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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
739049e4-b7de-4e3c-8c3f-3a041e703f7e | digiface-1m-1-million-digital-face-images-for | 2210.02579 | null | https://arxiv.org/abs/2210.02579v1 | https://arxiv.org/pdf/2210.02579v1.pdf | DigiFace-1M: 1 Million Digital Face Images for Face Recognition | State-of-the-art face recognition models show impressive accuracy, achieving over 99.8% on Labeled Faces in the Wild (LFW) dataset. Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet. Web-crawled face images are severely biased (in terms of race, ... | ['Jingjing Shen', 'Roberto Cipolla', 'Julien Valentin', 'Dong Chen', 'Charlie Hewitt', 'Tadas Baltrusaitis', 'Martin de La Gorce', 'Gwangbin Bae'] | 2022-10-05 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.71377081e-01 5.13594627e-01 2.53195047e-01 -9.21372950e-01
-9.12509501e-01 -6.10554814e-01 6.41431034e-01 -8.91133368e-01
-2.85860658e-01 7.10282087e-01 -5.75192198e-02 5.55091538e-03
5.66213667e-01 -6.72365308e-01 -9.12750959e-01 -3.68304044e-01
2.13401735e-01 5.25091469e-01 -5.48785031e-01 5.69476038... | [12.890278816223145, 0.621039867401123] |
aa07752b-7ca6-4c0d-b40e-90c8987a3213 | how-crucial-is-transformer-in-decision | 2211.14655 | null | https://arxiv.org/abs/2211.14655v1 | https://arxiv.org/pdf/2211.14655v1.pdf | How Crucial is Transformer in Decision Transformer? | Decision Transformer (DT) is a recently proposed architecture for Reinforcement Learning that frames the decision-making process as an auto-regressive sequence modeling problem and uses a Transformer model to predict the next action in a sequence of states, actions, and rewards. In this paper, we analyze how crucial th... | ['Jan Peters', 'Junning Huang', 'Boris Belousov', 'Max Siebenborn'] | 2022-11-26 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.10869294e-02 2.81842440e-01 -2.19776079e-01 -2.16732454e-02
-8.65990967e-02 -4.84164923e-01 6.18751466e-01 -2.04014644e-01
-3.21275890e-01 9.85748053e-01 -2.92387873e-01 -7.73013771e-01
-8.15595910e-02 -5.80459714e-01 -7.78277755e-01 -6.84827447e-01
-2.33501002e-01 3.17096591e-01 3.34017694e-01 -6.24711812... | [4.2574591636657715, 1.9062401056289673] |
21f97f27-04d5-445c-bc72-bac5ae722d7b | automatic-microscopic-cell-counting-by-use-of | 1903.00388 | null | http://arxiv.org/abs/1903.00388v2 | http://arxiv.org/pdf/1903.00388v2.pdf | Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression | Accurate cell counting in microscopic images is important for medical
diagnoses and biological studies. However, manual cell counting is very
time-consuming, tedious, and prone to subjective errors. We propose a new
density regression-based method for automatic cell counting that reduces the
need to manually annotate e... | ['Lilianna Solnica-Krezel', 'Kyaw Thu Minn', 'Shenghua He', 'Mark Anastasio', 'Hua Li'] | 2019-03-01 | null | null | null | null | ['automatic-cell-counting'] | ['miscellaneous'] | [ 8.87836888e-02 -8.59979615e-02 3.55632440e-03 -2.37390757e-01
-6.35607719e-01 -1.62818432e-01 2.69320518e-01 5.26169479e-01
-8.00804734e-01 1.23272216e+00 -4.14744824e-01 -2.76863948e-02
3.97551328e-01 -1.03217709e+00 -4.60062593e-01 -9.78759825e-01
3.60716343e-01 1.01861787e+00 3.93710077e-01 4.76128370... | [14.667957305908203, -3.191068649291992] |
9d77590d-c1fc-4781-b448-3d0823b6984b | equivariant-energy-guided-sde-for-inverse | 2209.15408 | null | https://arxiv.org/abs/2209.15408v3 | https://arxiv.org/pdf/2209.15408v3.pdf | Equivariant Energy-Guided SDE for Inverse Molecular Design | Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the g... | ['Jun Zhu', 'Chongxuan Li', 'Peiyao Li', 'Zhongkai Hao', 'Min Zhao', 'Fan Bao'] | 2022-09-30 | null | null | null | null | ['3d-molecule-generation'] | ['medical'] | [ 2.09600896e-01 7.94712547e-03 -2.24879816e-01 2.68751085e-02
-3.18034858e-01 -8.91561270e-01 6.55270517e-01 -5.16515598e-02
-1.45372534e-02 1.15453041e+00 3.33211631e-01 -4.04238284e-01
-3.79497528e-01 -9.90180433e-01 -8.56361508e-01 -1.13481820e+00
-9.64164957e-02 2.04881262e-02 -2.67832398e-01 -5.76605737... | [5.082918643951416, 5.684427261352539] |
aaa39187-adc6-4aef-9448-9ce457b4de4f | bplf-a-bi-parallel-linear-flow-model-for | 2106.07563 | null | https://arxiv.org/abs/2106.07563v1 | https://arxiv.org/pdf/2106.07563v1.pdf | BPLF: A Bi-Parallel Linear Flow Model for Facial Expression Generation from Emotion Set Images | The flow-based generative model is a deep learning generative model, which obtains the ability to generate data by explicitly learning the data distribution. Theoretically its ability to restore data is stronger than other generative models. However, its implementation has many limitations, including limited model desi... | ['Kunxian Shu', 'Xiaoming Yao', 'Shimei Xu', 'Lijia Yang', 'Siwei Liu', 'Yuanpeng Long', 'Gao Xu'] | 2021-05-27 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [-3.20651680e-01 -1.08273968e-01 3.61518800e-01 -5.39704084e-01
2.63675600e-01 -8.62327311e-03 3.81991148e-01 -1.13727534e+00
-3.87420692e-03 7.26963401e-01 2.59298474e-01 1.52767077e-01
6.96710125e-02 -1.25212324e+00 -3.95494133e-01 -1.05776465e+00
-3.34996171e-02 -3.26542296e-02 -3.56623411e-01 -4.63577867... | [13.576674461364746, 1.7141032218933105] |
b0774883-623a-41e0-893f-5368fbaac01f | empirically-validating-conformal-prediction | 2307.01088 | null | https://arxiv.org/abs/2307.01088v1 | https://arxiv.org/pdf/2307.01088v1.pdf | Empirically Validating Conformal Prediction on Modern Vision Architectures Under Distribution Shift and Long-tailed Data | Conformal prediction has emerged as a rigorous means of providing deep learning models with reliable uncertainty estimates and safety guarantees. Yet, its performance is known to degrade under distribution shift and long-tailed class distributions, which are often present in real world applications. Here, we characteri... | ['Graham W. Taylor', 'Kevin Kasa'] | 2023-07-03 | null | null | null | null | ['conformal-prediction', 'conformal-prediction'] | ['computer-vision', 'reasoning'] | [-2.78575607e-02 1.30662113e-01 -3.15526664e-01 -6.85235798e-01
-9.81632233e-01 -7.03672767e-01 6.27383292e-01 3.11281413e-01
-1.68337762e-01 9.72286761e-01 5.11936378e-03 -4.80839819e-01
-5.24760067e-01 -7.04662323e-01 -1.03650331e+00 -8.10386479e-01
-6.01209462e-01 5.96279442e-01 2.77388334e-01 1.50223970... | [7.8886260986328125, 4.0882182121276855] |
6866fe52-73dc-46c4-b365-b2618b827e50 | r2h-building-multimodal-navigation-helpers | 2305.14260 | null | https://arxiv.org/abs/2305.14260v1 | https://arxiv.org/pdf/2305.14260v1.pdf | R2H: Building Multimodal Navigation Helpers that Respond to Help | The ability to assist humans during a navigation task in a supportive role is crucial for intelligent agents. Such agents, equipped with environment knowledge and conversational abilities, can guide individuals through unfamiliar terrains by generating natural language responses to their inquiries, grounded in the visu... | ['Xin Eric Wang', 'Jing Gu', 'Kaizhi Zheng', 'Yue Fan'] | 2023-05-23 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-2.44766504e-01 5.37905753e-01 5.89261174e-01 -5.62193096e-01
-5.94990551e-01 -5.93377590e-01 9.55520689e-01 -1.74352840e-01
-4.46375579e-01 1.03425133e+00 7.89906681e-01 1.94867015e-01
1.57588676e-01 -7.27911472e-01 1.37194633e-01 -5.67260623e-01
1.46162540e-01 9.16383922e-01 -6.11731037e-02 -1.00304544... | [12.939884185791016, 7.835784912109375] |
9867f4ba-0217-43e9-a957-fb5ac76c107f | high-quality-diversification-for-task | 2106.00891 | null | https://arxiv.org/abs/2106.00891v2 | https://arxiv.org/pdf/2106.00891v2.pdf | High-Quality Diversification for Task-Oriented Dialogue Systems | Many task-oriented dialogue systems use deep reinforcement learning (DRL) to learn policies that respond to the user appropriately and complete the tasks successfully. Training DRL agents with diverse dialogue trajectories prepare them well for rare user requests and unseen situations. One effective diversification met... | ['Grace Hui Yang', 'Hrishikesh Kulkarni', 'Zhiwen Tang'] | 2021-06-02 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-2.84239709e-01 3.33899528e-01 5.06063178e-02 -2.88374633e-01
-3.42045635e-01 -6.31008744e-01 7.57980585e-01 -1.94834128e-01
-5.62679946e-01 1.09463286e+00 9.59596410e-02 -2.31826141e-01
1.48368880e-01 -7.44501591e-01 -2.89710701e-01 -7.05734015e-01
1.06582522e-01 1.15509605e+00 3.19803447e-01 -1.00976777... | [13.020994186401367, 8.062849998474121] |
015eade8-1e95-4644-9331-d36048d3e509 | stable-yaw-estimation-of-boats-from-the | 2306.14056 | null | https://arxiv.org/abs/2306.14056v1 | https://arxiv.org/pdf/2306.14056v1.pdf | Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs | Yaw estimation of boats from the viewpoint of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) or boats is a crucial task in various applications such as 3D scene rendering, trajectory prediction, and navigation. However, the lack of literature on yaw estimation of objects from the viewpoint of UAVs... | ['Andreas Zell', 'Timon Höfer', 'Benjamin Kiefer'] | 2023-06-24 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-1.79556563e-01 -3.70987505e-02 5.10660410e-01 -4.34773654e-01
-1.71863168e-01 -1.11608255e+00 4.99824584e-01 -2.61762410e-01
-4.53008980e-01 3.64804178e-01 -1.86582450e-02 -3.57903481e-01
1.15720909e-02 -6.77655399e-01 -9.91617858e-01 -5.29946983e-01
-3.23585898e-01 3.50075603e-01 7.55958617e-01 -3.62902254... | [7.551446437835693, -1.8543404340744019] |
fb2432e6-f140-464d-9ea4-027a03abc1c4 | deepvel-deep-learning-for-the-estimation-of | 1703.05128 | null | http://arxiv.org/abs/1703.05128v2 | http://arxiv.org/pdf/1703.05128v2.pdf | DeepVel: deep learning for the estimation of horizontal velocities at the solar surface | Many phenomena taking place in the solar photosphere are controlled by plasma
motions. Although the line-of-sight component of the velocity can be estimated
using the Doppler effect, we do not have direct spectroscopic access to the
components that are perpendicular to the line-of-sight. These components are
typically ... | ['I. S. Requerey', 'A. Asensio Ramos', 'N. Vitas'] | 2017-03-15 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [-3.82317334e-01 -1.82091966e-01 2.80634791e-01 -6.46224916e-02
1.68193698e-01 -5.53138435e-01 7.22697258e-01 6.23399317e-02
-3.93421561e-01 6.30027294e-01 -2.04140976e-01 -2.98332334e-01
-7.93302134e-02 -1.02320814e+00 -3.03632349e-01 -1.07800901e+00
-3.28568667e-01 8.44915330e-01 2.05886021e-01 -5.46397269... | [6.690825462341309, 3.246318817138672] |
de45e7a5-d176-4415-a7a6-935d9a6bf010 | a-bayesian-evaluation-framework-for-ground | 2007.06711 | null | https://arxiv.org/abs/2007.06711v2 | https://arxiv.org/pdf/2007.06711v2.pdf | A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks | An interesting development in automatic visual recognition has been the emergence of tasks where it is not possible to assign objective labels to images, yet still feasible to collect annotations that reflect human judgements about them. Machine learning-based predictors for these tasks rely on supervised training that... | ['Walter J. Scheirer', 'Mel McCurrie', 'Derek S. Prijatelj'] | 2020-06-20 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 4.62055653e-01 8.23246062e-01 -5.80902584e-02 -7.90989995e-01
-4.65159774e-01 -3.62903446e-01 6.49526119e-01 2.82657713e-01
-5.77324867e-01 6.86604440e-01 -2.09703986e-02 -1.50917202e-01
-1.71777546e-01 -3.49642545e-01 -6.98537230e-01 -8.02747369e-01
5.30758381e-01 5.83107829e-01 -6.56237006e-02 2.59421527... | [8.714601516723633, 5.110504150390625] |
69c6dd53-1939-4f40-9f88-1b94abd2854b | slk-ner-exploiting-second-order-lexicon | 2007.08416 | null | https://arxiv.org/abs/2007.08416v1 | https://arxiv.org/pdf/2007.08416v1.pdf | SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER | Although character-based models using lexicon have achieved promising results for Chinese named entity recognition (NER) task, some lexical words would introduce erroneous information due to wrongly matched words. Existing researches proposed many strategies to integrate lexicon knowledge. However, they performed with ... | ['Dou Hu', 'Lingwei Wei'] | 2020-07-16 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-4.44903262e-02 -2.23880902e-01 -3.26385647e-01 -1.63225919e-01
-3.82853180e-01 -5.83936691e-01 2.46930495e-01 4.22389150e-01
-7.33223081e-01 8.53288591e-01 3.72518778e-01 -4.04681653e-01
1.64667256e-02 -9.52630162e-01 -6.51514754e-02 -4.02085781e-01
3.15103143e-01 1.08891778e-01 5.23823023e-01 -4.26944524... | [9.78710651397705, 9.671896934509277] |
463c550a-9ae1-4ebf-b116-f0b0a470fbd4 | a-survey-of-vision-language-pre-training-from | 2306.07198 | null | https://arxiv.org/abs/2306.07198v1 | https://arxiv.org/pdf/2306.07198v1.pdf | A Survey of Vision-Language Pre-training from the Lens of Multimodal Machine Translation | Large language models such as BERT and the GPT series started a paradigm shift that calls for building general-purpose models via pre-training on large datasets, followed by fine-tuning on task-specific datasets. There is now a plethora of large pre-trained models for Natural Language Processing and Computer Vision. Re... | ['Kevin Duh', 'Jeremy Gwinnup'] | 2023-06-12 | null | null | null | null | ['visual-question-answering-1', 'image-captioning', 'multimodal-machine-translation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 5.67101538e-01 1.99441165e-01 -2.56421864e-01 -5.11357367e-01
-1.46339405e+00 -7.54100323e-01 1.10606277e+00 -1.41063690e-01
-5.48103452e-01 5.26663244e-01 4.81308579e-01 -6.47659183e-01
5.01530111e-01 -2.00546995e-01 -9.39471185e-01 -2.78167456e-01
