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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f69e9bdf-baec-4e88-bb0b-37310b4f17f8 | uboco-unsupervised-boundary-contrastive | 2111.14799 | null | https://arxiv.org/abs/2111.14799v2 | https://arxiv.org/pdf/2111.14799v2.pdf | UBoCo : Unsupervised Boundary Contrastive Learning for Generic Event Boundary Detection | Generic Event Boundary Detection (GEBD) is a newly suggested video understanding task that aims to find one level deeper semantic boundaries of events. Bridging the gap between natural human perception and video understanding, it has various potential applications, including interpretable and semantically valid video p... | ['Seon Joo Kim', 'Taehyun Kim', 'Jinwoo Kim', 'Hyolim Kang'] | 2021-11-29 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 5.53729057e-01 2.19173193e-01 -4.30597603e-01 -4.25822824e-01
-5.93866050e-01 -4.45997298e-01 5.19449234e-01 -3.45866494e-02
-9.23998877e-02 2.95689702e-01 5.71297288e-01 -1.88519627e-01
-2.31312457e-02 -4.88103837e-01 -1.03214526e+00 -5.73746741e-01
-5.35489246e-02 2.78299242e-01 5.28294683e-01 -5.99964820... | [9.300124168395996, 0.5792104601860046] |
25cc2347-18b9-4273-b9af-7e0d5baf5ede | non-autoregressive-end-to-end-approaches-for | 2304.10869 | null | https://arxiv.org/abs/2304.10869v1 | https://arxiv.org/pdf/2304.10869v1.pdf | Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding | This paper presents the use of non-autoregressive (NAR) approaches for joint automatic speech recognition (ASR) and spoken language understanding (SLU) tasks. The proposed NAR systems employ a Conformer encoder that applies connectionist temporal classification (CTC) to transcribe the speech utterance into raw ASR hypo... | ['Rama Doddipatla', 'Mohan Li'] | 2023-04-21 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.77942502e-01 2.96628565e-01 -1.76114552e-02 -6.98391020e-01
-1.40468490e+00 -3.23403478e-01 7.69554913e-01 -3.06332380e-01
-2.05834746e-01 4.35859293e-01 7.25681126e-01 -6.70252442e-01
5.46969056e-01 1.08677596e-01 -4.55506027e-01 -5.35844266e-01
1.54443488e-01 4.80894536e-01 -6.46280125e-02 -3.53772432... | [14.400197982788086, 6.874086380004883] |
5975a77c-642e-473c-848d-7046e037574d | experimenting-with-an-evaluation-framework | 2301.10888 | null | https://arxiv.org/abs/2301.10888v1 | https://arxiv.org/pdf/2301.10888v1.pdf | Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL) | Introduction Data imbalance is one of the crucial issues in big data analysis with fewer labels. For example, in real-world healthcare data, spam detection labels, and financial fraud detection datasets. Many data balance methods were introduced to improve machine learning algorithms' performance. Research claims SMOTE... | ['Xia Jiang', 'Chenyu Li'] | 2023-01-26 | null | null | null | null | ['imbalanced-classification', 'spam-detection'] | ['miscellaneous', 'natural-language-processing'] | [-5.55774681e-02 3.89943361e-01 -3.47691596e-01 -5.61672390e-01
-1.51599765e-01 1.35276914e-01 1.23496577e-01 4.26877141e-01
-3.72797430e-01 9.46236670e-01 1.59530178e-01 -4.37859654e-01
-1.37647673e-01 -1.11312878e+00 -5.22026062e-01 -5.09181261e-01
-5.38834259e-02 7.66958117e-01 -7.91078284e-02 -3.21442574... | [8.747355461120605, 4.344563961029053] |
63d828de-9636-4881-8dac-3a2b0e71af0b | transformer-meets-tracker-exploiting-temporal | 2103.11681 | null | https://arxiv.org/abs/2103.11681v2 | https://arxiv.org/pdf/2103.11681v2.pdf | Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking | In video object tracking, there exist rich temporal contexts among successive frames, which have been largely overlooked in existing trackers. In this work, we bridge the individual video frames and explore the temporal contexts across them via a transformer architecture for robust object tracking. Different from class... | ['Houqaing Li', 'Jie Wang', 'Wengang Zhou', 'Ning Wang'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_Transformer_Meets_Tracker_Exploiting_Temporal_Context_for_Robust_Visual_Tracking_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-object-tracking'] | ['computer-vision'] | [ 8.39126259e-02 -3.69849205e-01 -3.02976340e-01 -1.50987148e-01
-8.66496325e-01 -7.06456482e-01 8.24182630e-01 -4.05971497e-01
-4.70718324e-01 2.71493495e-01 4.06211853e-01 2.65724547e-02
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-7.94079825e-02 4.59870338e-01 8.62473130e-01 -4.94131632... | [6.286030292510986, -2.1046488285064697] |
fa291ec5-626d-4d34-bf97-3a61bf7712a2 | behavioral-epidemiology-an-economic-model-to | 2202.04174 | null | https://arxiv.org/abs/2202.04174v1 | https://arxiv.org/pdf/2202.04174v1.pdf | Behavioral epidemiology: An economic model to evaluate optimal policy in the midst of a pandemic | This paper combines a canonical epidemiology model of disease dynamics with government policy of lockdown and testing, and agents' decision to social distance in order to avoid getting infected. The model is calibrated with data on deaths and testing outcomes in the Unites States. It is shown that an intermediate but p... | ['Rohit Lamba', 'Ilia Krasikov', 'Shomak Chakrabarti'] | 2022-02-08 | null | null | null | null | ['epidemiology'] | ['medical'] | [-5.91271818e-02 7.80213833e-01 -2.18582332e-01 8.44059512e-02
-9.80012771e-03 -3.94481122e-01 6.53235853e-01 1.77114785e-01
-8.07940245e-01 8.04534912e-01 8.21858227e-01 -9.02788639e-01
-4.42176342e-01 -8.35372448e-01 6.16030842e-02 -9.66969907e-01
-1.62837014e-01 6.86014056e-01 -2.61196852e-01 -1.57660380... | [5.937751293182373, 4.403260231018066] |
eabe979c-8e98-499e-8899-1b5ceb331916 | memory-consistent-unsupervised-off-the-shelf | 2209.07910 | null | https://arxiv.org/abs/2209.07910v1 | https://arxiv.org/pdf/2209.07910v1.pdf | Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation | Unsupervised domain adaptation (UDA) has been a vital protocol for migrating information learned from a labeled source domain to facilitate the implementation in an unlabeled heterogeneous target domain. Although UDA is typically jointly trained on data from both domains, accessing the labeled source domain data is oft... | ['Jonghye Woo', 'Georges El Fakhri', 'Fangxu Xing', 'Xiaofeng Liu'] | 2022-09-16 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 3.64746273e-01 -2.61746459e-02 -3.69129270e-01 -6.78760827e-01
-1.27698350e+00 -6.62986159e-01 2.94814527e-01 3.06624025e-01
-7.23332703e-01 9.23612773e-01 6.48259223e-02 -3.72929245e-01
-2.55656615e-02 -4.78419960e-01 -7.04031944e-01 -9.89234090e-01
2.48192549e-01 6.84957922e-01 1.63902491e-01 1.86995134... | [14.595187187194824, -2.0241270065307617] |
102020e7-6e6f-493d-87e5-3035104d05fb | diffblender-scalable-and-composable | 2305.15194 | null | https://arxiv.org/abs/2305.15194v1 | https://arxiv.org/pdf/2305.15194v1.pdf | DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models | The recent progress in diffusion-based text-to-image generation models has significantly expanded generative capabilities via conditioning the text descriptions. However, since relying solely on text prompts is still restrictive for fine-grained customization, we aim to extend the boundaries of conditional generation t... | ['Namhyuk Ahn', 'Daesik Kim', 'Kibeom Hong', 'Junsoo Lee', 'Sungnyun Kim'] | 2023-05-24 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 4.67293799e-01 2.87940890e-01 -1.98699579e-01 -1.32436454e-01
-5.00360489e-01 -7.88584828e-01 1.31903708e+00 -1.48788989e-01
-1.94454387e-01 7.73830414e-01 5.45146883e-01 -2.11620584e-01
-2.52008773e-02 -1.08738291e+00 -5.59910893e-01 -4.26256239e-01
4.21100676e-01 4.46379185e-01 -1.07190460e-01 -2.67325044... | [11.363374710083008, -0.13457909226417542] |
ddd175a9-4fd5-4cda-9ec7-e1edaa4000c1 | robust-and-precise-facial-landmark-detection | 2112.12328 | null | https://arxiv.org/abs/2112.12328v1 | https://arxiv.org/pdf/2112.12328v1.pdf | Robust and Precise Facial Landmark Detection by Self-Calibrated Pose Attention Network | Current fully-supervised facial landmark detection methods have progressed rapidly and achieved remarkable performance. However, they still suffer when coping with faces under large poses and heavy occlusions for inaccurate facial shape constraints and insufficient labeled training samples. In this paper, we propose a ... | ['Hang Sun', 'Xu Wang', 'Witold Pedrycz', 'Zhihui Lai', 'Jie zhou', 'Hui Xi', 'Jun Wan'] | 2021-12-23 | null | null | null | null | ['facial-landmark-detection'] | ['computer-vision'] | [ 9.23120007e-02 1.74440220e-01 -2.57504016e-01 -8.10674012e-01
-6.57081306e-01 -2.47671101e-02 4.26770717e-01 -4.20327395e-01
-2.79564321e-01 3.66999209e-01 -2.39384938e-02 3.86612266e-01
3.54320630e-02 -4.90949512e-01 -8.23630631e-01 -7.65888751e-01
1.40638739e-01 3.42770398e-01 2.03862816e-01 -1.01016887... | [13.392620086669922, 0.43600261211395264] |
ed9e92f3-ed0f-4212-b226-abf43ae2b4d2 | rethinking-content-and-style-exploring-bias-1 | 2102.10544 | null | https://arxiv.org/abs/2102.10544v2 | https://arxiv.org/pdf/2102.10544v2.pdf | Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement | Content and style (C-S) disentanglement intends to decompose the underlying explanatory factors of objects into two independent subspaces. From the unsupervised disentanglement perspective, we rethink content and style and propose a formulation for unsupervised C-S disentanglement based on our assumption that different... | ['Wenjun Zeng', 'Yuwang Wang', 'Tao Yang', 'Xuanchi Ren'] | 2021-02-21 | rethinking-content-and-style-exploring-bias | https://openreview.net/forum?id=KjeUNkU2d26 | https://openreview.net/pdf?id=KjeUNkU2d26 | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [-7.18548968e-02 -1.45294428e-01 -4.47006166e-01 -3.45681101e-01
-4.45058465e-01 -7.59287059e-01 9.18800533e-01 -3.49868357e-01
-2.00512514e-01 3.66684318e-01 6.32252753e-01 6.14305548e-02
-9.73123536e-02 -4.95903820e-01 -7.20893323e-01 -9.50623572e-01
5.57882667e-01 5.92440963e-01 -3.44837159e-01 -1.16025209... | [11.208513259887695, 0.32151710987091064] |
7e7c9789-31eb-4dfa-b07c-665f592b59a9 | an-ensemble-based-system-for-microaneurysm | 1410.8577 | null | http://arxiv.org/abs/1410.8577v1 | http://arxiv.org/pdf/1410.8577v1.pdf | An Ensemble-based System for Microaneurysm Detection and Diabetic Retinopathy Grading | Reliable microaneurysm detection in digital fundus images is still an open
issue in medical image processing. We propose an ensemble-based framework to
improve microaneurysm detection. Unlike the well-known approach of considering
the output of multiple classifiers, we propose a combination of internal
components of mi... | ['Balint Antal', 'Andras Hajdu'] | 2014-10-30 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 1.76060602e-01 2.19572216e-01 1.31563514e-01 -3.46248865e-01
-9.80638325e-01 -4.09162253e-01 5.86622596e-01 4.75288182e-01
-8.60333979e-01 6.97042704e-01 3.17993879e-01 -3.32598627e-01
-3.14601332e-01 -7.59185076e-01 -4.29842532e-01 -7.34416008e-01
-6.03498630e-02 3.98265392e-01 5.03347337e-01 -4.24281519... | [15.830179214477539, -4.009278297424316] |
c2c9717d-28ac-448d-b083-8ea054088883 | neural-shuffle-exchange-networks-sequence | 1907.07897 | null | https://arxiv.org/abs/1907.07897v3 | https://arxiv.org/pdf/1907.07897v3.pdf | Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time | A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O($n^2$) complexity that leads to slow execution for long sequences. We introduce a new Shuffle-Exchange neural network model f... | ['Agris Šostaks', 'Emīls Ozoliņš', 'Kārlis Freivalds'] | 2019-07-18 | null | null | null | null | ['lambada'] | ['natural-language-processing'] | [ 4.97979105e-01 -3.56723249e-01 5.70472926e-02 -4.46765333e-01
-8.49034727e-01 -7.86512971e-01 2.12606728e-01 4.61612970e-01
-8.48750353e-01 5.97849786e-01 2.54447073e-01 -8.13438296e-01
7.36875609e-02 -8.59676242e-01 -1.08225942e+00 -2.93509096e-01
-2.75319129e-01 7.65976489e-01 3.44259322e-01 -7.51751602... | [10.886072158813477, 7.345756530761719] |
1792fb39-86b1-4d92-aac3-fbe8b4354451 | deep-learning-computer-vision-algorithms-for | 2211.01037 | null | https://arxiv.org/abs/2211.01037v1 | https://arxiv.org/pdf/2211.01037v1.pdf | Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing | This paper describes how advanced deep learning based computer vision algorithms are applied to enable real-time on-board sensor processing for small UAVs. Four use cases are considered: target detection, classification and localization, road segmentation for autonomous navigation in GNSS-denied zones, human body segme... | ['Pietro Andronico', 'Alessandro Palmas'] | 2022-11-02 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 1.05595618e-01 -1.64052323e-01 1.83352441e-01 -2.13080540e-01
