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260bffe2-9426-4702-8dd5-7d872aa16123 | predicting-survival-outcomes-in-the-presence | 2210.13891 | null | https://arxiv.org/abs/2210.13891v1 | https://arxiv.org/pdf/2210.13891v1.pdf | Predicting Survival Outcomes in the Presence of Unlabeled Data | Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial challenges, which can lead to reduced data collection and, in turn, can complicate subsequent analyses. In contrast, there is often plenty of baseline data available of patients with similar characteristics and background information, e.g., from patients that fall outside the study time window. In this article, we investigate whether we can benefit from the inclusion of such unlabeled data instances to predict accurate survival times. In other words, we introduce a third level of supervision in the context of survival analysis, apart from fully observed and censored instances, we also include unlabeled instances. We propose three approaches to deal with this novel setting and provide an empirical comparison over fifteen real-life clinical and gene expression survival datasets. Our results demonstrate that all approaches are able to increase the predictive performance over independent test data. We also show that integrating the partial supervision provided by censored data in a semi-supervised wrapper approach generally provides the best results, often achieving high improvements, compared to not using unlabeled data. | ['Celine Vens', 'Fateme Nateghi Haredasht'] | 2022-10-25 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 2.73374826e-01 -1.29499659e-01 -5.98876953e-01 -7.62433529e-01
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64ccf831-7b19-4d3b-86a6-6a9e1ce63f56 | deep-clustering-with-measure-propagation | 2104.08967 | null | https://arxiv.org/abs/2104.08967v3 | https://arxiv.org/pdf/2104.08967v3.pdf | Deep Clustering with Measure Propagation | Deep models have improved state-of-the-art for both supervised and unsupervised learning. For example, deep embedded clustering (DEC) has greatly improved the unsupervised clustering performance, by using stacked autoencoders for representation learning. However, one weakness of deep modeling is that the local neighborhood structure in the original space is not necessarily preserved in the latent space. To preserve local geometry, various methods have been proposed in the supervised and semi-supervised learning literature (e.g., spectral clustering and label propagation) using graph Laplacian regularization. In this paper, we combine the strength of deep representation learning with measure propagation (MP), a KL-divergence based graph regularization method originally used in the semi-supervised scenario. The main assumption of MP is that if two data points are close in the original space, they are likely to belong to the same class, measured by KL-divergence of class membership distribution. By taking the same assumption in the unsupervised learning scenario, we propose our Deep Embedded Clustering Aided by Measure Propagation (DECAMP) model. We evaluate DECAMP on short text clustering tasks. On three public datasets, DECAMP performs competitively with other state-of-the-art baselines, including baselines using additional data to generate word embeddings used in the clustering process. As an example, on the Stackoverflow dataset, DECAMP achieved a clustering accuracy of 79%, which is about 5% higher than all existing baselines. These empirical results suggest that DECAMP is a very effective method for unsupervised learning. | ['Patrick Haffner', 'Michael Johnston', 'Qiming Huang', 'Padmasundari Gopalakrishnan', 'Badrinath Jayakumar', 'Minhua Chen'] | 2021-04-18 | null | null | null | null | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-2.74035960e-01 -2.85147373e-02 -3.02485526e-02 -3.49235028e-01
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f0ef62c3-cc61-4699-946a-302a548f119c | mdp-a-generalized-framework-for-text-guided | 2303.16765 | null | https://arxiv.org/abs/2303.16765v2 | https://arxiv.org/pdf/2303.16765v2.pdf | MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path | Image generation using diffusion can be controlled in multiple ways. In this paper, we systematically analyze the equations of modern generative diffusion networks to propose a framework, called MDP, that explains the design space of suitable manipulations. We identify 5 different manipulations, including intermediate latent, conditional embedding, cross attention maps, guidance, and predicted noise. We analyze the corresponding parameters of these manipulations and the manipulation schedule. We show that some previous editing methods fit nicely into our framework. Particularly, we identified one specific configuration as a new type of control by manipulating the predicted noise, which can perform higher-quality edits than previous work for a variety of local and global edits. | ['Peter Wonka', 'Michael Birsak', 'Biao Zhang', 'Qian Wang'] | 2023-03-29 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 1.88132450e-01 1.60324693e-01 -1.61436066e-01 -8.64578784e-02
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bd786c7c-b7fd-41cb-99af-7ee29be8214f | efficient-majority-voting-in-digital-hardware | 2108.03979 | null | https://arxiv.org/abs/2108.03979v1 | https://arxiv.org/pdf/2108.03979v1.pdf | Efficient Majority Voting in Digital Hardware | In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impairing their efficient use in real-time systems, even when employing dedicated hardware accelerators. Ensemble learning methods are especially suitable for hardware acceleration since they can be constructed from individual learners of low complexity and thus offer large parallelization potential. For classification, the outputs of these learners are typically combined by majority voting, which often represents the bottleneck of a hardware accelerator for ensemble inference. In this work, we present a novel architecture that allows obtaining a majority decision in a number of clock cycles that is logarithmic in the number of inputs. We show, that for the example application of handwritten digit recognition a random forest processing engine employing this majority decision architecture implemented on an FPGA allows the classification of more than seven million images per second. | ['Michael Lunglmayr', 'Mario Huemer', 'Stefan Baumgartner'] | 2021-08-09 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 4.53742683e-01 -1.27473712e-01 7.98924267e-02 -4.20071632e-01
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ed97c43e-63d5-4f64-81b6-44c2c4a88173 | an-explainable-classification-model-for | 2105.10368 | null | https://arxiv.org/abs/2105.10368v3 | https://arxiv.org/pdf/2105.10368v3.pdf | Development and evaluation of an Explainable Prediction Model for Chronic Kidney Disease Patients based on Ensemble Trees | Chronic Kidney Disease (CKD), where delayed recognition implies premature mortality, is currently experiencing a globally increasing incidence and high cost to health systems. Data mining allows discovering subtle patterns in CKD indicators to contribute to an early diagnosis. This work presents the development and evaluation of an explainable prediction model that would support clinicians in the early diagnosis of CKD patients. The model development is based on a data management pipeline that detects the best combination of ensemble trees algorithms and features selected concerning classification performance. Furthermore, the main contribution of the paper involves an explainability-driven approach that allows selecting the best predictive model maintaining a balance between accuracy and explainability. Therefore, the most balanced explainable predictive model implements an extreme gradient boosting classifier over 3 features (packed cell value, specific gravity, and hypertension), achieving an accuracy of 99.2% and 97.5% with cross-validation technique and with new unseen data respectively. In addition, an analysis of the model's explainability shows that the packed cell value is the most relevant feature that influences the prediction results of the model, followed by specific gravity and hypertension. This small number of feature selected results in a reduced cost of the early diagnosis of CKD implying a promising solution for developing countries. | ['Pedro A. Moreno-Sanchez'] | 2021-05-21 | null | null | null | null | ['kidney-function'] | ['medical'] | [-1.67627871e-01 2.22609550e-01 -1.51060164e-01 -6.23956382e-01
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8b65d637-1637-402d-91de-07790af14a4e | detecting-backdoors-in-deep-text-classifiers | 2210.11264 | null | https://arxiv.org/abs/2210.11264v1 | https://arxiv.org/pdf/2210.11264v1.pdf | Detecting Backdoors in Deep Text Classifiers | Deep neural networks are vulnerable to adversarial attacks, such as backdoor attacks in which a malicious adversary compromises a model during training such that specific behaviour can be triggered at test time by attaching a specific word or phrase to an input. This paper considers the problem of diagnosing whether a model has been compromised and if so, identifying the backdoor trigger. We present the first robust defence mechanism that generalizes to several backdoor attacks against text classification models, without prior knowledge of the attack type, nor does our method require access to any (potentially compromised) training resources. Our experiments show that our technique is highly accurate at defending against state-of-the-art backdoor attacks, including data poisoning and weight poisoning, across a range of text classification tasks and model architectures. Our code will be made publicly available upon acceptance. | ['Trevor Cohn', 'Jun Wang', 'You Guo'] | 2022-10-11 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 5.94789982e-01 -6.35744706e-02 -1.59369946e-01 -1.23626009e-01
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977388ce-1e7d-4998-af4f-317d25f34a7e | exploiting-segment-level-semantics-for-online | 2111.11044 | null | https://arxiv.org/abs/2111.11044v3 | https://arxiv.org/pdf/2111.11044v3.pdf | Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos | Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing methods ignored a pivotal problem that surgical phases should be classified by learning segment-level semantics instead of solely relying on frame-wise information. This paper presents a segment-attentive hierarchical consistency network (SAHC) for surgical phase recognition from videos. The key idea is to extract hierarchical high-level semantic-consistent segments and use them to refine the erroneous predictions caused by ambiguous frames. To achieve it, we design a temporal hierarchical network to generate hierarchical high-level segments. Then, we introduce a hierarchical segment-frame attention module to capture relations between the low-level frames and high-level segments. By regularizing the predictions of frames and their corresponding segments via a consistency loss, the network can generate semantic-consistent segments and then rectify the misclassified predictions caused by ambiguous low-level frames. We validate SAHC on two public surgical video datasets, i.e., the M2CAI16 challenge dataset and the Cholec80 dataset. Experimental results show that our method outperforms previous state-of-the-arts and ablation studies prove the effectiveness of our proposed modules. Our code has been released at: https://github.com/xmed-lab/SAHC. | ['Xiaomeng Li', 'Xinpeng Ding'] | 2021-11-22 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 4.44091499e-01 5.18948674e-01 -8.37192178e-01 -3.61339539e-01
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416f3192-1c02-4c1f-bd78-04410f5d845a | evaluating-the-text-to-sql-capabilities-of | null | null | https://openreview.net/forum?id=lYli-bAuK54 | https://openreview.net/pdf?id=lYli-bAuK54 | Evaluating the Text-to-SQL Capabilities of Large Language Models | We perform an empirical evaluation of Text-to-SQL capabilities of the Codex language model. We find that, without any finetuning, Codex is a strong baseline on the Spider benchmark; we also analyze the failure modes of Codex in this setting. Furthermore, we demonstrate on the GeoQuery and Scholar benchmarks that a small number of in-domain examples provided in the prompt enables Codex to perform better than state-of-the-art models finetuned on such few-shot examples. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['text-to-sql'] | ['computer-code'] | [-6.21415079e-01 -1.01846218e-01 -9.04360235e-01 -4.68051553e-01
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ed0a16da-9336-4850-8cee-7976d2fe848d | emotional-talking-faces-making-videos-more | null | null | https://dl.acm.org/doi/10.1145/3551626.3564976 | https://dl.acm.org/doi/10.1145/3551626.3564976 | Emotional Talking Faces: Making Videos More Expressive and Realistic | Lip synchronization and talking face generation have gained a specific interest from the research community with the advent and need of digital communication in different fields. Prior works propose several elegant solutions to this problem. However, they often fail to create realistic-looking videos that account for people's expressions and emotions. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more real-looking and convincing. With a broad range of six emotions i.e., anger, disgust, fear, happiness, neutral, and sad, we show that our model generalizes across identities, emotions, and languages. | ['Rajiv Ratn Shah', 'Yifang Yin', 'Yi Yu', 'Sakshat Mali', 'Dhroov Goel', 'Sarthak Bhagat', 'Shagun Uppal', 'Sahil Goyal'] | 2022-12-13 | null | null | null | acm-multimedia-asia-2022-12 | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [-1.75299987e-01 1.23290330e-01 -1.36536509e-01 -7.08787382e-01
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a8e75f3c-11e6-4b7f-9910-868e71be30fd | unsupervised-domain-adaptation-with-6 | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Unsupervised_Domain_Adaptation_With_Hierarchical_Gradient_Synchronization_CVPR_2020_paper.pdf | Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization | Domain adaptation attempts to boost the performance on a target domain by borrowing knowledge from a well established source domain. To handle the distribution gap between two domains, the prominent approaches endeavor to extract domain-invariant features. It is known that after a perfect domain alignment the domain-invariant representations of two domains should share the same characteristics from perspective of the overview and also any local piece. Inspired by this, we propose a novel method called Hierarchical Gradient Synchronization to model the synchronization relationship among the local distribution pieces and global distribution, aiming for more precise domain-invariant features. Specifically, the hierarchical domain alignments including class-wise alignment, group-wise alignment and global alignment are first constructed. Then, these three types of alignment are constrained to be consistent to ensure better structure preservation. As a result, the obtained features are domain invariant and intrinsically structure preserved. As evaluated on extensive domain adaptation tasks, our proposed method achieves state-of-the-art classification performance on both vanilla unsupervised domain adaptation and partial domain adaptation.
