paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
8c4c721f-c893-4875-b1e1-108e43810b23 | liir-at-semeval-2020-task-12-a-cross-lingual | 2005.03695 | null | https://arxiv.org/abs/2005.03695v2 | https://arxiv.org/pdf/2005.03695v2.pdf | LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification | This paper presents our system entitled `LIIR' for SemEval-2020 Task 12 on Multilingual Offensive Language Identification in Social Media (OffensEval 2). We have participated in sub-task A for English, Danish, Greek, Arabic, and Turkish languages. We adapt and fine-tune the BERT and Multilingual Bert models made availa... | ['Marie-Francine Moens', 'Erfan Ghadery'] | 2020-05-07 | null | https://aclanthology.org/2020.semeval-1.274 | https://aclanthology.org/2020.semeval-1.274.pdf | semeval-2020 | ['abuse-detection'] | ['natural-language-processing'] | [-4.96145993e-01 -1.81603506e-01 -1.79123253e-01 -6.62234239e-03
-1.25459397e+00 -1.09028745e+00 8.36586595e-01 3.40105653e-01
-1.27281070e+00 9.35608447e-01 4.22419041e-01 -3.00588608e-01
9.26798284e-02 -3.83326471e-01 -3.71031970e-01 1.72989499e-02
-8.81469548e-02 8.03047955e-01 3.99458259e-02 -7.51642346... | [9.742698669433594, 10.64908504486084] |
89571c1c-39bf-4e7e-a6e0-ebcf5ebcc276 | neural-pre-processing-a-learning-framework | 2303.12148 | null | https://arxiv.org/abs/2303.12148v1 | https://arxiv.org/pdf/2303.12148v1.pdf | Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing | Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space. In this paper, we propose an end-to-end weakly supervised learning approach, called Neural Pre-processing (NPP), for solving all three sub-tasks simultaneously via a neural network, tr... | ['Mert R. Sabuncu', 'Alan Wang', 'Xinzi He'] | 2023-03-21 | null | null | null | null | ['reconstruction', 'skull-stripping'] | ['computer-vision', 'medical'] | [ 2.46503636e-01 4.34779078e-01 -1.96783021e-02 -8.65446389e-01
-1.04886520e+00 -4.03632253e-01 5.84559977e-01 -9.73397568e-02
-8.45855355e-01 5.58996320e-01 3.59121859e-01 -1.49408847e-01
-1.37996040e-02 -3.62752885e-01 -8.07490468e-01 -6.00202084e-01
-1.52617440e-01 3.96251559e-01 -8.71758759e-02 1.85260192... | [14.349220275878906, -2.2017626762390137] |
3eb92077-eef8-47de-aba4-7660dafdcbef | autodoviz-human-centered-automation-for | 2302.09688 | null | https://arxiv.org/abs/2302.09688v1 | https://arxiv.org/pdf/2302.09688v1.pdf | AutoDOViz: Human-Centered Automation for Decision Optimization | We present AutoDOViz, an interactive user interface for automated decision optimization (AutoDO) using reinforcement learning (RL). Decision optimization (DO) has classically being practiced by dedicated DO researchers where experts need to spend long periods of time fine tuning a solution through trial-and-error. Auto... | ['Daniel Haehn', 'Loraine Franke', 'Elizabeth M. Daly', 'Paulito Palmes', 'Radu Marinescu', 'Inge Vejsbjerg', 'Rahul Nair', 'Werner Geyer', 'Dharmashankar Subramanian', 'Long Vu', 'Owen Cornec', 'Cole Makuch', 'Abel N. Valente', 'Shazia Afzal', 'Daniel Karl I. Weidele'] | 2023-02-19 | null | null | null | null | ['automl'] | ['methodology'] | [-3.96711826e-01 5.36910951e-01 -1.03517160e-01 -3.27610940e-01
-5.39262414e-01 -9.71806347e-01 4.08606529e-01 3.67816836e-01
-4.52752531e-01 5.71726739e-01 2.52749801e-01 -8.62439930e-01
-4.23536777e-01 -4.90043700e-01 -2.12294340e-01 -2.17829362e-01
-4.04528938e-02 7.98118472e-01 -3.72046918e-01 -2.68311024... | [4.185873508453369, 1.716565489768982] |
2d7b4d4c-514b-4dbd-8df0-a59a45719d24 | review-on-the-feasibility-of-adversarial | 2303.07003 | null | https://arxiv.org/abs/2303.07003v1 | https://arxiv.org/pdf/2303.07003v1.pdf | Review on the Feasibility of Adversarial Evasion Attacks and Defenses for Network Intrusion Detection Systems | Nowadays, numerous applications incorporate machine learning (ML) algorithms due to their prominent achievements. However, many studies in the field of computer vision have shown that ML can be fooled by intentionally crafted instances, called adversarial examples. These adversarial examples take advantage of the intri... | ['Wim Mees', 'Jean-Michel Dricot', 'Thibault Debatty', 'Tayeb Kenaza', 'Benjamin Cochez', 'Islam Debicha'] | 2023-03-13 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 4.35317934e-01 3.03192616e-01 -1.25546783e-01 -1.05523564e-01
-2.65546411e-01 -9.11292255e-01 9.11486506e-01 -1.81563899e-01
-2.32862964e-01 5.35053194e-01 -6.50800109e-01 -7.27556586e-01
1.71820760e-01 -7.93982327e-01 -6.95498168e-01 -7.60442376e-01
-6.16944768e-02 -1.10488690e-01 2.41977185e-01 -2.20286772... | [5.689700603485107, 7.661377906799316] |
8251f7f5-e901-46e5-a994-a52958f0a96a | dynamic-texture-synthesis-by-incorporating | 2104.05940 | null | https://arxiv.org/abs/2104.05940v2 | https://arxiv.org/pdf/2104.05940v2.pdf | Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations | The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synthesis models comes from poor treatment of the long-range texture correlation and motion information. To address this problem, we incorporate ... | ['C. -C. Jay Kuo', 'Shiyu Mou', 'Ye Wang', 'Hong-Shuo Chen', 'Bin Wang', 'Kaitai Zhang'] | 2021-04-13 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.07505733e-01 -4.24221098e-01 -2.80673057e-01 4.40530293e-03
-4.81724024e-01 -2.56881446e-01 5.84880948e-01 -5.97861350e-01
2.85397798e-01 5.86887777e-01 8.18271860e-02 1.33317113e-01
-9.22942981e-02 -6.21905863e-01 -4.45172817e-01 -1.12432420e+00
-4.63051982e-02 -1.46564111e-01 6.95057869e-01 -2.60296375... | [11.0285062789917, -0.9663817882537842] |
dfec2368-b51d-45f2-a5f3-18c15aa9370f | active-learning-with-contrastive-pre-training | 2307.02744 | null | https://arxiv.org/abs/2307.02744v1 | https://arxiv.org/pdf/2307.02744v1.pdf | Active Learning with Contrastive Pre-training for Facial Expression Recognition | Deep learning has played a significant role in the success of facial expression recognition (FER), thanks to large models and vast amounts of labelled data. However, obtaining labelled data requires a tremendous amount of human effort, time, and financial resources. Even though some prior works have focused on reducing... | ['Ali Etemad', 'Shuvendu Roy'] | 2023-07-06 | null | null | null | null | ['facial-expression-recognition', 'active-learning', 'active-learning'] | ['computer-vision', 'methodology', 'natural-language-processing'] | [ 5.00627160e-01 4.39566284e-01 -4.34215784e-01 -7.52911150e-01
-1.01206839e+00 -1.63314328e-01 6.16105556e-01 4.37964126e-02
-6.73649728e-01 8.66963208e-01 3.62907380e-01 1.34267002e-01
1.12383477e-01 -5.54825962e-01 -5.94632268e-01 -7.61665165e-01
1.93047240e-01 5.01893878e-01 7.07122982e-02 -1.78586364... | [13.554035186767578, 1.6948810815811157] |
6b5e51a0-37e7-4e89-a4d4-74abdd074fb1 | dta-physical-camouflage-attacks-using | 2203.09831 | null | https://arxiv.org/abs/2203.09831v1 | https://arxiv.org/pdf/2203.09831v1.pdf | DTA: Physical Camouflage Attacks using Differentiable Transformation Network | To perform adversarial attacks in the physical world, many studies have proposed adversarial camouflage, a method to hide a target object by applying camouflage patterns on 3D object surfaces. For obtaining optimal physical adversarial camouflage, previous studies have utilized the so-called neural renderer, as it supp... | ['Howon Kim', 'Se-Yoon Oh', 'Hunmin Yang', 'Thi-Thu-Huong Le', 'Youngyeo Yun', 'Harashta Tatimma Larasati', 'Hyoeun Kang', 'Yongsu Kim', 'Naufal Suryanto'] | 2022-03-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Suryanto_DTA_Physical_Camouflage_Attacks_Using_Differentiable_Transformation_Network_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Suryanto_DTA_Physical_Camouflage_Attacks_Using_Differentiable_Transformation_Network_CVPR_2022_paper.pdf | cvpr-2022-1 | ['real-world-adversarial-attack'] | ['adversarial'] | [ 2.05780998e-01 4.71098125e-02 2.74196208e-01 4.49718773e-01
-5.46280086e-01 -1.02963245e+00 8.12804878e-01 -8.79127502e-01
-2.27565486e-02 4.30307090e-01 -4.55247819e-01 -6.82919681e-01
3.45877409e-01 -9.06670451e-01 -1.13609970e+00 -7.99118459e-01
-1.46015733e-01 -2.58573517e-02 4.72578704e-01 -3.15683335... | [5.4350905418396, 7.878684043884277] |
7043733f-bd2d-44e5-bfa0-31adbde2d794 | pedestrian-behavior-maps-for-safety | 2305.04506 | null | https://arxiv.org/abs/2305.04506v1 | https://arxiv.org/pdf/2305.04506v1.pdf | Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis | It is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking (AEB) systems which can trigger warnings and apply brakes as a pedestrian enters a vehicle's path. Unfortunately, ... | ['Mohan Trivedi', 'Afnan Alofi', 'Akshay Gopalkrishnan', 'Lulua Rakla', 'Samveed Desai', 'Ross Greer'] | 2023-05-08 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-4.31100667e-01 -3.23753178e-01 -1.87992439e-01 -4.38690871e-01
-8.38546872e-01 -5.84008336e-01 6.22212648e-01 5.60533106e-01
-7.47738004e-01 7.25913584e-01 2.51734287e-01 -9.45646584e-01
4.84840602e-01 -9.54081476e-01 -6.54376864e-01 -1.91312402e-01
-1.13919109e-01 1.91042423e-01 9.57635701e-01 -2.83266962... | [7.754637718200684, -0.8871783018112183] |
b5920e39-0d17-48d6-ad37-641ede583759 | deleter-leveraging-bert-to-perform | 1909.03223 | null | https://arxiv.org/abs/1909.03223v1 | https://arxiv.org/pdf/1909.03223v1.pdf | Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression | Text compression has diverse applications such as Summarization, Reading Comprehension and Text Editing. However, almost all existing approaches require either hand-crafted features, syntactic labels or parallel data. Even for one that achieves this task in an unsupervised setting, its architecture necessitates a task-... | ['Richard Socher', 'Tong Niu', 'Caiming Xiong'] | 2019-09-07 | null | null | null | null | ['sentence-compression', 'text-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.10108519e-01 2.44272828e-01 -3.14316750e-01 -3.33195984e-01
-8.52169335e-01 -3.82185400e-01 4.27689523e-01 5.17397583e-01
-6.80637419e-01 6.67874038e-01 6.10341370e-01 -4.23123926e-01
1.40094534e-01 -6.82172716e-01 -8.49422991e-01 -3.96824807e-01
1.63217336e-01 7.15420425e-01 -1.28869757e-01 -3.10108364... | [12.162691116333008, 9.247700691223145] |
ad161f5d-b50d-4639-96e3-7af359a35394 | rnn-transducers-for-nested-named-entity | 2203.03543 | null | https://arxiv.org/abs/2203.03543v1 | https://arxiv.org/pdf/2203.03543v1.pdf | RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences | Popular solutions to Named Entity Recognition (NER) include conditional random fields, sequence-to-sequence models, or utilizing the question-answering framework. However, they are not suitable for nested and overlapping spans with large ontologies and for predicting the position of the entities. To fill this gap, we i... | ['Laurent El Shafey', 'Mingqiu Wang', 'Izhak Shafran', 'Hagen Soltau'] | 2022-02-08 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 5.43124318e-01 4.08771545e-01 -2.26628155e-01 -6.39697433e-01
-1.02291751e+00 -7.04065382e-01 9.48199108e-02 2.14333192e-01
-6.96899116e-01 6.94691062e-01 3.45041156e-01 -3.39282900e-01
-2.68317331e-02 -7.11656451e-01 -6.91790581e-01 -3.10930818e-01
4.60644551e-02 5.14237881e-01 1.53286234e-01 -1.78991720... | [9.691828727722168, 9.438106536865234] |
4fcf62e4-1d1f-4f86-a07f-259f2f7cc2d6 | joinedtrans-prior-guided-multi-task | 2305.11504 | null | https://arxiv.org/abs/2305.11504v1 | https://arxiv.org/pdf/2305.11504v1.pdf | JOINEDTrans: Prior Guided Multi-task Transformer for Joint Optic Disc/Cup Segmentation and Fovea Detection | Deep learning-based image segmentation and detection models have largely improved the efficiency of analyzing retinal landmarks such as optic disc (OD), optic cup (OC), and fovea. However, factors including ophthalmic disease-related lesions and low image quality issues may severely complicate automatic OD/OC segmentat... | ['Xiaoying Tang', 'Pujin Cheng', 'Zhiyuan Cai', 'Li Lin', 'Huaqing He'] | 2023-05-19 | null | null | null | null | ['fovea-detection'] | ['medical'] | [ 5.91164641e-02 -6.65685907e-02 -1.11139275e-01 -1.90996170e-01
-6.33814692e-01 -3.86929929e-01 2.64910609e-01 -8.08766335e-02
-4.47223276e-01 4.94719326e-01 1.61239862e-01 -3.31477821e-01
-7.64749795e-02 -6.33560002e-01 -4.63620722e-01 -8.16093445e-01
2.87981004e-01 3.37625928e-02 5.87465167e-01 2.81574249... | [15.769103050231934, -3.965282440185547] |
5fbd7a3e-0b5d-4b3e-8ebd-f4afac308f2a | adaptive-personlization-in-federated-learning | 2207.03448 | null | https://arxiv.org/abs/2207.03448v1 | https://arxiv.org/pdf/2207.03448v1.pdf | Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data | Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patients. Although most medical centers conduct similar medical imaging tasks, their differences, such as specializations, number of patients, and... | ['Nassir Navab', 'Maximilian Frantzen', 'Richard Gaus', 'Johann Boschmann', 'Azade Farshad', 'Yousef Yeganeh'] | 2022-07-07 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [-3.09775472e-01 8.40313360e-02 -3.33775342e-01 -5.08908212e-01
-8.83804739e-01 -4.59930032e-01 9.28154290e-02 1.79429665e-01
-3.18774581e-01 5.97519338e-01 9.64212939e-02 -2.81245083e-01
-5.12027681e-01 -5.08200288e-01 -3.72932404e-01 -1.22301447e+00
-1.96439579e-01 8.10674489e-01 7.31908455e-02 3.49279165... | [6.0947136878967285, 6.445560455322266] |
