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218189b5-ea66-4310-8732-a6aa1f6eab47 | semantic-segmentation-on-vspw-dataset-through | 2109.01316 | null | https://arxiv.org/abs/2109.01316v1 | https://arxiv.org/pdf/2109.01316v1.pdf | Semantic Segmentation on VSPW Dataset through Aggregation of Transformer Models | Semantic segmentation is an important task in computer vision, from which some important usage scenarios are derived, such as autonomous driving, scene parsing, etc. Due to the emphasis on the task of video semantic segmentation, we participated in this competition. In this report, we briefly introduce the solutions of team 'BetterThing' for the ICCV2021 - Video Scene Parsing in the Wild Challenge. Transformer is used as the backbone for extracting video frame features, and the final result is the aggregation of the output of two Transformer models, SWIN and VOLO. This solution achieves 57.3% mIoU, which is ranked 3rd place in the Video Scene Parsing in the Wild Challenge. | ['Xiaotao Wang', 'Junhong Zou', 'Zixuan Chen'] | 2021-09-03 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 4.20437515e-01 3.00221115e-01 -1.10794650e-02 -6.04497731e-01
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0f884c12-8571-4131-a38d-cdd8fe908707 | task-specific-optimization-of-virtual-channel | 2112.13569 | null | https://arxiv.org/abs/2112.13569v1 | https://arxiv.org/pdf/2112.13569v1.pdf | Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification | Developing a single-microphone speech denoising or dereverberation front-end for robust automatic speaker verification (ASV) in noisy far-field speaking scenarios is challenging. To address this problem, we present a novel front-end design that involves a recently proposed extension of the weighted prediction error (WPE) speech dereverberation algorithm, the virtual acoustic channel expansion (VACE)-WPE. It is demonstrated experimentally in this study that unlike the conventional WPE algorithm, the VACE-WPE can be explicitly trained to cancel out both late reverberation and background noise. To build the front-end, the VACE-WPE is first independently (pre)trained to produce "noisy" dereverberated signals. Subsequently, given a pretrained speaker embedding model, the VACE-WPE is additionally fine-tuned within a task-specific optimization (TSO) framework, causing the speaker embedding extracted from the processed signal to be similar to that extracted from the "noise-free" target signal. Moreover, to extend the application of the proposed front-end to more general, unconstrained "in-the-wild" ASV scenarios beyond controlled far-field conditions, we propose a distortion regularization method for the VACE-WPE within the TSO framework. The effectiveness of the proposed approach is verified on both far-field and in-the-wild ASV benchmarks, demonstrating its superiority over fully neural front-ends and other TSO methods in various cases. | ['Joon-Hyuk Chang', 'Joon-Young Yang'] | 2021-12-27 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [ 3.09273720e-01 8.27827305e-03 6.72363400e-01 -3.98474604e-01
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bb32f28c-d156-4e0d-95e9-e316a147cd6c | 190503716 | 1905.03716 | null | https://arxiv.org/abs/1905.03716v1 | https://arxiv.org/pdf/1905.03716v1.pdf | Fully Parallel Architecture for Semi-global Stereo Matching with Refined Rank Method | Fully parallel architecture at disparity-level for efficient semi-global matching (SGM) with refined rank method is presented. The improved SGM algorithm is implemented with the non-parametric unified rank model which is the combination of Rank filter/AD and Rank SAD. Rank SAD is a novel definition by introducing the constraints of local image structure into the rank method. As a result, the unified rank model with Rank SAD can make up for the defects of Rank filter/AD. Experimental results show both excellent subjective quality and objective performance of the refined SGM algorithm. The fully parallel construction for hardware implementation of SGM is architected with reasonable strategies at disparity-level. The parallelism of the data-stream allows proper throughput for specific applications with acceptable maximum frequency. The results of RTL emulation and synthesis ensure that the proposed parallel architecture is suitable for VLSI implementation. | ['Yiwu Yao', 'Yuhua Cheng'] | 2019-05-07 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 4.14848536e-01 -1.40865251e-01 -2.96208113e-01 -5.38088083e-01
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610c3425-8917-4f47-9181-83cf4ee043f5 | animeceleb-large-scale-animation-celebfaces | 2111.07640 | null | https://arxiv.org/abs/2111.07640v2 | https://arxiv.org/pdf/2111.07640v2.pdf | AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment | We present a novel Animation CelebHeads dataset (AnimeCeleb) to address an animation head reenactment. Different from previous animation head datasets, we utilize 3D animation models as the controllable image samplers, which can provide a large amount of head images with their corresponding detailed pose annotations. To facilitate a data creation process, we build a semi-automatic pipeline leveraging an open 3D computer graphics software with a developed annotation system. After training with the AnimeCeleb, recent head reenactment models produce high-quality animation head reenactment results, which are not achievable with existing datasets. Furthermore, motivated by metaverse application, we propose a novel pose mapping method and architecture to tackle a cross-domain head reenactment task. During inference, a user can easily transfer one's motion to an arbitrary animation head. Experiments demonstrate the usefulness of the AnimeCeleb to train animation head reenactment models, and the superiority of our cross-domain head reenactment model compared to state-of-the-art methods. Our dataset and code are available at https://github.com/kangyeolk/AnimeCeleb. | ['Jaegul Choo', 'Junsoo Lee', 'Sunghyo Chung', 'Jaeseong Lee', 'Sunghyun Park', 'Kangyeol Kim'] | 2021-11-15 | null | null | null | null | ['talking-head-generation', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [-4.74245578e-01 1.67810872e-01 1.31078567e-02 -2.87621766e-01
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d6953f80-1063-4a4d-b3e4-93493f723e09 | a-survey-on-the-application-of-data-science | 2209.07528 | null | https://arxiv.org/abs/2209.07528v1 | https://arxiv.org/pdf/2209.07528v1.pdf | A Survey on the application of Data Science And Analytics in the field of Organised Sports | The application of Data Science and Analytics to optimize or predict outcomes is Ubiquitous in the Modern World. Data Science and Analytics have optimized almost every domain that exists in the market. In our survey, we focus on how the field of Analytics has been adopted in the field of sports, and how it has contributed to the transformation of the game right from the assessment of on-field players and their selection to the prediction of winning team and to the marketing of tickets and business aspects of big sports tournaments. We will present the analytical tools, algorithms, and methodologies adopted in the field of Sports Analytics for different sports and also present our views on the same and we will also compare and contrast these existing approaches. By doing so, we will also present the best tools, algorithms, and analytical methodologies to be considered by anyone who is looking to experiment with sports data and analyze various aspects of the game. | ['C Nandini', 'Prithvi HV', 'Sachin Kumar S'] | 2022-09-15 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-3.79142433e-01 6.99645886e-03 -3.13257694e-01 -3.08037370e-01
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a8cd36a9-d92a-46eb-8509-e41155cfffc1 | dynamic-dense-rgb-d-slam-using-learning-based | 2205.05916 | null | https://arxiv.org/abs/2205.05916v2 | https://arxiv.org/pdf/2205.05916v2.pdf | Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry | We propose a dense dynamic RGB-D SLAM pipeline based on a learning-based visual odometry, TartanVO. TartanVO, like other direct methods rather than feature-based, estimates camera pose through dense optical flow, which only applies to static scenes and disregards dynamic objects. Due to the color constancy assumption, optical flow is not able to differentiate between dynamic and static pixels. Therefore, to reconstruct a static map through such direct methods, our pipeline resolves dynamic/static segmentation by leveraging the optical flow output, and only fuse static points into the map. Moreover, we rerender the input frames such that the dynamic pixels are removed and iteratively pass them back into the visual odometry to refine the pose estimate. | ['Guangzhao Li', 'Jiayi Qiu', 'Yilin Cai', 'Shihao Shen'] | 2022-05-12 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-1.06417537e-01 -1.73832521e-01 -8.04640204e-02 -7.24121630e-02
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3b6de07f-94f5-4923-b62c-799438dc37f8 | proceedings-of-the-second-workshop-on-10 | null | null | https://aclanthology.org/W16-0800 | https://aclanthology.org/W16-0800.pdf | Proceedings of the Second Workshop on Computational Approaches to Deception Detection | null | [''] | 2016-06-01 | null | null | null | ws-2016-6 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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846601d3-5d89-4bfc-9211-76d1a7416f1b | sense-annotated-corpus-for-russian | null | null | https://aclanthology.org/2022.clib-1.15 | https://aclanthology.org/2022.clib-1.15.pdf | Sense-Annotated Corpus for Russian | We present a sense-annotated corpus for Russian. The resource was obtained my manually annotating texts from the OpenCorpora corpus, an open corpus for the Russian language, by senses of Russian wordnet RuWordNet. The annotation was used as a test collection for comparing unsupervised (Personalized Pagerank) and pseudo-labeling methods for Russian word sense disambiguation. | ['Dmitry Ilvovsky', 'Angelina Bolshina', 'Maksim Kulaev', 'Natalia Loukachevitch', 'Alexander Kirillovich'] | null | null | null | null | clib-2022-9 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.37335986e-01 5.75284779e-01 -5.62043905e-01 -2.37361193e-02
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cc191d34-dd65-4fa7-95fb-0e19758be17e | on-the-feasibility-of-attacking-thai-lpr | 2301.05506 | null | https://arxiv.org/abs/2301.05506v1 | https://arxiv.org/pdf/2301.05506v1.pdf | On the feasibility of attacking Thai LPR systems with adversarial examples | Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the \emph{semi-targeted} scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate. | ['Norrathep Rattanavipanon', 'Jakapan Suaboot', 'Chissanupong Jiamsuchon'] | 2023-01-13 | null | null | null | null | ['optical-character-recognition', 'license-plate-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.37074095e-01 -3.98127973e-01 1.06324330e-01 -1.62406713e-01
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83f28e49-f5b4-4f71-a366-392edb844051 | visual-speech-language-models | 1809.06800 | null | http://arxiv.org/abs/1809.06800v1 | http://arxiv.org/pdf/1809.06800v1.pdf | Visual Speech Language Models | Language models (LM) are very powerful in lipreading systems. Language models
built upon the ground truth utterances of datasets learn grammar and structure
rules of words and sentences (the latter in the case of continuous speech).
