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b69dc1a0-b324-4860-a747-9ad7495939b1 | scenescape-text-driven-consistent-scene | 2302.01133 | null | https://arxiv.org/abs/2302.01133v2 | https://arxiv.org/pdf/2302.01133v2.pdf | SceneScape: Text-Driven Consistent Scene Generation | We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pre-trained text-to-image model with the geometric priors learned by a pre-trained monocular depth prediction model. To tackle the pivotal challenge of achieving 3D consistency, i.e., synthesizing videos that depict geometrically-plausible scenes, we deploy an online test-time training to encourage the predicted depth map of the current frame to be geometrically consistent with the synthesized scene. The depth maps are used to construct a unified mesh representation of the scene, which is progressively constructed along the video generation process. In contrast to previous works, which are applicable only to limited domains, our method generates diverse scenes, such as walkthroughs in spaceships, caves, or ice castles. | ['Tali Dekel', 'Yoni Kasten', 'Amit Abecasis', 'Rafail Fridman'] | 2023-02-02 | null | null | null | null | ['video-generation', 'scene-generation', 'perpetual-view-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.6650567e-01 3.8490978e-01 2.7234721e-01 -2.1983030e-01
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1b79305b-50e7-4aef-b996-e66143242a62 | demystifying-code-summarization-models | 2102.04625 | null | https://arxiv.org/abs/2102.04625v3 | https://arxiv.org/pdf/2102.04625v3.pdf | WheaCha: A Method for Explaining the Predictions of Models of Code | Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use for the prediction. In this paper, we present a new method, called WheaCha, for explaining the predictions of code models. Although WheaCha employs the same mechanism of tracing model predictions back to the input features, it differs from all existing attribution methods in crucial ways. Specifically, WheaCha divides an input program into "wheat" (i.e., the defining features that are the reason for which models predict the label that they predict) and the rest "chaff" for any prediction of a learned code model. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show (1) HuoYan is efficient - taking on average under twenty seconds to compute the wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to predict input programs is made of simple syntactic or even lexical properties (i.e., identifier names); (3) Based on wheat, we present a novel approach to explaining the predictions of code models through the lens of training data. | ['Ke Wang', 'Linzhang Wang', 'Yu Wang'] | 2021-02-09 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 1.48386672e-01 3.19704145e-01 -5.86005628e-01 -5.87855756e-01
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d273d7f7-01c6-45e6-8725-1f06a141e7e9 | matra-a-multilingual-attentive | 2208.10801 | null | https://arxiv.org/abs/2208.10801v1 | https://arxiv.org/pdf/2208.10801v1.pdf | MATra: A Multilingual Attentive Transliteration System for Indian Scripts | Transliteration is a task in the domain of NLP where the output word is a similar-sounding word written using the letters of any foreign language. Today this system has been developed for several language pairs that involve English as either the source or target word and deployed in several places like Google Translate and chatbots. However, there is very little research done in the field of Indic languages transliterated to other Indic languages. This paper demonstrates a multilingual model based on transformers (with some modifications) that can give noticeably higher performance and accuracy than all existing models in this domain and get much better results than state-of-the-art models. This paper shows a model that can perform transliteration between any pair among the following five languages - English, Hindi, Bengali, Kannada and Tamil. It is applicable in scenarios where language is a barrier to communication in any written task. The model beats the state-of-the-art (for all pairs among the five mentioned languages - English, Hindi, Bengali, Kannada, and Tamil) and achieves a top-1 accuracy score of 80.7%, about 29.5% higher than the best current results. Furthermore, the model achieves 93.5% in terms of Phonetic Accuracy (transliteration is primarily a phonetic/sound-based task). | ['Bhavesh Laddagiri', 'Yash Raj'] | 2022-08-23 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-2.93740690e-01 -1.44146591e-01 -2.14455947e-01 -1.21224880e-01
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7d0af43c-1936-473a-9647-ee5416cfffc8 | teaching-large-language-models-to-self-debug | 2304.05128 | null | https://arxiv.org/abs/2304.05128v1 | https://arxiv.org/pdf/2304.05128v1.pdf | Teaching Large Language Models to Self-Debug | Large language models (LLMs) have achieved impressive performance on code generation. However, for complex programming tasks, generating the correct solution in one go becomes challenging, thus some prior works have designed program repair approaches to improve code generation performance. In this work, we propose Self-Debugging, which teaches a large language model to debug its predicted program via few-shot demonstrations. In particular, we demonstrate that Self-Debugging can teach the large language model to perform rubber duck debugging; i.e., without any feedback on the code correctness or error messages, the model is able to identify its mistakes by explaining the generated code in natural language. Self-Debugging achieves the state-of-the-art performance on several code generation benchmarks, including the Spider dataset for text-to-SQL generation, TransCoder for C++-to-Python translation, and MBPP for text-to-Python generation. On the Spider benchmark where there are no unit tests to verify the correctness of predictions, Self-Debugging with code explanation consistently improves the baseline by 2-3%, and improves the prediction accuracy on problems of the hardest label by 9%. On TransCoder and MBPP where unit tests are available, Self-Debugging improves the baseline accuracy by up to 12%. Meanwhile, by leveraging feedback messages and reusing failed predictions, Self-Debugging notably improves sample efficiency, and can match or outperform baseline models that generate more than 10x candidate programs. | ['Denny Zhou', 'Nathanael Schärli', 'Maxwell Lin', 'Xinyun Chen'] | 2023-04-11 | null | null | null | null | ['program-repair', 'text-to-sql', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning'] | [-3.67561206e-02 3.63566607e-01 -3.42760623e-01 -3.45008284e-01
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467e8231-88db-46f6-875a-9d20bd5e092f | personalized-federated-learning-via-amortized | 2307.02222 | null | https://arxiv.org/abs/2307.02222v1 | https://arxiv.org/pdf/2307.02222v1.pdf | Personalized Federated Learning via Amortized Bayesian Meta-Learning | Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data. However, the presence of statistical heterogeneity among clients poses a challenge, as the global model may struggle to perform well on each client's specific task. To address this issue, we introduce a new perspective on personalized federated learning through Amortized Bayesian Meta-Learning. Specifically, we propose a novel algorithm called \emph{FedABML}, which employs hierarchical variational inference across clients. The global prior aims to capture representations of common intrinsic structures from heterogeneous clients, which can then be transferred to their respective tasks and aid in the generation of accurate client-specific approximate posteriors through a few local updates. Our theoretical analysis provides an upper bound on the average generalization error and guarantees the generalization performance on unseen data. Finally, several empirical results are implemented to demonstrate that \emph{FedABML} outperforms several competitive baselines. | ['Yue Yu', 'Hui Wang', 'Zenglin Xu', 'Dun Zeng', 'Shaogao Lv', 'Shiyu Liu'] | 2023-07-05 | null | null | null | null | ['meta-learning', 'federated-learning', 'personalized-federated-learning'] | ['methodology', 'methodology', 'methodology'] | [-2.78749824e-01 6.89065382e-02 -1.95722222e-01 -7.05283940e-01
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c6e50a86-994b-4515-aa73-09d855ac6cf8 | modeling-biological-face-recognition-with | 2208.06681 | null | https://arxiv.org/abs/2208.06681v2 | https://arxiv.org/pdf/2208.06681v2.pdf | Modeling biological face recognition with deep convolutional neural networks | Deep convolutional neural networks (DCNNs) have become the state-of-the-art computational models of biological object recognition. Their remarkable success has helped vision science break new ground and consequently, recent efforts have started to transfer this achievement to research on biological face recognition. In this regard, face detection can be investigated by comparing face-selective biological neurons and brain areas to artificial neurons and model layers. Similarly, face identification can be examined by comparing in vivo and in silico multidimensional face spaces. In the present review, we summarize the first studies that used DCNNs to model biological face recognition. Based on this body of novel findings, we conclude that DCNNs are useful models that follow the general hierarchical organization of biological face recognition in the ventral visual pathway and the core face network. In two exemplary spotlights, we emphasize the unique scientific contributions of these models. Firstly, studies on face detection in DCNNs propose that elementary face-selectivity emerges automatically through feedforward processing. Secondly, studies on face identification in DCNNs suggest that experience and additional generative mechanisms facilitate this particular challenge. Taken together, as this novel computational approach enables close control of predisposition (i.e., architecture) and/or experience (i.e., training data), it may be suited to inform longstanding debates on the substrates of biological face recognition. | ['Walter R. Gruber', 'Leonard E. van Dyck'] | 2022-08-13 | null | null | null | null | ['face-detection', 'face-identification'] | ['computer-vision', 'computer-vision'] | [ 4.38401282e-01 3.28560881e-02 -3.75856049e-02 -3.81822795e-01
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48b78bbe-77f2-45bd-b024-f1768d12f6a8 | daliid-distortion-adaptive-learned-invariance | 2302.05753 | null | https://arxiv.org/abs/2302.05753v1 | https://arxiv.org/pdf/2302.05753v1.pdf | DaliID: Distortion-Adaptive Learned Invariance for Identification Models | In unconstrained scenarios, face recognition and person re-identification are subject to distortions such as motion blur, atmospheric turbulence, or upsampling artifacts. To improve robustness in these scenarios, we propose a methodology called Distortion-Adaptive Learned Invariance for Identification (DaliID) models. We contend that distortion augmentations, which degrade image quality, can be successfully leveraged to a greater degree than has been shown in the literature. Aided by an adaptive weighting schedule, a novel distortion augmentation is applied at severe levels during training. This training strategy increases feature-level invariance to distortions and decreases domain shift to unconstrained scenarios. At inference, we use a magnitude-weighted fusion of features from parallel models to retain robustness across the range of images. DaliID models achieve state-of-the-art (SOTA) for both face recognition and person re-identification on seven benchmark datasets, including IJB-S, TinyFace, DeepChange, and MSMT17. Additionally, we provide recaptured evaluation data at a distance of 750+ meters and further validate on real long-distance face imagery. | ['Terrance E. Boult', 'Gabriel Bertocco', 'Wes Robbins'] | 2023-02-11 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 3.90486538e-01 -4.08375025e-01 1.04727596e-01 -5.07084191e-01
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cc57e014-72d0-4f79-9615-7ecf0c260d13 | inesc-id-sentiment-analysis-without-hand | null | null | https://aclanthology.org/S15-2109 | https://aclanthology.org/S15-2109.pdf | INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces | null | ['Ramon Astudillo', 'Wang Ling', 'Silvio Amir', 'Isabel Trancoso', 'Mario J. Silva', 'Bruno Martins'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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e8699a3b-faeb-419a-95b8-8a199e40bc09 | generating-coherent-spontaneous-speech-and | 2101.05684 | null | https://arxiv.org/abs/2101.05684v1 | https://arxiv.org/pdf/2101.05684v1.pdf | Generating coherent spontaneous speech and gesture from text | Embodied human communication encompasses both verbal (speech) and non-verbal information (e.g., gesture and head movements). Recent advances in machine learning have substantially improved the technologies for generating synthetic versions of both of these types of data: On the speech side, text-to-speech systems are now able to generate highly convincing, spontaneous-sounding speech using unscripted speech audio as the source material. On the motion side, probabilistic motion-generation methods can now synthesise vivid and lifelike speech-driven 3D gesticulation. In this paper, we put these two state-of-the-art technologies together in a coherent fashion for the first time. Concretely, we demonstrate a proof-of-concept system trained on a single-speaker audio and motion-capture dataset, that is able to generate both speech and full-body gestures together from text input. In contrast to previous approaches for joint speech-and-gesture generation, we generate full-body gestures from speech synthesis trained on recordings of spontaneous speech from the same person as the motion-capture data. We illustrate our results by visualising gesture spaces and text-speech-gesture alignments, and through a demonstration video at https://simonalexanderson.github.io/IVA2020 . | ['Jonas Beskow', 'Taras Kucherenko', 'Gustav Eje Henter', 'Éva Székely', 'Simon Alexanderson'] | 2021-01-14 | null | null | null | null | ['gesture-generation'] | ['robots'] | [ 2.98414111e-01 4.72097993e-01 2.43565202e-01 -2.49488518e-01
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e693f4a8-c642-453c-9abf-976c0460f778 | discovering-entity-knowledge-bases-on-the-web | null | null | https://aclanthology.org/W16-1302 | https://aclanthology.org/W16-1302.pdf | Discovering Entity Knowledge Bases on the Web | null | ['Will Radford', 'Andrew Chisholm', 'Ben Hachey'] | 2016-06-01 | null | null | null | ws-2016-6 | ['person-recognition'] | ['computer-vision'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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8656c2b8-8ea5-4c16-8ca8-f9f09150e33f | adversarial-multitask-learning-for-joint-1 | 1910.12702 | null | https://arxiv.org/abs/1910.12702v1 | https://arxiv.org/pdf/1910.12702v1.pdf | Adversarial Multitask Learning for Joint Multi-Feature and Multi-Dialect Morphological Modeling | Morphological tagging is challenging for morphologically rich languages due to the large target space and the need for more training data to minimize model sparsity. Dialectal variants of morphologically rich languages suffer more as they tend to be more noisy and have less resources. In this paper we explore the use of multitask learning and adversarial training to address morphological richness and dialectal variations in the context of full morphological tagging. We use multitask learning for joint morphological modeling for the features within two dialects, and as a knowledge-transfer scheme for cross-dialectal modeling. We use adversarial training to learn dialect invariant features that can help the knowledge-transfer scheme from the high to low-resource variants. We work with two dialectal variants: Modern Standard Arabic (high-resource "dialect") and Egyptian Arabic (low-resource dialect) as a case study. Our models achieve state-of-the-art results for both. Furthermore, adversarial training provides more significant improvement when using smaller training datasets in particular. | ['Nizar Habash', 'Nasser Zalmout'] | 2019-10-28 | adversarial-multitask-learning-for-joint | https://aclanthology.org/P19-1173 | https://aclanthology.org/P19-1173.pdf | acl-2019-7 | ['morphological-tagging'] | ['natural-language-processing'] | [-2.06714034e-01 -8.20087343e-02 1.54337689e-01 -4.92287636e-01
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d0bd699f-7bbd-4a58-81db-2ac10c568cd7 | towards-controllable-protein-design-with | 2201.07338 | null | https://arxiv.org/abs/2201.07338v2 | https://arxiv.org/pdf/2201.07338v2.pdf | Controllable Protein Design with Language Models | The 21st century is presenting humankind with unprecedented environmental and medical challenges. The ability to design novel proteins tailored for specific purposes could transform our ability to respond timely to these issues. Recent advances in the field of artificial intelligence are now setting the stage to make this goal achievable. Protein sequences are inherently similar to natural languages: Amino acids arrange in a multitude of combinations to form structures that carry function, the same way as letters form words and sentences that carry meaning. Therefore, it is not surprising that throughout the history of Natural Language Processing (NLP), many of its techniques have been applied to protein research problems. In the last few years, we have witnessed revolutionary breakthroughs in the field of NLP. The implementation of Transformer pre-trained models has enabled text generation with human-like capabilities, including texts with specific properties such as style or subject. Motivated by its considerable success in NLP tasks, we expect dedicated Transformers to dominate custom protein sequence generation in the near future. Finetuning pre-trained models on protein families will enable the extension of their repertoires with novel sequences that could be highly divergent but still potentially functional. The combination of control tags such as cellular compartment or function will further enable the controllable design of novel protein functions. Moreover, recent model interpretability methods will allow us to open the 'black box' and thus enhance our understanding of folding principles. While early initiatives show the enormous potential of generative language models to design functional sequences, the field is still in its infancy. We believe that protein language models are a promising and largely unexplored field and discuss their foreseeable impact on protein design. | ['Birte Höcker', 'Noelia Ferruz'] | 2022-01-18 | null | null | null | null | ['protein-design'] | ['medical'] | [ 7.62822449e-01 2.52486378e-01 -5.03044836e-02 -5.69808841e-01
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97b7f0d5-4ad1-408a-ab15-f069a4036dcc | uniadapter-unified-parameter-efficient | 2302.06605 | null | https://arxiv.org/abs/2302.06605v2 | https://arxiv.org/pdf/2302.06605v2.pdf | UniAdapter: Unified Parameter-Efficient Transfer Learning for Cross-modal Modeling | Large-scale vision-language pre-trained models have shown promising transferability to various downstream tasks. As the size of these foundation models and the number of downstream tasks grow, the standard full fine-tuning paradigm becomes unsustainable due to heavy computational and storage costs. This paper proposes UniAdapter, which unifies unimodal and multimodal adapters for parameter-efficient cross-modal adaptation on pre-trained vision-language models. Specifically, adapters are distributed to different modalities and their interactions, with the total number of tunable parameters reduced by partial weight sharing. The unified and knowledge-sharing design enables powerful cross-modal representations that can benefit various downstream tasks, requiring only 1.0%-2.0% tunable parameters of the pre-trained model. Extensive experiments on 6 cross-modal downstream benchmarks (including video-text retrieval, image-text retrieval, VideoQA, and VQA) show that in most cases, UniAdapter not only outperforms the state-of-the-arts, but even beats the full fine-tuning strategy. Particularly, on the MSRVTT retrieval task, UniAdapter achieves 49.7% recall@1 with 2.2% model parameters, outperforming the latest competitors by 2.0%. The code and models are available at https://github.com/RERV/UniAdapter. | ['Wei Zhan', 'Masayoshi Tomizuka', 'Zhiwu Lu', 'Guoxing Yang', 'Yuqi Huo', 'Mingyu Ding', 'Haoyu Lu'] | 2023-02-13 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [-1.16545416e-01 -4.23895746e-01 -3.60157192e-01 -2.25719213e-01
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5587c862-5d72-4885-855b-4ce4d885eee3 | learning-canonical-shape-space-for-category | 2001.09322 | null | https://arxiv.org/abs/2001.09322v3 | https://arxiv.org/pdf/2001.09322v3.pdf | Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation | We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified representation for a large variety of instances of a certain object category. In particular, CASS is modeled as the latent space of a deep generative model of canonical 3D shapes with normalized pose. We train a variational auto-encoder (VAE) for generating 3D point clouds in the canonical space from an RGBD image. The VAE is trained in a cross-category fashion, exploiting the publicly available large 3D shape repositories. Since the 3D point cloud is generated in normalized pose (with actual size), the encoder of the VAE learns view-factorized RGBD embedding. It maps an RGBD image in arbitrary view into a pose-independent 3D shape representation. Object pose is then estimated via contrasting it with a pose-dependent feature of the input RGBD extracted with a separate deep neural networks. We integrate the learning of CASS and pose and size estimation into an end-to-end trainable network, achieving the state-of-the-art performance. | ['Zheng Wang', 'Jun Li', 'Dengsheng Chen', 'Kai Xu'] | 2020-01-25 | learning-canonical-shape-space-for-category-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Learning_Canonical_Shape_Space_for_Category-Level_6D_Object_Pose_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-shape-representation', 'generating-3d-point-clouds'] | ['computer-vision', 'computer-vision'] | [ 2.73128343e-03 2.51197487e-01 2.09642097e-01 -5.42478740e-01
