paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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7a5e8152-d046-4793-b20d-b48b965116be | multilingual-paraphrase-generation-for | null | null | https://aclanthology.org/2021.nlp4convai-1.4 | https://aclanthology.org/2021.nlp4convai-1.4.pdf | Multilingual Paraphrase Generation For Bootstrapping New Features in Task-Oriented Dialog Systems | The lack of labeled training data for new features is a common problem in rapidly changing real-world dialog systems. As a solution, we propose a multilingual paraphrase generation model that can be used to generate novel utterances for a target feature and target language. The generated utterances can be used to augme... | ['Patrick Lehnen', 'Tobias Falke', 'Caglar Tirkaz', 'Subhadarshi Panda'] | null | null | null | null | emnlp-nlp4convai-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 8.71177763e-02 5.27070582e-01 -2.67107248e-01 -7.54712760e-01
-8.83334994e-01 -9.26534235e-01 9.59452987e-01 -4.80448678e-02
-6.78056955e-01 1.16600287e+00 6.65267110e-01 -3.45359623e-01
6.17741942e-01 -4.39741492e-01 -2.40116581e-01 4.97240238e-02
3.92220318e-01 1.06818092e+00 2.67149461e-03 -8.82888973... | [12.678009986877441, 8.090580940246582] |
fb212340-9e91-4a41-953e-9d4bf20b0483 | multiple-news-headlines-generation-using-page | null | null | https://aclanthology.org/W19-8612 | https://aclanthology.org/W19-8612.pdf | Multiple News Headlines Generation using Page Metadata | Multiple headlines of a newspaper article have an important role to express the content of the article accurately and concisely. A headline depends on the content and intent of their article. While a single headline expresses the whole corresponding article, each of multiple headlines expresses different information in... | ['Kango Iwama', 'Yoshinobu Kano'] | 2019-10-01 | null | null | null | ws-2019-10 | ['headline-generation'] | ['natural-language-processing'] | [ 1.23073123e-01 3.18724692e-01 -2.39377260e-01 -1.75685227e-01
-1.16613412e+00 -7.97721565e-01 1.11766887e+00 3.85676235e-01
-1.66400373e-01 1.17059517e+00 1.00428402e+00 -5.44623472e-02
1.96808502e-01 -8.44680786e-01 -9.15004909e-01 -1.55680865e-01
1.98736742e-01 4.24470961e-01 4.67751980e-01 -5.58222532... | [12.2688570022583, 9.310391426086426] |
ef9ab5ba-4369-4024-89a4-865ba42752d4 | sparsity-and-coefficient-permutation-based | 2305.12986 | null | https://arxiv.org/abs/2305.12986v1 | https://arxiv.org/pdf/2305.12986v1.pdf | Sparsity and Coefficient Permutation Based Two-Domain AMP for Image Block Compressed Sensing | The learned denoising-based approximate message passing (LDAMP) algorithm has attracted great attention for image compressed sensing (CS) tasks. However, it has two issues: first, its global measurement model severely restricts its applicability to high-dimensional images, and its block-based measurement method exhibit... | ['Shuhao Bi', 'Huake Wang', 'Xingsong Hou', 'Junhui Li'] | 2023-05-22 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 5.27319670e-01 -4.57582474e-01 8.07004049e-02 -1.33091792e-01
-7.90816963e-01 4.07005489e-01 2.96525955e-01 -1.94207072e-01
-1.52537182e-01 4.04915452e-01 5.02133548e-01 -3.27877253e-02
-2.10876137e-01 -7.78144598e-01 -6.93105876e-01 -1.04460859e+00
2.10435212e-01 -3.82382959e-01 3.12383592e-01 -1.93364203... | [11.224644660949707, -2.1292672157287598] |
0e3fbef7-37a1-4e95-abc4-3b4eaf02c48b | semantic-sentence-matching-via-interacting | null | null | https://aclanthology.org/2022.coling-1.78 | https://aclanthology.org/2022.coling-1.78.pdf | Semantic Sentence Matching via Interacting Syntax Graphs | Studies have shown that the sentence’s syntactic structures are important for semantic sentence matching. A typical approach is encoding each sentence’s syntactic structure into an embedding vector, which can be combined with other features to predict the final matching scores. Though successes have been observed, embe... | ['Ji-Rong Wen', 'Zhenhua Dong', 'Jun Xu', 'Chen Xu'] | null | null | null | null | coling-2022-10 | ['graph-matching'] | ['graphs'] | [ 4.41387534e-01 2.73860157e-01 -1.46898478e-01 -9.19099689e-01
-5.02223432e-01 -3.90452296e-01 3.14983666e-01 6.02803171e-01
-2.27553084e-01 1.23724237e-01 6.10266089e-01 -2.15892747e-01
-1.22911252e-01 -8.92447770e-01 -6.63049459e-01 -4.36730832e-01
2.01940238e-01 3.53255451e-01 1.18070375e-02 -2.98516512... | [10.900505065917969, 8.58779525756836] |
17744caa-043e-42df-a213-e293b50634ce | collaborative-spatial-temporal-modeling-for | 2105.06818 | null | https://arxiv.org/abs/2105.06818v1 | https://arxiv.org/pdf/2105.06818v1.pdf | Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation | Language-queried video actor segmentation aims to predict the pixel-level mask of the actor which performs the actions described by a natural language query in the target frames. Existing methods adopt 3D CNNs over the video clip as a general encoder to extract a mixed spatio-temporal feature for the target frame. Thou... | ['Fei Wang', 'Jizhong Han', 'Wenguan Wang', 'Guanbin Li', 'Zihan Ding', 'Si Liu', 'Shaofei Huang', 'Tianrui Hui'] | 2021-05-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Hui_Collaborative_Spatial-Temporal_Modeling_for_Language-Queried_Video_Actor_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Hui_Collaborative_Spatial-Temporal_Modeling_for_Language-Queried_Video_Actor_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 2.84324974e-01 -1.72024861e-01 -4.17277068e-01 -6.63289070e-01
-1.02485454e+00 -4.96821940e-01 5.17071247e-01 -1.83456942e-01
-5.70686281e-01 1.88813820e-01 2.81629086e-01 6.56828061e-02
3.41223538e-01 -5.16317844e-01 -6.06334925e-01 -6.49273872e-01
-6.35351613e-03 1.44118309e-01 4.41296846e-01 2.45787531... | [9.704131126403809, 0.5738435387611389] |
c32083dc-29a2-47aa-81c6-afe422d581d9 | prototypical-quadruplet-for-few-shot-class | 2211.02947 | null | https://arxiv.org/abs/2211.02947v3 | https://arxiv.org/pdf/2211.02947v3.pdf | Prototypical quadruplet for few-shot class incremental learning | Scarcity of data and incremental learning of new tasks pose two major bottlenecks for many modern computer vision algorithms. The phenomenon of catastrophic forgetting, i.e., the model's inability to classify previously learned data after training with new batches of data, is a major challenge. Conventional methods add... | ['Subhasis Chaudhuri', 'Biplab Banerjee', 'Sanchar Palit'] | 2022-11-05 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 5.70093393e-01 2.18437403e-01 -2.35736594e-02 -1.29315332e-01
-5.25581419e-01 -6.28731310e-01 6.74364865e-01 -2.50240296e-01
-3.23732793e-01 1.11920762e+00 2.03289818e-02 4.64076698e-02
8.29768777e-02 -7.13322699e-01 -9.78304327e-01 -9.40102279e-01
4.32910979e-01 3.73648107e-01 1.82739034e-01 2.95408852... | [9.854318618774414, 3.3774940967559814] |
12f4761b-9487-4d27-9cc0-3e11685050d2 | finding-directions-in-gan-s-latent-space-for | 2202.00046 | null | https://arxiv.org/abs/2202.00046v2 | https://arxiv.org/pdf/2202.00046v2.pdf | Finding Directions in GAN's Latent Space for Neural Face Reenactment | This paper is on face/head reenactment where the goal is to transfer the facial pose (3D head orientation and expression) of a target face to a source face. Previous methods focus on learning embedding networks for identity and pose disentanglement which proves to be a rather hard task, degrading the quality of the gen... | ['Georgios Tzimiropoulos', 'Vasileios Argyriou', 'Stella Bounareli'] | 2022-01-31 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 1.79418668e-01 5.79120040e-01 2.03812122e-01 -4.09810603e-01
-6.38041139e-01 -7.79999912e-01 9.01045859e-01 -9.42839980e-01
2.81049479e-02 6.98562443e-01 3.31674337e-01 2.08308443e-01
3.48583311e-01 -7.00070143e-01 -8.43837798e-01 -9.40816224e-01
2.37068459e-01 4.72029209e-01 -4.84342426e-01 -5.56680799... | [12.648117065429688, -0.20455901324748993] |
e51da4df-4415-43bd-aeeb-ba0dd7754f16 | interactive-concept-learning-for-uncovering | 2305.05094 | null | https://arxiv.org/abs/2305.05094v1 | https://arxiv.org/pdf/2305.05094v1.pdf | Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections | Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniques such as topic models, or by following a manual theme discovery process. In this paper, we expand the definition of a theme to account for... | ['Dan Goldwasser', 'Ming Yin', 'Lyle Ungar', 'Tunazzina Islam', 'Maria Leonor Pacheco'] | 2023-05-08 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [ 8.86580944e-02 4.46417630e-01 -7.89451599e-02 -1.88459218e-01
-1.00659525e+00 -9.71799135e-01 5.48202753e-01 6.81488216e-01
-3.81865799e-01 5.56135178e-01 4.41760123e-01 -4.18769956e-01
-1.47273615e-01 -7.47916698e-01 -1.73396438e-01 -3.22457731e-01
2.93868631e-01 4.60881829e-01 2.23536059e-01 4.40263934... | [10.395886421203613, 7.121559143066406] |
00c3a6f6-3d85-40ce-b3a4-72afdafda803 | shape-aware-multi-person-pose-estimation-from-1 | 2110.02330 | null | https://arxiv.org/abs/2110.02330v1 | https://arxiv.org/pdf/2110.02330v1.pdf | Shape-aware Multi-Person Pose Estimation from Multi-View Images | In this paper we contribute a simple yet effective approach for estimating 3D poses of multiple people from multi-view images. Our proposed coarse-to-fine pipeline first aggregates noisy 2D observations from multiple camera views into 3D space and then associates them into individual instances based on a confidence-awa... | ['Otmar Hilliges', 'Chen Guo', 'Xu Chen', 'Jie Song', 'Zijian Dong'] | 2021-10-05 | shape-aware-multi-person-pose-estimation-from | http://openaccess.thecvf.com//content/ICCV2021/html/Dong_Shape-Aware_Multi-Person_Pose_Estimation_From_Multi-View_Images_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dong_Shape-Aware_Multi-Person_Pose_Estimation_From_Multi-View_Images_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-2.59535402e-01 -4.66150194e-02 -1.18425198e-01 -4.56712276e-01
-1.26082492e+00 -6.98579252e-01 6.57245219e-01 -1.52450249e-01
-4.29643899e-01 5.69819570e-01 3.52573216e-01 5.03938556e-01
1.18984558e-01 -3.77137214e-01 -7.98260629e-01 -3.66960287e-01
-1.64757166e-02 9.56091821e-01 2.93883413e-01 6.84403256... | [7.0407209396362305, -0.9714621305465698] |
5ae2c6e5-adb7-44cb-912f-9669e38bb874 | small-signal-stability-analysis-of-numerical | 2201.09529 | null | https://arxiv.org/abs/2201.09529v1 | https://arxiv.org/pdf/2201.09529v1.pdf | Small-Signal Stability Analysis of Numerical Integration Methods | The paper provides a novel framework to study the accuracy and stability of numerical integration schemes when employed for the time domain simulation of power systems. A matrix pencil-based approach is adopted to evaluate the error between the dynamic modes of the power system and the modes of the approximated discret... | ['Federico Milano', 'Ioannis Dassios', 'Georgios Tzounas'] | 2022-01-24 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.34275773e-01 -1.01705730e-01 3.67650181e-01 3.64567667e-01
-2.29422748e-01 -7.48672426e-01 5.63163877e-01 1.02647558e-01
-6.56043515e-02 1.14810705e+00 -5.96399665e-01 -6.41854465e-01
-7.68151939e-01 -6.03930771e-01 9.12757963e-02 -1.11883318e+00
-4.88917112e-01 3.40623438e-01 -4.40397449e-02 -6.90939844... | [5.724118232727051, 2.7604146003723145] |
79f3975d-a80b-450a-ae9c-d74d2c7c58e8 | evaluation-of-various-open-set-medical | 2110.10888 | null | https://arxiv.org/abs/2110.10888v1 | https://arxiv.org/pdf/2110.10888v1.pdf | Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks | The current generation of deep neural networks has achieved close-to-human results on "closed-set" image recognition; that is, the classes being evaluated overlap with the training classes. Many recent methods attempt to address the importance of the unknown, which are termed "open-set" recognition algorithms, try to r... | ['Xin Wang', 'ZongYuan Ge'] | 2021-10-21 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.39270794e-01 7.67547607e-01 -6.61846399e-02 -5.37318647e-01
-4.94646668e-01 -6.12741828e-01 5.61168909e-01 2.30012298e-01
-4.22480464e-01 8.56209457e-01 6.67196959e-02 -4.58104998e-01
-7.10291624e-01 -6.19289041e-01 -5.03168643e-01 -6.27880692e-01
1.33183271e-01 9.85768974e-01 -1.16080977e-01 -1.68720067... | [15.031068801879883, -2.370415210723877] |
d55f8374-50e5-4096-bfb3-66fc65a1baed | pivotal-tuning-for-latent-based-editing-of | 2106.05744 | null | https://arxiv.org/abs/2106.05744v1 | https://arxiv.org/pdf/2106.05744v1.pdf | Pivotal Tuning for Latent-based Editing of Real Images | Recently, a surge of advanced facial editing techniques have been proposed that leverage the generative power of a pre-trained StyleGAN. To successfully edit an image this way, one must first project (or invert) the image into the pre-trained generator's domain. As it turns out, however, StyleGAN's latent space induces... | ['Daniel Cohen-Or', 'Amit H. Bermano', 'Ron Mokady', 'Daniel Roich'] | 2021-06-10 | null | null | null | null | ['facial-editing'] | ['computer-vision'] | [ 6.41086102e-01 5.10125756e-01 2.02712640e-01 -4.04315412e-01
-3.49443704e-01 -8.81183088e-01 7.06883729e-01 -4.66009974e-01
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4.00994956e-01 -7.65180349e-01 -8.26213956e-01 -7.15147793e-01
2.11509869e-01 1.32622808e-01 -1.31938949e-01 -4.41090494... | [12.506314277648926, -0.21218331158161163] |
156b372a-fdb0-4bc7-b826-dba2750e1ad6 | rotation-equivariant-vector-field-networks | 1612.09346 | null | http://arxiv.org/abs/1612.09346v3 | http://arxiv.org/pdf/1612.09346v3.pdf | Rotation equivariant vector field networks | In many computer vision tasks, we expect a particular behavior of the output
with respect to rotations of the input image. If this relationship is
explicitly encoded, instead of treated as any other variation, the complexity