4.76913780e-01 8.20840061e-01 -3.41744721e-01 -1.89965576... | [11.310126304626465, 1.4156560897827148] |
5815b073-4ffa-4460-8bb2-1a8fdde856c3 | word-sense-extension | 2306.05609 | null | https://arxiv.org/abs/2306.05609v1 | https://arxiv.org/pdf/2306.05609v1.pdf | Word sense extension | Humans often make creative use of words to express novel senses. A long-standing effort in natural language processing has been focusing on word sense disambiguation (WSD), but little has been explored about how the sense inventory of a word may be extended toward novel meanings. We present a paradigm of word sense ext... | ['Yang Xu', 'Lei Yu'] | 2023-06-09 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 5.76648176e-01 8.82119983e-02 -2.32116371e-01 -4.67511117e-01
-4.47356790e-01 -9.88756955e-01 7.90963352e-01 5.37379801e-01
-8.69932294e-01 8.22459519e-01 7.60552764e-01 -4.85446066e-01
2.04407841e-01 -9.44273055e-01 -3.28129560e-01 -2.92934686e-01
2.27053776e-01 2.54097998e-01 2.89811641e-01 -7.44681895... | [10.398555755615234, 8.89050579071045] |
69ae2a3f-1799-40b6-b429-f3975b40254d | portable-speech-to-speech-translation-on-an | null | null | https://aclanthology.org/W18-1923 | https://aclanthology.org/W18-1923.pdf | Portable Speech-to-Speech Translation on an Android Smartphone: The MFLTS System | null | ['Sean Colbath', 'Ralf Meermeier', 'Martha Lillie'] | 2018-03-01 | null | null | null | ws-2018-3 | ['speech-to-speech-translation'] | ['speech'] | [-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... | [-7.3298139572143555, 3.642245054244995] |
44dd0a27-940b-47e9-9333-312787ce51d5 | large-margin-representation-learning-for | 2206.08537 | null | https://arxiv.org/abs/2206.08537v1 | https://arxiv.org/pdf/2206.08537v1.pdf | Large-Margin Representation Learning for Texture Classification | This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The core of such an approach is a loss function that computes the distances between instances of interest and support vectors. The objec... | ['Alessandro Lameiras Koerich', 'Alceu de Souza Britto Junior', 'Luiz Eduardo Soares de Oliveira', 'Jonathan de Matos'] | 2022-06-17 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 2.50629067e-01 2.01678157e-01 -2.94359297e-01 -6.44040525e-01
-6.44435763e-01 4.56771851e-02 2.11887777e-01 5.06343484e-01
-7.82796144e-01 7.68855453e-01 -4.37293500e-01 -2.60762841e-01
-3.75473291e-01 -8.34266245e-01 -5.15908360e-01 -7.84789622e-01
-2.93819159e-01 3.16425115e-01 3.67015421e-01 9.33321640... | [15.088761329650879, -2.7370591163635254] |
4c7f3b3b-54dc-414e-ab6e-9fcd428305d3 | a-proxy-free-strategy-for-practically | 2306.08313 | null | https://arxiv.org/abs/2306.08313v1 | https://arxiv.org/pdf/2306.08313v1.pdf | A Proxy-Free Strategy for Practically Improving the Poisoning Efficiency in Backdoor Attacks | Poisoning efficiency is a crucial factor in poisoning-based backdoor attacks. Attackers prefer to use as few poisoned samples as possible to achieve the same level of attack strength, in order to remain undetected. Efficient triggers have significantly improved poisoning efficiency, but there is still room for improvem... | ['Bin Li', 'Wei zhang', 'Xue Rui', 'Beihao Xia', 'Pengfei Xia', 'Hong Sun', 'Ziqiang Li'] | 2023-06-14 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-2.39368886e-01 -7.93836534e-01 -4.02370214e-01 2.08138958e-01
-9.02058542e-01 -1.01115048e+00 4.87258017e-01 4.43484485e-01
-8.96290720e-01 5.92923582e-01 1.56381175e-01 -2.91204840e-01
1.20654434e-01 -9.96582687e-01 -3.07970852e-01 -8.54107976e-01
-2.88120687e-01 4.26494181e-01 5.65173686e-01 -1.70083478... | [5.782889366149902, 7.593871116638184] |
5c3897e2-aacd-4a57-9332-f3022d2e06c4 | recurrent-convolutional-neural-networks-for | 1306.3584 | null | http://arxiv.org/abs/1306.3584v1 | http://arxiv.org/pdf/1306.3584v1.pdf | Recurrent Convolutional Neural Networks for Discourse Compositionality | The compositionality of meaning extends beyond the single sentence. Just as
words combine to form the meaning of sentences, so do sentences combine to form
the meaning of paragraphs, dialogues and general discourse. We introduce both a
sentence model and a discourse model corresponding to the two levels of
compositiona... | ['Nal Kalchbrenner', 'Phil Blunsom'] | 2013-06-15 | recurrent-convolutional-neural-networks-for-2 | https://aclanthology.org/W13-3214 | https://aclanthology.org/W13-3214.pdf | ws-2013-8 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 4.95638490e-01 7.36757100e-01 7.63976648e-02 -5.96435130e-01
-9.12156925e-02 -4.78596568e-01 1.17258167e+00 1.89262986e-01
-3.55782449e-01 3.87838572e-01 9.00076270e-01 -4.44200516e-01
3.14137697e-01 -8.57415617e-01 -3.34265947e-01 -5.24068236e-01
1.62291989e-01 3.20589781e-01 1.77051857e-01 -8.10045362... | [12.535086631774902, 7.560083866119385] |
a26d2350-92f9-4069-93d8-47ee7052923c | self-supervised-feature-learning-for-long | 2212.00122 | null | https://arxiv.org/abs/2212.00122v1 | https://arxiv.org/pdf/2212.00122v1.pdf | Self-Supervised Feature Learning for Long-Term Metric Visual Localization | Visual localization is the task of estimating camera pose in a known scene, which is an essential problem in robotics and computer vision. However, long-term visual localization is still a challenge due to the environmental appearance changes caused by lighting and seasons. While techniques exist to address appearance ... | ['Timothy D. Barfoot', 'Yuxuan Chen'] | 2022-11-30 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 6.00419492e-02 -4.43134576e-01 -6.82821544e-03 -6.54711008e-01
-8.05482507e-01 -8.48438263e-01 6.44274950e-01 -2.11154353e-02
-6.52140558e-01 4.96916801e-01 -8.69594142e-02 1.88891411e-01
7.88755044e-02 -4.90318030e-01 -1.29210556e+00 -4.51418996e-01
1.06668755e-01 5.79278290e-01 3.01981986e-01 -3.71351056... | [7.714034080505371, -2.1335225105285645] |
bc60f30d-02ce-453d-8287-1a4954dbf3d8 | weakly-supervised-hoi-detection-via-prior | 2303.01313 | null | https://arxiv.org/abs/2303.01313v1 | https://arxiv.org/pdf/2303.01313v1.pdf | Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning | Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challe... | ['Xuming He', 'Tinne Tuytelaars', 'Desen Zhou', 'Yongfei Liu', 'Bo Wan'] | 2023-03-02 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [ 2.69747764e-01 1.41017392e-01 -1.37377962e-01 -2.33400866e-01
-5.65163374e-01 -2.26896256e-01 4.12447602e-01 -3.77665609e-02
-4.70731914e-01 6.81882679e-01 3.79596613e-02 1.04494847e-01
1.12933457e-01 -3.80171090e-01 -9.22592580e-01 -6.37268007e-01
1.54366851e-01 4.98708814e-01 7.59466827e-01 -1.56067103... | [9.608762741088867, 1.3919748067855835] |
dce7d873-74e1-49d4-b517-b2b02fd48b5e | optical-flow-based-motion-detection-for | 2203.11693 | null | https://arxiv.org/abs/2203.11693v1 | https://arxiv.org/pdf/2203.11693v1.pdf | Optical Flow Based Motion Detection for Autonomous Driving | Motion detection is a fundamental but challenging task for autonomous driving. In particular scenes like highway, remote objects have to be paid extra attention for better controlling decision. Aiming at distant vehicles, we train a neural network model to classify the motion status using optical flow field information... | ['Ka Man Lo'] | 2022-03-03 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [-5.60985923e-01 -2.00140461e-01 -5.91976583e-01 -3.40303421e-01
-6.55296892e-02 -2.91873515e-01 4.59776729e-01 -4.44834441e-01
-6.23374581e-01 8.72998118e-01 4.09798883e-02 -6.45087659e-01
-1.01282205e-02 -8.27752233e-01 -6.05394065e-01 -7.28766561e-01
-1.16675586e-01 -7.72538632e-02 7.91995168e-01 -2.80797809... | [8.097597122192383, -1.3552002906799316] |
4b893367-31ce-49bd-aa61-1f4150585afa | mandoline-model-evaluation-under-distribution | 2107.00643 | null | https://arxiv.org/abs/2107.00643v2 | https://arxiv.org/pdf/2107.00643v2.pdf | Mandoline: Model Evaluation under Distribution Shift | Machine learning models are often deployed in different settings than they were trained and validated on, posing a challenge to practitioners who wish to predict how well the deployed model will perform on a target distribution. If an unlabeled sample from the target distribution is available, along with a labeled samp... | ['Nimit S. Sohoni', 'Christopher Ré', 'Kayvon Fatahalian', 'Fait Poms', 'Karan Goel', 'Mayee Chen'] | 2021-07-01 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 2.27321431e-01 -9.27657783e-02 -5.98613679e-01 -6.14103019e-01
-1.12934232e+00 -8.29082191e-01 5.89256525e-01 5.09670854e-01
-4.81726527e-01 8.22835207e-01 4.17853177e-01 -5.42148113e-01
-2.69789994e-01 -5.89713931e-01 -6.40203774e-01 -7.38470614e-01
1.29189149e-01 7.02739179e-01 -9.57196951e-03 3.07003230... | [8.58965015411377, 4.547660827636719] |
323666c3-253b-4d9d-a877-d1081d358a00 | a-survey-on-deep-learning-approaches-for-data | 2306.11740 | null | https://arxiv.org/abs/2306.11740v1 | https://arxiv.org/pdf/2306.11740v1.pdf | A survey on deep learning approaches for data integration in autonomous driving system | The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment. Recent advancements in deep learning have led to the rapid development of approaches that integrate multi-sensory measurements to enhance perception capabilities. This paper surveys the latest deep learning int... | ['Lei Chen', 'Yue Gong', 'Xiya Cao', 'Caifa Zhou', 'Likang Wang', 'Xi Zhu'] | 2023-06-17 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-1.64963201e-01 -1.96650252e-01 -4.99735266e-01 -5.76237321e-01
-5.31392992e-01 -3.82474869e-01 5.18771768e-01 1.19034043e-02
-4.05126572e-01 3.53377968e-01 -2.11371686e-02 -1.24790251e-01
-1.10175267e-01 -9.13284183e-01 -5.92254102e-01 -6.97727263e-01
4.30965871e-01 -8.92349612e-03 3.83672565e-01 -6.62742734... | [5.690496921539307, 0.8801959156990051] |
4103bbff-dc8d-49e5-90b1-33a0a2432422 | protein-interface-prediction-using-graph | null | null | http://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks | http://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks.pdf | Protein Interface Prediction using Graph Convolutional Networks | We consider the prediction of interfaces between proteins, a challenging problem with important applications in drug discovery and design, and examine the performance of existing and newly proposed spatial graph convolution operators for this task. By performing convolution over a local neighborhood of a node of intere... | ['Asa Ben-Hur', 'Jonathon Byrd', 'Alex Fout', 'Basir Shariat'] | 2017-12-01 | null | null | null | neurips-2017-12 | ['protein-interface-prediction'] | ['miscellaneous'] | [ 2.52486855e-01 1.34245947e-01 -1.46693110e-01 -3.02366674e-01
-2.49188870e-01 -4.47055399e-01 2.23298088e-01 7.16780186e-01
-4.69980806e-01 9.42390144e-01 -9.82419774e-02 -8.28449845e-01
-1.31519228e-01 -8.45546246e-01 -9.69623923e-01 -9.57377672e-01
-5.51189601e-01 6.27652109e-01 3.98718119e-01 -1.67750604... | [4.8799147605896, 5.673135280609131] |
bfdb6dde-7911-463a-97bb-50a1945b4ed3 | anomaly-detection-in-multiplex-dynamic | 2211.08378 | null | https://arxiv.org/abs/2211.08378v1 | https://arxiv.org/pdf/2211.08378v1.pdf | Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction | The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic interactions in the network. Most existing approaches usually study net... | ['Margo Seltzer', 'Ali Behrouz'] | 2022-11-15 | null | null | null | null | ['disease-prediction'] | ['medical'] | [ 1.67790204e-01 1.59885913e-01 -4.02027508e-03 -1.29752100e-01
7.47416854e-01 -5.65973639e-01 7.14792728e-01 4.51398998e-01
-7.82120377e-02 4.46911067e-01 4.27311212e-02 -2.16505870e-01
-3.68908137e-01 -1.06577241e+00 -5.24607301e-01 -5.45742273e-01
-9.51989114e-01 5.54604530e-01 6.42822504e-01 -3.71840745... | [7.055962562561035, 5.997568130493164] |
9c8d7e99-6e3b-45a3-845c-f669f0a80b10 | refineloc-iterative-refinement-for-weakly | 1904.00227 | null | https://arxiv.org/abs/1904.00227v3 | https://arxiv.org/pdf/1904.00227v3.pdf | RefineLoc: Iterative Refinement for Weakly-Supervised Action Localization | Video action detectors are usually trained using datasets with fully-supervised temporal annotations. Building such datasets is an expensive task. To alleviate this problem, recent methods have tried to leverage weak labeling, where videos are untrimmed and only a video-level label is available. In this paper, we propo... | ['Alejandro Pardo', 'Humam Alwassel', 'Fabian Caba Heilbron', 'Bernard Ghanem', 'Ali Thabet'] | 2019-03-30 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 2.33863905e-01 7.52040073e-02 -6.97263598e-01 -1.25552505e-01
-9.47226942e-01 -4.68170613e-01 6.59717619e-01 -2.10638165e-01
-5.86040556e-01 7.71179438e-01 4.58185256e-01 2.55399287e-01
2.62565345e-01 -2.70938188e-01 -8.14906120e-01 -6.15346074e-01
-3.33440870e-01 3.35202545e-01 1.02347422e+00 1.95633754... | [8.405200958251953, 0.546775221824646] |
1068477c-465e-4926-a311-5d97ca29d746 | trading-off-quality-for-efficiency-of | 2209.14825 | null | https://arxiv.org/abs/2209.14825v1 | https://arxiv.org/pdf/2209.14825v1.pdf | Trading off Quality for Efficiency of Community Detection: An Inductive Method across Graphs | Many network applications can be formulated as NP-hard combinatorial optimization problems of community detection (CD). Due to the NP-hardness, to balance the CD quality and efficiency remains a challenge. Most existing CD methods are transductive, which are independently optimized only for the CD on a single graph. So... | ['Dit-yan Yeung', 'Gong Zhang', 'Bo Bai', 'Chaorui Zhang', 'Meng Qin'] | 2022-09-29 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 2.05548689e-01 1.47854254e-01 -9.30898413e-02 3.08473650e-02
-8.83728027e-01 -9.99501109e-01 3.71697485e-01 3.72384071e-01
