paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1fb322c0-eb24-4ecb-b7d8-d9ba7446f73f | mid-space-independent-deformable-image | null | null | https://doi.org/10.1016/j.neuroimage.2017.02.055 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5432428/pdf/nihms859177.pdf | Mid-space-independent deformable image registration | Aligning images in a mid-space is a common approach to ensuring that deformable image registration is symmetric – that it does not depend on the arbitrary ordering of the input images. The results are, however, generally dependent on the mathematical definition of the mid-space. In particular, the set of possible solut... | ['B. Fischl', 'M. R. Sabuncu', 'M. Reuter', 'J. E. Iglesias', 'I. Aganj'] | 2017-05-15 | null | null | null | neuroimage-2017-5 | ['deformable-medical-image-registration'] | ['medical'] | [ 1.71216041e-01 2.04971030e-01 4.04199958e-02 -5.66077352e-01
-2.13403538e-01 -6.87204838e-01 4.44571495e-01 4.78710309e-02
-6.50423646e-01 5.34572124e-01 -4.25755493e-02 1.64027929e-01
-4.50482398e-01 -7.30747938e-01 -6.04258955e-01 -9.40008640e-01
-8.72860998e-02 4.17225301e-01 4.42526549e-01 -3.24994206... | [13.934399604797363, -2.530362367630005] |
b45fc68c-a416-41ab-9453-3f56012e1dba | supervised-and-unsupervised-deep-learning | 2304.14922 | null | https://arxiv.org/abs/2304.14922v1 | https://arxiv.org/pdf/2304.14922v1.pdf | Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction | Epilepsy affects more than 50 million people worldwide, making it one of the world's most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and st... | ['Shehroz S. Khan', 'Milos R. Popovic', 'Zakary Georgis-Yap'] | 2023-04-24 | null | null | null | null | ['seizure-prediction', 'seizure-detection'] | ['medical', 'medical'] | [ 4.21787910e-02 -6.19441122e-02 3.22944105e-01 -3.83736610e-01
-7.41144121e-01 -4.31848437e-01 3.66408974e-01 2.27754042e-01
-1.93614319e-01 7.64524460e-01 2.93817878e-01 -1.42931998e-01
-8.05278271e-02 -5.47204971e-01 -2.36253917e-01 -7.82267570e-01
-5.97133160e-01 3.95105511e-01 -1.62527442e-01 1.28026560... | [13.243268013000488, 3.542248249053955] |
d35e64f3-b69d-411e-a055-35773912c96b | make-a-choice-knowledge-base-question | 2305.13972 | null | https://arxiv.org/abs/2305.13972v1 | https://arxiv.org/pdf/2305.13972v1.pdf | Make a Choice! Knowledge Base Question Answering with In-Context Learning | Question answering over knowledge bases (KBQA) aims to answer factoid questions with a given knowledge base (KB). Due to the large scale of KB, annotated data is impossible to cover all fact schemas in KB, which poses a challenge to the generalization ability of methods that require a sufficient amount of annotated dat... | ['Wenliang Chen', 'Wenbiao Shao', 'Yuehe Chen', 'Chuanyuan Tan'] | 2023-05-23 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.32567981e-01 6.58236921e-01 -7.84578100e-02 -4.77563024e-01
-1.49529421e+00 -7.01580524e-01 4.09270704e-01 2.71537155e-02
-2.56310552e-01 1.40156829e+00 4.19491738e-01 -3.63045454e-01
-3.47622871e-01 -1.36894262e+00 -8.25454891e-01 -6.30434453e-02
4.26794976e-01 1.16617298e+00 7.54340947e-01 -1.02969134... | [10.623327255249023, 7.906917095184326] |
cd4924c3-0095-430e-a712-06ea604d8f16 | boxinst-high-performance-instance | 2012.02310 | null | https://arxiv.org/abs/2012.02310v1 | https://arxiv.org/pdf/2012.02310v1.pdf | BoxInst: High-Performance Instance Segmentation with Box Annotations | We present a high-performance method that can achieve mask-level instance segmentation with only bounding-box annotations for training. While this setting has been studied in the literature, here we show significantly stronger performance with a simple design (e.g., dramatically improving previous best reported mask AP... | ['Hao Chen', 'Xinlong Wang', 'Chunhua Shen', 'Zhi Tian'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Tian_BoxInst_High-Performance_Instance_Segmentation_With_Box_Annotations_CVPR_2021_paper.pdf | cvpr-2021-1 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.40803611e-01 5.28363168e-01 -1.86119422e-01 -5.76783895e-01
-1.01260769e+00 -7.45550632e-01 3.33163440e-01 -1.01199158e-01
-6.86561167e-01 6.99788630e-01 -2.70604283e-01 -2.95829445e-01
4.16641891e-01 -5.24459898e-01 -1.05261707e+00 -7.13845074e-01
1.83120251e-01 4.31513250e-01 5.88522494e-01 1.23892903... | [9.4433012008667, 0.3828722834587097] |
b84cfae6-4a2a-42d8-856c-7c12422dc8b0 | scenic-language-based-scene-generation | 1809.09310 | null | https://arxiv.org/abs/1809.09310v2 | https://arxiv.org/pdf/1809.09310v2.pdf | Scenic: A Language for Scenario Specification and Scene Generation | We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We ... | ['Sanjit A. Seshia', 'Alberto L. Sangiovanni-Vincentelli', 'Shromona Ghosh', 'Xiangyu Yue', 'Tommaso Dreossi', 'Daniel J. Fremont'] | 2018-09-25 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 1.32195652e-01 2.46194243e-01 2.94132829e-01 -5.96696973e-01
-1.49633139e-01 -5.13356030e-01 9.14547980e-01 2.08629623e-01
-3.04633498e-01 6.38385594e-01 -4.70146298e-01 -4.29195434e-01
-9.11257789e-02 -1.38242579e+00 -1.05459797e+00 -7.16007352e-01
-2.39845246e-01 8.45357895e-01 6.63831711e-01 -5.09348661... | [4.884744644165039, 1.6147022247314453] |
358443f4-ec91-42aa-a624-bb4034562d26 | gpt4tools-teaching-large-language-model-to | 2305.18752 | null | https://arxiv.org/abs/2305.18752v1 | https://arxiv.org/pdf/2305.18752v1.pdf | GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction | This paper aims to efficiently enable Large Language Models (LLMs) to use multimodal tools. Advanced proprietary LLMs, such as ChatGPT and GPT-4, have shown great potential for tool usage through sophisticated prompt engineering. Nevertheless, these models typically rely on prohibitive computational costs and publicly ... | ['Ying Shan', 'Xiu Li', 'Yixiao Ge', 'Sijie Zhao', 'Yanwei Li', 'Lin Song', 'Rui Yang'] | 2023-05-30 | null | null | null | null | ['instruction-following', 'prompt-engineering'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.41206121e-02 -1.94941238e-01 2.79502175e-03 -1.94829613e-01
-1.01781356e+00 -6.94774568e-01 5.99411249e-01 -2.68859208e-01
-2.70489186e-01 1.96191490e-01 -7.32387826e-02 -6.10563934e-01
3.15888971e-02 -4.96156663e-01 -7.20020354e-01 -4.01917279e-01
3.75461936e-01 4.86295670e-01 1.39806420e-01 -2.18942061... | [10.976948738098145, 8.081212997436523] |
26154f00-e461-4264-b7ef-9ec23e2bebab | translatotron-3-speech-to-speech-translation | 2305.17547 | null | https://arxiv.org/abs/2305.17547v2 | https://arxiv.org/pdf/2305.17547v2.pdf | Translatotron 3: Speech to Speech Translation with Monolingual Data | This paper presents Translatotron 3, a novel approach to train a direct speech-to-speech translation model from monolingual speech-text datasets only in a fully unsupervised manner. Translatotron 3 combines masked autoencoder, unsupervised embedding mapping, and back-translation to achieve this goal. Experimental resul... | ['Chulayuth Asawaroengchai', 'Michelle Tadmor Ramanovich', 'Heiga Zen', 'Yifan Ding', 'Alon Levkovitch', 'Eliya Nachmani'] | 2023-05-27 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [-1.07127585e-01 3.64083856e-01 -2.70360619e-01 -5.02988100e-01
-1.28904414e+00 -6.20801747e-01 7.24839389e-01 -2.27557778e-01
-2.44091913e-01 6.79874003e-01 5.92348099e-01 -7.53522098e-01
6.81295872e-01 -2.64723778e-01 -7.36694157e-01 -4.48652655e-01
2.82524467e-01 7.86957502e-01 -3.93999726e-01 -3.18928093... | [14.577853202819824, 7.11915397644043] |
7b27de5d-eeae-46fe-a10c-1243c17cf0fc | differentiable-dynamic-programming-for | 1802.03676 | null | http://arxiv.org/abs/1802.03676v2 | http://arxiv.org/pdf/1802.03676v2.pdf | Differentiable Dynamic Programming for Structured Prediction and Attention | Dynamic programming (DP) solves a variety of structured combinatorial
problems by iteratively breaking them down into smaller subproblems. In spite
of their versatility, DP algorithms are usually non-differentiable, which
hampers their use as a layer in neural networks trained by backpropagation. To
address this issue,... | ['Mathieu Blondel', 'Arthur Mensch'] | 2018-02-11 | differentiable-dynamic-programming-for-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2114 | http://proceedings.mlr.press/v80/mensch18a/mensch18a.pdf | icml-2018-7 | ['time-series-alignment'] | ['time-series'] | [ 7.66893923e-01 5.68434477e-01 -3.66418928e-01 -4.62632686e-01
-8.87182415e-01 -7.19261110e-01 7.20076263e-01 -1.11870669e-01
-3.35437536e-01 6.31372631e-01 1.66320652e-01 -6.76268876e-01
-2.74817377e-01 -5.69816887e-01 -9.54681575e-01 -7.83234596e-01
2.99085695e-02 6.42889380e-01 -8.58014077e-02 -5.93776889... | [11.418400764465332, 8.903435707092285] |
20e09e5d-502c-480e-af4d-2800ae01d2cd | modular-decomposition-of-protein-structure | 1809.06632 | null | http://arxiv.org/abs/1809.06632v1 | http://arxiv.org/pdf/1809.06632v1.pdf | Modular decomposition of protein structure using community detection | As the number of solved protein structures increases, the opportunities for
meta-analysis of this dataset increase too. Protein structures are known to be
formed of domains; structural and functional subunits that are often repeated
across sets of proteins. These domains generally form compact, globular
regions, and ar... | [] | 2018-09-18 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.43417042e-01 1.43546879e-01 -2.48087779e-01 -1.19486555e-01
-2.99041420e-01 -9.13366079e-01 3.18682045e-01 4.89656419e-01
-2.36713678e-01 9.82131720e-01 1.92857146e-01 -6.14511013e-01
-2.55393863e-01 -5.78421533e-01 -5.04039347e-01 -1.04259646e+00
-4.12037402e-01 8.85669410e-01 5.56229770e-01 -5.32728359... | [4.822559833526611, 5.314019680023193] |
adefb078-acfc-4eb5-9949-9891b9b293f4 | learning-to-solve-combinatorial-graph | 2205.14105 | null | https://arxiv.org/abs/2205.14105v1 | https://arxiv.org/pdf/2205.14105v1.pdf | Learning to Solve Combinatorial Graph Partitioning Problems via Efficient Exploration | From logistics to the natural sciences, combinatorial optimisation on graphs underpins numerous real-world applications. Reinforcement learning (RL) has shown particular promise in this setting as it can adapt to specific problem structures and does not require pre-solved instances for these, often NP-hard, problems. H... | ['Alexandre Laterre', 'Christopher W. F. Parsonson', 'Thomas D. Barrett'] | 2022-05-27 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [ 3.51544410e-01 3.27461213e-01 -2.46393785e-01 1.74262315e-01
-6.58055186e-01 -5.13606429e-01 5.86319149e-01 5.69372833e-01
-5.66181004e-01 8.49561095e-01 -1.73567042e-01 -6.93177342e-01
-5.62541068e-01 -1.06035268e+00 -6.41078055e-01 -7.92036474e-01
-5.95128000e-01 9.21600044e-01 7.93916360e-02 -3.71932238... | [5.232480525970459, 3.0331430435180664] |
d3ee40de-dd4d-4325-abe1-ab7387ae96e0 | clinical-longformer-and-clinical-bigbird | 2201.11838 | null | https://arxiv.org/abs/2201.11838v3 | https://arxiv.org/pdf/2201.11838v3.pdf | Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences | Transformers-based models, such as BERT, have dramatically improved the performance for various natural language processing tasks. The clinical knowledge enriched model, namely ClinicalBERT, also achieved state-of-the-art results when performed on clinical named entity recognition and natural language inference tasks. ... | ['Yuan Luo', 'Hanyin Wang', 'Faraz S. Ahmad', 'Ramsey M. Wehbe', 'Yikuan Li'] | 2022-01-27 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-1.91652149e-01 1.62634403e-01 -1.87101811e-01 -2.82390624e-01
-1.12719655e+00 -2.13896781e-01 3.05372596e-01 1.96692780e-01
-7.32332408e-01 8.90946507e-01 5.81424057e-01 -4.65683311e-01
-2.54737854e-01 -5.81665993e-01 -5.22153556e-01 -4.62466329e-01
-2.20116943e-01 6.71261132e-01 4.86114658e-02 -2.08156109... | [8.567428588867188, 8.756875038146973] |
c6e3c70d-e695-4cce-8fa1-14dc0b4a8307 | doubly-attentive-decoder-for-multi-modal | 1702.01287 | null | http://arxiv.org/abs/1702.01287v1 | http://arxiv.org/pdf/1702.01287v1.pdf | Doubly-Attentive Decoder for Multi-modal Neural Machine Translation | We introduce a Multi-modal Neural Machine Translation model in which a
doubly-attentive decoder naturally incorporates spatial visual features
obtained using pre-trained convolutional neural networks, bridging the gap
between image description and translation. Our decoder learns to attend to
source-language words and p... | ['Iacer Calixto', 'Qun Liu', 'Nick Campbell'] | 2017-02-04 | doubly-attentive-decoder-for-multi-modal-1 | https://aclanthology.org/P17-1175 | https://aclanthology.org/P17-1175.pdf | acl-2017-7 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 3.13247204e-01 2.92505801e-01 -2.50591576e-01 -3.32754523e-01
-1.42125332e+00 -7.04049110e-01 1.02032256e+00 -4.15576547e-01
-5.50299287e-01 6.02750897e-01 5.74275911e-01 -1.57924443e-01
8.65538836e-01 -5.71435213e-01 -1.28219748e+00 -2.46197045e-01
4.52638716e-01 7.08317518e-01 2.11041532e-02 -3.16078484... | [11.294177055358887, 1.4606715440750122] |
1cd0cb36-7879-4286-9d45-5b8439408a0f | adversarial-autoencoders-for-generating-3d | 1811.07605 | null | http://arxiv.org/abs/1811.07605v3 | http://arxiv.org/pdf/1811.07605v3.pdf | Adversarial Autoencoders for Compact Representations of 3D Point Clouds | Deep generative architectures provide a way to model not only images but also
complex, 3-dimensional objects, such as point clouds. In this work, we present
a novel method to obtain meaningful representations of 3D shapes that can be
used for challenging tasks including 3D points generation, reconstruction,
compression... | ['Tomasz Trzciński', 'Rafał Nowak', 'Piotr Klukowski', 'Maciej Zięba', 'Maciej Zamorski', 'Wojciech Stokowiec', 'Karol Kurach'] | 2018-11-19 | null | null | null | null | ['point-cloud-generation', 'generating-3d-point-clouds', '3d-object-retrieval'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.02902785e-01 1.26220912e-01 2.31479272e-01 -9.47928801e-02
-9.33276594e-01 -7.76370943e-01 9.77318704e-01 -2.32128844e-01