| [' Xilin Chen', ' Shiguang Shan', ' Meina Kan', 'Lanqing Hu'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['partial-domain-adaptation'] | ['methodology'] | [ 9.69666392e-02 -4.09825087e-01 -4.50360864e-01 -6.48959756e-01
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641b0d30-5152-4a23-b070-e3786f66bb57 | real-time-selfie-video-stabilization | 2009.02007 | null | https://arxiv.org/abs/2009.02007v2 | https://arxiv.org/pdf/2009.02007v2.pdf | Real-Time Selfie Video Stabilization | We propose a novel real-time selfie video stabilization method. Our method is completely automatic and runs at 26 fps. We use a 1D linear convolutional network to directly infer the rigid moving least squares warping which implicitly balances between the global rigidity and local flexibility. Our network structure is specifically designed to stabilize the background and foreground at the same time, while providing optional control of stabilization focus (relative importance of foreground vs. background) to the users. To train our network, we collect a selfie video dataset with 1005 videos, which is significantly larger than previous selfie video datasets. We also propose a grid approximation method to the rigid moving least squares warping that enables the real-time frame warping. Our method is fully automatic and produces visually and quantitatively better results than previous real-time general video stabilization methods. Compared to previous offline selfie video methods, our approach produces comparable quality with a speed improvement of orders of magnitude. | ['Jiyang Yu', 'Ravi Ramamoorthi', 'Ning Bi', 'Michel Sarkis', 'Keli Cheng'] | 2020-09-04 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yu_Real-Time_Selfie_Video_Stabilization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-stabilization'] | ['computer-vision'] | [ 2.50330064e-02 -1.61718652e-02 -1.58722922e-01 -3.69134755e-03
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74ca1df6-1b40-4933-9f97-d82c9836c7df | text-to-image-diffusion-model-in-generative | 2303.07909 | null | https://arxiv.org/abs/2303.07909v2 | https://arxiv.org/pdf/2303.07909v2.pdf | Text-to-image Diffusion Models in Generative AI: A Survey | This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance improves learning. Based on that, we present a review of state-of-the-art methods on text-conditioned image synthesis, i.e., text-to-image. We further summarize applications beyond text-to-image generation: text-guided creative generation and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions. | ['In So Kweon', 'Mengchun Zhang', 'Chaoning Zhang', 'Chenshuang Zhang'] | 2023-03-14 | null | null | null | null | ['text-guided-image-editing'] | ['computer-vision'] | [ 7.41123736e-01 2.84174919e-01 -1.68033212e-01 -1.71923339e-01
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5d2dacfd-f691-403d-a370-bb2d7a32b445 | modeling-multi-turn-conversation-with-deep | 1806.09102 | null | http://arxiv.org/abs/1806.09102v2 | http://arxiv.org/pdf/1806.09102v2.pdf | Modeling Multi-turn Conversation with Deep Utterance Aggregation | Multi-turn conversation understanding is a major challenge for building
intelligent dialogue systems. This work focuses on retrieval-based response
matching for multi-turn conversation whose related work simply concatenates the
conversation utterances, ignoring the interactions among previous utterances
for context modeling. In this paper, we formulate previous utterances into
context using a proposed deep utterance aggregation model to form a
fine-grained context representation. In detail, a self-matching attention is
first introduced to route the vital information in each utterance. Then the
model matches a response with each refined utterance and the final matching
score is obtained after attentive turns aggregation. Experimental results show
our model outperforms the state-of-the-art methods on three multi-turn
conversation benchmarks, including a newly introduced e-commerce dialogue
corpus. | ['Hai Zhao', 'Pengfei Zhu', 'Jiangtong Li', 'Zhuosheng Zhang', 'Gongshen Liu'] | 2018-06-24 | modeling-multi-turn-conversation-with-deep-2 | https://aclanthology.org/C18-1317 | https://aclanthology.org/C18-1317.pdf | coling-2018-8 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 2.80405849e-01 3.22235048e-01 8.23438689e-02 -8.41379642e-01
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5a88de7a-3fe2-4e27-bcb6-42003c108b25 | phrase-based-affordance-detection-via-cyclic | 2202.12076 | null | https://arxiv.org/abs/2202.12076v2 | https://arxiv.org/pdf/2202.12076v2.pdf | Phrase-Based Affordance Detection via Cyclic Bilateral Interaction | Affordance detection, which refers to perceiving objects with potential action possibilities in images, is a challenging task since the possible affordance depends on the person's purpose in real-world application scenarios. The existing works mainly extract the inherent human-object dependencies from image/video to accommodate affordance properties that change dynamically. In this paper, we explore to perceive affordance from a vision-language perspective and consider the challenging phrase-based affordance detection problem,i.e., given a set of phrases describing the action purposes, all the object regions in a scene with the same affordance should be detected. To this end, we propose a cyclic bilateral consistency enhancement network (CBCE-Net) to align language and vision features progressively. Specifically, the presented CBCE-Net consists of a mutual guided vision-language module that updates the common features of vision and language in a progressive manner, and a cyclic interaction module (CIM) that facilitates the perception of possible interaction with objects in a cyclic manner. In addition, we extend the public Purpose-driven Affordance Dataset (PAD) by annotating affordance categories with short phrases. The contrastive experimental results demonstrate the superiority of our method over nine typical methods from four relevant fields in terms of both objective metrics and visual quality. The related code and dataset will be released at \url{https://github.com/lulsheng/CBCE-Net}. | ['Yang Cao', 'Yu Kang', 'Hongchen Luo', 'Wei Zhai', 'Liangsheng Lu'] | 2022-02-24 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [ 1.14364386e-01 -3.99783671e-01 8.66082162e-02 -3.83368999e-01
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4c81f072-0653-4d55-a671-128558ef9247 | dasee-a-synthetic-database-of-domestic | 2104.13423 | null | https://arxiv.org/abs/2104.13423v2 | https://arxiv.org/pdf/2104.13423v2.pdf | DASEE A Synthetic Database of Domestic Acoustic Scenes and Events in Dementia Patients Environment | Access to informative databases is a crucial part of notable research developments. In the field of domestic audio classification, there have been significant advances in recent years. Although several audio databases exist, these can be limited in terms of the amount of information they provide, such as the exact location of the sound sources, and the associated noise levels. In this work, we detail our approach on generating an unbiased synthetic domestic audio database, consisting of sound scenes and events, emulated in both quiet and noisy environments. Data is carefully curated such that it reflects issues commonly faced in a dementia patients environment, and recreate scenarios that could occur in real-world settings. Similarly, the room impulse response generated is based on a typical one-bedroom apartment at Hebrew SeniorLife Facility. As a result, we present an 11-class database containing excerpts of clean and noisy signals at 5-seconds duration each, uniformly sampled at 16 kHz. Using our baseline model using Continues Wavelet Transform Scalograms and AlexNet, this yielded a weighted F1-score of 86.24 percent. | ['Nidhal Abdulaziz', 'Stefano Fasciani', 'Christian Ritz', 'Abigail Copiaco'] | 2021-04-27 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.37553939e-01 -3.02015275e-01 6.08141184e-01 -3.21011186e-01
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a98c0a1e-989c-44de-8a60-9a128bbdca86 | facial-uv-map-completion-for-pose-invariant | 2011.00912 | null | https://arxiv.org/abs/2011.00912v1 | https://arxiv.org/pdf/2011.00912v1.pdf | Facial UV Map Completion for Pose-invariant Face Recognition: A Novel Adversarial Approach based on Coupled Attention Residual UNets | Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods. | ['Sang Dinh', 'Dung Nguyen', 'Chung Tran', 'In Seop Na'] | 2020-11-02 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 2.91763037e-01 -1.59200445e-01 3.54004860e-01 -8.37059438e-01
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f5297c8c-43c6-4b18-ac46-78210ef2e040 | efficient-3d-semantic-segmentation-with-1 | 2306.08045 | null | https://arxiv.org/abs/2306.08045v1 | https://arxiv.org/pdf/2306.08045v1.pdf | Efficient 3D Semantic Segmentation with Superpoint Transformer | We introduce a novel superpoint-based transformer architecture for efficient semantic segmentation of large-scale 3D scenes. Our method incorporates a fast algorithm to partition point clouds into a hierarchical superpoint structure, which makes our preprocessing 7 times times faster than existing superpoint-based approaches. Additionally, we leverage a self-attention mechanism to capture the relationships between superpoints at multiple scales, leading to state-of-the-art performance on three challenging benchmark datasets: S3DIS (76.0% mIoU 6-fold validation), KITTI-360 (63.5% on Val), and DALES (79.6%). With only 212k parameters, our approach is up to 200 times more compact than other state-of-the-art models while maintaining similar performance. Furthermore, our model can be trained on a single GPU in 3 hours for a fold of the S3DIS dataset, which is 7x to 70x fewer GPU-hours than the best-performing methods. Our code and models are accessible at github.com/drprojects/superpoint_transformer. | ['Loic Landrieu', 'Hugo Raguet', 'Damien Robert'] | 2023-06-13 | efficient-3d-semantic-segmentation-with | http://arxiv.org/abs/2306.08045 | https://arxiv.org/pdf/2306.08045 | null | ['3d-semantic-segmentation'] | ['computer-vision'] | [-1.72555462e-01 -6.36636987e-02 -1.30149230e-01 -2.45542347e-01
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b63bd43b-061b-48ad-8a05-49c3c3fd006a | hope-speech-detection-on-social-media | 2212.07424 | null | https://arxiv.org/abs/2212.07424v1 | https://arxiv.org/pdf/2212.07424v1.pdf | Hope Speech Detection on Social Media Platforms | Since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded. In simple terms, harmful content is anything online which causes a person distress or harm. It may include hate speech, violent content, threats, non-hope speech, etc. The online content must be positive, uplifting and supportive. Over the past few years, many studies have focused on solving this problem through hate speech detection, but very few focused on identifying hope speech. This paper discusses various machine learning approaches to identify a sentence as Hope Speech, Non-Hope Speech, or a Neutral sentence. The dataset used in the study contains English YouTube comments and is released as a part of the shared task "EACL-2021: Hope Speech Detection for Equality, Diversity, and Inclusion". Initially, the dataset obtained from the shared task had three classes: Hope Speech, non-Hope speech, and not in English; however, upon deeper inspection, we discovered that dataset relabeling is required. A group of undergraduates was hired to help perform the entire dataset's relabeling task. We experimented with conventional machine learning models (such as Na\"ive Bayes, logistic regression and support vector machine) and pre-trained models (such as BERT) on relabeled data. According to the experimental results, the relabeled data has achieved a better accuracy for Hope speech identification than the original data set. | ['Shankar Biradar', 'Sunil Saumya', 'Shubham Sharma', 'Jagrut Nemade', 'Pasupuleti Chandana', 'Pranjal Aggarwal'] | 2022-11-14 | null | null | null | null | ['hate-speech-detection', 'hope-speech-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.85784274e-01 8.53950009e-02 -3.13089550e-01 -2.08135352e-01
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7235e0ca-4e34-473f-98c7-cf2a29eca85c | hdrfeat-a-feature-rich-network-for-high | 2211.04238 | null | https://arxiv.org/abs/2211.04238v1 | https://arxiv.org/pdf/2211.04238v1.pdf | HDRfeat: A Feature-Rich Network for High Dynamic Range Image Reconstruction | A major challenge for high dynamic range (HDR) image reconstruction from multi-exposed low dynamic range (LDR) images, especially with dynamic scenes, is the extraction and merging of relevant contextual features in order to suppress any ghosting and blurring artifacts from moving objects. To tackle this, in this work we propose a novel network for HDR reconstruction with deep and rich feature extraction layers, including residual attention blocks with sequential channel and spatial attention. For the compression of the rich-features to the HDR domain, a residual feature distillation block (RFDB) based architecture is adopted. In contrast to earlier deep-learning methods for HDR, the above contributions shift focus from merging/compression to feature extraction, the added value of which we demonstrate with ablation experiments. We present qualitative and quantitative comparisons on a public benchmark dataset, showing that our proposed method outperforms the state-of-the-art. | ['Orcun Göksel', 'Bozhi Liu', 'Fei Zhou', 'Lingkai Zhu'] | 2022-11-08 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 4.97951031e-01 -1.33161455e-01 1.84309587e-01 -3.64739239e-01
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1359cb7b-b309-4063-a663-c7595966c742 | deep-reinforcement-learning-applied-to-an | 2304.06567 | null | https://arxiv.org/abs/2304.06567v1 | https://arxiv.org/pdf/2304.06567v1.pdf | Deep reinforcement learning applied to an assembly sequence planning problem with user preferences | Deep reinforcement learning (DRL) has demonstrated its potential in solving complex manufacturing decision-making problems, especially in a context where the system learns over time with actual operation in the absence of training data. One interesting and challenging application for such methods is the assembly sequence planning (ASP) problem. In this paper, we propose an approach to the implementation of DRL methods in ASP. The proposed approach introduces in the RL environment parametric actions to improve training time and sample efficiency and uses two different reward signals: (1) user's preferences and (2) total assembly time duration. The user's preferences signal addresses the difficulties and non-ergonomic properties of the assembly faced by the human and the total assembly time signal enforces the optimization of the assembly. Three of the most powerful deep RL methods were studied, Advantage Actor-Critic (A2C), Deep Q-Learning (DQN), and Rainbow, in two different scenarios: a stochastic and a deterministic one. Finally, the performance of the DRL algorithms was compared to tabular Q-Learnings performance. After 10,000 episodes, the system achieved near optimal behaviour for the algorithms tabular Q-Learning, A2C, and Rainbow. Though, for more complex scenarios, the algorithm tabular Q-Learning is expected to underperform in comparison to the other 2 algorithms. The results support the potential for the application of deep reinforcement learning in assembly sequence planning problems with human interaction. | ['Pedro Neto', 'Miguel Neves'] | 2023-04-13 | null | null | null | null | ['q-learning'] | ['methodology'] | [-1.10181101e-01 3.58282208e-01 -1.33127809e-01 1.32358801e-02
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1703a8da-6b76-4a40-a9bc-e23dca4d1a40 | disjoint-cnn-for-multivariate-time-series | null | null | https://ieeexplore.ieee.org/abstract/document/9679860 | https://ieeexplore.ieee.org/abstract/document/9679860 | Disjoint-CNN for Multivariate Time Series Classification | Time series classification algorithms have been mainly dominated by non-deep learning models.
Deep learning for Multivariate Time Series Classification (MTSC) has gained huge interest in recent years.
Most state-of-the-art deep learning methods are convolutional-based where 1-dimensional (1D) convolutions are used to extract features from the 2-dimensional time series. This study shows that factorization of 1D convolution filters into disjoint temporal and spatial components yields significant accuracy improvements with almost no additional computational cost.
Based on our study on disjoint temporal-spatial filters, we have designed a novel filter block called ``1+1D", which emphasizes the interaction between dimensions to improve the model performance of the convolution-based on deep learning MTSC models. We also proposed a new and effective MTSC method called Disjoint-CNN using our proposed 1+1D filter blocks and through our extensive experiments show that our model (called Disjoint-CNN) outperforms the state-of-the-art MTSC models on 26 datasets in the UEA Multivariate time series archive, achieving the highest average rank among 9 MTSC benchmark models. | ['Mahsa Salehi', 'Chang Wei Tan', 'Navid Mohammadi Foumani'] | 2022-01-20 | null | null | null | 2021-international-conference-on-data-mining | ['time-series-classification'] | ['time-series'] | [-3.13742012e-01 -8.44289839e-01 -9.52762216e-02 -2.14818686e-01
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a4fce5a4-6311-40bb-a706-61088236e1e1 | learning-from-multi-perception-features-for | 2305.18547 | null | https://arxiv.org/abs/2305.18547v1 | https://arxiv.org/pdf/2305.18547v1.pdf | Learning from Multi-Perception Features for Real-Word Image Super-resolution | Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-estimation-based methods may be inaccurate in estimating the degradation, making them less applicable to real-world LR images. On the other hand, blind-based methods are often limited by their fixed single perception information, which hinders their ability to handle diverse perceptual characteristics. To overcome this limitation, we propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information and a series of newly-designed Cross-Perception Blocks (CPB) to combine this information for effective super-resolution reconstruction. Additionally, we introduce a contrastive regularization term (CR) that improves the model's learning capability by using newly generated HR and LR images as positive and negative samples for ground truth HR. Experimental results on challenging real-world SR datasets demonstrate that our approach significantly outperforms existing state-of-the-art methods in both qualitative and quantitative measures. | ['Yanning Zhang', 'In So Kweon', 'Jinqiu Sun', 'Pei Wang', 'Trung X. Pham', 'Kang Zhang', 'Axi Niu'] | 2023-05-26 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 4.69159722e-01 -4.00310606e-01 -2.92126179e-01 -2.44907647e-01
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dec3869a-f1ab-4039-8dc5-eaafb628c9c0 | hybrid-lemmatization-in-huspacy | 2306.07636 | null | https://arxiv.org/abs/2306.07636v1 | https://arxiv.org/pdf/2306.07636v1.pdf | Hybrid lemmatization in HuSpaCy | Lemmatization is still not a trivial task for morphologically rich languages. Previous studies showed that hybrid architectures usually work better for these languages and can yield great results. This paper presents a hybrid lemmatizer utilizing both a neural model, dictionaries and hand-crafted rules. We introduce a hybrid architecture along with empirical results on a widely used Hungarian dataset. The presented methods are published as three HuSpaCy models. | ['Richárd Farkas', 'Gergő Szabó', 'Zsolt Szántó', 'György Orosz', 'Péter Berkecz'] | 2023-06-13 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [-4.60708529e-01 6.93929121e-02 -3.39272380e-01 -4.83662277e-01
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f20b1d89-f5e1-47c3-a736-a8059eff369e | reducing-label-noise-in-anchor-free-object | 2008.01167 | null | https://arxiv.org/abs/2008.01167v2 | https://arxiv.org/pdf/2008.01167v2.pdf | Reducing Label Noise in Anchor-Free Object Detection | Current anchor-free object detectors label all the features that spatially fall inside a predefined central region of a ground-truth box as positive. This approach causes label noise during training, since some of these positively labeled features may be on the background or an occluder object, or they are simply not discriminative features. In this paper, we propose a new labeling strategy aimed to reduce the label noise in anchor-free detectors. We sum-pool predictions stemming from individual features into a single prediction. This allows the model to reduce the contributions of non-discriminatory features during training. We develop a new one-stage, anchor-free object detector, PPDet, to employ this labeling strategy during training and a similar prediction pooling method during inference. On the COCO dataset, PPDet achieves the best performance among anchor-free top-down detectors and performs on-par with the other state-of-the-art methods. It also outperforms all major one-stage and two-stage methods in small object detection (${AP}_{S}$ $31.4$). Code is available at https://github.com/nerminsamet/ppdet | ['Samet Hicsonmez', 'Nermin Samet', 'Emre Akbas'] | 2020-08-03 | reducing-label-noise-in-anchor-free-object-1 | null | null | bmvc-2020-8 | ['small-object-detection'] | ['computer-vision'] | [ 1.10477418e-01 1.10658512e-01 -3.74661922e-01 -4.38464403e-01