However, visual co-articulation effects in visual speech signals damage the
performance of visual speech LM's as visually, people do not utter what the
language model expects. These models are commonplace but while higher-order
N-gram LM's may improve classification rates, the cost of this model is
disproportionate to the common goal of developing more accurate classifiers. So
we compare which unit would best optimize a lipreading (visual speech) LM to
observe their limitations. We compare three units; visemes (visual speech
units) \cite{lan2010improving}, phonemes (audible speech units), and words. | [] | 2018-09-14 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-4.80004810e-02 4.19772536e-01 -4.27045524e-01 -1.05629787e-01
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72fb5b67-1e20-4fa0-bafd-fb5a18d89cdd | residual-attention-based-network-for-hand | 1901.05876 | null | http://arxiv.org/abs/1901.05876v1 | http://arxiv.org/pdf/1901.05876v1.pdf | Residual Attention based Network for Hand Bone Age Assessment | Computerized automatic methods have been employed to boost the productivity
as well as objectiveness of hand bone age assessment. These approaches make
predictions according to the whole X-ray images, which include other objects
that may introduce distractions. Instead, our framework is inspired by the
clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which
focuses on the key components of the hand. The proposed framework is composed
of two components: a Mask R-CNN subnet of pixelwise hand segmentation and a
residual attention network for hand bone age assessment. The Mask R-CNN subnet
segments the hands from X-ray images to avoid the distractions of other objects
(e.g., X-ray tags). The hierarchical attention components of the residual
attention subnet force our network to focus on the key components of the X-ray
images and generate the final predictions as well as the associated visual
supports, which is similar to the assessment procedure of clinicians. We
evaluate the performance of the proposed pipeline on the RSNA pediatric bone
age dataset and the results demonstrate its superiority over the previous
methods. | ['Shaoting Zhang', 'Junjie Bai', 'Yi Lu', 'Qi Song', 'Bin Kong', 'Siwei Lyu', 'Kunlin Cao', 'Youbing Yin', 'Feng Gao', 'Eric Wu', 'Xin Wang'] | 2018-12-21 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 5.65777905e-02 6.37688696e-01 2.11136658e-02 -3.35663527e-01
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52d42dfb-c0d1-4e3f-bb31-6f66d39f7a13 | seeking-commonness-and-inconsistencies-a | 2203.08060 | null | https://arxiv.org/abs/2203.08060v3 | https://arxiv.org/pdf/2203.08060v3.pdf | Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering | Multi-view subspace clustering aims to discover the hidden subspace structures from multiple views for robust clustering, and has been attracting considerable attention in recent years. Despite significant progress, most of the previous multi-view subspace clustering algorithms are still faced with two limitations. First, they usually focus on the consistency (or commonness) of multiple views, yet often lack the ability to capture the cross-view inconsistencies in subspace representations. Second, many of them overlook the local structures of multiple views and cannot jointly leverage multiple local structures to enhance the subspace representation learning. To address these two limitations, in this paper, we propose a jointly smoothed multi-view subspace clustering (JSMC) approach. Specifically, we simultaneously incorporate the cross-view commonness and inconsistencies into the subspace representation learning. The view-consensus grouping effect is presented to jointly exploit the local structures of multiple views to regularize the view-commonness representation, which is further associated with the low-rank constraint via the nuclear norm to strengthen its cluster structure. Thus the cross-view commonness and inconsistencies, the view-consensus grouping effect, and the low-rank representation are seamlessly incorporated into a unified objective function, upon which an alternating optimization algorithm is performed to achieve a robust subspace representation for clustering. Experimental results on a variety of real-world multi-view datasets confirm the superiority of our approach. Code available: https://github.com/huangdonghere/JSMC. | ['Chang-Dong Wang', 'Guang-Yu Zhang', 'Dong Huang', 'Xiaosha Cai'] | 2022-03-15 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.61682391e-01 -6.52080536e-01 -2.22756237e-01 -1.90697566e-01
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3d16b116-dd06-43bd-9243-76c6213aa410 | joint-appearance-and-motion-learning-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fan_Joint_Appearance_and_Motion_Learning_for_Efficient_Rolling_Shutter_Correction_CVPR_2023_paper.pdf | Joint Appearance and Motion Learning for Efficient Rolling Shutter Correction | Rolling shutter correction (RSC) is becoming increasingly popular for RS cameras that are widely used in commercial and industrial applications. Despite the promising performance, existing RSC methods typically employ a two-stage network structure that ignores intrinsic information interactions and hinders fast inference. In this paper, we propose a single-stage encoder-decoder-based network, named JAMNet, for efficient RSC. It first extracts pyramid features from consecutive RS inputs, and then simultaneously refines the two complementary information (i.e., global shutter appearance and undistortion motion field) to achieve mutual promotion in a joint learning decoder. To inject sufficient motion cues for guiding joint learning, we introduce a transformer-based motion embedding module and propose to pass hidden states across pyramid levels. Moreover, we present a new data augmentation strategy "vertical flip + inverse order" to release the potential of the RSC datasets. Experiments on various benchmarks show that our approach surpasses the state-of-the-art methods by a large margin, especially with a 4.7 dB PSNR leap on real-world RSC. Code is available at https://github.com/GitCVfb/JAMNet. | ['Qi Liu', 'Zhexiong Wan', 'Yuchao Dai', 'Yuxin Mao', 'Bin Fan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['unrolling'] | ['computer-vision'] | [ 3.59000593e-01 -3.28877926e-01 -5.45878112e-01 -2.52010554e-01
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b3dbd5e2-047d-4525-a08e-c86cc92b136c | deep-convolutional-generative-adversarial | 1812.10179 | null | http://arxiv.org/abs/1812.10179v1 | http://arxiv.org/pdf/1812.10179v1.pdf | Deep Convolutional Generative Adversarial Network Based Food Recognition Using Partially Labeled Data | Traditional machine learning algorithms using hand-crafted feature extraction
techniques (such as local binary pattern) have limited accuracy because of high
variation in images of the same class (or intra-class variation) for food
recognition task. In recent works, convolutional neural networks (CNN) have
been applied to this task with better results than all previously reported
methods. However, they perform best when trained with large amount of annotated
(labeled) food images. This is problematic when obtained in large volume,
because they are expensive, laborious and impractical. Our work aims at
developing an efficient deep CNN learning-based method for food recognition
alleviating these limitations by using partially labeled training data on
generative adversarial networks (GANs). We make new enhancements to the
unsupervised training architecture introduced by Goodfellow et al. (2014),
which was originally aimed at generating new data by sampling a dataset. In
this work, we make modifications to deep convolutional GANs to make them robust
and efficient for classifying food images. Experimental results on benchmarking
datasets show the superiority of our proposed method as compared to the
current-state-of-the-art methodologies even when trained with partially labeled
training data. | ['N. B. Puhan', 'Bappaditya Mandal', 'Avijit Verma'] | 2018-12-26 | null | null | null | null | ['food-recognition'] | ['computer-vision'] | [ 5.91715872e-01 -3.88138602e-03 -1.48101091e-01 -3.81711841e-01
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e9842706-6136-47a9-a235-181e25d7d8a9 | visual-semantics-allow-for-textual-reasoning-1 | 2112.12916 | null | https://arxiv.org/abs/2112.12916v1 | https://arxiv.org/pdf/2112.12916v1.pdf | Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition | Existing Scene Text Recognition (STR) methods typically use a language model to optimize the joint probability of the 1D character sequence predicted by a visual recognition (VR) model, which ignore the 2D spatial context of visual semantics within and between character instances, making them not generalize well to arbitrary shape scene text. To address this issue, we make the first attempt to perform textual reasoning based on visual semantics in this paper. Technically, given the character segmentation maps predicted by a VR model, we construct a subgraph for each instance, where nodes represent the pixels in it and edges are added between nodes based on their spatial similarity. Then, these subgraphs are sequentially connected by their root nodes and merged into a complete graph. Based on this graph, we devise a graph convolutional network for textual reasoning (GTR) by supervising it with a cross-entropy loss. GTR can be easily plugged in representative STR models to improve their performance owing to better textual reasoning. Specifically, we construct our model, namely S-GTR, by paralleling GTR to the language model in a segmentation-based STR baseline, which can effectively exploit the visual-linguistic complementarity via mutual learning. S-GTR sets new state-of-the-art on six challenging STR benchmarks and generalizes well to multi-linguistic datasets. Code is available at https://github.com/adeline-cs/GTR. | ['Bo Du', 'Chaoyue Wang', 'Fengxiang He', 'Juhua Liu', 'Jing Zhang', 'Chen Chen', 'Yue He'] | 2021-12-24 | visual-semantics-allow-for-textual-reasoning | https://arxiv.org/abs/2112.12916 | https://arxiv.org/pdf/2112.12916.pdf | aaai-2022-2021-12 | ['scene-text-recognition'] | ['computer-vision'] | [ 2.79875845e-01 1.62542522e-01 -2.13828698e-01 -4.66254622e-01
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ca6a310c-bfc1-4792-aacf-6395dadd0fe4 | photorealistic-lip-sync-with-adversarial | 2002.08700 | null | https://arxiv.org/abs/2002.08700v2 | https://arxiv.org/pdf/2002.08700v2.pdf | A Neural Lip-Sync Framework for Synthesizing Photorealistic Virtual News Anchors | Lip sync has emerged as a promising technique for generating mouth movements from audio signals. However, synthesizing a high-resolution and photorealistic virtual news anchor is still challenging. Lack of natural appearance, visual consistency, and processing efficiency are the main problems with existing methods. This paper presents a novel lip-sync framework specially designed for producing high-fidelity virtual news anchors. A pair of Temporal Convolutional Networks are used to learn the cross-modal sequential mapping from audio signals to mouth movements, followed by a neural rendering network that translates the synthetic facial map into a high-resolution and photorealistic appearance. This fully trainable framework provides end-to-end processing that outperforms traditional graphics-based methods in many low-delay applications. Experiments also show the framework has advantages over modern neural-based methods in both visual appearance and efficiency. | ['Zhou Zhu', 'Changjiang Ji', 'Ruobing Zheng', 'Bo Song'] | 2020-02-20 | null | null | null | null | ['lip-sync-1'] | ['computer-vision'] | [ 2.18845055e-01 -7.34313801e-02 1.82242766e-01 -3.54266286e-01
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59a8dd10-932d-4169-a6f0-bafce12211f3 | context-based-automated-scoring-of-complex | null | null | https://aclanthology.org/2020.bea-1.19 | https://aclanthology.org/2020.bea-1.19.pdf | Context-based Automated Scoring of Complex Mathematical Responses | The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately. In this paper we propose a method for automatically scoring responses that contain both text and algebraic expressions. Our method not only achieves high agreement with human raters, but also links explicitly to the scoring rubric {--} essentially providing explainable models and a way to potentially provide feedback to students in the future. | ['Dmytro Galochkin', 'Avijit Vajpayee', 'Aoife Cahill', 'Brian Riordan', 'James H Fife'] | 2020-07-01 | null | null | null | ws-2020-7 | ['explainable-models'] | ['computer-vision'] | [ 3.54892835e-02 3.72391015e-01 -1.50821730e-02 -7.53490031e-01
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79ddc3cc-8a5e-4719-b9e4-2103ffec6a69 | data-driven-computing-in-elasticity-via-1 | null | null | https://www.sciencedirect.com/science/article/pii/S2095034918302071 | https://www.sciencedirect.com/science/article/pii/S2095034918302071 | Data-driven computing in elasticity via kernel regression | This paper presents a simple nonparametric regression approach to data-driven computing in elasticity. We apply the kernel regression to the material data set, and formulate a system of nonlinear equations solved to obtain a static equilibrium state of an elastic structure. Preliminary numerical experiments illustrate that, compared with existing methods, the proposed method finds a reasonable solution even if data points distribute coarsely in a given material data set. | ['Yoshihiro Kanno'] | 2018-12-12 | null | null | null | theoretical-and-applied-mechanics-letters | ['stress-strain-relation'] | ['miscellaneous'] | [-3.16895962e-01 -2.43922040e-01 -1.81696981e-01 -3.36651027e-01
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f7d66832-0e54-4f52-a386-968390def924 | align-your-latents-high-resolution-video | 2304.08818 | null | https://arxiv.org/abs/2304.08818v1 | https://arxiv.org/pdf/2304.08818v1.pdf | Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models | Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We first pre-train an LDM on images only; then, we turn the image generator into a video generator by introducing a temporal dimension to the latent space diffusion model and fine-tuning on encoded image sequences, i.e., videos. Similarly, we temporally align diffusion model upsamplers, turning them into temporally consistent video super resolution models. We focus on two relevant real-world applications: Simulation of in-the-wild driving data and creative content creation with text-to-video modeling. In particular, we validate our Video LDM on real driving videos of resolution 512 x 1024, achieving state-of-the-art performance. Furthermore, our approach can easily leverage off-the-shelf pre-trained image LDMs, as we only need to train a temporal alignment model in that case. Doing so, we turn the publicly available, state-of-the-art text-to-image LDM Stable Diffusion into an efficient and expressive text-to-video model with resolution up to 1280 x 2048. We show that the temporal layers trained in this way generalize to different fine-tuned text-to-image LDMs. Utilizing this property, we show the first results for personalized text-to-video generation, opening exciting directions for future content creation. Project page: https://research.nvidia.com/labs/toronto-ai/VideoLDM/ | ['Karsten Kreis', 'Sanja Fidler', 'Seung Wook Kim', 'Tim Dockhorn', 'Huan Ling', 'Robin Rombach', 'Andreas Blattmann'] | 2023-04-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Blattmann_Align_Your_Latents_High-Resolution_Video_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Blattmann_Align_Your_Latents_High-Resolution_Video_Synthesis_With_Latent_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-super-resolution', 'video-generation', 'text-to-video-generation'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.82461816e-01 -2.71242112e-02 -1.47369161e-01 2.33964864e-02
