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1d11cfa4-77d2-4fb6-9d5a-de0a07c2e7f5 | boosting-fast-and-high-quality-speech | 2306.05708 | null | https://arxiv.org/abs/2306.05708v2 | https://arxiv.org/pdf/2306.05708v2.pdf | Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion | Denoising Diffusion Probabilistic Models have shown extraordinary ability on various generative tasks. However, their slow inference speed renders them impractical in speech synthesis. This paper proposes a linear diffusion model (LinDiff) based on an ordinary differential equation to simultaneously reach fast inference and high sample quality. Firstly, we employ linear interpolation between the target and noise to design a diffusion sequence for training, while previously the diffusion path that links the noise and target is a curved segment. When decreasing the number of sampling steps (i.e., the number of line segments used to fit the path), the ease of fitting straight lines compared to curves allows us to generate higher quality samples from a random noise with fewer iterations. Secondly, to reduce computational complexity and achieve effective global modeling of noisy speech, LinDiff employs a patch-based processing approach that partitions the input signal into small patches. The patch-wise token leverages Transformer architecture for effective modeling of global information. Adversarial training is used to further improve the sample quality with decreased sampling steps. We test proposed method with speech synthesis conditioned on acoustic feature (Mel-spectrograms). Experimental results verify that our model can synthesize high-quality speech even with only one diffusion step. Both subjective and objective evaluations demonstrate that our model can synthesize speech of a quality comparable to that of autoregressive models with faster synthesis speed (3 diffusion steps). | ['JianHua Tao', 'Ran He', 'Jie Cao', 'Tao Wang', 'Haogeng Liu'] | 2023-06-09 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.76916465e-01 2.43906826e-01 2.11107865e-01 9.76486877e-02
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b78f240c-e0b4-4c26-a974-ea3cda6d25db | towards-reducing-manual-workload-in | 2201.05648 | null | https://arxiv.org/abs/2201.05648v1 | https://arxiv.org/pdf/2201.05648v1.pdf | Towards Reducing Manual Workload in Technology-Assisted Reviews: Estimating Ranking Performance | Conducting a systematic review (SR) is comprised of multiple tasks: (i) collect documents (studies) that are likely to be relevant from digital libraries (eg., PubMed), (ii) manually read and label the documents as relevant or irrelevant, (iii) extract information from the relevant studies, and (iv) analyze and synthesize the information and derive a conclusion of SR. When researchers label studies, they can screen ranked documents where relevant documents are higher than irrelevant ones. This practice, known as screening prioritization (ie., document ranking approach), speeds up the process of conducting a SR as the documents labelled as relevant can move to the next tasks earlier. However, the approach is limited in reducing the manual workload because the total number of documents to screen remains the same. Towards reducing the manual workload in the screening process, we investigate the quality of document ranking of SR. This can signal researchers whereabouts in the ranking relevant studies are located and let them decide where to stop the screening. After extensive analysis on SR document rankings from different ranking models, we hypothesize 'topic broadness' as a factor that affects the ranking quality of SR. Finally, we propose a measure that estimates the topic broadness and demonstrate that the proposed measure is a simple yet effective method to predict the qualities of document rankings for SRs. | ['Aixin Sun', 'Grace E. Lee'] | 2022-01-14 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 5.11278391e-01 8.69738907e-02 -6.00495517e-01 -1.49705395e-01
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b489fea7-3b17-4ba8-b359-ba6144a2bafe | uob-at-semeval-2020-task-12-boosting-bert | 2008.08547 | null | https://arxiv.org/abs/2008.08547v1 | https://arxiv.org/pdf/2008.08547v1.pdf | UoB at SemEval-2020 Task 12: Boosting BERT with Corpus Level Information | Pre-trained language model word representation, such as BERT, have been extremely successful in several Natural Language Processing tasks significantly improving on the state-of-the-art. This can largely be attributed to their ability to better capture semantic information contained within a sentence. Several tasks, however, can benefit from information available at a corpus level, such as Term Frequency-Inverse Document Frequency (TF-IDF). In this work we test the effectiveness of integrating this information with BERT on the task of identifying abuse on social media and show that integrating this information with BERT does indeed significantly improve performance. We participate in Sub-Task A (abuse detection) wherein we achieve a score within two points of the top performing team and in Sub-Task B (target detection) wherein we are ranked 4 of the 44 participating teams. | ['Harish Tayyar Madabushi', 'Wah Meng Lim'] | 2020-08-19 | null | https://aclanthology.org/2020.semeval-1.295 | https://aclanthology.org/2020.semeval-1.295.pdf | semeval-2020 | ['abuse-detection'] | ['natural-language-processing'] | [ 1.20629549e-01 -1.93634316e-01 -1.55229755e-02 -3.51315856e-01
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5dad9383-f590-4924-971a-cd0db0509d48 | doubleu-net-a-deep-convolutional-neural | 2006.04868 | null | https://arxiv.org/abs/2006.04868v2 | https://arxiv.org/pdf/2006.04868v2.pdf | DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation | Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models. | ['Håvard D. Johansen', 'Pål Halvorsen', 'Dag Johansen', 'Michael A. Riegler', 'Debesh Jha'] | 2020-06-08 | null | null | null | null | ['skin-cancer-segmentation'] | ['medical'] | [ 5.10967791e-01 4.74618047e-01 -3.38332027e-01 -4.25006121e-01
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fa8877d7-63e8-4f75-9387-7ff4d2185d59 | object-detection-based-on-the-collection-of | 2306.14120 | null | https://arxiv.org/abs/2306.14120v1 | https://arxiv.org/pdf/2306.14120v1.pdf | Object Detection based on the Collection of Geometric Evidence | Artificial objects usually have very stable shape features, which are stable, persistent properties in geometry. They can provide evidence for object recognition. Shape features are more stable and more distinguishing than appearance features, color features, grayscale features, or gradient features. The difficulty with object recognition based on shape features is that objects may differ in color, lighting, size, position, pose, and background interference, and it is not currently possible to predict all possible conditions. The variety of objects and conditions renders object recognition based on geometric features very challenging. This paper provides a method based on shape templates, which involves the selection, collection, and combination discrimination of geometric evidence of the edge segments of images, to find out the target object accurately from background, and it is able to identify the semantic attributes of each line segment of the target object. In essence, the method involves solving a global optimal combinatorial optimization problem. Although the complexity of the global optimal combinatorial optimization problem seems to be very high, there is no need to define the complex feature vector and no need for any expensive training process. It has very good generalization ability and environmental adaptability, and more solid basis for cognitive psychology than other methods. The process of collecting geometric evidence, which is simple and universal, shows considerable prospects for practical use. The experimental results prove that the method has great advantages in response to changes in the environment, invariant recognition, pinpointing the geometry of objects, search efficiency, and efficient calculation. This attempt contributes to understanding of some types of universal processing during the process of object recognition. | ['Fu-yu Tang', 'Hui Wei'] | 2023-06-25 | null | null | null | null | ['object-recognition', 'combinatorial-optimization'] | ['computer-vision', 'methodology'] | [ 1.38948843e-01 -6.62645936e-01 -4.22257222e-02 -3.77375752e-01
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7cacc213-a731-494e-b65e-a7d56bd5f258 | apb2face-audio-guided-face-reenactment-with | 2004.14569 | null | https://arxiv.org/abs/2004.14569v1 | https://arxiv.org/pdf/2004.14569v1.pdf | APB2Face: Audio-guided face reenactment with auxiliary pose and blink signals | Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-resolution faces, which limits the application value. To solve those problems, we propose a novel deep neural network named APB2Face, which consists of GeometryPredictor and FaceReenactor modules. GeometryPredictor uses extra head pose and blink state signals as well as audio to predict the latent landmark geometry information, while FaceReenactor inputs the face landmark image to reenact the photorealistic face. A new dataset AnnVI collected from YouTube is presented to support the approach, and experimental results indicate the superiority of our method than state-of-the-arts, whether in authenticity or controllability. | ['Zhu-Cun Xue', 'Yong liu', 'Liang Liu', 'Jiangning Zhang'] | 2020-04-30 | null | null | null | null | ['face-reenactment'] | ['computer-vision'] | [ 4.78108972e-02 5.63785613e-01 2.62984157e-01 -4.44358557e-01
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fb12542e-b7fb-4112-b823-0b86f3ab7d9b | deep-ensemble-learning-with-frame-skipping | 2307.02858 | null | https://arxiv.org/abs/2307.02858v2 | https://arxiv.org/pdf/2307.02858v2.pdf | Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing | Face presentation attacks (PA), also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario. | ['Jorma Laaksonen', 'Mourad Oussalah', 'Md Ziaul Hoque', 'Usman Muhammad'] | 2023-07-06 | null | null | null | null | ['motion-prediction', 'ensemble-learning', 'face-anti-spoofing', 'ensemble-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 5.21949768e-01 -3.05883050e-01 -2.42233127e-01 -1.62807062e-01
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fdacc5fc-8c8b-4153-a5a7-32c58b863d74 | overview-of-the-medvidqa-2022-shared-task-on | null | null | https://aclanthology.org/2022.bionlp-1.25 | https://aclanthology.org/2022.bionlp-1.25.pdf | Overview of the MedVidQA 2022 Shared Task on Medical Video Question-Answering | In this paper, we present an overview of the MedVidQA 2022 shared task, collocated with the 21st BioNLP workshop at ACL 2022. The shared task addressed two of the challenges faced by medical video question answering: (I) a video classification task that explores new approaches to medical video understanding (labeling), and (ii) a visual answer localization task. Visual answer localization refers to the identification of the relevant temporal segments (start and end timestamps) in the video where the answer to the medical question is being shown or illustrated. A total of thirteen teams participated in the shared task challenges, with eleven system descriptions submitted to the workshop. The descriptions present monomodal and multi-modal approaches developed for medical video classification and visual answer localization. This paper describes the tasks, the datasets, evaluation metrics, and baseline systems for both tasks. Finally, the paper summarizes the techniques and results of the evaluation of the various approaches explored by the participating teams. | ['Dina Demner-Fushman', 'Deepak Gupta'] | null | null | null | null | bionlp-acl-2022-5 | ['video-question-answering'] | ['computer-vision'] | [ 2.50016540e-01 6.55993372e-02 -1.75436407e-01 -4.02960420e-01
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b4191672-7a91-4500-b051-2a67c45bd3aa | highly-confident-local-structure-based | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_Highly_Confident_Local_Structure_Based_Consensus_Graph_Learning_for_Incomplete_CVPR_2023_paper.pdf | Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering | Graph-based multi-view clustering has attracted extensive attention because of the powerful clustering-structure representation ability and noise robustness. Considering the reality of a large amount of incomplete data, in this paper, we propose a simple but effective method for incomplete multi-view clustering based on consensus graph learning, termed as HCLS_CGL. Unlike existing methods that utilize graph constructed from raw data to aid in the learning of consistent representation, our method directly learns a consensus graph across views for clustering. Specifically, we design a novel confidence graph and embed it to form a confidence structure driven consensus graph learning model. Our confidence graph is based on an intuitive similar-nearest-neighbor hypothesis, which does not require any additional information and can help the model to obtain a high-quality consensus graph for better clustering. Numerous experiments are performed to confirm the effectiveness of our method. | ['Yong Xu', 'Lunke Fei', 'Chao Huang', 'Zhihao Wu', 'Gehui Xu', 'Chengliang Liu', 'Jie Wen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-3.77852738e-01 -3.89121249e-02 -2.96073407e-01 -4.40729588e-01
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8667a03a-2dbe-43d8-8648-b7bbb3bcfd49 | unsupervised-learning-algorithms-for-keyword | 2206.12016 | null | https://arxiv.org/abs/2206.12016v1 | https://arxiv.org/pdf/2206.12016v1.pdf | Unsupervised Learning Algorithms for Keyword Extraction in an Undergraduate Thesis | The amount of data managed in many academic institutions has increased in recent years, particularly in all the research work done by undergraduate students, who simply use empirical techniques for keyword selection, forgetting existing technical methods to assist their students in this process. Information and communication technologies, such as the platform for integrated research and academic work with responsibility (PILAR), which records information about research projects, such as titles, summaries, and keywords in their various modalities, have gained relevance and importance in the management of these. We proved algorithms with these records of research projects that have been analysed in this study, and predictions were made for each of the nine (09) models of unsupervised machine learning algorithms that were implemented for each of the 7430 records from the dataset. The most efficient way of extracting keywords for this dataset was the TF-IDF method, obtaining 72% accuracy and [0.4786, SD 0.0501] in average extraction time for each thesis file processed by this model. | ['Marga I. Ingaluque', 'Irenio L. Chagua', 'William E. Arcaya', 'Edelfre Flores', 'Fred Torres-Cruz'] | 2022-06-23 | null | null | null | null | ['keyword-extraction'] | ['natural-language-processing'] | [ 3.47617194e-02 1.18383430e-01 -3.25009435e-01 1.15224846e-01
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d49fee98-229a-4b53-b6e1-35e2eb3d91a9 | pyramid-a-layered-model-for-nested-named | null | null | https://aclanthology.org/2020.acl-main.525 | https://aclanthology.org/2020.acl-main.525.pdf | Pyramid: A Layered Model for Nested Named Entity Recognition | This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER). In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape. Each time an embedding passes through a layer of the pyramid, its length is reduced by one. Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention. We also design an inverse pyramid to allow bidirectional interaction between layers. The proposed method achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE-2005, GENIA, and NNE, which are 80.27, 79.42, 77.78, and 93.70 with conventional embeddings, and 87.74, 86.34, 79.31, and 94.68 with pre-trained contextualized embeddings. In addition, our model can be used for the more general task of Overlapping Named Entity Recognition. A preliminary experiment confirms the effectiveness of our method in overlapping NER. | ['Jue Wang', 'Lidan Shou', 'Ke Chen', 'Gang Chen'] | 2020-07-01 | null | null | null | acl-2020-6 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-5.13477981e-01 2.18208313e-01 -4.51912023e-02 -2.47435600e-01
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39b0253b-3b87-41b7-9164-2f7e75f07b06 | a-unified-generative-framework-for-various | 2106.01223 | null | https://arxiv.org/abs/2106.01223v1 | https://arxiv.org/pdf/2106.01223v1.pdf | A Unified Generative Framework for Various NER Subtasks | Named Entity Recognition (NER) is the task of identifying spans that represent entities in sentences. Whether the entity spans are nested or discontinuous, the NER task can be categorized into the flat NER, nested NER, and discontinuous NER subtasks. These subtasks have been mainly solved by the token-level sequence labelling or span-level classification. However, these solutions can hardly tackle the three kinds of NER subtasks concurrently. To that end, we propose to formulate the NER subtasks as an entity span sequence generation task, which can be solved by a unified sequence-to-sequence (Seq2Seq) framework. Based on our unified framework, we can leverage the pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the special design of the tagging schema or ways to enumerate spans. We exploit three types of entity representations to linearize entities into a sequence. Our proposed framework is easy-to-implement and achieves state-of-the-art (SoTA) or near SoTA performance on eight English NER datasets, including two flat NER datasets, three nested NER datasets, and three discontinuous NER datasets. | ['Xipeng Qiu', 'Zheng Zhang', 'Qipeng Guo', 'Junqi Dai', 'Tao Gui', 'Hang Yan'] | 2021-06-02 | null | https://aclanthology.org/2021.acl-long.451 | https://aclanthology.org/2021.acl-long.451.pdf | acl-2021-5 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [ 2.44005471e-02 1.28890887e-01 1.82715073e-01 -3.58532995e-01
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001a51dc-3bf5-454a-8abd-726bea57e8b7 | maskgit-masked-generative-image-transformer | 2202.04200 | null | https://arxiv.org/abs/2202.04200v1 | https://arxiv.org/pdf/2202.04200v1.pdf | MaskGIT: Masked Generative Image Transformer | Generative transformers have experienced rapid popularity growth in the computer vision community in synthesizing high-fidelity and high-resolution images. The best generative transformer models so far, however, still treat an image naively as a sequence of tokens, and decode an image sequentially following the raster scan ordering (i.e. line-by-line). We find this strategy neither optimal nor efficient. This paper proposes a novel image synthesis paradigm using a bidirectional transformer decoder, which we term MaskGIT. During training, MaskGIT learns to predict randomly masked tokens by attending to tokens in all directions. At inference time, the model begins with generating all tokens of an image simultaneously, and then refines the image iteratively conditioned on the previous generation. Our experiments demonstrate that MaskGIT significantly outperforms the state-of-the-art transformer model on the ImageNet dataset, and accelerates autoregressive decoding by up to 64x. Besides, we illustrate that MaskGIT can be easily extended to various image editing tasks, such as inpainting, extrapolation, and image manipulation. | ['William T. Freeman', 'Ce Liu', 'Lu Jiang', 'Han Zhang', 'Huiwen Chang'] | 2022-02-08 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chang_MaskGIT_Masked_Generative_Image_Transformer_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-outpainting'] | ['computer-vision'] | [ 7.91096270e-01 2.43760929e-01 1.05267458e-01 -2.57956058e-01
