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0742dcf8-9cad-4f59-8a4b-d974370a1096 | real-time-geo-localization-using-satellite | 2108.03344 | null | https://arxiv.org/abs/2108.03344v1 | https://arxiv.org/pdf/2108.03344v1.pdf | Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles | The capabilities of autonomous flight with unmanned aerial vehicles (UAVs) have significantly increased in recent times. However, basic problems such as fast and robust geo-localization in GPS-denied environments still remain unsolved. Existing research has primarily concentrated on improving the accuracy of localizati... | ['Koushil Sreenath', 'Mark W. Mueller', 'Xiangyu Wu', 'Shuxiao Chen'] | 2021-08-07 | null | null | null | null | ['image-based-localization'] | ['computer-vision'] | [-9.68529563e-03 -6.29637659e-01 1.24093413e-01 -3.69256139e-01
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19e4a3db-b295-4481-b598-e8caff9717f9 | improving-open-set-semi-supervised-learning | 2301.10127 | null | https://arxiv.org/abs/2301.10127v2 | https://arxiv.org/pdf/2301.10127v2.pdf | Improving Open-Set Semi-Supervised Learning with Self-Supervision | Open-set semi-supervised learning (OSSL) is a realistic setting of semi-supervised learning where the unlabeled training set contains classes that are not present in the labeled set. Many existing OSSL methods assume that these out-of-distribution data are harmful and put effort into excluding data from unknown classes... | ['Lars Hammarstrand', 'Fredrik Kahl', 'Lennart Svensson', 'Erik Wallin'] | 2023-01-24 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 2.29888529e-01 4.48479533e-01 -6.66806221e-01 -7.15749323e-01
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73f359b2-6d54-4983-b62b-ff0a3111e6d4 | learning-empirical-bregman-divergence-for | 2304.07689 | null | https://arxiv.org/abs/2304.07689v3 | https://arxiv.org/pdf/2304.07689v3.pdf | Learning Empirical Bregman Divergence for Uncertain Distance Representation | Deep metric learning techniques have been used for visual representation in various supervised and unsupervised learning tasks through learning embeddings of samples with deep networks. However, classic approaches, which employ a fixed distance metric as a similarity function between two embeddings, may lead to subopti... | ['Anca L. Ralescu', 'Anna Zou', 'Ziru Liu', 'Zhiyuan Li'] | 2023-04-16 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-2.29422107e-01 -3.43439549e-01 -4.65630181e-02 -8.90126944e-01
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d719c2dc-d7d4-4ab2-8ae5-f3576a9e7b9c | supervised-and-unsupervised-categorization-of | null | null | https://link.springer.com/chapter/10.1007/978-3-030-98997-2_6 | https://link.springer.com/chapter/10.1007/978-3-030-98997-2_6 | Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset | The automatic categorization of crime news is useful to create statistics on the type of crimes occurring in a certain area. This assignment can be treated as a text categorization problem. Several studies have shown that the use of word embeddings improves outcomes in many Natural Language Processing (NLP), including ... | ['Laura Po', 'Giovanni Bonisoli', 'Federica Rollo'] | 2022-03-22 | null | null | null | lecture-notes-in-business-information | ['text-categorization', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-6.85253143e-02 1.24946542e-01 -2.57859856e-01 -2.65460491e-01
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2.08225965e-01 6.03318751e-01 1.86584890e-02 -3.45962465... | [10.252448081970215, 9.041481971740723] |
1bc5902d-c7c7-4f4a-830f-4ef36df2831b | one-shot-instance-segmentation | 1811.11507 | null | https://arxiv.org/abs/1811.11507v2 | https://arxiv.org/pdf/1811.11507v2.pdf | One-Shot Instance Segmentation | We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within a complex scene. To address this challenging new task, we propose Siamese Mask R-CNN. It extends Mask R-CNN by a Siamese backbone encoding ... | ['Ivan Ustyuzhaninov', 'Matthias Bethge', 'Claudio Michaelis', 'Alexander S. Ecker'] | 2018-11-28 | null | null | null | null | ['one-shot-object-detection', 'one-shot-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.26062810e-01 3.35033685e-01 -2.99216270e-01 -4.22968447e-01
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6.61385357e-02 6.91685975e-01 7.35029995e-01 4.02739644... | [9.442523002624512, 0.37658101320266724] |
e735786e-8d0b-4186-b47a-f6ee848f11ec | beamformer-guided-target-speaker-extraction | 2303.08702 | null | https://arxiv.org/abs/2303.08702v1 | https://arxiv.org/pdf/2303.08702v1.pdf | Beamformer-Guided Target Speaker Extraction | We propose a Beamformer-guided Target Speaker Extraction (BG-TSE) method to extract a target speaker's voice from a multi-channel recording informed by the direction of arrival of the target. The proposed method employs a front-end beamformer steered towards the target speaker to provide an auxiliary signal to a single... | ['Emanuël A. P. Habets', 'Srikanth Raj Chetupalli', 'Mohamed Elminshawi'] | 2023-03-15 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 1.08935207e-01 -1.82213873e-01 5.67385137e-01 -1.67994142e-01
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-2.26067811e-01 -3.20719779e-01 -1.71195686e-01 4.65936363... | [15.036004066467285, 5.781081676483154] |
a2633e47-d8bf-4984-9d9f-f38a1ef03a12 | mixtext-linguistically-informed-interpolation | 2004.12239 | null | https://arxiv.org/abs/2004.12239v1 | https://arxiv.org/pdf/2004.12239v1.pdf | MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification | This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix. TMix creates a large amount of augmented training samples by interpolating text in hidden space. Moreover, we leverage recent advances in data augmentation to guess... | ['Zichao Yang', 'Jiaao Chen', 'Diyi Yang'] | 2020-04-25 | mixtext-linguistically-informed-interpolation-1 | https://aclanthology.org/2020.acl-main.194 | https://aclanthology.org/2020.acl-main.194.pdf | acl-2020-6 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 1.93125784e-01 3.76487821e-01 -8.37200761e-01 -6.79807842e-01
-8.58929098e-01 -5.21709442e-01 9.46541727e-01 3.45963061e-01
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4.23029333e-01 8.76694202e-01 -3.75594109e-01 -2.48453319... | [10.690964698791504, 7.931811809539795] |
d3b769f0-8805-44ca-bb34-55409416dc41 | discrete-valued-neural-communication | 2107.02367 | null | https://arxiv.org/abs/2107.02367v3 | https://arxiv.org/pdf/2107.02367v3.pdf | Discrete-Valued Neural Communication | Deep learning has advanced from fully connected architectures to structured models organized into components, e.g., the transformer composed of positional elements, modular architectures divided into slots, and graph neural nets made up of nodes. In structured models, an interesting question is how to conduct dynamic a... | ['Dianbo Liu', 'Yoshua Bengio', 'Michael Curtis Mozer', 'Chen Sun', 'Anirudh Goyal', 'Kenji Kawaguchi', 'Alex Lamb'] | 2021-07-06 | null | http://proceedings.neurips.cc/paper/2021/hash/10907813b97e249163587e6246612e21-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/10907813b97e249163587e6246612e21-Paper.pdf | neurips-2021-12 | ['systematic-generalization'] | ['reasoning'] | [-7.33092651e-02 7.08790183e-01 -5.42329438e-02 -1.51288807e-01
2.37987824e-02 -5.87184191e-01 6.75006390e-01 1.29687831e-01
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-4.36751097e-01 7.66486406e-01 -2.84924451e-02 -1.58151776... | [6.977560997009277, 6.105581283569336] |
69d8df5a-1143-4762-b669-b2540cd6dfea | multi-talker-asr-for-an-unknown-number-of | 2006.02786 | null | https://arxiv.org/abs/2006.02786v3 | https://arxiv.org/pdf/2006.02786v3.pdf | Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR | Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown. To cope with this, we extend an iterative speech extraction system with mechanisms to count the number of sources and comb... | ['Reinhold Haeb-Umbach', 'Tomohiro Nakatani', 'Marc Delcroix', 'Thilo von Neumann', 'Keisuke Kinoshita', 'Lukas Drude', 'Christoph Boeddeker'] | 2020-06-04 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 1.42897606e-01 -4.66530174e-02 1.16999000e-01 -4.39559519e-01
-1.58038700e+00 -5.94604969e-01 5.56769490e-01 -1.75586477e-01
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-3.33397150e-01 8.64272475e-01 2.10502058e-01 -3.10816526... | [14.654824256896973, 6.197609901428223] |
7b96b6eb-14ba-4014-bbde-e8fe5a006fbb | complexity-guided-slimmable-decoder-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.pdf | Complexity-Guided Slimmable Decoder for Efficient Deep Video Compression | In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression. Specifically, given the target complexity constraints, in our cgSlimDecoder, we introduce a set of new channel width selection modules to aut... | ['Dong Xu', 'Zhihao Hu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-compensation'] | ['computer-vision'] | [ 1.25367001e-01 2.99267974e-02 -3.32184732e-01 -1.74491465e-01
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-1.05155610e-01 -2.61598453e-02 3.88449341e-01 3.80497165... | [11.313706398010254, -1.6344000101089478] |
e5a3647e-7de6-45d6-81ad-e83276e0eb6a | prioritized-sipp-for-multi-agent-path-finding | 2108.05145 | null | https://arxiv.org/abs/2108.05145v1 | https://arxiv.org/pdf/2108.05145v1.pdf | Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints | Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence in which one needs to find a set of collision-free paths for a group of mobile agents (robots) operating in the shared workspace. Due to its importance, the problem is well-studied and multiple optimal and approximate alg... | ['Konstantin Yakovlev', 'Zain Alabedeen Ali'] | 2021-08-11 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.62476671e-02 3.13096166e-01 -1.61027625e-01 -1.02388114e-02
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-6.72911227e-01 9.64519143e-01 3.80955935e-01 -5.32427907... | [4.930652141571045, 1.6921919584274292] |
0be64347-cac6-4c32-81fe-e17f48b917e9 | vocalist-an-audio-visual-synchronisation | 2204.0209 | null | https://arxiv.org/abs/2204.02090v2 | https://arxiv.org/pdf/2204.02090v2.pdf | VocaLiST: An Audio-Visual Synchronisation Model for Lips and Voices | In this paper, we address the problem of lip-voice synchronisation in videos containing human face and voice. Our approach is based on determining if the lips motion and the voice in a video are synchronised or not, depending on their audio-visual correspondence score. We propose an audio-visual cross-modal transformer... | ['Gloria Haro', 'Juan F. Montesinos', 'Venkatesh S. Kadandale'] | 2022-04-05 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization', 'music-source-separation'] | ['audio', 'computer-vision', 'music'] | [-2.85706744e-02 -8.25119987e-02 -2.12113619e-01 1.45719066e-01
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1.84376404e-01 1.85384989e-01 1.93406492e-01 2.41597798... | [14.405854225158691, 5.096757411956787] |
e3f2e677-8ad9-4bbe-9d8a-3dfa15659784 | rlss-a-deep-reinforcement-learning-algorithm | 2206.02544 | null | https://arxiv.org/abs/2206.02544v1 | https://arxiv.org/pdf/2206.02544v1.pdf | RLSS: A Deep Reinforcement Learning Algorithm for Sequential Scene Generation | We present RLSS: a reinforcement learning algorithm for sequential scene generation. This is based on employing the proximal policy optimization (PPO) algorithm for generative problems. In particular, we consider how to effectively reduce the action space by including a greedy search algorithm in the learning process. ... | ['Dominik L. Michels', 'Peter Wonka', 'Azimkhon Ostonov'] | 2022-06-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 0.5997109 0.4916908 -0.0358826 -0.046065 -0.64904994 -0.596285
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0.80442923 0.66765004 0.6853... | [4.144750595092773, 1.6303552389144897] |
2c0fdd4f-c075-414f-97c7-56b8b6ec1ea4 | smooth-ap-smoothing-the-path-towards-large | 2007.12163 | null | https://arxiv.org/abs/2007.12163v2 | https://arxiv.org/pdf/2007.12163v2.pdf | Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval | Optimising a ranking-based metric, such as Average Precision (AP), is notoriously challenging due to the fact that it is non-differentiable, and hence cannot be optimised directly using gradient-descent methods. To this end, we introduce an objective that optimises instead a smoothed approximation of AP, coined Smooth-... | ['Weidi Xie', 'Andrew Zisserman', 'Andrew Brown', 'Vicky Kalogeiton'] | 2020-07-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/929_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540647.pdf | eccv-2020-8 | ['image-instance-retrieval'] | ['computer-vision'] | [-2.42253974e-01 -1.33714899e-01 -4.49321382e-02 -7.76710927e-01
-1.36351860e+00 -5.97465694e-01 6.28283978e-01 9.20503512e-02
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-3.84645373e-01 6.84471011e-01 -9.00666639e-02 -2.24419788... | [9.45368480682373, 2.927333116531372] |