1.72386318e-01 -6.25284433e-01 4.76044208e-01 -6.41206726e-02
-7.64258623e-01 1.92917123e-01 -8.58995855e-01 -4.68562454e-01
-5.24402224e-02 -7.43850112e-01 -3.56558591e-01 -6.25205338e-01
-5.18240213e-01 4.47207063e-01 3.86739254e-01 -4.99445319... | [8.350276947021484, -1.1417721509933472] |
0d2c2394-208e-4b42-bbbb-3e3284017cea | disentangling-identity-and-pose-for-facial | 2208.08106 | null | https://arxiv.org/abs/2208.08106v1 | https://arxiv.org/pdf/2208.08106v1.pdf | Disentangling Identity and Pose for Facial Expression Recognition | Facial expression recognition (FER) is a challenging problem because the expression component is always entangled with other irrelevant factors, such as identity and head pose. In this work, we propose an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature r... | ['Weihong Deng', 'Jing Jiang'] | 2022-08-17 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.21975213e-01 -5.61074764e-02 -6.72013089e-02 -7.66181588e-01
-4.72836494e-01 -3.93672675e-01 4.25829560e-01 -6.97932720e-01
-2.76365340e-01 5.77813983e-01 4.51780185e-02 5.65202951e-01
2.53610849e-01 -3.57907742e-01 -5.81729233e-01 -1.22115004e+00
3.02286088e-01 8.23774189e-02 -6.44289911e-01 -1.64078668... | [13.034119606018066, 0.3192053735256195] |
46af13e6-cee8-40c0-a618-e25d2979db0c | learning-user-s-confidence-for-active | 2104.07791 | null | https://arxiv.org/abs/2104.07791v1 | https://arxiv.org/pdf/2104.07791v1.pdf | Learning User's confidence for active learning | In this paper, we study the applicability of active learning in operative scenarios: more particularly, we consider the well-known contradiction between the active learning heuristics, which rank the pixels according to their uncertainty, and the user's confidence in labeling, which is related to both the homogeneity o... | ['Jordi Munoz-Mari', 'Devis Tuia'] | 2021-04-15 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 8.62432942e-02 6.23669147e-01 -2.51230448e-01 -4.53469217e-01
-6.94312990e-01 -5.73061168e-01 3.91161352e-01 5.71640193e-01
-7.52962828e-01 7.82744706e-01 -1.25468150e-01 -2.15304524e-01
-5.66294432e-01 -1.13117373e+00 -5.28459966e-01 -9.28151548e-01
-2.01988444e-01 4.03222501e-01 6.79948330e-01 1.26890063... | [9.160164833068848, 1.0837054252624512] |
2c075ee9-0d2c-4906-9a3e-ce178f60be4a | deep-saliency-mapping-for-3d-meshes-and | null | null | https://dl.acm.org/doi/10.1145/3550073 | https://dl.acm.org/doi/pdf/10.1145/3550073 | Deep Saliency Mapping for 3D Meshes and Applications | Nowadays, three-dimensional (3D) meshes are widely used in various applications in different areas (e.g., industry, education, entertainment and safety). The 3D models are captured with multiple RGB-D sensors, and the sampled geometric manifolds are processed, compressed, simplified, stored, and transmitted to be recon... | ['Konstantinos Moustakas', 'Aris Lalos', 'Gerasimos Arvanitis', 'Stavros Nousias'] | 2023-02-06 | null | null | null | acm-transactions-on-multimedia-computing-2 | ['saliency-prediction'] | ['computer-vision'] | [ 6.36981487e-01 1.03409871e-01 -9.84559134e-02 1.57075152e-02
-3.51937592e-01 -6.05793707e-02 5.09378076e-01 5.89513779e-01
-2.02666774e-01 3.15385938e-01 1.74295798e-01 1.14090599e-01
-2.99732149e-01 -1.10745788e+00 -8.84536445e-01 -3.25064600e-01
-8.57944414e-02 2.54442871e-01 5.02102256e-01 -1.88075408... | [8.3895902633667, -3.2433652877807617] |
242cb5c6-19e5-4ba4-a0b8-0b8fd2da6c26 | obpose-leveraging-canonical-pose-for-object | 2206.03591 | null | https://arxiv.org/abs/2206.03591v3 | https://arxiv.org/pdf/2206.03591v3.pdf | ObPose: Leveraging Pose for Object-Centric Scene Inference and Generation in 3D | We present ObPose, an unsupervised object-centric inference and generation model which learns 3D-structured latent representations from RGB-D scenes. Inspired by prior art in 2D representation learning, ObPose considers a factorised latent space, separately encoding object location (where) and appearance (what). ObPose... | ['Ingmar Posner', 'Oiwi Parker Jones', 'Yizhe Wu'] | 2022-06-07 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.62004352e-01 5.12090147e-01 -3.39794927e-03 -4.63139981e-01
-5.56734324e-01 -9.92013097e-01 1.19155574e+00 -1.52872145e-01
-8.67835209e-02 2.13060528e-01 3.69981527e-01 6.29083067e-02
-1.83101267e-01 -9.74708855e-01 -1.30501199e+00 -7.78084219e-01
1.48035526e-01 8.10401976e-01 2.91307531e-02 6.10149764... | [9.021594047546387, -3.0720534324645996] |
e0dfd4d6-50b9-4153-9c5c-cff6ce803e05 | comparing-methods-for-twitter-sentiment | 1505.02973 | null | http://arxiv.org/abs/1505.02973v1 | http://arxiv.org/pdf/1505.02973v1.pdf | Comparing methods for Twitter Sentiment Analysis | This work extends the set of works which deal with the popular problem of
sentiment analysis in Twitter. It investigates the most popular document
("tweet") representation methods which feed sentiment evaluation mechanisms. In
particular, we study the bag-of-words, n-grams and n-gram graphs approaches and
for each of t... | ['Dimosthenis Anagnostopoulos', 'Evangelos Psomakelis', 'Theodora Varvarigou', 'Konstantinos Tserpes'] | 2015-05-12 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.54278332e-01 2.65295058e-01 -5.63847065e-01 -5.23503423e-01
-1.31334767e-01 -5.92965484e-01 1.03248990e+00 9.51970875e-01
-5.06155729e-01 6.28324389e-01 4.72149581e-01 -6.74259305e-01
-1.26707256e-01 -9.63562906e-01 -1.92656413e-01 -7.66540706e-01
-1.20950714e-01 5.89337766e-01 8.58142599e-02 -8.37260962... | [11.093843460083008, 6.9831318855285645] |
f2adeee1-4e95-4229-9e0a-8c06754a7b5e | action-gpt-leveraging-large-scale-language | 2211.15603 | null | https://arxiv.org/abs/2211.15603v3 | https://arxiv.org/pdf/2211.15603v3.pdf | Action-GPT: Leveraging Large-scale Language Models for Improved and Generalized Action Generation | We introduce Action-GPT, a plug-and-play framework for incorporating Large Language Models (LLMs) into text-based action generation models. Action phrases in current motion capture datasets contain minimal and to-the-point information. By carefully crafting prompts for LLMs, we generate richer and fine-grained descript... | ['Ravi Kiran Sarvadevabhatla', 'Shubh Maheshwari', 'Sai Shashank Kalakonda'] | 2022-11-28 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 2.16541137e-03 7.37300515e-02 -2.74825454e-01 -2.21027061e-02
-1.02005351e+00 -7.82663584e-01 1.06104183e+00 -2.30966777e-01
-1.84045121e-01 7.55744815e-01 9.15474355e-01 -1.98288947e-01
-1.67995095e-02 -6.64536178e-01 -6.53213143e-01 -4.82331097e-01
1.06017105e-01 3.50440830e-01 3.97789061e-01 -2.27682874... | [7.292294979095459, -0.11916562169790268] |
dc6911b1-5f54-44a6-aab9-3202fdd8942b | interpreting-hidden-semantics-in-the | 2303.06652 | null | https://arxiv.org/abs/2303.06652v1 | https://arxiv.org/pdf/2303.06652v1.pdf | Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network | Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We propose a novel approach, named Relevance Flow, to interpret the hidden semantics of 3D point cloud classification neural networks. It deli... | ['Cheng Wang', 'Shijun Zheng', 'Minghao Liu', 'Weiquan Liu'] | 2023-03-12 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-9.71395895e-02 2.25086734e-01 -1.07884794e-01 -4.89128530e-01
1.65612772e-02 -7.59802580e-01 3.07390124e-01 -8.93980861e-02
1.54338151e-01 -2.43346006e-01 -4.90258485e-01 -5.25257945e-01
9.20129120e-02 -9.49381232e-01 -1.05805278e+00 -7.54153728e-01
-1.80881381e-01 4.96825844e-01 4.80413675e-01 -6.54727444... | [7.901861190795898, -3.716900110244751] |
72ef2892-1060-4b51-af6b-82cffd578214 | crowdcam-dynamic-region-segmentation | 1811.11455 | null | https://arxiv.org/abs/1811.11455v2 | https://arxiv.org/pdf/1811.11455v2.pdf | CrowdCam: Dynamic Region Segmentation | We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-col... | ['Yael Moses', 'Shai Avidan', 'Nir Zarrabi'] | 2018-11-28 | null | null | null | null | ['dynamic-region-segmentation'] | ['computer-vision'] | [ 1.98256791e-01 -1.36692122e-01 3.85772079e-01 -4.57525134e-01
-3.47794265e-01 -8.73345673e-01 7.03395009e-01 2.55337417e-01
-4.55435812e-01 2.56323993e-01 1.10951602e-01 6.01979457e-02
3.14078778e-01 -7.07342803e-01 -7.68045545e-01 -4.78049934e-01
7.84345269e-02 5.54256499e-01 1.08823836e+00 -3.42073232... | [8.211233139038086, -1.479332447052002] |
32540187-c71d-4df9-b6cd-bda64b34701c | mid-tracking-and-identifying-people-with | null | null | https://doi.org/10.1109/DCOSS.2019.00028 | http://www.cs.ox.ac.uk/files/10889/%5BDCOSS19%5DmID.pdf | mID: Tracking and Identifying People with Millimeter Wave Radar | The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workpla... | ['Jianan Wang', 'and Andrew Markham', 'Niki Trigoni', 'Chris Xiaoxuan Lu', 'Wei Wang', 'Peijun Zhao', 'Changhao Chen'] | 2019-05-29 | null | null | null | 2019-15th-international-conference-on | ['rf-based-visual-tracking'] | ['computer-vision'] | [ 1.98866293e-01 -1.99782029e-01 1.77239954e-01 -1.89196337e-02
-8.41602683e-01 -7.88059950e-01 4.29572642e-01 -1.19639456e-01
-4.13867563e-01 8.51480246e-01 3.87266092e-02 -2.94696182e-01
-1.79726154e-01 -6.40058100e-01 -4.71045434e-01 -5.27954340e-01
-1.85899466e-01 2.67667115e-01 8.49495158e-02 3.25217068... | [6.851943016052246, 0.47998136281967163] |
bfb9f08d-0683-42ef-9210-b42939bbb0cc | lightweight-monocular-depth-estimation-via | 2306.05682 | null | https://arxiv.org/abs/2306.05682v1 | https://arxiv.org/pdf/2306.05682v1.pdf | Lightweight Monocular Depth Estimation via Token-Sharing Transformer | Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera can be deployable at a low cost and compact size. Due to its significant and growing needs, many lightweight monocular depth estimation networ... | ['Junmo Kim', 'Sung-Sik Cho', 'Yeong-Hun Park', 'Eojindl Yi', 'Hyounguk Shon', 'Jae Young Lee', 'Dong-Jae Lee'] | 2023-06-09 | null | null | null | null | ['depth-estimation', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [-3.08962256e-01 -3.79810214e-01 -1.03853561e-01 -3.49613130e-01
-3.66138592e-02 -1.21823035e-01 1.64954811e-01 -4.39968765e-01
-8.55182528e-01 4.58112031e-01 -4.98167902e-01 -3.20828438e-01
3.08290064e-01 -1.05504715e+00 -6.69151902e-01 -5.39752066e-01
1.01546042e-01 1.24554045e-01 6.77344859e-01 2.17956394... | [8.711319923400879, -2.3344287872314453] |
15211e35-e879-4548-a008-414db150cec6 | leveraging-unlabelled-data-in-multiple | 2305.00249 | null | https://arxiv.org/abs/2305.00249v1 | https://arxiv.org/pdf/2305.00249v1.pdf | Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions | Data-driven approaches for remote detection of Parkinson's Disease and its motor symptoms have proliferated in recent years, owing to the potential clinical benefits of early diagnosis. The holy grail of such approaches is the free-living scenario, in which data are collected continuously and unobtrusively during every... | ['Anastasios Delopoulos', 'Alexandros Papadopoulos'] | 2023-04-29 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.71589565e-01 3.49451602e-01 -1.92548707e-01 -2.98624158e-01
-1.44751692e+00 -3.42408627e-01 2.88390994e-01 -1.56134754e-01
-6.22160196e-01 1.08760643e+00 -5.84742101e-03 -1.48187084e-02
-2.79426277e-01 -3.98496509e-01 -7.30828285e-01 -8.41650307e-01
-3.30560595e-01 6.59038961e-01 2.50445336e-01 -2.65029609... | [14.633217811584473, -2.155263662338257] |
93a2ab0f-f6d8-4a8d-a423-21ab883b74a4 | online-unmixing-of-multitemporal | 1510.05893 | null | http://arxiv.org/abs/1510.05893v3 | http://arxiv.org/pdf/1510.05893v3.pdf | Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability | Hyperspectral unmixing is aimed at identifying the reference spectral
signatures composing an hyperspectral image and their relative abundance
fractions in each pixel. In practice, the identified signatures may vary
spectrally from an image to another due to varying acquisition conditions, thus
inducing possibly signif... | ['Jean-Yves Tourneret', 'Nicolas Dobigeon', 'Pierre-Antoine Thouvenin'] | 2015-10-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 1.03471565e+00 -7.60802627e-01 1.95080563e-01 -9.12646577e-02
-6.31760836e-01 -7.98317015e-01 6.08356953e-01 7.94987753e-02
-2.52237737e-01 8.22732449e-01 -4.20622081e-01 -1.91933095e-01
-4.38267946e-01 -5.62076092e-01 -5.02553463e-01 -1.33771443e+00
8.50813091e-03 4.51204300e-01 -5.20583749e-01 1.44665778... | [10.068131446838379, -2.0541434288024902] |