-2.74374746e-02 4.42986488e-01 -4.73084003e-01 -4.32911843e-01
-2.08637536e-01 -1.04644871e+00 -8.00912142e-01 -7.96097815e-01
-6.53592825e-01 7.44937181e-01 5.83202660e-01 4.91077714... | [7.190313816070557, 5.98133659362793] |
f3e27ace-c795-44a4-ba09-8be36c041faa | generative-adversarial-training-for-weakly | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Zou_Generative_Adversarial_Training_for_Weakly_Supervised_Cloud_Matting_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zou_Generative_Adversarial_Training_for_Weakly_Supervised_Cloud_Matting_ICCV_2019_paper.pdf | Generative Adversarial Training for Weakly Supervised Cloud Matting | The detection and removal of cloud in remote sensing images are essential for earth observation applications. Most previous methods consider cloud detection as a pixel-wise semantic segmentation process (cloud v.s. background), which inevitably leads to a category-ambiguity problem when dealing with semi-transparent cl... | [' Jieping Ye', ' Zhenwei Shi', ' Tianyang Shi', ' Wenyuan Li', 'Zhengxia Zou'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['cloud-detection'] | ['computer-vision'] | [ 7.11043179e-01 -1.21525936e-01 5.50846338e-01 -2.80189902e-01
-5.72724283e-01 -9.19300914e-01 6.64646447e-01 -3.35746706e-01
-1.33418739e-01 5.20442724e-01 -7.85729170e-01 -5.19363403e-01
2.65602589e-01 -1.18663752e+00 -8.31018984e-01 -1.18784273e+00
2.88543075e-01 5.46145976e-01 2.59774297e-01 -3.09209935... | [9.817062377929688, -1.7739253044128418] |
c79cd6f7-0bcb-4499-b326-4ae45a168ed5 | point-cloud-augmentation-with-weighted-local-1 | 2110.05379 | null | https://arxiv.org/abs/2110.05379v1 | https://arxiv.org/pdf/2110.05379v1.pdf | Point Cloud Augmentation with Weighted Local Transformations | Despite the extensive usage of point clouds in 3D vision, relatively limited data are available for training deep neural networks. Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature. In this paper, we propose a simple and eff... | ['Hyunwoo J. Kim', 'Seong Jae Hwang', 'Jaewon Lee', 'Dasol Hwang', 'Sanghyeok Lee', 'Sihyeon Kim'] | 2021-10-11 | point-cloud-augmentation-with-weighted-local | http://openaccess.thecvf.com//content/ICCV2021/html/Kim_Point_Cloud_Augmentation_With_Weighted_Local_Transformations_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kim_Point_Cloud_Augmentation_With_Weighted_Local_Transformations_ICCV_2021_paper.pdf | iccv-2021-1 | ['point-cloud-classification'] | ['computer-vision'] | [ 5.96995577e-02 2.37934798e-01 -2.30725557e-01 -5.07511199e-01
-7.68946409e-01 -4.51378673e-01 6.07341170e-01 -6.10666387e-02
-1.52390882e-01 2.21222281e-01 -2.70282984e-01 -2.26231158e-01
3.02466094e-01 -6.04102731e-01 -9.75976169e-01 -6.05729043e-01
3.25026691e-01 8.21762323e-01 2.64895886e-01 -3.95871466... | [8.027142524719238, -3.381268262863159] |
818c5eec-6393-461e-8266-7dd896ea205c | question-driven-span-labeling-model-for | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/17523 | https://ojs.aaai.org/index.php/AAAI/article/view/17523/17330 | Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction | Aspect term extraction and opinion word extraction are two fundamental subtasks of aspect-based sentiment analysis. The internal relationship between aspect terms and opinion words is typically ignored, and information for the decision-making of buyers and sellers is insufficient. In this paper, we explore an aspect–op... | ['Jianxin Liao', 'Lei Zhang', 'Jingyu Wang', 'Tongcun Liu', 'Yulong Wang', 'Lei Gao'] | 2021-05-18 | null | null | null | aaai-2021-5 | ['term-extraction', 'aspect-based-sentiment-analysis', 'reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.89335212e-01 2.29306981e-01 -1.95573285e-01 -5.97648799e-01
-1.03588116e+00 -7.65423357e-01 5.83491147e-01 6.61393285e-01
-3.91042888e-01 3.11379284e-01 4.06097770e-01 -5.87150395e-01
3.19200665e-01 -8.44787538e-01 -1.96478903e-01 -4.62622970e-01
4.00655925e-01 3.08518320e-01 1.57335743e-01 -4.54216480... | [11.48515510559082, 6.623805999755859] |
87dda7e0-168e-485e-80fd-65bfdc1d79b2 | an-exploratory-study-into-automated-pr-ecis | null | null | https://aclanthology.org/2020.lrec-1.50 | https://aclanthology.org/2020.lrec-1.50.pdf | An Exploratory Study into Automated Pr\'ecis Grading | Automated writing evaluation is a popular research field, but the main focus has been on evaluating argumentative essays. In this paper, we consider a different genre, namely pr{\'e}cis texts. A pr{\'e}cis is a written text that provides a coherent summary of main points of a spoken or written text. We present a corpus... | ['Senne Van Hoecke', 'Orphee De Clercq'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['automated-writing-evaluation'] | ['natural-language-processing'] | [ 1.51828468e-01 4.59225148e-01 -2.41791800e-01 -1.86517745e-01
-1.03202903e+00 -6.66905403e-01 8.37690115e-01 7.79035628e-01
-5.81042111e-01 1.01483655e+00 4.68329221e-01 -5.95491588e-01
-3.75617981e-01 -6.21372581e-01 -4.64355558e-01 -2.06257313e-01
6.34097934e-01 4.73403186e-01 6.17959425e-02 -4.14735019... | [11.797853469848633, 9.423715591430664] |
1909ce6c-4b50-418f-87ff-661d5ca23c15 | learning-spatio-temporal-features-with-two | 1905.02540 | null | https://arxiv.org/abs/1905.02540v2 | https://arxiv.org/pdf/1905.02540v2.pdf | Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading | We focus on the word-level visual lipreading, which requires recognizing the word being spoken, given only the video but not the audio. State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN (e.g., ResNet) as... | ['Kris Kitani', 'Xinshuo Weng'] | 2019-05-04 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-8.39303955e-02 -2.42472872e-01 -3.11630875e-01 -1.67993605e-01
-6.76298022e-01 -2.62966782e-01 3.86423409e-01 -6.43097162e-01
-6.16475523e-01 1.96959347e-01 2.78750509e-01 -6.19733810e-01
6.58971667e-01 -3.93763751e-01 -7.73389816e-01 -3.94767940e-01
1.30678490e-01 -2.53794849e-01 2.53968775e-01 -1.14846090... | [14.333357810974121, 5.034838676452637] |
2d99fa6a-83d3-47dd-96af-d8744a50636b | llms-for-knowledge-graph-construction-and | 2305.13168 | null | https://arxiv.org/abs/2305.13168v1 | https://arxiv.org/pdf/2305.13168v1.pdf | LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities | This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We employ eight distinct datasets that encompass aspects including entity, relation and event extraction, link prediction, and question answering. Empirically, o... | ['Ningyu Zhang', 'Huajun Chen', 'Shumin Deng', 'Yunzhi Yao', 'Yixin Ou', 'Shuofei Qiao', 'Jing Chen', 'Xiaohan Wang', 'Yuqi Zhu'] | 2023-05-22 | null | null | null | null | ['link-prediction', 'graph-construction', 'event-extraction'] | ['graphs', 'graphs', 'natural-language-processing'] | [-2.36020193e-01 8.31219196e-01 -4.93620843e-01 1.59189645e-02
-6.29736125e-01 -6.04691803e-01 8.71484518e-01 6.78012729e-01
-8.67323875e-02 1.04232132e+00 3.48153621e-01 -6.21239185e-01
-7.15201318e-01 -1.26116526e+00 -5.43016076e-01 -7.01354071e-03
-4.30203825e-01 7.27305412e-01 3.33617032e-01 -1.92263126... | [9.407784461975098, 8.15185832977295] |
9964db82-5b34-42ff-96e1-8328ffbb0496 | conditional-sequential-modulation-for-1 | 2009.10390 | null | https://arxiv.org/abs/2009.10390v1 | https://arxiv.org/pdf/2009.10390v1.pdf | Conditional Sequential Modulation for Efficient Global Image Retouching | Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-u... | ['Chao Dong', 'Yihao Liu', 'Yu Qiao', 'Jingwen He'] | 2020-09-22 | conditional-sequential-modulation-for | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2014_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580664.pdf | eccv-2020-8 | ['photo-retouching', 'image-retouching'] | ['computer-vision', 'computer-vision'] | [ 6.31332576e-01 -1.16116159e-01 -4.19470333e-02 4.51393100e-03
-3.20966452e-01 -9.12036672e-02 3.64473879e-01 -3.17452133e-01
-6.02187097e-01 7.57275760e-01 -1.58929303e-01 -3.58361095e-01
1.28038555e-01 -6.29386842e-01 -9.80032325e-01 -9.50410485e-01
3.26798826e-01 -7.00631440e-01 2.55320877e-01 -1.22974731... | [11.006936073303223, -2.2379114627838135] |
83fd0d4f-04bb-4bb4-9605-df4ba69ac2bb | constructing-highly-inductive-contexts-for | 2212.01810 | null | https://arxiv.org/abs/2212.01810v1 | https://arxiv.org/pdf/2212.01810v1.pdf | Constructing Highly Inductive Contexts for Dialogue Safety through Controllable Reverse Generation | Large pretrained language models can easily produce toxic or biased content, which is prohibitive for practical use. In order to detect such toxic generations, existing methods rely on templates, real-world data extraction, crowdsourcing workers, or automatic generation to construct adversarial contexts that are likely... | ['Minlie Huang', 'Lifeng Shang', 'Yasheng Wang', 'Fei Mi', 'Jiawen Deng', 'Hao Sun', 'Jiale Cheng', 'Zhexin Zhang'] | 2022-12-04 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.70821571e-01 -3.67588066e-02 -3.05943012e-01 -1.55725509e-01
-7.84582794e-01 -1.22748268e+00 7.81443596e-01 2.39730150e-01
-2.02252999e-01 1.08928740e+00 5.12149394e-01 -4.08137351e-01
2.04801977e-01 -1.01348758e+00 -6.16867781e-01 -6.77940845e-01
3.17186862e-01 2.66222119e-01 -4.87331441e-03 -6.67865157... | [6.1325860023498535, 8.158246994018555] |
8e7a0330-46a5-4c6e-9486-4798860e447f | towards-the-extraction-of-robust-sign | 2306.17558 | null | https://arxiv.org/abs/2306.17558v1 | https://arxiv.org/pdf/2306.17558v1.pdf | Towards the extraction of robust sign embeddings for low resource sign language recognition | Isolated Sign Language Recognition (SLR) has mostly been applied on relatively large datasets containing signs executed slowly and clearly by a limited group of signers. In real-world scenarios, however, we are met with challenging visual conditions, coarticulated signing, small datasets, and the need for signer indepe... | ['Joni Dambre', 'Anthony Ventresque', 'Ruth Holmes', 'Ellen Rushe', 'Mathieu De Coster'] | 2023-06-30 | null | null | null | null | ['sign-language-recognition', 'imputation', 'transfer-learning', 'imputation', 'imputation'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 2.76269037e-02 -3.51850331e-01 -2.91581362e-01 -1.68376699e-01
-1.08571053e+00 -7.73103893e-01 5.14677286e-01 -8.63981962e-01
-7.99859166e-01 5.06708980e-01 3.51330698e-01 6.25574738e-02
6.90651610e-02 -1.48050085e-01 -8.52721870e-01 -6.53118908e-01
4.25329758e-03 6.48980260e-01 2.83544600e-01 -2.54565924... | [9.144371032714844, -6.462841987609863] |
baf2c959-da03-4314-a7b4-148391921ecc | 0-1-phase-transitions-in-sparse-spiked-matrix | 1911.05030 | null | https://arxiv.org/abs/1911.05030v1 | https://arxiv.org/pdf/1911.05030v1.pdf | 0-1 phase transitions in sparse spiked matrix estimation | We consider statistical models of estimation of a rank-one matrix (the spike) corrupted by an additive gaussian noise matrix in the sparse limit. In this limit the underlying hidden vector (that constructs the rank-one matrix) has a number of non-zero components that scales sub-linearly with the total dimension of the ... | ['Jean Barbier', 'Nicolas Macris'] | 2019-11-12 | null | null | null | null | ['object-detection-in-aerial-images', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.97844607e-01 1.86354920e-01 -4.89857979e-03 2.58611385e-02
-7.94588745e-01 -2.91727632e-01 4.92919028e-01 -3.34208786e-01
-4.45399791e-01 7.70748854e-01 2.15915501e-01 1.56399652e-01
-4.10276383e-01 -2.11528420e-01 -8.04631054e-01 -1.21579957e+00
-7.02618539e-01 4.60389107e-01 -1.97073385e-01 3.35503630... | [6.96699333190918, 4.434881210327148] |
e5e7ea1d-e692-45d2-bedc-3838563bbb6c | anomaly-detection-in-emails-using-machine | 2203.10408 | null | https://arxiv.org/abs/2203.10408v1 | https://arxiv.org/pdf/2203.10408v1.pdf | Anomaly Detection in Emails using Machine Learning and Header Information | Anomalies in emails such as phishing and spam present major security risks such as the loss of privacy, money, and brand reputation to both individuals and organizations. Previous studies on email anomaly detection relied on a single type of anomaly and the analysis of the email body and subject content. A drawback of ... | ['Haruna Isah', 'Craig Beaman'] | 2022-03-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 9.08590481e-02 -1.65923640e-01 1.59679249e-01 -3.73173356e-01
-1.81872621e-01 -4.99764800e-01 6.39599383e-01 6.60349905e-01
-2.08646610e-01 5.29107690e-01 -2.59991497e-01 -6.81573510e-01
-9.46454480e-02 -7.73098588e-01 -5.30960113e-02 -5.54480255e-01
1.92234173e-01 3.79939109e-01 2.86693960e-01 -8.15165564... | [7.826504707336426, 10.008919715881348] |
83095bdd-3913-4986-9bb7-90bbf02e489c | estimation-of-speaker-age-and-height-from | 2203.11774 | null | https://arxiv.org/abs/2203.11774v1 | https://arxiv.org/pdf/2203.11774v1.pdf | Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model | The estimation of speaker characteristics such as age and height is a challenging task, having numerous applications in voice forensic analysis. In this work, we propose a bi-encoder transformer mixture model for speaker age and height estimation. Considering the wide differences in male and female voice characteristic... | ['Chng Eng Siong', 'Tran The Anh', 'Duc-Tuan Truong', 'Tarun Gupta'] | 2022-03-22 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.75580645e-01 1.10350531e-02 3.37039419e-02 -4.85879570e-01
-8.38526845e-01 -2.10039988e-01 5.08611381e-01 1.73684895e-01
-5.12990892e-01 4.65248287e-01 4.54018682e-01 -1.20207593e-01
2.24647522e-01 -5.77904522e-01 -3.49929303e-01 -6.69106960e-01
-1.50585487e-01 2.29068935e-01 -3.64425659e-01 1.76846862... | [14.167863845825195, 6.043668270111084] |