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4dbfb8c1-c4d2-42f3-81c5-ee0cf3d834a2 | boosting-image-outpainting-with-semantic | 2110.09267 | null | https://arxiv.org/abs/2110.09267v1 | https://arxiv.org/pdf/2110.09267v1.pdf | Boosting Image Outpainting with Semantic Layout Prediction | The objective of image outpainting is to extend image current border and generate new regions based on known ones. Previous methods adopt generative adversarial networks (GANs) to synthesize realistic images. However, the lack of explicit semantic representation leads to blurry and abnormal image pixels when the outpainting areas are complex and with various objects. In this work, we decompose the outpainting task into two stages. Firstly, we train a GAN to extend regions in semantic segmentation domain instead of image domain. Secondly, another GAN model is trained to synthesize real images based on the extended semantic layouts. The first model focuses on low frequent context such as sizes, classes and other semantic cues while the second model focuses on high frequent context like color and texture. By this design, our approach can handle semantic clues more easily and hence works better in complex scenarios. We evaluate our framework on various datasets and make quantitative and qualitative analysis. Experiments demonstrate that our method generates reasonable extended semantic layouts and images, outperforming state-of-the-art models. | ['Tong Lin', 'Yuning Jiang', 'Tiezheng Ge', 'Quan Chen', 'Min Zhou', 'Jin Ma', 'Ye Ma'] | 2021-10-18 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 6.81132972e-01 4.07028824e-01 2.65379399e-02 -3.41732591e-01
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2c680cfe-6fcc-4c90-b4cc-5ce40b5481e3 | heuristically-guided-compilation-for-multi | 2212.06940 | null | https://arxiv.org/abs/2212.06940v1 | https://arxiv.org/pdf/2212.06940v1.pdf | Heuristically Guided Compilation for Multi-Agent Path Finding | Multi-agent path finding (MAPF) is a task of finding non-conflicting paths connecting agents' specified initial and goal positions in a shared environment. We focus on compilation-based solvers in which the MAPF problem is expressed in a different well established formalism such as mixed-integer linear programming (MILP), Boolean satisfiability (SAT), or constraint programming (CP). As the target solvers for these formalisms act as black-boxes it is challenging to integrate MAPF specific heuristics in the MAPF compilation-based solvers. We show in this work how the build a MAPF encoding for the target SAT solver in which domain specific heuristic knowledge is reflected. The heuristic knowledge is transferred to the SAT solver by selecting candidate paths for each agent and by constructing the encoding only for these candidate paths instead of constructing the encoding for all possible paths for an agent. The conducted experiments show that heuristically guided compilation outperforms the vanilla variants of the SAT-based MAPF solver. | ['Pavel Surynek'] | 2022-12-13 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.13940261e-01 7.92759717e-01 -2.17601165e-01 -2.10915536e-01
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62d1b365-a292-4cc0-b2fa-9c58810515bd | an-audio-visual-speech-separation-model | 2212.10744 | null | https://arxiv.org/abs/2212.10744v1 | https://arxiv.org/pdf/2212.10744v1.pdf | An Audio-Visual Speech Separation Model Inspired by Cortico-Thalamo-Cortical Circuits | Audio-visual approaches involving visual inputs have laid the foundation for recent progress in speech separation. However, the optimization of the concurrent usage of auditory and visual inputs is still an active research area. Inspired by the cortico-thalamo-cortical circuit, in which the sensory processing mechanisms of different modalities modulate one another via the non-lemniscal sensory thalamus, we propose a novel cortico-thalamo-cortical neural network (CTCNet) for audio-visual speech separation (AVSS). First, the CTCNet learns hierarchical auditory and visual representations in a bottom-up manner in separate auditory and visual subnetworks, mimicking the functions of the auditory and visual cortical areas. Then, inspired by the large number of connections between cortical regions and the thalamus, the model fuses the auditory and visual information in a thalamic subnetwork through top-down connections. Finally, the model transmits this fused information back to the auditory and visual subnetworks, and the above process is repeated several times. The results of experiments on three speech separation benchmark datasets show that CTCNet remarkably outperforms existing AVSS methods with considerablely fewer parameters. These results suggest that mimicking the anatomical connectome of the mammalian brain has great potential for advancing the development of deep neural networks. Project repo is https://github.com/JusperLee/CTCNet. | ['Xiaolin Hu', 'Kexin Yuan', 'Hang Chen', 'Fenghua Xie', 'Kai Li'] | 2022-12-21 | null | null | null | null | ['speech-separation'] | ['speech'] | [-1.45446390e-01 -6.32608980e-02 1.08958751e-01 -6.37745410e-02
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5d0b2b87-c60e-4710-8624-94935cb26631 | modelling-sentence-pairs-with-tree-structured | 1610.02806 | null | http://arxiv.org/abs/1610.02806v1 | http://arxiv.org/pdf/1610.02806v1.pdf | Modelling Sentence Pairs with Tree-structured Attentive Encoder | We describe an attentive encoder that combines tree-structured recursive
neural networks and sequential recurrent neural networks for modelling sentence
pairs. Since existing attentive models exert attention on the sequential
structure, we propose a way to incorporate attention into the tree topology.
Specially, given a pair of sentences, our attentive encoder uses the
representation of one sentence, which generated via an RNN, to guide the
structural encoding of the other sentence on the dependency parse tree. We
evaluate the proposed attentive encoder on three tasks: semantic similarity,
paraphrase identification and true-false question selection. Experimental
results show that our encoder outperforms all baselines and achieves
state-of-the-art results on two tasks. | ['Yan Pan', 'Cong Liu', 'Yao Zhou'] | 2016-10-10 | modelling-sentence-pairs-with-tree-structured-1 | https://aclanthology.org/C16-1274 | https://aclanthology.org/C16-1274.pdf | coling-2016-12 | ['question-selection'] | ['natural-language-processing'] | [ 4.32608217e-01 4.81871367e-01 9.38497111e-02 -7.28101432e-01
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44df836c-e5fc-4e2a-b4cd-619fbb81dd72 | online-gaussian-lda-for-unsupervised-pattern | 1910.11599 | null | https://arxiv.org/abs/1910.11599v1 | https://arxiv.org/pdf/1910.11599v1.pdf | Online Gaussian LDA for Unsupervised Pattern Mining from Utility Usage Data | Non-intrusive load monitoring (NILM) aims at separating a whole-home energy signal into its appliance components. Such method can be harnessed to provide various services to better manage and control energy consumption (optimal planning and saving). NILM has been traditionally approached from signal processing and electrical engineering perspectives. Recently, machine learning has started to play an important role in NILM. While most work has focused on supervised algorithms, unsupervised approaches can be more interesting and of practical use in real case scenarios. Specifically, they do not require labelled training data to be acquired from individual appliances and the algorithm can be deployed to operate on the measured aggregate data directly. In this paper, we propose a fully unsupervised NILM framework based on Bayesian hierarchical mixture models. In particular, we develop a new method based on Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global components that summarise the energy signal. These components provide a representation of the consumption patterns. Designed to cope with big data, our algorithm, unlike existing NILM ones, does not focus on appliance recognition. To handle this massive data, GLDA works online. Another novelty of this work compared to the existing NILM is that the data involves different utilities (e.g, electricity, water and gas) as well as some sensors measurements. Finally, we propose different evaluation methods to analyse the results which show that our algorithm finds useful patterns. | ['Abdelhamid Bouchachia', 'Saad Mohamad'] | 2019-10-25 | null | null | null | null | ['non-intrusive-load-monitoring', 'electrical-engineering', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'miscellaneous', 'time-series'] | [ 1.85722783e-01 1.39859673e-02 -3.02318156e-01 -3.30284148e-01
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2144ddba-ee63-4beb-ba64-32385f0ce9c9 | mean-variance-efficient-collaborative | 2306.06590 | null | https://arxiv.org/abs/2306.06590v1 | https://arxiv.org/pdf/2306.06590v1.pdf | Mean-Variance Efficient Collaborative Filtering for Stock Recommendation | The rise of FinTech has transformed financial services onto online platforms, yet stock investment recommender systems have received limited attention compared to other industries. Personalized stock recommendations can significantly impact customer engagement and satisfaction within the industry. However, traditional investment recommendations focus on high-return stocks or highly diversified portfolios based on the modern portfolio theory, often neglecting user preferences. On the other hand, collaborative filtering (CF) methods also may not be directly applicable to stock recommendations, because it is inappropriate to just recommend stocks that users like. The key is to optimally blend users preference with the portfolio theory. However, research on stock recommendations within the recommender system domain remains comparatively limited, and no existing model considers both the preference of users and the risk-return characteristics of stocks. In this regard, we propose a mean-variance efficient collaborative filtering (MVECF) model for stock recommendations that consider both aspects. Our model is specifically designed to improve the pareto optimality (mean-variance efficiency) in a trade-off between the risk (variance of return) and return (mean return) by systemically handling uncertainties in stock prices. Such improvements are incorporated into the MVECF model using regularization, and the model is restructured to fit into the ordinary matrix factorization scheme to boost computational efficiency. Experiments on real-world fund holdings data show that our model can increase the mean-variance efficiency of suggested portfolios while sacrificing just a small amount of mean average precision and recall. Finally, we further show MVECF is easily applicable to the state-of-the-art graph-based ranking models. | ['Woo Chang Kim', 'YongJae lee', 'Munki Chung'] | 2023-06-11 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-6.39306009e-01 -6.78577870e-02 -5.27364910e-01 -4.13013667e-01
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dbf103d8-65e0-4e13-bce7-2065ca7dcf45 | large-scale-representation-learning-from | 1909.08782 | null | https://arxiv.org/abs/1909.08782v1 | https://arxiv.org/pdf/1909.08782v1.pdf | Large-scale representation learning from visually grounded untranscribed speech | Systems that can associate images with their spoken audio captions are an important step towards visually grounded language learning. We describe a scalable method to automatically generate diverse audio for image captioning datasets. This supports pretraining deep networks for encoding both audio and images, which we do via a dual encoder that learns to align latent representations from both modalities. We show that a masked margin softmax loss for such models is superior to the standard triplet loss. We fine-tune these models on the Flickr8k Audio Captions Corpus and obtain state-of-the-art results---improving recall in the top 10 from 29.6% to 49.5%. We also obtain human ratings on retrieval outputs to better assess the impact of incidentally matching image-caption pairs that were not associated in the data, finding that automatic evaluation substantially underestimates the quality of the retrieved results. | ['Yuan Zhang', 'Gabriel Ilharco', 'Jason Baldridge'] | 2019-09-19 | large-scale-representation-learning-from-2 | https://aclanthology.org/K19-1006 | https://aclanthology.org/K19-1006.pdf | conll-2019-11 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 5.14704287e-01 1.89782679e-01 -8.02201629e-02 -5.01982450e-01
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31074625-d2df-4cb1-aaab-b4228acf22a4 | blip-bootstrapping-language-image-pre | 2201.12086 | null | https://arxiv.org/abs/2201.12086v2 | https://arxiv.org/pdf/2201.12086v2.pdf | BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation | Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at https://github.com/salesforce/BLIP. | ['Steven Hoi', 'Caiming Xiong', 'Dongxu Li', 'Junnan Li'] | 2022-01-28 | null | null | null | null | ['open-vocabulary-attribute-detection'] | ['computer-vision'] | [ 2.05028504e-01 -1.40011981e-01 -1.10825278e-01 -3.60455483e-01
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2f85705b-b1e5-4ae8-a4a2-ba56672e30f8 | a-relation-specific-attention-network-for | null | null | https://www.ijcai.org/Proceedings/2020/561 | https://www.ijcai.org/Proceedings/2020/0561.pdf | A Relation-Specific Attention Network for Joint Entity and Relation Extraction | Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts. This is a big challenge due to some of the triplets extracted from one sentence may have overlapping entities. Most existing methods perform entity recognition followed by relation detection between every possible entity pairs, which usually suffers from numerous redundant operations. In this paper, we propose a relation-specific attention network (RSAN) to handle the issue. Our RSAN utilizes relation-aware attention mechanism to construct specific sentence representations for each relation, and then performs sequence labeling to extract its corresponding head and tail entities. Experiments on two public datasets show that our model can effectively extract overlapping triplets and achieve state-of-the-art performance. Our code is available at https://github.com/Anery/RSAN | ['Li Guo', 'Zeliang Song', 'Qiannan Zhu', 'Shirui Pan', 'Xiaofei Zhou', 'Yue Yuan'] | 2020-07-01 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.15252724e-02 2.09428906e-01 -2.59317398e-01 -4.13621813e-01
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d49fbcb5-a0c1-4f6a-87c9-ddde23fd7bd3 | geometer-graph-few-shot-class-incremental | 2205.13954 | null | https://arxiv.org/abs/2205.13954v2 | https://arxiv.org/pdf/2205.13954v2.pdf | Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representation | With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emergence of new nodes and edges. Novel classes appear incrementally along with few labeling due to its newly emergence or lack of exploration. In this paper, we focus on this challenging but practical graph few-shot class-incremental learning (GFSCIL) problem and propose a novel method called Geometer. Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype. Prototype is a vector representing a class in the metric space. With the pop-up of novel classes, Geometer learns and adjusts the attention-based prototypes by observing the geometric proximity, uniformity and separability. Teacher-student knowledge distillation and biased sampling are further introduced to mitigate catastrophic forgetting and unbalanced labeling problem respectively. Experimental results on four public datasets demonstrate that Geometer achieves a substantial improvement of 9.46% to 27.60% over state-of-the-art methods. | ['Xinbing Wang', 'Luoyi Fu', 'Weinan Zhang', 'Lina Yang', 'Xiaoying Gan', 'Bin Lu'] | 2022-05-27 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.23627165e-01 6.22174621e-01 -2.60044366e-01 -3.50921452e-01
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124f9d42-94cb-47ad-bb21-a8c157453cd1 | physics-informed-machine-learning-of-1 | 2209.09349 | null | https://arxiv.org/abs/2209.09349v1 | https://arxiv.org/pdf/2209.09349v1.pdf | Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference | Although the no-u-turn sampler (NUTS) is a widely adopted method for performing Bayesian inference, it requires numerous posterior gradients which can be expensive to compute in practice. Recently, there has been a significant interest in physics-based machine learning of dynamical (or Hamiltonian) systems and Hamiltonian neural networks (HNNs) is a noteworthy architecture. But these types of architectures have not been applied to solve Bayesian inference problems efficiently. We propose the use of HNNs for performing Bayesian inference efficiently without requiring numerous posterior gradients. We introduce latent variable outputs to HNNs (L-HNNs) for improved expressivity and reduced integration errors. We integrate L-HNNs in NUTS and further propose an online error monitoring scheme to prevent sampling degeneracy in regions where L-HNNs may have little training data. We demonstrate L-HNNs in NUTS with online error monitoring considering several complex high-dimensional posterior densities and compare its performance to NUTS. | ['Michael D. Shields', 'Yifeng Che', 'Somayajulu L. N. Dhulipala'] | 2022-09-19 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-1.32247239e-01 1.62844099e-02 5.54582365e-02 -2.87183762e-01
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469c6139-735b-488b-846c-62114cc04cb0 | ssp-based-construction-of-evaluation | null | null | https://aclanthology.org/2022.pandl-1.5 | https://aclanthology.org/2022.pandl-1.5.pdf | SSP-Based Construction of Evaluation-Annotated Data for Fine-Grained Aspect-Based Sentiment Analysis | We report the construction of a Korean evaluation-annotated corpus, hereafter called ‘Evaluation Annotated Dataset (EVAD)’, and its use in Aspect-Based Sentiment Analysis (ABSA) extended in order to cover e-commerce reviews containing sentiment and non-sentiment linguistic patterns. The annotation process uses Semi-Automatic Symbolic Propagation (SSP). We built extensive linguistic resources formalized as a Finite-State Transducer (FST) to annotate corpora with detailed ABSA components in the fashion e-commerce domain. The ABSA approach is extended, in order to analyze user opinions more accurately and extract more detailed features of targets, by including aspect values in addition to topics and aspects, and by classifying aspect-value pairs depending whether values are unary, binary, or multiple. For evaluation, the KoBERT and KcBERT models are trained on the annotated dataset, showing robust performances of F1 0.88 and F1 0.90, respectively, on recognition of aspect-value pairs. | ['Jeesun Nam', 'Eric Laporte', 'Gwanghoon Yoo', 'Changhoe Hwang', 'Shinwoo Kim', 'Suwon Choi'] | null | null | null | null | pandl-coling-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.61744520e-01 2.84322351e-01 -3.11064869e-01 -8.89823914e-01
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5d579f5d-75a8-4816-bba3-b9b07c3a1ef1 | texture-text-guided-texturing-of-3d-shapes | 2302.01721 | null | https://arxiv.org/abs/2302.01721v1 | https://arxiv.org/pdf/2302.01721v1.pdf | TEXTure: Text-Guided Texturing of 3D Shapes | In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model, TEXTure applies an iterative scheme that paints a 3D model from different viewpoints. Yet, while depth-to-image models can create plausible textures from a single viewpoint, the stochastic nature of the generation process can cause many inconsistencies when texturing an entire 3D object. To tackle these problems, we dynamically define a trimap partitioning of the rendered image into three progression states, and present a novel elaborated diffusion sampling process that uses this trimap representation to generate seamless textures from different views. We then show that one can transfer the generated texture maps to new 3D geometries without requiring explicit surface-to-surface mapping, as well as extract semantic textures from a set of images without requiring any explicit reconstruction. Finally, we show that TEXTure can be used to not only generate new textures but also edit and refine existing textures using either a text prompt or user-provided scribbles. We demonstrate that our TEXTuring method excels at generating, transferring, and editing textures through extensive evaluation, and further close the gap between 2D image generation and 3D texturing. | ['Daniel Cohen-Or', 'Raja Giryes', 'Yuval Alaluf', 'Gal Metzer', 'Elad Richardson'] | 2023-02-03 | null | null | null | null | ['text-guided-generation'] | ['computer-vision'] | [ 8.49918187e-01 2.23062679e-01 4.94625062e-01 -2.11341187e-01
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efe865f7-2d3f-4d31-8343-c8c7c157d935 | a-multi-task-dual-tree-network-for-aspect | null | null | https://aclanthology.org/2022.coling-1.616 | https://aclanthology.org/2022.coling-1.616.pdf | A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model. | ['Huijia Zhu', 'Jintao Du', 'Gongshen Liu', 'Kui Meng', 'Yichun Zhao'] | null | null | null | null | coling-2022-10 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.56919867e-01 -3.51227000e-02 -4.03940767e-01 -7.80672908e-01