9.73495767e-02 5.79393059e-02 -3.18581276e-02 -2.81152546e-01
-5.54699302e-02 -1.16404426e+00 -1.13868511e+00 -7.14112222e-01
6.33257776e-02 1.11247373e+00 -1.76446274e-01 -8.26577097... | [8.8107271194458, -3.688920259475708] |
20033521-52d3-4602-9583-e63ec5ad0d14 | autoregressive-unsupervised-image | 2007.08247 | null | https://arxiv.org/abs/2007.08247v1 | https://arxiv.org/pdf/2007.08247v1.pdf | Autoregressive Unsupervised Image Segmentation | In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspiration from autoregressive generative models that predict the current pixel from past pixels in a raster-scan ordering created with masked convo... | ['Céline Hudelot', 'Yassine Ouali', 'Myriam Tami'] | 2020-07-16 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/160_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520137.pdf | eccv-2020-8 | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 7.59416640e-01 4.34237510e-01 4.17184383e-02 -8.64348114e-01
-3.04027826e-01 -6.12764418e-01 6.78842187e-01 -1.53256685e-01
-4.05689448e-01 2.82912880e-01 -1.02637060e-01 -2.52206177e-01
-1.52212352e-01 -1.01087248e+00 -7.67382503e-01 -8.47228467e-01
2.47224838e-01 5.92585564e-01 3.79389733e-01 4.45758224... | [9.622199058532715, 0.6497306227684021] |
4d546baf-be26-4559-a6b5-ade8790514b5 | evaluation-metrics-for-cnns-compression | 2305.10616 | null | https://arxiv.org/abs/2305.10616v2 | https://arxiv.org/pdf/2305.10616v2.pdf | Evaluation Metrics for DNNs Compression | There is a lot of research effort into developing different techniques for neural networks compression. However, the community lacks standardised evaluation metrics, which are key to identifying the most suitable compression technique for different applications. This paper reviews existing neural network compression ev... | ['Samuel Budgett', 'Kerstin Eder', 'Phil Reiter', 'Hamid Asgari', 'Dieter Balemans', 'Abanoub Ghobrial'] | 2023-05-18 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 5.12458563e-01 -1.91259608e-01 -5.67631662e-01 -5.14590979e-01
-1.41080484e-01 -1.06953859e-01 5.10043085e-01 4.61225629e-01
-8.90992224e-01 6.26476467e-01 9.19077471e-02 -3.62798125e-01
-6.32705450e-01 -9.10532355e-01 -4.32024539e-01 -4.44407195e-01
-1.37852475e-01 3.60252231e-01 2.52014905e-01 1.52948266... | [8.504691123962402, 3.095745086669922] |
e84d9f18-7de8-41e6-9d5a-13f5e845eb10 | large-scale-cell-level-quality-of-service | 2212.14071 | null | https://arxiv.org/abs/2212.14071v2 | https://arxiv.org/pdf/2212.14071v2.pdf | Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques | This study presents a general machine learning framework to estimate the traffic-measurement-level experience rate at given throughput values in the form of a Key Performance Indicator for the cells on base stations across various cities, using busy-hour counter data, and several technical parameters together with the ... | ['Mahiye Uluyağmur Öztürk', 'Ufuk Uyan', 'M. Tuğberk İşyapar'] | 2022-12-28 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-4.91499782e-01 -7.28899762e-02 -2.68462121e-01 -4.85114485e-01
-9.20130908e-01 9.79994684e-02 3.43356520e-01 4.93321657e-01
-2.12619066e-01 1.13046229e+00 2.33146757e-01 -6.44253194e-01
-5.83732426e-01 -1.17803264e+00 -1.62500188e-01 -1.02105868e+00
-6.38318837e-01 4.24985796e-01 3.58235426e-02 -1.63914785... | [6.130830764770508, 1.5760794878005981] |
e4ad4c9d-50b7-4651-bbc1-21b44565d35d | boost-rs-boosted-embeddings-for-recommender | 2109.14766 | null | https://arxiv.org/abs/2109.14766v1 | https://arxiv.org/pdf/2109.14766v1.pdf | Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction | Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Recommender systems (RS), which are currently unexplored for the enzyme-substrate interaction prediction problem, can be utilized to provide enzyme recommendations for subst... | ['Soha Hassoun', 'Li-Ping Liu', 'Xinmeng Li'] | 2021-09-28 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 3.11917931e-01 -9.44441929e-02 -3.64144176e-01 -2.07848221e-01
-5.66305220e-01 -1.12611949e+00 8.02332997e-01 1.73317030e-01
-1.69703528e-01 7.40892470e-01 7.83711016e-01 -5.21221042e-01
-4.39886898e-01 -6.03631735e-01 -6.62215352e-01 -9.10856426e-01
-1.82005212e-01 2.39215061e-01 4.09268253e-02 -2.44228914... | [5.121079444885254, 5.873417854309082] |
f3a4e2ea-0e41-49ac-b980-390158873252 | aei-actors-environment-interaction-with | 2110.11474 | null | https://arxiv.org/abs/2110.11474v2 | https://arxiv.org/pdf/2110.11474v2.pdf | AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation | Humans typically perceive the establishment of an action in a video through the interaction between an actor and the surrounding environment. An action only starts when the main actor in the video begins to interact with the environment, while it ends when the main actor stops the interaction. Despite the great progres... | ['Minh-Triet Tran', 'Ngan Le', 'Kris Kitani', 'Sang Truong', 'Kashu Yamazaki', 'Hyekang Joo', 'Khoa Vo'] | 2021-10-21 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 5.60006201e-01 -9.60293785e-03 -2.43927628e-01 -1.55315027e-01
4.14475352e-02 -1.52736947e-01 1.06359339e+00 -4.96725738e-02
-5.54969609e-01 2.75966763e-01 3.37512016e-01 1.56828091e-02
9.62820947e-02 -7.07102418e-01 -6.02829635e-01 -6.18633449e-01
-4.38818671e-02 1.62881255e-01 6.87402606e-01 -1.27805203... | [8.441238403320312, 0.638586163520813] |
5b85b45d-4ec5-4533-b11c-3789bbcc9a44 | diff-id-an-explainable-identity-difference | 2303.18174 | null | https://arxiv.org/abs/2303.18174v1 | https://arxiv.org/pdf/2303.18174v1.pdf | Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection | Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions s... | ['Wenzhi Chen', 'Shouling Ji', 'Yang Xiang', 'Zonghui Wang', 'Senbo Yan', 'Yuxuan Duan', 'Xuhong Zhang', 'Chuer Yu'] | 2023-03-30 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 4.17027861e-01 -2.23065689e-01 1.79832295e-01 -4.32814658e-01
-5.34448266e-01 -6.80717826e-01 6.11420631e-01 -3.26665789e-01
-1.69854477e-01 5.16130209e-01 -1.43232331e-01 1.18345343e-01
-1.11746676e-01 -8.41920614e-01 -7.35874236e-01 -9.19879496e-01
-1.76703539e-02 -6.19283989e-02 -1.76396236e-01 -2.50660688... | [12.703771591186523, 1.0354273319244385] |
fe0e012f-f012-4754-a003-7c9ec8e545a7 | endpoints-weight-fusion-for-class-incremental | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xiao_Endpoints_Weight_Fusion_for_Class_Incremental_Semantic_Segmentation_CVPR_2023_paper.pdf | Endpoints Weight Fusion for Class Incremental Semantic Segmentation | Class incremental semantic segmentation (CISS) focuses on alleviating catastrophic forgetting to improve discrimination. Previous work mainly exploit regularization (e.g., knowledge distillation) to maintain previous knowledge in the current model. However, distillation alone often yields limited gain to the model ... | ['Ming-Ming Cheng', 'Joost Van de Weijer', 'Xialei Liu', 'Jiekang Feng', 'Chang-Bin Zhang', 'Jia-Wen Xiao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['class-incremental-learning', 'class-incremental-semantic-segmentation', 'incremental-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 2.97962010e-01 2.64511444e-02 -2.78431207e-01 -2.70390868e-01
-4.76124108e-01 -3.50925684e-01 2.65025795e-01 3.53422403e-01
-8.73256028e-01 8.72962177e-01 -1.37856871e-01 1.13490447e-02
-1.23967811e-01 -7.97875941e-01 -7.18750834e-01 -9.41497803e-01
3.40787560e-01 2.44306549e-01 7.05886304e-01 2.52188426... | [9.450156211853027, 2.349525213241577] |
a1bd76b9-3451-4b5a-b801-152b784f6792 | whole-slide-mitosis-detection-in-he-breast | 1808.05896 | null | http://arxiv.org/abs/1808.05896v1 | http://arxiv.org/pdf/1808.05896v1.pdf | Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks | Manual counting of mitotic tumor cells in tissue sections constitutes one of
the strongest prognostic markers for breast cancer. This procedure, however, is
time-consuming and error-prone. We developed a method to automatically detect
mitotic figures in breast cancer tissue sections based on convolutional neural
networ... | ['Jeroen van der Laak', 'Carla Wauters', 'Irene Otte-Holler', 'Willem Vreuls', 'Suzanne Mol', 'Rob van de Loo', 'Geert Litjens', 'Nico Karssemeijer', 'Maschenka Balkenhol', 'David Tellez', 'Rob Vogels', 'Peter Bult', 'Francesco Ciompi'] | 2018-08-17 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 2.64254183e-01 1.55511647e-01 -1.70848832e-01 -1.05139256e-01
-1.05749476e+00 -6.34251356e-01 2.90142864e-01 6.65356159e-01
-1.00221527e+00 8.21217895e-01 -1.93049461e-01 -5.28492987e-01
1.34164125e-01 -7.96180844e-01 -3.81458431e-01 -1.05133152e+00
1.17835082e-01 6.57839954e-01 3.55379909e-01 5.53858504... | [15.06419849395752, -3.1000282764434814] |
4cd5be3e-0320-40f3-8644-a39fe1ac0c10 | improving-video-instance-segmentation-via | 2107.13155 | null | https://arxiv.org/abs/2107.13155v2 | https://arxiv.org/pdf/2107.13155v2.pdf | Improving Video Instance Segmentation via Temporal Pyramid Routing | Video Instance Segmentation (VIS) is a new and inherently multi-task problem, which aims to detect, segment, and track each instance in a video sequence. Existing approaches are mainly based on single-frame features or single-scale features of multiple frames, where either temporal information or multi-scale informatio... | ['DaCheng Tao', 'Henghui Ding', 'Yibo Yang', 'Yunhai Tong', 'Guangliang Cheng', 'Kuiyuan Yang', 'Hao He', 'Xiangtai Li'] | 2021-07-28 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.04853922e-01 -4.87848341e-01 -4.19494063e-01 -2.55144715e-01
-8.27337027e-01 -5.01152158e-01 2.73419857e-01 2.38853004e-02
-4.00434822e-01 6.49229765e-01 -8.52220058e-02 1.12510370e-02
-9.50881466e-02 -7.83253074e-01 -5.89572072e-01 -7.91196644e-01
-9.72356945e-02 -8.83693993e-02 9.68760967e-01 3.26356255... | [9.138121604919434, -0.14822107553482056] |
c09d8ee2-f086-484d-a9b0-7ccc35a12c51 | superpixelwise-low-rank-approximation-based | null | null | https://ieeexplore.ieee.org/document/10136223 | https://ieeexplore.ieee.org/document/10136223 | Superpixelwise Low-Rank Approximation-Based Partial Label Learning for Hyperspectral Image Classification | Insufficient prior knowledge of a captured hyperspectral image (HSI) scene may lead the experts or the automatic labeling systems to offer incorrect labels or ambiguous labels (i.e., assigning each training sample to a group of candidate labels, among which only one of them is valid; this is also known as partial label... | ['Shujun Yang; Yu Zhang; Yao Ding; Danfeng Hong'] | 2023-05-25 | superpixelwise-low-rank-approximation-based-1 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10136223 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10136223 | journal-grsl-2023-5 | ['partial-label-learning'] | ['methodology'] | [ 8.64506721e-01 -1.25609515e-02 -1.99329048e-01 -4.11953002e-01
-7.14924514e-01 -5.15823960e-01 3.46578985e-01 1.55453339e-01
-3.08079839e-01 7.32335567e-01 -2.19314620e-01 -8.17479566e-02
-5.11448860e-01 -8.38527501e-01 -4.05691892e-01 -1.17755330e+00
4.25298810e-01 4.91042584e-01 3.46450418e-01 2.94689089... | [9.999271392822266, -1.7296231985092163] |
268fbc7f-32c1-4d68-8ce7-9036daca1963 | ita-image-text-alignments-for-multi-modal-1 | null | null | https://openreview.net/forum?id=AC8P4mj14AM | https://openreview.net/pdf?id=AC8P4mj14AM | ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition | Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obtained from a pretrained object detector and relies on an attention mechanism to model the interactions between image and text representation... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['multi-modal-named-entity-recognition'] | ['natural-language-processing'] | [ 1.17766187e-01 -2.29735095e-02 -1.54013529e-01 -3.71045113e-01
-8.22519541e-01 -6.29773200e-01 7.68963099e-01 -2.89865226e-01
-5.60913801e-01 2.20928729e-01 2.42300704e-01 -1.54326439e-01
3.68689388e-01 -6.10080957e-01 -1.05594528e+00 -6.65849090e-01
6.24554813e-01 4.39468384e-01 3.03718030e-01 5.80024086... | [10.8096923828125, 1.4408823251724243] |
92fdd178-8d1f-4d4f-9cf9-2b2c523242a4 | deep-rbfnet-point-cloud-feature-learning | 1812.04302 | null | http://arxiv.org/abs/1812.04302v2 | http://arxiv.org/pdf/1812.04302v2.pdf | Deep RBFNet: Point Cloud Feature Learning using Radial Basis Functions | Three-dimensional object recognition has recently achieved great progress
thanks to the development of effective point cloud-based learning frameworks,
such as PointNet and its extensions. However, existing methods rely heavily on
fully connected layers, which introduce a significant amount of parameters,
making the ne... | ['Chao Chen', 'Weikai Chen', 'Jun Xing', 'Xiaoguang Han', 'Guanbin Li', 'Yajie Zhao', 'Hao Li'] | 2018-12-11 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-3.58553469e-01 -4.71190363e-01 9.22296718e-02 -4.46290940e-01
-3.93520355e-01 -4.51383501e-01 5.11874914e-01 -3.58636975e-02
-4.58252937e-01 2.31020972e-01 -5.67114413e-01 -1.22113980e-01
-3.24578196e-01 -9.12101388e-01 -9.93429601e-01 -7.11552858e-01
-7.15115070e-02 4.32496279e-01 2.48760208e-01 -7.20082200... | [7.959822177886963, -3.646676540374756] |
368d166a-9569-443b-955a-427fb99ce3b9 | local-to-global-information-communication-for | 2302.08481 | null | https://arxiv.org/abs/2302.08481v1 | https://arxiv.org/pdf/2302.08481v1.pdf | Local-to-Global Information Communication for Real-Time Semantic Segmentation Network Search | Neural Architecture Search (NAS) has shown great potentials in automatically designing neural network architectures for real-time semantic segmentation. Unlike previous works that utilize a simplified search space with cell-sharing way, we introduce a new search space where a lightweight model can be more effectively s... | ['Peiwen Lin', 'Shuchang Lyu', 'Ting-Bing Xu', 'Peng Sun', 'Guangliang Cheng'] | 2023-02-16 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-1.54033735e-01 -3.22283134e-02 -1.36004150e-01 -4.44084883e-01
-4.50078338e-01 -3.52056921e-01 3.81519407e-01 1.66346759e-01