0a1e306b-3a8f-41b6-a678-7ae9cb411778 | the-effectiveness-of-morphology-aware | 2103.11189 | null | https://arxiv.org/abs/2103.11189v1 | https://arxiv.org/pdf/2103.11189v1.pdf | The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation | This paper evaluates the performance of several modern subword segmentation methods in a low-resource neural machine translation setting. We compare segmentations produced by applying BPE at the token or sentence level with morphologically-based segmentations from LMVR and MORSEL. We evaluate translation tasks between ... | ['Constantine Lignos', 'Jonne Sälevä'] | 2021-03-20 | null | https://aclanthology.org/2021.eacl-srw.22 | https://aclanthology.org/2021.eacl-srw.22.pdf | eacl-2021-2 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 2.08341435e-01 -7.61559159e-02 -1.75162822e-01 -3.20273638e-01
-1.38259554e+00 -9.79786396e-01 6.31153345e-01 1.80133685e-01
-8.82696450e-01 1.09528196e+00 4.13575292e-01 -9.33468103e-01
1.90936759e-01 -6.09977663e-01 -6.07180357e-01 -4.72889125e-01
5.36977053e-01 9.27421629e-01 -1.30252585e-01 -2.88380444... | [11.3802490234375, 10.30298900604248] |
b15a4d17-3f63-40d1-a36b-b8acbd5d4623 | towards-understanding-ecg-rhythm | null | null | http://proceedings.mlr.press/v85/goodfellow18a.html | http://proceedings.mlr.press/v85/goodfellow18a/goodfellow18a.pdf | Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings | Access to electronic health record (EHR) data has motivated computational advances in medical research. However, various concerns, particularly over privacy, can limit access to and collaborative use of EHR data. Sharing synthetic EHR data could mitigate risk. In this paper, we propose a new approach, medical Generativ... | ['Peter C. Laussen', 'Robert Greer', 'Sebastian D. Goodfellow', 'Mjaye Mazwi', 'Danny Eytan', 'Andrew Goodwin'] | 2018-08-17 | null | null | null | proceedings-of-the-3rd-machine-learning-for | ['arrhythmia-detection', 'ecg-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [ 1.60482481e-01 6.25702918e-01 2.46817112e-01 -6.60849571e-01
-1.08920252e+00 -5.40082037e-01 3.41578960e-01 2.43824735e-01
-3.53339404e-01 1.18960214e+00 3.79001081e-01 -4.45666999e-01
1.02335975e-01 -1.04675889e+00 -7.00089455e-01 -4.78820920e-01
-4.92877848e-02 4.86258864e-01 -9.55537975e-01 1.50705829... | [6.263184070587158, 6.7803568840026855] |
da37803f-6b55-4321-98fa-13811151dff9 | deep-reinforcement-learning-from-self-play-in | 1603.01121 | null | http://arxiv.org/abs/1603.01121v2 | http://arxiv.org/pdf/1603.01121v2.pdf | Deep Reinforcement Learning from Self-Play in Imperfect-Information Games | Many real-world applications can be described as large-scale games of
imperfect information. To deal with these challenging domains, prior work has
focused on computing Nash equilibria in a handcrafted abstraction of the
domain. In this paper we introduce the first scalable end-to-end approach to
learning approximate N... | ['David Silver', 'Johannes Heinrich'] | 2016-03-03 | null | null | null | null | ['game-of-poker', 'card-games'] | ['playing-games', 'playing-games'] | [-6.45977736e-01 1.57519326e-01 -9.04219598e-02 3.07695359e-01
-7.32122958e-01 -7.06125557e-01 2.50687122e-01 -5.06039858e-01
-7.40078568e-01 1.31798565e+00 -1.33315429e-01 -8.51956680e-02
-1.16329148e-01 -6.20942831e-01 -9.90803778e-01 -2.10815862e-01
-4.18896347e-01 9.76043820e-01 2.64709651e-01 -9.37156975... | [3.5965468883514404, 1.562039852142334] |
91cedda0-320f-4cff-8cb1-e2828d49efdb | video-based-computer-aided-arthroscopy-for | 1807.09627 | null | http://arxiv.org/abs/1807.09627v1 | http://arxiv.org/pdf/1807.09627v1.pdf | Video-based computer aided arthroscopy for patient specific reconstruction of the Anterior Cruciate Ligament | The Anterior Cruciate Ligament (ACL) tear is a common medical condition that
is treated using arthroscopy by pulling a tissue graft through a tunnel opened
with a drill. The correct anatomical position and orientation of this tunnel is
crucial for knee stability, and drilling an adequate bone tunnel is the most
technic... | ['Joao P. Barreto', 'Joao Oliveira', 'Cristovao Sousa', 'Pedro Marques', 'Luis Ribeiro', 'Carolina Raposo', 'Rui Melo', 'Fernando Fonseca'] | 2018-07-25 | null | null | null | null | ['medical-procedure'] | ['medical'] | [-2.68407494e-01 2.11874377e-02 -2.16809079e-01 4.48188692e-01
-2.62380868e-01 -3.63186717e-01 -2.29458287e-02 2.76744902e-01
-7.27917075e-01 5.01341522e-01 2.47292668e-01 -3.39310348e-01
8.62846300e-02 -2.09174603e-01 -3.34996939e-01 -2.45310843e-01
-2.97560662e-01 9.57152069e-01 7.16227233e-01 -1.71003014... | [13.757312774658203, -2.9336304664611816] |
05f13ec2-8a3b-42a3-86dc-96891ccc0dc7 | improving-mesh-based-motion-compensation-by | 2301.04836 | null | https://arxiv.org/abs/2301.04836v1 | https://arxiv.org/pdf/2301.04836v1.pdf | Improving mesh-based motion compensation by using edge adaptive graph-based compensated wavelet lifting for medical data sets | Medical applications like Computed Tomography (CT) or Magnetic Resonance Tomography (MRT) often require an efficient scalable representation of their huge output volumes in the further processing chain of medical routine. A downscaled version of such a signal can be obtained by using image and video coders based on wav... | ['André Kaup', 'Daniela Lanz'] | 2023-01-12 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 6.37824953e-01 9.70509723e-02 2.70034015e-01 -9.12450403e-02
-4.62179899e-01 8.30499977e-02 1.25447467e-01 3.43212485e-01
-6.63663626e-01 8.27220142e-01 1.71642363e-01 -8.60745832e-02
-4.74362262e-03 -9.61416543e-01 -3.35618228e-01 -6.31156564e-01
-2.25243270e-01 2.82441467e-01 6.28151238e-01 -9.00760517... | [11.511945724487305, -2.299452066421509] |
878b1631-70ea-482f-ab5c-333c58d18a42 | a-unified-dro-view-of-multi-class-loss | 2112.14869 | null | https://arxiv.org/abs/2112.14869v4 | https://arxiv.org/pdf/2112.14869v4.pdf | Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity | We study a family of loss functions named label-distributionally robust (LDR) losses for multi-class classification that are formulated from distributionally robust optimization (DRO) perspective, where the uncertainty in the given label information are modeled and captured by taking the worse case of distributional we... | ['Tianbao Yang', 'Yiming Ying', 'Dixian Zhu'] | 2021-12-30 | null | null | null | null | ['image-classification-shift-consistency'] | ['computer-vision'] | [-4.40261364e-02 -2.14473739e-01 -2.40110949e-01 -7.60815918e-01
-1.42869127e+00 -5.84084809e-01 5.05098850e-02 4.14582431e-01
-4.93361354e-01 9.57047403e-01 -2.93174952e-01 -1.35094717e-01
-6.03865147e-01 -6.80594742e-01 -6.15786195e-01 -9.89534438e-01
-1.70783296e-01 3.23106885e-01 -1.15393914e-01 1.65917631... | [9.222163200378418, 3.914842367172241] |
3fef91ff-70f0-40b0-8dfa-f410303b72ab | interpretable-deep-learning-methods-for | 2302.07930 | null | https://arxiv.org/abs/2302.07930v1 | https://arxiv.org/pdf/2302.07930v1.pdf | Interpretable Deep Learning Methods for Multiview Learning | Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. We propose iDeepViewLearn (Interpretable Deep Learning Method f... | ['Sandra E Safo', 'Ju Sun', 'Han Lu', 'Hengkang Wang'] | 2023-02-15 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-2.30588347e-01 3.18371169e-02 -4.09603924e-01 -6.89825654e-01
-4.98670310e-01 -1.82677701e-01 4.11342978e-01 3.25536609e-01
-1.34822447e-02 7.17662275e-01 3.83121550e-01 2.41525874e-01
-2.19635054e-01 -7.86890864e-01 -6.92225575e-01 -9.11063731e-01
-4.96703207e-01 3.17625821e-01 -5.98666191e-01 1.32808149... | [6.465691566467285, 5.506194591522217] |
e9fdb63a-994b-4fb4-b7b0-8343ccebc566 | globalflownet-video-stabilization-using-deep | 2210.13769 | null | https://arxiv.org/abs/2210.13769v3 | https://arxiv.org/pdf/2210.13769v3.pdf | GlobalFlowNet: Video Stabilization using Deep Distilled Global Motion Estimates | Videos shot by laymen using hand-held cameras contain undesirable shaky motion. Estimating the global motion between successive frames, in a manner not influenced by moving objects, is central to many video stabilization techniques, but poses significant challenges. A large body of work uses 2D affine transformations o... | ['Ajit Rajwade', 'Devansh Jain', 'Jerin Geo James'] | 2022-10-25 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-1.48849398e-01 -2.98857868e-01 -2.51514167e-01 -1.20982625e-01
-2.87526309e-01 -6.56773210e-01 1.80551916e-01 -4.20546412e-01
-3.51231605e-01 6.99932337e-01 5.06612323e-02 -6.19358942e-02
3.38570923e-02 -4.88403231e-01 -9.08811331e-01 -8.35565090e-01
-7.72418827e-03 -2.97351211e-01 6.06367409e-01 -1.72734186... | [10.610638618469238, -1.3948496580123901] |
d5b363aa-4652-4d05-99da-1296a287d024 | an-application-of-topological-graph | 1408.5634 | null | http://arxiv.org/abs/1408.5634v1 | http://arxiv.org/pdf/1408.5634v1.pdf | An application of topological graph clustering to protein function prediction | We use a semisupervised learning algorithm based on a topological data
analysis approach to assign functional categories to yeast proteins using
similarity graphs. This new approach to analyzing biological networks yields
results that are as good as or better than state of the art existing
approaches. | ["Danielle O'Donnol", 'R. Sean Bowman', 'Jesse Johnson', 'Douglas Heisterkamp'] | 2014-08-24 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 2.35633537e-01 5.48135638e-02 -2.06230700e-01 -4.89063352e-01
3.29578787e-01 -7.18054235e-01 4.43798184e-01 6.78919494e-01
-1.29226089e-01 1.15370572e+00 -2.02242866e-01 -6.70992553e-01
-7.44362772e-01 -8.04784417e-01 -3.23387474e-01 -6.11387312e-01
-6.64899051e-01 6.89217985e-01 7.03593075e-01 -2.48240486... | [6.573915958404541, 5.500241279602051] |
49356300-a4ff-4c68-972f-c23bef137899 | lightglue-local-feature-matching-at-light | 2306.13643 | null | https://arxiv.org/abs/2306.13643v1 | https://arxiv.org/pdf/2306.13643v1.pdf | LightGlue: Local Feature Matching at Light Speed | We introduce LightGlue, a deep neural network that learns to match local features across images. We revisit multiple design decisions of SuperGlue, the state of the art in sparse matching, and derive simple but effective improvements. Cumulatively, they make LightGlue more efficient - in terms of both memory and comput... | ['Marc Pollefeys', 'Paul-Edouard Sarlin', 'Philipp Lindenberger'] | 2023-06-23 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [-2.33680472e-01 -2.16492340e-01 -2.84489691e-01 -5.10831952e-01
-6.60953701e-01 -4.17141676e-01 4.45165187e-01 1.83756799e-02
-1.82329834e-01 2.89496839e-01 2.21869692e-01 -1.46399334e-01
-1.42752808e-02 -8.39495778e-01 -1.11183679e+00 -2.87488371e-01
-1.38060823e-01 3.94359440e-01 2.10179061e-01 -9.68367234... | [8.188416481018066, -1.999177098274231] |
7929b2e0-0760-41f3-9445-6872db177cb4 | identifying-therapist-conversational-actions | null | null | https://aclanthology.org/W19-3002 | https://aclanthology.org/W19-3002.pdf | Identifying therapist conversational actions across diverse psychotherapeutic approaches | While conversation in therapy sessions can vary widely in both topic and style, an understanding of the underlying techniques used by therapists can provide valuable insights into how therapists best help clients of different types. Dialogue act classification aims to identify the conversational {``}action{''} each spe... | ['Fei-Tzin Lee', 'Jacob Levine', 'Kathy McKeown', 'Derrick Hull', 'Bonnie Ray'] | 2019-06-01 | null | null | null | ws-2019-6 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 6.87706649e-01 6.18438900e-01 -6.48386061e-01 -7.84166038e-01
-5.20396352e-01 -6.31780446e-01 5.41750431e-01 3.64104033e-01
-7.87231773e-02 5.70249498e-01 1.15304613e+00 -2.14116558e-01
-4.10257548e-01 -1.88966289e-01 5.15297413e-01 -7.48186052e-01
2.41593212e-01 8.79954576e-01 -4.26649481e-01 -2.85713494... | [12.798222541809082, 8.009910583496094] |
18d67173-c4ed-410c-af22-333cc505aed7 | stglow-a-flow-based-generative-framework-with | 2211.11220 | null | https://arxiv.org/abs/2211.11220v2 | https://arxiv.org/pdf/2211.11220v2.pdf | STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction | Pedestrian trajectory prediction task is an essential component of intelligent systems, and its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, a... | ['Xia Li', 'Jiantao Zhou', 'Yuanman Li', 'Rongqin Liang'] | 2022-11-21 | null | null | null | null | ['robot-navigation'] | ['robots'] | [-3.58044684e-01 -2.62687117e-01 -1.68824032e-01 -2.89692342e-01
4.47472930e-02 -3.05944562e-01 6.46360040e-01 -4.17663813e-01
-3.71165201e-02 8.12286019e-01 3.02055240e-01 -3.59546959e-01
2.71786064e-01 -1.16650784e+00 -6.71334743e-01 -9.19978738e-01
6.43226802e-02 3.99584442e-01 5.63453972e-01 -3.43795896... | [6.416214466094971, 1.0386052131652832] |
3995b755-f472-4109-810f-8b911eae4ccf | cross-domain-few-shot-learning-by-1 | 2010.06498 | null | https://arxiv.org/abs/2010.06498v2 | https://arxiv.org/pdf/2010.06498v2.pdf | Cross-Domain Few-Shot Learning by Representation Fusion | In order to quickly adapt to new data, few-shot learning aims at learning from few examples, often by using already acquired knowledge. The new data often differs from the previously seen data due to a domain shift, that is, a change of the input-target distribution. While several methods perform well on small domain s... | ['Sepp Hochreiter', 'Günter Klambauer', 'Michael Kopp', 'David Kreil', 'Andreas Mayr', 'Michael Widrich', 'Johannes Brandstetter', 'Thomas Adler'] | 2020-10-13 | cross-domain-few-shot-learning-by | https://openreview.net/forum?id=w5bNwUzj33 | https://openreview.net/pdf?id=w5bNwUzj33 | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.79670954e-01 -1.84634522e-01 -2.21471757e-01 -1.38177678e-01