of the problem is decreased, leading to a reduction in the size of the required
model. In this ... | ['Devis Tuia', 'Nikos Komodakis', 'Michele Volpi', 'Diego Marcos'] | 2016-12-29 | rotation-equivariant-vector-field-networks-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Marcos_Rotation_Equivariant_Vector_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Marcos_Rotation_Equivariant_Vector_ICCV_2017_paper.pdf | iccv-2017-10 | ['patch-matching', 'colorectal-gland-segmentation', 'breast-tumour-classification', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical', 'medical', 'medical'] | [ 3.40581357e-01 1.63197562e-01 1.28516674e-01 -6.18483365e-01
2.93740015e-02 -6.65259898e-01 4.60679263e-01 -6.19479492e-02
-7.25351751e-01 4.35029179e-01 6.95498951e-04 -2.74887949e-01
-3.22314888e-01 -8.13528299e-01 -1.00307190e+00 -7.37223983e-01
-1.52925059e-01 1.01106830e-01 2.03671187e-01 -3.92743140... | [9.146623611450195, 2.250411033630371] |
bbd397a9-feac-4271-95e2-c5c9d96cacf4 | distilling-causal-effect-from-miscellaneous | 2210.03980 | null | https://arxiv.org/abs/2210.03980v1 | https://arxiv.org/pdf/2210.03980v1.pdf | Distilling Causal Effect from Miscellaneous Other-Class for Continual Named Entity Recognition | Continual Learning for Named Entity Recognition (CL-NER) aims to learn a growing number of entity types over time from a stream of data. However, simply learning Other-Class in the same way as new entity types amplifies the catastrophic forgetting and leads to a substantial performance drop. The main cause behind this ... | ['Qianli Ma', 'Haibin Chen', 'Zhanxian Liang', 'Junhao Zheng'] | 2022-10-08 | null | null | null | null | ['miscellaneous', 'continual-named-entity-recognition', 'fg-1-pg-1'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-1.56430408e-01 1.10535182e-01 -4.20367867e-01 -3.51572990e-01
-3.80589783e-01 -2.47523561e-01 3.77908528e-01 4.74220812e-01
-6.45759284e-01 1.07565415e+00 3.55401486e-01 4.48166542e-02
-1.04022190e-01 -9.86902118e-01 -1.08706653e+00 -6.15097821e-01
-5.45277167e-03 3.55253965e-01 5.15434623e-01 5.07568941... | [9.53813362121582, 9.049711227416992] |
9b0410f1-62a0-4955-8516-aeccb8c7d4ee | associative-learning-for-network-embedding | 2208.14376 | null | https://arxiv.org/abs/2208.14376v1 | https://arxiv.org/pdf/2208.14376v1.pdf | Associative Learning for Network Embedding | The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding meth... | ['Mohammed J. Zaki', 'Dmitry Krotov', 'Yuchen Liang'] | 2022-08-30 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.79717019e-01 3.94788116e-01 -2.07504719e-01 -1.44968361e-01
3.73673499e-01 -3.69100273e-01 5.99454224e-01 1.48712829e-01
-2.64777064e-01 3.32452118e-01 5.36958277e-01 -1.10083684e-01
-4.87871498e-01 -1.10179615e+00 -7.54838347e-01 -7.87802875e-01
-5.38835287e-01 4.47651088e-01 5.97732179e-02 -2.83866465... | [7.1599345207214355, 6.200343132019043] |
d7c1e9e0-cf18-4884-968c-ef54f65e4385 | chartreader-a-unified-framework-for-chart | 2304.02173 | null | https://arxiv.org/abs/2304.02173v1 | https://arxiv.org/pdf/2304.02173v1.pdf | ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules | Charts are a powerful tool for visually conveying complex data, but their comprehension poses a challenge due to the diverse chart types and intricate components. Existing chart comprehension methods suffer from either heuristic rules or an over-reliance on OCR systems, resulting in suboptimal performance. To address t... | ['Alexander G. Hauptmann', 'Teruko Mitamura', 'Jingdong Sun', 'SiYao Li', 'Qi Dai', 'Zhi-Qi Cheng'] | 2023-04-05 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 1.59157664e-01 -2.45462388e-01 -3.53639238e-02 -3.91445190e-01
-1.06953990e+00 -9.49473619e-01 3.83032769e-01 5.41807294e-01
7.89319053e-02 2.48847321e-01 3.32009405e-01 -8.50943863e-01
2.99111992e-01 -5.20217299e-01 -6.53604627e-01 -2.87748128e-01
2.93055326e-01 1.38298482e-01 7.43184164e-02 -1.31359883... | [11.31597900390625, 2.1517281532287598] |
4fed22e3-aaa2-42d0-9b54-30b4659ec998 | cross-lingual-inference-with-a-chinese-1 | 2203.06264 | null | https://arxiv.org/abs/2203.06264v1 | https://arxiv.org/pdf/2203.06264v1.pdf | Cross-lingual Inference with A Chinese Entailment Graph | Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples. In this paper, we present the first pipeline for building Chinese entailment graphs, which involves a novel high-recall open re... | ['Mark Steedman', 'Liane Guillou', 'Mohammad Javad Hosseini', 'Sabine Weber', 'Tianyi Li'] | 2022-03-11 | null | https://aclanthology.org/2022.findings-acl.96 | https://aclanthology.org/2022.findings-acl.96.pdf | findings-acl-2022-5 | ['entity-typing'] | ['natural-language-processing'] | [ 1.13787934e-01 7.42083788e-01 -2.82577783e-01 -3.63099217e-01
-1.12659955e+00 -8.25882077e-01 6.10898018e-01 6.07623100e-01
-3.37956429e-01 9.17356730e-01 4.00576621e-01 -8.63639951e-01
-2.25597948e-01 -8.29470754e-01 -9.99532223e-01 2.44335175e-01
-1.12604193e-01 8.73978257e-01 4.65577245e-01 -5.28502941... | [9.65932846069336, 8.675687789916992] |
4ec25850-dc54-4d2f-9249-dfd395c963fa | monoscene-monocular-3d-semantic-scene | 2112.00726 | null | https://arxiv.org/abs/2112.00726v2 | https://arxiv.org/pdf/2112.00726v2.pdf | MonoScene: Monocular 3D Semantic Scene Completion | MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its se... | ['Raoul de Charette', 'Anh-Quan Cao'] | 2021-12-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cao_MonoScene_Monocular_3D_Semantic_Scene_Completion_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cao_MonoScene_Monocular_3D_Semantic_Scene_Completion_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-semantic-scene-completion', 'single-view-3d-reconstruction', '3d-semantic-scene-completion-from-a-single', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.76984775e-01 2.20433682e-01 2.60676622e-01 -5.30877113e-01
-5.26928663e-01 -7.39499390e-01 6.77811027e-01 -2.43179649e-01
-2.95377746e-02 3.21493506e-01 3.50082815e-01 1.04218699e-01
-1.08615167e-01 -6.10451877e-01 -9.11092281e-01 -4.31474328e-01
4.74223226e-01 2.53125936e-01 1.97576806e-01 -1.53832197... | [8.696176528930664, -2.899186372756958] |
3c6136ca-d481-4bd2-be1e-6e1159741a43 | instance-aware-semantic-segmentation-via | 1512.04412 | null | http://arxiv.org/abs/1512.04412v1 | http://arxiv.org/pdf/1512.04412v1.pdf | Instance-aware Semantic Segmentation via Multi-task Network Cascades | Semantic segmentation research has recently witnessed rapid progress, but
many leading methods are unable to identify object instances. In this paper, we
present Multi-task Network Cascades for instance-aware semantic segmentation.
Our model consists of three networks, respectively differentiating instances,
estimating... | ['Jian Sun', 'Kaiming He', 'Jifeng Dai'] | 2015-12-14 | instance-aware-semantic-segmentation-via-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Dai_Instance-Aware_Semantic_Segmentation_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Dai_Instance-Aware_Semantic_Segmentation_CVPR_2016_paper.pdf | cvpr-2016-6 | ['multi-human-parsing'] | ['computer-vision'] | [ 4.89465475e-01 1.60950065e-01 -1.17150001e-01 -5.90695083e-01
-1.06054544e+00 -7.82244980e-01 4.04532373e-01 -1.55918822e-01
-7.33670294e-01 3.57495070e-01 -2.73277998e-01 -2.86066592e-01
3.87824655e-01 -5.32544315e-01 -1.02204168e+00 -5.11889815e-01
1.73429132e-01 5.16597509e-01 7.35851288e-01 -1.30035639... | [9.524362564086914, 0.3577296733856201] |
b75a927b-f060-4ace-b202-c208e043c11c | deep-attributed-graph-clustering-with-self | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0893608021002756 | https://www.sciencedirect.com/science/article/abs/pii/S0893608021002756 | Deep attributed graph clustering with self-separation regularization and parameter-free cluster estimation | Detecting clusters over attributed graphs is a fundamental task in the graph analysis field. The goal is
to partition nodes into dense clusters based on both their attributes and structures. Modern graph
neural networks provide facilitation to jointly capture the above information in attributed graphs
with a feature... | ['Minglong Lei', 'Ye Liang', 'Junzhong Ji'] | 2021-07-15 | null | null | null | neural-networks-2021-7 | ['graph-clustering'] | ['graphs'] | [-1.40686855e-01 2.40606248e-01 -2.25804970e-01 -7.04386950e-01
-2.52188802e-01 -3.28898579e-01 4.27771568e-01 5.46272993e-01
-1.71965957e-01 -3.41543742e-02 2.70049311e-02 6.01804927e-02
-2.40864724e-01 -8.53284478e-01 -6.73893273e-01 -8.02046955e-01
-5.53032815e-01 7.11526930e-01 -1.02576226e-01 1.98106706... | [7.386258602142334, 5.954362392425537] |
97f42ff6-c16f-4ada-a614-aa8b8fef9e99 | multi-predictor-fusion-combining-learning | 2307.01408 | null | https://arxiv.org/abs/2307.01408v1 | https://arxiv.org/pdf/2307.01408v1.pdf | Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors | Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn ... | ['Marco Pavone', 'Apoorva Sharma', 'Sushant Veer'] | 2023-07-03 | null | null | null | null | ['trajectory-prediction', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision'] | [-3.49325031e-01 2.09821954e-01 -7.45272338e-01 -3.94496709e-01
-7.66445577e-01 -3.91982734e-01 1.14786696e+00 1.36252940e-01
-1.99460432e-01 8.59555364e-01 2.03557551e-01 -5.00704825e-01
-4.16676164e-01 -7.81720817e-01 -8.47550392e-01 -6.61989331e-01
-1.90586999e-01 9.58371818e-01 7.82531321e-01 -2.70063847... | [5.725086212158203, 1.0311616659164429] |
b033c99e-1c92-48e3-8e1a-ddabe503e336 | domain-adaptive-semantic-segmentation-with-1 | 2110.05170 | null | https://arxiv.org/abs/2110.05170v3 | https://arxiv.org/pdf/2110.05170v3.pdf | Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization | Unsupervised domain adaptation (UDA) for semantic segmentation has been well-studied in recent years. However, most existing works largely neglect the local regional consistency across different domains and are less robust to changes in outdoor environments. In this paper, we propose a novel and fully end-to-end traina... | ['Lizhuang Ma', 'Ran Yi', 'Xuequan Lu', 'Chuyun Zhuang', 'Qianyu Zhou'] | 2021-10-11 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [-3.33931856e-02 -2.51074135e-01 -6.93248883e-02 -6.11304224e-01
-8.57422531e-01 -3.34150136e-01 2.47457013e-01 -2.38960698e-01
-6.02510214e-01 6.18404150e-01 -2.54215933e-02 1.79144755e-01
-1.60072371e-01 -4.49452370e-01 -7.53623426e-01 -9.71384466e-01
4.22580272e-01 3.39221954e-01 6.54932439e-01 -3.91885675... | [9.65636157989502, 1.3744609355926514] |
144953cb-429e-436a-99c3-8a968a9a9f45 | video-probabilistic-diffusion-models-in | 2302.07685 | null | https://arxiv.org/abs/2302.07685v2 | https://arxiv.org/pdf/2302.07685v2.pdf | Video Probabilistic Diffusion Models in Projected Latent Space | Despite the remarkable progress in deep generative models, synthesizing high-resolution and temporally coherent videos still remains a challenge due to their high-dimensionality and complex temporal dynamics along with large spatial variations. Recent works on diffusion models have shown their potential to solve this c... | ['Jinwoo Shin', 'Subin Kim', 'Kihyuk Sohn', 'Sihyun Yu'] | 2023-02-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yu_Video_Probabilistic_Diffusion_Models_in_Projected_Latent_Space_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yu_Video_Probabilistic_Diffusion_Models_in_Projected_Latent_Space_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-generation'] | ['computer-vision'] | [-6.93147108e-02 -3.17758560e-01 -9.64249820e-02 1.71018466e-01
-6.53456926e-01 -3.76814216e-01 8.99748385e-01 -1.08468032e+00
8.89975727e-02 8.33046198e-01 6.32721186e-01 1.02335811e-01
1.54178709e-01 -8.15315545e-01 -9.44560051e-01 -1.20965922e+00
1.05062090e-01 2.98074961e-01 1.46584809e-01 1.58185214... | [10.839008331298828, -0.621537983417511] |
c1869020-14a9-40ff-9dbc-2c323ae55b57 | eocsa-predicting-prognosis-of-epithelial | 2210.05258 | null | https://arxiv.org/abs/2210.05258v1 | https://arxiv.org/pdf/2210.05258v1.pdf | EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images | Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advi... | ['Leyi Wei', 'Xiuting Li', 'Changming Sun', 'Ran Su', 'Tianling Liu'] | 2022-10-11 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.35146797e-01 -7.40607232e-02 -4.04409856e-01 -1.51380897e-01
-7.28726506e-01 -1.92520488e-02 2.24692136e-01 4.55381960e-01
-4.17832166e-01 7.08441138e-01 8.46050233e-02 -2.68118888e-01
-3.19786370e-01 -7.14968979e-01 -3.29841554e-01 -1.13335323e+00
-3.46895367e-01 3.96455735e-01 -1.99376479e-01 -1.51666403... | [15.147439002990723, -2.944864273071289] |
df9d9da7-1f0b-40cd-80f2-ab5a75689c5b | fast-as-chita-neural-network-pruning-with | 2302.14623 | null | https://arxiv.org/abs/2302.14623v1 | https://arxiv.org/pdf/2302.14623v1.pdf | Fast as CHITA: Neural Network Pruning with Combinatorial Optimization | The sheer size of modern neural networks makes model serving a serious computational challenge. A popular class of compression techniques overcomes this challenge by pruning or sparsifying the weights of pretrained networks. While useful, these techniques often face serious tradeoffs between computational requirements ... | ['Rahul Mazumder', 'Zhe Zhao', 'Natalia Ponomareva', 'Hussein Hazimeh', 'Xiang Meng', 'Wenyu Chen', 'Riade Benbaki'] | 2023-02-28 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.03315902e-01 3.30117308e-02 -3.11943591e-01 -5.73571682e-01
-5.16433358e-01 5.26218899e-02 6.58213198e-02 1.98816881e-01
-8.74549210e-01 6.38652503e-01 4.05072607e-03 -2.09933102e-01
-4.41454530e-01 -6.70278788e-01 -7.45913029e-01 -4.51009661e-01
-3.74744087e-02 4.74816352e-01 2.02857807e-01 -3.50487866... | [8.577067375183105, 3.2441022396087646] |