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50760e37-3c5c-46c0-8a9b-c20be995850f | measuring-uncertainty-during-respiratory-rate | 1805.00082 | null | http://arxiv.org/abs/1805.00082v1 | http://arxiv.org/pdf/1805.00082v1.pdf | Measuring uncertainty during respiratory rate estimation using pressure-sensitive mats | We develop and evaluate a respiratory rate estimation algorithm that utilizes
data from pressure-sensitive mat (PSM) technology for continuous patient
monitoring in neonatal intensive care units (NICU). An analysis of the random
effect of drift and systematic effect of creep in the PSM data is presented,
showing that these are essentially dependent on the applied load and contact
surface. Uncertainty measurements are pivotal when estimating physiologic
parameters. The standard uncertainty in the PSM data is here represented by the
percent drift. Next, we evaluate the applicability of PSM technology to
estimate RR in neonatal patient simulator trials under five mixed effects
including internally and externally induced motion, mattress type, grunting,
laying position, and different breathing rates. We analyze the limits of
agreement on the mixed effects model to derive the uncertainty in the estimated
RR obtained through two estimation techniques. In comparison with the gold
standard RR values, we achieved a mean bias of 0.56 breaths per minute (bpm)
with an error bounded by a 95% confidence interval of [-2.26, 3.37] bpm. These
results meet the clinical accuracy requirements of RR within +/-5 bpm. | [] | 2018-04-30 | null | null | null | null | ['respiratory-rate-estimation'] | ['medical'] | [ 4.45136815e-01 1.62180364e-01 2.47181371e-01 -3.63482744e-01
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41568b12-c0da-4f54-a030-4aba593310b9 | eeg-based-epileptic-seizure-prediction-using | 2209.11172 | null | https://arxiv.org/abs/2209.11172v1 | https://arxiv.org/pdf/2209.11172v1.pdf | EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers | Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction would improve patient care, optimizing the quality of life and the treatment of epilepsy. Predicting an epileptic seizure implies the identification of two distinct states of EEG in a patient with epilepsy: the preictal and the interictal. In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals. Moreover, we accessed the impact of choosing different preictal duration, since its length is not a consensus among experts, and also evaluated how the sample size benefits each model. Our models are compared with fully connected, convolutional, and recurrent networks. The algorithms were patient-specific trained and evaluated on raw EEG signals from the CHB-MIT database. Experimental results and statistical validation demonstrated that our TMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure prediction. | ['Glauco A. P. Caurin', 'Marcelo Becker', 'Americo C. Sakamoto', 'Helio R. Machado', 'Frederico N. Nakano', 'Ricardo L. Saute', 'Gustavo J. G. Lahr', 'Paulo H. Polegato', 'Tharik J. S. Reis', 'Ricardo V. Godoy'] | 2022-09-18 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-5.58557399e-02 -2.32253760e-01 3.94765139e-01 -1.58739582e-01
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1edac68a-dc94-4a16-8bec-f58674c8f550 | integration-of-reinforcement-learning-based | 2304.08280 | null | https://arxiv.org/abs/2304.08280v1 | https://arxiv.org/pdf/2304.08280v1.pdf | Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving | Reinforcement learning has received high research interest for developing planning approaches in automated driving. Most prior works consider the end-to-end planning task that yields direct control commands and rarely deploy their algorithm to real vehicles. In this work, we propose a method to employ a trained deep reinforcement learning policy for dedicated high-level behavior planning. By populating an abstract objective interface, established motion planning algorithms can be leveraged, which derive smooth and drivable trajectories. Given the current environment model, we propose to use a built-in simulator to predict the traffic scene for a given horizon into the future. The behavior of automated vehicles in mixed traffic is determined by querying the learned policy. To the best of our knowledge, this work is the first to apply deep reinforcement learning in this manner, and as such lacks a state-of-the-art benchmark. Thus, we validate the proposed approach by comparing an idealistic single-shot plan with cyclic replanning through the learned policy. Experiments with a real testing vehicle on proving grounds demonstrate the potential of our approach to shrink the simulation to real world gap of deep reinforcement learning based planning approaches. Additional simulative analyses reveal that more complex multi-agent maneuvers can be managed by employing the cycling replanning approach. | ['Michael Buchholz', 'Benjamin Völz', 'Marvin Klimke'] | 2023-04-17 | null | null | null | null | ['motion-planning'] | ['robots'] | [-3.12941857e-02 5.72057009e-01 -2.81589866e-01 -2.34063193e-01
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b8f9eda2-4cae-4911-8159-b96b7204a5a0 | magnet-motif-agnostic-generation-of-molecules | 2305.19303 | null | https://arxiv.org/abs/2305.19303v1 | https://arxiv.org/pdf/2305.19303v1.pdf | MAGNet: Motif-Agnostic Generation of Molecules from Shapes | Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions. Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (motifs), from which they generate novel compounds. While motif representations greatly aid in learning molecular distributions, such methods struggle to represent substructures beyond their known motif set. To alleviate this issue and increase flexibility across datasets, we propose MAGNet, a graph-based model that generates abstract shapes before allocating atom and bond types. To this end, we introduce a novel factorisation of the molecules' data distribution that accounts for the molecules' global context and facilitates learning adequate assignments of atoms and bonds onto shapes. While the abstraction to shapes introduces greater complexity for distribution learning, we show the competitive performance of MAGNet on standard benchmarks. Importantly, we demonstrate that MAGNet's improved expressivity leads to molecules with more topologically distinct structures and, at the same time, diverse atom and bond assignments. | ['Stephan Günnemann', 'Fabian Theis', 'Bastian Rieck', 'Johanna Sommer', 'Leon Hetzel'] | 2023-05-30 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 3.89434338e-01 7.19364583e-02 -5.58159292e-01 -2.23099723e-01
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81e4596b-b367-4b46-8dac-f4b74009ca4d | an-anatomy-based-v1-model-extraction-of-low | 2302.09074 | null | https://arxiv.org/abs/2302.09074v1 | https://arxiv.org/pdf/2302.09074v1.pdf | An anatomy-based V1 model: Extraction of Low-level Features, Reduction of distortion and a V1-inspired SOM | We present a model of the primary visual cortex V1, guided by anatomical experiments. Unlike most machine learning systems our goal is not to maximize accuracy but to realize a system more aligned to biological systems. Our model consists of the V1 layers 4, 2/3, and 5, with inter-layer connections between them in accordance with the anatomy. We further include the orientation selectivity of the V1 neurons and lateral influences in each layer. Our V1 model, when applied to the BSDS500 ground truth images (indicating LGN contour detection before V1), can extract low-level features from the images and perform a significant amount of distortion reduction. As a follow-up to our V1 model, we propose a V1-inspired self-organizing map algorithm (V1-SOM), where the weight update of each neuron gets influenced by its neighbors. V1-SOM can tolerate noisy inputs as well as noise in the weight updates better than SOM and shows a similar level of performance when trained with high dimensional data such as the MNIST dataset. Finally, when we applied V1 processing to the MNIST dataset to extract low-level features and trained V1-SOM with the modified MNIST dataset, the quantization error was significantly reduced. Our results support the hypothesis that the ventral stream performs gradual untangling of input spaces. | ['Nikhil Ranjan Pal', 'Suvam Roy'] | 2023-02-18 | null | null | null | null | ['contour-detection', 'anatomy'] | ['computer-vision', 'miscellaneous'] | [ 1.10257573e-01 4.18976963e-01 3.35470960e-02 -1.20645151e-01
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2adcbbe6-ca01-4a54-82ec-4d27d92d8212 | high-order-tensor-pooling-with-attention-for | 2110.05216 | null | https://arxiv.org/abs/2110.05216v1 | https://arxiv.org/pdf/2110.05216v1.pdf | High-order Tensor Pooling with Attention for Action Recognition | We aim at capturing high-order statistics of feature vectors formed by a neural network, and propose end-to-end second- and higher-order pooling to form a tensor descriptor. Tensor descriptors require a robust similarity measure due to low numbers of aggregated vectors and the burstiness phenomenon, when a given feature appears more/less frequently than statistically expected. We show that the Heat Diffusion Process (HDP) on a graph Laplacian is closely related to the Eigenvalue Power Normalization (EPN) of the covariance/auto-correlation matrix, whose inverse forms a loopy graph Laplacian. We show that the HDP and the EPN play the same role, i.e., to boost or dampen the magnitude of the eigenspectrum thus preventing the burstiness. Finally, we equip higher-order tensors with EPN which acts as a spectral detector of higher-order occurrences to prevent burstiness. We prove that for a tensor of order r built from d dimensional feature descriptors, such a detector gives the likelihood if at least one higher-order occurrence is `projected' into one of binom(d,r) subspaces represented by the tensor; thus forming a tensor power normalization metric endowed with binom(d,r) such `detectors'. | ['Ke Sun', 'Lei Wang', 'Piotr Koniusz'] | 2021-10-11 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [-7.19699562e-02 5.80389462e-02 1.30882159e-01 1.60366789e-01
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bccf7059-53ac-4045-9900-67e50c2526a9 | hunting-group-clues-with-transformers-for | 2207.05254 | null | https://arxiv.org/abs/2207.05254v1 | https://arxiv.org/pdf/2207.05254v1.pdf | Hunting Group Clues with Transformers for Social Group Activity Recognition | This paper presents a novel framework for social group activity recognition. As an expanded task of group activity recognition, social group activity recognition requires recognizing multiple sub-group activities and identifying group members. Most existing methods tackle both tasks by refining region features and then summarizing them into activity features. Such heuristic feature design renders the effectiveness of features susceptible to incomplete person localization and disregards the importance of scene contexts. Furthermore, region features are sub-optimal to identify group members because the features may be dominated by those of people in the regions and have different semantics. To overcome these drawbacks, we propose to leverage attention modules in transformers to generate effective social group features. Our method is designed in such a way that the attention modules identify and then aggregate features relevant to social group activities, generating an effective feature for each social group. Group member information is embedded into the features and thus accessed by feed-forward networks. The outputs of feed-forward networks represent groups so concisely that group members can be identified with simple Hungarian matching between groups and individuals. Experimental results show that our method outperforms state-of-the-art methods on the Volleyball and Collective Activity datasets. | ['Ravigopal Vennelakanti', 'Rahul Vishwakarma', 'Masato Tamura'] | 2022-07-12 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 2.67991930e-01 1.00901462e-01 -2.01412424e-01 -4.30693448e-01
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84d01233-da5f-490f-a9e5-33b95b64f559 | plan-then-seam-towards-efficient-table-to | 2302.05138 | null | https://arxiv.org/abs/2302.05138v2 | https://arxiv.org/pdf/2302.05138v2.pdf | Plan-then-Seam: Towards Efficient Table-to-Text Generation | Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. However, they are computationally expensive due to the non-parallelizable nature of autoregressive decoding and the redundant parameters of two networks. In this paper, we propose the first totally non-autoregressive table-to-text model (Plan-then-Seam, PTS) that produces its outputs in parallel with one single network. PTS firstly writes and calibrates one plan of the content to be generated with a novel rethinking pointer predictor, and then takes the plan as the context for seaming to decode the description. These two steps share parameters and perform iteratively to capture token inter-dependency while keeping parallel decoding. Experiments on two public benchmarks show that PTS achieves 3.0~5.6 times speedup for inference time, reducing 50% parameters, while maintaining as least comparable performance against strong two-stage table-to-text competitors. | ['Yongbin Li', 'Binhua Li', 'Can Ma', 'Bing Li', 'Chengyang Fang', 'Ruiying Geng', 'Liang Li'] | 2023-02-10 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 5.23801386e-01 6.34904206e-01 -2.38512516e-01 -3.85328859e-01
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c29620b8-b7fe-46ff-9bfb-eabef93daa73 | question-answering-on-scholarly-knowledge | 2006.01527 | null | https://arxiv.org/abs/2006.01527v1 | https://arxiv.org/pdf/2006.01527v1.pdf | Question Answering on Scholarly Knowledge Graphs | Answering questions on scholarly knowledge comprising text and other artifacts is a vital part of any research life cycle. Querying scholarly knowledge and retrieving suitable answers is currently hardly possible due to the following primary reason: machine inactionable, ambiguous and unstructured content in publications. We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Such tables can be found in a variety of shapes in the scholarly literature (e.g., surveys, comparisons or results). Our system can retrieve direct answers to a variety of different questions asked on tabular data in articles. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions. This dataset is used as a benchmark for our system and can be reused by others. Additionally, JarvisQA is evaluated on two datasets against other baselines and shows an improvement of two to three folds in performance compared to related methods. | ['Sören Auer', 'Markus Stocker', 'Mohamad Yaser Jaradeh'] | 2020-06-02 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.41007298e-01 1.52464405e-01 -3.30373347e-01 -1.02410158e-02
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c6a18cee-82bf-4373-aafa-c645ed1bde80 | controlvideo-training-free-controllable-text | 2305.13077 | null | https://arxiv.org/abs/2305.13077v1 | https://arxiv.org/pdf/2305.13077v1.pdf | ControlVideo: Training-free Controllable Text-to-Video Generation | Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temporal modeling. Besides the training burden, the generated videos also suffer from appearance inconsistency and structural flickers, especially in long video synthesis. To address these challenges, we design a \emph{training-free} framework called \textbf{ControlVideo} to enable natural and efficient text-to-video generation. ControlVideo, adapted from ControlNet, leverages coarsely structural consistency from input motion sequences, and introduces three modules to improve video generation. Firstly, to ensure appearance coherence between frames, ControlVideo adds fully cross-frame interaction in self-attention modules. Secondly, to mitigate the flicker effect, it introduces an interleaved-frame smoother that employs frame interpolation on alternated frames. Finally, to produce long videos efficiently, it utilizes a hierarchical sampler that separately synthesizes each short clip with holistic coherency. Empowered with these modules, ControlVideo outperforms the state-of-the-arts on extensive motion-prompt pairs quantitatively and qualitatively. Notably, thanks to the efficient designs, it generates both short and long videos within several minutes using one NVIDIA 2080Ti. Code is available at https://github.com/YBYBZhang/ControlVideo. | ['Qi Tian', 'WangMeng Zuo', 'Xiaopeng Zhang', 'Dongsheng Jiang', 'Yuxiang Wei', 'Yabo Zhang'] | 2023-05-22 | null | null | null | null | ['video-generation', 'text-to-video-generation'] | ['computer-vision', 'natural-language-processing'] | [ 1.45971417e-01 -8.57386366e-02 -2.21129850e-01 7.30170235e-02
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fcb3ab91-484c-4062-93c3-531aa9e0e36d | let-graph-be-the-go-board-gradient-free-node | 2211.10782 | null | https://arxiv.org/abs/2211.10782v2 | https://arxiv.org/pdf/2211.10782v2.pdf | Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning | Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to essential applications requiring solid robustness or vigorous security standards, such as product recommendation and user behavior modeling. Under these scenarios, exploiting GNN's vulnerabilities and further downgrading its performance become extremely incentive for adversaries. Previous attackers mainly focus on structural perturbations or node injections to the existing graphs, guided by gradients from the surrogate models. Although they deliver promising results, several limitations still exist. For the structural perturbation attack, to launch a proposed attack, adversaries need to manipulate the existing graph topology, which is impractical in most circumstances. Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model. To bridge these gaps, in this paper, we study the problem of black-box node injection attack, without training a potentially misleading surrogate model. Specifically, we model the node injection attack as a Markov decision process and propose Gradient-free Graph Advantage Actor Critic, namely G2A2C, a reinforcement learning framework in the fashion of advantage actor critic. By directly querying the victim model, G2A2C learns to inject highly malicious nodes with extremely limited attacking budgets, while maintaining a similar node feature distribution. Through our comprehensive experiments over eight acknowledged benchmark datasets with different characteristics, we demonstrate the superior performance of our proposed G2A2C over the existing state-of-the-art attackers. Source code is publicly available at: https://github.com/jumxglhf/G2A2C}. | ['Yanfang Ye', 'Chuxu Zhang', 'Yujie Fan', 'Mingxuan Ju'] | 2022-11-19 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 1.79446280e-01 5.22036672e-01 -4.38699901e-01 2.23860577e-01
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e97d4f28-1330-4721-bb0c-224fc6cb451b | kashmir-a-computational-analysis-of-the-voice | 1909.12940 | null | https://arxiv.org/abs/1909.12940v4 | https://arxiv.org/pdf/1909.12940v4.pdf | Hope Speech Detection: A Computational Analysis of the Voice of Peace | The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an analysis on temporal trends of pro-peace and pro-war intent observing that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \emph{hope-speech detection}. | ['Jaime G. Carbonell', 'Ashiqur R. KhudaBukhsh', 'Shriphani Palakodety'] | 2019-09-11 | null | null | null | null | ['hope-speech-detection'] | ['natural-language-processing'] | [-2.55033791e-01 -1.38871104e-01 -2.66310006e-01 9.22013074e-02