-8.20141554e-01 -3.48291785e-01 5.81797659e-01 4.24788862e-01
-3.94571155e-01 9.59341466e-01 1.94702577e-02 1.08695202e-01
-2.20230147e-01 -9.46897328e-01 -9.34227586e-01 -7.61866927e-01
4.67107892e-02 6.13312662e-01 5.14555037e-01 -4.53920275... | [9.996828079223633, 2.9291234016418457] |
e7f579ef-ebdc-4aa5-adc8-be8eb4fb421b | adversarial-attacks-and-defences-for-skin | 2212.06822 | null | https://arxiv.org/abs/2212.06822v1 | https://arxiv.org/pdf/2212.06822v1.pdf | Adversarial Attacks and Defences for Skin Cancer Classification | There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentation in recent years. As a result, a rapid increase in the usage of such systems can be observed in th... | ['Shraddha Surtkar', 'Samina Attari', 'Ishaan Shivhare', 'Joy Purohit', 'Vinay Jogani'] | 2022-12-13 | null | null | null | null | ['adversarial-defense', 'skin-cancer-classification'] | ['adversarial', 'medical'] | [ 6.01817548e-01 3.26874286e-01 1.29798859e-01 -2.12120146e-01
-1.55933604e-01 -7.45550454e-01 5.13778210e-01 1.41481698e-01
-6.27552390e-01 3.96561712e-01 -2.25227773e-01 -7.28028893e-01
-7.07151964e-02 -8.10475230e-01 -5.12239873e-01 -6.34328663e-01
-1.46320775e-01 4.34015319e-02 1.56318799e-01 -3.59941393... | [5.591829776763916, 7.808200836181641] |
03ae97cc-62c2-498d-98a1-4c387c16d00d | labels-are-not-perfect-improving | 2008.04168 | null | https://arxiv.org/abs/2008.04168v1 | https://arxiv.org/pdf/2008.04168v1.pdf | Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty | Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive probability for bounding box regression in an un-supervised manner, or use simple heuristics to do uncertainty regularization. This leads to uns... | ['Klaus Dietmayer', 'Lars Rosenbaum', 'Di Feng', 'Fabian Timm'] | 2020-08-10 | null | null | null | null | ['robust-object-detection'] | ['computer-vision'] | [-8.97399150e-03 2.56783247e-01 -9.98333469e-02 -4.86656874e-01
-1.12053812e+00 -5.16662538e-01 4.97083783e-01 1.43970191e-01
-6.65536940e-01 8.96762729e-01 -5.26489079e-01 -3.94143075e-01
8.96502882e-02 -5.56143582e-01 -8.20056140e-01 -6.73585951e-01
4.16779034e-02 6.02142572e-01 9.59159374e-01 2.66450346... | [7.580136775970459, -1.1072161197662354] |
74f04822-3ff6-4da3-9862-f334a6e64282 | semantic-guided-context-modeling-for-indoor | 2305.12661 | null | https://arxiv.org/abs/2305.12661v1 | https://arxiv.org/pdf/2305.12661v1.pdf | Semantic-guided context modeling for indoor scene recognition | Exploring the semantic context in scene images is essential for indoor scene recognition. However, due to the diverse intra-class spatial layouts and the coexisting inter-class objects, modeling contextual relationships to adapt various image characteristics is a great challenge. Existing contextual modeling methods fo... | ['Yibin Li', 'Xin Ma', 'Hanbo Wu', 'Chuanxin Song'] | 2023-05-22 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.76069307e-01 -7.51946747e-01 5.76608926e-02 -6.48011029e-01
-1.57682315e-01 -4.58140165e-01 3.25398803e-01 -1.43507384e-02
-2.49692693e-01 3.14604312e-01 2.85808474e-01 -1.30093560e-01
-6.10675633e-01 -6.97715461e-01 -4.91127312e-01 -7.60374725e-01
3.23937476e-01 -3.44633549e-01 3.16807747e-01 6.56806231... | [9.487125396728516, -0.7156233787536621] |
da8a569e-7ba1-4e09-8096-4b330f5f522d | elf-an-extensive-lightweight-and-flexible | 1707.01067 | null | http://arxiv.org/abs/1707.01067v2 | http://arxiv.org/pdf/1707.01067v2.pdf | ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games | In this paper, we propose ELF, an Extensive, Lightweight and Flexible
platform for fundamental reinforcement learning research. Using ELF, we
implement a highly customizable real-time strategy (RTS) engine with three game
environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a
miniature version of S... | ['Yuandong Tian', 'Yuxin Wu', 'Wenling Shang', 'Qucheng Gong', 'C. Lawrence Zitnick'] | 2017-07-04 | elf-an-extensive-lightweight-and-flexible-1 | http://papers.nips.cc/paper/6859-elf-an-extensive-lightweight-and-flexible-research-platform-for-real-time-strategy-games | http://papers.nips.cc/paper/6859-elf-an-extensive-lightweight-and-flexible-research-platform-for-real-time-strategy-games.pdf | neurips-2017-12 | ['real-time-strategy-games'] | ['playing-games'] | [-5.48693776e-01 -2.61461467e-01 -1.13907252e-02 1.84363306e-01
-4.38387632e-01 -9.00305867e-01 3.16542149e-01 -3.55564773e-01
-1.12177682e+00 7.08239675e-01 -3.12200397e-01 -7.81323433e-01
-2.77239770e-01 -8.95709813e-01 -6.33406162e-01 -4.94906723e-01
-5.71488857e-01 7.08945096e-01 6.31984293e-01 -1.15641308... | [3.700322151184082, 1.4646884202957153] |
27b407e6-39b1-4348-badd-b7e6cc3290a5 | commonsense-knowledge-base-construction-in | 2105.01925 | null | https://arxiv.org/abs/2105.01925v1 | https://arxiv.org/pdf/2105.01925v1.pdf | Commonsense Knowledge Base Construction in the Age of Big Data | Compiling commonsense knowledge is traditionally an AI topic approached by manual labor. Recent advances in web data processing have enabled automated approaches. In this demonstration we will showcase three systems for automated commonsense knowledge base construction, highlighting each time one aspect of specific int... | ['Simon Razniewski'] | 2021-05-05 | null | null | null | null | ['commonsense-knowledge-base-construction'] | ['knowledge-base'] | [-1.79732274e-02 2.74712950e-01 5.09572737e-02 -3.35388035e-01
-3.27665687e-01 -8.80088866e-01 8.02255690e-01 4.18144286e-01
-2.69405186e-01 9.72031891e-01 1.36145532e-01 -5.09658158e-01
-6.84192598e-01 -1.03333414e+00 -7.72330642e-01 -2.41181716e-01
-1.21563174e-01 5.53279996e-01 -1.30572960e-01 -6.09580815... | [9.798919677734375, 8.190047264099121] |
4fa9bfb3-7edb-4764-abe6-2ef49d7dc8ef | inverted-pyramid-multi-task-transformer-for | 2203.07997 | null | https://arxiv.org/abs/2203.07997v3 | https://arxiv.org/pdf/2203.07997v3.pdf | InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding | Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction. Most existing works encounter a severe limitation of modeling in the locality due to heavy utilization of convolution operations, while learn... | ['Dan Xu', 'Hanrong Ye'] | 2022-03-15 | null | null | null | null | ['surface-normals-estimation', 'human-parsing', 'boundary-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.81709468e-01 -3.79243881e-01 2.54217833e-01 -5.29106975e-01
-9.61418211e-01 -1.37274697e-01 4.15371090e-01 9.54967830e-03
-2.94389218e-01 5.33933580e-01 1.45908371e-01 -4.75673079e-02
-1.32405937e-01 -7.43672967e-01 -1.05360854e+00 -5.69111347e-01
1.91763401e-01 3.41684759e-01 8.01345050e-01 -2.19782442... | [9.692279815673828, 1.2163903713226318] |
077cfce5-6e49-4bbe-a7f9-9a1ff351d18a | open-vocabulary-point-cloud-object-detection | 2304.00788 | null | https://arxiv.org/abs/2304.00788v2 | https://arxiv.org/pdf/2304.00788v2.pdf | Open-Vocabulary Point-Cloud Object Detection without 3D Annotation | The goal of open-vocabulary detection is to identify novel objects based on arbitrary textual descriptions. In this paper, we address open-vocabulary 3D point-cloud detection by a dividing-and-conquering strategy, which involves: 1) developing a point-cloud detector that can learn a general representation for localizin... | ['Shanghang Zhang', 'Kurt Keutzer', 'Masayoshi Tomizuka', 'Xiaodong Xie', 'Xiaobao Wei', 'Chenfeng Xu', 'Yuheng Lu'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lu_Open-Vocabulary_Point-Cloud_Object_Detection_Without_3D_Annotation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lu_Open-Vocabulary_Point-Cloud_Object_Detection_Without_3D_Annotation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['cloud-detection'] | ['computer-vision'] | [ 4.16399390e-02 -4.34771329e-02 -2.81141460e-04 -2.86682963e-01
-1.22798789e+00 -7.70363271e-01 8.01149011e-01 2.32637197e-01
-3.19578350e-01 -2.37707466e-01 -2.76807904e-01 -1.88381732e-01
4.54228818e-01 -5.29544353e-01 -1.08491123e+00 -6.00140035e-01
1.58090919e-01 5.28088927e-01 5.73020399e-01 -1.04504071... | [7.869972229003906, -2.9402194023132324] |
d01ca06d-f0e4-4e64-8b3f-f7bd8c8b4250 | deeper-text-understanding-for-ir-with | 1905.09217 | null | https://arxiv.org/abs/1905.09217v1 | https://arxiv.org/pdf/1905.09217v1.pdf | Deeper Text Understanding for IR with Contextual Neural Language Modeling | Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done on understanding the text content of a query or a document. This pa... | ['Zhuyun Dai', 'Jamie Callan'] | 2019-05-22 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 1.56286463e-01 1.19765371e-01 -1.02529001e+00 -5.29360950e-01
-8.20178628e-01 -3.66004884e-01 1.15931892e+00 4.83205438e-01
-7.13034511e-01 2.63611674e-01 9.67101455e-01 -6.35221720e-01
-3.97309512e-01 -8.02831769e-01 -3.71640056e-01 -5.66182807e-02
6.91363961e-02 6.14871740e-01 9.25913006e-02 -5.00375807... | [11.112361907958984, 8.064037322998047] |
ee469afc-8a5f-4abc-938c-b6cff3b715b5 | adversarial-deep-learning-in-eeg-biometrics | 1903.11673 | null | http://arxiv.org/abs/1903.11673v1 | http://arxiv.org/pdf/1903.11673v1.pdf | Adversarial Deep Learning in EEG Biometrics | Deep learning methods for person identification based on
electroencephalographic (EEG) brain activity encounters the problem of
exploiting the temporally correlated structures or recording session specific
variability within EEG. Furthermore, recent methods have mostly trained and
evaluated based on single session EEG ... | ['Toshiaki Koike-Akino', 'Ozan Ozdenizci', 'Deniz Erdogmus', 'Ye Wang'] | 2019-03-27 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 3.70484084e-01 -2.96312541e-01 4.13683772e-01 -7.73704410e-01
-7.90548205e-01 -6.25527501e-01 4.57554072e-01 -1.43191054e-01
-6.49340987e-01 1.06768012e+00 3.58612835e-01 1.58953443e-01
-4.52373087e-01 -2.62137473e-01 -8.34699869e-01 -5.42087615e-01
-7.69527912e-01 2.49677673e-01 -5.59253871e-01 -3.72633711... | [13.22960376739502, 3.4742350578308105] |
33922071-78e0-4724-b9ca-658c2866f57a | disentangling-semantic-features-of | 2106.14192 | null | https://arxiv.org/abs/2106.14192v1 | https://arxiv.org/pdf/2106.14192v1.pdf | Disentangling semantic features of macromolecules in Cryo-Electron Tomography | Cryo-electron tomography (Cryo-ET) is a 3D imaging technique that enables the systemic study of shape, abundance, and distribution of macromolecular structures in single cells in near-atomic resolution. However, the systematic and efficient $\textit{de novo}$ recognition and recovery of macromolecular structures captur... | ['Min Xu', 'Xiangrui Zeng', 'Yungeng Zhang', 'Jianye Pang', 'Kai Yi'] | 2021-06-27 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 3.90866809e-02 -4.96860951e-01 3.62658232e-01 -3.91129971e-01
-4.92135137e-01 -6.75676525e-01 5.28515160e-01 1.16781645e-01
-4.69595701e-01 1.02164817e+00 2.09566802e-01 -5.45426831e-02
-6.08488806e-02 -4.45927113e-01 -8.66437733e-01 -1.35396314e+00
-3.76393506e-03 8.77610624e-01 -1.74792111e-01 1.28805041... | [13.297715187072754, -3.0710034370422363] |
80a7869d-fa80-4d4f-a135-7d8ddbe1d70d | luke-graph-a-transformer-based-approach-with | 2303.06675 | null | https://arxiv.org/abs/2303.06675v1 | https://arxiv.org/pdf/2303.06675v1.pdf | LUKE-Graph: A Transformer-based Approach with Gated Relational Graph Attention for Cloze-style Reading Comprehension | Incorporating prior knowledge can improve existing pre-training models in cloze-style machine reading and has become a new trend in recent studies. Notably, most of the existing models have integrated external knowledge graphs (KG) and transformer-based models, such as BERT into a unified data structure. However, selec... | ['Kourosh Kiani', 'Shima Foolad'] | 2023-03-12 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [-1.25478348e-03 7.51200557e-01 -1.96151704e-01 -4.37804908e-01
-3.88703436e-01 -4.55498666e-01 4.41720486e-01 4.30041790e-01
-3.77500772e-01 3.35548550e-01 5.38585782e-01 -4.41621959e-01
-3.17524642e-01 -1.36485147e+00 -8.89326930e-01 -2.85749733e-01
3.09724122e-01 5.25257409e-01 4.59797055e-01 -4.80613828... | [10.365232467651367, 7.942054748535156] |
2b0a95e9-eb20-4c94-83f2-405e14483036 | poissonian-blurred-image-deconvolution-by | 2206.05283 | null | https://arxiv.org/abs/2206.05283v1 | https://arxiv.org/pdf/2206.05283v1.pdf | Poissonian Blurred Image Deconvolution by Framelet based Local Minimal Prior | Image production tools do not always create a clear image, noisy and blurry images are sometimes created. Among these cases, Poissonian noise is one of the most famous noises that appear in medical images and images taken in astronomy. Blurred image with Poissonian noise obscures important details that are of great imp... | ['Reza Parvaz'] | 2022-06-10 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.76711136e-01 -4.00013089e-01 4.67994362e-01 1.08192386e-02
2.73465961e-01 -1.93848997e-01 3.18795711e-01 -2.11397007e-01
-6.58044159e-01 1.27737367e+00 2.26407856e-01 8.28930289e-02
-3.15261006e-01 -5.61171949e-01 -2.83944219e-01 -9.39936221e-01
3.67178380e-01 4.82942276e-02 1.07827179e-01 2.05098093... | [11.626350402832031, -2.665297031402588] |
e2a8b6d4-a536-4af6-b57c-ed463e2ebb19 | free-hyperbolic-neural-networks-with-limited | 2107.11472 | null | https://arxiv.org/abs/2107.11472v4 | https://arxiv.org/pdf/2107.11472v4.pdf | Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers | Hyperbolic space can naturally embed hierarchies, unlike Euclidean space. Hyperbolic Neural Networks (HNNs) exploit such representational power by lifting Euclidean features into hyperbolic space for classification, outperforming Euclidean neural networks (ENNs) on datasets with known semantic hierarchies. However, HNN... | ['Stella X. Yu', 'Yubei Chen', 'Xudong Wang', 'Yunhui Guo'] | 2021-07-23 | free-hyperbolic-neural-networks-with-limited-1 | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Clipped_Hyperbolic_Classifiers_Are_Super-Hyperbolic_Classifiers_CVPR_2022_paper.pdf | cvpr-2022-1 | ['classification'] | ['methodology'] | [-2.32076988e-01 6.49622679e-01 9.84986499e-02 -3.23026836e-01
