provides an efficient and multi-scale solution that combines local and global information. Despite the success o... | ['Thomas Bashford-Rogers', 'Demetris Marnerides', 'Kurt Debattista'] | 2020-04-22 | null | null | null | null | ['tone-mapping', 'inverse-tone-mapping'] | ['computer-vision', 'computer-vision'] | [ 6.95191443e-01 -2.25784518e-02 3.81691992e-01 -1.55905083e-01
-3.00056994e-01 -7.70715475e-02 7.10454822e-01 -3.36215466e-01
-5.41869104e-01 6.06117487e-01 2.13890389e-01 -8.23871866e-02
-5.33327341e-01 -8.13613057e-01 -5.02172589e-01 -8.78785670e-01
-2.63446301e-01 -1.49717778e-01 6.21298254e-01 -5.13183594... | [11.048507690429688, -2.1691746711730957] |
e3901297-c760-4cf4-ae0d-6b10e5828701 | mmformer-multimodal-transformer-using | 2303.13101 | null | https://arxiv.org/abs/2303.13101v1 | https://arxiv.org/pdf/2303.13101v1.pdf | MMFormer: Multimodal Transformer Using Multiscale Self-Attention for Remote Sensing Image Classification | To benefit the complementary information between heterogeneous data, we introduce a new Multimodal Transformer (MMFormer) for Remote Sensing (RS) image classification using Hyperspectral Image (HSI) accompanied by another source of data such as Light Detection and Ranging (LiDAR). Compared with traditional Vision Trans... | ['Kaixing Zhao', 'Liang He', 'Yaqian Liu', 'Wei Feng', 'Zuheng Ming', 'Bo Zhang'] | 2023-03-23 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 5.68518758e-01 -4.91673768e-01 7.37428069e-02 -5.48199296e-01
-1.11633074e+00 -5.82085669e-01 5.63985407e-01 -2.63669699e-01
-1.96085602e-01 7.20681190e-01 7.81029314e-02 -5.20689249e-01
-3.02524090e-01 -1.11633146e+00 -8.64061117e-01 -7.98952699e-01
2.86656410e-01 9.88250040e-03 -1.79381996e-01 -2.64913082... | [9.943552017211914, -1.5338566303253174] |
f06ebd3d-0832-4701-aff5-7839551278d8 | multi-task-head-pose-estimation-in-the-wild-1 | 2202.02299 | null | https://arxiv.org/abs/2202.02299v1 | https://arxiv.org/pdf/2202.02299v1.pdf | Multi-task head pose estimation in-the-wild | We present a deep learning-based multi-task approach for head pose estimation in images. We contribute with a network architecture and training strategy that harness the strong dependencies among face pose, alignment and visibility, to produce a top performing model for all three tasks. Our architecture is an encoder-d... | ['Luis Baumela', 'José Miguel Buenaposada', 'Roberto Valle'] | 2022-02-04 | multi-task-head-pose-estimation-in-the-wild | https://dx.doi.org/10.1109/TPAMI.2020.3046323 | https://dx.doi.org/10.1109/TPAMI.2020.3046323 | null | ['head-pose-estimation', 'face-alignment'] | ['computer-vision', 'computer-vision'] | [-3.33606392e-01 3.56467277e-01 3.93731177e-01 -6.46561801e-01
-8.75933588e-01 1.92058105e-02 5.80842614e-01 -4.90688793e-02
-5.16557097e-01 3.38725835e-01 5.16195238e-01 1.38541520e-01
1.97193772e-01 -2.59444475e-01 -1.07086921e+00 -5.70335746e-01
-8.73191208e-02 6.24735117e-01 3.23890328e-01 -9.84052122... | [13.55038070678711, 0.3214949667453766] |
87de8655-db4b-44d4-b4bb-63fe157995bb | multimodal-grounding-for-sequence-to-sequence | 1811.03865 | null | http://arxiv.org/abs/1811.03865v2 | http://arxiv.org/pdf/1811.03865v2.pdf | Multimodal Grounding for Sequence-to-Sequence Speech Recognition | Humans are capable of processing speech by making use of multiple sensory
modalities. For example, the environment where a conversation takes place
generally provides semantic and/or acoustic context that helps us to resolve
ambiguities or to recall named entities. Motivated by this, there have been
many works studying... | ['Loïc Barrault', 'Shruti Palaskar', 'Ramon Sanabria', 'Ozan Caglayan', 'Florian Metze'] | 2018-11-09 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 2.73637384e-01 3.74205321e-01 2.26148263e-01 -5.02069056e-01
-1.12384176e+00 -5.35206437e-01 7.71846592e-01 2.04620600e-01
-7.07667172e-01 4.38591510e-01 3.92350227e-01 -4.61873651e-01
4.17698056e-01 -2.71598071e-01 -5.70284426e-01 -4.59661961e-01
4.52183336e-01 5.12610316e-01 3.69326293e-01 -4.72334325... | [14.349486351013184, 5.214905261993408] |
62db05ea-f299-4f63-969d-636d2fd55cf0 | feature-based-recursive-observer-design-for | 1606.03021 | null | http://arxiv.org/abs/1606.03021v1 | http://arxiv.org/pdf/1606.03021v1.pdf | Feature-based Recursive Observer Design for Homography Estimation | This paper presents a new algorithm for online estimation of a sequence of
homographies applicable to image sequences obtained from robotic vehicles
equipped with vision sensors. The approach taken exploits the underlying
Special Linear group structure of the set of homographies along with gyroscope
measurements and di... | ['Tarek Hamel', 'Minh-Duc Hua', 'Pascal Morin', 'Robert Mahony', 'Jochen Trumpf'] | 2016-06-09 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 1.52488142e-01 -1.42846823e-01 -5.99782169e-02 -2.03800142e-01
2.83523589e-01 -4.70895022e-01 8.92670095e-01 -5.25099993e-01
-3.71160954e-01 4.31998700e-01 -1.95456341e-01 -1.30523115e-01
-1.97436899e-01 -4.01162535e-01 -6.92537248e-01 -5.06921172e-01
-4.81198132e-02 3.60419214e-01 1.43525407e-01 -1.07545324... | [7.997910022735596, -2.234355926513672] |
f8eae272-cf1a-402f-94ae-4d1e30cf3548 | alphafold-distillation-for-improved-inverse | 2210.03488 | null | https://arxiv.org/abs/2210.03488v1 | https://arxiv.org/pdf/2210.03488v1.pdf | AlphaFold Distillation for Improved Inverse Protein Folding | Inverse protein folding, i.e., designing sequences that fold into a given three-dimensional structure, is one of the fundamental design challenges in bio-engineering and drug discovery. Traditionally, inverse folding mainly involves learning from sequences that have an experimentally resolved structure. However, the kn... | ['Vijil Chenthamarakshan', 'Payel Das', 'Aurelie Lozano', 'Igor Melnyk'] | 2022-10-05 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 3.27906728e-01 1.80735201e-01 -1.33873656e-01 -5.57124734e-01
-6.46474779e-01 -9.06845927e-01 4.22197096e-02 1.48356169e-01
-1.83672696e-01 1.06656575e+00 1.72042340e-01 -7.24989176e-01
1.94474176e-01 -4.96439457e-01 -1.31720459e+00 -8.98929775e-01
2.28883252e-01 4.73724872e-01 -1.13684177e-01 -2.62775779... | [4.726465225219727, 5.610795497894287] |
475ac60b-d217-4148-a7d2-dfc7b5d54f0e | adversarial-text-generation-via-sequence | null | null | https://aclanthology.org/2020.findings-emnlp.5 | https://aclanthology.org/2020.findings-emnlp.5.pdf | Adversarial Text Generation via Sequence Contrast Discrimination | In this paper, we propose a sequence contrast loss driven text generation framework, which learns the difference between real texts and generated texts and uses that difference. Specifically, our discriminator contains a discriminative sequence generator instead of a binary classifier, and measures the {`}relative real... | ['Xiaojun Wan', 'Ke Wang'] | 2020-11-01 | null | null | null | findings-of-the-association-for-computational | ['adversarial-text'] | ['adversarial'] | [ 4.42504495e-01 4.06130478e-02 1.21091923e-03 -2.55189449e-01
-1.03087890e+00 -6.50618732e-01 9.75473940e-01 -1.59172639e-01
-6.37877226e-01 1.26366365e+00 1.78385496e-01 -2.41017684e-01
3.08502048e-01 -9.16567385e-01 -6.20680571e-01 -7.29839087e-01
3.29794198e-01 4.62563634e-01 6.93164691e-02 -3.01347673... | [11.869787216186523, 9.275045394897461] |
4c185948-2442-4bd8-ab6e-c0dfb0e32e99 | adacc-cumulative-cost-sensitive-boosting-for | 2209.08309 | null | https://arxiv.org/abs/2209.08309v1 | https://arxiv.org/pdf/2209.08309v1.pdf | AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification | Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class. Cost-sensitive learning tackles this problem by treating the classes differently, formulated typically via a user-defined fixed misclassi... | ['Eirini Ntoutsi', 'Bodo Rosenhahn', 'Symeon Papadopoulos', 'Vasileios Iosifidis'] | 2022-09-17 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.31559122e-01 -2.69484706e-02 -4.75551665e-01 -6.41169310e-01
-9.35745597e-01 -5.80595076e-01 3.55359703e-01 9.33827162e-01
-4.83391106e-01 9.23894107e-01 -3.72963727e-01 -2.03346625e-01
-3.28790069e-01 -8.48263502e-01 -7.05463171e-01 -8.28427255e-01
6.48064464e-02 6.97726786e-01 3.04266870e-01 -1.36380732... | [8.756085395812988, 4.2277727127075195] |
1ecff834-3419-4ec8-a938-bd85a7f3785d | ms-lstm-exploring-spatiotemporal-multiscale | 2304.07724 | null | https://arxiv.org/abs/2304.07724v2 | https://arxiv.org/pdf/2304.07724v2.pdf | MS-LSTM: Exploring Spatiotemporal Multiscale Representations in Video Prediction Domain | The drastic variation of motion in spatial and temporal dimensions makes the video prediction task extremely challenging. Existing RNN models obtain higher performance by deepening or widening the model. They obtain the multi-scale features of the video only by stacking layers, which is inefficient and brings unbearabl... | ['Jie Liu', 'Hao Zhang', 'Zhifeng Ma'] | 2023-04-16 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-1.36880144e-01 -5.20366967e-01 -2.67907381e-01 -2.62513757e-01
-5.47711968e-01 -2.11005747e-01 3.64194214e-01 -4.04192358e-01
-4.33993608e-01 4.92179722e-01 5.03903687e-01 -2.65194029e-01
1.54119939e-01 -7.90144980e-01 -8.89261186e-01 -5.42706847e-01
-1.87091067e-01 -4.76939887e-01 6.39207602e-01 -1.21438235... | [8.903768539428711, 0.37421914935112] |
8e2be57d-df73-4106-8498-cf9face2fee0 | bayesian-hierarchical-dynamic-model-for-human | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Bayesian_Hierarchical_Dynamic_Model_for_Human_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Bayesian_Hierarchical_Dynamic_Model_for_Human_Action_Recognition_CVPR_2019_paper.pdf | Bayesian Hierarchical Dynamic Model for Human Action Recognition | Human action recognition remains as a challenging task partially due to the presence of large variations in the execution of action. To address this issue, we propose a probabilistic model called Hierarchical Dynamic Model (HDM). Leveraging on Bayesian framework, the model parameters are allowed to vary across differen... | [' Qiang Ji', ' Hui Su', ' Wanru Xu', 'Rui Zhao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 3.46909821e-01 -3.92368525e-01 -2.82191396e-01 -4.15941089e-01
-3.70469362e-01 -3.88834238e-01 6.83199823e-01 -2.75492817e-01
-3.17244947e-01 5.77359974e-01 3.24437797e-01 2.93166280e-01
-3.59607339e-01 -5.51282585e-01 -5.25281310e-01 -7.97361791e-01
2.08164603e-02 1.66314736e-01 5.92586219e-01 3.15493792... | [8.402231216430664, 0.7325824499130249] |
ad023f7e-177e-4e77-9fcb-8e547b3f318b | color-constancy-using-cnns | 1504.04548 | null | http://arxiv.org/abs/1504.04548v1 | http://arxiv.org/pdf/1504.04548v1.pdf | Color Constancy Using CNNs | In this work we describe a Convolutional Neural Network (CNN) to accurately
predict the scene illumination. Taking image patches as input, the CNN works in
the spatial domain without using hand-crafted features that are employed by
most previous methods. The network consists of one convolutional layer with max
pooling,... | ['Raimondo Schettini', 'Claudio Cusano', 'Simone Bianco'] | 2015-04-17 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.56265283e-01 -2.39840224e-01 1.18180383e-02 -6.13767803e-01
-2.92838395e-01 -3.30344230e-01 4.13010329e-01 -3.60832721e-01
-4.86993253e-01 5.85747302e-01 -1.45945922e-01 -2.06725299e-01
2.44345635e-01 -8.17809522e-01 -7.24627972e-01 -8.55200887e-01
1.83410212e-01 -5.11117160e-01 3.52009356e-01 8.48162733... | [10.32044792175293, -2.4517629146575928] |
976c0c48-5aa6-4138-8e37-d489abacdd39 | yedda-a-lightweight-collaborative-text-span | 1711.03759 | null | http://arxiv.org/abs/1711.03759v3 | http://arxiv.org/pdf/1711.03759v3.pdf | YEDDA: A Lightweight Collaborative Text Span Annotation Tool | In this paper, we introduce \textsc{Yedda}, a lightweight but efficient and
comprehensive open-source tool for text span annotation. \textsc{Yedda}
provides a systematic solution for text span annotation, ranging from
collaborative user annotation to administrator evaluation and analysis. It
overcomes the low efficienc... | ['Jie Yang', 'Yue Zhang', 'Linwei Li', 'Xingxuan Li'] | 2017-11-10 | yedda-a-lightweight-collaborative-text-span-1 | https://aclanthology.org/P18-4006 | https://aclanthology.org/P18-4006.pdf | acl-2018-7 | ['text-annotation'] | ['natural-language-processing'] | [-9.21200588e-02 1.76751956e-01 -2.70582251e-02 -3.48796338e-01