5cdf6eed-841e-40b5-b403-c9bee4766beb | review-of-medical-data-analysis-based-on | 2212.02234 | null | https://arxiv.org/abs/2212.02234v1 | https://arxiv.org/pdf/2212.02234v1.pdf | Review of medical data analysis based on spiking neural networks | Medical data mainly includes various biomedical signals and medical images, and doctors can make judgments on the physical condition of patients through medical data. However, the interpretation of medical data requires a lot of labor costs and may be misjudged, so many scholars use neural networks and deep learning to... | ['D. Zhao', 'L. Wang', 'X. Li'] | 2022-11-13 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 3.38100672e-01 -3.21622074e-01 -1.58145964e-01 -3.01220119e-01
1.91057086e-01 2.07443893e-01 -1.57296017e-01 3.07569164e-03
-6.14630282e-01 9.36836183e-01 -1.60967499e-01 -2.03152806e-01
-6.12366021e-01 -7.60579705e-01 6.37279749e-02 -7.73677945e-01
-3.06930006e-01 4.18513507e-01 -9.38888788e-02 1.11478128... | [13.75610065460205, 3.3306148052215576] |
7a97e31d-a81b-4f37-b7da-db2d10bdd131 | temporally-coherent-video-harmonization-using | 1809.01372 | null | http://arxiv.org/abs/1809.01372v1 | http://arxiv.org/pdf/1809.01372v1.pdf | Temporally Coherent Video Harmonization Using Adversarial Networks | Compositing is one of the most important editing operations for images and
videos. The process of improving the realism of composite results is often
called harmonization. Previous approaches for harmonization mainly focus on
images. In this work, we take one step further to attack the problem of video
harmonization. S... | ['Hao-Zhi Huang', 'Shi-Min Hu', 'Senzhe Xu', 'Junxiong Cai', 'Wei Liu'] | 2018-09-05 | null | null | null | null | ['video-harmonization'] | ['computer-vision'] | [ 2.88094848e-01 -1.28298223e-01 1.73399478e-01 -1.65424585e-01
-4.56827015e-01 -5.56599140e-01 6.13974571e-01 -2.49220148e-01
-4.30614680e-01 7.17384994e-01 -5.92121482e-03 -1.07969731e-01
2.00102687e-01 -8.86044025e-01 -1.09550071e+00 -5.64838052e-01
6.92372993e-02 -5.77056482e-02 3.83351862e-01 -3.97587091... | [11.256453514099121, -1.0648107528686523] |
a09b9a31-9af5-4a35-a58f-7d34d61a4fec | r-3-reverse-retrieve-and-rank-for-sarcasm | 2004.13248 | null | https://arxiv.org/abs/2004.13248v4 | https://arxiv.org/pdf/2004.13248v4.pdf | $R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge | We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context which could include shared commonsense or world knowledg... | ['Nanyun Peng', 'Debanjan Ghosh', 'Smaranda Muresan', 'Tuhin Chakrabarty'] | 2020-04-28 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.99807674e-01 7.95526147e-01 1.16468869e-01 -5.75130463e-01
-5.73338509e-01 -5.60210645e-01 7.47272015e-01 2.56768644e-01
-1.94630057e-01 7.34637260e-01 1.01847160e+00 2.11694971e-01
5.33894539e-01 -5.74366927e-01 -5.18848449e-02 -3.21280599e-01
9.25161421e-01 6.32457554e-01 -2.29943693e-02 -9.60761905... | [13.073317527770996, 7.729450702667236] |
7b8bd8f3-275a-4c1e-b53b-ed31e587052c | heartbeat-classification-fusing-temporal-and | null | null | https://www.researchgate.net/publication/327263145_Heartbeat_classification_fusing_temporal_and_morphological_information_of_ECGs_via_ensemble_of_classifiers | https://www.researchgate.net/publication/327263145_Heartbeat_classification_fusing_temporal_and_morphological_information_of_ECGs_via_ensemble_of_classifiers | Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers | A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets... | ['V.Mondéjar-Guerr', 'J.Rouco', 'J.Novo', 'M.Ortega', 'M.G.Penedo'] | 2019-01-01 | null | null | null | biomedical-signal-processing-and-control-2019 | ['heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.87591237e-01 -1.36183083e-01 1.01573437e-01 -3.39754075e-01
-4.04653639e-01 -3.41542512e-01 5.04917562e-01 8.54870915e-01
-4.09506649e-01 9.23283875e-01 -4.12308276e-01 -2.43320167e-01
-4.83702302e-01 -6.29021406e-01 4.04584641e-03 -8.77526522e-01
-1.29393876e-01 4.71382946e-01 5.31200826e-01 -6.15694150... | [14.182726860046387, 3.2487668991088867] |
8779bb3a-e536-49f7-ab79-cea1ed4b6887 | multi-slice-low-rank-tensor-decomposition | 2102.12056 | null | https://arxiv.org/abs/2102.12056v3 | https://arxiv.org/pdf/2102.12056v3.pdf | Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation | Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. However, both the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. In particular, for the common clinical cases where the... | ['Heng-Da Cheng', 'Haotian Wang', 'Xiancheng Zhou', 'Min Xian', 'Changfa Shi'] | 2021-02-24 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-1.46672741e-01 -1.82272136e-01 -4.96543348e-02 -1.92506790e-01
-9.87067819e-01 -5.49341440e-01 2.02117637e-01 1.78780064e-01
-2.29501259e-02 2.60528505e-01 4.87228572e-01 -2.35705495e-01
-3.00682873e-01 -4.31180686e-01 -1.91694990e-01 -1.08849669e+00
-2.05468878e-01 7.14885831e-01 2.53862530e-01 1.50740325... | [14.400650978088379, -2.6542952060699463] |
95594b7f-d09a-4777-8da1-1d92fab57209 | one-shot-learning-for-autonomous-aerial | 2206.01411 | null | https://arxiv.org/abs/2206.01411v1 | https://arxiv.org/pdf/2206.01411v1.pdf | One-shot Learning for Autonomous Aerial Manipulation | This paper is concerned with learning transferable contact models for aerial manipulation tasks. We investigate a contact-based approach for enabling unmanned aerial vehicles with cable-suspended passive grippers to compute the attach points on novel payloads for aerial transportation. This is the first time that the p... | ['Eliseo Ferrante', 'Claudio Zito'] | 2022-06-03 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 3.04421484e-01 2.51958877e-01 1.02124631e-01 1.08611383e-01
-5.16212225e-01 -1.09902275e+00 5.20599604e-01 -7.18322471e-02
-2.81453758e-01 1.01448512e+00 -5.25978029e-01 -1.62733734e-01
-7.75422633e-01 -6.95000172e-01 -1.21611738e+00 -8.10695648e-01
-6.19823515e-01 6.40113294e-01 3.12140733e-01 -4.71590698... | [4.979844093322754, 0.5540348887443542] |
bfd08953-b624-44ae-b716-6a8d6d65d7b4 | large-scale-category-structure-aware-image | null | null | http://papers.nips.cc/paper/4347-large-scale-category-structure-aware-image-categorization | http://papers.nips.cc/paper/4347-large-scale-category-structure-aware-image-categorization.pdf | Large-Scale Category Structure Aware Image Categorization | Most previous research on image categorization has focused on medium-scale data sets, while large-scale image categorization with millions of images from thousands of categories remains a challenge. With the emergence of structured large-scale dataset such as the ImageNet, rich information about the conceptual relation... | ['Bin Zhao', 'Fei Li', 'Eric P. Xing'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['image-categorization'] | ['computer-vision'] | [ 1.52553514e-01 -2.86768466e-01 -2.03241587e-01 -7.97578812e-01
-3.67523998e-01 -6.68059528e-01 4.89351243e-01 5.69953740e-01
-4.76810485e-01 1.66129336e-01 2.78363854e-01 -1.34937137e-01
-3.12339425e-01 -7.89113402e-01 -4.74179357e-01 -3.60803068e-01
-1.25764519e-01 9.47149396e-02 3.60091269e-01 -4.42436226... | [9.756978034973145, 2.045459270477295] |
c4a077a3-6f59-4327-b362-d98591a3e6a3 | preserving-locality-in-vision-transformers | 2304.06971 | null | https://arxiv.org/abs/2304.06971v1 | https://arxiv.org/pdf/2304.06971v1.pdf | Preserving Locality in Vision Transformers for Class Incremental Learning | Learning new classes without forgetting is crucial for real-world applications for a classification model. Vision Transformers (ViT) recently achieve remarkable performance in Class Incremental Learning (CIL). Previous works mainly focus on block design and model expansion for ViTs. However, in this paper, we find that... | ['De-Chuan Zhan', 'Han-Jia Ye', 'Da-Wei Zhou', 'Bowen Zheng'] | 2023-04-14 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 8.27205628e-02 6.89724535e-02 -2.69803017e-01 -4.26208466e-01
-3.37177545e-01 -2.37868503e-01 5.86497605e-01 1.80670783e-01
-4.54064161e-01 6.72507226e-01 3.85564357e-01 -2.18275517e-01
1.19714707e-01 -7.72220910e-01 -1.05868173e+00 -7.71455884e-01
-1.78345609e-02 -8.85918215e-02 6.88497186e-01 -9.77369174... | [9.601052284240723, 1.7705763578414917] |
f5fb1371-9bd4-4249-8cd5-c0c38e58f280 | semi-blind-source-separation-via-sparse | 1212.0451 | null | http://arxiv.org/abs/1212.0451v2 | http://arxiv.org/pdf/1212.0451v2.pdf | Semi-blind Source Separation via Sparse Representations and Online Dictionary Learning | This work examines a semi-blind single-channel source separation problem. Our
specific aim is to separate one source whose local structure is approximately
known, from another a priori unspecified background source, given only a single
linear combination of the two sources. We propose a separation technique based
on lo... | ['Sirisha Rambhatla', 'Jarvis D. Haupt'] | 2012-12-03 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.36029583e-01 -3.20326507e-01 -1.09315254e-01 5.43693863e-02
-1.32245302e+00 -6.37046218e-01 3.78437042e-01 1.12375900e-01
4.53301892e-02 6.29148543e-01 4.38335329e-01 5.19334599e-02
-1.96151823e-01 -2.02701092e-01 -4.61909533e-01 -9.96800005e-01
-9.30113271e-02 2.90896177e-01 -2.32906967e-01 -1.15979195... | [15.229649543762207, 5.73895788192749] |
ab369f46-86af-44ab-81ec-c72b7acd3dc0 | graphrel-modeling-text-as-relational-graphs | null | null | https://aclanthology.org/P19-1136 | https://aclanthology.org/P19-1136.pdf | GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction | In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. In contrast to previous baselines, we consider the interaction between named entities and relations via a 2nd-phase relation-weighted GCN to better ex... | ['Wei-Yun Ma', 'Peng-Hsuan Li', 'Tsu-Jui Fu'] | 2019-07-01 | null | null | null | acl-2019-7 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-6.51533976e-02 9.17534888e-01 -5.71747899e-01 -4.39279079e-01
-6.41824186e-01 -5.61273932e-01 7.08902478e-01 7.54096270e-01
-4.41172987e-01 8.02955806e-01 3.03671837e-01 -3.46725464e-01
-1.75042808e-01 -1.15380204e+00 -4.55631882e-01 1.66572090e-02
-4.63100195e-01 7.20549285e-01 3.38059276e-01 -3.24056029... | [9.251664161682129, 8.613909721374512] |
baf14636-2f46-4ade-8967-5bc00ec4d597 | what-else-can-fool-deep-learning-addressing-1 | 1912.06960 | null | https://arxiv.org/abs/1912.06960v1 | https://arxiv.org/pdf/1912.06960v1.pdf | What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance | There is active research targeting local image manipulations that can fool deep neural networks (DNNs) into producing incorrect results. This paper examines a type of global image manipulation that can produce similar adverse effects. Specifically, we explore how strong color casts caused by incorrectly applied computa... | ['Michael S. Brown', 'Mahmoud Afifi'] | 2019-12-15 | what-else-can-fool-deep-learning-addressing | http://openaccess.thecvf.com/content_ICCV_2019/html/Afifi_What_Else_Can_Fool_Deep_Learning_Addressing_Color_Constancy_Errors_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Afifi_What_Else_Can_Fool_Deep_Learning_Addressing_Color_Constancy_Errors_ICCV_2019_paper.pdf | iccv-2019-10 | ['color-constancy'] | ['computer-vision'] | [ 5.44156015e-01 -1.53452203e-01 3.01730782e-01 -6.93755150e-01
-4.41739112e-01 -4.21337485e-01 3.94921333e-01 -2.37612367e-01
-7.18843341e-01 5.26407242e-01 -4.42049131e-02 -6.16476297e-01
2.99395978e-01 -5.01878500e-01 -1.04973853e+00 -4.93640751e-01
3.24408203e-01 -3.04906011e-01 2.10993901e-01 -7.07975850... | [11.237035751342773, -0.6990960240364075] |
c76a4767-44ba-48b4-a434-8580b8de7570 | bop-benchmark-for-6d-object-pose-estimation | 1808.08319 | null | http://arxiv.org/abs/1808.08319v1 | http://arxiv.org/pdf/1808.08319v1.pdf | BOP: Benchmark for 6D Object Pose Estimation | We propose a benchmark for 6D pose estimation of a rigid object from a single
RGB-D input image. The training data consists of a texture-mapped 3D object
model or images of the object in known 6D poses. The benchmark comprises of: i)
eight datasets in a unified format that cover different practical scenarios,
including... | ['Tae-Kyun Kim', 'Xenophon Zabulis', 'Stephan Ihrke', 'Jiri Matas', 'Federico Tombari', 'Fabian Manhardt', 'Dirk Kraft', 'Tomas Hodan', 'Frank Michel', 'Caner Sahin', 'Bertram Drost', 'Anders Glent Buch', 'Wadim Kehl', 'Eric Brachmann', 'Carsten Rother', 'Joel Vidal'] | 2018-08-24 | bop-benchmark-for-6d-object-pose-estimation-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Tomas_Hodan_PESTO_6D_Object_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Tomas_Hodan_PESTO_6D_Object_ECCV_2018_paper.pdf | eccv-2018-9 | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 2.54695296e-01 -2.95979287e-02 -6.12533167e-02 -2.58583248e-01
-9.14264977e-01 -7.09064662e-01 7.47943819e-01 -1.80681869e-01
-3.35296720e-01 3.07685256e-01 -8.55473280e-02 1.42542988e-01
-4.24349666e-01 -3.56603414e-01 -8.07101548e-01 -5.82851827e-01
-1.78423766e-02 1.26235211e+00 4.70596820e-01 -1.43679023... | [7.571387767791748, -2.618903160095215] |
c38a5065-791a-499d-b5eb-49cd7cbf7e38 | snippet-policy-network-for-multi-class-varied | 2107.13361 | null | https://arxiv.org/abs/2107.13361v1 | https://arxiv.org/pdf/2107.13361v1.pdf | Snippet Policy Network for Multi-class Varied-length ECG Early Classification | Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases. The prevailing studies formulate arrhythmia detection from ECG as a time series classification problem. Meanwhile, early detection of arrhythmia presents a real-world demand for early prevention an... | ['Vincent S. Tseng', 'Gary G. Yen', 'Yu Huang'] | 2021-07-28 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 1.47277296e-01 -4.29181904e-01 -2.11253539e-01 -9.91592482e-02
-6.23298347e-01 -2.72714287e-01 -2.81694438e-03 3.47897321e-01
-4.56230968e-01 7.49804080e-01 -4.52396274e-01 -3.15056652e-01
-6.62197828e-01 -5.24353802e-01 -1.31820574e-01 -8.46947134e-01