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ca1b71d4-80e9-401d-9aca-8c0649bc7c74 | a-framework-for-depth-estimation-and-relative | 1908.00309 | null | https://arxiv.org/abs/1908.00309v1 | https://arxiv.org/pdf/1908.00309v1.pdf | A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision | The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple vision-controlled robots. The depth estimation problem aims at recovering the 3D information of the environment. The relative localization problem consists of estimating the relative pose between two robots, by sensing each other's pose or sharing information about the perceived environment. Most solutions for these problems use a set of discrete data without taking into account the chronological order of the events. This paper builds on recent results on continuous estimation to propose a framework that estimates the depth and relative pose between two non-holonomic vehicles. The basic idea consists in estimating the depth of the points by explicitly considering the dynamics of the camera mounted on a ground robot, and feeding the estimates of 3D points observed by both cameras in a filter that computes the relative pose between the robots. We evaluate the convergence for a set of simulated scenarios and show experimental results validating the proposed framework. | ['Romulo T. Rodrigues', 'A. Pedro Aguiar', 'Pedro Miraldo', 'Dimos V. Dimarogonas'] | 2019-08-01 | null | null | null | null | ['3d-depth-estimation'] | ['computer-vision'] | [-1.14880748e-01 1.90119475e-01 2.20766664e-01 -3.12938631e-01
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c073a96a-85ce-4a76-b88d-b486768d5f4c | the-effect-of-wearing-a-face-mask-on-face | 2110.11283 | null | https://arxiv.org/abs/2110.11283v4 | https://arxiv.org/pdf/2110.11283v4.pdf | The Effect of Wearing a Face Mask on Face Image Quality | Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. The effect of wearing a face mask on the different components of the face recognition system in a collaborative environment is a problem that is still to be fully studied. This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-the-art face image quality assessment methods of different natures. This aims at providing better understanding on the effect of face masks on the operation of face recognition as a whole system. In addition, we further studied the effect of simulated masks on face image utility in comparison to real face masks. We discuss the correlation between the mask effect on face image quality and that on the face verification performance by automatic systems and human experts, indicating a consistent trend between both factors. The evaluation is conducted on the database containing (1) no-masked faces, (2) real face masks, and (3) simulated face masks, by synthetically generating digital facial masks on no-masked faces. Finally, a visual interpretation of the face areas contributing to the quality score of a selected set of quality assessment methods is provided to give a deeper insight into the difference of network decisions in masked and non-masked faces, among other variations. | ['Naser Damer', 'Florian Kirchbuchner', 'Biying Fu'] | 2021-10-21 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.73142180e-01 8.35526213e-02 3.42416316e-01 -4.31681305e-01
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b8efc4bb-9562-4d8f-99d4-8aac95052477 | an-end-to-end-multi-module-audio-deepfake | 2307.00729 | null | https://arxiv.org/abs/2307.00729v1 | https://arxiv.org/pdf/2307.00729v1.pdf | An End-to-End Multi-Module Audio Deepfake Generation System for ADD Challenge 2023 | The task of synthetic speech generation is to generate language content from a given text, then simulating fake human voice.The key factors that determine the effect of synthetic speech generation mainly include speed of generation, accuracy of word segmentation, naturalness of synthesized speech, etc. This paper builds an end-to-end multi-module synthetic speech generation model, including speaker encoder, synthesizer based on Tacotron2, and vocoder based on WaveRNN. In addition, we perform a lot of comparative experiments on different datasets and various model structures. Finally, we won the first place in the ADD 2023 challenge Track 1.1 with the weighted deception success rate (WDSR) of 44.97%. | ['Zhuoyue Chen', 'Yibo Duan', 'Qilong Yuan', 'Sheng Zhao'] | 2023-07-03 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.31415480e-02 3.52293521e-01 1.43357351e-01 -2.17416003e-01
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a7806385-baa2-4e7f-8d32-b449123d6f26 | gender-biases-in-automatic-evaluation-metrics | 2305.14711 | null | https://arxiv.org/abs/2305.14711v1 | https://arxiv.org/pdf/2305.14711v1.pdf | Gender Biases in Automatic Evaluation Metrics: A Case Study on Image Captioning | Pretrained model-based evaluation metrics have demonstrated strong performance with high correlations with human judgments in various natural language generation tasks such as image captioning. Despite the impressive results, their impact on fairness is under-explored -- it is widely acknowledged that pretrained models can encode societal biases, and utilizing them for evaluation purposes may inadvertently manifest and potentially amplify biases. In this paper, we conduct a systematic study in gender biases of model-based evaluation metrics with a focus on image captioning tasks. Specifically, we first identify and quantify gender biases in different evaluation metrics regarding profession, activity, and object concepts. Then, we demonstrate the negative consequences of using these biased metrics, such as favoring biased generation models in deployment and propagating the biases to generation models through reinforcement learning. We also present a simple but effective alternative to reduce gender biases by combining n-gram matching-based and pretrained model-based evaluation metrics. | ['Nanyun Peng', 'Asli Celikyilmaz', 'Tianlu Wang', 'Zi-Yi Dou', 'Haoyi Qiu'] | 2023-05-24 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 3.68233770e-01 4.95414078e-01 -4.35779244e-01 -8.83080602e-01
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5dfb005b-6dbc-4738-b746-8e23547462fc | a-user-centered-interactive-human-in-the-loop | 2304.01774 | null | https://arxiv.org/abs/2304.01774v1 | https://arxiv.org/pdf/2304.01774v1.pdf | A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System | Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling. | ['Rob Procter', 'Yulan He', 'Du Liu', 'Lama Alqazlan', 'Zheng Fang'] | 2023-04-04 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.93687975e-01 5.55310965e-01 5.48444688e-02 -6.23565733e-01
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6ce34e36-319f-4475-a786-83765e27889e | deep-reinforcement-learning-with-stacked | 2010.11655 | null | https://arxiv.org/abs/2010.11655v3 | https://arxiv.org/pdf/2010.11655v3.pdf | Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games | We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to represent the environment information and language actions, existing RL agents are not empowered with any reasoning capabilities to deal with textual games. In this work, we aim to conduct explicit reasoning with knowledge graphs for decision making, so that the actions of an agent are generated and supported by an interpretable inference procedure. We propose a stacked hierarchical attention mechanism to construct an explicit representation of the reasoning process by exploiting the structure of the knowledge graph. We extensively evaluate our method on a number of man-made benchmark games, and the experimental results demonstrate that our method performs better than existing text-based agents. | ['Chengqi Zhang', 'Joey Tianyi Zhou', 'Yali Du', 'Ling Chen', 'Meng Fang', 'Yunqiu Xu'] | 2020-10-22 | null | http://proceedings.neurips.cc/paper/2020/hash/bf65417dcecc7f2b0006e1f5793b7143-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/bf65417dcecc7f2b0006e1f5793b7143-Paper.pdf | neurips-2020-12 | ['text-based-games'] | ['playing-games'] | [-8.21372196e-02 5.79125822e-01 -3.97920758e-02 -1.06434144e-01
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8ed2bb36-d9f2-4857-9a0a-6fdf7d2bfbc2 | similarities-and-differences-between-stimulus | 1612.06975 | null | http://arxiv.org/abs/1612.06975v1 | http://arxiv.org/pdf/1612.06975v1.pdf | Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks | Deep convolutional neural networks (CNNs) trained for object classification
have a number of striking similarities with the primate ventral visual stream.
In particular, activity in early, intermediate, and late layers is closely
related to activity in V1, V4, and the inferotemporal cortex (IT). This study
further compares activity in late layers of object-classification CNNs to
activity patterns reported in the IT electrophysiology literature. There are a
number of close similarities, including the distributions of population
response sparseness across stimuli, and the distribution of size tuning
bandwidth. Statisics of scale invariance, responses to clutter and occlusion,
and orientation tuning are less similar. Statistics of object selectivity are
quite different. These results agree with recent studies that highlight strong
parallels between object-categorization CNNs and the ventral stream, and also
highlight differences that could perhaps be reduced in future CNNs. | [] | 2016-12-21 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.11418523e-01 -4.82603461e-01 -3.65699120e-02 -4.25632715e-01
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e075b844-1b25-4968-8b7a-1bf20fea6fed | vid2seq-large-scale-pretraining-of-a-visual | 2302.14115 | null | https://arxiv.org/abs/2302.14115v2 | https://arxiv.org/pdf/2302.14115v2.pdf | Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning | In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the same output sequence. Such a unified model requires large-scale training data, which is not available in current annotated datasets. We show that it is possible to leverage unlabeled narrated videos for dense video captioning, by reformulating sentence boundaries of transcribed speech as pseudo event boundaries, and using the transcribed speech sentences as pseudo event captions. The resulting Vid2Seq model pretrained on the YT-Temporal-1B dataset improves the state of the art on a variety of dense video captioning benchmarks including YouCook2, ViTT and ActivityNet Captions. Vid2Seq also generalizes well to the tasks of video paragraph captioning and video clip captioning, and to few-shot settings. Our code is publicly available at https://antoyang.github.io/vid2seq.html. | ['Cordelia Schmid', 'Josef Sivic', 'Ivan Laptev', 'Jordi Pont-Tuset', 'Antoine Miech', 'Paul Hongsuck Seo', 'Arsha Nagrani', 'Antoine Yang'] | 2023-02-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.82069319e-01 4.09065425e-01 -5.83283365e-01 -3.05028379e-01
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b343024a-9649-4036-931c-66a767cd31eb | kimera-an-open-source-library-for-real-time | 1910.02490 | null | https://arxiv.org/abs/1910.02490v3 | https://arxiv.org/pdf/1910.02490v3.pdf | Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping | We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM). The library goes beyond existing visual and visual-inertial SLAM libraries (e.g., ORB-SLAM, VINS- Mono, OKVIS, ROVIO) by enabling mesh reconstruction and semantic labeling in 3D. Kimera is designed with modularity in mind and has four key components: a visual-inertial odometry (VIO) module for fast and accurate state estimation, a robust pose graph optimizer for global trajectory estimation, a lightweight 3D mesher module for fast mesh reconstruction, and a dense 3D metric-semantic reconstruction module. The modules can be run in isolation or in combination, hence Kimera can easily fall back to a state-of-the-art VIO or a full SLAM system. Kimera runs in real-time on a CPU and produces a 3D metric-semantic mesh from semantically labeled images, which can be obtained by modern deep learning methods. We hope that the flexibility, computational efficiency, robustness, and accuracy afforded by Kimera will build a solid basis for future metric-semantic SLAM and perception research, and will allow researchers across multiple areas (e.g., VIO, SLAM, 3D reconstruction, segmentation) to benchmark and prototype their own efforts without having to start from scratch. | ['Luca Carlone', 'Antoni Rosinol', 'Yun Chang', 'Marcus Abate'] | 2019-10-06 | null | null | null | null | ['semantic-slam'] | ['computer-vision'] | [-3.26387644e-01 -2.92878002e-01 -1.99781433e-01 -3.13853145e-01
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ff5ea051-5264-4331-b118-325591bcbf6d | probabilistic-time-series-forecasting-for | 2211.13729 | null | https://arxiv.org/abs/2211.13729v2 | https://arxiv.org/pdf/2211.13729v2.pdf | Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments | With increasingly more computation being shifted to the edge of the network, monitoring of critical infrastructures, such as intermediate processing nodes in autonomous driving, is further complicated due to the typically resource-constrained environments. In order to reduce the resource overhead on the network link imposed by monitoring, various methods have been discussed that either follow a filtering approach for data-emitting devices or conduct dynamic sampling based on employed prediction models. Still, existing methods are mainly requiring adaptive monitoring on edge devices, which demands device reconfigurations, utilizes additional resources, and limits the sophistication of employed models. In this paper, we propose a sampling-based and cloud-located approach that internally utilizes probabilistic forecasts and hence provides means of quantifying model uncertainties, which can be used for contextualized adaptations of sampling frequencies and consequently relieves constrained network resources. We evaluate our prototype implementation for the monitoring pipeline on a publicly available streaming dataset and demonstrate its positive impact on resource efficiency in a method comparison. | ['Lauritz Thamsen', 'Odej Kao', 'Soeren Becker', 'Babak Sistani Zadeh Aghdam', 'Dominik Scheinert'] | 2022-11-24 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 2.96341062e-01 1.38847679e-01 -5.38404472e-02 -1.94097817e-01
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576d916e-b409-46e1-9102-bc82b6307aff | correlating-edge-pose-with-parsing | 2005.01431 | null | https://arxiv.org/abs/2005.01431v1 | https://arxiv.org/pdf/2005.01431v1.pdf | Correlating Edge, Pose with Parsing | According to existing studies, human body edge and pose are two beneficial factors to human parsing. The effectiveness of each of the high-level features (edge and pose) is confirmed through the concatenation of their features with the parsing features. Driven by the insights, this paper studies how human semantic boundaries and keypoint locations can jointly improve human parsing. Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses. To capture such correlations, we propose a Correlation Parsing Machine (CorrPM) employing a heterogeneous non-local block to discover the spatial affinity among feature maps from the edge, pose and parsing. The proposed CorrPM allows us to report new state-of-the-art accuracy on three human parsing datasets. Importantly, comparative studies confirm the advantages of feature correlation over the concatenation. | ['Xiaodong Xie', 'Chi Su', 'Ziwei Zhang', 'Liang Zheng'] | 2020-05-04 | correlating-edge-pose-with-parsing-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Correlating_Edge_Pose_With_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Correlating_Edge_Pose_With_Parsing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 9.11063701e-02 1.44527495e-01 -3.83250862e-01 -4.37510282e-01
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-2.42053494e-02 -1.35923862e+00 -6.62950575e-02 -1.20931666e-03] | [7.9938225746154785, -0.3066132068634033] |
7f9ee0cb-1321-44df-b66b-2213f7c3bbdc | specifying-object-attributes-and-relations-in | 1909.05379 | null | https://arxiv.org/abs/1909.05379v2 | https://arxiv.org/pdf/1909.05379v2.pdf | Specifying Object Attributes and Relations in Interactive Scene Generation | We introduce a method for the generation of images from an input scene graph. The method separates between a layout embedding and an appearance embedding. The dual embedding leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs. In addition, the embedding scheme supports multiple and diverse output images per scene graph, which can be further controlled by the user. We demonstrate two modes of per-object control: (i) importing elements from other images, and (ii) navigation in the object space, by selecting an appearance archetype. Our code is publicly available at https://www.github.com/ashual/scene_generation | ['Oron Ashual', 'Lior Wolf'] | 2019-09-11 | specifying-object-attributes-and-relations-in-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Ashual_Specifying_Object_Attributes_and_Relations_in_Interactive_Scene_Generation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ashual_Specifying_Object_Attributes_and_Relations_in_Interactive_Scene_Generation_ICCV_2019_paper.pdf | iccv-2019-10 | ['layout-to-image-generation', 'scene-generation'] | ['computer-vision', 'computer-vision'] | [ 2.36733183e-01 1.11516409e-01 1.22000456e-01 -3.74010384e-01
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a2db815e-2a79-4317-8bd4-9556294b39e9 | endowing-language-models-with-multimodal | 2206.13163 | null | https://arxiv.org/abs/2206.13163v1 | https://arxiv.org/pdf/2206.13163v1.pdf | Endowing Language Models with Multimodal Knowledge Graph Representations | We propose a method to make natural language understanding models more parameter efficient by storing knowledge in an external knowledge graph (KG) and retrieving from this KG using a dense index. Given (possibly multilingual) downstream task data, e.g., sentences in German, we retrieve entities from the KG and use their multimodal representations to improve downstream task performance. We use the recently released VisualSem KG as our external knowledge repository, which covers a subset of Wikipedia and WordNet entities, and compare a mix of tuple-based and graph-based algorithms to learn entity and relation representations that are grounded on the KG multimodal information. We demonstrate the usefulness of the learned entity representations on two downstream tasks, and show improved performance on the multilingual named entity recognition task by $0.3\%$--$0.7\%$ F1, while we achieve up to $2.5\%$ improvement in accuracy on the visual sense disambiguation task. All our code and data are available in: \url{https://github.com/iacercalixto/visualsem-kg}. | ['Iacer Calixto', 'Clara Vania', 'Kyunghyun Cho', 'Houda Alberts', 'Yibo Liu', 'Yash R. Deshpande', 'Ningyuan Huang'] | 2022-06-27 | null | null | null | null | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-3.59579742e-01 5.18111229e-01 -2.67472506e-01 -2.57299066e-01
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017a7f89-b157-40a2-91ac-088d63580ce8 | meta-learning-of-interface-conditions-for | 2210.12669 | null | https://arxiv.org/abs/2210.12669v2 | https://arxiv.org/pdf/2210.12669v2.pdf | Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks | Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply different PINNs to solve the problem in each subdomain, and stitch the subdomains at the interface. Thereby, they can further alleviate the problem complexity, reduce the computational cost, and allow parallelization. However, the performance of multi-domain PINNs is sensitive to the choice of the interface conditions. While quite a few conditions have been proposed, there is no suggestion about how to select the conditions according to specific problems. To address this gap, we propose META Learning of Interface Conditions (METALIC), a simple, efficient yet powerful approach to dynamically determine appropriate interface conditions for solving a family of parametric PDEs. Specifically, we develop two contextual multi-arm bandit (MAB) models. The first one applies to the entire training course, and online updates a Gaussian process (GP) reward that given the PDE parameters and interface conditions predicts the performance. We prove a sub-linear regret bound for both UCB and Thompson sampling, which in theory guarantees the effectiveness of our MAB. The second one partitions the training into two stages, one is the stochastic phase and the other deterministic phase; we update a GP reward for each phase to enable different condition selections at the two stages to further bolster the flexibility and performance. We have shown the advantage of METALIC on four bench-mark PDE families. | ['Shandian Zhe', 'Robert M. Kirby', 'Akil Narayan', 'Conor Tillinghast', 'Yiming Xu', 'Michael Penwarden', 'Shibo Li'] | 2022-10-23 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-4.65329736e-02 -1.63755164e-01 -3.58533412e-01 2.03714937e-01