1d793400-9c22-4787-8c5d-2227289f19d3 | voice-conversion-with-just-nearest-neighbors | 2305.18975 | null | https://arxiv.org/abs/2305.18975v1 | https://arxiv.org/pdf/2305.18975v1.pdf | Voice Conversion With Just Nearest Neighbors | Any-to-any voice conversion aims to transform source speech into a target voice with just a few examples of the target speaker as a reference. Recent methods produce convincing conversions, but at the cost of increased complexity -- making results difficult to reproduce and build on. Instead, we keep it simple. We prop... | ['Herman Kamper', 'Benjamin van Niekerk', 'Matthew Baas'] | 2023-05-30 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [ 2.11620212e-01 5.28678223e-02 -1.14083745e-01 -2.27692619e-01
-1.35883367e+00 -7.40874469e-01 3.54789972e-01 -3.31954271e-01
1.38399899e-01 6.01654232e-01 8.21036756e-01 -2.95569599e-01
3.39061052e-01 -4.38235164e-01 -5.38021863e-01 -4.48339283e-01
4.04392779e-01 4.41008508e-02 -2.57741399e-02 -2.46423557... | [14.922013282775879, 6.564141273498535] |
e6aa28b3-0253-418e-88c1-e7d10d34aec0 | spam-t5-benchmarking-large-language-models | 2304.01238 | null | https://arxiv.org/abs/2304.01238v3 | https://arxiv.org/pdf/2304.01238v3.pdf | Spam-T5: Benchmarking Large Language Models for Few-Shot Email Spam Detection | This paper investigates the effectiveness of large language models (LLMs) in email spam detection by comparing prominent models from three distinct families: BERT-like, Sentence Transformers, and Seq2Seq. Additionally, we examine well-established machine learning techniques for spam detection, such as Na\"ive Bayes and... | ['Sean Moran', 'Maxime Labonne'] | 2023-04-03 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-7.78590422e-03 -6.09379053e-01 -1.75537914e-01 -2.94729233e-01
-9.30504739e-01 -5.25312066e-01 9.14740026e-01 -7.54669085e-02
-7.04138219e-01 6.06630743e-01 2.49232396e-01 -5.89414597e-01
1.23251043e-01 -4.89015192e-01 -2.05261990e-01 -3.08413208e-01
1.38810888e-01 4.75517005e-01 5.79044759e-01 -5.26874244... | [7.870345592498779, 9.9771728515625] |
6d61b2be-e133-4195-8676-969097a25696 | techniques-for-automated-machine-learning | 1907.08908 | null | https://arxiv.org/abs/1907.08908v1 | https://arxiv.org/pdf/1907.08908v1.pdf | Techniques for Automated Machine Learning | Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without e... | ['Yi-Wei Chen', 'Qingquan Song', 'Xia Hu'] | 2019-07-21 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-3.42639565e-01 5.36532961e-02 -1.34796292e-01 -3.04217637e-01
-7.94821680e-01 -4.22820896e-01 1.31585106e-01 -9.69231874e-02
-3.23447168e-01 9.06977296e-01 -5.16517460e-01 -8.10938850e-02
-6.13056600e-01 -6.06755257e-01 -5.41899323e-01 -7.84569621e-01
1.23636145e-02 8.45818162e-01 -2.47846410e-01 3.43335308... | [6.534095287322998, 3.9928340911865234] |
a16c8b0c-0438-4cda-8d36-dbf89f5c4a74 | ensemble-based-transfer-learning-for-low | 2105.07622 | null | https://arxiv.org/abs/2105.07622v1 | https://arxiv.org/pdf/2105.07622v1.pdf | Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation | Quality Estimation (QE) of Machine Translation (MT) is a task to estimate the quality scores for given translation outputs from an unknown MT system. However, QE scores for low-resource languages are usually intractable and hard to collect. In this paper, we focus on the Sentence-Level QE Shared Task of the Fifth Confe... | ['Yi-Chieh Liu', 'Yung-An Hsieh', 'Ting-Wei Wu'] | 2021-05-17 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-4.23406772e-02 -4.33647513e-01 -2.92688400e-01 -3.38563263e-01
-1.95005679e+00 -8.24793577e-01 4.87148970e-01 -3.06562424e-01
-4.49484050e-01 1.24631584e+00 3.70656215e-02 -7.58871853e-01
3.67803514e-01 -2.40905777e-01 -1.09784126e+00 -3.55266184e-01
1.58364177e-01 6.19145811e-01 -2.66448170e-01 -4.30069566... | [11.64050006866455, 10.295262336730957] |
5fa320ef-420f-47d8-8b9f-db76136432c3 | ntire-2021-challenge-on-quality-enhancement | 2104.10782 | null | https://arxiv.org/abs/2104.10782v5 | https://arxiv.org/pdf/2104.10782v5.pdf | NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study | This paper introduces a novel dataset for video enhancement and studies the state-of-the-art methods of the NTIRE 2021 challenge on quality enhancement of compressed video. The challenge is the first NTIRE challenge in this direction, with three competitions, hundreds of participants and tens of proposed solutions. Our... | ['Radu Timofte', 'Ren Yang'] | 2021-04-21 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [-1.09278830e-02 -6.75451219e-01 -3.25442046e-01 -1.93242997e-01
-8.37025702e-01 -3.65834713e-01 2.96009660e-01 -4.58286256e-01
-4.74544078e-01 6.28161967e-01 8.33072245e-01 -4.87604886e-02
2.33653691e-02 -3.67480576e-01 -5.82545340e-01 -2.22055897e-01
-5.76159894e-01 -3.95341933e-01 2.66299695e-01 -6.85352147... | [11.607414245605469, -1.7890113592147827] |
b2c70e8f-02bc-4642-81c4-a8ca8789fe31 | 4seasons-benchmarking-visual-slam-and-long | 2301.01147 | null | https://arxiv.org/abs/2301.01147v1 | https://arxiv.org/pdf/2301.01147v1.pdf | 4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions | In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The proposed benchmark provides drastic appearance variations caused by seasonal changes and diverse weather and illumination conditions. While s... | ['Daniel Cremers', 'Niclas Zeller', 'Rui Wang', 'Nan Yang', 'Patrick Wenzel'] | 2022-12-31 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-4.84862715e-01 -4.46369261e-01 -2.84295857e-01 -6.23218000e-01
-8.31475317e-01 -6.36508822e-01 7.32876539e-01 1.49891777e-02
-5.69445431e-01 9.09601808e-01 -1.92394063e-01 -3.05066317e-01
2.11686045e-01 -5.27030468e-01 -8.21503818e-01 -4.91393149e-01
-3.01320702e-01 7.22390294e-01 4.41376507e-01 -7.13505328... | [7.354974746704102, -2.0825212001800537] |
8a7c2fa1-638e-41ab-810a-ee72db84b7fd | recap-retrieval-enhanced-context-aware-prefix | 2306.07206 | null | https://arxiv.org/abs/2306.07206v1 | https://arxiv.org/pdf/2306.07206v1.pdf | RECAP: Retrieval-Enhanced Context-Aware Prefix Encoder for Personalized Dialogue Response Generation | Endowing chatbots with a consistent persona is essential to an engaging conversation, yet it remains an unresolved challenge. In this work, we propose a new retrieval-enhanced approach for personalized response generation. Specifically, we design a hierarchical transformer retriever trained on dialogue domain data to p... | ['Jonathan May', 'Xuezhe Ma', 'Marjorie Freedman', 'Hyundong J. Cho', 'Shuai Liu'] | 2023-06-12 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 2.65817821e-01 3.70045722e-01 6.65729493e-02 -6.41852975e-01
-1.48110604e+00 -5.07006347e-01 8.76666963e-01 -5.92975855e-01
-2.96675414e-01 1.07343245e+00 1.03936553e+00 7.67588913e-02
2.59426296e-01 -4.59741771e-01 -2.53293484e-01 -1.20311633e-01
4.32978392e-01 9.13029373e-01 5.53938672e-02 -8.87207568... | [12.609753608703613, 8.204733848571777] |
c9d741a4-6d90-4991-8558-21824e24d160 | exploiting-open-ie-for-deriving-multiple | null | null | https://aclanthology.org/R19-1144 | https://aclanthology.org/R19-1144.pdf | Exploiting Open IE for Deriving Multiple Premises Entailment Corpus | Natural language inference (NLI) is a key part of natural language understanding. The NLI task is defined as a decision problem whether a given sentence {--} hypothesis {--} can be inferred from a given text. Typically, we deal with a text consisting of just a single premise/single sentence, which is called a single pr... | ["Jakub Kl{\\'\\i}mek", "Martin V{\\'\\i}ta"] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['open-information-extraction'] | ['natural-language-processing'] | [ 5.72227895e-01 7.12207198e-01 9.44680721e-02 -6.83705688e-01
-9.06187952e-01 -5.53667605e-01 9.83599067e-01 4.83567506e-01
-5.76382577e-01 1.20203340e+00 1.46141991e-01 -4.86931860e-01
1.63202584e-02 -6.69295073e-01 -1.10511005e+00 -1.91973656e-01
2.48216525e-01 6.63524747e-01 3.91256005e-01 -2.75326610... | [9.993429183959961, 8.601542472839355] |
62703e8b-61c6-44f9-8b8b-2db2e4b89214 | streaming-submodular-maximization-under-a-k | 2002.03352 | null | https://arxiv.org/abs/2002.03352v1 | https://arxiv.org/pdf/2002.03352v1.pdf | Streaming Submodular Maximization under a $k$-Set System Constraint | In this paper, we propose a novel framework that converts streaming algorithms for monotone submodular maximization into streaming algorithms for non-monotone submodular maximization. This reduction readily leads to the currently tightest deterministic approximation ratio for submodular maximization subject to a $k$-ma... | ['Amin Karbasi', 'Moran Feldman', 'Ran Haba', 'Ehsan Kazemi'] | 2020-02-09 | null | null | null | null | ['movie-recommendation', 'data-summarization'] | ['miscellaneous', 'miscellaneous'] | [ 1.10535488e-01 5.32860756e-01 -5.94585955e-01 -3.47130030e-01
-8.71742964e-01 -9.43384171e-01 -3.45304757e-01 4.29586411e-01
-2.07125366e-01 8.45363021e-01 3.07141066e-01 5.47186397e-02
-8.01173627e-01 -9.61295128e-01 -7.79440582e-01 -5.94367027e-01
-6.21136725e-01 7.98696399e-01 -4.26657163e-02 -3.87835503... | [6.533705711364746, 4.9190263748168945] |
cad533f8-c6bb-499a-aa97-061f20f0a444 | exploiting-pseudo-image-captions-for | 2305.05496 | null | https://arxiv.org/abs/2305.05496v1 | https://arxiv.org/pdf/2305.05496v1.pdf | Exploiting Pseudo Image Captions for Multimodal Summarization | Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI... | ['Shikun Zhang', 'Jinan Sun', 'Wei Ye', 'Rui Xie', 'Chaoya Jiang'] | 2023-05-09 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 4.43277985e-01 1.04926057e-01 -1.20099835e-01 -4.47880089e-01
-1.39302945e+00 -5.49997628e-01 7.86540449e-01 1.14985727e-01
-9.51397121e-01 6.13510489e-01 6.69875816e-02 -2.03338981e-01
-1.36439011e-01 -3.75354618e-01 -8.60925853e-01 -7.86711216e-01
-3.58892530e-02 1.50074646e-01 -5.49956970e-02 9.17122290... | [10.801566123962402, 1.5196815729141235] |
87ff4cac-6a22-4a81-b391-1ec30a9e12e0 | stylizednerf-consistent-3d-scene-stylization | 2205.12183 | null | https://arxiv.org/abs/2205.12183v2 | https://arxiv.org/pdf/2205.12183v2.pdf | StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning | 3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered from different views. Directly applying methods for image or video stylization to 3D scenes cannot achieve such consistency. Thanks to recentl... | ['Lin Gao', 'Yu-Kun Lai', 'Yu-Jie Yuan', 'Yue He', 'Yi-Hua Huang'] | 2022-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Huang_StylizedNeRF_Consistent_3D_Scene_Stylization_As_Stylized_NeRF_via_2D-3D_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Huang_StylizedNeRF_Consistent_3D_Scene_Stylization_As_Stylized_NeRF_via_2D-3D_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-stylization'] | ['computer-vision'] | [ 2.56393045e-01 1.51323736e-01 -2.37731114e-02 -3.58895063e-01
-4.62955385e-01 -6.31150186e-01 6.71089530e-01 -6.03704453e-01
3.70207950e-02 4.97312248e-01 5.53593924e-03 -7.83623308e-02
2.50439614e-01 -9.38724935e-01 -1.16150868e+00 -5.92317283e-01
5.18637955e-01 5.33095539e-01 1.00392848e-01 1.08733162... | [9.300220489501953, -3.2195825576782227] |
b442f93c-e899-4827-85ec-5021bcb1fe03 | virtual-to-real-reinforcement-learning-for | 1704.03952 | null | http://arxiv.org/abs/1704.03952v4 | http://arxiv.org/pdf/1704.03952v4.pdf | Virtual to Real Reinforcement Learning for Autonomous Driving | Reinforcement learning is considered as a promising direction for driving
policy learning. However, training autonomous driving vehicle with
reinforcement learning in real environment involves non-affordable
trial-and-error. It is more desirable to first train in a virtual environment
and then transfer to the real envi... | ['Ziyan Wang', 'Yurong You', 'Cewu Lu', 'Xinlei Pan'] | 2017-04-13 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [-3.28355581e-02 4.06138867e-01 -1.43349618e-01 -3.93542320e-01
-2.70286560e-01 -3.95128548e-01 5.84127843e-01 -6.74695253e-01
-6.50693655e-01 1.05311954e+00 -2.74514407e-01 -6.85082138e-01
3.78822654e-01 -9.97938156e-01 -1.33199239e+00 -5.45623899e-01
4.51945625e-02 4.68673199e-01 5.22606194e-01 -7.39539444... | [5.057966709136963, 1.2331249713897705] |
c6bf80cc-4819-4259-a98e-5b45f0b2c4fb | improving-accuracy-of-zero-shot-action | 2301.08874 | null | https://arxiv.org/abs/2301.08874v2 | https://arxiv.org/pdf/2301.08874v2.pdf | Improving Zero-Shot Action Recognition using Human Instruction with Text Description | Zero-shot action recognition, which recognizes actions in videos without having received any training examples, is gaining wide attention considering it can save labor costs and training time. Nevertheless, the performance of zero-shot learning is still unsatisfactory, which limits its practical application. To solve t... | ['Kazuhiko Kawamoto', 'Hiroshi Kera', 'Nan Wu'] | 2023-01-21 | null | null | null | null | ['zero-shot-action-recognition', 'text-matching'] | ['computer-vision', 'natural-language-processing'] | [ 3.10404450e-01 -3.07906955e-01 -4.69492048e-01 -4.02742893e-01
