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6c782a41-13ad-44ec-b29e-1d0b0a4f43e1 | hl-dataset-grounding-high-level-linguistic | 2302.12189 | null | https://arxiv.org/abs/2302.12189v1 | https://arxiv.org/pdf/2302.12189v1.pdf | HL Dataset: Grounding High-Level Linguistic Concepts in Vision | Current captioning datasets, focus on object-centric captions, describing the visible objects in the image, often ending up stating the obvious (for humans), e.g. "people eating food in a park". Although these datasets are useful to evaluate the ability of Vision & Language models to recognize the visual content, they ... | ['Albert Gatt', 'Kees Van Deemter', 'Michele Cafagna'] | 2023-02-23 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.67836690e-01 5.93854666e-01 -1.18947200e-01 -5.34337699e-01
-4.41396713e-01 -7.75350749e-01 1.29462492e+00 4.41683084e-01
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3.16813797e-01 6.24409974e-01 -2.56975740e-01 -2.79349536... | [10.701358795166016, 1.529900312423706] |
8a5c9c99-f4c7-44a9-b9d2-410d38831103 | proactive-query-expansion-for-streaming-data | 2201.06592 | null | https://arxiv.org/abs/2201.06592v1 | https://arxiv.org/pdf/2201.06592v1.pdf | Proactive Query Expansion for Streaming Data Using External Source | Query expansion is the process of reformulating the original query by adding relevant words. Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system. Adding words that can... | ['Ilya Safro', 'Alexander Herzog', 'Yuheng Du', 'Amy Apon', 'Farah Alshanik'] | 2022-01-17 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.91560071e-02 -1.61328748e-01 -1.95635334e-01 3.35585028e-02
-8.04652750e-01 -4.31831032e-01 8.41524184e-01 8.07194948e-01
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-3.24958473e-01 7.92979121e-01 6.98014677e-01 -4.85516191... | [10.402411460876465, 7.3660054206848145] |
4d517158-d550-4d69-a074-16d59c0b87d6 | crackseg9k-a-collection-and-benchmark-for | 2208.13054 | null | https://arxiv.org/abs/2208.13054v1 | https://arxiv.org/pdf/2208.13054v1.pdf | CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks | The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. Howeve... | ['Sai Chowdeswara Rao Korlapati', 'Saipraneeth Devunuri', 'Siddharth Sharma', 'Dhananjay Balakrishnan', 'Shreyas Singh', 'Shreyas Kulkarni'] | 2022-08-27 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.33969229e-01 -1.51247755e-01 3.26715022e-01 -1.02955282e-01
-8.87628853e-01 -4.90048617e-01 1.07251806e-02 4.29108590e-01
-2.45002791e-01 7.46992975e-02 -1.41165748e-01 -1.46184132e-01
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6.95203096e-02 1.14978224e-01 8.71747017e-01 -7.94372857... | [7.473919868469238, 1.6094114780426025] |
b0bf25c4-13a5-4cb1-959c-44c6d382c95b | dacs-domain-adaptation-via-cross-domain-mixed | 2007.08702 | null | https://arxiv.org/abs/2007.08702v2 | https://arxiv.org/pdf/2007.08702v2.pdf | DACS: Domain Adaptation via Cross-domain Mixed Sampling | Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when going from synthetic to real data. In this paper we address the problem ... | ['Wilhelm Tranheden', 'Viktor Olsson', 'Lennart Svensson', 'Juliano Pinto'] | 2020-07-17 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.73173070e-01 1.90918818e-01 -2.69371811e-02 -7.11811423e-01
-1.09490013e+00 -6.61750197e-01 7.13134885e-01 -2.48551682e-01
-5.55875182e-01 8.36359859e-01 -2.48316333e-01 1.04201280e-01
2.08286285e-01 -7.52727568e-01 -9.13681388e-01 -7.64968872e-01
3.81798446e-01 1.10487044e+00 4.22473699e-01 1.75436307... | [9.742074012756348, 1.3998373746871948] |
4fa26c72-5877-4855-83de-aa2ea8a6a7ca | matching-feature-sets-for-few-shot-image | 2204.00949 | null | https://arxiv.org/abs/2204.00949v1 | https://arxiv.org/pdf/2204.00949v1.pdf | Matching Feature Sets for Few-Shot Image Classification | In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue... | ['Christian Gagné', 'Jean-François Lalonde', 'Hugo Larochelle', 'Arman Afrasiyabi'] | 2022-04-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['set-matching', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.48832160e-01 -8.49549994e-02 -2.46211275e-01 -7.69696116e-01
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-6.43805981e-01 4.34917271e-01 7.14584906e-03 8.07956755e-02
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2.39877492e-01 1.27449974e-01 3.92775655e-01 -4.20283347... | [9.949841499328613, 2.6949548721313477] |
93ad11bd-f411-4d7b-b146-477de5261738 | real-time-3d-tracking-of-articulated-tools | 1605.03483 | null | http://arxiv.org/abs/1605.03483v3 | http://arxiv.org/pdf/1605.03483v3.pdf | Real-time 3D Tracking of Articulated Tools for Robotic Surgery | In robotic surgery, tool tracking is important for providing safe tool-tissue
interaction and facilitating surgical skills assessment. Despite recent
advances in tool tracking, existing approaches are faced with major
difficulties in real-time tracking of articulated tools. Most algorithms are
tailored for offline proc... | ['Guang-Zhong Yang', 'Menglong Ye', 'Stamatia Giannarou', 'Lin Zhang'] | 2016-05-11 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [ 7.86306038e-02 1.08165979e-01 8.76319483e-02 2.26119801e-01
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-4.77678597e-01 2.73079183e-02 -4.45476353e-01 -6.36856258e-01
-1.82407215e-01 6.46625876e-01 3.35548133e-01 -3.55594754... | [13.729935646057129, -3.034227132797241] |
9858b599-fd08-4f31-bb86-ba4f5781b732 | softmatch-distance-a-novel-distance-for | 2303.04737 | null | https://arxiv.org/abs/2303.04737v1 | https://arxiv.org/pdf/2303.04737v1.pdf | SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images | General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models ... | ['Licheng Jiao', 'Jingjing Ma', 'Xiangrong Zhang', 'Xu Tang', 'Yuqun Yang'] | 2023-03-08 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.28079334e-01 -2.58698970e-01 -2.14422405e-01 -5.22262931e-01
-3.76446515e-01 -5.51063180e-01 7.97580302e-01 3.68137896e-01
-3.47889930e-01 5.83289504e-01 1.82923391e-01 -2.70319074e-01
6.43304512e-02 -8.57187748e-01 -5.44115961e-01 -8.50824773e-01
2.70675391e-01 6.69804513e-02 6.85709476e-01 -2.26285502... | [9.662845611572266, -1.0279372930526733] |
d6a40d81-9c82-44f1-9765-5f35fadfb70a | patchwork-a-patch-wise-attention-network-for | 1904.01784 | null | https://arxiv.org/abs/1904.01784v2 | https://arxiv.org/pdf/1904.01784v2.pdf | Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams | Recent advances in single-frame object detection and segmentation techniques have motivated a wide range of works to extend these methods to process video streams. In this paper, we explore the idea of hard attention aimed for latency-sensitive applications. Instead of reasoning about every frame separately, our method... | ['Yuning Chai'] | 2019-04-03 | patchwork-a-patch-wise-attention-network-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Chai_Patchwork_A_Patch-Wise_Attention_Network_for_Efficient_Object_Detection_and_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Chai_Patchwork_A_Patch-Wise_Attention_Network_for_Efficient_Object_Detection_and_ICCV_2019_paper.pdf | iccv-2019-10 | ['hard-attention'] | ['methodology'] | [ 4.32154417e-01 2.02985965e-02 -2.47276366e-01 -1.86568961e-01
-6.73701227e-01 -2.44829744e-01 1.75148547e-01 1.66537941e-01
-8.64075184e-01 5.17706692e-01 -3.40198249e-01 -2.37605855e-01
2.18952179e-01 -6.54758930e-01 -8.18496168e-01 -6.69994831e-01
-1.77095965e-01 2.69065320e-01 1.11468256e+00 1.78766206... | [9.096659660339355, -0.030858732759952545] |
942f741b-78e6-4516-b316-7337f575d00f | s4nd-modeling-images-and-videos-as | 2210.06583 | null | https://arxiv.org/abs/2210.06583v2 | https://arxiv.org/pdf/2210.06583v2.pdf | S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces | Visual data such as images and videos are typically modeled as discretizations of inherently continuous, multidimensional signals. Existing continuous-signal models attempt to exploit this fact by modeling the underlying signals of visual (e.g., image) data directly. However, these models have not yet been able to achi... | ['Christopher Ré', 'Stephen A. Baccus', 'Tri Dao', 'Preey Shah', 'Gordon W. Downs', 'Albert Gu', 'Karan Goel', 'Eric Nguyen'] | 2022-10-12 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 1.47041649e-01 2.85269674e-02 -2.25244328e-01 -1.69050351e-01
-9.47198033e-01 -4.03070897e-01 6.20974958e-01 -5.18682420e-01
-4.94068682e-01 4.17264462e-01 1.36120126e-01 -6.56830221e-02
1.56016842e-01 -5.35381615e-01 -1.20119965e+00 -3.29605997e-01
-3.16367537e-01 -9.37614515e-02 4.13161159e-01 -5.74672148... | [9.23874568939209, 0.9530038833618164] |
0aff286b-8410-4019-a9cd-090553950577 | detecting-pulmonary-embolism-from-computed | 2206.01344 | null | https://arxiv.org/abs/2206.01344v1 | https://arxiv.org/pdf/2206.01344v1.pdf | Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network | The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of ... | ['Chin Kuo', 'Yun-Chien Cheng', 'Chia-Hung Yang'] | 2022-06-03 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [ 1.62313227e-02 -2.69302219e-01 -3.05508040e-02 1.05765015e-01
-3.51873130e-01 -4.49506313e-01 -1.47349581e-01 5.55618346e-01
-7.30038762e-01 6.33396626e-01 -4.34903540e-02 -9.35120404e-01
-2.07188085e-01 -1.10493648e+00 -1.26416624e-01 -6.10805035e-01
-1.26181617e-01 8.70613277e-01 6.55332983e-01 5.43229222... | [15.226829528808594, -2.0432801246643066] |
aa4e1b38-e78e-465d-beae-d878d97d4a5d | why-deep-learning-generalizes | 2211.09639 | null | https://arxiv.org/abs/2211.09639v2 | https://arxiv.org/pdf/2211.09639v2.pdf | Why Deep Learning Generalizes | Very large deep learning models trained using gradient descent are remarkably resistant to memorization given their huge capacity, but are at the same time capable of fitting large datasets of pure noise. Here methods are introduced by which models may be trained to memorize datasets that normally are generalized. We f... | ['Benjamin L. Badger'] | 2022-11-17 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-9.44624469e-02 2.68058181e-01 -1.24710336e-01 -3.47710580e-01
-2.02832699e-01 -3.45786452e-01 5.95059335e-01 7.39644766e-02
-7.81078517e-01 9.17472005e-01 8.98080841e-02 -4.64199811e-01
-1.34670362e-01 -1.01603866e+00 -1.01841390e+00 -6.73712075e-01
-2.24494904e-01 3.95893335e-01 1.70793124e-02 -3.75823319... | [8.640959739685059, 3.3391811847686768] |
ad7fc92b-f3f3-4ed4-9f21-10e4ed219fd5 | uncovering-the-missing-pattern-unified | 2303.16005 | null | https://arxiv.org/abs/2303.16005v1 | https://arxiv.org/pdf/2303.16005v1.pdf | Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction | Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. Thi... | ['Yun Fu', 'Chiho Choi', 'Hyung-gun Chi', 'Armin Bazarjani', 'Yi Xu'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['trajectory-prediction'] | ['computer-vision'] | [ 2.08239689e-01 -1.97416514e-01 -5.45672417e-01 -3.42770606e-01
-5.62489212e-01 -3.08813423e-01 4.37988669e-01 -6.91899583e-02
-3.97295579e-02 1.01450467e+00 4.77190107e-01 -4.55273062e-01
-3.28742802e-01 -8.13423753e-01 -9.39229310e-01 -5.98909020e-01
-1.60155773e-01 3.58933322e-02 1.51854858e-01 2.68347040... | [6.623457908630371, 1.930039882659912] |
d67eb62f-aa4f-4d6a-8bea-0354410c6b89 | clip-td-clip-targeted-distillation-for-vision | 2201.05729 | null | https://arxiv.org/abs/2201.05729v3 | https://arxiv.org/pdf/2201.05729v3.pdf | CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks | Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks. While early concurrent works have begun to study this potential on a subset of tasks, important questions remain: 1) What is the benefit of... | ['Lu Yuan', 'Shih-Fu Chang', 'Haoxuan You', 'Bin Xiao', 'Xiyang Dai', 'Jianwei Yang', 'Luowei Zhou', 'Yen-Chun Chen', 'Noel Codella', 'Zhecan Wang'] | 2022-01-15 | null | null | null | null | ['visual-commonsense-reasoning', 'visual-entailment'] | ['reasoning', 'reasoning'] | [ 3.97842407e-01 -4.74073216e-02 -2.25793570e-01 -2.40434512e-01
-1.05491662e+00 -6.20470226e-01 8.56739938e-01 -2.88929194e-01
-8.27593148e-01 5.00969529e-01 1.99636281e-01 -5.32233417e-01