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e9feabf5-60d6-4b3d-a621-1ca8e7d4d1dd | a-unified-sequence-to-sequence-front-end | 1911.04111 | null | https://arxiv.org/abs/1911.04111v1 | https://arxiv.org/pdf/1911.04111v1.pdf | A unified sequence-to-sequence front-end model for Mandarin text-to-speech synthesis | In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech. Building a typical pipeline-based front-end which consists of multiple individual components requires extensive efforts. In this paper, we proposed a unified sequence-to-sequence front-end model for Mandarin TTS that converts raw texts to linguistic features directly. Compared to the pipeline-based front-end, our unified front-end can achieve comparable performance in polyphone disambiguation and prosody word prediction, and improve intonation phrase prediction by 0.0738 in F1 score. We also implemented the unified front-end with Tacotron and WaveRNN to build a Mandarin TTS system. The synthesized speech by that got a comparable MOS (4.38) with the pipeline-based front-end (4.37) and close to human recordings (4.49). | ['Shichao Liu', 'Zhiling Zhang', 'Yuxuan Wang', 'Yang Zhang', 'Zejun Ma', 'Xiang Yin', 'Junjie Pan'] | 2019-11-11 | null | null | null | null | ['polyphone-disambiguation'] | ['natural-language-processing'] | [ 4.43951301e-02 -8.84623826e-02 3.09029996e-01 -5.26864469e-01
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8549e099-9467-492a-bc85-af0a1738aa9a | bayesian-metric-learning-for-uncertainty | 2302.01332 | null | https://arxiv.org/abs/2302.01332v2 | https://arxiv.org/pdf/2302.01332v2.pdf | Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval | We propose the first Bayesian encoder for metric learning. Rather than relying on neural amortization as done in prior works, we learn a distribution over the network weights with the Laplace Approximation. We actualize this by first proving that the contrastive loss is a valid log-posterior. We then propose three methods that ensure a positive definite Hessian. Lastly, we present a novel decomposition of the Generalized Gauss-Newton approximation. Empirically, we show that our Laplacian Metric Learner (LAM) estimates well-calibrated uncertainties, reliably detects out-of-distribution examples, and yields state-of-the-art predictive performance. | ['Soren Hauberg', 'Silas Brack', 'Marco Miani', 'Frederik Warburg'] | 2023-02-02 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-8.43350887e-02 3.13959867e-01 -7.41092190e-02 -6.77758992e-01
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2e109c73-722d-4011-87e7-0ef71d2325e7 | comparison-of-u-net-based-convolutional | 1810.04017 | null | http://arxiv.org/abs/1810.04017v1 | http://arxiv.org/pdf/1810.04017v1.pdf | Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT | Various approaches for liver segmentation in CT have been proposed: Besides
statistical shape models, which played a major role in this research area,
novel approaches on the basis of convolutional neural networks have been
introduced recently. Using a set of 219 liver CT datasets with reference
segmentations from liver surgery planning, we evaluate the performance of
several neural network classifiers based on 2D and 3D U-net architectures. An
interesting observation is that slice-wise approaches perform surprisingly
well, with mean and median Dice coefficients above 0.97, and may be preferable
over 3D approaches given current hardware and software limitations. | ['Andrea Schenk', 'Grzegorz Chlebus', 'Hans Meine', 'Mohsen Ghafoorian', 'Itaru Endo'] | 2018-10-09 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [-4.48933452e-01 1.34904608e-01 -3.37712020e-01 -6.33244812e-01
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9f0634d4-28ad-4a61-aac3-56edc69cce28 | 190910367 | 1909.10367 | null | https://arxiv.org/abs/1909.10367v2 | https://arxiv.org/pdf/1909.10367v2.pdf | Learning Temporal Attention in Dynamic Graphs with Bilinear Interactions | Reasoning about graphs evolving over time is a challenging concept in many domains, such as bioinformatics, physics, and social networks. We consider a common case in which edges can be short term interactions (e.g., messaging) or long term structural connections (e.g., friendship). In practice, long term edges are often specified by humans. Human-specified edges can be both expensive to produce and suboptimal for the downstream task. To alleviate these issues, we propose a model based on temporal point processes and variational autoencoders that learns to infer temporal attention between nodes by observing node communication. As temporal attention drives between-node feature propagation, using the dynamics of node interactions to learn this key component provides more flexibility while simultaneously avoiding issues associated with human-specified edges. We also propose a bilinear transformation layer for pairs of node features instead of concatenation, typically used in prior work, and demonstrate its superior performance in all cases. In experiments on two datasets in the dynamic link prediction task, our model often outperforms the baseline model that requires a human-specified graph. Moreover, our learned attention is semantically interpretable and infers connections similar to actual graphs. | ['Graham W. Taylor', 'Carolyn Augusta', 'Boris Knyazev'] | 2019-09-23 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-8.89468864e-02 3.46887141e-01 -1.03908099e-01 -4.07609016e-01
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bd3d7ae3-5e28-412b-b4b7-ed0e165bd733 | estimating-the-causal-effect-of-early | 2306.13891 | null | https://arxiv.org/abs/2306.13891v1 | https://arxiv.org/pdf/2306.13891v1.pdf | Estimating the Causal Effect of Early ArXiving on Paper Acceptance | What is the effect of releasing a preprint of a paper before it is submitted for peer review? No randomized controlled trial has been conducted, so we turn to observational data to answer this question. We use data from the ICLR conference (2018--2022) and apply methods from causal inference to estimate the effect of arXiving a paper before the reviewing period (early arXiving) on its acceptance to the conference. Adjusting for 18 confounders such as topic, authors, and quality, we may estimate the causal effect. However, since quality is a challenging construct to estimate, we use the negative outcome control method, using paper citation count as a control variable to debias the quality confounding effect. Our results suggest that early arXiving may have a small effect on a paper's chances of acceptance. However, this effect (when existing) does not differ significantly across different groups of authors, as grouped by author citation count and institute rank. This suggests that early arXiving does not provide an advantage to any particular group. | ['Noah A. Smith', 'Bo Zhang', 'David Wadden', 'Jiayao Zhang', 'Yanai Elazar'] | 2023-06-24 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [-2.08950043e-01 1.39497042e-01 -5.56640685e-01 -7.81228021e-02
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76f3f425-31e9-4f45-b963-2162939c9082 | learning-sparse-temporal-video-mapping-for | 2301.06103 | null | https://arxiv.org/abs/2301.06103v1 | https://arxiv.org/pdf/2301.06103v1.pdf | Learning Sparse Temporal Video Mapping for Action Quality Assessment in Floor Gymnastics | Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance. It is crucial to comprehend local discriminative spatial dependencies and global semantics for accurate evaluation. However, existing benchmark datasets mainly incorporate sports where the performance lasts only a few seconds. Consequently, state-ofthe-art sports quality assessment methods specifically focus on spatial structure. Although they achieve high performance in short-term sports, they are unable to model prolonged video sequences and fail to achieve similar performance in long-term sports. To facilitate such analysis, we introduce a new dataset, coined AGF-Olympics, that incorporates artistic gymnastic floor routines. AFG-Olympics provides highly challenging scenarios with extensive background, viewpoint, and scale variations over an extended sample duration of up to 2 minutes. In addition, we propose a discriminative attention module to map the dense feature space into a sparse representation by disentangling complex associations. Extensive experiments indicate that our proposed module provides an effective way to embed long-range spatial and temporal correlation semantics. | ['Ajmal Mian', 'Ghulam Mubashar Hassan', 'Sania Zahan'] | 2023-01-15 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [-3.04962367e-01 -8.39361012e-01 -2.58080572e-01 -4.41556454e-01
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e54e642a-3c6c-4ade-be56-d8964536a3e4 | hardware-efficient-joint-radar-communications | 2104.08127 | null | https://arxiv.org/abs/2104.08127v1 | https://arxiv.org/pdf/2104.08127v1.pdf | Hardware Efficient Joint Radar-Communications with Hybrid Precoding and RF Chain Optimization | In this paper, we aim to achieve energy efficient design with minimum hardware requirement for hybrid precoding, which enables a large number of antennas with minimal number of RF chains, and sub-arrayed multiple-input multiple-output (MIMO) radar based joint radar-communication (JRC) systems. A dynamic active RF chain selection mechanism is implemented in the baseband processing and the energy efficiency (EE) maximization problem is solved using fractional programming to obtain the optimal number of RF chains at the current channel state. Subsequently hybrid precoders are computed employing a sub-arrayed MIMO structure for EE maximization with weighted formulation of the communication and radar metrics, and the solution is based on alternating minimization. The simulation results show that the proposed method with minimum hardware achieves the best EE while maintaining the rate performance, and an efficient trade-off between sensing and communication. | ['Fan Liu', 'Christos Masouros', 'Aryan Kaushik'] | 2021-04-16 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 7.17953682e-01 1.25744909e-01 -2.01142028e-01 -2.52532959e-01
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c10e88d2-8077-4f6d-b558-81013d3caa59 | vidstyleode-disentangled-video-editing-via | 2304.06020 | null | https://arxiv.org/abs/2304.06020v1 | https://arxiv.org/pdf/2304.06020v1.pdf | VidStyleODE: Disentangled Video Editing via StyleGAN and NeuralODEs | We propose $\textbf{VidStyleODE}$, a spatiotemporally continuous disentangled $\textbf{Vid}$eo representation based upon $\textbf{Style}$GAN and Neural-$\textbf{ODE}$s. Effective traversal of the latent space learned by Generative Adversarial Networks (GANs) has been the basis for recent breakthroughs in image editing. However, the applicability of such advancements to the video domain has been hindered by the difficulty of representing and controlling videos in the latent space of GANs. In particular, videos are composed of content (i.e., appearance) and complex motion components that require a special mechanism to disentangle and control. To achieve this, VidStyleODE encodes the video content in a pre-trained StyleGAN $\mathcal{W}_+$ space and benefits from a latent ODE component to summarize the spatiotemporal dynamics of the input video. Our novel continuous video generation process then combines the two to generate high-quality and temporally consistent videos with varying frame rates. We show that our proposed method enables a variety of applications on real videos: text-guided appearance manipulation, motion manipulation, image animation, and video interpolation and extrapolation. Project website: https://cyberiada.github.io/VidStyleODE | ['Aykut Erdem', 'Erkut Erdem', 'Levent Karacan', 'Duygu Ceylan', 'Tolga Birdal', 'Andrew Bond', 'Moayed Haji Ali'] | 2023-04-12 | null | null | null | null | ['video-generation', 'image-animation'] | ['computer-vision', 'computer-vision'] | [ 3.18147272e-01 -5.36770374e-02 4.07973193e-02 8.17867368e-02
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45ad64dd-a0e3-48bb-9d9d-d7fdb8a3c7fe | posterior-sampling-for-deep-reinforcement | 2305.00477 | null | https://arxiv.org/abs/2305.00477v2 | https://arxiv.org/pdf/2305.00477v2.pdf | Posterior Sampling for Deep Reinforcement Learning | Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency by building an environment model that can be used for planning. Posterior Sampling for Reinforcement Learning is such a model-based algorithm that has attracted significant interest due to its performance in the tabular setting. This paper introduces Posterior Sampling for Deep Reinforcement Learning (PSDRL), the first truly scalable approximation of Posterior Sampling for Reinforcement Learning that retains its model-based essence. PSDRL combines efficient uncertainty quantification over latent state space models with a specially tailored continual planning algorithm based on value-function approximation. Extensive experiments on the Atari benchmark show that PSDRL significantly outperforms previous state-of-the-art attempts at scaling up posterior sampling while being competitive with a state-of-the-art (model-based) reinforcement learning method, both in sample efficiency and computational efficiency. | ['Paulo Rauber', 'Michelangelo Conserva', 'Remo Sasso'] | 2023-04-30 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-1.77736267e-01 3.71903837e-01 -8.79098117e-01 -1.42770022e-01
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1f958c04-03ae-431c-8ac2-b32c8e0dbdad | chad-charlotte-anomaly-dataset | 2212.09258 | null | https://arxiv.org/abs/2212.09258v3 | https://arxiv.org/pdf/2212.09258v3.pdf | CHAD: Charlotte Anomaly Dataset | In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of a video contain abnormal behaviors. However, video anomaly detection is particularly context-specific, and the availability of representative datasets heavily limits real-world accuracy. Additionally, the metrics currently reported by most state-of-the-art methods often do not reflect how well the model will perform in real-world scenarios. In this article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a high-resolution, multi-camera anomaly dataset in a commercial parking lot setting. In addition to frame-level anomaly labels, CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor. This is especially beneficial for skeleton-based anomaly detection, which is useful for its lower computational demand in real-world settings. CHAD is also the first anomaly dataset to contain multiple views of the same scene. With four camera views and over 1.15 million frames, CHAD is the largest fully annotated anomaly detection dataset including person annotations, collected from continuous video streams from stationary cameras for smart video surveillance applications. To demonstrate the efficacy of CHAD for training and evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection algorithms on CHAD and provide comprehensive analysis, including both quantitative results and qualitative examination. The dataset is available at https://github.com/TeCSAR-UNCC/CHAD. | ['Hamed Tabkhi', 'Christopher Neff', 'Babak Rahimi Ardabili', 'Ghazal Alinezhad Noghre', 'Armin Danesh Pazho'] | 2022-12-19 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-1.20881140e-01 -6.36432588e-01 -4.92109507e-02 -1.48755312e-01
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e0e0cc9d-b0e5-432f-8fa2-be4309add77e | deep-neural-network-concepts-for-background | 1811.05255 | null | http://arxiv.org/abs/1811.05255v1 | http://arxiv.org/pdf/1811.05255v1.pdf | Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation | Conventional neural networks show a powerful framework for background
subtraction in video acquired by static cameras. Indeed, the well-known SOBS
method and its variants based on neural networks were the leader methods on the
largescale CDnet 2012 dataset during a long time. Recently, convolutional
neural networks which belong to deep learning methods were employed with
success for background initialization, foreground detection and deep learned
features. Currently, the top current background subtraction methods in CDnet
2014 are based on deep neural networks with a large gap of performance in
comparison on the conventional unsupervised approaches based on multi-features
or multi-cues strategies. Furthermore, a huge amount of papers was published
since 2016 when Braham and Van Droogenbroeck published their first work on CNN
applied to background subtraction providing a regular gain of performance. In
this context, we provide the first review of deep neural network concepts in
background subtraction for novices and experts in order to analyze this success
and to provide further directions. For this, we first surveyed the methods used
background initialization, background subtraction and deep learned features.