-6.77028120e-01 -1.00083208e+00 2.88026016e-02 6.91498637e-01
-5.38224936e-01 8.17834258e-01 3.73723954e-01 -3.94066811e-01
-2.63041705e-01 -4.93515700e-01 4.34971042e-03 -3.50256525e-02
5.03933847e-01 7.49386728e-01 4.44467366e-01 -6.13794066... | [9.330724716186523, 8.973881721496582] |
0460a9ba-ecd4-40a9-90a7-83f899f5b2bb | clip-vg-self-paced-curriculum-adapting-of | 2305.08685 | null | https://arxiv.org/abs/2305.08685v2 | https://arxiv.org/pdf/2305.08685v2.pdf | CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding | Visual Grounding (VG) is a crucial topic in the field of vision and language, which involves locating a specific region described by expressions within an image. To reduce the reliance on manually labeled data, unsupervised methods have been developed to locate regions using pseudo-labels. However, the performance of e... | ['Changsheng Xu', 'YaoWei Wang', 'Ming Yan', 'Fang Peng', 'Xiaoshan Yang', 'Linhui Xiao'] | 2023-05-15 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 9.38065648e-02 -6.55267164e-02 -3.61040652e-01 -5.66991806e-01
-1.22885478e+00 -6.81284368e-01 4.63663012e-01 -4.55869129e-03
-4.34681237e-01 4.30149436e-01 1.16734147e-01 5.75992540e-02
3.56660932e-01 -4.12817150e-01 -8.91806781e-01 -5.31732440e-01
3.49821061e-01 3.56707215e-01 3.37579399e-01 -9.11385007... | [10.08920669555664, 1.2441757917404175] |
a70049c9-29ce-4f2b-bd06-d8fceb1e9593 | simple-contrastive-graph-clustering | 2205.07865 | null | https://arxiv.org/abs/2205.07865v3 | https://arxiv.org/pdf/2205.07865v3.pdf | Simple Contrastive Graph Clustering | Contrastive learning has recently attracted plenty of attention in deep graph clustering for its promising performance. However, complicated data augmentations and time-consuming graph convolutional operation undermine the efficiency of these methods. To solve this problem, we propose a Simple Contrastive Graph Cluster... | ['Xinwang Liu', 'Sihang Zhou', 'Xihong Yang', 'Yue Liu'] | 2022-05-11 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-1.03886910e-01 -7.83252344e-03 1.82835404e-02 -4.63807225e-01
-2.94631541e-01 -2.88356572e-01 4.71475959e-01 1.29669011e-01
-5.55765033e-01 2.00712204e-01 -1.29374105e-03 -1.22469224e-01
-9.61542204e-02 -7.04860985e-01 -7.62786508e-01 -1.12279475e+00
5.97132649e-03 2.95304239e-01 1.11839838e-01 -1.02036912... | [7.417293548583984, 6.01495885848999] |
1794013a-c272-4e9c-bed4-3710a706ff28 | a-spatiotemporal-oriented-energy-network-for | 1708.06690 | null | http://arxiv.org/abs/1708.06690v1 | http://arxiv.org/pdf/1708.06690v1.pdf | A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition | This paper presents a novel hierarchical spatiotemporal orientation
representation for spacetime image analysis. It is designed to combine the
benefits of the multilayer architecture of ConvNets and a more controlled
approach to spacetime analysis. A distinguishing aspect of the approach is that
unlike most contemporar... | ['Isma Hadji', 'Richard P. Wildes'] | 2017-08-22 | a-spatiotemporal-oriented-energy-network-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Hadji_A_Spatiotemporal_Oriented_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hadji_A_Spatiotemporal_Oriented_ICCV_2017_paper.pdf | iccv-2017-10 | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 2.16707468e-01 -1.66428369e-02 -9.81210172e-02 -2.79682249e-01
4.93662432e-02 -5.30082166e-01 8.21615815e-01 4.76874709e-02
-4.18790132e-01 2.00808749e-01 2.47630760e-01 -3.19974929e-01
-3.78276348e-01 -7.94090629e-01 -2.61294186e-01 -9.99649584e-01
-6.77071631e-01 -3.67956668e-01 4.61031228e-01 -3.40537459... | [9.106705665588379, 2.291059732437134] |
c98be523-588d-4629-a54e-3e04e005996c | dan-a-segmentation-free-document-attention | 2203.12273 | null | https://arxiv.org/abs/2203.12273v4 | https://arxiv.org/pdf/2203.12273v4.pdf | DAN: a Segmentation-free Document Attention Network for Handwritten Document Recognition | Unconstrained handwritten text recognition is a challenging computer vision task. It is traditionally handled by a two-step approach, combining line segmentation followed by text line recognition. For the first time, we propose an end-to-end segmentation-free architecture for the task of handwritten document recognitio... | ['Thierry Paquet', 'Clément Chatelain', 'Denis Coquenet'] | 2022-03-23 | null | null | null | null | ['handwritten-document-recognition'] | ['computer-vision'] | [ 4.79606539e-01 -3.72282937e-02 -2.47793496e-01 -5.71726918e-01
-9.40028369e-01 -7.57831395e-01 7.05279946e-01 -1.26285595e-03
-6.05606556e-01 1.48615822e-01 4.03329097e-02 -5.36457539e-01
5.36735654e-01 -3.61980557e-01 -8.94791722e-01 -5.01340508e-01
5.26869059e-01 6.25434577e-01 1.89473704e-01 3.64231318... | [11.930961608886719, 2.4331233501434326] |
49c449a4-7c0f-4c76-ac8d-b5fa6cf82129 | d-rex-dialogue-relation-extraction-with | 2109.05126 | null | https://arxiv.org/abs/2109.05126v2 | https://arxiv.org/pdf/2109.05126v2.pdf | D-REX: Dialogue Relation Extraction with Explanations | Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only part... | ['William Yang Wang', 'Lise Getoor', 'Yi-Lin Tuan', 'Varun Embar', 'Alon Albalak'] | 2021-09-10 | null | https://aclanthology.org/2022.nlp4convai-1.4 | https://aclanthology.org/2022.nlp4convai-1.4.pdf | nlp4convai-acl-2022-5 | ['dialog-relation-extraction', 'relation-explanation'] | ['natural-language-processing', 'natural-language-processing'] | [ 0.3006738 1.4259088 -0.8775344 -0.62125635 -0.99367887 -0.55858374
1.0329695 0.47742265 0.03433529 1.273645 0.8352474 -0.7938943
-0.21263568 -0.49417362 -0.25793666 0.14201707 0.11849755 1.0142808
0.17064695 -0.29601765 -0.07004463 0.23215598 -0.99284136 0.67733926
0.78055114 0.48140967 -0.54... | [12.293376922607422, 8.156768798828125] |
8b393f3d-c253-4052-a0b3-364265046b31 | iart-a-search-engine-for-art-historical | 2108.01542 | null | https://arxiv.org/abs/2108.01542v1 | https://arxiv.org/pdf/2108.01542v1.pdf | iART: A Search Engine for Art-Historical Images to Support Research in the Humanities | In this paper, we introduce iART: an open Web platform for art-historical research that facilitates the process of comparative vision. The system integrates various machine learning techniques for keyword- and content-based image retrieval as well as category formation via clustering. An intuitive GUI supports users to... | ['Ralph Ewerth', 'Hubertus Kohle', 'Eyke Hüllermeier', 'Javad Rahnama', 'Stefanie Schneider', 'Matthias Springstein'] | 2021-08-03 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-3.56447846e-01 -6.89528883e-01 -4.08817947e-01 8.00728053e-02
-1.03548312e+00 -8.56719017e-01 1.16104746e+00 4.17832375e-01
-6.23118103e-01 3.52387965e-01 6.97130039e-02 -2.20318899e-01
-5.35354972e-01 -7.33849347e-01 -2.62067914e-01 -5.47377348e-01
1.59240607e-02 1.02476227e+00 1.12701999e-02 -8.95620957... | [10.961947441101074, 0.5976201891899109] |
7deccfdf-35d1-4174-8e8f-9ab335860498 | test-time-adaptation-with-regularized-loss | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Veksler_Test_Time_Adaptation_With_Regularized_Loss_for_Weakly_Supervised_Salient_CVPR_2023_paper.pdf | Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection | It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained model to that image. Indeed nothing can be closer to the test data than the test image itself. The main difficulty of test-time a... | ['Olga Veksler'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['salient-object-detection-1'] | ['computer-vision'] | [ 3.85647774e-01 2.92957425e-01 -1.00397646e-01 -5.31937063e-01
-7.60063946e-01 -4.56775129e-01 3.27535391e-01 1.29723057e-01
-7.83146799e-01 8.83429825e-01 -3.89749587e-01 -1.38853282e-01
4.99243177e-02 -8.29291284e-01 -1.17300868e+00 -6.72371924e-01
2.51981735e-01 7.60133028e-01 7.15188265e-01 -1.52260780... | [9.551761627197266, 1.5626001358032227] |
22dc8073-a248-4a39-9301-85ca9316ed5c | a-neural-acoustic-echo-canceller-optimized | 2106.00856 | null | https://arxiv.org/abs/2106.00856v1 | https://arxiv.org/pdf/2106.00856v1.pdf | A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data | We consider the problem of recognizing speech utterances spoken to a device which is generating a known sound waveform; for example, recognizing queries issued to a digital assistant which is generating responses to previous user inputs. Previous work has proposed building acoustic echo cancellation (AEC) models for th... | ['Rohit Prabhavalkar', 'Alexander Gruenstein', 'Turaj Zakizadeh Shabestary', 'Alex Park', 'Nathan Howard'] | 2021-06-01 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 9.58029091e-01 1.30670920e-01 6.50650084e-01 -6.69093132e-01
-1.49879920e+00 -5.64407527e-01 5.76349974e-01 -1.48863107e-01
-6.34216428e-01 2.56435722e-01 4.62817043e-01 -5.81757903e-01
4.05462891e-01 -1.74748197e-01 -7.61978745e-01 -3.35924029e-01
-3.02932877e-02 5.64162210e-02 1.99766651e-01 -4.37100172... | [14.77049446105957, 6.1860127449035645] |
f520a5b2-5cfe-41a5-b175-5e9dbc815674 | transfer-free-data-efficient-multilingual | 2305.13528 | null | https://arxiv.org/abs/2305.13528v1 | https://arxiv.org/pdf/2305.13528v1.pdf | Transfer-Free Data-Efficient Multilingual Slot Labeling | Slot labeling (SL) is a core component of task-oriented dialogue (ToD) systems, where slots and corresponding values are usually language-, task- and domain-specific. Therefore, extending the system to any new language-domain-task configuration requires (re)running an expensive and resource-intensive data annotation pr... | ['Anna Korhonen', 'Ivan Vulić', 'Evgeniia Razumovskaia'] | 2023-05-22 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [ 5.05312867e-02 2.10808650e-01 -3.43084008e-01 -4.52672243e-01
-1.24728942e+00 -7.58150876e-01 5.57580948e-01 1.68722137e-04
-8.33164155e-01 1.28056884e+00 1.76868498e-01 -7.16793239e-01
3.72583538e-01 -4.04405862e-01 -4.69956517e-01 -3.31308991e-01
2.21287534e-01 1.01932466e+00 2.95723468e-01 -5.11873722... | [12.370340347290039, 8.454245567321777] |
aeeaa132-3f6a-4296-bd0b-8abbf3fd3237 | probing-for-bridging-inference-in-transformer | 2104.09400 | null | https://arxiv.org/abs/2104.09400v1 | https://arxiv.org/pdf/2104.09400v1.pdf | Probing for Bridging Inference in Transformer Language Models | We probe pre-trained transformer language models for bridging inference. We first investigate individual attention heads in BERT and observe that attention heads at higher layers prominently focus on bridging relations in-comparison with the lower and middle layers, also, few specific attention heads concentrate consis... | ['Yufang Hou', 'Onkar Pandit'] | 2021-04-19 | null | https://aclanthology.org/2021.naacl-main.327 | https://aclanthology.org/2021.naacl-main.327.pdf | naacl-2021-4 | ['cloze-test', 'bridging-anaphora-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [-2.84576267e-01 6.73542023e-01 -4.95769262e-01 -2.42578685e-01
-9.62953806e-01 -2.97555357e-01 9.05732155e-01 2.17311040e-01
-4.06749249e-01 8.42308283e-01 8.18866909e-01 -3.75957608e-01
-5.66594526e-02 -9.14029002e-01 -6.94592059e-01 -2.18821630e-01
1.12640485e-01 9.64673102e-01 3.53954822e-01 -7.11143017... | [9.416792869567871, 9.518204689025879] |
40e55493-eaac-4819-bf5b-cd3a5cf1e5f0 | learning-matchable-colorspace-transformations | 1904.01080 | null | https://arxiv.org/abs/1904.01080v5 | https://arxiv.org/pdf/1904.01080v5.pdf | Learning Matchable Image Transformations for Long-term Metric Visual Localization | Long-term metric self-localization is an essential capability of autonomous mobile robots, but remains challenging for vision-based systems due to appearance changes caused by lighting, weather, or seasonal variations. While experience-based mapping has proven to be an effective technique for bridging the `appearance g... | ['Lee Clement', 'Mona Gridseth', 'Justin Tomasi', 'Jonathan Kelly'] | 2019-04-01 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-1.14522666e-01 -3.81341934e-01 1.97353199e-01 -6.62268102e-01
-1.00760937e+00 -8.57782185e-01 4.98930931e-01 1.19341768e-01
-6.32214665e-01 4.82873499e-01 -6.20345660e-02 -7.08269402e-02
4.38823402e-02 -4.63080555e-01 -9.27626312e-01 -2.64633983e-01