-5.68156183e-01 -6.66882336e-01 5.79992175e-01 -1.18854769e-01
-7.33771682e-01 5.83525836e-01 3.94060999e-01 -2.24797040e-01
-4.57879454e-01 -9.70725954e-01 -7.21971095e-01 -7.07692623e-01
-4.18760866e-01 1.22739412e-01 4.61814225e-01 -5.21028876... | [9.185223579406738, 3.1686761379241943] |
e8e8fe29-368d-4446-ab48-7d8ba4c5112a | healthedge-a-machine-learning-based-smart | 2301.10450 | null | https://arxiv.org/abs/2301.10450v1 | https://arxiv.org/pdf/2301.10450v1.pdf | HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System | Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for typ... | ['Leila Ismail', 'Huned Materwala', 'Alain Hennebelle'] | 2023-01-25 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 7.09629655e-02 -1.38333887e-01 -5.70166647e-01 -6.27099276e-01
-1.20173924e-01 2.66349524e-01 1.31611332e-01 7.38601089e-01
-8.31808820e-02 8.89871597e-01 1.23749092e-01 -5.43366075e-01
-3.82592618e-01 -9.66436803e-01 -2.88371816e-02 -7.19682515e-01
-1.49388149e-01 8.86630595e-01 -4.05172884e-01 2.89891511... | [8.40965747833252, 4.972101211547852] |
8332ab17-73ec-4b26-a602-ab962011ac74 | virtual-multi-view-fusion-for-3d-semantic | 2007.13138 | null | https://arxiv.org/abs/2007.13138v1 | https://arxiv.org/pdf/2007.13138v1.pdf | Virtual Multi-view Fusion for 3D Semantic Segmentation | Semantic segmentation of 3D meshes is an important problem for 3D scene understanding. In this paper we revisit the classic multiview representation of 3D meshes and study several techniques that make them effective for 3D semantic segmentation of meshes. Given a 3D mesh reconstructed from RGBD sensors, our method effe... | ['Xiaoqi Yin', 'Thomas Funkhouser', 'David Ross', 'Caroline Pantofaru', 'Abhijit Kundu', 'Brian Brewington', 'Alireza Fathi'] | 2020-07-26 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4670_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123690511.pdf | eccv-2020-8 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 4.05499786e-01 4.64386702e-01 -1.29597187e-01 -7.62655914e-01
-8.62106383e-01 -6.73986495e-01 5.60028851e-01 2.68283993e-01
7.35658929e-02 -8.73311684e-02 -1.62169084e-01 -1.73803568e-01
1.51490510e-01 -1.16037667e+00 -1.27105629e+00 -1.81005895e-02
2.80469626e-01 1.05783522e+00 6.73024893e-01 -1.43327966... | [8.341943740844727, -2.9837286472320557] |
eabdd415-e1c0-4d37-a281-a5df34f121ef | conffusion-confidence-intervals-for-diffusion | 2211.09795 | null | https://arxiv.org/abs/2211.09795v1 | https://arxiv.org/pdf/2211.09795v1.pdf | Conffusion: Confidence Intervals for Diffusion Models | Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting. Current diffusion-based methods do not provide statistical guarantees regarding the generated results, often preventing their use in high-stakes situations. ... | ['Yedid Hoshen', 'Eliahu Horwitz'] | 2022-11-17 | null | null | null | null | ['facial-inpainting', 'image-inpainting', 'prediction-intervals'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 2.53501803e-01 2.48100698e-01 -2.85817355e-01 -1.77744582e-01
-1.22602677e+00 -7.36284435e-01 8.61234725e-01 5.82580678e-02
-3.96295398e-01 1.05439901e+00 3.02869171e-01 -2.95305401e-01
2.57736742e-01 -8.61449361e-01 -7.56485581e-01 -6.33194566e-01
1.69057399e-01 5.62659204e-01 2.61433899e-01 3.90613019... | [11.19501781463623, -0.33948659896850586] |
27c0aabf-fad8-473f-a053-676f5231f970 | image-dehazing-transformer-with-transmission | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Image_Dehazing_Transformer_With_Transmission-Aware_3D_Position_Embedding_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Image_Dehazing_Transformer_With_Transmission-Aware_3D_Position_Embedding_CVPR_2022_paper.pdf | Image Dehazing Transformer With Transmission-Aware 3D Position Embedding | Despite single image dehazing has been made promising progress with Convolutional Neural Networks (CNNs), the inherent equivariance and locality of convolution still bottleneck dehazing performance. Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is ... | ['Chongyi Li', 'Wenqi Ren', 'Runmin Cong', 'Saeed Anwar', 'Qixin Yan', 'Chun-Le Guo'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-dehazing'] | ['computer-vision'] | [ 4.60358769e-01 -6.26676679e-02 1.52807951e-01 -8.49340260e-02
-3.69558126e-01 -2.54668951e-01 7.04579413e-01 -2.38800049e-01
-1.88913032e-01 4.13805395e-01 1.99299112e-01 -2.83547074e-01
-1.25280246e-01 -9.20649052e-01 -9.01047111e-01 -1.24341583e+00
1.98949501e-01 -5.19141257e-01 4.63089168e-01 -5.37106514... | [10.944387435913086, -3.0819332599639893] |
03795bf3-a087-4067-b6cd-b0a6f2b26953 | boosting-offline-reinforcement-learning-via | 2210.09241 | null | https://arxiv.org/abs/2210.09241v1 | https://arxiv.org/pdf/2210.09241v1.pdf | Boosting Offline Reinforcement Learning via Data Rebalancing | Offline reinforcement learning (RL) is challenged by the distributional shift between learning policies and datasets. To address this problem, existing works mainly focus on designing sophisticated algorithms to explicitly or implicitly constrain the learned policy to be close to the behavior policy. The constraint app... | ['Shuicheng Yan', 'Gao Huang', 'Zhongwen Xu', 'Xiao Ma', 'Bingyi Kang', 'Yang Yue'] | 2022-10-17 | null | null | null | null | ['d4rl'] | ['robots'] | [-7.51574263e-02 9.89802480e-02 -7.46959686e-01 -4.35538799e-01
-6.87854290e-01 -6.98437989e-01 4.78570938e-01 2.12614432e-01
-8.85879338e-01 9.16337430e-01 1.73328251e-01 -4.48186755e-01
-4.21312004e-02 -7.23647177e-01 -8.96744490e-01 -8.27246666e-01
2.44108066e-02 5.01045346e-01 3.13069493e-01 -5.21470197... | [4.069770812988281, 2.2325141429901123] |
3070f70c-0921-4f87-b006-1536110fdc03 | coherent-parametric-contours-for-interactive | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Lu_Coherent_Parametric_Contours_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Lu_Coherent_Parametric_Contours_CVPR_2016_paper.pdf | Coherent Parametric Contours for Interactive Video Object Segmentation | Interactive video segmentation systems aim at producing sub-pixel-level object boundaries for visual effect applications. Recent approaches mainly focus on using sparse user input (i.e. scribbles) for efficient segmentation; however, the quality of the final object boundaries is not satisfactory for the following reaso... | ['Jue Wang', 'Linda Shapiro', 'Yao Lu', 'Xue Bai'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['interactive-video-object-segmentation'] | ['computer-vision'] | [ 2.30780914e-01 -3.27089339e-01 -1.72455803e-01 -2.35359326e-01
-6.24715686e-01 -7.40496039e-01 1.81926087e-01 1.88442260e-01
-4.37432140e-01 6.12222016e-01 -1.44252300e-01 -9.78899151e-02
-4.08619130e-03 -3.27376455e-01 -6.76896751e-01 -4.75910723e-01
5.93603067e-02 1.61245003e-01 1.26661491e+00 -1.57905340... | [9.28069019317627, -0.4160038232803345] |
6a04ac50-88fd-41da-8c78-9fbc2ecdd90a | promptmix-text-to-image-diffusion-models | 2301.12914 | null | https://arxiv.org/abs/2301.12914v2 | https://arxiv.org/pdf/2301.12914v2.pdf | PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks | Many deep learning tasks require annotations that are too time consuming for human operators, resulting in small dataset sizes. This is especially true for dense regression problems such as crowd counting which requires the location of every person in the image to be annotated. Techniques such as data augmentation and ... | ['Alexandros Iosifidis', 'Qi Zhang', 'Arian Bakhtiarnia'] | 2023-01-30 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.21972728e-01 3.46908748e-01 3.23188931e-01 -4.83928293e-01
-3.88475835e-01 -4.43006516e-01 7.95836926e-01 8.39508623e-02
-9.04315412e-01 7.87930131e-01 1.03557870e-01 -2.16921851e-01
5.99927247e-01 -7.14890718e-01 -7.86210299e-01 -4.65208828e-01
-9.06940997e-02 8.98276210e-01 5.11153638e-01 -6.53303191... | [9.960013389587402, 1.1280572414398193] |
74f8f98e-7cd9-41c1-9c45-4e36c5d47ece | mace-an-efficient-model-agnostic-framework | 2205.15540 | null | https://arxiv.org/abs/2205.15540v1 | https://arxiv.org/pdf/2205.15540v1.pdf | MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation | Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning model is differentiable and treat categorical attributes as continuous ones, which restricts ... | ['Steven C. H. Hoi', 'Caiming Xiong', 'Jia Li', 'Wenzhuo Yang'] | 2022-05-31 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.00345540e-01 5.76670587e-01 -9.43877637e-01 -5.66074073e-01
-6.39033079e-01 -2.15379387e-01 7.14963078e-01 -7.56208077e-02
-2.49678567e-02 1.16043568e+00 5.37618577e-01 -8.42126608e-01
-3.03317875e-01 -7.15554416e-01 -1.12263978e+00 -2.34596819e-01
-2.30139241e-01 5.66307664e-01 -5.42302251e-01 -2.35776082... | [8.699493408203125, 5.6237406730651855] |
2162c58d-1576-4691-ac60-c627b32b2ab9 | rf-based-3d-skeletons | null | null | https://doi.org/10.1145/3230543.3230579 | https://people.csail.mit.edu/mingmin/papers/rfpose3d-sigcomm-zhao.pdf | RF-based 3D skeletons | This paper introduces RF-Pose3D, the first system that infers 3D human skeletons from RF signals. It requires no sensors on the body, and works with multiple people and across walls and occlusions. Further, it generates dynamic skeletons that follow the people as they move, walk or sit. As such, RF-Pose3D provides a si... | ['Ming-Min Zhao', 'Yonglong Tian', 'Tianhong Li', 'Hang Zhao', 'Antonio Torralba', 'Zachary Kabelac', 'Rumen Hristov', 'Dina Katabi', 'Mohammad Abu Alsheikh'] | 2018-08-20 | null | null | null | sigcomm-18-proceedings-of-the-2018-conference | ['rf-based-pose-estimation'] | ['computer-vision'] | [ 7.97594041e-02 1.23767525e-01 1.05657540e-01 -4.65468429e-02
3.69789228e-02 -2.60648370e-01 1.58869445e-01 -4.16726798e-01
-2.09753245e-01 5.13023734e-01 2.94886291e-01 4.13286202e-02
-6.77404478e-02 -9.66455042e-01 -5.94181180e-01 -4.50015455e-01
-5.89821398e-01 4.35052037e-01 2.15378612e-01 -5.90791442... | [6.869505405426025, 0.2985776960849762] |
a911ab1e-19bd-408a-bc9d-e83ad9a41e62 | speakers-account-for-asymmetries-in-visual | 1807.09000 | null | https://arxiv.org/abs/1807.09000v4 | https://arxiv.org/pdf/1807.09000v4.pdf | The division of labor in communication: Speakers help listeners account for asymmetries in visual perspective | Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking can be relatively effortful. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observat... | ['Noah D. Goodman', 'Robert D. Hawkins', 'Hyowon Gweon'] | 2018-07-24 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [ 2.75186867e-01 5.56655824e-01 8.35307837e-02 -9.63039756e-01
-5.47491312e-01 -7.15975225e-01 6.19644582e-01 3.24897885e-01
-8.88523102e-01 2.82211185e-01 1.01848698e+00 -2.36569971e-01
-6.93402737e-02 -5.96011758e-01 -1.46984458e-01 -4.04305965e-01
2.61268258e-01 4.02708471e-01 -2.77667850e-01 -5.80594316... | [10.324118614196777, 8.55572509765625] |
8fece3c3-5164-40bd-846a-fcde358fe000 | unsupervised-domain-adaptation-for-automated | 2212.07023 | null | https://arxiv.org/abs/2212.07023v1 | https://arxiv.org/pdf/2212.07023v1.pdf | Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification | Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: For this retrospective study, we collected 3,166 three-dimensional (3D) double-echo steady-state magne... | ['Weitian Chen', 'James F. Griffith', 'Michael Tim-Yun Ong', 'Kevin Ki-Wai Ho', 'Jack Lee', 'Siyue Li', 'Fan Xiao', 'Donal G. Cahill', 'Yongcheng Yao', 'Junru Zhong'] | 2022-12-14 | null | null | null | null | ['phenotype-classification'] | ['medical'] | [ 3.21439505e-01 -2.46061221e-01 -2.59962291e-01 -2.26642236e-01
-1.29373574e+00 -1.77495807e-01 2.07066938e-01 3.26061219e-01
-7.36714125e-01 8.15334380e-01 3.99661690e-01 -5.39733991e-02
-5.08186102e-01 -4.37838048e-01 -2.27061808e-01 -6.65896952e-01
-6.98502004e-01 9.88219321e-01 4.77346510e-01 2.25819319... | [14.532155990600586, -1.7705219984054565] |
bcaeff3c-230f-4ec5-9443-86444b89ea4c | an-aerial-image-recognition-framework-using | 1410.2188 | null | http://arxiv.org/abs/1410.2188v1 | http://arxiv.org/pdf/1410.2188v1.pdf | An Aerial Image Recognition Framework using Discrimination and Redundancy Quality Measure | Aerial image categorization plays an indispensable role in remote sensing and
artificial intelligence. In this paper, we propose a new aerial image
categorization framework, focusing on organizing the local patches of each
aerial image into multiple discriminative subgraphs. The subgraphs reflect both
the geometric pro... | ['Luming Zhang', 'Yuxin Hu'] | 2014-10-06 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 3.42653036e-01 -1.96304813e-01 -2.18475133e-01 -3.02438319e-01
3.07182241e-02 -7.38497853e-01 1.16603389e-01 4.31844264e-01
2.39477530e-01 4.08930361e-01 9.22715068e-02 -8.30154568e-02
-6.17236137e-01 -1.19097984e+00 -1.69349551e-01 -8.58111084e-01
-2.50606835e-01 -2.75322109e-01 3.01173449e-01 -2.17713583... | [10.024248123168945, -1.5069433450698853] |
3ed6c174-ecc2-41b1-9a88-fa21c70e84b2 | actor-mimic-deep-multitask-and-transfer | 1511.06342 | null | http://arxiv.org/abs/1511.06342v4 | http://arxiv.org/pdf/1511.06342v4.pdf | Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning | The ability to act in multiple environments and transfer previous knowledge
to new situations can be considered a critical aspect of any intelligent agent.