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6a2cbcc6-3f1d-4518-9ef7-506fbb0f83cf | vecchia-gaussian-process-ensembles-on | 2305.17063 | null | https://arxiv.org/abs/2305.17063v1 | https://arxiv.org/pdf/2305.17063v1.pdf | Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks | For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification, while deep neural networks (DNNs) excel at representation learning. We propose to synergistically combine these two approaches in a hybrid method consisting of an ensemble of GPs built on the output of hidden layers of a DNN. GP scalability is achieved via Vecchia approximations that exploit nearest-neighbor conditional independence. The resulting deep Vecchia ensemble not only imbues the DNN with uncertainty quantification but can also provide more accurate and robust predictions. We demonstrate the utility of our model on several datasets and carry out experiments to understand the inner workings of the proposed method. | ['Matthias Katzfuss', 'Felix Jimenez'] | 2023-05-26 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [-2.83073664e-01 3.03764135e-01 1.94011450e-01 -4.78119522e-01
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2cd8cb59-60ce-4f84-887e-8a45cae8e9cb | a-graphical-point-process-framework-for | 2302.06075 | null | https://arxiv.org/abs/2302.06075v1 | https://arxiv.org/pdf/2302.06075v1.pdf | A Graphical Point Process Framework for Understanding Removal Effects in Multi-Touch Attribution | Marketers employ various online advertising channels to reach customers, and they are particularly interested in attribution for measuring the degree to which individual touchpoints contribute to an eventual conversion. The availability of individual customer-level path-to-purchase data and the increasing number of online marketing channels and types of touchpoints bring new challenges to this fundamental problem. We aim to tackle the attribution problem with finer granularity by conducting attribution at the path level. To this end, we develop a novel graphical point process framework to study the direct conversion effects and the full relational structure among numerous types of touchpoints simultaneously. Utilizing the temporal point process of conversion and the graphical structure, we further propose graphical attribution methods to allocate proper path-level conversion credit, called the attribution score, to individual touchpoints or corresponding channels for each customer's path to purchase. Our proposed attribution methods consider the attribution score as the removal effect, and we use the rigorous probabilistic definition to derive two types of removal effects. We examine the performance of our proposed methods in extensive simulation studies and compare their performance with commonly used attribution models. We also demonstrate the performance of the proposed methods in a real-world attribution application. | ['Lingzhou Xue', 'Amirhossein Meisami', 'Arava Sai Kumar', 'James W. Snyder Jr.', 'Qian Chen', 'Jun Tao'] | 2023-02-13 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 2.57442109e-02 -2.83188939e-01 -9.00411189e-01 -5.59186578e-01
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7cfcb90a-6526-48b4-9e20-b9e0a017ccd2 | camera-calibration-from-a-single-imaged | 2307.00689 | null | https://arxiv.org/abs/2307.00689v1 | https://arxiv.org/pdf/2307.00689v1.pdf | Camera Calibration from a Single Imaged Ellipsoid: A Moon Calibration Algorithm | This work introduces a method that applies images of the extended bodies in the solar system to spacecraft camera calibration. The extended bodies consist of planets and moons that are well-modeled by triaxial ellipsoids. When imaged, the triaxial ellipsoid projects to a conic section which is generally an ellipse. This work combines the imaged ellipse with information on the observer's target-relative state to achieve camera calibration from a single imaged ellipsoid. As such, this work is the first to accomplish camera calibration from a single, non-spherical imaged ellipsoid. The camera calibration algorithm is applied to synthetic images of ellipsoids as well as planetary images of Saturn's moons as captured by the Cassini spacecraft. From a single image, the algorithm estimates the focal length and principal point of Cassini's Narrow Angle Camera within 1.0 mm and 10 pixels, respectively. With multiple images, the one standard deviation uncertainty in focal length and principal point estimates reduce to 0.5 mm and 3.1 pixels, respectively. Though created for spacecraft camera calibration in mind, this work also generalizes to terrestrial camera calibration using any number of imaged ellipsoids. | ['Mason A. Peck', 'Kalani R. Danas Rivera'] | 2023-07-02 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 4.71555889e-02 2.96376526e-01 2.77606845e-01 -3.88269946e-02
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bc46ea45-e9e6-45bf-b288-42c4a07d8abf | representation-flow-for-action-recognition | 1810.01455 | null | https://arxiv.org/abs/1810.01455v3 | https://arxiv.org/pdf/1810.01455v3.pdf | Representation Flow for Action Recognition | In this paper, we propose a convolutional layer inspired by optical flow algorithms to learn motion representations. Our representation flow layer is a fully-differentiable layer designed to capture the `flow' of any representation channel within a convolutional neural network for action recognition. Its parameters for iterative flow optimization are learned in an end-to-end fashion together with the other CNN model parameters, maximizing the action recognition performance. Furthermore, we newly introduce the concept of learning `flow of flow' representations by stacking multiple representation flow layers. We conducted extensive experimental evaluations, confirming its advantages over previous recognition models using traditional optical flows in both computational speed and performance. Code/models available here: https://piergiaj.github.io/rep-flow-site/ | ['Michael S. Ryoo', 'AJ Piergiovanni'] | 2018-10-02 | representation-flow-for-action-recognition-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Piergiovanni_Representation_Flow_for_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Piergiovanni_Representation_Flow_for_Action_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['activity-recognition-in-videos'] | ['computer-vision'] | [ 1.03606479e-02 -3.00506204e-01 -6.31993294e-01 -2.52106577e-01
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d81aa905-b5b0-447d-8f3e-92e216473418 | fine-grained-temporal-relation-extraction-1 | null | null | https://aclanthology.org/2021.wnut-1.5 | https://aclanthology.org/2021.wnut-1.5.pdf | Fine-grained Temporal Relation Extraction with Ordered-Neuron LSTM and Graph Convolutional Networks | Fine-grained temporal relation extraction (FineTempRel) aims to recognize the durations and timeline of event mentions in text. A missing part in the current deep learning models for FineTempRel is their failure to exploit the syntactic structures of the input sentences to enrich the representation vectors. In this work, we propose to fill this gap by introducing novel methods to integrate the syntactic structures into the deep learning models for FineTempRel. The proposed model focuses on two types of syntactic information from the dependency trees, i.e., the syntax-based importance scores for representation learning of the words and the syntactic connections to identify important context words for the event mentions. We also present two novel techniques to facilitate the knowledge transfer between the subtasks of FineTempRel, leading to a novel model with the state-of-the-art performance for this task. | ['Thien Huu Nguyen', 'Minh Van Nguyen', 'Minh Tran Phu'] | null | null | null | null | wnut-acl-2021-11 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-1.70539960e-01 3.57633919e-01 -3.09509337e-01 -7.43709266e-01
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5f263d46-1b2d-4c90-b724-0e3070cf8ed3 | combatant-tamilnlp-acl2022-fine-grained | null | null | https://aclanthology.org/2022.dravidianlangtech-1.34 | https://aclanthology.org/2022.dravidianlangtech-1.34.pdf | COMBATANT@TamilNLP-ACL2022: Fine-grained Categorization of Abusive Comments using Logistic Regression | With the widespread usage of social media and effortless internet access, millions of posts and comments are generated every minute. Unfortunately, with this substantial rise, the usage of abusive language has increased significantly in these mediums. This proliferation leads to many hazards such as cyber-bullying, vulgarity, online harassment and abuse. Therefore, it becomes a crucial issue to detect and mitigate the usage of abusive language. This work presents our system developed as part of the shared task to detect the abusive language in Tamil. We employed three machine learning (LR, DT, SVM), two deep learning (CNN+BiLSTM, CNN+BiLSTM with FastText) and a transformer-based model (Indic-BERT). The experimental results show that Logistic regression (LR) and CNN+BiLSTM models outperformed the others. Both Logistic Regression (LR) and CNN+BiLSTM with FastText achieved the weighted F_1-score of 0.39. However, LR obtained a higher recall value (0.44) than CNN+BiLSTM (0.36). This leads us to stand the 2^{nd} rank in the shared task competition. | ['Mohammed Moshiul Hoque', 'Omar Sharif', 'Eftekhar Hossain', 'Mahathir Bishal', 'Alamgir Hossain'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['abusive-language'] | ['natural-language-processing'] | [-3.58455956e-01 -2.45813683e-01 -7.86939338e-02 4.23277915e-02
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6c5d0527-3b68-41c6-9f7e-3bf8d2b095b5 | event-intensity-stereo-estimating-depth-by | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Mostafavi_Event-Intensity_Stereo_Estimating_Depth_by_the_Best_of_Both_Worlds_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Mostafavi_Event-Intensity_Stereo_Estimating_Depth_by_the_Best_of_Both_Worlds_ICCV_2021_paper.pdf | Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds | Event cameras can report scene movements as an asynchronous stream of data called the events. Unlike traditional cameras, event cameras have very low latency (microseconds vs milliseconds) very high dynamic range (140dB vs 60 dB), and low power consumption, as they report changes of a scene and not a complete frame. As they re-port per pixel feature-like events and not the whole intensity frame they are immune to motion blur. However, event cameras require movement between the scene and camera to fire events ,i.e., they have no output when the scene is relatively static. Traditional cameras, however, report the whole frame of pixels at once in fixed intervals but have lower dynamic range and are prone to motion blur in case of rapid movements. We get the best from both worlds and use events and intensity images together in our complementary design and estimate dense disparity from this combination. The proposed end-to-end design combines events and images in a sequential manner and correlates them to esti-mate dense depth values. Our various experimental settings in real-world and simulated scenarios exploit the superiority of our method in predicting accurate depth values with fine details. We further extend our method to extreme cases of missing the left or right event or stereo pair and also investigate stereo depth estimation with inconsistent dynamic ranges or event thresholds on the left and right pairs | ['Jonghyun Choi', 'Kuk-Jin Yoon', 'Mohammad Mostafavi'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['stereo-depth-estimation'] | ['computer-vision'] | [ 5.78152001e-01 -5.16076446e-01 1.49447545e-01 -4.89292353e-01
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60d7fbcc-0675-4980-a582-1160bac4ea57 | haar-wavelet-feature-compression-for | 2110.04824 | null | https://arxiv.org/abs/2110.04824v1 | https://arxiv.org/pdf/2110.04824v1.pdf | Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks | Graph Convolutional Networks (GCNs) are widely used in a variety of applications, and can be seen as an unstructured version of standard Convolutional Neural Networks (CNNs). As in CNNs, the computational cost of GCNs for large input graphs (such as large point clouds or meshes) can be high and inhibit the use of these networks, especially in environments with low computational resources. To ease these costs, quantization can be applied to GCNs. However, aggressive quantization of the feature maps can lead to a significant degradation in performance. On a different note, Haar wavelet transforms are known to be one of the most effective and efficient approaches to compress signals. Therefore, instead of applying aggressive quantization to feature maps, we propose to utilize Haar wavelet compression and light quantization to reduce the computations and the bandwidth involved with the network. We demonstrate that this approach surpasses aggressive feature quantization by a significant margin, for a variety of problems ranging from node classification to point cloud classification and part and semantic segmentation. | ['Eran Treister', 'Benjamin Bodner', 'Moshe Eliasof'] | 2021-10-10 | null | null | null | null | ['feature-compression', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 2.43582159e-01 1.61267251e-01 -1.32126614e-01 -1.52912870e-01
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a7e70e25-2346-4ee9-8351-0ea927f2c4f1 | person-identification-and-body-mass-index-a | 1811.07173 | null | http://arxiv.org/abs/1811.07173v2 | http://arxiv.org/pdf/1811.07173v2.pdf | Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers | Obtaining a smart surveillance requires a sensing system that can capture
accurate and detailed information for the human walking style. The radar
micro-Doppler ($\boldsymbol{\mu}$-D) analysis is proved to be a reliable metric
for studying human locomotions. Thus, $\boldsymbol{\mu}$-D signatures can be
used to identify humans based on their walking styles. Additionally, the
signatures contain information about the radar cross section (RCS) of the
moving subject. This paper investigates the effect of human body
characteristics on human identification based on their $\boldsymbol{\mu}$-D
signatures. In our proposed experimental setup, a treadmill is used to collect
$\boldsymbol{\mu}$-D signatures of 22 subjects with different genders and body
characteristics. Convolutional autoencoders (CAE) are then used to extract the
latent space representation from the $\boldsymbol{\mu}$-D signatures. It is
then interpreted in two dimensions using t-distributed stochastic neighbor
embedding (t-SNE). Our study shows that the body mass index (BMI) has a
correlation with the $\boldsymbol{\mu}$-D signature of the walking subject. A
50-layer deep residual network is then trained to identify the walking subject
based on the $\boldsymbol{\mu}$-D signature. We achieve an accuracy of 98% on
the test set with high signal-to-noise-ratio (SNR) and 84% in case of different
SNR levels. | ['Fady Aziz', 'Urs Schneider', 'Sherif Abdulatif', 'Karim Armanious', 'Bernhard Kleiner', 'Bin Yang'] | 2018-11-17 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.17994286e-01 -2.75289029e-01 1.74649045e-01 -3.44259918e-01
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2cdb0a0e-579f-43ac-9602-241a62f400d6 | graph-to-tree-learning-for-solving-math-word | null | null | https://aclanthology.org/2020.acl-main.362 | https://aclanthology.org/2020.acl-main.362.pdf | Graph-to-Tree Learning for Solving Math Word Problems | While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs. We conduct extensive experiments on two available datasets. Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly. We also discuss case studies and empirically examine Graph2Tree{'}s effectiveness in translating the MWP text into solution expressions. | ['Ee-Peng Lim', 'Roy Ka-Wei Lee', 'Yi Bin', 'Jipeng Zhang', 'Jie Shao', 'Yan Wang', 'Lei Wang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.75735797e-03 1.26334623e-01 -2.47793317e-01 -4.41616476e-01
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362b2a6d-2edd-44ae-8893-3fba67d213da | towards-real-time-6d-pose-estimation-of | 2211.03211 | null | https://arxiv.org/abs/2211.03211v1 | https://arxiv.org/pdf/2211.03211v1.pdf | Towards real-time 6D pose estimation of objects in single-view cone-beam X-ray | Deep learning-based pose estimation algorithms can successfully estimate the pose of objects in an image, especially in the field of color images. 6D Object pose estimation based on deep learning models for X-ray images often use custom architectures that employ extensive CAD models and simulated data for training purposes. Recent RGB-based methods opt to solve pose estimation problems using small datasets, making them more attractive for the X-ray domain where medical data is scarcely available. We refine an existing RGB-based model (SingleShotPose) to estimate the 6D pose of a marked cube from grayscale X-ray images by creating a generic solution trained on only real X-ray data and adjusted for X-ray acquisition geometry. The model regresses 2D control points and calculates the pose through 2D/3D correspondences using Perspective-n-Point(PnP), allowing a single trained model to be used across all supporting cone-beam-based X-ray geometries. Since modern X-ray systems continuously adjust acquisition parameters during a procedure, it is essential for such a pose estimation network to consider these parameters in order to be deployed successfully and find a real use case. With a 5-cm/5-degree accuracy of 93% and an average 3D rotation error of 2.2 degrees, the results of the proposed approach are comparable with state-of-the-art alternatives, while requiring significantly less real training examples and being applicable in real-time applications. | ['Fons van der Sommen', 'Peter H. N. de With', 'Lena Filatova', 'Joel de Bruijn', 'Christiaan G. A. Viviers'] | 2022-11-06 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.43411714e-02 1.74407646e-01 2.51933057e-02 -3.84689599e-01
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ae2e4605-3e80-46d0-b98e-dcee03e1d43f | nerf-textbf-2-neural-radio-frequency-radiance | 2305.06118 | null | https://arxiv.org/abs/2305.06118v1 | https://arxiv.org/pdf/2305.06118v1.pdf | NeRF$^\textbf{2}$: Neural Radio-Frequency Radiance Fields | Although Maxwell discovered the physical laws of electromagnetic waves 160 years ago, how to precisely model the propagation of an RF signal in an electrically large and complex environment remains a long-standing problem. The difficulty is in the complex interactions between the RF signal and the obstacles (e.g., reflection, diffraction, etc.). Inspired by the great success of using a neural network to describe the optical field in computer vision, we propose a neural radio-frequency radiance field, NeRF$^\textbf{2}$, which represents a continuous volumetric scene function that makes sense of an RF signal's propagation. Particularly, after training with a few signal measurements, NeRF$^\textbf{2}$ can tell how/what signal is received at any position when it knows the position of a transmitter. As a physical-layer neural network, NeRF$^\textbf{2}$ can take advantage of the learned statistic model plus the physical model of ray tracing to generate a synthetic dataset that meets the training demands of application-layer artificial neural networks (ANNs). Thus, we can boost the performance of ANNs by the proposed turbo-learning, which mixes the true and synthetic datasets to intensify the training. Our experiment results show that turbo-learning can enhance performance with an approximate 50% increase. We also demonstrate the power of NeRF$^\textbf{2}$ in the field of indoor localization and 5G MIMO. | ['Lei Yang', 'Qingrui Pan', 'Zhenlin An', 'Xiaopeng Zhao'] | 2023-05-10 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 2.48550773e-01 -7.46181682e-02 4.74049926e-01 -1.74311191e-01
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4cd1dfbd-8550-4198-bd31-53403662e7ab | machine-learning-approaches-for-non-intrusive | 2203.16538 | null | https://arxiv.org/abs/2203.16538v1 | https://arxiv.org/pdf/2203.16538v1.pdf | Machine Learning Approaches for Non-Intrusive Home Absence Detection Based on Appliance Electrical Use | Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios include but are not limited to: elderly people living alone, people suffering from dementia, home quarantine. The majority of published papers focus on either pressure / door sensors or cameras in order to detect outing events. Although the aforementioned approaches provide solid results, they are intrusive and require modifications for sensor placement. In our work, appliance electrical use is investigated as a means for detecting the presence or absence of residents. The energy use is the result of power disaggregation, a non intrusive / non invasive sensing method. Since a dataset providing energy data and ground truth for home absence is not available, artificial outing events were introduced on the UK-DALE dataset, a well known dataset for Non Intrusive Load Monitoring (NILM). Several machine learning algorithms were evaluated using the generated dataset. Benchmark results have shown that home absence detection using appliance power consumption is feasible. | ['Dimitris Vrakas', 'Athanasios Lentzas'] | 2022-03-30 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 4.16196018e-01 8.13553408e-02 -5.54227903e-02 -1.23678699e-01
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547f7aa3-eaac-4348-9053-dddb89b92b8b | deep-learning-for-extracting-protein-protein | 1706.01556 | null | http://arxiv.org/abs/1706.01556v2 | http://arxiv.org/pdf/1706.01556v2.pdf | Deep learning for extracting protein-protein interactions from biomedical literature | State-of-the-art methods for protein-protein interaction (PPI) extraction are
primarily feature-based or kernel-based by leveraging lexical and syntactic
information. But how to incorporate such knowledge in the recent deep learning
methods remains an open question. In this paper, we propose a multichannel
dependency-based convolutional neural network model (McDepCNN). It applies one
channel to the embedding vector of each word in the sentence, and another
channel to the embedding vector of the head of the corresponding word.