e4ac344b-418c-416a-b93d-e13924e1888d | a-large-scale-analysis-of-mixed-initiative-in | 2104.07096 | null | https://arxiv.org/abs/2104.07096v2 | https://arxiv.org/pdf/2104.07096v2.pdf | A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search | Conversational search is a relatively young area of research that aims at automating an information-seeking dialogue. In this paper we help to position it with respect to other research areas within conversational Artificial Intelligence (AI) by analysing the structural properties of an information-seeking dialogue. To... | ['Maarten de Rijke', 'Evangelos Kanoulas', 'Svitlana Vakulenko'] | 2021-04-14 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 4.69209641e-01 9.85586524e-01 -1.77274734e-01 -4.71691221e-01
-7.96126485e-01 -9.51637805e-01 1.44965851e+00 3.82947475e-01
-3.29872042e-01 6.80960178e-01 1.17461467e+00 -6.64065540e-01
-4.29007441e-01 -2.02830032e-01 3.33909333e-01 -2.73093402e-01
1.02447189e-01 1.02955794e+00 1.21714007e-02 -8.08270335... | [12.383711814880371, 7.906962871551514] |
39f7c223-adf5-4d00-bf04-7d7f15e6e7af | a-generative-language-model-for-few-shot-1 | 2204.05356 | null | https://arxiv.org/abs/2204.05356v1 | https://arxiv.org/pdf/2204.05356v1.pdf | A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis | Sentiment analysis is an important task in natural language processing. In recent works, pre-trained language models are often used to achieve state-of-the-art results, especially when training data is scarce. It is common to fine-tune on the downstream task, usually by adding task-specific layers on top of the model. ... | ['Caiming Xiong', 'Wenhao Liu', 'Ehsan Hosseini-Asl'] | 2022-04-11 | null | https://aclanthology.org/2022.findings-naacl.58 | https://aclanthology.org/2022.findings-naacl.58.pdf | findings-naacl-2022-7 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 5.16275644e-01 1.34545028e-01 -1.68392107e-01 -4.59625870e-01
-1.12622702e+00 -5.69730818e-01 8.98379922e-01 -7.26127401e-02
-3.28728050e-01 6.37411892e-01 3.41798037e-01 -1.85897678e-01
3.27652037e-01 -6.95236087e-01 -5.06664932e-01 -7.80047596e-01
4.23829854e-01 4.89251047e-01 5.82879118e-04 -6.24730527... | [11.51556396484375, 6.721602439880371] |
fe8a2f10-ecdb-4d25-a2a9-a7226c4d9bc6 | dynamic-spatio-temporal-specialization | 2209.01425 | null | https://arxiv.org/abs/2209.01425v1 | https://arxiv.org/pdf/2209.01425v1.pdf | Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition | The goal of fine-grained action recognition is to successfully discriminate between action categories with subtle differences. To tackle this, we derive inspiration from the human visual system which contains specialized regions in the brain that are dedicated towards handling specific tasks. We design a novel Dynamic ... | ['Jun Liu', 'Jinghua Wang', 'Anran Wang', 'Hossein Rahmani', 'Qiuhong Ke', 'Lin Geng Foo', 'Tianjiao Li'] | 2022-09-03 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 3.41209978e-01 -4.01570231e-01 -3.19027126e-01 -6.30084395e-01
-3.84874016e-01 -5.22290647e-01 6.31608129e-01 -2.36013159e-01
-5.63471258e-01 4.62696224e-01 5.04104435e-01 2.68051296e-01
-2.01328292e-01 -5.75751066e-01 -7.12442517e-01 -7.84914970e-01
-1.50952250e-01 1.56792730e-01 6.66593313e-01 1.78836230... | [8.42962646484375, 0.6623556613922119] |
96ce757f-ba81-4fbc-9113-c5bc0bfbee49 | single-image-haze-removal-using-dark-channel | null | null | https://ieeexplore.ieee.org/abstract/document/5567108/authors#authors | http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.672.3815&rep=rep1&type=pdf | Single Image Haze Removal Using Dark Channel Prior | In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is ver... | ['Jian Sun', 'Xiaoou Tang', 'Kaiming He'] | 2010-09-09 | null | null | null | ieee-transactions-on-pattern-analysis-and-10 | ['single-image-haze-removal'] | ['computer-vision'] | [ 2.73165017e-01 -3.66558224e-01 7.28233933e-01 -3.15561146e-01
-3.63991559e-01 -1.20314680e-01 1.72693968e-01 -4.88499403e-01
-1.74020752e-01 8.63182664e-01 -1.22283511e-01 -5.61630800e-02
1.69083089e-01 -1.05642486e+00 -5.54066122e-01 -1.41768658e+00
9.11072269e-02 -1.47619233e-01 6.70836270e-01 -4.49287146... | [10.864924430847168, -3.178379774093628] |
72f4b9f0-1e1d-4d2b-ab70-5599e967b29a | samu-xlsr-semantically-aligned-multimodal | 2205.08180 | null | https://arxiv.org/abs/2205.08180v1 | https://arxiv.org/pdf/2205.08180v1.pdf | SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation | We propose the SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation learning framework. Unlike previous works on speech representation learning, which learns multilingual contextual speech embedding at the resolution of an acoustic frame (10-20ms), this work focuses on learning... | ['James Glass', 'Antoine Laurent', 'Sameer Khurana'] | 2022-05-17 | null | null | null | null | ['speech-to-text-translation', 'speech-to-speech-translation'] | ['natural-language-processing', 'speech'] | [ 3.24702919e-01 1.48950994e-01 -3.15041274e-01 -4.60276991e-01
-2.01901817e+00 -5.62534451e-01 8.98683488e-01 6.91009983e-02
-4.84039307e-01 3.92643541e-01 6.29213274e-01 -7.73869634e-01
2.33664826e-01 -2.85664052e-01 -9.79194820e-01 -3.05429369e-01
2.91979045e-01 6.12192154e-01 -1.77678823e-01 -2.94955283... | [14.420610427856445, 7.078566551208496] |
e02e1a77-a336-4db1-9ef8-0e1d168a724b | collective-relevance-labeling-for-passage | 2205.03273 | null | https://arxiv.org/abs/2205.03273v2 | https://arxiv.org/pdf/2205.03273v2.pdf | Collective Relevance Labeling for Passage Retrieval | Deep learning for Information Retrieval (IR) requires a large amount of high-quality query-document relevance labels, but such labels are inherently sparse. Label smoothing redistributes some observed probability mass over unobserved instances, often uniformly, uninformed of the true distribution. In contrast, we propo... | ['Minsoo Kim', 'Seung-won Hwang', 'Jihyuk Kim'] | 2022-05-06 | null | https://aclanthology.org/2022.naacl-main.305 | https://aclanthology.org/2022.naacl-main.305.pdf | naacl-2022-7 | ['passage-retrieval'] | ['natural-language-processing'] | [-1.48372129e-01 5.02703190e-01 -7.72169054e-01 -5.46071887e-01
-1.79575062e+00 -6.57390058e-01 6.46299481e-01 1.80428639e-01
-5.32745063e-01 9.04396296e-01 3.22774202e-01 -3.98190945e-01
-3.91407281e-01 -6.26663387e-01 -8.48554075e-01 -5.08196592e-01
-4.57067154e-02 1.17209244e+00 3.17018293e-02 1.87976956... | [11.398509979248047, 7.669262409210205] |
64cf5a11-e625-48ff-aeea-d837a9a18e6f | radarformer-lightweight-and-accurate-real | 2304.08447 | null | https://arxiv.org/abs/2304.08447v1 | https://arxiv.org/pdf/2304.08447v1.pdf | RadarFormer: Lightweight and Accurate Real-Time Radar Object Detection Model | The performance of perception systems developed for autonomous driving vehicles has seen significant improvements over the last few years. This improvement was associated with the increasing use of LiDAR sensors and point cloud data to facilitate the task of object detection and recognition in autonomous driving. Howev... | ['Hisham Cholakkal', 'Jean Lahoud', 'Yahia Dalbah'] | 2023-04-17 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 8.23619068e-02 -4.12587076e-01 9.27420631e-02 -7.06310153e-01
-6.87355220e-01 -3.28717470e-01 8.47386897e-01 -1.26136959e-01
-7.54385948e-01 4.16063279e-01 -5.14154673e-01 -4.68077093e-01
-1.10180311e-01 -1.02087057e+00 -6.30248904e-01 -7.41012752e-01
-4.09284653e-03 5.70205986e-01 4.52723652e-01 -3.98906648... | [7.860193252563477, -1.4780597686767578] |
2b7a84df-6802-4939-a1ed-b07e57652e48 | transedrp-dual-transformer-model-with-edge | 2210.17401 | null | https://arxiv.org/abs/2210.17401v1 | https://arxiv.org/pdf/2210.17401v1.pdf | TransEDRP: Dual Transformer model with Edge Emdedded for Drug Respond Prediction | GNN-based methods have achieved excellent results as a mainstream task in drug response prediction tasks in recent years. Traditional GNN methods use only the atoms in a drug molecule as nodes to obtain the representation of the molecular graph through node information passing, whereas the method using the transformer ... | ['Hu Wenbin', 'Li Kun'] | 2022-10-23 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 1.13987893e-01 -1.11382738e-01 -4.53782141e-01 -6.15485348e-02
-9.39868316e-02 -4.27055508e-01 3.48350585e-01 3.50314856e-01
-1.49196789e-01 8.43633771e-01 2.86288559e-01 -3.28820229e-01
3.27487811e-02 -1.04620409e+00 -9.80019212e-01 -9.71391857e-01
1.15821369e-01 1.95487961e-01 -1.10575026e-02 -3.17615777... | [5.149643898010254, 5.903248310089111] |
389bd6e5-cdfc-4fa2-ab96-8b868be9d67b | bert4eth-a-pre-trained-transformer-for | 2303.18138 | null | https://arxiv.org/abs/2303.18138v1 | https://arxiv.org/pdf/2303.18138v1.pdf | BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection | As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repet... | ['Ling Liu', 'Bingsheng He', 'Shengliang Lu', 'Bingqiao Luo', 'Zhen Zhang', 'Sihao Hu'] | 2023-03-29 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-9.16881338e-02 -3.19944024e-02 -3.33733171e-01 -2.87284464e-01
-3.95284712e-01 -8.76370966e-01 5.31696439e-01 2.76597887e-01
-1.82505220e-01 5.79838634e-01 1.84391811e-01 -8.29856277e-01
5.52347414e-02 -8.77133965e-01 -4.31587994e-01 -1.18615776e-01
-3.16863954e-01 6.65023744e-01 -7.26154000e-02 -2.81992942... | [6.748358726501465, 6.551349639892578] |
7e80f694-dac6-45fc-bdc9-860f564bd269 | adversary-aware-continual-learning | 2304.14483 | null | https://arxiv.org/abs/2304.14483v1 | https://arxiv.org/pdf/2304.14483v1.pdf | Adversary Aware Continual Learning | Class incremental learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, it has been shown that such approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent ... | ['Robi Polikar', 'Muhammad Umer'] | 2023-04-27 | null | null | null | null | ['class-incremental-learning', 'incremental-learning', 'misinformation'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 5.06304920e-01 1.35746866e-01 -8.85466039e-02 7.06259459e-02
-4.54741955e-01 -1.44243383e+00 7.62943923e-01 3.62581521e-01
-6.05081856e-01 8.17163348e-01 -4.41460967e-01 -7.57572651e-01
-2.03684375e-01 -9.89313126e-01 -1.36713016e+00 -8.88695061e-01
-4.19632941e-01 1.48982301e-01 4.77617174e-01 -2.98314691... | [5.7422099113464355, 7.525333404541016] |
ee5ea0ba-5778-441a-9889-f702ce85cd20 | design-based-conformal-prediction | 2303.01422 | null | https://arxiv.org/abs/2303.01422v1 | https://arxiv.org/pdf/2303.01422v1.pdf | Design-based conformal prediction | Conformal prediction is an assumption-lean approach to generating distribution-free prediction intervals or sets, for nearly arbitrary predictive models, with guaranteed finite-sample coverage. Conformal methods are an active research topic in statistics and machine learning, but only recently have they been extended t... | ['Jerzy Wieczorek'] | 2023-03-02 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 5.30144095e-01 5.48117876e-01 -7.05511808e-01 -7.92781174e-01
-1.01903462e+00 -4.76742417e-01 5.31477749e-01 4.08729874e-02
-2.90537477e-02 1.14593637e+00 4.92957085e-01 -8.62481177e-01
-5.91153741e-01 -1.01470017e+00 -6.49098158e-01 -2.92700589e-01
-2.96948552e-01 7.69735277e-01 -1.76039979e-01 1.53068736... | [7.732021331787109, 4.550881862640381] |
6d45ea89-760a-49d8-8069-8d5428613a8d | camouflaged-instance-segmentation-dataset-and | 2103.17123 | null | https://arxiv.org/abs/2103.17123v4 | https://arxiv.org/pdf/2103.17123v4.pdf | Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite | This paper pushes the envelope on decomposing camouflaged regions in an image into meaningful components, namely, camouflaged instances. To promote the new task of camouflaged instance segmentation of in-the-wild images, we introduce a dataset, dubbed CAMO++, that extends our preliminary CAMO dataset (camouflaged objec... | ['Khanh-Duy Nguyen', 'Minh-Quan Le', 'Tam V. Nguyen', 'Minh-Triet Tran', 'Thanh-Toan Do', 'Tan-Cong Nguyen', 'Yubo Cao', 'Trung-Nghia Le'] | 2021-03-31 | null | null | null | null | ['camouflaged-object-segmentation'] | ['computer-vision'] | [ 5.86888969e-01 4.92099859e-02 -4.41303879e-01 5.85853541e-03
-8.11658144e-01 -7.75396883e-01 5.14917493e-01 -4.95115876e-01
-1.37470849e-02 6.61645591e-01 -2.81119972e-01 -3.81179154e-01
3.14581901e-01 -4.93323952e-01 -6.96097374e-01 -7.92427242e-01
2.55533457e-01 2.67154843e-01 5.15209734e-01 -9.18367431... | [9.654020309448242, -0.16972748935222626] |
526f8f0c-2e8f-44e2-a509-e991cec4bbe2 | representation-learning-via-invariant-causal-1 | 2010.07922 | null | https://arxiv.org/abs/2010.07922v1 | https://arxiv.org/pdf/2010.07922v1.pdf | Representation Learning via Invariant Causal Mechanisms | Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of t... | ['Charles Blundell', 'Lars Buesing', 'Jacob Walker', 'Brian McWilliams', 'Jovana Mitrovic'] | 2020-10-15 | representation-learning-via-invariant-causal | https://arxiv.org/abs/2010.07922 | https://arxiv.org/pdf/2010.07922.pdf | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.65406978e-01 6.24598324e-01 -7.42797434e-01 -6.68936372e-01
-6.60955727e-01 -3.27716917e-01 9.64985907e-01 4.02281471e-02
-4.90673512e-01 8.59687805e-01 5.06726384e-01 -9.20159295e-02
-2.67978549e-01 -5.38871229e-01 -9.67885911e-01 -5.57687521e-01
-1.21792063e-01 4.45310026e-01 -9.36558545e-02 -1.54132843... | [9.371954917907715, 2.767833709716797] |
67ee3f0c-f1b9-417d-b65b-9e0497762004 | maser-multi-agent-reinforcement-learning-with | 2206.10607 | null | https://arxiv.org/abs/2206.10607v1 | https://arxiv.org/pdf/2206.10607v1.pdf | MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer | In this paper, we consider cooperative multi-agent reinforcement learning (MARL) with sparse reward. To tackle this problem, we propose a novel method named MASER: MARL with subgoals generated from experience replay buffer. Under the widely-used assumption of centralized training with decentralized execution and consis... | ['Youngchul Sung', 'Whiyoung Jung', 'Woojun Kim', 'Jeewon Jeon'] | 2022-06-20 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-6.45452619e-01 2.62160063e-01 -4.57206100e-01 2.53157675e-01