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3f6e3e99-c253-40aa-8061-503ba5ed5781 | ma-vit-modality-agnostic-vision-transformers | 2304.07549 | null | https://arxiv.org/abs/2304.07549v1 | https://arxiv.org/pdf/2304.07549v1.pdf | MA-ViT: Modality-Agnostic Vision Transformers for Face Anti-Spoofing | The existing multi-modal face anti-spoofing (FAS) frameworks are designed based on two strategies: halfway and late fusion. However, the former requires test modalities consistent with the training input, which seriously limits its deployment scenarios. And the latter is built on multiple branches to process different modalities independently, which limits their use in applications with low memory or fast execution requirements. In this work, we present a single branch based Transformer framework, namely Modality-Agnostic Vision Transformer (MA-ViT), which aims to improve the performance of arbitrary modal attacks with the help of multi-modal data. Specifically, MA-ViT adopts the early fusion to aggregate all the available training modalities data and enables flexible testing of any given modal samples. Further, we develop the Modality-Agnostic Transformer Block (MATB) in MA-ViT, which consists of two stacked attentions named Modal-Disentangle Attention (MDA) and Cross-Modal Attention (CMA), to eliminate modality-related information for each modal sequences and supplement modality-agnostic liveness features from another modal sequences, respectively. Experiments demonstrate that the single model trained based on MA-ViT can not only flexibly evaluate different modal samples, but also outperforms existing single-modal frameworks by a large margin, and approaches the multi-modal frameworks introduced with smaller FLOPs and model parameters. | ['Yanyan Liang', 'Ajian Liu'] | 2023-04-15 | null | null | null | null | ['face-anti-spoofing'] | ['computer-vision'] | [ 3.90108585e-01 -4.42024678e-01 -2.31560975e-01 -4.57931235e-02
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6d44f01d-1d1c-490d-8839-d68948fe072b | residual-attention-a-simple-but-effective | 2108.02456 | null | https://arxiv.org/abs/2108.02456v2 | https://arxiv.org/pdf/2108.02456v2.pdf | Residual Attention: A Simple but Effective Method for Multi-Label Recognition | Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. To effectively capture different spatial regions occupied by objects from different categories, we propose an embarrassingly simple module, named class-specific residual attention (CSRA). CSRA generates class-specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature. CSRA achieves state-of-the-art results on multilabel recognition, and at the same time is much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads to consistent improvement across many diverse pretrained models and datasets without any extra training. CSRA is both easy to implement and light in computations, which also enjoys intuitive explanations and visualizations. | ['Jianxin Wu', 'Ke Zhu'] | 2021-08-05 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_Residual_Attention_A_Simple_but_Effective_Method_for_Multi-Label_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_Residual_Attention_A_Simple_but_Effective_Method_for_Multi-Label_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['multi-label-image-classification'] | ['computer-vision'] | [ 2.98599124e-01 -2.35930651e-01 -1.32858694e-01 -6.42472386e-01
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38d9a8ce-8d18-4c39-9059-62789e6d7580 | pitch-preservation-in-singing-voice-synthesis | 2110.05033 | null | https://arxiv.org/abs/2110.05033v2 | https://arxiv.org/pdf/2110.05033v2.pdf | Pitch Preservation In Singing Voice Synthesis | Suffering from limited singing voice corpus, existing singing voice synthesis (SVS) methods that build encoder-decoder neural networks to directly generate spectrogram could lead to out-of-tune issues during the inference phase. To attenuate these issues, this paper presents a novel acoustic model with independent pitch encoder and phoneme encoder, which disentangles the phoneme and pitch information from music score to fully utilize the corpus. Specifically, according to equal temperament theory, the pitch encoder is constrained by a pitch metric loss that maps distances between adjacent input pitches into corresponding frequency multiples between the encoder outputs. For the phoneme encoder, based on the analysis that same phonemes corresponding to varying pitches can produce similar pronunciations, this encoder is followed by an adversarially trained pitch classifier to enforce the identical phonemes with different pitches mapping into the same phoneme feature space. By these means, the sparse phonemes and pitches in original input spaces can be transformed into more compact feature spaces respectively, where the same elements cluster closely and cooperate mutually to enhance synthesis quality. Then, the outputs of the two encoders are summed together to pass through the following decoder in the acoustic model. Experimental results indicate that the proposed approaches can characterize intrinsic structure between pitch inputs to obtain better pitch synthesis accuracy and achieve superior singing synthesis performance against the advanced baseline system. | ['Huajun Wang', 'Kun Wang', 'Hai Zhu', 'Shujun Liu'] | 2021-10-11 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 5.43889292e-02 9.32872519e-02 -2.52337512e-02 -1.83462705e-02
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c7607d58-de6b-484d-b3c7-19972c32933e | illumination-estimation-challenge-experience | 2012.15779 | null | https://arxiv.org/abs/2012.15779v1 | https://arxiv.org/pdf/2012.15779v1.pdf | Illumination Estimation Challenge: experience of past two years | Illumination estimation is the essential step of computational color constancy, one of the core parts of various image processing pipelines of modern digital cameras. Having an accurate and reliable illumination estimation is important for reducing the illumination influence on the image colors. To motivate the generation of new ideas and the development of new algorithms in this field, the 2nd Illumination estimation challenge~(IEC\#2) was conducted. The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased. The challenge had several tracks: general, indoor, and two-illuminant with each of them focusing on different parameters of the scenes. Other main features of it are a new large dataset of images (about 5000) taken with the same camera sensor model, a manual markup accompanying each image, diverse content with scenes taken in numerous countries under a huge variety of illuminations extracted by using the SpyderCube calibration object, and a contest-like markup for the images from the Cube+ dataset that was used in IEC\#1. This paper focuses on the description of the past two challenges, algorithms which won in each track, and the conclusions that were drawn based on the results obtained during the 1st and 2nd challenge that can be useful for similar future developments. | ['Dmitry Nikolaev', 'Sven Lončarić', 'Raimondo Schettini', 'Simone Bianco', 'Riccardo Riva', 'Marco Buzzelli', 'Yanlin Qian', 'Artem Nikonorov', 'Daria Senshina', 'Arseniy Terekhin', 'Zhihao LI', 'Alexander Belokopytov', 'Marko Subašić', 'Karlo Koscević', 'Nikola Banić', 'Ilya Semenkov', 'Alex Savchik', 'Egor Ershov'] | 2020-12-31 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 2.78548062e-01 -6.94363475e-01 4.68090475e-01 -5.68329036e-01
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256577b5-41a8-4c8b-82e4-d0fab0b81b56 | a-new-paradigm-for-device-free-indoor | 2304.06490 | null | https://arxiv.org/abs/2304.06490v1 | https://arxiv.org/pdf/2304.06490v1.pdf | A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems | The demand for device-free indoor localization using commercial Wi-Fi devices has rapidly increased in various fields due to its convenience and versatile applications. However, random frequency offset (RFO) in wireless channels poses challenges to the accuracy of indoor localization when using fluctuating channel state information (CSI). To mitigate the RFO problem, an error vector spectrum (EVS) is conceived thanks to its higher resolution of signal and robustness to RFO. To address these challenges, this paper proposed a novel error vector assisted learning (EVAL) for device-free indoor localization. The proposed EVAL scheme employs deep neural networks to classify the location of a person in the indoor environment by extracting ample channel features from the physical layer signals. We conducted realistic experiments based on OpenWiFi project to extract both EVS and CSI to examine the performance of different device-free localization techniques. Experimental results show that our proposed EVAL scheme outperforms conventional machine learning methods and benchmarks utilizing either CSI amplitude or phase information. Compared to most existing CSI-based localization schemes, a new paradigm with higher positioning accuracy by adopting EVS is revealed by our proposed EVAL system. | ['Kai-Ten Feng', 'Li-Hsiang Shen', 'An-Hung Hsiao', 'Wen Liu'] | 2023-03-25 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 1.65330112e-01 -4.23645556e-01 -1.73510715e-01 -4.23934877e-01
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6604f7eb-1072-479c-b663-712b784bab5a | renderdiffusion-image-diffusion-for-3d | 2211.09869 | null | https://arxiv.org/abs/2211.09869v2 | https://arxiv.org/pdf/2211.09869v2.pdf | RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation | Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D generation or single-view object reconstruction. In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision. Central to our method is a novel image denoising architecture that generates and renders an intermediate three-dimensional representation of a scene in each denoising step. This enforces a strong inductive structure within the diffusion process, providing a 3D consistent representation while only requiring 2D supervision. The resulting 3D representation can be rendered from any view. We evaluate RenderDiffusion on FFHQ, AFHQ, ShapeNet and CLEVR datasets, showing competitive performance for generation of 3D scenes and inference of 3D scenes from 2D images. Additionally, our diffusion-based approach allows us to use 2D inpainting to edit 3D scenes. | ['Titas Anciukevicius', 'Paul Guerrero', 'Niloy J. Mitra', 'Hakan Bilen', 'Paul Henderson', 'Matthew Fisher', 'Zexiang Xu'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Anciukevicius_RenderDiffusion_Image_Diffusion_for_3D_Reconstruction_Inpainting_and_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['object-reconstruction'] | ['computer-vision'] | [ 1.23001963e-01 3.85351986e-01 2.17544556e-01 -5.47204137e-01
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ad82a5e7-da45-4efe-9b6a-13a33a2cd322 | fast-multi-layer-laplacian-enhancement | 1606.07396 | null | http://arxiv.org/abs/1606.07396v1 | http://arxiv.org/pdf/1606.07396v1.pdf | Fast Multi-Layer Laplacian Enhancement | A novel, fast and practical way of enhancing images is introduced in this
paper. Our approach builds on Laplacian operators of well-known edge-aware
kernels, such as bilateral and nonlocal means, and extends these filter's
capabilities to perform more effective and fast image smoothing, sharpening and
tone manipulation. We propose an approximation of the Laplacian, which does not
require normalization of the kernel weights. Multiple Laplacians of the
affinity weights endow our method with progressive detail decomposition of the
input image from fine to coarse scale. These image components are blended by a
structure mask, which avoids noise/artifact magnification or detail loss in the
output image. Contributions of the proposed method to existing image editing
tools are: (1) Low computational and memory requirements, making it appropriate
for mobile device implementations (e.g. as a finish step in a camera pipeline),
(2) A range of filtering applications from detail enhancement to denoising with
only a few control parameters, enabling the user to apply a combination of
various (and even opposite) filtering effects. | ['Peyman Milanfar', 'Hossein Talebi'] | 2016-06-23 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 4.93470371e-01 -1.04872286e-01 2.91868269e-01 -3.23061049e-01
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11d7202a-9ebe-41d5-8cc5-af66cb10f826 | artificial-neural-network-based-breast-cancer | 2006.01767 | null | https://arxiv.org/abs/2006.01767v1 | https://arxiv.org/pdf/2006.01767v1.pdf | Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review | Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people. For predicting breast cancer, several automated systems are already developed using different medical imaging modalities. This paper provides a systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography. The advantages and limitations of different ANN models including spiking neural network (SNN), deep belief network (DBN), convolutional neural network (CNN), multilayer neural network (MLNN), stacked autoencoders (SAE), and stacked de-noising autoencoders (SDAE) are described in this review. The review also shows that the studies related to breast cancer detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies. It is found that the best performance was achieved by residual neural network (ResNet)-50 and ResNet-101 models of CNN algorithm. | ['Subrato Bharati', 'Prajoy Podder', 'M. Rubaiyat Hossain Mondal'] | 2020-05-29 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 1.96201980e-01 2.22080991e-01 -1.03817917e-02 -2.84898877e-01
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9f8433db-a295-4b73-bfad-479345c90f45 | the-loss-of-the-property-of-locality-of-the | 2211.11170 | null | https://arxiv.org/abs/2211.11170v1 | https://arxiv.org/pdf/2211.11170v1.pdf | The loss of the property of locality of the kernel in high-dimensional Gaussian process regression on the example of the fitting of molecular potential energy surfaces | Kernel based methods including Gaussian process regression (GPR) and generally kernel ridge regression (KRR) have been finding increasing use in computational chemistry, including the fitting of potential energy surfaces and density functionals in high-dimensional feature spaces. Kernels of the Matern family such as Gaussian-like kernels (basis functions) are often used, which allows imparting them the meaning of covariance functions and formulating GPR as an estimator of the mean of a Gaussian distribution. The notion of locality of the kernel is critical for this interpretation. It is also critical to the formulation of multi-zeta type basis functions widely used in computational chemistry We show, on the example of fitting of molecular potential energy surfaces of increasing dimensionality, the practical disappearance of the property of locality of a Gaussian-like kernel in high dimensionality. We also formulate a multi-zeta approach to the kernel and show that it significantly improves the quality of regression in low dimensionality but loses any advantage in high dimensionality, which is attributed to the loss of the property of locality. | ['Manabu Ihara', 'Sergei Manzhos'] | 2022-11-21 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-1.57569833e-02 -8.69879201e-02 1.14669621e-01 -3.19550574e-01
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5dcdd2d4-f4c9-46c1-80bd-6f1c662128f8 | efficient-bayesian-physics-informed-neural | 2303.07392 | null | https://arxiv.org/abs/2303.07392v1 | https://arxiv.org/pdf/2303.07392v1.pdf | Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion | Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on partial differential equations. However, the overparameterized nature of neural networks poses a computational challenge for high-dimensional posterior inference. Existing inference approaches, such as particle-based or variance inference methods, are either computationally expensive for high-dimensional posterior inference or provide unsatisfactory uncertainty estimates. In this paper, we present a new efficient inference algorithm for B-PINNs that uses Ensemble Kalman Inversion (EKI) for high-dimensional inference tasks. We find that our proposed method can achieve inference results with informative uncertainty estimates comparable to Hamiltonian Monte Carlo (HMC)-based B-PINNs with a much reduced computational cost. These findings suggest that our proposed approach has great potential for uncertainty quantification in physics-informed machine learning for practical applications. | ['Xueyu Zhu', 'Andrew Pensoneault'] | 2023-03-13 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.18514776e-01 -1.13917939e-01 1.57724127e-01 -4.29361105e-01
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c8942559-49c0-4b8e-80a4-dc03e4ff966a | lico-net-linearized-convolution-network-for | 2211.04635 | null | https://arxiv.org/abs/2211.04635v1 | https://arxiv.org/pdf/2211.04635v1.pdf | LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting | This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting. It is optimized specifically for low-power processor units like microcontrollers. ML operators exhibit heterogeneous efficiency profiles on power-efficient hardware. Given the exact theoretical computation cost, int8 operators are more computation-effective than float operators, and linear layers are often more efficient than other layers. The proposed LiCo-Net is a dual-phase system that uses the efficient int8 linear operators at the inference phase and applies streaming convolutions at the training phase to maintain a high model capacity. The experimental results show that LiCo-Net outperforms single-value decomposition filter (SVDF) on hardware efficiency with on-par detection performance. Compared to SVDF, LiCo-Net reduces cycles by 40% on HiFi4 DSP. | ['Vikas Chandra', 'Raghuraman Krishnamoorthi', 'Xin Lei', 'Ming Sun', 'Raziel Alvarez', 'Limin Tang', 'Ivaylo Enchev', 'Yiteng Huang', 'Yangyang Shi', 'Biqiao Zhang', 'Li Wan', 'Zhaojun Yang', 'Haichuan Yang'] | 2022-11-09 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [-9.39291567e-02 -3.02007586e-01 -4.65187997e-01 -1.48234218e-01
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61f79ded-70fe-4e97-87a4-67e371e7511d | graph-based-features-for-automatic-online | 1708.01060 | null | https://arxiv.org/abs/1708.01060v1 | https://arxiv.org/pdf/1708.01060v1.pdf | Graph-based Features for Automatic Online Abuse Detection | While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach. | ['Georges Linares', 'Richard Dufour', 'Vincent Labatut', 'Etienne Papegnies'] | 2017-08-03 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [ 2.50376403e-01 4.04149294e-01 3.82204615e-02 -1.44154847e-01
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61e48ce6-00df-46ee-8dc1-705fa4dc143f | mpchat-towards-multimodal-persona-grounded | 2305.17388 | null | https://arxiv.org/abs/2305.17388v1 | https://arxiv.org/pdf/2305.17388v1.pdf | MPCHAT: Towards Multimodal Persona-Grounded Conversation | In order to build self-consistent personalized dialogue agents, previous research has mostly focused on textual persona that delivers personal facts or personalities. However, to fully describe the multi-faceted nature of persona, image modality can help better reveal the speaker's personal characteristics and experiences in episodic memory (Rubin et al., 2003; Conway, 2009). In this work, we extend persona-based dialogue to the multimodal domain and make two main contributions. First, we present the first multimodal persona-based dialogue dataset named MPCHAT, which extends persona with both text and images to contain episodic memories. Second, we empirically show that incorporating multimodal persona, as measured by three proposed multimodal persona-grounded dialogue tasks (i.e., next response prediction, grounding persona prediction, and speaker identification), leads to statistically significant performance improvements across all tasks. Thus, our work highlights that multimodal persona is crucial for improving multimodal dialogue comprehension, and our MPCHAT serves as a high-quality resource for this research. | ['Gunhee Kim', 'Sangdoo Yun', 'Yeda Song', 'Jaewoo Ahn'] | 2023-05-27 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-1.12914048e-01 5.31265974e-01 -4.34049591e-03 -5.42932093e-01
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2fc8915f-ecb7-4bbf-9aac-295986e23853 | llm-itself-can-read-and-generate-cxr-images | 2305.11490 | null | https://arxiv.org/abs/2305.11490v2 | https://arxiv.org/pdf/2305.11490v2.pdf | LLM Itself Can Read and Generate CXR Images | Building on the recent remarkable development of large language models (LLMs), active attempts are being made to extend the utility of LLMs to multimodal tasks. There have been previous efforts to link language and visual information, and attempts to add visual capabilities to LLMs are ongoing as well. However, existing attempts use LLMs only as image decoders and no attempt has been made to generate images in the same line as the natural language. By adopting a VQ-GAN framework in which latent representations of images are treated as a kind of text tokens, we present a novel method to fine-tune a pre-trained LLM to read and generate images like text without any structural changes, extra training objectives, or the need for training an ad-hoc network while still preserving the of the instruction-following capability of the LLM. We apply this framework to chest X-ray (CXR) image and report generation tasks as it is a domain in which translation of complex information between visual and language domains is important. The code is available at https://github.com/hyn2028/llm-cxr. | ['Jong Chul Ye', 'Won Jun Kim', 'Suhyeon Lee'] | 2023-05-19 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [ 7.15230703e-01 5.28948724e-01 -6.92612603e-02 -4.93455887e-01