2.88576812e-01 -3.92349064e-01 -7.59316325e-01 -2.86767364e-01
2.04598695e-01 4.65488285e-01 4.57982391e-01 -3.47441375... | [10.693106651306152, 1.73996901512146] |
c7d7bf19-5b4b-4d0a-95b5-6f349278403b | dublin-document-understanding-by-language | 2305.14218 | null | https://arxiv.org/abs/2305.14218v3 | https://arxiv.org/pdf/2305.14218v3.pdf | DUBLIN -- Document Understanding By Language-Image Network | Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images. Existing models often rely on manual feature engineering or domain-specific pipelines, which limit their generalization ability across different document types and languages. In this paper, ... | ['Saurabh Tiwary', 'Subhojit Som', 'Hardik Hansrajbhai Chauhan', 'Vishrav Chaudhary', 'Monojit Choudhury', 'Qiang Liu', 'Owais Mohammed Khan', 'Kumar Tanmay', 'Aditi Khandelwal', 'Kriti Aggarwal'] | 2023-05-23 | null | null | null | null | ['optical-character-recognition', 'feature-engineering', 'document-classification', 'key-information-extraction', 'reading-comprehension'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.92540962e-01 -2.24432230e-01 3.19591835e-02 -2.82558352e-01
-1.12216580e+00 -1.15081060e+00 9.20618057e-01 2.38610297e-01
-9.18114185e-03 3.16110742e-03 2.12841213e-01 -7.72480130e-01
1.12581655e-01 -7.20384717e-01 -9.20125663e-01 -2.26395175e-01
3.90558958e-01 3.83996636e-01 3.77708822e-01 -2.64691144... | [11.426072120666504, 2.2264139652252197] |
a0bb9e1e-672a-48a9-ac21-edb2a939a5c5 | sketch2saliency-learning-to-detect-salient | 2303.11502 | null | https://arxiv.org/abs/2303.11502v3 | https://arxiv.org/pdf/2303.11502v3.pdf | Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings | Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sketches - that they are also salient. This is intuitive as sketching is a natural attentive process at its core. More specifically, we aim to s... | ['Yi-Zhe Song', 'Tao Xiang', 'Pinaki Nath Chowdhury', 'Aneeshan Sain', 'Amandeep Kumar', 'Subhadeep Koley', 'Ayan Kumar Bhunia'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.pdf | cvpr-2023-1 | ['saliency-detection'] | ['computer-vision'] | [ 3.37032557e-01 2.66125798e-01 -1.82686508e-01 -1.88362718e-01
-1.87446564e-01 -3.79100382e-01 9.30869401e-01 3.26992460e-02
-1.12718688e-02 5.72645903e-01 3.64554495e-01 7.65582845e-02
1.85067281e-01 -7.55245924e-01 -6.73899710e-01 -3.69026452e-01
3.10209751e-01 1.56011820e-01 4.78718817e-01 -3.58194649... | [11.665284156799316, 0.338863730430603] |
8f14f6dd-3830-44c9-ac30-04031e88f07a | dvi-depth-guided-video-inpainting-for | 2007.08854 | null | https://arxiv.org/abs/2007.08854v1 | https://arxiv.org/pdf/2007.08854v1.pdf | DVI: Depth Guided Video Inpainting for Autonomous Driving | To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud. By building a dense 3D map from stitched point clouds, frames within a video... | ['Sibo Zhang', 'Ruigang Yang', 'Feixiang Lu', 'Wei Li', 'Miao Liao', 'Dingfu Zhou'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3620_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660001.pdf | eccv-2020-8 | ['video-inpainting'] | ['computer-vision'] | [ 1.45194829e-01 -1.11852080e-01 1.95086494e-01 -1.72481239e-01
-7.43452787e-01 -4.01431829e-01 2.62953013e-01 -3.91145617e-01
-3.55287611e-01 9.42230821e-01 -3.03310931e-01 -2.47759335e-02
2.33363405e-01 -7.74053514e-01 -1.13023901e+00 -6.16476953e-01
2.08158940e-01 4.60052669e-01 6.00645125e-01 -1.53742015... | [8.785969734191895, -2.2898051738739014] |
908e8381-99f5-4c0c-8b58-1f0c18d6862a | multi-person-extreme-motion-prediction-with | 2105.08825 | null | https://arxiv.org/abs/2105.08825v7 | https://arxiv.org/pdf/2105.08825v7.pdf | Multi-Person Extreme Motion Prediction | Human motion prediction aims to forecast future poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackled for single humans in isolation. In this paper, we explore this problem when dealing with humans performing collaborative tasks, we seek t... | ['Francesc Moreno-Noguer', 'Xavier Alameda-Pineda', 'Xiaoyu Bie', 'Wen Guo'] | 2021-05-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['multi-person-pose-forecasting'] | ['computer-vision'] | [ 1.65552631e-01 1.18505850e-01 -7.13260993e-02 -3.77240002e-01
-4.67327803e-01 -2.33530879e-01 6.48806810e-01 -6.26623511e-01
-6.85594201e-01 6.80021703e-01 6.33102179e-01 2.82250196e-01
5.48464432e-02 -2.01820537e-01 -7.46106386e-01 -2.61164308e-01
-5.66046715e-01 1.00029421e+00 4.18908536e-01 -3.17289919... | [7.277703285217285, -0.333547979593277] |
3f2a41b3-fc6a-4b5b-b624-177a9afd2ea2 | an-energy-and-gpu-computation-efficient | 1904.09730 | null | http://arxiv.org/abs/1904.09730v1 | http://arxiv.org/pdf/1904.09730v1.pdf | An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection | As DenseNet conserves intermediate features with diverse receptive fields by
aggregating them with dense connection, it shows good performance on the object
detection task. Although feature reuse enables DenseNet to produce strong
features with a small number of model parameters and FLOPs, the detector with
DenseNet ba... | ['Joong-won Hwang', 'Sangrok Lee', 'Jongyoul Park', 'Yuseok Bae', 'Youngwan Lee'] | 2019-04-22 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-4.52931046e-01 -1.66932687e-01 -9.66790915e-02 -1.50600806e-01
2.83523053e-01 -2.14488834e-01 2.62632370e-01 -1.22449405e-01
-9.12159324e-01 5.75019777e-01 -3.90836075e-02 2.00901836e-01
-9.09074116e-03 -1.14551282e+00 -4.18545574e-01 -6.55951977e-01
-1.76619068e-01 3.88679141e-03 9.38848436e-01 -9.84252840... | [8.747252464294434, -0.28912025690078735] |
5cab777d-b90e-4f6a-8e18-9163f00738f7 | intermittent-upwelling-events-trigger-delayed | null | null | https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022GL102651 | https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2022GL102651 | Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic Waters | Pico-nanophytoplankton organisms are dominant in oceanic oligotrophic areas but their adaptive growth rates make their contribution to the carbon cycle difficult to estimate. Here we address their response capacities after sporadic wind gusts causing upwelling events in a coastal Mediterranean station. When the water c... | ['M. Thyssen', 'O. Grosso', 'C. Pinazo', 'N. Bensoussan', 'C. Caille', 'V. Rossi', 'R. Fuchs'] | 2023-03-02 | null | null | null | geophysical-research-letters-2023-3 | ['pico'] | ['natural-language-processing'] | [ 1.02824710e-01 -5.10869801e-01 5.02162635e-01 3.25628698e-01
3.15388203e-01 -9.25597787e-01 9.32772458e-01 6.42346621e-01
-6.70050681e-01 1.24394619e+00 3.77266616e-01 -1.34096667e-01
4.08731662e-02 -7.36946583e-01 -3.20866555e-01 -1.05194998e+00
-5.57376266e-01 3.50144327e-01 3.81707400e-01 -3.57856005... | [6.322652339935303, 3.127035140991211] |
36935611-db02-43f1-89f7-269385c8b853 | jct-at-semeval-2021-task-1-context-aware | null | null | https://aclanthology.org/2021.semeval-1.13 | https://aclanthology.org/2021.semeval-1.13.pdf | JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction | In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT cont... | ['Shmuel Liebeskind', 'Otniel Elkayam', 'Chaya Liebeskind'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-6.03979789e-02 2.48075593e-02 -2.51441330e-01 -3.88756603e-01
-9.57461894e-02 -4.18212473e-01 5.30290008e-01 1.06936681e+00
-9.77369606e-01 1.42594874e-01 7.62425542e-01 -4.01309431e-01
1.28351420e-01 -7.62194514e-01 -2.11858466e-01 -2.41906289e-02
-2.26343535e-02 1.78610563e-01 6.00894094e-02 -2.71311790... | [10.646849632263184, 10.439587593078613] |
15952cd0-b882-4b12-810d-1483c69c2b08 | zoom-vqa-patches-frames-and-clips-integration | 2304.06440 | null | https://arxiv.org/abs/2304.06440v1 | https://arxiv.org/pdf/2304.06440v1.pdf | Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment | Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content. To effectively model these complicated quality-related factors, in this paper, we decompose video into three levels (\ie,... | ['Xing Wen', 'Ming Sun', 'Kun Yuan', 'Kai Zhao'] | 2023-04-13 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [-2.50049084e-01 -7.14964092e-01 -1.51622251e-01 -3.34311873e-01
-1.05986023e+00 -4.17663425e-01 2.71335959e-01 -2.10469868e-02
-1.23656258e-01 3.68145585e-01 6.23096526e-01 -4.18982506e-02
-4.33651209e-02 -7.17504442e-01 -6.23473585e-01 -4.29326922e-01
-2.10300058e-01 -1.78952321e-01 4.30530250e-01 -4.94456857... | [11.694884300231934, -1.8111896514892578] |
aae75246-0afa-42e5-8281-7c19f5601900 | gkd-a-general-knowledge-distillation | 2306.06629 | null | https://arxiv.org/abs/2306.06629v1 | https://arxiv.org/pdf/2306.06629v1.pdf | GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model | Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices. However, the deployment of knowledge distillation systems faces great challenges in real-world industrial-strength appl... | ['Jie Tang', 'Peng Zhang', 'Shu Zhao', 'Jingang Wang', 'Jiahao Liu', 'Keqing He', 'Hongyin Tang', 'Yang Yang', 'Wenwen Gong', 'Yuanchun Wang', 'Weng Lam Tam', 'Shicheng Tan'] | 2023-06-11 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-3.32340002e-01 -1.56788483e-01 -4.33678001e-01 4.67732176e-02
-4.45797205e-01 -5.76303780e-01 6.94414556e-01 -1.33626908e-01
-7.88019061e-01 6.62131667e-01 -2.14277238e-01 -1.05453205e+00
3.56796145e-01 -8.30827236e-01 -5.93051255e-01 -2.44781300e-01
3.64183277e-01 7.85847664e-01 6.11324072e-01 -1.15099020... | [8.780856132507324, 3.6362485885620117] |
1743635a-7300-4f66-88a9-a650f2d95964 | traffic-sign-detection-under-challenging | 1908.11262 | null | https://arxiv.org/abs/1908.11262v1 | https://arxiv.org/pdf/1908.11262v1.pdf | Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics | Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. Existing traffic sign datasets are limited in terms of type and severity of challenging conditions. Metadata correspondi... | ['Min-Hung Chen', 'Ghassan AlRegib', 'Dogancan Temel'] | 2019-08-29 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 1.86050102e-01 -7.55868852e-01 -1.06868207e-01 -7.35189840e-02
-7.30826557e-01 -6.46771312e-01 6.96531653e-01 -2.89717168e-01
-3.41795623e-01 6.11823618e-01 2.22273581e-02 -5.49527109e-01
-4.39788252e-02 -5.74724019e-01 -7.23434389e-01 -6.72919035e-01
-2.41684079e-01 -1.69451997e-01 6.47748172e-01 -9.52685997... | [7.937602519989014, -0.8030827045440674] |
3164b07a-1bfb-4438-bf1c-202bc549ce5e | z-lavi-zero-shot-language-solver-fueled-by | 2210.12261 | null | https://arxiv.org/abs/2210.12261v1 | https://arxiv.org/pdf/2210.12261v1.pdf | Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination | Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks. However, they generally suffer from reporting bias, the phenomenon describing the lack of explicit commonsense knowledge in written text, e.g., ''an orange is orange''. To overcome this limitation, ... | ['Jianshu Chen', 'Dong Yu', 'Xiaoyang Wang', 'Hongming Zhang', 'Wenlin Yao', 'Yue Yang'] | 2022-10-21 | null | null | null | null | ['visual-commonsense-tests'] | ['natural-language-processing'] | [ 2.41649389e-01 2.98340380e-01 -1.45736068e-01 -1.77841440e-01
-6.48713946e-01 -6.51200175e-01 1.01029360e+00 -1.20359175e-01
-3.19929540e-01 5.31822741e-01 4.12058473e-01 -5.51384032e-01
2.83091545e-01 -8.22053492e-01 -9.86259758e-01 -3.42971593e-01
6.35968268e-01 1.40539303e-01 -3.88533235e-01 -2.30273783... | [10.775814056396484, 1.7104625701904297] |
62963dc7-c29c-43b9-8f50-0b821072807d | coins-dynamically-generating-contextualized | 2106.02497 | null | https://arxiv.org/abs/2106.02497v1 | https://arxiv.org/pdf/2106.02497v1.pdf | COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion | Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In thi... | ['Anette Frank', 'Debjit Paul'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.395 | https://aclanthology.org/2021.acl-long.395.pdf | acl-2021-5 | ['story-completion'] | ['natural-language-processing'] | [ 7.55032599e-01 8.45931113e-01 -8.00702497e-02 -3.96297574e-01
-8.85465503e-01 -7.44195640e-01 1.17151821e+00 5.16885258e-02
5.84634878e-02 1.09760356e+00 1.20348155e+00 -6.14732623e-01
1.49653256e-01 -8.95663917e-01 -6.91233099e-01 8.71668682e-02