-9.87696171e-01 -4.71344322e-01 4.21290725e-01 3.73652428e-01
-6.39158905e-01 1.42325032e+00 8.33027139e-02 1.98115796e-01
-5.25077164e-01 -7.51621068e-01 -5.56811035e-01 -8.76845479e-01
-4.32426631e-01 6.16651237e-01 1.63869411e-01 -5.64497352... | [3.812676191329956, 2.023343086242676] |
fd4b5b16-2db9-4ad5-9528-6b0129f8df9f | malceiver-perceiver-with-hierarchical-and | 2204.05994 | null | https://arxiv.org/abs/2204.05994v1 | https://arxiv.org/pdf/2204.05994v1.pdf | Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection | We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android APK file. To reach a malware classification decision the model combines hierarchical features extract... | ['Niall McLaughlin'] | 2022-04-12 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 5.93239129e-01 -5.05296066e-02 -5.48372328e-01 -2.79783934e-01
-4.27275151e-01 -8.59469175e-01 7.01807737e-01 -4.44363281e-02
-2.48960614e-01 -6.16204105e-02 5.85029647e-02 -7.82617569e-01
2.16971815e-01 -6.40588284e-01 -6.94738388e-01 -3.86137187e-01
-1.68587655e-01 7.92034790e-02 3.88652980e-01 -2.50619203... | [14.447157859802246, 9.70645523071289] |
4eb8d1bb-6137-409e-b70b-0fe77c9100d0 | proximal-gradient-based-unfolding-for-massive | 2212.01839 | null | https://arxiv.org/abs/2212.01839v1 | https://arxiv.org/pdf/2212.01839v1.pdf | Proximal Gradient-Based Unfolding for Massive Random Access in IoT Networks | Grant-free random access is an effective technology for enabling low-overhead and low-latency massive access, where joint activity detection and channel estimation (JADCE) is a critical issue. Although existing compressive sensing algorithms can be applied for JADCE, they usually fail to simultaneously harvest the foll... | ['Yonina C. Eldar', 'Xu Chen', 'Yong Zhou', 'Yinan Zou'] | 2022-12-04 | null | null | null | null | ['compressive-sensing', 'activity-detection'] | ['computer-vision', 'computer-vision'] | [ 2.75900543e-01 -4.99109030e-01 -5.67650378e-01 -1.80185914e-01
-7.80817389e-01 -1.03798181e-01 -3.21381278e-02 -5.74260771e-01
-2.39701286e-01 8.29951346e-01 4.33967113e-01 -7.06739008e-01
-2.20696628e-01 -3.44473302e-01 -6.08011425e-01 -7.16148913e-01
-6.26715899e-01 -2.52802700e-01 -4.08263594e-01 -1.92203000... | [6.3526387214660645, 1.4119846820831299] |
f8e58509-8f1a-4133-97ad-3cfcc1eb97d4 | fine-grained-off-road-semantic-segmentation | 2103.03651 | null | https://arxiv.org/abs/2103.03651v1 | https://arxiv.org/pdf/2103.03651v1.pdf | Fine-Grained Off-Road Semantic Segmentation and Mapping via Contrastive Learning | Road detection or traversability analysis has been a key technique for a mobile robot to traverse complex off-road scenes. The problem has been mainly formulated in early works as a binary classification one, e.g. associating pixels with road or non-road labels. Whereas understanding scenes with fine-grained labels are... | ['Huijing Zhao', 'Xijun Zhao', 'Shaochi Hu', 'Biao Gao'] | 2021-03-05 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 4.11001205e-01 2.25265607e-01 -3.75107974e-01 -8.41839015e-01
-5.78401327e-01 -4.10721570e-01 3.79751593e-01 1.51077732e-01
-2.83797592e-01 6.29412234e-01 -3.32266808e-01 -3.85741115e-01
-4.49953824e-01 -1.21630883e+00 -6.69381678e-01 -3.70769292e-01
2.83699972e-03 7.54243195e-01 5.76348066e-01 -4.55645382... | [8.295819282531738, -1.8684076070785522] |
f66a9162-4dfa-4785-b3df-cd43a9d5725e | temporally-coherent-person-matting-trained-on | 2109.04843 | null | https://arxiv.org/abs/2109.04843v1 | https://arxiv.org/pdf/2109.04843v1.pdf | Temporally Coherent Person Matting Trained on Fake-Motion Dataset | We propose a novel neural-network-based method to perform matting of videos depicting people that does not require additional user input such as trimaps. Our architecture achieves temporal stability of the resulting alpha mattes by using motion-estimation-based smoothing of image-segmentation algorithm outputs, combine... | ['Dmitry Vatolin', 'Andrey Moskalenko', 'Mikhail Erofeev', 'Ivan Molodetskikh'] | 2021-09-10 | null | null | null | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 3.18336278e-01 2.28312507e-01 1.19419694e-01 -3.57194155e-01
-5.05509377e-01 -4.28617597e-01 3.92196178e-01 -7.49484599e-01
-5.13437033e-01 6.24151111e-01 -5.36506958e-02 -1.87488109e-01
6.18245959e-01 -7.33436346e-01 -1.42885649e+00 -4.56472784e-01
-1.96732014e-01 3.02811384e-01 3.29412669e-01 1.75604627... | [10.706969261169434, -0.8086175322532654] |
baac132c-cfad-4dbb-a0af-237f3340be63 | revisiting-gray-pixel-for-statistical | 1803.08326 | null | http://arxiv.org/abs/1803.08326v4 | http://arxiv.org/pdf/1803.08326v4.pdf | Revisiting Gray Pixel for Statistical Illumination Estimation | We present a statistical color constancy method that relies on novel gray
pixel detection and mean shift clustering. The method, called Mean Shifted Grey
Pixel -- MSGP, is based on the observation: true-gray pixels are aligned
towards one single direction. Our solution is compact, easy to compute and
requires no traini... | ['Joni-Kristian Kämäräinen', 'Jarno Nikkanen', 'Yanlin Qian', 'Jiri Matas', 'Said Pertuz'] | 2018-03-22 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.42820787e-01 -3.13654184e-01 -1.77540109e-01 -1.17396265e-01
-7.18507469e-01 -5.26100099e-01 4.74728405e-01 -2.97816545e-01
-3.79245847e-01 5.97208500e-01 -5.65369785e-01 -3.25018317e-01
3.71734679e-01 -2.67877519e-01 -8.56632590e-01 -1.24420238e+00
3.45463939e-02 -2.00972538e-02 6.95927858e-01 -2.28752159... | [10.47368049621582, -2.5575246810913086] |
df0e8615-058a-4ef2-9163-f592b6ca60c8 | modeling-user-behavior-with-graph-convolution | 2202.06081 | null | https://arxiv.org/abs/2202.06081v1 | https://arxiv.org/pdf/2202.06081v1.pdf | Modeling User Behavior with Graph Convolution for Personalized Product Search | User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to m... | ['Keping Yang', 'Taiwei Jin', 'Sen Li', 'Guli Lin', 'Fuyu Lv', 'Xiaotong Zhang', 'Xiao-Ming Wu', 'Bo Liu', 'Qimai Li', 'Fan Lu'] | 2022-02-12 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [ 6.62576780e-02 -2.77210653e-01 -8.42674613e-01 -7.09534407e-01
-3.59773487e-01 -6.00062013e-01 7.56165385e-01 4.54146117e-01
-1.99243680e-01 8.08821172e-02 6.37760937e-01 -2.15778753e-01
-5.05123496e-01 -8.12912464e-01 -2.93642163e-01 -3.15742135e-01
-2.30503097e-01 5.78149915e-01 -1.09748496e-02 -3.77640545... | [10.151416778564453, 5.6860575675964355] |
e383ca6c-98e5-4863-9d88-c40fd8309044 | autoencoder-based-anomaly-detection-in | 2305.08977 | null | https://arxiv.org/abs/2305.08977v1 | https://arxiv.org/pdf/2305.08977v1.pdf | Autoencoder-based Anomaly Detection in Streaming Data with Incremental Learning and Concept Drift Adaptation | In our digital universe nowadays, enormous amount of data are produced in a streaming manner in a variety of application areas. These data are often unlabelled. In this case, identifying infrequent events, such as anomalies, poses a great challenge. This problem becomes even more difficult in non-stationary environment... | ['Marios M. Polycarpou', 'Kleanthis Malialis', 'Jin Li'] | 2023-05-15 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 1.21089078e-01 -5.81383348e-01 -1.42521843e-01 -3.76928955e-01
-3.89182329e-01 -1.22856110e-01 3.42454374e-01 5.53985715e-01
-2.85463810e-01 8.52477849e-01 9.11890343e-03 -5.85793965e-02
-2.89224446e-01 -7.73389459e-01 -4.84119117e-01 -7.28985310e-01
-3.02539259e-01 3.00867021e-01 3.05989355e-01 -5.89740761... | [7.375077724456787, 2.85905385017395] |
96610dd2-01a1-49a0-90a2-a52dabc809a1 | handheld-mobile-photography-in-very-low-light | 1910.11336 | null | https://arxiv.org/abs/1910.11336v1 | https://arxiv.org/pdf/1910.11336v1.pdf | Handheld Mobile Photography in Very Low Light | Taking photographs in low light using a mobile phone is challenging and rarely produces pleasing results. Aside from the physical limits imposed by read noise and photon shot noise, these cameras are typically handheld, have small apertures and sensors, use mass-produced analog electronics that cannot easily be cooled,... | ['Tim Brooks', 'Yun-Ta Tsai', 'Qiurui He', 'Nikhil Karnad', 'Marc Levoy', 'Dillon Sharlet', 'Orly Liba', 'Jonathan T. Barron', 'Yael Pritch', 'Samuel W. Hasinoff', 'Kiran Murthy', 'Tianfan Xue', 'Ryan Geiss'] | 2019-10-24 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 6.47656620e-01 -4.77474838e-01 3.04266274e-01 -4.57866602e-02
-2.52792627e-01 -9.26203728e-01 2.26948038e-01 -4.44708347e-01
-5.91109633e-01 8.08342218e-01 -2.43778735e-01 -3.08067858e-01
3.29699129e-01 -4.94048119e-01 -4.79465157e-01 -7.16168940e-01
4.23474908e-01 -2.49546513e-01 5.23513913e-01 1.34219155... | [10.545787811279297, -2.5930142402648926] |
77629c39-f65a-4a82-991e-5ec8f2d8d3c9 | neuron-coverage-guided-domain-generalization | 2103.00229 | null | https://arxiv.org/abs/2103.00229v2 | https://arxiv.org/pdf/2103.00229v2.pdf | Neuron Coverage-Guided Domain Generalization | This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN... | ['Shiqi Wang', 'Yang Liu', 'Xiaofei Xie', 'Haoliang Li', 'Chris Xing Tian'] | 2021-02-27 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 2.38469869e-01 1.08160302e-01 -1.67793900e-01 -4.18359101e-01
1.82058647e-01 -7.33673751e-01 1.54516205e-01 -1.36219323e-01
-1.28565609e-01 8.86420250e-01 -3.58442277e-01 -4.68766719e-01
-2.91568786e-01 -1.06747746e+00 -1.00641716e+00 -5.50755084e-01
1.72102869e-01 2.80908942e-01 2.09370598e-01 -1.01358138... | [6.5683207511901855, 7.630397796630859] |
17b78cbc-0806-4723-9efa-88b491947a8f | self-constructing-graph-convolutional | 2003.06932 | null | https://arxiv.org/abs/2003.06932v2 | https://arxiv.org/pdf/2003.06932v2.pdf | Self-Constructing Graph Convolutional Networks for Semantic Labeling | Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings... | ['Arnt-Børre Salberg', 'Michael Kampffmeyer', 'Qinghui Liu', 'Robert Jenssen'] | 2020-03-15 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [ 4.84275818e-02 3.98421079e-01 6.02085982e-03 -3.35368603e-01
-2.52010852e-01 -6.65935755e-01 3.74306977e-01 1.62537545e-01
-2.03135386e-01 4.30731446e-01 -1.03044743e-03 -3.45474035e-01
-3.09190929e-01 -1.06661296e+00 -8.08013797e-01 -6.31142795e-01
-1.67513028e-01 4.01576132e-01 1.31572232e-01 3.36823091... | [9.627215385437012, 0.7686147689819336] |
a1cf99b2-0675-429b-a6db-c191bbe4cd4b | learning-from-bootstrapping-and-stepwise | 2205.09324 | null | https://arxiv.org/abs/2205.09324v1 | https://arxiv.org/pdf/2205.09324v1.pdf | Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer | Text style transfer is an important task in controllable language generation. Supervised approaches have pushed performance improvement on style-oriented rewriting such as formality conversion. However, challenges remain due to the scarcity of large-scale parallel data in many domains. While unsupervised approaches do ... | ['Nancy F. Chen', 'Zhengyuan Liu'] | 2022-05-19 | null | https://aclanthology.org/2022.findings-naacl.201 | https://aclanthology.org/2022.findings-naacl.201.pdf | findings-naacl-2022-7 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 8.04824352e-01 2.04672664e-01 -2.91659921e-01 -4.68801707e-01
-1.10045099e+00 -6.36578381e-01 8.19315314e-01 2.33115461e-02
-3.09573501e-01 1.15459919e+00 2.84902692e-01 -1.48309261e-01
2.37457618e-01 -6.89602435e-01 -7.90151834e-01 -4.70592469e-01
4.07542974e-01 6.62280858e-01 -4.30982700e-03 -5.66487610... | [11.67066764831543, 9.306537628173828] |
c34572a3-5780-4684-834f-96e21e1078e0 | dreamartist-towards-controllable-one-shot | 2211.11337 | null | https://arxiv.org/abs/2211.11337v3 | https://arxiv.org/pdf/2211.11337v3.pdf | DreamArtist: Towards Controllable One-Shot Text-to-Image Generation via Positive-Negative Prompt-Tuning | Large-scale text-to-image generation models have achieved remarkable progress in synthesizing high-quality, feature-rich images with high resolution guided by texts. However, these models often struggle with novel concepts, eg, new styles, object entities, etc. Although recent attempts have employed fine-tuning or prom... | ['Liang Lin', 'Pengxu Wei', 'Ziyi Dong'] | 2022-11-21 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 5.06547272e-01 -1.29767790e-01 7.20391944e-02 -2.56489366e-01
-4.77625191e-01 -4.08481777e-01 8.80984187e-01 -2.94175774e-01
-2.76027679e-01 7.54225552e-01 3.80355239e-01 2.96907067e-01
-3.90769914e-02 -9.38071728e-01 -6.47044778e-01 -8.06547344e-01
5.51319301e-01 3.88721913e-01 1.11440271e-01 -6.05438888... | [11.472586631774902, -0.37425002455711365] |
6031d38d-e48d-4a79-a422-e8256f4fe9e0 | automatic-detection-and-rectification-of | 2303.05763 | null | https://arxiv.org/abs/2303.05763v1 | https://arxiv.org/pdf/2303.05763v1.pdf | Automatic Detection and Rectification of Paper Receipts on Smartphones | We describe the development of a real-time smartphone app that allows the user to digitize paper receipts in a novel way by "waving" their phone over the receipts and letting the app automatically detect and rectify the receipts for subsequent text recognition. We show that traditional computer vision algorithms for ed... | ['Ikuo Kitagishi', 'Masashi Tanaka', 'Edward Whittaker'] | 2023-03-10 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 4.61817682e-01 -3.90124410e-01 2.95566052e-01 1.28882721e-01
-5.02681375e-01 -7.44168460e-01 5.41705728e-01 -5.22786789e-02
-2.87319213e-01 1.80630505e-01 -3.53548914e-01 -4.09352750e-01
4.61953938e-01 -5.78781724e-01 -9.53610063e-01 -3.47610146e-01
7.37639964e-02 3.39554280e-01 5.96988440e-01 -1.21582737... | [8.549877166748047, -1.7416656017303467] |