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4093f185-d317-4533-b715-d606b1fd8946 | tu-wien-trec-deep-learning-19-simple | 1912.01385 | null | https://arxiv.org/abs/1912.01385v1 | https://arxiv.org/pdf/1912.01385v1.pdf | TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking | The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpretable at the same time. However, in most instances we have to choose which property is most important to us. We used the opportunity of the TREC 2019 Deep Learning track to evaluate the effectiveness of a balanced neural re-ranking approach. We submitted results of the TK (Transformer-Kernel) model: a neural re-ranking model for ad-hoc search using an efficient contextualization mechanism. TK employs a very small number of lightweight Transformer layers to contextualize query and document word embeddings. To score individual term interactions, we use a document-length enhanced kernel-pooling, which enables users to gain insight into the model. Our best result for the passage ranking task is: 0.420 MAP, 0.671 nDCG, 0.598 P@10 (TUW19-p3 full). Our best result for the document ranking task is: 0.271 MAP, 0.465 nDCG, 0.730 P@10 (TUW19-d3 re-ranking). | ['Markus Zlabinger', 'Sebastian Hofstätter', 'Allan Hanbury'] | 2019-12-03 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-2.12144107e-01 -1.71801001e-01 2.48206034e-02 -4.24599916e-01
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5072e2ba-7d23-48f7-9c51-b56986e65e20 | multilingual-negation-scope-resolution-for | null | null | https://aclanthology.org/2021.louhi-1.2 | https://aclanthology.org/2021.louhi-1.2.pdf | Multilingual Negation Scope Resolution for Clinical Text | Negation scope resolution is key to high-quality information extraction from clinical texts, but so far, efforts to make encoders used for information extraction negation-aware have been limited to English. We present a universal approach to multilingual negation scope resolution, that overcomes the lack of training data by relying on disparate resources in different languages and domains. We evaluate two approaches to learn from these resources, training on combined data and training in a multi-task learning setup. Our experiments show that zero-shot scope resolution in clinical text is possible, and that combining available resources improves performance in most cases. | ['Anders Søgaard', 'Mareike Hartmann'] | null | null | null | null | eacl-louhi-2021-4 | ['negation-scope-resolution'] | ['natural-language-processing'] | [ 4.39172477e-01 4.09669578e-01 -8.05017233e-01 -3.20169538e-01
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7995cf05-5e39-4b56-94d7-3547015c9f23 | genpose-generative-category-level-object-pose | 2306.10531 | null | https://arxiv.org/abs/2306.10531v1 | https://arxiv.org/pdf/2306.10531v1.pdf | GenPose: Generative Category-level Object Pose Estimation via Diffusion Models | Object pose estimation plays a vital role in embodied AI and computer vision, enabling intelligent agents to comprehend and interact with their surroundings. Despite the practicality of category-level pose estimation, current approaches encounter challenges with partially observed point clouds, known as the multihypothesis issue. In this study, we propose a novel solution by reframing categorylevel object pose estimation as conditional generative modeling, departing from traditional point-to-point regression. Leveraging score-based diffusion models, we estimate object poses by sampling candidates from the diffusion model and aggregating them through a two-step process: filtering out outliers via likelihood estimation and subsequently mean-pooling the remaining candidates. To avoid the costly integration process when estimating the likelihood, we introduce an alternative method that trains an energy-based model from the original score-based model, enabling end-to-end likelihood estimation. Our approach achieves state-of-the-art performance on the REAL275 dataset, surpassing 50% and 60% on strict 5d2cm and 5d5cm metrics, respectively. Furthermore, our method demonstrates strong generalizability to novel categories sharing similar symmetric properties without fine-tuning and can readily adapt to object pose tracking tasks, yielding comparable results to the current state-of-the-art baselines. | ['Hao Dong', 'Mingdong Wu', 'Jiyao Zhang'] | 2023-06-18 | null | null | null | null | ['pose-tracking', 'pose-estimation', '6d-pose-estimation-using-rgbd', '6d-pose-estimation-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.37787044e-01 -9.76922214e-02 4.40696441e-02 -3.69501710e-01
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4cbcd469-149b-45a8-a185-ca500bc1696a | asner-annotated-dataset-and-baseline-for-1 | null | null | https://aclanthology.org/2022.lrec-1.706 | https://aclanthology.org/2022.lrec-1.706.pdf | AsNER - Annotated Dataset and Baseline for Assamese Named Entity recognition | We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available. | ['Priyankoo Sarmah', 'Sukumar Nandi', 'Dhrubajyoti Pathak'] | null | null | null | null | lrec-2022-6 | ['xlm-r'] | ['natural-language-processing'] | [-3.21324021e-01 1.91120803e-01 -9.57401991e-02 -3.61480981e-01
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7177700b-521a-491f-9c0d-7b9012ca0e69 | consistent-open-ended-question-generation | 2210.11536 | null | https://arxiv.org/abs/2210.11536v1 | https://arxiv.org/pdf/2210.11536v1.pdf | CONSISTENT: Open-Ended Question Generation From News Articles | Recent work on question generation has largely focused on factoid questions such as who, what, where, when about basic facts. Generating open-ended why, how, what, etc. questions that require long-form answers have proven more difficult. To facilitate the generation of open-ended questions, we propose CONSISTENT, a new end-to-end system for generating open-ended questions that are answerable from and faithful to the input text. Using news articles as a trustworthy foundation for experimentation, we demonstrate our model's strength over several baselines using both automatic and human=based evaluations. We contribute an evaluation dataset of expert-generated open-ended questions.We discuss potential downstream applications for news media organizations. | ['Smaranda Muresan', 'Justin Lewis', 'Tuhin Chakrabarty'] | 2022-10-20 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-7.74821192e-02 1.11213446e+00 1.33648902e-01 -5.32218456e-01
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bd9fab31-86a3-4c97-b389-f6e3bf198af2 | learning-to-rectify-for-robust-learning-with | 2111.04239 | null | https://arxiv.org/abs/2111.04239v1 | https://arxiv.org/pdf/2111.04239v1.pdf | Learning to Rectify for Robust Learning with Noisy Labels | Label noise significantly degrades the generalization ability of deep models in applications. Effective strategies and approaches, \textit{e.g.} re-weighting, or loss correction, are designed to alleviate the negative impact of label noise when training a neural network. Those existing works usually rely on the pre-specified architecture and manually tuning the additional hyper-parameters. In this paper, we propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network within the meta-learning scenario. In contrast to the deterministic models, WarPI is formulated as a hierarchical probabilistic model by learning an amortization meta-network, which can resolve sample ambiguity and be therefore more robust to serious label noise. Unlike the existing approximated weighting function of directly generating weight values from losses, our meta-network is learned to estimate a rectifying vector from the input of the logits and labels, which has the capability of leveraging sufficient information lying in them. This provides an effective way to rectify the learning procedure for the classification network, demonstrating a significant improvement of the generalization ability. Besides, modeling the rectifying vector as a latent variable and learning the meta-network can be seamlessly integrated into the SGD optimization of the classification network. We evaluate WarPI on four benchmarks of robust learning with noisy labels and achieve the new state-of-the-art under variant noise types. Extensive study and analysis also demonstrate the effectiveness of our model. | ['Yilong Yin', 'Zhongyi Han', 'Qi Wei', 'Chenhui Guo', 'Haoliang Sun'] | 2021-11-08 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.51710737e-01 2.68440306e-01 -2.84484863e-01 -6.96609437e-01
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cbc6a177-4aa8-4de5-aecf-14d8e56b72a8 | a-low-rank-approximation-approach-to-learning | null | null | https://aclanthology.org/N16-1008 | https://aclanthology.org/N16-1008.pdf | A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization | null | ['a', 'William Yang Wang', 'Yashar Mehdad', 'Dragomir R. Radev', 'Am Stent'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['timeline-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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26253a9d-086f-4bd7-bf08-957a7fa2433b | spectral-image-clustering-on-dual-energy-ct | 2201.13398 | null | https://arxiv.org/abs/2201.13398v1 | https://arxiv.org/pdf/2201.13398v1.pdf | Spectral image clustering on dual-energy CT scans using functional regression mixtures | Dual-energy computed tomography (DECT) is an advanced CT scanning technique enabling material characterization not possible with conventional CT scans. It allows the reconstruction of energy decay curves at each 3D image voxel, representing varying image attenuation at different effective scanning energy levels. In this paper, we develop novel functional data analysis (FDA) techniques and adapt them to the analysis of DECT decay curves. More specifically, we construct functional mixture models that integrate spatial context in mixture weights, with mixture component densities being constructed upon the energy decay curves as functional observations. We design unsupervised clustering algorithms by developing dedicated expectation maximization (EM) algorithms for the maximum likelihood estimation of the model parameters. To our knowledge, this is the first article to adapt statistical FDA tools and model-based clustering to take advantage of the full spectral information provided by DECT. We evaluate our methods on 91 head and neck cancer DECT scans. We compare our unsupervised clustering results to tumor contours traced manually by radiologists, as well as to several baseline algorithms. Given the inter-rater variability even among experts at delineating head and neck tumors, and given the potential importance of tissue reactions surrounding the tumor itself, our proposed methodology has the potential to add value in downstream machine learning applications for clinical outcome prediction based on DECT data in head and neck cancer. | ['Peter Savadjiev', 'Reza Forghani', 'Mark Coates', 'Faicel Chamroukhi', 'Segolene Brivet'] | 2022-01-31 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.08813488e-01 -1.59169659e-02 -3.06589454e-01 -2.68589497e-01
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557366f7-b8c8-424d-80e4-f940d8badafa | clevrtex-a-texture-rich-benchmark-for | 2111.10265 | null | https://arxiv.org/abs/2111.10265v1 | https://arxiv.org/pdf/2111.10265v1.pdf | ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation | There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. It includes 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings. Dataset and code are available at https://www.robots.ox.ac.uk/~vgg/research/clevrtex. | ['Christian Rupprecht', 'Iro Laina', 'Laurynas Karazija'] | 2021-11-19 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 5.57857513e-01 -1.08167984e-01 1.78998828e-01 -3.66765499e-01
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6e54e172-5383-449c-a095-38afe2d60970 | knowledge-based-multilingual-language-model-1 | 2111.10962 | null | https://arxiv.org/abs/2111.10962v4 | https://arxiv.org/pdf/2111.10962v4.pdf | Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples | Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utilization of multilingual knowledge graph (KG) data for language model (LM) pretraining. They often train LMs with KGs in indirect ways, relying on extra entity/relation embeddings to facilitate knowledge injection. In this work, we explore methods to make better use of the multilingual annotation and language agnostic property of KG triples, and present novel knowledge based multilingual language models (KMLMs) trained directly on the knowledge triples. We first generate a large amount of multilingual synthetic sentences using the Wikidata KG triples. Then based on the intra- and inter-sentence structures of the generated data, we design pretraining tasks to enable the LMs to not only memorize the factual knowledge but also learn useful logical patterns. Our pretrained KMLMs demonstrate significant performance improvements on a wide range of knowledge-intensive cross-lingual tasks, including named entity recognition (NER), factual knowledge retrieval, relation classification, and a newly designed logical reasoning task. | ['Luo Si', 'Shafiq Joty', 'Lidong Bing', 'Ruidan He', 'Xin Li', 'Linlin Liu'] | 2021-11-22 | knowledge-based-multilingual-language-model | https://openreview.net/forum?id=SCSonHu4p0W | https://openreview.net/pdf?id=SCSonHu4p0W | null | ['relation-classification'] | ['natural-language-processing'] | [-4.12186801e-01 4.86699551e-01 -5.78583419e-01 -3.88777673e-01
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23e5d5d1-2bf7-475d-8290-9d06a74341c2 | fast-spatially-varying-indoor-lighting-1 | 1906.03799 | null | https://arxiv.org/abs/1906.03799v1 | https://arxiv.org/pdf/1906.03799v1.pdf | Fast Spatially-Varying Indoor Lighting Estimation | We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location in less than 20ms on a laptop mobile graphics card. While existing approaches estimate a single, global lighting representation or require depth as input, our method reasons about local lighting without requiring any geometry information. We demonstrate, through quantitative experiments including a user study, that our results achieve lower lighting estimation errors and are preferred by users over the state-of-the-art. Our approach can be used directly for augmented reality applications, where a virtual object is relit realistically at any position in the scene in real-time. | ['Jean-François Lalonde', 'Sunil Hadap', 'Mathieu Garon', 'Kalyan Sunkavalli', 'Nathan Carr'] | 2019-06-10 | fast-spatially-varying-indoor-lighting | http://openaccess.thecvf.com/content_CVPR_2019/html/Garon_Fast_Spatially-Varying_Indoor_Lighting_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Garon_Fast_Spatially-Varying_Indoor_Lighting_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['lighting-estimation'] | ['computer-vision'] | [ 3.37473564e-02 -1.36470914e-01 3.62088174e-01 -5.09311020e-01
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3ad0bc84-53a3-46a3-a0a8-bd78d9e2dece | efficient-multi-domain-dictionary-learning | 1811.00274 | null | http://arxiv.org/abs/1811.00274v1 | http://arxiv.org/pdf/1811.00274v1.pdf | Efficient Multi-Domain Dictionary Learning with GANs | In this paper, we propose the multi-domain dictionary learn- ing (MDDL) to
make dictionary learning-based classification more robust to data representing
in different domains. We use adversarial neural networks to generate data in
different styles, and collect all the generated data into a miscellaneous
dictionary. To tackle the dictionary learning with many sam- ples, we compute
the weighting matrix that compress the mis- cellaneous dictionary from
multi-sample per class to single sample per class. We show that the time
complexity solv- ing the proposed MDDL with weighting matrix is the same as
solving the dictionary with single sample per class. More- over, since the
weighting matrix could help the solver rely more on the training data, which
possibly lie in the same do- main with the testing data, the classification
could be more accurate. | ['Ulrich Neumann', 'Cho Ying Wu'] | 2018-11-01 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 9.70183685e-02 -7.78587535e-02 -1.81174502e-01 -1.88953325e-01
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c6d457f3-2614-4336-891a-456b50cf419c | document-image-shadow-removal-guided-by-color | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Document_Image_Shadow_Removal_Guided_by_Color-Aware_Background_CVPR_2023_paper.pdf | Document Image Shadow Removal Guided by Color-Aware Background | Existing works on document image shadow removal mostly depend on learning and leveraging a constant background (the color of the paper) from the image. However, the constant background is less representative and frequently ignores other background colors, such as the printed colors, resulting in distorted results. In this paper, we present a color-aware background extraction network (CBENet) for extracting a spatially varying background image that accurately depicts the background colors of the document. Furthermore, we propose a background-guided document images shadow removal network (BGShadowNet) using the predicted spatially varying background as auxiliary information, which consists of two stages. At Stage I, a background-constrained decoder is designed to promote a coarse result. Then, the coarse result is refined with a background-based attention module (BAModule) to maintain a consistent appearance and a detail improvement module (DEModule) to enhance the texture details at Stage II. Experiments on two benchmark datasets qualitatively and quantitatively validate the superiority of the proposed approach over state-of-the-arts. | ['Chunxia Xiao', 'Xiaolong Zhang', 'Zheng Liu', 'Qing Zhang', 'Yinghao He', 'Ling Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 6.71312988e-01 -3.45092654e-01 1.85589001e-01 -3.03534776e-01
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5e4b2cfd-10c6-48dd-b08c-727d9e63677a | leveraging-line-point-consistence-to-preserve | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Jia_Leveraging_Line-Point_Consistence_To_Preserve_Structures_for_Wide_Parallax_Image_CVPR_2021_paper.pdf | Leveraging Line-Point Consistence To Preserve Structures for Wide Parallax Image Stitching | Generating high-quality stitched images with natural structures is a challenging task in computer vision. In this paper, we succeed in preserving both local and global geometric structures for wide parallax images, while reducing artifacts and distortions. A projective invariant, Characteristic Number, is used to match co-planar local sub-regions for input images. The homography between these well-matched sub-regions produces consistent line and point pairs, suppressing artifacts in overlapping areas. We explore and introduce global collinear structures into an objective function to specify and balance the desired characters for image warping, which can preserve both local and global structures while alleviating distortions. We also develop comprehensive measures for stitching quality to quantify the collinearity of points and the discrepancy of matched line pairs by considering the sensitivity to linear structures for human vision. Extensive experiments demonstrate the superior performance of the proposed method over the state-of-the-art by presenting sharp textures and preserving prominent natural structures in stitched images. Especially, our method not only exhibits lower errors but also the least divergence across all test images. Code is available at https://github.com/dut-media-lab/Image-Stitching | ['Longin Jan Latecki', 'Xinchen Ye', 'Shiyu Teng', 'Haotian Zhao', 'Xin Fan', 'ZhengJun Li', 'Qi Jia'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['image-stitching'] | ['computer-vision'] | [ 5.16893446e-01 -3.75817776e-01 2.67502256e-02 -5.32838423e-03
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4a957f4b-c89f-4a3e-a016-df8e4b069e3f | cross-domain-sparse-coding | 1311.7080 | null | http://arxiv.org/abs/1311.7080v1 | http://arxiv.org/pdf/1311.7080v1.pdf | Cross-Domain Sparse Coding | Sparse coding has shown its power as an effective data representation method.