-6.75571144e-01 6.68367624e-01 -3.71213704e-01 -2.38723248e-01
-2.85797238e-01 -1.16693234e+00 -7.03304350e-01 -7.08984196e-01
4.22696210e-02 4.92910981e-01 8.54984224e-01 -8.38580057... | [9.281209945678711, -0.4930656850337982] |
c8a34143-d331-49f6-b2d8-e7640a3f7413 | spatial-feature-calibration-and-temporal | 2104.05606 | null | https://arxiv.org/abs/2104.05606v1 | https://arxiv.org/pdf/2104.05606v1.pdf | Spatial Feature Calibration and Temporal Fusion for Effective One-stage Video Instance Segmentation | Modern one-stage video instance segmentation networks suffer from two limitations. First, convolutional features are neither aligned with anchor boxes nor with ground-truth bounding boxes, reducing the mask sensitivity to spatial location. Second, a video is directly divided into individual frames for frame-level insta... | ['Lei Zhang', 'Lida Li', 'Shuai Li', 'Minghan Li'] | 2021-04-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_Spatial_Feature_Calibration_and_Temporal_Fusion_for_Effective_One-Stage_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.84045613e-01 -9.37623307e-02 -3.62638831e-01 -3.42673898e-01
-6.67315781e-01 -7.20034480e-01 -5.33791631e-03 -3.62579763e-01
-3.92151713e-01 6.32062078e-01 -1.92065701e-01 -2.53796671e-02
2.45733216e-01 -4.42374229e-01 -9.70536351e-01 -5.76208293e-01
-1.95488244e-01 -5.88219650e-02 7.83478498e-01 1.89130232... | [9.195935249328613, -0.1229557916522026] |
97e64bcc-8f2a-4df6-808b-b805559b420c | challenges-of-using-real-world-sensory-inputs | 2306.09281 | null | https://arxiv.org/abs/2306.09281v1 | https://arxiv.org/pdf/2306.09281v1.pdf | Challenges of Using Real-World Sensory Inputs for Motion Forecasting in Autonomous Driving | Motion forecasting plays a critical role in enabling robots to anticipate future trajectories of surrounding agents and plan accordingly. However, existing forecasting methods often rely on curated datasets that are not faithful to what real-world perception pipelines can provide. In reality, upstream modules that are ... | ['Patrick Pérez', 'Matthieu Cord', 'Mickaël Chen', 'Éloi Zablocki', 'Loïck Chambon', 'Yihong Xu'] | 2023-06-15 | null | null | null | null | ['motion-forecasting'] | ['computer-vision'] | [ 8.36547185e-03 1.37346327e-01 2.00286210e-01 -3.61923426e-01
-4.44942921e-01 -8.63540471e-01 9.52582419e-01 2.78740108e-01
-4.30826247e-01 6.65820599e-01 4.98991311e-01 -3.48734111e-01
1.06070615e-01 -1.07416523e+00 -7.53982127e-01 -4.02216345e-01
-4.56503510e-01 4.79790777e-01 6.71351969e-01 -3.72212857... | [5.739987850189209, 0.7904168963432312] |
69e14027-93d7-4de8-9ef4-0bb6f63d7f03 | robustness-of-edited-neural-networks | 2303.00046 | null | https://arxiv.org/abs/2303.00046v1 | https://arxiv.org/pdf/2303.00046v1.pdf | Robustness of edited neural networks | Successful deployment in uncertain, real-world environments requires that deep learning models can be efficiently and reliably modified in order to adapt to unexpected issues. However, the current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden. For this reason, there ... | ['Henry Kvinge', 'Jonathan Tu', 'Cody Nizinski', 'Charles Godfrey', 'Davis Brown'] | 2023-02-28 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 1.93495214e-01 -7.21927807e-02 3.53501514e-02 -4.36130196e-01
-6.00904822e-01 -6.23560667e-01 5.40215075e-01 5.66962771e-02
-3.44323933e-01 7.89378941e-01 1.19669124e-01 -1.72808960e-01
-3.90002608e-01 -4.03038800e-01 -9.15166974e-01 -7.02520013e-01
-2.26258915e-02 2.66836733e-01 -3.55775617e-02 -2.32919604... | [5.761196136474609, 7.713463306427002] |
73dc8b37-898f-4c9c-a895-d77fb310b9fc | deep-occupancy-predictive-representations-for | 2303.04218 | null | https://arxiv.org/abs/2303.04218v1 | https://arxiv.org/pdf/2303.04218v1.pdf | Deep Occupancy-Predictive Representations for Autonomous Driving | Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural motion planners for autonomous vehicles. Instead, this work proposes to learn which features are task-relevant. Given its immediate relevance ... | ['Matthias Althoff', 'Lars Frederik Peiss', 'Eivind Meyer'] | 2023-03-07 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 4.60361168e-02 4.70885158e-01 -6.50073111e-01 -3.67098838e-01
-5.24137557e-01 -3.22022378e-01 9.68096316e-01 -2.27289170e-01
-5.32835364e-01 9.28977132e-01 3.06816101e-01 -5.27567327e-01
-2.92087376e-01 -1.08296824e+00 -8.30129445e-01 -5.11798978e-01
-3.58258486e-01 7.51578212e-01 5.04426539e-01 -4.62332696... | [5.235077381134033, 1.0270967483520508] |
9d5c4d0f-6a71-4d3d-b05f-e2de25804bea | long-term-hourly-scenario-generation-for | 2306.16427 | null | https://arxiv.org/abs/2306.16427v1 | https://arxiv.org/pdf/2306.16427v1.pdf | Long-Term Hourly Scenario Generation for Correlated Wind and Solar Power combining Variational Autoencoders with Radial Basis Function Kernels | Accurate generation of realistic future scenarios of renewable energy generation is crucial for long-term planning and operation of electrical systems, especially considering the increasing focus on sustainable energy and the growing penetration of renewable generation in energy matrices. These predictions enable power... | ['Julio Alberto Silva Dias'] | 2023-06-27 | null | null | null | null | ['decision-making', 'energy-management'] | ['reasoning', 'time-series'] | [-2.99890101e-01 -4.25273627e-01 2.59564221e-02 1.86485171e-01
-3.52730393e-01 -6.55557871e-01 7.60310471e-01 -3.48244533e-02
1.66542545e-01 1.35706365e+00 3.82246286e-01 -2.07746625e-01
-4.69650269e-01 -1.34320092e+00 -3.60993385e-01 -1.09964597e+00
-6.73713088e-02 6.32988811e-02 -5.89100242e-01 -1.51094273... | [6.200638294219971, 2.7800402641296387] |
01034960-b9b9-4444-9103-21ce000038bf | knowledge-based-end-to-end-memory-networks | 1804.08204 | null | http://arxiv.org/abs/1804.08204v1 | http://arxiv.org/pdf/1804.08204v1.pdf | Knowledge-based end-to-end memory networks | End-to-end dialog systems have become very popular because they hold the
promise of learning directly from human to human dialog interaction. Retrieval
and Generative methods have been explored in this area with mixed results. A
key element that is missing so far, is the incorporation of a-priori knowledge
about the ta... | ['Jatin Ganhotra', 'Lazaros Polymenakos'] | 2018-04-23 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-1.78407803e-01 6.07574046e-01 6.45125583e-02 -6.82174027e-01
-5.81307113e-01 -6.76903546e-01 1.05605149e+00 -2.68833712e-02
-6.99100018e-01 1.19315863e+00 6.27485812e-01 -1.64515331e-01
2.89512263e-03 -7.40786314e-01 -2.20347151e-01 -2.29442120e-01
2.03021258e-01 1.24363816e+00 4.74253148e-01 -6.48532033... | [12.76264476776123, 8.000736236572266] |
3487eba1-d682-4f98-856f-4c7e555bf41b | deep-conditional-transformation-models-for | 2210.11366 | null | https://arxiv.org/abs/2210.11366v2 | https://arxiv.org/pdf/2210.11366v2.pdf | Deep conditional transformation models for survival analysis | An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of electronic health records. Recently, several neural-network based solutions have been proposed, some of which are binary classifiers. Parame... | ['Thomas J. Fuchs', 'Torsten Hothorn', 'Ida Häggström', 'Lucas Kook', 'Gabriele Campanella'] | 2022-10-20 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.09248832e-01 -9.93649736e-02 -8.01742196e-01 -8.21885586e-01
-8.51732373e-01 -4.44890589e-01 4.07825083e-01 4.01456207e-01
-3.73748600e-01 1.32368267e+00 2.92187303e-01 -7.85850942e-01
-5.17643392e-01 -8.20757806e-01 -4.52316761e-01 -6.22348130e-01
-5.24514973e-01 9.55739319e-01 -2.40335509e-01 2.51304001... | [7.789129734039307, 5.621460914611816] |
1c4ecbfc-6feb-4318-96b8-6f5efc6dd3d6 | ppo-ue-proximal-policy-optimization-via | 2212.06343 | null | https://arxiv.org/abs/2212.06343v1 | https://arxiv.org/pdf/2212.06343v1.pdf | PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration | Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach. However, we observe that the homogeneous exploration process in PPO could cause an unexpected stability issue in the training phase. To address this issue, we propose PPO-UE, a PPO variant equipped with self-... | ['Jin-Hee Cho', 'Dong H. Jeong', 'Feng Chen', 'Lance M. Kaplan', 'Audun Jøsang', 'Zhen Guo', 'Qisheng Zhang'] | 2022-12-13 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-5.30164480e-01 2.30358690e-02 -5.07057786e-01 1.90996781e-01
-6.29506171e-01 -3.48609209e-01 6.72895074e-01 4.41438109e-02
-7.52614617e-01 1.37561643e+00 -5.16843237e-02 -3.34670246e-01
-4.87954527e-01 -6.30284607e-01 -1.04544985e+00 -8.56743336e-01
-3.70270669e-01 1.86386690e-01 1.47670597e-01 -3.78130317... | [4.204695701599121, 2.255730628967285] |
21a4e771-3c15-4941-828f-84f6e6df73c1 | heterogeneous-graph-learning-for-visual | 1910.11475 | null | https://arxiv.org/abs/1910.11475v1 | https://arxiv.org/pdf/1910.11475v1.pdf | Heterogeneous Graph Learning for Visual Commonsense Reasoning | Visual commonsense reasoning task aims at leading the research field into solving cognition-level reasoning with the ability of predicting correct answers and meanwhile providing convincing reasoning paths, resulting in three sub-tasks i.e., Q->A, QA->R and Q->AR. It poses great challenges over the proper semantic alig... | ['Weijiang Yu', 'Xiaodan Liang', 'Weihao Yu', 'Jingwen Zhou', 'Nong Xiao'] | 2019-10-25 | heterogeneous-graph-learning-for-visual-1 | http://papers.nips.cc/paper/8544-heterogeneous-graph-learning-for-visual-commonsense-reasoning | http://papers.nips.cc/paper/8544-heterogeneous-graph-learning-for-visual-commonsense-reasoning.pdf | neurips-2019-12 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 4.06804979e-02 3.85248572e-01 -6.61513209e-02 -3.65476817e-01
-5.31125367e-01 -6.78520918e-01 7.90332913e-01 1.67596668e-01
-1.40033022e-01 3.42542917e-01 2.70575911e-01 -7.11479008e-01
-1.50320664e-01 -9.82836246e-01 -5.45310736e-01 -2.52381325e-01
4.74045813e-01 3.00868988e-01 5.08152187e-01 -6.58455372... | [10.701131820678711, 1.7724852561950684] |
47e07a04-b993-4fe7-b410-1732b4a0ead7 | automatic-annotation-and-evaluation-of-error | null | null | https://aclanthology.org/P17-1074 | https://aclanthology.org/P17-1074.pdf | Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction | Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corr... | ['Ted Briscoe', 'Mariano Felice', 'Christopher Bryant'] | 2017-07-01 | null | null | null | acl-2017-7 | ['annotated-code-search', 'table-annotation', 'table-annotation', 'news-annotation'] | ['computer-code', 'knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 3.88397336e-01 4.49661642e-01 6.06540322e-01 -7.36672401e-01
-8.68911386e-01 -7.28294015e-01 1.52047396e-01 1.21511221e+00
-7.81267345e-01 8.25146437e-01 7.53024146e-02 -5.62423706e-01
-8.80228058e-02 -5.38351774e-01 -7.94089913e-01 6.84258267e-02
3.33292782e-01 5.52692175e-01 1.65203348e-01 -1.76358610... | [11.074201583862305, 10.73856258392334] |
5465fbd4-16bc-4ecf-ab0f-34ec15ebfff7 | accurate-temporal-action-proposal-generation | 2003.04145 | null | https://arxiv.org/abs/2003.04145v1 | https://arxiv.org/pdf/2003.04145v1.pdf | Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network | Accurate temporal action proposals play an important role in detecting actions from untrimmed videos. The existing approaches have difficulties in capturing global contextual information and simultaneously localizing actions with different durations. To this end, we propose a Relation-aware pyramid Network (RapNet) to ... | ['Yufeng Yuan', 'Guanshuo Wang', 'Xi Zhou', 'Jiani Li', 'Zhixiang Shi', 'Shiming Ge', 'Jialin Gao'] | 2020-03-09 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 3.68049502e-01 -3.70244026e-01 -5.41747093e-01 -3.42473537e-01
-6.57351434e-01 -3.68045926e-01 7.48484492e-01 6.10838793e-02
-4.75209504e-01 6.22624636e-01 6.43850803e-01 2.02317029e-01
-2.54443794e-01 -6.03623271e-01 -2.85030216e-01 -5.30590177e-01
-4.38486934e-01 -2.59995516e-02 1.15163696e+00 -9.01791230... | [8.387701988220215, 0.510066032409668] |
9095fa24-a9a9-44b8-9ff7-8c87f3f7bdbe | poisson-image-deconvolution-by-a-plug-and | 2010.09321 | null | https://arxiv.org/abs/2010.09321v3 | https://arxiv.org/pdf/2010.09321v3.pdf | Poisson Image Deconvolution by a Plug-and-Play Quantum Denoising Scheme | This paper introduces a new Plug-and-Play (PnP) alternating direction of multipliers (ADMM) scheme based on a recently proposed denoiser using the Schroedinger equation's solutions of quantum physics. The efficiency of the proposed algorithm is evaluated for Poisson image deconvolution, which is very common for imaging... | ['Denis Kouamé', 'Bertrand Georgeot', 'Adrian Basarab', 'Sayantan Dutta'] | 2020-10-19 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.72331315e-01 -4.37603891e-03 5.85614800e-01 -1.96493119e-01
-5.84101439e-01 1.00208364e-01 5.68689048e-01 -1.67511834e-03
-1.01387072e+00 9.98242617e-01 2.25160792e-02 -1.74862072e-01
-3.68860483e-01 -6.04967237e-01 -3.56801420e-01 -1.24140429e+00
1.63067251e-01 3.52276802e-01 1.51188269e-01 -3.23998094... | [12.078582763671875, -2.540260076522827] |
94024c38-4bfb-4ece-990e-3b919cc0d9e0 | similarity-contrastive-estimation-for-image | 2212.11187 | null | https://arxiv.org/abs/2212.11187v1 | https://arxiv.org/pdf/2212.11187v1.pdf | Similarity Contrastive Estimation for Image and Video Soft Contrastive Self-Supervised Learning | Contrastive representation learning has proven to be an effective self-supervised learning method for images and videos. Most successful approaches are based on Noise Contrastive Estimation (NCE) and use different views of an instance as positives that should be contrasted with other instances, called negatives, that a... | ['Romain Hérault', 'Astrid Orcesi', 'Jaonary Rabarisoa', 'Julien Denize'] | 2022-12-21 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 4.53858137e-01 -8.70324895e-02 -3.26665640e-01 -5.19976735e-01
-7.25881577e-01 -4.06678289e-01 8.26359987e-01 1.88882917e-01
-5.29055715e-01 6.18318677e-01 1.57545343e-01 1.49902135e-01
-1.24768704e-01 -7.31853068e-01 -1.12775254e+00 -8.10096800e-01