-4.59319949e-01 2.68775970e-01 2.15574935e-01 -3.03202838... | [7.609611988067627, -2.057589054107666] |
0a458334-94b0-40b0-a62e-c9f0e1d097f7 | nested-named-entity-recognition-for-chinese | null | null | https://aclanthology.org/2021.rocling-1.3 | https://aclanthology.org/2021.rocling-1.3.pdf | Nested Named Entity Recognition for Chinese Electronic Health Records with QA-based Sequence Labeling | This study presents a novel QA-based sequence labeling (QASL) approach to naturally tackle both flat and nested Named Entity Recogntion (NER) tasks on a Chinese Electronic Health Records (CEHRs) dataset. This proposed QASL approach parallelly asks a corresponding natural language question for each specific named entity... | ['Keh-Yih Su', 'Cheng-Lung Sung', 'Chih-Hao Lin', 'Yu-Lun Chiang'] | null | null | null | null | rocling-2021-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 2.37133771e-01 2.29181781e-01 1.00064673e-01 -1.71830341e-01
-1.18629074e+00 -7.26621687e-01 1.87985227e-01 6.24942660e-01
-9.99139011e-01 1.06337881e+00 1.25478789e-01 -4.30718899e-01
2.35052202e-02 -8.65546286e-01 -5.03057957e-01 -5.40836275e-01
1.81934759e-01 4.18294579e-01 4.56279486e-01 -1.46710366... | [9.562531471252441, 9.458250999450684] |
6735141f-087a-4982-b0f2-0f4d14fddeb4 | diversevul-a-new-vulnerable-source-code | 2304.00409 | null | https://arxiv.org/abs/2304.00409v1 | https://arxiv.org/pdf/2304.00409v1.pdf | DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection | We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 150 CWEs, 26,635 vulnerable functions, and 352,606 non-vulnerable functions extracted... | ['David Wagner', 'Xinyun Chen', 'Zhoujie Ding', 'Yizheng Chen'] | 2023-04-01 | null | null | null | null | ['feature-engineering', 'vulnerability-detection'] | ['methodology', 'miscellaneous'] | [-3.59981924e-01 3.78006622e-02 -1.54999867e-01 -9.36782807e-02
-7.16267824e-01 -9.63236570e-01 -5.30402660e-02 3.31606686e-01
9.66373086e-03 1.88189074e-01 2.26539914e-02 -9.05126631e-01
8.64041131e-03 -1.06337059e+00 -7.75436699e-01 4.54145484e-03
-4.47664261e-01 -1.62825719e-01 3.79260749e-01 -4.29773808... | [7.098455429077148, 7.774828910827637] |
590e5932-80ef-46bc-9331-90eb718739f1 | image-processing-operations-identification | 1709.02908 | null | http://arxiv.org/abs/1709.02908v1 | http://arxiv.org/pdf/1709.02908v1.pdf | Image Processing Operations Identification via Convolutional Neural Network | In recent years, image forensics has attracted more and more attention, and
many forensic methods have been proposed for identifying image processing
operations. Up to now, most existing methods are based on hand crafted
features, and just one specific operation is considered in their methods. In
many forensic scenario... | ['Haodong Li', 'Bolin Chen', 'Weiqi Luo'] | 2017-09-09 | null | null | null | null | ['steganalysis', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 3.48637968e-01 -6.26311839e-01 1.67891175e-01 -1.45346224e-01
-2.72636682e-01 -1.98358849e-01 3.82041246e-01 -4.07973044e-02
-6.34691179e-01 1.89706236e-01 -3.07974339e-01 -3.52272600e-01
6.68376451e-03 -8.99134636e-01 -3.44850957e-01 -1.03639722e+00
4.25208136e-02 -3.30335796e-01 3.87685478e-01 -4.48672771... | [12.361282348632812, 0.9630408883094788] |
ab118e03-a351-427b-9c7e-fff4bf976a39 | causality-based-ctr-prediction-using-graph | 2301.12762 | null | https://arxiv.org/abs/2301.12762v1 | https://arxiv.org/pdf/2301.12762v1.pdf | Causality-based CTR Prediction using Graph Neural Networks | As a prevalent problem in online advertising, CTR prediction has attracted plentiful attention from both academia and industry. Recent studies have been reported to establish CTR prediction models in the graph neural networks (GNNs) framework. However, most of GNNs-based models handle feature interactions in a complete... | ['Chunjie Zhang', 'Yanwu Yang', 'Panyu Zhai'] | 2023-01-30 | null | null | null | null | ['causal-discovery', 'click-through-rate-prediction'] | ['knowledge-base', 'miscellaneous'] | [ 1.50264293e-01 2.07356974e-01 -6.82982564e-01 -6.70661509e-01
-1.22740656e-01 -2.13768587e-01 6.71813965e-01 2.48712599e-01
3.47182065e-01 4.24036235e-01 5.51215529e-01 -7.11166084e-01
-7.37785876e-01 -1.17352676e+00 -7.35215068e-01 -2.41232440e-01
-6.38267457e-01 1.92285687e-01 1.76240250e-01 -4.82421100... | [10.201714515686035, 5.666111469268799] |
54dcdd6d-c305-4fc4-a707-a20a3671a949 | ma-nerf-motion-assisted-neural-radiance | 2306.10350 | null | https://arxiv.org/abs/2306.10350v2 | https://arxiv.org/pdf/2306.10350v2.pdf | MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images | We address the problem of photorealistic 3D face avatar synthesis from sparse images. Existing Parametric models for face avatar reconstruction struggle to generate details that originate from inputs. Meanwhile, although current NeRF-based avatar methods provide promising results for novel view synthesis, they fail to ... | ['Chun Yuan', 'Wensen Feng', 'Yukang Cao', 'Xiang Zhou', 'Weichen Zhang'] | 2023-06-17 | null | null | null | null | ['novel-view-synthesis', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 6.86018243e-02 4.74074066e-01 -7.91029856e-02 -5.11408210e-01
-6.34546995e-01 -4.60145593e-01 6.65099621e-01 -1.03846204e+00
3.56629044e-01 5.30052662e-01 6.60963714e-01 2.90364116e-01
3.33935618e-01 -5.46512187e-01 -7.48563945e-01 -5.85005999e-01
5.85553825e-01 6.18011773e-01 -2.72037834e-01 -3.37833613... | [12.679352760314941, -0.3857371211051941] |
c29bbdf3-a74c-4eef-8532-cb541a968ac1 | when-accuracy-meets-privacy-two-stage | 2203.12803 | null | https://arxiv.org/abs/2203.12803v2 | https://arxiv.org/pdf/2203.12803v2.pdf | A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study | COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for co... | ['Naomi Fengqi Li', 'Alexandros Shikun Zhang'] | 2022-03-24 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.64430225e-01 1.73128303e-02 -3.11231464e-01 -3.23202670e-01
-5.81256568e-01 -4.65658575e-01 -3.67522836e-02 1.26705959e-01
-7.75200129e-01 8.97433460e-01 -1.28394127e-01 -6.05127513e-01
-1.92946926e-01 -8.44663978e-01 -6.63092852e-01 -6.98835611e-01
-1.54184401e-01 5.76418400e-01 -1.26134470e-01 2.90787816... | [6.143313884735107, 6.519535541534424] |
431b5f87-241e-48f8-9088-7570d9cd40fb | improving-cross-domain-cross-lingual-and | null | null | https://aclanthology.org/2022.acl-srw.30 | https://aclanthology.org/2022.acl-srw.30.pdf | Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection | With the increase of deception and misinformation especially in social media, it has become crucial to be able to develop machine learning methods to automatically identify deceptive language. In this proposal, we identify key challenges underlying deception detection in cross-domain, cross-lingual and multi-modal sett... | ['Sarah Ita Levitan', 'Subhadarshi Panda'] | null | null | null | null | acl-2022-5 | ['deception-detection'] | ['miscellaneous'] | [-2.44888544e-01 -6.20779037e-01 -2.40933374e-01 -3.66941601e-01
-1.27674866e+00 -1.02149415e+00 9.18946564e-01 2.06543401e-01
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-2.29733158e-02 -1.33813471e-01 -1.74725547e-01 -2.94857383e-01
3.72766972e-01 1.30356461e-01 -4.52113688e-01 -1.19078897... | [8.23892879486084, 10.424702644348145] |
d202fa15-7477-4244-a090-c06bfc3e27c0 | evopose-a-recursive-transformer-for-3d-human | 2306.09615 | null | https://arxiv.org/abs/2306.09615v1 | https://arxiv.org/pdf/2306.09615v1.pdf | EVOPOSE: A Recursive Transformer For 3D Human Pose Estimation With Kinematic Structure Priors | Transformer is popular in recent 3D human pose estimation, which utilizes long-term modeling to lift 2D keypoints into the 3D space. However, current transformer-based methods do not fully exploit the prior knowledge of the human skeleton provided by the kinematic structure. In this paper, we propose a novel transforme... | ['Nenghai Yu', 'Qi Chu', 'Zhiwei Zhao', 'Bin Liu', 'Yan Lu', 'Yaqi Zhang'] | 2023-06-16 | null | null | null | null | ['pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.09351146e-01 4.58519459e-02 -1.67333752e-01 -1.93714979e-03
-3.53150338e-01 4.05660085e-02 4.61744547e-01 -3.96424621e-01
-3.54043216e-01 5.86535573e-01 5.82722843e-01 5.06579518e-01
3.92756537e-02 -5.95882356e-01 -7.39626586e-01 -3.47916901e-01
-2.10517317e-01 9.25685823e-01 5.81877172e-01 -5.12387276... | [7.058581829071045, -0.6238043904304504] |
42679272-fa6a-407b-9ec1-84528afbf160 | 3d-aware-face-swapping | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_3D-Aware_Face_Swapping_CVPR_2023_paper.pdf | 3D-Aware Face Swapping | Face swapping is an important research topic in computer vision with wide applications in entertainment and privacy protection. Existing methods directly learn to swap 2D facial images, taking no account of the geometric information of human faces. In the presence of large pose variance between the source and the t... | ['Xiaokang Yang', 'Wenhan Zhu', 'Yichao Yan', 'Chao Ma', 'Yixuan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['face-swapping'] | ['computer-vision'] | [ 4.60013188e-03 -6.78180670e-03 1.98250487e-01 -4.55113083e-01
-4.68602002e-01 -7.34872103e-01 5.24932504e-01 -7.70966709e-01
2.39955708e-01 3.89517635e-01 3.41799259e-01 2.69871503e-01
4.70030494e-02 -5.76230824e-01 -7.63290882e-01 -8.29661727e-01
4.03833210e-01 2.29566231e-01 -1.73187345e-01 -5.67340776... | [12.88776683807373, -0.04353645443916321] |
2c2f90de-449b-4856-8288-d32ca5229dcc | quotienting-impertinent-camera-kinematics-for | 1903.09073 | null | https://arxiv.org/abs/1903.09073v2 | https://arxiv.org/pdf/1903.09073v2.pdf | Quotienting Impertinent Camera Kinematics for 3D Video Stabilization | With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows are more robust than the sparse feature matching techniques used by existing 3D stabilization methods, able to better handle large camera dis... | ['Jin Seob Kim', 'Gregory S. Chirikjian', 'Sipu Ruan', 'Christian Wuelker', 'Thomas W. Mitchel'] | 2019-03-21 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-2.29324222e-01 -3.76739651e-01 -1.17940515e-01 8.44677538e-02
-2.91359961e-01 -7.26128757e-01 6.50946677e-01 4.13796864e-02
-3.02355796e-01 5.73291421e-01 2.55052805e-01 7.03017265e-02
-8.12499225e-02 -5.49501657e-01 -5.68948150e-01 -6.72502935e-01
5.53342067e-02 1.56122863e-01 6.24146223e-01 -2.91218489... | [8.773978233337402, -1.8535462617874146] |
baaf0105-66e2-4f72-ba38-933f868c595c | joint-learning-based-heterogeneous-graph | null | null | https://aclanthology.org/2022.naacl-main.301 | https://aclanthology.org/2022.naacl-main.301.pdf | Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization | Previous studies on the timeline summarization (TLS) task ignored the information interaction between sentences and dates, and adopted pre-defined unlearnable representations for them. They also considered date selection and event detection as two independent tasks, which makes it impossible to integrate their advantag... | ['Manabu Okumura', 'Kotaro Funakoshi', 'Hidetaka Kamigaito', 'Dongyuan Li', 'Jingyi You'] | null | null | null | null | naacl-2022-7 | ['timeline-summarization'] | ['natural-language-processing'] | [-7.83255994e-02 2.35479474e-01 -3.30500960e-01 -2.49193311e-01
-9.22369659e-01 -6.35262609e-01 6.62782907e-01 6.67339861e-01
-4.10991073e-01 6.02077246e-01 8.48352492e-01 -1.98357046e-01
4.25290577e-02 -8.19007456e-01 -5.92087746e-01 -2.38309965e-01
-2.02005744e-01 4.14104521e-01 3.35449427e-01 -1.11993581... | [12.473531723022461, 9.466474533081055] |
b3c2cbaf-f8e3-44b8-a341-791b97f7777f | persona-based-conversational-ai-state-of-the | 2212.03699 | null | https://arxiv.org/abs/2212.03699v1 | https://arxiv.org/pdf/2212.03699v1.pdf | Persona-Based Conversational AI: State of the Art and Challenges | Conversational AI has become an increasingly prominent and practical application of machine learning. However, existing conversational AI techniques still suffer from various limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand c... | ['Ranga Raju Vatsavai', 'Christopher Symons', 'Junfeng Liu'] | 2022-12-04 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.92935485e-01 4.16010499e-01 -1.63520887e-01 -7.69312561e-01
-7.25098193e-01 -3.27620834e-01 1.12840140e+00 -3.14206392e-01