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caf88221-b425-4396-8bc8-cec10eaf3a0d | iris-interpretable-rubric-informed | 2303.09097 | null | https://arxiv.org/abs/2303.09097v1 | https://arxiv.org/pdf/2303.09097v1.pdf | IRIS: Interpretable Rubric-Informed Segmentation for Action Quality Assessment | AI-driven Action Quality Assessment (AQA) of sports videos can mimic Olympic judges to help score performances as a second opinion or for training. However, these AI methods are uninterpretable and do not justify their scores, which is important for algorithmic accountability. Indeed, to account for their decisions, instead of scoring subjectively, sports judges use a consistent set of criteria - rubric - on multiple actions in each performance sequence. Therefore, we propose IRIS to perform Interpretable Rubric-Informed Segmentation on action sequences for AQA. We investigated IRIS for scoring videos of figure skating performance. IRIS predicts (1) action segments, (2) technical element score differences of each segment relative to base scores, (3) multiple program component scores, and (4) the summed final score. In a modeling study, we found that IRIS performs better than non-interpretable, state-of-the-art models. In a formative user study, practicing figure skaters agreed with the rubric-informed explanations, found them useful, and trusted AI judgments more. This work highlights the importance of using judgment rubrics to account for AI decisions. | ['Brian Y. Lim', 'Nobuo Kawaguchi', 'Hitoshi Matsuyama'] | 2023-03-16 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 2.64389545e-01 2.58438975e-01 -3.44935596e-01 -7.04717636e-01
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f9ba7c8c-c6bb-4b16-bbf9-d7fa4ab89e52 | designing-explainable-predictive-machine | 2306.11771 | null | https://arxiv.org/abs/2306.11771v1 | https://arxiv.org/pdf/2306.11771v1.pdf | Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration | Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe this issue to the widely held view on advanced machine learning algorithms as "black boxes" whose complexity does not allow for uncovering the factors that drive the output of a corresponding system. To contribute to overcome this adoption barrier, we argue that research in information systems should devote more attention to the design of prototypical prediction-oriented machine learning applications (i.e., artifacts) whose predictions can be explained to human decision-makers. However, despite the recent emergence of a variety of tools that facilitate the development of such artifacts, there has so far been little research on their development. We attribute this research gap to the lack of methodological guidance to support the creation of these artifacts. For this reason, we develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state-of-the-art approaches to explainable artificial intelligence. Moreover, we showcase the methodology using the example of price prediction in the sharing economy (i.e., on Airbnb). | ['Peter Kowalczyk', 'Giacomo Welsch'] | 2023-06-20 | null | null | null | null | ['explainable-artificial-intelligence'] | ['computer-vision'] | [ 1.01194613e-01 5.46507061e-01 -6.50555551e-01 -3.21512133e-01
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640c11a3-648f-4508-955c-34900205058f | visual-transformers-for-primates | 2212.10093 | null | https://arxiv.org/abs/2212.10093v1 | https://arxiv.org/pdf/2212.10093v1.pdf | Visual Transformers for Primates Classification and Covid Detection | We apply the vision transformer, a deep machine learning model build around the attention mechanism, on mel-spectrogram representations of raw audio recordings. When adding mel-based data augmentation techniques and sample-weighting, we achieve comparable performance on both (PRS and CCS challenge) tasks of ComParE21, outperforming most single model baselines. We further introduce overlapping vertical patching and evaluate the influence of parameter configurations. Index Terms: audio classification, attention, mel-spectrogram, unbalanced data-sets, computational paralinguistics | ['Claudia-Linnhoff Popien', 'Andreas Sedlmeier', 'Robert Müller', 'Steffen Illium'] | 2022-12-20 | null | null | null | null | ['audio-classification'] | ['audio'] | [ 3.76632124e-01 -1.74453080e-01 7.11436048e-02 -1.69222459e-01
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8b7f5159-deff-494b-9452-ac53c376d5c3 | a-structurally-regularized-cnn-architecture | 2306.16604 | null | https://arxiv.org/abs/2306.16604v1 | https://arxiv.org/pdf/2306.16604v1.pdf | A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition | We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization. Our proposed CNN architecture is fully compatible with the end-to-end learning mechanism of typical CNN architectures and learns the subband decomposition from the input dataset. We show that the proposed CNN architecture has attractive properties, such as robustness to input and weight-and-bias quantization noise, compared to regular full-band CNN architectures. Importantly, the proposed architecture significantly reduces computational costs, while maintaining state-of-the-art classification accuracy. Experiments on image classification tasks using the MNIST, CIFAR-10/100, Caltech-101, and ImageNet-2012 datasets show that the proposed architecture allows accuracy surpassing state-of-the-art results. On the ImageNet-2012 dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and 69.73%, respectively. Notably, the proposed architecture offers over 90% reduction in computation cost in the inference path and approximately 75% reduction in back-propagation (per iteration) with just a single-layer subband decomposition. With a 2-layer subband decomposition, the computational gains are even more significant with comparable accuracy results to the single-layer decomposition. | ['Zeljko Zilic', 'Ioannis Psaromiligkos', 'Pavel Sinha'] | 2023-06-29 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.27491996e-01 3.34161520e-02 -2.52732009e-01 -5.72892368e-01
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f9597019-31c2-4b60-8297-47e5eb95490f | temporal-graph-benchmark-for-machine-learning | 2307.01026 | null | https://arxiv.org/abs/2307.01026v1 | https://arxiv.org/pdf/2307.01026v1.pdf | Temporal Graph Benchmark for Machine Learning on Temporal Graphs | We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/ . | ['Reihaneh Rabbany', 'Guillaume Rabusseau', 'Michael Bronstein', 'Jure Leskovec', 'Emanuele Rossi', 'Weihua Hu', 'Matthias Fey', 'Jacob Danovitch', 'Farimah Poursafaei', 'Shenyang Huang'] | 2023-07-03 | null | null | null | null | ['property-prediction'] | ['medical'] | [-2.04321533e-01 -1.23594262e-01 -7.13878989e-01 -3.02607596e-01
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d0df6b48-ec7d-414b-a742-d7281a88872d | moving-tiger-beyond-sentence-level | null | null | https://aclanthology.org/L18-1348 | https://aclanthology.org/L18-1348.pdf | Moving TIGER beyond Sentence-Level | null | ['Jonas Kuhn', 'Agnieszka Falenska', 'Kerstin Eckart'] | 2018-05-01 | moving-tiger-beyond-sentence-level-1 | https://aclanthology.org/L18-1348 | https://aclanthology.org/L18-1348.pdf | lrec-2018-5 | ['morphological-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c58cbac1-1b8b-4a3f-8a04-d06e07232594 | an-unsupervised-neural-attention-model-for | null | null | https://aclanthology.org/P17-1036 | https://aclanthology.org/P17-1036.pdf | An Unsupervised Neural Attention Model for Aspect Extraction | Aspect extraction is an important and challenging task in aspect-based sentiment analysis. Existing works tend to apply variants of topic models on this task. While fairly successful, these methods usually do not produce highly coherent aspects. In this paper, we present a novel neural approach with the aim of discovering coherent aspects. The model improves coherence by exploiting the distribution of word co-occurrences through the use of neural word embeddings. Unlike topic models which typically assume independently generated words, word embedding models encourage words that appear in similar contexts to be located close to each other in the embedding space. In addition, we use an attention mechanism to de-emphasize irrelevant words during training, further improving the coherence of aspects. Experimental results on real-life datasets demonstrate that our approach discovers more meaningful and coherent aspects, and substantially outperforms baseline methods on several evaluation tasks. | ['Wee Sun Lee', 'Hwee Tou Ng', 'Daniel Dahlmeier', 'Ruidan He'] | 2017-07-01 | null | null | null | acl-2017-7 | ['aspect-extraction'] | ['natural-language-processing'] | [-4.02038544e-02 1.38820812e-01 -5.91607928e-01 -5.24408877e-01
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eb3201bb-dbe2-4911-a790-e51cd5006fc4 | tensor-based-intrinsic-subspace | 2010.09193 | null | https://arxiv.org/abs/2010.09193v7 | https://arxiv.org/pdf/2010.09193v7.pdf | Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering | As a hot research topic, many multi-view clustering approaches are proposed over the past few years. Nevertheless, most existing algorithms merely take the consensus information among different views into consideration for clustering. Actually, it may hinder the multi-view clustering performance in real-life applications, since different views usually contain diverse statistic properties. To address this problem, we propose a novel Tensor-based Intrinsic Subspace Representation Learning (TISRL) for multi-view clustering in this paper. Concretely, the rank preserving decomposition is proposed firstly to effectively deal with the diverse statistic information contained in different views. Then, to achieve the intrinsic subspace representation, the tensor-singular value decomposition based low-rank tensor constraint is also utilized in our method. It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint. The objective function can be optimized by an augmented Lagrangian multiplier based alternating direction minimization algorithm. Experimental results on nine common used real-world multi-view datasets illustrate the superiority of TISRL. | ['Shuangxun Ma', 'Haoyu Tang', 'Jihua Zhu', 'Yu Zhang', 'Zhongyu Li', 'Qinghai Zheng'] | 2020-10-19 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.89356583e-01 -6.84162080e-01 -1.58732384e-01 -1.23474963e-01
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6eaae324-af63-4c3e-8a5f-620eace56f55 | powerbev-a-powerful-yet-lightweight-framework | 2306.10761 | null | https://arxiv.org/abs/2306.10761v1 | https://arxiv.org/pdf/2306.10761v1.pdf | PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View | Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic. While bird's-eye view (BEV) representations are commonplace in perception for autonomous driving, their potential in a motion prediction setting is less explored. Existing approaches for BEV instance prediction from surround cameras rely on a multi-task auto-regressive setup coupled with complex post-processing to predict future instances in a spatio-temporally consistent manner. In this paper, we depart from this paradigm and propose an efficient novel end-to-end framework named POWERBEV, which differs in several design choices aimed at reducing the inherent redundancy in previous methods. First, rather than predicting the future in an auto-regressive fashion, POWERBEV uses a parallel, multi-scale module built from lightweight 2D convolutional networks. Second, we show that segmentation and centripetal backward flow are sufficient for prediction, simplifying previous multi-task objectives by eliminating redundant output modalities. Building on this output representation, we propose a simple, flow warping-based post-processing approach which produces more stable instance associations across time. Through this lightweight yet powerful design, POWERBEV outperforms state-of-the-art baselines on the NuScenes Dataset and poses an alternative paradigm for BEV instance prediction. We made our code publicly available at: https://github.com/EdwardLeeLPZ/PowerBEV. | ['Juergen Gall', 'Marius Cordts', 'Niklas Hanselmann', 'Xieyuanli Chen', 'Shuxiao Ding', 'Peizheng Li'] | 2023-06-19 | null | null | null | null | ['motion-prediction', 'autonomous-vehicles', 'navigate'] | ['computer-vision', 'computer-vision', 'reasoning'] | [-3.39494571e-02 -2.32546777e-01 -3.90228122e-01 -5.22422433e-01
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2929ac99-30e3-4f54-afaa-5c17cc876ec9 | slot-dependency-modeling-for-zero-shot-cross | null | null | https://aclanthology.org/2022.coling-1.42 | https://aclanthology.org/2022.coling-1.42.pdf | Slot Dependency Modeling for Zero-Shot Cross-Domain Dialogue State Tracking | Zero-shot learning for Dialogue State Tracking (DST) focuses on generalizing to an unseen domain without the expense of collecting in domain data. However, previous zero-shot DST methods ignore the slot dependencies in a multidomain dialogue, resulting in sub-optimal performances when adapting to unseen domains. In this paper, we utilize slot prompts combination, slot values demonstration, and slot constraint object to model the slot-slot dependencies, slot-value dependency and slot-context dependency respectively. Specifically, each slot prompt consists of a slot-specific prompt and a slot-shared prompt to capture the shared knowledge across different domains. Experimental results show the effectiveness of our proposed method over existing state-of-art generation methods under zero-shot/few-shot settings. | ['Li Guo', 'Zheng Lin', 'Yanhe Fu', 'Piji Li', 'Yanan Cao', 'Qingyue Wang'] | null | null | null | null | coling-2022-10 | ['dialogue-state-tracking'] | ['natural-language-processing'] | [ 6.15943968e-02 3.32920820e-01 -3.67161602e-01 -4.67024624e-01
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d18b3901-02d1-46a4-811f-fa220736c5ac | attentive-state-space-modeling-of-disease | null | null | http://papers.nips.cc/paper/9311-attentive-state-space-modeling-of-disease-progression | http://papers.nips.cc/paper/9311-attentive-state-space-modeling-of-disease-progression.pdf | Attentive State-Space Modeling of Disease Progression | Models of disease progression are instrumental for predicting patient outcomes and understanding disease dynamics. Existing models provide the patient with pragmatic (supervised) predictions of risk, but do not provide the clinician with intelligible (unsupervised) representations of disease pathophysiology. In this paper, we develop the attentive state-space model, a deep probabilistic model that learns accurate and interpretable structured representations for disease trajectories. Unlike Markovian state-space models, in which the dynamics are memoryless, our model uses an attention mechanism to create "memoryful" dynamics, whereby attention weights determine the dependence of future disease states on past medical history. To learn the model parameters from medical records, we develop an infer ence algorithm that simultaneously learns a compiled inference network and the model parameters, leveraging the attentive state-space representation to construct a "Rao-Blackwellized" variational approximation of the posterior state distribution. Experiments on data from the UK Cystic Fibrosis registry show that our model demonstrates superior predictive accuracy and provides insights into the progression of chronic disease. | ['Mihaela van der Schaar', 'Ahmed M. Alaa'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['predicting-patient-outcomes'] | ['medical'] | [ 1.79317981e-01 4.28031653e-01 -4.49922174e-01 -6.15246892e-01
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fa250790-7ade-4153-a831-5364cc807a68 | recognition-of-basic-hand-movements-using | 1810.10062 | null | http://arxiv.org/abs/1810.10062v1 | http://arxiv.org/pdf/1810.10062v1.pdf | Recognition of basic hand movements using Electromyography | The aim of this work was to identify six basic movements of the hand using
two systems. Being an interdisciplinary topic, there has been conducted
studying in the anatomy of forearm muscles, biosignals, the method of
electromyography (EMG) and methods of pattern recognition. Moreover, the signal
contained enough noise and had to be analyzed, using EMD, to extract features
and to reduce its dimensionality, using RELIEF and PCA, to improve the success
rate of classification. The first part uses an EMG system of Delsys initially
for an individual and then for six people with the average successful
classification, for these six movements at rates of over 80%. The second part
involves the construction of an autonomous system EMG using an Arduino
microcontroller, EMG sensors and electrodes, which are arranged in an elastic
glove. Classification results in this case reached 75% of success. | [] | 2018-10-23 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 1.97464570e-01 1.38661236e-01 1.46556526e-01 2.12569848e-01
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547e8c97-a538-47fc-9ace-111b84a417cb | detecting-propaganda-techniques-in-code | 2305.14534 | null | https://arxiv.org/abs/2305.14534v1 | https://arxiv.org/pdf/2305.14534v1.pdf | Detecting Propaganda Techniques in Code-Switched Social Media Text | Propaganda is a form of communication intended to influence the opinions and the mindset of the public to promote a particular agenda. With the rise of social media, propaganda has spread rapidly, leading to the need for automatic propaganda detection systems. Most work on propaganda detection has focused on high-resource languages, such as English, and little effort has been made to detect propaganda for low-resource languages. Yet, it is common to find a mix of multiple languages in social media communication, a phenomenon known as code-switching. Code-switching combines different languages within the same text, which poses a challenge for automatic systems. With this in mind, here we propose the novel task of detecting propaganda techniques in code-switched text. To support this task, we create a corpus of 1,030 texts code-switching between English and Roman Urdu, annotated with 20 propaganda techniques, which we make publicly available. We perform a number of experiments contrasting different experimental setups, and we find that it is important to model the multilinguality directly (rather than using translation) as well as to use the right fine-tuning strategy. The code and the dataset are publicly available at https://github.com/mbzuai-nlp/propaganda-codeswitched-text | ['Preslav Nakov', 'Shady Shehata', 'Asif Hanif', 'Muhammad Umar Salman'] | 2023-05-23 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [-1.32642195e-01 -2.00924277e-01 -3.05543542e-01 3.28589864e-02
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cd6bb9b7-6368-45c5-a685-e8550e3236a4 | stock-price-prediction-under-anomalous | 2109.15059 | null | https://arxiv.org/abs/2109.15059v1 | https://arxiv.org/pdf/2109.15059v1.pdf | Stock Price Prediction Under Anomalous Circumstances | The stock market is volatile and complicated, especially in 2020. Because of a series of global and regional "black swans," such as the COVID-19 pandemic, the U.S. stock market triggered the circuit breaker three times within one week of March 9 to 16, which is unprecedented throughout history. Affected by the whole circumstance, the stock prices of individual corporations also plummeted by rates that were never predicted by any pre-developed forecasting models. It reveals that there was a lack of satisfactory models that could predict the changes in stocks prices when catastrophic, highly unlikely events occur. To fill the void of such models and to help prevent investors from heavy losses during uncertain times, this paper aims to capture the movement pattern of stock prices under anomalous circumstances. First, we detect outliers in sequential stock prices by fitting a standard ARIMA model and identifying the points where predictions deviate significantly from actual values. With the selected data points, we train ARIMA and LSTM models at the single-stock level, industry level, and general market level, respectively. Since the public moods affect the stock market tremendously, a sentiment analysis is also incorporated into the models in the form of sentiment scores, which are converted from comments about specific stocks on Reddit. Based on 100 companies' stock prices in the period of 2016 to 2020, the models achieve an average prediction accuracy of 98% which can be used to optimize existing prediction methodologies. | ['Jiebo Luo', 'Wei Wu', 'Jinlong Ruan'] | 2021-09-14 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.92525482e-01 -2.39796743e-01 -1.91035375e-01 5.12591004e-02
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6b38d272-b123-4bbc-ad38-c6a70d31b757 | umsiforeseer-at-semeval-2020-task-11 | null | null | https://aclanthology.org/2020.semeval-1.242 | https://aclanthology.org/2020.semeval-1.242.pdf | UMSIForeseer at SemEval-2020 Task 11: Propaganda Detection by Fine-Tuning BERT with Resampling and Ensemble Learning | We describe our participation at the SemEval 2020 {``}Detection of Propaganda Techniques in News Articles{''} - Techniques Classification (TC) task, designed to categorize textual fragments into one of the 14 given propaganda techniques. Our solution leverages pre-trained BERT models. We present our model implementations, evaluation results and analysis of these results. We also investigate the potential of combining language models with resampling and ensemble learning methods to deal with data imbalance and improve performance. | ['Qiaozhu Mei', 'Cristina Garbacea', 'Yunzhe Jiang'] | 2020-12-01 | null | null | null | semeval-2020 | ['propaganda-detection'] | ['natural-language-processing'] | [ 7.30690360e-02 4.67748120e-02 -7.55194843e-01 -4.10268456e-01