Towards this goal, we define a novel method of multitask and transfer learning
that enables an autonomous agent to learn how to behave in multiple tasks
simultaneou... | ['Ruslan Salakhutdinov', 'Jimmy Lei Ba', 'Emilio Parisotto'] | 2015-11-19 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 4.96740907e-01 3.00493568e-01 4.29843329e-02 -2.25882366e-01
-4.46062386e-01 -8.71655047e-01 7.52662599e-01 -9.70146507e-02
-5.54056346e-01 1.01383674e+00 -2.66676039e-01 -2.95483232e-01
-4.44025069e-01 -7.60597348e-01 -8.01474214e-01 -7.68000722e-01
-3.36601660e-02 9.07922626e-01 4.66892898e-01 -5.56341290... | [4.051812171936035, 1.5673893690109253] |
9ddaa99b-5e0c-4602-ba3e-b7f027434856 | can-deepfakes-be-created-by-novice-users | 2304.14576 | null | https://arxiv.org/abs/2304.14576v1 | https://arxiv.org/pdf/2304.14576v1.pdf | Can deepfakes be created by novice users? | Recent advancements in machine learning and computer vision have led to the proliferation of Deepfakes. As technology democratizes over time, there is an increasing fear that novice users can create Deepfakes, to discredit others and undermine public discourse. In this paper, we conduct user studies to understand wheth... | ['Brendan Dolan-Gavitt', 'Rachel Greenstadt', 'Siddharth Garg', 'Progga Deb', 'Brian Timmerman', 'Kevin Gallagher', 'Gauri Jagatap', 'Pulak Mehta'] | 2023-04-28 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 4.58588824e-02 2.53856152e-01 1.88276526e-02 1.37250468e-01
-6.92535281e-01 -1.02137101e+00 5.15506864e-01 -9.25335214e-02
-3.72895837e-01 1.09689146e-01 -3.59009691e-02 -8.87002945e-01
3.71864051e-01 -3.64030987e-01 -6.34134412e-01 -1.94218054e-01
5.68275034e-01 -6.69217557e-02 1.51933223e-01 -1.64316036... | [12.527132034301758, 1.1952327489852905] |
f2bbb6cd-bdad-4961-a6c1-fcfdc23a9bac | corola-a-sequential-solution-to-moving-object | 1505.03566 | null | http://arxiv.org/abs/1505.03566v2 | http://arxiv.org/pdf/1505.03566v2.pdf | COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation | Extracting moving objects from a video sequence and estimating the background
of each individual image are fundamental issues in many practical applications
such as visual surveillance, intelligent vehicle navigation, and traffic
monitoring. Recently, some methods have been proposed to detect moving objects
in a video ... | ['Hong Zhang', 'Moein Shakeri'] | 2015-05-13 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.31164432e-01 -6.40471160e-01 -1.25956208e-01 -1.62521765e-01
-7.32570410e-01 -2.74281800e-01 3.18111360e-01 -2.83973157e-01
-3.88224632e-01 3.65913749e-01 1.04159266e-01 -1.50053818e-02
-1.28193749e-02 -1.55220479e-01 -8.26829016e-01 -8.42221200e-01
-3.84873748e-01 1.62113667e-01 9.27765071e-01 3.63346398... | [8.98952865600586, -0.8048866987228394] |
b9bbec46-1590-4756-8ab4-23467149701d | fourier-contour-embedding-for-arbitrary | 2104.10442 | null | https://arxiv.org/abs/2104.10442v2 | https://arxiv.org/pdf/2104.10442v2.pdf | Fourier Contour Embedding for Arbitrary-Shaped Text Detection | One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances. Most of existing methods model text instances in image spatial domain via masks or contour point sequences in the Cartesian or the polar coordina... | ['Zhanghui Kuang', 'Wayne Zhang', 'Lianwen Jin', 'Lingyu Liang', 'Jianyong Chen', 'Yiqin Zhu'] | 2021-04-21 | fourier-contour-embedding-for-arbitrary-1 | https://arxiv.org/abs/2104.10442 | https://arxiv.org/pdf/2104.10442.pdf | cvpr-2021-2021-4 | ['scene-text-detection'] | ['computer-vision'] | [ 4.19606358e-01 -2.27212116e-01 2.04036906e-01 -1.48853853e-01
-2.61668682e-01 -6.26269162e-01 7.45169878e-01 -4.08188939e-01
-4.66791019e-02 -3.01755704e-02 4.47084643e-02 -2.32683450e-01
-2.37346023e-01 -8.71539176e-01 -5.23307323e-01 -5.75212896e-01
1.55447811e-01 6.52276695e-01 5.41030645e-01 -2.11785093... | [12.088459014892578, 2.2674155235290527] |
9f9dc97d-2116-4a85-8524-868606a6b44d | robust-integrated-sensing-and-communication | 2303.07652 | null | https://arxiv.org/abs/2303.07652v1 | https://arxiv.org/pdf/2303.07652v1.pdf | Robust Integrated Sensing and Communication Beamforming for Dual-functional Radar and Communications: Method and Insights | This work presents a novel robust beamforming design dedicated for dual-functional radar and communication (DFRC) base stations (BSs) in the context of integrated sensing and communications (ISAC). The architecture is intended for circumstances with imperfect channel state information (CSI). Our suggested approach demo... | ['Marwa Chafii', 'Ahmad Bazzi'] | 2023-03-14 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 3.29077214e-01 1.23243161e-01 5.73203862e-02 -6.38643652e-02
-7.27746248e-01 -8.49846423e-01 3.84335339e-01 -5.75977623e-01
1.31495940e-02 7.23179162e-01 2.72031695e-01 -7.32390523e-01
-8.67536724e-01 -5.51630139e-01 5.44173550e-03 -1.20695436e+00
-5.45598447e-01 -1.09637856e-01 -5.99137843e-01 1.33333923... | [6.281074523925781, 1.2495671510696411] |
6cb4672c-b25e-4d34-9a17-a4cb5188f07d | inductive-and-unsupervised-representation | null | null | https://openreview.net/forum?id=rkem91rtDB | https://openreview.net/pdf?id=rkem91rtDB | Inductive and Unsupervised Representation Learning on Graph Structured Objects | Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain. It is also challenging to make graph learning inductive and unsupervised at the same time, as learning processes guided by reconstruction error based loss funct... | ['Wei Cheng', 'Qianqian Ma', 'Dongjin Song', 'Yanchi Liu', 'Wenchao Yu', 'Lichen Wang', 'Bo Zong', 'Yun Fu', 'Jingchao Ni', 'Haifeng Chen'] | 2020-05-01 | null | null | null | iclr-2020-1 | ['graph-similarity'] | ['graphs'] | [ 4.95089412e-01 5.63183784e-01 -4.84813571e-01 -2.26455316e-01
-7.28924930e-01 -5.42818606e-01 5.32691777e-01 8.11458766e-01
5.68164978e-03 4.98536319e-01 1.58936635e-01 -2.55941570e-01
-3.88452947e-01 -1.10385442e+00 -7.42261589e-01 -6.91606760e-01
-3.89905035e-01 7.65707374e-01 -1.48248628e-01 2.87564635... | [7.200651168823242, 6.22418212890625] |
f78f8686-002b-4d3d-91a8-6b30f1ca19f7 | forensic-video-steganalysis-in-spatial-domain | 2305.18070 | null | https://arxiv.org/abs/2305.18070v1 | https://arxiv.org/pdf/2305.18070v1.pdf | Forensic Video Steganalysis in Spatial Domain by Noise Residual Convolutional Neural Network | This research evaluates a convolutional neural network (CNN) based approach to forensic video steganalysis. A video steganography dataset is created to train a CNN to conduct forensic steganalysis in the spatial domain. We use a noise residual convolutional neural network to detect embedded secrets since a steganograph... | ['Meike Kombrink', 'Zeno Geradts', 'Mart Keizer'] | 2023-05-29 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 4.82039720e-01 -5.95950149e-02 3.87134910e-01 2.71850765e-01
8.23837370e-02 -2.24883214e-01 3.01684707e-01 -6.38258815e-01
-2.31465191e-01 4.40969944e-01 -2.76661903e-01 -9.30724144e-01
4.60257560e-01 -1.07329774e+00 -5.96438110e-01 -7.08764195e-01
-6.55695379e-01 -2.94926256e-01 3.51363093e-01 -5.21552444... | [4.275097846984863, 8.09187126159668] |
8f1f7598-cbf4-4c6c-9820-e0429d7841b5 | unovost-unsupervised-offline-video-object | 2001.05425 | null | https://arxiv.org/abs/2001.05425v1 | https://arxiv.org/pdf/2001.05425v1.pdf | UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking | We address Unsupervised Video Object Segmentation (UVOS), the task of automatically generating accurate pixel masks for salient objects in a video sequence and of tracking these objects consistently through time, without any input about which objects should be tracked. Towards solving this task, we present UnOVOST (Uns... | ['Idil Esen Zulfikar', 'Jonathon Luiten', 'Bastian Leibe'] | 2020-01-15 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 3.43977600e-01 -1.45964399e-01 -3.88087690e-01 -2.05618441e-01
-7.52582908e-01 -8.89704823e-01 5.34269333e-01 3.54189873e-01
-6.19742215e-01 4.05253381e-01 -1.49871916e-01 -1.00337468e-01
-3.59811671e-02 -5.08384705e-01 -1.00245953e+00 -5.20079672e-01
-3.88638228e-01 8.18676054e-01 1.09543121e+00 2.51931429... | [9.158447265625, -0.23414117097854614] |
dfed0638-585b-40a7-ba44-abc93bdc86d9 | kgtn-ens-few-shot-image-classification-with | 2211.03199 | null | https://arxiv.org/abs/2211.03199v1 | https://arxiv.org/pdf/2211.03199v1.pdf | KGTN-ens: Few-Shot Image Classification with Knowledge Graph Ensembles | We propose KGTN-ens, a framework extending the recent Knowledge Graph Transfer Network (KGTN) in order to incorporate multiple knowledge graph embeddings at a small cost. We evaluate it with different combinations of embeddings in a few-shot image classification task. We also construct a new knowledge source - Wikidata... | ['Agata Filipowska', 'Anna Fensel', 'Dominik Filipiak'] | 2022-11-06 | null | null | null | null | ['few-shot-image-classification', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['computer-vision', 'graphs', 'methodology'] | [-1.25842780e-01 2.43622988e-01 -4.95605946e-01 -6.75999969e-02
-2.43706152e-01 -2.20391497e-01 5.84074438e-01 8.91538113e-02
-7.36650467e-01 6.08593822e-01 3.50808918e-01 -7.09018260e-02
-3.67515624e-01 -1.07266438e+00 -7.52671540e-01 -3.02648813e-01
-4.48173061e-02 3.34723175e-01 5.78012586e-01 -8.16913173... | [8.733317375183105, 7.798191070556641] |
e0b6cb47-0608-44b7-b9e3-d5a469db0956 | cam-cad-point-cloud-part-segmentation-via-few | 2207.01218 | null | https://arxiv.org/abs/2207.01218v2 | https://arxiv.org/pdf/2207.01218v2.pdf | CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning | 3D part segmentation is an essential step in advanced CAM/CAD workflow. Precise 3D segmentation contributes to lower defective rate of work-pieces produced by the manufacturing equipment (such as computer controlled CNCs), thereby improving work efficiency and attaining the attendant economic benefits. A large class of... | ['Tong Heng Lee', 'Vadakkepat Prahlad', 'Abdullah Al Mamun', 'Haoren Guo', 'Haiyue Zhu', 'Jiahui Wang'] | 2022-07-04 | null | null | null | null | ['3d-part-segmentation'] | ['computer-vision'] | [ 2.64496505e-01 6.28577396e-02 -3.24497849e-01 -1.84670538e-01
-1.82214588e-01 -2.09521919e-01 1.77253619e-01 3.87999900e-02
-6.90511540e-02 4.85728383e-01 -4.75718290e-01 4.07517441e-02
-5.65694749e-01 -8.96229208e-01 -4.36980635e-01 -7.27097690e-01
2.37327382e-01 7.56368935e-01 4.31150764e-01 -3.14509600... | [7.347378253936768, 1.984575867652893] |
c63e7a0b-a30c-4726-857c-80583e3ef64e | bazinga-a-dataset-for-multi-party-dialogues | null | null | https://aclanthology.org/2022.lrec-1.367 | https://aclanthology.org/2022.lrec-1.367.pdf | Bazinga! A Dataset for Multi-Party Dialogues Structuring | We introduce a dataset built around a large collection of TV (and movie) series. Those are filled with challenging multi-party dialogues. Moreover, TV series come with a very active fan base that allows the collection of metadata and accelerates annotation. With 16 TV and movie series, Bazinga! amounts to 400+ hours of... | ['Claude Barras', 'Ruiqing Yin', 'Léo Galmant', 'Aman Berhe', 'Martin Bouteiller', 'Sharleyne Lefevre', 'Benjamin Maurice', 'Hervé Bredin', 'Camille Guinaudeau', 'Juliette Bergoënd', 'Paul Lerner'] | null | null | null | null | lrec-2022-6 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 2.82769836e-02 4.27930027e-01 -5.16439714e-02 -6.76868558e-01
-1.45391977e+00 -8.74049246e-01 7.01077163e-01 2.61734903e-01
-5.86147964e-01 8.49490941e-01 8.12068760e-01 7.57992938e-02
3.85379285e-01 -3.08891058e-01 -5.17842174e-01 -2.04182088e-01
-9.83899981e-02 9.53349650e-01 -5.43579347e-02 -4.83001232... | [13.713031768798828, 7.149754524230957] |
aee8e49e-6277-4152-b7de-0dcc549b3573 | sensor-data-driven-analysis-for | 2301.06300 | null | https://arxiv.org/abs/2301.06300v1 | https://arxiv.org/pdf/2301.06300v1.pdf | Sensor data-driven analysis for identification of causal relationships between exposure to air pollution and respiratory rate in asthmatics | According to the Lancet report on the global burden of disease published in October 2020, air pollution is among the five highest risk factors for global health, reducing life expectancy on average by 20 months. This paper describes a data-driven method for establishing causal relationships within and between two multi... | ['S Maiya', 'D K Arvind'] | 2023-01-16 | null | null | null | null | ['causal-discovery', 'epidemiology'] | ['knowledge-base', 'medical'] | [ 5.84627151e-01 -6.83751553e-02 -1.32007182e-01 -2.11023092e-01