-3.05102885e-01 8.42540503e-01 1.04590535e+00 6.17069229e-02
-3.49816643e-02 -4.82700825e-01 -2.31324300e-01 -4.34643716e-01
1.74674571e-01 9.90265250e-01 -2.45971173e-01 -7.11137533... | [12.548169136047363, 7.9916768074035645] |
bd3b3035-f604-4661-a9c5-a2249def0b07 | simple-pose-rethinking-and-improving-a-bottom | 1911.10529 | null | https://arxiv.org/abs/1911.10529v1 | https://arxiv.org/pdf/1911.10529v1.pdf | Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation | We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one. The improved approach surpasses the baseline significantly thanks to (1) an intuitional yet more sensible representation, which we refer to as body parts to encode the connection information between keypoints, (2) an... | ['Zengfu Wang', 'Wen Su', 'Jia Li'] | 2019-11-24 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.45600691e-01 1.95301011e-01 -2.92808652e-01 -1.18311942e-01
-9.35447276e-01 -3.87040496e-01 5.78404307e-01 2.66720265e-01
-6.14456236e-01 5.65221369e-01 5.48612475e-01 3.44027966e-01
-1.84000030e-01 -3.38114679e-01 -1.06791806e+00 -3.41340870e-01
-4.31344956e-01 7.28373408e-01 4.58541662e-01 -3.38991016... | [7.147174835205078, -0.7769943475723267] |
3494b090-a141-4e9f-9255-dfa88820c672 | kinematic-data-based-action-segmentation-for | 2303.07814 | null | https://arxiv.org/abs/2303.07814v1 | https://arxiv.org/pdf/2303.07814v1.pdf | Kinematic Data-Based Action Segmentation for Surgical Applications | Action segmentation is a challenging task in high-level process analysis, typically performed on video or kinematic data obtained from various sensors. In the context of surgical procedures, action segmentation is critical for workflow analysis algorithms. This work presents two contributions related to action segmenta... | ['Shlomi Laufer', 'Carla M Pugh', 'Or Rubin', 'Omer Shubi', 'Adam Goldbraikh'] | 2023-03-14 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 4.40861046e-01 2.03707635e-01 -6.02960467e-01 -1.99410319e-01
-8.01689446e-01 -3.54334623e-01 3.14416051e-01 -1.98373478e-02
-8.26937139e-01 2.55906224e-01 5.86170971e-01 -7.04048991e-01
-1.03649050e-01 -2.21480787e-01 -8.86866450e-01 -7.50035882e-01
-2.75480701e-03 2.62131155e-01 2.07334816e-01 -3.88158888... | [14.061981201171875, -3.357175827026367] |
5aae6add-1a77-4c60-bb90-1c2010516665 | multimodal-fusion-of-emg-and-vision-for-human | 2104.03893 | null | https://arxiv.org/abs/2104.03893v3 | https://arxiv.org/pdf/2104.03893v3.pdf | Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control | Objective: For lower arm amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. Current control methods based on physiological signals such as electromyography (EMG) are prone to yielding poor inference outcomes due to motion artifacts, muscle fatigue, and many more. Vis... | ['Gunar Schirner', 'Deniz Erdogmus', 'Taskin Padir', 'Cagdas Onal', 'Paolo Bonato', 'Mathew Yarossi', 'Mariusz P. Furmanek', 'Sezen Yagmur Gunay', 'Mohammadreza Sharif', 'Mo Han', 'Mehrshad Zandigohar'] | 2021-04-08 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 2.50372738e-01 -2.24087715e-01 -3.96186590e-01 5.21267056e-02
-6.62147284e-01 -2.01732144e-01 2.71283120e-01 -4.09491390e-01
-3.22886348e-01 9.20865715e-01 2.68612057e-01 2.95272619e-01
-4.78601992e-01 -1.54801548e-01 -7.16620266e-01 -7.30575919e-01
-1.64663717e-01 8.76132399e-02 -6.79055378e-02 1.54208243... | [6.816800594329834, 0.15788176655769348] |
ad684251-cbe1-47bc-a004-b1765100ae5d | authorship-attribution-using-text-distortion-1 | null | null | https://1library.net/document/zpnew20y-authorship-attribution-using-text-distortion.html?utm_source=related_list | https://www.aclweb.org/anthology/E17-1107.pdf | Authorship Attribution Using Text Distortion | Authorship attribution is associated with important applications in forensics and humanities research. A crucial point in this field is to quantify the personal style of writing, ideally in a way that is not affected by changes in topic or genre. In this paper, we present a novel method that enhances authorship attribu... | ['Efstathios Stamatatos'] | 2017-04-03 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 1.39636740e-01 -1.46012217e-01 -1.46517679e-01 -1.45925850e-01
-3.07935774e-01 -8.28416646e-01 7.85753787e-01 4.62786436e-01
-5.62467754e-01 7.62456536e-01 1.77630916e-01 -7.59812221e-02
-1.82882801e-01 -3.90217632e-01 -1.76267758e-01 -3.42048585e-01
5.22115469e-01 4.92876023e-01 8.79463404e-02 1.64163515... | [9.566183090209961, 10.608369827270508] |
9cc7c2c6-e4f1-4f01-8cf6-d2c1441dc945 | deep-homography-estimation-in-dynamic | 2109.15098 | null | https://arxiv.org/abs/2109.15098v2 | https://arxiv.org/pdf/2109.15098v2.pdf | Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction | Current laparoscopic camera motion automation relies on rule-based approaches or only focuses on surgical tools. Imitation Learning (IL) methods could alleviate these shortcomings, but have so far been applied to oversimplified setups. Instead of extracting actions from oversimplified setups, in this work we introduce ... | ['Tom Vercauteren', 'Christos Bergeles', 'Sébastien Ourselin', 'Martin Huber'] | 2021-09-30 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 1.42269894e-01 9.54767913e-02 -1.45754457e-01 5.82327880e-02
-2.63191551e-01 -8.18255663e-01 6.67802811e-01 -4.62599754e-01
-6.76832974e-01 5.07425010e-01 -6.35228381e-02 -2.55671591e-01
-8.51122290e-02 -2.86364079e-01 -1.10706902e+00 -6.52849495e-01
4.59448881e-02 6.06129467e-01 -3.70319039e-02 -1.29853010... | [14.03707504272461, -3.333566904067993] |
5cff55ed-3f37-4976-bcfd-fe667aa05d0d | copulaboost-additive-modeling-with-copula | 2208.04669 | null | https://arxiv.org/abs/2208.04669v1 | https://arxiv.org/pdf/2208.04669v1.pdf | Copulaboost: additive modeling with copula-based model components | We propose a type of generalised additive models with of model components based on pair-copula constructions, with prediction as a main aim. The model components are designed such that our model may capture potentially complex interaction effects in the relationship between the response covariates. In addition, our mod... | ['Ingrid Hobæk Haff', 'Simon Boge Brant'] | 2022-08-09 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 6.54816721e-03 6.65471032e-02 -1.18544303e-01 -5.38252473e-01
-6.58718288e-01 -1.68136597e-01 4.89395231e-01 4.97538298e-01
-3.65514457e-01 9.90497470e-01 9.59700495e-02 -5.26462734e-01
-5.52203000e-01 -9.37267005e-01 -7.96731412e-01 -8.49687040e-01
-4.63311762e-01 7.61641800e-01 4.64234501e-02 -3.74520421... | [7.850579261779785, 4.925848484039307] |
0f9331c8-1ece-49f5-9ce1-44d482ba3427 | a-bottom-up-approach-for-automatic-pancreas | 1407.8497 | null | http://arxiv.org/abs/1407.8497v1 | http://arxiv.org/pdf/1407.8497v1.pdf | A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans | Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD)
system to detect pathologies and perform quantitative analysis. For
anatomically high-variability abdominal organs such as the pancreas, previous
segmentation works report low accuracies when comparing to organs like the
heart or liver. In this p... | ['Le Lu', 'Amal Farag', 'Jiamin Liu', 'Ronald M. Summers', 'Evrim Turkbey'] | 2014-07-31 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 3.03685993e-01 3.58576447e-01 -2.51477420e-01 -3.61166388e-01
-8.84530962e-01 -7.47097492e-01 1.21687204e-01 9.05352890e-01
-2.88421988e-01 3.78621042e-01 1.58575401e-02 -3.57232571e-01
-6.44063279e-02 -5.47002912e-01 -3.42749715e-01 -9.24976110e-01
-5.64610660e-01 8.71510386e-01 4.97102648e-01 6.78303003... | [14.51269245147705, -2.6887989044189453] |
294bb99e-be5c-4f0b-b979-131db7e52c3a | totally-ordered-sequential-rules-for-utility | 2209.13501 | null | https://arxiv.org/abs/2209.13501v1 | https://arxiv.org/pdf/2209.13501v1.pdf | Totally-ordered Sequential Rules for Utility Maximization | High utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High utility sequential rule mining (HUSRM) discovers high utility and... | ['Philip S. Yu', 'Wensheng Gan', 'Maohua Lyu', 'Chunkai Zhang'] | 2022-09-27 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 4.29735035e-01 9.58548710e-02 -5.74563146e-01 -7.93408006e-02
-1.02796115e-01 -1.57557800e-01 4.57996596e-03 1.16131634e-01
-2.03414813e-01 1.20982969e+00 -3.12512904e-01 -7.26228833e-01
-4.91920859e-01 -1.48543417e+00 -4.21173185e-01 -4.57911968e-01
-3.00115913e-01 6.58544540e-01 7.37032354e-01 -1.55612260... | [8.284422874450684, 6.2959089279174805] |
54298ece-364c-4b90-984c-fa203d11952b | learning-like-a-child-fast-novel-visual | 1504.06692 | null | http://arxiv.org/abs/1504.06692v2 | http://arxiv.org/pdf/1504.06692v2.pdf | Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images | In this paper, we address the task of learning novel visual concepts, and
their interactions with other concepts, from a few images with sentence
descriptions. Using linguistic context and visual features, our method is able
to efficiently hypothesize the semantic meaning of new words and add them to
its word dictionar... | ['Wei Xu', 'Zhiheng Huang', 'Yi Yang', 'Junhua Mao', 'Alan Yuille', 'Jiang Wang'] | 2015-04-25 | learning-like-a-child-fast-novel-visual-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Mao_Learning_Like_a_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Mao_Learning_Like_a_ICCV_2015_paper.pdf | iccv-2015-12 | ['novel-concepts'] | ['reasoning'] | [ 2.69043922e-01 1.34706855e-01 -1.83506846e-01 -5.13212562e-01
-4.71562117e-01 -4.11969543e-01 5.37894011e-01 1.25184909e-01
-4.85748231e-01 8.75575066e-01 2.68361449e-01 -1.10124424e-01
4.25505430e-01 -7.50207663e-01 -9.99897778e-01 -6.69635713e-01
1.09056547e-01 2.05177993e-01 1.22507215e-01 -1.28772423... | [10.220171928405762, 1.9996099472045898] |
bed0c449-99fc-4bcd-9ba8-ce1c502397ad | simultaneous-or-sequential-training-how | 2306.02972 | null | https://arxiv.org/abs/2306.02972v1 | https://arxiv.org/pdf/2306.02972v1.pdf | Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System | Speech representation learning with self-supervised algorithms has resulted in notable performance boosts in many downstream tasks. Recent work combined self-supervised learning (SSL) and visually grounded speech (VGS) processing mechanisms for representation learning. The joint training with SSL and VGS mechanisms pro... | ['Okko Räsänen', 'Tuomas Virtanen', 'María Andrea Cruz Blandón', 'Khazar Khorrami'] | 2023-06-05 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.69996336e-01 -9.87671092e-02 -1.18248150e-01 -2.10035458e-01
-1.07523000e+00 -4.70787227e-01 7.41140306e-01 3.74826610e-01
-4.67822284e-01 4.06213701e-01 4.44890678e-01 -3.41296226e-01
2.48717126e-02 -6.16055489e-01 -6.45968854e-01 -6.52326107e-01
1.41647801e-01 3.35635424e-01 1.47034407e-01 -2.37893000... | [14.280762672424316, 5.07403564453125] |
341fbe2a-4030-4f1f-88ba-f6cd6b483d4c | lavt-language-aware-vision-transformer-for | 2112.02244 | null | https://arxiv.org/abs/2112.02244v2 | https://arxiv.org/pdf/2112.02244v2.pdf | LAVT: Language-Aware Vision Transformer for Referring Image Segmentation | Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key challenges behind this task is leveraging the referring expression for highlighting relevant positions in the image. A paradigm for tackling th... | ['Philip H. S. Torr', 'Hengshuang Zhao', 'Kai Chen', 'Yansong Tang', 'Jiaqi Wang', 'Zhao Yang'] | 2021-12-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_LAVT_Language-Aware_Vision_Transformer_for_Referring_Image_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalized-referring-expression-segmentation', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.68661153e-01 2.35875487e-01 -2.21613161e-02 -3.76024544e-01
-1.13294482e+00 -6.23117983e-01 8.05322230e-01 -8.91669989e-02
-5.01285553e-01 2.11099878e-01 1.47607803e-01 -2.17666537e-01
9.20439288e-02 -4.69295084e-01 -8.70837092e-01 -6.00996971e-01
4.51381445e-01 3.41382086e-01 4.29572612e-01 -4.03380156... | [10.430869102478027, 1.4801390171051025] |
d8f9e79a-37c8-4640-96a4-774b2b356b06 | heart-rate-estimation-from | 1809.03174 | null | http://arxiv.org/abs/1809.03174v1 | http://arxiv.org/pdf/1809.03174v1.pdf | Heart Rate Estimation from Ballistocardiography Based on Hilbert Transform and Phase Vocoder | This paper presents a robust method to monitor heart rate (HR) from BCG