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6bc9383e-1d33-4a9f-94d2-6d06c4a4fd4e | muboost-an-effective-method-for-solving-indic | 2206.10280 | null | https://arxiv.org/abs/2206.10280v1 | https://arxiv.org/pdf/2206.10280v1.pdf | muBoost: An Effective Method for Solving Indic Multilingual Text Classification Problem | Text Classification is an integral part of many Natural Language Processing tasks such as sarcasm detection, sentiment analysis and many more such applications. Many e-commerce websites, social-media/entertainment platforms use such models to enhance user-experience to generate traffic and thus, revenue on their platforms. In this paper, we are presenting our solution to Multilingual Abusive Comment Identification Problem on Moj, an Indian video-sharing social networking service, powered by ShareChat. The problem dealt with detecting abusive comments, in 13 regional Indic languages such as Hindi, Telugu, Kannada etc., on the videos on Moj platform. Our solution utilizes the novel muBoost, an ensemble of CatBoost classifier models and Multilingual Representations for Indian Languages (MURIL) model, to produce SOTA performance on Indic text classification tasks. We were able to achieve a mean F1-score of 89.286 on the test data, an improvement over baseline MURIL model with a F1-score of 87.48. | ['Aditya Jain', 'Manish Pathak'] | 2022-06-21 | null | null | null | null | ['multilingual-text-classification'] | ['miscellaneous'] | [-4.43818808e-01 -2.79006660e-01 -3.35740894e-01 -2.15656832e-01
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8d01266a-286f-403e-aebf-1772bb74dc27 | data-valuation-for-medical-imaging-using | 2010.08006 | null | https://arxiv.org/abs/2010.08006v1 | https://arxiv.org/pdf/2010.08006v1.pdf | Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset | The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset. We characterized the effectiveness of data Shapley in identifying low quality versus valuable data for pneumonia detection. We found that removing training data with high Shapley values decreased the pneumonia detection performance, whereas removing data with low Shapley values improved the model performance. Furthermore, there were more mislabeled examples in low Shapley value data and more true pneumonia cases in high Shapley value data. Our results suggest that low Shapley value indicates mislabeled or poor quality images, whereas high Shapley value indicates data that are valuable for pneumonia detection. Our method can serve as a framework for using data Shapley to denoise large-scale medical imaging datasets. | ['Daniel L. Rubin', 'James Zou', 'Jared A. Dunnmon', 'Sameer Rehman', 'Rikiya Yamashita', 'Amirata Ghorbani', 'Siyi Tang'] | 2020-10-15 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 3.21569383e-01 -1.13156168e-02 -3.90916198e-01 -5.84223807e-01
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d82a626d-33b8-4753-bc71-36c4b2bc62af | end-to-end-nilm-system-using-high-frequency | 2004.13905 | null | http://arxiv.org/abs/2004.13905v1 | http://arxiv.org/pdf/2004.13905v1.pdf | End-to-end NILM System Using High Frequency Data and Neural Networks | Improving energy efficiency is a necessity in the fight against climate
change. Non Intrusive Load Monitoring (NILM) systems give important information
about the household consumption that can be used by the electric utility or the
end users. In this work the implementation of an end-to-end NILM system is
presented, which comprises a custom high frequency meter and neural-network
based algorithms. The present article presents a novel way to include high
frequency information as input of neural network models by means of
multivariate time series that include carefully selected features. Furthermore,
it provides a detailed assessment of the generalization error and shows that
this class of models generalize well to new instances of seen-in-training
appliances. An evaluation database formed of measurements in two Uruguayan
homes is collected and discussion on general unsupervised approaches is
provided. | [] | 2020-04-29 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 6.44071773e-02 -9.75869596e-02 -2.36082748e-01 -7.45772898e-01
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4ed5a436-31f8-4e89-9fe0-8b8be508abf5 | generative-models-for-multi-illumination | 2109.00863 | null | https://arxiv.org/abs/2109.00863v1 | https://arxiv.org/pdf/2109.00863v1.pdf | Generative Models for Multi-Illumination Color Constancy | In this paper, the aim is multi-illumination color constancy. However, most of the existing color constancy methods are designed for single light sources. Furthermore, datasets for learning multiple illumination color constancy are largely missing. We propose a seed (physics driven) based multi-illumination color constancy method. GANs are exploited to model the illumination estimation problem as an image-to-image domain translation problem. Additionally, a novel multi-illumination data augmentation method is proposed. Experiments on single and multi-illumination datasets show that our methods outperform sota methods. | ['Theo Gevers', 'Sezer Karaoglu', 'Yang Liu', 'Partha Das'] | 2021-09-02 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.96511114e-01 -5.96190333e-01 -9.49057266e-02 -4.15813923e-01
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dbf17254-95b2-481d-8e12-80858e97a147 | knowledge-prompting-for-few-shot-action | 2211.12030 | null | https://arxiv.org/abs/2211.12030v1 | https://arxiv.org/pdf/2211.12030v1.pdf | Knowledge Prompting for Few-shot Action Recognition | Few-shot action recognition in videos is challenging for its lack of supervision and difficulty in generalizing to unseen actions. To address this task, we propose a simple yet effective method, called knowledge prompting, which leverages commonsense knowledge of actions from external resources to prompt a powerful pre-trained vision-language model for few-shot classification. We first collect large-scale language descriptions of actions, defined as text proposals, to build an action knowledge base. The collection of text proposals is done by filling in handcraft sentence templates with external action-related corpus or by extracting action-related phrases from captions of Web instruction videos.Then we feed these text proposals into the pre-trained vision-language model along with video frames to generate matching scores of the proposals to each frame, and the scores can be treated as action semantics with strong generalization. Finally, we design a lightweight temporal modeling network to capture the temporal evolution of action semantics for classification.Extensive experiments on six benchmark datasets demonstrate that our method generally achieves the state-of-the-art performance while reducing the training overhead to 0.001 of existing methods. | ['Hanxi Lin', 'Xinxiao wu', 'Yuheng Shi'] | 2022-11-22 | null | null | null | null | ['few-shot-action-recognition', 'action-recognition-in-videos-2'] | ['computer-vision', 'computer-vision'] | [ 3.78264040e-01 -1.24920145e-01 -7.10265040e-01 -5.41145802e-01
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405a5cdb-938a-495b-94aa-9a44d933c71f | learning-a-structural-causal-model-for | 2305.17727 | null | https://arxiv.org/abs/2305.17727v1 | https://arxiv.org/pdf/2305.17727v1.pdf | Learning a Structural Causal Model for Intuition Reasoning in Conversation | Reasoning, a crucial aspect of NLP research, has not been adequately addressed by prevailing models including Large Language Model. Conversation reasoning, as a critical component of it, remains largely unexplored due to the absence of a well-designed cognitive model. In this paper, inspired by intuition theory on conversation cognition, we develop a conversation cognitive model (CCM) that explains how each utterance receives and activates channels of information recursively. Besides, we algebraically transformed CCM into a structural causal model (SCM) under some mild assumptions, rendering it compatible with various causal discovery methods. We further propose a probabilistic implementation of the SCM for utterance-level relation reasoning. By leveraging variational inference, it explores substitutes for implicit causes, addresses the issue of their unobservability, and reconstructs the causal representations of utterances through the evidence lower bounds. Moreover, we constructed synthetic and simulated datasets incorporating implicit causes and complete cause labels, alleviating the current situation where all available datasets are implicit-causes-agnostic. Extensive experiments demonstrate that our proposed method significantly outperforms existing methods on synthetic, simulated, and real-world datasets. Finally, we analyze the performance of CCM under latent confounders and propose theoretical ideas for addressing this currently unresolved issue. | ['Xinyu Yang', 'Wenjing Zhu', 'Jing Luo', 'Bingyu Liao', 'Hang Chen'] | 2023-05-28 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.01118580e-01 8.13576937e-01 -5.03195226e-01 -3.69338840e-01
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04ebb097-fe4c-4cdb-9542-7de2636a25be | small-footprint-keyword-spotting-with-multi | 2010.09960 | null | https://arxiv.org/abs/2010.09960v1 | https://arxiv.org/pdf/2010.09960v1.pdf | Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution | Keyword Spotting (KWS) plays a vital role in human-computer interaction for smart on-device terminals and service robots. It remains challenging to achieve the trade-off between small footprint and high accuracy for KWS task. In this paper, we explore the application of multi-scale temporal modeling to the small-footprint keyword spotting task. We propose a multi-branch temporal convolution module (MTConv), a CNN block consisting of multiple temporal convolution filters with different kernel sizes, which enriches temporal feature space. Besides, taking advantage of temporal and depthwise convolution, a temporal efficient neural network (TENet) is designed for KWS system. Based on the purposed model, we replace standard temporal convolution layers with MTConvs that can be trained for better performance. While at the inference stage, the MTConv can be equivalently converted to the base convolution architecture, so that no extra parameters and computational costs are added compared to the base model. The results on Google Speech Command Dataset show that one of our models trained with MTConv performs the accuracy of 96.8% with only 100K parameters. | ['Xiaowei Qin', 'Xiaodong Wei', 'Ximin Li'] | 2020-10-20 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [-2.39474233e-02 -8.31799284e-02 -9.72261727e-02 -5.22553086e-01
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f2edf70a-0521-4c35-8574-944c9172c786 | scene-understanding-networks-for-autonomous | 1805.07029 | null | http://arxiv.org/abs/1805.07029v1 | http://arxiv.org/pdf/1805.07029v1.pdf | Scene Understanding Networks for Autonomous Driving based on Around View Monitoring System | Modern driver assistance systems rely on a wide range of sensors (RADAR,
LIDAR, ultrasound and cameras) for scene understanding and prediction. These
sensors are typically used for detecting traffic participants and scene
elements required for navigation. In this paper we argue that relying on camera
based systems, specifically Around View Monitoring (AVM) system has great
potential to achieve these goals in both parking and driving modes with
decreased costs. The contributions of this paper are as follows: we present a
new end-to-end solution for delimiting the safe drivable area for each frame by
means of identifying the closest obstacle in each direction from the driving
vehicle, we use this approach to calculate the distance to the nearest
obstacles and we incorporate it into a unified end-to-end architecture capable
of joint object detection, curb detection and safe drivable area detection.
Furthermore, we describe the family of networks for both a high accuracy
solution and a low complexity solution. We also introduce further augmentation
of the base architecture with 3D object detection. | ['Andrei Leica', 'Andrei Petreanu', 'Vlad Paunescu', 'Ioana Veronica Chelu', 'YunSung Soh', 'Alexandru Ghiuta', 'Livia Iordache', 'HyunJoo Ryu', 'ByeongMoon Jeon', 'JeongYeol Baek'] | 2018-05-18 | null | null | null | null | ['drivable-area-detection'] | ['computer-vision'] | [ 1.20623894e-01 1.54467717e-01 2.78611258e-02 -7.03336060e-01
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fb860263-491f-4cdb-9586-a34ae133709c | learning-cross-context-entity-representations-1 | 2001.03765 | null | https://arxiv.org/abs/2001.03765v1 | https://arxiv.org/pdf/2001.03765v1.pdf | Learning Cross-Context Entity Representations from Text | Language modeling tasks, in which words, or word-pieces, are predicted on the basis of a local context, have been very effective for learning word embeddings and context dependent representations of phrases. Motivated by the observation that efforts to code world knowledge into machine readable knowledge bases or human readable encyclopedias tend to be entity-centric, we investigate the use of a fill-in-the-blank task to learn context independent representations of entities from the text contexts in which those entities were mentioned. We show that large scale training of neural models allows us to learn high quality entity representations, and we demonstrate successful results on four domains: (1) existing entity-level typing benchmarks, including a 64% error reduction over previous work on TypeNet (Murty et al., 2018); (2) a novel few-shot category reconstruction task; (3) existing entity linking benchmarks, where we match the state-of-the-art on CoNLL-Aida without linking-specific features and obtain a score of 89.8% on TAC-KBP 2010 without using any alias table, external knowledge base or in domain training data and (4) answering trivia questions, which uniquely identify entities. Our global entity representations encode fine-grained type categories, such as Scottish footballers, and can answer trivia questions such as: Who was the last inmate of Spandau jail in Berlin? | ['David Weiss', 'Thibault Févry', 'Tom Kwiatkowski', 'Livio Baldini Soares', 'Nicholas FitzGerald', 'Jeffrey Ling', 'Zifei Shan'] | 2020-01-11 | null | https://openreview.net/forum?id=HygwvC4tPH | https://openreview.net/pdf?id=HygwvC4tPH | null | ['learning-word-embeddings'] | ['methodology'] | [-2.93940127e-01 1.62010968e-01 -6.69802189e-01 -2.76821386e-02
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d11c9142-a42f-4122-a38f-8e4f55f787a6 | utilizing-resource-rich-language-datasets-for | 2111.12276 | null | https://arxiv.org/abs/2111.12276v1 | https://arxiv.org/pdf/2111.12276v1.pdf | Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages | This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the encoder-decoder model based on Transformer. To train a highly accurate end-to-end model, we need to prepare a large image-to-text paired dataset for the target language. However, it is difficult to collect this data, especially for resource-poor languages. To overcome this difficulty, our proposed method utilizes well-prepared large datasets in resource-rich languages such as English, to train the resource-poor encoder-decoder model. Our key idea is to build a model in which the encoder reflects knowledge of multiple languages while the decoder specializes in knowledge of just the resource-poor language. To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition. The proposed method also pre-trains the decoder by using the resource-poor language's dataset to make the decoder better suited to the resource-poor language. Experiments on Japanese scene text recognition using a small, publicly available dataset demonstrate the effectiveness of the proposed method. | ['Ryo Masumura', 'Tomohiro Tanaka', 'Akihiko Takashima', 'Mana Ihori', 'Naoki Makishima', 'Yoshihiro Yamazaki', 'Shota Orihashi'] | 2021-11-24 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.11272502e-01 -4.96221393e-01 -6.88667083e-03 -5.06820679e-01
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61de4378-7663-4313-92e2-35ceddc3a590 | low-rank-isomap-algorithm | 2103.04060 | null | https://arxiv.org/abs/2103.04060v1 | https://arxiv.org/pdf/2103.04060v1.pdf | Low-Rank Isomap Algorithm | The Isomap is a well-known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. Although the reduction of the computational complexity of the graphing stage has been investigated, yet the eigenvalue decomposition stage remains a bottleneck in the problem. In this paper, we propose the Low-Rank Isomap algorithm by introducing a projection operator on the embedded graph from the ambient space to a low-rank latent space to facilitate applying the partial eigenvalue decomposition. This approach leads to reducing the complexity of Isomap to a linear order while preserving the structural information during the dimensionality reduction process. The superiority of the Low-Rank Isomap algorithm compared to some state-of-art algorithms is experimentally verified on facial image clustering in terms of speed and accuracy. | ['Mohammad Hossein Kahaei', 'Eysan Mehrbani'] | 2021-03-06 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 4.75056350e-01 1.63816020e-01 3.24779421e-01 8.23395047e-03
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d43dc5da-b18f-4e87-9683-50ad19f43429 | a-multiple-choices-reading-comprehension | 2303.18162 | null | https://arxiv.org/abs/2303.18162v1 | https://arxiv.org/pdf/2303.18162v1.pdf | A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education | Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language. | ['Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Tuong Quang Pham', 'Khoi Trong Hoang', 'Son T. Luu'] | 2023-03-31 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.49227428e-01 2.95081615e-01 1.99263021e-01 -3.09181899e-01
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5a69650f-d171-4121-a8d4-f61ae46bae55 | a-cost-based-multi-layer-network-approach-for | 2209.09032 | null | https://arxiv.org/abs/2209.09032v2 | https://arxiv.org/pdf/2209.09032v2.pdf | A cost-based multi-layer network approach for the discovery of patient phenotypes | Clinical records frequently include assessments of the characteristics of patients, which may include the completion of various questionnaires. These questionnaires provide a variety of perspectives on a patient's current state of well-being. Not only is it critical to capture the heterogeneity given by these perspectives, but there is also a growing demand for developing cost-effective technologies for clinical phenotyping. Filling out many questionnaires may be a strain for the patients and therefore costly. In this work, we propose COBALT -- a cost-based layer selector model for detecting phenotypes using a community detection approach. Our goal is to minimize the number of features used to build these phenotypes while preserving its quality. We test our model using questionnaire data from chronic tinnitus patients and represent the data in a multi-layer network structure. The model is then evaluated by predicting post-treatment data using baseline features (age, gender, and pre-treatment data) as well as the identified phenotypes as a feature. For some post-treatment variables, predictors using phenotypes from COBALT as features outperformed those using phenotypes detected by traditional clustering methods. Moreover, using phenotype data to predict post-treatment data proved beneficial in comparison with predictors that were solely trained with baseline features. | ['Myra Spiliopoulou', 'Winfried Schlee', 'Uli Niemann', 'Clara Puga'] | 2022-09-19 | null | null | null | null | ['community-detection'] | ['graphs'] | [ 4.08659056e-02 7.02149328e-03 -6.80625588e-02 -5.61801910e-01
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4aa3309b-52a0-4d3c-a6f8-e78ed207a294 | energy-transformer | 2302.07253 | null | https://arxiv.org/abs/2302.07253v1 | https://arxiv.org/pdf/2302.07253v1.pdf | Energy Transformer | Transformers have become the de facto models of choice in machine learning, typically leading to impressive performance on many applications. At the same time, the architectural development in the transformer world is mostly driven by empirical findings, and the theoretical understanding of their architectural building blocks is rather limited. In contrast, Dense Associative Memory models or Modern Hopfield Networks have a well-established theoretical foundation, but have not yet demonstrated truly impressive practical results. We propose a transformer architecture that replaces the sequence of feedforward transformer blocks with a single large Associative Memory model. Our novel architecture, called Energy Transformer (or ET for short), has many of the familiar architectural primitives that are often used in the current generation of transformers. However, it is not identical to the existing architectures. The sequence of transformer layers in ET is purposely designed to minimize a specifically engineered energy function, which is responsible for representing the relationships between the tokens. As a consequence of this computational principle, the attention in ET is different from the conventional attention mechanism. In this work, we introduce the theoretical foundations of ET, explore it's empirical capabilities using the image completion task, and obtain strong quantitative results on the graph anomaly detection task. | ['Dmitry Krotov', 'Mohammed J. Zaki', 'Duen Horng Chau', 'Hendrik Strobelt', 'Rameswar Panda', 'Bao Pham', 'Yuchen Liang', 'Benjamin Hoover'] | 2023-02-14 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.02742709e-01 2.37026423e-01 1.13349542e-01 -6.93725869e-02
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da9f4109-193f-410c-9ea4-fef3841e3219 | identifiability-of-the-simplex-volume | 1406.5273 | null | http://arxiv.org/abs/1406.5273v2 | http://arxiv.org/pdf/1406.5273v2.pdf | Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case | In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known
to be powerful in enabling simple and effective blind HU solutions. However,
the pure-pixel assumption is not always satisfied in an exact sense, especially
for scenarios where pixels are heavily mixed. In the no pure-pixel case, a good
blind HU approach to consider is the minimum volume enclosing simplex (MVES).