0ddfdedd-1523-4c17-9180-2744e0987c00 | deep-ahs-a-deep-learning-approach-to-acoustic | 2302.09252 | null | https://arxiv.org/abs/2302.09252v1 | https://arxiv.org/pdf/2302.09252v1.pdf | Deep AHS: A Deep Learning Approach to Acoustic Howling Suppression | In this paper, we formulate acoustic howling suppression (AHS) as a supervised learning problem and propose a deep learning approach, called Deep AHS, to address it. Deep AHS is trained in a teacher forcing way which converts the recurrent howling suppression process into an instantaneous speech separation process to s... | ['Dong Yu', 'Meng Yu', 'Hao Zhang'] | 2023-02-18 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 2.95073569e-01 6.47619814e-02 2.33035877e-01 -3.49134728e-02
-8.64088297e-01 -8.80365893e-02 3.29155564e-01 -2.18827501e-01
-2.35348552e-01 1.96257696e-01 5.77262819e-01 -2.87926674e-01
-1.68980613e-01 -4.19119537e-01 -5.62937856e-01 -8.98947001e-01
2.11279705e-01 -3.37701082e-01 7.77899772e-02 -1.51166812... | [15.015043258666992, 5.902990341186523] |
991ec2ed-6b07-410d-a681-6696a8fec149 | refusion-enabling-large-size-realistic-image | 2304.08291 | null | https://arxiv.org/abs/2304.08291v1 | https://arxiv.org/pdf/2304.08291v1.pdf | Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models | This work aims to improve the applicability of diffusion models in realistic image restoration. Specifically, we enhance the diffusion model in several aspects such as network architecture, noise level, denoising steps, training image size, and optimizer/scheduler. We show that tuning these hyperparameters allows us to... | ['Thomas B. Schön', 'Jens Sjölund', 'Zheng Zhao', 'Fredrik K. Gustafsson', 'Ziwei Luo'] | 2023-04-17 | null | null | null | null | ['shadow-removal', 'bokeh-effect-rendering', 'image-dehazing', 'image-shadow-removal', 'stereo-image-super-resolution'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.73614001e-01 -1.47553474e-01 -6.54841540e-03 -1.64386034e-01
-9.18657780e-01 -1.94368616e-01 4.30706620e-01 -6.44375861e-01
-2.86914110e-01 6.37089133e-01 6.39412642e-01 -2.80717343e-01
3.48259369e-03 -7.89561450e-01 -7.45205164e-01 -1.06350422e+00
7.79349655e-02 -1.46666663e-02 1.77571699e-01 -3.80608439... | [11.27173900604248, -2.1838276386260986] |
d9d4d5b2-a315-4e42-af96-c8bfccc9f0c0 | data-driven-summarization-of-scientific | 1804.08875 | null | http://arxiv.org/abs/1804.08875v1 | http://arxiv.org/pdf/1804.08875v1.pdf | Data-driven Summarization of Scientific Articles | Data-driven approaches to sequence-to-sequence modelling have been
successfully applied to short text summarization of news articles. Such models
are typically trained on input-summary pairs consisting of only a single or a
few sentences, partially due to limited availability of multi-sentence training
data. Here, we p... | ['Michael Pfeiffer', 'Richard H. R. Hahnloser', 'Nikola I. Nikolov'] | 2018-04-24 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 8.34260046e-01 3.64646077e-01 -4.73360151e-01 -3.23868871e-01
-1.23147249e+00 -5.47306478e-01 8.04409385e-01 8.65007937e-01
-4.42794412e-01 1.23293066e+00 1.02258503e+00 -3.71064693e-01
3.41433026e-02 -4.77910250e-01 -7.73339212e-01 -1.03670128e-01
2.33013451e-01 6.01079047e-01 3.16707231e-02 -3.60506415... | [12.501297950744629, 9.522834777832031] |
b437fcbc-fe96-4fc8-b0b6-e192c0afbc17 | decomposed-prototype-learning-for-few-shot | 2303.10863 | null | https://arxiv.org/abs/2303.10863v1 | https://arxiv.org/pdf/2303.10863v1.pdf | Decomposed Prototype Learning for Few-Shot Scene Graph Generation | Today's scene graph generation (SGG) models typically require abundant manual annotations to learn new predicate types. Thus, it is difficult to apply them to real-world applications with a long-tailed distribution of predicates. In this paper, we focus on a new promising task of SGG: few-shot SGG (FSSGG). FSSGG encour... | ['Jun Xiao', 'Yi Yang', 'Yinfu Feng', 'Guikun Chen', 'Long Chen', 'Xingchen Li'] | 2023-03-20 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 3.89929622e-01 8.48413482e-02 -3.44502240e-01 -4.88105386e-01
-4.06368852e-01 -4.92480248e-01 7.30657458e-01 2.88933426e-01
1.67714387e-01 5.97240031e-01 1.64875403e-01 -1.26320183e-01
-1.89329997e-01 -9.25669551e-01 -6.24593616e-01 -5.42576313e-01
2.56688390e-02 5.72324097e-01 5.94507694e-01 -1.74015462... | [10.182536125183105, 1.9371331930160522] |
93ff669c-99d8-44ae-8844-30c3682d6cf1 | verifiable-and-provably-secure-machine | 2210.09126 | null | https://arxiv.org/abs/2210.09126v2 | https://arxiv.org/pdf/2210.09126v2.pdf | Verifiable and Provably Secure Machine Unlearning | Machine unlearning aims to remove points from the training dataset of a machine learning model after training; for example when a user requests their data to be deleted. While many machine unlearning methods have been proposed, none of them enable users to audit the procedure. Furthermore, recent work shows a user is u... | ['Nicolas Papernot', 'Olga Ohrimenko', 'Esha Ghosh', 'Varun Chandrasekaran', 'Doreen Riepel', 'Thorsten Eisenhofer'] | 2022-10-17 | null | null | null | null | ['snarks'] | ['natural-language-processing'] | [ 3.30212563e-01 2.95943528e-01 -4.29021269e-01 -1.90802023e-01
-4.71923411e-01 -1.23194575e+00 3.16631466e-01 3.51690471e-01
-6.07492864e-01 9.05538440e-01 -7.34569907e-01 -1.07425773e+00
9.96838138e-03 -1.16063166e+00 -1.40824485e+00 -7.22114146e-01
-1.40522972e-01 3.97092760e-01 1.62285671e-01 2.99387932... | [5.912057399749756, 7.226564407348633] |
53fe9363-374e-4bcb-bada-08e0ee4331b0 | curriculum-learning-for-compositional-visual | 2303.15006 | null | https://arxiv.org/abs/2303.15006v1 | https://arxiv.org/pdf/2303.15006v1.pdf | Curriculum Learning for Compositional Visual Reasoning | Visual Question Answering (VQA) is a complex task requiring large datasets and expensive training. Neural Module Networks (NMN) first translate the question to a reasoning path, then follow that path to analyze the image and provide an answer. We propose an NMN method that relies on predefined cross-modal embeddings to... | ['Michel Crucianu', 'Marin Ferecatu', 'Wafa Aissa'] | 2023-03-27 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 2.11235046e-01 2.22072318e-01 -5.20102158e-02 -3.54314983e-01
-7.94317067e-01 -7.45402217e-01 5.52808583e-01 2.51411140e-01
-7.65306294e-01 5.02756119e-01 -1.57441109e-01 -5.10191023e-01
-1.58757955e-01 -1.06710792e+00 -8.69950473e-01 -5.16019344e-01
4.40052181e-01 5.23036003e-01 3.57140392e-01 -1.59218401... | [10.722506523132324, 1.9020401239395142] |
d1d9e289-ed80-4d09-a921-a47623bb08c4 | deep-reinforcement-learning-based-path | 2301.05980 | null | https://arxiv.org/abs/2301.05980v1 | https://arxiv.org/pdf/2301.05980v1.pdf | Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as Observation | Industrial robots are widely used in various manufacturing environments due to their efficiency in doing repetitive tasks such as assembly or welding. A common problem for these applications is to reach a destination without colliding with obstacles or other robot arms. Commonly used sampling-based path planning approa... | ['Jens Lambrecht', 'Benno Kutschank', 'Yifan Hu', 'Linh Kästner', 'Teham Bhuiyan'] | 2023-01-14 | null | null | null | null | ['industrial-robots', 'motion-planning'] | ['robots', 'robots'] | [ 1.23001739e-01 -1.86945394e-01 -4.65211645e-02 -1.42780006e-01
-3.79132032e-01 -3.49232763e-01 1.92640156e-01 2.32454211e-01
-5.67803442e-01 8.45216036e-01 -5.38111687e-01 -2.54299730e-01
-5.99269211e-01 -1.03393865e+00 -5.54246902e-01 -8.05186570e-01
-1.80677086e-01 9.21126902e-01 2.63249248e-01 -4.70274061... | [4.83468770980835, 1.3807153701782227] |
a9ab733f-8a1c-444f-898c-99dbc73eab5f | xf2t-cross-lingual-fact-to-text-generation | 2209.11252 | null | https://arxiv.org/abs/2209.11252v1 | https://arxiv.org/pdf/2209.11252v1.pdf | XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages | Multiple business scenarios require an automated generation of descriptive human-readable text from structured input data. Hence, fact-to-text generation systems have been developed for various downstream tasks like generating soccer reports, weather and financial reports, medical reports, person biographies, etc. Unfo... | ['Vasudeva Varma', 'Manish Gupta', 'Anubhav Sharma', 'Bhavyajeet Singh', 'Tushar Abhishek', 'Shivprasad Sagare'] | 2022-09-22 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 9.69130844e-02 3.26585323e-01 -1.19151406e-01 -4.57180887e-01
-1.16177237e+00 -6.51620686e-01 1.23122549e+00 9.54022557e-02
-1.85933128e-01 1.36679971e+00 8.15992296e-01 -5.52967668e-01
1.47609845e-01 -1.09542418e+00 -6.81228518e-01 -1.68585047e-01
2.17203572e-01 8.42501819e-01 -1.98319077e-01 -5.34913838... | [11.506692886352539, 9.423125267028809] |
b2141344-616a-42b9-bd83-580425348123 | semeval-2015-task-6-clinical-tempeval | null | null | https://aclanthology.org/S15-2136 | https://aclanthology.org/S15-2136.pdf | SemEval-2015 Task 6: Clinical TempEval | null | ['Marc Verhagen', 'Leon Derczynski', 'Guergana Savova', 'Steven Bethard', 'James Pustejovsky'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.273398399353027, 3.7930564880371094] |
d6fc5618-1277-44bf-a452-85fc5f8486ee | named-entity-recognition-in-the-style-of | 2101.11122 | null | https://arxiv.org/abs/2101.11122v1 | https://arxiv.org/pdf/2101.11122v1.pdf | Named Entity Recognition in the Style of Object Detection | In this work, we propose a two-stage method for named entity recognition (NER), especially for nested NER. We borrowed the idea from the two-stage Object Detection in computer vision and the way how they construct the loss function. First, a region proposal network generates region candidates and then a second-stage mo... | ['Bing Li'] | 2021-01-26 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-3.37051868e-01 2.49349385e-01 1.48774996e-01 -6.91617370e-01
-9.07419086e-01 -6.86839819e-01 4.20657843e-01 1.52092233e-01
-1.14943361e+00 7.80038595e-01 6.78153336e-02 -3.36971492e-01
3.31352323e-01 -7.83033669e-01 -6.60948634e-01 -3.80243003e-01
-1.99943095e-01 6.22268617e-01 5.20247877e-01 -2.70142611... | [9.674333572387695, 9.600103378295898] |
58961f36-2d76-42ab-9021-6fccd3cfa532 | identifying-copied-fragments-in-a-18th | null | null | https://aclanthology.org/2022.lrec-1.631 | https://aclanthology.org/2022.lrec-1.631.pdf | Identifying Copied Fragments in a 18th Century Dutch Chronicle | We apply computational stylometric techniques to an 18th century Dutch chronicle to determine which fragments of the manuscript represent the author’s own original work and which show signs of external source use through either direct copying or paraphrasing. Through stylometric methods the majority of text fragments i... | ['Erika Kuijpers', 'Alie Lassche', 'Lianne Wilhelmus', 'Eleanor L. T. Smith', 'Roser Morante'] | null | null | null | null | lrec-2022-6 | ['authorship-verification'] | ['natural-language-processing'] | [ 5.56313470e-02 -9.96974576e-03 -4.12900925e-01 2.77211726e-01
-4.36728150e-01 -1.02227736e+00 1.02677846e+00 3.98349017e-01
-4.69738513e-01 8.84379089e-01 5.41648746e-01 -6.78155124e-01
-1.24571860e-01 -5.19397557e-01 -2.48022333e-01 -2.18879879e-01
8.47525001e-01 7.23440647e-01 -1.16386609e-02 -1.67837054... | [9.588790893554688, 10.574206352233887] |
efa237eb-99ea-4018-b911-914ba6690cb5 | self-knowledge-distillation-based-self | 2206.03009 | null | https://arxiv.org/abs/2206.03009v1 | https://arxiv.org/pdf/2206.03009v1.pdf | Self-Knowledge Distillation based Self-Supervised Learning for Covid-19 Detection from Chest X-Ray Images | The global outbreak of the Coronavirus 2019 (COVID-19) has overloaded worldwide healthcare systems. Computer-aided diagnosis for COVID-19 fast detection and patient triage is becoming critical. This paper proposes a novel self-knowledge distillation based self-supervised learning method for COVID-19 detection from ches... | ['Miki Haseyama', 'Takahiro Ogawa', 'Ren Togo', 'Guang Li'] | 2022-06-07 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-5.85679673e-02 -2.60035187e-01 -1.92462340e-01 -3.01984936e-01
-5.80779731e-01 -4.89724219e-01 1.66338384e-01 7.96520174e-01
-4.81289148e-01 6.27661645e-01 -1.98541135e-01 -3.15463543e-01
-3.80440295e-01 -3.90284330e-01 -2.28302434e-01 -5.33514678e-01
-6.41126454e-01 8.46779585e-01 1.64147481e-01 5.01478076... | [15.57408618927002, -1.6978635787963867] |
45f2797c-26d0-4d00-81e9-a4b94ad14b3e | common-pets-in-3d-dynamic-new-view-synthesis | 2211.03889 | null | https://arxiv.org/abs/2211.03889v1 | https://arxiv.org/pdf/2211.03889v1.pdf | Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories | Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on sparse rigid object reconstruction successfully learned such priors from large datasets such as CO3D. In this paper, we extend this approach... | ['David Novotny', 'Andrea Vedaldi', 'Natalia Neverova', 'Ignacio Rocco', 'Jeremy Reizenstein', 'Roman Shapovalov', 'Samarth Sinha'] | 2022-11-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sinha_Common_Pets_in_3D_Dynamic_New-View_Synthesis_of_Real-Life_Deformable_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sinha_Common_Pets_in_3D_Dynamic_New-View_Synthesis_of_Real-Life_Deformable_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-reconstruction'] | ['computer-vision'] | [-1.47590131e-01 1.25426218e-01 1.19737998e-01 -2.15230137e-01
-7.95858324e-01 -8.79117012e-01 9.08540308e-01 -6.99736774e-01
2.02199340e-01 4.02045637e-01 6.14357829e-01 2.09125593e-01
4.48761225e-01 -2.47229293e-01 -1.26205564e+00 -4.08061087e-01
1.39258504e-01 9.77813423e-01 6.01003766e-01 6.54625520... | [8.629915237426758, -2.8107123374938965] |