(Ballistocardiography) signal, which is acquired from the sensor embedded in a
chair or a mattress. The proposed algorithm addresses the shortfalls in
traditional Fast Fourier Transform (FFT) based approaches by introducing
Hilbert Transform to ext... | [] | 2018-09-10 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 6.78411007e-01 2.71448176e-02 1.88446954e-01 -1.17109790e-01
-4.99851286e-01 -3.36588979e-01 1.37980744e-01 -2.20865514e-02
-5.27564824e-01 1.25314152e+00 -1.26370668e-01 -1.22057006e-01
-5.70547432e-02 -2.76409358e-01 -1.31106958e-01 -6.55332804e-01
-3.91166329e-01 -4.92420137e-01 -1.53409064e-01 2.65701383... | [13.981937408447266, 3.0069472789764404] |
1149f575-1262-46bd-bac6-d9682c7f980f | uncovering-the-background-induced-bias-in-rgb | 2304.08230 | null | https://arxiv.org/abs/2304.08230v1 | https://arxiv.org/pdf/2304.08230v1.pdf | Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation | In recent years, there has been a growing trend of using data-driven methods in industrial settings. These kinds of methods often process video images or parts, therefore the integrity of such images is crucial. Sometimes datasets, e.g. consisting of images, can be sophisticated for various reasons. It becomes critical... | ['Marko Bertogna', 'Micaela Verucchi', 'Paola Ardòn', 'Giorgia Franchini', 'Tobia Poppi', 'Carmelo Scribano', 'Davide Sapienza', 'Elena Govi'] | 2023-04-17 | null | null | null | null | ['6d-pose-estimation-1'] | ['computer-vision'] | [ 4.07023579e-01 2.09214211e-01 -4.20174263e-02 -1.31569207e-01
-1.57055616e-01 -4.74014729e-01 6.61617041e-01 -1.44932009e-02
-3.22637320e-01 5.90430260e-01 1.05140451e-02 -1.54213766e-02
-3.83808285e-01 -4.92459506e-01 -1.31729925e+00 -6.07356071e-01
-1.18628561e-01 3.84215087e-01 2.96522379e-01 -1.86607793... | [7.634875774383545, -1.0336214303970337] |
d0d271e4-4aa2-4c20-b99d-3acfa9cccb59 | an-account-of-opinion-implicatures | 1404.6491 | null | http://arxiv.org/abs/1404.6491v1 | http://arxiv.org/pdf/1404.6491v1.pdf | An Account of Opinion Implicatures | While previous sentiment analysis research has concentrated on the
interpretation of explicitly stated opinions and attitudes, this work initiates
the computational study of a type of opinion implicature (i.e.,
opinion-oriented inference) in text. This paper described a rule-based
framework for representing and analyzi... | ['Lingjia Deng', 'Janyce Wiebe'] | 2014-04-23 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [ 1.01520315e-01 7.28959858e-01 -4.37133104e-01 -8.82080197e-01
2.31582165e-01 -7.89484799e-01 7.54097521e-01 1.04668987e+00
-1.73969075e-01 8.55578959e-01 8.32361102e-01 -8.37854207e-01
3.59189719e-01 -9.18484867e-01 -4.25324410e-01 -3.49460989e-01
3.94852251e-01 2.73853093e-01 6.13442324e-02 -7.23041177... | [11.328592300415039, 6.780926704406738] |
956cac1b-0d3c-4daf-a943-248a2a5a8afc | improving-seasonal-forecast-using | 2010.14610 | null | https://arxiv.org/abs/2010.14610v1 | https://arxiv.org/pdf/2010.14610v1.pdf | Improving seasonal forecast using probabilistic deep learning | The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model ... | ['Jiwoo Lee', 'CEline J. W. Bonfils', 'Donald D. Lucas', 'Andre Goncalves', 'Gemma J. Anderson', 'Baoxiang Pan'] | 2020-10-27 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.11826050e-01 -2.01450542e-01 -1.94299184e-02 -3.84263903e-01
-3.44292939e-01 -6.35302782e-01 9.31751788e-01 -2.47803658e-01
2.62489617e-01 8.85793865e-01 4.48801190e-01 -7.98254669e-01
-2.63743103e-01 -8.84994268e-01 -2.83453584e-01 -9.64308619e-01
-2.29851693e-01 4.58043337e-01 -4.43965614e-01 -6.68215692... | [6.563406467437744, 2.9689297676086426] |
5eace04d-6def-4b4e-a004-34552d61c33f | using-natural-language-and-program | 2205.11558 | null | https://arxiv.org/abs/2205.11558v3 | https://arxiv.org/pdf/2205.11558v3.pdf | Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines | Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on ... | ['Thomas L. Griffiths', 'Karthik Narasimhan', 'Jonathan D. Cohen', 'Nathaniel D. Daw', 'Robert D. Hawkins', 'Michael Y. Hu', 'Raja Marjieh', 'Ishita Dasgupta', 'Carlos G. Correa', 'Sreejan Kumar'] | 2022-05-23 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 2.07328185e-01 6.86145663e-01 -4.57462847e-01 -5.26572049e-01
-7.63067305e-02 -4.71851110e-01 1.17591238e+00 3.13810855e-01
-6.60571814e-01 7.94310689e-01 3.84445548e-01 -3.20597798e-01
1.83293507e-01 -1.18557262e+00 -8.60717893e-01 -3.42325658e-01
-3.17738831e-01 9.86207426e-01 -2.71453615e-02 -5.63331246... | [4.2356367111206055, 1.2941441535949707] |
bfb4a2b7-a303-4442-a59a-429ddd9dd6f6 | text-aware-single-image-specular-highlight | 2108.06881 | null | https://arxiv.org/abs/2108.06881v1 | https://arxiv.org/pdf/2108.06881v1.pdf | Text-Aware Single Image Specular Highlight Removal | Removing undesirable specular highlight from a single input image is of crucial importance to many computer vision and graphics tasks. Existing methods typically remove specular highlight for medical images and specific-object images, however, they cannot handle the images with text. In addition, the impact of specular... | ['Dong-Ming Yan', 'Jingen Jiang', 'Weize Quan', 'Chaoqun Wang', 'Shiyu Hou'] | 2021-08-16 | null | null | null | null | ['highlight-detection', 'highlight-removal'] | ['computer-vision', 'computer-vision'] | [ 7.19110668e-01 -6.55193269e-01 1.53039679e-01 -2.57596403e-01
-3.96916568e-01 -3.62674206e-01 3.55346680e-01 -1.03693716e-01
-1.70131102e-01 3.68065774e-01 7.82036707e-02 -4.91454639e-02
3.34663510e-01 -4.61657047e-01 -3.94100964e-01 -1.07674980e+00
6.16347849e-01 -2.92848706e-01 4.88434196e-01 2.85442621... | [11.979948997497559, 2.135601043701172] |
0106d422-f9e9-405d-ada9-e2fed8d3d304 | audiovisual-transfer-learning-for-audio | 2106.05408 | null | https://arxiv.org/abs/2106.05408v1 | https://arxiv.org/pdf/2106.05408v1.pdf | Audiovisual transfer learning for audio tagging and sound event detection | We study the merit of transfer learning for two sound recognition problems, i.e., audio tagging and sound event detection. Employing feature fusion, we adapt a baseline system utilizing only spectral acoustic inputs to also make use of pretrained auditory and visual features, extracted from networks built for different... | ['Hugo Van hamme', 'Wim Boes'] | 2021-06-09 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 4.43749905e-01 -1.50801808e-01 2.63165414e-01 -3.03314626e-01
-1.29588914e+00 -8.41706693e-01 5.27717769e-01 3.76598030e-01
-5.86766481e-01 5.86953938e-01 4.07420874e-01 -7.61265382e-02
-3.21459360e-02 -4.56021339e-01 -4.52938676e-01 -7.69830883e-01
-2.60591567e-01 1.89878806e-01 5.09317219e-01 2.35219467... | [15.207425117492676, 5.128678798675537] |
26f24fd8-f0c0-4958-a4d4-80978956b565 | a-novel-sleep-stage-classification-using-cnn | 2110.15277 | null | https://arxiv.org/abs/2110.15277v3 | https://arxiv.org/pdf/2110.15277v3.pdf | A Novel Sleep Stage Classification Using CNN Generated by an Efficient Neural Architecture Search with a New Data Processing Trick | With the development of automatic sleep stage classification (ASSC) techniques, many classical methods such as k-means, decision tree, and SVM have been used in automatic sleep stage classification. However, few methods explore deep learning on ASSC. Meanwhile, most deep learning methods require extensive expertise and... | ['Adam Slowik', 'Ziming Yuan', 'Yu Xue'] | 2021-10-27 | null | null | null | null | ['automatic-sleep-stage-classification'] | ['medical'] | [-9.18138176e-02 -4.02622968e-01 2.53922269e-02 -2.76373953e-01
2.19068080e-01 -1.90179169e-01 4.87497970e-02 -1.76991984e-01
-9.05181050e-01 4.60858405e-01 -2.18741700e-01 -4.00109798e-01
-2.31020421e-01 -7.81115234e-01 -4.09324706e-01 -9.94310915e-01
3.86932552e-01 -8.01949948e-02 3.44341397e-01 -3.78816575... | [8.512314796447754, 3.0636603832244873] |
49de5ced-21cf-4efa-a335-ec642328b46f | fast-video-salient-object-detection-via | 2010.10027 | null | https://arxiv.org/abs/2010.10027v2 | https://arxiv.org/pdf/2010.10027v2.pdf | Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation | Since the wide employment of deep learning frameworks in video salient object detection, the accuracy of the recent approaches has made stunning progress. These approaches mainly adopt the sequential modules, based on optical flow or recurrent neural network (RNN), to learn robust spatiotemporal features. These modules... | ['Wenbin Zou', 'Yuanman Li', 'Yi Tang'] | 2020-10-20 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [-1.32314295e-01 -2.15257376e-01 -3.28495950e-01 -2.44102161e-03
-1.47474959e-01 -1.12815440e-01 4.75024015e-01 -1.19248219e-01
-4.89877552e-01 5.73648334e-01 2.93429106e-01 -5.13043031e-02
-8.04667547e-02 -6.36924386e-01 -7.07542837e-01 -7.78080940e-01
-6.80485554e-03 -6.21974766e-01 8.54208887e-01 -2.05906570... | [9.592036247253418, -0.35304877161979675] |
3db1fe53-18ce-4c93-b0b7-028399c8f96c | social-media-personal-event-notifier-using | 2210.05001 | null | https://arxiv.org/abs/2210.05001v1 | https://arxiv.org/pdf/2210.05001v1.pdf | Social Media Personal Event Notifier Using NLP and Machine Learning | Social media apps have become very promising and omnipresent in daily life. Most social media apps are used to deliver vital information to those nearby and far away. As our lives become more hectic, many of us strive to limit our usage of social media apps because they are too addictive, and the majority of us have go... | ['Vetriselvi A', 'Ashwin Kumar BR', 'Sharan Padmanabhan', 'Pavithiran G'] | 2022-10-10 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-1.11458264e-01 1.58666596e-01 -2.85773665e-01 -4.03379947e-01
-3.50834519e-01 -4.23250198e-01 3.00039142e-01 6.74081624e-01
-7.99087822e-01 9.20430541e-01 4.96902168e-01 -3.73739749e-01
-1.74849518e-02 -1.02083743e+00 2.18817458e-01 -3.32850128e-01
4.27374721e-01 3.52453142e-01 4.06983286e-01 -4.00098979... | [9.778203964233398, 8.752239227294922] |
4ce6269a-52ef-4c9a-993e-b0d54422938d | cross-domain-collaborative-learning-for | 2305.08078 | null | https://arxiv.org/abs/2305.08078v1 | https://arxiv.org/pdf/2305.08078v1.pdf | Cross-domain Collaborative Learning for Recognizing Multiple Retinal Diseases from Wide-Field Fundus Images | This paper addresses the emerging task of recognizing multiple retinal diseases from wide-field (WF) and ultra-wide-field (UWF) fundus images. For an effective reuse of existing labeled color fundus photo (CFP) data, we propose Cross-domain Collaborative Learning (CdCL). Inspired by the success of fixed-ratio based mix... | ['Xirong Li', 'Dayong Ding', 'Youxin Chen', 'Niranchana Manivannan', 'Sheng Yang', 'Xinyu Zhao', 'Jianchun Zhao', 'Jinrui Wang', 'Bo wang', 'Jingyuan Yang', 'Qijie Wei'] | 2023-05-14 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 2.76378900e-01 -2.29085572e-02 -3.17065269e-01 -3.56246918e-01
-8.18891466e-01 -5.56539655e-01 3.77599686e-01 -3.21841538e-01
-4.46751535e-01 8.26908410e-01 4.90890175e-01 -2.50606030e-01
-3.45309019e-01 -4.48655248e-01 -4.58751231e-01 -7.51819432e-01
1.46876574e-01 -6.57503307e-02 4.69458073e-01 2.69656200... | [15.791440963745117, -3.9594621658325195] |
edd388f1-0913-4c2b-bfa4-a2c911da5b1a | predictive-coding-based-deep-dynamic-neural | 1706.02444 | null | http://arxiv.org/abs/1706.02444v1 | http://arxiv.org/pdf/1706.02444v1.pdf | Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning | This study presents a dynamic neural network model based on the predictive
coding framework for perceiving and predicting the dynamic visuo-proprioceptive
patterns. In our previous study [1], we have shown that the deep dynamic neural
network model was able to coordinate visual perception and action generation in
a sea... | ['Jungsik Hwang', 'Minkyu Choi', 'Jinhyung Kim', 'Ahmadreza Ahmadi', 'Jun Tani'] | 2017-06-08 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 3.96091253e-01 3.82221282e-01 2.17383616e-02 1.59291048e-02
6.22637153e-01 -1.60049900e-01 1.13839769e+00 -4.92421627e-01
-2.16245994e-01 4.98649478e-01 4.15182292e-01 1.24441877e-01
-4.10829365e-01 -8.85567248e-01 -1.12956536e+00 -7.00074971e-01
-1.29120559e-01 3.07357281e-01 -8.64450783e-02 -2.64166504... | [4.443124771118164, 1.0494452714920044] |