2.79884636e-01 6.57852352e-01 5.42981625e-02 -5.00936210... | [11.610455513000488, 8.884175300598145] |
4541665b-ae36-4ee5-9ff4-c85c68b3d434 | dhivya-hope-detection-lt-edi-eacl2021 | null | null | https://aclanthology.org/2021.ltedi-1.9 | https://aclanthology.org/2021.ltedi-1.9.pdf | dhivya-hope-detection@LT-EDI-EACL2021: Multilingual Hope Speech Detection for Code-mixed and Transliterated Texts | In this paper we work with a hope speech detection corpora that includes English, Tamil, and Malayalam datasets. We present a two phase mechanism to detect hope speech. In the first phase we build a classifier to identify the language of the text. In the second phase, we build a classifier to detect hope speech, non ho... | ['Dhivya Chinnappa'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [ 5.50790727e-02 2.46400356e-01 -2.85360545e-01 -4.16050941e-01
-1.12631249e+00 -4.89156902e-01 1.13166571e+00 1.15709096e-01
-1.20459713e-01 4.07732993e-01 8.90807271e-01 -6.77677929e-01
4.22982186e-01 -8.17154825e-01 3.75656933e-02 -4.16346520e-01
3.19656163e-01 4.76311028e-01 3.40165734e-01 -4.04683977... | [9.389021873474121, 10.690409660339355] |
30f4a6e3-ac58-47b6-8f45-7c216c81df4b | learning-to-tokenize-for-generative-retrieval | 2304.04171 | null | https://arxiv.org/abs/2304.04171v1 | https://arxiv.org/pdf/2304.04171v1.pdf | Learning to Tokenize for Generative Retrieval | Conventional document retrieval techniques are mainly based on the index-retrieve paradigm. It is challenging to optimize pipelines based on this paradigm in an end-to-end manner. As an alternative, generative retrieval represents documents as identifiers (docid) and retrieves documents by generating docids, enabling e... | ['Zhaochun Ren', 'Maarten de Rijke', 'Dawei Yin', 'Zhumin Chen', 'Pengjie Ren', 'Haichao Zhu', 'Shuaiqiang Wang', 'Zheng Chen', 'Lingyong Yan', 'Weiwei Sun'] | 2023-04-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 8.91724154e-02 -4.40690935e-01 -4.48348895e-02 -4.16585058e-01
-1.41907918e+00 -8.18554640e-01 9.91721570e-01 9.55022797e-02
-4.00127769e-01 1.28515348e-01 3.45187664e-01 -1.74484327e-01
-9.93693769e-02 -7.55770445e-01 -7.18591332e-01 -5.93706906e-01
1.47194907e-01 9.43981111e-01 -9.30490047e-02 -1.26549974... | [11.437678337097168, 7.672786712646484] |
cacfb01d-882d-43d1-a4ce-a1a5e4e541c5 | morphological-disambiguation-of-south-s-ami | null | null | https://aclanthology.org/2020.sltu-1.5 | https://aclanthology.org/2020.sltu-1.5.pdf | Morphological Disambiguation of South S\'ami with FSTs and Neural Networks | We present a method for conducting morphological disambiguation for South S{\'a}mi, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the ... | ['Mika H{\\"a}m{\\"a}l{\\"a}inen', 'Linda Wiechetek'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 4.64244932e-01 2.71533340e-01 -1.51055399e-03 -3.97147477e-01
-5.63194871e-01 -9.72465038e-01 5.88495076e-01 6.88024700e-01
-1.00232697e+00 6.08921647e-01 2.06962422e-01 -1.05878878e+00
-1.94930643e-01 -9.95304704e-01 -2.58442909e-01 -5.75194955e-01
-2.42096204e-02 4.49347407e-01 1.34712979e-01 -9.33643341... | [10.41865348815918, 10.12356185913086] |
adc3caac-9066-457f-b5c2-ed42b4f2495b | improving-multi-class-classifier-using | 2210.16033 | null | https://arxiv.org/abs/2210.16033v1 | https://arxiv.org/pdf/2210.16033v1.pdf | Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization | The universal-set naive Bayes classifier (UNB)~\cite{Komiya:13}, defined using likelihood ratios (LRs), was proposed to address imbalanced classification problems. However, the LR estimator used in the UNB overestimates LRs for low-frequency data, degrading the classification performance. Our previous study~\cite{Kikuc... | ['Tadachika Ozono', 'Masato Kikuchi'] | 2022-10-28 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.35553122e-01 -2.15130314e-01 -6.23534739e-01 -8.95863116e-01
-6.20283663e-01 -8.86338726e-02 -1.41521916e-01 3.93428594e-01
-3.93068403e-01 1.06724930e+00 -1.42047033e-01 -3.54259670e-01
-3.07634771e-01 -1.07526016e+00 -3.61379206e-01 -6.72348320e-01
8.26857761e-02 3.77058268e-01 4.59286511e-01 -6.89706281... | [8.690126419067383, 4.2738118171691895] |
58844f03-8e43-45bb-9877-d02e9b3bd70b | autoshot-a-short-video-dataset-and-state-of-1 | 2304.06116 | null | https://arxiv.org/abs/2304.06116v1 | https://arxiv.org/pdf/2304.06116v1.pdf | AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection | The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot bounda... | ['Ji Liu', 'Zhangyang Wang', 'Debing Zhang', 'Jincan Deng', 'Wenxian Liu', 'Xiufeng Xie', 'Yufang Huang', 'Wentao Zhu'] | 2023-04-12 | autoshot-a-short-video-dataset-and-state-of | https://openreview.net/pdf?id=u89Eq-_3oE4 | https://openreview.net/pdf?id=u89Eq-_3oE4 | submitted-to-iclr-2022-9 | ['boundary-detection', 'architecture-search'] | ['computer-vision', 'methodology'] | [-2.78676242e-01 -3.21220160e-01 -2.70140529e-01 -4.78911996e-02
-5.38443387e-01 -4.02236342e-01 3.38195562e-01 -4.10908997e-01
-3.70594829e-01 5.25285959e-01 3.02659065e-01 1.00381158e-01
-4.27375697e-02 -5.10823488e-01 -7.42427766e-01 -3.82748157e-01
-2.10249320e-01 -1.27424151e-01 5.49031615e-01 -7.36994892... | [9.355207443237305, 0.258888304233551] |
1039b2a3-cfe2-4c46-817e-2ae1c8987860 | from-width-based-model-checking-to-width | 2205.10995 | null | https://arxiv.org/abs/2205.10995v2 | https://arxiv.org/pdf/2205.10995v2.pdf | From Width-Based Model Checking to Width-Based Automated Theorem Proving | In the field of parameterized complexity theory, the study of graph width measures has been intimately connected with the development of width-based model checking algorithms for combinatorial properties on graphs. In this work, we introduce a general framework to convert a large class of width-based model-checking alg... | ['Farhad Vadiee', 'Mateus de Oliveira Oliveira'] | 2022-05-23 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.19747144e-01 5.94561815e-01 -3.52648765e-01 -4.60504368e-02
-4.37044084e-01 -6.37618244e-01 -4.26736884e-02 7.17419565e-01
-1.17002718e-01 4.98482972e-01 -6.63180053e-01 -9.82194245e-01
-4.44634169e-01 -1.41473019e+00 -6.79586828e-01 -4.32451963e-01
-5.98964453e-01 6.90281212e-01 9.89276648e-01 -2.52083302... | [6.880578517913818, 5.246801376342773] |
073ca9e1-b7a7-4e41-afdf-48a484c4a17f | li-yong-attentivelai-gai-shan-duan-dui-duan | null | null | https://aclanthology.org/2019.rocling-1.36 | https://aclanthology.org/2019.rocling-1.36.pdf | 利用Attentive來改善端對端中文語篇剖析遞迴類神經網路系統(Using Attentive to improve Recursive LSTM End-to-End Chinese Discourse Parsing) | null | ['Chia-Hui Chang', 'Yu-Jen Wang'] | null | null | null | null | rocling-2019-10 | ['discourse-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.316679954528809, 3.7287871837615967] |
ede3f739-1eac-4adf-8475-edfebb5e188b | mumuqa-multimedia-multi-hop-news-question | 2112.10728 | null | https://arxiv.org/abs/2112.10728v2 | https://arxiv.org/pdf/2112.10728v2.pdf | MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding | Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images is often limited to just picking the answer from a pre-defined set of options. In addition, images in the real world, especially in news, h... | ['Heng Ji', 'Alexander Schwing', 'Shih-Fu Chang', 'Avirup Sil', 'Mohit Bansal', 'Lifu Huang', 'Jaemin Cho', 'Haoyang Wen', 'Xudong Lin', 'Manling Li', 'Xilin Rui', 'Revanth Gangi Reddy'] | 2021-12-20 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 5.57507694e-01 3.07603538e-01 8.91958475e-02 -5.64181983e-01
-1.48641634e+00 -6.72427595e-01 7.58205593e-01 1.95391759e-01
-4.15113151e-01 4.71284777e-01 5.38298011e-01 -2.55796909e-01
3.49399000e-01 -6.83495104e-01 -1.22688210e+00 -2.96190441e-01
3.66530776e-01 8.09672117e-01 6.09730363e-01 -2.90602744... | [10.896629333496094, 1.5543662309646606] |
76df5bcc-bccf-4f8d-9b74-4c35a4d1b681 | a-hint-from-arithmetic-on-systematic | 2103.01403 | null | https://arxiv.org/abs/2103.01403v3 | https://arxiv.org/pdf/2103.01403v3.pdf | A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics | Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with lea... | ['Song-Chun Zhu', 'Ying Nian Wu', 'Yixin Zhu', 'Yining Hong', 'Siyuan Huang', 'Qing Li'] | 2021-03-02 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 4.62389886e-01 -8.32653493e-02 1.62899375e-01 -5.74355841e-01
-3.67703229e-01 -7.36290932e-01 6.11370504e-01 3.81742090e-01
-3.66345316e-01 5.24965048e-01 1.80202499e-01 -6.44854248e-01
-1.00174375e-01 -9.10485864e-01 -7.64157712e-01 -3.36142510e-01
-2.82516688e-01 3.91005546e-01 3.70703667e-01 -5.29113054... | [9.670561790466309, 7.220230579376221] |
4077159d-2f37-4c21-80d5-66dab5826869 | a-survey-on-embedding-dynamic-graphs | 2101.01229 | null | https://arxiv.org/abs/2101.01229v2 | https://arxiv.org/pdf/2101.01229v2.pdf | A Survey on Embedding Dynamic Graphs | Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and di... | ['Artur Ziviani', 'Alex B. Vieira', 'Matheus R. F. Mendonça', 'Claudio D. T. Barros'] | 2021-01-04 | null | null | null | null | ['dynamic-link-prediction', 'dynamic-graph-embedding'] | ['graphs', 'graphs'] | [-2.87044615e-01 -7.62289166e-02 -4.18030262e-01 9.18667018e-02
5.72291732e-01 -8.04266751e-01 7.13981330e-01 5.31621218e-01
1.73958749e-01 1.86714560e-01 3.23939770e-01 -6.60860658e-01
-7.61243105e-01 -1.13312781e+00 -3.30129601e-02 -7.40908086e-01
-1.11237907e+00 5.09197176e-01 1.65095374e-01 -4.05615717... | [7.150038242340088, 6.085655689239502] |
cc59abc4-70f8-4cb3-8475-23a753712b8d | point-cloud-completion-on-structured-feature | 2202.08583 | null | https://arxiv.org/abs/2202.08583v2 | https://arxiv.org/pdf/2202.08583v2.pdf | Point cloud completion via structured feature maps using a feedback network | In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global str... | ['Ruizhen Hu', 'Hui Huang', 'Chongyang Ma', 'Haibin Huang', 'Zejia Su'] | 2022-02-17 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-7.11014867e-02 -5.81413321e-02 1.43890336e-01 -4.92478162e-01
-8.59215915e-01 -4.70411807e-01 6.37645245e-01 1.68246925e-01
7.75192529e-02 2.88447618e-01 -5.65885752e-02 2.19427884e-01
-9.88385752e-02 -1.19478667e+00 -1.04212439e+00 -3.44944596e-01
-6.04315922e-02 6.41055942e-01 2.51682431e-01 -2.89736509... | [8.375130653381348, -3.5992441177368164] |
47557bce-73d0-49ae-bf37-e427412e469d | when-not-to-trust-language-models | 2212.10511 | null | https://arxiv.org/abs/2212.10511v4 | https://arxiv.org/pdf/2212.10511v4.pdf | When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories | Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing fa... | ['Hannaneh Hajishirzi', 'Daniel Khashabi', 'Rajarshi Das', 'Victor Zhong', 'Akari Asai', 'Alex Mallen'] | 2022-12-20 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-5.15691996e-01 3.00634325e-01 -2.95078635e-01 -6.23924844e-02
-1.41515005e+00 -9.51278865e-01 6.50762081e-01 2.28055209e-01
-7.75170922e-01 1.27888989e+00 3.28825027e-01 -5.65634012e-01
-4.63917851e-01 -1.08633423e+00 -1.07121933e+00 -1.49382442e-01
-1.30971685e-01 9.69600439e-01 5.17821729e-01 -4.61720765... | [10.720710754394531, 8.024425506591797] |
56c9a019-7f51-490d-af70-c143443b6669 | empowering-molecule-discovery-for-molecule | 2306.06615 | null | https://arxiv.org/abs/2306.06615v1 | https://arxiv.org/pdf/2306.06615v1.pdf | Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective | Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs. Traditional methods for molecule discovery follow a trial-and-error process, which are both time-consuming and costly, while computational approaches such as artificial intelligence (AI) have emer... | ['Qing Li', 'Jiliang Tang', 'Hui Liu', 'Xiao-Yong Wei', 'Wenqi Fan', 'Yunqing Liu', 'Jiatong Li'] | 2023-06-11 | null | null | null | null | ['molecule-captioning', 'text-based-de-novo-molecule-generation'] | ['medical', 'medical'] | [ 5.36118746e-01 -2.60657072e-01 -7.35663295e-01 -1.99276134e-01
-9.65023816e-01 -5.77768564e-01 5.92999339e-01 6.29676759e-01
-4.60036919e-02 1.18301952e+00 2.93929526e-03 -6.31916046e-01
-5.17612435e-02 -1.02198827e+00 -1.01755869e+00 -6.94703043e-01