-6.33678198e-01 3.51421535e-01 -2.34476291e-02 5.09614460... | [14.288296699523926, 3.2729508876800537] |
66bd27e8-c80c-41cc-8197-5a331463f8d2 | style-over-substance-evaluation-biases-for | 2307.03025 | null | https://arxiv.org/abs/2307.03025v1 | https://arxiv.org/pdf/2307.03025v1.pdf | Style Over Substance: Evaluation Biases for Large Language Models | As large language models (LLMs) continue to advance, accurately and comprehensively evaluating their performance becomes increasingly challenging. Conventionally, human evaluations are considered the gold standard in natural language generation. Recent advancements incorporate state-of-the-art LLMs as proxies for human... | ['Alham Fikri Aji', 'Minghao Wu'] | 2023-07-06 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [-8.78643766e-02 3.48418504e-01 1.86676607e-01 -3.60270143e-01
-1.21126366e+00 -8.06828976e-01 7.74389625e-01 5.93775451e-01
-7.21493781e-01 9.01187003e-01 4.01585281e-01 -3.59896004e-01
1.32054046e-01 -8.25839579e-01 -4.32192534e-01 -2.16891631e-01
6.54093266e-01 5.98942697e-01 -1.26247808e-01 -3.26654881... | [11.772787094116211, 8.853104591369629] |
fd94cc55-c384-4dde-9c8d-160a25f2c01d | freepoint-unsupervised-point-cloud-instance | 2305.06973 | null | https://arxiv.org/abs/2305.06973v1 | https://arxiv.org/pdf/2305.06973v1.pdf | FreePoint: Unsupervised Point Cloud Instance Segmentation | Instance segmentation of point clouds is a crucial task in 3D field with numerous applications that involve localizing and segmenting objects in a scene. However, achieving satisfactory results requires a large number of manual annotations, which is a time-consuming and expensive process. To alleviate dependency on ann... | ['Gui-Song Xia', 'Dengxin Dai', 'Li Jiang', 'Jian Ding', 'Zhikai Zhang'] | 2023-05-11 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 2.32977316e-01 3.16497922e-01 -1.78641200e-01 -6.21652305e-01
-8.90809774e-01 -8.41274083e-01 5.36084175e-01 2.25485861e-01
-2.59808242e-01 2.82283932e-01 -6.58036470e-01 -2.64501184e-01
1.22056909e-01 -8.03716898e-01 -1.00386369e+00 -4.65650737e-01
1.20892137e-01 1.03117073e+00 6.09194040e-01 -3.84468073... | [8.028698921203613, -3.1820857524871826] |
5857fbf8-04f5-4f45-bcc8-1a4a7fa2c34f | lmd-a-learnable-mask-network-to-detect | 2211.00825 | null | https://arxiv.org/abs/2211.00825v2 | https://arxiv.org/pdf/2211.00825v2.pdf | LMD: A Learnable Mask Network to Detect Adversarial Examples for Speaker Verification | Although the security of automatic speaker verification (ASV) is seriously threatened by recently emerged adversarial attacks, there have been some countermeasures to alleviate the threat. However, many defense approaches not only require the prior knowledge of the attackers but also possess weak interpretability. To a... | ['Kunde Yang', 'Wei-Qiang Zhang', 'Xiao-Lei Zhang', 'Jie Wang', 'Xing Chen'] | 2022-11-02 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 2.90743977e-01 1.39345601e-01 2.00989962e-01 -3.14060807e-01
-1.04904270e+00 -9.29390609e-01 3.62920523e-01 -3.90141040e-01
3.70900445e-02 4.78194445e-01 2.14325890e-01 -4.61613029e-01
1.98815674e-01 -3.80365849e-01 -4.60456491e-01 -9.36569929e-01
-1.42546207e-01 -1.80176690e-01 1.14653662e-01 -4.07407671... | [14.012434959411621, 5.833187580108643] |
a137f7bc-7f8e-4d9a-ae77-5a49bedb7f0a | when-sam-meets-medical-images-an | 2304.08506 | null | https://arxiv.org/abs/2304.08506v5 | https://arxiv.org/pdf/2304.08506v5.pdf | When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation | Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analys... | ['Xinde Li', 'Shenghong Ju', 'Tianyi Xia', 'Chuanfei Hu'] | 2023-04-17 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.12086871e-01 5.66574037e-01 -5.80650508e-01 -2.50718087e-01
-1.24073291e+00 -3.90018731e-01 7.81858116e-02 3.27997983e-01
-3.83629590e-01 3.09466898e-01 4.88373488e-02 -3.17086220e-01
1.01781890e-01 -3.63794982e-01 -7.36220032e-02 -8.78723979e-01
2.28118613e-01 8.63856733e-01 6.52647257e-01 1.53551057... | [14.69363021850586, -2.2842347621917725] |
6824218a-eabc-4103-9538-1f5de6db6a12 | on-evaluating-multilingual-compositional | 2306.11420 | null | https://arxiv.org/abs/2306.11420v1 | https://arxiv.org/pdf/2306.11420v1.pdf | On Evaluating Multilingual Compositional Generalization with Translated Datasets | Compositional generalization allows efficient learning and human-like inductive biases. Since most research investigating compositional generalization in NLP is done on English, important questions remain underexplored. Do the necessary compositional generalization abilities differ across languages? Can models composit... | ['Daniel Hershcovich', 'Zi Wang'] | 2023-06-20 | null | null | null | null | ['machine-translation', 'semantic-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.53532046e-01 -1.96540169e-02 -4.13799286e-01 -6.65241778e-01
-8.63630891e-01 -1.03387475e+00 5.80075383e-01 2.77322866e-02
-4.83472049e-01 7.83505797e-01 4.31464165e-01 -8.64996910e-01
1.14171311e-01 -7.29705632e-01 -9.90786016e-01 -4.50002253e-01
2.51367927e-01 8.02576661e-01 4.11517881e-02 -3.90213370... | [10.83095932006836, 9.408235549926758] |
3ee1cdb2-c699-4035-bf3d-c7933b429eb8 | da2-deep-attention-adapter-for-memory | 2012.01362 | null | https://arxiv.org/abs/2012.01362v3 | https://arxiv.org/pdf/2012.01362v3.pdf | $DA^3$:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning | Nowadays, one practical limitation of deep neural network (DNN) is its high degree of specialization to a single task or domain (e.g., one visual domain). It motivates researchers to develop algorithms that can adapt DNN model to multiple domains sequentially, while still performing well on the past domains, which is k... | ['Deliang Fan', 'Adnan Siraj Rakin', 'Li Yang'] | 2020-12-02 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-6.26830989e-03 -4.48577791e-01 -2.65816808e-01 -2.30077296e-01
-3.72028530e-01 -5.71295977e-01 8.42646360e-02 -2.50972867e-01
-6.99743271e-01 8.25612187e-01 -1.83383986e-01 -3.68726939e-01
-4.83324863e-02 -9.74651396e-01 -9.30131495e-01 -7.83004105e-01
5.88908136e-01 3.29031467e-01 5.65696836e-01 -6.87149391... | [8.77720832824707, 2.909540891647339] |
6c522662-a8a6-4ae4-9da7-123818bb089a | pymaf-x-towards-well-aligned-full-body-model | 2207.06400 | null | https://arxiv.org/abs/2207.06400v3 | https://arxiv.org/pdf/2207.06400v3.pdf | PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images | We present PyMAF-X, a regression-based approach to recovering parametric full-body models from monocular images. This task is very challenging since minor parametric deviation may lead to noticeable misalignment between the estimated mesh and the input image. Moreover, when integrating part-specific estimations into th... | ['Yebin Liu', 'Zhenan Sun', 'Liang An', 'Mengcheng Li', 'Yuxiang Zhang', 'Yating Tian', 'Hongwen Zhang'] | 2022-07-13 | null | null | null | null | ['markerless-motion-capture', '3d-human-pose-and-shape-estimation', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.99698204e-01 2.63138592e-01 -3.81971657e-01 -3.51854146e-01
-7.07866013e-01 -6.07700013e-02 1.72372848e-01 -4.65267777e-01
1.11152627e-01 4.57547605e-01 4.35111493e-01 4.46904123e-01
-1.12512819e-01 -5.53624988e-01 -9.96950507e-01 -4.38674748e-01
2.93237627e-01 6.02328598e-01 4.71957326e-02 -3.15914541... | [7.0965375900268555, -1.1946470737457275] |
a3df19cf-7c1c-412f-a9af-afdfe862f056 | seeing-what-you-said-talking-face-generation | 2303.17480 | null | https://arxiv.org/abs/2303.17480v1 | https://arxiv.org/pdf/2303.17480v1.pdf | Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert | Talking face generation, also known as speech-to-lip generation, reconstructs facial motions concerning lips given coherent speech input. The previous studies revealed the importance of lip-speech synchronization and visual quality. Despite much progress, they hardly focus on the content of lip movements i.e., the visu... | ['Haizhou Li', 'Robby T. Tan', 'Malu Zhang', 'Xinyuan Qian', 'Jiadong Wang'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Seeing_What_You_Said_Talking_Face_Generation_Guided_by_a_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Seeing_What_You_Said_Talking_Face_Generation_Guided_by_a_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 5.10458350e-02 1.14619665e-01 -3.10960382e-01 -4.04604971e-02
-1.18904936e+00 -6.86943009e-02 3.73628318e-01 -6.53258979e-01
-1.54844061e-01 6.40663087e-01 5.19716620e-01 1.41838044e-01
1.26169622e-01 -2.01670200e-01 -6.39556646e-01 -8.81134927e-01
4.91237670e-01 -1.01022452e-01 1.19266346e-01 2.26243529... | [14.36989974975586, 5.000560283660889] |
abe6fc8f-e8dc-4d2d-8d35-0ebc4ae3457d | enhanced-vehicle-re-identification-for-its-a | 2208.06579 | null | https://arxiv.org/abs/2208.06579v1 | https://arxiv.org/pdf/2208.06579v1.pdf | Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning | In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolu... | ['Radhika M. Pai', 'Ujjwal Verma', 'Manohara Pai M. M', 'Ashutosh Holla B'] | 2022-08-13 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [ 4.25039195e-02 -4.95008409e-01 -1.49558008e-01 -4.63559508e-01
-4.62169141e-01 -6.01972580e-01 8.85628760e-01 -2.29749262e-01
-4.29995805e-01 4.47566628e-01 -1.98533908e-01 -2.94853240e-01
3.40519249e-02 -6.40363574e-01 -6.79432154e-01 -6.92297876e-01
5.07847190e-01 9.18964073e-02 1.50641054e-01 -1.13753267... | [8.096098899841309, -0.9949317574501038] |
7511162b-c8b9-4413-b759-39f2be05dce0 | learning-universe-model-for-partial-matching | 2210.10374 | null | https://arxiv.org/abs/2210.10374v1 | https://arxiv.org/pdf/2210.10374v1.pdf | Learning Universe Model for Partial Matching Networks over Multiple Graphs | We consider the general setting for partial matching of two or multiple graphs, in the sense that not necessarily all the nodes in one graph can find their correspondences in another graph and vice versa. We take a universe matching perspective to this ubiquitous problem, whereby each node is either matched into an anc... | ['Junchi Yan', 'Tianzhe Wang', 'Jiaxin Lu', 'Zetian Jiang'] | 2022-10-19 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-8.11081287e-03 2.60693967e-01 2.81997807e-02 -1.11680463e-01
-8.50911498e-01 -4.01681036e-01 2.30286852e-01 5.39713264e-01
9.98364687e-02 2.11441427e-01 -1.99289933e-01 -3.97998318e-02
-2.94669956e-01 -7.96171963e-01 -8.82103205e-01 -6.40887618e-01
-3.20211589e-01 8.23237062e-01 2.67303914e-01 -1.10187314... | [7.190425872802734, 6.382202625274658] |
45e960c6-c5f7-47d3-a571-8dd47d368c23 | a-generalized-parametric-3d-shape | 1803.01780 | null | http://arxiv.org/abs/1803.01780v1 | http://arxiv.org/pdf/1803.01780v1.pdf | A generalized parametric 3D shape representation for articulated pose estimation | We present a novel parametric 3D shape representation, Generalized sum of
Gaussians (G-SoG), which is particularly suitable for pose estimation of
articulated objects. Compared with the original sum-of-Gaussians (SoG), G-SoG
can handle both isotropic and anisotropic Gaussians, leading to a more flexible
and adaptable s... | ['Meng Ding', 'Guoliang Fan'] | 2018-03-05 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-1.95992634e-01 2.94687837e-01 3.30330968e-01 -8.70858431e-02
-5.58764040e-01 -5.11097074e-01 2.77754843e-01 -3.02949339e-01
-2.34789729e-01 4.58655059e-01 9.37116593e-02 1.49807796e-01
-4.61241990e-01 -5.29889345e-01 -4.15854067e-01 -8.37934196e-01
-8.78184885e-02 1.16596460e+00 4.89418030e-01 1.51669487... | [7.0356550216674805, -1.2235417366027832] |
cfb0b0ac-b356-45c5-89df-0bd7fa70fecb | you-only-need-one-model-for-open-domain | 2112.07381 | null | https://arxiv.org/abs/2112.07381v2 | https://arxiv.org/pdf/2112.07381v2.pdf | You Only Need One Model for Open-domain Question Answering | Recent approaches to Open-domain Question Answering refer to an external knowledge base using a retriever model, optionally rerank passages with a separate reranker model and generate an answer using another reader model. Despite performing related tasks, the models have separate parameters and are weakly-coupled durin... | ['Kyoung-Gu Woo', 'Christopher D. Manning', 'Ashwin Paranjape', 'Jongwon Lee', 'Akhil Kedia', 'Haejun Lee'] | 2021-12-14 | null | null | null | null | ['hard-attention', 'triviaqa'] | ['methodology', 'miscellaneous'] | [-9.40997992e-03 5.34664690e-01 8.05641264e-02 -1.90219462e-01
-1.47753072e+00 -8.67274761e-01 6.66024685e-01 2.72430360e-01
-6.32728338e-01 6.85772479e-01 5.35925686e-01 -4.94821936e-01
-1.43407226e-01 -9.17381704e-01 -1.03209555e+00 -2.70904712e-02
2.55046487e-01 1.12845004e+00 8.47606540e-01 -6.37510777... | [11.271342277526855, 7.959482669830322] |
9f77aa55-4a9b-4d60-a92d-bb8cb64d47a1 | autoassign-differentiable-label-assignment | 2007.03496 | null | https://arxiv.org/abs/2007.03496v3 | https://arxiv.org/pdf/2007.03496v3.pdf | AutoAssign: Differentiable Label Assignment for Dense Object Detection | Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledge and achieves appearance-aware through a fully differentiable weighting mechanism. During training, to both satisfy the prior distribution ... | ['Jian-Feng Wang', 'Benjin Zhu', 'Songtao Liu', 'Zeming Li', 'Jian Sun', 'Fuhang Zong', 'Zhengkai Jiang'] | 2020-07-07 | null | null | null | null | ['dense-object-detection'] | ['computer-vision'] | [ 4.44471352e-02 -1.95353568e-01 -4.23189789e-01 -6.18903399e-01
-6.57632947e-01 -5.96351445e-01 4.10384983e-01 -1.02476224e-01
-5.64877689e-01 4.08098578e-01 -3.34431738e-01 5.47807217e-02
4.08176184e-01 -4.37180489e-01 -6.19146645e-01 -6.24089003e-01
1.29504070e-01 3.64743173e-01 9.26373661e-01 4.39759418... | [9.234554290771484, 1.3202623128890991] |