-5.87752104e-01 -2.72188466e-02 3.07667345e-01 -1.88732237e-01
-3.86701971e-01 5.15534878e-01 2.46617168e-01 5.98254874e-02
4.56551053e-02 -6.78533673e-01 -2.49334350e-01 -7.98973024e-01
3.70629787e-01 4.51990142e-02 6.13572240e-01 6.60858676... | [8.484374046325684, 0.7875861525535583] |
c7b3490a-0a54-4c86-94f4-e55929c460b5 | cloze-test-helps-effective-video-anomaly | 2008.11988 | null | https://arxiv.org/abs/2008.11988v1 | https://arxiv.org/pdf/2008.11988v1.pdf | Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events | As a vital topic in media content interpretation, video anomaly detection (VAD) has made fruitful progress via deep neural network (DNN). However, existing methods usually follow a reconstruction or frame prediction routine. They suffer from two gaps: (1) They cannot localize video activities in a both precise and comp... | ['Zhiping Cai', 'Siqi Wang', 'En Zhu', 'Jianping Yin', 'Guang Yu', 'Chuanfu Xu', 'Marius Kloft'] | 2020-08-27 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [ 9.31607038e-02 -4.86237645e-01 -2.12977692e-01 -1.90537512e-01
-5.07091105e-01 -4.41206455e-01 6.55165017e-01 -2.85714474e-02
-2.23761395e-01 4.52290952e-01 4.84351903e-01 -2.05102488e-01
2.99990386e-01 -5.80221117e-01 -8.51213038e-01 -5.35871625e-01
-8.39101449e-02 -2.61560649e-01 4.56515223e-01 -6.74980134... | [8.23250961303711, 1.158563256263733] |
86c9ce52-d4a4-4762-a90f-285beda0f411 | mkiou-loss-towards-accurate-oriented-object | 2206.15109 | null | https://arxiv.org/abs/2206.15109v1 | https://arxiv.org/pdf/2206.15109v1.pdf | MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images | Oriented bounding box regression is crucial for oriented object detection. However, regression-based methods often suffer from boundary problems and the inconsistency between loss and evaluation metrics. In this paper, a modulated Kalman IoU loss of approximate SkewIoU is proposed, named MKIoU. To avoid boundary proble... | ['Linlin Ou', 'Mi Lin', 'Jiangping Lu', 'Xinyi Yu'] | 2022-06-30 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.18276432e-01 -3.60450476e-01 2.51547426e-01 -4.52662617e-01
-4.51690048e-01 -3.45966399e-01 2.73102790e-01 1.76442526e-02
-3.92415226e-01 4.89730448e-01 -2.88339138e-01 -6.71094358e-02
-2.26016998e-01 -9.63292956e-01 -4.75931853e-01 -9.68334138e-01
-5.31907529e-02 -2.05116898e-01 6.85586274e-01 -1.69953838... | [8.720860481262207, -0.8007519841194153] |
f3848a3f-3d2c-442b-a520-360d3b011417 | listening-to-the-world-improves-speech | 1710.08377 | null | http://arxiv.org/abs/1710.08377v1 | http://arxiv.org/pdf/1710.08377v1.pdf | Listening to the World Improves Speech Command Recognition | We study transfer learning in convolutional network architectures applied to
the task of recognizing audio, such as environmental sound events and speech
commands. Our key finding is that not only is it possible to transfer
representations from an unrelated task like environmental sound classification
to a voice-focuse... | ['Brian McMahan', 'Delip Rao'] | 2017-10-23 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 1.85606539e-01 -7.56425783e-02 4.91679877e-01 -4.93509263e-01
-1.03092790e+00 -5.62313139e-01 3.64993632e-01 -7.37649128e-02
-6.21491909e-01 4.52441543e-01 4.22776073e-01 -4.99800682e-01
6.33276403e-02 -7.66838968e-01 -9.36457694e-01 -3.87805969e-01
-3.12951922e-01 5.13765663e-02 4.74027991e-01 -1.57736808... | [15.30974006652832, 5.374879837036133] |
e2808306-4ad1-4739-b798-fc8a0d890708 | handmime-sign-language-fingerspelling | 2209.05135 | null | https://arxiv.org/abs/2209.05135v3 | https://arxiv.org/pdf/2209.05135v3.pdf | Signs of Language: Embodied Sign Language Fingerspelling Acquisition from Demonstrations for Human-Robot Interaction | Learning fine-grained movements is a challenging topic in robotics, particularly in the context of robotic hands. One specific instance of this challenge is the acquisition of fingerspelling sign language in robots. In this paper, we propose an approach for learning dexterous motor imitation from video examples without... | ['Angelo Cangelosi', 'Aphrodite Galata', 'Federico Tavella'] | 2022-09-12 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.10944742e-01 2.05007419e-01 -6.49878085e-02 1.13764912e-01
-4.83523279e-01 -6.97417915e-01 6.46311283e-01 -9.41668391e-01
-5.45821548e-01 6.96842432e-01 -8.46890956e-02 -9.78566483e-02
-3.06294769e-01 -4.09639664e-02 -1.23916328e+00 -6.08659208e-01
1.01220518e-01 6.66953206e-01 2.50683039e-01 -2.25491881... | [4.697591304779053, 0.6375033259391785] |
815ddae2-f890-4143-9f3f-dc9dcb7d1cc4 | recovering-arrhythmic-eeg-transients-from | 2303.07683 | null | https://arxiv.org/abs/2303.07683v1 | https://arxiv.org/pdf/2303.07683v1.pdf | Recovering Arrhythmic EEG Transients from Their Stochastic Interference | Traditionally, the neuronal dynamics underlying electroencephalograms (EEG) have been understood as arising from \textit{rhythmic oscillators with varying degrees of synchronization}. This dominant metaphor employs frequency domain EEG analysis to identify the most prominent populations of neuronal current sources in t... | ['Kaspar E. Vogt', 'Masashi Yanagisawa', 'Juan-Carlos Letelier', 'Xifang Hayashi', 'Olga Malyshevskaya', 'GoEun Han', 'Hiroyasu Ando', 'Javier Díaz'] | 2023-03-14 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.00972456e-01 -3.43652308e-01 5.89853227e-01 9.46537554e-02
-1.32470071e-01 -6.39058292e-01 4.69619393e-01 3.17699537e-02
-3.81242752e-01 8.85545850e-01 -4.00582403e-02 3.79061006e-04
-5.32767832e-01 -4.92912292e-01 -5.30207753e-01 -1.35881126e+00
-5.75279951e-01 -1.70510560e-01 2.74473112e-02 -2.76061267... | [12.951003074645996, 3.4734387397766113] |
6d8132ff-e78c-424b-af84-14af871e7518 | hypliloc-towards-effective-lidar-pose | 2304.00932 | null | https://arxiv.org/abs/2304.00932v2 | https://arxiv.org/pdf/2304.00932v2.pdf | HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion | LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high computation storage costs and can lead to globally inaccurate pose estimations if the database is too sparse. On the other hand, pose regress... | ['Wee Peng Tay', 'Yang song', 'Kai Zhao', 'Wei Wang', 'Rui She', 'Qiyu Kang', 'Sijie Wang'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_HypLiLoc_Towards_Effective_LiDAR_Pose_Regression_With_Hyperbolic_Fusion_CVPR_2023_paper.pdf | cvpr-2023-1 | ['lidar-absolute-pose-regression', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [-2.02351838e-01 -4.85749662e-01 -3.02261591e-01 -4.78913099e-01
-1.18010795e+00 -4.85842437e-01 5.18552899e-01 3.66387255e-02
-4.78451818e-01 5.36180019e-01 -7.51056224e-02 -1.93575650e-01
-2.32290402e-01 -8.93740594e-01 -8.44860494e-01 -6.54345036e-01
2.72682726e-01 3.90347630e-01 1.80025548e-01 -2.01884359... | [7.552072048187256, -2.274656057357788] |
8adfe2b2-6a61-451c-ae09-cd0d3df0556f | learning-multi-modal-brain-tumor-segmentation | 2208.12781 | null | https://arxiv.org/abs/2208.12781v1 | https://arxiv.org/pdf/2208.12781v1.pdf | Learning Multi-Modal Brain Tumor Segmentation from Privileged Semi-Paired MRI Images with Curriculum Disentanglement Learning | Due to the difficulties of obtaining multimodal paired images in clinical practice, recent studies propose to train brain tumor segmentation models with unpaired images and capture complementary information through modality translation. However, these models cannot fully exploit the complementary information from diffe... | ['Rui Li', 'Jia Wei', 'Zecheng Liu'] | 2022-08-26 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 9.01438117e-01 1.11994542e-01 -5.63840926e-01 -5.01462102e-01
-1.30396259e+00 -7.01629758e-01 5.33887088e-01 1.46808904e-02
-7.09116876e-01 8.09053123e-01 2.11105317e-01 -3.75270009e-01
-8.50378051e-02 -4.65305895e-01 -6.69358194e-01 -9.16572630e-01
4.54003900e-01 3.50604147e-01 -1.10086612e-01 1.94119900... | [14.511727333068848, -2.1272292137145996] |
90c3ff36-aa2f-4b2a-ab82-1c202adc8524 | soccernet-a-scalable-dataset-for-action | 1804.04527 | null | http://arxiv.org/abs/1804.04527v2 | http://arxiv.org/pdf/1804.04527v2.pdf | SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos | In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from on... | ['Tarek Dghaily', 'Silvio Giancola', 'Mohieddine Amine', 'Bernard Ghanem'] | 2018-04-12 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [-6.15672953e-02 -4.66210037e-01 -6.30350113e-01 -1.26287252e-01
-1.15316486e+00 -7.23326325e-01 5.36723554e-01 1.35104150e-01
-7.70349026e-01 6.48447931e-01 3.91182840e-01 2.95847416e-01
9.17498767e-02 -4.24294770e-01 -8.26404154e-01 -3.98178041e-01
-4.52984333e-01 1.78733751e-01 8.83720577e-01 -2.51554012... | [7.972524642944336, 0.2156038135290146] |
e032a6f1-e1b3-452b-aa76-222e74b8d310 | taxoexpan-self-supervised-taxonomy-expansion | 2001.09522 | null | https://arxiv.org/abs/2001.09522v1 | https://arxiv.org/pdf/2001.09522v1.pdf | TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network | Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous eff... | ['Chenyan Xiong', 'Kuansan Wang', 'Jiawei Han', 'Zhihong Shen', 'Jiaming Shen', 'Chi Wang'] | 2020-01-26 | null | null | null | null | ['product-recommendation', 'taxonomy-expansion'] | ['miscellaneous', 'natural-language-processing'] | [ 7.71555584e-03 -2.16840245e-02 -7.26357937e-01 -5.63830614e-01
-4.23899256e-02 -6.60587788e-01 2.73990184e-01 4.81077343e-01
-1.81899205e-01 2.97025830e-01 1.42085642e-01 -4.35387701e-01
-3.37968916e-01 -1.25865209e+00 -4.12408680e-01 -2.17140734e-01
4.62233201e-02 7.65272677e-01 3.03984463e-01 -4.43915188... | [9.2421875, 7.9513983726501465] |
1babe5f0-2671-49a0-9ae8-3178e435e7c4 | data-expansion-using-wordnet-based-semantic | null | null | https://aclanthology.org/2022.lrec-1.187 | https://aclanthology.org/2022.lrec-1.187.pdf | Data Expansion Using WordNet-based Semantic Expansion and Word Disambiguation for Cyberbullying Detection | Automatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent structure of cyberbullying and the lack of labeled large-scale corpus, enabling efficient machine-learning-based tools including neural networks. This paper advocates a data augmentat... | ['Muhidin Mohamed', 'Mourad Oussalah', 'Djamila Romaissa Beddiar', 'Md Saroar Jahan'] | null | null | null | null | lrec-2022-6 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.09521151e-01 3.77788961e-01 -2.30173081e-01 -2.74146080e-01
-5.83710194e-01 -1.19307257e-01 3.65349323e-01 6.08756661e-01
-8.16248834e-01 6.56017363e-01 3.01547110e-01 -1.90271869e-01
-2.92669922e-01 -7.89055705e-01 -4.12075728e-01 -3.62859607e-01
3.51961218e-02 2.62185961e-01 -1.08117551e-01 -6.09197259... | [8.788865089416504, 10.503437042236328] |
dfbf7af2-bc91-4c8a-addd-37e317a789e2 | collaborative-filtering-via-heterogeneous | null | null | http://fange.pro/files/2021Collaborative.pdf | http://fange.pro/files/2021Collaborative.pdf | Collaborative filtering via heterogeneous neural networks | After being proved extremely useful in many applications, the network embedding has played a critical role in the network analysis. Most of recent works usually model the network by minimizing the joint probability that the target node co-occurs with its neighboring nodes. These methods may fail to capture the personal... | ['Wei Zeng', 'Changjie Fan', 'Kai Wang', 'Jianrong Tao', 'Biao Geng', 'Ge Fan'] | 2022-02-01 | null | null | null | neurocomputing-2022-2 | ['network-embedding', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-1.01079218e-01 1.79904044e-01 -7.87872195e-01 -2.15167522e-01
3.91601659e-02 -4.57681179e-01 4.67550755e-01 3.68557632e-01
-4.66942713e-02 5.79023957e-01 2.86161751e-01 -1.21803962e-01
-6.86260104e-01 -1.13921714e+00 -6.76495016e-01 -7.02106833e-01
-4.94718701e-01 6.77150071e-01 2.73589700e-01 -1.36319265... | [7.257582187652588, 6.217521667480469] |
ed1a7e38-58b8-46c8-bb1e-4e6d9295f5e7 | understanding-the-challenges-and | 2303.05463 | null | https://arxiv.org/abs/2303.05463v1 | https://arxiv.org/pdf/2303.05463v1.pdf | Understanding the Challenges and Opportunities of Pose-based Anomaly Detection | Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. Utilizing pose data alleviates privacy and ethical issues. Also, computation-wise, the complexity of pose-based models is lower than pixel-based approaches. How... | ['Hamed Tabkhi', 'Vinit Katariya', 'Armin Danesh Pazho', 'Ghazal Alinezhad Noghre'] | 2023-03-09 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 3.57816964e-01 -4.54164326e-01 4.54473030e-03 -3.58863473e-01
-6.17639244e-01 -6.02079451e-01 4.37755466e-01 4.15211231e-01
-4.87027586e-01 3.48932713e-01 1.25303343e-01 -4.97200303e-02
-1.30309284e-01 -5.10664582e-01 -6.94065273e-01 -7.12808371e-01