3.40081662e-01 6.45060480e-01 1.10370278e-01 -4.76895332... | [4.998213768005371, 5.886322021484375] |
f264ebbc-5647-4839-b4ef-0de261e5e83b | fingerprint-image-quality-estimation-and-its | 2211.13557 | null | https://arxiv.org/abs/2211.13557v1 | https://arxiv.org/pdf/2211.13557v1.pdf | Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification | Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of ... | ['Joaquin Gonzalez-Rodriguez', 'Javier Ortega-Garcia', 'Fernando Alonso-Fernandez', 'Julian Fierrez', 'Josef Bigun', 'Klaus Kollreider', 'Hartwig Fronthaler'] | 2022-11-24 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 5.09824812e-01 -2.50075668e-01 1.28803790e-01 -6.89915299e-01
-8.39865208e-01 -4.68363643e-01 2.94610560e-01 2.05310598e-01
-5.48909724e-01 7.71739900e-01 1.80724058e-02 1.70855030e-01
-8.40612352e-01 -7.09725022e-01 -5.11104345e-01 -8.23183179e-01
1.16064802e-01 1.90882519e-01 -9.25510451e-02 -1.26145855... | [12.924084663391113, 0.9905920028686523] |
00883e42-40d8-4f6f-aebf-92ab491f8fe9 | histalign-improving-context-dependency-in | 2305.04782 | null | https://arxiv.org/abs/2305.04782v1 | https://arxiv.org/pdf/2305.04782v1.pdf | HistAlign: Improving Context Dependency in Language Generation by Aligning with History | Language models (LMs) can generate hallucinations and incoherent outputs, which highlights their weak context dependency. Cache-LMs, which augment LMs with a memory of recent history, can increase context dependency and have shown remarkable performance in diverse language generation tasks. However, we find that even w... | ['Mohit Bansal', 'Shiyue Zhang', 'David Wan'] | 2023-05-08 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.55286103e-01 1.13248721e-01 -2.15247363e-01 -9.73628387e-02
-1.10826933e+00 -4.51778919e-01 9.05023396e-01 2.13482633e-01
-1.57335281e-01 1.08814645e+00 1.18165588e+00 -2.42928818e-01
4.09922242e-01 -6.49516463e-01 -7.08201587e-01 -4.78955358e-01
3.80721502e-02 3.34442019e-01 1.55288100e-01 -4.22082514... | [11.87903118133545, 9.061873435974121] |
c0b70381-4ae7-4ae1-abbc-bb8bfde16c17 | graph-sampling-with-determinantal-processes | 1703.01594 | null | http://arxiv.org/abs/1703.01594v1 | http://arxiv.org/pdf/1703.01594v1.pdf | Graph sampling with determinantal processes | We present a new random sampling strategy for k-bandlimited signals defined
on graphs, based on determinantal point processes (DPP). For small graphs, ie,
in cases where the spectrum of the graph is accessible, we exhibit a DPP
sampling scheme that enables perfect recovery of bandlimited signals. For large
graphs, ie, ... | ['Simon Barthelmé', 'Pierre-Olivier Amblard', 'Nicolas Tremblay'] | 2017-03-05 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.48527843e-01 4.00144428e-01 2.40111604e-01 3.76999348e-01
-4.61920977e-01 -5.94374418e-01 4.16929051e-02 2.76278108e-02
4.34399545e-02 9.90041375e-01 -2.37747338e-02 -5.60873628e-01
-2.40771785e-01 -1.03269506e+00 -5.37967384e-01 -8.45493019e-01
-7.82319188e-01 1.89108372e-01 3.34649056e-01 -2.20462233... | [6.890629768371582, 5.076262474060059] |
5ff96a8d-2794-454a-8751-a656bd0084b0 | a-survey-on-unsupervised-industrial-anomaly | 2204.11161 | null | https://arxiv.org/abs/2204.11161v4 | https://arxiv.org/pdf/2204.11161v4.pdf | A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images | In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vis... | ['Shiguo Lian', 'Zhaoxiang Liu', 'Yajie Cui'] | 2022-04-24 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.66440332e-01 -1.79149777e-01 -2.00830717e-02 -1.32611647e-01
-1.98319376e-01 -2.30202809e-01 1.25855386e-01 3.94074380e-01
1.44745380e-01 9.40913111e-02 -5.52361667e-01 -2.19477251e-01
-1.64839163e-01 -7.28317797e-01 -3.63141567e-01 -8.03877175e-01
-5.23732305e-02 2.08925709e-01 6.12225197e-02 -2.79236902... | [7.512690544128418, 2.0122528076171875] |
a2adfcc6-8331-4ec3-9086-7849ed77a9b4 | extracting-victim-counts-from-text | 2302.12367 | null | https://arxiv.org/abs/2302.12367v1 | https://arxiv.org/pdf/2302.12367v1.pdf | Extracting Victim Counts from Text | Decision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information about such victim counts is often only available within full-text event descriptions from newspapers and othe... | ['Niklas Stoehr', 'Shehzaad Dhuliawala', 'Mian Zhong'] | 2023-02-23 | null | null | null | null | ['dependency-parsing', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.00690824e-01 -1.52172521e-01 -4.52801287e-01 -2.90772200e-01
-1.41296911e+00 -8.90221298e-01 4.55485195e-01 1.08700550e+00
-1.00870037e+00 1.16129458e+00 8.16013157e-01 -6.54377282e-01
-3.51840436e-01 -1.01489019e+00 -2.39892319e-01 -3.07742774e-01
1.61600187e-01 8.67632151e-01 -5.22009581e-02 -3.73279721... | [8.919193267822266, 9.38196086883545] |
dfdeec3e-8671-47a7-8a82-d4e6f34843e5 | matrix-cofactorization-for-joint-spatial | 1907.08511 | null | https://arxiv.org/abs/1907.08511v2 | https://arxiv.org/pdf/1907.08511v2.pdf | Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images | Hyperspectral unmixing aims at identifying a set of elementary spectra and the corresponding mixture coefficients for each pixel of an image. As the elementary spectra correspond to the reflectance spectra of real materials, they are often very correlated yielding an ill-conditioned problem. To enrich the model and to ... | ['Stéphane May', 'Mathieu Fauvel', 'Adrien Lagrange', 'Nicolas Dobigeon'] | 2019-07-19 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.91361785e-01 -4.55338150e-01 2.47752026e-01 -1.70525700e-01
-5.70457280e-01 -5.52426338e-01 5.90972483e-01 1.15338497e-01
-3.12266141e-01 8.22737575e-01 1.38945486e-02 2.08159462e-01
-4.67131466e-01 -6.99305713e-01 -3.48972589e-01 -1.23895431e+00
2.77137190e-01 1.66007385e-01 -1.52952880e-01 1.01071578... | [10.043022155761719, -2.060952663421631] |
09464276-7944-4de5-aff2-cd1f7f418404 | sat-solvers-and-computer-algebra-systems-a | 1907.04408 | null | https://arxiv.org/abs/1907.04408v2 | https://arxiv.org/pdf/1907.04408v2.pdf | SAT Solvers and Computer Algebra Systems: A Powerful Combination for Mathematics | Over the last few decades, many distinct lines of research aimed at automating mathematics have been developed, including computer algebra systems (CASs) for mathematical modelling, automated theorem provers for first-order logic, SAT/SMT solvers aimed at program verification, and higher-order proof assistants for chec... | ['Ilias Kotsireas', 'Vijay Ganesh', 'Curtis Bright'] | 2019-07-09 | null | null | null | null | ['mathematical-proofs', 'mathematical-reasoning'] | ['miscellaneous', 'natural-language-processing'] | [ 1.47392675e-02 3.45305145e-01 -1.86590999e-02 4.12065424e-02
-5.77427864e-01 -1.00690925e+00 6.33173347e-01 1.94279909e-01
2.15288728e-01 8.53328407e-01 -5.62779903e-01 -1.19353056e+00
-5.62902927e-01 -1.20715845e+00 -5.92102647e-01 -1.40567318e-01
-3.01750183e-01 7.70781696e-01 3.71001601e-01 -6.08383298... | [8.817094802856445, 6.914913654327393] |
5bcbd940-61a7-4952-9302-c6f3d0feabb9 | empirical-study-of-text-augmentation-on-1 | null | null | https://aclanthology.org/2020.paclic-1.53 | https://aclanthology.org/2020.paclic-1.53.pdf | Empirical Study of Text Augmentation on Social Media Text in Vietnamese | null | ['Ngan Nguyen', 'Kiet Nguyen', 'Son Luu'] | null | null | null | null | paclic-2020-10 | ['text-augmentation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.225844860076904, 3.739696741104126] |
438c53dd-e538-482b-8475-af63e5ccde1e | cortical-mirror-system-activation-during-real | 1902.09189 | null | http://arxiv.org/abs/1902.09189v2 | http://arxiv.org/pdf/1902.09189v2.pdf | Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study | Analogous to the mirror neuron system repeatedly described in monkeys as a
possible substrate for imitation learning and/or action understanding, a
neuronal execution/observation matching system (OEMS) is assumed in humans, but
little is known to what extent this system is activated in non-experimental,
real-life condi... | [] | 2019-03-27 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 1.71524912e-01 1.43088564e-01 -9.28942338e-02 3.51685137e-01
1.57278806e-01 -2.29470655e-01 9.84182239e-01 -2.34714910e-01
-8.58989477e-01 6.35133386e-01 3.24289292e-01 1.60642013e-01
-2.45952860e-01 -2.07801908e-01 -6.46775663e-01 -6.12479687e-01
-6.00154579e-01 6.86922222e-02 1.09465279e-01 -1.28773168... | [13.04062557220459, 3.411558151245117] |
232a1b8c-36d8-41b8-b9b2-f913a1d7f817 | nl-cs-net-deep-learning-with-non-local-prior | 2305.03899 | null | https://arxiv.org/abs/2305.03899v1 | https://arxiv.org/pdf/2305.03899v1.pdf | NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing | Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes ... | ['Yueyang Teng', 'YuDong Yao', 'Chen Li', 'Shouliang Qi', 'Shuai Bian'] | 2023-05-06 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.96583819e-01 -1.75435364e-01 -3.50356437e-02 -2.75066137e-01
-6.77950561e-01 5.47241084e-02 1.36896238e-01 -2.44626597e-01
-6.56251013e-01 6.29395962e-01 6.31987453e-02 -9.90772918e-02
-3.08831573e-01 -4.67993975e-01 -7.44180322e-01 -9.09063101e-01
-1.44150645e-01 1.23834372e-01 -4.54951450e-02 -9.94783416... | [11.244744300842285, -2.144561529159546] |
aec1b343-40bc-47de-8ee9-ecbe9fc29ba2 | jetseg-efficient-real-time-semantic | 2305.11419 | null | https://arxiv.org/abs/2305.11419v1 | https://arxiv.org/pdf/2305.11419v1.pdf | JetSeg: Efficient Real-Time Semantic Segmentation Model for Low-Power GPU-Embedded Systems | Real-time semantic segmentation is a challenging task that requires high-accuracy models with low-inference times. Implementing these models on embedded systems is limited by hardware capability and memory usage, which produces bottlenecks. We propose an efficient model for real-time semantic segmentation called JetSeg... | ['Oscar Montiel', 'Daniel Alejandro Lopez', 'Miguel Lopez-Montiel'] | 2023-05-19 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-8.64435807e-02 -4.76376154e-02 -2.08846956e-01 -4.38000053e-01
-5.97053230e-01 -2.40270093e-01 2.60429289e-02 -2.11205930e-01
-8.72161567e-01 4.11682308e-01 -2.93574452e-01 -6.68799639e-01
4.91790533e-01 -9.38021898e-01 -8.48736167e-01 -5.94103515e-01
3.34943861e-01 3.69912863e-01 9.05871332e-01 -6.05945811... | [9.38294506072998, -0.22042660415172577] |
14e2d021-52c2-4422-afbf-ebeab2f03bd6 | dual-arm-adversarial-robot-learning | 2110.08066 | null | https://arxiv.org/abs/2110.08066v1 | https://arxiv.org/pdf/2110.08066v1.pdf | Dual-Arm Adversarial Robot Learning | Robot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have been explored in simulation, real-world robot learning research seems to be sti... | ['Elie Aljalbout'] | 2021-10-15 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 8.86005610e-02 4.90845174e-01 1.58213466e-01 -4.67858426e-02
-2.02479079e-01 -6.97946429e-01 2.29219213e-01 -6.67278022e-02
-5.75356662e-01 8.63966346e-01 -6.05461776e-01 -2.05370754e-01
-2.49277264e-01 -6.12444103e-01 -9.49540198e-01 -7.09471285e-01
-5.30674756e-01 4.65910643e-01 1.38770193e-01 -4.35786009... | [4.559243679046631, 1.299855351448059] |
7cf26d3a-342c-4d19-9a3c-9a8f25ece5f6 | glioma-classification-using-multimodal | 2011.05410 | null | https://arxiv.org/abs/2011.05410v1 | https://arxiv.org/pdf/2011.05410v1.pdf | Glioma Classification Using Multimodal Radiology and Histology Data | Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-cons... | ['Yinyin Yuan', 'Otar Akanyeti', 'Maryam Afzali', 'Tomasz Pieciak', 'Azam Hamidinekoo'] | 2020-11-10 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-1.25948608e-01 9.47079062e-02 5.11725098e-02 -4.67021018e-01
-1.34250832e+00 -5.07558107e-01 5.64486802e-01 7.25193799e-01
-4.74677563e-01 6.49474859e-01 3.77868414e-01 -3.93354177e-01
-1.34953782e-01 -7.66059637e-01 2.82530576e-01 -1.29881334e+00
-1.36593193e-01 6.84373498e-01 4.02823299e-01 2.89130747... | [14.762613296508789, -2.586230516433716] |