bfb466ee-5b16-41d1-a057-fef28448e8d4 | semi-offline-reinforcement-learning-for | 2306.09712 | null | https://arxiv.org/abs/2306.09712v1 | https://arxiv.org/pdf/2306.09712v1.pdf | Semi-Offline Reinforcement Learning for Optimized Text Generation | In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time cost, and offline methods efficiently obtain reward signals by sacrificing exploration capability. We propose semi-offline RL, a novel paradig... | ['Rui Yan', 'Yi Liu', 'Jie Cao', 'Li Dong', 'Victor Ye Dong', 'Yiqiao Jin', 'Xiting Wang', 'Changyu Chen'] | 2023-06-16 | null | null | null | null | ['text-generation', 'offline-rl'] | ['natural-language-processing', 'playing-games'] | [-2.94054955e-01 6.26658872e-02 -6.25081539e-01 2.52080038e-02
-8.84434760e-01 -8.37037385e-01 2.90847272e-01 1.23743623e-01
-8.54143739e-01 8.98964286e-01 -2.36688778e-01 -4.66229945e-01
-2.30608627e-01 -4.82201934e-01 -7.36710548e-01 -4.14461017e-01
-6.49317324e-01 3.25254411e-01 -9.97650176e-02 -1.72233865... | [4.0844221115112305, 2.1860949993133545] |
fedd411a-5cf0-4d3b-8ad6-94f6d813f308 | catchbackdoor-backdoor-testing-by-critical | 2112.13064 | null | https://arxiv.org/abs/2112.13064v2 | https://arxiv.org/pdf/2112.13064v2.pdf | CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing | The success of deep neural networks (DNNs) in real-world applications has benefited from abundant pre-trained models. However, the backdoored pre-trained models can pose a significant trojan threat to the deployment of downstream DNNs. Existing DNN testing methods are mainly designed to find incorrect corner case behav... | ['Zhaoyan Ming', 'Yue Yu', 'Ting Wang', 'Chong Fu', 'Yao Cheng', 'Jinyin Chen', 'Ruoxi Chen', 'Haibo Jin'] | 2021-12-24 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [ 2.27153718e-01 -1.50634870e-01 -1.62724108e-01 1.15833774e-01
-2.95748353e-01 -1.19503701e+00 5.46826422e-01 -3.18315506e-01
-8.44079554e-02 6.69160485e-01 -5.42420864e-01 -9.20111179e-01
-2.27883365e-02 -7.15599418e-01 -1.36861110e+00 -8.01655591e-01
-1.90539181e-01 -1.64491162e-01 5.86486816e-01 -3.87716740... | [5.709142208099365, 7.698341369628906] |
5e9f1cb8-47a0-4bb2-abf1-4ca4d7d9825b | automated-graph-generation-at-sentence-level | null | null | https://aclanthology.org/2020.coling-main.240 | https://aclanthology.org/2020.coling-main.240.pdf | Automated Graph Generation at Sentence Level for Reading Comprehension Based on Conceptual Graphs | This paper proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the form of a directed graph. We adopt the idea of conceptual graphs to frame for the miscellaneous textual information into conceptual compactness. We first empirically observe that this graph representat... | ['Chun-Shien Lu', 'Wan-Hsuan Lin'] | 2020-12-01 | null | null | null | coling-2020-8 | ['miscellaneous'] | ['miscellaneous'] | [ 3.19360226e-01 9.57990110e-01 -9.57575887e-02 -3.87437463e-01
-4.36165154e-01 -4.71153170e-01 8.40190232e-01 8.23964596e-01
-1.77026585e-01 4.55487013e-01 4.09945577e-01 -8.97711635e-01
-6.51884973e-01 -1.21297419e+00 -4.50442553e-01 -2.39063144e-01
-1.83997266e-02 7.00586855e-01 3.81222665e-01 -5.76007783... | [10.494146347045898, 8.013036727905273] |
312f6bfb-b3c9-44d2-bf03-a84edd647966 | cern-confidence-energy-recurrent-network-for | 1704.03058 | null | http://arxiv.org/abs/1704.03058v1 | http://arxiv.org/pdf/1704.03058v1.pdf | CERN: Confidence-Energy Recurrent Network for Group Activity Recognition | This work is about recognizing human activities occurring in videos at
distinct semantic levels, including individual actions, interactions, and group
activities. The recognition is realized using a two-level hierarchy of Long
Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture,
which can be tra... | ['Song-Chun Zhu', 'Sinisa Todorovic', 'Tianmin Shu'] | 2017-04-10 | cern-confidence-energy-recurrent-network-for-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Shu_CERN_Confidence-Energy_Recurrent_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Shu_CERN_Confidence-Energy_Recurrent_CVPR_2017_paper.pdf | cvpr-2017-7 | ['group-activity-recognition'] | ['computer-vision'] | [ 4.36141521e-01 2.97047943e-01 -2.57694095e-01 -4.02998924e-01
-7.73169518e-01 -6.50423095e-02 6.32113159e-01 -1.93400651e-01
-5.04850149e-01 7.11628377e-01 5.08067608e-01 7.43003860e-02
-3.46949309e-01 -4.34111416e-01 -1.11422658e+00 -7.91510761e-01
-4.32354689e-01 1.97002530e-01 3.35423499e-02 2.79640228... | [8.282123565673828, 0.5472071170806885] |
fe0a905f-c649-4635-9d30-3ab985a2d5c7 | icartoonface-a-benchmark-of-cartoon-person | 1907.13394 | null | https://arxiv.org/abs/1907.13394v3 | https://arxiv.org/pdf/1907.13394v3.pdf | Cartoon Face Recognition: A Benchmark Dataset | Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dat... | ['Junhui Liu', 'Yifan Zhao', 'Yi Zheng', 'Xiangju Lu', 'Mengyuan Ren', 'Jia Li', 'He Yan'] | 2019-07-31 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 3.44411641e-01 -4.71682608e-01 -1.07599616e-01 -3.68724257e-01
-6.01739764e-01 -7.59289265e-01 5.77159464e-01 -4.73509789e-01
9.82513800e-02 7.43209779e-01 -1.79900214e-01 7.39657730e-02
2.69705236e-01 -3.76039088e-01 -7.71773517e-01 -9.51591313e-01
1.06916584e-01 4.19397324e-01 -1.10269785e-01 9.37863588... | [13.282976150512695, 0.6846533417701721] |
ea6da077-d339-47ce-b7eb-e49458886522 | on-the-complexity-of-representation-learning | 2212.09429 | null | https://arxiv.org/abs/2212.09429v1 | https://arxiv.org/pdf/2212.09429v1.pdf | On the Complexity of Representation Learning in Contextual Linear Bandits | In contextual linear bandits, the reward function is assumed to be a linear combination of an unknown reward vector and a given embedding of context-arm pairs. In practice, the embedding is often learned at the same time as the reward vector, thus leading to an online representation learning problem. Existing approache... | ['Alessandro Lazaric', 'Matteo Pirotta', 'Andrea Tirinzoni'] | 2022-12-19 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 5.32978714e-01 2.90314078e-01 -1.11923218e+00 -1.49678066e-01
-1.22893488e+00 -1.00225902e+00 3.65085661e-01 1.41347885e-01
3.01714018e-02 1.01738155e+00 1.32720426e-01 -6.58834875e-01
-7.20052421e-01 -9.44204926e-01 -1.17699099e+00 -8.91416550e-01
-2.54798383e-01 5.88018179e-01 -4.31635916e-01 -8.70811939... | [4.5696702003479, 3.3141748905181885] |
7606bb63-7821-4ca3-a730-0381e7cb88e0 | tad-a-large-scale-benchmark-for-traffic | 2209.12386 | null | https://arxiv.org/abs/2209.12386v1 | https://arxiv.org/pdf/2209.12386v1.pdf | TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance | Automatic traffic accidents detection has appealed to the machine vision community due to its implications on the development of autonomous intelligent transportation systems (ITS) and importance to traffic safety. Most previous studies on efficient analysis and prediction of traffic accidents, however, have used small... | ['Shiguo Lian', 'Yibing Nan', 'Chuwen Huang', 'Yajun Xu'] | 2022-09-26 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 9.71919596e-02 -4.72346395e-01 -2.99324870e-01 -2.24273518e-01
-5.23680091e-01 -7.46991113e-02 4.67729390e-01 -3.11786503e-01
-6.02019727e-01 6.89938366e-01 1.26509055e-01 -2.99617141e-01
-6.56764209e-02 -8.03922057e-01 -3.65927905e-01 -7.92882144e-01
2.57718921e-01 1.71480909e-01 8.25339675e-01 -3.74954641... | [7.7908782958984375, -0.49544262886047363] |
1ee9a0f3-c7df-41be-80d4-9d09635acab4 | multivariate-multi-frequency-and-multimodal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Multivariate_Multi-Frequency_and_Multimodal_Rethinking_Graph_Neural_Networks_for_Emotion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Multivariate_Multi-Frequency_and_Multimodal_Rethinking_Graph_Neural_Networks_for_Emotion_CVPR_2023_paper.pdf | Multivariate, Multi-Frequency and Multimodal: Rethinking Graph Neural Networks for Emotion Recognition in Conversation | Complex relationships of high arity across modality and context dimensions is a critical challenge in the Emotion Recognition in Conversation (ERC) task. Yet, previous works tend to encode multimodal and contextual relationships in a loosely-coupled manner, which may harm relationship modelling. Recently, Graph Neu... | ['Heng Tao Shen', 'Shuyuan Zhu', 'Jie Shao', 'Feiyu Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.62401229e-01 -7.34133869e-02 7.95318633e-02 -4.81076568e-01
-3.15757573e-01 -2.50334680e-01 6.30391002e-01 2.67817795e-01
-1.88948959e-01 4.91733223e-01 5.80571890e-01 6.46673888e-03
-6.76893830e-01 -4.97046471e-01 -2.30722949e-01 -6.45211399e-01
-4.73074138e-01 2.76736498e-01 -3.85494411e-01 -7.24176288... | [13.14797306060791, 5.271522045135498] |
53acfa5d-985c-42a5-b67c-4280a9733b3f | autotrigger-named-entity-recognition-with | 2109.04726 | null | https://arxiv.org/abs/2109.04726v3 | https://arxiv.org/pdf/2109.04726v3.pdf | AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction | Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In... | ['Jay Pujara', 'Fred Morstatter', 'Xiang Ren', 'James Allan', 'Elizabeth Boschee', 'Bill Yuchen Lin', 'Sheikh Muhammad Sarwar', 'Ravi Kiran Selvam', 'Dong-Ho Lee'] | 2021-09-10 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 6.82501346e-02 6.52441084e-01 -1.61543131e-01 -7.65181601e-01
-9.90319848e-01 -8.47052395e-01 6.35084033e-01 6.42304242e-01
-8.38557661e-01 9.83245254e-01 4.82112288e-01 -3.85389209e-01
3.05512697e-01 -7.32332885e-01 -8.62139702e-01 -2.05290869e-01
1.22946806e-01 5.28491795e-01 -2.92948812e-01 1.93295404... | [9.70616626739502, 9.460282325744629] |
002cb121-b557-43ea-bdeb-054f93bc320a | fuzzy-jets | 1509.02216 | null | http://arxiv.org/abs/1509.02216v1 | http://arxiv.org/pdf/1509.02216v1.pdf | Fuzzy Jets | Collimated streams of particles produced in high energy physics experiments
are organized using clustering algorithms to form jets. To construct jets, the
experimental collaborations based at the Large Hadron Collider (LHC) primarily
use agglomerative hierarchical clustering schemes known as sequential
recombination. W... | ['Lester Mackey', 'Benjamin Nachman', 'Conrad Stansbury', 'Ariel Schwartzman'] | 2015-09-07 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-6.97574675e-01 -3.04272324e-01 -9.69031602e-02 -2.23982900e-01
-3.17161709e-01 -8.74296486e-01 8.93440485e-01 5.17152965e-01
-4.49456006e-01 6.16865635e-01 7.88066164e-02 -3.05842996e-01
-2.24241704e-01 -8.72451782e-01 -1.75126642e-01 -9.46435571e-01
-7.81475753e-02 1.59047735e+00 1.06809378e+00 2.23772570... | [15.70753002166748, 2.9174845218658447] |
5af461e3-d13f-4e12-a19c-1d39971a9f79 | a-question-type-driven-and-copy-loss-enhanced | 2005.11665 | null | https://arxiv.org/abs/2005.11665v1 | https://arxiv.org/pdf/2005.11665v1.pdf | A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation | The answer-agnostic question generation is a significant and challenging task, which aims to automatically generate questions for a given sentence but without an answer. In this paper, we propose two new strategies to deal with this task: question type prediction and copy loss mechanism. The question type module is to ... | ['Nan Jiang', 'Yunfang Wu', 'Xiuyu Wu'] | 2020-05-24 | a-question-type-driven-and-copy-loss-enhanced-1 | https://aclanthology.org/2020.ngt-1.8 | https://aclanthology.org/2020.ngt-1.8.pdf | ws-2020-7 | ['type-prediction'] | ['computer-code'] | [ 0.22092012 0.5264859 0.24585132 -0.30950218 -1.1985079 -0.84336394
0.52272916 0.14950958 -0.2276954 1.0711993 0.5363187 -0.40046278
0.10883699 -1.0760618 -0.69595456 0.00930414 0.74285287 0.5203562
0.65015966 -0.6993538 0.59438586 -0.06628245 -1.5141984 0.8666496
1.2501278 0.66487914 0.57... | [11.53770923614502, 8.206329345703125] |
eae62db8-221c-4791-a413-98e13678f335 | dhrl-a-graph-based-approach-for-long-horizon | 2210.05150 | null | https://arxiv.org/abs/2210.05150v3 | https://arxiv.org/pdf/2210.05150v3.pdf | DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning | Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction. However, previous HRL algorithms often suffer from serious data inefficiency as environments get large. The extended components, $i.e.$, goal space and length of episodes, impose a burden on ... | ['H. Jin Kim', 'Inkyu Jang', 'Jigang Kim', 'Seungjae Lee'] | 2022-10-11 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.79358536e-01 1.66239142e-01 -3.82282615e-01 1.57501131e-01
-6.22773826e-01 -4.96794194e-01 4.24868137e-01 3.00530493e-01
-6.11058712e-01 1.19237542e+00 1.37697071e-01 -1.28326371e-01
-2.88829893e-01 -9.38261807e-01 -7.46790469e-01 -6.85875833e-01
-5.82522452e-01 2.04884484e-01 6.91933811e-01 -4.02995884... | [4.164243221282959, 1.5676871538162231] |
5b1ecf6b-3d82-4e20-95a5-436ae5f14e7f | robust-neural-malware-detection-models-for | 1806.10741 | null | http://arxiv.org/abs/1806.10741v1 | http://arxiv.org/pdf/1806.10741v1.pdf | Robust Neural Malware Detection Models for Emulation Sequence Learning | Malicious software, or malware, presents a continuously evolving challenge in
computer security. These embedded snippets of code in the form of malicious
files or hidden within legitimate files cause a major risk to systems with
their ability to run malicious command sequences. Malware authors even use
polymorphism to ... | ['Karthik Selvaraj', 'Mady Marinescu', 'Jack W. Stokes', 'Rakshit Agrawal'] | 2018-06-28 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.67642522e-01 -6.47641718e-01 -2.03660622e-01 -2.15401743e-02
-2.86536038e-01 -1.07211924e+00 7.49948382e-01 1.77154601e-01
-5.65970957e-01 1.05482429e-01 -3.67723942e-01 -9.75272417e-01
2.39872709e-01 -7.77235925e-01 -8.17499101e-01 -5.55315912e-01
-6.80274904e-01 2.40616530e-01 4.41312104e-01 3.33873071... | [14.394762992858887, 9.659435272216797] |