24632c69-f528-412d-9457-aedd2cb6b127 | trading-quality-for-efficiency-of-graph | null | null | https://openreview.net/forum?id=e6MWIbNeW1 | https://openreview.net/pdf?id=e6MWIbNeW1 | Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs | Many applications of network systems can be formulated as several NP-hard combinatorial optimization problems regarding graph partitioning (GP), e.g., modularity maximization and NCut minimization. Due to the NP-hardness, to balance the quality and efficiency of GP remains a challenge. Existing methods use machine lear... | ['Dit-yan Yeung', 'Gong Zhang', 'Bo Bai', 'Chaorui Zhang', 'Meng Qin'] | 2021-09-29 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 3.42620052e-02 2.43439436e-01 -2.34458402e-01 1.02125280e-01
-7.05872595e-01 -7.82622039e-01 1.82798147e-01 1.71442464e-01
3.16772163e-01 6.02692962e-01 -4.84798640e-01 -4.27870363e-01
-5.55990338e-01 -1.18176866e+00 -9.83977973e-01 -9.91708338e-01
-6.09548509e-01 8.23319674e-01 2.73167223e-01 1.31589502... | [7.1703362464904785, 6.034206867218018] |
10699954-3d9e-494e-9fd2-e2d4dd039f75 | equivalence-of-dataflow-graphs-via-rewrite | 2002.06799 | null | https://arxiv.org/abs/2002.06799v2 | https://arxiv.org/pdf/2002.06799v2.pdf | Equivalence of Dataflow Graphs via Rewrite Rules Using a Graph-to-Sequence Neural Model | In this work we target the problem of provably computing the equivalence between two programs represented as dataflow graphs. To this end, we formalize the problem of equivalence between two programs as finding a set of semantics-preserving rewrite rules from one into the other, such that after the rewrite the two prog... | ['Louis-Noël Pouchet', 'Théo Barollet', 'Steve Kommrusch'] | 2020-02-17 | null | null | null | null | ['graph-to-sequence'] | ['natural-language-processing'] | [ 5.26871800e-01 3.82086903e-01 -3.15916777e-01 -3.09017152e-01
-5.53360879e-01 -9.64129329e-01 3.12220275e-01 4.49003190e-01
1.64928943e-01 3.91747564e-01 -1.40700504e-01 -1.36756301e+00
2.74181277e-01 -1.41052628e+00 -1.43001068e+00 1.77952856e-01
-3.50616783e-01 3.50315928e-01 2.62752444e-01 -3.05536062... | [8.624372482299805, 7.2467241287231445] |
dce24ac1-dc86-424d-be38-ea0673e4c0df | mick-a-meta-learning-framework-for-few-shot | 2004.14164 | null | https://arxiv.org/abs/2004.14164v2 | https://arxiv.org/pdf/2004.14164v2.pdf | MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data | Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We pr... | ['Yinggong Zhao', 'Libin Shen', 'Xiaoqing Geng', 'Xiwen Chen', 'Kenny Q. Zhu'] | 2020-04-26 | null | null | null | null | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 4.87952381e-01 5.89222550e-01 -9.25446868e-01 -4.92377371e-01
-1.10687780e+00 -4.56894822e-02 4.15171057e-01 6.12435460e-01
-3.04887891e-01 8.90697002e-01 9.30825099e-02 -2.26209685e-02
-3.81295860e-01 -9.48771536e-01 -4.08783227e-01 -3.34047884e-01
-9.98161137e-02 8.77857447e-01 4.66979772e-01 -5.92444956... | [9.19176197052002, 8.553003311157227] |
ef71eda7-097c-448f-9739-4f77cb322941 | attention-based-open-ran-slice-management | 2306.09490 | null | https://arxiv.org/abs/2306.09490v1 | https://arxiv.org/pdf/2306.09490v1.pdf | Attention-based Open RAN Slice Management using Deep Reinforcement Learning | As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing has emerged as a potential solution to address the different service requirements. However, managing network slices while maintaining qualit... | ['Jonathan Ashdown', 'Fatemeh Afghah', 'Fatemeh Lotfi'] | 2023-06-15 | null | null | null | null | ['management'] | ['miscellaneous'] | [-3.75178993e-01 5.10440730e-02 -8.24716747e-01 -5.69882929e-01
-4.70440537e-01 -6.23260200e-01 -4.85924929e-02 -2.67623872e-01
2.08790734e-01 1.17818248e+00 -1.00453824e-01 -4.73949373e-01
-5.70820212e-01 -8.82376969e-01 2.55589157e-01 -7.94413984e-01
-7.17058897e-01 9.08886731e-01 1.89405549e-02 -1.52268082... | [5.8682475090026855, 1.7020667791366577] |
d6483444-d1e8-4ce7-aec0-949530cb82da | deep-learning-for-table-detection-and | 2211.08469 | null | https://arxiv.org/abs/2211.08469v1 | https://arxiv.org/pdf/2211.08469v1.pdf | Deep learning for table detection and structure recognition: A survey | Tables are everywhere, from scientific journals, papers, websites, and newspapers all the way to items we buy at the supermarket. Detecting them is thus of utmost importance to automatically understanding the content of a document. The performance of table detection has substantially increased thanks to the rapid devel... | ['Islam Taj-Eddin', 'Daniyar Nurseitov', 'Mohamed Hamada', 'Mohamed Mahmoud', 'Mahmoud Abdalla', 'Ebrahem Elkady', 'Alexander Berendeyev', 'Abdelrahman Abdallah', 'Mahmoud Kasem'] | 2022-11-15 | null | null | null | null | ['table-recognition', 'table-detection', 'table-extraction'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [-1.43195257e-01 -1.84010595e-01 -4.53957528e-01 -2.30697632e-01
-7.42977381e-01 -8.85615706e-01 2.71290660e-01 6.32937372e-01
1.01488054e-01 5.64004660e-01 2.83780187e-01 -8.76355618e-02
-3.16393338e-02 -1.10230219e+00 -6.89918101e-01 -3.08605969e-01
-1.99393839e-01 5.76927781e-01 1.84016563e-02 -3.98264199... | [11.684002876281738, 3.024055004119873] |
696eccdf-172b-495c-92ac-dfda7a07f265 | diffusion-transport-alignment | 2206.07305 | null | https://arxiv.org/abs/2206.07305v1 | https://arxiv.org/pdf/2206.07305v1.pdf | Diffusion Transport Alignment | The integration of multimodal data presents a challenge in cases when the study of a given phenomena by different instruments or conditions generates distinct but related domains. Many existing data integration methods assume a known one-to-one correspondence between domains of the entire dataset, which may be unrealis... | ['Kevin R. Moon', 'Guy Wolf', 'Andres F. Duque'] | 2022-06-15 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 1.56749457e-01 -1.01624802e-01 -2.12853357e-01 -2.63230443e-01
-1.04164684e+00 -1.04497719e+00 1.01596355e+00 4.32395726e-01
-1.67373538e-01 5.38403273e-01 1.81277737e-01 5.96473590e-02
-4.66052592e-01 -8.56089056e-01 -6.20109856e-01 -8.30547512e-01
3.82517427e-01 1.11652911e+00 6.09450899e-02 -3.58583689... | [7.979042053222656, 4.0665178298950195] |
030c6599-2ae6-4b90-9c1b-51b7bfd25123 | medical-knowledge-guided-deep-learning-for | 2111.10620 | null | https://arxiv.org/abs/2111.10620v2 | https://arxiv.org/pdf/2111.10620v2.pdf | Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification | Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that lever... | ['Shandong Wu', 'Margarita L. Zuley', 'Ashok Panigrahy', 'Dooman Arefan', 'Chang Liu', 'Long Gao'] | 2021-11-20 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 4.33947325e-01 1.65959194e-01 -5.41221797e-01 -7.53891826e-01
-9.80633914e-01 -1.00804176e-02 2.22606033e-01 4.60766375e-01
-4.69256938e-01 5.37880301e-01 1.53468832e-01 -5.12428522e-01
-1.74823686e-01 -5.56955874e-01 -5.84861815e-01 -3.84998918e-01
2.55972147e-02 6.59067154e-01 3.33528556e-02 -1.11713737... | [15.033632278442383, -2.4173150062561035] |
4d95d1d6-8161-4653-aabd-b02431e1c7b6 | training-compact-neural-networks-via | 1909.02214 | null | https://arxiv.org/abs/1909.02214v2 | https://arxiv.org/pdf/1909.02214v2.pdf | Auxiliary Learning for Deep Multi-task Learning | Multi-task learning (MTL) is an efficient solution to solve multiple tasks simultaneously in order to get better speed and performance than handling each single-task in turn. The most current methods can be categorized as either: (i) hard parameter sharing where a subset of the parameters is shared among tasks while ot... | ['Wei Yin', 'Chunhua Shen', 'Yifan Liu', 'Hao Chen', 'Bohan Zhuang'] | 2019-09-05 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.64529610e-01 2.97896326e-01 -2.29690999e-01 -3.22178960e-01
-7.33096242e-01 -1.64180264e-01 2.47126028e-01 -1.39148340e-01
-7.20648050e-01 6.41227901e-01 -2.57235527e-01 -1.70767888e-01
-1.11809067e-01 -4.53309685e-01 -8.13285589e-01 -1.12726831e+00
3.98073643e-01 3.69782031e-01 6.16568267e-01 1.93305120... | [9.429102897644043, 1.2990567684173584] |
af8b8310-a2da-41f3-86ad-644c51ad17d2 | cross-category-video-highlight-detection-via | 2108.11770 | null | https://arxiv.org/abs/2108.11770v1 | https://arxiv.org/pdf/2108.11770v1.pdf | Cross-category Video Highlight Detection via Set-based Learning | Autonomous highlight detection is crucial for enhancing the efficiency of video browsing on social media platforms. To attain this goal in a data-driven way, one may often face the situation where highlight annotations are not available on the target video category used in practice, while the supervision on another vid... | ['Changhu Wang', 'Zhenbang Sun', 'Riheng Zhu', 'Bingbing Ni', 'Hang Wang', 'Minghao Xu'] | 2021-08-26 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xu_Cross-Category_Video_Highlight_Detection_via_Set-Based_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xu_Cross-Category_Video_Highlight_Detection_via_Set-Based_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 2.70506710e-01 -2.74860084e-01 -3.28720182e-01 -7.00968131e-02
-7.85901308e-01 -4.96655583e-01 4.18437213e-01 2.30913088e-01
-2.23259851e-01 4.65457767e-01 3.55698690e-02 -2.19438374e-02
-1.16582148e-01 -5.95602095e-01 -7.11134136e-01 -9.03204858e-01
3.44117098e-02 -3.26020598e-01 5.49783051e-01 -1.82757955... | [10.025540351867676, 0.4146043360233307] |
0bf71363-1007-401e-b304-df5b37ac974e | efficient-and-accurate-scene-text-detection | 2306.15142 | null | https://arxiv.org/abs/2306.15142v1 | https://arxiv.org/pdf/2306.15142v1.pdf | Efficient and Accurate Scene Text Detection with Low-Rank Approximation Network | Recently, regression-based methods, which predict parameter curves for localizing texts, are popular in scene text detection. However, these methods struggle to balance concise structure and fast post-processing, and the existing parameter curves are still not ideal for modeling arbitrary-shaped texts, leading to a cha... | ['Yuchen Su'] | 2023-06-27 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.34036252e-02 -6.42507195e-01 -2.07109600e-01 -7.36874193e-02
-7.57813334e-01 -3.23089063e-01 5.49953282e-01 2.31611822e-02
-1.93983659e-01 1.89341918e-01 4.09451008e-01 -4.57686111e-02
1.57899186e-01 -4.80330318e-01 -3.09963554e-01 -6.47817791e-01
5.64792991e-01 4.62063342e-01 5.87446809e-01 6.73967302... | [12.062270164489746, 2.240041971206665] |
6fb7ecb7-d66f-473b-a309-ad6e023ce370 | textsc-ambipun-generating-humorous-puns-with | null | null | https://openreview.net/forum?id=MXqSsBbZkF- | https://openreview.net/pdf?id=MXqSsBbZkF- | $\textsc{AmbiPun}$: Generating Humorous Puns with Ambiguous Context | In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['reverse-dictionary'] | ['natural-language-processing'] | [ 3.01369458e-01 -7.73051307e-02 1.35074677e-02 7.20536560e-02
-1.04262817e+00 -8.07285666e-01 6.13326013e-01 2.07826287e-01
-5.73899209e-01 1.05559468e+00 5.51728070e-01 -5.49443960e-02
3.06631267e-01 -9.61302698e-01 -4.49140191e-01 -2.93150693e-01
5.43711722e-01 6.97384357e-01 -1.66932553e-01 -8.41993511... | [11.378156661987305, 9.027750015258789] |
f2921773-a6d0-4cd8-a0f4-f239273c84f0 | supervised-classification-based-stock | 1406.0824 | null | http://arxiv.org/abs/1406.0824v1 | http://arxiv.org/pdf/1406.0824v1.pdf | Supervised classification-based stock prediction and portfolio optimization | As the number of publicly traded companies as well as the amount of their
financial data grows rapidly, it is highly desired to have tracking, analysis,
and eventually stock selections automated. There have been few works focusing
on estimating the stock prices of individual companies. However, many of those
have worke... | ['Adam Goldberg', 'Sercan Arik', 'Sukru Burc Eryilmaz'] | 2014-06-03 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.63798630e-01 -3.84990513e-01 -4.38003093e-01 -2.37950623e-01
-3.52577686e-01 -9.76186872e-01 6.02011859e-01 7.44470507e-02
-3.21468800e-01 8.27559948e-01 4.82006818e-02 -5.83201587e-01
-2.74266511e-01 -9.34826136e-01 -2.89926440e-01 -4.29235429e-01
-1.42876968e-01 6.35164618e-01 3.46999228e-01 -3.30526739... | [4.577200889587402, 4.182484149932861] |