However, up to now, all the sparse coding approaches are limited within the
single domain learning problem. In this paper, we extend the sparse coding to
cross domain learning problem, which tries to learn from a source domain to a
target domain with significant different distribution. We impose the Maximum
Mean Discrepancy (MMD) criterion to reduce the cross-domain distribution
difference of sparse codes, and also regularize the sparse codes by the class
labels of the samples from both domains to increase the discriminative ability.
The encouraging experiment results of the proposed cross-domain sparse coding
algorithm on two challenging tasks --- image classification of photograph and
oil painting domains, and multiple user spam detection --- show the advantage
of the proposed method over other cross-domain data representation methods. | ['Jim Jing-Yan Wang'] | 2013-11-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 3.01870197e-01 -4.48154688e-01 -3.55507642e-01 -3.92788291e-01
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1e1f48dc-4d57-4021-96d2-275ddcaec971 | dimbert-learning-vision-language-grounded | 2210.16431 | null | https://arxiv.org/abs/2210.16431v1 | https://arxiv.org/pdf/2210.16431v1.pdf | DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention | Vision-and-language (V-L) tasks require the system to understand both vision content and natural language, thus learning fine-grained joint representations of vision and language (a.k.a. V-L representations) is of paramount importance. Recently, various pre-trained V-L models are proposed to learn V-L representations and achieve improved results in many tasks. However, the mainstream models process both vision and language inputs with the same set of attention matrices. As a result, the generated V-L representations are entangled in one common latent space. To tackle this problem, we propose DiMBERT (short for Disentangled Multimodal-Attention BERT), which is a novel framework that applies separated attention spaces for vision and language, and the representations of multi-modalities can thus be disentangled explicitly. To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format. In this manner, visual concepts help to bridge the gap between the two modalities. We pre-train DiMBERT on a large amount of image-sentence pairs on two tasks: bidirectional language modeling and sequence-to-sequence language modeling. After pre-train, DiMBERT is further fine-tuned for the downstream tasks. Experiments show that DiMBERT sets new state-of-the-art performance on three tasks (over four datasets), including both generation tasks (image captioning and visual storytelling) and classification tasks (referring expressions). The proposed DiM (short for Disentangled Multimodal-Attention) module can be easily incorporated into existing pre-trained V-L models to boost their performance, up to a 5% increase on the representative task. Finally, we conduct a systematic analysis and demonstrate the effectiveness of our DiM and the introduced visual concepts. | ['Yuexian Zou', 'Xu sun', 'Wei Fan', 'Xuancheng Ren', 'Shen Ge', 'Xian Wu', 'Fenglin Liu'] | 2022-10-28 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.56344473e-02 -1.22949421e-01 -2.27576107e-01 -2.00109035e-01
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ee900e5a-4d48-4eca-8695-e901ae209531 | interactive-error-resolution-strategies-for | null | null | https://aclanthology.org/W13-4022 | https://aclanthology.org/W13-4022.pdf | Interactive Error Resolution Strategies for Speech-to-Speech Translation Systems | null | ['Sanjika Hewavitharana', 'Matthew Roy', 'Frederick Choi', 'Rohit Kumar', 'Sankaranarayanan Ananthakrishnan'] | 2013-08-01 | null | null | null | ws-2013-8 | ['speech-to-speech-translation'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7cf6cb56-91e2-4927-98cf-112a378490df | improving-ecg-based-covid-19-diagnosis-and | 2211.10431 | null | https://arxiv.org/abs/2211.10431v2 | https://arxiv.org/pdf/2211.10431v2.pdf | Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale | Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited available pandemic data. This study shows this approach -- pre-train deep learning models with pre-pandemic data -- can work effectively, by demonstrating substantial performance improvement over three different COVID-19 related diagnostic and prognostic prediction tasks. Similar transfer learning strategies can be useful for developing timely artificial intelligence solutions in future pandemic outbreaks. | ['Nariman Sepehrvand', 'Padma Kaul', 'Russell Greiner', 'Abram Hindle', 'Amir Salimi', 'Zihan Wang', 'Luan Manh Chu', 'Sunil Vasu Kalmady', 'Weijie Sun'] | 2022-11-14 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.34496051e-01 -5.72696477e-02 -2.49917097e-02 -3.68102044e-01
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675ff0ba-17d7-488f-9566-684f17c144a7 | refining-amortized-posterior-approximations | 2305.08733 | null | https://arxiv.org/abs/2305.08733v1 | https://arxiv.org/pdf/2305.08733v1.pdf | Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics | We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in maximally informative summary statistics. Amortized variational inference is restricted by the expressive power of the chosen variational distribution and the availability of training data in the form of joint data and parameter samples, which often lead to approximation errors such as the amortization gap. To address this issue, we propose an iterative framework that refines the current amortized posterior approximation at each step. Our approach involves alternating between two steps: (1) constructing a training dataset consisting of pairs of summarized data residuals and parameters, where the summarized data residual is generated using a gradient-based summary statistic, and (2) training a conditional generative model -- a normalizing flow in our examples -- on this dataset to obtain a probabilistic update of the unknown parameter. This procedure leads to iterative refinement of the amortized posterior approximations without the need for extra training data. We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration. Additionally, we showcase the capability of our method in tackling realistically sized problems by applying it to transcranial ultrasound, a high-dimensional, nonlinear inverse problem governed by wave physics, and observe enhanced posterior quality through better image reconstruction with the posterior mean. | ['Felix J. Herrmann', 'Mathias Louboutin', 'Ali Siahkoohi', 'Rafael Orozco'] | 2023-05-15 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 3.40743542e-01 2.47790754e-01 1.67077109e-01 -8.30534175e-02
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4e021a9f-7f8c-4b33-b2b8-a0dfa2925940 | detectron2-object-detection-manipulating | null | null | https://www.ijert.org/detectron2-object-detection-manipulating-images-using-cartoonization | https://www.ijert.org/research/detectron2-object-detection-manipulating-images-using-cartoonization-IJERTV10IS080122.pdf | Detectron2 Object Detection & Manipulating Images using Cartoonization | In today's world, there is a rapid increase in the autonomous vehicle. There are various levels of autonomous vehicles depending upon the degree of autonomy-for the lower degree of autonomy driver has more power and functionality for managing, on coming to the fully automated vehicle like Tesla are expected to have full control over the functions. These advances cooperate to plan the vehicle's position and its nearness to everything around it. Because of this, there is popularity for these vehicles, since they give a great deal of advantages to individuals utilizing them. We use the Facebook AI Research software system that implements object detection algorithms, Caffe2 deep learning framework for advanced object detection by offering speedy training. We have also manipulated images to derive insights addressing the issues companies face when making the step from research to production. We have implemented detectron2 object detection for faster detection of objects. There is labeling of the object & we used manipulation of images using cartoonization. | ['Sonali Kotni', 'Allena Venkata Sai Abhishek'] | 2021-08-01 | null | null | null | international-journal-of-engineering-research | ['panoptic-segmentation', 'object-recognition', 'image-augmentation', 'real-time-object-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-6.93049014e-01 2.34157518e-01 -1.35410890e-01 -5.52000046e-01
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95c58cab-f303-4318-bcf3-beca1b1b9448 | ucas-iie-nlp-at-semeval-2023-task-12 | 2306.01093 | null | https://arxiv.org/abs/2306.01093v1 | https://arxiv.org/pdf/2306.01093v1.pdf | UCAS-IIE-NLP at SemEval-2023 Task 12: Enhancing Generalization of Multilingual BERT for Low-resource Sentiment Analysis | This paper describes our system designed for SemEval-2023 Task 12: Sentiment analysis for African languages. The challenge faced by this task is the scarcity of labeled data and linguistic resources in low-resource settings. To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages. Specifically, we design a lexicon-based multilingual BERT to facilitate language adaptation and sentiment-aware representation learning. Besides, we apply a supervised adversarial contrastive learning technique to learn sentiment-spread structured representations and enhance model generalization. Our system achieved competitive results, largely outperforming baselines on both multilingual and zero-shot sentiment classification subtasks. Notably, the system obtained the 1st rank on the zero-shot classification subtask in the official ranking. Extensive experiments demonstrate the effectiveness of our system. | ['Songlin Hu', 'Wei Zhou', 'Yaxin Liu', 'Lingwei Wei', 'Dou Hu'] | 2023-06-01 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-1.69393614e-01 -3.73371273e-01 -4.20859724e-01 -5.31776428e-01
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e7f6c5c2-1fc9-4743-9f46-30001e53651c | combating-confirmation-bias-a-unified-pseudo | 2307.02075 | null | https://arxiv.org/abs/2307.02075v1 | https://arxiv.org/pdf/2307.02075v1.pdf | Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment | Entity alignment (EA) aims at identifying equivalent entity pairs across different knowledge graphs (KGs) that refer to the same real-world identity. To systematically combat confirmation bias for pseudo-labeling-based entity alignment, we propose a Unified Pseudo-Labeling framework for Entity Alignment (UPL-EA) that explicitly eliminates pseudo-labeling errors to boost the accuracy of entity alignment. UPL-EA consists of two complementary components: (1) The Optimal Transport (OT)-based pseudo-labeling uses discrete OT modeling as an effective means to enable more accurate determination of entity correspondences across two KGs and to mitigate the adverse impact of erroneous matches. A simple but highly effective criterion is further devised to derive pseudo-labeled entity pairs that satisfy one-to-one correspondences at each iteration. (2) The cross-iteration pseudo-label calibration operates across multiple consecutive iterations to further improve the pseudo-labeling precision rate by reducing the local pseudo-label selection variability with a theoretical guarantee. The two components are respectively designed to eliminate Type I and Type II pseudo-labeling errors identified through our analyse. The calibrated pseudo-labels are thereafter used to augment prior alignment seeds to reinforce subsequent model training for alignment inference. The effectiveness of UPL-EA in eliminating pseudo-labeling errors is both theoretically supported and experimentally validated. The experimental results show that our approach achieves competitive performance with limited prior alignment seeds. | ['Junbin Gao', 'Daokun Zhang', 'Jie Yin', 'Qijie Ding'] | 2023-07-05 | null | null | null | null | ['entity-alignment', 'knowledge-graphs', 'pseudo-label', 'entity-alignment'] | ['knowledge-base', 'knowledge-base', 'miscellaneous', 'natural-language-processing'] | [ 2.23843291e-01 3.94472808e-01 -4.98918056e-01 -4.25620824e-01
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de444ad5-ac95-4347-9446-15392e05f09b | seqformer-a-frustratingly-simple-model-for | 2112.08275 | null | https://arxiv.org/abs/2112.08275v2 | https://arxiv.org/pdf/2112.08275v2.pdf | SeqFormer: Sequential Transformer for Video Instance Segmentation | In this work, we present SeqFormer for video instance segmentation. SeqFormer follows the principle of vision transformer that models instance relationships among video frames. Nevertheless, we observe that a stand-alone instance query suffices for capturing a time sequence of instances in a video, but attention mechanisms shall be done with each frame independently. To achieve this, SeqFormer locates an instance in each frame and aggregates temporal information to learn a powerful representation of a video-level instance, which is used to predict the mask sequences on each frame dynamically. Instance tracking is achieved naturally without tracking branches or post-processing. On YouTube-VIS, SeqFormer achieves 47.4 AP with a ResNet-50 backbone and 49.0 AP with a ResNet-101 backbone without bells and whistles. Such achievement significantly exceeds the previous state-of-the-art performance by 4.6 and 4.4, respectively. In addition, integrated with the recently-proposed Swin transformer, SeqFormer achieves a much higher AP of 59.3. We hope SeqFormer could be a strong baseline that fosters future research in video instance segmentation, and in the meantime, advances this field with a more robust, accurate, neat model. The code is available at https://github.com/wjf5203/SeqFormer. | ['Xiang Bai', 'Wenqing Zhang', 'Song Bai', 'Yi Jiang', 'Junfeng Wu'] | 2021-12-15 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.43389493e-01 2.61834245e-02 -4.46477801e-01 -2.71095634e-01
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bba4fdeb-a0bf-4708-b19a-92cdeb2fecbf | a-unified-framework-for-slot-based-response | 2305.17433 | null | https://arxiv.org/abs/2305.17433v1 | https://arxiv.org/pdf/2305.17433v1.pdf | A Unified Framework for Slot based Response Generation in a Multimodal Dialogue System | Natural Language Understanding (NLU) and Natural Language Generation (NLG) are the two critical components of every conversational system that handles the task of understanding the user by capturing the necessary information in the form of slots and generating an appropriate response in accordance with the extracted information. Recently, dialogue systems integrated with complementary information such as images, audio, or video have gained immense popularity. In this work, we propose an end-to-end framework with the capability to extract necessary slot values from the utterance and generate a coherent response, thereby assisting the user to achieve their desired goals in a multimodal dialogue system having both textual and visual information. The task of extracting the necessary information is dependent not only on the text but also on the visual cues present in the dialogue. Similarly, for the generation, the previous dialog context comprising multimodal information is significant for providing coherent and informative responses. We employ a multimodal hierarchical encoder using pre-trained DialoGPT and also exploit the knowledge base (Kb) to provide a stronger context for both the tasks. Finally, we design a slot attention mechanism to focus on the necessary information in a given utterance. Lastly, a decoder generates the corresponding response for the given dialogue context and the extracted slot values. Experimental results on the Multimodal Dialogue Dataset (MMD) show that the proposed framework outperforms the baselines approaches in both the tasks. The code is available at https://github.com/avinashsai/slot-gpt. | ['Asif Ekbal', 'Avinash Madasu', 'Mauajama Firdaus'] | 2023-05-27 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 3.10528129e-01 4.29851413e-01 2.98299119e-02 -4.30339634e-01
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537ab7f1-7998-4276-9957-3d1b61f82339 | performance-comparison-of-deep-rl-algorithms | 2208.00728 | null | https://arxiv.org/abs/2208.00728v1 | https://arxiv.org/pdf/2208.00728v1.pdf | Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling | Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems' operational cost and technical constraints (e.g, generation-demand power balance) DRL algorithms must consider a trade-off when designing the reward function. This trade-off introduces extra hyperparameters that impact the DRL algorithms' performance and capability of providing feasible solutions. In this paper, a performance comparison of different DRL algorithms, including DDPG, TD3, SAC, and PPO, are presented. We aim to provide a fair comparison of these DRL algorithms for energy systems optimal scheduling problems. Results show DRL algorithms' capability of providing in real-time good-quality solutions, even in unseen operational scenarios, when compared with a mathematical programming model of the energy system optimal scheduling problem. Nevertheless, in the case of large peak consumption, these algorithms failed to provide feasible solutions, which can impede their practical implementation. | ['Peter Palensky', 'Pedro P. Vergara', 'Edgar Mauricio Salazar', 'Hou Shengren'] | 2022-08-01 | null | null | null | null | ['energy-management'] | ['time-series'] | [-3.65093648e-01 7.83633068e-02 -3.80748302e-01 5.51839098e-02
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407ea8c3-b6c7-45d0-a66f-6700a9d85955 | effective-use-of-variational-embedding | 1906.03402 | null | https://arxiv.org/abs/1906.03402v3 | https://arxiv.org/pdf/1906.03402v3.pdf | Effective Use of Variational Embedding Capacity in Expressive End-to-End Speech Synthesis | Recent work has explored sequence-to-sequence latent variable models for expressive speech synthesis (supporting control and transfer of prosody and style), but has not presented a coherent framework for understanding the trade-offs between the competing methods. In this paper, we propose embedding capacity (the amount of information the embedding contains about the data) as a unified method of analyzing the behavior of latent variable models of speech, comparing existing heuristic (non-variational) methods to variational methods that are able to explicitly constrain capacity using an upper bound on representational mutual information. In our proposed model (Capacitron), we show that by adding conditional dependencies to the variational posterior such that it matches the form of the true posterior, the same model can be used for high-precision prosody transfer, text-agnostic style transfer, and generation of natural-sounding prior samples. For multi-speaker models, Capacitron is able to preserve target speaker identity during inter-speaker prosody transfer and when drawing samples from the latent prior. Lastly, we introduce a method for decomposing embedding capacity hierarchically across two sets of latents, allowing a portion of the latent variability to be specified and the remaining variability sampled from a learned prior. Audio examples are available on the web. | ['RJ Skerry-Ryan', 'Daisy Stanton', 'Soroosh Mariooryad', 'Eric Battenberg', 'David Kao', 'Matt Shannon', 'Tom Bagby'] | 2019-06-08 | null | https://openreview.net/forum?id=SJgBQaVKwH | https://openreview.net/pdf?id=SJgBQaVKwH | null | ['expressive-speech-synthesis'] | ['speech'] | [ 3.60732317e-01 5.25892198e-01 -3.71066153e-01 -3.48538041e-01