-2.26978675e-01 5.02963245e-01 3.04236501e-01 -3.79360229... | [9.54870319366455, 2.658583402633667] |
08b86f05-e328-4209-afd3-94f7ccb9e089 | replication-study-development-and-validation | 1803.04337 | null | http://arxiv.org/abs/1803.04337v3 | http://arxiv.org/pdf/1803.04337v3.pdf | Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs | Replication studies are essential for validation of new methods, and are
crucial to maintain the high standards of scientific publications, and to use
the results in practice. We have attempted to replicate the main method in
'Development and validation of a deep learning algorithm for detection of
diabetic retinopathy... | ['Kajsa Møllersen', 'Mike Voets', 'Lars Ailo Bongo'] | 2018-03-12 | null | null | null | null | ['diabetic-retinopathy-detection', 'mitosis-detection'] | ['medical', 'medical'] | [-3.17046762e-01 -1.46863073e-01 -1.70490950e-01 -3.95478189e-01
-7.97005892e-01 -5.11019051e-01 -9.77056846e-02 8.47066939e-02
-7.17410624e-01 8.31207216e-01 3.17143857e-01 -8.14689100e-01
-2.55846798e-01 -6.20698988e-01 -6.61845565e-01 -6.50075018e-01
-1.58089757e-01 1.00030996e-01 3.30879152e-01 3.02702993... | [15.82508373260498, -3.996168851852417] |
46fc1abb-52ce-4083-bdc4-ab2011d3c1a9 | thompson-sampling-regret-bounds-for | 2304.13593 | null | https://arxiv.org/abs/2304.13593v1 | https://arxiv.org/pdf/2304.13593v1.pdf | Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards | In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by Neu et al. and their concept of lifted information ratio. First, we prove a comprehensive bound on the Thompson Sampling expected cumulative regret that depends on the mutual inf... | ['Mikael Skoglund', 'Tobias J. Oechtering', 'Borja Rodríguez-Gálvez', 'Amaury Gouverneur'] | 2023-04-26 | null | null | null | null | ['thompson-sampling', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [ 2.87980020e-01 2.57870913e-01 -7.78723776e-01 -3.02009493e-01
-1.25539947e+00 -7.15671599e-01 1.43676654e-01 1.18587039e-01
-3.72044057e-01 1.51110852e+00 -9.70275849e-02 -6.03303254e-01
-8.11180353e-01 -7.17469156e-01 -1.05837393e+00 -8.54828596e-01
-2.15920970e-01 7.61242747e-01 1.36535987e-02 2.37469018... | [4.4666829109191895, 3.2461252212524414] |
b9a85bca-9eb0-45b4-8dc6-fa3f91a30d5d | semi-local-3d-lane-detection-and-uncertainty | 2003.05257 | null | https://arxiv.org/abs/2003.05257v1 | https://arxiv.org/pdf/2003.05257v1.pdf | Semi-Local 3D Lane Detection and Uncertainty Estimation | We propose a novel camera-based DNN method for 3D lane detection with uncertainty estimation. Our method is based on a semi-local, BEV, tile representation that breaks down lanes into simple lane segments. It combines learning a parametric model for the segments along with a deep feature embedding that is then used to ... | ['Max Bluvstein', 'Netalee Efrat', 'Shaul Oron', 'Noa Garnett', 'Dan Levi', 'Bat El Shlomo'] | 2020-03-11 | null | null | null | null | ['3d-lane-detection'] | ['computer-vision'] | [-1.98821247e-01 1.87556654e-01 -3.97131056e-01 -5.54781020e-01
-9.99846995e-01 -9.26972270e-01 8.10806096e-01 -2.60929137e-01
-1.21702053e-01 5.84100544e-01 3.88583481e-01 -4.66310978e-01
1.69526249e-01 -7.19231725e-01 -9.85687733e-01 -3.72207314e-01
-2.07602397e-01 5.03432512e-01 4.79030460e-01 -7.71258213... | [7.950685024261475, -1.7593162059783936] |
4b0c5154-20bf-40cf-95b4-8a5f03c02daa | transformer-capsule-model-for-intent | null | null | https://www.aaai.org/Papers/AAAI/2020GB/SA-ObuchowskiA.549.pdf | https://www.aaai.org/Papers/AAAI/2020GB/SA-ObuchowskiA.549.pdf | Transformer-Capsule Model for Intent Detection | Intent recognition is one of the most crucial tasks in NLUsystems, which are nowadays especially important for design-ing intelligent conversation. We propose a novel approach to intent recognition which involves combining transformer architecture with capsule networks. Our results show that such architecture... | ['Michał Lew', 'Aleksander Obuchowski'] | 2020-02-07 | null | null | null | thirty-fourth-aaai-conference-on-artificial | ['intent-recognition'] | ['natural-language-processing'] | [-4.37033266e-01 -1.76582008e-03 -6.88585401e-01 -4.03649032e-01
-3.62774789e-01 -7.22174227e-01 8.60664785e-01 -2.54279584e-01
-1.25231311e-01 5.10562897e-01 7.13484645e-01 -7.34735012e-01
4.44100238e-02 -4.46778715e-01 -4.13648784e-01 9.68880579e-02
-1.76535845e-01 7.02314854e-01 1.96745366e-01 -5.07175565... | [12.466658592224121, 7.493104934692383] |
9ba29fc7-c3a2-4244-b526-bdef82580444 | recurrent-neural-networks-for-multivariate | 1606.01865 | null | http://arxiv.org/abs/1606.01865v2 | http://arxiv.org/pdf/1606.01865v2.pdf | Recurrent Neural Networks for Multivariate Time Series with Missing Values | Multivariate time series data in practical applications, such as health care,
geoscience, and biology, are characterized by a variety of missing values. In
time series prediction and other related tasks, it has been noted that missing
values and their missing patterns are often correlated with the target labels,
a.k.a.... | ['David Sontag', 'Sanjay Purushotham', 'Kyunghyun Cho', 'Zhengping Che', 'Yan Liu'] | 2016-06-06 | null | null | null | null | ['multivariate-time-series-imputation'] | ['time-series'] | [ 2.08478063e-01 -6.11170530e-01 -3.84714663e-01 -4.59805518e-01
-6.69406712e-01 -6.60899878e-02 8.42332914e-02 -4.16836760e-04
5.77568486e-02 9.08517480e-01 4.77717221e-01 -5.33200443e-01
-1.83922127e-01 -6.42647207e-01 -7.16038465e-01 -8.73191297e-01
-4.00834560e-01 1.01315469e-01 -3.46156567e-01 -1.91969752... | [7.0935492515563965, 3.19378662109375] |
af4d0194-25ab-4772-84f4-e8bc7cca4a70 | regularized-densely-connected-pyramid-network | 2008.12416 | null | https://arxiv.org/abs/2008.12416v2 | https://arxiv.org/pdf/2008.12416v2.pdf | Regularized Densely-connected Pyramid Network for Salient Instance Segmentation | Much of the recent efforts on salient object detection (SOD) have been devoted to producing accurate saliency maps without being aware of their instance labels. To this end, we propose a new pipeline for end-to-end salient instance segmentation (SIS) that predicts a class-agnostic mask for each detected salient instanc... | ['Ming-Ming Cheng', 'Le Zhang', 'Yu-Huan Wu', 'Wang Gao', 'Yun Liu'] | 2020-08-28 | null | null | null | null | ['salient-object-detection'] | ['computer-vision'] | [ 3.85996222e-01 4.73401159e-01 -4.48166996e-01 -5.73583126e-01
-9.41893816e-01 -2.15921640e-01 2.23689571e-01 -5.80891743e-02
-3.43481302e-01 5.04982412e-01 2.78498471e-01 9.84448716e-02
3.04455727e-01 -4.55840945e-01 -8.87370944e-01 -4.35315996e-01
6.68466613e-02 9.34874564e-02 7.63517857e-01 -1.15721293... | [9.695951461791992, 0.05088011547923088] |
3c03fc08-9140-4511-9e92-f9238299a2c5 | fast-and-accurate-shift-reduce-constituent | null | null | https://aclanthology.org/P13-1043 | https://aclanthology.org/P13-1043.pdf | Fast and Accurate Shift-Reduce Constituent Parsing | null | ['Jingbo Zhu', 'Muhua Zhu', 'Yue Zhang', 'Wenliang Chen', 'Min Zhang'] | 2013-08-01 | null | null | null | acl-2013-8 | ['transition-based-dependency-parsing'] | ['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... | [-7.410432815551758, 3.749617576599121] |
73a51bfe-0803-4ed5-9ee8-ba7a63f3ca9c | speech-denoising-with-deep-feature-losses | 1806.10522 | null | http://arxiv.org/abs/1806.10522v1 | http://arxiv.org/pdf/1806.10522v1.pdf | Speech Denoising with Deep Feature Losses | We present an end-to-end deep learning approach to denoising speech signals
by processing the raw waveform directly. Given input audio containing speech
corrupted by an additive background signal, the system aims to produce a
processed signal that contains only the speech content. Recent approaches have
shown promising... | ['Vladlen Koltun', 'Francois G. Germain', 'Qifeng Chen'] | 2018-06-27 | null | null | null | null | ['audio-tagging', 'speech-denoising'] | ['audio', 'speech'] | [ 3.48961800e-01 -3.37514617e-02 8.00522685e-01 -4.09512043e-01
-1.28140604e+00 -3.85722846e-01 2.68385112e-01 1.29420936e-01
-6.84332550e-01 4.23186332e-01 3.62740934e-01 5.28374724e-02
-8.30749571e-02 -3.51890117e-01 -7.48680532e-01 -9.06247973e-01
-2.01734990e-01 -7.89049491e-02 1.12387046e-01 -3.43253285... | [15.113655090332031, 5.779756546020508] |
e12b7320-e74a-4b29-bc55-6d97cd3fa5e9 | patch-craft-self-supervised-training-for | 2211.09919 | null | https://arxiv.org/abs/2211.09919v1 | https://arxiv.org/pdf/2211.09919v1.pdf | Patch-Craft Self-Supervised Training for Correlated Image Denoising | Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoisi... | ['Michael Elad', 'Gregory Vaksman'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Vaksman_Patch-Craft_Self-Supervised_Training_for_Correlated_Image_Denoising_CVPR_2023_paper.pdf | cvpr-2023-1 | ['patch-matching'] | ['computer-vision'] | [ 6.51424587e-01 -4.43711102e-01 1.74850181e-01 -2.37647697e-01
-6.28729820e-01 -2.42266774e-01 3.95713955e-01 7.37205148e-02
-3.46560240e-01 7.32798517e-01 -9.42982882e-02 1.05639145e-01
-2.94004738e-01 -8.43903005e-01 -8.60351682e-01 -1.22552156e+00
9.35938582e-02 1.07562400e-01 1.83667958e-01 -2.75876999... | [11.459199905395508, -2.4497969150543213] |
6bb321f7-3fb3-4247-b525-977173f64ec5 | vstar-a-video-grounded-dialogue-dataset-for | 2305.18756 | null | https://arxiv.org/abs/2305.18756v1 | https://arxiv.org/pdf/2305.18756v1.pdf | VSTAR: A Video-grounded Dialogue Dataset for Situated Semantic Understanding with Scene and Topic Transitions | Video-grounded dialogue understanding is a challenging problem that requires machine to perceive, parse and reason over situated semantics extracted from weakly aligned video and dialogues. Most existing benchmarks treat both modalities the same as a frame-independent visual understanding task, while neglecting the int... | ['Dongyan Zhao', 'Yueqian Wang', 'Jinpeng Li', 'Xueliang Zhao', 'Zilong Zheng', 'Yuxuan Wang'] | 2023-05-30 | null | null | null | null | ['scene-segmentation', 'dialogue-generation', 'dialogue-understanding', 'dialogue-generation'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 5.12777388e-01 5.67331076e-01 2.46066079e-02 -6.94532335e-01
-1.14194763e+00 -7.56776631e-01 9.55930471e-01 -1.71344087e-01
-9.54326466e-02 6.52909219e-01 8.21667433e-01 -6.38123900e-02
5.01201630e-01 -4.55664128e-01 -6.95582092e-01 -5.27922034e-01
1.01637416e-01 6.49940848e-01 2.81416118e-01 -5.84136963... | [10.818465232849121, 1.1480141878128052] |
1b1ad75b-18bf-4e58-b05f-163ef5cdfbd4 | language-independent-neuro-symbolic-semantic | 2305.04460 | null | https://arxiv.org/abs/2305.04460v1 | https://arxiv.org/pdf/2305.04460v1.pdf | Language Independent Neuro-Symbolic Semantic Parsing for Form Understanding | Recent works on form understanding mostly employ multimodal transformers or large-scale pre-trained language models. These models need ample data for pre-training. In contrast, humans can usually identify key-value pairings from a form only by looking at layouts, even if they don't comprehend the language used. No prio... | ['Fatemeh Shiri', 'Lizhen Qu', 'Bhanu Prakash Voutharoja'] | 2023-05-08 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 1.50621325e-01 2.63427943e-01 -3.17442358e-01 -5.02554953e-01
-5.59478343e-01 -1.02354026e+00 3.33338827e-01 6.09419882e-01
-1.19862050e-01 3.48831564e-01 1.04666539e-01 -9.24546123e-01
4.23792079e-02 -1.41245484e+00 -8.34815860e-01 1.58539593e-01
1.84204839e-02 5.34609020e-01 -1.07357219e-01 -8.17655846... | [9.463780403137207, 7.9547319412231445] |
3debb294-4ed0-44aa-ba7f-9c07bd2bc3ee | c-2-sp-net-joint-compression-and | 2110.13674 | null | https://arxiv.org/abs/2110.13674v2 | https://arxiv.org/pdf/2110.13674v2.pdf | C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction | Recent development in brain-machine interface technology has made seizure prediction possible. However, the communication of large volume of electrophysiological signals between sensors and processing apparatus and related computation become two major bottlenecks for seizure prediction systems due to the constrained ba... | ['Mohamad Sawan', 'Jie Yang', 'Ziyu Wang', 'Yi Shi', 'Di wu'] | 2021-10-26 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 7.94461250e-01 -7.75843337e-02 2.03237180e-02 -2.50103265e-01
-8.79097641e-01 2.85783783e-02 -1.30286992e-01 9.82243717e-02
-4.04801250e-01 8.40733945e-01 1.88416168e-01 -5.94785251e-02
-3.79365295e-01 -2.62733489e-01 -4.48051989e-01 -6.38367832e-01
-3.92003983e-01 -7.80301318e-02 -2.86007337e-02 3.22672397... | [13.283967018127441, 3.4514951705932617] |
4d5e9d2f-deb2-4347-829d-8d4133df4e7a | algo-synthesizing-algorithmic-programs-with | 2305.14591 | null | https://arxiv.org/abs/2305.14591v1 | https://arxiv.org/pdf/2305.14591v1.pdf | ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers | Large language models (LLMs) excel at implementing code from functionality descriptions, but struggle with algorithmic problems that require not only implementation but also identification of the suitable algorithm. Moreover, LLM-generated programs lack guaranteed correctness and require human verification. To address ... | ['Lei LI', 'William Yang Wang', 'Jingtao Xia', 'Danqing Wang', 'Kexun Zhang'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-7.15298131e-02 1.02260299e-01 -3.30024600e-01 -2.73548096e-01
-1.28627574e+00 -1.21500325e+00 3.37676376e-01 1.19866915e-01
-2.03096420e-01 3.79045993e-01 -3.05287331e-01 -1.20748568e+00
1.00666933e-01 -8.07580292e-01 -1.06675947e+00 -1.17403671e-01
8.34517777e-02 6.45949900e-01 1.96372122e-01 -1.22984797... | [7.858436584472656, 7.660763263702393] |
a60e4bf7-73cd-42f1-8e07-2f50b3281309 | active-deep-learning-for-classification-of | 1611.10031 | null | http://arxiv.org/abs/1611.10031v1 | http://arxiv.org/pdf/1611.10031v1.pdf | Active Deep Learning for Classification of Hyperspectral Images | Active deep learning classification of hyperspectral images is considered in
this paper. Deep learning has achieved success in many applications, but
good-quality labeled samples are needed to construct a deep learning network.