de02d7e7-0ded-430f-93f8-c06685027b0e | ovis-open-vocabulary-visual-instance-search | 2108.03704 | null | https://arxiv.org/abs/2108.03704v1 | https://arxiv.org/pdf/2108.03704v1.pdf | OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning | We introduce the task of open-vocabulary visual instance search (OVIS). Given an arbitrary textual search query, Open-vocabulary Visual Instance Search (OVIS) aims to return a ranked list of visual instances, i.e., image patches, that satisfies the search intent from an image database. The term "open vocabulary" means ... | ['Zicheng Liu', 'Junsong Yuan', 'Lijuan Wang', 'Kevin Lin', 'Sheng Liu'] | 2021-08-08 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 2.04224572e-01 -6.88032359e-02 -4.99472052e-01 -2.31579870e-01
-1.26025867e+00 -6.94174469e-01 6.81814253e-01 1.52396843e-01
-3.94104630e-01 1.59317762e-01 3.80904078e-02 -6.36224970e-02
1.28661260e-01 -4.82080072e-01 -1.19806254e+00 -2.73201764e-01
1.69609115e-01 5.46512604e-01 2.85021544e-01 -1.63473506... | [10.369733810424805, 1.5753036737442017] |
2452cdde-c8f6-4572-a32e-98c740623e37 | investigating-catastrophic-overfitting-in | 2302.11963 | null | https://arxiv.org/abs/2302.11963v2 | https://arxiv.org/pdf/2302.11963v2.pdf | Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective | Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into da... | ['Xiaolin Huang', 'Sizhe Chen', 'Tao Li', 'Zhengbao He'] | 2023-02-23 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.48467457e-01 9.38072428e-03 2.01496556e-01 1.15716299e-02
-6.55927896e-01 -8.61320436e-01 3.26866895e-01 -1.23339169e-01
-1.91638380e-01 7.22219586e-01 -1.65318344e-02 -3.05737376e-01
1.56899571e-01 -8.76594901e-01 -1.19606054e+00 -1.06903064e+00
4.16229963e-02 -2.27476835e-01 6.85532466e-02 -5.59581280... | [5.598830223083496, 7.915475845336914] |
c8fcc563-3d8b-4f4c-8981-33a67c45d1ed | isointense-infant-brain-mri-segmentation-with | 1708.02757 | null | http://arxiv.org/abs/1708.02757v1 | http://arxiv.org/pdf/1708.02757v1.pdf | Isointense infant brain MRI segmentation with a dilated convolutional neural network | Quantitative analysis of brain MRI at the age of 6 months is difficult
because of the limited contrast between white matter and gray matter. In this
study, we use a dilated triplanar convolutional neural network in combination
with a non-dilated 3D convolutional neural network for the segmentation of
white matter, gray... | ['Josien P. W. Pluim', 'Pim Moeskops'] | 2017-08-09 | null | null | null | null | ['infant-brain-mri-segmentation'] | ['medical'] | [-1.82681337e-01 1.14800215e-01 2.98928082e-01 -5.39955437e-01
-2.43485570e-01 -3.19605291e-01 -2.28486478e-01 2.45053366e-01
-7.33625114e-01 4.26801801e-01 1.92886740e-01 -5.92006147e-01
1.08592898e-01 -4.61233854e-01 -6.65959001e-01 -1.03733838e-01
-6.43594444e-01 7.26603448e-01 1.91875666e-01 8.23796540... | [14.185531616210938, -2.359025478363037] |
7cc3a439-110e-4c20-8c66-ec01e20cc92b | evaluating-deep-convolutional-neural-networks | 1703.04101 | null | http://arxiv.org/abs/1703.04101v2 | http://arxiv.org/pdf/1703.04101v2.pdf | Evaluating Deep Convolutional Neural Networks for Material Classification | Determining the material category of a surface from an image is a demanding
task in perception that is drawing increasing attention. Following the recent
remarkable results achieved for image classification and object detection
utilising Convolutional Neural Networks (CNNs), we empirically study material
classification... | ['Klaus D. McDonald-Maier', 'Grigorios Kalliatakis', 'Shoaib Ehsan', 'Juergen Gall', 'Georgios Stamatiadis', 'Ales Leonardis', 'Anca Sticlaru'] | 2017-03-12 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 6.68703675e-01 -2.43627787e-01 3.54130156e-02 -2.72515357e-01
-4.26781416e-01 -2.69194633e-01 7.67384827e-01 3.04429442e-01
-5.02614677e-01 3.87084812e-01 -2.62821466e-01 8.27719048e-02
-3.67044777e-01 -1.05474973e+00 -1.14601886e+00 -6.01442337e-01
-1.96477488e-01 1.12343945e-01 4.62937325e-01 -3.06943923... | [10.204272270202637, -0.16151997447013855] |
22196d79-93a6-4d31-9a33-4d2a3247bec5 | towards-realistic-semi-supervised-learning | 2207.02269 | null | https://arxiv.org/abs/2207.02269v2 | https://arxiv.org/pdf/2207.02269v2.pdf | Towards Realistic Semi-Supervised Learning | Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of un... | ['Mubarak Shah', 'Navid Kardan', 'Mamshad Nayeem Rizve'] | 2022-07-05 | null | null | null | null | ['novel-class-discovery', 'open-world-semi-supervised-learning', 'novel-class-discovery'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.80792779e-01 3.93461347e-01 -4.99464840e-01 -7.69074678e-01
-1.11280954e+00 -5.71489334e-01 4.38785940e-01 7.19528571e-02
-5.52080810e-01 1.15584576e+00 -1.77018121e-01 -7.11968467e-02
1.41370595e-01 -4.77059752e-01 -1.08748758e+00 -6.68810189e-01
2.65608639e-01 8.96499574e-01 2.87619442e-01 3.32287312... | [9.504281044006348, 3.437788486480713] |
60f37686-5812-4c90-a09e-cebbf9ec0596 | optical-flow-estimation-using-a-spatial | 1611.00850 | null | http://arxiv.org/abs/1611.00850v2 | http://arxiv.org/pdf/1611.00850v2.pdf | Optical Flow Estimation using a Spatial Pyramid Network | We learn to compute optical flow by combining a classical spatial-pyramid
formulation with deep learning. This estimates large motions in a
coarse-to-fine approach by warping one image of a pair at each pyramid level by
the current flow estimate and computing an update to the flow. Instead of the
standard minimization ... | ['Michael J. Black', 'Anurag Ranjan'] | 2016-11-03 | optical-flow-estimation-using-a-spatial-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ranjan_Optical_Flow_Estimation_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ranjan_Optical_Flow_Estimation_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [-1.49087161e-01 -3.06756258e-01 -4.76009659e-02 1.62369356e-01
-8.56838152e-02 -6.49858952e-01 4.95088905e-01 -1.22204512e-01
-5.15580416e-01 8.05601120e-01 4.69890505e-01 -7.24646375e-02
1.39441013e-01 -1.05073202e+00 -6.81883931e-01 -5.63314676e-01
-3.29485327e-01 -1.21261612e-01 7.69048989e-01 -1.86900422... | [8.810277938842773, -1.8304117918014526] |
62afa6cd-21e2-49a0-9275-d4e7d7f38e4e | temporally-smooth-online-action-detection | 2104.08030 | null | https://arxiv.org/abs/2104.08030v1 | https://arxiv.org/pdf/2104.08030v1.pdf | Temporally smooth online action detection using cycle-consistent future anticipation | Many video understanding tasks work in the offline setting by assuming that the input video is given from the start to the end. However, many real-world problems require the online setting, making a decision immediately using only the current and the past frames of videos such as in autonomous driving and surveillance ... | ['Seon Joo Kim', 'Seonghyeon Nam', 'Young Hwi Kim'] | 2021-04-16 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.24306422e-01 1.05395868e-01 -1.74228922e-01 -5.74419558e-01
-4.20318395e-01 -1.20320767e-01 5.02576053e-01 -3.12671721e-01
-7.01148450e-01 3.89613241e-01 2.74235129e-01 -2.65133977e-01
1.94190770e-01 -4.85951364e-01 -8.46920311e-01 -6.29633784e-01
-3.96409005e-01 7.42417527e-03 8.28140199e-01 3.16522941... | [8.25935173034668, 0.410014271736145] |
772254a5-5abe-4572-9d03-010f1afd23a6 | rendnet-unified-2d-3d-recognizer-with-latent-1 | 2206.10066 | null | https://arxiv.org/abs/2206.10066v1 | https://arxiv.org/pdf/2206.10066v1.pdf | RendNet: Unified 2D/3D Recognizer With Latent Space Rendering | Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods is to first render the VG into raster graphics (RG) and then conduct recognition based on RG formats. However, this procedure discards the st... | ['Dongsheng Li', 'Yansen Wang', 'Caihua Shan', 'Xinyang Jiang', 'Ruoxi Shi'] | 2022-06-21 | rendnet-unified-2d-3d-recognizer-with-latent | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.85001010e-01 -3.88680190e-01 3.18237394e-01 -3.48005712e-01
-1.72286034e-01 -3.83974820e-01 7.11193323e-01 -2.19783396e-01
1.26221403e-01 4.32191603e-02 -2.09495127e-01 -4.57526565e-01
-5.23344539e-02 -1.46019244e+00 -3.92842799e-01 -5.29107153e-01
2.72302568e-01 1.67564020e-01 5.09303510e-01 -3.37787241... | [8.015900611877441, -3.178344964981079] |
98d6dd06-bfbc-4f8f-81bf-5ee744ede85a | md-hit-machine-learning-for-materials | 2307.04351 | null | https://arxiv.org/abs/2307.04351v1 | https://arxiv.org/pdf/2307.04351v1.pdf | MD-HIT: Machine learning for materials property prediction with dataset redundancy control | Materials datasets are usually featured by the existence of many redundant (highly similar) materials due to the tinkering material design practice over the history of materials research. For example, the materials project database has many perovskite cubic structure materials similar to SrTiO$_3$. This sample redundan... | ['Jianjun Hu', 'Sadman Sadeed Omee', 'Nihang Fu', 'Qin Li'] | 2023-07-10 | null | null | null | null | ['property-prediction', 'protein-function-prediction'] | ['medical', 'medical'] | [ 2.98130274e-01 -1.97549567e-01 -2.05000237e-01 -3.31952542e-01
-5.90124905e-01 -1.11797981e-01 1.62913695e-01 2.64069945e-01
-1.04172938e-01 1.15637624e+00 2.51035430e-02 -1.69730559e-01
-3.50581080e-01 -9.46541667e-01 -7.44351566e-01 -1.12032855e+00
1.13200203e-01 6.08746290e-01 4.56394017e-01 -2.23970935... | [5.172454833984375, 5.28926420211792] |
2f42ac73-9cd0-41d4-8ffa-4b5e822a973d | self-paced-contrastive-learning-for-semi | 2107.13741 | null | https://arxiv.org/abs/2107.13741v2 | https://arxiv.org/pdf/2107.13741v2.pdf | Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels | Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e.g., image classification. However, in domains such as medical imaging, collecting unlabeled data can be challenging and expensive. In this work, we p... | ['Marco Pedersoli', 'Chrisitian Desrosiers', 'Ping Wang', 'Jizong Peng'] | 2021-07-29 | null | http://proceedings.neurips.cc/paper/2021/hash/8b5c8441a8ff8e151b191c53c1842a38-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8b5c8441a8ff8e151b191c53c1842a38-Paper.pdf | neurips-2021-12 | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 6.54592872e-01 1.94960847e-01 -4.32739913e-01 -7.41041064e-01
-9.78322148e-01 -4.68620896e-01 4.13948625e-01 3.46181571e-01
-8.04117024e-01 5.33439040e-01 -5.12833148e-02 -3.52461725e-01
2.67367542e-01 -3.65055859e-01 -7.67991662e-01 -7.28820562e-01
3.90226915e-02 6.70785308e-01 3.64546716e-01 1.70129240... | [14.695396423339844, -2.2191972732543945] |
6c6d8c58-faad-4e44-a2ee-47fb735ba961 | ontoed-low-resource-event-detection-with | 2105.10922 | null | https://arxiv.org/abs/2105.10922v4 | https://arxiv.org/pdf/2105.10922v4.pdf | OntoED: Low-resource Event Detection with Ontology Embedding | Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To ... | ['Huajun Chen', 'Fei Huang', 'Mosha Chen', 'Huaixiao Tou', 'Hui Chen', 'Luoqiu Li', 'Ningyu Zhang', 'Shumin Deng'] | 2021-05-23 | null | https://aclanthology.org/2021.acl-long.220 | https://aclanthology.org/2021.acl-long.220.pdf | acl-2021-5 | ['ontology-embedding'] | ['knowledge-base'] | [ 5.55384555e-04 1.41351238e-01 -4.13798839e-01 -2.84054786e-01
-2.88502604e-01 -5.56999922e-01 7.05494046e-01 8.59087944e-01
-4.60720092e-01 6.84291005e-01 7.84259439e-01 -1.38831288e-02
-4.88875866e-01 -1.50663269e+00 -3.07240754e-01 -2.38037646e-01
-3.11360657e-01 4.72673148e-01 4.28098887e-01 -2.42901564... | [9.062151908874512, 9.189180374145508] |
e045dd7e-0f14-4356-abed-bec874d972dc | how-human-is-human-evaluation-improving-the | 2205.11930 | null | https://arxiv.org/abs/2205.11930v2 | https://arxiv.org/pdf/2205.11930v2.pdf | The Authenticity Gap in Human Evaluation | Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotators, and rank NLG systems by their average scores. However, little consideration has been given as to whether this approach faithfully captures human preferences. Analyzing this st... | ['Dan Jurafsky', 'Kawin Ethayarajh'] | 2022-05-24 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 8.48670155e-02 4.27968174e-01 -2.81332344e-01 -3.75447571e-01
-1.04925573e+00 -1.18454039e+00 4.58624244e-01 2.60268420e-01
-5.91854990e-01 8.91827285e-01 5.03748894e-01 -4.93147343e-01
-1.67830795e-01 -5.50148785e-01 -5.70432127e-01 -3.68164480e-01
3.83421153e-01 5.85149527e-01 -2.07600072e-01 -4.66258489... | [11.647194862365723, 8.876633644104004] |