c31824fa-13e4-4fb5-bfea-47a936451d66 | defense-against-adversarial-cloud-attack-on | 2306.17431 | null | https://arxiv.org/abs/2306.17431v2 | https://arxiv.org/pdf/2306.17431v2.pdf | Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection | Detecting the salient objects in a remote sensing image has wide applications for the interdisciplinary research. Many existing deep learning methods have been proposed for Salient Object Detection (SOD) in remote sensing images and get remarkable results. However, the recent adversarial attack examples, generated by c... | ['Hongkai Yu', 'Yuewei Lin', 'Tianyun Zhang', 'Zibo Meng', 'Qing Guo', 'Jinlong Li', 'Lan Fu', 'Huiming Sun'] | 2023-06-30 | null | null | null | null | ['adversarial-attack', 'object-detection', 'salient-object-detection-1'] | ['adversarial', 'computer-vision', 'computer-vision'] | [ 4.41904306e-01 -8.20006132e-02 5.10844588e-01 7.85121247e-02
-4.28288639e-01 -9.16436136e-01 5.21614730e-01 -2.56948829e-01
-2.56710261e-01 3.64947826e-01 8.89679790e-02 -4.27134722e-01
1.36598840e-01 -9.91920769e-01 -9.10148680e-01 -1.10763812e+00
-1.42097965e-01 -8.22289288e-02 2.93229014e-01 -6.88505292... | [5.505424499511719, 7.94519567489624] |
ba26f0e0-59b0-466f-8f04-de56db6a25a7 | development-of-a-thermodynamics-of-human | 2212.12795 | null | https://arxiv.org/abs/2212.12795v2 | https://arxiv.org/pdf/2212.12795v2.pdf | Development of a Thermodynamics of Human Cognition and Human Culture | Inspired by foundational studies in classical and quantum physics, and by information retrieval studies in quantum information theory, we prove that the notions of 'energy' and 'entropy' can be consistently introduced in human language and, more generally, in human culture. More explicitly, if energy is attributed to w... | ['Sandro Sozzo', 'Lester Beltran', 'Jonito Aerts Arguëlles', 'Diederik Aerts'] | 2022-12-24 | null | null | null | null | ['culture'] | ['speech'] | [ 1.99135467e-01 1.06608726e-01 2.85190791e-01 -7.08789006e-02
2.23806828e-01 -7.89454103e-01 1.03512096e+00 5.10632277e-01
-6.63035810e-01 8.30808163e-01 3.84190470e-01 -2.67011881e-01
-2.55477011e-01 -1.10338962e+00 -2.97661781e-01 -9.59147274e-01
2.16608614e-01 3.12302202e-01 1.19965509e-01 -5.86769104... | [5.792938709259033, 5.018588066101074] |
aa58301a-bc44-4ff7-b9b5-5070185fc935 | transition-is-a-process-pair-to-video-change | null | null | https://ieeexplore.ieee.org/document/9975266 | https://ieeexplore.ieee.org/document/9975266 | Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images | As an important yet challenging task in Earth observation, change detection (CD) is undergoing a technological revolution, given the broadening application of deep learning. Nevertheless, existing deep learning-based CD methods still suffer from two salient issues: 1) incomplete temporal modeling, and 2) space-time cou... | ['Hongyan zhang', 'Guangyi Yang', 'Manhui Lin'] | 2022-12-07 | null | null | null | ieee-transactions-on-image-processing-2022-12 | ['change-detection', 'video-understanding', 'change-detection-for-remote-sensing-images', 'building-change-detection-for-remote-sensing'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous'] | [ 1.17086032e-02 -3.89441788e-01 -8.64605159e-02 -1.78718299e-01
-3.97184998e-01 -5.74813366e-01 9.34915245e-01 7.61755416e-03
-2.28617758e-01 3.28996480e-01 7.43076876e-02 -2.82621562e-01
-1.15769863e-01 -7.62477338e-01 -7.41925359e-01 -7.54605055e-01
-4.03940350e-01 -5.97623326e-02 5.11533797e-01 -6.24853559... | [9.259491920471191, -0.7665409445762634] |
76dcae2d-2abb-4a88-8613-4e676d6cee4d | scgc-self-supervised-contrastive-graph | 2204.12656 | null | https://arxiv.org/abs/2204.12656v1 | https://arxiv.org/pdf/2204.12656v1.pdf | SCGC : Self-Supervised Contrastive Graph Clustering | Graph clustering discovers groups or communities within networks. Deep learning methods such as autoencoders (AE) extract effective clustering and downstream representations but cannot incorporate rich structural information. While Graph Neural Networks (GNN) have shown great success in encoding graph structure, typica... | ['Shekhar S. Chandra', 'Marius Portmann', 'Gayan K. Kulatilleke'] | 2022-04-27 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.09033711e-01 3.29955131e-01 -9.58945081e-02 -2.54802972e-01
-1.41620547e-01 -5.62618554e-01 6.16460145e-01 2.00341746e-01
-2.31307223e-01 4.96557266e-01 1.34474292e-01 -4.26797718e-01
-2.86328018e-01 -1.07733023e+00 -9.42793787e-01 -6.72160387e-01
-4.66937512e-01 4.66274053e-01 -1.09586157e-01 1.14659369... | [6.9586029052734375, 6.182433605194092] |
25b474e7-774f-4623-ba0a-802d2203881f | video-pose-distillation-for-few-shot-fine | 2109.01305 | null | https://arxiv.org/abs/2109.01305v1 | https://arxiv.org/pdf/2109.01305v1.pdf | Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition | Human pose is a useful feature for fine-grained sports action understanding. However, pose estimators are often unreliable when run on sports video due to domain shift and factors such as motion blur and occlusions. This leads to poor accuracy when downstream tasks, such as action recognition, depend on pose. End-to-en... | ['Kayvon Fatahalian', 'Michaël Gharbi', 'Matthew Fisher', 'James Hong'] | 2021-09-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Hong_Video_Pose_Distillation_for_Few-Shot_Fine-Grained_Sports_Action_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Hong_Video_Pose_Distillation_for_Few-Shot_Fine-Grained_Sports_Action_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['action-understanding', 'fine-grained-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.56480342e-01 -1.73760474e-01 -4.52306181e-01 -4.29865241e-01
-1.10162210e+00 -7.60279775e-01 3.13662767e-01 -2.76356433e-02
-6.38156891e-01 5.38395584e-01 5.43904424e-01 5.81557274e-01
4.88539860e-02 -2.66997993e-01 -1.10111880e+00 -4.98277396e-01
-1.52412653e-01 6.15067124e-01 5.35913885e-01 -7.58343562... | [7.881523132324219, 0.13941660523414612] |
dab71276-9bbe-41a6-8eec-4c3e5636743b | a-robust-design-of-time-varying-internal | 2304.06451 | null | https://arxiv.org/abs/2304.06451v1 | https://arxiv.org/pdf/2304.06451v1.pdf | A robust design of time-varying internal model principle-based control for ultra-precision tracking in a direct-drive servo stage | This paper proposes a robust design of the time-varying internal model principle-based control (TV-IMPC) for tracking sophisticated references generated by linear time-varying (LTV) autonomous systems. The existing TV-IMPC design usually requires a complete knowledge of the plant I/O (input/output) model, leading to th... | ['Zhen Zhang', 'Yue Cao'] | 2023-04-13 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 1.43719181e-01 6.86852038e-01 -3.98487151e-01 5.66711009e-01
-1.93930436e-02 -6.66837871e-01 3.93368274e-01 -2.64961928e-01
-1.44251334e-02 8.13955843e-01 -5.21232188e-01 -7.65657842e-01
-6.47078574e-01 -1.18635632e-01 -6.44181788e-01 -8.98128688e-01
3.38037342e-01 -1.90117314e-01 -2.80293282e-02 -3.80361855... | [5.3046488761901855, 2.457193613052368] |
a8d0d04b-980c-4a82-b4d7-fe09cab34f63 | prediction-of-icd-codes-with-clinical-bert | 2008.10492 | null | https://arxiv.org/abs/2008.10492v1 | https://arxiv.org/pdf/2008.10492v1.pdf | Prediction of ICD Codes with Clinical BERT Embeddings and Text Augmentation with Label Balancing using MIMIC-III | This paper achieves state of the art results for the ICD code prediction task using the MIMIC-III dataset. This was achieved through the use of Clinical BERT (Alsentzer et al., 2019). embeddings and text augmentation and label balancing to improve F1 scores for both ICD Chapter as well as ICD disease codes. We attribut... | ['Haifeng Lin', 'Gaurav Desai', 'Brent Biseda', 'Anish Philip'] | 2020-08-24 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 2.03771561e-01 7.12355494e-01 -3.99427980e-01 -4.01131958e-01
-9.91056442e-01 -2.82669187e-01 4.46553200e-01 7.40354061e-01
-4.55983132e-01 6.83050156e-01 7.02796638e-01 -6.11866593e-01
-1.43437654e-01 -4.08486903e-01 -3.61554325e-01 -3.00678253e-01
-4.96254295e-01 8.08346391e-01 -2.28517935e-01 -1.37766972... | [8.031961441040039, 6.852139949798584] |
b402e27e-e30f-4da3-989d-13f538256235 | improved-active-fire-detection-using | 2304.09721 | null | https://arxiv.org/abs/2304.09721v1 | https://arxiv.org/pdf/2304.09721v1.pdf | Improved Active Fire Detection using Operational U-Nets | As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide. Warmer temperatures and drier conditions can cause quickly spreading fires and make them harder to control; therefore, early detection and accurate locating of active fires are crucial i... | ['Moncef Gabbouj', 'Turker Ince', 'Fahad Sohrab', 'Mete Ahishali', 'Ozer Can Devecioglu'] | 2023-04-19 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 4.43675399e-01 -4.95561123e-01 -3.83106440e-01 3.16083319e-02
8.69292393e-03 -2.71154165e-01 5.75279832e-01 2.98671961e-01
-7.97259510e-01 8.70590627e-01 -4.62173261e-02 -4.33808059e-01
-1.47320583e-01 -1.36027551e+00 -1.72620699e-01 -8.34697783e-01
-6.42109096e-01 -7.59646529e-03 3.92154992e-01 -1.39478505... | [9.308920860290527, -1.369176983833313] |
15cf4019-9aca-4583-8645-719e006eb51e | discriminative-multi-modality-speech | 2005.05592 | null | https://arxiv.org/abs/2005.05592v2 | https://arxiv.org/pdf/2005.05592v2.pdf | Discriminative Multi-modality Speech Recognition | Vision is often used as a complementary modality for audio speech recognition (ASR), especially in the noisy environment where performance of solo audio modality significantly deteriorates. After combining visual modality, ASR is upgraded to the multi-modality speech recognition (MSR). In this paper, we propose a two-s... | ['Jacob Wang', 'Yandong Guo', 'Bo Xu', 'Cheng Lu'] | 2020-05-12 | discriminative-multi-modality-speech-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Xu_Discriminative_Multi-Modality_Speech_Recognition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_Discriminative_Multi-Modality_Speech_Recognition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 1.70216188e-01 -4.54165518e-01 8.88924394e-03 -2.98258606e-02
-9.69920516e-01 -4.12345305e-02 4.01898414e-01 -6.24232531e-01
-4.75276530e-01 1.73362628e-01 5.58598578e-01 -5.16256392e-01
3.66563052e-01 -1.59531102e-01 -4.90550160e-01 -8.83731902e-01
3.77753943e-01 -1.86949790e-01 3.13373983e-01 -3.27456534... | [14.34458065032959, 5.219583511352539] |
2eacdd81-81db-4cfa-b41c-f0875f548d6c | multi-stage-temporal-difference-learning-for | 1606.07374 | null | http://arxiv.org/abs/1606.07374v2 | http://arxiv.org/pdf/1606.07374v2.pdf | Multi-Stage Temporal Difference Learning for 2048-like Games | Szubert and Jaskowski successfully used temporal difference (TD) learning
together with n-tuple networks for playing the game 2048. However, we observed
a phenomenon that the programs based on TD learning still hardly reach large
tiles. In this paper, we propose multi-stage TD (MS-TD) learning, a kind of
hierarchical r... | ['Chao-Chin Liang', 'Chia-Chuan Chang', 'Chu-Hsuan Hsueh', 'I-Chen Wu', 'Kun-Hao Yeh', 'Han Chiang'] | 2016-06-23 | null | null | null | null | ['2048'] | ['playing-games'] | [-4.15161461e-01 -1.21990256e-01 -4.22559291e-01 3.20103616e-01
-8.82462859e-01 -4.41599041e-01 3.60193580e-01 -1.14668813e-02
-7.40380526e-01 9.18209195e-01 -2.26746887e-01 -6.77565813e-01
-2.23570779e-01 -1.25119150e+00 -8.85872841e-01 -5.35485029e-01
-4.91164178e-01 3.36217165e-01 9.53572690e-01 -6.15521789... | [3.6176819801330566, 1.56121826171875] |
c3aaae69-0ced-4604-8298-93c21bd61559 | ian-the-individual-aggregation-network-for | 1705.05552 | null | http://arxiv.org/abs/1705.05552v1 | http://arxiv.org/pdf/1705.05552v1.pdf | IAN: The Individual Aggregation Network for Person Search | Person search in real-world scenarios is a new challenging computer version
task with many meaningful applications. The challenge of this task mainly comes
from: (1) unavailable bounding boxes for pedestrians and the model needs to
search for the person over the whole gallery images; (2) huge variance of
visual appeara... | ['Kai-Zhu Huang', 'Jiashi Feng', 'Jimin Xiao', 'Tammam Tillo', 'Yunchao Wei', 'Yanchun Xie'] | 2017-05-16 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-4.55346495e-01 -5.07115662e-01 7.88233355e-02 -2.91778475e-01
-4.72462624e-01 -2.31175467e-01 3.32591891e-01 -2.89398193e-01
-6.95595264e-01 6.54769719e-01 1.70849398e-01 3.07251215e-01
2.18477368e-01 -6.31093383e-01 -6.11707330e-01 -7.77511239e-01
1.87639281e-01 2.88155526e-01 3.96017611e-01 -1.06023811... | [14.780203819274902, 0.8364280462265015] |
9f967cc5-0f96-4430-9a8f-3687038d5462 | facial-expression-recognition-and-image | 2208.06117 | null | https://arxiv.org/abs/2208.06117v1 | https://arxiv.org/pdf/2208.06117v1.pdf | Facial Expression Recognition and Image Description Generation in Vietnamese | This paper discusses a facial expression recognition model and a description generation model to build descriptive sentences for images and facial expressions of people in images. Our study shows that YOLOv5 achieves better results than a traditional CNN for all emotions on the KDEF dataset. In particular, the accuraci... | ['Jugal Kalita', 'Loc Huu Nguy', 'Kim-Ngoc Thi Nguyen', 'Khang Nhut Lam'] | 2022-08-12 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [-4.30550069e-01 -8.10266957e-02 2.24602073e-01 -7.52926588e-01
-3.98549080e-01 -3.73037070e-01 4.26777482e-01 -3.55281353e-01
-3.66635740e-01 7.40204513e-01 2.46615306e-01 5.13217151e-01
4.94414270e-01 -6.14961028e-01 -5.54324448e-01 -8.07632089e-01
1.43394217e-01 -2.24562269e-02 -5.55680275e-01 -1.86210006... | [13.282247543334961, 4.98299503326416] |