6d224a40-a388-47a4-97bd-da766d3a084e | learning-feature-recovery-transformer-for | 2301.01879 | null | https://arxiv.org/abs/2301.01879v1 | https://arxiv.org/pdf/2301.01879v1.pdf | Learning Feature Recovery Transformer for Occluded Person Re-identification | One major issue that challenges person re-identification (Re-ID) is the ubiquitous occlusion over the captured persons. There are two main challenges for the occluded person Re-ID problem, i.e., the interference of noise during feature matching and the loss of pedestrian information brought by the occlusions. In this p... | ['Zhenan Sun', 'Jian Liang', 'Lingxiao He', 'Boqiang Xu'] | 2023-01-05 | null | null | null | null | ['person-re-identification', 'graph-similarity', 'graph-matching'] | ['computer-vision', 'graphs', 'graphs'] | [-1.60160720e-01 -4.67570305e-01 2.25389495e-01 -1.58649743e-01
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2.82845497e-01 9.01296362e-02 7.54265040e-02 -2.02815346... | [14.701699256896973, 0.9133080840110779] |
af537d18-24a7-4d81-b1a3-8e390cc36ed2 | a-theoretical-understanding-of-neural-network | 2206.05604 | null | https://arxiv.org/abs/2206.05604v2 | https://arxiv.org/pdf/2206.05604v2.pdf | A Theoretical Understanding of Neural Network Compression from Sparse Linear Approximation | The goal of model compression is to reduce the size of a large neural network while retaining a comparable performance. As a result, computation and memory costs in resource-limited applications may be significantly reduced by dropping redundant weights, neurons, or layers. There have been many model compression algori... | ['Yuhong Yang', 'Jie Ding', 'Ganghua Wang', 'Wenjing Yang'] | 2022-06-11 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 6.45323634e-01 7.26755559e-02 -2.29700357e-01 -3.65739524e-01
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-1.20378666e-01 2.14154631e-01 2.30402261e-01 5.41524822... | [8.501059532165527, 3.147736072540283] |
f90148a2-d01a-4f14-9cad-2b6a0e9ffdbe | modular-storygan-with-background-and-theme | null | null | https://link.springer.com/chapter/10.1007/978-3-031-09037-0_23 | https://link.springer.com/chapter/10.1007/978-3-031-09037-0_23 | Modular StoryGAN with Background and Theme Awareness for Story Visualization | Story visualization is a novel topic that intersects computer vision and natural language processing. In this task, given a series of natural language sentences that compose a story, a sequence of images should be generated that correspond to the sentences. Prior works have introduced recurrent generative models which ... | ['Modafar Al-Shouha', 'Gábor Szűcs'] | 2022-06-02 | null | null | null | icprai-2022-3rd-international-conference-on | ['story-visualization'] | ['computer-vision'] | [-4.29717824e-02 1.32379040e-01 7.04550445e-02 -1.29590884e-01
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2.16526330e-01 1.02727041e-01 4.52116728e-01 -2.29439065... | [11.129517555236816, 0.630122721195221] |
76d4b20e-d03f-4eb6-9e26-558e6d2ba554 | graph-augmented-learning-to-rank-for-querying | 2111.10541 | null | https://arxiv.org/abs/2111.10541v4 | https://arxiv.org/pdf/2111.10541v4.pdf | Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph | Knowledge graph question answering (KGQA) based on information retrieval aims to answer a question by retrieving answer from a large-scale knowledge graph. Most existing methods first roughly retrieve the knowledge subgraphs (KSG) that may contain candidate answer, and then search for the exact answer in the KSG. Howev... | ['Bo Long', 'Fangli Xu', 'Zhihua Wei', 'Po Hu', 'Lingfei Wu', 'Hanning Gao'] | 2021-11-20 | null | null | null | null | ['graph-question-answering', 'answer-selection'] | ['graphs', 'natural-language-processing'] | [-1.39901653e-01 3.52836162e-01 -2.66212970e-01 -6.91271275e-02
-1.09071243e+00 -4.91270244e-01 9.79951322e-02 4.22322094e-01
1.91509604e-01 6.80036128e-01 1.91888675e-01 -2.82214433e-02
-8.28607678e-01 -1.36835682e+00 -7.46767521e-01 -3.20332021e-01
-2.51259077e-02 6.41251504e-01 1.21394348e+00 -2.46697605... | [10.439177513122559, 7.8678789138793945] |
30783302-94b1-4f9f-bb07-5ff27453a50a | transforming-ecg-diagnosis-an-in-depth-review | 2306.01249 | null | https://arxiv.org/abs/2306.01249v1 | https://arxiv.org/pdf/2306.01249v1.pdf | Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection | The emergence of deep learning has significantly enhanced the analysis of electrocardiograms (ECGs), a non-invasive method that is essential for assessing heart health. Despite the complexity of ECG interpretation, advanced deep learning models outperform traditional methods. However, the increasing complexity of ECG d... | ['Zibin Zhao'] | 2023-06-02 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 4.77341682e-01 -2.82088429e-01 2.47430131e-02 -4.26085293e-01
-7.99083054e-01 -5.42822242e-01 -2.97352344e-01 4.88707751e-01
-2.46751070e-01 6.22615814e-01 3.07845157e-02 -6.61284268e-01
-5.32914042e-01 -3.82142931e-01 -5.47823904e-04 -6.38505280e-01
-8.76536131e-01 3.91359538e-01 -3.62116069e-01 -9.57309380... | [14.316734313964844, 3.285630941390991] |
cc63f21d-e96e-4868-b34b-89a1a5de5561 | nested-graph-neural-networks | 2110.13197 | null | https://arxiv.org/abs/2110.13197v1 | https://arxiv.org/pdf/2110.13197v1.pdf | Nested Graph Neural Networks | Graph neural network (GNN)'s success in graph classification is closely related to the Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features to a center node, both 1-WL and GNN obtain a node representation that encodes a rooted subtree around the center node. These rooted subtree repr... | ['Pan Li', 'Muhan Zhang'] | 2021-10-25 | null | http://proceedings.neurips.cc/paper/2021/hash/8462a7c229aea03dde69da754c3bbcc4-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8462a7c229aea03dde69da754c3bbcc4-Paper.pdf | neurips-2021-12 | ['graph-property-prediction'] | ['graphs'] | [ 2.43066102e-01 6.47923350e-01 -4.24940288e-01 -2.26844087e-01
-3.84323925e-01 -5.03566980e-01 9.66536775e-02 2.16554448e-01
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-1.80570230e-01 -1.36253738e+00 -7.78714359e-01 -7.19297409e-01
-4.67624456e-01 2.22594798e-01 4.02156621e-01 -1.67946786... | [6.970025062561035, 6.232250690460205] |
c36d2465-6f76-4e1a-a400-499cb8f62396 | 2305-15001 | 2305.15001 | null | https://arxiv.org/abs/2305.15001v2 | https://arxiv.org/pdf/2305.15001v2.pdf | Contrastive Training of Complex-Valued Autoencoders for Object Discovery | Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of slots is hardwired; all slots have equal capacity; training has high computational cost; there are no object-level relational factors within s... | ['Jürgen Schmidhuber', 'Kazuki Irie', 'Anand Gopalakrishnan', 'Aleksandar Stanić'] | 2023-05-24 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 1.90759841e-02 1.15829697e-02 -2.85019606e-01 -7.55443946e-02
-4.07431751e-01 -3.97559136e-01 6.45558298e-01 1.77494213e-01
-3.64752650e-01 5.63875079e-01 -3.08730036e-01 2.12325498e-01
-5.09146035e-01 -6.66986644e-01 -5.86312473e-01 -8.64656508e-01
-5.70347786e-01 8.69671345e-01 7.78972387e-01 -1.84226081... | [9.694581985473633, 1.0760847330093384] |
db1869b2-acf9-4fe0-9275-e9694a753845 | learned-video-compression-via-heterogeneous | 2207.04589 | null | https://arxiv.org/abs/2207.04589v3 | https://arxiv.org/pdf/2207.04589v3.pdf | Learned Video Compression via Heterogeneous Deformable Compensation Network | Learned video compression has recently emerged as an essential research topic in developing advanced video compression technologies, where motion compensation is considered one of the most challenging issues. In this paper, we propose a learned video compression framework via heterogeneous deformable compensation strat... | ['Chang Wen Chen', 'Zhenzhong Chen', 'Huairui Wang'] | 2022-07-11 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 3.71562898e-01 -5.80763996e-01 -5.80007657e-02 -3.93237889e-01
-6.09581947e-01 -7.30095431e-02 4.23493415e-01 -3.91418874e-01
-5.69027126e-01 5.17390847e-01 6.39971495e-01 2.94623554e-01
-4.53309149e-01 -6.13112450e-01 -5.95226109e-01 -1.03197563e+00
1.98454395e-01 -7.64214918e-02 3.94191384e-01 -2.92389002... | [11.131961822509766, -1.7553147077560425] |
63bc377d-f400-42a0-9ea1-4a274112f341 | impact-of-deep-learning-libraries-on-online | 2305.00595 | null | https://arxiv.org/abs/2305.00595v2 | https://arxiv.org/pdf/2305.00595v2.pdf | Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection | Providing online adaptive lightweight time series anomaly detection without human intervention and domain knowledge is highly valuable. Several such anomaly detection approaches have been introduced in the past years, but all of them were only implemented in one deep learning library. With the development of deep learn... | ['Jia-Chun Lin', 'Ming-Chang Lee'] | 2023-04-30 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-5.64936876e-01 -5.84376812e-01 4.48923618e-01 -3.20504755e-01
-8.83518308e-02 -7.13669956e-01 6.41210198e-01 8.11691284e-01
-5.51645815e-01 6.33391738e-02 -1.73113406e-01 -5.56942403e-01
-1.57651201e-01 -8.52135181e-01 -5.74597955e-01 -5.29050648e-01
-5.53813100e-01 4.11889791e-01 5.03400385e-01 -2.97780424... | [7.41025447845459, 2.618990182876587] |
64e448f8-b96a-414d-9fed-91a6fddec5fc | abstractive-document-summarization-with-a | null | null | https://aclanthology.org/P17-1108 | https://aclanthology.org/P17-1108.pdf | Abstractive Document Summarization with a Graph-Based Attentional Neural Model | Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive docu... | ['Jiwei Tan', 'Xiaojun Wan', 'Jianguo Xiao'] | 2017-07-01 | null | null | null | acl-2017-7 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 7.31830657e-01 4.99544859e-01 -2.13196769e-01 -7.45312795e-02
-7.77661383e-01 -3.03770095e-01 7.09732294e-01 4.38209504e-01
-3.21589708e-01 9.51503992e-01 1.08633804e+00 -2.19973132e-01
1.35878831e-01 -4.67849553e-01 -6.76366091e-01 -3.65962118e-01
1.95444092e-01 4.76060480e-01 9.46940705e-02 -4.70236272... | [12.495403289794922, 9.471222877502441] |
0139d90c-5af3-4ff4-8e5e-52102499b8af | commonaccent-exploring-large-acoustic | 2305.18283 | null | https://arxiv.org/abs/2305.18283v1 | https://arxiv.org/pdf/2305.18283v1.pdf | CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice | Despite the recent advancements in Automatic Speech Recognition (ASR), the recognition of accented speech still remains a dominant problem. In order to create more inclusive ASR systems, research has shown that the integration of accent information, as part of a larger ASR framework, can lead to the mitigation of accen... | ['Cem Subakan', 'Danielius Visockas', 'Sara Ahmed', 'Juan Zuluaga-Gomez'] | 2023-05-29 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [-2.44920701e-01 -6.87630698e-02 7.73389786e-02 -6.18165016e-01
-9.04192924e-01 -8.54254901e-01 3.07883561e-01 6.63094893e-02
-5.75100601e-01 3.84940892e-01 6.90388203e-01 -7.81869948e-01
1.42728165e-01 -3.22247237e-01 -3.24882567e-01 -5.36498606e-01
3.29017371e-01 4.11539853e-01 -2.67492175e-01 -5.47737718... | [14.305221557617188, 6.743173122406006] |
ba5d4edf-fe75-4c08-ad70-75cd7d6a0a5c | systematic-categorization-of-influencing | 2105.00279 | null | https://arxiv.org/abs/2105.00279v1 | https://arxiv.org/pdf/2105.00279v1.pdf | Systematic Categorization of Influencing Factors on Radar-Based Perception to Facilitate Complex Real-World Data Evaluation | For the assessment of machine perception for automated driving it is important to understand the influence of certain environment factors on the sensors used. Especially when investigating large amounts of real-world data to find and understand perception uncertainties, a smart concept is needed to structure and catego... | ['Lutz Eckstein', 'Maike Scholtes'] | 2021-05-01 | null | null | null | null | ['sensor-modeling'] | ['computer-vision'] | [ 4.16646034e-01 1.56141028e-01 2.08952054e-02 -8.01920712e-01
-3.39131087e-01 -5.09335041e-01 5.00644803e-01 5.76638103e-01
-4.76776630e-01 2.48583764e-01 1.29209504e-01 -8.74599695e-01
-6.80090964e-01 -9.08243537e-01 -3.62535417e-01 -6.18198693e-01
1.35525838e-01 2.55685091e-01 3.51162761e-01 -4.86059517... | [5.666130542755127, 1.2024738788604736] |
21ccf4af-3401-41e2-a205-76eff732fba6 | rice-leaf-disease-classification-and | 2209.01579 | null | https://arxiv.org/abs/2209.01579v1 | https://arxiv.org/pdf/2209.01579v1.pdf | Rice Leaf Disease Classification and Detection Using YOLOv5 | A staple food in more than a hundred nations worldwide is rice (Oryza sativa). The cultivation of rice is vital to global economic growth. However, the main issue facing the agricultural industry is rice leaf disease. The quality and quantity of the crops have declined, and this is the main cause. As farmers in any cou... | ['Manoranjan Paul', 'Samiul Ul Hoque', 'Iftekhar Junaeid', 'Ashikur Rahman', 'Md Ershadul Haque'] | 2022-09-04 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-2.88005948e-01 -4.95865494e-01 -1.49461254e-01 -2.39710733e-02
5.22100292e-02 -6.84079587e-01 -1.30734578e-01 2.03528345e-01
-7.82287642e-02 4.86348689e-01 -3.80226433e-01 -4.14857775e-01
-2.04410572e-02 -1.03131127e+00 -1.32491365e-01 -1.11335945e+00
1.00070201e-01 -5.75396530e-02 2.25503251e-01 -2.13128969... | [9.238602638244629, -1.5583606958389282] |
0bd62158-b971-4ed7-985e-f4a61c405898 | state-of-the-art-economic-load-dispatch-of | 1812.1161 | null | http://arxiv.org/abs/1812.11610v1 | http://arxiv.org/pdf/1812.11610v1.pdf | State-of-the-Art Economic Load Dispatch of Power Systems Using Particle Swarm Optimization | Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one