87a1a657-c381-483e-b93d-e518dcb28cdd | complementary-intrinsics-from-neural-radiance | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Complementary_Intrinsics_From_Neural_Radiance_Fields_and_CNNs_for_Outdoor_CVPR_2023_paper.pdf | Complementary Intrinsics From Neural Radiance Fields and CNNs for Outdoor Scene Relighting | Relighting an outdoor scene is challenging due to the diverse illuminations and salient cast shadows. Intrinsic image decomposition on outdoor photo collections could partly solve this problem by weakly supervised labels with albedo and normal consistency from multi-view stereo. With neural radiance fields (NeRFs),... | ['Boxin Shi', 'Zhaofei Yu', 'Si Li', 'Jiajun Tang', 'Yongjie Zhu', 'Xuanning Cui', 'Siqi Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 6.25907362e-01 2.11474016e-01 6.23711765e-01 -7.49016762e-01
-3.30774695e-01 -7.89480746e-01 5.61217010e-01 -3.72535974e-01
1.83776662e-01 8.96682322e-01 1.08639441e-01 -1.54913008e-01
3.86094749e-01 -9.30131555e-01 -1.10988164e+00 -6.82038307e-01
4.02038991e-01 3.28024536e-01 3.82479541e-02 -2.80573428... | [9.746278762817383, -3.0403757095336914] |
22a93f28-fb76-490c-8701-62b14a92098c | write-a-speaker-text-based-emotional-and | 2104.07995 | null | https://arxiv.org/abs/2104.07995v2 | https://arxiv.org/pdf/2104.07995v2.pdf | Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation | In this paper, we propose a novel text-based talking-head video generation framework that synthesizes high-fidelity facial expressions and head motions in accordance with contextual sentiments as well as speech rhythm and pauses. To be specific, our framework consists of a speaker-independent stage and a speaker-specif... | ['Suzhen Wang', 'Lincheng Li', 'Changjie Fan', 'Xin Yu', 'Yixing Zheng', 'Yu Ding', 'Zhimeng Zhang'] | 2021-04-16 | null | null | null | null | ['talking-head-generation', 'face-model'] | ['computer-vision', 'computer-vision'] | [-9.42532420e-02 1.16708688e-05 7.95953721e-02 -5.72554886e-01
-7.66376972e-01 -4.27863032e-01 5.40595055e-01 -9.38744307e-01
2.68235430e-03 2.91380942e-01 6.44725502e-01 3.66827697e-01
4.54019845e-01 -3.61756712e-01 -5.82196236e-01 -7.48444796e-01
1.95098534e-01 1.44110784e-01 -2.35173315e-01 -1.86862573... | [13.194897651672363, -0.4235260486602783] |
4054d52c-ddbc-491a-9f5b-d85e52dc653a | frenchmedmcqa-a-french-multiple-choice-1 | 2304.04280 | null | https://arxiv.org/abs/2304.04280v1 | https://arxiv.org/pdf/2304.04280v1.pdf | FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain | This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dat... | ['Pierre-Antoine Gourraud', 'Béatrice Daille', 'Emmanuel Morin', 'Mickael Rouvier', 'Richard Dufour', 'Adrien Bazoge', 'Yanis Labrak'] | 2023-04-09 | frenchmedmcqa-a-french-multiple-choice | https://hal.archives-ouvertes.fr/hal-03824241v1 | https://hal.archives-ouvertes.fr/hal-03824241/document | louhi-2022-10 | ['multiple-choice-qa'] | ['natural-language-processing'] | [ 2.70756572e-01 5.89709997e-01 2.46709466e-01 -5.19030094e-01
-1.50006783e+00 -8.47288966e-01 4.87284154e-01 6.71784937e-01
-5.79819024e-01 1.03883147e+00 4.39569443e-01 -6.64049447e-01
-5.66876948e-01 -5.72088838e-01 -6.63906455e-01 -1.11131467e-01
4.58314151e-01 1.03288019e+00 3.34411234e-01 -5.59736490... | [9.03846549987793, 8.500308990478516] |
ea4d9434-da3d-40c2-9819-8663885678f7 | reinforcement-learning-for-datacenter | 2102.09337 | null | https://arxiv.org/abs/2102.09337v2 | https://arxiv.org/pdf/2102.09337v2.pdf | Reinforcement Learning for Datacenter Congestion Control | We approach the task of network congestion control in datacenters using Reinforcement Learning (RL). Successful congestion control algorithms can dramatically improve latency and overall network throughput. Until today, no such learning-based algorithms have shown practical potential in this domain. Evidently, the most... | ['Shie Mannor', 'Gal Chechik', 'Benjamin Fuhrer', 'Doron Haritan Kazakov', 'Amit Mandelbaum', 'Gal Dalal', 'Yuval Shpigelman', 'Chen Tessler'] | 2021-02-18 | null | null | null | null | ['network-congestion-control'] | ['miscellaneous'] | [-4.42740947e-01 -5.94223924e-02 -4.55106378e-01 -7.52519220e-02
-1.13852426e-01 -4.33729172e-01 2.63120711e-01 2.74204731e-01
-4.48445022e-01 1.37563825e+00 -4.77336764e-01 -8.76856506e-01
-4.61680144e-01 -6.28787696e-01 -3.30676466e-01 -5.75788379e-01
-6.97260857e-01 6.31508470e-01 4.04424250e-01 -3.00485641... | [5.312305450439453, 1.763676643371582] |
55de216b-0756-485a-9743-ed8dcbc30084 | adversarial-learning-semantic-volume-for-2d | null | null | https://openreview.net/pdf?id=gafjGfv8uR | https://openreview.net/pdf?id=gafjGfv8uR | Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild | Regression-based methods have revolutionized 2D landmark localization with the exploitation of deep neural networks and massive annotated datasets in the wild. However, it remains challenging for 3D landmark localization due to the lack of annotated datasets and the ambiguous nature of landmarks under the 3D perspectiv... | ['Zhenan Sun', 'Qi Li', 'Hongwen Zhang'] | 2019-04-19 | null | null | null | ieee-transactions-on-image-processing-2019-4 | ['face-alignment', '3d-facial-landmark-localization'] | ['computer-vision', 'computer-vision'] | [-1.59594551e-01 3.54862362e-01 -2.48512238e-01 -6.07443452e-01
-1.03604388e+00 -3.38796645e-01 7.41463363e-01 -2.24332780e-01
-2.06687197e-01 4.39575493e-01 -4.73761484e-02 -1.52593590e-02
2.49602318e-01 -5.17069876e-01 -6.98307157e-01 -6.51522160e-01
-1.15604319e-01 5.42303801e-01 -3.24535221e-02 -2.07975488... | [13.402384757995605, 0.2760089933872223] |
42b49029-098f-4ee3-892b-eadbcbf13850 | bistnet-semantic-image-prior-guided | 2212.02268 | null | https://arxiv.org/abs/2212.02268v1 | https://arxiv.org/pdf/2212.02268v1.pdf | BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization | How to effectively explore the colors of reference exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this paper, we present an effective BiSTNet to explore colors of reference exemplars and utilize them to help video colorization by a bidirectional temporal feature f... | ['Jinshan Pan', 'Jinhui Tang', 'Zhulin Tao', 'Xiaoyu Du', 'Zhongzheng Peng', 'Yixin Yang'] | 2022-12-05 | null | null | null | null | ['colorization'] | ['computer-vision'] | [-1.14951812e-01 -6.99713886e-01 3.93260159e-02 -1.63147271e-01
-4.93619680e-01 -4.47339207e-01 1.84379369e-01 -3.95356059e-01
-2.10860774e-01 6.38202071e-01 1.10063627e-01 5.81944883e-02
-1.71636231e-02 -6.40725315e-01 -7.79584825e-01 -8.74807477e-01
1.11550711e-01 -2.11680993e-01 3.27296048e-01 -2.69666702... | [11.12484073638916, -1.224510669708252] |
4cad60af-ce4a-45cc-9a01-005b5de05277 | masked-multi-step-probabilistic-forecasting | 2302.06818 | null | https://arxiv.org/abs/2302.06818v1 | https://arxiv.org/pdf/2302.06818v1.pdf | Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand | Predicting the demand for electricity with uncertainty helps in planning and operation of the grid to provide reliable supply of power to the consumers. Machine learning (ML)-based demand forecasting approaches can be categorized into (1) sample-based approaches, where each forecast is made independently, and (2) time ... | ['Honggang Wang', 'Nurali Virani', 'Yiwei Fu'] | 2023-02-14 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-3.02854359e-01 -2.70291507e-01 -2.76502192e-01 -8.38187933e-01
-7.59845734e-01 -6.48003817e-01 9.81417060e-01 1.81008577e-01
2.06786290e-01 1.24487627e+00 3.25572491e-01 -7.15382457e-01
-2.29312956e-01 -1.37347710e+00 -3.54749501e-01 -9.59728360e-01
-3.61923099e-01 7.99111247e-01 -1.24750867e-01 -5.57147302... | [6.1647820472717285, 2.9017534255981445] |
d7c7343b-7b41-4b0b-82a4-826585f24fcd | scicml-information-theoretic-co-clustering | 2205.09523 | null | https://arxiv.org/abs/2205.09523v1 | https://arxiv.org/pdf/2205.09523v1.pdf | scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data | Modern high-throughput sequencing technologies have enabled us to profile multiple molecular modalities from the same single cell, providing unprecedented opportunities to assay celluar heterogeneity from multiple biological layers. However, the datasets generated from these technologies tend to have high level of nois... | ['Zhixiang Lin', 'Pengcheng Zeng'] | 2022-05-19 | null | null | null | null | ['multi-view-learning', 'data-integration'] | ['computer-vision', 'knowledge-base'] | [-3.97744700e-02 -8.62924337e-01 -3.70646507e-01 -1.82379082e-01
-8.22195590e-01 -7.87411213e-01 3.68024379e-01 8.22119296e-01
1.17090531e-01 5.83303928e-01 3.28381717e-01 3.91910493e-01
-4.71268266e-01 -5.07240474e-01 -3.46886307e-01 -1.12836409e+00
-3.26257125e-02 5.22508502e-01 -2.45588422e-01 3.99829388... | [6.461132526397705, 5.390803813934326] |
07348c43-cfa9-4501-8ad7-0f36fb31842d | mgimn-multi-grained-interactive-matching | 2204.04952 | null | https://arxiv.org/abs/2204.04952v3 | https://arxiv.org/pdf/2204.04952v3.pdf | MGIMN: Multi-Grained Interactive Matching Network for Few-shot Text Classification | Text classification struggles to generalize to unseen classes with very few labeled text instances per class. In such a few-shot learning (FSL) setting, metric-based meta-learning approaches have shown promising results. Previous studies mainly aim to derive a prototype representation for each class. However, they negl... | ['Ji Zhang', 'Yuanhang Zheng', 'Xing Gao', 'Mieradilijiang Maimaiti', 'Jianhai Zhang'] | 2022-04-11 | null | https://aclanthology.org/2022.naacl-main.141 | https://aclanthology.org/2022.naacl-main.141.pdf | naacl-2022-7 | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 3.20347816e-01 -3.40547234e-01 -4.23780978e-01 -7.05203354e-01
-9.20857728e-01 -1.84039459e-01 7.75754809e-01 6.56905055e-01
-3.99683893e-01 6.35369003e-01 5.30623533e-02 1.18051626e-01
-4.42463249e-01 -9.81582046e-01 -5.11992536e-02 -5.25447607e-01
2.75772780e-01 6.09798610e-01 4.36334074e-01 -1.53446212... | [10.188546180725098, 3.508753776550293] |
686054a5-009d-4982-9668-bd5be51ebcd2 | refractive-light-field-features-for-curved | 2103.15349 | null | https://arxiv.org/abs/2103.15349v2 | https://arxiv.org/pdf/2103.15349v2.pdf | Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion | Curved refractive objects are common in the human environment, and have a complex visual appearance that can cause robotic vision algorithms to fail. Light-field cameras allow us to address this challenge by capturing the view-dependent appearance of such objects in a single exposure. We propose a novel image feature f... | ['Donald G. Dansereau', 'Thierry Peynot', 'Peter Corke', 'Dorian Tsai'] | 2021-03-29 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 3.88519704e-01 -1.31486848e-01 5.33166528e-01 -3.19666266e-01
1.29514962e-01 -7.78507471e-01 3.47988904e-01 -4.68602687e-01
-8.20941254e-02 9.47589055e-02 -2.44142503e-01 6.74088821e-02
-8.56172442e-02 -3.60670358e-01 -6.93462491e-01 -5.18313408e-01
1.37757242e-01 5.60301244e-01 3.85435998e-01 -2.98435867... | [7.014835357666016, -1.9922986030578613] |
0be97c76-0d74-425b-aa16-030d3ea54889 | chatgpt-is-a-remarkable-tool-for-experts | 2306.03102 | null | https://arxiv.org/abs/2306.03102v1 | https://arxiv.org/pdf/2306.03102v1.pdf | ChatGPT is a Remarkable Tool -- For Experts | This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare. We explore the potential of ChatGPT to enhance productivity, streamline problem-solving processes, and improve writing style. Furthermore, ... | ['Shulamit Reches', 'Rina Azoulay', 'Amos Azaria'] | 2023-06-02 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 7.79857785e-02 3.87258619e-01 1.50085419e-01 -2.19974190e-01
-2.78409958e-01 -7.84435213e-01 8.71767104e-02 3.66972029e-01
-2.53123492e-01 9.06427205e-01 -1.34505600e-01 -1.03766906e+00
-4.51048881e-01 -3.57104897e-01 -3.76292497e-01 -1.87521100e-01
4.65399295e-01 2.44800940e-01 -3.11634421e-01 -1.19423559... | [10.04763126373291, 7.230997562408447] |
b10574dd-ea8f-495b-a36e-735d0d20a01c | constrained-convolutional-neural-networks-a | null | null | https://ieeexplore.ieee.org/abstract/document/8335799 | https://misl.ece.drexel.edu/wp-content/uploads/2018/04/BayarStammTIFS01.pdf | Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection | Identifying the authenticity and processing history
of an image is an important task in multimedia forensics.