d4511e11-08ab-4c36-8511-1c291b3a5195 | gfte-graph-based-financial-table-extraction | 2003.07560 | null | https://arxiv.org/abs/2003.07560v1 | https://arxiv.org/pdf/2003.07560v1.pdf | GFTE: Graph-based Financial Table Extraction | Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison. However, in financial industry and many other fields tables are often disclosed in unstructured digital files, e.g. Portable Document Format (PDF) and images, which ar... | ['Zheng Huang', 'Yiren Li', 'Xianhui Liu', 'Junchi Yan', 'Yi Zhou', 'Fan Ye'] | 2020-03-17 | null | null | null | null | ['table-extraction'] | ['miscellaneous'] | [-3.23083133e-01 -2.89764494e-01 -4.59634364e-01 -2.19594091e-01
-4.41734701e-01 -6.09749973e-01 2.38392591e-01 6.59448743e-01
-2.13461727e-01 9.60937202e-01 4.44988251e-01 -6.68426812e-01
-2.92197578e-02 -1.31387258e+00 -7.43937492e-01 -1.50685102e-01
1.36182848e-02 3.90487134e-01 2.67967582e-02 -3.18520784... | [11.629671096801758, 3.130505084991455] |
2ff4af69-2df1-42f0-8987-22b538a4dd97 | semantic-rule-web-based-diagnosis-and | 2301.03013 | null | https://arxiv.org/abs/2301.03013v2 | https://arxiv.org/pdf/2301.03013v2.pdf | Semantic rule Web-based Diagnosis and Treatment of Vector-Borne Diseases using SWRL rules | Vector-borne diseases (VBDs) are a kind of infection caused through the transmission of vectors generated by the bites of infected parasites, bacteria, and viruses, such as ticks, mosquitoes, triatomine bugs, blackflies, and sandflies. If these diseases are not properly treated within a reasonable time frame, the morta... | ['Navjot Singh', 'Sonali Agarwal', 'Sadhana Tiwari', 'Ritesh Chandra'] | 2023-01-08 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.43475342e-01 -7.46519342e-02 -4.18870628e-01 -1.80000946e-01
1.67817548e-01 -3.44002128e-01 4.81271863e-01 8.63963187e-01
-3.93370658e-01 7.43632197e-01 5.18393040e-01 -4.55776215e-01
-5.08087456e-01 -1.16061842e+00 -9.73515119e-03 -3.97884905e-01
-1.65045917e-01 7.38961279e-01 1.84424311e-01 -2.46416941... | [8.51614761352539, 8.465448379516602] |
b2d554eb-1cc0-48a8-b113-23fab57079ee | recurrent-neural-network-transducer-for-audio | 1911.04890 | null | https://arxiv.org/abs/1911.04890v1 | https://arxiv.org/pdf/1911.04890v1.pdf | Recurrent Neural Network Transducer for Audio-Visual Speech Recognition | This work presents a large-scale audio-visual speech recognition system based on a recurrent neural network transducer (RNN-T) architecture. To support the development of such a system, we built a large audio-visual (A/V) dataset of segmented utterances extracted from YouTube public videos, leading to 31k hours of audi... | ['Hank Liao', 'Otavio Braga', 'Basilio Garcia', 'Yannis Assael', 'Takaki Makino', 'Olivier Siohan', 'Brendan Shillingford'] | 2019-11-08 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [-1.40320053e-02 -2.59474684e-02 8.03736672e-02 -3.03732485e-01
-1.26990211e+00 -3.81693363e-01 4.33528364e-01 -3.77449691e-01
-2.77010560e-01 1.24798603e-01 3.74111354e-01 -3.98981154e-01
7.05191672e-01 1.13969659e-02 -8.55216980e-01 -5.37680864e-01
-1.50752477e-02 1.69363528e-01 3.80613357e-01 5.63603677... | [14.34278678894043, 5.134781360626221] |
9941a522-1527-4ed9-b6c1-4f7e07801f66 | information-retrieval-recent-advances-and | 2301.08801 | null | https://arxiv.org/abs/2301.08801v1 | https://arxiv.org/pdf/2301.08801v1.pdf | Information Retrieval: Recent Advances and Beyond | In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related ... | ['Hugo Proenca', 'Kailash A. Hambarde'] | 2023-01-20 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 1.57934517e-01 -7.13171512e-02 -7.12070525e-01 -1.26950085e-01
-7.57521093e-01 -4.38213646e-01 7.56656468e-01 7.25839257e-01
-6.25493705e-01 1.88265458e-01 9.68317166e-02 -6.86741918e-02
-7.12173343e-01 -7.71263659e-01 -6.76578283e-02 -4.57428008e-01
-2.63059556e-01 6.79975986e-01 7.78536201e-02 -4.40934449... | [11.41093635559082, 7.690741539001465] |
6eba21d9-7c4f-405c-968b-0b793283247d | qualities-challenges-and-future-of-genetic | 2011.05277 | null | https://arxiv.org/abs/2011.05277v3 | https://arxiv.org/pdf/2011.05277v3.pdf | Qualities, challenges and future of genetic algorithms: a literature review | Genetic algorithms, computer programs that simulate natural evolution, are increasingly applied across many disciplines. They have been used to solve various optimisation problems from neural network architecture search to strategic games, and to model phenomena of adaptation and learning. Expertise on the qualities an... | ['Doyne J. Farmer', 'Alissa M. Kleinnijenhuis', 'Aymeric Vie'] | 2020-11-05 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.24382475e-01 -1.88376367e-01 -3.02551743e-02 3.51711750e-01
3.00230086e-01 -5.12069166e-01 5.26365280e-01 9.50967055e-03
-6.28576756e-01 1.10216129e+00 -3.90288591e-01 -2.30596259e-01
-7.82941401e-01 -8.60106885e-01 -1.20705113e-01 -1.17922044e+00
-2.69019276e-01 5.14877498e-01 -1.40170408e-02 -8.65867496... | [5.731607913970947, 3.8675084114074707] |
79f0d305-d1a5-48e6-aa2c-6be15654b5da | cutmib-boosting-light-field-super-resolution | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xiao_CutMIB_Boosting_Light_Field_Super-Resolution_via_Multi-View_Image_Blending_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xiao_CutMIB_Boosting_Light_Field_Super-Resolution_via_Multi-View_Image_Blending_CVPR_2023_paper.pdf | CutMIB: Boosting Light Field Super-Resolution via Multi-View Image Blending | Data augmentation (DA) is an efficient strategy for improving the performance of deep neural networks. Recent DA strategies have demonstrated utility in single image super-resolution (SR). Little research has, however, focused on the DA strategy for light field SR, in which multi-view information utilization is req... | ['Zhiwei Xiong', 'Ruisheng Gao', 'Yutong Liu', 'Zeyu Xiao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 4.24767286e-01 -3.26284081e-01 2.28888076e-02 -3.57710183e-01
-5.24048686e-01 -1.55418217e-01 1.27015844e-01 -6.63209736e-01
-6.51422739e-02 7.20093608e-01 1.47349268e-01 1.96849093e-01
-1.38756558e-01 -9.58608329e-01 -7.25001812e-01 -9.97844040e-01
5.63874483e-01 -1.12828836e-01 3.55176240e-01 -1.65660590... | [10.690568923950195, -2.3334834575653076] |
0e1489e6-96a3-492f-aab6-95acf567488a | unsupervised-landmark-detection-based | 2109.14805 | null | https://arxiv.org/abs/2109.14805v3 | https://arxiv.org/pdf/2109.14805v3.pdf | Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images | Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially ... | ['Jinman Kim', 'Qian Wang', 'Ruiyan Zhang', 'Dagan Feng', 'Zhengbin Zhu', 'Liyun Chen', 'Dongming Wei', 'Lei Bi', 'Yuyu Guo'] | 2021-09-30 | null | null | null | null | ['unsupervised-landmark-detection'] | ['computer-vision'] | [-5.10842875e-02 -3.35175335e-01 -3.72257292e-01 -5.76941222e-02
-7.39920199e-01 -4.37925875e-01 1.61872149e-01 -1.80212721e-01
-3.58960450e-01 3.87882233e-01 6.08048677e-01 4.33725603e-02
-2.21268743e-01 -4.65863228e-01 -2.38536909e-01 -1.00958502e+00
-5.49251065e-02 1.68733999e-01 3.37144732e-01 2.69243777... | [14.1820707321167, -2.498248338699341] |
42849620-079a-414b-b0ea-84bac7fade33 | cascadexml-rethinking-transformers-for-end-to | 2211.00640 | null | https://arxiv.org/abs/2211.00640v1 | https://arxiv.org/pdf/2211.00640v1.pdf | CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification | Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent approaches, such as XR-Transformer and LightXML, leverage a transformer instance to achieve state-of-the-art performance. However, in this pr... | ['Rohit Babbar', 'Erik Schultheis', 'Atmadeep Banerjee', 'Siddhant Kharbanda'] | 2022-10-29 | null | null | null | null | ['multi-label-text-classification', 'extreme-multi-label-classification', 'multi-label-text-classification'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 2.72201419e-01 1.30008971e-02 -1.45043790e-01 -7.20285594e-01
-1.44331515e+00 -6.39740944e-01 5.99044383e-01 2.30234832e-01
-3.81740123e-01 5.68613112e-01 1.85910270e-01 -2.84346610e-01
-3.21374722e-02 -5.25940955e-01 -5.39204240e-01 -3.43311697e-01
4.41601008e-01 7.29707301e-01 -1.38326779e-01 1.13899551... | [9.606673240661621, 4.422499179840088] |
b63fb2c6-f278-4840-b8e2-c432d9b6d446 | multi-view-data-classification-with-a-label | 2201.00714 | null | https://arxiv.org/abs/2201.00714v1 | https://arxiv.org/pdf/2201.00714v1.pdf | Multi-view Data Classification with a Label-driven Auto-weighted Strategy | Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality v... | ['Qibin Zhao', 'Shengli Xie', 'Haonan Huang', 'Guoxu Zhou', 'Yuyuan Yu'] | 2022-01-03 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-5.65535165e-02 -3.87836620e-02 -4.70472038e-01 -5.49305737e-01
-8.47359002e-01 -4.76353586e-01 4.19653654e-01 -3.45798433e-02
-1.64848894e-01 4.58434373e-01 4.78378683e-01 1.73761696e-01
-3.80746990e-01 -6.75580919e-01 -2.95573473e-01 -9.30014908e-01
4.32855010e-01 4.21227396e-01 1.03727669e-01 8.02674517... | [8.548012733459473, 4.498826503753662] |
a55cb40c-7e9b-4431-9ef4-2bddca1b29c0 | few-shot-domain-adaptation-for-in-situ | 2007.15422 | null | https://arxiv.org/abs/2007.15422v1 | https://arxiv.org/pdf/2007.15422v1.pdf | Few shot domain adaptation for in situ macromolecule structural classification in cryo-electron tomograms | Motivation: Cryo-Electron Tomography (cryo-ET) visualizes structure and spatial organization of macromolecules and their interactions with other subcellular components inside single cells in the close-to-native state at sub-molecular resolution. Such information is critical for the accurate understanding of cellular pr... | ['Xiangrui Zeng', 'Ge Yang', 'Rui Jiang', 'Ran Li', 'Jie Jin', 'Hongyi Wang', 'Min Xu', 'Liangyong Yu'] | 2020-07-30 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 1.24460034e-01 -4.63609874e-01 -9.28890407e-02 -2.81102449e-01
-1.03052270e+00 -6.21068656e-01 4.36370194e-01 1.26652867e-01
-3.11897099e-01 1.35069299e+00 -2.03143746e-01 -4.74329442e-02
1.26791820e-01 -6.35897100e-01 -6.35435700e-01 -1.30860686e+00
2.79885769e-01 7.48008132e-01 2.93907911e-01 5.46615571... | [13.474211692810059, -3.1197354793548584] |
2fd59b15-7567-417b-a4ac-e204aa0bfe86 | novel-class-discovery-without-forgetting | 2207.10659 | null | https://arxiv.org/abs/2207.10659v1 | https://arxiv.org/pdf/2207.10659v1.pdf | Novel Class Discovery without Forgetting | Humans possess an innate ability to identify and differentiate instances that they are not familiar with, by leveraging and adapting the knowledge that they have acquired so far. Importantly, they achieve this without deteriorating the performance on their earlier learning. Inspired by this, we identify and formulate a... | ['Vineeth N Balasubramanian', 'Kai Han', 'Piyush Rai', 'Soma Biswas', 'Gaurav Aggarwal', 'Sujoy Paul', 'K J Joseph'] | 2022-07-21 | null | null | null | null | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 3.84617597e-01 1.50769427e-01 -7.98424110e-02 -5.09558022e-01
-3.59280050e-01 -6.02480173e-01 5.64247370e-01 2.70313352e-01
-6.95130229e-01 1.08667624e+00 1.21923663e-01 -6.11553118e-02
-2.58642852e-01 -7.99544513e-01 -6.47763491e-01 -6.72715068e-01
-2.29606867e-01 7.03578949e-01 2.87375569e-01 2.60209978... | [9.837520599365234, 3.3080875873565674] |
93887b33-6794-4468-be2a-142ed1b01ea0 | intrinsic-scene-decomposition-from-rgb-d | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Hachama_Intrinsic_Scene_Decomposition_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Hachama_Intrinsic_Scene_Decomposition_ICCV_2015_paper.pdf | Intrinsic Scene Decomposition From RGB-D images | In this paper, we address the problem of computing an intrinsic decomposition of the colors of a surface into an albedo and a shading term. The surface is reconstructed from a single or multiple RGB-D images of a static scene obtained from different views. We thereby extend and improve existing works in the area of int... | ['Peter Wonka', 'Mohammed Hachama', 'Bernard Ghanem'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.92000484e-01 -4.20763820e-01 4.23743188e-01 -3.45957637e-01
-3.45587999e-01 -3.98807138e-01 5.94231248e-01 -6.42018840e-02
-4.30193037e-01 4.68791157e-01 -2.46649864e-03 1.15078717e-01
1.45363554e-01 -8.77752125e-01 -7.36915112e-01 -1.22520018e+00
6.53110921e-01 1.44748732e-01 1.34329215e-01 -3.02064568... | [9.925837516784668, -2.9478325843811035] |
4def3508-df31-4b92-84af-3fae05f0f77a | adversarial-synthesis-based-data-augmentation | 2205.15747 | null | https://arxiv.org/abs/2205.15747v2 | https://arxiv.org/pdf/2205.15747v2.pdf | Adversarial synthesis based data-augmentation for code-switched spoken language identification | Spoken Language Identification (LID) is an important sub-task of Automatic Speech Recognition(ASR) that is used to classify the language(s) in an audio segment. Automatic LID plays an useful role in multilingual countries. In various countries, identifying a language becomes hard, due to the multilingual scenario where... | ['Dr. Hardik Sailor', 'Dr. Abhishek Bhatt', 'Dr. Shrinivas Mahajan', 'Poorval Wanere', 'Chirag Patil', 'Parth Shastri'] | 2022-05-30 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 1.80242643e-01 -7.57848620e-02 1.71072870e-01 -3.14157568e-02
-1.01673532e+00 -7.36677051e-01 6.15776181e-01 -1.63449183e-01
-2.36510739e-01 8.57741416e-01 4.61106509e-01 -4.40099090e-01
5.42531013e-01 -4.75523919e-01 -3.40529710e-01 -8.33725154e-01
3.25976193e-01 4.74678338e-01 -2.95187116e-01 -3.61211389... | [14.248126029968262, 6.529244899749756] |
4ffc4e95-237c-43cd-8007-e55154c470ba | modelling-uncertainty-in-deep-learning-for | 1509.05909 | null | http://arxiv.org/abs/1509.05909v2 | http://arxiv.org/pdf/1509.05909v2.pdf | Modelling Uncertainty in Deep Learning for Camera Relocalization | We present a robust and real-time monocular six degree of freedom visual
relocalization system. We use a Bayesian convolutional neural network to
regress the 6-DOF camera pose from a single RGB image. It is trained in an
end-to-end manner with no need of additional engineering or graph optimisation.