-6.44718826e-01 -1.32669620e-02 5.03708601e-01 -1.67809755... | [7.864928245544434, 1.411323070526123] |
7e4be5b0-9f7d-44b4-bd70-804e289dd166 | global-feature-aggregation-for-accident | 2006.08942 | null | https://arxiv.org/abs/2006.08942v1 | https://arxiv.org/pdf/2006.08942v1.pdf | Global Feature Aggregation for Accident Anticipation | Anticipation of accidents ahead of time in autonomous and non-autonomous vehicles aids in accident avoidance. In order to recognize abnormal events such as traffic accidents in a video sequence, it is important that the network takes into account interactions of objects in a given frame. We propose a novel Feature Aggr... | ['Muhammad Umar Karim Khan', 'Chong Min Kyung', 'Mishal Fatima'] | 2020-06-16 | null | null | null | null | ['accident-anticipation', 'parameter-prediction'] | ['computer-vision', 'miscellaneous'] | [ 7.99387023e-02 6.71361834e-02 2.72816986e-01 -6.30755424e-01
-6.53568864e-01 2.82814384e-01 5.59257030e-01 5.18579066e-01
-9.90095317e-01 7.17474997e-01 2.92130321e-01 -1.21500015e-01
-3.46792996e-01 -6.76993072e-01 -8.84034932e-01 -5.23124337e-01
-6.89341247e-01 2.30879441e-01 8.23383570e-01 -1.25112817... | [7.284457206726074, 0.28887468576431274] |
9c431fea-d18d-4a67-bf0f-b24a43338e66 | what-you-need-is-a-more-professional-teacher | 1906.02517 | null | https://arxiv.org/abs/1906.02517v5 | https://arxiv.org/pdf/1906.02517v5.pdf | Guided learning for weakly-labeled semi-supervised sound event detection | We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-supervised sound event detection (SED). There are two sub-targets implied in weakly-labeled SED: audio tagging and boundary detection. Instead of designing a single model by considering a trade-off between the two sub-targets, we de... | ['Liwei Lin', 'Hong Liu', 'Yueliang Qian', 'Xiangdong Wang'] | 2019-06-06 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 1.55824870e-01 6.09801233e-01 -2.22342834e-01 -3.30071926e-01
-1.23531830e+00 -4.38554913e-01 4.56901789e-01 1.97798491e-01
-3.61956507e-01 2.49387562e-01 3.15906614e-01 -1.65018514e-01
-4.71542031e-02 -4.88478005e-01 -4.85593021e-01 -6.49376512e-01
-4.12256241e-01 2.06688911e-01 6.46728218e-01 1.63070604... | [15.150815963745117, 5.204653739929199] |
d6a550d7-37e7-43ed-b5c3-cb32d861b4b0 | privacy-in-practice-private-covid-19 | 2211.11434 | null | https://arxiv.org/abs/2211.11434v4 | https://arxiv.org/pdf/2211.11434v4.pdf | Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version) | Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images. To perform data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous works exploring private COVID-19 models are in part based on small dataset... | ['Erhard Rahm', 'Peter Christen', 'Maja Schneider', 'Lucas Lange'] | 2022-11-21 | null | null | null | null | ['membership-inference-attack', 'privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 2.37926707e-01 2.67517865e-01 -4.43970650e-01 -3.22335035e-01
-8.30250680e-01 -1.08566844e+00 3.71314108e-01 5.20054758e-01
-6.60355568e-01 6.51058257e-01 1.88531980e-01 -1.14486730e+00
-3.36656272e-01 -6.36633337e-01 -4.77790892e-01 -4.55524534e-01
-4.83261377e-01 3.18605065e-01 -1.55482322e-01 1.99412212... | [6.006861209869385, 6.941809177398682] |
d039fe74-efbf-4380-9171-7a2844c6c61f | localizing-semantic-patches-for-accelerating | 2206.03367 | null | https://arxiv.org/abs/2206.03367v1 | https://arxiv.org/pdf/2206.03367v1.pdf | Localizing Semantic Patches for Accelerating Image Classification | Existing works often focus on reducing the architecture redundancy for accelerating image classification but ignore the spatial redundancy of the input image. This paper proposes an efficient image classification pipeline to solve this problem. We first pinpoint task-aware regions over the input image by a lightweight ... | ['Yongjun Xu', 'Zhulin An', 'Chuanguang Yang'] | 2022-06-07 | null | null | null | null | ['classification'] | ['methodology'] | [ 8.09162185e-02 2.72994697e-01 -3.23475838e-01 -5.01536012e-01
-4.66683477e-01 -5.65053284e-01 3.53954881e-01 -8.39769244e-02
-3.35039467e-01 4.17102724e-01 -4.23414297e-02 -4.01451439e-01
-5.70788793e-02 -8.56848359e-01 -1.07997513e+00 -5.36159754e-01
2.36041799e-01 1.10604681e-01 5.71416140e-01 1.08858034... | [9.484792709350586, 0.8775036931037903] |
b098e1f6-3012-4f21-b324-b902e5e91e8e | causal-graph-discovery-from-self-and-mutually | 2301.11197 | null | https://arxiv.org/abs/2301.11197v2 | https://arxiv.org/pdf/2301.11197v2.pdf | Causal Graph Discovery from Self and Mutually Exciting Time Series | We present a generalized linear structural causal model, coupled with a novel data-adaptive linear regularization, to recover causal directed acyclic graphs (DAGs) from time series. By leveraging a recently developed stochastic monotone Variational Inequality (VI) formulation, we cast the causal discovery problem as a ... | ['Rishikesan Kamaleswaran', 'Christopher S. Josef', 'Yao Xie', 'Song Wei'] | 2023-01-26 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.47060114e-01 2.94726551e-01 -4.49433178e-01 -4.68641132e-01
-9.83170450e-01 -5.22358477e-01 1.98042005e-01 4.12193984e-01
-4.13737670e-02 1.10490024e+00 4.66184735e-01 -6.66243553e-01
-9.59460855e-01 -5.75523376e-01 -1.06077516e+00 -7.69244969e-01
-7.13416874e-01 3.15408170e-01 -1.41986981e-01 3.65439147... | [7.806352138519287, 5.29641056060791] |
d514eb88-ae33-4936-b1e3-880605f4a409 | how-do-seq2seq-models-perform-on-end-to-end-1 | null | null | https://aclanthology.org/2022.acl-long.531 | https://aclanthology.org/2022.acl-long.531.pdf | How Do Seq2Seq Models Perform on End-to-End Data-to-Text Generation? | With the rapid development of deep learning, Seq2Seq paradigm has become prevalent for end-to-end data-to-text generation, and the BLEU scores have been increasing in recent years. However, it is widely recognized that there is still a gap between the quality of the texts generated by models and the texts written by hu... | ['Xiaojun Wan', 'Xunjian Yin'] | null | null | null | null | acl-2022-5 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 4.31972109e-02 -1.94536503e-02 1.56888202e-01 -3.29367876e-01
-9.70140219e-01 -6.48577094e-01 4.88873333e-01 -1.57478210e-02
-3.44827145e-01 1.07934237e+00 7.29233861e-01 -5.28631285e-02
4.10405546e-02 -6.68542445e-01 -6.10613644e-01 -2.94981271e-01
3.92316043e-01 6.49009526e-01 7.44027123e-02 -5.37028134... | [11.770543098449707, 9.054878234863281] |
8f5c0683-1744-4dab-89e0-7c7f9564ade3 | tive-a-toolbox-for-identifying-video-instance | 2210.08856 | null | https://arxiv.org/abs/2210.08856v1 | https://arxiv.org/pdf/2210.08856v1.pdf | TIVE: A Toolbox for Identifying Video Instance Segmentation Errors | Since first proposed, Video Instance Segmentation(VIS) task has attracted vast researchers' focus on architecture modeling to boost performance. Though great advances achieved in online and offline paradigms, there are still insufficient means to identify model errors and distinguish discrepancies between methods, as w... | ['Qing Song', 'Yilin Zhou', 'Wenyi Zhao', 'Zilong Jia', 'Lu Yang', 'Wenhe Jia'] | 2022-10-17 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-7.33254850e-02 -3.16910028e-01 -3.38782400e-01 -3.49596143e-01
-6.73013389e-01 -7.25426078e-01 2.54384995e-01 8.75509977e-02
-2.09023356e-01 3.25941622e-01 -1.69723541e-01 -2.47517377e-01
-3.21250677e-01 -4.60946381e-01 -7.98562825e-01 -3.88660103e-01
-4.07256037e-01 6.89968392e-02 5.04568517e-01 1.10458478... | [9.137256622314453, 0.06550868600606918] |
b6121bd8-e086-4e0b-94c9-ad852d92ddae | driven-to-distraction-self-supervised | 1711.06623 | null | http://arxiv.org/abs/1711.06623v2 | http://arxiv.org/pdf/1711.06623v2.pdf | Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments | We present a self-supervised approach to ignoring "distractors" in camera
images for the purposes of robustly estimating vehicle motion in cluttered
urban environments. We leverage offline multi-session mapping approaches to
automatically generate a per-pixel ephemerality mask and depth map for each
input image, which ... | ['Will Maddern', 'Ingmar Posner', 'Geoffrey Pascoe', 'Dan Barnes'] | 2017-11-17 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.18782707e-01 1.05440378e-01 4.65405658e-02 -5.89736462e-01
-7.17961848e-01 -8.15042377e-01 7.11276054e-01 -4.93955165e-01
-6.39283240e-01 3.51398706e-01 -3.48501317e-02 -1.64502397e-01
5.12778640e-01 -5.02968252e-01 -1.12359607e+00 -3.46281379e-01
7.23156929e-02 6.04042947e-01 4.32841033e-01 -1.35196835... | [7.980929374694824, -2.178434371948242] |
7c6b212e-2b0a-402f-bdc5-da8ffc78f931 | persistent-homology-captures-the | 2106.00012 | null | https://arxiv.org/abs/2106.00012v1 | https://arxiv.org/pdf/2106.00012v1.pdf | Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set | The training of neural networks is usually monitored with a validation (holdout) set to estimate the generalization of the model. This is done instead of measuring intrinsic properties of the model to determine whether it is learning appropriately. In this work, we suggest studying the training of neural networks with ... | ['Marta Villegas', 'Jordi Armengol-Estapé', 'David Pérez-Fernández', 'Asier Gutiérrez-Fandiño'] | 2021-05-31 | persistent-homology-captures-the-1 | https://openreview.net/forum?id=BM64dm9HvN | https://openreview.net/pdf?id=BM64dm9HvN | neurips-2021-12 | ['holdout-set'] | ['computer-vision'] | [ 3.54462326e-01 6.32417619e-01 -6.58569038e-02 -2.28758588e-01
1.88636586e-01 -6.10281289e-01 8.26957345e-01 6.99070618e-02
-4.80318546e-01 7.76138902e-01 -6.14758909e-01 -3.39089930e-01
-3.47634941e-01 -1.18093121e+00 -1.12804711e+00 -9.33124721e-01
-3.54273498e-01 6.63436651e-01 4.71034378e-01 -3.91827434... | [7.84873104095459, 3.723123550415039] |
e1909470-4ded-42e7-8549-457a2e72952c | piano-a-parametric-hand-bone-model-from | 2106.10893 | null | https://arxiv.org/abs/2106.10893v1 | https://arxiv.org/pdf/2106.10893v1.pdf | PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging | Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the firs... | ['Jingyi Yu', 'Lan Xu', 'Yuyao Zhang', 'Minye Wu', 'Yuwei Li'] | 2021-06-21 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [-2.62125999e-01 2.50524133e-01 -2.47002885e-01 -2.71489173e-02
-2.41367832e-01 -4.02759999e-01 2.68255081e-02 -4.63038355e-01
-1.28601221e-02 7.60459781e-01 2.36895427e-01 -1.53138161e-01
-1.16120107e-01 -8.38658333e-01 -8.06852221e-01 -5.36082506e-01
-1.39188826e-01 8.62252712e-01 4.44056422e-01 -1.89954624... | [6.969552040100098, -1.1965065002441406] |
3eafbc89-4f45-489c-8365-5d2bfe094ca5 | unsupervised-domain-adaptation-for-spatio | 2010.09211 | null | https://arxiv.org/abs/2010.09211v1 | https://arxiv.org/pdf/2010.09211v1.pdf | Unsupervised Domain Adaptation for Spatio-Temporal Action Localization | Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features. This problem is typically formulated in the context of supervised learning, where the learned classifiers operate... | ['Ming-Hsuan Yang', 'Behzad Dariush', 'Yi-Ting Chen', 'Nakul Agarwal'] | 2020-10-19 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 5.09956002e-01 -4.20713186e-01 -4.26734269e-01 -5.10272920e-01
-5.55899978e-01 -3.99155378e-01 5.90851068e-01 3.53809685e-01
-8.27814698e-01 7.12924778e-01 -1.23165445e-02 1.63849562e-01
-2.55498469e-01 -5.15320957e-01 -7.18504131e-01 -7.82572031e-01
-2.79591292e-01 2.73639351e-01 7.56915867e-01 2.90509701... | [8.405145645141602, 0.731173574924469] |
d18ab2d4-4a9d-4389-a622-9cd215df063c | adatag-multi-attribute-value-extraction-from | 2106.02318 | null | https://arxiv.org/abs/2106.02318v1 | https://arxiv.org/pdf/2106.02318v1.pdf | AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding | Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate... | ['Xin Luna Dong', 'Xiang Ren', 'Christan Grant', 'Yan Liang', 'Nasser Zalmout', 'Jun Yan'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.362 | https://aclanthology.org/2021.acl-long.362.pdf | acl-2021-5 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.22901702e-02 4.64291215e-01 -6.92350864e-01 -8.56029332e-01
-6.83986247e-01 -9.39696670e-01 3.33892375e-01 2.75155455e-01
-4.48567361e-01 4.84924465e-01 2.34404311e-01 -8.46529678e-02
7.85092637e-02 -1.12852049e+00 -5.72659016e-01 -3.08430582e-01
1.98597573e-02 1.03852856e+00 9.73121598e-02 -2.86848128... | [9.978684425354004, 6.312695026397705] |
80c167b7-ccd5-42db-98d5-daf7d25b27c2 | deep-multi-modal-classification-of | 1710.09779 | null | http://arxiv.org/abs/1710.09779v3 | http://arxiv.org/pdf/1710.09779v3.pdf | Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis | Pancreatic cancer has the poorest prognosis among all cancer types.
Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically
identifiable precursors to pancreatic cancer; hence, early detection and
precise risk assessment of IPMN are vital. In this work, we propose a
Convolutional Neural Network (CNN) base... | ['Candice W. Bolan', 'Juan E. Corral', 'Sarfaraz Hussein', 'Ulas Bagci', 'Michael B. Wallace', 'Pujan Kandel'] | 2017-10-26 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 1.42497540e-01 -6.37141541e-02 -3.00221950e-01 -3.84664625e-01
-8.25426877e-01 -2.46731192e-01 4.80499923e-01 3.37562144e-01
-4.90522653e-01 4.01511192e-01 2.18199775e-01 -4.04524148e-01
-3.87899458e-01 -7.78253675e-01 -3.73112530e-01 -8.65101278e-01
-4.50385332e-01 5.98519802e-01 6.90608565e-03 1.11716248... | [14.890851020812988, -2.606868028640747] |
1ca63cfc-ad01-43ba-b79e-e0d407c2eadc | classifying-the-ideological-orientation-of | null | null | https://ieeexplore.ieee.org/document/10069289 | https://ieeexplore.ieee.org/document/10069289 | Classifying the Ideological Orientation of User-Submitted Texts in Social Media | With the long-term goal of understanding how language is used and evolves within online communities, this work explores the application of natural language processing techniques to classify text articles according to their ideological orientation (i.e., conservative or liberal). We first collect a balanced corpus of te... | ['Rickard Ewetz', 'Adan Ernesto Vela', 'Kamalakkannan Ravi'] | 2022-12-12 | null | null | null | ieee-international-conference-on-machine-1 | ['news-classification'] | ['natural-language-processing'] | [-1.22414948e-02 -8.61635581e-02 -8.52354586e-01 -2.16909185e-01
-3.58579010e-01 -8.61753702e-01 1.43858635e+00 7.01664209e-01
-6.11953020e-01 3.88835102e-01 7.61310399e-01 -8.34991932e-01
-4.97381724e-02 -7.32701302e-01 -1.47521257e-01 -3.37536395e-01
-9.03938338e-02 3.51720870e-01 5.83050177e-02 -4.06339616... | [9.045135498046875, 9.975153923034668] |
c8cd7990-5c68-470d-9db8-84b8ab815526 | the-current-state-of-summarization | 2305.04853 | null | https://arxiv.org/abs/2305.04853v1 | https://arxiv.org/pdf/2305.04853v1.pdf | The Current State of Summarization | With the explosive growth of textual information, summarization systems have become increasingly important. This work aims at indicating the current state of the art in abstractive text summarization concisely. As part of this, we outline the current paradigm shifts towards pre-trained encoder-decoder models and large ... | ['Fabian Retkowski'] | 2023-05-08 | null | null | null | null | ['abstractive-text-summarization', 'text-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.41300845e-01 3.51158202e-01 -3.11336040e-01 -1.96491227e-01
-1.26859748e+00 -1.39301121e-01 6.50826037e-01 5.58705270e-01
-3.97355318e-01 8.41566563e-01 1.21955729e+00 -1.22023828e-01
3.01813036e-01 -3.47326636e-01 -3.13626260e-01 -5.77843226e-02
-1.67632829e-02 4.82822031e-01 -1.64109588e-01 -5.83922565... | [12.518937110900879, 9.453408241271973] |
b154244f-6d92-4bd5-9ae0-2b7f359ab77e | finding-a-balanced-degree-of-automation-for | 2109.11503 | null | https://arxiv.org/abs/2109.11503v1 | https://arxiv.org/pdf/2109.11503v1.pdf | Finding a Balanced Degree of Automation for Summary Evaluation | Human evaluation for summarization tasks is reliable but brings in issues of reproducibility and high costs. Automatic metrics are cheap and reproducible but sometimes poorly correlated with human judgment. In this work, we propose flexible semiautomatic to automatic summary evaluation metrics, following the Pyramid hu... | ['Mohit Bansal', 'Shiyue Zhang'] | 2021-09-23 | null | https://aclanthology.org/2021.emnlp-main.531 | https://aclanthology.org/2021.emnlp-main.531.pdf | emnlp-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 2.31204808e-01 1.33986875e-01 -3.32201779e-01 -3.64866227e-01
-1.23172998e+00 -8.73494446e-01 8.51009130e-01 5.35200477e-01
-4.65480238e-01 9.15255189e-01 7.50409603e-01 -1.23190895e-01
-2.04985470e-01 -4.03427452e-01 -4.56769854e-01 -1.70196369e-01
4.32345688e-01 3.72417092e-01 2.15680331e-01 -1.66547194... | [11.954314231872559, 9.192255020141602] |
bfc80bba-6f75-4eed-ac6e-734e3bd2a7eb | mention-centered-graph-neural-network-for | 2103.08200 | null | https://arxiv.org/abs/2103.08200v1 | https://arxiv.org/pdf/2103.08200v1.pdf | Mention-centered Graph Neural Network for Document-level Relation Extraction | Document-level relation extraction aims to discover relations between entities across a whole document. How to build the dependency of entities from different sentences in a document remains to be a great challenge. Current approaches either leverage syntactic trees to construct document-level graphs or aggregate infer... | ['Yiyan Zhang', 'Min Peng', 'Jiaxin Pan'] | 2021-03-15 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 9.75372717e-02 5.67234755e-01 -4.20878530e-01 -4.90964144e-01
-7.34179676e-01 -8.94559681e-01 7.53844202e-01 6.49306834e-01
-9.49058756e-02 9.38473642e-01 5.80761135e-01 -4.68120009e-01
-2.36919373e-01 -1.16037679e+00 -6.61331177e-01 -1.63211256e-01
-3.87751698e-01 2.19938636e-01 4.55760717e-01 -3.04726750... | [9.270279884338379, 8.632672309875488] |
4a86d48b-94a0-4802-9855-ec10efc2f9ea | niki-neural-inverse-kinematics-with | 2305.08590 | null | https://arxiv.org/abs/2305.08590v1 | https://arxiv.org/pdf/2305.08590v1.pdf | NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation | With the progress of 3D human pose and shape estimation, state-of-the-art methods can either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases. However, they cannot obtain robustness and mesh-image alignment at the same time. In this work, we present NIKI (Neural Inverse Kinematics with In... | ['Cewu Lu', 'Fan Wang', 'Jiasheng Tang', 'Qi Liu', 'Siyuan Bian', 'Jiefeng Li'] | 2023-05-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_NIKI_Neural_Inverse_Kinematics_With_Invertible_Neural_Networks_for_3D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', '3d-human-pose-and-shape-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.12549058e-02 1.08627744e-01 -2.53954548e-02 -3.13737571e-01
-4.62798804e-01 -1.53042093e-01 3.60166043e-01 -4.89809364e-01
-1.50176540e-01 5.05780160e-01 9.60413963e-02 -5.59817590e-02
-2.90271968e-01 -6.34959221e-01 -1.02881074e+00 -4.84144628e-01
1.21455058e-01 7.22484648e-01 -3.21762711e-02 -2.46285573... | [7.0722270011901855, -1.1602345705032349] |
fa1209dc-12cd-49e2-8426-6cfd44de81dc | learning-by-tracking-siamese-cnn-for-robust | 1604.07866 | null | http://arxiv.org/abs/1604.07866v3 | http://arxiv.org/pdf/1604.07866v3.pdf | Learning by tracking: Siamese CNN for robust target association | This paper introduces a novel approach to the task of data association within
the context of pedestrian tracking, by introducing a two-stage learning scheme
to match pairs of detections. First, a Siamese convolutional neural network
(CNN) is trained to learn descriptors encoding local spatio-temporal structures
between... | ['Cristian Canton Ferrer', 'Laura Leal-Taixé', 'Konrad Schindler'] | 2016-04-26 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [ 6.38464885e-03 -4.63876992e-01 -1.18763909e-01 -3.69067848e-01
-4.39626873e-01 -4.50298369e-01 7.79146612e-01 3.46916229e-01
-9.55231428e-01 7.88403451e-01 1.29372710e-02 2.29933754e-01
1.84362262e-01 -6.50740981e-01 -7.88562536e-01 -5.97154558e-01
-4.66356158e-01 3.34357738e-01 6.68554366e-01 2.68577915... | [6.476890563964844, -1.9716781377792358] |
89387ea8-2a72-492b-8196-0bdf8f1cfd05 | polite-teacher-semi-supervised-instance | 2211.03850 | null | https://arxiv.org/abs/2211.03850v1 | https://arxiv.org/pdf/2211.03850v1.pdf | Polite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding | We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation. The proposed architecture relies on the Teacher-Student mutual learning framework. To filter out noisy pseudo-labels, we use confidence thresholding for bounding boxes and mask scoring for masks. The approach... | ['Marek Cygan', 'Anna Fensel', 'Piotr Tempczyk', 'Andrzej Zapała', 'Dominik Filipiak'] | 2022-11-07 | null | null | null | null | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [ 3.65951717e-01 6.64528131e-01 -2.63754398e-01 -5.05287409e-01
-1.33002281e+00 -6.13023460e-01 6.37484550e-01 1.74414009e-01
-6.98444903e-01 6.07269406e-01 -5.22317767e-01 -3.34955186e-01
1.82774976e-01 -2.52599865e-01 -8.83365571e-01 -6.30738497e-01
1.14100650e-01 1.00789607e+00 6.21708333e-01 2.64743745... | [9.38200569152832, 0.7400688529014587] |
2a116527-c557-4730-8c66-4cb0a7b15e7f | sivd-dataset-of-iranian-vehicles-for-real | null | null | https://ieeexplore.ieee.org/document/10043932 | https://www.researchgate.net/profile/Farbod-Siahkali/publication/368731575_SIVD_Dataset_of_Iranian_Vehicles_for_Real-Time_Multi-Camera_Video_Tracking_and_Recognition/links/63fb9251b1704f343f84f46d/SIVD-Dataset-of-Iranian-Vehicles-for-Real-Time-Multi-Camera-Video-Tracking-and-Recognition.pdf?origin=publication_detail | SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition | In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small num... | ['Mehdi Tale Masouleh', 'Seyed Amirmahdi Alavi', 'Farbod Siahkali'] | 2023-02-22 | null | null | null | icspis-2023-2 | ['object-tracking', 'vehicle-re-identification'] | ['computer-vision', 'computer-vision'] | [-3.78394783e-01 -5.99239528e-01 -2.59850830e-01 -2.03999937e-01
-2.73031980e-01 -3.17989767e-01 7.26046741e-01 -4.62056667e-01
-5.40455043e-01 6.23927534e-01 -5.28434277e-01 -5.13104677e-01
-9.11084414e-02 -1.00842369e+00 -4.06743228e-01 -7.31011093e-01
1.10444210e-01 7.54814267e-01 4.17722225e-01 -2.14788407... | [8.142127990722656, -1.0050636529922485] |
6939abc7-2709-4074-bd28-80834ca28cad | dynamic-slam-semantic-monocular-visual | null | null | https://www.sciencedirect.com/science/article/abs/pii/S0921889018308029 | https://www.researchgate.net/profile/Linhui-Xiao/publication/332149941_Dynamic-SLAM_Semantic_monocular_visual_localization_and_mapping_based_on_deep_learning_in_dynamic_environment/links/6013f1fa45851517ef22eb7d/Dynamic-SLAM-Semantic-monocular-visual-localization-and-mapping-based-on-deep-learning-in-dynamic-environmen... | Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment | When working in dynamic environment, traditional SLAM framework performs poorly due to interference from dynamic objects. By taking advantages of deep learning in object detection, a semantic simultaneous localization and mapping framework named Dynamic-SLAM is proposed, in order to solve the problem of SLAM in dynamic... | ['Xudong Zou', 'Zheng Rong', 'Xiaosong Qiu', 'Jinge Wang', 'Linhui Xiao'] | 2019-07-06 | null | null | null | robotics-and-autonomous-systems-2019-7 | ['semantic-slam'] | ['computer-vision'] | [-2.83880055e-01 -4.28668171e-01 1.30943015e-01 -2.01783374e-01
-2.55155027e-01 -1.69894844e-01 2.79874384e-01 -1.71619862e-01
-8.03873599e-01 3.52888316e-01 -3.29990238e-01 1.53522730e-01
2.37648226e-02 -6.57447994e-01 -7.46301353e-01 -4.62318718e-01
-5.99591620e-02 5.48967957e-01 1.06278682e+00 -2.70059109... | [7.4192047119140625, -2.091444492340088] |
6799f6bc-cdf9-461c-9c4e-19adafd232a5 | visual-representation-learning-from-unlabeled | 2303.12001 | null | https://arxiv.org/abs/2303.12001v1 | https://arxiv.org/pdf/2303.12001v1.pdf | Visual Representation Learning from Unlabeled Video using Contrastive Masked Autoencoders | Masked Autoencoders (MAEs) learn self-supervised representations by randomly masking input image patches and a reconstruction loss. Alternatively, contrastive learning self-supervised methods encourage two versions of the same input to have a similar representation, while pulling apart the representations for different... | ['Vicente Ordonez', 'Ruben Villegas', 'Jefferson Hernandez'] | 2023-03-21 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 3.32070619e-01 -1.42203197e-02 -3.70307356e-01 -3.20433557e-01
-8.51717651e-01 -3.36437076e-01 7.66640782e-01 -4.03635293e-01
-6.22488439e-01 6.02916539e-01 2.51813889e-01 3.84636521e-02
1.75972223e-01 -4.68478978e-01 -1.57314777e+00 -7.59851933e-01
-2.89438099e-01 7.29179308e-02 2.51160711e-01 -1.95893109... | [9.227002143859863, 1.0360664129257202] |
9291034b-21b7-458a-ab2b-6a7cba6e8f06 | optimising-2d-pose-representation-improve | 2209.00618 | null | https://arxiv.org/abs/2209.00618v1 | https://arxiv.org/pdf/2209.00618v1.pdf | Optimising 2D Pose Representation: Improve Accuracy, Stability and Generalisability Within Unsupervised 2D-3D Human Pose Estimation | This paper addresses the problem of 2D pose representation during unsupervised 2D to 3D pose lifting to improve the accuracy, stability and generalisability of 3D human pose estimation (HPE) models. All unsupervised 2D-3D HPE approaches provide the entire 2D kinematic skeleton to a model during training. We argue that ... | ['Hansung Kim', 'Srinandan Dasmahapatra', 'Peter Hardy'] | 2022-09-01 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 1.13160104e-01 6.56728268e-01 -4.23215926e-02 -2.78109275e-02
-5.73803484e-01 -5.42197585e-01 3.45089078e-01 -2.52604365e-01
-5.35983384e-01 6.27729893e-01 1.43127605e-01 -1.32454215e-02
-4.63565700e-02 -5.16384184e-01 -1.06501341e+00 -5.63358247e-01
-5.01907051e-01 7.92354047e-01 1.00824043e-01 -4.64331597... | [6.922865390777588, -1.057049036026001] |
67374fdc-5c0a-4f91-9ba8-e188e3cbf4ba | do-machine-learning-models-learn-common-sense | 2303.01433 | null | https://arxiv.org/abs/2303.01433v2 | https://arxiv.org/pdf/2303.01433v2.pdf | Do Machine Learning Models Learn Statistical Rules Inferred from Data? | Machine learning models can make critical errors that are easily hidden within vast amounts of data. Such errors often run counter to rules based on human intuition. However, rules based on human knowledge are challenging to scale or to even formalize. We thereby seek to infer statistical rules from the data and quanti... | ['Eric Wong', 'Mayur Naik', 'Yinjun Wu', 'Aaditya Naik'] | 2023-03-02 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.89249301e-01 4.57611501e-01 -3.72345120e-01 -9.11429882e-01
-8.21545720e-01 -3.86614949e-01 3.64020944e-01 2.03800172e-01
-1.50973737e-01 7.91865051e-01 -1.94737926e-01 -5.65984309e-01
-2.03991711e-01 -5.26895702e-01 -1.34630489e+00 -2.14419570e-02
4.63726372e-01 6.34643853e-01 9.94079858e-02 1.54759854... | [9.071939468383789, 6.534579753875732] |
5b85c55e-d891-4698-ab5b-3627dd119612 | jointly-modeling-aspect-and-polarity-for | 2109.07680 | null | https://arxiv.org/abs/2109.07680v3 | https://arxiv.org/pdf/2109.07680v3.pdf | Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews | Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-ta... | ['Jafar Razmara', 'Milad Vazan'] | 2021-09-16 | null | null | null | null | ['aspect-category-polarity', 'persian-sentiment-anlysis', 'aspect-category-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 4.08694223e-02 -1.75747886e-01 -1.73067912e-01 -6.31437480e-01
-5.74314654e-01 -7.32467115e-01 9.14045393e-01 5.74046552e-01