Therefore, the model can use richer information obtained from different
channels. Experiments on two public benchmarking datasets, AIMed and BioInfer,
demonstrate that McDepCNN compares favorably to the state-of-the-art
rich-feature and single-kernel based methods. In addition, McDepCNN achieves
24.4% relative improvement in F1-score over the state-of-the-art methods on
cross-corpus evaluation and 12% improvement in F1-score over kernel-based
methods on "difficult" instances. These results suggest that McDepCNN
generalizes more easily over different corpora, and is capable of capturing
long distance features in the sentences. | ['Yifan Peng', 'Zhiyong Lu'] | 2017-06-05 | deep-learning-for-extracting-protein-protein-1 | https://aclanthology.org/W17-2304 | https://aclanthology.org/W17-2304.pdf | ws-2017-8 | ['cross-corpus'] | ['computer-vision'] | [-2.02307284e-01 -3.17909569e-01 -3.54179561e-01 -5.49909472e-01
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27d681a7-744f-43fb-aca5-99451dc562b0 | cafs-class-adaptive-framework-for-semi | 2303.11606 | null | https://arxiv.org/abs/2303.11606v1 | https://arxiv.org/pdf/2303.11606v1.pdf | CAFS: Class Adaptive Framework for Semi-Supervised Semantic Segmentation | Semi-supervised semantic segmentation learns a model for classifying pixels into specific classes using a few labeled samples and numerous unlabeled images. The recent leading approach is consistency regularization by selftraining with pseudo-labeling pixels having high confidences for unlabeled images. However, using only highconfidence pixels for self-training may result in losing much of the information in the unlabeled datasets due to poor confidence calibration of modern deep learning networks. In this paper, we propose a class-adaptive semisupervision framework for semi-supervised semantic segmentation (CAFS) to cope with the loss of most information that occurs in existing high-confidence-based pseudolabeling methods. Unlike existing semi-supervised semantic segmentation frameworks, CAFS constructs a validation set on a labeled dataset, to leverage the calibration performance for each class. On this basis, we propose a calibration aware class-wise adaptive thresholding and classwise adaptive oversampling using the analysis results from the validation set. Our proposed CAFS achieves state-ofthe-art performance on the full data partition of the base PASCAL VOC 2012 dataset and on the 1/4 data partition of the Cityscapes dataset with significant margins of 83.0% and 80.4%, respectively. The code is available at https://github.com/cjf8899/CAFS. | ['Dong-Geol Choi', 'Minseok Seo', 'Yooseung Wang', 'Hyeoncheol Noh', 'Jingi Ju'] | 2023-03-21 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 3.09788316e-01 4.57894623e-01 -5.51829875e-01 -1.05474186e+00
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0c82cc7a-3ada-4f2b-92e4-9e8e72383193 | spatio-temporal-prediction-in-video-coding-by-1 | 2207.09729 | null | https://arxiv.org/abs/2207.09729v1 | https://arxiv.org/pdf/2207.09729v1.pdf | Spatio-temporal prediction in video coding by non-local means refined motion compensation | The prediction step is a very important part of hybrid video codecs. In this contribution, a novel spatio-temporal prediction algorithm is introduced. For this, the prediction is carried out in two steps. Firstly, a preliminary temporal prediction is conducted by motion compensation. Afterwards, spatial refinement is carried out for incorporating spatial redundancies from already decoded neighboring blocks. Thereby, the spatial refinement is achieved by applying Non-Local Means de-noising to the union of the motion compensated block and the already decoded blocks. Including the spatial refinement into H.264/AVC, a rate reduction of up to 14 % or respectively a gain of up to 0.7 dB PSNR compared to unrefined motion compensated prediction can be achieved. | ['André Kaup', 'Thomas Richter', 'Jürgen Seiler'] | 2022-07-20 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.60609519e-01 9.02089998e-02 -5.00137098e-02 -8.29023123e-02
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54b0305a-c4d8-46f9-8b83-83c24198e66f | nerfbk-a-high-quality-benchmark-for-nerf | 2306.06300 | null | https://arxiv.org/abs/2306.06300v2 | https://arxiv.org/pdf/2306.06300v2.pdf | NERFBK: A High-Quality Benchmark for NERF-Based 3D Reconstruction | This paper introduces a new real and synthetic dataset called NeRFBK specifically designed for testing and comparing NeRF-based 3D reconstruction algorithms. High-quality 3D reconstruction has significant potential in various fields, and advancements in image-based algorithms make it essential to evaluate new advanced techniques. However, gathering diverse data with precise ground truth is challenging and may not encompass all relevant applications. The NeRFBK dataset addresses this issue by providing multi-scale, indoor and outdoor datasets with high-resolution images and videos and camera parameters for testing and comparing NeRF-based algorithms. This paper presents the design and creation of the NeRFBK benchmark, various examples and application scenarios, and highlights its potential for advancing the field of 3D reconstruction. | ['Fabio Remondino', 'Ziyang Yan', 'Gabriele Mazzacca', 'Simone Rigon', 'Ali Karami'] | 2023-06-09 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [-7.36554414e-02 -5.80262065e-01 1.99265748e-01 -3.07258844e-01
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088b4a8d-e3cc-44cf-abcf-22947ef83f6f | learning-logic-programs-by-discovering-where | 2202.09806 | null | https://arxiv.org/abs/2202.09806v2 | https://arxiv.org/pdf/2202.09806v2.pdf | Learning logic programs by discovering where not to search | The goal of inductive logic programming (ILP) is to search for a hypothesis that generalises training examples and background knowledge (BK). To improve performance, we introduce an approach that, before searching for a hypothesis, first discovers where not to search. We use given BK to discover constraints on hypotheses, such as that a number cannot be both even and odd. We use the constraints to bootstrap a constraint-driven ILP system. Our experiments on multiple domains (including program synthesis and game playing) show that our approach can (i) substantially reduce learning times by up to 97%, and (ii) scale to domains with millions of facts. | ['Céline Hocquette', 'Andrew Cropper'] | 2022-02-20 | null | null | null | null | ['inductive-logic-programming'] | ['methodology'] | [ 2.22248331e-01 5.00228763e-01 -6.62918329e-01 -3.40844005e-01
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a28cd2e7-253b-44d1-9eb8-42ab0d703bb4 | partially-latent-factors-based-multi-view | 2201.01050 | null | https://arxiv.org/abs/2201.01050v2 | https://arxiv.org/pdf/2201.01050v2.pdf | Partially latent factors based multi-view subspace learning | Multi-view subspace clustering always performs well in high-dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two stage fusion strategy is proposed to embed representation learning into the process of multi-view subspace clustering. This paper first propose a novel matrix factorization method that can separate the coupling consistent and complementary information from observations of multiple views. Based on the obtained latent representations, we further propose two subspace clustering strategies: feature-level fusion and subspace-level hierarchical strategy. Feature-level method concatenates all kinds of latent representations from multiple views, and the original problem therefore degenerates to a single-view subspace clustering process. Subspace-level hierarchical method performs different self-expressive reconstruction processes on the corresponding complementary and consistent latent representations coming from each view, i.e. the prior constraints imposed on different types of subspace representations are related to the appropriate input factors. Finally, extensive experimental results on real-world datasets demonstrate the superiority of our proposed methods by comparing against some state-of-the-art subspace clustering algorithms. | ['Jian-wei Liu', 'Jin-zhong Chen', 'Ze-Yu Liu', 'Run-kun Lu'] | 2022-01-04 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.54016399e-01 -5.63235700e-01 -1.56146288e-01 -1.72570392e-01
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26d0b87a-fb75-4f66-b1d1-2ed6ce9cdd0b | vision-based-pose-estimation-for-augmented | 1806.09316 | null | http://arxiv.org/abs/1806.09316v1 | http://arxiv.org/pdf/1806.09316v1.pdf | Vision-based Pose Estimation for Augmented Reality : A Comparison Study | Augmented reality aims to enrich our real world by inserting 3D virtual
objects. In order to accomplish this goal, it is important that virtual
elements are rendered and aligned in the real scene in an accurate and visually
acceptable way. The solution of this problem can be related to a pose
estimation and 3D camera localization. This paper presents a survey on
different approaches of 3D pose estimation in augmented reality and gives
classification of key-points-based techniques. The study given in this paper
may help both developers and researchers in the field of augmented reality. | ['Nadia Zenati', 'Samir Otmane', 'Hayet Belghit', 'Abdelkader Bellarbi'] | 2018-06-25 | null | null | null | null | ['camera-localization'] | ['computer-vision'] | [-2.07713190e-02 -1.57878980e-01 2.47879907e-01 -1.63160250e-01
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7136c879-2159-40b9-972f-76846aae9c65 | improving-the-transferability-of-adversarial-6 | 2303.15735 | null | https://arxiv.org/abs/2303.15735v1 | https://arxiv.org/pdf/2303.15735v1.pdf | Improving the Transferability of Adversarial Samples by Path-Augmented Method | Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon negatively affects their deployment in real-world scenarios, especially security-related ones. To evaluate the robustness of a target model in practice, transfer-based attacks craft adversarial samples with a local model and have attracted increasing attention from researchers due to their high efficiency. The state-of-the-art transfer-based attacks are generally based on data augmentation, which typically augments multiple training images from a linear path when learning adversarial samples. However, such methods selected the image augmentation path heuristically and may augment images that are semantics-inconsistent with the target images, which harms the transferability of the generated adversarial samples. To overcome the pitfall, we propose the Path-Augmented Method (PAM). Specifically, PAM first constructs a candidate augmentation path pool. It then settles the employed augmentation paths during adversarial sample generation with greedy search. Furthermore, to avoid augmenting semantics-inconsistent images, we train a Semantics Predictor (SP) to constrain the length of the augmentation path. Extensive experiments confirm that PAM can achieve an improvement of over 4.8% on average compared with the state-of-the-art baselines in terms of the attack success rates. | ['Michael R. Lyu', 'Yuxin Su', 'Xiaosen Wang', 'Weibin Wu', 'Yichen Li', 'Wenxuan Wang', 'Jen-tse Huang', 'Jianping Zhang'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Improving_the_Transferability_of_Adversarial_Samples_by_Path-Augmented_Method_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-augmentation'] | ['computer-vision'] | [ 4.97802228e-01 -8.93664062e-02 -1.00036100e-01 -7.45568350e-02
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5c0ea7da-528d-402f-8378-8fd0c0758afb | ace-an-actor-ensemble-algorithm-for | 1811.02696 | null | http://arxiv.org/abs/1811.02696v1 | http://arxiv.org/pdf/1811.02696v1.pdf | ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search | In this paper, we propose an actor ensemble algorithm, named ACE, for
continuous control with a deterministic policy in reinforcement learning. In
ACE, we use actor ensemble (i.e., multiple actors) to search the global maxima
of the critic. Besides the ensemble perspective, we also formulate ACE in the
option framework by extending the option-critic architecture with deterministic
intra-option policies, revealing a relationship between ensemble and options.