-4.97916996e-01 -5.18978477e-01 5.51939070e-01 6.02271318e-01
-3.55691820e-01 9.81803119e-01 1.31126165e-01 -4.83647346e-01
-7.98125267e-01 -1.17075479e+00 -7.28909135e-01 -5.91460407e-01
-3.18765849e-01 3.22058111e-01 1.52260259e-01 5.94461322... | [6.190474033355713, 2.605403423309326] |
d13cf24f-e992-4a0d-968c-866b6f1b35e0 | large-scale-multi-modal-person-identification | 1912.12134 | null | https://arxiv.org/abs/1912.12134v1 | https://arxiv.org/pdf/1912.12134v1.pdf | Large-scale Multi-modal Person Identification in Real Unconstrained Environments | Person identification (P-ID) under real unconstrained noisy environments is a huge challenge. In multiple-feature learning with Deep Convolutional Neural Networks (DCNNs) or Machine Learning method for large-scale person identification in the wild, the key is to design an appropriate strategy for decision layer fusion ... | ['Jiajie Ye', 'Yisheng Guan', 'Xinghong Huang', 'Hong Zhang', 'Junfa Liu'] | 2019-12-17 | null | null | null | null | ['person-identification', 'multi-modal-person-identification'] | ['computer-vision', 'miscellaneous'] | [ 1.43716354e-02 -6.74242914e-01 3.26263100e-01 -5.92117310e-01
-7.90640295e-01 -3.33438218e-01 3.60115469e-01 -7.44167566e-02
-6.83137119e-01 7.01578140e-01 4.83382314e-01 3.81484032e-01
-4.05189753e-01 -6.30181551e-01 -1.79902911e-01 -7.91404009e-01
8.22676048e-02 2.20052510e-01 -2.83029705e-01 -8.54741037... | [14.568195343017578, 1.021847128868103] |
7a55559d-846a-4c09-97e3-e773f8c1f049 | embracing-consistency-a-one-stage-approach | 2209.13306 | null | https://arxiv.org/abs/2209.13306v2 | https://arxiv.org/pdf/2209.13306v2.pdf | Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding | Spatio-Temporal video grounding (STVG) focuses on retrieving the spatio-temporal tube of a specific object depicted by a free-form textual expression. Existing approaches mainly treat this complicated task as a parallel frame-grounding problem and thus suffer from two types of inconsistency drawbacks: feature alignment... | ['Yadong Mu', 'Zehuan Yuan', 'Yongzhi Li', 'Yang Jin'] | 2022-09-27 | null | null | null | null | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [ 1.14627652e-01 -1.35233566e-01 -2.73190767e-01 -3.71584505e-01
-9.23722625e-01 -3.69442374e-01 6.56132698e-01 -3.84816796e-01
-1.22587174e-01 3.83256525e-01 3.06381106e-01 -1.90935239e-01
1.88530475e-01 -4.19292271e-01 -9.49018121e-01 -4.76150006e-01
3.44333231e-01 -4.92783934e-02 4.85552549e-01 -9.36901197... | [9.714398384094238, 0.6525529623031616] |
e21c4099-811d-4605-baff-096149654743 | learning-weakly-supervised-audio-visual | 2305.18797 | null | https://arxiv.org/abs/2305.18797v2 | https://arxiv.org/pdf/2305.18797v2.pdf | Learning Weakly Supervised Audio-Visual Violence Detection in Hyperbolic Space | In recent years, the task of weakly supervised audio-visual violence detection has gained considerable attention. The goal of this task is to identify violent segments within multimodal data based on video-level labels. Despite advances in this field, traditional Euclidean neural networks, which have been used in prior... | ['Zizhao Wu', 'Yigang Wang', 'Keyang Yu', 'Xiao Zhou', 'Yikai Luo', 'Hao Wen', 'Xiaogang Peng'] | 2023-05-30 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.81506896e-01 -1.19144998e-01 -1.49842510e-02 -1.75614968e-01
-1.12388945e+00 -4.63165671e-01 6.33607686e-01 2.64875740e-01
-4.64613646e-01 1.97810933e-01 5.62964499e-01 1.36341527e-01
-3.25107694e-01 -3.56523573e-01 -2.96723396e-01 -6.99341893e-01
-2.59761900e-01 -4.45514545e-02 -1.22941341e-02 -2.02397168... | [13.526847839355469, 4.668430328369141] |
c86133b8-deb6-4fde-808d-95e64d9307b5 | approximate-bayes-optimal-pseudo-label | 2302.08883 | null | https://arxiv.org/abs/2302.08883v5 | https://arxiv.org/pdf/2302.08883v5.pdf | Approximately Bayes-Optimal Pseudo Label Selection | Semi-supervised learning by self-training heavily relies on pseudo-label selection (PLS). The selection often depends on the initial model fit on labeled data. Early overfitting might thus be propagated to the final model by selecting instances with overconfident but erroneous predictions, often referred to as confirma... | ['Thomas Augustin', 'Thomas Nagler', 'Emilio Dorigatti', 'Jann Goschenhofer', 'Julian Rodemann'] | 2023-02-17 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.95433724e-01 5.72943449e-01 -1.81565434e-01 -6.90469623e-01
-9.17638898e-01 -3.80919993e-01 3.37766349e-01 2.26737037e-01
-3.54230016e-01 1.30766201e+00 -4.10868168e-01 -2.28754401e-01
-3.53880942e-01 -4.83848661e-01 -6.71929002e-01 -7.76176989e-01
2.73481179e-02 7.47729897e-01 9.76479650e-02 5.50434589... | [7.913333892822266, 4.296858310699463] |
88601cb5-af10-4c33-82d3-50d22ebd01d9 | fire-detection-in-a-still-image-using-colour | 1803.03828 | null | http://arxiv.org/abs/1803.03828v1 | http://arxiv.org/pdf/1803.03828v1.pdf | Fire detection in a still image using colour information | Colour analysis is a crucial step in image-based fire detection algorithms.
Many of the proposed fire detection algorithms in a still image are prone to
false alarms caused by objects with a colour similar to fire. To design a
colour-based system with a better false alarm rate, a new
colour-differentiating conversion m... | ['Abdsamad Benkrid', 'Oluwarotimi Giwa'] | 2018-03-10 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 7.27200389e-01 -5.93932152e-01 6.27586246e-01 2.58156449e-01
-1.99268788e-01 -4.99524146e-01 7.79408574e-01 1.63671419e-01
-7.53363431e-01 5.68048358e-01 -4.42368001e-01 -1.26190633e-01
-7.04442620e-01 -9.55657423e-01 -2.35521290e-02 -1.23352480e+00
1.74112618e-01 3.92270654e-01 5.64276993e-01 2.96376012... | [9.245498657226562, -1.2770963907241821] |
18a9b814-7bf8-4230-8e1c-5710228b10eb | subtype-former-a-deep-learning-approach-for | 2207.14639 | null | https://arxiv.org/abs/2207.14639v1 | https://arxiv.org/pdf/2207.14639v1.pdf | Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data | Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large a... | ['Zhe Wang', 'Jing Zhang', 'Dongdong Li', 'Xiaoyang Fang', 'Yi Jiang', 'Yuhang Sheng', 'Hai Yang'] | 2022-07-28 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.24703772e-01 -6.17020428e-01 -9.28927004e-01 -1.06527485e-01
-7.86528587e-01 -3.89309853e-01 1.35793716e-01 4.86738473e-01
8.44342858e-02 9.39356327e-01 3.91803384e-01 -5.06951511e-01
-4.99531478e-01 -8.29429150e-01 -2.59810328e-01 -1.23593974e+00
1.08526938e-01 5.92527986e-01 -4.90223318e-01 -1.71483904... | [5.951557636260986, 5.694282054901123] |
7165f6c0-d995-4266-9ecf-ed8398d0b787 | physics-informed-machine-learning-of | 2208.00880 | null | https://arxiv.org/abs/2208.00880v1 | https://arxiv.org/pdf/2208.00880v1.pdf | Physics-informed Machine Learning of Parameterized Fundamental Diagrams | Fundamental diagrams describe the relationship between speed, flow, and density for some roadway (or set of roadway) configuration(s). These diagrams typically do not reflect, however, information on how speed-flow relationships change as a function of exogenous variables such as curb configuration, weather or other ex... | ['Chase Dowling', 'Andisheh Ranjbari', 'Vinay Amatya', 'Thomas Maxner', 'James Koch'] | 2022-08-01 | null | null | null | null | ['physics-informed-machine-learning', 'machine-learning', 'machine-learning'] | ['graphs', 'methodology', 'miscellaneous'] | [ 1.48216158e-01 -2.26343218e-02 -1.85427785e-01 -4.02075261e-01
5.63210621e-02 -5.04149795e-01 7.77096212e-01 1.17460288e-01
-3.51859003e-01 8.86768401e-01 -1.82868645e-01 -8.30316722e-01
-7.15954721e-01 -9.96333957e-01 -9.71786439e-01 -6.64991736e-01
-6.53582141e-02 2.99578547e-01 1.94301426e-01 -5.74510813... | [5.628015518188477, 1.2593824863433838] |
998ad58c-8832-44ea-b9eb-f9d16d16bce2 | neural-code-summarization-how-far-are-we | 2107.07112 | null | https://arxiv.org/abs/2107.07112v2 | https://arxiv.org/pdf/2107.07112v2.pdf | On the Evaluation of Neural Code Summarization | Source code summaries are important for program comprehension and maintenance. However, there are plenty of programs with missing, outdated, or mismatched summaries. Recently, deep learning techniques have been exploited to automatically generate summaries for given code snippets. To achieve a profound understanding of... | ['Hongbin Sun', 'Dongmei Zhang', 'Hongyu Zhang', 'Shi Han', 'Junjie Chen', 'Lun Du', 'Yanlin Wang', 'Ensheng Shi'] | 2021-07-15 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 2.46233176e-02 -2.47969061e-01 -2.73721606e-01 -2.84165472e-01
-6.60533369e-01 -4.10824805e-01 2.14426905e-01 3.94468009e-01
-2.49982044e-01 3.70195150e-01 5.88645637e-01 -4.85307425e-01
-5.88323660e-02 -5.09975195e-01 -6.34064615e-01 -3.05940449e-01
1.04123158e-02 -1.42671123e-01 1.47120342e-01 -2.68950641... | [7.671576976776123, 7.938797473907471] |
c502ed40-5771-4947-b560-a14b3aabe76d | an-abstract-specification-of-voxml-as-an | 2305.13076 | null | https://arxiv.org/abs/2305.13076v1 | https://arxiv.org/pdf/2305.13076v1.pdf | An Abstract Specification of VoxML as an Annotation Language | VoxML is a modeling language used to map natural language expressions into real-time visualizations using commonsense semantic knowledge of objects and events. Its utility has been demonstrated in embodied simulation environments and in agent-object interactions in situated multimodal human-agent collaboration and comm... | ['James Pustejovsky', 'Nikhil Krishnaswamy', 'Kiyong Lee'] | 2023-05-22 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-8.44840035e-02 7.92152762e-01 1.82115704e-01 -3.70980650e-01
1.28617093e-01 -5.13928592e-01 1.54430234e+00 4.40701872e-01
-2.45652646e-01 6.18170261e-01 6.35753214e-01 -3.92932177e-01
-1.75347686e-01 -8.75434875e-01 -2.71066695e-01 -2.51629800e-01
-4.13254946e-01 5.34521997e-01 1.87617064e-01 -5.68957508... | [5.091229438781738, 0.5443795919418335] |
a2acb812-754f-45da-bf9a-75e45affa0a6 | unsupervised-abstractive-summarization-of | 2102.04490 | null | https://arxiv.org/abs/2102.04490v2 | https://arxiv.org/pdf/2102.04490v2.pdf | Unsupervised Abstractive Summarization of Bengali Text Documents | Abstractive summarization systems generally rely on large collections of document-summary pairs. However, the performance of abstractive systems remains a challenge due to the unavailability of parallel data for low-resource languages like Bengali. To overcome this problem, we propose a graph-based unsupervised abstrac... | ['Taufiqul Jannat', 'Md. Saifur Rahman Chowdhury', 'Tahsin Tasnim Mim', 'Mir Tafseer Nayeem', 'Radia Rayan Chowdhury'] | 2021-01-26 | null | https://aclanthology.org/2021.eacl-main.224 | https://aclanthology.org/2021.eacl-main.224.pdf | eacl-2021-2 | ['unsupervised-extractive-summarization'] | ['natural-language-processing'] | [ 5.13283670e-01 4.24610734e-01 4.09783283e-03 -3.19040239e-01
-1.40495765e+00 -8.10615480e-01 8.77385795e-01 8.60359728e-01
-4.68900621e-01 7.46460319e-01 9.87272203e-01 -2.55618185e-01
1.54895335e-01 -5.03806353e-01 -4.57402736e-01 -2.74416894e-01
5.94834909e-02 9.06215787e-01 3.91189575e-01 -4.46653098... | [12.516236305236816, 9.519414901733398] |
10a1fdbf-f26b-4088-aeae-459f9be0e5a1 | vision-transformer-based-model-for-describing | 2210.02762 | null | https://arxiv.org/abs/2210.02762v2 | https://arxiv.org/pdf/2210.02762v2.pdf | Vision Transformer Based Model for Describing a Set of Images as a Story | Visual Story-Telling is the process of forming a multi-sentence story from a set of images. Appropriately including visual variation and contextual information captured inside the input images is one of the most challenging aspects of visual storytelling. Consequently, stories developed from a set of images often lack ... | ['Ajmal Mian', 'Ghulam Mubashar Hassan', 'Zainy M. Malakan'] | 2022-10-06 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 5.42759001e-01 -3.37257594e-01 -2.78928448e-02 -3.29449832e-01
-4.11676675e-01 -4.08318222e-01 9.06335175e-01 -2.35641405e-01
-9.13242577e-04 5.40424347e-01 5.15874267e-01 1.23994380e-01
2.20535100e-01 -7.16909170e-01 -9.85062957e-01 -6.81020260e-01
4.90431905e-01 1.23756807e-02 3.18120480e-01 -1.64328173... | [11.088898658752441, 0.6752956509590149] |
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