-4.12106663e-01 6.53481722e-01 2.35533178e-01 -2.31896803e-01
1.30747184e-01 -6.64186716e-01 -1.80235058e-01 -6.74449563e-01
1.98703498e-01 5.04600108e-01 -2.14660436e-01 -4.15689677... | [11.248103141784668, 6.854100227355957] |
02fd5844-12fc-470d-9614-227247a97e7c | representation-learning-on-hyper-relational | 2305.18256 | null | https://arxiv.org/abs/2305.18256v2 | https://arxiv.org/pdf/2305.18256v2.pdf | Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers | A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing auxiliary information for a triplet. While existing hyper-relational knowledge graph embedding methods assume that the entities are discrete... | ['Joyce Jiyoung Whang', 'Jaejun Lee', 'Chanyoung Chung'] | 2023-05-29 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [-1.22548454e-01 4.24650311e-01 -6.88970029e-01 -6.17618084e-01
-1.11794025e-01 -4.20520395e-01 4.40910906e-01 6.34651124e-01
-1.93949983e-01 7.82710612e-01 3.64271075e-01 -2.92498410e-01
-6.14288211e-01 -1.66243982e+00 -8.65965962e-01 -4.47312504e-01
-3.34434122e-01 6.97999597e-01 -1.46260299e-02 -4.34962690... | [8.762855529785156, 7.844595909118652] |
4fdc350b-967f-4119-820b-298d1138fe1a | lukthung-classification-using-neural-networks | 1908.08769 | null | https://arxiv.org/abs/1908.08769v2 | https://arxiv.org/pdf/1908.08769v2.pdf | Lukthung Classification Using Neural Networks on Lyrics and Audios | Music genre classification is a widely researched topic in music information retrieval (MIR). Being able to automatically tag genres will benefit music streaming service providers such as JOOX, Apple Music, and Spotify for their content-based recommendation. However, most studies on music classification have been done ... | ['Kasina Euchukanonchai', 'Naruemon Pratanwanich', 'Kawisorn Kamtue', 'Dittaya Wanvarie'] | 2019-08-23 | null | null | null | null | ['genre-classification', 'music-classification'] | ['computer-vision', 'music'] | [-9.26576778e-02 -7.47345567e-01 -5.34369588e-01 -9.69236046e-02
-8.44445825e-01 -7.00889051e-01 1.78426728e-01 -1.54143646e-01
-1.15152128e-01 3.55562150e-01 6.65534079e-01 1.71632782e-01
-4.44605827e-01 -7.79459417e-01 -3.11461806e-01 -6.59909248e-01
-1.36316732e-01 2.42187142e-01 -2.07920209e-01 -2.99749672... | [15.899423599243164, 5.187716960906982] |
78a7591c-172c-42f2-b76c-189c7e6b4605 | ihs-rd-belarus-at-semeval-2016-task-9 | null | null | https://aclanthology.org/S16-1187 | https://aclanthology.org/S16-1187.pdf | IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping. | null | ['Maria Yermakovich', 'Artsiom Artsymenia', 'Palina Dounar'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.1982879638671875, 3.854064464569092] |
f4354f48-37b5-4acf-890f-a13053171108 | judge-localize-and-edit-ensuring-visual | 2212.03507 | null | https://arxiv.org/abs/2212.03507v2 | https://arxiv.org/pdf/2212.03507v2.pdf | Judge, Localize, and Edit: Ensuring Visual Commonsense Morality for Text-to-Image Generation | Text-to-image generation methods produce high-resolution and high-quality images, but these methods should not produce immoral images that may contain inappropriate content from the commonsense morality perspective. Conventional approaches often neglect these ethical concerns, and existing solutions are limited in avoi... | ['Jinkyu Kim', 'Suhong Moon', 'Seongbeom Park'] | 2022-12-07 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.02096438e-01 4.51022685e-01 7.37449527e-02 -2.08111003e-01
-3.90329331e-01 -6.69753134e-01 9.34803843e-01 -4.39681411e-01
-1.96283415e-01 8.36619735e-01 1.67955399e-01 -3.36025536e-01
1.84233770e-01 -8.31129730e-01 -7.32497752e-01 -5.91936231e-01
6.01675749e-01 2.35405684e-01 -2.84699172e-01 -3.25906932... | [12.080665588378906, 0.932635486125946] |
4449b304-7a3f-422b-909a-febbfa3f267a | multi-task-learning-for-sparsity-pattern | 2212.08697 | null | https://arxiv.org/abs/2212.08697v1 | https://arxiv.org/pdf/2212.08697v1.pdf | Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach | We extend best-subset selection to linear Multi-Task Learning (MTL), where a set of linear models are jointly trained on a collection of datasets (``tasks''). Allowing the regression coefficients of tasks to have different sparsity patterns (i.e., different supports), we propose a modeling framework for MTL that encour... | ['Rahul Mazumder', 'Giovanni Parmigiani', 'Kenneth T. Kishida', 'Kayhan Behdin', 'Gabriel Loewinger'] | 2022-12-16 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 3.95162910e-01 -2.47226357e-01 -8.06918383e-01 -7.41919339e-01
-1.14167833e+00 -5.78560472e-01 1.29641175e-01 4.57755364e-02
-4.44333941e-01 1.10677576e+00 1.46137968e-01 -1.58005878e-01
-3.65505785e-01 -1.04770944e-01 -1.05611849e+00 -7.10644782e-01
-1.40548393e-01 4.45623457e-01 -2.65094116e-02 2.77462780... | [8.403820991516113, 4.1729841232299805] |
f07db327-89ad-4c7e-919b-5050d3581d4b | domain-generalization-for-domain-linked | 2306.00879 | null | https://arxiv.org/abs/2306.00879v1 | https://arxiv.org/pdf/2306.00879v1.pdf | Domain Generalization for Domain-Linked Classes | Domain generalization (DG) focuses on transferring domain-invariant knowledge from multiple source domains (available at train time) to an, a priori, unseen target domain(s). This requires a class to be expressed in multiple domains for the learning algorithm to break the spurious correlations between domain and class.... | ['Sirisha Rambhatla', 'Saad Hossain', 'Kimathi Kaai'] | 2023-06-01 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 4.23542053e-01 1.95551589e-02 -3.29595029e-01 -6.96774244e-01
-8.56754780e-01 -9.41504776e-01 4.76973355e-01 -6.76966459e-02
-1.15390485e-02 1.11305177e+00 4.60654646e-02 1.18415458e-02
-2.22147793e-01 -8.80635619e-01 -9.16410089e-01 -6.08493805e-01
3.25598791e-02 6.74482703e-01 9.20716301e-02 -3.12967926... | [10.275910377502441, 3.0127720832824707] |
bdc744c4-c93e-40cc-ad75-b8c90da78b0c | data-types-as-a-more-ergonomic-frontend-for | 2210.04826 | null | https://arxiv.org/abs/2210.04826v1 | https://arxiv.org/pdf/2210.04826v1.pdf | Data types as a more ergonomic frontend for Grammar-Guided Genetic Programming | Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI). Grammar-Guided Genetic Programming (GGGP) appr... | ['Alcides Fonseca', 'Pedro Barbosa', 'Paulo Canelas', 'Leon Ingelse', 'Guilherme Espada'] | 2022-10-10 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.47093046e-02 4.73786116e-01 -9.50413719e-02 -1.57224804e-01
-2.02297702e-01 -6.56508267e-01 4.66168970e-01 6.74238354e-02
-4.26110737e-02 6.74287856e-01 -4.80568945e-01 -8.44609141e-01
-4.43017453e-01 -1.23314238e+00 -7.42021084e-01 -3.37793916e-01
-3.84993672e-01 3.51012945e-01 4.72175032e-01 -5.66232443... | [8.039837837219238, 7.321506023406982] |
2f0aa9dd-2175-45e1-bc3e-d222fb0a2cf3 | video-relation-detection-with-trajectory | 2101.08165 | null | https://arxiv.org/abs/2101.08165v1 | https://arxiv.org/pdf/2101.08165v1.pdf | Video Relation Detection with Trajectory-aware Multi-modal Features | Video relation detection problem refers to the detection of the relationship between different objects in videos, such as spatial relationship and action relationship. In this paper, we present video relation detection with trajectory-aware multi-modal features to solve this task. Considering the complexity of doing vi... | ['Si Liu', 'Guanghui Ren', 'Wentao Xie'] | 2021-01-20 | null | null | null | null | ['video-visual-relation-detection'] | ['computer-vision'] | [ 6.65546358e-02 -2.49494329e-01 -2.76475430e-01 -2.97258310e-02
-5.79568803e-01 -5.03321767e-01 8.43432605e-01 1.15814403e-01
-2.14476198e-01 2.30584309e-01 3.04040849e-01 -1.29472792e-01
-2.63682187e-01 -4.92570430e-01 -7.88323104e-01 -2.60490090e-01
-5.34009218e-01 2.02611670e-01 9.98121023e-01 -1.35164514... | [9.174217224121094, 0.6966190934181213] |
9b8c7e97-28b4-4338-adfa-6aa45c0d2f1a | emotion-recognition-from-multiple-modalities | 2108.10152 | null | https://arxiv.org/abs/2108.10152v1 | https://arxiv.org/pdf/2108.10152v1.pdf | Emotion Recognition from Multiple Modalities: Fundamentals and Methodologies | Humans are emotional creatures. Multiple modalities are often involved when we express emotions, whether we do so explicitly (e.g., facial expression, speech) or implicitly (e.g., text, image). Enabling machines to have emotional intelligence, i.e., recognizing, interpreting, processing, and simulating emotions, is bec... | ['Kurt Keutzer', 'Guiguang Ding', 'Jufeng Yang', 'Guoli Jia', 'Sicheng Zhao'] | 2021-08-18 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 3.39635164e-01 -1.55130759e-01 -5.10841608e-02 -8.36963177e-01
-4.35631216e-01 -6.40933514e-01 2.87736982e-01 4.52450067e-02
-3.52518141e-01 7.52545059e-01 2.71195889e-01 5.04984498e-01
2.90246636e-01 -5.01518011e-01 -8.22790340e-02 -6.39095783e-01
3.15715559e-02 1.86462238e-01 -7.79522419e-01 -3.48333895... | [13.201221466064453, 5.35120153427124] |
e0c655a1-77ad-49dc-872a-4049d8efc47c | improving-supervised-drug-protein-relation | null | null | https://aclanthology.org/2022.bionlp-1.16 | https://aclanthology.org/2022.bionlp-1.16.pdf | Improving Supervised Drug-Protein Relation Extraction with Distantly Supervised Models | This paper proposes novel drug-protein relation extraction models that indirectly utilize distant supervision data. Concretely, instead of adding distant supervision data to the manually annotated training data, our models incorporate distantly supervised models that are relation extraction models trained with distant ... | ['Yutaka Sasaki', 'Makoto Miwa', 'Naoki Iinuma'] | null | null | null | null | bionlp-acl-2022-5 | ['drugprot'] | ['natural-language-processing'] | [ 2.92718261e-01 5.05550683e-01 -6.11125529e-01 -7.06491530e-01
-6.96926534e-01 -1.71309263e-01 3.96808714e-01 4.58458096e-01
-3.45506817e-01 1.33269799e+00 1.73468456e-01 -1.71431676e-01
-1.01021491e-01 -6.53433263e-01 -6.70438111e-01 -6.32016301e-01
3.19165528e-01 6.89844012e-01 2.90219069e-01 -1.27981886... | [9.131158828735352, 8.586711883544922] |
c6a34b34-05e3-4448-abdd-1729491aed04 | implicit-spoken-language-diarization | 2306.12913 | null | https://arxiv.org/abs/2306.12913v1 | https://arxiv.org/pdf/2306.12913v1.pdf | Implicit spoken language diarization | Spoken language diarization (LD) and related tasks are mostly explored using the phonotactic approach. Phonotactic approaches mostly use explicit way of language modeling, hence requiring intermediate phoneme modeling and transcribed data. Alternatively, the ability of deep learning approaches to model temporal dynamic... | ['S. R. Mahadeva Prasanna', 'Amartya Chowdhury', 'Jagabandhu Mishra'] | 2023-06-22 | null | null | null | null | ['speaker-diarization'] | ['speech'] | [-8.35962370e-02 3.30408633e-01 -9.25511494e-02 -4.54644829e-01
-9.05038416e-01 -4.39585149e-01 7.44503498e-01 2.83257500e-03
-5.61088204e-01 4.42036599e-01 5.26099086e-01 -2.07501113e-01
2.66771406e-01 -4.09712732e-01 -3.73811662e-01 -6.82658255e-01
-3.17936301e-01 5.03550351e-01 -2.29391053e-01 -2.84779221... | [14.38928508758545, 6.271712303161621] |
2067b712-766c-46cd-8f64-ba62af100bca | results-of-semtab-2021 | null | null | http://ceur-ws.org/Vol-3103/ | http://ceur-ws.org/Vol-3103/paper0.pdf | Results of SemTab 2021 | SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop. SemTab provides a common framework to conduct a systematic evaluation of state-of-... | ['Nora Abdelmageed', 'Kavitha Srinivas', 'Juan Sequeda', 'Ernesto Jimenez-Ruiz', 'Oktie Hassanzadeh', 'Vasilis Efthymiou', 'Jiaoyan Chen', 'Vincenzo Cutrona'] | 2021-10-27 | null | null | null | iswc-2021-10 | ['ontology-matching', 'table-annotation', 'table-annotation'] | ['knowledge-base', 'knowledge-base', 'natural-language-processing'] | [-7.98054878e-03 5.49871027e-01 -5.40946662e-01 -5.08502051e-02
-1.66763842e-01 -6.25529826e-01 9.11711156e-01 6.71915531e-01
-1.29095882e-01 4.07892317e-01 3.39285791e-01 -2.29931086e-01
-8.84372294e-01 -1.14776826e+00 -2.82963365e-01 6.27998650e-01
5.46764508e-02 9.86519277e-01 7.90097952e-01 -6.92802072... | [9.231491088867188, 8.009546279907227] |
102a1c55-6113-4da8-8b45-5ecf3445d73e | learning-video-object-segmentation-with | 1704.05737 | null | http://arxiv.org/abs/1704.05737v2 | http://arxiv.org/pdf/1704.05737v2.pdf | Learning Video Object Segmentation with Visual Memory | This paper addresses the task of segmenting moving objects in unconstrained
videos. We introduce a novel two-stream neural network with an explicit memory
module to achieve this. The two streams of the network encode spatial and
temporal features in a video sequence respectively, while the memory module
captures the ev... | ['Cordelia Schmid', 'Pavel Tokmakov', 'Karteek Alahari'] | 2017-04-19 | learning-video-object-segmentation-with-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Tokmakov_Learning_Video_Object_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Tokmakov_Learning_Video_Object_ICCV_2017_paper.pdf | iccv-2017-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 1.91746637e-01 -2.84691304e-01 -3.08577955e-01 -2.27056205e-01
-5.60199976e-01 -4.91533488e-01 5.02264619e-01 -1.90915138e-01
-6.87867880e-01 3.39570165e-01 -5.51048703e-02 2.15452854e-02
4.53779250e-01 -7.48331964e-01 -1.15474808e+00 -9.23769593e-01
-3.24644983e-01 1.52460020e-02 7.58679450e-01 2.55882651... | [8.944693565368652, -0.03861088678240776] |
7a0c6d00-1143-4c28-930e-ec644d34949b | a-crystal-specific-pre-training-framework-for | 2306.05344 | null | https://arxiv.org/abs/2306.05344v2 | https://arxiv.org/pdf/2306.05344v2.pdf | A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction | Crystal property prediction is a crucial aspect of developing novel materials. However, there are two technical challenges to be addressed for speeding up the investigation of crystals. First, labeling crystal properties is intrinsically difficult due to the high cost and time involved in physical simulations or lab ex... | ['Bin Yang', 'Chenjuan Guo', 'Jilin Hu', 'Yanru Song', 'Haomin Yu'] | 2023-06-08 | null | null | null | null | ['property-prediction', 'physical-simulations'] | ['medical', 'miscellaneous'] | [ 4.77542698e-01 -1.43693417e-01 -5.22441685e-01 -4.83168066e-01
-5.06238878e-01 -2.19428062e-01 5.78933299e-01 4.05341089e-02
-5.41466698e-02 8.61708581e-01 1.26935109e-01 5.14603592e-02
-7.20645487e-02 -7.56233096e-01 -8.24676037e-01 -1.09261322e+00
2.15339243e-01 4.23589110e-01 1.86223537e-01 -6.80041760... | [5.159276485443115, 5.530156135559082] |
fb009fd8-6099-4a35-af91-3b01ab623de0 | improved-anomaly-detection-in-crowded-scenes | 1304.0886 | null | http://arxiv.org/abs/1304.0886v1 | http://arxiv.org/pdf/1304.0886v1.pdf | Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture | A robust and efficient anomaly detection technique is proposed, capable of
dealing with crowded scenes where traditional tracking based approaches tend to
fail. Initial foreground segmentation of the input frames confines the analysis
to foreground objects and effectively ignores irrelevant background dynamics.