The algorithm can o... | ['Roberto Cipolla', 'Alex Kendall'] | 2015-09-19 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 1.65182769e-01 1.60790503e-01 1.06560625e-01 -5.24751067e-01
-7.57850766e-01 -8.26866984e-01 4.97693628e-01 -1.11699410e-01
-6.15424812e-01 6.31233037e-01 -1.25440195e-01 -3.24639171e-01
-1.20026484e-01 -4.44119215e-01 -1.30876505e+00 -3.93666834e-01
8.16666260e-02 6.35674536e-01 1.49173275e-01 2.46008962... | [7.657641887664795, -2.270331859588623] |
2ac93ef3-5045-4764-966c-4be99fa39668 | cqe-a-comprehensive-quantity-extractor | 2305.08853 | null | https://arxiv.org/abs/2305.08853v1 | https://arxiv.org/pdf/2305.08853v1.pdf | CQE: A Comprehensive Quantity Extractor | Quantities are essential in documents to describe factual information. They are ubiquitous in application domains such as finance, business, medicine, and science in general. Compared to other information extraction approaches, interestingly only a few works exist that describe methods for a proper extraction and repre... | ['Michael Gertz', 'Philip Göldner', 'Vivian Kazakova', 'Satya Almasian'] | 2023-05-15 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-1.68892398e-01 -2.47404784e-01 -3.57814342e-01 -2.21707702e-01
-5.89331150e-01 -7.61674702e-01 9.52469468e-01 1.06621552e+00
-4.69533890e-01 7.51319706e-01 1.56617373e-01 -4.03201610e-01
-2.11709023e-01 -1.03583705e+00 -3.53832334e-01 -4.30745721e-01
-8.16749223e-03 4.77428973e-01 2.28010237e-01 -2.89505988... | [9.428722381591797, 8.730173110961914] |
9f399398-0445-4ea4-9d56-5a8a53b5dfad | a-variational-perspective-on-solving-inverse | 2305.04391 | null | https://arxiv.org/abs/2305.04391v1 | https://arxiv.org/pdf/2305.04391v1.pdf | A Variational Perspective on Solving Inverse Problems with Diffusion Models | Diffusion models have emerged as a key pillar of foundation models in visual domains. One of their critical applications is to universally solve different downstream inverse tasks via a single diffusion prior without re-training for each task. Most inverse tasks can be formulated as inferring a posterior distribution o... | ['Arash Vahdat', 'Jan Kautz', 'Jiaming Song', 'Morteza Mardani'] | 2023-05-07 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 3.89774710e-01 4.16663624e-02 2.64113754e-01 1.63343437e-02
-8.92332852e-01 -4.32142317e-01 7.55395949e-01 -5.22991419e-01
-3.07074368e-01 6.19930804e-01 3.80200148e-01 -5.14687486e-02
-2.72385001e-01 -5.61087310e-01 -7.01056719e-01 -1.05147576e+00
3.17642808e-01 3.63003165e-01 1.12977549e-01 -2.74176389... | [11.698702812194824, -2.395348072052002] |
ef7759fb-58de-441e-a8fb-1fa060e9444d | robot-navigation-in-constrained-pedestrian | 2010.08600 | null | https://arxiv.org/abs/2010.08600v2 | https://arxiv.org/pdf/2010.08600v2.pdf | Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning | Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation has focused mainly on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional chal... | ['Silvio Savarese', 'Roberto Martín-Martín', 'Patrick Goebel', 'Can Liu', "Claudia Pérez-D'Arpino"] | 2020-10-16 | null | null | null | null | ['social-navigation'] | ['robots'] | [-1.14877731e-01 2.49350056e-01 2.62026668e-01 -3.36733669e-01
-1.08329266e-01 -7.01647162e-01 6.67746663e-01 9.71521139e-02
-8.36379766e-01 1.15357327e+00 3.86180371e-01 -6.24709606e-01
-3.39393526e-01 -1.11000574e+00 -8.51426721e-01 -6.61859930e-01
-7.68301487e-01 9.08779323e-01 5.50148189e-01 -8.83987665... | [4.76202917098999, 0.8568902015686035] |
5b1b4363-3916-4473-b212-62b278293a38 | meta-batch-instance-normalization-for | 2011.14670 | null | https://arxiv.org/abs/2011.14670v2 | https://arxiv.org/pdf/2011.14670v2.pdf | Meta Batch-Instance Normalization for Generalizable Person Re-Identification | Although supervised person re-identification (Re-ID) methods have shown impressive performance, they suffer from a poor generalization capability on unseen domains. Therefore, generalizable Re-ID has recently attracted growing attention. Many existing methods have employed an instance normalization technique to reduce ... | ['Changick Kim', 'Hyoungseob Park', 'Minki Jeong', 'Taekyung Kim', 'Seokeon Choi'] | 2020-11-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Choi_Meta_Batch-Instance_Normalization_for_Generalizable_Person_Re-Identification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['generalizable-person-re-identification'] | ['computer-vision'] | [-3.59722041e-02 -3.62304360e-01 -4.11805660e-02 -6.56526327e-01
-3.21954846e-01 -4.20757145e-01 6.47526026e-01 -2.19182104e-01
-6.01750493e-01 6.81793094e-01 4.57170084e-02 1.99246287e-01
-1.42560318e-01 -6.74319386e-01 -5.23539901e-01 -5.23211241e-01
4.44557160e-01 5.13524175e-01 -1.80143975e-02 -4.18803483... | [14.748296737670898, 1.0680232048034668] |
5d270299-e4db-44e3-a13c-7d4d2cc57b1e | flowlens-seeing-beyond-the-fov-via-flow | 2211.11293 | null | https://arxiv.org/abs/2211.11293v1 | https://arxiv.org/pdf/2211.11293v1.pdf | FlowLens: Seeing Beyond the FoV via Flow-guided Clip-Recurrent Transformer | Limited by hardware cost and system size, camera's Field-of-View (FoV) is not always satisfactory. However, from a spatio-temporal perspective, information beyond the camera's physical FoV is off-the-shelf and can actually be obtained "for free" from the past. In this paper, we propose a novel task termed Beyond-FoV Es... | ['Kaiwei Wang', 'Xiaoting Yin', 'Kailun Yang', 'Qi Jiang', 'Hao Shi'] | 2022-11-21 | null | null | null | null | ['seeing-beyond-the-visible', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [-2.82524943e-01 -3.53246242e-01 -1.58146083e-01 -2.10308149e-01
-4.67954427e-01 -4.04855847e-01 3.78735900e-01 -5.94103098e-01
-1.12819493e-01 4.86775666e-01 4.27836686e-01 -2.68553555e-01
8.03562924e-02 -4.56723422e-01 -8.01390946e-01 -5.00046194e-01
1.53788313e-01 -5.23853362e-01 2.63601750e-01 3.55129950... | [10.75388240814209, -1.4666528701782227] |
2177af99-69bc-43eb-ad38-c718dd6fefe4 | collision-avoidance-robotics-via-meta | 2007.08616 | null | https://arxiv.org/abs/2007.08616v1 | https://arxiv.org/pdf/2007.08616v1.pdf | Collision Avoidance Robotics Via Meta-Learning (CARML) | This paper presents an approach to exploring a multi-objective reinforcement learning problem with Model-Agnostic Meta-Learning. The environment we used consists of a 2D vehicle equipped with a LIDAR sensor. The goal of the environment is to reach some pre-determined target location but also effectively avoid any obsta... | ['Abhiram Iyer', 'Aravind Mahadevan'] | 2020-07-16 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.80568919e-02 2.67581671e-01 -4.69875038e-01 -7.27166161e-02
-9.31844234e-01 -4.01156634e-01 5.17999828e-01 1.10833198e-01
-1.03509855e+00 1.25102198e+00 -5.52085936e-01 -4.87693667e-01
-6.10963643e-01 -9.63504672e-01 -7.57247210e-01 -7.67202079e-01
-4.69841748e-01 1.02229154e+00 3.54208022e-01 -5.18865108... | [5.003396511077881, 1.3238885402679443] |
185f2540-1b9d-4a7d-a4c6-dffcf767221f | slic-hf-sequence-likelihood-calibration-with | 2305.10425 | null | https://arxiv.org/abs/2305.10425v1 | https://arxiv.org/pdf/2305.10425v1.pdf | SLiC-HF: Sequence Likelihood Calibration with Human Feedback | Learning from human feedback has been shown to be effective at aligning language models with human preferences. Past work has often relied on Reinforcement Learning from Human Feedback (RLHF), which optimizes the language model using reward scores assigned from a reward model trained on human preference data. In this w... | ['Peter J. Liu', 'Mohammad Saleh', 'Misha Khalman', 'Tianqi Liu', 'Rishabh Joshi', 'Yao Zhao'] | 2023-05-17 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 3.37003142e-01 1.89915821e-01 -5.68223476e-01 -5.65546215e-01
-1.32049060e+00 -8.31514239e-01 6.11213624e-01 2.77725101e-01
-8.98258090e-01 1.03049791e+00 8.27351630e-01 -4.39464092e-01
1.89834833e-01 -1.67558640e-01 -5.91349900e-01 -4.42726344e-01
1.54689014e-01 9.04223204e-01 -2.72073820e-02 -2.82532036... | [11.80644416809082, 8.904767036437988] |
17234e82-ef8f-45f1-a305-6884295c3aac | lstfe-net-long-short-term-feature-enhancement | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xiao_LSTFE-NetLong_Short-Term_Feature_Enhancement_Network_for_Video_Small_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xiao_LSTFE-NetLong_Short-Term_Feature_Enhancement_Network_for_Video_Small_Object_Detection_CVPR_2023_paper.pdf | LSTFE-Net:Long Short-Term Feature Enhancement Network for Video Small Object Detection | Video small object detection is a difficult task due to the lack of object information. Recent methods focus on adding more temporal information to obtain more potent high-level features, which often fail to specify the most vital information for small objects, resulting in insufficient or inappropriate features. S... | ['Jiayi Ma', 'Zhongyuan Wang', 'Shurui Wang', 'Yunhua Chen', 'Yuanxu Wu', 'Jinsheng Xiao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['small-object-detection'] | ['computer-vision'] | [-1.15647294e-01 -6.16313756e-01 -2.70407051e-01 -4.82207805e-01
-6.16047144e-01 -4.31082785e-01 4.14897352e-01 -3.72986123e-02
-5.27024269e-01 5.07900953e-01 2.21340805e-01 2.55956560e-01
6.59086779e-02 -8.26423407e-01 -7.08579659e-01 -6.74343705e-01
-3.01364571e-01 -3.46981376e-01 9.75408316e-01 -2.71060914... | [9.071020126342773, -0.3598281741142273] |
8ae81d02-6af0-4760-a99b-f0d33ea998f9 | exploiting-spatial-information-with-the | 2210.15512 | null | https://arxiv.org/abs/2210.15512v2 | https://arxiv.org/pdf/2210.15512v2.pdf | Exploiting spatial information with the informed complex-valued spatial autoencoder for target speaker extraction | In conventional multichannel audio signal enhancement, spatial and spectral filtering are often performed sequentially. In contrast, it has been shown that for neural spatial filtering a joint approach of spectro-spatial filtering is more beneficial. In this contribution, we investigate the spatial filtering performed ... | ['Walter Kellermann', 'Mhd Modar Halimeh', 'Annika Briegleb'] | 2022-10-27 | null | null | null | null | ['target-speaker-extraction'] | ['audio'] | [ 4.62955952e-01 -1.55715466e-01 3.85278374e-01 -2.68403232e-01
-8.74294341e-01 -6.17708921e-01 6.72184408e-01 -7.10505545e-02
-6.73388362e-01 6.45074010e-01 3.20117176e-01 -5.76026700e-02
-4.95102763e-01 -3.50470513e-01 -6.82421625e-01 -1.00122511e+00
-2.15759888e-01 -1.00182585e-01 2.81720161e-01 -1.54374778... | [15.279093742370605, 5.68610954284668] |
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