16e0d1b8-8e96-47ac-a28b-22005b1810d1 | simple-yet-powerful-an-overlooked-1 | null | null | https://aclanthology.org/2022.coling-1.184 | https://aclanthology.org/2022.coling-1.184.pdf | Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. Traditional NER systems ignore nested entities, which are entities contained in other entity mentions. Although several methods have been proposed to address this case,... | ['Jocelyn Dunstan', 'Felipe Bravo-Marquez', 'Matias Rojas'] | null | null | null | null | coling-2022-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-2.93461502e-01 2.93828696e-01 -5.66540100e-02 -4.45180923e-01
-8.89731288e-01 -7.74991930e-01 8.47438276e-01 5.04943669e-01
-1.06403220e+00 9.10652697e-01 5.59389353e-01 -5.26011169e-01
1.13482118e-01 -6.41943038e-01 -5.14747441e-01 -2.35737175e-01
-1.63557976e-01 6.68179274e-01 3.30421597e-01 -2.98565060... | [9.831923484802246, 9.628555297851562] |
500f5cee-85a2-425c-9bc0-96b7b3e314f9 | uninext-exploring-a-unified-architecture-for | 2304.13700 | null | https://arxiv.org/abs/2304.13700v2 | https://arxiv.org/pdf/2304.13700v2.pdf | UniNeXt: Exploring A Unified Architecture for Vision Recognition | Vision Transformers have shown great potential in computer vision tasks. Most recent works have focused on elaborating the spatial token mixer for performance gains. However, we observe that a well-designed general architecture can significantly improve the performance of the entire backbone, regardless of which spatia... | ['Zhibin Wang', 'Fan Wang', 'Sitong Wu', 'Jianlong Yuan', 'Fangjian Lin'] | 2023-04-26 | null | null | null | null | ['spatial-token-mixer'] | ['computer-vision'] | [-1.47957252e-02 4.70343195e-02 -6.41011447e-02 1.25790715e-01
-2.90485859e-01 -4.06303674e-01 8.08413982e-01 -3.74973923e-01
-3.35051596e-01 5.47762997e-02 2.83641368e-01 -3.95051539e-01
1.06156610e-01 -7.37357795e-01 -7.35384941e-01 -7.88805306e-01
3.31316262e-01 -1.28149584e-01 7.67763674e-01 -3.85104239... | [9.62668228149414, 1.349242091178894] |
750e3b71-233d-46b0-8a6c-ec31de5cdf8c | machine-learning-and-the-future-of-bayesian | 2304.11251 | null | https://arxiv.org/abs/2304.11251v1 | https://arxiv.org/pdf/2304.11251v1.pdf | Machine Learning and the Future of Bayesian Computation | Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly perfor... | ['David B. Dunson', 'Sanvesh Srivastava', 'Lizhen Lin', 'Trevor Campbell', 'Steven Winter'] | 2023-04-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.13783218e-01 2.40930673e-02 -4.15713847e-01 -6.14332318e-01
-7.75971293e-01 -5.27101457e-01 7.63573647e-01 4.80150729e-02
-1.41028211e-01 8.82421076e-01 6.21832788e-01 -5.65954089e-01
-5.30407310e-01 -7.49368608e-01 -1.51448533e-01 -6.11102581e-01
-1.68962985e-01 7.48323321e-01 -1.38562545e-01 4.64977801... | [6.978137016296387, 4.009216785430908] |
32e92953-26f5-4dd3-9619-10aaa762479b | multimodal-intent-discovery-from-livestream | null | null | https://aclanthology.org/2022.findings-naacl.36 | https://aclanthology.org/2022.findings-naacl.36.pdf | Multimodal Intent Discovery from Livestream Videos | Individuals, educational institutions, and businesses are prolific at generating instructional video content such as “how-to” and tutorial guides. While significant progress has been made in basic video understanding tasks, identifying procedural intent within these instructional videos is a challenging and important t... | ['Mohit Bansal', 'Walter Chang', 'Trung Bui', 'Seunghyun Yoon', 'Franck Dernoncourt', 'Quan Tran', 'Adyasha Maharana'] | null | null | null | null | findings-naacl-2022-7 | ['intent-discovery'] | ['natural-language-processing'] | [ 3.68240654e-01 -1.83955297e-01 -6.59646988e-01 -3.15267295e-01
-9.90527391e-01 -7.44924068e-01 5.60738564e-01 2.16737732e-01
-2.54397124e-01 1.82858169e-01 9.94938433e-01 -3.46137077e-01
-8.43946859e-02 -2.67575197e-02 -9.55402017e-01 -4.79007453e-01
-9.36710089e-02 -1.93225414e-01 -1.80529859e-02 -8.38228688... | [10.328659057617188, 0.7886306047439575] |
381b1226-0005-41bd-abe8-d39dde357180 | robust-speech-recognition-using-consensus | 1507.06023 | null | http://arxiv.org/abs/1507.06023v1 | http://arxiv.org/pdf/1507.06023v1.pdf | Robust speech recognition using consensus function based on multi-layer networks | The clustering ensembles mingle numerous partitions of a specified data into
a single clustering solution. Clustering ensemble has emerged as a potent
approach for ameliorating both the forcefulness and the stability of
unsupervised classification results. One of the major problems in clustering
ensembles is to find th... | ['Abir Smiti', 'Rimah Amami', 'Ghaith Manita'] | 2015-07-22 | null | null | null | null | ['clustering-ensemble', 'robust-speech-recognition'] | ['graphs', 'speech'] | [-3.63490060e-02 -2.59356976e-01 4.96405125e-01 -4.36079234e-01
-3.67593437e-01 -5.03689885e-01 2.91201383e-01 2.71874875e-01
-3.72263491e-01 5.84107637e-01 2.06712224e-02 8.43437910e-02
-6.27898574e-01 -5.32530010e-01 -5.27688824e-02 -9.93151248e-01
-5.14332429e-02 5.34637392e-01 1.47687018e-01 -2.37587094... | [7.582937717437744, 4.458425998687744] |
0024eab2-1f35-4da3-aafc-f25567dde7a1 | gest-the-graph-of-events-in-space-and-time-as | 2305.12940 | null | https://arxiv.org/abs/2305.12940v1 | https://arxiv.org/pdf/2305.12940v1.pdf | GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language | One of the essential human skills is the ability to seamlessly build an inner representation of the world. By exploiting this representation, humans are capable of easily finding consensus between visual, auditory and linguistic perspectives. In this work, we set out to understand and emulate this ability through an ex... | ['Marius Leordeanu', 'Traian Rebedea', 'Nicolae Cudlenco', 'Mihai Masala'] | 2023-05-22 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 5.87013438e-02 1.36874110e-01 2.17468157e-01 -2.63057858e-01
-2.55101144e-01 -5.29395938e-01 1.35983491e+00 6.23539805e-01
-1.43127546e-01 2.78637558e-01 5.70628583e-01 1.49101362e-01
-1.14946745e-01 -8.11909497e-01 -5.14742315e-01 -3.24066840e-02
1.72146261e-01 2.56018698e-01 6.94330633e-02 -3.80213112... | [10.687324523925781, 1.7211514711380005] |
f5c98847-b635-4609-b2a1-d17cb6c80c69 | smsmix-sense-maintained-sentence-mixup-for | 2212.07072 | null | https://arxiv.org/abs/2212.07072v2 | https://arxiv.org/pdf/2212.07072v2.pdf | SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation | Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. Although current systems have attained unprecedented performances for such tasks, the nonuniform distribution of word senses during training generally results in systems performing ... | ['Chang D. Yoo', 'Mark Hasegawa-Johnson', 'Sunjae Yoon', 'John Harvill', 'Eunseop Yoon', 'Hee Suk Yoon'] | 2022-12-14 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.12269819e-01 1.16803788e-01 -3.47780824e-01 -3.63854945e-01
-6.38566196e-01 -5.91999888e-01 4.40144479e-01 8.00813258e-01
-7.00124502e-01 7.38253057e-01 4.30475712e-01 -2.92789876e-01
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4.21400934e-01 1.46836475e-01 3.09662163e-01 -6.86320424... | [10.28640079498291, 9.066695213317871] |
beaebbb0-dde1-44eb-9182-935768137c60 | breaking-the-architecture-barrier-a-method | 2212.13970 | null | https://arxiv.org/abs/2212.13970v1 | https://arxiv.org/pdf/2212.13970v1.pdf | Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks | Transfer learning is a popular technique for improving the performance of neural networks. However, existing methods are limited to transferring parameters between networks with same architectures. We present a method for transferring parameters between neural networks with different architectures. Our method, called D... | ['Kamil Piechowiak', 'Daniel Nowak', 'Maciej A. Czyzewski'] | 2022-12-28 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [-5.88302724e-02 -1.69212863e-01 -2.45638728e-01 -5.81849694e-01
-1.95038036e-01 -8.79425228e-01 1.71856165e-01 -2.72684216e-01
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-1.31366104e-01 -7.35670447e-01 -8.40319574e-01 -4.63470757e-01
1.34305730e-01 6.19753063e-01 5.46077490e-01 -4.36759181... | [8.718208312988281, 3.2207257747650146] |
0c4ae1e6-cb7b-4c4e-bfe5-f9c9b7356b36 | an-mrc-framework-for-semantic-role-labeling | 2109.06660 | null | https://arxiv.org/abs/2109.06660v2 | https://arxiv.org/pdf/2109.06660v2.pdf | An MRC Framework for Semantic Role Labeling | Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we pro... | ['Jun He', 'Guoyin Wang', 'Ziyao Wang', 'Han Qiu', 'Xiaofei Sun', 'Yuxian Meng', 'Jiwei Li', 'Nan Wang'] | 2021-09-14 | null | https://aclanthology.org/2022.coling-1.191 | https://aclanthology.org/2022.coling-1.191.pdf | coling-2022-10 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.64446855e-01 5.50447166e-01 -3.36639404e-01 -5.09616554e-01
-8.46698046e-01 -8.89497697e-01 6.24946415e-01 7.81801283e-01
-4.47208345e-01 6.80281937e-01 5.71926355e-01 -4.99097228e-01
-2.99434483e-01 -9.55157101e-01 -4.46332663e-01 -4.88154978e-01
4.33005065e-01 4.65579748e-01 6.15113378e-01 -3.96386474... | [10.200650215148926, 9.199841499328613] |
03cd1af3-cd71-4dcd-a3b3-8c23b0b23a47 | fair-and-diverse-dpp-based-data-summarization | 1802.04023 | null | http://arxiv.org/abs/1802.04023v1 | http://arxiv.org/pdf/1802.04023v1.pdf | Fair and Diverse DPP-based Data Summarization | Sampling methods that choose a subset of the data proportional to its
diversity in the feature space are popular for data summarization. However,
recent studies have noted the occurrence of bias (under- or over-representation
of a certain gender or race) in such data summarization methods. In this paper
we initiate a s... | ['Tarun Kathuria', 'Amit Deshpande', 'Vijay Keswani', 'L. Elisa Celis', 'Damian Straszak', 'Nisheeth K. Vishnoi'] | 2018-02-12 | fair-and-diverse-dpp-based-data-summarization-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2176 | http://proceedings.mlr.press/v80/celis18a/celis18a.pdf | icml-2018-7 | ['data-summarization'] | ['miscellaneous'] | [ 5.28728902e-01 2.96761870e-01 -5.36310792e-01 -6.93460763e-01
-8.24943900e-01 -4.96184647e-01 8.40740621e-01 2.24021047e-01
-4.37931031e-01 1.15339637e+00 5.82782388e-01 -1.18013494e-01
-2.85186589e-01 -8.65035474e-01 -5.26780009e-01 -7.64895380e-01
7.59957582e-02 7.80860245e-01 -2.25853641e-02 -1.01972828... | [6.970812797546387, 4.866064548492432] |
5d453971-9abd-4a9b-bfb5-d9f23c4d7854 | hyp-2-loss-beyond-hypersphere-metric-space | 2208.06866 | null | https://arxiv.org/abs/2208.06866v1 | https://arxiv.org/pdf/2208.06866v1.pdf | HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval | Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval. In this paper, we carried out in-depth investigations on metric learning in deep hashing for establishing a powerful m... | ['Jue Wang', 'Yanbo Fan', 'Chun Yuan', 'Zhengzhuo Xu', 'Zenghao Chai', 'Chengyin Xu'] | 2022-08-14 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [-2.00757295e-01 -4.35680449e-01 -2.43328586e-01 -5.08315563e-01
-1.47972274e+00 -3.28209281e-01 3.28489065e-01 3.18226546e-01
-5.18194497e-01 7.10801899e-01 -9.97976884e-02 5.31453919e-03
-6.35317445e-01 -7.86337733e-01 -5.39263368e-01 -1.10217202e+00
-2.26172015e-01 5.77433169e-01 9.40948054e-02 -1.00673951... | [11.32977294921875, 0.9705618619918823] |
edd1b409-4c51-4c94-aab4-07acad3fa016 | uncovering-political-hate-speech-during | 2306.14764 | null | https://arxiv.org/abs/2306.14764v2 | https://arxiv.org/pdf/2306.14764v2.pdf | Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines | The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages. To address this issue, we introduce a new dataset named IEHate, which contains 11,457 manually annotated Hindi tweets related to the Indian Assembly Election Campaign from November ... | ['Imran Razzak', 'Usman Naseem', 'Kritesh Rauniyar', 'Surendrabikram Thapa', 'Mohammad Aman Siddiqui', 'Farhan Ahmad Jafri'] | 2023-06-26 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.83796108e-01 4.92761433e-02 -1.87390506e-01 -7.00652748e-02
-9.30458903e-01 -8.87966335e-01 1.03443491e+00 3.31160545e-01
-4.24803495e-01 6.17135465e-01 8.38599622e-01 -4.86840010e-01
4.11992967e-01 -5.46030760e-01 -2.23311067e-01 -6.29167080e-01