Then, we discuss the adequacy of deep neural networks for background
subtraction. Finally, experimental results are presented on the CDnet 2014
dataset. | ['Soon Ki Jung', 'Thierry Bouwmans', 'Sajid Javed', 'Maryam Sultana'] | 2018-11-13 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 3.88141304e-01 -5.66696465e-01 2.29444355e-01 -1.72885686e-01
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e3ab7dd2-a7e9-4b02-99be-001c62d88df6 | multi-crop-contrastive-learning-for | 2304.12235 | null | https://arxiv.org/abs/2304.12235v3 | https://arxiv.org/pdf/2304.12235v3.pdf | Multi-cropping Contrastive Learning and Domain Consistency for Unsupervised Image-to-Image Translation | Recently, unsupervised image-to-image translation methods based on contrastive learning have achieved state-of-the-art results in many tasks. However, in the previous works, the negatives are sampled from the input image itself, which inspires us to design a data augmentation method to improve the quality of the selected negatives. Moreover, the previous methods only preserve the content consistency via patch-wise contrastive learning in the embedding space, which ignores the domain consistency between the generated images and the real images of the target domain. In this paper, we propose a novel unsupervised image-to-image translation framework based on multi-cropping contrastive learning and domain consistency, called MCDUT. Specifically, we obtain the multi-cropping views via the center-cropping and the random-cropping with the aim of further generating the high-quality negative examples. To constrain the embeddings in the deep feature space, we formulate a new domain consistency loss, which encourages the generated images to be close to the real images in the embedding space of the same domain. Furthermore, we present a dual coordinate attention network by embedding positional information into the channel, which called DCA. We employ the DCA network in the design of generator, which makes the generator capture the horizontal and vertical global information of dependency. In many image-to-image translation tasks, our method achieves state-of-the-art results, and the advantages of our method have been proven through extensive comparison experiments and ablation research. | ['Cheng-Wei Hu', 'Zheng Yuan', 'Wei-Ling Cai', 'Chen Zhao'] | 2023-04-24 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 3.87203276e-01 -6.36049211e-02 -1.79179192e-01 -3.43187988e-01
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470e4f06-dceb-42ad-a258-4f6285917989 | learning-and-visualizing-localized-geometric | 1711.04851 | null | http://arxiv.org/abs/1711.04851v3 | http://arxiv.org/pdf/1711.04851v3.pdf | Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes | 3D Convolutional Neural Networks (3D-CNN) have been used for object
recognition based on the voxelized shape of an object. However, interpreting
the decision making process of these 3D-CNNs is still an infeasible task. In
this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation
Mapping method (3D-GradCAM) for visual explanations of the distinct local
geometric features of interest within an object. To enable efficient learning
of 3D geometries, we augment the voxel data with surface normals of the object
boundary. We then train a 3D-CNN with this augmented data and identify the
local features critical for decision-making using 3D GradCAM. An application of
this feature identification framework is to recognize difficult-to-manufacture
drilled hole features in a complex CAD geometry. The framework can be extended
to identify difficult-to-manufacture features at multiple spatial scales
leading to a real-time design for manufacturability decision support system. | ['Adarsh Krishnamurthy', 'Sambit Ghadai', 'Aditya Balu', 'Soumik Sarkar'] | 2017-11-13 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-5.86510263e-02 2.54737228e-01 1.47083610e-01 -5.08804023e-01
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55d56ecf-b834-4ea9-b473-8474e46667ae | structure-and-automatic-segmentation-of | 2008.00756 | null | https://arxiv.org/abs/2008.00756v1 | https://arxiv.org/pdf/2008.00756v1.pdf | Structure and Automatic Segmentation of Dhrupad Vocal Bandish Audio | A Dhrupad vocal concert comprises a composition section that is interspersed with improvised episodes of increased rhythmic activity involving the interaction between the vocals and the percussion. Tracking the changing rhythmic density, in relation to the underlying metric tempo of the piece, thus facilitates the detection and labeling of the improvised sections in the concert structure. This work concerns the automatic detection of the musically relevant rhythmic densities as they change in time across the bandish (composition) performance. An annotated dataset of Dhrupad bandish concert sections is presented. We investigate a CNN-based system, trained to detect local tempo relationships, and follow it with temporal smoothing. We also employ audio source separation as a pre-processing step to the detection of the individual surface densities of the vocals and the percussion. This helps us obtain the complete musical description of the concert sections in terms of capturing the changing rhythmic interaction of the two performers. | ['Rohit M. A.', 'Preeti Rao'] | 2020-08-03 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.04268008e-01 -3.87183487e-01 3.56967151e-01 2.54241556e-01
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6fb6107b-0893-422f-88da-5f643bdd833b | environment-sound-classification-using | 1908.11219 | null | https://arxiv.org/abs/1908.11219v9 | https://arxiv.org/pdf/1908.11219v9.pdf | Environment Sound Classification using Multiple Feature Channels and Attention based Deep Convolutional Neural Network | In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coefficients (MFCC), Gammatone Frequency Cepstral Coefficients (GFCC), the Constant Q-transform (CQT) and Chromagram. Such multiple features have never been used before for signal or audio processing. And, we employ a deeper CNN (DCNN) compared to previous models, consisting of spatially separable convolutions working on time and feature domain separately. Alongside, we use attention modules that perform channel and spatial attention together. We use some data augmentation techniques to further boost performance. Our model is able to achieve state-of-the-art performance on all three benchmark environment sound classification datasets, i.e. the UrbanSound8K (97.52%), ESC-10 (95.75%) and ESC-50 (88.50%). To the best of our knowledge, this is the first time that a single environment sound classification model is able to achieve state-of-the-art results on all three datasets. For ESC-10 and ESC-50 datasets, the accuracy achieved by the proposed model is beyond human accuracy of 95.7% and 81.3% respectively. | ['Ole-Christoffer Granmo', 'Jivitesh Sharma', 'Morten Goodwin'] | 2019-08-28 | null | null | null | null | ['sound-classification'] | ['audio'] | [-6.79348735e-03 -3.89487147e-01 7.69152343e-01 -5.59652373e-02
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326b2612-4a5c-4e1f-8cbd-a28aa4e88778 | cell-detection-with-star-convex-polygons | 1806.03535 | null | http://arxiv.org/abs/1806.03535v2 | http://arxiv.org/pdf/1806.03535v2.pdf | Cell Detection with Star-convex Polygons | Automatic detection and segmentation of cells and nuclei in microscopy images
is important for many biological applications. Recent successful learning-based
approaches include per-pixel cell segmentation with subsequent pixel grouping,
or localization of bounding boxes with subsequent shape refinement. In
situations of crowded cells, these can be prone to segmentation errors, such as
falsely merging bordering cells or suppressing valid cell instances due to the
poor approximation with bounding boxes. To overcome these issues, we propose to
localize cell nuclei via star-convex polygons, which are a much better shape
representation as compared to bounding boxes and thus do not need shape
refinement. To that end, we train a convolutional neural network that predicts
for every pixel a polygon for the cell instance at that position. We
demonstrate the merits of our approach on two synthetic datasets and one
challenging dataset of diverse fluorescence microscopy images. | ['Martin Weigert', 'Uwe Schmidt', 'Gene Myers', 'Coleman Broaddus'] | 2018-06-09 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 4.49740380e-01 7.77325854e-02 4.88000184e-01 -2.51107723e-01
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e1b26f15-b5e9-4c84-a203-576b02d38134 | sparse-representation-classification-via | 1906.01601 | null | https://arxiv.org/abs/1906.01601v1 | https://arxiv.org/pdf/1906.01601v1.pdf | Sparse Representation Classification via Screening for Graphs | The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency for random graphs drawn from stochastic blockmodels. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance but significantly faster. | ['Cencheng Shen', 'Carey Priebe', 'Yuexiao Dong', 'Li Chen'] | 2019-06-04 | null | null | null | null | ['image-classification-shift-consistency'] | ['computer-vision'] | [ 3.67220104e-01 -2.18454935e-02 -6.45274818e-01 -3.03666592e-01
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5527ef69-6337-42da-aedb-230ae88ced04 | eye-tracking-as-a-source-of-implicit-feedback | 2305.07516 | null | https://arxiv.org/abs/2305.07516v1 | https://arxiv.org/pdf/2305.07516v1.pdf | Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis | Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest. | ['Maria Bielikova', 'Robert Moro', 'Santiago de Leon-Martinez'] | 2023-05-12 | null | null | null | null | ['movie-recommendation', 'collaborative-filtering'] | ['miscellaneous', 'miscellaneous'] | [-4.09682304e-01 -2.11252302e-01 -2.30589911e-01 -4.26643372e-01
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9a24483a-7eb3-4621-b550-759d870ec657 | training-ibm-watson-using-automatically | 1611.03932 | null | http://arxiv.org/abs/1611.03932v1 | http://arxiv.org/pdf/1611.03932v1.pdf | Training IBM Watson using Automatically Generated Question-Answer Pairs | IBM Watson is a cognitive computing system capable of question answering in
natural languages. It is believed that IBM Watson can understand large corpora
and answer relevant questions more effectively than any other
question-answering system currently available. To unleash the full power of
Watson, however, we need to train its instance with a large number of
well-prepared question-answer pairs. Obviously, manually generating such pairs
in a large quantity is prohibitively time consuming and significantly limits
the efficiency of Watson's training. Recently, a large-scale dataset of over 30
million question-answer pairs was reported. Under the assumption that using
such an automatically generated dataset could relieve the burden of manual
question-answer generation, we tried to use this dataset to train an instance
of Watson and checked the training efficiency and accuracy. According to our
experiments, using this auto-generated dataset was effective for training
Watson, complementing manually crafted question-answer pairs. To the best of
the authors' knowledge, this work is the first attempt to use a large-scale
dataset of automatically generated question-answer pairs for training IBM
Watson. We anticipate that the insights and lessons obtained from our
experiments will be useful for researchers who want to expedite Watson training
leveraged by automatically generated question-answer pairs. | ['Minseok Kim', 'Jaeyoon Yoo', 'Gyuwan Kim', 'Sungroh Yoon', 'Jangho Lee', 'Changwoo Jung'] | 2016-11-12 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 1.67969003e-01 3.66896659e-01 6.32658482e-01 -3.70294601e-01
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07067e94-be42-4996-9fbc-baf7c2312944 | from-random-to-regular-variation-in-the | 1910.10210 | null | http://arxiv.org/abs/1910.10210v1 | http://arxiv.org/pdf/1910.10210v1.pdf | From Random to Regular: Variation in the Patterning of Retinal Mosaics | The various types of retinal neurons are each positioned at their respective
depths within the retina where they are believed to be assembled as orderly
mosaics, in which like-type neurons minimize proximity to one another. Two
common statistical analyses for assessing the spatial properties of retinal
mosaics include the nearest neighbor analysis, from which an index of their
"regularity" is commonly calculated, and the density recovery profile derived
from auto-correlation analysis, revealing the presence of an exclusion zone
indicative of anti-clustering. While each of the spatial statistics derived
from these analyses, the regularity index and the effective radius, can be
useful in characterizing such properties of orderly retinal mosaics, they are
rarely sufficient for conveying the natural variation in the self-spacing
behavior of different types of retinal neurons and the extent to which that
behavior generates uniform intercellular spacing across the mosaic. We consider
the strengths and limitations of different spatial statistical analyses for
assessing the patterning in retinal mosaics, highlighting a number of
misconceptions and their frequent misuse. Rather than being diagnostic criteria
for determining simply whether a population is "regular", they should be
treated as descriptive statistics that convey variation in the factors that
influence neuronal positioning. We subsequently apply multiple spatial
statistics to the analysis of eight different mosaics in the mouse retina,
demonstrating conspicuous variability in the degree of patterning present, from
essentially random to notably regular. This variability in patterning has both
a developmental as well as a functional significance, reflecting the rules
governing the positioning of different types of neurons as the architecture of
the retina is assembled (abstract truncated). | [] | 2019-10-22 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 3.35095018e-01 -2.04635426e-01 1.75228477e-01 -2.95169592e-01
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f49dcc3d-3ba1-4baf-a27c-4ec18f9b37ac | face-space-action-recognition-by-face-object | 1601.04293 | null | http://arxiv.org/abs/1601.04293v1 | http://arxiv.org/pdf/1601.04293v1.pdf | Face-space Action Recognition by Face-Object Interactions | Action recognition in still images has seen major improvement in recent years
due to advances in human pose estimation, object recognition and stronger
feature representations. However, there are still many cases in which
performance remains far from that of humans. In this paper, we approach the
problem by learning explicitly, and then integrating three components of
transitive actions: (1) the human body part relevant to the action (2) the
object being acted upon and (3) the specific form of interaction between the
person and the object. The process uses class-specific features and relations
not used in the past for action recognition and which use inherently two cycles
in the process unlike most standard approaches. We focus on face-related
actions (FRA), a subset of actions that includes several currently challenging
categories. We present an average relative improvement of 52% over state-of-the
art. We also make a new benchmark publicly available. | ['Shimon Ullman', 'Amir Rosenfeld'] | 2016-01-17 | null | null | null | null | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 6.51402414e-01 1.72515154e-01 -2.39938036e-01 -5.39953291e-01
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677388a2-ff44-4f38-95a0-c69ba55115cb | transformer-based-model-for-monocular-visual | 2305.06121 | null | https://arxiv.org/abs/2305.06121v1 | https://arxiv.org/pdf/2305.06121v1.pdf | Transformer-based model for monocular visual odometry: a video understanding approach | Estimating the camera pose given images of a single camera is a traditional task in mobile robots and autonomous vehicles. This problem is called monocular visual odometry and it often relies on geometric approaches that require engineering effort for a specific scenario. Deep learning methods have shown to be generalizable after proper training and a considerable amount of available data. Transformer-based architectures have dominated the state-of-the-art in natural language processing and computer vision tasks, such as image and video understanding. In this work, we deal with the monocular visual odometry as a video understanding task to estimate the 6-DoF camera's pose. We contribute by presenting the TSformer-VO model based on spatio-temporal self-attention mechanisms to extract features from clips and estimate the motions in an end-to-end manner. Our approach achieved competitive state-of-the-art performance compared with geometry-based and deep learning-based methods on the KITTI visual odometry dataset, outperforming the DeepVO implementation highly accepted in the visual odometry community. | ['Marcos R. O. A. Maximo', 'André O. Françani'] | 2023-05-10 | null | null | null | null | ['video-understanding', 'monocular-visual-odometry', 'visual-odometry'] | ['computer-vision', 'robots', 'robots'] | [-4.52088892e-01 -4.03597653e-02 -2.59135991e-01 -4.44026709e-01
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f7d9e7f7-87e2-4af2-8aa3-419ca1346951 | identifying-well-formed-natural-language | 1808.09419 | null | http://arxiv.org/abs/1808.09419v1 | http://arxiv.org/pdf/1808.09419v1.pdf | Identifying Well-formed Natural Language Questions | Understanding search queries is a hard problem as it involves dealing with
"word salad" text ubiquitously issued by users. However, if a query resembles a
well-formed question, a natural language processing pipeline is able to perform
more accurate interpretation, thus reducing downstream compounding errors.