08005743-93fc-4ec4-a05f-2a3a41ff1c53 | tf-gnn-graph-neural-networks-in-tensorflow | 2207.03522 | null | https://arxiv.org/abs/2207.03522v1 | https://arxiv.org/pdf/2207.03522v1.pdf | TF-GNN: Graph Neural Networks in TensorFlow | TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. Many production models at Google use TF-GNN and it has been recently released as an open source pr... | ['Bryan Perozzi', 'David Wong', 'Lisa Wang', 'Kevin Villela', 'Anton Tsitsulin', 'Jennifer She', 'Mihir Paradkar', 'John Palowitch', 'Vahab Mirrokni', 'Brandon Mayer', 'André Linhares', 'Silvio Lattanzi', 'Filipe Miguel Gonçalves de Almeida', 'Jonathan Halcrow', 'Neslihan Bulut', 'Peter Battaglia', 'Sami Abu-El-Haija',... | 2022-07-07 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-7.63839126e-01 -1.85604066e-01 -5.64844251e-01 -3.32018971e-01
3.88440222e-01 -3.19172978e-01 6.14629269e-01 1.02133594e-01
-2.01171748e-02 3.53687167e-01 2.40773141e-01 -1.05063331e+00
-6.87121879e-03 -1.05078530e+00 -4.21743095e-01 -2.35261843e-01
-6.07856333e-01 4.71040398e-01 2.62202710e-01 -4.26041871... | [6.97572135925293, 5.796433448791504] |
3b45a4ab-f47e-47c3-8b72-ab171535ad07 | deep-generative-models-in-engineering-design | 2110.10863 | null | https://arxiv.org/abs/2110.10863v4 | https://arxiv.org/pdf/2110.10863v4.pdf | Deep Generative Models in Engineering Design: A Review | Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to design engineering may enable such automated design synthesis and is a rese... | ['Faez Ahmed', 'Amin Heyrani Nobari', 'Lyle Regenwetter'] | 2021-10-21 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [ 1.45057857e-01 1.90663144e-01 -1.78865522e-01 -2.86130141e-02
-1.69908464e-01 -5.94473422e-01 4.50065494e-01 -5.00871778e-01
5.15317082e-01 8.12862098e-01 2.53100246e-01 -3.67844403e-01
-4.76419926e-01 -1.22581995e+00 -6.96138799e-01 -7.34699428e-01
3.04025441e-01 4.53681976e-01 -7.49343634e-01 -3.94676536... | [5.80819034576416, 3.299329996109009] |
e0d9aa05-714d-46e1-a62f-ebb0403533d3 | metacomp-learning-to-adapt-for-online-depth | 2207.10623 | null | https://arxiv.org/abs/2207.10623v1 | https://arxiv.org/pdf/2207.10623v1.pdf | MetaComp: Learning to Adapt for Online Depth Completion | Relying on deep supervised or self-supervised learning, previous methods for depth completion from paired single image and sparse depth data have achieved impressive performance in recent years. However, facing a new environment where the test data occurs online and differs from the training data in the RGB image conte... | ['Liping Xie', 'Mingming Gong', 'Wei Ji', 'Shanshan Zhao', 'Yang Chen'] | 2022-07-21 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 5.04533410e-01 2.72677224e-02 -6.42189905e-02 -3.72203410e-01
-4.36476886e-01 -3.99375111e-01 2.91089058e-01 -1.34250760e-01
-4.45832878e-01 5.69253862e-01 8.76169205e-02 4.66061048e-02
1.05280630e-01 -8.87840450e-01 -8.31502795e-01 -7.42128372e-01
1.82958096e-01 3.81134808e-01 4.11869526e-01 -1.23102508... | [8.6929931640625, -2.337602376937866] |
6c83d004-463e-4263-9a11-52134e126f90 | san-bert-extractive-summarization-for | 2304.01894 | null | https://arxiv.org/abs/2304.01894v1 | https://arxiv.org/pdf/2304.01894v1.pdf | San-BERT: Extractive Summarization for Sanskrit Documents using BERT and it's variants | In this work, we develop language models for the Sanskrit language, namely Bidirectional Encoder Representations from Transformers (BERT) and its variants: A Lite BERT (ALBERT), and Robustly Optimized BERT (RoBERTa) using Devanagari Sanskrit text corpus. Then we extracted the features for the given text from these mode... | ['Mahabala Rao M G', 'Jammi Kunal', 'Sampath Lonka', 'Kartik Bhatnagar'] | 2023-04-04 | null | null | null | null | ['extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.67713219e-01 1.94899529e-01 3.29418406e-02 -3.73333037e-01
-9.69262958e-01 -8.32585871e-01 9.80467379e-01 1.55090794e-01
-4.13700610e-01 1.10952401e+00 1.34478545e+00 -1.69044554e-01
-4.34708476e-01 -5.06219566e-01 -3.06051463e-01 -6.28109217e-01
5.66971526e-02 9.18356299e-01 -2.68932343e-01 -2.66412973... | [12.481362342834473, 9.522987365722656] |
9902f73f-4723-4c32-8797-c5290ab5694a | semi-supervised-few-shot-atomic-action | 2011.08410 | null | https://arxiv.org/abs/2011.08410v1 | https://arxiv.org/pdf/2011.08410v1.pdf | Semi-Supervised Few-Shot Atomic Action Recognition | Despite excellent progress has been made, the performance on action recognition still heavily relies on specific datasets, which are difficult to extend new action classes due to labor-intensive labeling. Moreover, the high diversity in Spatio-temporal appearance requires robust and representative action feature aggreg... | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Sizhe Song', 'Xiaoyuan Ni'] | 2020-11-17 | null | null | null | null | ['atomic-action-recognition'] | ['computer-vision'] | [ 5.89139998e-01 -1.48166418e-01 -6.86419010e-01 -3.21094960e-01
-9.30771232e-01 -1.21311285e-01 7.13367939e-01 -3.18669766e-01
-3.80751938e-01 6.89446747e-01 5.17305255e-01 4.58529174e-01
-1.68091372e-01 -1.41028762e-01 -4.70161259e-01 -7.88547099e-01
-8.65364373e-02 2.83518314e-01 4.12513375e-01 6.90201968... | [8.315871238708496, 0.6965299844741821] |
877dd124-de10-4113-baef-bad4ca038dc1 | learning-graph-models-for-template-free-1 | null | null | https://arxiv.org/abs/2006.07038 | https://arxiv.org/pdf/2006.07038.pdf | Learning Graph Models for Template-Free Retrosynthesis | Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule. A key consideration in building neural models for this task is aligning model design with strategies adopted by chemists. Building on this viewpoin... | ['Regina Barzilay', 'Andreas Krause', 'Connor W. Coley', 'Charlotte Bunne', 'Vignesh Ram Somnath'] | 2021-06-04 | null | null | null | arxiv-2021-6 | ['retrosynthesis'] | ['medical'] | [ 7.86230922e-01 6.41957283e-01 -4.98002976e-01 -2.09251702e-01
-2.88923364e-02 -9.91334558e-01 7.70056844e-01 6.07207000e-01
3.04574780e-02 8.99201989e-01 2.15291008e-01 -6.64770603e-01
2.04672322e-01 -9.11345303e-01 -9.75593805e-01 -6.49506450e-01
2.99484823e-02 4.40226257e-01 5.74637875e-02 -3.61684889... | [4.523787498474121, 6.084957122802734] |
14b7ac01-9bbe-477b-8979-61fdc319f9d3 | through-the-fairness-lens-experimental | 2307.02726 | null | https://arxiv.org/abs/2307.02726v1 | https://arxiv.org/pdf/2307.02726v1.pdf | Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching | Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias and its societal impacts. Despite extensive research on these two topics, little attention has been paid to the fairness of entity matching.... | ['Divesh Srivastava', 'Abolfazl Asudeh', 'Fatemeh Nargesian', 'Nikola Danevski', 'Nima Shahbazi'] | 2023-07-06 | null | null | null | null | ['fairness', 'fairness'] | ['computer-vision', 'miscellaneous'] | [-7.89577365e-02 2.90733904e-01 -4.26933467e-01 -4.82583404e-01
-1.26805127e-01 -4.04647976e-01 5.79850972e-01 7.13955641e-01
-7.74144769e-01 9.63676512e-01 3.78448278e-01 -2.62071550e-01
-8.76763538e-02 -1.07232010e+00 -3.06630284e-01 -2.60374069e-01
-9.50419754e-02 4.21073496e-01 -3.21597070e-01 -1.40116870... | [8.877676010131836, 5.348504543304443] |
70e0de97-8daf-491a-a726-13ccf90ef453 | towards-end-to-end-car-license-plate-location | 2008.10916 | null | https://arxiv.org/abs/2008.10916v2 | https://arxiv.org/pdf/2008.10916v2.pdf | Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios | Benefiting from the rapid development of convolutional neural networks, the performance of car license plate detection and recognition has been largely improved. Nonetheless, most existing methods solve detection and recognition problems separately, and focus on specific scenarios, which hinders the deployment for real... | ['Shuxin Qin', 'Sijiang Liu'] | 2020-08-25 | null | null | null | null | ['license-plate-detection'] | ['computer-vision'] | [ 2.12218806e-01 -9.67684388e-01 -1.45224437e-01 -3.47634614e-01
-8.27338398e-01 -6.88295782e-01 3.25615585e-01 -6.76634312e-01
-5.29903591e-01 3.38346362e-01 -5.38587451e-01 -3.08219492e-01
4.58802998e-01 -5.52084863e-01 -5.47705650e-01 -1.03254044e+00
4.66421753e-01 1.22636735e-01 6.95514679e-01 3.21974903... | [9.78979206085205, -4.985507488250732] |
69118286-3f1f-4e7a-8f68-aa58d1a17ff7 | texture-generation-with-neural-cellular | 2105.07299 | null | https://arxiv.org/abs/2105.07299v1 | https://arxiv.org/pdf/2105.07299v1.pdf | Texture Generation with Neural Cellular Automata | Neural Cellular Automata (NCA) have shown a remarkable ability to learn the required rules to "grow" images, classify morphologies, segment images, as well as to do general computation such as path-finding. We believe the inductive prior they introduce lends itself to the generation of textures. Textures in the natural... | ['Ettore Randazzo', 'Eyvind Niklasson', 'Alexander Mordvintsev'] | 2021-05-15 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.44623113e-01 3.94413948e-01 3.59442919e-01 1.76234975e-01
-2.74312198e-01 -8.44409645e-01 1.21473682e+00 -2.17773877e-02
4.28084321e-02 1.06257689e+00 1.45802004e-02 -1.67017654e-01
-2.33059719e-01 -1.28273261e+00 -1.02873373e+00 -1.44826615e+00
-2.78102100e-01 4.68953848e-01 3.22904319e-01 -4.18703854... | [11.454760551452637, -0.3632754683494568] |
eb1d6c9d-18c5-4907-942b-929572e77a38 | data-augmentation-via-dependency-tree-1 | 1903.09460 | null | http://arxiv.org/abs/1903.09460v1 | http://arxiv.org/pdf/1903.09460v1.pdf | Data Augmentation via Dependency Tree Morphing for Low-Resource Languages | Neural NLP systems achieve high scores in the presence of sizable training
dataset. Lack of such datasets leads to poor system performances in the case
low-resource languages. We present two simple text augmentation techniques
using dependency trees, inspired from image processing. We crop sentences by
removing depende... | ['Mark Steedman', 'Gözde Gül Şahin'] | 2019-03-22 | data-augmentation-via-dependency-tree | https://aclanthology.org/D18-1545 | https://aclanthology.org/D18-1545.pdf | emnlp-2018-10 | ['text-augmentation'] | ['natural-language-processing'] | [ 4.58295703e-01 1.38489127e-01 -2.29429051e-01 -3.80466342e-01
-7.28566647e-01 -7.49746621e-01 8.02548885e-01 -5.87292835e-02
-1.05608809e+00 1.03295887e+00 5.75735509e-01 -4.44833875e-01
4.74899143e-01 -4.03135628e-01 -5.52475274e-01 -4.46737081e-01
1.57502502e-01 5.92589200e-01 2.10636452e-01 -4.85454023... | [10.367616653442383, 9.933441162109375] |
09d6a6ef-80dd-4c62-bff3-3edc175f1656 | parallel-multi-dimensional-lstm-with | 1506.07452 | null | http://arxiv.org/abs/1506.07452v1 | http://arxiv.org/pdf/1506.07452v1.pdf | Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation | Convolutional Neural Networks (CNNs) can be shifted across 2D images or 3D
videos to segment them. They have a fixed input size and typically perceive
only small local contexts of the pixels to be classified as foreground or
background. In contrast, Multi-Dimensional Recurrent NNs (MD-RNNs) can perceive
the entire spat... | ['Wonmin Byeon', 'Marijn F. Stollenga', 'Marcus Liwicki', 'Juergen Schmidhuber'] | 2015-06-24 | parallel-multi-dimensional-lstm-with-1 | http://papers.nips.cc/paper/5642-parallel-multi-dimensional-lstm-with-application-to-fast-biomedical-volumetric-image-segmentation | http://papers.nips.cc/paper/5642-parallel-multi-dimensional-lstm-with-application-to-fast-biomedical-volumetric-image-segmentation.pdf | neurips-2015-12 | ['brain-image-segmentation'] | ['medical'] | [ 5.84039629e-01 1.78728044e-01 5.05419895e-02 -3.54685426e-01
-6.76721632e-01 -3.46449167e-01 3.09309423e-01 -1.15040570e-01
-9.40589666e-01 4.67187166e-01 5.57537824e-02 -5.15832543e-01
2.24646062e-01 -6.60643756e-01 -9.57907259e-01 -9.02010918e-01
-2.77735054e-01 3.45196813e-01 6.12142146e-01 1.58088818... | [14.44774341583252, -2.5688815116882324] |
7d098c14-4850-42ba-9459-37665524016d | agile3d-attention-guided-interactive-multi | 2306.00977 | null | https://arxiv.org/abs/2306.00977v1 | https://arxiv.org/pdf/2306.00977v1.pdf | AGILE3D: Attention Guided Interactive Multi-object 3D Segmentation | During interactive segmentation, a model and a user work together to delineate objects of interest in a 3D point cloud. In an iterative process, the model assigns each data point to an object (or the background), while the user corrects errors in the resulting segmentation and feeds them back into the model. From a mac... | ['Theodora Kontogianni', 'Konrad Schindler', 'Bastian Leibe', 'Francis Engelmann', 'Jonas Schult', 'Sabarinath Mahadevan', 'Yuanwen Yue'] | 2023-06-01 | null | null | null | null | ['interactive-segmentation'] | ['computer-vision'] | [ 1.67203307e-01 -7.06588924e-02 1.71562843e-02 -2.65510291e-01
-6.79224789e-01 -7.48722136e-01 1.12881690e-01 4.22855824e-01
-4.80602711e-01 1.02578782e-01 -3.50880504e-01 -4.22385186e-01
1.91114232e-01 -7.52337158e-01 -8.41662765e-01 -5.63447356e-01
9.54355896e-02 1.01280284e+00 9.34872806e-01 2.67434776... | [9.347145080566406, -0.2928410768508911] |
7d58862f-bfe2-4770-90d8-09bc097bb4b2 | knowledge-hypergraph-embedding-meets | 2102.09557 | null | https://arxiv.org/abs/2102.09557v1 | https://arxiv.org/pdf/2102.09557v1.pdf | Knowledge Hypergraph Embedding Meets Relational Algebra | Embedding-based methods for reasoning in knowledge hypergraphs learn a representation for each entity and relation. Current methods do not capture the procedural rules underlying the relations in the graph. We propose a simple embedding-based model called ReAlE that performs link prediction in knowledge hypergraphs (ge... | ['David Poole', 'David Vazquez', 'Perouz Taslakian', 'Bahare Fatemi'] | 2021-02-18 | null | null | null | null | ['hypergraph-embedding'] | ['graphs'] | [-1.48726046e-01 1.00061333e+00 -4.69407856e-01 2.56120833e-03