2.39530459e-01 1.25296578e-01 -2.94162780e-01 -5.88364363... | [8.73317813873291, 10.525480270385742] |
eb258463-36fa-42fb-9fbb-556a97dc6abb | collaborative-representation-based | 1204.2358 | null | https://arxiv.org/abs/1204.2358v2 | https://arxiv.org/pdf/1204.2358v2.pdf | Collaborative Representation based Classification for Face Recognition | By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm spars... | ['David Zhang', 'Xiangchu Feng', 'Yi Ma', 'Lei Zhang', 'Meng Yang'] | 2012-04-11 | null | null | null | null | ['robust-face-recognition', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 2.92250544e-01 -5.80440350e-02 -3.11446667e-01 -5.19458294e-01
-3.96807253e-01 -1.33741856e-01 3.31974298e-01 -1.79659203e-01
3.05747036e-02 4.42656040e-01 2.19286725e-01 1.69872358e-01
-4.23527837e-01 -7.21618235e-01 -2.56017298e-01 -1.02128530e+00
-4.32870872e-02 -3.26489419e-01 -1.41658604e-01 -2.47422203... | [12.54605770111084, 0.4261248707771301] |
e27ccf21-e2e9-469e-8b7c-9d842995f4a6 | colada-a-collaborative-label-denoising | 2305.14913 | null | https://arxiv.org/abs/2305.14913v1 | https://arxiv.org/pdf/2305.14913v1.pdf | CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition | Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-language labeled data or performing knowledge distillation on target-languag... | ['Chin-Yew Lin', 'Tiejun Zhao', 'Börje F. Karlsson', 'Huiqiang Jiang', 'Qianhui Wu', 'Tingting Ma'] | 2023-05-24 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 2.27228906e-02 -1.72545135e-01 -1.74980015e-02 -6.63747013e-01
-1.35654926e+00 -6.82306170e-01 5.04325509e-01 7.41690248e-02
-6.13262415e-01 7.12098539e-01 3.87158900e-01 -8.98990333e-02
2.87070572e-01 -6.84893668e-01 -4.97307241e-01 -8.55259836e-01
5.91602325e-01 2.99771458e-01 -2.65099555e-01 5.53727336... | [9.952903747558594, 9.61655044555664] |
4ea4c80f-fc93-4cce-8a7a-acc142695d31 | how-far-are-we-from-robust-voice-conversion-a | 2011.12063 | null | https://arxiv.org/abs/2011.12063v3 | https://arxiv.org/pdf/2011.12063v3.pdf | How Far Are We from Robust Voice Conversion: A Survey | Voice conversion technologies have been greatly improved in recent years with the help of deep learning, but their capabilities of producing natural sounding utterances in different conditions remain unclear. In this paper, we gave a thorough study of the robustness of known VC models. We also modified these models, su... | ['Hung-Yi Lee', 'Chien-yu Huang', 'Jheng-Hao Lin', 'Tzu-Hsien Huang'] | 2020-11-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-3.11072797e-01 -2.64867038e-01 2.34275199e-02 -2.64979333e-01
-6.41574621e-01 -5.82873404e-01 4.98818129e-01 -5.43262005e-01
-1.74950138e-01 6.22423530e-01 3.93094420e-01 -4.32349324e-01
3.14105004e-01 -5.19075215e-01 -4.05434638e-01 -6.27107322e-01
1.50230169e-01 -7.50137344e-02 2.14834481e-01 -2.71340638... | [14.873407363891602, 6.539517879486084] |
4ed67227-9fc1-4ac8-9c95-d0528fd76800 | llm-empowered-chatbots-for-psychiatrist-and | 2305.13614 | null | https://arxiv.org/abs/2305.13614v1 | https://arxiv.org/pdf/2305.13614v1.pdf | LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation | Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiatric outpatient scenarios. In this work, we focus on exploring the potential of ChatGPT in powering chatbots for psychiatrist and patient sim... | ['Lyuchun Cui', 'Zhiling Zhang', 'Kunyao Lan', 'Kenny Q. Zhu', 'Mengyue Wu', 'Siyuan Chen'] | 2023-05-23 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-2.31017500e-01 1.05630517e+00 1.08242393e-01 -5.64159811e-01
-5.97316742e-01 -3.67899597e-01 1.25336289e-01 -4.53091264e-02
-4.47047651e-01 8.42101216e-01 8.13446224e-01 -3.54952812e-01
-1.18245631e-01 -6.98764175e-02 9.39706922e-01 -3.23191047e-01
-1.43089578e-01 9.67051029e-01 -3.23689908e-01 -2.94223607... | [12.553348541259766, 7.844502925872803] |
2a634609-e5e0-4737-ba1d-4f22393376c0 | findings-of-the-2016-conference-on-machine | null | null | https://aclanthology.org/W16-2301 | https://aclanthology.org/W16-2301.pdf | Findings of the 2016 Conference on Machine Translation | null | ['Lucia Specia', "Aur{\\'e}lie N{\\'e}v{\\'e}ol", 'Philipp Koehn', 'Rajen Chatterjee', 'Ond{\\v{r}}ej Bojar', 'Varvara Logacheva', 'Raphael Rubino', 'Marco Turchi', 'Marcos Zampieri', 'Matt Post', 'Christof Monz', 'Carolina Scarton', 'Barry Haddow', 'Martin Popel', 'Antonio Jimeno Yepes', 'Yvette Graham', 'Matthias Huc... | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.38292932510376, 3.681321382522583] |
401a3bae-593e-4abb-b4a7-f88b9a5b227e | an-unsupervised-extractive-summarization | 2112.03203 | null | https://arxiv.org/abs/2112.03203v4 | https://arxiv.org/pdf/2112.03203v4.pdf | An unsupervised extractive summarization method based on multi-round computation | Text summarization methods have attracted much attention all the time. In recent years, deep learning has been applied to text summarization, and it turned out to be pretty effective. Most of the current text summarization methods based on deep learning are supervised methods which need large-scale datasets. However, l... | ['Kevin Song', 'Jin He', 'Yongfeng Huang', 'Zhongliang Yang', 'Yingzhu Xiong', 'Dehao Tao'] | 2021-12-06 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.37687400e-01 -1.60965979e-01 -1.02655165e-01 -2.41853029e-01
-4.80844647e-01 3.52711193e-02 4.66275126e-01 5.90361238e-01
-4.11452651e-01 7.88939893e-01 1.01555729e+00 9.11211371e-02
-4.66323011e-02 -8.85018528e-01 -1.75928354e-01 -5.18356860e-01
1.85720742e-01 4.02769268e-01 3.32654446e-01 -4.77034867... | [12.675464630126953, 9.55978012084961] |
6f9e7378-eb31-43ba-a399-f1439c0c0b8b | learning-dynamic-guidance-for-depth-image | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Gu_Learning_Dynamic_Guidance_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Gu_Learning_Dynamic_Guidance_CVPR_2017_paper.pdf | Learning Dynamic Guidance for Depth Image Enhancement | The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizi... | ['WangMeng Zuo', 'Shuhang Gu', 'Yunjin Chen', 'Shi Guo', 'Chongyu Chen', 'Lei Zhang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['depth-image-upsampling'] | ['computer-vision'] | [ 4.86604869e-01 -1.40020475e-01 -1.74455851e-01 -7.20415175e-01
-6.91706359e-01 -6.51416481e-02 2.23733529e-01 -3.37334350e-02
-5.19115865e-01 4.18387294e-01 2.76576549e-01 2.10255444e-01
-4.53291625e-01 -7.61945903e-01 -2.68006563e-01 -9.25129175e-01
6.12611510e-02 -6.54374287e-02 2.31821790e-01 -1.25144467... | [9.39005184173584, -2.317049980163574] |
7c660457-1531-4440-9bf0-095d0c4a7e0a | efficiently-aligned-cross-lingual-transfer | 2304.01295 | null | https://arxiv.org/abs/2304.01295v2 | https://arxiv.org/pdf/2304.01295v2.pdf | Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning | Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the high cost of obtaining non-English conversational data, which results in limited coverage. In this wo... | ['Yingbo Zhou', 'Caiming Xiong', 'Wenhao Liu', 'Shafiq Joty', 'Semih Yavuz', 'Jin Qu', 'Lifu Tu'] | 2023-04-03 | null | null | null | null | ['cross-lingual-transfer', 'intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.11944519e-01 1.85243860e-01 -5.30020952e-01 -5.00128746e-01
-1.27255821e+00 -6.18796885e-01 8.97644341e-01 -1.51798591e-01
-5.60420454e-01 1.13031673e+00 6.04063272e-01 -6.02742136e-01
3.91370893e-01 -5.01060724e-01 -4.21282649e-01 -2.61670262e-01
1.70981921e-02 8.89365137e-01 -1.04929730e-01 -6.56616092... | [12.386980056762695, 8.408698081970215] |
c959520b-b6fa-4e2a-8114-83dd6a4661ce | flow-lenia-mass-conservation-for-the-study-of | 2212.07906 | null | https://arxiv.org/abs/2212.07906v2 | https://arxiv.org/pdf/2212.07906v2.pdf | Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization | The design of complex self-organising systems producing life-like phenomena, such as the open-ended evolution of virtual creatures, is one of the main goals of artificial life. Lenia, a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states, has attracted a lot of atten... | ['Bert Wang-Chak Chan', 'Clément Moulin-Frier', 'Pierre-Yves Oudeyer', 'Mayalen Etcheverry', 'Gautier Hamon', 'Erwan Plantec'] | 2022-12-14 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.20313950e-01 5.07820174e-02 3.87773365e-01 6.65801108e-01
6.09332204e-01 -9.40588474e-01 8.48031044e-01 1.07033715e-01
-1.91473559e-01 1.16970277e+00 -1.71448022e-01 -5.47972806e-02
-3.70597214e-01 -1.22562325e+00 -4.77599591e-01 -1.13920200e+00
-6.34699464e-01 7.40736544e-01 7.51463056e-01 -8.12564850... | [5.599849224090576, 4.126174449920654] |
d5249218-d0fc-4c56-87f7-ba2594c9d3d0 | an-overview-of-ai-and-blockchain-integration | 2305.03928 | null | https://arxiv.org/abs/2305.03928v1 | https://arxiv.org/pdf/2305.03928v1.pdf | An Overview of AI and Blockchain Integration for Privacy-Preserving | With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This ... | ['Wenkai Li', 'Xiaoqi Li', 'Hongli Peng', 'Yuanzheng Niu', 'Dechao Kong', 'Zongwei Li'] | 2023-05-06 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 1.01657473e-01 1.02723040e-01 -7.43038297e-01 -4.18358654e-01
-2.32958868e-01 -1.22641778e+00 7.64608026e-01 4.73645508e-01
-2.52505571e-01 1.02789044e+00 2.85119653e-01 -8.08718443e-01
-8.49935487e-02 -8.08186293e-01 -1.77609682e-01 -8.64641309e-01
1.16598770e-01 3.63459170e-01 -4.84760404e-01 -9.92233828... | [5.924837112426758, 6.60598087310791] |
c32834a9-a081-42c1-b7e2-51e25b04f25d | calico-self-supervised-camera-lidar | 2306.00349 | null | https://arxiv.org/abs/2306.00349v1 | https://arxiv.org/pdf/2306.00349v1.pdf | CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception | Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous... | ['Chaowei Xiao', 'Z. Morley Mao', 'Atul Prakash', 'Qingzhao Zhang', 'Haizhong Zheng', 'Jiachen Sun'] | 2023-06-01 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [ 7.80990273e-02 2.43220665e-02 -2.87840962e-01 -4.22537953e-01
-9.04250443e-01 -8.27866495e-01 8.25570464e-01 1.93834171e-01
-7.04330504e-01 1.37779728e-01 -3.08093429e-01 -5.72197378e-01
3.88139278e-01 -7.63148189e-01 -9.52833176e-01 -5.66031337e-01
2.55231053e-01 3.20895046e-01 6.18789852e-01 -4.50798064... | [7.899035930633545, -2.4875283241271973] |
d3df7760-63a6-4824-a7dd-c3604bd29994 | naturalinversion-data-free-image-synthesis | 2306.16661 | null | https://arxiv.org/abs/2306.16661v1 | https://arxiv.org/pdf/2306.16661v1.pdf | NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency | We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature... | ['Suhyun Kim', 'Dohee Kim', 'Dogyun Park', 'Yujin Kim'] | 2023-06-29 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 4.50742006e-01 1.32680997e-01 2.40027145e-01 -5.23450851e-01
-6.47872269e-01 -4.80016321e-01 5.43068290e-01 -4.85572338e-01
-4.26824182e-01 8.86332214e-01 1.34393096e-01 4.96877171e-02
-8.66352245e-02 -8.88524890e-01 -1.18916106e+00 -6.79429293e-01
2.66694248e-01 4.08843994e-01 5.50521649e-02 -1.51034117... | [11.58896255493164, -0.5637441873550415] |
2334dff5-c3f6-468e-86f6-87d172d9e32b | high-order-tensor-formulation-for | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Bibi_High_Order_Tensor_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Bibi_High_Order_Tensor_ICCV_2017_paper.pdf | High Order Tensor Formulation for Convolutional Sparse Coding | Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct single-feature 2D images independently. However, learning multi-dimensional dictionaries and sparse... | ['Adel Bibi', 'Bernard Ghanem'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['video-reconstruction'] | ['computer-vision'] | [-3.17025324e-03 -4.33403671e-01 4.37385105e-02 1.43310996e-02
-6.37808740e-01 -4.56222147e-01 4.22604978e-01 -1.53509006e-01
-6.03660867e-02 3.78441423e-01 5.00116229e-01 -9.55303609e-02
-2.72665739e-01 -3.36186200e-01 -4.92122859e-01 -7.07399189e-01
-2.41703898e-01 3.95674795e-01 -4.29595262e-02 -5.54011166... | [11.441937446594238, -1.979193925857544] |