Empirical experience has suggested that MVES algorithms can perform well
without pure pixels, although it was not totally clear why this is true from a
theoretical viewpoint. This paper aims to address the latter issue. We develop
an analysis framework wherein the perfect endmember identifiability of MVES is
studied under the noiseless case. We prove that MVES is indeed robust against
lack of pure pixels, as long as the pixels do not get too heavily mixed and too
asymmetrically spread. The theoretical results are verified by numerical
simulations. | ['Chong-Yung Chi', 'Wei-Chiang Li', 'Wing-Kin Ma', 'Chia-Hsiang Lin', 'ArulMurugan Ambikapathi'] | 2014-06-20 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.34251219e-01 -2.22950846e-01 8.82421434e-02 2.95979649e-01
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bf7765a2-e4a9-4ed9-ae71-5ce4bf8074ec | chalearn-looking-at-people-and-faces-of-the | null | null | https://ieeexplore.ieee.org/document/7789583 | https://sergioescalera.com/wp-content/uploads/2016/05/18.pdf | ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge 2016 | We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org. | ['Michel Valstar', 'Georgios Tzimiropoulos', 'Xavier Baró', 'Brais Martínez', 'Marc Oliu', 'Ciprian Corneanu', 'Sergio Escalera', 'Isabelle Guyon', 'Mohammad Ali Bagheri', 'Mercedes Torres Torres', 'Hugo Jair Escalante'] | 2016-12-19 | null | null | null | 2016-ieee-conference-on-computer-vision-and-1 | ['gender-prediction'] | ['computer-vision'] | [-2.78741658e-01 7.01392442e-02 1.44571558e-01 -6.48701847e-01
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59aff169-3139-4d68-88ec-9f7421bb27d6 | robust-hate-speech-detection-in-social-media | 2307.01680 | null | https://arxiv.org/abs/2307.01680v1 | https://arxiv.org/pdf/2307.01680v1.pdf | Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation | The automatic detection of hate speech online is an active research area in NLP. Most of the studies to date are based on social media datasets that contribute to the creation of hate speech detection models trained on them. However, data creation processes contain their own biases, and models inherently learn from these dataset-specific biases. In this paper, we perform a large-scale cross-dataset comparison where we fine-tune language models on different hate speech detection datasets. This analysis shows how some datasets are more generalisable than others when used as training data. Crucially, our experiments show how combining hate speech detection datasets can contribute to the development of robust hate speech detection models. This robustness holds even when controlling by data size and compared with the best individual datasets. | ['Jose Camacho-Collados', 'Dimosthenis Antypas'] | 2023-07-04 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.09750919e-01 -1.64925009e-01 -2.32811630e-01 -1.10383384e-01
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558437cb-f8f7-47e1-b39a-1c98ebc30043 | metagenomic-analysis-using-phylogenetic | 2202.03534 | null | https://arxiv.org/abs/2202.03534v2 | https://arxiv.org/pdf/2202.03534v2.pdf | Metagenomic Analysis using Phylogenetic Placement -- A Review of the First Decade | Phylogenetic placement refers to a family of tools and methods to analyze, visualize, and interpret the tsunami of metagenomic sequencing data generated by high-throughput sequencing. Compared to alternative (e. g., similarity-based) methods, it puts metabarcoding sequences into a phylogenetic context using a set of known reference sequences and taking evolutionary history into account. Thereby, one can increase the accuracy of metagenomic surveys and eliminate the requirement for having exact or close matches with existing sequence databases. Phylogenetic placement constitutes a valuable analysis tool per se, but also entails a plethora of downstream tools to interpret its results. A common use case is to analyze species communities obtained from metagenomic sequencing, for example via taxonomic assignment, diversity quantification, sample comparison, and identification of correlations with environmental variables. In this review, we provide an overview over the methods developed during the first ten years. In particular, the goals of this review are (i) to motivate the usage of phylogenetic placement and illustrate some of its use cases, (ii) to outline the full workflow, from raw sequences to publishable figures, including best practices, (iii) to introduce the most common tools and methods and their capabilities, (iv) to point out common placement pitfalls and misconceptions,(v) to showcase typical placement-based analyses, and how they can help to analyze, visualize, and interpret phylogenetic placement data. | ['Pierre Barbera', 'Micah Dunthorn', 'Alexandros Stamatakis', 'Lucas Czech'] | 2022-02-07 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 6.83180034e-01 -7.99311280e-01 1.95144385e-01 2.07893580e-01
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9fec7dff-47c0-451a-9108-9ee9f4618760 | isaac-gym-high-performance-gpu-based-physics | 2108.10470 | null | https://arxiv.org/abs/2108.10470v2 | https://arxiv.org/pdf/2108.10470v2.pdf | Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning | Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks. We host the results and videos at \url{https://sites.google.com/view/isaacgym-nvidia} and isaac gym can be downloaded at \url{https://developer.nvidia.com/isaac-gym}. | ['Gavriel State', 'Ankur Handa', 'Arthur Allshire', 'Nikita Rudin', 'David Hoeller', 'Miles Macklin', 'Kier Storey', 'Michelle Lu', 'Yunrong Guo', 'Lukasz Wawrzyniak', 'Viktor Makoviychuk'] | 2021-08-24 | null | null | null | null | ['omniverse-isaac-gym', 'isaac-gym-preview'] | ['robots', 'robots'] | [-6.95932984e-01 -1.87639624e-01 4.09882888e-02 -8.35077390e-02
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57f8c782-02c8-4dac-b8ea-11309bd10867 | an-efficient-speech-separation-network-based | 2306.05887 | null | https://arxiv.org/abs/2306.05887v1 | https://arxiv.org/pdf/2306.05887v1.pdf | An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention | We present an efficient speech separation neural network, ARFDCN, which combines dilated convolutions, multi-scale fusion (MSF), and channel attention to overcome the limited receptive field of convolution-based networks and the high computational cost of transformer-based networks. The suggested network architecture is encoder-decoder based. By using dilated convolutions with gradually increasing dilation value to learn local and global features and fusing them at adjacent stages, the model can learn rich feature content. Meanwhile, by adding channel attention modules to the network, the model can extract channel weights, learn more important features, and thus improve its expressive power and robustness. Experimental results indicate that the model achieves a decent balance between performance and computational efficiency, making it a promising alternative to current mainstream models for practical applications. | ['Junyu Wang'] | 2023-06-09 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 6.66018277e-02 -2.84337372e-01 -2.19912767e-01 -2.34697253e-01
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97b5747a-3b1e-498f-a1e9-e439c46afb84 | on-the-connection-between-temperature-and | 2303.15164 | null | https://arxiv.org/abs/2303.15164v1 | https://arxiv.org/pdf/2303.15164v1.pdf | On the Connection between Temperature and Volatility in Ideal Agent Systems | Models for spin systems known from statistical physics are applied by analogy in econometrics in the form of agent-based models. Researchers suggest that the state variable temperature $T$ corresponds to volatility $\sigma$ in capital market theory problems. To the best of our knowledge, this has not yet been theoretically derived, for example, for an ideal agent system. In the present paper, we derive the exact algebraic relation between $T$ and $\sigma$ for an ideal agent system and discuss implications and limitations. | ['John H. Stiebel', 'Ingo Hoffmann', 'Christoph J. Börner'] | 2023-03-27 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-4.77275789e-01 1.19594507e-01 -1.69825524e-01 -1.79836497e-01
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e1be84e9-a1e9-4073-8ba5-6166036067e3 | supervised-opinion-aspect-extraction-by | 1612.07940 | null | http://arxiv.org/abs/1612.07940v1 | http://arxiv.org/pdf/1612.07940v1.pdf | Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results | One of the key tasks of sentiment analysis of product reviews is to extract
product aspects or features that users have expressed opinions on. In this
work, we focus on using supervised sequence labeling as the base approach to
performing the task. Although several extraction methods using sequence
labeling methods such as Conditional Random Fields (CRF) and Hidden Markov
Models (HMM) have been proposed, we show that this supervised approach can be
significantly improved by exploiting the idea of concept sharing across
multiple domains. For example, "screen" is an aspect in iPhone, but not only
iPhone has a screen, many electronic devices have screens too. When "screen"
appears in a review of a new domain (or product), it is likely to be an aspect
too. Knowing this information enables us to do much better extraction in the
new domain. This paper proposes a novel extraction method exploiting this idea
in the context of supervised sequence labeling. Experimental results show that
it produces markedly better results than without using the past information. | ['Annice Kim', 'Bing Liu', 'Hu Xu', 'Lei Shu'] | 2016-12-23 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 5.16246498e-01 3.51252019e-01 -4.47027832e-01 -6.99431896e-01
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26170773-b2af-4aa1-b8de-e1d2cd9776f0 | fpnn-field-probing-neural-networks-for-3d | 1605.06240 | null | http://arxiv.org/abs/1605.06240v3 | http://arxiv.org/pdf/1605.06240v3.pdf | FPNN: Field Probing Neural Networks for 3D Data | Building discriminative representations for 3D data has been an important
task in computer graphics and computer vision research. Convolutional Neural
Networks (CNNs) have shown to operate on 2D images with great success for a
variety of tasks. Lifting convolution operators to 3D (3DCNNs) seems like a
plausible and promising next step. Unfortunately, the computational complexity
of 3D CNNs grows cubically with respect to voxel resolution. Moreover, since
most 3D geometry representations are boundary based, occupied regions do not
increase proportionately with the size of the discretization, resulting in
wasted computation. In this work, we represent 3D spaces as volumetric fields,
and propose a novel design that employs field probing filters to efficiently
extract features from them. Each field probing filter is a set of probing
points --- sensors that perceive the space. Our learning algorithm optimizes
not only the weights associated with the probing points, but also their
locations, which deforms the shape of the probing filters and adaptively
distributes them in 3D space. The optimized probing points sense the 3D space
"intelligently", rather than operating blindly over the entire domain. We show
that field probing is significantly more efficient than 3DCNNs, while providing
state-of-the-art performance, on classification tasks for 3D object recognition
benchmark datasets. | ['Yangyan Li', 'Leonidas J. Guibas', 'Hao Su', 'Soeren Pirk', 'Charles R. Qi'] | 2016-05-20 | fpnn-field-probing-neural-networks-for-3d-1 | http://papers.nips.cc/paper/6416-fpnn-field-probing-neural-networks-for-3d-data | http://papers.nips.cc/paper/6416-fpnn-field-probing-neural-networks-for-3d-data.pdf | neurips-2016-12 | ['3d-object-recognition'] | ['computer-vision'] | [ 6.66243806e-02 -3.32633182e-02 -1.11041311e-03 -2.71546304e-01
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6717453d-ef76-4333-931b-84f03a73b8bb | sgg-learning-to-select-guide-and-generate-for | 2105.02544 | null | https://arxiv.org/abs/2105.02544v2 | https://arxiv.org/pdf/2105.02544v2.pdf | SGG: Learning to Select, Guide, and Generate for Keyphrase Generation | Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. In this paper, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrase generation separately with different mechanisms. Specifically, SGG is a hierarchical neural network which consists of a pointing-based selector at low layer concentrated on present keyphrase generation, a selection-guided generator at high layer dedicated to absent keyphrase generation, and a guider in the middle to transfer information from selector to generator. Experimental results on four keyphrase generation benchmarks demonstrate the effectiveness of our model, which significantly outperforms the strong baselines for both present and absent keyphrases generation. Furthermore, we extend SGG to a title generation task which indicates its extensibility in natural language generation tasks. | ['BoWen Zhou', 'Xiaodong He', 'Youzheng Wu', 'Yifan Wang', 'Junwei Bao', 'Jing Zhao'] | 2021-05-06 | null | https://aclanthology.org/2021.naacl-main.455 | https://aclanthology.org/2021.naacl-main.455.pdf | naacl-2021-4 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.29747945e-01 1.65522844e-01 -3.18801075e-01 1.62127301e-01
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42a64134-d68f-4c48-9055-38abad965ec5 | medleyvox-an-evaluation-dataset-for-multiple | 2211.07302 | null | https://arxiv.org/abs/2211.07302v2 | https://arxiv.org/pdf/2211.07302v2.pdf | MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation | Separation of multiple singing voices into each voice is a rarely studied area in music source separation research. The absence of a benchmark dataset has hindered its progress. In this paper, we present an evaluation dataset and provide baseline studies for multiple singing voices separation. First, we introduce MedleyVox, an evaluation dataset for multiple singing voices separation. We specify the problem definition in this dataset by categorizing it into i) unison, ii) duet, iii) main vs. rest, and iv) N-singing separation. Second, to overcome the absence of existing multi-singing datasets for a training purpose, we present a strategy for construction of multiple singing mixtures using various single-singing datasets. Third, we propose the improved super-resolution network (iSRNet), which greatly enhances initial estimates of separation networks. Jointly trained with the Conv-TasNet and the multi-singing mixture construction strategy, the proposed iSRNet achieved comparable performance to ideal time-frequency masks on duet and unison subsets of MedleyVox. Audio samples, the dataset, and codes are available on our website (https://github.com/jeonchangbin49/MedleyVox). | ['Kyogu Lee', 'Ben Sangbae Chon', 'Keunwoo Choi', 'Hyeongi Moon', 'Chang-Bin Jeon'] | 2022-11-14 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.18727408e-01 -5.88236392e-01 -1.99315604e-02 2.22158045e-01