Hence, identifying whether or not a query is well formed can enhance query
understanding. Here, we introduce a new task of identifying a well-formed
natural language question. We construct and release a dataset of 25,100
publicly available questions classified into well-formed and non-wellformed
categories and report an accuracy of 70.7% on the test set. We also show that
our classifier can be used to improve the performance of neural
sequence-to-sequence models for generating questions for reading comprehension. | ['Manaal Faruqui', 'Dipanjan Das'] | 2018-08-28 | identifying-well-formed-natural-language-1 | https://aclanthology.org/D18-1091 | https://aclanthology.org/D18-1091.pdf | emnlp-2018-10 | ['query-wellformedness'] | ['natural-language-processing'] | [ 4.98464584e-01 2.96180785e-01 1.03301667e-02 -4.98428494e-01
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55949da2-0200-466e-bc9e-31b17357613f | learning-descriptive-image-captioning-via | 2306.13460 | null | https://arxiv.org/abs/2306.13460v2 | https://arxiv.org/pdf/2306.13460v2.pdf | Learning Descriptive Image Captioning via Semipermeable Maximum Likelihood Estimation | Image captioning aims to describe visual content in natural language. As 'a picture is worth a thousand words', there could be various correct descriptions for an image. However, with maximum likelihood estimation as the training objective, the captioning model is penalized whenever its prediction mismatches with the label. For instance, when the model predicts a word expressing richer semantics than the label, it will be penalized and optimized to prefer more concise expressions, referred to as conciseness optimization. In contrast, predictions that are more concise than labels lead to richness optimization. Such conflicting optimization directions could eventually result in the model generating general descriptions. In this work, we introduce Semipermeable MaxImum Likelihood Estimation (SMILE), which allows richness optimization while blocking conciseness optimization, thus encouraging the model to generate longer captions with more details. Extensive experiments on two mainstream image captioning datasets MSCOCO and Flickr30K demonstrate that SMILE significantly enhances the descriptiveness of generated captions. We further provide in-depth investigations to facilitate a better understanding of how SMILE works. | ['Qin Jin', 'Liang Zhang', 'Anwen Hu', 'Zihao Yue'] | 2023-06-23 | null | null | null | null | ['image-captioning', 'blocking'] | ['computer-vision', 'natural-language-processing'] | [ 5.38993537e-01 7.32284546e-01 -3.50972652e-01 -6.62443221e-01
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2f716b1f-7b3d-4329-a78a-69fd68fbbe4d | gshot-few-shot-generative-modeling-of-labeled | 2306.03480 | null | https://arxiv.org/abs/2306.03480v1 | https://arxiv.org/pdf/2306.03480v1.pdf | GSHOT: Few-shot Generative Modeling of Labeled Graphs | Deep graph generative modeling has gained enormous attraction in recent years due to its impressive ability to directly learn the underlying hidden graph distribution. Despite their initial success, these techniques, like much of the existing deep generative methods, require a large number of training samples to learn a good model. Unfortunately, large number of training samples may not always be available in scenarios such as drug discovery for rare diseases. At the same time, recent advances in few-shot learning have opened door to applications where available training data is limited. In this work, we introduce the hitherto unexplored paradigm of few-shot graph generative modeling. Towards this, we develop GSHOT, a meta-learning based framework for few-shot labeled graph generative modeling. GSHOT learns to transfer meta-knowledge from similar auxiliary graph datasets. Utilizing these prior experiences, GSHOT quickly adapts to an unseen graph dataset through self-paced fine-tuning. Through extensive experiments on datasets from diverse domains having limited training samples, we establish that GSHOT generates graphs of superior fidelity compared to existing baselines. | ['Srikanta Bedathur', 'Sayan Ranu', 'Shubham Gupta', 'Sahil Manchanda'] | 2023-06-06 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 6.82205185e-02 3.50004464e-01 -3.19387048e-01 -2.62893379e-01
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8cb1ffca-b564-47dc-bf03-cefb497f2139 | phased-progressive-learning-with-coupling | 2205.12117 | null | https://arxiv.org/abs/2205.12117v3 | https://arxiv.org/pdf/2205.12117v3.pdf | Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification | Deep convolutional neural networks often perform poorly when faced with datasets that suffer from quantity imbalances and classification difficulties. Despite advances in the field, existing two-stage approaches still exhibit dataset bias or domain shift. To counter this, a phased progressive learning schedule has been proposed that gradually shifts the emphasis from representation learning to training the upper classifier. This approach is particularly beneficial for datasets with larger imbalances or fewer samples. Another new method a coupling-regulation-imbalance loss function is proposed, which combines three parts: a correction term, Focal loss, and LDAM loss. This loss is effective in addressing quantity imbalances and outliers, while regulating the focus of attention on samples with varying classification difficulties. These approaches have yielded satisfactory results on several benchmark datasets, including Imbalanced CIFAR10, Imbalanced CIFAR100, ImageNet-LT, and iNaturalist 2018, and can be easily generalized to other imbalanced classification models. | ['Ronald X. Xu', 'Peng Yao', 'Shuwei Shen', 'Peng Liu', 'Pengfei Shao', 'Bingxuan Wu', 'Fan Zhang', 'Yi Cheng', 'Liang Xu'] | 2022-05-24 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 1.98593717e-02 -2.21336871e-01 -4.90078449e-01 -6.56482518e-01
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115cdd2a-5148-4d73-b0c6-77d66f38c6b2 | is-bert-really-robust-natural-language-attack | 1907.11932 | null | https://arxiv.org/abs/1907.11932v6 | https://arxiv.org/pdf/1907.11932v6.pdf | Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment | Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present TextFooler, a simple but strong baseline to generate natural adversarial text. By applying it to two fundamental natural language tasks, text classification and textual entailment, we successfully attacked three target models, including the powerful pre-trained BERT, and the widely used convolutional and recurrent neural networks. We demonstrate the advantages of this framework in three ways: (1) effective---it outperforms state-of-the-art attacks in terms of success rate and perturbation rate, (2) utility-preserving---it preserves semantic content and grammaticality, and remains correctly classified by humans, and (3) efficient---it generates adversarial text with computational complexity linear to the text length. *The code, pre-trained target models, and test examples are available at https://github.com/jind11/TextFooler. | ['Joey Tianyi Zhou', 'Zhijing Jin', 'Peter Szolovits', 'Di Jin'] | 2019-07-27 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 5.63835502e-01 2.77743310e-01 3.17173123e-01 -2.26880550e-01
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dea543d0-feaa-42d0-bb88-300c664d20ea | on-the-relationship-between-counterfactual | 2207.04317 | null | https://arxiv.org/abs/2207.04317v2 | https://arxiv.org/pdf/2207.04317v2.pdf | On the Relationship Between Counterfactual Explainer and Recommender | Recommender systems employ machine learning models to learn from historical data to predict the preferences of users. Deep neural network (DNN) models such as neural collaborative filtering (NCF) are increasingly popular. However, the tangibility and trustworthiness of the recommendations are questionable due to the complexity and lack of explainability of the models. To enable explainability, recent techniques such as ACCENT and FIA are looking for counterfactual explanations that are specific historical actions of a user, the removal of which leads to a change to the recommendation result. In this work, we present a general framework for both DNN and non-DNN models so that the counterfactual explainers all belong to it with specific choices of components. This framework first estimates the influence of a certain historical action after its removal and then uses search algorithms to find the minimal set of such actions for the counterfactual explanation. With this framework, we are able to investigate the relationship between the explainers and recommenders. We empirically study two recommender models (NCF and Factorization Machine) and two datasets (MovieLens and Yelp). We analyze the relationship between the performance of the recommender and the quality of the explainer. We observe that with standard evaluation metrics, the explainers deliver worse performance when the recommendations are more accurate. This indicates that having good explanations to correct predictions is harder than having them to wrong predictions. The community needs more fine-grained evaluation metrics to measure the quality of counterfactual explanations to recommender systems. | ['Meng Jiang', 'Zheng Ning', 'Zhihan Zhang', 'Gang Liu'] | 2022-07-09 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [-1.43550918e-01 2.63739496e-01 -3.03397119e-01 -5.75951099e-01
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484c9017-81d6-4ac2-a295-aaed916f743b | stochastic-action-prediction-for-imitation | 2101.01055 | null | https://arxiv.org/abs/2101.01055v1 | https://arxiv.org/pdf/2101.01055v1.pdf | Stochastic Action Prediction for Imitation Learning | Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. When performing demonstrations, experts are not always consistent and might accomplish the same task in slightly different ways. In this paper, we demonstrate inherent stochasticity in demonstrations collected for tasks including line following with a remote-controlled car and manipulation tasks including reaching, pushing, and picking and placing an object. We model stochasticity in the data distribution using autoregressive action generation, generative adversarial nets, and variational prediction and compare the performance of these approaches. We find that accounting for stochasticity in the expert data leads to substantial improvement in the success rate of task completion. | ['Bharadwaj Amrutur', 'Shishir Kolathaya', 'Nihesh Rathod', 'Sagar Gubbi Venkatesh'] | 2020-12-26 | null | null | null | null | ['action-generation'] | ['computer-vision'] | [ 7.07599223e-02 -6.65321201e-02 4.83378880e-02 -3.61675054e-01
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48409748-f43f-430f-999b-07d0cae8a167 | lights-light-specularity-dataset-for-specular | 2101.10772 | null | https://arxiv.org/abs/2101.10772v1 | https://arxiv.org/pdf/2101.10772v1.pdf | LIGHTS: LIGHT Specularity Dataset for specular detection in Multi-view | Specular highlights are commonplace in images, however, methods for detecting them and in turn removing the phenomenon are particularly challenging. A reason for this, is due to the difficulty of creating a dataset for training or evaluation, as in the real-world we lack the necessary control over the environment. Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Our dataset consists of 18 high quality architectural scenes, where each scene is rendered with multiple views. In total we have 2,603 views with an average of 145 views per scene. Additionally we propose a simple aggregation based method for specular highlight detection that outperforms prior work by 3.6% in two orders of magnitude less time on our dataset. | ['Stuart James', 'Alessio Del Bue', 'Theodore Tsesmelis', 'Mohamed Dahy Elkhouly'] | 2021-01-26 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 4.29540336e-01 -5.33082664e-01 5.77811837e-01 -9.17591378e-02
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d1b88146-bbca-4799-b607-8569d397a08d | subset-labeled-lda-for-large-scale-multi | 1709.05480 | null | http://arxiv.org/abs/1709.05480v1 | http://arxiv.org/pdf/1709.05480v1.pdf | Subset Labeled LDA for Large-Scale Multi-Label Classification | Labeled Latent Dirichlet Allocation (LLDA) is an extension of the standard
unsupervised Latent Dirichlet Allocation (LDA) algorithm, to address
multi-label learning tasks. Previous work has shown it to perform in par with
other state-of-the-art multi-label methods. Nonetheless, with increasing label
sets sizes LLDA encounters scalability issues. In this work, we introduce
Subset LLDA, a simple variant of the standard LLDA algorithm, that not only can
effectively scale up to problems with hundreds of thousands of labels but also
improves over the LLDA state-of-the-art. We conduct extensive experiments on
eight data sets, with label sets sizes ranging from hundreds to hundreds of
thousands, comparing our proposed algorithm with the previously proposed LLDA
algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme
multi-label classification. The results show a steady advantage of our method
over the other LLDA algorithms and competitive results compared to the extreme
multi-label classification algorithms. | ['Grigorios Tsoumakas', 'Yannis Papanikolaou'] | 2017-09-16 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [-1.48826733e-01 8.11415389e-02 -1.52236193e-01 -4.15785670e-01
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8979a301-bb7e-46d5-af5c-b9606b6ca483 | a-comparative-study-of-hierarchical-risk | 2210.00984 | null | https://arxiv.org/abs/2210.00984v1 | https://arxiv.org/pdf/2210.00984v1.pdf | A Comparative Study of Hierarchical Risk Parity Portfolio and Eigen Portfolio on the NIFTY 50 Stocks | Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a systematic approach to portfolio optimization using two approaches, the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors of the Indian stock market. The portfolios are built following the two approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021, to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of its Eigen counterpart on both training and test data for the majority of the sectors studied. | ['Abhishek Dutta', 'Jaydip Sen'] | 2022-10-03 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.51366574e-01 -3.14318687e-01 8.63752440e-02 -1.26061082e-01
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10668414-5b83-482b-9da6-a92ffc5e0236 | conditional-wavegan | 1809.10636 | null | http://arxiv.org/abs/1809.10636v1 | http://arxiv.org/pdf/1809.10636v1.pdf | Conditional WaveGAN | Generative models are successfully used for image synthesis in the recent
years. But when it comes to other modalities like audio, text etc little
progress has been made. Recent works focus on generating audio from a
generative model in an unsupervised setting. We explore the possibility of
using generative models conditioned on class labels. Concatenation based
conditioning and conditional scaling were explored in this work with various
hyper-parameter tuning methods. In this paper we introduce Conditional WaveGANs
(cWaveGAN). Find our implementation at https://github.com/acheketa/cwavegan | ['Woo-Jin Han', 'Gue Jun Jung', 'Anoop Toffy', 'Chae Young Lee'] | 2018-09-27 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 3.90682399e-01 2.63202071e-01 1.10889666e-01 -2.60390192e-01
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5e3d9a5c-6435-4921-94a2-871f6b9e116c | state-based-episodic-memory-for-multi-agent | 2110.09817 | null | https://arxiv.org/abs/2110.09817v1 | https://arxiv.org/pdf/2110.09817v1.pdf | State-based Episodic Memory for Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) algorithms have made promising progress in recent years by leveraging the centralized training and decentralized execution (CTDE) paradigm. However, existing MARL algorithms still suffer from the sample inefficiency problem. In this paper, we propose a simple yet effective approach, called state-based episodic memory (SEM), to improve sample efficiency in MARL. SEM adopts episodic memory (EM) to supervise the centralized training procedure of CTDE in MARL. To the best of our knowledge, SEM is the first work to introduce EM into MARL. We can theoretically prove that, when using for MARL, SEM has lower space complexity and time complexity than state and action based EM (SAEM), which is originally proposed for single-agent reinforcement learning. Experimental results on StarCraft multi-agent challenge (SMAC) show that introducing episodic memory into MARL can improve sample efficiency and SEM can reduce storage cost and time cost compared with SAEM. | ['Wu-Jun Li', 'Xiao Ma'] | 2021-10-19 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.66033578e-01 -2.33108029e-01 -2.12082237e-01 1.66082337e-01