Furthermore, we perform a look-ahead tree search with those actors and a
learned value prediction model, resulting in a refined value estimation. We
demonstrate a significant performance boost of ACE over DDPG and its variants
in challenging physical robot simulators. | ['Shangtong Zhang', 'Hao Chen', 'Hengshuai Yao'] | 2018-11-06 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-6.65178150e-02 4.68085438e-01 -3.97027194e-01 -1.01584233e-01
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58327c45-4562-4d7e-9f28-d5e784836ba5 | query2particles-knowledge-graph-reasoning | 2204.12847 | null | https://arxiv.org/abs/2204.12847v1 | https://arxiv.org/pdf/2204.12847v1.pdf | Query2Particles: Knowledge Graph Reasoning with Particle Embeddings | Answering complex logical queries on incomplete knowledge graphs (KGs) with missing edges is a fundamental and important task for knowledge graph reasoning. The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space. Then the answer entities are selected according to the similarities between the entity embeddings and the query embedding. As the answers to a complex query are obtained from a combination of logical operations over sub-queries, the embeddings of the answer entities may not always follow a uni-modal distribution in the embedding space. Thus, it is challenging to simultaneously retrieve a set of diverse answers from the embedding space using a single and concentrated query representation such as a vector or a hyper-rectangle. To better cope with queries with diversified answers, we propose Query2Particles (Q2P), a complex KG query answering method. Q2P encodes each query into multiple vectors, named particle embeddings. By doing so, the candidate answers can be retrieved from different areas over the embedding space using the maximal similarities between the entity embeddings and any of the particle embeddings. Meanwhile, the corresponding neural logic operations are defined to support its reasoning over arbitrary first-order logic queries. The experiments show that Query2Particles achieves state-of-the-art performance on the complex query answering tasks on FB15k, FB15K-237, and NELL knowledge graphs. | ['Yangqiu Song', 'Hongming Zhang', 'ZiHao Wang', 'Jiaxin Bai'] | 2022-04-27 | null | https://aclanthology.org/2022.findings-naacl.207 | https://aclanthology.org/2022.findings-naacl.207.pdf | findings-naacl-2022-7 | ['complex-query-answering'] | ['knowledge-base'] | [-4.39568043e-01 2.58675307e-01 -4.96890843e-01 -3.86260867e-01
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8a858e92-f321-4bb0-b421-b591ca1cc0a2 | clusternet-deep-hierarchical-cluster-network | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_ClusterNet_Deep_Hierarchical_Cluster_Network_With_Rigorously_Rotation-Invariant_Representation_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_ClusterNet_Deep_Hierarchical_Cluster_Network_With_Rigorously_Rotation-Invariant_Representation_for_CVPR_2019_paper.pdf | ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis | Current neural networks for 3D object recognition are vulnerable to 3D rotation. Existing works mostly rely on massive amounts of rotation-augmented data to alleviate the problem, which lacks solid guarantee of the 3D rotation invariance. In this paper, we address the issue by introducing a novel point cloud representation that can be mathematically proved rigorously rotation-invariant, i.e., identical point clouds in different orientations are unified as a unique and consistent representation. Moreover, the proposed representation is conditional information-lossless, because it retains all necessary information of point cloud except for orientation information. In addition, the proposed representation is complementary with existing network architectures for point cloud and fundamentally improves their robustness against rotation transformation. Finally, we propose a deep hierarchical cluster network called ClusterNet to better adapt to the proposed representation. We employ hierarchical clustering to explore and exploit the geometric structure of point cloud, which is embedded in a hierarchical structure tree. Extensive experimental results have shown that our proposed method greatly outperforms the state-of-the-arts in rotation robustness on rotation-augmented 3D object classification benchmarks.
| [' Liang Lin', ' Meng Wang', ' Tianshui Chen', ' Ruijia Xu', ' Guanbin Li', 'Chao Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['3d-object-classification', '3d-object-recognition'] | ['computer-vision', 'computer-vision'] | [-3.50803971e-01 -2.52990752e-01 -2.43196279e-01 -2.17882931e-01
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78de44e9-8799-4d9e-95d3-de86294ab627 | end-to-end-document-classification-and-key | 2306.00750 | null | https://arxiv.org/abs/2306.00750v1 | https://arxiv.org/pdf/2306.00750v1.pdf | End-to-End Document Classification and Key Information Extraction using Assignment Optimization | We propose end-to-end document classification and key information extraction (KIE) for automating document processing in forms. Through accurate document classification we harness known information from templates to enhance KIE from forms. We use text and layout encoding with a cosine similarity measure to classify visually-similar documents. We then demonstrate a novel application of mixed integer programming by using assignment optimization to extract key information from documents. Our approach is validated on an in-house dataset of noisy scanned forms. The best performing document classification approach achieved 0.97 f1 score. A mean f1 score of 0.94 for the KIE task suggests there is significant potential in applying optimization techniques. Abation results show that the method relies on document preprocessing techniques to mitigate Type II errors and achieve optimal performance. | ["Mairead O'Cuinn", 'Rachel Heyburn', 'Bradley Savage', 'Liam Madigan', 'Joana Cavadas', 'Ciaran Cooney'] | 2023-06-01 | null | null | null | null | ['document-classification', 'key-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.25561321e-01 -5.28433025e-02 -7.64791444e-02 -3.38162184e-01
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a2d50fd9-9657-43d8-a374-74ac29f6767c | dyrep-learning-representations-over-dynamic | null | null | https://openreview.net/forum?id=HyePrhR5KX | https://openreview.net/pdf?id=HyePrhR5KX | DyRep: Learning Representations over Dynamic Graphs | Representation Learning over graph structured data has received significant attention recently due to its ubiquitous applicability. However, most advancements have been made in static graph settings while efforts for jointly learning dynamic of the graph and dynamic on the graph are still in an infant stage. Two fundamental questions arise in learning over dynamic graphs: (i) How to elegantly model dynamical processes over graphs? (ii) How to leverage such a model to effectively encode evolving graph information into low-dimensional representations? We present DyRep - a novel modeling framework for dynamic graphs that posits representation learning as a latent mediation process bridging two observed processes namely -- dynamics of the network (realized as topological evolution) and dynamics on the network (realized as activities between nodes). Concretely, we propose a two-time scale deep temporal point process model that captures the interleaved dynamics of the observed processes. This model is further parameterized by a temporal-attentive representation network that encodes temporally evolving structural information into node representations which in turn drives the nonlinear evolution of the observed graph dynamics. Our unified framework has inductive capability to generalize over unseen nodes and we design an efficient unsupervised procedure for end-to-end training. We demonstrate that DyRep outperforms state-of-art baselines in quantitative analysis using dynamic link prediction and time prediction tasks. We further present extensive qualitative insights into our framework to discern indispensable role of various components of our framework. | ['Prasenjeet Biswal', 'Hongyuan Zha', 'Rakshit Trivedi', 'Mehrdad Farajtabar'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['dynamic-link-prediction'] | ['graphs'] | [ 1.69458538e-01 4.61583465e-01 -2.05899075e-01 2.41443608e-03
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18c24a5d-a148-4b94-b2ac-cb0632df565e | deepsnr-a-deep-learning-foundation-for | 2207.04749 | null | https://arxiv.org/abs/2207.04749v1 | https://arxiv.org/pdf/2207.04749v1.pdf | DeepSNR: A deep learning foundation for offline gravitational wave detection | All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipelines in experiments at LIGO is the matched-filter algorithm, which produces a signal-to-noise-ratio-based statistic for ranking candidate observations. Existing deep-learning-based attempts to detect gravitational waves, which have shown promise in both signal sensitivity and computational efficiency, output probability scores. However, probability scores are not easily integrated into discovery workflows, limiting the use of deep learning thus far to non-discovery-oriented applications. In this paper, the Deep Learning Signal-to-Noise Ratio (DeepSNR) detection pipeline, which uses a novel method for generating a signal-to-noise ratio ranking statistic from deep learning classifiers, is introduced, providing the first foundation for the use of deep learning algorithms in discovery-oriented pipelines. The performance of DeepSNR is demonstrated by identifying binary black hole merger candidates versus noise sources in open LIGO data from the first observation run. High-fidelity simulations of the LIGO detector responses are used to present the first sensitivity estimates of deep learning models in terms of physical observables. The robustness of DeepSNR under various experimental considerations is also investigated. The results pave the way for DeepSNR to be used in the scientific discovery of gravitational waves and rare signals in broader contexts, potentially enabling the detection of fainter signals and never-before-observed phenomena. | ['Haydn Vestal', 'Gianni Martire', 'Alexey Bobrick', 'Luke Sellers', 'Manfred Paulini', 'Michael Andrews'] | 2022-07-11 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [-2.76883274e-01 -1.99053168e-01 2.90745497e-01 -3.12880039e-01
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987ef3fe-78b0-40db-bc2b-78eff76ca8e1 | reliability-scores-from-saliency-map-clusters | 2305.15149 | null | https://arxiv.org/abs/2305.15149v1 | https://arxiv.org/pdf/2305.15149v1.pdf | Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower | Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation, errors can occur due to field-variability and limited training data. In this paper, we analyze the reliability of a harvest-readiness classifier with interpretable machine learning. By identifying clusters of saliency maps, we derive reliability scores for each classification result using knowledge about the domain and the image properties. For unseen data, the reliability can be used to (i) inform farmers to improve their decision-making and (ii) increase the model prediction accuracy. Using RGB images of single cauliflower plants at different developmental stages from the GrowliFlower dataset, we investigate various saliency mapping approaches and find that they result in different quality of reliability scores. With the most suitable interpretation tool, we adjust the classification result and achieve a 15.72% improvement of the overall accuracy to 88.14% and a 15.44% improvement of the average class accuracy to 88.52% for the GrowliFlower dataset. | ['Ribana Roscher', 'Jana Kierdorf'] | 2023-05-24 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [ 2.79446155e-01 2.41464406e-01 -4.90614146e-01 -4.11164522e-01
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ba6a716a-2e9f-4e54-90e2-93ee934dafd0 | unsupervised-deep-one-class-classification | 2302.06048 | null | https://arxiv.org/abs/2302.06048v1 | https://arxiv.org/pdf/2302.06048v1.pdf | Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics | One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In practice, however, abnormal samples are often mixed in a training dataset, and they detrimentally affect the training of deep models, which limits their applicability. For robust normality learning of deep practical models, we propose an unsupervised deep one-class classification that learns normality from pseudo-labeled normal samples, i.e., outlier detection in single cluster scenarios. To this end, we propose a pseudo-labeling method by an adaptive threshold selected by ranking-based training dynamics. The experiments on 10 anomaly detection benchmarks show that our method effectively improves performance on anomaly detection by sizable margins. | ['Jun Kyun Choi', 'Jongmin Yu', 'Junsik Kim', 'Minkyung Kim'] | 2023-02-13 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.41185576e-02 -2.09199414e-01 2.06417684e-02 -7.71557033e-01
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b153efa0-da9e-446a-a193-d8cec569d6bd | frame-rate-up-conversion-detection-based-on | 2103.13674 | null | https://arxiv.org/abs/2103.13674v1 | https://arxiv.org/pdf/2103.13674v1.pdf | Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features | With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continuity of videos with a lower frame-rate and is used by malicious counterfeiters in video tampering such as generating fake frame-rate video without improving the quality or mixing temporally spliced videos. FRUC is based on frame interpolation schemes and subtle artifacts that remain in interpolated frames are often difficult to distinguish. Hence, detecting such forgery traces is a critical issue in video forensics. This paper proposes a frame-rate conversion detection network (FCDNet) that learns forensic features caused by FRUC in an end-to-end fashion. The proposed network uses a stack of consecutive frames as the input and effectively learns interpolation artifacts using network blocks to learn spatiotemporal features. This study is the first attempt to apply a neural network to the detection of FRUC. Moreover, it can cover the following three types of frame interpolation schemes: nearest neighbor interpolation, bilinear interpolation, and motion-compensated interpolation. In contrast to existing methods that exploit all frames to verify integrity, the proposed approach achieves a high detection speed because it observes only six frames to test its authenticity. Extensive experiments were conducted with conventional forensic methods and neural networks for video forensic tasks to validate our research. The proposed network achieved state-of-the-art performance in terms of detecting the interpolated artifacts of FRUC. The experimental results also demonstrate that our trained model is robust for an unseen dataset, unlearned frame-rate, and unlearned quality factor. | ['Heung-Kyu Lee', 'Myung-Joon Kwon', 'Wonhyuk Ahn', 'In-Jae Yu', 'Seung-Hun Nam', 'Minseok Yoon'] | 2021-03-25 | null | null | null | null | ['video-forensics'] | ['computer-vision'] | [ 2.18458220e-01 -6.27162397e-01 -2.23625228e-01 4.26722690e-02
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00661dfc-d586-4fca-8423-d311941a913d | composite-optimization-algorithms-for-sigmoid | 2303.00589 | null | https://arxiv.org/abs/2303.00589v3 | https://arxiv.org/pdf/2303.00589v3.pdf | Composite Optimization Algorithms for Sigmoid Networks | In this paper, we use composite optimization algorithms to solve sigmoid networks. We equivalently transfer the sigmoid networks to a convex composite optimization and propose the composite optimization algorithms based on the linearized proximal algorithms and the alternating direction method of multipliers. Under the assumptions of the weak sharp minima and the regularity condition, the algorithm is guaranteed to converge to a globally optimal solution of the objective function even in the case of non-convex and non-smooth problems. Furthermore, the convergence results can be directly related to the amount of training data and provide a general guide for setting the size of sigmoid networks. Numerical experiments on Franke's function fitting and handwritten digit recognition show that the proposed algorithms perform satisfactorily and robustly. | ['Qi Ye', 'Huixiong Chen'] | 2023-03-01 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.37690723e-01 1.14920512e-01 -1.84887558e-01 -2.75352508e-01
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f48376dc-0505-4699-a6a1-12c9299ce46d | counterfactual-analysis-in-dynamic-models | 2205.13832 | null | https://arxiv.org/abs/2205.13832v4 | https://arxiv.org/pdf/2205.13832v4.pdf | Counterfactual Analysis in Dynamic Latent State Models | We provide an optimization-based framework to perform counterfactual analysis in a dynamic model with hidden states. Our framework is grounded in the ``abduction, action, and prediction'' approach to answer counterfactual queries and handles two key challenges where (1) the states are hidden and (2) the model is dynamic. Recognizing the lack of knowledge on the underlying causal mechanism and the possibility of infinitely many such mechanisms, we optimize over this space and compute upper and lower bounds on the counterfactual quantity of interest. Our work brings together ideas from causality, state-space models, simulation, and optimization, and we apply it on a breast cancer case study. To the best of our knowledge, we are the first to compute lower and upper bounds on a counterfactual query in a dynamic latent-state model. | ['Raghav Singal', 'Martin Haugh'] | 2022-05-27 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 1.49659708e-01 5.94815493e-01 -8.88615191e-01 -1.22849112e-02
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4573bad9-8e27-49ce-91f2-5b6f4b673a3d | dereverberation-of-autoregressive-envelopes | 2108.05520 | null | https://arxiv.org/abs/2108.05520v2 | https://arxiv.org/pdf/2108.05520v2.pdf | Dereverberation of Autoregressive Envelopes for Far-field Speech Recognition | The task of speech recognition in far-field environments is adversely affected by the reverberant artifacts that elicit as the temporal smearing of the sub-band envelopes. In this paper, we develop a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The sub-band envelopes are derived using frequency domain linear prediction (FDLP) which performs an autoregressive estimation of the Hilbert envelopes. The neural dereverberation model estimates the envelope gain which when applied to reverberant signals suppresses the late reflection components in the far-field signal. The dereverberated envelopes are used for feature extraction in speech recognition. Further, the sequence of steps involved in envelope dereverberation, feature extraction and acoustic modeling for ASR can be implemented as a single neural processing pipeline which allows the joint learning of the dereverberation network and the acoustic model. Several experiments are performed on the REVERB challenge dataset, CHiME-3 dataset and VOiCES dataset. In these experiments, the joint learning of envelope dereverberation and acoustic model yields significant performance improvements over the baseline ASR system based on log-mel spectrogram as well as other past approaches for dereverberation (average relative improvements of 10-24% over the baseline system). A detailed analysis on the choice of hyper-parameters and the cost function involved in envelope dereverberation is also provided. | ['Sriram Ganapathy', 'Rohit Kumar', 'Anirudh Sreeram', 'Anurenjan Purushothaman'] | 2021-08-12 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.86963379e-01 -4.00655597e-01 8.10385406e-01 -2.30090916e-01
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4db229e6-5da7-4ea8-bf53-4231ca1c8ee4 | the-clip-model-is-secretly-an-image-to-prompt | 2305.12716 | null | https://arxiv.org/abs/2305.12716v1 | https://arxiv.org/pdf/2305.12716v1.pdf | The CLIP Model is Secretly an Image-to-Prompt Converter | The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP). However, text prompts have limitations when it comes to incorporating implicit information from reference images. Existing methods have attempted to address this limitation by employing expensive training procedures involving millions of training samples for image-to-image generation. In contrast, this paper demonstrates that the CLIP model, as utilized in Stable Diffusion, inherently possesses the ability to instantaneously convert images into text prompts. Such an image-to-prompt conversion can be achieved by utilizing a linear projection matrix that is calculated in a closed form. Moreover, the paper showcases that this capability can be further enhanced by either utilizing a small amount of similar-domain training data (approximately 100 images) or incorporating several online training steps (around 30 iterations) on the reference images. By leveraging these approaches, the proposed method offers a simple and flexible solution to bridge the gap between images and text prompts. This methodology can be applied to various tasks such as image variation and image editing, facilitating more effective and seamless interaction between images and textual prompts. | ['Lingqiao Liu', 'Haoxuan Ding', 'Chunna Tian', 'Yuxuan Ding'] | 2023-05-22 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [ 8.08558881e-01 -2.18925737e-02 -1.90963671e-02 -2.36515984e-01
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3718bb34-7c21-492e-8b60-78c52eeea53b | hierarchically-attentive-rnn-for-album | 1708.02977 | null | http://arxiv.org/abs/1708.02977v1 | http://arxiv.org/pdf/1708.02977v1.pdf | Hierarchically-Attentive RNN for Album Summarization and Storytelling | We address the problem of end-to-end visual storytelling. Given a photo
album, our model first selects the most representative (summary) photos, and
then composes a natural language story for the album. For this task, we make
use of the Visual Storytelling dataset and a model composed of three
hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album
photos, select representative (summary) photos, and compose the story.