-3.96907032e-02 -7.19201028e-01 8.67545605e-01 5.42149365e-01
6.08605109e-02 6.72314167e-01 3.84770542e-01 -5.50061524e-01
-8.03875327e-01 -1.77983761e+00 -8.99617672e-01 -3.07645708e-01
-7.63925672e-01 1.32078850e+00 4.08906519e-01 -4.70874339... | [8.742385864257812, 7.675741195678711] |
b164b1a9-4ca2-49a7-80b8-894c30db3560 | optimal-and-efficient-binary-questioning-for | 2307.01578 | null | https://arxiv.org/abs/2307.01578v1 | https://arxiv.org/pdf/2307.01578v1.pdf | Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation | Even though data annotation is extremely important for interpretability, research and development of artificial intelligence solutions, most research efforts such as active learning or few-shot learning focus on the sample efficiency problem. This paper studies the neglected complementary problem of getting annotated d... | ['Gabriele Facciolo', 'Josselin Kherroubi', 'Jean-Michel Morel', 'Franco Marchesoni-Acland'] | 2023-07-04 | null | null | null | null | ['active-learning', 'few-shot-learning', 'active-learning'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 5.96069396e-01 6.88398838e-01 -4.41309363e-01 -5.21333218e-01
-9.07822192e-01 -5.21903574e-01 1.46472692e-01 6.77223086e-01
-6.43958926e-01 9.30591881e-01 -8.58384296e-02 -3.96206528e-01
-4.48795468e-01 -6.96654499e-01 -3.08202595e-01 -5.26412427e-01
1.63717270e-01 7.73808956e-01 1.89546749e-01 1.92656498... | [9.643092155456543, 4.531122207641602] |
3efaad36-d0e9-43eb-9b9e-5ebea80d7a54 | swift-and-sure-hardness-aware-contrastive | 2201.00565 | null | https://arxiv.org/abs/2201.00565v2 | https://arxiv.org/pdf/2201.00565v2.pdf | Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings | Knowledge graph embedding (KGE) has shown great potential in automatic knowledge graph (KG) completion and knowledge-driven tasks. However, recent KGE models suffer from high training cost and large storage space, thus limiting their practicality in real-world applications. To address this challenge, based on the lates... | ['Quan Z. Sheng', 'Yu Liu', 'Kai Wang'] | 2022-01-03 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.93769664e-01 5.30171357e-02 -5.09381235e-01 -1.67619195e-02
-6.57695770e-01 -3.35683405e-01 2.20402688e-01 2.89017558e-01
-4.95036066e-01 4.67687458e-01 2.86193956e-02 -3.41347009e-01
-4.84302521e-01 -1.13568187e+00 -7.71001935e-01 -6.49153233e-01
-1.97093546e-01 5.29632807e-01 3.82887602e-01 -1.27903074... | [8.7445068359375, 7.838283061981201] |
7eddf43a-1973-4c8c-b03f-01cc1be0d570 | em-k-indexing-for-approximate-query-matching | 2111.04070 | null | https://arxiv.org/abs/2111.04070v1 | https://arxiv.org/pdf/2111.04070v1.pdf | Em-K Indexing for Approximate Query Matching in Large-scale ER | Accurate and efficient entity resolution (ER) is a significant challenge in many data mining and analysis projects requiring integrating and processing massive data collections. It is becoming increasingly important in real-world applications to develop ER solutions that produce prompt responses for entity queries on l... | ['Gary Glonek', 'Matthew Roughan', 'Samudra Herath'] | 2021-11-07 | null | null | null | null | ['entity-resolution'] | ['natural-language-processing'] | [-2.83483509e-02 -2.87228048e-01 -3.62874925e-01 -4.78737324e-01
-8.06115270e-01 -4.91830081e-01 1.26461685e-01 7.22067535e-01
-7.85759270e-01 7.81156063e-01 -5.01843281e-02 -1.03164628e-01
-7.57864952e-01 -1.59624410e+00 -5.31685174e-01 -7.81347603e-02
-2.50244915e-01 8.51419628e-01 7.83945560e-01 -2.38282621... | [8.235560417175293, 6.7888994216918945] |
28ed3f7d-17b2-4e60-94d8-78ce99818813 | varnet-exploring-variations-for-unsupervised | null | null | https://ieeexplore.ieee.org/document/8594264 | https://ieeexplore.ieee.org/document/8594264 | VarNet: Exploring Variations for Unsupervised Video Prediction | Unsupervised video prediction is a very challenging task due to the complexity and diversity in natural scenes. Prior works directly predicting pixels or optical flows either have the blurring problem or require additional assumptions. We highlight that the crux for video frame prediction lies in precisely capturing th... | ['Jing Ye', 'Shice Liu', 'Qiankun Tang', 'Yiming Zeng', 'Yu Hu', 'Beibei Jin'] | 2018-10-01 | null | null | null | ieee-rsj-international-conference-on-4 | ['video-prediction'] | ['computer-vision'] | [ 6.80522546e-02 -4.49415743e-01 -1.06802091e-01 -3.29994112e-01
-1.54475257e-01 -1.95666067e-02 2.80802667e-01 -5.03064275e-01
-3.04710746e-01 8.87289762e-01 1.77917615e-01 9.83442664e-02
5.61689883e-02 -6.16615593e-01 -8.00168812e-01 -9.37691033e-01
-2.53879458e-01 -3.85599583e-01 5.77681303e-01 -1.73179120... | [10.470507621765137, -1.2027567625045776] |
6c8c2b6b-5592-4b72-af30-b932bc821999 | multi-scale-evolutionary-neural-architecture | 2304.10749 | null | https://arxiv.org/abs/2304.10749v2 | https://arxiv.org/pdf/2304.10749v2.pdf | Emergence of Brain-inspired Small-world Spiking Neural Network through Neuroevolution | Human brain is the product of evolution during hundreds over millions of years and can engage in multiple advanced cognitive functions with low energy consumption. Brain-inspired artificial intelligence serves as a computational continuation of this natural evolutionary process, is imperative to take inspiration from t... | ['Yiting Dong', 'Yi Zeng', 'Bing Han', 'Feifei Zhao', 'Wenxuan Pan'] | 2023-04-21 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 5.60149811e-02 -1.83364347e-01 4.41847205e-01 -7.84712136e-02
6.71081603e-01 -3.93328547e-01 6.05590343e-01 -2.75731772e-01
-3.58465016e-01 1.01475251e+00 -1.71247050e-01 -3.34478989e-02
-3.36749524e-01 -6.63029969e-01 -4.83242393e-01 -1.07040417e+00
-2.91434199e-01 2.15600818e-01 4.87517267e-01 -7.47700274... | [8.038875579833984, 2.78082013130188] |
1ee5a48c-9465-49b7-81cb-0335fd911b40 | influence-of-various-text-embeddings-on | 2305.03144 | null | https://arxiv.org/abs/2305.03144v1 | https://arxiv.org/pdf/2305.03144v1.pdf | Influence of various text embeddings on clustering performance in NLP | With the advent of e-commerce platforms, reviews are crucial for customers to assess the credibility of a product. The star ratings do not always match the review text written by the customer. For example, a three star rating (out of five) may be incongruous with the review text, which may be more suitable for a five s... | ['Rohan Saha'] | 2023-05-04 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-5.70499599e-01 -2.23089412e-01 -1.59625351e-01 -5.51216662e-01
-4.49773401e-01 -7.48369455e-01 6.31681740e-01 8.91858101e-01
-4.90339696e-01 -1.09701499e-01 5.20423234e-01 -4.19708073e-01
-5.62601447e-01 -6.74156427e-01 -7.87914917e-02 -7.63247132e-01
2.57939756e-01 7.35700428e-01 1.87780544e-01 -6.09966666... | [10.374053955078125, 7.254085540771484] |
3d62d4e3-0de1-48c4-99df-66ad10d56118 | hylda-end-to-end-hybrid-learning-domain | 2201.05585 | null | https://arxiv.org/abs/2201.05585v2 | https://arxiv.org/pdf/2201.05585v2.pdf | Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning | In this paper we address the challenging problem of domain adaptation in LiDAR semantic segmentation. We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples. We propose a domain adaption framework that mitigates the issue of d... | ['Liu Bingbing', 'Yang Liu', 'Shubhra Aich', 'Yannis Y. He', 'Mrigank Rochan', 'Eduardo R. Corral-Soto'] | 2022-01-14 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 6.62244022e-01 5.06934404e-01 -4.77231205e-01 -7.96490848e-01
-1.13661301e+00 -6.66628003e-01 5.98806679e-01 -5.08953035e-01
-4.22843516e-01 8.07864368e-01 -1.96785048e-01 -1.96603760e-01
4.38519299e-01 -8.68555784e-01 -1.01006794e+00 -3.68813485e-01
6.50711000e-01 1.14647377e+00 3.74670357e-01 -4.00082693... | [9.772359848022461, 1.2888256311416626] |
3d69f946-8c0d-471b-a894-bdfd7a909edb | code-translation-with-compiler | 2207.03578 | null | https://arxiv.org/abs/2207.03578v5 | https://arxiv.org/pdf/2207.03578v5.pdf | Code Translation with Compiler Representations | In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted rules, which limits their applicability and produces unnatural-looking code. Applying neural machine translation (NMT) approaches to code has... | ['Francois Charton', 'Hugh Leather', 'Gabriel Synnaeve', 'Patrick Labatut', 'Baptiste Roziere', 'Marc Szafraniec'] | 2022-06-30 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 4.03880537e-01 1.91245005e-02 -6.28587604e-01 -1.43532544e-01
-1.23805606e+00 -8.86771917e-01 4.05696630e-01 5.94125688e-02
-3.17701721e-03 4.16177750e-01 2.86054343e-01 -1.11784685e+00
4.88324732e-01 -9.13933337e-01 -1.11865127e+00 -6.85534105e-02
2.11475641e-01 1.65081084e-01 -9.21857059e-02 -5.16548634... | [7.71728515625, 7.849681377410889] |
693e1164-dc23-49ec-ae7d-574088aa0473 | lifelong-person-re-identification-via-1 | 2211.16201 | null | https://arxiv.org/abs/2211.16201v1 | https://arxiv.org/pdf/2211.16201v1.pdf | Lifelong Person Re-Identification via Knowledge Refreshing and Consolidation | Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently. However, a key challenge for LReID is how to incrementally preserve old knowledge and gradually add new cap... | ['Jingya Wang', 'Shenghua Gao', 'Zimo Liu', 'Ye Shi', 'Chunlin Yu'] | 2022-11-29 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-2.94826776e-01 -3.43069285e-01 -1.28736511e-01 -9.12214816e-02
1.22997113e-01 -2.02450678e-01 5.06149530e-01 3.09451103e-01
-9.13060546e-01 9.66892838e-01 1.77098230e-01 -1.02621481e-01
-3.69691521e-01 -9.88539279e-01 -6.78696215e-01 -6.66410983e-01
3.01624206e-03 2.98361033e-01 5.60211897e-01 -2.15784937... | [9.815508842468262, 3.186896562576294] |
25f879b7-7394-4a5e-aed6-4376cfa6357e | enhancing-semantic-code-search-with | 2204.03293 | null | https://arxiv.org/abs/2204.03293v3 | https://arxiv.org/pdf/2204.03293v3.pdf | CoCoSoDa: Effective Contrastive Learning for Code Search | Code search aims to retrieve semantically relevant code snippets for a given natural language query. Recently, many approaches employing contrastive learning have shown promising results on code representation learning and greatly improved the performance of code search. However, there is still a lot of room for improv... | ['Wenchao Gu', 'Hongbin Sun', 'Dongmei Zhang', 'Shi Han', 'Hongyu Zhang', 'Lun Du', 'Yanlin Wang', 'Ensheng Shi'] | 2022-04-07 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-4.32808883e-02 -4.02915120e-01 -5.69052994e-01 -2.59845942e-01
-1.20459461e+00 -6.16653323e-01 4.82948184e-01 1.64166242e-01
-3.81128401e-01 2.12370232e-01 2.57567316e-01 -5.08406162e-01
1.64751664e-01 -4.73741829e-01 -7.43667305e-01 -3.70157897e-01
-1.32319346e-01 2.78813481e-01 3.13910425e-01 -1.86637849... | [7.553072929382324, 8.002159118652344] |
207ddc76-4e4e-441c-a9d3-02eb84354d42 | novel-visual-category-discovery-with-dual | 2107.03358 | null | https://arxiv.org/abs/2107.03358v2 | https://arxiv.org/pdf/2107.03358v2.pdf | Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation | In this paper, we tackle the problem of novel visual category discovery, i.e., grouping unlabelled images from new classes into different semantic partitions by leveraging a labelled dataset that contains images from other different but relevant categories. This is a more realistic and challenging setting than conventi... | ['Kai Han', 'Bingchen Zhao'] | 2021-07-07 | null | http://proceedings.neurips.cc/paper/2021/hash/c203d8a151612acf12457e4d67635a95-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c203d8a151612acf12457e4d67635a95-Paper.pdf | neurips-2021-12 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 5.36114156e-01 3.64620984e-01 -4.65814024e-01 -6.66332126e-01
-8.79326582e-01 -8.03139448e-01 7.84908116e-01 2.86771536e-01
-2.02149108e-01 6.62675142e-01 1.67736217e-01 -8.53129402e-02
-1.53997079e-01 -5.95607877e-01 -6.42632365e-01 -9.78457570e-01
1.13821916e-01 7.81954408e-01 3.18802327e-01 4.01693791... | [9.689432144165039, 2.883833646774292] |
85c4c2d6-27cc-4f46-aff2-9a0023ae7a23 | unsupervised-sentence-compression-using | 1809.02669 | null | http://arxiv.org/abs/1809.02669v1 | http://arxiv.org/pdf/1809.02669v1.pdf | Unsupervised Sentence Compression using Denoising Auto-Encoders | In sentence compression, the task of shortening sentences while retaining the
original meaning, models tend to be trained on large corpora containing pairs
of verbose and compressed sentences. To remove the need for paired corpora, we
emulate a summarization task and add noise to extend sentences and train a
denoising ... | ['Thibault Févry', 'Jason Phang'] | 2018-09-07 | unsupervised-sentence-compression-using-1 | https://aclanthology.org/K18-1040 | https://aclanthology.org/K18-1040.pdf | conll-2018-10 | ['unsupervised-abstractive-sentence-compression'] | ['natural-language-processing'] | [ 8.04146826e-01 9.22759116e-01 2.51955409e-02 -6.93545043e-01
-1.40009916e+00 -6.12448215e-01 5.81850827e-01 5.19748032e-01
-4.38011140e-01 8.51280451e-01 1.01763582e+00 -1.82306319e-01
3.02474290e-01 -5.50932527e-01 -9.09021854e-01 -6.51445463e-02
2.21342668e-01 4.52603161e-01 -2.72985488e-01 -3.07496607... | [12.351906776428223, 9.388152122497559] |
1c031807-f9b9-45c7-8d9c-32d4607f7577 | improving-distantly-supervised-relation-1 | 1804.06987 | null | http://arxiv.org/abs/1804.06987v1 | http://arxiv.org/pdf/1804.06987v1.pdf | Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention | Relation extraction is the problem of classifying the relationship between
two entities in a given sentence. Distant Supervision (DS) is a popular
technique for developing relation extractors starting with limited supervision.