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da4e4966-be48-4f4a-851e-61be646987bf | lidarmutlinet-unifying-lidar-semantic | 2206.11428 | null | https://arxiv.org/abs/2206.11428v2 | https://arxiv.org/pdf/2206.11428v2.pdf | LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network | This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022. Our network, termed LidarMultiNet, unifies the major LiDAR perception tasks such as 3D semantic segmentation, object detection, and panoptic segmentation in a single framework. At the core of LidarMultiNet is a strong 3D voxel-based encoder-decoder network with a novel Global Context Pooling (GCP) module extracting global contextual features from a LiDAR frame to complement its local features. An optional second stage is proposed to refine the first-stage segmentation or generate accurate panoptic segmentation results. Our solution achieves a mIoU of 71.13 and is the best for most of the 22 classes on the Waymo 3D semantic segmentation test set, outperforming all the other 3D semantic segmentation methods on the official leaderboard. We demonstrate for the first time that major LiDAR perception tasks can be unified in a single strong network that can be trained end-to-end. | ['Hassan Foroosh', 'Panqu Wang', 'Yu Wang', 'Yufei Xie', 'Zixiang Zhou', 'Weijia Chen', 'Dongqiangzi Ye'] | 2022-06-23 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 3.13747942e-01 2.97138333e-01 -2.37645879e-01 -7.29559422e-01
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262b3bab-51f5-40a4-8501-7072eb79908f | a-survey-on-anti-spoofing-methods-for-face | 2010.04145 | null | https://arxiv.org/abs/2010.04145v1 | https://arxiv.org/pdf/2010.04145v1.pdf | A Survey On Anti-Spoofing Methods For Face Recognition with RGB Cameras of Generic Consumer Devices | The widespread deployment of face recognition-based biometric systems has made face Presentation Attack Detection (face anti-spoofing) an increasingly critical issue. This survey thoroughly investigates the face Presentation Attack Detection (PAD) methods, that only require RGB cameras of generic consumer devices, over the past two decades. We present an attack scenario-oriented typology of the existing face PAD methods and we provide a review of over 50 of the most recent face PAD methods and their related issues. We adopt a comprehensive presentation of the methods that have most influenced face PAD following the proposed typology, and in chronological order. By doing so, we depict the main challenges, evolutions and current trends in the field of face PAD, and provide insights on its future research. From an experimental point of view, this survey paper provides a summarized overview of the available public databases and extensive comparative experimental results of different PAD methods. | ['Jean-Christophe Burie', 'Muhammad Muzzamil Luqman', 'Muriel Visani', 'Zuheng Ming'] | 2020-10-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 5.37968218e-01 -1.95027366e-01 -1.53010219e-01 -6.26563579e-02
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beed1761-cf4d-40f1-aed7-263dbd94c57e | differentiable-divergences-between-time | 2010.08354 | null | https://arxiv.org/abs/2010.08354v3 | https://arxiv.org/pdf/2010.08354v3.pdf | Differentiable Divergences Between Time Series | Computing the discrepancy between time series of variable sizes is notoriously challenging. While dynamic time warping (DTW) is popularly used for this purpose, it is not differentiable everywhere and is known to lead to bad local optima when used as a "loss". Soft-DTW addresses these issues, but it is not a positive definite divergence: due to the bias introduced by entropic regularization, it can be negative and it is not minimized when the time series are equal. We propose in this paper a new divergence, dubbed soft-DTW divergence, which aims to correct these issues. We study its properties; in particular, under conditions on the ground cost, we show that it is a valid divergence: it is non-negative and minimized if and only if the two time series are equal. We also propose a new "sharp" variant by further removing entropic bias. We showcase our divergences on time series averaging and demonstrate significant accuracy improvements compared to both DTW and soft-DTW on 84 time series classification datasets. | ['Jean-Philippe Vert', 'Arthur Mensch', 'Mathieu Blondel'] | 2020-10-16 | null | null | null | null | ['time-series-averaging'] | ['time-series'] | [ 2.40996137e-01 -2.71020204e-01 4.76024747e-02 -2.51196861e-01
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48142a91-eb4b-419e-91a5-d26173abcc99 | softpool-an-encoder-decoder-network-for-point | 2205.03899 | null | https://arxiv.org/abs/2205.03899v1 | https://arxiv.org/pdf/2205.03899v1.pdf | SoftPool++: An Encoder-Decoder Network for Point Cloud Completion | We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not require any max-pooling or voxelization operation. Instead, the proposed operator used to learn the point cloud embedding in the encoder extracts permutation-invariant features from the point cloud via a soft-pooling of feature activations, which are able to preserve fine-grained geometric details. These features are then passed on to a decoder architecture. Due to the compression in the encoder, a typical limitation of this type of architectures is that they tend to lose parts of the input shape structure. We propose to overcome this limitation by using skip connections specifically devised for point clouds, where links between corresponding layers in the encoder and the decoder are established. As part of these connections, we introduce a transformation matrix that projects the features from the encoder to the decoder and vice-versa. The quantitative and qualitative results on the task of object completion from partial scans on the ShapeNet dataset show that incorporating our approach achieves state-of-the-art performance in shape completion both at low and high resolutions. | ['Federico Tombari', 'Nassir Navab', 'David Joseph Tan', 'Yida Wang'] | 2022-05-08 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.28583738e-01 3.80889803e-01 3.34212393e-01 -5.72163403e-01
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69a26e92-fb2b-454a-87b8-2e53781ee872 | leverage-unlabeled-data-for-abstractive | 2007.15296 | null | https://arxiv.org/abs/2007.15296v2 | https://arxiv.org/pdf/2007.15296v2.pdf | Leverage Unlabeled Data for Abstractive Speech Summarization with Self-Supervised Learning and Back-Summarization | Supervised approaches for Neural Abstractive Summarization require large annotated corpora that are costly to build. We present a French meeting summarization task where reports are predicted based on the automatic transcription of the meeting audio recordings. In order to build a corpus for this task, it is necessary to obtain the (automatic or manual) transcription of each meeting, and then to segment and align it with the corresponding manual report to produce training examples suitable for training. On the other hand, we have access to a very large amount of unaligned data, in particular reports without corresponding transcription. Reports are professionally written and well formatted making pre-processing straightforward. In this context, we study how to take advantage of this massive amount of unaligned data using two approaches (i) self-supervised pre-training using a target-side denoising encoder-decoder model; (ii) back-summarization i.e. reversing the summarization process by learning to predict the transcription given the report, in order to align single reports with generated transcription, and use this synthetic dataset for further training. We report large improvements compared to the previous baseline (trained on aligned data only) for both approaches on two evaluation sets. Moreover, combining the two gives even better results, outperforming the baseline by a large margin of +6 ROUGE-1 and ROUGE-L and +5 ROUGE-2 on two evaluation sets | ['Yannick Estève', 'François Hernandez', 'Vincent Nguyen', 'Paul Tardy', 'Louis de Seynes', 'David Janiszek'] | 2020-07-30 | null | null | null | null | ['meeting-summarization'] | ['natural-language-processing'] | [ 7.09016621e-01 6.22099936e-01 1.04103900e-01 -3.64414006e-01
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45d2b4b2-af66-4f47-89da-447af327dda6 | lesion-guided-explainable-few-weak-shot | 2211.08732 | null | https://arxiv.org/abs/2211.08732v2 | https://arxiv.org/pdf/2211.08732v2.pdf | Lesion Guided Explainable Few Weak-shot Medical Report Generation | Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care. However, most existing approaches to automatic report generation require sufficient labeled data for training. In addition, the learned model can only generate reports for the training classes, lacking the ability to adapt to previously unseen novel diseases. To this end, we propose a lesion guided explainable few weak-shot medical report generation framework that learns correlation between seen and novel classes through visual and semantic feature alignment, aiming to generate medical reports for diseases not observed in training. It integrates a lesion-centric feature extractor and a Transformer-based report generation module. Concretely, the lesion-centric feature extractor detects the abnormal regions and learns correlations between seen and novel classes with multi-view (visual and lexical) embeddings. Then, features of the detected regions and corresponding embeddings are concatenated as multi-view input to the report generation module for explainable report generation, including text descriptions and corresponding abnormal regions detected in the images. We conduct experiments on FFA-IR, a dataset providing explainable annotations, showing that our framework outperforms others on report generation for novel diseases. | ['Yefeng Zheng', 'Liansheng Wang', 'Dong Wei', 'Jinghan Sun'] | 2022-11-16 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 3.76269788e-01 7.48890996e-01 -3.86543691e-01 -5.46715558e-01
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bb4b69a3-431e-4d98-ba13-f4c5cabe4f61 | vtc-improving-video-text-retrieval-with-user | 2210.10820 | null | https://arxiv.org/abs/2210.10820v1 | https://arxiv.org/pdf/2210.10820v1.pdf | VTC: Improving Video-Text Retrieval with User Comments | Multi-modal retrieval is an important problem for many applications, such as recommendation and search. Current benchmarks and even datasets are often manually constructed and consist of mostly clean samples where all modalities are well-correlated with the content. Thus, current video-text retrieval literature largely focuses on video titles or audio transcripts, while ignoring user comments, since users often tend to discuss topics only vaguely related to the video. Despite the ubiquity of user comments online, there is currently no multi-modal representation learning datasets that includes comments. In this paper, we a) introduce a new dataset of videos, titles and comments; b) present an attention-based mechanism that allows the model to learn from sometimes irrelevant data such as comments; c) show that by using comments, our method is able to learn better, more contextualised, representations for image, video and audio representations. Project page: https://unitaryai.github.io/vtc-paper. | ['Christian Rupprecht', 'Yuki M. Asano', 'James Thewlis', 'Laura Hanu'] | 2022-10-19 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.00932576e-01 -3.04775059e-01 -6.66007221e-01 -1.41496718e-01
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90180a8d-a635-4522-85ef-3f2970d1a987 | underground-diagnosis-based-on-gpr-and | 2211.15480 | null | https://arxiv.org/abs/2211.15480v1 | https://arxiv.org/pdf/2211.15480v1.pdf | Underground Diagnosis Based on GPR and Learning in the Model Space | Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or abnormals automatically. In this paper, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model. | ['Huanhuan Chen', 'Yizhan Fan', 'Xiren Zhou', 'Ao Chen'] | 2022-11-25 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 1.16749197e-01 1.72744974e-01 3.06947500e-01 -5.69929898e-01
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666688f9-47d9-4abc-a1de-b7d1c0ff5de5 | negative-data-augmentation-1 | 2102.05113 | null | https://arxiv.org/abs/2102.05113v1 | https://arxiv.org/pdf/2102.05113v1.pdf | Negative Data Augmentation | Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA)that intentionally create out-of-distribution samples. We show that such negative out-of-distribution samples provide information on the support of the data distribution, and can be leveraged for generative modeling and representation learning. We introduce a new GAN training objective where we use NDA as an additional source of synthetic data for the discriminator. We prove that under suitable conditions, optimizing the resulting objective still recovers the true data distribution but can directly bias the generator towards avoiding samples that lack the desired structure. Empirically, models trained with our method achieve improved conditional/unconditional image generation along with improved anomaly detection capabilities. Further, we incorporate the same negative data augmentation strategy in a contrastive learning framework for self-supervised representation learning on images and videos, achieving improved performance on downstream image classification, object detection, and action recognition tasks. These results suggest that prior knowledge on what does not constitute valid data is an effective form of weak supervision across a range of unsupervised learning tasks. | ['Stefano Ermon', 'Hongxia Jin', 'Burak Uzkent', 'Jiaming Song', 'Kumar Ayush', 'Abhishek Sinha'] | 2021-02-09 | negative-data-augmentation | https://openreview.net/forum?id=Ovp8dvB8IBH | https://openreview.net/pdf?id=Ovp8dvB8IBH | iclr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 9.18719769e-01 5.93516648e-01 -5.29150307e-01 -3.83945227e-01
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80569c0c-1c7c-4c95-a34f-2b2964b899a9 | lrs3-ted-a-large-scale-dataset-for-visual | 1809.00496 | null | http://arxiv.org/abs/1809.00496v2 | http://arxiv.org/pdf/1809.00496v2.pdf | LRS3-TED: a large-scale dataset for visual speech recognition | This paper introduces a new multi-modal dataset for visual and audio-visual
speech recognition. It includes face tracks from over 400 hours of TED and TEDx
videos, along with the corresponding subtitles and word alignment boundaries.