Automatic and human evaluations show our model achieves better performance on
selection, generation, and retrieval than baselines. | ['Tamara L. Berg', 'Licheng Yu', 'Mohit Bansal'] | 2017-08-09 | hierarchically-attentive-rnn-for-album-1 | https://aclanthology.org/D17-1101 | https://aclanthology.org/D17-1101.pdf | emnlp-2017-9 | ['visual-storytelling'] | ['natural-language-processing'] | [ 0.28775823 0.09659904 -0.08084155 -0.38920984 -1.3287857 -0.4789913
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d91ff9e8-0819-4a33-ba4f-e6a12713f495 | general-automatic-human-shape-and-motion | 1607.08659 | null | http://arxiv.org/abs/1607.08659v2 | http://arxiv.org/pdf/1607.08659v2.pdf | General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues | Markerless motion capture algorithms require a 3D body with properly
personalized skeleton dimension and/or body shape and appearance to
successfully track a person. Unfortunately, many tracking methods consider
model personalization a different problem and use manual or semi-automatic
model initialization, which greatly reduces applicability. In this paper, we
propose a fully automatic algorithm that jointly creates a rigged actor model
commonly used for animation - skeleton, volumetric shape, appearance, and
optionally a body surface - and estimates the actor's motion from multi-view
video input only. The approach is rigorously designed to work on footage of
general outdoor scenes recorded with very few cameras and without background
subtraction. Our method uses a new image formation model with analytic
visibility and analytically differentiable alignment energy. For
reconstruction, 3D body shape is approximated as Gaussian density field. For
pose and shape estimation, we minimize a new edge-based alignment energy
inspired by volume raycasting in an absorbing medium. We further propose a new
statistical human body model that represents the body surface, volumetric
Gaussian density, as well as variability in skeleton shape. Given any
multi-view sequence, our method jointly optimizes the pose and shape parameters
of this model fully automatically in a spatiotemporal way. | ['Hans-Peter Seidel', 'Christian Richardt', 'Nadia Robertini', 'Helge Rhodin', 'Dan Casas', 'Christian Theobalt'] | 2016-07-28 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 4.25417610e-02 6.80757174e-03 1.87438160e-01 5.79826161e-02
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baa4941f-0943-430a-ace2-f6a951ea6acb | dynamic-neural-network-decoupling | 1906.01166 | null | https://arxiv.org/abs/1906.01166v2 | https://arxiv.org/pdf/1906.01166v2.pdf | Interpretable Neural Network Decoupling | The remarkable performance of convolutional neural networks (CNNs) is entangled with their huge number of uninterpretable parameters, which has become the bottleneck limiting the exploitation of their full potential. Towards network interpretation, previous endeavors mainly resort to the single filter analysis, which however ignores the relationship between filters. In this paper, we propose a novel architecture decoupling method to interpret the network from a perspective of investigating its calculation paths. More specifically, we introduce a novel architecture controlling module in each layer to encode the network architecture by a vector. By maximizing the mutual information between the vectors and input images, the module is trained to select specific filters to distill a unique calculation path for each input. Furthermore, to improve the interpretability and compactness of the decoupled network, the output of each layer is encoded to align the architecture encoding vector with the constraint of sparsity regularization. Unlike conventional pixel-level or filter-level network interpretation methods, we propose a path-level analysis to explore the relationship between the combination of filter and semantic concepts, which is more suitable to interpret the working rationale of the decoupled network. Extensive experiments show that the decoupled network achieves several applications, i.e., network interpretation, network acceleration, and adversarial samples detection. | ['Ling Shao', 'Yuchao Li', 'Yongjian Wu', 'Baochang Zhang', 'Shaohui Lin', 'Rongrong Ji', 'Feiyue Huang', 'Chenqian Yan'] | 2019-06-04 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2426_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600647.pdf | eccv-2020-8 | ['network-interpretation'] | ['computer-vision'] | [ 5.41771770e-01 1.87127829e-01 -1.23632848e-01 -2.84773976e-01
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0c92f779-226d-46a8-afe3-4b1ee26ac07c | a-search-based-dynamic-reranking-model-for | null | null | https://aclanthology.org/P16-1132 | https://aclanthology.org/P16-1132.pdf | A Search-Based Dynamic Reranking Model for Dependency Parsing | null | ['Shu-Jian Huang', 'Jia-Jun Chen', 'Xin-yu Dai', 'Yue Zhang', 'Hao Zhou', 'Junsheng Zhou'] | 2016-08-01 | null | null | null | acl-2016-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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df760dc7-0744-4980-bff3-4c1aeddaaf6b | meta-ordinal-weighting-net-for-improving-lung | 2102.00456 | null | https://arxiv.org/abs/2102.00456v1 | https://arxiv.org/pdf/2102.00456v1.pdf | Meta ordinal weighting net for improving lung nodule classification | The progression of lung cancer implies the intrinsic ordinal relationship of lung nodules at different stages-from benign to unsure then to malignant. This problem can be solved by ordinal regression methods, which is between classification and regression due to its ordinal label. However, existing convolutional neural network (CNN)-based ordinal regression methods only focus on modifying classification head based on a randomly sampled mini-batch of data, ignoring the ordinal relationship resided in the data itself. In this paper, we propose a Meta Ordinal Weighting Network (MOW-Net) to explicitly align each training sample with a meta ordinal set (MOS) containing a few samples from all classes. During the training process, the MOW-Net learns a mapping from samples in MOS to the corresponding class-specific weight. In addition, we further propose a meta cross-entropy (MCE) loss to optimize the network in a meta-learning scheme. The experimental results demonstrate that the MOW-Net achieves better accuracy than the state-of-the-art ordinal regression methods, especially for the unsure class. | ['Junping Zhang', 'Hongming Shan', 'Yiming Lei'] | 2021-01-31 | null | null | null | null | ['lung-nodule-classification'] | ['medical'] | [ 4.34336454e-01 2.35148221e-01 -1.04017460e+00 -6.39623284e-01
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717abd79-d7c8-4f98-8254-b5f645bc4e0a | unarxive-a-large-scholarly-data-set-with | null | null | https://link.springer.com/article/10.1007%2Fs11192-020-03382-z | https://www.aifb.kit.edu/images/f/f9/UnarXive_Scientometrics2020.pdf | unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata | In recent years, scholarly data sets have been used for various purposes, such as paper recommendation, citation recommendation, citation context analysis, and citation context-based document summarization. The evaluation of approaches to such tasks and their applicability in real-world scenarios heavily depend on the used data set. However, existing scholarly data sets are limited in several regards. In this paper, we propose a new data set based on all publications from all scientific disciplines available on arXiv.org. Apart from providing the papers' plain text, in-text citations were annotated via global identifiers. Furthermore, citing and cited publications were linked to the Microsoft Academic Graph, providing access to rich metadata. Our data set consists of over one million documents and 29.2 million citation contexts. The data set, which is made freely available for research purposes, not only can enhance the future evaluation of research paper-based and citation context-based approaches, but also serve as a basis for new ways to analyze in-text citations, as we show prototypically in this article. | ['Michael Färber', 'Tarek Saier'] | 2020-03-02 | null | null | null | scientometrics-2020-3 | ['scientific-results-extraction', 'scientific-concept-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-2.85298616e-01 -2.38319457e-01 -6.73198760e-01 4.88735139e-02
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7deed062-9903-4a86-9a6b-3af3f1644bf7 | an-aerial-weed-detection-system-for-green | null | null | https://doi.org/10.37221/eaef.13.2_42 | https://www.jstage.jst.go.jp/article/eaef/13/2/13_42/_pdf/-char/en | An Aerial Weed Detection System for Green Onion Crops Using the You Only Look Once (YOLOv3) Deep Learning Algorithm | The real-time object detection system You Only Look Once (specifically YOLOv3) has recently shown remarkable speed, making it potentially suitable for Unmanned Aerial Vehicle (UAV) precision spraying. In this study, YOLO-WEED, a weed detection system based on YOLOv3, was developed. The dataset, derived from a five-minute UAV video, was split into a 69 : 17 : 13 ratio for training, validation, and testing, respectively. YOLO-WEED demonstrated a real-time detection speed (up to 24.4 FPS) and high performance using NVIDIA GeForce GTX 1060, with a mean average precision of 93.81 % and an F1 score of 0.94. These results successfully show the effectiveness of the YOLO-WEED system for real-time UAV weed detection, given its high speed and high accuracy in detection. | ['Tofael Ahamed', 'Addie Ira Borja Parico'] | 2020-01-01 | null | null | null | engineering-in-agriculture-environment-and | ['real-time-object-detection'] | ['computer-vision'] | [-4.46425751e-02 -7.08838940e-01 -1.17634073e-01 4.53300804e-01
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7c645db4-e8b7-425c-9c10-aa4c9e781644 | deciphering-speech-a-zero-resource-approach | 2111.06799 | null | https://arxiv.org/abs/2111.06799v3 | https://arxiv.org/pdf/2111.06799v3.pdf | Deciphering Speech: a Zero-Resource Approach to Cross-Lingual Transfer in ASR | We present a method for cross-lingual training an ASR system using absolutely no transcribed training data from the target language, and with no phonetic knowledge of the language in question. Our approach uses a novel application of a decipherment algorithm, which operates given only unpaired speech and text data from the target language. We apply this decipherment to phone sequences generated by a universal phone recogniser trained on out-of-language speech corpora, which we follow with flat-start semi-supervised training to obtain an acoustic model for the new language. To the best of our knowledge, this is the first practical approach to zero-resource cross-lingual ASR which does not rely on any hand-crafted phonetic information. We carry out experiments on read speech from the GlobalPhone corpus, and show that it is possible to learn a decipherment model on just 20 minutes of data from the target language. When used to generate pseudo-labels for semi-supervised training, we obtain WERs that range from 32.5% to just 1.9% absolute worse than the equivalent fully supervised models trained on the same data. | ['Peter Bell', 'Electra Wallington', 'Ondrej Klejch'] | 2021-11-12 | null | null | null | null | ['decipherment'] | ['natural-language-processing'] | [ 6.17351353e-01 3.95679444e-01 2.47330591e-01 -5.15648961e-01
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7e5a19e7-c8b4-4038-a4cd-27a0c328bb23 | fenet-a-frequency-extraction-network-for | 2101.02873 | null | https://arxiv.org/abs/2101.02873v1 | https://arxiv.org/pdf/2101.02873v1.pdf | FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection | Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of detecting OSA, requires multiple specialized sensors for signal collection, hence patients have to physically visit hospitals and bear the costly treatment for a single detection. Recently, many single-sensor alternatives have been proposed to improve the cost efficiency and convenience. Among these methods, solutions based on RR-interval (i.e., the interval between two consecutive pulses) signals reach a satisfactory balance among comfort, portability and detection accuracy. In this paper, we advance RR-interval based OSA detection by considering its real-world practicality from energy perspectives. As photoplethysmogram (PPG) pulse sensors are commonly equipped on smart wrist-worn wearable devices (e.g., smart watches and wristbands), the energy efficiency of the detection model is crucial to fully support an overnight observation on patients. This creates challenges as the PPG sensors are unable to keep collecting continuous signals due to the limited battery capacity on smart wrist-worn devices. Therefore, we propose a novel Frequency Extraction Network (FENet), which can extract features from different frequency bands of the input RR-interval signals and generate continuous detection results with downsampled, discontinuous RR-interval signals. With the help of the one-to-multiple structure, FENet requires only one-third of the operation time of the PPG sensor, thus sharply cutting down the energy consumption and enabling overnight diagnosis. Experimental results on real OSA datasets reveal the state-of-the-art performance of FENet. | ['Xiangliang Zhang', 'Lizhen Cui', 'Hongxu Chen', 'Tong Chen', 'Hongzhi Yin', 'Guanhua Ye'] | 2021-01-08 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 1.74508423e-01 -2.71326303e-01 -2.62370706e-01 -1.49262846e-01
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0246edf7-c6fd-4819-903f-1d521bb4f02c | de-identification-of-french-unstructured | 2209.09631 | null | https://arxiv.org/abs/2209.09631v1 | https://arxiv.org/pdf/2209.09631v1.pdf | De-Identification of French Unstructured Clinical Notes for Machine Learning Tasks | Unstructured textual data are at the heart of health systems: liaison letters between doctors, operating reports, coding of procedures according to the ICD-10 standard, etc. The details included in these documents make it possible to get to know the patient better, to better manage him or her, to better study the pathologies, to accurately remunerate the associated medical acts\ldots All this seems to be (at least partially) within reach of today by artificial intelligence techniques. However, for obvious reasons of privacy protection, the designers of these AIs do not have the legal right to access these documents as long as they contain identifying data. De-identifying these documents, i.e. detecting and deleting all identifying information present in them, is a legally necessary step for sharing this data between two complementary worlds. Over the last decade, several proposals have been made to de-identify documents, mainly in English. While the detection scores are often high, the substitution methods are often not very robust to attack. In French, very few methods are based on arbitrary detection and/or substitution rules. In this paper, we propose a new comprehensive de-identification method dedicated to French-language medical documents. Both the approach for the detection of identifying elements (based on deep learning) and their substitution (based on differential privacy) are based on the most proven existing approaches. The result is an approach that effectively protects the privacy of the patients at the heart of these medical documents. The whole approach has been evaluated on a French language medical dataset of a French public hospital and the results are very encouraging. | ['Christophe Guyeux', 'Azzedine Rahmani', 'Philippe Selles', 'David Laiymani', 'Maxime Coulmeau', 'Jean-François Couchot', 'Yakini Tchouka'] | 2022-09-16 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 2.17496291e-01 4.42425698e-01 -2.29640193e-02 -2.50190794e-01
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63ddfddb-32f9-44d5-81c8-8331e97fe41b | dptext-detr-towards-better-scene-text | 2207.04491 | null | https://arxiv.org/abs/2207.04491v2 | https://arxiv.org/pdf/2207.04491v2.pdf | DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer | Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling.In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://github.com/ymy-k/DPText-DETR. | ['DaCheng Tao', 'Bo Du', 'Juhua Liu', 'Shanshan Zhao', 'Jing Zhang', 'Maoyuan Ye'] | 2022-07-10 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.70771554e-02 -3.11783224e-01 -8.25064778e-02 2.91183982e-02
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b16953a4-009b-414d-8542-c7b33eb7aadb | dp-gan-diversity-promoting-generative | 1802.01345 | null | http://arxiv.org/abs/1802.01345v3 | http://arxiv.org/pdf/1802.01345v3.pdf | DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text | Existing text generation methods tend to produce repeated and "boring"
expressions. To tackle this problem, we propose a new text generation model,
called Diversity-Promoting Generative Adversarial Network (DP-GAN). The
proposed model assigns low reward for repeatedly generated text and high reward
for "novel" and fluent text, encouraging the generator to produce diverse and
informative text. Moreover, we propose a novel language-model based
discriminator, which can better distinguish novel text from repeated text
without the saturation problem compared with existing classifier-based
discriminators. The experimental results on review generation and dialogue
generation tasks demonstrate that our model can generate substantially more
diverse and informative text than existing baselines. The code is available at
https://github.com/lancopku/DPGAN | ['Xu sun', 'Jingjing Xu', 'Junyang Lin', 'Xuancheng Ren'] | 2018-02-05 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [ 2.92057872e-01 4.09712583e-01 -2.10299179e-01 -1.40631393e-01
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4a86567c-ba5c-46ba-9f87-2525aaa195ea | multimodal-few-shot-object-detection-with | 2204.07841 | null | https://arxiv.org/abs/2204.07841v3 | https://arxiv.org/pdf/2204.07841v3.pdf | Multi-Modal Few-Shot Object Detection with Meta-Learning-Based Cross-Modal Prompting | We study multi-modal few-shot object detection (FSOD) in this paper, using both few-shot visual examples and class semantic information for detection, which are complementary to each other by definition. Most of the previous works on multi-modal FSOD are fine-tuning-based which are inefficient for online applications. Moreover, these methods usually require expertise like class names to extract class semantic embedding, which are hard to get for rare classes. Our approach is motivated by the high-level conceptual similarity of (metric-based) meta-learning and prompt-based learning to learn generalizable few-shot and zero-shot object detection models respectively without fine-tuning. Specifically, we combine the few-shot visual classifier and text classifier learned via meta-learning and prompt-based learning respectively to build the multi-modal classifier and detection models. In addition, to fully exploit the pre-trained language models, we propose meta-learning-based cross-modal prompting to generate soft prompts for novel classes present in few-shot visual examples, which are then used to learn the text classifier. Knowledge distillation is introduced to learn the soft prompt generator without using human prior knowledge of class names, which may not be available for rare classes. Our insight is that the few-shot support images naturally include related context information and semantics of the class. We comprehensively evaluate the proposed multi-modal FSOD models on multiple few-shot object detection benchmarks, achieving promising results. | ['Shiyuan Huang', 'Jiawei Ma', 'Long Chen', 'Shih-Fu Chang', 'Rama Chellappa', 'Guangxing Han'] | 2022-04-16 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 3.36238027e-01 -1.28458768e-01 -4.61668074e-01 -4.01495218e-01
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61ca9b8e-b663-4e60-8ef9-6f8a3fccc58b | vcvw-3d-a-virtual-construction-vehicles-and | 2305.17927 | null | https://arxiv.org/abs/2305.17927v1 | https://arxiv.org/pdf/2305.17927v1.pdf | VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations | Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligence (AI) applications only applicable to some scenarios that only require 2D information. However, most advanced applications usually require AI agents to perceive 3D spatial information, which limits the further development of the current computer vision (CV) in construction. The lack of 3D annotated datasets for construction object detection worsens the situation. Therefore, this study creates and releases a virtual dataset with 3D annotations named VCVW-3D, which covers 15 construction scenes and involves ten categories of construction vehicles and workers. The VCVW-3D dataset is characterized by multi-scene, multi-category, multi-randomness, multi-viewpoint, multi-annotation, and binocular vision. Several typical 2D and monocular 3D object detection models are then trained and evaluated on the VCVW-3D dataset to provide a benchmark for subsequent research. The VCVW-3D is expected to bring considerable economic benefits and practical significance by reducing the costs of data construction, prototype development, and exploration of space-awareness applications, thus promoting the development of CV in construction, especially those of 3D applications. | ['Xiaowei Luo', 'Yuexiong Ding'] | 2023-05-29 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 4.82260995e-02 -1.15387484e-01 1.00530095e-01 -4.08344269e-02
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30028906-d58b-4343-834c-bd91e689a007 | calibration-assessment-and-boldness | 2305.03780 | null | https://arxiv.org/abs/2305.03780v2 | https://arxiv.org/pdf/2305.03780v2.pdf | Calibration Assessment and Boldness-Recalibration for Binary Events | Probability predictions are essential to inform decision making in medicine, economics, image classification, sports analytics, entertainment, and many other fields. Ideally, probability predictions are (i) well calibrated, (ii) accurate, and (iii) bold, i.e., far from the base rate of the event. Predictions that satisfy these three criteria are informative for decision making. However, there is a fundamental tension between calibration and boldness, since calibration metrics can be high when predictions are overly cautious, i.e., non-bold. The purpose of this work is to develop a hypothesis test and Bayesian model selection approach to assess calibration, and a strategy for boldness-recalibration that enables practitioners to responsibly embolden predictions subject to their required level of calibration. Specifically, we allow the user to pre-specify their desired posterior probability of calibration, then maximally embolden predictions subject to this constraint. We verify the performance of our procedures via simulation, then demonstrate the breadth of applicability by applying these methods to real world case studies in each of the fields mentioned above. We find that very slight relaxation of calibration probability (e.g., from 0.99 to 0.95) can often substantially embolden predictions (e.g., widening Hockey predictions' range from .25-.75 to .10-.90) | ['Christopher T. Franck', 'Adeline P. Guthrie'] | 2023-05-05 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 4.35978293e-01 2.17838779e-01 -4.88316387e-01 -5.23300231e-01