We note that most of the sentences in the distant supervision relation
extraction setting are... | ['Siddhesh Khandelwal', 'Sharmistha Jat', 'Partha Talukdar'] | 2018-04-19 | null | null | null | null | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 3.51822257e-01 7.74027169e-01 -4.64033157e-01 -4.44350839e-01
-8.93525958e-01 -1.85258210e-01 7.48891771e-01 4.12787527e-01
-3.27684432e-01 9.12867665e-01 7.40734160e-01 -6.06557310e-01
-3.76025438e-02 -8.20876956e-01 -4.62543219e-01 -4.23616678e-01
-3.62152606e-02 4.16675597e-01 2.45178536e-01 -5.89486718... | [9.285841941833496, 8.637656211853027] |
27a0c9a7-6f16-4e27-b14b-64e76ec14334 | crossway-diffusion-improving-diffusion-based | 2307.01849 | null | https://arxiv.org/abs/2307.01849v1 | https://arxiv.org/pdf/2307.01849v1.pdf | Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning | Sequence modeling approaches have shown promising results in robot imitation learning. Recently, diffusion models have been adopted for behavioral cloning, benefiting from their exceptional capabilities in modeling complex data distribution. In this work, we propose Crossway Diffusion, a method to enhance diffusion-bas... | ['Michael S. Ryoo', 'Jinghuan Shang', 'Varun Belagali', 'Xiang Li'] | 2023-07-04 | null | null | null | null | ['self-supervised-learning', 'imitation-learning'] | ['computer-vision', 'methodology'] | [ 7.69108161e-02 9.99958441e-02 -4.63214576e-01 7.95698259e-03
-2.65049934e-01 -2.29067385e-01 1.01995826e+00 -4.01253253e-01
-9.14558351e-01 1.08725834e+00 3.13941479e-01 -1.52258307e-01
-4.56696637e-02 -3.82453322e-01 -7.80875385e-01 -1.05090809e+00
-2.38861859e-01 4.76907164e-01 2.54354179e-01 -8.39200988... | [4.342376708984375, 1.2459006309509277] |
e873a185-f0c3-4e5d-bc86-0c856130e502 | learning-3d-human-shape-and-pose-from-dense | 1912.13344 | null | https://arxiv.org/abs/1912.13344v2 | https://arxiv.org/pdf/1912.13344v2.pdf | Learning 3D Human Shape and Pose from Dense Body Parts | Reconstructing 3D human shape and pose from monocular images is challenging despite the promising results achieved by the most recent learning-based methods. The commonly occurred misalignment comes from the facts that the mapping from images to the model space is highly non-linear and the rotation-based pose represent... | ['Zhenan Sun', 'Wanli Ouyang', 'Jie Cao', 'Hongwen Zhang', 'Guo Lu'] | 2019-12-31 | null | null | null | null | ['3d-human-pose-and-shape-estimation', '3d-human-reconstruction', 'human-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.67286009e-01 1.06432125e-01 -8.44117552e-02 -3.18305701e-01
-4.49837089e-01 -1.90699231e-02 3.87832612e-01 -2.26580739e-01
-1.83665082e-01 5.20336151e-01 3.55742097e-01 5.77520311e-01
-4.61542718e-02 -7.50915527e-01 -9.97726679e-01 -4.87343937e-01
-7.39270821e-02 7.78083205e-01 2.84004807e-01 -2.71571577... | [7.056962490081787, -1.0247230529785156] |
22ba9740-e191-44e1-8dac-3d9e76385279 | dsmnet-deep-high-precision-3d-surface | 2304.04200 | null | https://arxiv.org/abs/2304.04200v1 | https://arxiv.org/pdf/2304.04200v1.pdf | DSMNet: Deep High-precision 3D Surface Modeling from Sparse Point Cloud Frames | Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this demand, we first independently construct a set of LiDAR system with an optical stage, and then we build a HPMB dataset based on the constructed L... | ['Weiquan Liu', 'Cheng Wang', 'Yu Zang', 'Xiuhong Lin', 'Zhiyong Wang', 'Changjie Qiu'] | 2023-04-09 | null | null | null | null | ['simultaneous-localization-and-mapping', 'point-cloud-registration'] | ['computer-vision', 'computer-vision'] | [-2.75911212e-01 -4.32171226e-01 -7.29517564e-02 -3.86388302e-01
-7.28970945e-01 -1.65075794e-01 5.64417720e-01 -1.21321678e-01
-1.36912495e-01 5.11688232e-01 -3.67811888e-01 5.25350459e-02
-9.09616873e-02 -1.22396004e+00 -9.39754844e-01 -4.61663991e-01
9.40773115e-02 1.29028690e+00 7.44823396e-01 -6.98200017... | [8.034125328063965, -2.920042037963867] |
edfeff20-60f2-4cab-af00-268fd3fddf7c | topology-aware-uncertainty-for-image | 2306.05671 | null | https://arxiv.org/abs/2306.05671v1 | https://arxiv.org/pdf/2306.05671v1.pdf | Topology-Aware Uncertainty for Image Segmentation | Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelerate large scale annotation, one has to adopt semi-automatic approaches such as proofreading by experts. In this work, we focus on uncertainty... | ['Chao Chen', 'Prateek Prasanna', 'Xiaoling Hu', 'Yikai Zhang', 'Saumya Gupta'] | 2023-06-09 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 5.90363108e-02 4.61991370e-01 1.64057434e-01 -1.31135613e-01
-8.21037471e-01 -8.14126253e-01 3.80493462e-01 4.93130118e-01
3.50220241e-02 8.43201399e-01 4.71554957e-02 -3.83746445e-01
-2.48368666e-01 -8.57082903e-01 -8.98807943e-01 -6.02192879e-01
-6.03475124e-02 6.56874120e-01 5.62392414e-01 3.35499763... | [9.348950386047363, -0.30207139253616333] |
ba43d44b-f255-4302-a5fa-a8c0d957b396 | use-image-clustering-to-facilitate-technology | 2112.08604 | null | https://arxiv.org/abs/2112.08604v1 | https://arxiv.org/pdf/2112.08604v1.pdf | Use Image Clustering to Facilitate Technology Assisted Review | During the past decade breakthroughs in GPU hardware and deep neural networks technologies have revolutionized the field of computer vision, making image analytical potentials accessible to a range of real-world applications. Technology Assisted Review (TAR) in electronic discovery though traditionally has dominantly d... | ['Adam Dabrowski', 'Han Qin', 'Hilary Quatinetz', 'Fusheng Wei', 'Haozhen Zhao'] | 2021-12-16 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-8.47155377e-02 -4.37089294e-01 9.86058861e-02 -3.92927349e-01
-6.35206938e-01 -6.20620012e-01 5.45019984e-01 3.01995903e-01
-4.87164348e-01 -1.68789640e-01 -3.10117062e-02 -5.40847301e-01
-1.19417779e-01 -4.12654102e-01 -6.44698322e-01 -4.89976704e-01
-1.05521567e-01 1.94383115e-01 1.90710589e-01 1.06601395... | [9.23216438293457, 1.9962278604507446] |
1be86529-4d20-442c-a9a1-39369b0e8903 | model-and-data-agreement-for-learning-with | 2212.01054 | null | https://arxiv.org/abs/2212.01054v2 | https://arxiv.org/pdf/2212.01054v2.pdf | Model and Data Agreement for Learning with Noisy Labels | Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learning approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulat... | ['Dongchao Wen', 'Hongzhi Shi', 'Yunfeng Yin', 'Xingchen Cui', 'Weihong Deng', 'Yuhang Zhang'] | 2022-12-02 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [-4.75456342e-02 -1.98816419e-01 9.08373222e-02 -7.57379830e-01
-1.37002861e+00 -3.97035301e-01 1.64791316e-01 1.83351308e-01
-7.23820269e-01 9.93020833e-01 -1.33975551e-01 1.56292737e-01
8.77548605e-02 -5.39351523e-01 -7.39747047e-01 -9.68638122e-01
3.63240242e-01 3.82622302e-01 8.70384723e-02 7.14725032... | [9.411529541015625, 3.8753931522369385] |
74125783-67c1-4b8c-84e8-ea9b069a01de | understanding-image-retrieval-re-ranking-a | 2012.07620 | null | https://arxiv.org/abs/2012.07620v2 | https://arxiv.org/pdf/2012.07620v2.pdf | Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective | The re-ranking approach leverages high-confidence retrieved samples to refine retrieval results, which have been widely adopted as a post-processing tool for image retrieval tasks. However, we notice one main flaw of re-ranking, i.e., high computational complexity, which leads to an unaffordable time cost for real-worl... | ['Yi Yang', 'Errui Ding', 'Xiao Tan', 'Zhedong Zheng', 'Minyue Jiang', 'Xuanmeng Zhang'] | 2020-12-14 | null | null | null | null | ['drone-view-target-localization'] | ['computer-vision'] | [ 2.68426351e-03 -5.59140325e-01 -2.54265994e-01 -4.39099595e-02
-1.19742656e+00 -5.48930049e-01 6.91304982e-01 5.18456936e-01
-4.86082852e-01 3.94166172e-01 1.68238714e-01 -4.00062054e-01
-2.81217039e-01 -1.03800547e+00 -6.22897804e-01 -6.61464155e-01
-1.98862091e-01 2.87163466e-01 2.12102890e-01 -1.80870205... | [10.822872161865234, 0.6888476014137268] |
85015ecd-0100-41ab-b550-b5acf0635b67 | revitalizing-optimization-for-3d-human-pose | 2105.13965 | null | https://arxiv.org/abs/2105.13965v2 | https://arxiv.org/pdf/2105.13965v2.pdf | Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation | We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation. Our optimization method, SCOPE (Sparse Constrained Optimization for 3D human Pose and shapE estimation), is orders of magnitude faster (avg. 4 ms convergence) than existing optimi... | ['Mustafa Mukadam', 'Todd Murphey', 'Weipeng Xu', 'Donglai Xiang', 'Kalyan Vasudev Alwala', 'Taosha Fan'] | 2021-05-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Fan_Revitalizing_Optimization_for_3D_Human_Pose_and_Shape_Estimation_A_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Fan_Revitalizing_Optimization_for_3D_Human_Pose_and_Shape_Estimation_A_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-2.84199025e-02 -1.74986228e-01 -5.70716187e-02 -7.42980465e-02
-7.33070791e-01 -4.64566439e-01 2.80542135e-01 -3.09090286e-01
-6.37947679e-01 4.10330355e-01 4.72935140e-01 2.38375276e-01
3.18675071e-01 -2.80848872e-02 -8.65857601e-01 -1.85417563e-01
-2.25763053e-01 1.02290165e+00 1.00579485e-01 -2.11036637... | [7.038627624511719, -0.880335807800293] |
2dc7a97a-3e03-4b35-942b-e6573be26c07 | point-cloud-quality-assessment-large-scale | 2012.11895 | null | https://arxiv.org/abs/2012.11895v4 | https://arxiv.org/pdf/2012.11895v4.pdf | Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric | Full-reference (FR) point cloud quality assessment (PCQA) has achieved impressive progress in recent years. However, in many cases, obtaining the reference point clouds is difficult, so no-reference (NR) metrics have become a research hotspot. Few researches about NR-PCQA are carried out due to the lack of a large-scal... | ['Le Yang', 'Yiling Xu', 'Qi Yang', 'Yipeng Liu'] | 2020-12-22 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 3.71925184e-03 -7.33442783e-01 1.88496843e-01 -2.66859293e-01
-1.03686082e+00 -2.38941029e-01 1.25794917e-01 4.70521674e-02
5.62291332e-02 4.97946948e-01 1.74639553e-01 5.38339429e-02
-2.73372173e-01 -9.12332594e-01 -6.32572114e-01 -7.19204664e-01
1.20462403e-01 3.49029191e-02 2.06230208e-01 -3.82565945... | [11.8110933303833, -1.8576875925064087] |
8d0cd889-6efb-4638-972d-947442f320c1 | forecasting-crime-with-deep-learning | 1806.01486 | null | http://arxiv.org/abs/1806.01486v1 | http://arxiv.org/pdf/1806.01486v1.pdf | Forecasting Crime with Deep Learning | The objective of this work is to take advantage of deep neural networks in
order to make next day crime count predictions in a fine-grain city partition.