By analyzing traces left by different image manipulations, researchers have been able to develop several algorithms capable
of detecting targeted editing operations. While this approach has
led to the devel... | ['Matthew C. Stamm', 'Belhassen Bayar'] | 2018-04-11 | null | null | null | ieee-transactions-on-information-forensics-5 | ['image-manipulation-detection'] | ['computer-vision'] | [ 3.67290318e-01 -6.10193014e-01 1.03879929e-01 -1.11051731e-01
-6.19154215e-01 -4.80133593e-01 5.11711299e-01 2.37071916e-01
-5.16608655e-01 9.07903835e-02 -4.08909708e-01 -1.32432058e-01
1.57446951e-01 -8.01763654e-01 -9.01980639e-01 -4.63766783e-01
-1.23396873e-01 2.94626858e-02 5.95779240e-01 7.00236037... | [12.372001647949219, 1.0003180503845215] |
03217aea-6324-42e5-8e9a-3d09bf360b6f | low-resource-neural-machine-translation-a-1 | null | null | https://aclanthology.org/2022.vardial-1.4 | https://aclanthology.org/2022.vardial-1.4.pdf | Low-Resource Neural Machine Translation: A Case Study of Cantonese | The development of Natural Language Processing (NLP) applications for Cantonese, a language with over 85 million speakers, is lagging compared to other languages with a similar number of speakers. In this paper, we present, to our best knowledge, the first benchmark of multiple neural machine translation (NMT) systems ... | ['Evelyn Kai-Yan Liu'] | null | null | null | null | vardial-coling-2022-10 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 4.53217179e-02 -5.62442169e-02 -2.07671970e-01 -4.27319229e-01
-1.19466829e+00 -6.90251350e-01 9.32896852e-01 -6.82500824e-02
-7.52384126e-01 1.07073200e+00 3.62852782e-01 -9.73811626e-01
4.39929724e-01 -4.43903655e-01 -8.80521357e-01 -1.90641612e-01
2.01737568e-01 8.03692102e-01 -3.64187241e-01 -5.05399346... | [11.46831226348877, 10.363056182861328] |
58c3de40-470a-49d5-bbed-87dd26edb586 | isolation-scheme-for-virtual-network | 2211.14158 | null | https://arxiv.org/abs/2211.14158v2 | https://arxiv.org/pdf/2211.14158v2.pdf | An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning | Virtualization technologies are the foundation of modern ICT infrastructure, enabling service providers to create dedicated virtual networks (VNs) that can support a wide range of smart city applications. These VNs continuously generate massive amounts of data, necessitating stringent reliability and security requireme... | ['Sanghwan Lee', 'Chunming Rong', 'Ali Gohar'] | 2022-11-25 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.86599016e-01 -1.73075214e-01 -4.35728431e-01 3.20930868e-01
8.93211141e-02 -5.55144370e-01 2.88662493e-01 -1.09680302e-01
-1.89039141e-01 1.18447268e+00 -3.45581323e-01 -8.66693854e-01
-6.16657674e-01 -1.09130740e+00 -7.05313161e-02 -8.55828226e-01
-4.20206249e-01 7.74342954e-01 1.99709311e-01 2.67763790... | [5.873384952545166, 1.7092770338058472] |
f526d9b4-f4ea-43bf-973f-ffaf74ecb8c0 | a-feature-rich-constituent-context-model-for | null | null | https://aclanthology.info/papers/P12-2004/p12-2004 | https://www.aclweb.org/anthology/P12-2004 | A Feature-Rich Constituent Context Model for Grammar Induction | null | ['Jakob Uszkoreit', 'Dave Golland', 'John DeNero'] | 2012-07-01 | null | https://aclanthology.org/P12-2004 | https://aclanthology.org/P12-2004.pdf | acl-2012-7 | ['dependency-grammar-induction'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5392200946807861, 15.869219779968262] |
812653a6-3090-417e-a455-e17ec3eb2fae | appearance-and-structure-aware-robust-deep | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ren_Appearance_and_Structure_Aware_Robust_Deep_Visual_Graph_Matching_Attack_CVPR_2022_paper.pdf | Appearance and Structure Aware Robust Deep Visual Graph Matching: Attack, Defense and Beyond | Despite the recent breakthrough of high accuracy deep graph matching (GM) over visual images, the robustness of deep GM models is rarely studied which yet has been revealed an important issue in modern deep nets, ranging from image recognition to graph learning tasks. We first show that an adversarial attack on key... | ['Junchi Yan', 'Runzhong Wang', 'Qingquan Bao', 'Qibing Ren'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['graph-matching'] | ['graphs'] | [ 2.31656656e-02 2.22153828e-01 -9.52157006e-02 -1.26642764e-01
-7.66363859e-01 -7.22127497e-01 5.78129113e-01 3.29159722e-02
-9.67532173e-02 1.97987676e-01 -3.46541330e-02 -5.59529126e-01
2.80487746e-01 -8.34792435e-01 -1.08994985e+00 -6.26914322e-01
-1.23262584e-01 6.84286356e-02 3.84678483e-01 -2.18791991... | [6.355424404144287, 7.12902307510376] |
3cc433b9-1d1a-4c9f-a728-4a1e35b562f7 | macedonian-speech-synthesis-for-assistive | 2205.09198 | null | https://arxiv.org/abs/2205.09198v2 | https://arxiv.org/pdf/2205.09198v2.pdf | Macedonian Speech Synthesis for Assistive Technology Applications | Speech technology is becoming ever more ubiquitous with the advance of speech enabled devices and services. The use of speech synthesis in Augmentative and Alternative Communication tools, has facilitated inclusion of individuals with speech impediments allowing them to communicate with their surroundings using speech.... | ['Branislav Gerazov', 'Dimitar Tashkovski', 'Zoran Ivanovski', 'Toni Bachvarovski', 'Kristijan Lazarev', 'Stefan Janev', 'Risto Chavdarov', 'Tea Veljkovikj', 'Violeta Argirova', 'Martin Velichkovski', 'Elena Velovska', 'Bojan Sofronievski'] | 2022-05-18 | null | null | null | null | ['pitch-control'] | ['audio'] | [-2.08972171e-01 4.43503231e-01 8.55077058e-02 -6.05211668e-02
-7.99023867e-01 -2.97892988e-01 4.75208163e-01 -4.18746263e-01
-3.97032231e-01 8.50327909e-01 8.30105484e-01 -4.78143811e-01
-9.25355256e-02 -3.22957695e-01 1.55064046e-01 -4.34451103e-01
1.07691713e-01 5.89528918e-01 9.56561118e-02 -6.57891095... | [14.484779357910156, 6.358744144439697] |
c8bd75bd-eab7-4552-bf8b-61389f0e9581 | proxy-indicators-for-the-quality-of-open | null | null | https://aclanthology.org/2021.emnlp-main.618 | https://aclanthology.org/2021.emnlp-main.618.pdf | Proxy Indicators for the Quality of Open-domain Dialogues | The automatic evaluation of open-domain dialogues remains a largely unsolved challenge. Despite the abundance of work done in the field, human judges have to evaluate dialogues’ quality. As a consequence, performing such evaluations at scale is usually expensive. This work investigates using a deep-learning model train... | ['Ricardo Usbeck', 'Jens Lehmann', 'Rostislav Nedelchev'] | null | null | null | null | emnlp-2021-11 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-2.33427182e-01 4.83218491e-01 1.39920011e-01 -6.75106406e-01
-1.02273941e+00 -6.88024819e-01 8.36624444e-01 4.63389486e-01
-5.51410258e-01 8.15792561e-01 5.88654697e-01 -1.20329738e-01
-1.00111105e-01 -6.87192559e-01 -1.45223022e-01 -3.08922023e-01
1.93669185e-01 8.42443168e-01 -7.06960484e-02 -6.31239593... | [12.796598434448242, 8.120006561279297] |
fb21b81c-37c0-4799-9d85-770cddbbfe3f | concreteness-and-subjectivity-as-dimensions | null | null | https://aclanthology.org/P14-2118 | https://aclanthology.org/P14-2118.pdf | Concreteness and Subjectivity as Dimensions of Lexical Meaning | null | ['Felix Hill', 'Anna Korhonen'] | 2014-06-01 | null | null | null | acl-2014-6 | ['subjectivity-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... | [-7.3644208908081055, 3.753366231918335] |
0d3356db-c281-4055-a5bf-efb55ef6032f | resource-constrained-station-keeping-for | 2303.01173 | null | https://arxiv.org/abs/2303.01173v1 | https://arxiv.org/pdf/2303.01173v1.pdf | Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning | High altitude balloons have proved useful for ecological aerial surveys, atmospheric monitoring, and communication relays. However, due to weight and power constraints, there is a need to investigate alternate modes of propulsion to navigate in the stratosphere. Very recently, reinforcement learning has been proposed a... | ['Wenbin Li', 'Alan Hunter', 'Özgür Şimşek', 'Loïc Prenevost', 'Jack Saunders'] | 2023-03-02 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.36554340e-01 8.71523917e-02 -2.00329974e-01 1.96986914e-01
1.71854779e-01 -8.37116838e-01 3.98311019e-01 1.32041126e-01
-5.88229656e-01 1.11239004e+00 -3.92013818e-01 -4.81523663e-01
-6.30307674e-01 -1.01768124e+00 -5.14694452e-01 -9.10613775e-01
-5.19777119e-01 -2.66915932e-02 3.51706833e-01 -7.92349279... | [4.840404510498047, 1.9626718759536743] |
504839b3-b8c7-488b-89f0-e26e44ac272b | glu-net-global-local-universal-network-for | 1912.05524 | null | https://arxiv.org/abs/1912.05524v3 | https://arxiv.org/pdf/1912.05524v3.pdf | GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences | Establishing dense correspondences between a pair of images is an important and general problem, covering geometric matching, optical flow and semantic correspondences. While these applications share fundamental challenges, such as large displacements, pixel-accuracy, and appearance changes, they are currently addresse... | ['Radu Timofte', 'Martin Danelljan', 'Prune Truong'] | 2019-12-11 | glu-net-global-local-universal-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Truong_GLU-Net_Global-Local_Universal_Network_for_Dense_Flow_and_Correspondences_CVPR_2020_paper.pdf | cvpr-2020-6 | ['geometric-matching', 'dense-pixel-correspondence-estimation'] | ['computer-vision', 'computer-vision'] | [ 8.40104371e-02 -2.50373542e-01 -1.04666203e-01 -2.27923915e-01
-4.41364110e-01 -4.61872160e-01 5.35450161e-01 -3.39049771e-02
-4.99941111e-01 5.07363379e-01 -9.72853005e-02 -4.77610677e-02
-2.21902594e-01 -8.73397052e-01 -6.32858872e-01 -4.79617238e-01
1.93499308e-02 3.02089483e-01 4.19746280e-01 -2.45677799... | [8.590497016906738, -2.108123302459717] |
44e56024-ba73-4823-bdd6-010d0a19ff0c | aboships-an-inshore-and-offshore-maritime | 2102.05869 | null | https://arxiv.org/abs/2102.05869v1 | https://arxiv.org/pdf/2102.05869v1.pdf | ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations | Availability of domain-specific datasets is an essential problem in object detection. Maritime vessel detection of inshore and offshore datasets is no exception, there is a limited number of studies addressing this need. For that reason, we collected a dataset of images of maritime vessels taking into account different... | ['Johan Lilius', 'Luca Zelioli', 'Valentin Soloviev', 'Bogdan Iancu'] | 2021-02-11 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.42790312e-01 -8.35733339e-02 7.01811612e-01 -2.62601525e-01
-5.48422694e-01 -1.14239597e+00 5.06698310e-01 1.68060750e-01
-7.63743818e-01 3.72204512e-01 -2.68398076e-01 -2.74460405e-01
-3.07060987e-01 -5.79519749e-01 -6.05570197e-01 -7.17806458e-01
-3.75111163e-01 2.28282213e-01 8.53526354e-01 -1.85717478... | [8.628669738769531, -0.8195075392723083] |
31e52af9-19d4-4644-8b26-b0735d1255c2 | domain-adaptive-semantic-segmentation-by | 2303.16435 | null | https://arxiv.org/abs/2303.16435v1 | https://arxiv.org/pdf/2303.16435v1.pdf | Domain Adaptive Semantic Segmentation by Optimal Transport | Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling... | ['Shihui Ying', 'Ce Li', 'Xin Wang', 'Yaqian Guo'] | 2023-03-29 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 3.27825576e-01 -2.09261760e-01 -2.23968029e-02 -5.81848145e-01
-1.12560995e-01 -2.82702446e-01 3.62081856e-01 -2.94167623e-02
-4.61035311e-01 4.87825781e-01 -7.08812755e-03 -1.38803348e-01
-8.04701820e-02 -9.64437962e-01 -6.40842378e-01 -6.31284773e-01
4.13709164e-01 1.03796557e-01 7.42009044e-01 -3.16724360... | [9.586420059204102, -0.23782223463058472] |
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