The new dataset is substantially larger in scale compared to other public
datasets that are available for general research. | ['Triantafyllos Afouras', 'Joon Son Chung', 'Andrew Zisserman'] | 2018-09-03 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [-5.69254085e-02 -2.16292500e-01 -6.06512487e-01 -8.35589051e-01
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620f96bb-536d-40f3-afcb-32d805c07853 | tracking-legislators-expressed-policy-agendas | null | null | https://osf.io/preprints/socarxiv/ync87/ | https://files.osf.io/v1/resources/ync87/providers/osfstorage/61e71a74bc925b0694d4bc4d | Tracking Legislators’ Expressed Policy Agendas in Real Time | We develop a real-time scalable method to analyze strategic communication by political actors on salient policy issues through their tweets. Using word embeddings and supervised machine learning models, we classify legislators' tweets according to whether or not they reference policy issues as well as what positions they promote. This allows us to measure the microdynamics of elite communication with a high level of temporal granularity in a manner that is scalable across diverse issue areas and legislatures. As a proof of concept, here we use this method to track the multi-year evolution of positions that members of Congress express on immigration and climate change. Validation with issue-specific vote scores suggests that our method performs with a satisfactory level of accuracy and enables us to identify legislators whose online rhetoric differs substantively from their voting behavior. We also display the immigration analysis on an automatically updated publicly available interactive website, enabling researchers, journalists, and policy makers alike to explore legislators' shifting rhetoric on immigration in real time. | ['Jens Hainmueller', 'Jeremy Weinstein', 'Duncan Lawrence', 'David Laitin', 'Alexandra Siegel'] | 2022-01-18 | null | null | null | socarxiv-2022-1 | ['political-salient-issue-orientation-detection'] | ['natural-language-processing'] | [-1.61263049e-01 -7.83841535e-02 -9.14551079e-01 -2.24297523e-01
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e38d0112-8ce1-4e15-b93b-1874642c314d | fast-private-kernel-density-estimation-via | 2307.01877 | null | https://arxiv.org/abs/2307.01877v1 | https://arxiv.org/pdf/2307.01877v1.pdf | Fast Private Kernel Density Estimation via Locality Sensitive Quantization | We study efficient mechanisms for differentially private kernel density estimation (DP-KDE). Prior work for the Gaussian kernel described algorithms that run in time exponential in the number of dimensions $d$. This paper breaks the exponential barrier, and shows how the KDE can privately be approximated in time linear in $d$, making it feasible for high-dimensional data. We also present improved bounds for low-dimensional data. Our results are obtained through a general framework, which we term Locality Sensitive Quantization (LSQ), for constructing private KDE mechanisms where existing KDE approximation techniques can be applied. It lets us leverage several efficient non-private KDE methods -- like Random Fourier Features, the Fast Gauss Transform, and Locality Sensitive Hashing -- and ``privatize'' them in a black-box manner. Our experiments demonstrate that our resulting DP-KDE mechanisms are fast and accurate on large datasets in both high and low dimensions. | ['Nina Mishra', 'Yonatan Naamad', 'Tal Wagner'] | 2023-07-04 | null | null | null | null | ['quantization', 'density-estimation'] | ['methodology', 'methodology'] | [-7.59664655e-01 -3.49647641e-01 -3.43523234e-01 -3.61627340e-01
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6484ab77-0b07-4836-94f3-4e64ac01efc5 | senpoi-at-semeval-2022-task-10-point-me-to | null | null | https://aclanthology.org/2022.semeval-1.183 | https://aclanthology.org/2022.semeval-1.183.pdf | SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi | Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70%. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting. | ['Andreas Hotho', 'Sebastian Wankerl', 'Jan Pfister'] | null | null | null | null | semeval-naacl-2022-7 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 5.31483769e-01 3.87010336e-01 -4.49366540e-01 -5.08165836e-01
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84d9c4c6-0c0e-4571-815a-fc4d07a2cdba | testing-the-reliability-of-chatgpt-for-text | 2304.11085 | null | https://arxiv.org/abs/2304.11085v1 | https://arxiv.org/pdf/2304.11085v1.pdf | Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark | Recent studies have demonstrated promising potential of ChatGPT for various text annotation and classification tasks. However, ChatGPT is non-deterministic which means that, as with human coders, identical input can lead to different outputs. Given this, it seems appropriate to test the reliability of ChatGPT. Therefore, this study investigates the consistency of ChatGPT's zero-shot capabilities for text annotation and classification, focusing on different model parameters, prompt variations, and repetitions of identical inputs. Based on the real-world classification task of differentiating website texts into news and not news, results show that consistency in ChatGPT's classification output can fall short of scientific thresholds for reliability. For example, even minor wording alterations in prompts or repeating the identical input can lead to varying outputs. Although pooling outputs from multiple repetitions can improve reliability, this study advises caution when using ChatGPT for zero-shot text annotation and underscores the need for thorough validation, such as comparison against human-annotated data. The unsupervised application of ChatGPT for text annotation and classification is not recommended. | ['Michael V. Reiss'] | 2023-04-17 | null | null | null | null | ['text-annotation'] | ['natural-language-processing'] | [ 2.31354311e-01 7.73497745e-02 2.37631544e-01 -4.44426686e-01
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8ffe5b1d-668c-48a6-8a26-427a6b745f4f | multi-scale-hourglass-hierarchical-fusion | 2104.12100 | null | https://arxiv.org/abs/2104.12100v2 | https://arxiv.org/pdf/2104.12100v2.pdf | Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining | Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we present a Multi-scale Hourglass Hierarchical Fusion Network (MH2F-Net) in end-to-end manner, to exactly captures rain streak features with multi-scale extraction, hierarchical distillation and information aggregation. For better extracting the features, a novel Multi-scale Hourglass Extraction Block (MHEB) is proposed to get local and global features across different scales through down- and up-sample process. Besides, a Hierarchical Attentive Distillation Block (HADB) then employs the dual attention feature responses to adaptively recalibrate the hierarchical features and eliminate the redundant ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy to progressively discriminate feature learning and aggregate different features instead of directly concatenating or adding. Extensive experiments on both synthetic and real rainy datasets demonstrate the effectiveness of the designed MH2F-Net by comparing with recent state-of-the-art deraining algorithms. Our source code will be available on the GitHub: https://github.com/cxtalk/MH2F-Net. | ['Lei Xu', 'Yufeng Huang', 'Xiang Chen'] | 2021-04-25 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [-3.24409544e-01 -5.25357485e-01 4.47060198e-01 -6.16783679e-01
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2e09eef9-cae7-4a44-a61c-9313a760b0f4 | instaindoor-and-multi-modal-deep-learning-for | 2112.12409 | null | https://arxiv.org/abs/2112.12409v1 | https://arxiv.org/pdf/2112.12409v1.pdf | InstaIndoor and Multi-modal Deep Learning for Indoor Scene Recognition | Indoor scene recognition is a growing field with great potential for behaviour understanding, robot localization, and elderly monitoring, among others. In this study, we approach the task of scene recognition from a novel standpoint, using multi-modal learning and video data gathered from social media. The accessibility and variety of social media videos can provide realistic data for modern scene recognition techniques and applications. We propose a model based on fusion of transcribed speech to text and visual features, which is used for classification on a novel dataset of social media videos of indoor scenes named InstaIndoor. Our model achieves up to 70% accuracy and 0.7 F1-Score. Furthermore, we highlight the potential of our approach by benchmarking on a YouTube-8M subset of indoor scenes as well, where it achieves 74% accuracy and 0.74 F1-Score. We hope the contributions of this work pave the way to novel research in the challenging field of indoor scene recognition. | ['Estefania Talavera', 'Andreea Glavan'] | 2021-12-23 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.54573816e-01 -1.87526718e-01 -8.11848864e-02 -5.99636018e-01
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ea2b677d-70ba-4e7d-b45a-7230821c688f | affine-medical-image-registration-with-coarse | 2203.15216 | null | https://arxiv.org/abs/2203.15216v2 | https://arxiv.org/pdf/2203.15216v2.pdf | Affine Medical Image Registration with Coarse-to-Fine Vision Transformer | Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone performance of the affine subnetwork is less explored. Moreover, existing CNN-based affine registration approaches focus either on the local misalignment or the global orientation and position of the input to predict the affine transformation matrix, which are sensitive to spatial initialization and exhibit limited generalizability apart from the training dataset. In this paper, we present a fast and robust learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method naturally leverages the global connectivity and locality of the convolutional vision transformer and the multi-resolution strategy to learn the global affine registration. We evaluate our method on 3D brain atlas registration and template-matching normalization. Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods. The source code is available at https://github.com/cwmok/C2FViT. | ['Albert C. S. Chung', 'Tony C. W. Mok'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mok_Affine_Medical_Image_Registration_With_Coarse-To-Fine_Vision_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['template-matching'] | ['computer-vision'] | [-1.16531640e-01 -2.18838975e-01 -3.06023061e-01 -5.99701047e-01
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f868b5ae-44ff-401a-be95-681ebc872a88 | universal-semantic-annotator-the-first | null | null | https://aclanthology.org/2022.lrec-1.282 | https://aclanthology.org/2022.lrec-1.282.pdf | Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing | In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing. Together, such annotations can be used to provide users with rich and diverse semantic information, help second-language learners, and allow researchers to integrate explicit semantic knowledge into downstream tasks and real-world applications. | ['Roberto Navigli', 'Stefano Faralli', 'Simone Conia', 'Riccardo Orlando'] | null | null | null | null | lrec-2022-6 | ['word-sense-disambiguation', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.98523852e-02 4.72751647e-01 -4.73248243e-01 -4.29522514e-01
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48d8dc82-97a6-48b1-ba61-b37cefddcca0 | dynamic-causal-explanation-based-diffusion | 2305.09703 | null | https://arxiv.org/abs/2305.09703v1 | https://arxiv.org/pdf/2305.09703v1.pdf | Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting | Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explainability for the causal relationship between the neighbour nodes of the dynamically generated graphs, which can easily lead to a risk in subsequent decisions. Moreover, few of them consider the uncertainty and noise of dynamic graphs based on the time series datasets, which are ubiquitous in real-world graph structure networks. In this paper, we propose a novel Dynamic Diffusion-Variational Graph Neural Network (DVGNN) for spatio-temporal forecasting. For dynamic graph construction, an unsupervised generative model is devised. Two layers of graph convolutional network (GCN) are applied to calculate the posterior distribution of the latent node embeddings in the encoder stage. Then, a diffusion model is used to infer the dynamic link probability and reconstruct causal graphs in the decoder stage adaptively. The new loss function is derived theoretically, and the reparameterization trick is adopted in estimating the probability distribution of the dynamic graphs by Evidence Lower Bound during the backpropagation period. After obtaining the generated graphs, dynamic GCN and temporal attention are applied to predict future states. Experiments are conducted on four real-world datasets of different graph structures in different domains. The results demonstrate that the proposed DVGNN model outperforms state-of-the-art approaches and achieves outstanding Root Mean Squared Error result while exhibiting higher robustness. Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs. | ['Fernando Alonso-Fernandez', 'Stefan Byttner', 'Sławomir Nowaczyk', 'Prayag Tiwari', 'Guojun Liang'] | 2023-05-16 | null | null | null | null | ['graph-construction', 'spatio-temporal-forecasting'] | ['graphs', 'time-series'] | [-8.43083039e-02 1.38077170e-01 -1.74177080e-01 -1.63123399e-01
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2cd7130b-c066-4ee5-af2b-a0aea6f1b97e | marble-music-audio-representation-benchmark | 2306.10548 | null | https://arxiv.org/abs/2306.10548v2 | https://arxiv.org/pdf/2306.10548v2.pdf | MARBLE: Music Audio Representation Benchmark for Universal Evaluation | In the era of extensive intersection between art and Artificial Intelligence (AI), such as image generation and fiction co-creation, AI for music remains relatively nascent, particularly in music understanding. This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark. To address this issue, we introduce the Music Audio Representation Benchmark for universaL Evaluation, termed MARBLE. It aims to provide a benchmark for various Music Information Retrieval (MIR) tasks by defining a comprehensive taxonomy with four hierarchy levels, including acoustic, performance, score, and high-level description. We then establish a unified protocol based on 14 tasks on 8 public-available datasets, providing a fair and standard assessment of representations of all open-sourced pre-trained models developed on music recordings as baselines. Besides, MARBLE offers an easy-to-use, extendable, and reproducible suite for the community, with a clear statement on copyright issues on datasets. Results suggest recently proposed large-scale pre-trained musical language models perform the best in most tasks, with room for further improvement. The leaderboard and toolkit repository are published at https://marble-bm.shef.ac.uk to promote future music AI research. | ['Roger Dannenbert', 'Norbert Gyenge', 'Anton Ragni', 'Emmanouil Benetos', 'Chenghua Lin', 'Jie Fu', 'Yike Guo', 'Ruibo Liu', 'Shi Wang', 'Si Liu', 'Wei Xue', 'Gus Xia', 'Wenhu Chen', 'Ningzhi Wang', 'Binyue Deng', 'Zeyue Tian', 'Jiawen Huang', 'Yiqi Liu', 'Le Zhuo', 'Hanzhi Yin', 'Xingran Chen', 'Ge Zhang', 'Yizhi Li', 'Yinghao Ma', 'Ruibin Yuan'] | 2023-06-18 | null | null | null | null | ['music-information-retrieval', 'information-retrieval'] | ['music', 'natural-language-processing'] | [ 4.05045629e-01 -2.86267281e-01 -1.34583309e-01 2.70555556e-01
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38ae11bd-4eb7-4ce8-9826-c8dd05e1ca3f | beam-search-decoding-using-manner-of | 1811.07720 | null | http://arxiv.org/abs/1811.07720v1 | http://arxiv.org/pdf/1811.07720v1.pdf | Beam Search Decoding using Manner of Articulation Detection Knowledge Derived from Connectionist Temporal Classification | Manner of articulation detection using deep neural networks require a priori
knowledge of the attribute discriminative features or the decent phoneme
alignments. However generating an appropriate phoneme alignment is complex and
its performance depends on the choice of optimal number of senones, Gaussians,
etc. In the first part of our work, we exploit the manner of articulation
detection using connectionist temporal classification (CTC) which doesn't need
any phoneme alignment. Later we modify the state-of-the-art character based
posteriors generated by CTC using the manner of articulation CTC detector. Beam
search decoding is performed on the modified posteriors and it's impact on open
source datasets such as AN4 and LibriSpeech is observed. | ['Sreenivasa Rao K', 'Pradeep Rangan'] | 2018-11-16 | null | null | null | null | ['manner-of-articulation-detection'] | ['speech'] | [ 2.49702290e-01 -1.08507842e-01 8.74314755e-02 -1.86533809e-01
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202bb707-33a7-4afe-bc94-7fc04f2aa32f | convolutional-neural-network-cnn-to-reduce | 2209.03475 | null | https://arxiv.org/abs/2209.03475v2 | https://arxiv.org/pdf/2209.03475v2.pdf | Convolutional Neural Network (CNN) to reduce construction loss in JPEG compression caused by Discrete Fourier Transform (DFT) | In recent decades, digital image processing has gained enormous popularity. Consequently, a number of data compression strategies have been put forth, with the goal of minimizing the amount of information required to represent images. Among them, JPEG compression is one of the most popular methods that has been widely applied in multimedia and digital applications. The periodic nature of DFT makes it impossible to meet the periodic condition of an image's opposing edges without producing severe artifacts, which lowers the image's perceptual visual quality. On the other hand, deep learning has recently achieved outstanding results for applications like speech recognition, image reduction, and natural language processing. Convolutional Neural Networks (CNN) have received more attention than most other types of deep neural networks. The use of convolution in feature extraction results in a less redundant feature map and a smaller dataset, both of which are crucial for image compression. In this work, an effective image compression method is purposed using autoencoders. The study's findings revealed a number of important trends that suggested better reconstruction along with good compression can be achieved using autoencoders. | ['Suman Kunwar'] | 2022-08-26 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 2.17484087e-01 -3.12161118e-01 -7.18485788e-02 -1.86251104e-01
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0fe5c81a-11c0-4d70-86d4-49c1e5f54d45 | deep-rts-a-game-environment-for-deep | 1808.05032 | null | http://arxiv.org/abs/1808.05032v1 | http://arxiv.org/pdf/1808.05032v1.pdf | Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games | Reinforcement learning (RL) is an area of research that has blossomed
tremendously in recent years and has shown remarkable potential for artificial
intelligence based opponents in computer games. This success is primarily due
to the vast capabilities of convolutional neural networks, that can extract
useful features from noisy and complex data. Games are excellent tools to test
and push the boundaries of novel RL algorithms because they give valuable
insight into how well an algorithm can perform in isolated environments without
the real-life consequences. Real-time strategy games (RTS) is a genre that has
tremendous complexity and challenges the player in short and long-term
planning. There is much research that focuses on applied RL in RTS games, and
novel advances are therefore anticipated in the not too distant future.
However, there are to date few environments for testing RTS AIs. Environments
in the literature are often either overly simplistic, such as microRTS, or
complex and without the possibility for accelerated learning on consumer
hardware like StarCraft II. This paper introduces the Deep RTS game environment
for testing cutting-edge artificial intelligence algorithms for RTS games. Deep
RTS is a high-performance RTS game made specifically for artificial
intelligence research. It supports accelerated learning, meaning that it can
learn at a magnitude of 50 000 times faster compared to existing RTS games.
Deep RTS has a flexible configuration, enabling research in several different
RTS scenarios, including partially observable state-spaces and map complexity.