It is expensive getting good labeled samples in hyperspectral images for remote
sensing appl... | ['HUI ZHANG', 'Peng Liu', 'Kie B. Eom'] | 2016-11-30 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 6.28955841e-01 -7.25203380e-02 -5.03000677e-01 -6.31256521e-01
-7.61161804e-01 -2.12977722e-01 1.78488135e-01 3.70729685e-01
-7.91764855e-01 7.78706670e-01 -3.36268485e-01 -8.60457718e-02
-5.60499728e-01 -1.18333662e+00 -1.24962509e-01 -1.31750703e+00
-1.57994881e-01 7.55487561e-01 -4.91571665e-01 3.00906390... | [9.861486434936523, -1.5353949069976807] |
4cb5f111-d439-4cc6-a9f5-59004e37a527 | online-adaptive-image-reconstruction-onair | 1809.01817 | null | https://arxiv.org/abs/1809.01817v3 | https://arxiv.org/pdf/1809.01817v3.pdf | Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models | Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements. Dictionary or transform learning methods are useful in applications such as denoising, inpainting, and medical image reconstruction. This paper proposes a framework for online (or time-sequential)... | ['Brian E. Moore', 'Saiprasad Ravishankar', 'Raj Rao Nadakuditi', 'Jeffrey A. Fessler'] | 2018-09-06 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 7.06556976e-01 -1.95116460e-01 -1.91600531e-01 -1.81446239e-01
-8.14523101e-01 -1.65936142e-01 8.48980621e-02 -2.59873062e-01
-4.96195436e-01 7.81517267e-01 3.79440695e-01 1.75729215e-01
-2.01056689e-01 -2.25248262e-01 -7.98474669e-01 -9.91849840e-01
-1.52495906e-01 3.49147648e-01 -1.35556623e-01 1.50416389... | [11.574626922607422, -2.200669050216675] |
bdbcb51c-a413-4c74-beb9-fcdb3cd981c7 | transfer-reward-learning-for-policy-gradient | 1909.03622 | null | https://arxiv.org/abs/1909.03622v1 | https://arxiv.org/pdf/1909.03622v1.pdf | Transfer Reward Learning for Policy Gradient-Based Text Generation | Task-specific scores are often used to optimize for and evaluate the performance of conditional text generation systems. However, such scores are non-differentiable and cannot be used in the standard supervised learning paradigm. Hence, policy gradient methods are used since the gradient can be computed without requiri... | ['Danushka Bollegala', "James O' Neill"] | 2019-09-09 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 5.82668185e-01 3.47999692e-01 -3.25758189e-01 -6.98185802e-01
-1.55767989e+00 -5.12999117e-01 8.81870151e-01 1.93733022e-01
-8.93398821e-01 1.06776834e+00 2.98287839e-01 -2.75392592e-01
5.99726103e-02 -5.91166973e-01 -9.13968265e-01 -6.13731146e-01
1.16224699e-01 4.49337572e-01 6.15300564e-03 -2.56886959... | [11.809609413146973, 9.051454544067383] |
ae108f02-19a2-4008-9ae5-1e2afb164e72 | see-eye-to-eye-a-lidar-agnostic-3d-detection | 2111.09450 | null | https://arxiv.org/abs/2111.09450v2 | https://arxiv.org/pdf/2111.09450v2.pdf | See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation | Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D detectors trained for one lidar are tested on other types of lidars. Remarkable progress in lidar manufacturing has brought about advances i... | ['Eduardo Nebot', 'Stewart Worrall', 'Mao Shan', 'Julie Stephany Berrio', 'Darren Tsai'] | 2021-11-17 | null | null | null | null | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 2.78270930e-01 -2.69672126e-01 -2.40835816e-01 -7.64416277e-01
-8.40238631e-01 -6.75635040e-01 4.01905149e-01 5.66533022e-03
-3.18125516e-01 1.25183478e-01 -5.38451314e-01 -3.74177337e-01
-1.01825073e-01 -9.06957030e-01 -8.87171924e-01 -1.18593216e-01
3.46540570e-01 1.16718066e+00 7.49781132e-01 1.06370866... | [7.86998176574707, -2.7026267051696777] |
a59cb9b0-d1cb-4cba-a2c6-a42e61ff151f | disn-deep-implicit-surface-network-for-high | 1905.10711 | null | https://arxiv.org/abs/1905.10711v4 | https://arxiv.org/pdf/1905.10711v4.pdf | DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction | Reconstructing 3D shapes from single-view images has been a long-standing research problem. In this paper, we present DISN, a Deep Implicit Surface Network which can generate a high-quality detail-rich 3D mesh from an 2D image by predicting the underlying signed distance fields. In addition to utilizing global image fe... | ['Radomir Mech', 'Duygu Ceylan', 'Weiyue Wang', 'Qiangeng Xu', 'Ulrich Neumann'] | 2019-05-26 | disn-deep-implicit-surface-network-for-high-1 | http://papers.nips.cc/paper/8340-disn-deep-implicit-surface-network-for-high-quality-single-view-3d-reconstruction | http://papers.nips.cc/paper/8340-disn-deep-implicit-surface-network-for-high-quality-single-view-3d-reconstruction.pdf | neurips-2019-12 | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-5.50829507e-02 9.38018337e-02 -4.82227504e-02 -5.07118523e-01
-8.03460181e-01 -3.25640440e-01 4.27043647e-01 -1.45358369e-01
-6.43495619e-02 5.29330254e-01 2.44366065e-01 -3.20088677e-02
-3.03954761e-02 -8.03045630e-01 -8.73782337e-01 -4.52785969e-01
6.47549555e-02 7.35746086e-01 2.43181065e-01 -1.21861763... | [8.780627250671387, -3.180751085281372] |
d7421c7f-1f90-4f3c-94e3-f1e0b719ba63 | sample-efficient-learning-of-novel-visual | 2306.09482 | null | https://arxiv.org/abs/2306.09482v1 | https://arxiv.org/pdf/2306.09482v1.pdf | Sample-Efficient Learning of Novel Visual Concepts | Despite the advances made in visual object recognition, state-of-the-art deep learning models struggle to effectively recognize novel objects in a few-shot setting where only a limited number of examples are provided. Unlike humans who excel at such tasks, these models often fail to leverage known relationships between... | ['Katia Sycara', 'Joseph Campbell', 'Simon Stepputtis', 'Sarthak Bhagat'] | 2023-06-15 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.60424548e-01 2.93260068e-01 -1.96127892e-01 -2.79899925e-01
-4.89452705e-02 -6.22778058e-01 9.82059419e-01 6.64725065e-01
-3.66437316e-01 5.34154773e-01 5.84160797e-02 -5.14231771e-02
-4.60524529e-01 -7.98562169e-01 -8.70071590e-01 -2.14955717e-01
-1.32638872e-01 4.36599851e-01 4.93223995e-01 -2.96690404... | [10.189692497253418, 2.420544147491455] |
8b75b963-5ead-4715-a558-aef0313d44a8 | remoteclip-a-vision-language-foundation-model | 2306.11029 | null | https://arxiv.org/abs/2306.11029v1 | https://arxiv.org/pdf/2306.11029v1.pdf | RemoteCLIP: A Vision Language Foundation Model for Remote Sensing | General-purpose foundation models have become increasingly important in the field of artificial intelligence. While self-supervised learning (SSL) and Masked Image Modeling (MIM) have led to promising results in building such foundation models for remote sensing, these models primarily learn low-level features, require... | ['Jun Zhou', 'Jiale Zhu', 'Xiaocong Zhou', 'Zhangqingyun Guan', 'Delong Chen', 'Fan Liu'] | 2023-06-19 | null | null | null | null | ['object-counting', 'classification-1'] | ['computer-vision', 'methodology'] | [ 4.39079225e-01 -2.83590406e-01 -4.19401854e-01 -4.79348332e-01
-9.16868210e-01 -6.34638906e-01 8.35850060e-01 2.96946287e-01
-5.84682286e-01 1.41684055e-01 2.96897709e-01 -4.14336026e-01
-3.19582447e-02 -9.38327312e-01 -7.30200171e-01 -3.57705176e-01
7.79781267e-02 1.59609467e-01 1.70315281e-01 -4.78731245... | [9.346728324890137, -0.9699073433876038] |
3be2c56a-8128-4422-9e20-dc51cdd52e7b | deep-metric-color-embeddings-for-splicing | 2206.10737 | null | https://arxiv.org/abs/2206.10737v1 | https://arxiv.org/pdf/2206.10737v1.pdf | Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images | One common task in image forensics is to detect spliced images, where multiple source images are composed to one output image. Most of the currently best performing splicing detectors leverage high-frequency artifacts. However, after an image underwent strong compression, most of the high frequency artifacts are not av... | ['Christian Riess', 'Benjamin Hadwiger'] | 2022-06-21 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 3.17625016e-01 -3.80385667e-01 3.41729999e-01 1.28054051e-02
-6.97934628e-01 -8.33683133e-01 5.81569493e-01 9.06688422e-02
-4.02619869e-01 5.31992376e-01 7.83003308e-03 -1.44403549e-02
-4.62439284e-02 -6.19397402e-01 -8.76971900e-01 -8.98470402e-01
1.33117050e-01 1.00251369e-01 2.64327615e-01 1.35006398... | [12.339727401733398, 0.9688510298728943] |
3ed02215-91f7-4d10-a17b-761eacae09d0 | ntire-2021-depth-guided-image-relighting | 2104.13365 | null | https://arxiv.org/abs/2104.13365v1 | https://arxiv.org/pdf/2104.13365v1.pdf | NTIRE 2021 Depth Guided Image Relighting Challenge | Image relighting is attracting increasing interest due to its various applications. From a research perspective, image relighting can be exploited to conduct both image normalization for domain adaptation, and also for data augmentation. It also has multiple direct uses for photo montage and aesthetic enhancement. In t... | ['Radu Timofte', 'Sabine Susstrunk', 'Ruofan Zhou', 'Majed El Helou'] | 2021-04-27 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 6.03480101e-01 -9.67408717e-02 1.56833246e-01 -6.46439075e-01
-5.51578224e-01 -6.95659459e-01 6.31241024e-01 -2.22652897e-01
-5.26330411e-01 4.37907875e-01 1.80629075e-01 1.50245324e-01
5.41621625e-01 -2.95875937e-01 -8.10590267e-01 -6.90830946e-01
6.97218955e-01 1.09509975e-01 -1.14957444e-01 -3.07550013... | [10.664925575256348, -2.2249667644500732] |
ff97a4a1-d5ac-4092-bb32-9a018f80bfe9 | nuclei-segmentation-via-a-deep-panoptic-model | null | null | https://www.researchgate.net/publication/334843766_Nuclei_Segmentation_via_a_Deep_Panoptic_Model_with_Semantic_Feature_Fusion | https://www.ijcai.org/proceedings/2019/0121.pdf | Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion | Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis. Due to the high variability of nuclei appearances and numerous overlapping objects, this task still remains challenging.