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25722f89-22d8-4f89-9b5e-62b3b45a35be | emotionally-enhanced-talking-face-generation | 2303.11548 | null | https://arxiv.org/abs/2303.11548v2 | https://arxiv.org/pdf/2303.11548v2.pdf | Emotionally Enhanced Talking Face Generation | Several works have developed end-to-end pipelines for generating lip-synced talking faces with various real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, these methods' effectiveness largely depends on the faces in the training dataset, which means they may not perform well on unseen faces. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing. With a broad range of six emotions, i.e., \emph{happiness}, \emph{sadness}, \emph{fear}, \emph{anger}, \emph{disgust}, and \emph{neutral}, we show that our model can adapt to arbitrary identities, emotions, and languages. Our proposed framework is equipped with a user-friendly web interface with a real-time experience for talking face generation with emotions. We also conduct a user study for subjective evaluation of our interface's usability, design, and functionality. Project page: https://midas.iiitd.edu.in/emo/ | ['Rajiv Ratn Shah', 'Yifang Yin', 'Yi Yu', 'Sarthak Bhagat', 'Shagun Uppal', 'Sahil Goyal'] | 2023-03-21 | null | null | null | null | ['talking-head-generation', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.45073995e-01 -3.86868343e-02 1.86675102e-01 -6.54073775e-01
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0766df42-a3e3-42a2-92a0-3c19bb90741e | teaching-independent-parts-separately-tips | 2205.05980 | null | https://arxiv.org/abs/2205.05980v4 | https://arxiv.org/pdf/2205.05980v4.pdf | "Teaching Independent Parts Separately" (TIPSy-GAN) : Improving Accuracy and Stability in Unsupervised Adversarial 2D to 3D Pose Estimation | We present TIPSy-GAN, a new approach to improve the accuracy and stability in unsupervised adversarial 2D to 3D human pose estimation. In our work we demonstrate that the human kinematic skeleton should not be assumed as a single spatially codependent structure; in fact, we posit when a full 2D pose is provided during training, there is an inherent bias learned where the 3D coordinate of a keypoint is spatially codependent on the 2D coordinates of all other keypoints. To investigate our hypothesis we follow previous adversarial approaches but train two generators on spatially independent parts of the kinematic skeleton, the torso and the legs. We find that improving the self-consistency cycle is key to lowering the evaluation error and therefore introduce new consistency constraints during training. A TIPSy model is produced via knowledge distillation from these generators which can predict the 3D ordinates for the entire 2D pose with improved results. Furthermore, we address an unanswered question in prior work of how long to train in a truly unsupervised scenario. We show that for two independent generators training adversarially has improved stability than that of a solo generator which collapses. TIPSy decreases the average error by 17\% when compared to that of a baseline solo generator on the Human3.6M dataset. TIPSy improves upon other unsupervised approaches while also performing strongly against supervised and weakly-supervised approaches during evaluation on both the Human3.6M and MPI-INF-3DHP datasets. | ['Hansung Kim', 'Srinandan Dasmahapatra', 'Peter Hardy'] | 2022-05-12 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 7.01329559e-02 5.61768413e-01 5.00670895e-02 -1.27575770e-01
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6706d2a0-9f12-4df6-9a1f-4abc785682d9 | discern-and-answer-mitigating-the-impact-of | 2305.01579 | null | https://arxiv.org/abs/2305.01579v1 | https://arxiv.org/pdf/2305.01579v1.pdf | Discern and Answer: Mitigating the Impact of Misinformation in Retrieval-Augmented Models with Discriminators | Most existing retrieval-augmented language models (LMs) for question answering assume all retrieved information is factually correct. In this work, we study a more realistic scenario in which retrieved documents may contain misinformation, causing conflicts among them. We observe that the existing models are highly brittle to such information in both fine-tuning and in-context few-shot learning settings. We propose approaches to make retrieval-augmented LMs robust to misinformation by explicitly fine-tuning a discriminator or prompting to elicit discrimination capability in GPT-3. Our empirical results on open-domain question answering show that these approaches significantly improve LMs' robustness to knowledge conflicts. We also provide our findings on interleaving the fine-tuned model's decision with the in-context learning process, paving a new path to leverage the best of both worlds. | ['Joyce Jiyoung Whang', 'Sung-Hyon Myaeng', 'Junmo Kang', 'Jeonghwan Kim', 'Giwon Hong'] | 2023-05-02 | null | null | null | null | ['misinformation', 'open-domain-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 2.27819905e-01 2.60018289e-01 -3.70726854e-01 -3.12816083e-01
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3d974af6-9d1b-4394-9025-1c2d48c35fa4 | cross-age-speaker-verification-learning-age | 2207.05929 | null | https://arxiv.org/abs/2207.05929v1 | https://arxiv.org/pdf/2207.05929v1.pdf | Cross-Age Speaker Verification: Learning Age-Invariant Speaker Embeddings | Automatic speaker verification has achieved remarkable progress in recent years. However, there is little research on cross-age speaker verification (CASV) due to insufficient relevant data. In this paper, we mine cross-age test sets based on the VoxCeleb dataset and propose our age-invariant speaker representation(AISR) learning method. Since the VoxCeleb is collected from the YouTube platform, the dataset consists of cross-age data inherently. However, the meta-data does not contain the speaker age label. Therefore, we adopt the face age estimation method to predict the speaker age value from the associated visual data, then label the audio recording with the estimated age. We construct multiple Cross-Age test sets on VoxCeleb (Vox-CA), which deliberately select the positive trials with large age-gap. Also, the effect of nationality and gender is considered in selecting negative pairs to align with Vox-H cases. The baseline system performance drops from 1.939\% EER on the Vox-H test set to 10.419\% on the Vox-CA20 test set, which indicates how difficult the cross-age scenario is. Consequently, we propose an age-decoupling adversarial learning (ADAL) method to alleviate the negative effect of the age gap and reduce intra-class variance. Our method outperforms the baseline system by over 10\% related EER reduction on the Vox-CA20 test set. The source code and trial resources are available on https://github.com/qinxiaoyi/Cross-Age_Speaker_Verification | ['Ming Li', 'Dan Su', 'Chao Weng', 'Na Li', 'Xiaoyi Qin'] | 2022-07-13 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-1.28598973e-01 -1.38571113e-01 -2.79933494e-02 -5.67811549e-01
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e424ed37-397c-422b-a713-6c186a66f011 | bidirectional-lstm-crf-for-clinical-concept | 1611.08373 | null | http://arxiv.org/abs/1611.08373v1 | http://arxiv.org/pdf/1611.08373v1.pdf | Bidirectional LSTM-CRF for Clinical Concept Extraction | Automated extraction of concepts from patient clinical records is an
essential facilitator of clinical research. For this reason, the 2010 i2b2/VA
Natural Language Processing Challenges for Clinical Records introduced a
concept extraction task aimed at identifying and classifying concepts into
predefined categories (i.e., treatments, tests and problems). State-of-the-art
concept extraction approaches heavily rely on handcrafted features and
domain-specific resources which are hard to collect and define. For this
reason, this paper proposes an alternative, streamlined approach: a recurrent
neural network (the bidirectional LSTM with CRF decoding) initialized with
general-purpose, off-the-shelf word embeddings. The experimental results
achieved on the 2010 i2b2/VA reference corpora using the proposed framework
outperform all recent methods and ranks closely to the best submission from the
original 2010 i2b2/VA challenge. | ['Massimo Piccardi', 'Ehsan Zare Borzeshi', 'Raghavendra Chalapathy'] | 2016-11-25 | null | null | null | null | ['clinical-concept-extraction'] | ['medical'] | [ 3.46257150e-01 8.27304795e-02 -5.82756162e-01 -3.51376206e-01
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5df31e9e-e826-4b2f-a64b-b1b68200438d | 191013406 | 1910.13406 | null | https://arxiv.org/abs/1910.13406v2 | https://arxiv.org/pdf/1910.13406v2.pdf | Generalization of Reinforcement Learners with Working and Episodic Memory | Memory is an important aspect of intelligence and plays a role in many deep reinforcement learning models. However, little progress has been made in understanding when specific memory systems help more than others and how well they generalize. The field also has yet to see a prevalent consistent and rigorous approach for evaluating agent performance on holdout data. In this paper, we aim to develop a comprehensive methodology to test different kinds of memory in an agent and assess how well the agent can apply what it learns in training to a holdout set that differs from the training set along dimensions that we suggest are relevant for evaluating memory-specific generalization. To that end, we first construct a diverse set of memory tasks that allow us to evaluate test-time generalization across multiple dimensions. Second, we develop and perform multiple ablations on an agent architecture that combines multiple memory systems, observe its baseline models, and investigate its performance against the task suite. | ['Joel Z. Leibo', 'Charles Blundell', 'Gavin Buttimore', 'Adrià Puigdomènech Badia', 'Steven Hansen', 'Ryan Faulkner', 'Melissa Tan', 'Charlie Deck', 'Meire Fortunato'] | 2019-10-29 | generalization-of-reinforcement-learners-with | http://papers.nips.cc/paper/9411-generalization-of-reinforcement-learners-with-working-and-episodic-memory | http://papers.nips.cc/paper/9411-generalization-of-reinforcement-learners-with-working-and-episodic-memory.pdf | neurips-2019-12 | ['holdout-set'] | ['computer-vision'] | [-4.22810316e-02 -1.96679845e-01 -1.57930572e-02 -2.20584258e-01
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005ed206-d4df-4e52-9326-4f2b1c058e04 | towards-detection-of-subjective-bias-using | 2002.06644 | null | https://arxiv.org/abs/2002.06644v1 | https://arxiv.org/pdf/2002.06644v1.pdf | Towards Detection of Subjective Bias using Contextualized Word Embeddings | Subjective bias detection is critical for applications like propaganda detection, content recommendation, sentiment analysis, and bias neutralization. This bias is introduced in natural language via inflammatory words and phrases, casting doubt over facts, and presupposing the truth. In this work, we perform comprehensive experiments for detecting subjective bias using BERT-based models on the Wiki Neutrality Corpus(WNC). The dataset consists of $360k$ labeled instances, from Wikipedia edits that remove various instances of the bias. We further propose BERT-based ensembles that outperform state-of-the-art methods like $BERT_{large}$ by a margin of $5.6$ F1 score. | ['Tanvi Dadu', 'Kartikey Pant', 'Radhika Mamidi'] | 2020-02-16 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 5.18898759e-03 2.77362585e-01 -7.78325737e-01 -5.54362774e-01
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51ea9084-897d-4dfb-91e8-0e18a8423fde | damia-leveraging-domain-adaptation-as-a | 2005.08016 | null | https://arxiv.org/abs/2005.08016v1 | https://arxiv.org/pdf/2005.08016v1.pdf | DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks | Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability. Our experiment also excludes factors that may hinder the performance of DAMIA, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner. | ['Weiqi Luo', 'Yue Zhang', 'Anjia Yang', 'Jian Weng', 'Hongwei Huang', 'Guoqiang Zeng'] | 2020-05-16 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.19145250e-02 8.01947042e-02 -2.89156675e-01 -3.09000075e-01
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615b7420-b35f-48c8-9e8d-e5160240c91f | dense-video-object-captioning-from-disjoint | 2306.11729 | null | https://arxiv.org/abs/2306.11729v1 | https://arxiv.org/pdf/2306.11729v1.pdf | Dense Video Object Captioning from Disjoint Supervision | We propose a new task and model for dense video object captioning -- detecting, tracking, and captioning trajectories of all objects in a video. This task unifies spatial and temporal understanding of the video, and requires fine-grained language description. Our model for dense video object captioning is trained end-to-end and consists of different modules for spatial localization, tracking, and captioning. As such, we can train our model with a mixture of disjoint tasks, and leverage diverse, large-scale datasets which supervise different parts of our model. This results in noteworthy zero-shot performance. Moreover, by finetuning a model from this initialization, we can further improve our performance, surpassing strong image-based baselines by a significant margin. Although we are not aware of other work performing this task, we are able to repurpose existing video grounding datasets for our task, namely VidSTG and VLN. We show our task is more general than grounding, and models trained on our task can directly be applied to grounding by finding the bounding box with the maximum likelihood of generating the query sentence. Our model outperforms dedicated, state-of-the-art models for spatial grounding on both VidSTG and VLN. | ['Cordelia Schmid', 'Chen Sun', 'Anurag Arnab', 'Xingyi Zhou'] | 2023-06-20 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [ 1.09921329e-01 1.64452553e-01 -2.96078861e-01 -8.97843912e-02
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fdc4164f-1c1b-41ba-91b2-6514fd89095b | deep-reinforcement-learning-for-doom-using | 1807.01960 | null | http://arxiv.org/abs/1807.01960v1 | http://arxiv.org/pdf/1807.01960v1.pdf | Deep Reinforcement Learning for Doom using Unsupervised Auxiliary Tasks | Recent developments in deep reinforcement learning have enabled the creation
of agents for solving a large variety of games given a visual input. These
methods have been proven successful for 2D games, like the Atari games, or for
simple tasks, like navigating in mazes. It is still an open question, how to
address more complex environments, in which the reward is sparse and the state
space is huge. In this paper we propose a divide and conquer deep reinforcement
learning solution and we test our agent in the first person shooter (FPS) game
of Doom. Our work is based on previous works in deep reinforcement learning and
in Doom agents. We also present how our agent is able to perform better in
unknown environments compared to a state of the art reinforcement learning
algorithm. | ['Pericles A. Mitkas', 'Georgios Papoudakis', 'Kyriakos C. Chatzidimitriou'] | 2018-07-05 | null | null | null | null | ['game-of-doom'] | ['playing-games'] | [-2.99851596e-01 3.45452614e-02 1.01862438e-01 2.37429351e-01
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4805f0e0-5f77-4d25-960f-0afc912c8db2 | only-text-only-image-or-both-predicting | null | null | https://aclanthology.org/2020.icon-main.60 | https://aclanthology.org/2020.icon-main.60.pdf | Only text? only image? or both? Predicting sentiment of internet memes | Nowadays, the spread of Internet memes on online social media platforms such as Instagram, Facebook, Reddit, and Twitter is very fast. Analyzing the sentiment of memes can provide various useful insights. Meme sentiment classification is a new area of research that is not explored yet. Recently SemEval provides a dataset for meme sentiment classification. As this dataset is highly imbalanced, we extend this dataset by annotating new instances and use a sampling strategy to build a meme sentiment classifier. We propose a multi-modal framework for meme sentiment classification by utilizing textual and visual features of the meme. We found that for meme sentiment classification, only textual or only visual features are not sufficient. Our proposed framework utilizes textual as well as visual features together. We propose to use the attention mechanism to improve meme classification performance. Our proposed framework achieves macro F1 and accuracy of 34.23 and 50.02, respectively. It increases the accuracy by 6.77 and 7.86 compared to only textual and visual features, respectively. | ['Asif Ekbal', 'Mamta .', 'Pranati Behera'] | null | null | null | null | icon-2020-12 | ['meme-classification'] | ['natural-language-processing'] | [-3.38305831e-01 -4.65887398e-01 -1.85939878e-01 -2.63796687e-01
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e2a76d41-6be3-4ea0-a530-e48231f5c477 | integrating-both-visual-and-audio-cues-for | 1711.08097 | null | http://arxiv.org/abs/1711.08097v2 | http://arxiv.org/pdf/1711.08097v2.pdf | Integrating both Visual and Audio Cues for Enhanced Video Caption | Video caption refers to generating a descriptive sentence for a specific
short video clip automatically, which has achieved remarkable success recently.