We show that Deep RTS lives up to our promises by comparing its performance
with microRTS, ELF, and StarCraft II on high-end consumer hardware. Using Deep
RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of
the time. Deep RTS is publicly available at https://github.com/cair/DeepRTS. | ['Ole-Christoffer Granmo', 'Per-Arne Andersen', 'Morten Goodwin'] | 2018-08-15 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.61292696e-01 8.83502364e-02 -1.51238471e-01 6.70757815e-02
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3dfdf28e-55a5-464b-8202-bc96709ea3d6 | sentence-classification-with-imbalanced-data | null | null | https://aclanthology.org/2020.smm4h-1.25 | https://aclanthology.org/2020.smm4h-1.25.pdf | Sentence Classification with Imbalanced Data for Health Applications | Identifying and extracting reports of medications, their abuse or adverse effects from social media is a challenging task. In social media, relevant reports are very infrequent, causes imbalanced class distribution for machine learning algorithms. Learning algorithms typically designed to optimize the overall accuracy without considering the relative distribution of each class. Thus, imbalanced class distribution is problematic as learning algorithms have low predictive accuracy for the infrequent class. Moreover, social media represents natural linguistic variation in creative language expressions. In this paper, we have used a combination of data balancing and neural language representation techniques to address the challenges. Specifically, we participated the shared tasks 1, 2 (all languages), 4, and 3 (only the span detection, no normalization was attempted) in Social Media Mining for Health applications (SMM4H) 2020 (Klein et al., 2020). The results show that with the proposed methodology recall scores are better than the precision scores for the shared tasks. The recall score is also better compared to the mean score of the total submissions. However, the F1-score is worse than the mean score except for task 2 (French). | ['Farhana Ferdousi Liza'] | null | null | null | null | smm4h-coling-2020-12 | ['sentence-classification'] | ['natural-language-processing'] | [ 6.20669834e-02 2.55017638e-01 -6.24154508e-01 -2.10949421e-01
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9f853034-1201-47dd-87ff-26775673b5c1 | improving-contextual-representation-with-1 | 2205.06603 | null | https://arxiv.org/abs/2205.06603v1 | https://arxiv.org/pdf/2205.06603v1.pdf | Improving Contextual Representation with Gloss Regularized Pre-training | Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of pre-training and inference from the perspective of word probability distribution. We discover that BERT risks neglecting the contextual word similarity in pre-training. To tackle this issue, we propose an auxiliary gloss regularizer module to BERT pre-training (GR-BERT), to enhance word semantic similarity. By predicting masked words and aligning contextual embeddings to corresponding glosses simultaneously, the word similarity can be explicitly modeled. We design two architectures for GR-BERT and evaluate our model in downstream tasks. Experimental results show that the gloss regularizer benefits BERT in word-level and sentence-level semantic representation. The GR-BERT achieves new state-of-the-art in lexical substitution task and greatly promotes BERT sentence representation in both unsupervised and supervised STS tasks. | ['Zejun Ma', 'Peihao Wu', 'Zhecheng An', 'Yu Lin'] | 2022-05-13 | null | https://aclanthology.org/2022.findings-naacl.68 | https://aclanthology.org/2022.findings-naacl.68.pdf | findings-naacl-2022-7 | ['word-similarity'] | ['natural-language-processing'] | [ 3.78862351e-01 3.83301169e-01 -4.14465606e-01 -5.97086430e-01
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6d4f5b76-ac94-41a4-b3b7-32bc3278abad | interactive-control-over-temporal-consistency | 2301.00750 | null | https://arxiv.org/abs/2301.00750v2 | https://arxiv.org/pdf/2301.00750v2.pdf | Interactive Control over Temporal Consistency while Stylizing Video Streams | Image stylization has seen significant advancement and widespread interest over the years, leading to the development of a multitude of techniques. Extending these stylization techniques, such as Neural Style Transfer (NST), to videos is often achieved by applying them on a per-frame basis. However, per-frame stylization usually lacks temporal consistency, expressed by undesirable flickering artifacts. Most of the existing approaches for enforcing temporal consistency suffer from one or more of the following drawbacks: They (1) are only suitable for a limited range of techniques, (2) do not support online processing as they require the complete video as input, (3) cannot provide consistency for the task of stylization, or (4) do not provide interactive consistency control. Domain-agnostic techniques for temporal consistency aim to eradicate flickering completely but typically disregard aesthetic aspects. For stylization tasks, however, consistency control is an essential requirement as a certain amount of flickering adds to the artistic look and feel. Moreover, making this control interactive is paramount from a usability perspective. To achieve the above requirements, we propose an approach that stylizes video streams in real-time at full HD resolutions while providing interactive consistency control. We develop a lite optical-flow network that operates at 80 FPS on desktop systems with sufficient accuracy. Further, we employ an adaptive combination of local and global consistency features and enable interactive selection between them. Objective and subjective evaluations demonstrate that our method is superior to state-of-the-art video consistency approaches. | ['Matthias Trapp', 'Jürgen Döllner', 'Amir Semmo', 'Moritz Hilscher', 'Max Reimann', 'Sumit Shekhar'] | 2023-01-02 | null | null | null | null | ['image-stylization', 'video-temporal-consistency', 'video-stabilization'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.45043457e-01 -4.21807557e-01 -1.24306120e-01 -8.46194252e-02
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448cee51-4f3b-4d1d-8b5b-0c0eb83bcef9 | dynamic-kernels-and-channel-attention-with | 2211.02000 | null | https://arxiv.org/abs/2211.02000v2 | https://arxiv.org/pdf/2211.02000v2.pdf | Dynamic Kernels and Channel Attention for Low Resource Speaker Verification | State-of-the-art speaker verification frameworks have typically focused on developing models with increasingly deeper (more layers) and wider (number of channels) models to improve their verification performance. Instead, this paper proposes an approach to increase the model resolution capability using attention-based dynamic kernels in a convolutional neural network to adapt the model parameters to be feature-conditioned. The attention weights on the kernels are further distilled by channel attention and multi-layer feature aggregation to learn global features from speech. This approach provides an efficient solution to improving representation capacity with lower data resources. This is due to the self-adaptation to inputs of the structures of the model parameters. The proposed dynamic convolutional model achieved 1.62\% EER and 0.18 miniDCF on the VoxCeleb1 test set and has a 17\% relative improvement compared to the ECAPA-TDNN using the same training resources. | ['Thomas Hain', 'Md Asif Jalal', 'Anna Ollerenshaw'] | 2022-11-03 | null | null | null | null | ['speaker-verification'] | ['speech'] | [-2.35912371e-02 2.86439717e-01 4.45727371e-02 -5.37460268e-01
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0c1cf55f-c555-4464-88d3-bc4381f21312 | reducing-sequence-length-by-predicting-edit | 2305.11862 | null | https://arxiv.org/abs/2305.11862v1 | https://arxiv.org/pdf/2305.11862v1.pdf | Reducing Sequence Length by Predicting Edit Operations with Large Language Models | Large Language Models (LLMs) have demonstrated remarkable performance in various tasks and gained significant attention. LLMs are also used for local sequence transduction tasks, including grammatical error correction (GEC) and formality style transfer, where most tokens in a source text are kept unchanged. However, it is inefficient to generate all target tokens because a prediction error of a target token may cause a catastrophe in predicting subsequent tokens and because the computational cost grows quadratically with the target sequence length. This paper proposes to predict a set of edit operations for the source text for local sequence transduction tasks. Representing an edit operation with a span of the source text and changed tokens, we can reduce the length of the target sequence and thus the computational cost for inference. We apply instruction tuning for LLMs on the supervision data of edit operations. Experiments show that the proposed method achieves comparable performance to the baseline in four tasks, paraphrasing, formality style transfer, GEC, and text simplification, despite reducing the length of the target text by as small as 21\%. Furthermore, we report that the instruction tuning with the proposed method achieved the state-of-the-art performance in the four tasks. | ['Naoaki Okazaki', 'Masahiro Kaneko'] | 2023-05-19 | null | null | null | null | ['style-transfer', 'grammatical-error-correction', 'formality-style-transfer'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 7.75971115e-01 4.28333022e-02 9.58089978e-02 -4.18840647e-01
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32da5d47-687d-4ee3-8b4f-64efb68e591e | auto-avsr-audio-visual-speech-recognition | 2303.14307 | null | https://arxiv.org/abs/2303.14307v3 | https://arxiv.org/pdf/2303.14307v3.pdf | Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels | Audio-visual speech recognition has received a lot of attention due to its robustness against acoustic noise. Recently, the performance of automatic, visual, and audio-visual speech recognition (ASR, VSR, and AV-ASR, respectively) has been substantially improved, mainly due to the use of larger models and training sets. However, accurate labelling of datasets is time-consuming and expensive. Hence, in this work, we investigate the use of automatically-generated transcriptions of unlabelled datasets to increase the training set size. For this purpose, we use publicly-available pre-trained ASR models to automatically transcribe unlabelled datasets such as AVSpeech and VoxCeleb2. Then, we train ASR, VSR and AV-ASR models on the augmented training set, which consists of the LRS2 and LRS3 datasets as well as the additional automatically-transcribed data. We demonstrate that increasing the size of the training set, a recent trend in the literature, leads to reduced WER despite using noisy transcriptions. The proposed model achieves new state-of-the-art performance on AV-ASR on LRS2 and LRS3. In particular, it achieves a WER of 0.9% on LRS3, a relative improvement of 30% over the current state-of-the-art approach, and outperforms methods that have been trained on non-publicly available datasets with 26 times more training data. | ['Maja Pantic', 'Stavros Petridis', 'Honglie Chen', 'Adriana Fernandez-Lopez', 'Alexandros Haliassos', 'Pingchuan Ma'] | 2023-03-25 | null | null | null | null | ['lipreading', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [ 3.96112084e-01 1.25099421e-01 1.83259994e-01 -3.36842984e-01
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57784deb-8e11-4eb5-a3fe-3c6e4372c5f9 | zs-mstm-zero-shot-style-transfer-for-gesture | 2305.12887 | null | https://arxiv.org/abs/2305.12887v1 | https://arxiv.org/pdf/2305.12887v1.pdf | ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding | In this study, we address the importance of modeling behavior style in virtual agents for personalized human-agent interaction. We propose a machine learning approach to synthesize gestures, driven by prosodic features and text, in the style of different speakers, even those unseen during training. Our model incorporates zero-shot multimodal style transfer using multimodal data from the PATS database, which contains videos of diverse speakers. We recognize style as a pervasive element during speech, influencing the expressivity of communicative behaviors, while content is conveyed through multimodal signals and text. By disentangling content and style, we directly infer the style embedding, even for speakers not included in the training phase, without the need for additional training or fine-tuning. Objective and subjective evaluations are conducted to validate our approach and compare it against two baseline methods. | ['Nicolas Obin', 'Catherine Pelachaud', 'Mireille Fares'] | 2023-05-22 | null | null | null | null | ['style-transfer', 'disentanglement'] | ['computer-vision', 'methodology'] | [ 2.98789054e-01 3.23207766e-01 -6.62851557e-02 -6.63374484e-01
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0bcf272a-d87c-499a-a83f-f20c05ac6135 | semi-supervised-auto-encoder-graph-network | null | null | https://ieeexplore.ieee.org/abstract/document/9567704 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9567704 | Semi-Supervised Auto-Encoder Graph Network for Diabetic Retinopathy Grading | Diabetic Retinopathy (DR) causes quite a few blindness worldwide, which can be refrained by the timely diagnosis on retinal images. Recently, researches on deep learning-based retinal image classification have accelerated outstanding improvements in DR grading task. However, existing DR grading works are mostly limited to a supervised manner. They require accurately annotated data labeled by professional experts, and the annotating work is very laborious and time-consuming. We propose a Semi-supervised Auto-encoder Graph Network (SAGN) for the challenging DR diagnosis to relax this constraint. Precisely, SAGN consists of three major modules: auto-encoder feature learning, neighbor correlation mining, and graph representation. Firstly, our model learns to extract representations from retinal images and reconstruct them as close to original inputs as possible. Then neighbor correlations among labeled and unlabeled samples are established by their similarities, calculated by the radial basis function. Finally, we operate Graph Convolutional Neural Network (GCN) to grade retinal samples from extracted features and their correlations. To evaluate the performance of SAGN, we conduct sufficient comparative experiments on APTOS 2019 dataset, trained from EyePACS. Results demonstrate that our SAGN model can achieve comparable performance with limited labeled retinal images with the help of large amounts of unlabeled data. | ['Wenpei Kang', 'Sungtae Jung', 'Sunkyoung Kang', 'Zhang Song', 'Yujie Li'] | 2021-10-11 | null | null | null | ieee-2021-10 | ['diabetic-retinopathy-grading'] | ['medical'] | [-9.73277315e-02 2.02287495e-01 -2.28245661e-01 -6.62164867e-01
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59043bce-6271-4758-aef2-5b0c60492815 | mind-reasoning-manners-enhancing-type | 2301.02983 | null | https://arxiv.org/abs/2301.02983v1 | https://arxiv.org/pdf/2301.02983v1.pdf | Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text | Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. | ['Lingling Zhang', 'Jian Zhang', 'Tianzhe Zhao', 'Qika Lin', 'Jun Liu', 'Fangzhi Xu'] | 2023-01-08 | null | null | null | null | ['logical-reasoning', 'type'] | ['reasoning', 'speech'] | [ 1.37949735e-01 3.22260052e-01 -2.98195273e-01 -4.95104700e-01
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e0958397-6c39-4001-ad9c-c6897a543352 | query-based-instance-discrimination-network | 2211.01797 | null | https://arxiv.org/abs/2211.01797v1 | https://arxiv.org/pdf/2211.01797v1.pdf | Query-based Instance Discrimination Network for Relational Triple Extraction | Joint entity and relation extraction has been a core task in the field of information extraction. Recent approaches usually consider the extraction of relational triples from a stereoscopic perspective, either learning a relation-specific tagger or separate classifiers for each relation type. However, they still suffer from error propagation, relation redundancy and lack of high-level connections between triples. To address these issues, we propose a novel query-based approach to construct instance-level representations for relational triples. By metric-based comparison between query embeddings and token embeddings, we can extract all types of triples in one step, thus eliminating the error propagation problem. In addition, we learn the instance-level representation of relational triples via contrastive learning. In this way, relational triples can not only enclose rich class-level semantics but also access to high-order global connections. Experimental results show that our proposed method achieves the state of the art on five widely used benchmarks. | ['Yueting Zhuang', 'Weiming Lu', 'Xiaoxia Cheng', 'Wenqi Zhang', 'Xuming Hu', 'Yongliang Shen', 'Zeqi Tan'] | 2022-11-03 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.98174253e-01 3.11221302e-01 -5.57775676e-01 -3.72944683e-01
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e0db9046-52ba-4b43-a63f-66f3358beed1 | framerank-a-text-processing-approach-to-video | 1904.05544 | null | http://arxiv.org/abs/1904.05544v2 | http://arxiv.org/pdf/1904.05544v2.pdf | FrameRank: A Text Processing Approach to Video Summarization | Video summarization has been extensively studied in the past decades.
However, user-generated video summarization is much less explored since there
lack large-scale video datasets within which human-generated video summaries
are unambiguously defined and annotated. Toward this end, we propose a
user-generated video summarization dataset - UGSum52 - that consists of 52
videos (207 minutes). In constructing the dataset, because of the subjectivity
of user-generated video summarization, we manually annotate 25 summaries for
each video, which are in total 1300 summaries. To the best of our knowledge, it
is currently the largest dataset for user-generated video summarization.
Based on this dataset, we present FrameRank, an unsupervised video
summarization method that employs a frame-to-frame level affinity graph to
identify coherent and informative frames to summarize a video. We use the
Kullback-Leibler(KL)-divergence-based graph to rank temporal segments according
to the amount of semantic information contained in their frames. We illustrate
the effectiveness of our method by applying it to three datasets SumMe, TVSum
and UGSum52 and show it achieves state-of-the-art results. | ['Qian Zhang', 'Guoping Qiu', 'Zhuo Lei', 'Chao Zhang'] | 2019-04-11 | null | null | null | null | ['unsupervised-video-summarization'] | ['computer-vision'] | [ 2.70935953e-01 1.96295734e-02 -3.74091297e-01 -1.79415777e-01
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1a668995-263f-4f44-a196-33f927115d87 | bag-of-color-features-for-color-constancy | 1906.04445 | null | https://arxiv.org/abs/1906.04445v1 | https://arxiv.org/pdf/1906.04445v1.pdf | Bag of Color Features For Color Constancy | In this paper, we propose a novel color constancy approach, called Bag of Color Features (BoCF), building upon Bag-of-Features pooling. The proposed method substantially reduces the number of parameters needed for illumination estimation. At the same time, the proposed method is consistent with the color constancy assumption stating that global spatial information is not relevant for illumination estimation and local information ( edges, etc.) is sufficient. Furthermore, BoCF is consistent with color constancy statistical approaches and can be interpreted as a learning-based generalization of many statistical approaches. To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention. BoCF approach and its variants achieve competitive, compared to the state of the art, results while requiring much fewer parameters on three benchmark datasets: ColorChecker RECommended, INTEL-TUT version 2, and NUS8. | ['Alexandros Iosifidis', 'Nikolaos Passalis', 'Moncef Gabbouj', 'Jenni Raitoharju', 'Jarno Nikkanen', 'Firas Laakom', 'Anastasios Tefas'] | 2019-06-11 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-1.09963328e-01 -5.94691753e-01 5.60590029e-02 -5.88570952e-01
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1b6e22c9-eee5-4fcd-9b61-9261635cae93 | evolutionary-game-theoretical-analysis-for | 2206.11114 | null | https://arxiv.org/abs/2206.11114v1 | https://arxiv.org/pdf/2206.11114v1.pdf | Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games | Evolutionary game theory has been a successful tool to combine classical game theory with learning-dynamical descriptions in multiagent systems. Provided some symmetric structures of interacting players, many studies have been focused on using a simplified heuristic payoff table as input to analyse the dynamics of interactions. Nevertheless, even for the state-of-the-art method, there are two limits. First, there is inaccuracy when analysing the simplified payoff table. Second, no existing work is able to deal with 2-population multiplayer asymmetric games. In this paper, we fill the gap between heuristic payoff table and dynamic analysis without any inaccuracy. In addition, we propose a general framework for $m$ versus $n$ 2-population multiplayer asymmetric games. Then, we compare our method with the state-of-the-art in some classic games. Finally, to illustrate our method, we perform empirical game-theoretical analysis on Wolfpack as well as StarCraft II, both of which involve complex multiagent interactions. | ['Wenxin Li', 'Haifeng Wang', 'Yushan Zhou', 'Peng Peng', 'Xinyu Zhang'] | 2022-06-22 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-2.61635572e-01 1.69953872e-02 4.99869347e-01 3.96687597e-01
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