eep learning based semantic and instance segmentation models hav... | ['Weidong Cai', 'Lauren O’Donnell', 'Fan Zhang', 'Yang song', 'Donghao Zhang', 'Dongnan Liu', 'Chaoyi Zhang'] | 2019-08-10 | null | null | null | international-joint-conference-on-artificial | ['nuclear-segmentation'] | ['medical'] | [ 2.78478861e-01 1.54391646e-01 -2.40062132e-01 -3.71614814e-01
-1.00725842e+00 -4.01077032e-01 3.86456519e-01 6.39114380e-01
-4.96309578e-01 6.58489168e-01 -3.07338327e-01 2.28154689e-01
-1.41166449e-01 -7.91861057e-01 -1.67053401e-01 -1.35410428e+00
2.67367005e-01 5.37313402e-01 8.17359328e-01 1.34807825... | [14.905284881591797, -3.055500030517578] |
52b38477-07ae-4389-b4b0-8f9ef4b97036 | interpreting-neural-cwi-classifiers-weights | null | null | https://aclanthology.org/2020.bea-1.17 | https://aclanthology.org/2020.bea-1.17.pdf | Interpreting Neural CWI Classifiers' Weights as Vocabulary Size | Complex Word Identification (CWI) is a task for the identification of words that are challenging for second-language learners to read. Even though the use of neural classifiers is now common in CWI, the interpretation of their parameters remains difficult. This paper analyzes neural CWI classifiers and shows that some ... | ['Yo Ehara'] | 2020-07-01 | null | null | null | ws-2020-7 | ['complex-word-identification'] | ['natural-language-processing'] | [ 1.78119645e-01 3.26821893e-01 -1.69251397e-01 -4.52624857e-01
-4.23271716e-01 -8.55898738e-01 5.89552283e-01 6.46475673e-01
-9.15497959e-01 6.28380120e-01 8.03461969e-02 -5.57046056e-01
-1.79612070e-01 -6.28985465e-01 -5.21268070e-01 -2.10058391e-01
6.36962414e-01 8.40507984e-01 1.84179872e-01 -4.37015563... | [10.799487113952637, 10.343113899230957] |
59233b51-f074-4077-8c76-fac3010ab578 | accomontage2-a-complete-harmonization-and | 2209.00353 | null | https://arxiv.org/abs/2209.00353v1 | https://arxiv.org/pdf/2209.00353v1.pdf | AccoMontage2: A Complete Harmonization and Accompaniment Arrangement System | We propose AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melody. Following AccoMontage, this study focuses on generating piano arrangements for popular/folk songs and it carries on the generalized template-based retrieval method. The novelties of th... | ['Gus Xia', 'Jingwei Zhao', 'Haochen Hu', 'Li Yi'] | 2022-09-01 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.30616892e-02 -4.81319457e-01 -6.71066642e-02 5.26843518e-02
-1.15812016e+00 -1.01156735e+00 3.44942153e-01 -3.70560773e-02
-8.98491740e-02 4.90511745e-01 4.14164722e-01 2.77625859e-01
-3.14811587e-01 -7.48417854e-01 -2.19169572e-01 -5.39362490e-01
3.55150513e-02 4.87198859e-01 2.88470060e-01 -6.77508950... | [15.9785737991333, 5.494420051574707] |
6aa9ff76-edc5-4759-a0da-417c385efa25 | content-aware-directed-propagation-network | 2107.13144 | null | https://arxiv.org/abs/2107.13144v3 | https://arxiv.org/pdf/2107.13144v3.pdf | Content-aware Directed Propagation Network with Pixel Adaptive Kernel Attention | Convolutional neural networks (CNNs) have been not only widespread but also achieved noticeable results on numerous applications including image classification, restoration, and generation. Although the weight-sharing property of convolutions makes them widely adopted in various tasks, its content-agnostic characterist... | ['Sung-Jea Ko', 'Seung-Won Jung', 'Yoon-Jae Yeo', 'Min-Cheol Sagong'] | 2021-07-28 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 3.96595716e-01 1.13462672e-01 9.60630924e-02 -3.95502687e-01
-6.14065647e-01 -2.00626329e-01 4.66130465e-01 -7.44573474e-02
-6.58693612e-01 5.23643613e-01 1.65770784e-01 -1.80209666e-01
-2.49265566e-01 -8.65840077e-01 -7.75747716e-01 -7.32419968e-01
2.10389748e-01 7.15380982e-02 5.72010636e-01 -1.02151871... | [10.015022277832031, -0.3073491156101227] |
f7965b37-9fd7-4229-8e69-b76542b7a142 | shift-variance-in-scene-text-detection | 2208.09231 | null | https://arxiv.org/abs/2208.09231v1 | https://arxiv.org/pdf/2208.09231v1.pdf | Shift Variance in Scene Text Detection | Theory of convolutional neural networks suggests the property of shift equivariance, i.e., that a shifted input causes an equally shifted output. In practice, however, this is not always the case. This poses a great problem for scene text detection for which a consistent spatial response is crucial, irrespective of the... | ['Philipp Härtinger', 'Jan-Hendrik Neudeck', 'Markus Glitzner'] | 2022-08-19 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.18031490e-01 -2.88493931e-01 5.13998747e-01 -2.01581344e-01
-1.78496867e-01 -7.06544638e-01 8.90397191e-01 5.13276994e-01
-6.97915912e-01 2.25134239e-01 3.18998029e-03 -1.71110228e-01
1.80547535e-02 -6.54204011e-01 -7.84909427e-01 -6.57065749e-01
2.05509141e-01 5.87511696e-02 7.45671034e-01 -4.24672455... | [11.81978702545166, 2.3758773803710938] |
974ad4ed-6b33-43a4-bffd-e645362ee703 | efficient-evolutionary-methods-for-game-agent | 1901.00723 | null | http://arxiv.org/abs/1901.00723v1 | http://arxiv.org/pdf/1901.00723v1.pdf | Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best | This paper introduces a simple and fast variant of Planet Wars as a test-bed
for statistical planning based Game AI agents, and for noisy hyper-parameter
optimisation. Planet Wars is a real-time strategy game with simple rules but
complex game-play. The variant introduced in this paper is designed for speed
to enable e... | ['Diego Perez-Liebana', 'John Woodward', 'Vanessa Volz', 'Simon M. Lucas', 'Raluca D. Gaina', 'Jialin Liu', 'Ivan Bravi'] | 2019-01-03 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [ 6.57302365e-02 -2.57500075e-02 5.60724847e-02 1.60518289e-01
-1.01941097e+00 -8.71309340e-01 6.04783297e-01 -4.16117102e-01
-9.13597286e-01 1.03965425e+00 3.26141678e-02 -4.85441059e-01
-9.70983148e-01 -7.87647545e-01 -1.13164119e-01 -1.03580320e+00
-4.53871310e-01 1.47881687e+00 4.10388499e-01 -6.74604356... | [3.5061960220336914, 1.520886778831482] |
8fba9231-af1e-4529-b373-3ff9c0d72598 | dense-pose-transfer | 1809.01995 | null | http://arxiv.org/abs/1809.01995v1 | http://arxiv.org/pdf/1809.01995v1.pdf | Dense Pose Transfer | In this work we integrate ideas from surface-based modeling with neural
synthesis: we propose a combination of surface-based pose estimation and deep
generative models that allows us to perform accurate pose transfer, i.e.
synthesize a new image of a person based on a single image of that person and
the image of a pose... | ['Riza Alp Guler', 'Natalia Neverova', 'Iasonas Kokkinos'] | 2018-09-06 | dense-pose-transfer-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Natalia_Neverova_Two_Stream__ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Natalia_Neverova_Two_Stream__ECCV_2018_paper.pdf | eccv-2018-9 | ['pose-transfer'] | ['computer-vision'] | [ 5.67779958e-01 4.44848865e-01 3.81614476e-01 -4.72571433e-01
-1.18771410e+00 -5.95542371e-01 7.68330455e-01 -2.58990645e-01
-5.58509111e-01 7.36518323e-01 1.29027143e-01 5.36246002e-01
4.55872416e-01 -7.53561854e-01 -1.25210190e+00 -5.94522238e-01
3.61354798e-01 1.04974878e+00 7.03060180e-02 -1.72317594... | [11.712124824523926, -0.7626749277114868] |
621a2a08-966e-4fab-acbb-ecfadc63a5a6 | cross-lingual-transfer-with-maml-on-trees | null | null | https://aclanthology.org/2021.adaptnlp-1.8 | https://aclanthology.org/2021.adaptnlp-1.8.pdf | Cross-Lingual Transfer with MAML on Trees | In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks that have a hierarchical structure. Our research extends... | ['Alberto Bernacchia', 'Da-Shan Shiu', 'Ye Tian', 'Feng-Ting Liao', 'Tim Nieradzik', 'Jamie McGowan', 'Federica Freddi', 'Jezabel Garcia'] | null | null | null | null | eacl-adaptnlp-2021-4 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.60426751e-01 1.38225913e-01 -2.81531632e-01 -6.48521185e-01
-5.29987872e-01 -6.97190464e-01 7.20566273e-01 2.12935269e-01
-8.09193373e-01 8.58344376e-01 2.99063772e-01 -2.30763197e-01
-1.52887955e-01 -5.40368617e-01 -7.61959791e-01 -5.00948310e-01
2.41878442e-02 7.22872734e-01 3.78252715e-01 -8.75850916... | [10.89146900177002, 9.44340705871582] |
6e4e09ef-bac6-49b2-b4eb-ac0d6e43d79d | pl-eesr-perceptual-loss-based-end-to-end | 2110.00940 | null | https://arxiv.org/abs/2110.00940v1 | https://arxiv.org/pdf/2110.00940v1.pdf | PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction | Speech enhancement aims to improve the perceptual quality of the speech signal by suppression of the background noise. However, excessive suppression may lead to speech distortion and speaker information loss, which degrades the performance of speaker embedding extraction. To alleviate this problem, we propose an end-t... | ['Haizhou Li', 'Ville Hautamaki', 'Kong Aik Lee', 'Yi Ma'] | 2021-10-03 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 1.07168369e-01 -2.58610159e-01 4.00209844e-01 -4.84594405e-01
-8.84843767e-01 -4.03853893e-01 2.72274673e-01 -1.25081182e-01
-3.53913873e-01 3.49335551e-01 7.71695375e-01 -9.97196808e-02
1.50838584e-01 -3.24172109e-01 -4.09730464e-01 -9.30279076e-01
1.26578435e-01 -6.07378960e-01 -1.35035276e-01 -1.59655645... | [14.705174446105957, 6.034103870391846] |
bc4e55bb-7a2f-4bb9-8b50-79518a1c2c4a | data-driven-smart-ponzi-scheme-detection | 2108.09305 | null | https://arxiv.org/abs/2108.09305v1 | https://arxiv.org/pdf/2108.09305v1.pdf | Data-driven Smart Ponzi Scheme Detection | A smart Ponzi scheme is a new form of economic crime that uses Ethereum smart contract account and cryptocurrency to implement Ponzi scheme. The smart Ponzi scheme has harmed the interests of many investors, but researches on smart Ponzi scheme detection is still very limited. The existing smart Ponzi scheme detection ... | ['Feiyang Wang', 'Kai Lei', 'Weijing Wu', 'Yuzhi Liang'] | 2021-08-20 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [ 8.60468149e-02 -2.12565307e-02 -2.70379633e-01 5.55047505e-02
-3.20333280e-02 -5.56226313e-01 8.63230228e-01 -2.06622034e-01
-1.88541979e-01 4.35724109e-01 2.15133396e-03 -4.02093560e-01
-2.66679138e-01 -1.25409424e+00 1.17127486e-01 -6.20962858e-01
-1.28601357e-01 8.35690558e-01 4.00761604e-01 -7.93249249... | [6.798694610595703, 7.246587753295898] |
e0f44449-e437-455c-8873-43f98c36d458 | particle-swarm-optimization-for-time-series | 1501.07399 | null | http://arxiv.org/abs/1501.07399v1 | http://arxiv.org/pdf/1501.07399v1.pdf | Particle swarm optimization for time series motif discovery | Efficiently finding similar segments or motifs in time series data is a
fundamental task that, due to the ubiquity of these data, is present in a wide
range of domains and situations. Because of this, countless solutions have been
devised but, to date, none of them seems to be fully satisfactory and flexible.
In this a... | ['Joan Serrà', 'Josep Lluis Arcos'] | 2015-01-29 | null | null | null | null | ['time-series-streams'] | ['time-series'] | [ 2.40573093e-01 -4.76485938e-01 -9.69036296e-02 9.64714810e-02
-2.42337435e-01 -5.82103431e-01 6.24163628e-01 4.85987812e-01
-3.92028362e-01 6.34455323e-01 -1.95077866e-01 -1.86906412e-01
-8.92363489e-01 -6.45396531e-01 -5.06149948e-01 -9.60034728e-01
-5.07299721e-01 4.94595855e-01 1.75454751e-01 -4.87262279... | [7.252933502197266, 3.319857120513916] |
f26db4d3-e51d-49f4-af3c-9a7fd1b10c70 | image-stitching-with-perspective-preserving | 1605.05019 | null | http://arxiv.org/abs/1605.05019v1 | http://arxiv.org/pdf/1605.05019v1.pdf | Image stitching with perspective-preserving warping | Image stitching algorithms often adopt the global transformation, such as
homography, and work well for planar scenes or parallax free camera motions.