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c73c1f39-370f-4c6e-9c02-911542f16d5f | a-graph-based-lattice-dependency-parser-for | null | null | https://aclanthology.org/Q15-1026 | https://aclanthology.org/Q15-1026.pdf | A Graph-based Lattice Dependency Parser for Joint Morphological Segmentation and Syntactic Analysis | Space-delimited words in Turkish and Hebrew text can be further segmented into meaningful units, but syntactic and semantic context is necessary to predict segmentation. At the same time, predicting correct syntactic structures relies on correct segmentation. We present a graph-based lattice dependency parser that operates on morphological lattices to represent different segmentations and morphological analyses for a given input sentence. The lattice parser predicts a dependency tree over a path in the lattice and thus solves the joint task of segmentation, morphological analysis, and syntactic parsing. We conduct experiments on the Turkish and the Hebrew treebank and show that the joint model outperforms three state-of-the-art pipeline systems on both data sets. Our work corroborates findings from constituency lattice parsing for Hebrew and presents the first results for full lattice parsing on Turkish. | ['{\\"O}zlem {\\c{C}}etino{\\u{g}}lu', 'Wolfgang Seeker'] | 2015-01-01 | null | null | null | tacl-2015-1 | ['morphological-tagging'] | ['natural-language-processing'] | [-4.26746123e-02 3.91946971e-01 -1.85155377e-01 -7.28111863e-01
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145406cd-1fca-4971-8655-6f884618ad6c | nerf-loc-visual-localization-with-conditional | 2304.07979 | null | https://arxiv.org/abs/2304.07979v1 | https://arxiv.org/pdf/2304.07979v1.pdf | NeRF-Loc: Visual Localization with Conditional Neural Radiance Field | We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene representation in our pipeline, which supports continuous 3D descriptors generation and neural rendering. By unifying the feature matching and the scene coordinate regression to the same framework, our model learns both generalizable knowledge and scene prior respectively during two training stages. Furthermore, to improve the localization robustness when domain gap exists between training and testing phases, we propose an appearance adaptation layer to explicitly align styles between the 3D model and the query image. Experiments show that our method achieves higher localization accuracy than other learning-based approaches on multiple benchmarks. Code is available at \url{https://github.com/JenningsL/nerf-loc}. | ['Chengjie Wang', 'Yong liu', 'Qiang Nie', 'Jianlin Liu'] | 2023-04-17 | null | null | null | null | ['neural-rendering', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [-6.03610687e-02 -3.85504901e-01 -2.37453982e-01 -6.65124893e-01
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517af0c7-36b5-4853-afd7-1a4cbbb94518 | chatgpt-evaluation-on-sentence-level | 2304.14827 | null | https://arxiv.org/abs/2304.14827v2 | https://arxiv.org/pdf/2304.14827v2.pdf | ChatGPT Evaluation on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations | This paper aims to quantitatively evaluate the performance of ChatGPT, an interactive large language model, on inter-sentential relations such as temporal relations, causal relations, and discourse relations. Given ChatGPT's promising performance across various tasks, we conduct extensive evaluations on the whole test sets of 13 datasets, including temporal and causal relations, PDTB2.0-based and dialogue-based discourse relations, and downstream applications on discourse understanding. To achieve reliable results, we adopt three tailored prompt templates for each task, including the zero-shot prompt template, zero-shot prompt engineering (PE) template, and in-context learning (ICL) prompt template, to establish the initial baseline scores for all popular sentence-pair relation classification tasks for the first time. We find that ChatGPT exhibits strong performance in detecting and reasoning about causal relations, while it may not be proficient in identifying the temporal order between two events. It can recognize most discourse relations with existing explicit discourse connectives, but the implicit discourse relation still remains a challenging task. Meanwhile, ChatGPT performs poorly in the dialogue discourse parsing task that requires structural understanding in a dialogue before being aware of the discourse relation. | ['Yangqiu Song', 'Xin Liu', 'Tianqing Fang', 'Yuxin Jiang', 'Weiqi Wang', 'Jiayang Cheng', 'Chunkit Chan'] | 2023-04-28 | null | null | null | null | ['discourse-parsing', 'prompt-engineering', 'relation-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.70916581e-01 7.75433779e-01 -3.59764189e-01 -3.74640942e-01
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cd8c24b8-6bf6-4f25-9817-249c888defc7 | the-application-of-preconditioned-alternating | 1711.07721 | null | http://arxiv.org/abs/1711.07721v1 | http://arxiv.org/pdf/1711.07721v1.pdf | The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack | Post capture refocusing effect in smartphone cameras is achievable by using
focal stacks. However, the accuracy of this effect is totally dependent on the
combination of the depth layers in the stack. The accuracy of the extended
depth of field effect in this application can be improved significantly by
computing an accurate depth map which has been an open issue for decades. To
tackle this issue, in this paper, a framework is proposed based on
Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth
from the focal stack and synthetic defocus application. In addition to its
ability to provide high structural accuracy and occlusion handling, the
optimization function of the proposed method can, in fact, converge faster and
better than state of the art methods. The evaluation has been done on 21 sets
of focal stacks and the optimization function has been compared against 5 other
methods. Preliminary results indicate that the proposed method has a better
performance in terms of structural accuracy and optimization in comparison to
the current state of the art methods. | ['Hossein Javidnia', 'Peter Corcoran'] | 2017-11-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 3.85246783e-01 -1.39247045e-01 5.79277039e-01 -2.45459780e-01
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0d4e93e6-6108-4252-8346-5ed0bcac4758 | identifying-corresponding-patches-in-sar-and | 1801.08467 | null | http://arxiv.org/abs/1801.08467v1 | http://arxiv.org/pdf/1801.08467v1.pdf | Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN | In this letter, we propose a pseudo-siamese convolutional neural network
(CNN) architecture that enables to solve the task of identifying corresponding
patches in very-high-resolution (VHR) optical and synthetic aperture radar
(SAR) remote sensing imagery. Using eight convolutional layers each in two
parallel network streams, a fully connected layer for the fusion of the
features learned in each stream, and a loss function based on binary
cross-entropy, we achieve a one-hot indication if two patches correspond or
not. The network is trained and tested on an automatically generated dataset
that is based on a deterministic alignment of SAR and optical imagery via
previously reconstructed and subsequently co-registered 3D point clouds. The
satellite images, from which the patches comprising our dataset are extracted,
show a complex urban scene containing many elevated objects (i.e. buildings),
thus providing one of the most difficult experimental environments. The
achieved results show that the network is able to predict corresponding patches
with high accuracy, thus indicating great potential for further development
towards a generalized multi-sensor key-point matching procedure. Index
Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep
learning, convolutional neural networks (CNN), image matching, deep matching | ['Lloyd H. Hughes', 'Yuanyuan Wang', 'Xiao Xiang Zhu', 'Michael Schmitt', 'Lichao Mou'] | 2018-01-25 | null | null | null | null | ['key-point-matching'] | ['natural-language-processing'] | [ 7.14951098e-01 -3.37249190e-01 2.42880449e-01 -7.14816928e-01
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c23c9c1c-6454-49c4-849c-a7fc01404b91 | boosting-video-super-resolution-with-patch | 2207.08674 | null | https://arxiv.org/abs/2207.08674v3 | https://arxiv.org/pdf/2207.08674v3.pdf | Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization | The success of existing video super-resolution (VSR) algorithms stems mainly exploiting the temporal information from the neighboring frames. However, none of these methods have discussed the influence of the temporal redundancy in the patches with stationary objects and background and usually use all the information in the adjacent frames without any discrimination. In this paper, we observe that the temporal redundancy will bring adverse effect to the information propagation,which limits the performance of the most existing VSR methods. Motivated by this observation, we aim to improve existing VSR algorithms by handling the temporal redundancy patches in an optimized manner. We develop two simple yet effective plug and play methods to improve the performance of existing local and non-local propagation-based VSR algorithms on widely-used public videos. For more comprehensive evaluating the robustness and performance of existing VSR algorithms, we also collect a new dataset which contains a variety of public videos as testing set. Extensive evaluations show that the proposed methods can significantly improve the performance of existing VSR methods on the collected videos from wild scenarios while maintain their performance on existing commonly used datasets. The code is available at https://github.com/HYHsimon/Boosted-VSR. | ['Fei Wang', 'Lean Fu', 'Ding Liu', 'Yu Guo', 'Chao Zhu', 'Jinshan Pan', 'Hang Dong', 'Yuhao Huang'] | 2022-07-18 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 7.50949457e-02 -6.83798552e-01 -2.29297474e-01 -1.63503811e-01
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91011176-61a0-4eb0-b6c6-4bf3c85c4966 | targeted-data-generation-finding-and-fixing | 2305.17804 | null | https://arxiv.org/abs/2305.17804v1 | https://arxiv.org/pdf/2305.17804v1.pdf | Targeted Data Generation: Finding and Fixing Model Weaknesses | Even when aggregate accuracy is high, state-of-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust. Additional data collection may not help in addressing these weaknesses, as such challenging subgroups may be unknown to users, and underrepresented in the existing and new data. We propose Targeted Data Generation (TDG), a framework that automatically identifies challenging subgroups, and generates new data for those subgroups using large language models (LLMs) with a human in the loop. TDG estimates the expected benefit and potential harm of data augmentation for each subgroup, and selects the ones most likely to improve within group performance without hurting overall performance. In our experiments, TDG significantly improves the accuracy on challenging subgroups for state-of-the-art sentiment analysis and natural language inference models, while also improving overall test accuracy. | ['Fereshte Khani', 'Marco Tulio Ribeiro', 'Zexue He'] | 2023-05-28 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [ 4.33056243e-02 6.45949662e-01 -6.84633493e-01 -6.80986822e-01
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80ddccdb-f6f1-4f02-a903-5843c245f9c5 | revisiting-and-advancing-fast-adversarial-1 | 2112.12376 | null | https://arxiv.org/abs/2112.12376v6 | https://arxiv.org/pdf/2112.12376v6.pdf | Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization | Adversarial training (AT) is a widely recognized defense mechanism to gain the robustness of deep neural networks against adversarial attacks. It is built on min-max optimization (MMO), where the minimizer (i.e., defender) seeks a robust model to minimize the worst-case training loss in the presence of adversarial examples crafted by the maximizer (i.e., attacker). However, the conventional MMO method makes AT hard to scale. Thus, Fast-AT (Wong et al., 2020) and other recent algorithms attempt to simplify MMO by replacing its maximization step with the single gradient sign-based attack generation step. Although easy to implement, Fast-AT lacks theoretical guarantees, and its empirical performance is unsatisfactory due to the issue of robust catastrophic overfitting when training with strong adversaries. In this paper, we advance Fast-AT from the fresh perspective of bi-level optimization (BLO). We first show that the commonly-used Fast-AT is equivalent to using a stochastic gradient algorithm to solve a linearized BLO problem involving a sign operation. However, the discrete nature of the sign operation makes it difficult to understand the algorithm performance. Inspired by BLO, we design and analyze a new set of robust training algorithms termed Fast Bi-level AT (Fast-BAT), which effectively defends sign-based projected gradient descent (PGD) attacks without using any gradient sign method or explicit robust regularization. In practice, we show our method yields substantial robustness improvements over baselines across multiple models and datasets. Codes are available at https://github.com/OPTML-Group/Fast-BAT. | ['Guanhua Zhang', 'Sijia Liu', 'Shiyu Chang', 'Mingyi Hong', 'Prashant Khanduri', 'Yihua Zhang'] | 2021-12-23 | revisiting-and-advancing-fast-adversarial | https://openreview.net/forum?id=gzeruP-0J29 | https://openreview.net/pdf?id=gzeruP-0J29 | null | ['adversarial-defense'] | ['adversarial'] | [ 9.17232856e-02 -5.43247210e-03 1.05656935e-02 -1.99218079e-01
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8e203f4f-33dd-48c5-9142-e69286124e12 | blind-surveillance-image-quality-assessment | 2206.04318 | null | https://arxiv.org/abs/2206.04318v1 | https://arxiv.org/pdf/2206.04318v1.pdf | Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency | The intelligent video surveillance system (IVSS) can automatically analyze the content of the surveillance image (SI) and reduce the burden of the manual labour. However, the SIs may suffer quality degradations in the procedure of acquisition, compression, and transmission, which makes IVSS hard to understand the content of SIs. In this paper, we first conduct an example experiment (i.e. the face detection task) to demonstrate that the quality of the SIs has a crucial impact on the performance of the IVSS, and then propose a saliency-based deep neural network for the blind quality assessment of the SIs, which helps IVSS to filter the low-quality SIs and improve the detection and recognition performance. Specifically, we first compute the saliency map of the SI to select the most salient local region since the salient regions usually contain rich semantic information for machine vision and thus have a great impact on the overall quality of the SIs. Next, the convolutional neural network (CNN) is adopted to extract quality-aware features for the whole image and local region, which are then mapped into the global and local quality scores through the fully connected (FC) network respectively. Finally, the overall quality score is computed as the weighted sum of the global and local quality scores. Experimental results on the SI quality database (SIQD) show that the proposed method outperforms all compared state-of-the-art BIQA methods. | ['Guangtao Zhai', 'Tao Wang', 'ZiCheng Zhang', 'Xiongkuo Min', 'Wenhan Zhu', 'Wei Sun', 'Wei Lu'] | 2022-06-09 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 2.53613681e-01 -3.34721416e-01 -2.28370540e-02 -1.62352517e-01
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e089db61-4de1-449a-89f2-7124ee33a4c1 | learning-the-prediction-distribution-for-semi | 2007.02745 | null | https://arxiv.org/abs/2007.02745v1 | https://arxiv.org/pdf/2007.02745v1.pdf | Learning the Prediction Distribution for Semi-Supervised Learning with Normalising Flows | As data volumes continue to grow, the labelling process increasingly becomes a bottleneck, creating demand for methods that leverage information from unlabelled data. Impressive results have been achieved in semi-supervised learning (SSL) for image classification, nearing fully supervised performance, with only a fraction of the data labelled. In this work, we propose a probabilistically principled general approach to SSL that considers the distribution over label predictions, for labels of different complexity, from "one-hot" vectors to binary vectors and images. Our method regularises an underlying supervised model, using a normalising flow that learns the posterior distribution over predictions for labelled data, to serve as a prior over the predictions on unlabelled data. We demonstrate the general applicability of this approach on a range of computer vision tasks with varying output complexity: classification, attribute prediction and image-to-image translation. | ['Ivana Balažević', 'Timothy Hospedales', 'Carl Allen'] | 2020-07-06 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 1.01927125e+00 6.93655372e-01 -6.20932162e-01 -9.67906296e-01
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64a5773d-86b3-47f8-8893-be7ca63cba92 | a-time-dependent-markovian-model-of-a-limit | 2302.00846 | null | https://arxiv.org/abs/2302.00846v1 | https://arxiv.org/pdf/2302.00846v1.pdf | A time-dependent Markovian model of a limit order book | This paper considers a Markovian model of a limit order book where time-dependent rates are allowed. With the objective of understanding the mechanisms through which a microscopic model of an orderbook can converge to more general diffusion than a Brownian motion with constant coefficient, a simple time-dependent model is proposed. The model considered here starts by describing the processes that govern the arrival of the different orders such as limit orders, market orders and cancellations. In this sense, this is a microscopic model rather than a ``mesoscopic'' model where the starting point is usually the point processes describing the times at which the price changes occur and aggregate in these all the information pertaining to the arrival of individual orders. Furthermore, several empirical studies are performed to shed some light into the validity of the modeling assumptions and to verify whether certain stocks satisfy the conditions for their price process to converge to a more complex diffusion. | ['Jonathan A. Chávez-Casillas'] | 2023-02-02 | null | null | null | null | ['point-processes'] | ['methodology'] | [-3.57166678e-01 -5.01834452e-01 -4.50585522e-02 9.51274633e-02
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e31efc40-f46e-413a-9548-19fc9b0f43c2 | transfer-learning-for-melanoma-detection | 1703.05235 | null | http://arxiv.org/abs/1703.05235v1 | http://arxiv.org/pdf/1703.05235v1.pdf | Transfer Learning for Melanoma Detection: Participation in ISIC 2017 Skin Lesion Classification Challenge | This manuscript describes our participation in the International Skin Imaging
Collaboration's 2017 Skin Lesion Analysis Towards Melanoma Detection
competition. We participated in Part 3: Lesion Classification. The two stated
goals of this binary image classification challenge were to distinguish between
(a) melanoma and (b) nevus and seborrheic keratosis, followed by distinguishing
between (a) seborrheic keratosis and (b) nevus and melanoma. We chose a deep
neural network approach with a transfer learning strategy, using a pre-trained
Inception V3 network as both a feature extractor to provide input for a
multi-layer perceptron as well as fine-tuning an augmented Inception network.