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173452bf-841e-4a51-95d1-b6b40ebfaaa6 | defocus-blur-detection-via-salient-region | 2011.09677 | null | https://arxiv.org/abs/2011.09677v1 | https://arxiv.org/pdf/2011.09677v1.pdf | Defocus Blur Detection via Salient Region Detection Prior | Defocus blur always occurred in photos when people take photos by Digital Single Lens Reflex Camera(DSLR), giving salient region and aesthetic pleasure. Defocus blur Detection aims to separate the out-of-focus and depth-of-field areas in photos, which is an important work in computer vision. Current works for defocus blur detection mainly focus on the designing of networks, the optimizing of the loss function, and the application of multi-stream strategy, meanwhile, these works do not pay attention to the shortage of training data. In this work, to address the above data-shortage problem, we turn to rethink the relationship between two tasks: defocus blur detection and salient region detection. In an image with bokeh effect, it is obvious that the salient region and the depth-of-field area overlap in most cases. So we first train our network on the salient region detection tasks, then transfer the pre-trained model to the defocus blur detection tasks. Besides, we propose a novel network for defocus blur detection. Experiments show that our transfer strategy works well on many current models, and demonstrate the superiority of our network. | ['Liguo Weng', 'Zhiwei Wang', 'Chunyi Sun', 'Min Xia', 'Ming Qian'] | 2020-11-19 | null | null | null | null | ['defocus-blur-detection'] | ['computer-vision'] | [ 3.58124197e-01 -5.64781070e-01 5.65658472e-02 -4.28159416e-01
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a2b3153b-bc48-45b5-bcf2-d66736b81d3e | matte-anything-interactive-natural-image | 2306.04121 | null | https://arxiv.org/abs/2306.04121v1 | https://arxiv.org/pdf/2306.04121v1.pdf | Matte Anything: Interactive Natural Image Matting with Segment Anything Models | Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimaps often requires significant labor, which limits the widespread application of matting algorithms on a large scale. To address the issue, we propose Matte Anything model (MatAny), an interactive natural image matting model which could produce high-quality alpha-matte with various simple hints. The key insight of MatAny is to generate pseudo trimap automatically with contour and transparency prediction. We leverage task-specific vision models to enhance the performance of natural image matting. Specifically, we use the segment anything model (SAM) to predict high-quality contour with user interaction and an open-vocabulary (OV) detector to predict the transparency of any object. Subsequently, a pretrained image matting model generates alpha mattes with pseudo trimaps. MatAny is the interactive matting algorithm with the most supported interaction methods and the best performance to date. It consists of orthogonal vision models without any additional training. We evaluate the performance of MatAny against several current image matting algorithms, and the results demonstrate the significant potential of our approach. | ['Wenyu Liu', 'Lang Ye', 'Xinggang Wang', 'Jingfeng Yao'] | 2023-06-07 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 4.32170182e-01 4.34311241e-01 2.55403876e-01 -2.51038611e-01
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c96bf3b0-03ec-4967-893d-0512292cb558 | partitioning-distributed-compute-jobs-with | 2301.13799 | null | https://arxiv.org/abs/2301.13799v1 | https://arxiv.org/pdf/2301.13799v1.pdf | Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks | From natural language processing to genome sequencing, large-scale machine learning models are bringing advances to a broad range of fields. Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices. This has motivated the research of new compute and network systems capable of handling such tasks. In particular, recent work has focused on developing management schemes which decide how to allocate distributed resources such that some overall objective, such as minimising the job completion time (JCT), is optimised. However, such studies omit explicit consideration of how much a job should be distributed, usually assuming that maximum distribution is desirable. In this work, we show that maximum parallelisation is sub-optimal in relation to user-critical metrics such as throughput and blocking rate. To address this, we propose PAC-ML (partitioning for asynchronous computing with machine learning). PAC-ML leverages a graph neural network and reinforcement learning to learn how much to partition computation graphs such that the number of jobs which meet arbitrary user-defined JCT requirements is maximised. In experiments with five real deep learning computation graphs on a recently proposed optical architecture across four user-defined JCT requirement distributions, we demonstrate PAC-ML achieving up to 56.2% lower blocking rates in dynamic job arrival settings than the canonical maximum parallelisation strategy used by most prior works. | ['Georgios Zervas', 'Alessandro Ottino', 'Zacharaya Shabka', 'Christopher W. F. Parsonson'] | 2023-01-31 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 2.88283467e-01 7.70371184e-02 -1.65669486e-01 -4.80346233e-01
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e36b19a2-10eb-4f5d-b056-cc213508d266 | modelling-the-development-of-counting-with | 2105.10577 | null | https://arxiv.org/abs/2105.10577v1 | https://arxiv.org/pdf/2105.10577v1.pdf | Modelling the development of counting with memory-augmented neural networks | Learning to count is an important example of the broader human capacity for systematic generalization, and the development of counting is often characterized by an inflection point when children rapidly acquire proficiency with the procedures that support this ability. We aimed to model this process by training a reinforcement learning agent to select N items from a binary vector when instructed (known as the give-$N$ task). We found that a memory-augmented modular network architecture based on the recently proposed Emergent Symbol Binding Network (ESBN) exhibited an inflection during learning that resembled human development. This model was also capable of systematic extrapolation outside the range of its training set - for example, trained only to select between 1 and 10 items, it could succeed at selecting 11 to 15 items as long as it could make use of an arbitrary count sequence of at least that length. The close parallels to child development and the capacity for extrapolation suggest that our model could shed light on the emergence of systematicity in humans. | ['Jonathan Cohen', 'Taylor Webb', 'Zack Dulberg'] | 2021-05-21 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 3.26246142e-01 1.52645379e-01 1.84781644e-02 -3.29934627e-01
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8d530700-bea4-4f08-8bed-7e3ee2afb474 | benchmark-for-uncertainty-robustness-in-self | 2212.12411 | null | https://arxiv.org/abs/2212.12411v1 | https://arxiv.org/pdf/2212.12411v1.pdf | Benchmark for Uncertainty & Robustness in Self-Supervised Learning | Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars. In addition to a lack of labeled data, these applications also suffer from distributional shifts. Therefore, an SSL method should provide robust generalization and uncertainty estimation in the test dataset to be considered a reliable model in such high-stakes domains. However, existing approaches often focus on generalization, without evaluating the model's uncertainty. The ability to compare SSL techniques for improving these estimates is therefore critical for research on the reliability of self-supervision models. In this paper, we explore variants of SSL methods, including Jigsaw Puzzles, Context, Rotation, Geometric Transformations Prediction for vision, as well as BERT and GPT for language tasks. We train SSL in auxiliary learning for vision and pre-training for language model, then evaluate the generalization (in-out classification accuracy) and uncertainty (expected calibration error) across different distribution covariate shift datasets, including MNIST-C, CIFAR-10-C, CIFAR-10.1, and MNLI. Our goal is to create a benchmark with outputs from experiments, providing a starting point for new SSL methods in Reliable Machine Learning. All source code to reproduce results is available at https://github.com/hamanhbui/reliable_ssl_baselines. | ['Iliana Maifeld-Carucci', 'Ha Manh Bui'] | 2022-12-23 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-4.85953800e-02 1.72895819e-01 -3.91451418e-01 -8.24008822e-01
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6d722cd6-14d4-4910-a1d4-daa1c723149c | combining-compressions-for-multiplicative | 2208.09684 | null | https://arxiv.org/abs/2208.09684v1 | https://arxiv.org/pdf/2208.09684v1.pdf | Combining Compressions for Multiplicative Size Scaling on Natural Language Tasks | Quantization, knowledge distillation, and magnitude pruning are among the most popular methods for neural network compression in NLP. Independently, these methods reduce model size and can accelerate inference, but their relative benefit and combinatorial interactions have not been rigorously studied. For each of the eight possible subsets of these techniques, we compare accuracy vs. model size tradeoffs across six BERT architecture sizes and eight GLUE tasks. We find that quantization and distillation consistently provide greater benefit than pruning. Surprisingly, except for the pair of pruning and quantization, using multiple methods together rarely yields diminishing returns. Instead, we observe complementary and super-multiplicative reductions to model size. Our work quantitatively demonstrates that combining compression methods can synergistically reduce model size, and that practitioners should prioritize (1) quantization, (2) knowledge distillation, and (3) pruning to maximize accuracy vs. model size tradeoffs. | ['Chris DuBois', 'Ajay Gupta', 'Shayne Longpre', 'Jinhao Lei', 'Rajiv Movva'] | 2022-08-20 | null | https://aclanthology.org/2022.coling-1.252 | https://aclanthology.org/2022.coling-1.252.pdf | coling-2022-10 | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 2.31988922e-01 1.25684023e-01 -4.47160006e-01 -4.82396394e-01
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c88f4d9c-6ffe-4dfb-8600-29be64d7ada9 | fast-bi-layer-neural-synthesis-of-one-shot | 2008.10174 | null | https://arxiv.org/abs/2008.10174v1 | https://arxiv.org/pdf/2008.10174v1.pdf | Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars | We propose a neural rendering-based system that creates head avatars from a single photograph. Our approach models a person's appearance by decomposing it into two layers. The first layer is a pose-dependent coarse image that is synthesized by a small neural network. The second layer is defined by a pose-independent texture image that contains high-frequency details. The texture image is generated offline, warped and added to the coarse image to ensure a high effective resolution of synthesized head views. We compare our system to analogous state-of-the-art systems in terms of visual quality and speed. The experiments show significant inference speedup over previous neural head avatar models for a given visual quality. We also report on a real-time smartphone-based implementation of our system. | ['Aliaksandra Shysheya', 'Egor Zakharov', 'Victor Lempitsky', 'Aleksei Ivakhnenko'] | 2020-08-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1637_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570511.pdf | eccv-2020-8 | ['talking-head-generation'] | ['computer-vision'] | [ 3.04676145e-01 5.31779587e-01 5.99080741e-01 -6.16457999e-01
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3accc0d9-7f78-4325-8cc3-c7765bbe5d51 | deep-semi-supervised-and-self-supervised | 2208.02408 | null | https://arxiv.org/abs/2208.02408v1 | https://arxiv.org/pdf/2208.02408v1.pdf | Deep Semi-Supervised and Self-Supervised Learning for Diabetic Retinopathy Detection | Diabetic retinopathy (DR) is one of the leading causes of blindness in the working-age population of developed countries, caused by a side effect of diabetes that reduces the blood supply to the retina. Deep neural networks have been widely used in automated systems for DR classification on eye fundus images. However, these models need a large number of annotated images. In the medical domain, annotations from experts are costly, tedious, and time-consuming; as a result, a limited number of annotated images are available. This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy. The proposed method uses unsupervised pretraining via self-supervised learning followed by supervised fine-tuning with a small set of labeled images and knowledge distillation to increase the performance in classification task. This method was evaluated on the EyePACS test and Messidor-2 dataset achieving 0.94 and 0.89 AUC respectively using only 2% of EyePACS train labeled images. | ['Fabio A. González', 'Oscar Perdómo', 'Jose Miguel Arrieta Ramos'] | 2022-08-04 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [ 2.60300431e-02 3.17199767e-01 -1.79923490e-01 -7.97913015e-01
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f601f579-5b3d-4078-ac67-63f7ce6be42e | color-image-steganography-using-deep | 2211.09409 | null | https://arxiv.org/abs/2211.09409v1 | https://arxiv.org/pdf/2211.09409v1.pdf | Color Image steganography using Deep convolutional Autoencoders based on ResNet architecture | In this paper, a deep learning color image steganography scheme combining convolutional autoencoders and ResNet architecture is proposed. Traditional steganography methods suffer from some critical defects such as low capacity, security, and robustness. In recent decades, image hiding and image extraction were realized by autoencoder convolutional neural networks to solve the aforementioned challenges. The contribution of this paper is introducing a new scheme for color image steganography inspired by ResNet architecture. The reverse ResNet architecture is utilized to extract the secret image from the stego image. In the proposed method, all images are passed through the prepossess model which is a convolutional deep neural network with the aim of feature extraction. Then, the operational model generates stego and extracted images. In fact, the operational model is an autoencoder based on ResNet structure that produces an image from feature maps. The advantage of proposed structure is identity of models in embedding and extraction phases. The performance of the proposed method is studied using COCO and CelebA datasets. For quantitative comparisons with previous related works, peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM) and hiding capacity are evaluated. The experimental results verify that the proposed scheme performs better than traditional and pervious deep steganography methods. The PSNR and SSIM are more than 40 dB and 0.98, respectively that implies high imperceptibility of the proposed method. Also, this method can hide a color image of the same size in another color image, which can be inferred that the relative capacity of the proposed method is 8 bits per pixel. | ['Saeed Khorashadizadeh', 'Mohammad-Hassan Majidi', 'Seyed Hesam Odin Hashemi'] | 2022-11-17 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 3.30019772e-01 -4.90260571e-02 3.55992407e-01 2.98628271e-01
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a3fbd58f-d57e-4f57-9052-832753bafff7 | 2003-13328 | 2003.13328 | null | https://arxiv.org/abs/2003.13328v1 | https://arxiv.org/pdf/2003.13328v1.pdf | Strip Pooling: Rethinking Spatial Pooling for Scene Parsing | Spatial pooling has been proven highly effective in capturing long-range contextual information for pixel-wise prediction tasks, such as scene parsing. In this paper, beyond conventional spatial pooling that usually has a regular shape of NxN, we rethink the formulation of spatial pooling by introducing a new pooling strategy, called strip pooling, which considers a long but narrow kernel, i.e., 1xN or Nx1. Based on strip pooling, we further investigate spatial pooling architecture design by 1) introducing a new strip pooling module that enables backbone networks to efficiently model long-range dependencies, 2) presenting a novel building block with diverse spatial pooling as a core, and 3) systematically comparing the performance of the proposed strip pooling and conventional spatial pooling techniques. Both novel pooling-based designs are lightweight and can serve as an efficient plug-and-play module in existing scene parsing networks. Extensive experiments on popular benchmarks (e.g., ADE20K and Cityscapes) demonstrate that our simple approach establishes new state-of-the-art results. Code is made available at https://github.com/Andrew-Qibin/SPNet. | ['Ming-Ming Cheng', 'Li Zhang', 'Jiashi Feng', 'Qibin Hou'] | 2020-03-30 | strip-pooling-rethinking-spatial-pooling-for | http://openaccess.thecvf.com/content_CVPR_2020/html/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hou_Strip_Pooling_Rethinking_Spatial_Pooling_for_Scene_Parsing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-parsing'] | ['computer-vision'] | [ 1.20071448e-01 1.81491688e-01 -2.47442216e-01 -5.62014520e-01
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604ebf0b-e0b8-44a0-a5f4-49c49ad1765c | aerial-monocular-3d-object-detection | 2208.03974 | null | https://arxiv.org/abs/2208.03974v1 | https://arxiv.org/pdf/2208.03974v1.pdf | Aerial Monocular 3D Object Detection | Drones equipped with cameras can significantly enhance human ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for drones has always been conducted in the 2D image space, which fundamentally limits their ability to understand 3D scenes. Furthermore, existing 3D object detection methods developed for autonomous driving cannot be directly applied to drones due to the lack of deformation modeling, which is essential for the distant aerial perspective with sensitive distortion and small objects. To fill the gap, this work proposes a dual-view detection system named DVDET to achieve aerial monocular object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module that can properly warp information from the drone's perspective to the BEV. Compared to the monocular methods for cars, our transformation includes a learnable deformable network for explicitly revising the severe deviation. To address the dataset challenge, we propose a new large-scale simulation dataset named AM3D-Sim, generated by the co-simulation of AirSIM and CARLA, and a new real-world aerial dataset named AM3D-Real, collected by DJI Matrice 300 RTK, in both datasets, high-quality annotations for 3D object detection are provided. Extensive experiments show that i) aerial monocular 3D object detection is feasible; ii) the model pre-trained on the simulation dataset benefits real-world performance, and iii) DVDET also benefits monocular 3D object detection for cars. To encourage more researchers to investigate this area, we will release the dataset and related code in https://sjtu-magic.github.io/dataset/AM3D/. | ['Siheng Chen', 'Weidi Xie', 'Shaoheng Fang', 'Yue Hu'] | 2022-08-08 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [-1.14193216e-01 -3.27742040e-01 1.93704039e-01 -7.54016489e-02
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c8a51ec3-1acd-466b-8f70-d1b7c3375f54 | visinger-variational-inference-with | 2110.08813 | null | https://arxiv.org/abs/2110.08813v2 | https://arxiv.org/pdf/2110.08813v2.pdf | VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis | In this paper, we propose VISinger, a complete end-to-end high-quality singing voice synthesis (SVS) system that directly generates audio waveform from lyrics and musical score. Our approach is inspired by VITS, which adopts VAE-based posterior encoder augmented with normalizing flow-based prior encoder and adversarial decoder to realize complete end-to-end speech generation. VISinger follows the main architecture of VITS, but makes substantial improvements to the prior encoder based on the characteristics of singing. First, instead of using phoneme-level mean and variance of acoustic features, we introduce a length regulator and a frame prior network to get the frame-level mean and variance on acoustic features, modeling the rich acoustic variation in singing. Second, we further introduce an F0 predictor to guide the frame prior network, leading to stabler singing performance. Finally, to improve the singing rhythm, we modify the duration predictor to specifically predict the phoneme to note duration ratio, helped with singing note normalization. Experiments on a professional Mandarin singing corpus show that VISinger significantly outperforms FastSpeech+Neural-Vocoder two-stage approach and the oracle VITS; ablation study demonstrates the effectiveness of different contributions. | ['Mengxiao Bi', 'Pengcheng Zhu', 'Lei Xie', 'Heyang Xue', 'Jian Cong', 'Yongmao Zhang'] | 2021-10-17 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.51036203e-01 -1.04585417e-01 1.18647799e-01 -1.18208982e-01
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3595c779-a8ce-464d-868b-be7fce17058c | grounded-language-learning-in-a-simulated-3d | 1706.06551 | null | http://arxiv.org/abs/1706.06551v2 | http://arxiv.org/pdf/1706.06551v2.pdf | Grounded Language Learning in a Simulated 3D World | We are increasingly surrounded by artificially intelligent technology that
takes decisions and executes actions on our behalf. This creates a pressing
need for general means to communicate with, instruct and guide artificial
agents, with human language the most compelling means for such communication.