However, these conditions are easily violated in practice. With casual camera
motions, variable taken views, large depth change, or complex structures, it is
a challengi... | ['Tian-Zhu Xiang', 'Gui-Song Xia', 'Liangpei Zhang'] | 2016-05-17 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 4.27512139e-01 -5.40556371e-01 -2.73684226e-02 -2.62970813e-02
-3.12050641e-01 -8.88452530e-01 5.65688789e-01 -4.40050483e-01
3.08581665e-02 4.03565288e-01 2.58262306e-01 1.01527184e-01
1.66341325e-03 -5.51659107e-01 -4.67537105e-01 -1.01026237e+00
5.60971379e-01 1.40950173e-01 4.41258609e-01 -2.92959899... | [9.332110404968262, -2.3578977584838867] |
b91d7c17-f783-472b-b1ab-e7ba9f3fd235 | restormer-efficient-transformer-for-high | 2111.09881 | null | https://arxiv.org/abs/2111.09881v2 | https://arxiv.org/pdf/2111.09881v2.pdf | Restormer: Efficient Transformer for High-Resolution Image Restoration | Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks. Recently, another class of neural architectures, Transformers, have shown significant performance gains on natural lang... | ['Ming-Hsuan Yang', 'Fahad Shahbaz Khan', 'Munawar Hayat', 'Salman Khan', 'Aditya Arora', 'Syed Waqas Zamir'] | 2021-11-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zamir_Restormer_Efficient_Transformer_for_High-Resolution_Image_Restoration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['color-image-denoising', 'single-image-deraining', 'grayscale-image-denoising'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.77649218e-01 -3.42428744e-01 1.42988414e-01 -2.23927230e-01
-7.85232306e-01 -1.49564341e-01 4.73347813e-01 -4.55862314e-01
-4.48131830e-01 5.02592504e-01 5.05422890e-01 -2.58641273e-01
-8.40692141e-04 -5.99560976e-01 -1.00585830e+00 -1.04254246e+00
3.30653429e-01 -2.30956733e-01 1.56542748e-01 -2.95720637... | [11.208958625793457, -2.297423839569092] |
336319d8-0399-4c64-b245-ff4b8c51dd13 | open-category-human-object-interaction-pre | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.pdf | Open-Category Human-Object Interaction Pre-Training via Language Modeling Framework | Human-object interaction (HOI) has long been plagued by the conflict between limited supervised data and a vast number of possible interaction combinations in real life. Current methods trained from closed-set data predict HOIs as fixed-dimension logits, which restricts their scalability to open-set categories. To ... | ['Qin Jin', 'Boshen Xu', 'Sipeng Zheng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 2.79403239e-01 2.00441986e-01 -2.71946549e-01 -6.25736296e-01
-5.64740956e-01 -4.51188236e-01 8.05078804e-01 -3.58361840e-01
-2.76623875e-01 5.86556375e-01 3.40531319e-01 -7.72183314e-02
1.28816858e-01 -6.57273471e-01 -9.60763872e-01 -3.13817680e-01
8.80862027e-02 8.23437631e-01 7.80368820e-02 -1.88389182... | [9.987635612487793, 1.616268515586853] |
9e016c52-8346-42ff-b29a-420cb50027cd | latent-shift-latent-diffusion-with-temporal | 2304.08477 | null | https://arxiv.org/abs/2304.08477v2 | https://arxiv.org/pdf/2304.08477v2.pdf | Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation | We propose Latent-Shift -- an efficient text-to-video generation method based on a pretrained text-to-image generation model that consists of an autoencoder and a U-Net diffusion model. Learning a video diffusion model in the latent space is much more efficient than in the pixel space. The latter is often limited to fi... | ['Xi Yin', 'Jiebo Luo', 'Jia-Bin Huang', 'Sonal Gupta', 'Harry Yang', 'Songyang Zhang', 'Jie An'] | 2023-04-17 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 4.51467991e-01 1.43704802e-01 3.83296050e-02 -6.18389845e-02
-5.94128013e-01 -4.03847307e-01 9.28958476e-01 -5.31215608e-01
-3.52799773e-01 6.56358898e-01 4.09497023e-01 -1.08908311e-01
3.51630449e-01 -9.48452473e-01 -9.13178146e-01 -7.80673087e-01
1.45677328e-01 -1.13667455e-02 4.55006093e-01 -6.14494868... | [10.865438461303711, -0.7334749102592468] |
32c583fe-6585-4932-9fdf-b9cc1a8d7767 | blockwise-principal-component-analysis-for | 2305.06042 | null | https://arxiv.org/abs/2305.06042v1 | https://arxiv.org/pdf/2305.06042v1.pdf | Blockwise Principal Component Analysis for monotone missing data imputation and dimensionality reduction | Monotone missing data is a common problem in data analysis. However, imputation combined with dimensionality reduction can be computationally expensive, especially with the increasing size of datasets. To address this issue, we propose a Blockwise principal component analysis Imputation (BPI) framework for dimensionali... | ['Binh T. Nguyen', 'Pål Halvorsen', 'Michael A. Riegler', 'Steven A. Hicks', 'Thu Nguyen', 'Hoang Thien Ly', 'Mai Anh Vu', 'Tu T. Do'] | 2023-05-10 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.95790425e-01 -3.85570794e-01 -1.12718306e-01 -4.47656393e-01
-6.68823183e-01 -3.58452857e-01 -1.05138034e-01 -1.69811457e-01
-2.58585125e-01 9.23205256e-01 6.29242301e-01 -1.82293698e-01
-4.64258641e-01 -8.84914100e-01 -7.62531459e-01 -8.29679847e-01
1.02916934e-01 7.80572295e-01 -6.16502464e-01 3.08337301... | [7.686771869659424, 4.851105213165283] |
c3579d0a-5505-4e5e-b379-0871c955a5b2 | evaluation-of-deep-convolutional-generative | 2009.01181 | null | https://arxiv.org/abs/2009.01181v1 | https://arxiv.org/pdf/2009.01181v1.pdf | Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images | Medical image datasets are usually imbalanced, due to the high costs of obtaining the data and time-consuming annotations. Training deep neural network models on such datasets to accurately classify the medical condition does not yield desired results and often over-fits the data on majority class samples. In order to ... | ['Sagar Kora Venu'] | 2020-09-02 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.90021789e-01 2.75671959e-01 -1.94271188e-02 -4.74356443e-01
-4.83453393e-01 -3.39345127e-01 3.49430442e-01 4.03756917e-01
-3.56035471e-01 7.93193638e-01 -2.59892223e-03 -3.51251096e-01
8.34564194e-02 -8.67620170e-01 -4.71524686e-01 -6.67951107e-01
9.83716324e-02 5.99299371e-01 -2.06029207e-01 -2.28200145... | [14.268848419189453, -1.994086742401123] |
1fce4013-e227-485d-9d36-99f060fab184 | ensemble-nonlinear-model-predictive-control | 2303.10393 | null | https://arxiv.org/abs/2303.10393v1 | https://arxiv.org/pdf/2303.10393v1.pdf | Ensemble Nonlinear Model Predictive Control for Residential Solar-Battery Energy Management | In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange. Providing a schedule of this bidirectional power dispatch can facilitate the operational planning for the grid operator and bri... | ['Changfu Zou', 'Chih Feng Lee', 'Daniel E. Quevedo', 'D. Mahinda Vilathgamuwa', 'Yang Li'] | 2023-03-18 | null | null | null | null | ['energy-management'] | ['time-series'] | [-2.79692411e-01 -3.15885633e-01 -1.77915189e-02 -2.67335288e-02
-1.46033019e-01 -8.50744069e-01 4.49972898e-01 3.59258540e-02
2.44271129e-01 1.38291860e+00 -1.42475292e-01 1.22456625e-02
-7.22106457e-01 -1.05878627e+00 -2.42746755e-01 -1.23823094e+00
-9.18787345e-02 7.69637525e-01 -5.95066667e-01 -2.13960811... | [5.678208351135254, 2.5233519077301025] |
203627d0-d946-43bc-86c3-226bba4c2b4f | ive-got-a-construction-looks-funny | null | null | https://aclanthology.org/2020.udw-1.16 | https://aclanthology.org/2020.udw-1.16.pdf | I’ve got a construction looks funny – representing and recovering non-standard constructions in UD | The UD framework defines guidelines for a crosslingual syntactic analysis in the framework of dependency grammar, with the aim of providing a consistent treatment across languages that not only supports multilingual NLP applications but also facilitates typological studies. Until now, the UD framework has mostly focuss... | ['Ines Rehbein', 'Josef Ruppenhofer'] | null | null | null | null | udw-coling-2020-12 | ['multilingual-nlp'] | ['natural-language-processing'] | [-4.89199191e-01 1.91476464e-01 -3.11533421e-01 -5.04122257e-01
-6.66584671e-01 -8.89190316e-01 6.65616155e-01 4.22750115e-01
-1.65469632e-01 7.77594090e-01 7.67737269e-01 -7.70325959e-01
-3.12633574e-01 -4.97485667e-01 -2.43396491e-01 -4.07026976e-01
9.79669541e-02 3.96491319e-01 2.12065533e-01 -4.39874738... | [10.404139518737793, 9.824941635131836] |
df45156e-7512-4b4d-8cf6-35b1146fd2dc | towards-resolving-the-challenge-of-long-tail | 2011.03822 | null | https://arxiv.org/abs/2011.03822v1 | https://arxiv.org/pdf/2011.03822v1.pdf | Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection | Existing methods for object detection in UAV images ignored an important challenge - imbalanced class distribution in UAV images - which leads to poor performance on tail classes. We systematically investigate existing solutions to long-tail problems and unveil that re-balancing methods that are effective on natural im... | ['Chen Chen', 'Taojiannan Yang', 'Weiping Yu'] | 2020-11-07 | null | null | null | null | ['head-detection', 'image-cropping'] | ['computer-vision', 'computer-vision'] | [ 1.52784614e-02 -4.76819992e-01 -1.76289797e-01 -3.96604240e-02
-3.02618980e-01 -9.37533319e-01 5.38902938e-01 -1.65279999e-01
-2.78616726e-01 5.36906302e-01 -4.32862729e-01 -3.68748724e-01
-3.25669423e-02 -6.85972989e-01 -7.38556266e-01 -8.06510091e-01
1.38276285e-02 4.23695534e-01 9.10636961e-01 -2.38926545... | [8.709639549255371, -0.6539978981018066] |
372af700-189c-4f63-b904-0b3085a3578a | graph-neural-network-for-fraud-detection-via | null | null | https://ieeexplore.ieee.org/abstract/document/9204584/ | https://ieeexplore.ieee.org/abstract/document/9204584/ | Graph Neural Network for Fraud Detection via Spatial-Temporal Attention | Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fr... | ['Liqing Zhang', 'Ying Zhang', 'Xiaoyang Wang', 'Dawei Cheng'] | 2020-09-23 | null | null | null | tkde-2020-9 | ['fraud-detection'] | ['miscellaneous'] | [-3.95065248e-01 -5.85911393e-01 5.89968190e-02 -3.89973134e-01
5.33714797e-03 -1.56474382e-01 1.02137253e-01 2.53299206e-01
-4.29532111e-01 1.67600110e-01 -2.71984991e-02 -4.98713315e-01
-7.29112402e-02 -1.18299520e+00 -3.64291668e-01 -2.91437417e-01
-4.70467716e-01 3.08993250e-01 1.53032348e-01 -3.08565378... | [7.315569877624512, 5.849272727966309] |
e6044117-7455-4390-abe4-1953f92298fd | short-term-memory-convolutions | 2302.04331 | null | https://arxiv.org/abs/2302.04331v1 | https://arxiv.org/pdf/2302.04331v1.pdf | Short-Term Memory Convolutions | The real-time processing of time series signals is a critical issue for many real-life applications. The idea of real-time processing is especially important in audio domain as the human perception of sound is sensitive to any kind of disturbance in perceived signals, especially the lag between auditory and visual moda... | ['Artur Szumaczuk', 'Bartłomiej Jasik', 'Paweł Daniluk', 'Krzysztof Arendt', 'Grzegorz Stefański'] | 2023-02-08 | null | null | null | null | ['acoustic-scene-classification', 'scene-classification', 'speech-separation'] | ['audio', 'computer-vision', 'speech'] | [ 3.62319261e-01 -2.52480060e-01 6.92514718e-01 -2.34443277e-01
-4.87619221e-01 -3.90346676e-01 4.52980548e-01 4.39691514e-01
-8.41371596e-01 4.88567978e-01 -2.67987400e-01 -4.00539517e-01
-3.17167252e-01 -7.64632344e-01 -7.40590811e-01 -5.84758043e-01
-2.97263354e-01 -3.52016576e-02 5.73583066e-01 -2.01891810... | [15.198286056518555, 5.461155414581299] |
eef98170-471c-451c-ac18-d5378aa799c8 | decision-making-under-uncertainty-an | 1911.00946 | null | https://arxiv.org/abs/1911.00946v3 | https://arxiv.org/pdf/1911.00946v3.pdf | Decision Making under Uncertainty: An Experimental Study in Market Settings | We implement nonparametric revealed-preference tests of subjective expected utility theory and its generalizations. We find that a majority of subjects' choices are consistent with the maximization of some utility function. They respond to price changes in the direction subjective expected utility theory predicts, but ... | ['Kota Saito', 'Taisuke Imai', 'Federico Echenique'] | 2019-11-03 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [-3.60448599e-01 1.10896744e-01 -7.67855227e-01 -7.66889155e-01
-1.72473058e-01 -9.58983898e-01 2.14383975e-01 -2.84671169e-02
-8.92870009e-01 1.48495281e+00 8.86680733e-04 -4.30840909e-01
-2.37768069e-01 -9.10253227e-01 -4.07982707e-01 -4.08257604e-01
-9.54763591e-02 4.26661968e-01 2.13037372e-01 -1.91714689... | [4.347177028656006, 2.9975407123565674] |
7b718387-1978-48c9-b2d2-2dd9fc030dfc | a-deep-multiscale-framework-for-video | null | null | https://ieeexplore.ieee.org/abstract/document/10086041 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10086041 | A Deep Multiscale Framework for Video Watermarking | Video watermarking embeds a message into a cover
video in an imperceptible manner, which can be retrieved
even if the video undergoes certain modifications or distortions. Traditional watermarking methods are often manually designed for particular types of distortions and thus
cannot simultaneously handle a broad ... | ['and Feng Yang', 'Peyman Milanfar1', 'Ce Liu1', 'Huiwen Chang1', 'Yinxiao Li1', 'Xiyang Luo1'] | 2023-03-28 | null | null | null | ieee-transactions-on-image-processing-2023-3 | ['video-editing'] | ['computer-vision'] | [ 4.63501573e-01 -3.70755285e-01 -5.40536404e-01 2.90844589e-01
-1.04945421e+00 -7.51008093e-01 4.10008311e-01 -1.61082551e-01
-7.26012662e-02 4.52668518e-01 2.75532812e-01 -1.94421798e-01
3.64695013e-01 -4.79888529e-01 -9.52795684e-01 -7.70119250e-01
-5.52923441e-01 -3.86797339e-01 2.01288119e-01 -1.87862203... | [5.3277435302734375, 7.935269832611084] |
0f6e1e0e-0015-402c-8667-c67c7eb773a7 | single-stage-instance-shadow-detection-with | null | null | https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf | Single-Stage Instance Shadow Detection with Bidirectional Relation Learning | Instance shadow detection aims to find shadow instances paired with the objects that cast the shadows. The previous work adopts a two-stage framework to first predict shadow instances, object instances, and shadow-object associations from the region proposals, then leverage a post-processing to match the predictions to... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Xiaowei Hu', 'Tianyu Wang'] | 2021-06-19 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Single-Stage_Instance_Shadow_Detection_With_Bidirectional_Relation_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['shadow-detection'] | ['computer-vision'] | [ 4.13534760e-01 4.01160896e-01 -5.69225550e-02 -9.33037877e-01
-4.64009523e-01 -2.05267757e-01 7.28105724e-01 -1.37957662e-01
-1.42242625e-01 4.71219867e-01 1.15781808e-02 -1.89606518e-01
2.95666307e-01 -6.60360992e-01 -7.66689241e-01 -5.38085103e-01
-4.18216176e-02 7.79420197e-01 1.07138598e+00 1.30675450... | [10.851622581481934, -4.117460250854492] |