of the most promising optimization techniques in solving highly constrained
non-linear and non-convex optimization problems in different areas of
electrical engineering. Economic operation of the power system is one of the
most important areas... | ['Mahamad Nabab Alam'] | 2018-12-30 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-2.41587952e-01 -4.97591317e-01 -2.19865113e-01 1.48646221e-01
4.01363343e-01 -6.06396735e-01 2.33282387e-01 1.99663624e-01
-1.68510273e-01 1.49252772e+00 -5.82837343e-01 8.57741162e-02
-8.74117613e-01 -8.70352983e-01 2.38569885e-01 -1.32656860e+00
-3.97222996e-01 8.00375581e-01 -7.71398097e-02 -5.87258875... | [5.666371822357178, 3.302734136581421] |
28cc559e-ded4-40ea-bbf4-290f2d0a3b03 | learning-to-resolve-conflicts-for-multi-agent | 2012.06005 | null | https://arxiv.org/abs/2012.06005v1 | https://arxiv.org/pdf/2012.06005v1.pdf | Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search | Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. At the high level, CBS repeatedly detects conflicts and resolves one of them by splitting the current problem into two subproblems. Previous work chooses the conflict to resolve by categorizing the conflict into three classes and ... | ['Sven Koenig', 'Bistra Dilkina', 'Taoan Huang'] | 2020-12-10 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.77316030e-02 4.95846793e-02 -5.27766228e-01 -4.85191643e-02
-8.30529392e-01 -7.28452444e-01 3.20105016e-01 3.14927369e-01
-4.21126455e-01 1.05896902e+00 -1.78085566e-01 -5.41978061e-01
-3.58410209e-01 -1.05571771e+00 -3.84616673e-01 -6.18552566e-01
-2.86886990e-01 1.22287917e+00 9.07908797e-01 -3.99596304... | [4.978606224060059, 1.9114363193511963] |
8e478bbf-03b1-447a-852c-5d77588a2587 | semantic-context-encoding-for-accurate-3d | null | null | https://ieeexplore.ieee.org/document/9136884 | https://ieeexplore.ieee.org/document/9136884 | Semantic Context Encoding for Accurate 3D Point Cloud Segmentation | Semantic context plays a significant role in image segmentation. However, few prior works have explored semantic contexts for 3D point cloud segmentation. In this paper, we propose a simple yet effective Point Context Encoding (PointCE) module to capture semantic contexts of a point cloud and adaptively highlight inter... | ['and Gongjian Wen', 'Yinjie Lei', 'Y anni Ma', 'Y ulan Guo', 'Hao liu'] | 2020-08-01 | null | null | null | ieee-2020-8 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 1.80869251e-01 -6.56180084e-02 -9.07730088e-02 -7.26612985e-01
-5.12547493e-01 -4.38913375e-01 4.56654072e-01 1.84664264e-01
-3.12509447e-01 9.25395265e-02 -4.16165084e-01 -3.33426595e-01
-1.08004533e-01 -8.08173597e-01 -6.63102686e-01 -3.00003976e-01
-1.88776150e-01 2.47969061e-01 8.98240089e-01 4.92786579... | [7.9693450927734375, -3.2593421936035156] |
c2903a67-2324-4191-aa65-403fc2fd2495 | using-synthetic-data-for-conversational | 2204.02653 | null | https://arxiv.org/abs/2204.02653v1 | https://arxiv.org/pdf/2204.02653v1.pdf | Using Synthetic Data for Conversational Response Generation in Low-resource Settings | Response generation is a task in natural language processing (NLP) where a model is trained to respond to human statements. Conversational response generators take this one step further with the ability to respond within the context of previous responses. While there are existing techniques for training such models, th... | ['Charibeth Cheng', 'Jan Christian Blaise Cruz', 'Denzel Adrian Co', 'Schuyler Ng', 'Adrian Paule Ty', 'Gabriel Louis Tan'] | 2022-04-06 | null | null | null | null | ['conversational-response-generation'] | ['natural-language-processing'] | [ 3.99538934e-01 5.06525397e-01 3.38571578e-01 -4.85389471e-01
-1.02848816e+00 -7.33469248e-01 1.11918414e+00 1.25033379e-01
-3.32723558e-01 1.18054295e+00 6.63586915e-01 -2.43602946e-01
4.31977034e-01 -8.00572872e-01 -2.01746956e-01 -2.85709649e-01
1.79115355e-01 8.31153989e-01 -8.71088356e-02 -7.40815938... | [12.758450508117676, 8.163408279418945] |
93809d26-cd00-4578-9434-cad0cb4a6451 | mastering-pair-trading-with-risk-aware | 2304.00364 | null | https://arxiv.org/abs/2304.00364v1 | https://arxiv.org/pdf/2304.00364v1.pdf | Mastering Pair Trading with Risk-Aware Recurrent Reinforcement Learning | Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requires RL methods to make thousands of correct actions that nevertheless have no obvious relations to th... | ['Min Peng', 'Yanzhao Lai', 'Boyi Zhang', 'Qianqian Xie', 'Jimin Huang', 'Weiguang Han'] | 2023-04-01 | null | null | null | null | ['pair-trading'] | ['time-series'] | [-6.91967964e-01 2.20412597e-01 -1.31530628e-01 6.93849400e-02
-4.12524819e-01 -5.56961656e-01 3.83638740e-01 -1.62407130e-01
-4.63508785e-01 9.11919475e-01 -2.05204070e-01 -2.10955024e-01
-3.01132739e-01 -1.14195395e+00 -7.72254646e-01 -6.21170521e-01
-4.49148417e-01 6.02532029e-01 1.40557691e-01 -4.38455015... | [4.4307074546813965, 3.9391422271728516] |
7cb8a2ea-92a4-4045-98f2-291ad64583de | sparseformer-sparse-visual-recognition-via | 2304.03768 | null | https://arxiv.org/abs/2304.03768v1 | https://arxiv.org/pdf/2304.03768v1.pdf | SparseFormer: Sparse Visual Recognition via Limited Latent Tokens | Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly. However, most current vision networks follow a dense paradigm, processing every single visual unit (e.g,, pixel or patch) in a uniform manner. In this paper, we challenge thi... | ['Mike Zheng Shou', 'LiMin Wang', 'Zhan Tong', 'Ziteng Gao'] | 2023-04-07 | null | null | null | null | ['video-classification', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 2.63061017e-01 -1.72434136e-01 -2.24175841e-01 -5.36510885e-01
-4.20309216e-01 -3.36533606e-01 5.15146792e-01 -2.65005857e-01
-3.96826714e-01 4.98951197e-01 2.67547250e-01 -6.29882142e-02
2.27806121e-01 -6.41279876e-01 -8.60896766e-01 -7.31949091e-01
2.60459691e-01 1.42103553e-01 -6.17297851e-02 4.80762810... | [9.624855041503906, 0.7990595698356628] |
178f2bd6-6dc8-4d21-a02d-29b76bf00754 | class-incremental-learning-based-on-label | 2306.12619 | null | https://arxiv.org/abs/2306.12619v1 | https://arxiv.org/pdf/2306.12619v1.pdf | Class-Incremental Learning based on Label Generation | Despite the great success of pre-trained language models, it is still a challenge to use these models for continual learning, especially for the class-incremental learning (CIL) setting due to catastrophic forgetting (CF). This paper reports our finding that if we formulate CIL as a continual label generation problem, ... | ['Bing Liu', 'Dongyan Zhao', 'Yiduo Guo', 'Yijia Shao'] | 2023-06-22 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 4.19073343e-01 2.48340920e-01 -5.93477428e-01 -3.05925369e-01
-8.79644632e-01 -5.01369119e-01 9.14919078e-01 4.19978867e-04
-4.35082376e-01 1.13435590e+00 4.13754880e-01 -2.95680344e-01
3.09916198e-01 -4.68774617e-01 -1.00382233e+00 -2.89903969e-01
2.20667705e-01 6.64692879e-01 1.40779451e-01 -1.85267061... | [9.893242835998535, 3.5174577236175537] |
b361ce77-9f32-411a-af6b-31be76dbff29 | unsupervised-keyphrase-extraction-via | null | null | https://openreview.net/forum?id=u2Pt8ULp7Kd | https://openreview.net/pdf?id=u2Pt8ULp7Kd | Unsupervised Keyphrase Extraction via Interpretable Neural Networks | Keyphrase extraction aims at automatically extracting a list of "important'' phrases which represent the key concepts in a document. Traditionally, it has been approached from an information-theoretic angle using phrase co-occurrence statistics. This work proposes a novel unsupervised approach to keyphrase extraction t... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['keyphrase-extraction'] | ['natural-language-processing'] | [ 4.38629925e-01 5.02027750e-01 -7.30789304e-01 -1.66168902e-02
-8.22811067e-01 -5.48322260e-01 9.42717731e-01 1.11528397e+00
-7.75220573e-01 7.67091393e-01 9.29465353e-01 -5.94630361e-01
-4.14094567e-01 -5.34468949e-01 -7.24206269e-01 -5.00910580e-01
-9.49497744e-02 3.84319395e-01 -2.08113343e-01 -3.67558897... | [12.199066162109375, 8.929367065429688] |
63b66629-d30a-488d-bca3-4bcf2ea32bfa | event-driven-headline-generation | null | null | https://aclanthology.org/P15-1045 | https://aclanthology.org/P15-1045.pdf | Event-Driven Headline Generation | null | ['Meishan Zhang', 'Yue Zhang', 'Donghong Ji', 'Rui Sun'] | 2015-07-01 | event-driven-headline-generation-1 | https://aclanthology.org/P15-1045 | https://aclanthology.org/P15-1045.pdf | ijcnlp-2015-7 | ['headline-generation'] | ['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... | [-7.270991802215576, 3.7096095085144043] |
f593f07b-f795-47cc-9dc5-64f80af4cb01 | an-interleaving-semantics-of-the-timed | 2306.07675 | null | https://arxiv.org/abs/2306.07675v2 | https://arxiv.org/pdf/2306.07675v2.pdf | An Interleaving Semantics of the Timed Concurrent Language for Argumentation to Model Debates and Dialogue Games | Time is a crucial factor in modelling dynamic behaviours of intelligent agents: activities have a determined temporal duration in a real-world environment, and previous actions influence agents' behaviour. In this paper, we propose a language for modelling concurrent interaction between agents that also allows the spec... | ['Carlo Taticchi', 'Maria Chiara Meo', 'Stefano Bistarelli'] | 2023-06-13 | null | null | null | null | ['abstract-argumentation', 'abstract-argumentation'] | ['natural-language-processing', 'reasoning'] | [ 2.33453244e-01 5.80594063e-01 -2.31802533e-03 -3.47256869e-01
2.05037639e-01 -7.53917933e-01 1.37430000e+00 3.84939879e-01
-7.13975847e-01 8.09548676e-01 -1.38728455e-01 -6.44539654e-01
-2.34844163e-01 -1.35322964e+00 -2.90991068e-01 -5.51061273e-01
-3.43073279e-01 8.98708582e-01 8.84505093e-01 -3.61359030... | [8.56231689453125, 6.7142333984375] |
11261b4d-b1d8-422a-bbf4-29d92ec991a6 | meta-rppg-remote-heart-rate-estimation-using | 2007.06786 | null | https://arxiv.org/abs/2007.06786v1 | https://arxiv.org/pdf/2007.06786v1.pdf | Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner | Remote heart rate estimation is the measurement of heart rate without any physical contact with the subject and is accomplished using remote photoplethysmography (rPPG) in this work. rPPG signals are usually collected using a video camera with a limitation of being sensitive to multiple contributing factors, e.g. varia... | ['Chen-Yi Lee', 'Eugene Lee', 'Evan Chen'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5772_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720392.pdf | eccv-2020-8 | ['heart-rate-estimation'] | ['medical'] | [ 3.48127663e-01 1.93787403e-02 -2.87882566e-01 -7.16161609e-01
-7.94687927e-01 -3.50620598e-01 5.27720265e-02 -2.51004219e-01
-4.58151370e-01 8.12005639e-01 1.07646056e-01 2.21456252e-02
1.88766599e-01 -4.45443720e-01 -4.90176231e-01 -8.18730235e-01
-3.86237502e-02 2.87643522e-01 -1.78987280e-01 8.09201822... | [13.868300437927246, 2.694096565246582] |
1c264cf8-ef70-43d7-bab1-063f7d34e11c | bs3d-building-scale-3d-reconstruction-from | 2301.01057 | null | https://arxiv.org/abs/2301.01057v1 | https://arxiv.org/pdf/2301.01057v1.pdf | BS3D: Building-scale 3D Reconstruction from RGB-D Images | Various datasets have been proposed for simultaneous localization and mapping (SLAM) and related problems. Existing datasets often include small environments, have incomplete ground truth, or lack important sensor data, such as depth and infrared images. We propose an easy-to-use framework for acquiring building-scale ... | ['Janne Heikkilä', 'Li Liu', 'Esa Rahtu', 'Juho Kannala', 'Janne Mustaniemi'] | 2023-01-03 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-8.19075182e-02 -2.50903100e-01 -3.47610116e-02 -5.02472043e-01
-7.31732488e-01 -6.57226264e-01 3.42881531e-01 -1.25109911e-01
-4.56842124e-01 7.86629677e-01 1.14394434e-01 -2.02175394e-01
3.50184053e-01 -9.99624729e-01 -7.37759113e-01 -4.04595196e-01
1.17531314e-01 6.99719250e-01 2.95634300e-01 -3.36543471... | [7.497402667999268, -2.2139317989349365] |
a2d4461d-8b64-40d5-bf5b-8d9850e60e39 | progressively-complementarity-aware-fusion | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Chen_Progressively_Complementarity-Aware_Fusion_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_Progressively_Complementarity-Aware_Fusion_CVPR_2018_paper.pdf | Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection | How to incorporate cross-modal complementarity sufficiently is the cornerstone question for RGB-D salient object detection. Previous works mainly address this issue by simply concatenating multi-modal features or combining unimodal predictions. In this paper, we answer this question from two perspectives: (1) We argue ... | ['Youfu Li', 'Hao Chen'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.08185068e-01 2.94430494e-01 -1.44099712e-01 -2.20459864e-01
-9.29003954e-01 -4.15487081e-01 6.14856482e-01 4.11842130e-02
-1.22778632e-01 4.43616897e-01 3.63981485e-01 -1.18639693e-01
-5.35338260e-02 -6.47523344e-01 -9.08279896e-01 -7.88303018e-01
1.37069777e-01 -2.88210381e-02 6.73793674e-01 -4.27412957... | [9.73672866821289, -0.8124123215675354] |