However, most of the existing methods focus more on visual information while
ignoring the synchronized audio cues. We propose three multimodal deep fusion
strategies to maximize the benefits of visual-audio resonance information. The
first one explores the impact on cross-modalities feature fusion from low to
high order. The second establishes the visual-audio short-term dependency by
sharing weights of corresponding front-end networks. The third extends the
temporal dependency to long-term through sharing multimodal memory across
visual and audio modalities. Extensive experiments have validated the
effectiveness of our three cross-modalities fusion strategies on two benchmark
datasets, including Microsoft Research Video to Text (MSRVTT) and Microsoft
Video Description (MSVD). It is worth mentioning that sharing weight can
coordinate visual-audio feature fusion effectively and achieve the state-of-art
performance on both BELU and METEOR metrics. Furthermore, we first propose a
dynamic multimodal feature fusion framework to deal with the part modalities
missing case. Experimental results demonstrate that even in the audio absence
mode, we can still obtain comparable results with the aid of the additional
audio modality inference module. | ['Zhao-Xiang Zhang', 'Wangli Hao', 'He Guan', 'Guibo Zhu'] | 2017-11-22 | null | null | null | null | ['video-description'] | ['computer-vision'] | [ 6.71920627e-02 -3.07627141e-01 -4.26425859e-02 -2.70287812e-01
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ff41cb52-d1cb-4584-8d2a-79fb30462a7b | edaps-enhanced-domain-adaptive-panoptic | 2304.14291 | null | https://arxiv.org/abs/2304.14291v1 | https://arxiv.org/pdf/2304.14291v1.pdf | EDAPS: Enhanced Domain-Adaptive Panoptic Segmentation | With autonomous industries on the rise, domain adaptation of the visual perception stack is an important research direction due to the cost savings promise. Much prior art was dedicated to domain-adaptive semantic segmentation in the synthetic-to-real context. Despite being a crucial output of the perception stack, panoptic segmentation has been largely overlooked by the domain adaptation community. Therefore, we revisit well-performing domain adaptation strategies from other fields, adapt them to panoptic segmentation, and show that they can effectively enhance panoptic domain adaptation. Further, we study the panoptic network design and propose a novel architecture (EDAPS) designed explicitly for domain-adaptive panoptic segmentation. It uses a shared, domain-robust transformer encoder to facilitate the joint adaptation of semantic and instance features, but task-specific decoders tailored for the specific requirements of both domain-adaptive semantic and instance segmentation. As a result, the performance gap seen in challenging panoptic benchmarks is substantially narrowed. EDAPS significantly improves the state-of-the-art performance for panoptic segmentation UDA by a large margin of 25% on SYNTHIA-to-Cityscapes and even 72% on the more challenging SYNTHIA-to-Mapillary Vistas. The implementation is available at https://github.com/susaha/edaps. | ['Luc van Gool', 'Dengxin Dai', 'Anton Obukhov', 'Lukas Hoyer', 'Suman Saha'] | 2023-04-27 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 3.26380193e-01 1.04632460e-01 -1.09573655e-01 -5.18236816e-01
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1347e14a-d88e-41ca-b2fe-8e462cdd8d3b | umt-unified-multi-modal-transformers-for | 2203.12745 | null | https://arxiv.org/abs/2203.12745v2 | https://arxiv.org/pdf/2203.12745v2.pdf | UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection | Finding relevant moments and highlights in videos according to natural language queries is a natural and highly valuable common need in the current video content explosion era. Nevertheless, jointly conducting moment retrieval and highlight detection is an emerging research topic, even though its component problems and some related tasks have already been studied for a while. In this paper, we present the first unified framework, named Unified Multi-modal Transformers (UMT), capable of realizing such joint optimization while can also be easily degenerated for solving individual problems. As far as we are aware, this is the first scheme to integrate multi-modal (visual-audio) learning for either joint optimization or the individual moment retrieval task, and tackles moment retrieval as a keypoint detection problem using a novel query generator and query decoder. Extensive comparisons with existing methods and ablation studies on QVHighlights, Charades-STA, YouTube Highlights, and TVSum datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method under various settings. Source code and pre-trained models are available at https://github.com/TencentARC/UMT. | ['XiaoHu Qie', 'Ying Shan', 'Chang Wen Chen', 'Yang Wu', 'Siyuan Li', 'Ye Liu'] | 2022-03-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_UMT_Unified_Multi-Modal_Transformers_for_Joint_Video_Moment_Retrieval_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['highlight-detection', 'moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 6.44523725e-02 -5.05640805e-01 -4.02469397e-01 8.53960663e-02
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bfebf544-7901-4ba9-9ee1-ded57edf0790 | missing-modality-robustness-in-semi | 2304.10756 | null | https://arxiv.org/abs/2304.10756v1 | https://arxiv.org/pdf/2304.10756v1.pdf | Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation | Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propose a simple yet efficient multi-modal fusion mechanism Linear Fusion, that performs better than the state-of-the-art multi-modal models even with limited supervision. Second, we propose M3L: Multi-modal Teacher for Masked Modality Learning, a semi-supervised framework that not only improves the multi-modal performance but also makes the model robust to the realistic missing modality scenario using unlabeled data. We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities. Our proposal shows an absolute improvement of up to 10% on robust mIoU above the most competitive baselines. Our code is available at https://github.com/harshm121/M3L | ['Zsolt Kira', 'Yen-Cheng Liu', 'Harsh Maheshwari'] | 2023-04-21 | null | null | null | null | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 4.10949349e-01 5.31269014e-02 -2.97551811e-01 -3.78241658e-01
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334859e5-d030-4fbc-8d3d-0dc646ec9a6b | contextual-similarity-aggregation-with-self | 2110.13430 | null | https://arxiv.org/abs/2110.13430v1 | https://arxiv.org/pdf/2110.13430v1.pdf | Contextual Similarity Aggregation with Self-attention for Visual Re-ranking | In content-based image retrieval, the first-round retrieval result by simple visual feature comparison may be unsatisfactory, which can be refined by visual re-ranking techniques. In image retrieval, it is observed that the contextual similarity among the top-ranked images is an important clue to distinguish the semantic relevance. Inspired by this observation, in this paper, we propose a visual re-ranking method by contextual similarity aggregation with self-attention. In our approach, for each image in the top-K ranking list, we represent it into an affinity feature vector by comparing it with a set of anchor images. Then, the affinity features of the top-K images are refined by aggregating the contextual information with a transformer encoder. Finally, the affinity features are used to recalculate the similarity scores between the query and the top-K images for re-ranking of the latter. To further improve the robustness of our re-ranking model and enhance the performance of our method, a new data augmentation scheme is designed. Since our re-ranking model is not directly involved with the visual feature used in the initial retrieval, it is ready to be applied to retrieval result lists obtained from various retrieval algorithms. We conduct comprehensive experiments on four benchmark datasets to demonstrate the generality and effectiveness of our proposed visual re-ranking method. | ['Houqiang Li', 'Wengang Zhou', 'Min Wang', 'Hui Wu', 'Jianbo Ouyang'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/18d10dc6e666eab6de9215ae5b3d54df-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/18d10dc6e666eab6de9215ae5b3d54df-Paper.pdf | neurips-2021-12 | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.42414868e-01 -4.94716644e-01 -2.79917926e-01 -2.23476917e-01
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603903db-5c3f-4ab6-87e9-c214967bdba8 | membership-inference-attack-using-self | 2205.13680 | null | https://arxiv.org/abs/2205.13680v1 | https://arxiv.org/pdf/2205.13680v1.pdf | Membership Inference Attack Using Self Influence Functions | Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model. Thus, MI is a major privacy threat to models trained on private sensitive data, such as medical records. In MI attacks one may consider the black-box settings, where the model's parameters and activations are hidden from the adversary, or the white-box case where they are available to the attacker. In this work, we focus on the latter and present a novel MI attack for it that employs influence functions, or more specifically the samples' self-influence scores, to perform the MI prediction. We evaluate our attack on CIFAR-10, CIFAR-100, and Tiny ImageNet datasets, using versatile architectures such as AlexNet, ResNet, and DenseNet. Our attack method achieves new state-of-the-art results for both training with and without data augmentations. Code is available at https://github.com/giladcohen/sif_mi_attack. | ['Raja Giryes', 'Gilad Cohen'] | 2022-05-26 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [ 3.83768946e-01 4.27157521e-01 -2.90152162e-01 -6.25658691e-01
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47aecdbe-b966-40c0-bf03-06e756afab68 | generative-modeling-in-structural-hankel | 2211.13857 | null | https://arxiv.org/abs/2211.13857v1 | https://arxiv.org/pdf/2211.13857v1.pdf | Generative Modeling in Structural-Hankel Domain for Color Image Inpainting | In recent years, some researchers focused on using a single image to obtain a large number of samples through multi-scale features. This study intends to a brand-new idea that requires only ten or even fewer samples to construct the low-rank structural-Hankel matrices-assisted score-based generative model (SHGM) for color image inpainting task. During the prior learning process, a certain amount of internal-middle patches are firstly extracted from several images and then the structural-Hankel matrices are constructed from these patches. To better apply the score-based generative model to learn the internal statistical distribution within patches, the large-scale Hankel matrices are finally folded into the higher dimensional tensors for prior learning. During the iterative inpainting process, SHGM views the inpainting problem as a conditional generation procedure in low-rank environment. As a result, the intermediate restored image is acquired by alternatively performing the stochastic differential equation solver, alternating direction method of multipliers, and data consistency steps. Experimental results demonstrated the remarkable performance and diversity of SHGM. | ['Qiegen Liu', 'Yuhao Wang', 'Wenbo Wan', 'Shenglin Wu', 'CHUNHUA WU', 'Zihao Li'] | 2022-11-25 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.61824721e-01 -1.14768326e-01 2.78932899e-01 -1.30664349e-01
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eb841b5b-820d-493c-bf72-dce397dd6527 | almanac-knowledge-grounded-language-models | 2303.01229 | null | https://arxiv.org/abs/2303.01229v2 | https://arxiv.org/pdf/2303.01229v2.pdf | Almanac: Retrieval-Augmented Language Models for Clinical Medicine | Large-language models have recently demonstrated impressive zero-shot capabilities in a variety of natural language tasks such as summarization, dialogue generation, and question-answering. Despite many promising applications in clinical medicine, adoption of these models in real-world settings has been largely limited by their tendency to generate incorrect and sometimes even toxic statements. In this study, we develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios (n = 130) evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality (mean of 18% at p-value < 0.05) across all specialties, with improvements in completeness and safety. Our results demonstrate the potential for large language models to be effective tools in the clinical decision-making process, while also emphasizing the importance of careful testing and deployment to mitigate their shortcomings. | ['William Hiesinger', 'Joanna Nelson', 'Curt Langlotz', 'Karen Hirsch', 'Kathleen Boyd', 'Jack Boyd', 'Euan Ashley', 'Kevin Alexander', 'Michael Moor', 'Jennifer L. Kim', 'Alex R. Dalal', 'Rohan Shad', 'Akash Chaurasia', 'Cyril Zakka'] | 2023-03-01 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [ 3.06654125e-01 7.14907825e-01 -5.51379621e-01 -2.71464765e-01
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5cc9cffb-322d-4e5a-8761-e44dc9ddf56b | blind-deblurring-of-hyperspectral-document | 2303.05130 | null | https://arxiv.org/abs/2303.05130v1 | https://arxiv.org/pdf/2303.05130v1.pdf | Blind deblurring of hyperspectral document images | Most computer vision and machine learning-based approaches for historical document analysis are tailored to grayscale or RGB images and thus, mostly exploit their spatial information. Multispectral (MS) and hyperspectral (HS) images contain, next to the spatial information, much richer spectral information than RGB images (usually spreading beyond the visible spectral range) that can facilitate more effective feature extraction, more accurate classification and recognition, and thus, improved analysis. Although utilization of rich spectral information can improve historical document analysis tremendously, there are still some potential limitations of HS imagery such as camera-induced noise and blur that require a carefully designed preprocessing step. Here, we propose novel blind HS image deblurring methods tailored to document images. We exploit a low-rank property of HS images (i.e., by projecting an HS image to a lower dimensional subspace) and utilize a text tailor image prior to performing a PSF estimation and deblurring of subspace components. The preliminary results show that the proposed approach gives good results over all spectral bands, removing successfully image artefacts introduced by blur and noise and significantly increasing the number of bands that can be used in further analysis. | ['A. Traviglia', 'G. Franceschin', 'P. Guzzonato', 'M. Ljubenovic'] | 2023-03-09 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 7.11444795e-01 -7.73076653e-01 1.15125321e-01 1.30528808e-01