c85122b2-eecf-4606-9572-a470d7087baa | a-survey-of-loss-functions-for-semantic | 2006.14822 | null | https://arxiv.org/abs/2006.14822v4 | https://arxiv.org/pdf/2006.14822v4.pdf | A survey of loss functions for semantic segmentation | Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. In the past five years, various papers came up with different objective loss functions used in different cases such as biased data, sparse segmentation, etc. In ... | ['Shruti Jadon'] | 2020-06-26 | null | null | null | null | ['skull-stripping'] | ['medical'] | [-1.99909374e-01 2.38796119e-02 -2.55416274e-01 -7.06335664e-01
-9.02299047e-01 -2.66785651e-01 3.53007644e-01 1.75684065e-01
-7.28931785e-01 8.11297059e-01 -1.05131023e-01 -6.90402389e-02
-1.19048476e-01 -6.46342576e-01 -5.44028997e-01 -7.58195996e-01
-1.39009356e-01 7.24133670e-01 6.72973931e-01 -1.10322736... | [14.37387466430664, -2.3632688522338867] |
4f78c002-075a-45e4-9c17-464d3eb1285e | data-augmentation-for-cross-domain-named | 2109.01758 | null | https://arxiv.org/abs/2109.01758v1 | https://arxiv.org/pdf/2109.01758v1.pdf | Data Augmentation for Cross-Domain Named Entity Recognition | Current work in named entity recognition (NER) shows that data augmentation techniques can produce more robust models. However, most existing techniques focus on augmenting in-domain data in low-resource scenarios where annotated data is quite limited. In contrast, we study cross-domain data augmentation for the NER ta... | ['Thamar Solorio', 'Leonardo Neves', 'Gustavo Aguilar', 'Shuguang Chen'] | 2021-09-04 | null | https://aclanthology.org/2021.emnlp-main.434 | https://aclanthology.org/2021.emnlp-main.434.pdf | emnlp-2021-11 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 4.07312751e-01 9.62709561e-02 -1.62577420e-01 -7.53246725e-01
-6.44476473e-01 -8.05506706e-01 5.83998621e-01 1.45749658e-01
-9.77434278e-01 8.80767465e-01 6.12900674e-01 -1.30305871e-01
3.47588509e-01 -8.00726473e-01 -5.72870076e-01 -8.59623551e-02
3.84502947e-01 6.46101832e-01 -1.29150540e-01 -2.72843540... | [9.79371166229248, 9.537885665893555] |
eef447b3-1bd0-40be-8de9-b4869ec27a2c | unsupervised-object-segmentation-in-video-by | 1704.05674 | null | http://arxiv.org/abs/1704.05674v1 | http://arxiv.org/pdf/1704.05674v1.pdf | Unsupervised object segmentation in video by efficient selection of highly probable positive features | We address an essential problem in computer vision, that of unsupervised
object segmentation in video, where a main object of interest in a video
sequence should be automatically separated from its background. An efficient
solution to this task would enable large-scale video interpretation at a high
semantic level in t... | ['Marius Leordeanu', 'Emanuela Haller'] | 2017-04-19 | unsupervised-object-segmentation-in-video-by-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Haller_Unsupervised_Object_Segmentation_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Haller_Unsupervised_Object_Segmentation_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 6.07826769e-01 -5.77259734e-02 -1.16508588e-01 -3.26165140e-01
-6.56449378e-01 -6.80198193e-01 4.44349587e-01 2.20071048e-01
-5.45810580e-01 4.68410045e-01 -1.68055519e-01 8.00010115e-02
-7.21992701e-02 -4.64061499e-01 -9.13423240e-01 -1.05057812e+00
-2.30021462e-01 4.70252335e-01 9.00222063e-01 2.39262328... | [8.995625495910645, -0.23641222715377808] |
5e4434e3-1702-4305-adb3-d8b60eb33e40 | image-restoration-using-joint-statistical | 1405.3173 | null | http://arxiv.org/abs/1405.3173v1 | http://arxiv.org/pdf/1405.3173v1.pdf | Image Restoration Using Joint Statistical Modeling in Space-Transform Domain | This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in ... | ['Siwei Ma', 'Debin Zhao', 'Wen Gao', 'Ruiqin Xiong', 'Jian Zhang'] | 2014-05-11 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 4.14391786e-01 -5.64788520e-01 4.31192890e-02 -2.57556111e-01
-1.05071986e+00 -1.21951088e-01 2.82169044e-01 -4.61828232e-01
-2.01442942e-01 7.04985142e-01 2.53974766e-01 1.42338306e-01
-5.42480886e-01 -3.05062354e-01 -6.01635575e-01 -1.09237385e+00
9.20409560e-02 -3.37381363e-01 -1.61387846e-01 -1.17035650... | [11.461050033569336, -2.553382158279419] |
1a1d7e8f-cbd2-4b76-a2f1-6fd81e235565 | remote-sensing-image-change-detection-with | 2307.02007 | null | https://arxiv.org/abs/2307.02007v1 | https://arxiv.org/pdf/2307.02007v1.pdf | Remote Sensing Image Change Detection with Graph Interaction | Modern remote sensing image change detection has witnessed substantial advancements by harnessing the potent feature extraction capabilities of CNNs and Transforms.Yet,prevailing change detection techniques consistently prioritize extracting semantic features related to significant alterations,overlooking the viability... | ['Chenglong Liu'] | 2023-07-05 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.91601968e-01 -2.20484853e-01 1.46726936e-01 -2.00990379e-01
-1.87508777e-01 -3.30921352e-01 7.78345644e-01 8.88654664e-02
-3.45266968e-01 2.16212809e-01 3.13812762e-01 -9.49342251e-02
-5.01302540e-01 -1.26135945e+00 -4.62895125e-01 -7.30569541e-01
-3.80141914e-01 -3.58099163e-01 1.81683272e-01 -6.01127088... | [9.705276489257812, -1.3288804292678833] |
0c2584ab-fb25-4f8a-9df9-9277a06c3be0 | spectral-feature-scaling-method-for | 1805.07006 | null | http://arxiv.org/abs/1805.07006v1 | http://arxiv.org/pdf/1805.07006v1.pdf | Spectral feature scaling method for supervised dimensionality reduction | Spectral dimensionality reduction methods enable linear separations of
complex data with high-dimensional features in a reduced space. However, these
methods do not always give the desired results due to irregularities or
uncertainties of the data. Thus, we consider aggressively modifying the scales
of the features to ... | ['Tetsuya Sakurai', 'Momo Matsuda', 'Keiichi Morikuni'] | 2018-05-18 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 2.69521475e-01 -1.39908165e-01 -1.98530734e-01 -3.43278915e-01
-4.40109521e-01 -5.70108056e-01 3.90451014e-01 -1.22781746e-01
-1.77344769e-01 5.98983347e-01 -6.02165982e-02 2.07295746e-01
-7.60510623e-01 -4.81616557e-01 -1.78936154e-01 -1.25075769e+00
-1.13926224e-01 4.70012695e-01 -1.05920412e-01 8.32402706... | [7.8754706382751465, 4.250045299530029] |
535da6f8-0320-459f-b8fc-65642582d9ac | attanet-attention-augmented-network-for-fast | 2103.05930 | null | https://arxiv.org/abs/2103.05930v1 | https://arxiv.org/pdf/2103.05930v1.pdf | AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing | Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture both factors always leads to high computational complexity, which is problematic in real-time scenarios. In this paper, we propose a new mo... | ['Rui Huang', 'Kangfu Mei', 'Qi Song'] | 2021-03-10 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 3.65678251e-01 -3.43157426e-02 7.06862332e-03 -3.51455301e-01
-6.18283629e-01 -3.55545133e-02 2.92665124e-01 4.35466200e-01
-7.31284916e-01 4.64942306e-01 -8.10164884e-02 -1.65492594e-01
-3.40786204e-02 -1.03634882e+00 -5.42446852e-01 -7.45798230e-01
1.68151200e-01 1.06466599e-01 7.02238381e-01 -1.59206107... | [9.350677490234375, -0.4286232590675354] |
d217ca04-61f4-43bd-8338-d61fa7b9d1ab | diffusionseg-adapting-diffusion-towards | 2303.09813 | null | https://arxiv.org/abs/2303.09813v1 | https://arxiv.org/pdf/2303.09813v1.pdf | DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery | Learning from a large corpus of data, pre-trained models have achieved impressive progress nowadays. As popular generative pre-training, diffusion models capture both low-level visual knowledge and high-level semantic relations. In this paper, we propose to exploit such knowledgeable diffusion models for mainstream dis... | ['Yanfeng Wang', 'Ya zhang', 'Yu Wang', 'Jinxiang Liu', 'Fei Zhang', 'Chen Ju', 'Yuhuan Yang', 'Chaofan Ma'] | 2023-03-17 | null | null | null | null | ['object-discovery'] | ['computer-vision'] | [ 5.41516066e-01 2.33263075e-01 -3.65439594e-01 -3.98974299e-01
-5.93841314e-01 -4.13441330e-01 6.39296949e-01 -7.89622813e-02
-4.62949067e-01 6.54770315e-01 1.67692065e-01 -1.45039773e-02
-8.44562501e-02 -7.13549674e-01 -6.23888195e-01 -7.39560246e-01
4.94767517e-01 2.71076083e-01 5.15971601e-01 6.63722083... | [9.570834159851074, 0.829123854637146] |
46f36ec6-4e63-4d2d-b75b-1c2c3909d6e8 | on-unifying-multi-view-self-representations | 1610.07126 | null | http://arxiv.org/abs/1610.07126v3 | http://arxiv.org/pdf/1610.07126v3.pdf | On Unifying Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization | In this paper, we address the multi-view subspace clustering problem. Our
method utilizes the circulant algebra for tensor, which is constructed by
stacking the subspace representation matrices of different views and then
rotating, to capture the low rank tensor subspace so that the refinement of the
view-specific subs... | ['Yanyun Qu', 'DaCheng Tao', 'Lei Zhang', 'Yuan Xie', 'Yan Liu', 'Wensheng Zhang'] | 2016-10-23 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.61566007e-01 -6.32817328e-01 -9.98321325e-02 1.08845115e-01
-5.79446733e-01 -7.54108310e-01 2.78156608e-01 -6.30895138e-01
-4.42633666e-02 1.09358586e-01 5.43742836e-01 1.56893916e-02
-6.77784145e-01 -5.03869392e-02 -1.97649494e-01 -1.23602879e+00
-2.97724921e-02 2.62822300e-01 -9.05271396e-02 -1.10159263... | [8.217004776000977, 4.619556903839111] |
d45b089b-5193-4742-ac16-b9e5656fa09f | multi-level-gated-recurrent-neural-network-1 | 1910.01822 | null | https://arxiv.org/abs/1910.01822v1 | https://arxiv.org/pdf/1910.01822v1.pdf | Multi-level Gated Recurrent Neural Network for Dialog Act Classification | In this paper we focus on the problem of dialog act (DA) labelling. This problem has recently attracted a lot of attention as it is an important sub-part of an automatic question answering system, which is currently in great demand. Traditional methods tend to see this problem as a sequence labelling task and deals wit... | ['Yunfang Wu', 'Wei Li'] | 2019-10-04 | multi-level-gated-recurrent-neural-network | https://aclanthology.org/C16-1185 | https://aclanthology.org/C16-1185.pdf | coling-2016-12 | ['dialog-act-classification'] | ['natural-language-processing'] | [ 4.20682341e-01 3.01468790e-01 1.04157984e-01 -6.88073397e-01
-5.17264366e-01 -5.99393249e-01 8.75889659e-01 -4.57604928e-03
-6.69804156e-01 9.10413444e-01 6.58521354e-01 -6.76827192e-01
4.44941998e-01 -6.32100880e-01 2.52166808e-01 -5.61123431e-01
3.47441226e-01 7.81039715e-01 4.42016870e-01 -8.08726609... | [12.763679504394531, 7.73839807510376] |
2bc496f2-3df8-4dc4-af72-6a9d097fbffc | content-authentication-for-neural-imaging | 1812.01516 | null | http://arxiv.org/abs/1812.01516v2 | http://arxiv.org/pdf/1812.01516v2.pdf | Content Authentication for Neural Imaging Pipelines: End-to-end Optimization of Photo Provenance in Complex Distribution Channels | Forensic analysis of digital photo provenance relies on intrinsic traces left
in the photograph at the time of its acquisition. Such analysis becomes
unreliable after heavy post-processing, such as down-sampling and
re-compression applied upon distribution in the Web. This paper explores
end-to-end optimization of the ... | ['Pawel Korus', 'Nasir Memon'] | 2018-12-04 | content-authentication-for-neural-imaging-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Korus_Content_Authentication_for_Neural_Imaging_Pipelines_End-To-End_Optimization_of_Photo_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Korus_Content_Authentication_for_Neural_Imaging_Pipelines_End-To-End_Optimization_of_Photo_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-manipulation-detection'] | ['computer-vision'] | [ 3.99144083e-01 -4.73183393e-02 1.35354236e-01 -3.93144518e-01
-1.09760225e+00 -8.98492098e-01 4.50445086e-01 3.61086786e-01
-5.80687940e-01 9.41409841e-02 -5.67541048e-02 -5.28179884e-01
1.06595568e-01 -5.32841504e-01 -1.10926640e+00 -1.25591025e-01
-8.10509101e-02 -9.99131724e-02 2.88424939e-01 6.34537041... | [12.375832557678223, 1.0166833400726318] |
17a12218-f0aa-47d7-8895-a239f1eb8c74 | an-audio-visual-attention-based-multimodal | 2203.05178 | null | https://arxiv.org/abs/2203.05178v1 | https://arxiv.org/pdf/2203.05178v1.pdf | An Audio-Visual Attention Based Multimodal Network for Fake Talking Face Videos Detection | DeepFake based digital facial forgery is threatening the public media security, especially when lip manipulation has been used in talking face generation, the difficulty of fake video detection is further improved. By only changing lip shape to match the given speech, the facial features of identity is hard to be discr... | ['Yanning Zhang', 'Yufei zha', 'Wei Huang', 'Lei Xie', 'Peng Zhang', 'Ganglai Wang'] | 2022-03-10 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 3.40171829e-02 8.10625181e-02 1.29819706e-01 1.11808181e-02
-4.25236195e-01 -1.69900417e-01 4.43911880e-01 -5.02483249e-01
-9.19507146e-02 4.14860368e-01 1.51573554e-01 4.42878418e-02
2.28739202e-01 -5.29255152e-01 -5.74621737e-01 -7.62716949e-01
3.54772866e-01 -3.76563400e-01 5.72039299e-02 -2.16672793... | [13.01039981842041, 1.1686722040176392] |
4e15ca2e-f275-43d1-bcdd-1b5ad51a9c86 | codeattack-code-based-adversarial-attacks-for | 2206.00052 | null | https://arxiv.org/abs/2206.00052v3 | https://arxiv.org/pdf/2206.00052v3.pdf | CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models | Pre-trained programming language (PL) models (such as CodeT5, CodeBERT, GraphCodeBERT, etc.,) have the potential to automate software engineering tasks involving code understanding and code generation. However, these models operate in the natural channel of code, i.e., they are primarily concerned with the human unders... | ['Chandan K. Reddy', 'Akshita Jha'] | 2022-05-31 | null | null | null | null | ['code-translation'] | ['computer-code'] | [-1.72960646e-02 2.51499504e-01 -1.11621618e-01 8.41332823e-02
-1.02679646e+00 -1.17311585e+00 6.37404442e-01 2.11714834e-01
3.12375933e-01 4.60876524e-02 1.39798284e-01 -1.00141597e+00
5.75839221e-01 -7.15368629e-01 -1.23857307e+00 -2.21474301e-02
-1.85249329e-01 1.10698286e-02 6.74404204e-02 -3.05323035... | [7.211448669433594, 7.872186660766602] |
a6ebcb0f-6ef6-4b6f-b3b6-1a8bc970b95e | holistic-instance-level-human-parsing | 1709.03612 | null | http://arxiv.org/abs/1709.03612v1 | http://arxiv.org/pdf/1709.03612v1.pdf | Holistic, Instance-Level Human Parsing | Object parsing -- the task of decomposing an object into its semantic parts
-- has traditionally been formulated as a category-level segmentation problem.
Consequently, when there are multiple objects in an image, current methods
cannot count the number of objects in the scene, nor can they determine which
part belongs... | ['Philip H. S. Torr', 'Anurag Arnab', 'Qizhu Li'] | 2017-09-11 | null | null | null | null | ['multi-human-parsing', 'human-parsing'] | ['computer-vision', 'computer-vision'] | [ 5.97210288e-01 4.08773363e-01 -1.36401486e-02 -3.29286605e-01
-7.58332968e-01 -5.13792574e-01 3.41694802e-01 3.88542980e-01
-5.56804776e-01 2.95921296e-01 -4.71636534e-01 5.40669402e-03
1.63433149e-01 -1.03266323e+00 -9.25073385e-01 -6.02403045e-01
1.19118057e-01 9.43457365e-01 7.44736135e-01 2.57349759... | [9.431353569030762, 0.4975510835647583] |
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