0db6cf31-fac1-4e22-8ce9-07de65e445ee | semi-supervised-domain-adaptation-for-cross | 2211.00677 | null | https://arxiv.org/abs/2211.00677v3 | https://arxiv.org/pdf/2211.00677v3.pdf | Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection | In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other da... | ['Stefan M. Wild', 'Gabriel N. Perdue', 'Brian Nord', 'Sandeep Madireddy', 'Kevin Pedro', 'Ashia Lewis', 'Aleksandra Ćiprijanović'] | 2022-11-01 | null | null | null | null | ['morphology-classification', 'universal-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 1.00819856e-01 -8.65346864e-02 2.63343215e-01 -4.42740947e-01
-3.55580032e-01 -1.05525243e+00 9.88644898e-01 5.74161820e-02
-3.21086973e-01 4.61942524e-01 -2.16712594e-01 -5.97648144e-01
-2.86333978e-01 -7.32463479e-01 -4.69612658e-01 -9.77195084e-01
-4.29628864e-02 1.30503953e+00 7.33339012e-01 -2.14879006... | [7.955251216888428, 2.842731475830078] |
3cccf8b5-f449-4e22-9561-44f177cd7394 | variational-quantum-algorithms-for-chemical | 2211.07854 | null | https://arxiv.org/abs/2211.07854v1 | https://arxiv.org/pdf/2211.07854v1.pdf | Variational Quantum Algorithms for Chemical Simulation and Drug Discovery | Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest pr... | ['Srinjoy Ganguly', 'Prateek Jain', 'Sai Nandan Morapakula', 'Hasan Mustafa'] | 2022-11-15 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 2.90906820e-02 -3.10623765e-01 2.04679608e-01 -1.32161751e-01
-3.04223031e-01 -6.00811064e-01 1.59141093e-01 3.66066515e-01
-4.66493696e-01 1.10940158e+00 -4.23142076e-01 -5.26499569e-01
2.17684731e-01 -9.92996752e-01 -7.11523354e-01 -1.05041790e+00
4.41420749e-02 5.71020961e-01 2.33981267e-01 -6.24929726... | [5.540566444396973, 4.925580978393555] |
3a951797-cd45-4ae3-b74a-7095d13fabd9 | quality-signals-in-generated-stories | null | null | https://aclanthology.org/S18-2024 | https://aclanthology.org/S18-2024.pdf | Quality Signals in Generated Stories | We study the problem of measuring the quality of automatically-generated stories. We focus on the setting in which a few sentences of a story are provided and the task is to generate the next sentence ({``}continuation{''}) in the story. We seek to identify what makes a story continuation interesting, relevant, and hav... | ['Lifu Tu', 'Kevin Gimpel', 'Manasvi Sagarkar', 'John Wieting'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['story-continuation'] | ['computer-vision'] | [ 3.39615077e-01 6.43212974e-01 -2.38412783e-01 -4.71360862e-01
-1.31663084e+00 -7.95431137e-01 8.62843931e-01 2.69401729e-01
-1.63552865e-01 9.63962197e-01 1.03740311e+00 1.80763349e-01
2.94864267e-01 -8.66348267e-01 -9.35771644e-01 -1.33584276e-01
3.10593516e-01 4.29463655e-01 4.04085107e-02 -2.85591781... | [11.728670120239258, 8.832683563232422] |
821f5779-fd9f-420d-8fb9-bd92b3044045 | wavenilm-a-causal-neural-network-for-power | 1902.08736 | null | https://arxiv.org/abs/1902.08736v2 | https://arxiv.org/pdf/1902.08736v2.pdf | Wavenilm: A causal neural network for power disaggregation from the complex power signal | Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems; however, many of them are not causal which is important for real-time ... | ['Ivan V. Bajić', 'Alon Harell', 'Stephen Makonin'] | 2019-02-23 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-1.45606725e-02 -7.56383091e-02 -4.86351311e-01 -1.72000855e-01
-3.45354259e-01 -2.53544599e-01 3.47023070e-01 3.98796499e-02
1.35798812e-01 9.49792266e-01 3.72528702e-01 -1.01882353e-01
-4.77878004e-01 -8.55278969e-01 -3.39282215e-01 -7.15014756e-01
-5.83791912e-01 1.37261182e-01 -4.47461724e-01 -1.09756649... | [16.067657470703125, 7.580373764038086] |
cc940561-fcf3-4047-9acb-b05ed6883337 | multi-task-learning-improves-synthetic-speech | null | null | https://ieeexplore.ieee.org/abstract/document/9746059 | https://ieeexplore.ieee.org/abstract/document/9746059 | Multi-task learning improves synthetic speech detection | With the development of deep learning, synthetic speech has become more and more realistic and easier to spoof Automatic Speaker Verification (ASV) devices. Based on mining more effective hand-crafted features and proposing more powerful networks, many algorithms have been proposed to detect this malicious attack. In t... | ['Shilin Wang', 'Yichuan Mo'] | 2022-04-27 | null | null | null | 2022-ieee-international-conference-on | ['voice-conversion', 'synthetic-speech-detection', 'voice-conversion', 'speaker-verification'] | ['audio', 'audio', 'speech', 'speech'] | [ 3.20338249e-01 -1.65815011e-01 -1.72350705e-01 -2.46148095e-01
-1.02351809e+00 -5.94193935e-01 6.99203670e-01 -3.44601750e-01
-2.60467917e-01 2.39793628e-01 1.53490663e-01 -6.90944314e-01
2.21848816e-01 -2.65107036e-01 -4.11265641e-01 -7.44185567e-01
-1.00262240e-01 3.37807804e-01 4.58402872e-01 -5.32463372... | [14.073442459106445, 5.867691516876221] |
c7706bd5-7c2f-4892-87ce-e74927251f29 | deepedit-deep-editable-learning-for | 2305.10655 | null | https://arxiv.org/abs/2305.10655v1 | https://arxiv.org/pdf/2305.10655v1.pdf | DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images | Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators. In this paper, we introduce DeepEdit, a deep learning-based method for volumetric medic... | ['M. Jorge Cardoso', 'Sebastien Ourselin', 'Abood Quraini', 'Andrew Feng', 'Tom Vercauteren', 'Prerna Dogra', 'Daguang Xu', 'Holger R. Roth', 'Steve Pieper', 'Ron Alkalay', 'Csaba Pinter', 'Daniel Palkovics', 'Michela Antonelli', 'Alvin Ihsani', 'Vishwesh Nath', 'Richard Brown', 'Muhammad Asad', 'Sachidanand Alle', 'Pr... | 2023-05-18 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [-2.71833986e-02 7.95411646e-01 -2.76625603e-01 -6.53003514e-01
-1.39041865e+00 -9.25285637e-01 3.01125288e-01 5.57556689e-01
-6.38586998e-01 5.23597658e-01 -2.50580609e-02 -6.59469903e-01
-3.57715003e-02 -6.98004603e-01 -6.17387176e-01 -5.81614852e-01
-1.66104600e-01 1.41042864e+00 4.34342891e-01 3.19327056... | [14.661181449890137, -2.2540619373321533] |
35972599-3a44-4968-a809-87bbe6b9bafa | adversarial-multi-task-learning-for-end-to | 2305.16638 | null | https://arxiv.org/abs/2305.16638v1 | https://arxiv.org/pdf/2305.16638v1.pdf | Adversarial Multi-task Learning for End-to-end Metaphor Detection | Metaphor detection (MD) suffers from limited training data. In this paper, we started with a linguistic rule called Metaphor Identification Procedure and then proposed a novel multi-task learning framework to transfer knowledge in basic sense discrimination (BSD) to MD. BSD is constructed from word sense disambiguation... | ['Ying Liu', 'Shenglong Zhang'] | 2023-05-26 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.78231716e-01 -6.26057461e-02 -2.57861614e-01 -2.88082659e-01
-1.02252138e+00 -9.23475742e-01 7.39665806e-01 1.82862669e-01
-5.60082555e-01 6.10040784e-01 5.70203841e-01 -3.06270778e-01
7.93556571e-02 -6.85465872e-01 -2.97849238e-01 -4.45621073e-01
5.94380014e-02 2.40903616e-01 -2.94571698e-01 -6.10617936... | [10.448270797729492, 8.897456169128418] |
0918046f-6d35-49db-ab20-ac46c852b25e | unsupervised-space-time-network-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Meunier_Unsupervised_Space-Time_Network_for_Temporally-Consistent_Segmentation_of_Multiple_Motions_CVPR_2023_paper.pdf | Unsupervised Space-Time Network for Temporally-Consistent Segmentation of Multiple Motions | Motion segmentation is one of the main tasks in computer vision and is relevant for many applications. The optical flow (OF) is the input generally used to segment every frame of a video sequence into regions of coherent motion. Temporal consistency is a key feature of motion segmentation, but it is often neglected... | ['Patrick Bouthemy', 'Etienne Meunier'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['motion-segmentation'] | ['computer-vision'] | [ 5.40121645e-02 -3.10465023e-02 -2.12634832e-01 -2.19975904e-01
-2.12958485e-01 -4.54534233e-01 5.51125705e-01 -2.54210651e-01
-5.91190398e-01 6.66119754e-01 -6.57817647e-02 -7.14094266e-02
-6.06572926e-02 -6.09950006e-01 -7.12443709e-01 -7.31871784e-01
-2.78617918e-01 2.22669616e-01 7.31493056e-01 6.26041591... | [9.106066703796387, -0.4969991445541382] |
a3a3f8c9-1b86-429d-a0e0-fd39dd5114cb | a-relaxed-optimization-approach-for-1 | 2306.08492 | null | https://arxiv.org/abs/2306.08492v1 | https://arxiv.org/pdf/2306.08492v1.pdf | A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models | In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversarial examples that are semantically similar to the original sentences but destroy the translation generated by the target NMT model. This optim... | ['Pascal Frossard', 'Ljiljana Dolamic', 'Clément Barbier', 'Sahar Sadrizadeh'] | 2023-06-14 | null | null | null | null | ['adversarial-attack', 'nmt', 'machine-translation', 'semantic-textual-similarity', 'semantic-similarity'] | ['adversarial', 'computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.92254460e-01 3.16214681e-01 7.10222591e-03 -2.42076471e-01
-1.31176126e+00 -1.03186584e+00 8.34313512e-01 -3.30462813e-01
-4.05644983e-01 7.59136736e-01 3.98404337e-02 -3.54665846e-01
4.43456382e-01 -7.42263317e-01 -1.26283360e+00 -6.61338389e-01
3.49940896e-01 7.45276213e-01 -1.62053838e-01 -3.57231289... | [6.03780460357666, 8.143766403198242] |
be6f331b-f6c8-4e2a-b48f-031a776ecd34 | complicated-background-suppression-of-visar | 2209.10431 | null | https://arxiv.org/abs/2209.10431v1 | https://arxiv.org/pdf/2209.10431v1.pdf | Complicated Background Suppression of ViSAR Image For Moving Target Shadow Detection | The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability. To solve this problem, we propose a method to suppress complicated background ... | ['Xu Zhan', 'Xiaoling Zhang', 'Zhenyu Yang'] | 2022-09-21 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.89478111e-01 -8.16981792e-01 4.80490237e-01 3.38440128e-02
-1.34536639e-01 -3.35953385e-01 4.82582837e-01 -7.21174121e-01
-4.15654331e-01 8.03911567e-01 -2.77017683e-01 -2.25505084e-01
-2.57801749e-02 -9.47784901e-01 -2.27937281e-01 -1.35250330e+00
-7.63106570e-02 1.05409034e-01 8.53803277e-01 -1.61556467... | [8.479512214660645, -1.1002789735794067] |
bca03064-e6d2-4068-bdf2-60c1c63c1c09 | an-end-to-end-framework-for-marketing | 2302.04477 | null | https://arxiv.org/abs/2302.04477v1 | https://arxiv.org/pdf/2302.04477v1.pdf | An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint | Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level budget allocation is an essential task in marketing campaigns. Recent advances in this field often address the budget allocation problem using... | ['Peng Jiang', 'Jingjian Lin', 'Guorui Zhou', 'Shusen Wang', 'Ziang Yan'] | 2023-02-09 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.25186870e-01 3.17575522e-02 -1.25174844e+00 -5.79459488e-01
-8.25574815e-01 -3.91147017e-01 4.44517843e-02 1.12616360e-01
-4.44738925e-01 5.28707206e-01 3.19024682e-01 -5.39700031e-01
-1.64997011e-01 -7.94378996e-01 -7.14568377e-01 -4.55968589e-01
8.30098093e-02 2.67791420e-01 -3.18670571e-01 -2.74548102... | [9.453394889831543, 5.356531620025635] |
ddcf95d2-d1c7-46ad-99a9-52ee992aec9c | active-learning-with-pseudo-labels-for-multi | 2112.13709 | null | https://arxiv.org/abs/2112.13709v2 | https://arxiv.org/pdf/2112.13709v2.pdf | Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-Training | Pose estimation of the human body and hands is a fundamental problem in computer vision, and learning-based solutions require a large amount of annotated data. In this work, we improve the efficiency of the data annotation process for 3D pose estimation problems with Active Learning (AL) in a multi-view setting. AL sel... | ['Yuting Ye', 'Cem Keskin', 'He Wen', 'Kun He', 'Qi Feng'] | 2021-12-27 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 6.47753999e-02 1.55739352e-01 -3.35502476e-01 -3.26651901e-01
-1.21761239e+00 -7.91407287e-01 2.56783932e-01 -1.01318032e-01
-5.61943352e-01 3.48162204e-01 2.59008616e-01 2.32064769e-01
2.58436859e-01 -2.59806722e-01 -6.89471245e-01 -4.85605121e-01
4.04657386e-02 1.03986299e+00 3.53113532e-01 6.85864463... | [6.960638046264648, -0.8164288997650146] |
7af1ea1e-ae42-40f2-b4fa-3d5fddac7abe | automated-feature-extraction-on-asmap-for | 2201.12055 | null | https://arxiv.org/abs/2201.12055v2 | https://arxiv.org/pdf/2201.12055v2.pdf | Automated Feature Extraction on AsMap for Emotion Classification using EEG | Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in th... | ['Ebrahim Ghaderpour', 'Souvik Phadikar', 'Nidul Sinha', 'Md. Zaved Iqubal Ahmed'] | 2022-01-28 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-1.40622770e-02 -2.55633175e-01 4.55262780e-01 -4.92296755e-01
-2.49884158e-01 -1.79246217e-01 2.16185063e-01 1.62632465e-01
-5.84896803e-01 9.15471792e-01 -3.42008309e-03 2.56518364e-01
-4.98264790e-01 -4.37148333e-01 -5.84012046e-02 -8.11476409e-01
-4.28644389e-01 -1.88068569e-01 -3.89628917e-01 -9.62756872... | [13.249611854553223, 3.373905658721924] |
d6a82889-7e9b-49d3-9478-c1932a923863 | towards-locally-consistent-object-counting | 1904.03373 | null | http://arxiv.org/abs/1904.03373v1 | http://arxiv.org/pdf/1904.03373v1.pdf | Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks | High-density object counting in surveillance scenes is challenging mainly due
to the drastic variation of object scales. The prevalence of deep learning has
largely boosted the object counting accuracy on several benchmark datasets.