31cba3ab-e0af-4546-84d4-8376714e5b40 | revisiting-discriminative-entropy-clustering | 2301.11405 | null | https://arxiv.org/abs/2301.11405v2 | https://arxiv.org/pdf/2301.11405v2.pdf | Discriminative Entropy Clustering and its Relation to K-means and SVM | Maximization of mutual information between the model's input and output is formally related to "decisiveness" and "fairness" of the softmax predictions, motivating such unsupervised entropy-based losses for discriminative models. Recent self-labeling methods based on such losses represent the state of the art in deep c... | ['Yuri Boykov', 'Zhongwen Zhang'] | 2023-01-26 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 1.52808562e-01 6.05586052e-01 -4.57884550e-01 -8.41452301e-01
-5.01945794e-01 -6.03367329e-01 8.17577064e-01 3.26633543e-01
-4.34507638e-01 7.82803118e-01 1.20630190e-01 2.46964544e-02
-4.75469083e-01 -5.50164282e-01 -4.60584641e-01 -1.12984264e+00
-1.13409944e-01 6.31705523e-01 -6.41254336e-02 1.95745096... | [9.27514934539795, 3.362274169921875] |
49002979-ac55-4b8b-a52f-4f26f77e4b94 | stylemelgan-an-efficient-high-fidelity | 2011.01557 | null | https://arxiv.org/abs/2011.01557v2 | https://arxiv.org/pdf/2011.01557v2.pdf | StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization | In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech. Computationally heavy models like WaveNet and WaveGlow achieve best results, while lightweight GAN models, e.g. MelGAN and Parallel WaveGAN, remain inferior in terms of... | ['Guillaume Fuchs', 'Nicola Pia', 'Ahmed Mustafa'] | 2020-11-03 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 3.87696356e-01 3.56807172e-01 3.90313745e-01 9.62408707e-02
-1.11440158e+00 -6.22672319e-01 6.94165170e-01 -6.49853826e-01
-2.40659773e-01 7.89165020e-01 4.11970556e-01 -4.01773214e-01
4.88546461e-01 -7.74715126e-01 -7.84765184e-01 -9.32749689e-01
1.80942610e-01 2.06285819e-01 -2.56590456e-01 -2.10203931... | [15.330933570861816, 6.195594787597656] |
c8e192d5-b51b-4ead-84d6-01e8c487b0b1 | reinforcement-learning-in-robotic-motion | 2306.06754 | null | https://arxiv.org/abs/2306.06754v1 | https://arxiv.org/pdf/2306.06754v1.pdf | Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning | High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks. For real robots, it is challenging to collect enough qualified data either as demonstrations for IL or experiences for RL due to safety considerations in environments with obs... | ['Lambert Schomaker', 'Sha Luo'] | 2023-06-11 | null | null | null | null | ['imitation-learning', 'motion-planning'] | ['methodology', 'robots'] | [-1.05679028e-01 2.09082991e-01 -3.76636386e-01 -4.18348722e-02
-7.61735678e-01 -3.68658841e-01 5.92654884e-01 -2.03354433e-01
-8.11215460e-01 1.17081511e+00 5.89015484e-02 -2.25113735e-01
-9.70145389e-02 -5.51358759e-01 -8.23570073e-01 -7.31649041e-01
-4.47661459e-01 5.53693712e-01 3.95904630e-01 -2.46460170... | [4.541464328765869, 1.1342498064041138] |
0cf36688-65cf-44e8-9e0b-38d13aef6be1 | transfer-learning-across-several-centuries | 2306.14592 | null | https://arxiv.org/abs/2306.14592v1 | https://arxiv.org/pdf/2306.14592v1.pdf | Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary | A named entity recognition and classification plays the first and foremost important role in capturing semantics in data and anchoring in translation as well as downstream study for history. However, NER in historical text has faced challenges such as scarcity of annotated corpus, multilanguage variety, various noise, ... | ['Jaehyuk Lee', 'Hyungil Lee', 'Joonmo Ahn', 'Taehong Jang', 'Sojung Lucia Kim'] | 2023-06-26 | null | null | null | null | ['transfer-learning', 'cg'] | ['miscellaneous', 'natural-language-processing'] | [-3.20299774e-01 -2.51662880e-01 -4.68997151e-01 -2.83995450e-01
-9.26251709e-01 -1.10407567e+00 8.37154686e-01 1.99712753e-01
-1.00371456e+00 1.28231490e+00 8.33055139e-01 -5.68777204e-01
-6.56837132e-03 -5.96152306e-01 -4.74639684e-01 -3.67113024e-01
-3.31151277e-01 5.97685218e-01 2.43103765e-02 -4.63776380... | [9.997235298156738, 9.850567817687988] |
e94ad713-3a08-414b-8f00-4a3b91be0687 | high-fidelity-pseudo-labels-for-boosting | 2304.02621 | null | https://arxiv.org/abs/2304.02621v1 | https://arxiv.org/pdf/2304.02621v1.pdf | High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation | The task of image-level weakly-supervised semantic segmentation (WSSS) has gained popularity in recent years, as it reduces the vast data annotation cost for training segmentation models. The typical approach for WSSS involves training an image classification network using global average pooling (GAP) on convolutional ... | ['Michael Felsberg', 'Yushan Zhang', 'Arvi Jonnarth'] | 2023-04-05 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 5.35346448e-01 2.98324078e-01 -2.67721564e-01 -5.12658775e-01
-9.52992857e-01 -6.03723943e-01 6.49906874e-01 2.23240733e-01
-7.65832722e-01 5.13346136e-01 -1.58513114e-01 -1.51605085e-01
3.39922309e-01 -6.95525706e-01 -1.05114543e+00 -8.16744566e-01
2.53333628e-01 3.33940268e-01 8.06066692e-01 8.66013318... | [9.520405769348145, 0.3635052740573883] |
26001202-5786-4149-8f5d-f99f90ecd365 | design-and-implementation-of-image-processing | 1607.04760 | null | http://arxiv.org/abs/1607.04760v1 | http://arxiv.org/pdf/1607.04760v1.pdf | Design and implementation of image processing system for Lumen social robot-humanoid as an exhibition guide for Electrical Engineering Days 2015 | Lumen Social Robot is a humanoid robot development with the purpose that it
could be a good friend to all people. In this year, the Lumen Social Robot is
being developed into a guide in the exhibition and in the seminar of the Final
Exam of undergraduate and graduate students in Electrical Engineering ITB,
named Electr... | ['Setyaki Sholata Sya', 'Ary Setijadi Prihatmanto'] | 2016-07-16 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-1.74387515e-01 3.12580645e-01 3.41893166e-01 -5.83455712e-02
2.92885661e-01 -4.16084498e-01 3.80008399e-01 -4.97769684e-01
-3.00046831e-01 4.16067779e-01 -5.21756470e-01 -1.90768525e-01
1.00023329e-01 -1.07952261e+00 -6.86502278e-01 -8.55297923e-01
-3.11933532e-02 7.00661063e-01 9.44900885e-02 -3.06642205... | [13.305502891540527, 0.8247893452644348] |
9940c1b8-3538-4114-8592-ff5e0fe81677 | entity-linking-meets-deep-learning-techniques | 2109.12520 | null | https://arxiv.org/abs/2109.12520v1 | https://arxiv.org/pdf/2109.12520v1.pdf | Entity Linking Meets Deep Learning: Techniques and Solutions | Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields of knowledge engineering and data mining, underlying a variety of downstream applications such as knowledge base population, content analysi... | ['Xiaojie Yuan', 'Jianyong Wang', 'Jiawei Han', 'Yinan Liu', 'Yuhan Li', 'Wei Shen'] | 2021-09-26 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.46150690e-01 1.78628594e-01 -8.91261101e-01 -2.36488394e-02
-5.78258872e-01 -5.83859563e-01 5.28491378e-01 7.11980402e-01
-4.03342664e-01 9.89518344e-01 2.81918943e-01 -3.04748595e-01
-4.50755835e-01 -1.24281359e+00 -5.25021970e-01 -2.82480985e-01
-3.72978151e-01 6.02295041e-01 2.34825477e-01 -2.80349821... | [9.164265632629395, 8.43586254119873] |
972398ed-24df-4486-b862-5bfbd6bcaf09 | u-net-vs-transformer-is-u-net-outdated-in | 2208.04939 | null | https://arxiv.org/abs/2208.04939v2 | https://arxiv.org/pdf/2208.04939v2.pdf | U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration? | Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies.... | ['Jinming Duan', 'Zhaowen Qiu', 'Wenqi Lu', 'Tianyang Zhang', 'Joseph Bartlett', 'Xi Jia'] | 2022-08-07 | null | null | null | null | ['medical-image-registration', 'long-range-modeling'] | ['medical', 'natural-language-processing'] | [-1.30858585e-01 2.89070487e-01 5.80109470e-02 -4.42105949e-01
-9.09283400e-01 -3.88194740e-01 4.97841179e-01 -1.17682710e-01
-6.80088282e-01 6.24571919e-01 2.56230086e-01 -2.17944950e-01
-3.43321562e-02 -7.23540187e-01 -9.99381006e-01 -5.64142168e-01
-3.20731997e-01 5.39910138e-01 4.05276388e-01 -4.31157678... | [14.008038520812988, -2.570021390914917] |
f1b0b92d-40c2-4083-81f2-7cacf3e4769f | the-evolution-of-sentiment-analysis-a-review | 1612.01556 | null | http://arxiv.org/abs/1612.01556v4 | http://arxiv.org/pdf/1612.01556v4.pdf | The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers | Sentiment analysis is one of the fastest growing research areas in computer
science, making it challenging to keep track of all the activities in the area.
We present a computer-assisted literature review, where we utilize both text
mining and qualitative coding, and analyze 6,996 papers from Scopus. We find
that the r... | ['Daniel Graziotin', 'Mika Viking Mäntylä', 'Miikka Kuutila'] | 2016-12-05 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [-3.68284643e-01 -4.39693853e-02 -8.15380812e-01 2.37777680e-01
-1.20760776e-01 -8.32997203e-01 4.76739198e-01 9.46979761e-01
-7.20971286e-01 5.02756059e-01 3.83111507e-01 -5.78052104e-01
3.70067060e-02 -7.10003197e-01 -1.94319025e-01 -7.43702129e-02
4.15437311e-01 -1.31708980e-01 -6.32888153e-02 -6.41764760... | [10.738103866577148, 6.960362911224365] |
c20164d5-4dc9-487a-9fb4-4ff1b580c9f3 | asymmetric-cross-guided-attention-network-for | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Asymmetric_Cross-Guided_Attention_Network_for_Actor_and_Action_Video_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Asymmetric_Cross-Guided_Attention_Network_for_Actor_and_Action_Video_Segmentation_ICCV_2019_paper.pdf | Asymmetric Cross-Guided Attention Network for Actor and Action Video Segmentation From Natural Language Query | Actor and action video segmentation from natural language query aims to selectively segment the actor and its action in a video based on an input textual description. Previous works mostly focus on learning simple correlation between two heterogeneous features of vision and language via dynamic convolution or fully con... | [' Dacheng Tao', ' Junchi Yan', ' Cheng Deng', 'Hao Wang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 2.50732034e-01 -1.79193050e-01 -4.06286269e-01 -5.29405057e-01
-8.23785663e-01 -4.19851094e-01 5.56314886e-01 -2.01097131e-01
-7.49454618e-01 5.19115388e-01 3.47074002e-01 -9.33065638e-03
3.11928481e-01 -4.08987164e-01 -7.84852564e-01 -5.89216709e-01
4.47344571e-01 1.49965659e-01 6.11119926e-01 2.88096443... | [9.97340202331543, 0.7907313704490662] |
cf1fdaa0-d4b4-41cf-a11b-2042bd919b86 | efficient-dynamic-filter-for-robust-and-low | 2205.01304 | null | https://arxiv.org/abs/2205.01304v2 | https://arxiv.org/pdf/2205.01304v2.pdf | Efficient dynamic filter for robust and low computational feature extraction | Unseen noise signal which is not considered in a model training process is difficult to anticipate and would lead to performance degradation. Various methods have been investigated to mitigate unseen noise. In our previous work, an Instance-level Dynamic Filter (IDF) and a Pixel Dynamic Filter (PDF) were proposed to ex... | ['Hanseok Ko', 'David K. Han', 'Jeong-gi Kwak', 'Bokyeung Lee', 'Gwantae Kim', 'Donghyeon Kim'] | 2022-05-03 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.01055929e-01 -3.23689312e-01 3.36078078e-01 -5.06497025e-01
-6.84695363e-01 -4.49359596e-01 6.09146595e-01 -1.99942254e-02
-8.62552464e-01 4.22375739e-01 3.89649689e-01 -1.48505956e-01
-1.31579369e-01 -4.63447064e-01 -5.77579618e-01 -8.54825437e-01
1.84775159e-01 -5.21667182e-01 2.89976269e-01 6.25667647... | [14.624354362487793, 5.987997531890869] |
a442ce0b-bac4-4881-91b9-8e90bf0810a6 | a-survey-on-compiler-autotuning-using-machine | 1801.04405 | null | http://arxiv.org/abs/1801.04405v5 | http://arxiv.org/pdf/1801.04405v5.pdf | A Survey on Compiler Autotuning using Machine Learning | Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: opti... | ['John Cavazos', 'Amir H. Ashouri', 'Cristina Silvano', 'Gianluca Palermo', 'William Killian'] | 2018-01-13 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-2.48252526e-02 -3.82849246e-01 -9.16992784e-01 -3.30250502e-01
-6.02554500e-01 -4.56094593e-01 6.30430937e-01 5.77284515e-01
-2.90650189e-01 4.04819697e-01 2.24832952e-01 -6.45208895e-01
7.28155002e-02 -6.34380639e-01 -3.48143637e-01 -4.38297361e-01
-1.85999691e-01 5.58863163e-01 3.40291560e-02 -4.46204334... | [7.779078960418701, 7.478159427642822] |
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