This approach yielded validation set AUC's of 0.84 on the second task and 0.76
on the first task, for an average AUC of 0.80. We joined the competition
unfortunately late, and we look forward to improving on these results. | ['Dennis H. Murphree', 'Che Ngufor'] | 2017-03-15 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.98336780e-01 1.08215429e-01 -2.58252144e-01 -3.28040391e-01
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9235ccc2-95a3-4ad9-9cd5-e1628a18d2d9 | spatio-temporal-person-retrieval-via-natural | 1704.07945 | null | http://arxiv.org/abs/1704.07945v2 | http://arxiv.org/pdf/1704.07945v2.pdf | Spatio-temporal Person Retrieval via Natural Language Queries | In this paper, we address the problem of spatio-temporal person retrieval
from multiple videos using a natural language query, in which we output a tube
(i.e., a sequence of bounding boxes) which encloses the person described by the
query. For this problem, we introduce a novel dataset consisting of videos
containing people annotated with bounding boxes for each second and with five
natural language descriptions. To retrieve the tube of the person described by
a given natural language query, we design a model that combines methods for
spatio-temporal human detection and multimodal retrieval. We conduct
comprehensive experiments to compare a variety of tube and text representations
and multimodal retrieval methods, and present a strong baseline in this task as
well as demonstrate the efficacy of our tube representation and multimodal
feature embedding technique. Finally, we demonstrate the versatility of our
model by applying it to two other important tasks. | ['Tatsuya Harada', 'Masataka Yamaguchi', 'Kuniaki Saito', 'Yoshitaka Ushiku'] | 2017-04-26 | spatio-temporal-person-retrieval-via-natural-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Yamaguchi_Spatio-Temporal_Person_Retrieval_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Yamaguchi_Spatio-Temporal_Person_Retrieval_ICCV_2017_paper.pdf | iccv-2017-10 | ['person-retrieval'] | ['computer-vision'] | [ 8.69486015e-03 -5.76581061e-01 -3.11966594e-02 -2.47036487e-01
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bc28f22d-47fa-4167-ae35-73f069996027 | synthesising-clinically-realistic-chest-x | 2010.03975 | null | https://arxiv.org/abs/2010.03975v2 | https://arxiv.org/pdf/2010.03975v2.pdf | Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs | Chest x-rays are a vital tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled data to develop new diagnostic tools, however this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals. Previous work has sought to address these concerns by creating class-specific GANs that synthesise images to augment training data. These approaches cannot be scaled as they introduce computational trade offs between model size and class number which places fixed limits on the quality that such generates can achieve. We address this concern by introducing latent class optimisation which enables efficient, multi-modal sampling from a GAN and with which we synthesise a large archive of labelled generates. We apply a PGGAN to the task of unsupervised x-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples. We provide an in depth review of the properties of varying pathologies seen on generates as well as an overview of the extent of disease diversity captured by the model. We validate the application of the Fr\'echet Inception Distance (FID) to measure the quality of x-ray generates and find that they are similar to other high resolution tasks. We quantify x-ray clinical realism by asking radiologists to distinguish between real and fake scans and find that generates are more likely to be classed as real than by chance, but there is still progress required to achieve true realism. We confirm these findings by evaluating synthetic classification model performance on real scans. We conclude by discussing the limitations of PGGAN generates and how to achieve controllable, realistic generates. | ['Adam Pantanowitz', 'Grace Rubin', 'David M. Rubin', 'Bradley Segal'] | 2020-10-07 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 8.25027406e-01 8.46427560e-01 -1.12102017e-01 -3.55047137e-01
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becbe4b6-9d12-45b5-b012-44dc65ff78d1 | improving-fast-slow-encoder-based-transducer | 2212.07650 | null | https://arxiv.org/abs/2212.07650v1 | https://arxiv.org/pdf/2212.07650v1.pdf | Improving Fast-slow Encoder based Transducer with Streaming Deliberation | This paper introduces a fast-slow encoder based transducer with streaming deliberation for end-to-end automatic speech recognition. We aim to improve the recognition accuracy of the fast-slow encoder based transducer while keeping its latency low by integrating a streaming deliberation model. Specifically, the deliberation model leverages partial hypotheses from the streaming fast encoder and implicitly learns to correct recognition errors. We modify the parallel beam search algorithm for fast-slow encoder based transducer to be efficient and compatible with the deliberation model. In addition, the deliberation model is designed to process streaming data. To further improve the deliberation performance, a simple text augmentation approach is explored. We also compare LSTM and Conformer models for encoding partial hypotheses. Experiments on Librispeech and in-house data show relative WER reductions (WERRs) from 3% to 5% with a slight increase in model size and negligible extra token emission latency compared with fast-slow encoder based transducer. Compared with vanilla neural transducers, the proposed deliberation model together with fast-slow encoder based transducer obtains relative 10-11% WERRs on Librispeech and around relative 6% WERR on in-house data with smaller emission delays. | ['Duc Le', 'Michael L. Seltzer', 'Ozlem Kalinli', 'Gil Keren', 'Yangyang Shi', 'Jinxi Guo', 'Jay Mahadeokar', 'Ke Li'] | 2022-12-15 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 6.40336037e-01 5.03050268e-01 2.76392400e-01 -6.45471394e-01
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8f680d91-a51f-47ce-92cb-7f62bd90ed3f | automatic-distractor-generation-for-multiple | 2011.13100 | null | https://arxiv.org/abs/2011.13100v1 | https://arxiv.org/pdf/2011.13100v1.pdf | Automatic Distractor Generation for Multiple Choice Questions in Standard Tests | To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art. | ['Wei Fan', 'Xian Wu', 'Zhaopeng Qiu'] | 2020-11-26 | null | https://aclanthology.org/2020.coling-main.189 | https://aclanthology.org/2020.coling-main.189.pdf | coling-2020-8 | ['distractor-generation'] | ['natural-language-processing'] | [-1.43240765e-01 7.04883784e-03 2.82759547e-01 -5.51554970e-02
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818ae85e-b9e3-46eb-9340-379964cfb24c | a-brief-prehistory-of-double-descent | 2004.04328 | null | https://arxiv.org/abs/2004.04328v1 | https://arxiv.org/pdf/2004.04328v1.pdf | A Brief Prehistory of Double Descent | In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners. Given a fixed training sample size $n$, such curves show the risk of a learner as a function of some (approximate) measure of its complexity $N$. With $N$ the number of features, these curves are also referred to as feature curves. A salient observation in [1] is that these curves can display, what they call, double descent: with increasing $N$, the risk initially decreases, attains a minimum, and then increases until $N$ equals $n$, where the training data is fitted perfectly. Increasing $N$ even further, the risk decreases a second and final time, creating a peak at $N=n$. This twofold descent may come as a surprise, but as opposed to what [1] reports, it has not been overlooked historically. Our letter draws attention to some original, earlier findings, of interest to contemporary machine learning. | ['Jesse H. Krijthe', 'Tom Viering', 'Marco Loog', 'David M. J. Tax', 'Alexander Mey'] | 2020-04-07 | null | null | null | null | ['prehistory'] | ['miscellaneous'] | [ 5.71272783e-02 2.48770043e-01 -2.36726895e-01 -2.97698289e-01
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5ace8ee5-85d1-404a-8de9-f8a92f12100f | low-dimensional-manifold-constrained | 2007.03882 | null | https://arxiv.org/abs/2007.03882v1 | https://arxiv.org/pdf/2007.03882v1.pdf | Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction | Deep neural network based methods have achieved promising results for CT metal artifact reduction (MAR), most of which use many synthesized paired images for training. As synthesized metal artifacts in CT images may not accurately reflect the clinical counterparts, an artifact disentanglement network (ADN) was proposed with unpaired clinical images directly, producing promising results on clinical datasets. However, without sufficient supervision, it is difficult for ADN to recover structural details of artifact-affected CT images based on adversarial losses only. To overcome these problems, here we propose a low-dimensional manifold (LDM) constrained disentanglement network (DN), leveraging the image characteristics that the patch manifold is generally low-dimensional. Specifically, we design an LDM-DN learning algorithm to empower the disentanglement network through optimizing the synergistic network loss functions while constraining the recovered images to be on a low-dimensional patch manifold. Moreover, learning from both paired and unpaired data, an efficient hybrid optimization scheme is proposed to further improve the MAR performance on clinical datasets. Extensive experiments demonstrate that the proposed LDM-DN approach can consistently improve the MAR performance in paired and/or unpaired learning settings, outperforming competing methods on synthesized and clinical datasets. | ['Mengzhou Li', 'Fenglei Fan', 'Hongming Shan', 'Ge Wang', 'Jimin Liang', 'Wenxiang Cong', 'Chuang Niu'] | 2020-07-08 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 3.34170252e-01 9.98404692e-04 -1.05564306e-02 -2.95239598e-01
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341f5626-1144-4f3a-8392-76b7bebe61e9 | research-and-implementation-of-drug-target | 2306.00041 | null | https://arxiv.org/abs/2306.00041v1 | https://arxiv.org/pdf/2306.00041v1.pdf | Research And Implementation Of Drug Target Interaction Confidence Measurement Method Based On Causal Intervention | The identification and discovery of drug-target Interaction (DTI) is an important step in the field of Drug research and development, which can help scientists discover new drugs and accelerate the development process. KnowledgeGraph and the related knowledge graph Embedding (KGE) model develop rapidly and show good performance in the field of drug discovery in recent years. In the task of drug target identification, the lack of authenticity and accuracy of the model will lead to the increase of misjudgment rate and the low efficiency of drug development. To solve the above problems, this study focused on the problem of drug target link prediction with knowledge mapping as the core technology, and adopted the confidence measurement method based on causal intervention to measure the triplet score, so as to improve the accuracy of drug target interaction prediction model. By comparing with the traditional Softmax and Sigmod confidence measurement methods on different KGE models, the results show that the confidence measurement method based on causal intervention can effectively improve the accuracy of DTI link prediction, especially for high-precision models. The predicted results are more conducive to guiding the design and development of followup experiments of drug development, so as to improve the efficiency of drug development. | ['Zaiwen Feng', 'Debo Cheng', 'Yang Xie', 'Bowen Wang', 'Wenting Ye'] | 2023-05-31 | null | null | null | null | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'drug-discovery'] | ['graphs', 'graphs', 'graphs', 'medical'] | [-1.39672056e-01 -2.51655489e-01 -6.55923069e-01 -7.46564195e-02
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a6bd4e7b-af91-4a34-886f-af474fe64e74 | individual-fairness-in-bayesian-neural | 2304.10828 | null | https://arxiv.org/abs/2304.10828v1 | https://arxiv.org/pdf/2304.10828v1.pdf | Individual Fairness in Bayesian Neural Networks | We study Individual Fairness (IF) for Bayesian neural networks (BNNs). Specifically, we consider the $\epsilon$-$\delta$-individual fairness notion, which requires that, for any pair of input points that are $\epsilon$-similar according to a given similarity metrics, the output of the BNN is within a given tolerance $\delta>0.$ We leverage bounds on statistical sampling over the input space and the relationship between adversarial robustness and individual fairness to derive a framework for the systematic estimation of $\epsilon$-$\delta$-IF, designing Fair-FGSM and Fair-PGD as global,fairness-aware extensions to gradient-based attacks for BNNs. We empirically study IF of a variety of approximately inferred BNNs with different architectures on fairness benchmarks, and compare against deterministic models learnt using frequentist techniques. Interestingly, we find that BNNs trained by means of approximate Bayesian inference consistently tend to be markedly more individually fair than their deterministic counterparts. | ['Andrea Patane', 'Luca Laurenti', 'Matthew Wicker', 'Alice Doherty'] | 2023-04-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-2.13267922e-01 3.65698278e-01 -1.72447816e-01 -1.00159454e+00
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451726b1-2a54-4d32-806c-d36dfeb87b18 | automatic-tooth-segmentation-from-3d-dental | 2209.08132 | null | https://arxiv.org/abs/2209.08132v1 | https://arxiv.org/pdf/2209.08132v1.pdf | Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model | 3D tooth segmentation is an important task for digital orthodontics. Several Deep Learning methods have been proposed for automatic tooth segmentation from 3D dental models or intraoral scans. These methods require annotated 3D intraoral scans. Manually annotating 3D intraoral scans is a laborious task. One approach is to devise self-supervision methods to reduce the manual labeling effort. Compared to other types of point cloud data like scene point cloud or shape point cloud data, 3D tooth point cloud data has a very regular structure and a strong shape prior. We look at how much representative information can be learnt from a single 3D intraoral scan. We evaluate this quantitatively with the help of ten different methods of which six are generic point cloud segmentation methods whereas the other four are tooth segmentation specific methods. Surprisingly, we find that with a single 3D intraoral scan training, the Dice score can be as high as 0.86 whereas the full training set gives Dice score of 0.94. We conclude that the segmentation methods can learn a great deal of information from a single 3D tooth point cloud scan under suitable conditions e.g. data augmentation. We are the first to quantitatively evaluate and demonstrate the representation learning capability of Deep Learning methods from a single 3D intraoral scan. This can enable building self-supervision methods for tooth segmentation under extreme data limitation scenario by leveraging the available data to the fullest possible extent. | ['Dimitris Metaxas', 'Hrebesh Molly Subhash', 'Ananya Jana'] | 2022-09-16 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.06147686e-01 9.13573563e-01 -4.41918105e-01 -7.47979760e-01
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