However, does the global counts really count? Armed with this question we dive
into the... | ['Jian Zhang', 'Wenjun Zhang', 'Muming Zhao', 'Chongyang Zhang'] | 2019-04-06 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-6.75123334e-02 -2.30813622e-01 -1.20001033e-01 -3.91563326e-01
-6.46755576e-01 -2.90500075e-01 7.31945813e-01 8.56739283e-02
-5.76744795e-01 8.62386942e-01 -1.17952358e-02 -2.67139170e-02
-2.40344107e-02 -1.08686399e+00 -9.72176313e-01 -7.24986196e-01
1.05812408e-01 7.04726636e-01 4.72212404e-01 1.75429881... | [8.67809009552002, -0.07646612077951431] |
b1ee191a-bebb-45f6-bcfb-f27a43965ac6 | the-npu-system-for-the-2020-personalized | 2102.13552 | null | https://arxiv.org/abs/2102.13552v1 | https://arxiv.org/pdf/2102.13552v1.pdf | The NPU System for the 2020 Personalized Voice Trigger Challenge | This paper describes the system developed by the NPU team for the 2020 personalized voice trigger challenge. Our submitted system consists of two independently trained subsystems: a small footprint keyword spotting (KWS) system and a speaker verification (SV) system. For the KWS system, a multi-scale dilated temporal c... | ['Lei Xie', 'Qijie Shao', 'Zhanheng Yang', 'Qing Wang', 'Yihui Fu', 'Li Zhang', 'Jingyong Hou'] | 2021-02-26 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 9.36984345e-02 2.87972480e-01 -7.68594369e-02 -4.48360741e-01
-1.58245480e+00 -6.04282260e-01 3.41102064e-01 -1.42347351e-01
-3.07467699e-01 2.02828422e-01 3.44243735e-01 -4.51963753e-01
2.68611759e-01 -9.31411460e-02 -5.23579776e-01 -6.60650551e-01
3.72126214e-02 2.54125714e-01 4.54449236e-01 2.07730904... | [14.446983337402344, 6.262013912200928] |
26fd59ce-77ee-4a4a-a98c-91e008bc47b4 | a-reinforcement-learning-framework-for-online | 2302.10924 | null | https://arxiv.org/abs/2302.10924v1 | https://arxiv.org/pdf/2302.10924v1.pdf | A Reinforcement Learning Framework for Online Speaker Diarization | Speaker diarization is a task to label an audio or video recording with the identity of the speaker at each given time stamp. In this work, we propose a novel machine learning framework to conduct real-time multi-speaker diarization and recognition without prior registration and pretraining in a fully online and reinfo... | ['Xinxin Zhang', 'Baihan Lin'] | 2023-02-21 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 2.10366696e-01 8.09455812e-02 4.93138423e-03 -5.85343897e-01
-1.19897532e+00 -6.54758632e-01 4.06891763e-01 -4.32069749e-02
-5.54571688e-01 3.65571409e-01 -1.16368212e-01 -2.85232306e-01
-8.29599276e-02 -3.82259339e-01 -4.90084291e-01 -8.38899970e-01
-3.37977767e-01 8.31287444e-01 -9.62618738e-02 -1.63853168... | [14.376927375793457, 6.133562088012695] |
f5296e93-f32b-4c0b-a92b-2eed06fc67cb | overview-of-the-icassp-2023-general-meeting | 2303.13932 | null | https://arxiv.org/abs/2303.13932v1 | https://arxiv.org/pdf/2303.13932v1.pdf | Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG) | ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings. MUG includes five tracks, including ... | ['Zhou Zhao', 'Yi Ren', 'Jinglin Liu', 'Zhijie Yan', 'Wen Wang', 'Qian Chen', 'Hai Yu', 'Jiaqing Liu', 'Chong Deng', 'Qinglin Zhang'] | 2023-03-24 | null | null | null | null | ['extractive-summarization', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.02490795e-01 6.12458527e-01 -2.13903755e-01 -2.35412776e-01
-1.75956917e+00 -7.76143372e-01 8.98275971e-01 9.30875182e-01
-2.50337925e-02 8.69272828e-01 1.32863045e+00 -1.15850858e-01
-4.03945474e-03 3.85496356e-02 -1.41567528e-01 -1.01983845e-01
-2.91222602e-01 4.64702964e-01 1.98069643e-02 -5.14243022... | [12.653422355651855, 9.404962539672852] |
16da0ee6-f73b-4d5e-936f-97aded8fdafc | co-embedding-of-nodes-and-edges-with-graph | 2010.13242 | null | https://arxiv.org/abs/2010.13242v1 | https://arxiv.org/pdf/2010.13242v1.pdf | Co-embedding of Nodes and Edges with Graph Neural Networks | Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of machine learning tasks. Graph embedding is a way to transform and encode... | ['Sheng Li', 'Pengsheng Ji', 'Ronghang Zhu', 'Xiaodong Jiang'] | 2020-10-25 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 1.02391653e-01 2.38737628e-01 -2.37596795e-01 -2.70554662e-01
1.93742752e-01 -5.03874362e-01 6.64841652e-01 5.29482782e-01
-1.50684223e-01 5.78320861e-01 -9.76721421e-02 -6.17800951e-01
-3.54286820e-01 -1.34855664e+00 -6.62187636e-01 -7.54227161e-01
-5.26372313e-01 3.79566908e-01 5.66166304e-02 -2.16327876... | [7.0745415687561035, 6.252328872680664] |
7db0192f-25b6-4356-a8e4-115a8aebd731 | lp-dif-learning-local-pattern-specific-deep | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_LP-DIF_Learning_Local_Pattern-Specific_Deep_Implicit_Function_for_3D_Objects_CVPR_2023_paper.pdf | LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and Scenes | Deep Implicit Function (DIF) has gained much popularity as an efficient 3D shape representation. To capture geometry details, current mainstream methods divide 3D shapes into local regions and then learn each one with a local latent code via a decoder, where the decoder shares the geometric similarities among diffe... | ['Zhizhong Han', 'Yi Fang', 'Kanle Shi', 'Yue Gao', 'Yu-Shen Liu', 'Meng Wang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-representation', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [-3.50118250e-01 -2.96560898e-02 -3.23124290e-01 -2.79073209e-01
-8.92398477e-01 -6.63520038e-01 3.09251100e-01 4.11559604e-02
3.84160846e-01 1.61263168e-01 4.74869460e-01 1.20592445e-01
1.52425438e-01 -1.11288726e+00 -7.05589354e-01 -8.00718188e-01
1.67647377e-01 5.49327075e-01 4.34286624e-01 2.02833757... | [8.191658973693848, -3.556230068206787] |
2036a32a-587c-4234-99e0-4f3c4712f0f7 | document-level-event-extraction-via | 2105.14924 | null | https://arxiv.org/abs/2105.14924v1 | https://arxiv.org/pdf/2105.14924v1.pdf | Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker | Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model. In this paper, we... | ['Baobao Chang', 'Lei LI', 'Tianyu Liu', 'Runxin Xu'] | 2021-05-31 | null | https://aclanthology.org/2021.acl-long.274 | https://aclanthology.org/2021.acl-long.274.pdf | acl-2021-5 | ['document-level-event-extraction'] | ['natural-language-processing'] | [-1.23999305e-01 1.81634933e-01 -3.42124492e-01 -2.96910852e-02
-8.53125036e-01 -7.95869112e-01 8.01448703e-01 6.60042048e-01
-8.63112211e-02 6.68146968e-01 5.92791617e-01 -4.90622483e-02
-3.14993680e-01 -8.26245487e-01 -6.57351971e-01 -2.94959664e-01
-2.09643975e-01 2.41712376e-01 6.44184649e-01 -5.51423840... | [9.10024356842041, 9.114121437072754] |
8a0e8e22-502f-4bf8-84df-2c0c92ac9333 | deep-polarimetric-hdr-reconstruction | 2203.1419 | null | https://arxiv.org/abs/2203.14190v1 | https://arxiv.org/pdf/2203.14190v1.pdf | Deep Polarimetric HDR Reconstruction | This paper proposes a novel learning based high-dynamic-range (HDR) reconstruction method using a polarization camera. We utilize a previous observation that polarization filters with different orientations can attenuate natural light differently, and we treat the multiple images acquired by the polarization camera as ... | ['Hong Zhang', 'Moein Shakeri', 'Juiwen Ting'] | 2022-03-27 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 1.35084704e-01 -1.56659886e-01 2.72208035e-01 -4.32123244e-01
-6.89495862e-01 -1.53336555e-01 5.62658608e-01 -1.00868344e+00
-2.42889643e-01 8.95462155e-01 3.89788091e-01 1.18401460e-01
-1.17945224e-01 -1.06828451e+00 -8.71165812e-01 -1.14038312e+00
2.18675300e-01 1.81766272e-01 -2.37242475e-01 -3.41350317... | [10.480483055114746, -2.2534842491149902] |
85ba3d72-81c7-418e-a1c0-eac766dadbda | bidirectional-cross-modal-knowledge | 2301.00182 | null | https://arxiv.org/abs/2301.00182v2 | https://arxiv.org/pdf/2301.00182v2.pdf | Bidirectional Cross-Modal Knowledge Exploration for Video Recognition with Pre-trained Vision-Language Models | Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still lim... | ['Wanli Ouyang', 'Yi Yang', 'Jingdong Wang', 'Haipeng Luo', 'Xiaohan Wang', 'Wenhao Wu'] | 2022-12-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.pdf | cvpr-2023-1 | ['zero-shot-action-recognition', 'video-recognition', 'action-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.01706696e-02 -5.62128067e-01 -6.51930451e-01 -2.42800593e-01
-7.61964798e-01 -3.08363527e-01 8.41088891e-01 -3.57469589e-01
-3.69565099e-01 4.95673031e-01 3.52139711e-01 -1.18583618e-02
1.92336693e-01 -3.65319878e-01 -9.24653351e-01 -5.95253527e-01
1.18846543e-01 9.30417106e-02 3.96588326e-01 -1.14289731... | [9.9492826461792, 0.915420651435852] |
3518df0e-2654-44f5-8f73-2914a2f527d5 | sequential-changepoint-approach-for-online | 1407.5978 | null | http://arxiv.org/abs/1407.5978v3 | http://arxiv.org/pdf/1407.5978v3.pdf | Sequential Changepoint Approach for Online Community Detection | We present new algorithms for detecting the emergence of a community in large
networks from sequential observations. The networks are modeled using
Erdos-Renyi random graphs with edges forming between nodes in the community
with higher probability. Based on statistical changepoint detection
methodology, we develop thre... | ['David Marangoni-Simonsen', 'Yao Xie'] | 2014-07-22 | null | null | null | null | ['online-community-detection'] | ['graphs'] | [-1.77349113e-02 -5.01494221e-02 -8.77640694e-02 4.56055611e-01
9.95325297e-03 -7.50293612e-01 4.79618758e-01 4.33679312e-01
-1.85841605e-01 7.32744396e-01 -6.39688790e-01 -3.19887072e-01
-3.59300375e-01 -1.08810449e+00 -2.70726204e-01 -1.13668144e+00
-7.30747283e-01 6.20162070e-01 9.16103482e-01 1.03440553... | [6.950352191925049, 5.158266544342041] |
65d17c69-8083-489e-b358-b918b128f1e8 | sparse-graph-learning-for-spatiotemporal-time | 2205.13492 | null | https://arxiv.org/abs/2205.13492v2 | https://arxiv.org/pdf/2205.13492v2.pdf | Sparse Graph Learning from Spatiotemporal Time Series | Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures. Often, however, the relational information characterizing the underlying data-generating process is unavailable and the ... | ['Cesare Alippi', 'Daniele Zambon', 'Andrea Cini'] | 2022-05-26 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.51701719e-01 1.78836480e-01 1.83436479e-02 -3.83714765e-01
-6.01258695e-01 -4.28255141e-01 6.90791965e-01 3.63728732e-01
-1.57170668e-01 3.61468375e-01 -1.36238784e-02 -6.71020150e-01
-5.94166219e-01 -9.41246390e-01 -9.31843281e-01 -7.56270885e-01
-6.03403389e-01 6.12085819e-01 1.38901547e-01 -3.21117222... | [6.8058905601501465, 3.098037004470825] |
e6135a3d-8c1f-4652-94db-368dd92aa404 | sign-language-translation-with-hierarchical | 2111.07258 | null | https://arxiv.org/abs/2111.07258v1 | https://arxiv.org/pdf/2111.07258v1.pdf | Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural Network | Sign language translation (SLT), which generates text in a spoken language from visual content in a sign language, is important to assist the hard-of-hearing community for their communications. Inspired by neural machine translation (NMT), most existing SLT studies adopted a general sequence to sequence learning strate... | ['Zhiyong Wang', 'Mohammed Bennamounm', 'Ah Chung Tsoi', 'Markus Hagenbuchner', 'Kun Hu', 'Jichao Kan'] | 2021-11-14 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 1.78085878e-01 8.79864767e-02 -1.44765556e-01 -2.17461780e-01
-2.36941576e-01 -1.63815603e-01 6.14522338e-01 -4.35779214e-01
7.16912299e-02 5.05344868e-01 5.82122684e-01 2.44394075e-02
-2.22702269e-02 -7.33123064e-01 -6.58425987e-01 -7.62264907e-01
-8.60398337e-02 3.92581016e-01 3.01042229e-01 -2.11108014... | [9.252396583557129, -6.550869464874268] |
63521fc7-ba43-4680-96e4-12992a85ab40 | creating-and-evaluating-resources-for | null | null | https://aclanthology.org/2021.wassa-1.20 | https://aclanthology.org/2021.wassa-1.20.pdf | Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi | In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers. The positive or negative sentiment score is assigned to each Sindhi opinion word. Afterwards, we de... | ['Zenglin Xu', 'Saifullah Tumrani', 'Jay Kumar', 'Yong Dai', 'Naveed Ali', 'Wazir Ali'] | null | null | null | null | eacl-wassa-2021-4 | ['subjectivity-analysis', 'text-annotation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.73434066e-03 -2.24794708e-02 -5.18489599e-01 -2.89549500e-01