To achieve this in a scalable fashion, agents must be able to relate language
to the world and to actions; that is, their understanding of language must be
grounded and embodied. However, learning grounded language is a notoriously
challenging problem in artificial intelligence research. Here we present an
agent that learns to interpret language in a simulated 3D environment where it
is rewarded for the successful execution of written instructions. Trained via a
combination of reinforcement and unsupervised learning, and beginning with
minimal prior knowledge, the agent learns to relate linguistic symbols to
emergent perceptual representations of its physical surroundings and to
pertinent sequences of actions. The agent's comprehension of language extends
beyond its prior experience, enabling it to apply familiar language to
unfamiliar situations and to interpret entirely novel instructions. Moreover,
the speed with which this agent learns new words increases as its semantic
knowledge grows. This facility for generalising and bootstrapping semantic
knowledge indicates the potential of the present approach for reconciling
ambiguous natural language with the complexity of the physical world. | ['Chris Apps', 'Marcus Wainwright', 'David Szepesvari', 'Wojciech Marian Czarnecki', 'Ryan Faulkner', 'Fumin Wang', 'Felix Hill', 'Denis Teplyashin', 'Hubert Soyer', 'Simon Green', 'Karl Moritz Hermann', 'Demis Hassabis', 'Phil Blunsom', 'Max Jaderberg'] | 2017-06-20 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [ 4.52732265e-01 2.97290444e-01 1.58917531e-01 -3.16239893e-01
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8dd5b330-f2a6-47c5-9bdc-30d43bf89b88 | stereotypical-bias-removal-for-hate-speech | 2001.05495 | null | https://arxiv.org/abs/2001.05495v1 | https://arxiv.org/pdf/2001.05495v1.pdf | Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations | With the ever-increasing cases of hate spread on social media platforms, it is critical to design abuse detection mechanisms to proactively avoid and control such incidents. While there exist methods for hate speech detection, they stereotype words and hence suffer from inherently biased training. Bias removal has been traditionally studied for structured datasets, but we aim at bias mitigation from unstructured text data. In this paper, we make two important contributions. First, we systematically design methods to quantify the bias for any model and propose algorithms for identifying the set of words which the model stereotypes. Second, we propose novel methods leveraging knowledge-based generalizations for bias-free learning. Knowledge-based generalization provides an effective way to encode knowledge because the abstraction they provide not only generalizes content but also facilitates retraction of information from the hate speech detection classifier, thereby reducing the imbalance. We experiment with multiple knowledge generalization policies and analyze their effect on general performance and in mitigating bias. Our experiments with two real-world datasets, a Wikipedia Talk Pages dataset (WikiDetox) of size ~96k and a Twitter dataset of size ~24k, show that the use of knowledge-based generalizations results in better performance by forcing the classifier to learn from generalized content. Our methods utilize existing knowledge-bases and can easily be extended to other tasks | ['Vasudeva Varma', 'Manish Gupta', 'Pinkesh Badjatiya'] | 2020-01-15 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 1.01429485e-01 2.66307089e-02 -6.03508234e-01 -2.77527213e-01
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acb40ed5-91c1-4fe7-9c45-de60691930a5 | generalizing-natural-language-analysis-1 | 1911.03822 | null | https://arxiv.org/abs/1911.03822v2 | https://arxiv.org/pdf/1911.03822v2.pdf | Generalizing Natural Language Analysis through Span-relation Representations | Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks. We perform extensive experiments to test this insight on 10 disparate tasks spanning dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving performance comparable to state-of-the-art specialized models. We further demonstrate benefits of multi-task learning, and also show that the proposed method makes it easy to analyze differences and similarities in how the model handles different tasks. Finally, we convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis. | ['Wei Xu', 'Zhengbao Jiang', 'Jun Araki', 'Graham Neubig'] | 2019-11-10 | generalizing-natural-language-analysis-2 | https://aclanthology.org/2020.acl-main.192 | https://aclanthology.org/2020.acl-main.192.pdf | acl-2020-6 | ['constituency-parsing', 'semantic-role-labeling-predicted-predicates'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.13711017e-01 3.65571827e-01 -4.82639432e-01 -8.27316046e-01
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693e30be-88c2-43dd-8ef3-4d133653b3eb | hierarchical-semi-supervised-contrastive | 2207.11789 | null | https://arxiv.org/abs/2207.11789v1 | https://arxiv.org/pdf/2207.11789v1.pdf | Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection | Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. However, when contaminated with unlabeled abnormal samples in training set under semi-supervised settings, current contrastive-based methods generally 1) ignore the comprehensive relation between training data, leading to suboptimal performance, and 2) require fine-tuning, resulting in low efficiency. To address the above two issues, in this paper, we propose a novel hierarchical semi-supervised contrastive learning (HSCL) framework, for contamination-resistant anomaly detection. Specifically, HSCL hierarchically regulates three complementary relations: sample-to-sample, sample-to-prototype, and normal-to-abnormal relations, enlarging the discrimination between normal and abnormal samples with a comprehensive exploration of the contaminated data. Besides, HSCL is an end-to-end learning approach that can efficiently learn discriminative representations without fine-tuning. HSCL achieves state-of-the-art performance in multiple scenarios, such as one-class classification and cross-dataset detection. Extensive ablation studies further verify the effectiveness of each considered relation. The code is available at https://github.com/GaoangW/HSCL. | ['Klara Nahrstedt', 'Mingli Song', 'Xinchao Wang', 'Yibing Zhan', 'Gaoang Wang'] | 2022-07-24 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 2.15939835e-01 -4.06514972e-01 -3.96530479e-02 -4.73011732e-01
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f0d72633-494b-4963-b289-6886d00bc3ae | detecting-localising-and-classifying-polyps | 2101.03285 | null | https://arxiv.org/abs/2101.03285v1 | https://arxiv.org/pdf/2101.03285v1.pdf | Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning | In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set is highly imbalanced with the large majority of frames consisting of normal images and a small minority comprising frames with polyps. Colonoscopy videos may contain blurry images and frames displaying feces and water jet sprays to clean the colon -- such frames can mistakenly be detected as anomalies, so we have implemented a classifier to reject these two types of frames before polyp detection takes place. Next, given a frame containing a polyp, our method localises (with a bounding box around the polyp) and classifies it into five different classes. Furthermore, we study a method to improve the reliability and interpretability of the classification result using uncertainty estimation and classification calibration. Classification uncertainty and calibration not only help improve classification accuracy by rejecting low-confidence and high-uncertain results, but can be used by doctors to decide how to decide on the classification of a polyp. All the proposed detection, localisation and classification methods are tested using large data sets and compared with relevant baseline approaches. | ['Gustavo Carneiro', 'Rajvinder Singh', 'Seon Ho Shin', 'Alastair D. Burt', 'Johan W. Verjans', 'Gabriel Maicas', 'Yuyuan Liu', 'Leonardo Zorron Cheng Tao Pu', 'Yu Tian'] | 2021-01-09 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 5.69492996e-01 3.04725021e-01 1.50391772e-01 -2.84761041e-01
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3712c2e1-6f16-4c8a-9acf-5c28311f6d99 | student-t-networks-for-melody-estimation | 2110.07419 | null | https://arxiv.org/abs/2110.07419v2 | https://arxiv.org/pdf/2110.07419v2.pdf | Student-t Networks for Melody Estimation | Melody estimation or melody extraction refers to the extraction of the primary or fundamental dominant frequency in a melody. This sequence of frequencies obtained represents the pitch of the dominant melodic line from recorded music audio signals. The music signal may be monophonic or polyphonic. The melody extraction problem from audio signals gets complicated when we start dealing with polyphonic audio data. This is because in generalized audio signals,the sounds are highly correlated over both frequency and time domains. This complex overlap of many sounds, makes identification of predominant frequency challenging. | ['Avi', 'Bhavesh Jain', 'Udhav Gupta'] | 2021-10-14 | null | null | null | null | ['melody-extraction'] | ['music'] | [ 4.00745749e-01 -6.41532004e-01 9.51002017e-02 4.38215554e-01
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aa4521e8-0b62-42f6-b543-835d7feafa2c | towards-a-comprehensive-taxonomy-and-large | null | null | https://aclanthology.org/2020.alw-1.17 | https://aclanthology.org/2020.alw-1.17.pdf | Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage | Abusive language classifiers have been shown to exhibit bias against women and racial minorities. Since these models are trained on data that is collected using keywords, they tend to exhibit a high sensitivity towards pejoratives. As a result, comments written by victims of abuse are frequently labelled as hateful, even if they discuss or reclaim slurs. Any attempt to address bias in keyword-based corpora requires a better understanding of pejorative language, as well as an equitable representation of targeted users in data collection. We make two main contributions to this end. First, we provide an annotation guide that outlines 4 main categories of online slur usage, which we further divide into a total of 12 sub-categories. Second, we present a publicly available corpus based on our taxonomy, with 39.8k human annotated comments extracted from Reddit. This corpus was annotated by a diverse cohort of coders, with Shannon equitability indices of 0.90, 0.92, and 0.87 across sexuality, ethnicity, and gender. Taken together, our taxonomy and corpus allow researchers to evaluate classifiers on a wider range of speech containing slurs. | ['Derek Ruths', 'Haji Mohammad Saleem', 'Jana Kurrek'] | null | null | null | null | emnlp-alw-2020-11 | ['abusive-language'] | ['natural-language-processing'] | [-1.35538056e-01 1.41585112e-01 -4.86533821e-01 -4.21627074e-01
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68186653-3437-4055-babc-0152c0bfdbab | using-partial-monotonicity-in-submodular | 2202.03051 | null | https://arxiv.org/abs/2202.03051v2 | https://arxiv.org/pdf/2202.03051v2.pdf | Using Partial Monotonicity in Submodular Maximization | Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications. Traditionally, the study of submodular functions was based on binary function properties. However, such properties have an inherit weakness, namely, if an algorithm assumes functions that have a particular property, then it provides no guarantee for functions that violate this property, even when the violation is very slight. Therefore, recent works began to consider continuous versions of function properties. Probably the most significant among these (so far) are the submodularity ratio and the curvature, which were studied extensively together and separately. The monotonicity property of set functions plays a central role in submodular maximization. Nevertheless, and despite all the above works, no continuous version of this property has been suggested to date (as far as we know). This is unfortunate since submoduar functions that are almost monotone often arise in machine learning applications. In this work we fill this gap by defining the monotonicity ratio, which is a continues version of the monotonicity property. We then show that for many standard submodular maximization algorithms one can prove new approximation guarantees that depend on the monotonicity ratio; leading to improved approximation ratios for the common machine learning applications of movie recommendation, quadratic programming and image summarization. | ['Moran Feldman', 'Loay Mualem'] | 2022-02-07 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 2.24559039e-01 4.16914672e-01 -4.07108873e-01 -2.63394028e-01
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0c011544-5d97-4cc7-b85c-4138414836de | ice-monitoring-in-swiss-lakes-from-optical | 2010.14300 | null | https://arxiv.org/abs/2010.14300v1 | https://arxiv.org/pdf/2010.14300v1.pdf | Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams using Machine Learning | Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources to monitor lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores >93%, for both sensors. It also generalises well across lakes and winters with mIoU scores >78% and >80% respectively. On average, our webcam approach achieves mIoU values of 87% (approx.) and generalisation scores of 71% (approx.) and 69% (approx.) across different cameras and winters respectively. Additionally, we put forward a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras. | ['Konrad Schindler', 'Laura Leal-Taixe', 'Emmanuel Baltsavias', 'Tianyu Wu', 'Rajanie Prabha', 'Manu Tom'] | 2020-10-27 | null | null | null | null | ['lake-ice-detection', 'lake-ice-detection'] | ['computer-vision', 'miscellaneous'] | [ 4.63816486e-02 -3.15325141e-01 1.59429640e-01 -5.17826557e-01
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d5cc8128-1ade-46fe-b270-2554379256a5 | identification-of-novel-diagnostic | 2305.18841 | null | https://arxiv.org/abs/2305.18841v1 | https://arxiv.org/pdf/2305.18841v1.pdf | Identification of Novel Diagnostic Neuroimaging Biomarkers for Autism Spectrum Disorder Through Convolutional Neural Network-Based Analysis of Functional, Structural, and Diffusion Tensor Imaging Data Towards Enhanced Autism Diagnosis | Autism Spectrum Disorder is one of the leading neurodevelopmental disorders in our world, present in over 1% of the population and rapidly increasing in prevalence, yet the condition lacks a robust, objective, and efficient diagnostic. Clinical diagnostic criteria rely on subjective behavioral assessments, which are prone to misdiagnosis as they face limitations in terms of their heterogeneity, specificity, and biases. This study proposes a novel convolutional-neural-network based classification tool that aims to identify the potential of different neuroimaging features as autism biomarkers. The model is constructed using a set of sequential layers specifically designed to extract relevant features from brain scans. Trained and tested on over 300,000 distinct features across three imaging types, the model shows promising results, achieving an accuracy of 95.4% and outperforming metrics of current gold standard diagnostics. 32 optimal features from the imaging data were identified and classified as candidate biomarkers using an independent samples t-test, in which functional features such as neural activity and connectivity in various brain regions exhibited the highest differences in the mean values between individuals with autism and typical control subjects. The p-values of these biomarkers were < 0.001, proving the statistical significance of the results and indicating that this research could pave the way towards the usage of neuroimaging in conjunction with behavioral criteria in clinics. Furthermore, the salient features discovered in the brain structure of individuals with autism could lead to a more profound understanding of the underlying neurobiological mechanisms of the disorder, which remains one of the most substantial enigmas in the field even today. | ['Annie Adhikary'] | 2023-05-30 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.33553633e-01 -3.70885502e-03 2.70665027e-02 -3.07181507e-01
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2de9c9af-3f24-4a54-bab0-3b2a15dc32ae | neural-graph-embedding-methods-for-natural | 1911.03042 | null | https://arxiv.org/abs/1911.03042v3 | https://arxiv.org/pdf/1911.03042v3.pdf | Neural Graph Embedding Methods for Natural Language Processing | Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs. However, all of them tend to be sparse with very few facts per entity. In the first part of the thesis, we propose two solutions to alleviate this problem: (1) KG Canonicalization, i.e., identifying and merging duplicate entities in a KG, (2) Relation Extraction which involves automating the process of extracting semantic relationships between entities from unstructured text. Traditional Neural Networks like CNNs and RNNs are constrained to handle Euclidean data. However, graphs in Natural Language Processing (NLP) are prominent. Recently, Graph Convolutional Networks (GCNs) have been proposed to address this shortcoming and have been successfully applied for several problems. In the second part of the thesis, we utilize GCNs for Document Timestamping problem and for learning word embeddings using dependency context of a word instead of sequential context. In this third part of the thesis, we address two limitations of existing GCN models, i.e., (1) The standard neighborhood aggregation scheme puts no constraints on the number of nodes that can influence the representation of a target node. This leads to a noisy representation of hub-nodes which coves almost the entire graph in a few hops. (2) Most of the existing GCN models are limited to handle undirected graphs. However, a more general and pervasive class of graphs are relational graphs where each edge has a label and direction associated with it. Existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representation of nodes only. | ['Shikhar Vashishth'] | 2019-11-08 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-2.01694041e-01 4.28740919e-01 -2.55055517e-01 -1.94301248e-01
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