Input f... | ['Vikas Reddy', 'Conrad Sanderson', 'Brian C. Lovell'] | 2013-04-03 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 1.94241732e-01 -4.10290033e-01 2.90151119e-01 3.52527872e-02
-3.98298502e-01 -3.63768578e-01 8.02328527e-01 4.59581703e-01
-7.33067453e-01 6.40901566e-01 -1.43902466e-01 3.81270014e-02
3.61605547e-02 -5.32208323e-01 -3.53842676e-01 -1.16883898e+00
-3.45652699e-01 6.43979609e-01 9.36686099e-01 2.75392085... | [8.835230827331543, -0.7451316714286804] |
b2ded9cf-c9e0-448d-856d-a031306b0e1b | hififace-3d-shape-and-semantic-prior-guided | 2106.09965 | null | https://arxiv.org/abs/2106.09965v1 | https://arxiv.org/pdf/2106.09965v1.pdf | HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping | In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identi... | ['Rongrong Ji', 'Feiyue Huang', 'Yongjian Wu', 'Jilin Li', 'Chengjie Wang', 'Ying Tai', 'Wenqing Chu', 'Junwei Zhu', 'Xu Chen', 'YuHan Wang'] | 2021-06-18 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [-0.09077884 0.14758494 0.18703309 -0.48331594 -0.31828627 -0.42190543
0.48327428 -0.9204073 0.20052691 0.52239215 0.351698 0.3144194
0.3646981 -0.87821084 -0.76938355 -0.65728843 0.53329086 0.19197097
-0.20953372 -0.267768 0.02958886 0.7690824 -1.7744706 0.31784543
0.80141926 1.181167 -0.0... | [12.76079273223877, -0.07820997387170792] |
4821d5bb-802c-4e00-a65e-fe6330d8736f | a-synthetic-hyperspectral-array-video | 2301.07551 | null | https://arxiv.org/abs/2301.07551v3 | https://arxiv.org/pdf/2301.07551v3.pdf | Synthetic Hyperspectral Array Video Database with Applications to Cross-Spectral Reconstruction and Hyperspectral Video Coding | In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse applications. For all scenes, depth maps are provided as well to yield the position of a pix... | ['André Kaup', 'Jürgen Seiler', 'Frank Sippel'] | 2023-01-18 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 7.50459313e-01 -4.31636989e-01 1.17006838e-01 4.02682312e-02
-6.84225202e-01 -8.87761772e-01 2.57617235e-01 5.43009415e-02
-2.58456856e-01 7.23111689e-01 -1.20702974e-01 -6.39579892e-02
-3.71971875e-01 -8.72985125e-01 -5.65776467e-01 -1.11062419e+00
-2.58719236e-01 -3.25537622e-01 1.15556799e-01 -2.14288875... | [10.194881439208984, -2.1206154823303223] |
9f2dfa28-7af5-4274-8854-00541eac14d3 | parallelizing-optical-flow-estimation-on-an | 2305.13055 | null | https://arxiv.org/abs/2305.13055v1 | https://arxiv.org/pdf/2305.13055v1.pdf | Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation | Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed t... | ['Luca Benini', 'Michele Magno', 'Jonas Kühne'] | 2023-05-22 | null | null | null | null | ['autonomous-navigation'] | ['computer-vision'] | [-5.38917556e-02 -3.56894404e-01 8.06699228e-03 3.21404964e-01
2.66990244e-01 -6.40111804e-01 2.93396413e-01 4.35456634e-02
-9.33660686e-01 3.19642127e-01 -5.20034790e-01 -8.12288344e-01
3.29406470e-01 -7.18542933e-01 -1.34970009e-01 -3.32919836e-01
-2.23392874e-01 -8.25422779e-02 5.37275314e-01 -3.61162201... | [8.493110656738281, -1.206519365310669] |
cf34c359-2695-4bcb-b38d-6337db3fe86d | speech-enhancement-in-adverse-environments | 1803.00396 | null | http://arxiv.org/abs/1803.00396v1 | http://arxiv.org/pdf/1803.00396v1.pdf | Speech Enhancement in Adverse Environments Based on Non-stationary Noise-driven Spectral Subtraction and SNR-dependent Phase Compensation | A two-step enhancement method based on spectral subtraction and phase
spectrum compensation is presented in this paper for noisy speeches in adverse
environments involving non-stationary noise and medium to low levels of SNR.
The magnitude of the noisy speech spectrum is modified in the first step of the
proposed metho... | [] | 2018-02-19 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 8.56123805e-01 -3.44240516e-01 5.73582172e-01 -3.41432840e-02
-7.90007889e-01 -3.75710428e-01 4.09864008e-01 2.72079498e-01
-6.27048850e-01 7.42472112e-01 3.29829127e-01 -2.31857672e-01
-3.62177968e-01 -5.62887967e-01 1.74113512e-02 -1.11895621e+00
1.97724462e-01 -3.37166280e-01 3.69955540e-01 -3.50520790... | [15.0031156539917, 5.746849536895752] |
60fac3b7-dc43-43b1-9147-c9204352b8c4 | evaluating-variants-of-wav2vec-2-0-on | null | null | https://ieeexplore.ieee.org/document/10096552 | https://ieeexplore.ieee.org/document/10096552 | Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst Tasks | The search for emotional biomarkers within the human voice is a challenging research area. Previous studies focused on predicting affective state from speech; this study explores various tasks on affective vocal bursts. Borrowing the success of self-supervised learning in automatic speech recognition, we extracted acou... | ['Akira Sasou', 'Bagus Tris Atmaja'] | 2023-05-05 | null | null | null | icassp-2023-5 | ['culture', 'type'] | ['speech', 'speech'] | [-2.47771785e-01 2.97064126e-01 2.23338544e-01 -3.77354383e-01
-8.33828151e-01 -4.22680438e-01 3.88689697e-01 2.98086721e-02
-5.20863175e-01 5.37271440e-01 4.96468574e-01 1.61309928e-01
1.35546535e-01 -1.97610274e-01 -3.04757878e-02 -6.36449993e-01
-3.25510293e-01 2.63396204e-01 1.23075053e-01 -2.95949221... | [13.63438892364502, 5.753809452056885] |
16e0b0bb-bd8c-4f0c-89f9-0bb19681a5ab | roomdreamer-text-driven-3d-indoor-scene | 2305.11337 | null | https://arxiv.org/abs/2305.11337v1 | https://arxiv.org/pdf/2305.11337v1.pdf | RoomDreamer: Text-Driven 3D Indoor Scene Synthesis with Coherent Geometry and Texture | The techniques for 3D indoor scene capturing are widely used, but the meshes produced leave much to be desired. In this paper, we propose "RoomDreamer", which leverages powerful natural language to synthesize a new room with a different style. Unlike existing image synthesis methods, our work addresses the challenge of... | ['Yang Zhao', 'Junsong Yuan', 'Feng Tang', 'Kai Kang', 'Hongyu Xu', 'Liangliang Cao', 'Liangchen Song'] | 2023-05-18 | null | null | null | null | ['indoor-scene-synthesis'] | ['computer-vision'] | [ 6.21787548e-01 -4.91789021e-02 4.20427471e-01 -4.03477073e-01
-4.08794791e-01 -5.98510206e-01 5.99732697e-01 -3.05762202e-01
2.82521963e-01 5.80054581e-01 3.16717952e-01 -1.63749442e-01
8.04459453e-02 -1.07343662e+00 -7.74589300e-01 -7.26631343e-01
6.14588916e-01 6.91334018e-03 9.05281082e-02 -1.73216626... | [9.344184875488281, -3.074092149734497] |
d5d5a083-f61c-4a9e-9443-2d431918cf4e | summarizing-and-exploring-tabular-data-in | 2005.11490 | null | https://arxiv.org/abs/2005.11490v3 | https://arxiv.org/pdf/2005.11490v3.pdf | Summarizing and Exploring Tabular Data in Conversational Search | Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we ... | ['Zhuyun Dai', 'Jamie Callan', 'Shuo Zhang', 'Krisztian Balog'] | 2020-05-23 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 7.54549503e-02 5.64251661e-01 -5.51068127e-01 -1.73816860e-01
-1.58620405e+00 -1.09017015e+00 7.36833870e-01 7.23649621e-01
-2.19396651e-01 1.20722878e+00 1.34218371e+00 -1.83040828e-01
2.13313088e-01 -6.01415217e-01 -2.79866606e-01 3.03483397e-01
3.71452928e-01 8.31590891e-01 4.18782204e-01 -7.10605502... | [12.395846366882324, 9.278860092163086] |
fd76666b-d3b3-4606-ada1-9e2da60cf8f8 | semeval-2019-shared-task-cross-lingual | 1805.12386 | null | https://arxiv.org/abs/1805.12386v4 | https://arxiv.org/pdf/1805.12386v4.pdf | SemEval 2019 Shared Task: Cross-lingual Semantic Parsing with UCCA - Call for Participation | We announce a shared task on UCCA parsing in English, German and French, and call for participants to submit their systems. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsi... | ['Elior Sulem', 'Daniel Hershcovich', 'Omri Abend', 'Leshem Choshen', 'Zohar Aizenbud', 'Ari Rappoport'] | 2018-05-31 | null | null | null | null | ['ucca-parsing'] | ['natural-language-processing'] | [ 5.67940697e-02 5.09091496e-01 -2.38392845e-01 -6.91092610e-01
-1.23846424e+00 -1.05193841e+00 3.33161443e-01 3.62747997e-01
-2.43219480e-01 6.71438754e-01 7.20947266e-01 -4.55023497e-01
4.04968262e-01 -8.44344437e-01 -5.30393660e-01 -1.62517384e-01
2.25326240e-01 7.89084196e-01 2.86085099e-01 -4.05064911... | [10.315911293029785, 9.478090286254883] |
29add106-c125-4ed7-8a79-617011662963 | text-mining-of-stocktwits-data-for-predicting | 2103.16388 | null | https://arxiv.org/abs/2103.16388v1 | https://arxiv.org/pdf/2103.16388v1.pdf | Text Mining of Stocktwits Data for Predicting Stock Prices | Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model t... | ['Matloob Khushi', 'Usman Naseem', 'Shreya Narang', 'Priyanka Mandal', 'Mukul Jaggi'] | 2021-03-13 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-9.36046243e-01 -4.65400033e-02 -5.58699548e-01 -4.80303228e-01
-2.03407869e-01 -1.09344280e+00 9.56480861e-01 4.64123860e-02
-3.68177682e-01 7.69628644e-01 1.36826962e-01 -3.77622962e-01
1.25604346e-01 -1.13671386e+00 -3.90943080e-01 -3.19703877e-01
-3.34229827e-01 8.03639054e-01 5.09294748e-01 -6.12574399... | [4.407070159912109, 4.285129070281982] |
539a3043-4b61-43c7-9817-8fd511d88b58 | star-sql-guided-pre-training-for-context | 2210.11888 | null | https://arxiv.org/abs/2210.11888v2 | https://arxiv.org/pdf/2210.11888v2.pdf | STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing | In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural language (NL) utterance and table schema representations for text-to-SQL conversations. Concretely, we propose two novel pre-training objectives wh... | ['Yongbin Li', 'Luo Si', 'Fei Huang', 'Weijie Li', 'Zheng Cao', 'Binhua Li', 'Bowen Li', 'Min Yang', 'Binyuan Hui', 'Xiangyu Li', 'ZeFeng Cai'] | 2022-10-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 9.96451974e-02 3.21381181e-01 -2.36976579e-01 -1.00060284e+00
-1.54938257e+00 -9.56980050e-01 4.78906989e-01 4.18171495e-01
-1.26336545e-01 3.58219206e-01 9.33236361e-01 -6.87047124e-01
2.27528647e-01 -6.10265136e-01 -1.11712742e+00 -7.72896484e-02
2.19299253e-02 8.66288245e-01 3.83322448e-01 -5.48510194... | [10.039424896240234, 7.885897159576416] |
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