-5.23504138e-01 -9.47548449e-01 4.39014703e-01 4.86330479e-01
-7.76682317e-01 9.98525918e-01 7.88303018e-01 -1.25381321e-01
3.13706428e-01 -8.96853864e-01 1.86130494e-01 -3.43005478e-01
4.50718313e-01 5.69642365e-01 -1.30154386e-01 -1.03430974... | [11.1445951461792, 6.919198036193848] |
2a72043a-a29e-4bd7-989e-8565844bfcb9 | practical-fixed-parameter-algorithms-for | 2112.13175 | null | https://arxiv.org/abs/2112.13175v1 | https://arxiv.org/pdf/2112.13175v1.pdf | Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs | Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated as a Stackelberg game between one defender and one attacker. The attack graph contains one destinatio... | ['Hung Nguyen', 'Frank Neumann', 'Aneta Neumann', 'Jialiang Li', 'Mingyu Guo'] | 2021-12-25 | null | null | null | null | ['tree-decomposition'] | ['graphs'] | [-1.12541273e-01 4.11160469e-01 -5.32208622e-01 3.13820451e-01
-4.35022235e-01 -1.30254638e+00 4.84345928e-02 2.68786997e-01
-4.45863724e-01 3.43732387e-01 -2.81250507e-01 -1.15571153e+00
-4.25614357e-01 -1.17357624e+00 -2.32090920e-01 -6.92127705e-01
-9.03071642e-01 7.13501930e-01 8.59066486e-01 -5.34416258... | [5.988329887390137, 7.178386211395264] |
63a1cc35-aee5-4887-be16-2d3935ff86b2 | improved-mutual-information-estimation | null | null | https://openreview.net/forum?id=S1lslCEYPB | https://openreview.net/pdf?id=S1lslCEYPB | Improved Mutual Information Estimation | We propose a new variational lower bound on the KL divergence and show that the Mutual Information (MI) can be estimated by maximizing this bound using a witness function on a hypothesis function class and an auxiliary scalar variable. If the function class is in a Reproducing Kernel Hilbert Space (RKHS), this leads to... | ['Tom Sercu*', 'Jerret Ross*', 'Pierre Dognin*', 'Igor Melnyk*', 'Youssef Mroueh*'] | 2019-09-25 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.22770500e-01 4.28133249e-01 -2.92436719e-01 -4.20717061e-01
-1.31087756e+00 -5.04066765e-01 3.49552661e-01 -2.12227315e-01
-5.75964034e-01 8.40973139e-01 7.23929778e-02 -2.27699518e-01
-1.00084603e-01 -5.00771582e-01 -8.84501874e-01 -8.62695634e-01
-3.25730950e-01 1.95220456e-01 -3.72508168e-01 1.09008953... | [7.434401988983154, 4.035598278045654] |
2e72337b-8183-49a2-a0e7-793d864b4a21 | local-region-learning-modules-for-point-cloud | 2303.17338 | null | https://arxiv.org/abs/2303.17338v1 | https://arxiv.org/pdf/2303.17338v1.pdf | Local region-learning modules for point cloud classification | Data organization via forming local regions is an integral part of deep learning networks that process 3D point clouds in a hierarchical manner. At each level, the point cloud is sampled to extract representative points and these points are used to be centers of local regions. The organization of local regions is of co... | ['Helin Dutagaci', 'Kaya Turgut'] | 2023-03-30 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.27167413e-01 7.02913776e-02 1.46532714e-01 -7.50157416e-01
-3.59226346e-01 -3.72927874e-01 5.51572502e-01 5.59310913e-01
-4.58698779e-01 5.91932125e-02 -6.17350712e-02 1.78006262e-01
-2.71457255e-01 -8.68228018e-01 -1.11524951e+00 -6.37102485e-01
-3.11019838e-01 6.87972069e-01 5.23081064e-01 7.05851540... | [7.973719120025635, -3.5620193481445312] |
f06fcc19-343b-4d8f-be24-92e364cb41b6 | compositional-3d-scene-generation-using | 2303.12218 | null | https://arxiv.org/abs/2303.12218v2 | https://arxiv.org/pdf/2303.12218v2.pdf | Compositional 3D Scene Generation using Locally Conditioned Diffusion | Designing complex 3D scenes has been a tedious, manual process requiring domain expertise. Emerging text-to-3D generative models show great promise for making this task more intuitive, but existing approaches are limited to object-level generation. We introduce \textbf{locally conditioned diffusion} as an approach to c... | ['Gordon Wetzstein', 'Ryan Po'] | 2023-03-21 | null | null | null | null | ['scene-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 4.84608293e-01 2.78154165e-01 1.80723146e-01 -6.19861424e-01
-1.12396324e+00 -8.49522412e-01 1.17949712e+00 -3.35711926e-01
1.26239985e-01 2.80926138e-01 8.41774881e-01 -3.81532013e-01
4.71736759e-01 -6.91823125e-01 -6.94373012e-01 -3.73328239e-01
4.14928526e-01 8.37734520e-01 1.72377512e-01 -3.90781581... | [11.15704345703125, -0.2851477265357971] |
0fc161a5-c7fb-4400-967d-0b64b8036796 | multi-agent-path-finding-with-delay | 1612.05309 | null | http://arxiv.org/abs/1612.05309v1 | http://arxiv.org/pdf/1612.05309v1.pdf | Multi-Agent Path Finding with Delay Probabilities | Several recently developed Multi-Agent Path Finding (MAPF) solvers scale to
large MAPF instances by searching for MAPF plans on 2 levels: The high-level
search resolves collisions between agents, and the low-level search plans paths
for single agents under the constraints imposed by the high-level search. We
make the f... | ['Sven Koenig', 'T. K. Satish Kumar', 'Hang Ma'] | 2016-12-15 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 6.73272386e-02 8.71072531e-01 -3.13678622e-01 -1.82431743e-01
-8.77673924e-01 -7.47673869e-01 5.29037535e-01 5.63948035e-01
-4.71170723e-01 1.07103956e+00 2.97483414e-01 -2.10612327e-01
-9.78765428e-01 -1.30595529e+00 -7.61272371e-01 -3.50329816e-01
-9.37041342e-01 1.50971735e+00 6.43301249e-01 -3.64766628... | [4.961398601531982, 1.7703958749771118] |
93d631d8-a896-47d1-9207-cc8fb51115d7 | efficient-deep-embedded-subspace-clustering | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cai_Efficient_Deep_Embedded_Subspace_Clustering_CVPR_2022_paper.pdf | Efficient Deep Embedded Subspace Clustering | Recently deep learning methods have shown significant progress in data clustering tasks. Deep clustering methods (including distance-based methods and subspace-based methods) integrate clustering and feature learning into a unified framework, where there is a mutual promotion between clustering and representation. ... | ['Zhao Zhang', 'Yunhe Zhang', 'Shiping Wang', 'Wenzhong Guo', 'Jicong Fan', 'Jinyu Cai'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['online-clustering'] | ['computer-vision'] | [-5.26309788e-01 -5.97638249e-01 4.69357185e-02 -3.49383205e-01
-6.58054769e-01 -6.04963899e-01 4.77778763e-01 -1.57983154e-01
-4.16397750e-01 1.02205552e-01 2.99380630e-01 1.65122256e-01
-4.64366466e-01 -5.49904764e-01 -3.67628604e-01 -1.15863121e+00
-4.35326286e-02 8.12256813e-01 -4.65589575e-02 2.08196267... | [9.0462007522583, 3.3610332012176514] |
f3f83344-05e8-4e80-adba-ab78d4aab5e9 | x-clip-end-to-end-multi-grained-contrastive | 2207.07285 | null | https://arxiv.org/abs/2207.07285v2 | https://arxiv.org/pdf/2207.07285v2.pdf | X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval | Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is ... | ['Rongrong Ji', 'Ji Zhang', 'Ming Yan', 'Xiaoshuai Sun', 'Guohai Xu', 'Yiwei Ma'] | 2022-07-15 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 3.22825946e-02 -1.04251754e+00 -2.49530803e-02 -9.73152220e-02
-1.01299250e+00 -2.62959242e-01 9.60482955e-01 2.34454006e-01
-6.26368165e-01 2.41143167e-01 4.48180497e-01 3.02370161e-01
-3.14579606e-01 -7.23205686e-01 -3.10176224e-01 -6.50889456e-01
5.72410747e-02 1.10958211e-01 3.59524876e-01 -3.35514337... | [10.426486015319824, 0.897409200668335] |
064b5900-dcfa-46f9-8a3f-4cc6b6d0e012 | transfer-learning-improves-french-cross | null | null | https://aclanthology.org/2022.vardial-1.12 | https://aclanthology.org/2022.vardial-1.12.pdf | Transfer Learning Improves French Cross-Domain Dialect Identification: NRC @ VarDial 2022 | We describe the systems developed by the National Research Council Canada for the French Cross-Domain Dialect Identification shared task at the 2022 VarDial evaluation campaign. We evaluated two different approaches to this task: SVM and probabilistic classifiers exploiting n-grams as features, and trained from scratch... | ['Cyril Goutte', 'Serge Leger', 'Gabriel Bernier-Colborne'] | null | null | null | null | vardial-coling-2022-10 | ['dialect-identification'] | ['natural-language-processing'] | [-2.68704385e-01 -2.40198765e-02 -1.08441815e-01 -9.52406287e-01
-1.40010166e+00 -1.11454558e+00 8.77357185e-01 1.37076035e-01
-5.43103993e-01 1.01393461e+00 4.79966581e-01 -4.38921958e-01
2.04865262e-01 -5.54337025e-01 -2.48875797e-01 -3.27532232e-01
-4.57156971e-02 7.72236228e-01 1.57686472e-01 -5.36131859... | [10.186490058898926, 10.72949504852295] |
bdf2308c-c7bd-4f90-808f-1a94e8945b77 | learning-from-self-sampled-correct-and | 2205.14318 | null | https://arxiv.org/abs/2205.14318v2 | https://arxiv.org/pdf/2205.14318v2.pdf | Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions | Pretrained language models have shown superior performance on many natural language processing tasks, yet they still struggle at multi-step formal reasoning tasks like grade school math problems. One key challenge of finetuning them to solve such math reasoning problems is that many existing datasets only contain one r... | ['Jianfeng Gao', 'Dragomir Radev', 'Christopher Meek', 'Oleksandr Polozov', 'Chenglong Wang', 'Jeevana Priya Inala', 'Ansong Ni'] | 2022-05-28 | null | null | null | null | ['program-synthesis', 'gsm8k', 'arithmetic-reasoning'] | ['computer-code', 'natural-language-processing', 'reasoning'] | [ 7.29148602e-03 7.85457641e-02 -4.75057870e-01 -5.08471608e-01
-1.00421810e+00 -8.65776300e-01 2.19176710e-01 4.33217704e-01
-3.31696749e-01 8.21048558e-01 9.89759490e-02 -5.68398833e-01
-3.27963084e-01 -1.20802236e+00 -9.36706543e-01 -8.01351219e-02
2.45299160e-01 5.48305213e-01 2.78809220e-01 -2.62037903... | [9.781089782714844, 7.359528541564941] |
09cd4a7f-4d5f-451d-8cd4-a3f1cc7cbd37 | segmentation-free-direct-iris-localization | 2210.10403 | null | https://arxiv.org/abs/2210.10403v1 | https://arxiv.org/pdf/2210.10403v1.pdf | Segmentation-free Direct Iris Localization Networks | This paper proposes an efficient iris localization method without using iris segmentation and circle fitting. Conventional iris localization methods first extract iris regions by using semantic segmentation methods such as U-Net. Afterward, the inner and outer iris circles are localized using the traditional circle fit... | ['Masato Tsukada', 'Koichi Takahashi', 'Takahiro Toizumi'] | 2022-10-19 | null | null | null | null | ['iris-recognition', 'iris-segmentation'] | ['computer-vision', 'medical'] | [ 1.44115254e-01 -3.08104515e-01 -3.99413228e-01 -2.09274113e-01
-1.53074339e-01 -3.58836114e-01 -7.51537755e-02 -1.16191141e-01
-3.69595289e-01 4.35703188e-01 2.82478915e-03 -8.41787979e-02
-7.65420496e-02 -5.19609511e-01 -4.29651409e-01 -5.74369550e-01
4.11064655e-01 1.36422873e-01 1.22431070e-01 5.20848811... | [3.792997121810913, -3.5873122215270996] |
d518948b-5b9d-4803-9623-2dbb558c18fd | meta-auxiliary-learning-for-facial-action | 2105.0662 | null | https://arxiv.org/abs/2105.06620v1 | https://arxiv.org/pdf/2105.06620v1.pdf | Meta Auxiliary Learning for Facial Action Unit Detection | Despite the success of deep neural networks on facial action unit (AU) detection, better performance depends on a large number of training images with accurate AU annotations. However, labeling AU is time-consuming, expensive, and error-prone. Considering AU detection and facial expression recognition (FER) are two hig... | ['Shiguang Shan', 'Yong Li'] | 2021-05-14 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection', 'auxiliary-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 4.41501349e-01 -1.12684583e-02 -1.39339328e-01 -5.32985449e-01
-9.95793402e-01 1.48404166e-01 2.75681496e-01 -3.09176624e-01
-6.28794730e-01 5.46320200e-01 -8.02194253e-02 6.65616512e-01
3.18589985e-01 -5.67659974e-01 -5.91680467e-01 -9.69454050e-01
2.69187868e-01 2.95987934e-01 5.35209253e-02 -3.37292343... | [13.631171226501465, 1.6016614437103271] |