We make predictions using Chicago and Portland crime data, which is augmented
with additional datasets covering weather, census data, and public
transportation. The c... | ['Diego Klabjan', 'Alexander Stec'] | 2018-06-05 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-2.46134654e-01 -8.52961168e-02 7.89420605e-02 -5.55632353e-01
-5.11979997e-01 -2.22352698e-01 5.73041916e-01 5.26081324e-01
-7.74988472e-01 1.06285739e+00 7.53931522e-01 -4.61893886e-01
-3.04852247e-01 -1.54370570e+00 -5.66299975e-01 -2.65253652e-02
-3.23725998e-01 6.70146585e-01 -1.00000598e-01 -4.07078177... | [6.742462635040283, 1.9540774822235107] |
f26a386b-214c-4596-a526-5fdc18efb4f3 | bias-in-automated-image-colorization-metrics | 2202.08143 | null | https://arxiv.org/abs/2202.08143v1 | https://arxiv.org/pdf/2202.08143v1.pdf | Bias in Automated Image Colorization: Metrics and Error Types | We measure the color shifts present in colorized images from the ADE20K dataset, when colorized by the automatic GAN-based DeOldify model. We introduce fine-grained local and regional bias measurements between the original and the colorized images, and observe many colorization effects. We confirm a general desaturatio... | ['Doina Bucur', 'Floris Weers', 'Frank Stapel'] | 2022-02-16 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 1.25348419e-01 -4.10545170e-01 -8.23617801e-02 -4.92952526e-01
-7.79570818e-01 -1.03209460e+00 7.47114658e-01 -4.04082716e-01
-4.69552457e-01 7.19909251e-01 8.70552808e-02 -5.44482796e-03
2.62887627e-01 -3.68856132e-01 -7.22832799e-01 -9.94403064e-01
1.56136245e-01 1.94997817e-01 1.00408174e-01 -9.55611318... | [11.29468059539795, -1.2415286302566528] |
9a8d28fd-df4f-4c26-bc23-6cc76ec47f17 | constructing-effective-personalized-policies | 1612.08082 | null | http://arxiv.org/abs/1612.08082v3 | http://arxiv.org/pdf/1612.08082v3.pdf | Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features | This paper proposes a novel approach for constructing effective personalized
policies when the observed data lacks counter-factual information, is biased
and possesses many features. The approach is applicable in a wide variety of
settings from healthcare to advertising to education to finance. These settings
have in c... | ['Mihaela van der Schaar', 'William R. Zame', 'Qiaojun Feng', 'Onur Atan'] | 2016-12-23 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.52355981e-01 3.29301476e-01 -8.85418594e-01 -4.93213177e-01
-5.84987104e-01 -3.20619285e-01 7.86814094e-01 5.08480668e-01
-8.57585847e-01 1.29055202e+00 5.23682237e-01 -4.82433349e-01
-6.20787859e-01 -8.77511203e-01 -8.18290651e-01 -9.15845096e-01
-2.52559811e-01 8.34060609e-01 -1.30321518e-01 2.30130136... | [8.393753051757812, 5.441229820251465] |
77bc4c55-0356-4df6-92dc-ab4324434a36 | partial-feature-selection-and-alignment-for | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Fu_Partial_Feature_Selection_and_Alignment_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.pdf | Partial Feature Selection and Alignment for Multi-Source Domain Adaptation | Multi-Source Domain Adaptation (MSDA), which dedicates to transfer the knowledge learned from multiple source domains to an unlabeled target domain, has drawn increasing attention in the research community. By assuming that the source and target domains share consistent key feature representations and identical lab... | ['Huimin Lu', 'Kai Zuo', 'Yanli Ji', 'Chao Ma', 'Zuo Cao', 'Xing Xu', 'Ming Zhang', 'Yangye Fu'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['partial-domain-adaptation'] | ['methodology'] | [ 2.52641708e-01 -3.13869864e-01 -3.95160973e-01 -4.98626500e-01
-6.87991083e-01 -6.87136650e-01 4.56420064e-01 5.51149398e-02
-1.44331977e-01 7.83590436e-01 2.93903351e-02 3.13379407e-01
-3.11226219e-01 -7.82290161e-01 -5.83969474e-01 -1.02050936e+00
3.71701658e-01 5.21586359e-01 3.09981972e-01 -5.18707512... | [10.380990982055664, 3.117393970489502] |
af459993-94de-4432-9853-775917537470 | self-attentive-3d-human-pose-and-shape | 2103.14182 | null | https://arxiv.org/abs/2103.14182v2 | https://arxiv.org/pdf/2103.14182v2.pdf | Self-Attentive 3D Human Pose and Shape Estimation from Videos | We consider the task of estimating 3D human pose and shape from videos. While existing frame-based approaches have made significant progress, these methods are independently applied to each image, thereby often leading to inconsistent predictions. In this work, we present a video-based learning algorithm for 3D human p... | ['Ming-Hsuan Yang', 'Robinson Piramuthu', 'Marco Piccirilli', 'Yun-Chun Chen'] | 2021-03-26 | null | null | null | null | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-1.61925346e-01 -1.64682552e-01 -9.67056379e-02 -3.45855743e-01
-4.97968137e-01 -6.94317520e-02 4.09424484e-01 -2.01572821e-01
-4.51714754e-01 4.97912526e-01 3.75935197e-01 3.44358146e-01
2.71043271e-01 -2.32380465e-01 -6.21535540e-01 -3.23855639e-01
-3.66767317e-01 4.57882524e-01 6.09844506e-01 -1.40857846... | [7.257760524749756, -0.51537024974823] |
e1daab01-6e1e-4379-96ee-1a90f354f31e | krm-based-dialogue-management | 1912.00669 | null | https://arxiv.org/abs/1912.00669v1 | https://arxiv.org/pdf/1912.00669v1.pdf | KRM-based Dialogue Management | A KRM-based dialogue management (DM) is proposed using to implement human-computer dialogue system in complex scenarios. KRM-based DM has a well description ability and it can ensure the logic of the dialogue process. Then a complex application scenario in the Internet of Things (IOT) industry and a dialogue system imp... | ['Wei Zheng', 'Xiaoyu Chi', 'Wenwu Qu'] | 2019-12-02 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-3.34046483e-01 6.16702199e-01 3.13674241e-01 -6.23128653e-01
2.75340490e-02 -5.51975965e-01 9.16112483e-01 1.63882058e-02
5.59642762e-02 6.27164245e-01 2.43151724e-01 -2.99398661e-01
-3.80436122e-01 -9.26396906e-01 5.58124006e-01 -2.16094911e-01
2.21866682e-01 1.22885609e+00 7.51262963e-01 -9.30368662... | [12.979720115661621, 7.984541416168213] |
4cea371a-2ec2-4b26-b4bf-de25a2db5d25 | multires-netvlad-augmenting-place-recognition | 2202.09146 | null | https://arxiv.org/abs/2202.09146v1 | https://arxiv.org/pdf/2202.09146v1.pdf | MultiRes-NetVLAD: Augmenting Place Recognition Training with Low-Resolution Imagery | Visual Place Recognition (VPR) is a crucial component of 6-DoF localization, visual SLAM and structure-from-motion pipelines, tasked to generate an initial list of place match hypotheses by matching global place descriptors. However, commonly-used CNN-based methods either process multiple image resolutions after traini... | ['Sourav Garg', 'Michael Milford', 'Ahmad Khaliq'] | 2022-02-18 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-2.33033985e-01 -5.72523296e-01 -2.39555880e-01 -3.16143662e-01
-1.25773847e+00 -8.17355454e-01 8.86557281e-01 4.58390951e-01
-5.62431633e-01 4.09834176e-01 4.22366202e-01 1.52058616e-01
-1.30335584e-01 -7.23709822e-01 -8.38789523e-01 -4.02207702e-01
9.42434892e-02 3.51077318e-01 5.05369902e-01 -3.10595930... | [7.670206546783447, -1.995654821395874] |
1cedc504-42f5-4844-8d78-f44f3557151a | minimum-description-length-clustering-to | 2306.14937 | null | https://arxiv.org/abs/2306.14937v2 | https://arxiv.org/pdf/2306.14937v2.pdf | Minimum Description Length Clustering to Measure Meaningful Image Complexity | Existing image complexity metrics cannot distinguish meaningful content from noise. This means that white noise images, which contain no meaningful information, are judged as highly complex. We present a new image complexity metric through hierarchical clustering of patches. We use the minimum description length princi... | ['Thomas Lukasiewicz', 'Louis Mahon'] | 2023-06-26 | null | null | null | null | ['clustering'] | ['methodology'] | [ 2.39548787e-01 -1.22850470e-01 3.21756691e-01 -1.80331022e-01
-5.78309000e-01 -7.83429086e-01 3.76214296e-01 4.61219490e-01
-3.48134220e-01 5.80372773e-02 2.38048375e-01 -1.02380499e-01
-4.85931247e-01 -5.90289116e-01 -2.09234029e-01 -7.25957692e-01
-2.20550746e-01 1.17000841e-01 6.14612997e-01 -8.80082697... | [11.460186958312988, -2.2179174423217773] |
a7975424-bfab-438e-817f-27f0528214b1 | a-versatile-multi-agent-reinforcement | 2306.07542 | null | https://arxiv.org/abs/2306.07542v1 | https://arxiv.org/pdf/2306.07542v1.pdf | A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management | Multi-agent reinforcement learning (MARL) models multiple agents that interact and learn within a shared environment. This paradigm is applicable to various industrial scenarios such as autonomous driving, quantitative trading, and inventory management. However, applying MARL to these real-world scenarios is impeded by... | ['Jiang Bian', 'Lei Song', 'Li Zhao', 'Chuheng Zhang', 'Wei Jiang', 'Zhihao Liu', 'Xianliang Yang'] | 2023-06-13 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-6.03913665e-01 -2.89040387e-01 -6.36973530e-02 2.64524132e-01
-2.71159112e-01 -6.82997286e-01 4.60751444e-01 1.90463260e-01
-4.09315020e-01 1.46374369e+00 -5.33930361e-01 -2.49823421e-01
-6.13347173e-01 -7.00894237e-01 -6.71226501e-01 -6.75699830e-01
-8.47208798e-01 1.21502328e+00 2.63827205e-01 -7.68169582... | [3.7728445529937744, 1.969111442565918] |
7bf484fc-2da5-4e47-bc08-6d09cbee0c9b | on-the-complexity-of-dark-chinese-chess | 2112.02989 | null | https://arxiv.org/abs/2112.02989v1 | https://arxiv.org/pdf/2112.02989v1.pdf | On the complexity of Dark Chinese Chess | This paper provides a complexity analysis for the game of dark Chinese chess (a.k.a. "JieQi"), a variation of Chinese chess. Dark Chinese chess combines some of the most complicated aspects of board and card games, such as long-term strategy or planning, large state space, stochastic, and imperfect-information, which m... | ['Tongwei Lu', 'Cong Wang'] | 2021-12-06 | null | null | null | null | ['card-games'] | ['playing-games'] | [-4.76973385e-01 -1.77704707e-01 2.48303235e-01 2.32907474e-01
-2.53808498e-01 -8.42788696e-01 1.75499376e-02 -1.72078967e-01
-7.60767698e-01 1.16869080e+00 -2.75301844e-01 -8.69424820e-01
-5.63504696e-01 -1.06739104e+00 -6.48508668e-02 -5.99706948e-01
-3.20593894e-01 6.50761902e-01 9.33947206e-01 -9.78605449... | [3.498565912246704, 1.5135672092437744] |
8b277f60-64bc-455c-b742-430b30a328a9 | drone-based-volume-estimation-in-indoor | 2211.08013 | null | https://arxiv.org/abs/2211.08013v1 | https://arxiv.org/pdf/2211.08013v1.pdf | Drone-based Volume Estimation in Indoor Environments | Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incor... | ['John Lygeros', 'Alisa Rupenyan', 'Stefan Stevšić', 'Dominic Liao-McPherson', 'Samuel Balula'] | 2022-11-15 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-4.66785580e-02 9.93049219e-02 3.54396760e-01 -6.58688426e-01
-6.27005517e-01 -5.45755267e-01 3.21710318e-01 7.63067901e-01
-5.41101158e-01 7.92493224e-01 -4.50388640e-01 -4.35782135e-01
-4.34169322e-01 -1.14188981e+00 -9.85599041e-01 -6.07998848e-01
-3.31568092e-01 1.03155887e+00 9.58379731e-02 -5.66847771... | [7.198177337646484, -2.015690565109253] |
489856e6-a305-405f-8a10-76dc93b2748b | mtna-a-neural-multi-task-model-for-aspect | null | null | https://aclanthology.org/I17-2026 | https://aclanthology.org/I17-2026.pdf | MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews | Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated... | ['Wubai Zhou', 'Wei Xue', 'Tao Li', 'Qing Wang'] | 2017-11-01 | mtna-a-neural-multi-task-model-for-aspect-1 | https://aclanthology.org/I17-2026 | https://aclanthology.org/I17-2026.pdf | ijcnlp-2017-11 | ['extract-aspect'] | ['natural-language-processing'] | [ 1.48829758e-01 6.37952536e-02 -4.08944964e-01 -6.89477324e-01
-1.02239203e+00 -5.59137940e-01 3.38933855e-01 4.09444690e-01
-3.12935978e-01 4.73145068e-01 3.14885944e-01 -4.26302522e-01
1.19856820e-01 -7.91909754e-01 -5.50042391e-01 -5.72262824e-01
4.01717037e-01 5.84385395e-01 -1.96177572e-01 -2.54439294... | [11.395238876342773, 6.681830406188965] |
5950bf6c-ada0-4466-9b75-ea1a5f5b565b | position-guided-text-prompt-for-vision | 2212.09737 | null | https://arxiv.org/abs/2212.09737v2 | https://arxiv.org/pdf/2212.09737v2.pdf | Position-guided Text Prompt for Vision-Language Pre-training | Vision-Language Pre-Training (VLP) has shown promising capabilities to align image and text pairs, facilitating a broad variety of cross-modal learning tasks. However, we observe that VLP models often lack the visual grounding/localization capability which is critical for many downstream tasks such as visual reasoning.... | ['Shuicheng Yan', 'Mike Zheng Shou', 'Pan Zhou', 'Alex Jinpeng Wang'] | 2022-12-19 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Position-Guided_Text_Prompt_for_Vision-Language_Pre-Training_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Position-Guided_Text_Prompt_for_Vision-Language_Pre-Training_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-reasoning', 'zero-shot-cross-modal-retrieval', 'visual-reasoning'] | ['computer-vision', 'miscellaneous', 'reasoning'] | [-1.11007787e-01 -1.24325827e-01 -4.83320266e-01 -1.56404167e-01
-1.04505658e+00 -8.53841722e-01 8.49725246e-01 2.77590062e-02
-3.75436753e-01 3.50523621e-01 2.57216860e-02 -4.80272591e-01
1.81098163e-01 -6.15168989e-01 -1.16261995e+00 -7.00530469e-01
4.06139016e-01 6.22720242e-01 3.39345485e-01 8.04632716... | [10.521842956542969, 1.4499952793121338] |
eb314ea3-ad19-4f0c-b1eb-8315966e768c | bridging-the-visual-gap-wide-range-image | 2103.15149 | null | https://arxiv.org/abs/2103.15149v2 | https://arxiv.org/pdf/2103.15149v2.pdf | Bridging the Visual Gap: Wide-Range Image Blending | In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them. Although such problem is closely related to the topics of image inpainting, ... | ['Wei-Chen Chiu', 'Ya-Chu Chang', 'Chia-Ni Lu'] | 2021-03-28 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lu_Bridging_the_Visual_Gap_Wide-Range_Image_Blending_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-outpainting'] | ['computer-vision'] | [ 5.46664655e-01 8.74202624e-02 -3.13228779e-02 -2.82958180e-01
-9.63392973e-01 -1.97047859e-01 6.58449113e-01 -2.59013683e-01
-7.90667683e-02 5.89244962e-01 1.91207245e-01 -8.07290450e-02
-2.62706243e-02 -8.64412963e-01 -1.23317134e+00 -6.96299672e-01
3.97499561e-01 -1.45437106e-01 5.88904927e-03 -4.10295427... | [11.387192726135254, -1.0996010303497314] |
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