66ff9e86-a329-4a4f-b565-f828c1c8bf19 | unsupervised-person-re-identification-with | 2103.0458 | null | https://arxiv.org/abs/2103.04580v1 | https://arxiv.org/pdf/2103.04580v1.pdf | Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering | Although unsupervised person re-identification (Re-ID) has drawn increasing research attention recently, it remains challenging to learn discriminative features without annotations across disjoint camera views. In this paper, we address the unsupervised person Re-ID with a conceptually novel yet simple framework, terme... | ['Qi Hao', 'Yu Qiao', 'Xiaojiang Peng', 'Qing Li'] | 2021-03-08 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.06811598e-01 -4.69538122e-01 -1.71438962e-01 -5.90437770e-01
-6.40341043e-01 -4.12435442e-01 6.43864691e-01 1.86111659e-01
-6.08319163e-01 3.55009168e-01 6.26410127e-01 7.27823019e-01
-5.82955629e-02 -5.77847362e-01 -2.71020651e-01 -7.36111045e-01
1.43835187e-01 6.96118593e-01 1.21459253e-01 2.73264736... | [14.782512664794922, 1.0398015975952148] |
c8b9c2b8-3605-4e17-9f65-70424336362c | grammatical-analysis-of-pretrained-sentence | 1901.03438 | null | https://arxiv.org/abs/1901.03438v4 | https://arxiv.org/pdf/1901.03438v4.pdf | Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments | Recent work on evaluating grammatical knowledge in pretrained sentence encoders gives a fine-grained view of a small number of phenomena. We introduce a new analysis dataset that also has broad coverage of linguistic phenomena. We annotate the development set of the Corpus of Linguistic Acceptability (CoLA; Warstadt et... | ['Alex Warstadt', 'Samuel R. Bowman'] | 2019-01-11 | null | null | null | null | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 5.83602861e-02 5.00039399e-01 9.82967839e-02 -9.00247693e-01
-9.69556928e-01 -9.73796129e-01 8.44834328e-01 4.17126536e-01
-6.58325911e-01 6.83231235e-01 5.65877855e-01 -9.22292233e-01
-6.45173667e-03 -8.86515379e-01 -9.32346642e-01 -3.97213608e-01
-1.47161603e-01 5.56590319e-01 8.67806152e-02 -6.79099798... | [10.667143821716309, 9.37912654876709] |
e9f8a4c3-73da-4e71-a7af-620cf0ac95e3 | maximal-cliques-on-multi-frame-proposal-graph | 2301.12352 | null | https://arxiv.org/abs/2301.12352v1 | https://arxiv.org/pdf/2301.12352v1.pdf | Maximal Cliques on Multi-Frame Proposal Graph for Unsupervised Video Object Segmentation | Unsupervised Video Object Segmentation (UVOS) aims at discovering objects and tracking them through videos. For accurate UVOS, we observe if one can locate precise segment proposals on key frames, subsequent processes are much simpler. Hence, we propose to reason about key frame proposals using a graph built with the o... | ['Li Fuxin', 'Chanho Kim', 'Hung Nguyen', 'Jay Patravali', 'Jialin Yuan'] | 2023-01-29 | null | null | null | null | ['video-instance-segmentation', 'video-object-segmentation', 'video-semantic-segmentation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.87393421e-01 4.31837946e-01 -6.01385713e-01 -9.84347314e-02
-1.02568984e+00 -6.61112189e-01 4.21820104e-01 2.72842556e-01
-4.68025148e-01 5.19642353e-01 -2.67262012e-01 8.41645673e-02
9.68880206e-02 -5.28727889e-01 -1.03511786e+00 -6.01736009e-01
-2.72486662e-03 9.65016067e-01 1.29243350e+00 3.74555498... | [9.112608909606934, -0.18866419792175293] |
1c299687-ab4f-4562-b6bd-7c21ea23593a | lakebench-benchmarks-for-data-discovery-over | 2307.04217 | null | https://arxiv.org/abs/2307.04217v1 | https://arxiv.org/pdf/2307.04217v1.pdf | LakeBench: Benchmarks for Data Discovery over Data Lakes | Within enterprises, there is a growing need to intelligently navigate data lakes, specifically focusing on data discovery. Of particular importance to enterprises is the ability to find related tables in data repositories. These tables can be unionable, joinable, or subsets of each other. There is a dearth of benchmark... | ['Horst Samulowitz', 'Subhajit Chaudhury', 'Tejaswini Pedapati', 'Aamod Khatiwada', 'Harsha Kokel', 'Oktie Hassanzadeh', 'Ibrahim Abdelaziz', 'Julian Dolby', 'Kavitha Srinivas'] | 2023-07-09 | null | null | null | null | ['navigate'] | ['reasoning'] | [-5.33577204e-01 3.61640632e-01 -3.40942740e-01 -4.09227759e-01
-7.47225106e-01 -7.34296381e-01 5.82159877e-01 6.88773870e-01
-1.51865885e-01 7.21051276e-01 6.78873897e-01 -6.61433160e-01
-5.06525338e-01 -1.13073671e+00 -6.30543709e-01 5.28939515e-02
-2.66976327e-01 8.64805400e-01 2.56335199e-01 -3.62014055... | [9.380705833435059, 7.927925109863281] |
01682951-7fdb-4856-8c79-4d183c886bb9 | aero-audio-super-resolution-in-the-spectral | 2211.12232 | null | https://arxiv.org/abs/2211.12232v2 | https://arxiv.org/pdf/2211.12232v2.pdf | AERO: Audio Super Resolution in the Spectral Domain | We present AERO, a audio super-resolution model that processes speech and music signals in the spectral domain. AERO is based on an encoder-decoder architecture with U-Net like skip connections. We optimize the model using both time and frequency domain loss functions. Specifically, we consider a set of reconstruction ... | ['Yossi Adi', 'Or Tal', 'Moshe Mandel'] | 2022-11-22 | null | null | null | null | ['bandwidth-extension', 'audio-super-resolution', 'audio-super-resolution', 'bandwidth-extension'] | ['audio', 'audio', 'music', 'speech'] | [ 2.59396970e-01 -1.92464948e-01 -8.03909525e-02 -2.86671855e-02
-1.53196299e+00 -7.44127870e-01 3.72986257e-01 -1.80108413e-01
-1.99230790e-01 6.66754663e-01 5.20641208e-01 2.11153422e-02
9.42775141e-03 -5.32181025e-01 -7.06145167e-01 -5.56502223e-01
-1.90302283e-01 -2.67172545e-01 -7.07985312e-02 -2.71193355... | [15.430612564086914, 5.874926567077637] |
36cd9217-673c-4e9f-8c3e-567411c65760 | sparsestep-approximating-the-counting-norm | 1701.06967 | null | https://arxiv.org/abs/1701.06967v1 | https://arxiv.org/pdf/1701.06967v1.pdf | SparseStep: Approximating the Counting Norm for Sparse Regularization | The SparseStep algorithm is presented for the estimation of a sparse parameter vector in the linear regression problem. The algorithm works by adding an approximation of the exact counting norm as a constraint on the model parameters and iteratively strengthening this approximation to arrive at a sparse solution. Theor... | ['Andreas Alfons', 'Patrick J. F. Groenen', 'Gerrit J. J. van den Burg'] | 2017-01-24 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 2.04869494e-01 7.61276260e-02 -6.34042084e-01 -4.09820050e-01
-1.26461029e+00 -3.35611373e-01 2.60152817e-01 -1.99920207e-01
-2.76475072e-01 8.52937162e-01 1.76728249e-01 -2.87185818e-01
-2.94295460e-01 -4.41371761e-02 -7.81164050e-01 -9.15273666e-01
2.40252949e-02 4.61778134e-01 -2.65729815e-01 -6.92904294... | [7.047167778015137, 4.383917808532715] |
580059ad-6d0f-45b3-84fa-66fac51641d6 | efficient-modeling-of-morphing-wing-flight | 2110.01057 | null | https://arxiv.org/abs/2110.01057v1 | https://arxiv.org/pdf/2110.01057v1.pdf | Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules | Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very complex, particularly when the rules from insect flight dynamics (fast flapping dynamics and light wings) are not applicable. In these situations, widely used averaging techniques can fail quickly. The primary motivation is to find efficient models... | ['Alireza Ramezani', 'Deniz Erdogmus', 'Yunus Bicer', 'Paul Ghanem'] | 2021-10-03 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-3.62125903e-01 -5.84607542e-01 3.05829883e-01 4.22486454e-01
2.24022686e-01 -8.45177889e-01 4.56356227e-01 -4.45066094e-01
-2.56807536e-01 9.68930125e-01 -3.20481151e-01 -1.23217201e-03
-1.41703114e-01 -8.35126460e-01 -6.03754818e-01 -9.28307593e-01
-4.31046009e-01 2.96498269e-01 5.31425655e-01 -4.33319628... | [6.507774353027344, 3.576676845550537] |
545370b8-95d0-4ca9-adda-6153a60a56c0 | monte-carlo-for-protein-structures | 2307.02177 | null | https://arxiv.org/abs/2307.02177v2 | https://arxiv.org/pdf/2307.02177v2.pdf | Monte Carlo for Protein Structures | While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previo... | ['Sanne Abeln', 'K. Anton Feenstra', 'Jocelyne Vreede', 'Arriën Symon Rauh', 'Halima Mouhib', 'Maurits Dijkstra', 'Juami H. M. van Gils'] | 2023-07-05 | null | null | null | null | ['protein-structure-prediction'] | ['miscellaneous'] | [ 2.02359796e-01 2.51474939e-02 -2.54723337e-02 -6.42176121e-02
1.49375154e-02 -5.03930032e-01 2.15099126e-01 3.62849116e-01
-3.37558657e-01 1.15241814e+00 -4.51854199e-01 -5.49263239e-01
-1.07733496e-01 -5.37777662e-01 -5.53399265e-01 -1.29181147e+00
-1.32740125e-01 6.46969140e-01 4.31533545e-01 -7.18724191... | [4.739238262176514, 5.270280361175537] |
b6b07d2a-2770-49f7-b185-f3acb94bbd83 | flow-based-perturbation-for-cause-effect | null | null | https://scholar.google.com/citations?view_op=view_citation&hl=en&user=I1HeYbAAAAAJ&sortby=pubdate&citation_for_view=I1HeYbAAAAAJ:KlAtU1dfN6UC | https://dl.acm.org/doi/pdf/10.1145/3511808.3557326 | Flow-based Perturbation for Cause-effect Inference | A new causal discovery method is introduced to solve the bivariate causal discovery problem. The proposed algorithm leverages the expressive power of flow-based models and tries to learn the complex relationship between two variables. Algorithms have been developed to infer the causal direction according to empirical p... | ['Ping Li', 'Shaogang Ren'] | 2022-10-17 | null | null | null | proceedings-of-the-31st-acm-international | ['causal-discovery', 'causal-identification'] | ['knowledge-base', 'reasoning'] | [ 2.22942859e-01 1.69552624e-01 -8.36793065e-01 -3.78790647e-01
-3.44542444e-01 -2.27084458e-01 6.69738412e-01 4.10855114e-02
3.50898504e-01 1.26471305e+00 3.36611748e-01 -5.07892430e-01
-9.95244026e-01 -9.57216024e-01 -6.64321125e-01 -5.79339206e-01
-1.24163890e+00 1.43527076e-01 -1.84535280e-01 1.65031180... | [7.804073333740234, 5.323843955993652] |
8ede2d4c-0ee1-4129-9bfa-092751b5a539 | bb_twtr-at-semeval-2017-task-4-twitter | 1704.06125 | null | http://arxiv.org/abs/1704.06125v1 | http://arxiv.org/pdf/1704.06125v1.pdf | BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs | In this paper we describe our attempt at producing a state-of-the-art Twitter
sentiment classifier using Convolutional Neural Networks (CNNs) and Long Short
Term Memory (LSTMs) networks. Our system leverages a large amount of unlabeled
data to pre-train word embeddings. We then use a subset of the unlabeled data
to fin... | ['Mathieu Cliche'] | 2017-04-20 | null | null | null | semeval-2017-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.88061208e-01 4.44326140e-02 -3.37529600e-01 -8.34268153e-01
-7.02840328e-01 -6.05290830e-01 9.31248069e-01 3.14462781e-01
-1.12856364e+00 6.18081510e-01 5.61155677e-01 -4.70841795e-01
4.10416335e-01 -7.24367797e-01 -6.77613854e-01 -2.38803104e-01
1.50085194e-03 4.48220879e-01 1.23644672e-01 -6.67655468... | [10.804430961608887, 7.596992492675781] |
f43bb5b4-9de0-45ab-8043-61eb34492b63 | posegan-a-pose-to-image-translation-framework | 2006.12712 | null | https://arxiv.org/abs/2006.12712v1 | https://arxiv.org/pdf/2006.12712v1.pdf | PoseGAN: A Pose-to-Image Translation Framework for Camera Localization | Camera localization is a fundamental requirement in robotics and computer vision. This paper introduces a pose-to-image translation framework to tackle the camera localization problem. We present PoseGANs, a conditional generative adversarial networks (cGANs) based framework for the implementation of pose-to-image tran... | ['Qing Li', 'Guoping Qiu', 'Kanglin Liu'] | 2020-06-23 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [ 2.40815207e-01 9.74214897e-02 -6.06227629e-02 5.86434044e-02
-8.71161699e-01 -9.09386992e-01 7.44074404e-01 -6.41389608e-01
-3.29489261e-01 6.63318396e-01 -2.58345306e-01 -1.72519013e-01
2.88942665e-01 -7.67440438e-01 -1.36384356e+00 -9.54919696e-01
2.35396519e-01 4.16027367e-01 6.90948665e-02 -1.03633478... | [8.092283248901367, -2.231893539428711] |
aa122251-217a-4f83-81f1-e7cb12d055d8 | time-series-prediction-about-air-quality | 2111.11848 | null | https://arxiv.org/abs/2111.11848v1 | https://arxiv.org/pdf/2111.11848v1.pdf | Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping | This systematic mapping study investigates the use of Long short-term memory networks to predict time series data about air quality, trying to understand the reasons, characteristics and methods available in the scientific literature, identify gaps in the researched area and potential approaches that can be exploited o... | ['Vinicius F. S. Mota', 'Lucas L. S. Sachetti'] | 2021-11-22 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 1.14003226e-01 -1.50750458e-01 -6.22377634e-01 -2.96240479e-01
1.46774739e-01 -4.31650788e-01 3.32354188e-01 2.59648561e-01
-2.91355669e-01 5.68997920e-01 3.66838574e-01 -7.83348203e-01
-1.26922584e+00 -1.04883790e+00 -4.05498981e-01 -4.73149329e-01
-1.01181008e-01 -2.98921894e-02 -1.75388172e-01 6.11627810... | [6.350470542907715, 2.690047025680542] |
4dad19ba-3943-4d37-8a23-e3ef9cbd4552 | introducing-explicit-gaze-constraints-to-face | 2305.16138 | null | https://arxiv.org/abs/2305.16138v1 | https://arxiv.org/pdf/2305.16138v1.pdf | Introducing Explicit Gaze Constraints to Face Swapping | Face swapping combines one face's identity with another face's non-appearance attributes (expression, head pose, lighting) to generate a synthetic face. This technology is rapidly improving, but falls flat when reconstructing some attributes, particularly gaze. Image-based loss metrics that consider the full face do no... | ['Eakta Jain', 'Frederick Shic', 'Ethan Wilson'] | 2023-05-25 | null | null | null | null | ['deepfake-detection', 'face-swapping', 'eye-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.96637857e-01 4.08116996e-01 -1.48860306e-01 -7.42510617e-01
-4.61345792e-01 -5.50764620e-01 3.53324771e-01 -7.13364840e-01
1.81450173e-02 6.32592499e-01 1.71169683e-01 -1.02467194e-01
2.97289371e-01 -2.84003973e-01 -6.03814363e-01 -5.82439244e-01
1.32721722e-01 -4.71023023e-02 -3.73934925e-01 5.86752146... | [14.06353759765625, 0.027484333142638206] |
6dc4bba8-bab7-4b52-b83d-af534970ba22 | causal-neural-graph-collaborative-filtering | 2307.04384 | null | https://arxiv.org/abs/2307.04384v1 | https://arxiv.org/pdf/2307.04384v1.pdf | Causal Neural Graph Collaborative Filtering | Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF) models. One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings fo... | ['Guandong Xu', 'Wei Huang', 'Dianer Yu', 'Qian Li', 'Xiangmeng Wang'] | 2023-07-10 | null | null | null | null | ['graph-learning', 'representation-learning', 'graph-representation-learning', 'recommendation-systems', 'collaborative-filtering'] | ['graphs', 'methodology', 'methodology', 'miscellaneous', 'miscellaneous'] | [-3.61494541e-01 3.75541821e-02 -5.47395468e-01 -2.72542268e-01
2.24154890e-01 -4.12292808e-01 5.41648269e-01 5.57491779e-01
1.53730258e-01 3.11260551e-01 8.40186417e-01 -6.66835189e-01
-6.22214377e-01 -1.37633348e+00 -5.57463825e-01 -2.72465885e-01
-3.04956883e-01 1.91995576e-02 1.44659758e-01 -3.97714913... | [10.2056884765625, 5.635709762573242] |
1b4ccf4f-b3da-4683-b02b-6f88d0b2504d | improving-paraphrase-generation-models-with | null | null | https://openreview.net/forum?id=IYnDDdoHCyO | https://openreview.net/pdf?id=IYnDDdoHCyO | Improving Paraphrase Generation models with machine translation generated pre-training | Paraphrase generation is a fundamental and longstanding problem in the Natural Language Processing field. With the huge success of pre-trained transformers, the pre-train–fine-tune approach has become a standard choice. At the same time, popular task-agnostic pre-trainings usually require terabyte datasets and hundreds... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 1.78851727e-02 -4.64191556e-01 -1.45616114e-01 -3.63420665e-01
-9.12984550e-01 -7.66579986e-01 8.59972537e-01 3.67357075e-01
-7.15215921e-01 7.01959491e-01 1.60835788e-01 -2.77862251e-01
1.89859912e-01 -8.03419828e-01 -7.91880965e-01 -2.92743504e-01
4.93535459e-01 6.14810765e-01 1.92646414e-01 -5.00785172... | [11.398902893066406, 8.944530487060547] |
b57ba8a5-081f-4ff7-a43b-301c6cb36904 | habitat-matterport-3d-dataset-hm3d-1000-large | 2109.08238 | null | https://arxiv.org/abs/2109.08238v1 | https://arxiv.org/pdf/2109.08238v1.pdf | Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI | We present the Habitat-Matterport 3D (HM3D) dataset. HM3D is a large-scale dataset of 1,000 building-scale 3D reconstructions from a diverse set of real-world locations. Each scene in the dataset consists of a textured 3D mesh reconstruction of interiors such as multi-floor residences, stores, and other private indoor ... | ['Dhruv Batra', 'Yili Zhao', 'Manolis Savva', 'Angel X. Chang', 'Andrew Westbury', 'Wojciech Galuba', 'Eric Undersander', 'John Turner', 'Alex Clegg', 'Oleksandr Maksymets', 'Erik Wijmans', 'Aaron Gokaslan', 'Santhosh K. Ramakrishnan'] | 2021-09-16 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-2.60377049e-01 1.64384797e-01 3.03771704e-01 1.62964277e-02
-6.14459395e-01 -5.54764271e-01 6.59889281e-01 -9.82052833e-02
-2.73985863e-01 5.28482318e-01 3.49966794e-01 -3.69491220e-01
-1.16201341e-01 -1.24289000e+00 -1.07936943e+00 -4.31650281e-01
-4.70606923e-01 5.41949391e-01 2.45767817e-01 -6.35582745... | [4.629292011260986, 0.5935952663421631] |
c05afc99-ea9d-4c1f-929b-95ce59fac17a | prototypical-pseudo-label-denoising-and | 2101.10979 | null | https://arxiv.org/abs/2101.10979v2 | https://arxiv.org/pdf/2101.10979v2.pdf | Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation | Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on represe... | ['Fang Wen', 'Yong Wang', 'Dong Chen', 'Ting Zhang', 'Bo Zhang', 'Pan Zhang'] | 2021-01-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Prototypical_Pseudo_Label_Denoising_and_Target_Structure_Learning_for_Domain_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 2.81569153e-01 1.78562969e-01 -5.84345222e-01 -8.13349545e-01
-8.97047818e-01 -9.01968539e-01 3.42786074e-01 9.33324471e-02
-4.49358702e-01 6.25690460e-01 1.33093104e-01 2.03204438e-01
-3.44044459e-03 -3.96158516e-01 -6.34591520e-01 -7.29390383e-01
5.17349720e-01 8.14600766e-01 5.37715375e-01 2.22168267... | [9.703758239746094, 1.3946657180786133] |
ddc1f08b-8073-4ad0-a1b5-ee48f9a6a83c | mrf-zoom-a-fast-dictionary-searching | 1506.05393 | null | http://arxiv.org/abs/1506.05393v1 | http://arxiv.org/pdf/1506.05393v1.pdf | MRF-ZOOM: A Fast Dictionary Searching Algorithm for Magnetic Resonance Fingerprinting | Magnetic resonance fingerprinting (MRF) is a new technique for simultaneously
quantifying multiple MR parameters using one temporally resolved MR scan. But
its brute-force dictionary generating and searching (DGS) process causes a huge
disk space demand and computational burden, prohibiting it from a practical
multiple... | ['Ze Wang'] | 2015-06-17 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 1.97118744e-01 -4.33206946e-01 1.82315275e-01 -2.78170615e-01
-8.76334965e-01 -2.97730595e-01 9.23980996e-02 1.78521574e-01
-6.38739347e-01 6.89175427e-01 1.95978098e-02 -3.61235678e-01
-5.35435200e-01 -6.01744831e-01 -3.27653527e-01 -9.19092119e-01
-4.27751273e-01 6.20259702e-01 6.80293918e-01 -1.25916125... | [13.539961814880371, -2.424652576446533] |
4109893b-0960-496e-b7f8-955bd3c356e2 | interactive-video-object-segmentation-in-the | 1801.00269 | null | http://arxiv.org/abs/1801.00269v1 | http://arxiv.org/pdf/1801.00269v1.pdf | Interactive Video Object Segmentation in the Wild | In this paper we present our system for human-in-the-loop video object
segmentation. The backbone of our system is a method for one-shot video object
segmentation. While fast, this method requires an accurate pixel-level
segmentation of one (or several) frames as input. As manually annotating such a
segmentation is imp... | ['Arnaud Benard', 'Michael Gygli'] | 2017-12-31 | null | null | null | null | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 2.36342892e-01 -2.08860333e-03 -1.22139297e-01 -4.16039884e-01
-8.83313835e-01 -9.52799797e-01 -5.06694168e-02 1.66908950e-01
-7.28936553e-01 3.53756815e-01 -3.09221029e-01 -3.81741971e-01
4.80699748e-01 -5.61468124e-01 -9.55019057e-01 -1.38884440e-01
2.04039335e-01 5.73280632e-01 1.04352021e+00 1.39360324... | [9.250718116760254, -0.18268102407455444] |
6218c370-84a6-43c3-a5e4-eca3f4245eb0 | displacement-filed-calculation-of-large-scale | 2303.15868 | null | https://arxiv.org/abs/2303.15868v2 | https://arxiv.org/pdf/2303.15868v2.pdf | Displacement field calculation of large-scale structures using computer vision with physical constraints | Because of the advantages of easy deployment, low cost and non-contact, computer vision-based structural displacement acquisition technique has received wide attention and research in recent years. However, the displacement field acquisition of large-scale structures is a challenging topic due to the contradiction of c... | ['Shunlong Li', 'Hao Di', 'Fanzeng Meng', 'Yi Zhuo', 'Peng Zhong', 'Yapeng Guo'] | 2023-03-28 | null | null | null | null | ['template-matching', 'image-stitching'] | ['computer-vision', 'computer-vision'] | [ 2.61494875e-01 -3.10348481e-01 4.61508155e-01 1.13350123e-01
-5.95308244e-01 -2.12775722e-01 8.95255730e-02 -1.37404844e-01
-4.81846541e-01 6.58068419e-01 -3.54992598e-01 5.29716134e-01
-7.31588304e-01 -9.09894884e-01 -2.49667645e-01 -8.00316632e-01
2.35373855e-01 6.47651434e-01 7.87498653e-01 -2.92843848... | [9.148574829101562, -2.4101035594940186] |
aa3c33c6-512b-418a-9cfc-f92be284bf17 | a-study-on-cross-corpus-speech-emotion | 2201.03511 | null | https://arxiv.org/abs/2201.03511v1 | https://arxiv.org/pdf/2201.03511v1.pdf | A study on cross-corpus speech emotion recognition and data augmentation | Models that can handle a wide range of speakers and acoustic conditions are essential in speech emotion recognition (SER). Often, these models tend to show mixed results when presented with speakers or acoustic conditions that were not visible during training. This paper investigates the impact of cross-corpus data com... | ['Svetlana Stoyanchev', 'Simon Keizer', 'Rama Doddipatla', 'Norbert Braunschweiler'] | 2022-01-10 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [ 1.93925560e-01 1.09343499e-01 4.60792392e-01 -5.69253147e-01
-6.75461292e-01 -3.82487804e-01 7.59347320e-01 2.30014324e-01
-5.99873722e-01 5.14748216e-01 2.55206108e-01 2.56070551e-02
3.63717437e-01 9.26016942e-02 -2.31199205e-01 -5.49294889e-01
-1.19005568e-01 2.96446145e-01 3.64463739e-02 -5.53883076... | [13.734454154968262, 5.899652004241943] |
f90bf90d-51b7-4145-b83d-985f53c16120 | contrastive-predictive-autoencoders-for | 2305.12959 | null | https://arxiv.org/abs/2305.12959v1 | https://arxiv.org/pdf/2305.12959v1.pdf | Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning | We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal repre... | ['Gang Xiao', 'Zhiqiang Shen', 'Xiaoxiao Sheng'] | 2023-05-22 | null | null | null | null | ['colorization', 'gesture-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.19524729e-01 -4.18161780e-01 -3.23441088e-01 -5.30192435e-01
-7.58681774e-01 -4.69889313e-01 7.85333216e-01 -8.64718035e-02
-1.71841100e-01 3.95296395e-01 2.79208571e-01 6.54317513e-02
3.08831781e-01 -6.73718333e-01 -1.01013029e+00 -9.32649970e-01
-4.38681357e-02 3.81632417e-01 3.11881274e-01 -3.35361771... | [8.496264457702637, 0.14268068969249725] |
2aba6bed-5941-4db6-8a45-5fb5b49e4179 | reconstruction-driven-dynamic-refinement | 2304.04581 | null | https://arxiv.org/abs/2304.04581v1 | https://arxiv.org/pdf/2304.04581v1.pdf | Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation | Glaucoma is one of the leading causes of irreversible blindness. Segmentation of optic disc (OD) and optic cup (OC) on fundus images is a crucial step in glaucoma screening. Although many deep learning models have been constructed for this task, it remains challenging to train an OD/OC segmentation model that could be ... | ['Yong Xia', 'Yongsheng Pan', 'Ziyang Chen'] | 2023-04-10 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 3.55046839e-01 2.40668282e-01 -2.59587616e-02 -2.41476223e-01
-4.43563610e-01 -1.57311663e-01 1.27264321e-01 -5.63735485e-01
-3.95952970e-01 7.09757507e-01 2.55858421e-01 -3.46551776e-01
-8.29701312e-03 -5.65711796e-01 -4.87516642e-01 -8.50380599e-01
2.09960312e-01 1.06975742e-01 4.31484818e-01 -8.35903287... | [15.764628410339355, -3.944196939468384] |
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