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e02ee229-77ac-4d59-aaac-6fb8c7864b8b | towards-end-to-end-speaker-diarization-in-the | 2211.01299 | null | https://arxiv.org/abs/2211.01299v1 | https://arxiv.org/pdf/2211.01299v1.pdf | Towards End-to-end Speaker Diarization in the Wild | Speaker diarization algorithms address the "who spoke when" problem in audio recordings. Algorithms trained end-to-end have proven superior to classical modular-cascaded systems in constrained scenarios with a small number of speakers. However, their performance for in-the-wild recordings containing more speakers with shorter utterance lengths remains to be investigated. In this paper, we address this gap, showing that an attractor-based end-to-end system can also perform remarkably well in the latter scenario when first pre-trained on a carefully-designed simulated dataset that matches the distribution of in-the-wild recordings. We also propose to use an attention mechanism to increase the network capacity in decoding more speaker attractors, and to jointly train the attractors on a speaker recognition task to improve the speaker attractor representation. Even though the model we propose is audio-only, we find it significantly outperforms both audio-only and audio-visual baselines on the AVA-AVD benchmark dataset, achieving state-of-the-art results with an absolute reduction in diarization error of 23.3%. | ['Jonathan Le Roux', 'Aswin Subramanian', 'François G. Germain', 'Gordon Wichern', 'Zexu Pan'] | 2022-11-02 | null | null | null | null | ['speaker-recognition'] | ['speech'] | [ 1.01840779e-01 2.70276308e-01 2.37333804e-01 -3.55437249e-01
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16cda6ba-d6da-4972-b96d-b9b67652c555 | hdnet-hierarchical-dynamic-network-for-gait | 2211.00312 | null | https://arxiv.org/abs/2211.00312v1 | https://arxiv.org/pdf/2211.00312v1.pdf | HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar | Gait recognition is widely used in diversified practical applications. Currently, the most prevalent approach is to recognize human gait from RGB images, owing to the progress of computer vision technologies. Nevertheless, the perception capability of RGB cameras deteriorates in rough circumstances, and visual surveillance may cause privacy invasion. Due to the robustness and non-invasive feature of millimeter wave (mmWave) radar, radar-based gait recognition has attracted increasing attention in recent years. In this research, we propose a Hierarchical Dynamic Network (HDNet) for gait recognition using mmWave radar. In order to explore more dynamic information, we propose point flow as a novel point clouds descriptor. We also devise a dynamic frame sampling module to promote the efficiency of computation without deteriorating performance noticeably. To prove the superiority of our methods, we perform extensive experiments on two public mmWave radar-based gait recognition datasets, and the results demonstrate that our model is superior to existing state-of-the-art methods. | ['Zhiguo Shi', 'Cheng Zhuo', 'Yu Fu', 'Chaojie Gu', 'Kun Shi', 'Yong Wang', 'Yanyan Huang'] | 2022-11-01 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [-8.16310346e-02 -7.66585767e-01 1.51040763e-01 -1.92986459e-01
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78186105-39fd-4ca7-9c6a-bea7841d17d0 | lips-don-t-lie-a-generalisable-and-robust | 2012.07657 | null | https://arxiv.org/abs/2012.07657v3 | https://arxiv.org/pdf/2012.07657v3.pdf | Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection | Although current deep learning-based face forgery detectors achieve impressive performance in constrained scenarios, they are vulnerable to samples created by unseen manipulation methods. Some recent works show improvements in generalisation but rely on cues that are easily corrupted by common post-processing operations such as compression. In this paper, we propose LipForensics, a detection approach capable of both generalising to novel manipulations and withstanding various distortions. LipForensics targets high-level semantic irregularities in mouth movements, which are common in many generated videos. It consists in first pretraining a spatio-temporal network to perform visual speech recognition (lipreading), thus learning rich internal representations related to natural mouth motion. A temporal network is subsequently finetuned on fixed mouth embeddings of real and forged data in order to detect fake videos based on mouth movements without overfitting to low-level, manipulation-specific artefacts. Extensive experiments show that this simple approach significantly surpasses the state-of-the-art in terms of generalisation to unseen manipulations and robustness to perturbations, as well as shed light on the factors responsible for its performance. Code is available on GitHub. | ['Maja Pantic', 'Stavros Petridis', 'Konstantinos Vougioukas', 'Alexandros Haliassos'] | 2020-12-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Haliassos_Lips_Dont_Lie_A_Generalisable_and_Robust_Approach_To_Face_CVPR_2021_paper.pdf | cvpr-2021-1 | ['lipreading'] | ['computer-vision'] | [ 3.36967736e-01 -7.71498680e-02 2.84464974e-02 -1.66646644e-01
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6d2a4193-4c17-4925-8f9a-23e9d1ba2b86 | capsgan-using-dynamic-routing-for-generative | 1806.03968 | null | http://arxiv.org/abs/1806.03968v1 | http://arxiv.org/pdf/1806.03968v1.pdf | CapsGAN: Using Dynamic Routing for Generative Adversarial Networks | In this paper, we propose a novel technique for generating images in the 3D
domain from images with high degree of geometrical transformations. By
coalescing two popular concurrent methods that have seen rapid ascension to the
machine learning zeitgeist in recent years: GANs (Goodfellow et. al.) and
Capsule networks (Sabour, Hinton et. al.) - we present: \textbf{CapsGAN}. We
show that CapsGAN performs better than or equal to traditional CNN based GANs
in generating images with high geometric transformations using rotated MNIST.
In the process, we also show the efficacy of using capsules architecture in the
GANs domain. Furthermore, we tackle the Gordian Knot in training GANs - the
performance control and training stability by experimenting with using
Wasserstein distance (gradient clipping, penalty) and Spectral Normalization.
The experimental findings of this paper should propel the application of
capsules and GANs in the still exciting and nascent domain of 3D image
generation, and plausibly video (frame) generation. | ['Sal Vivona', 'Raeid Saqur'] | 2018-06-07 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 3.41924518e-01 6.86297357e-01 3.84801954e-01 -4.40976694e-02
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a5a0122b-a479-4bc6-b4d6-e6940b257cdd | misspelling-correction-with-pre-trained | 2101.03204 | null | https://arxiv.org/abs/2101.03204v1 | https://arxiv.org/pdf/2101.03204v1.pdf | Misspelling Correction with Pre-trained Contextual Language Model | Spelling irregularities, known now as spelling mistakes, have been found for several centuries. As humans, we are able to understand most of the misspelled words based on their location in the sentence, perceived pronunciation, and context. Unlike humans, computer systems do not possess the convenient auto complete functionality of which human brains are capable. While many programs provide spelling correction functionality, many systems do not take context into account. Moreover, Artificial Intelligence systems function in the way they are trained on. With many current Natural Language Processing (NLP) systems trained on grammatically correct text data, many are vulnerable against adversarial examples, yet correctly spelled text processing is crucial for learning. In this paper, we investigate how spelling errors can be corrected in context, with a pre-trained language model BERT. We present two experiments, based on BERT and the edit distance algorithm, for ranking and selecting candidate corrections. The results of our experiments demonstrated that when combined properly, contextual word embeddings of BERT and edit distance are capable of effectively correcting spelling errors. | ['Julia Taylor Rayz', 'Youlim Ko', 'Xiaonan Jing', 'Yifei Hu'] | 2021-01-08 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 4.37801272e-01 -1.69918641e-01 2.06051216e-01 -4.53358144e-01
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8dddad41-b85e-405b-bef9-afcca441b324 | neurips-22-cross-domain-metadl-competition | 2208.14686 | null | https://arxiv.org/abs/2208.14686v1 | https://arxiv.org/pdf/2208.14686v1.pdf | NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results | We present the design and baseline results for a new challenge in the ChaLearn meta-learning series, accepted at NeurIPS'22, focusing on "cross-domain" meta-learning. Meta-learning aims to leverage experience gained from previous tasks to solve new tasks efficiently (i.e., with better performance, little training data, and/or modest computational resources). While previous challenges in the series focused on within-domain few-shot learning problems, with the aim of learning efficiently N-way k-shot tasks (i.e., N class classification problems with k training examples), this competition challenges the participants to solve "any-way" and "any-shot" problems drawn from various domains (healthcare, ecology, biology, manufacturing, and others), chosen for their humanitarian and societal impact. To that end, we created Meta-Album, a meta-dataset of 40 image classification datasets from 10 domains, from which we carve out tasks with any number of "ways" (within the range 2-20) and any number of "shots" (within the range 1-20). The competition is with code submission, fully blind-tested on the CodaLab challenge platform. The code of the winners will be open-sourced, enabling the deployment of automated machine learning solutions for few-shot image classification across several domains. | ['Wenwu Zhu', 'Xin Wang', 'Ihsan Ullah', 'Isabelle Guyon', 'Chaoyu Guan', 'Sergio Escalera', 'Adrian El Baz', 'Hong Chen', 'Dustin Carrión-Ojeda'] | 2022-08-31 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.20204277e-01 -2.51628458e-02 -1.80581138e-01 -1.49276152e-01
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1a29cef5-e1ec-4b22-b9bd-de0aeb5e9074 | a-hybrid-ann-snn-architecture-for-low-power | 2303.14176 | null | https://arxiv.org/abs/2303.14176v1 | https://arxiv.org/pdf/2303.14176v1.pdf | A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception | Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificial neural networks (ANNs). These states converge only after long transient periods, and quickly decay without input data, leading to higher latency, power consumption, and lower accuracy. This work addresses this issue by initializing the state with an auxiliary ANN running at a low rate. The SNN then uses the state to generate predictions with high temporal resolution until the next initialization phase. Our hybrid ANN-SNN model thus combines the best of both worlds: It does not suffer from long state transients and state decay thanks to the ANN, and can generate predictions with high temporal resolution, low latency, and low power thanks to the SNN. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts when run at the same inference rate. Moreover, when compared to SNNs, our method achieves a 74% lower error. This research thus provides a new understanding of how ANNs and SNNs can be used to maximize their respective benefits. | ['Davide Scaramuzza', 'Daniel Gehrig', 'Mathias Gehrig', 'Asude Aydin'] | 2023-03-24 | null | null | null | null | ['3d-human-pose-estimation'] | ['computer-vision'] | [ 4.55362916e-01 1.35300294e-01 6.39029732e-03 4.46437206e-03
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df6c1fba-0e9e-484f-ae74-b0466ff2ed6c | decentralized-social-navigation-with-non | 2306.08815 | null | https://arxiv.org/abs/2306.08815v1 | https://arxiv.org/pdf/2306.08815v1.pdf | Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization | This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiating right of way at a corridor intersection. Our contribution is a new realtime bi-level optimization algorithm, in which the top-level optimization consists of computing a fair and collision-free ordering followed by the bottom-level optimization which plans optimal trajectories conditioned on the ordering. We show that, given such a priority order, we can impose simple kinodynamic constraints on each robot that are sufficient for it to plan collision-free trajectories with minimal deviation from their preferred velocities, similar to how humans navigate in these scenarios. We successfully deploy the proposed algorithm in the real world using F$1/10$ robots, a Clearpath Jackal, and a Boston Dynamics Spot as well as in simulation using the SocialGym 2.0 multi-agent social navigation simulator, in the doorway and corridor intersection scenarios. We compare with state-of-the-art social navigation methods using multi-agent reinforcement learning, collision avoidance algorithms, and crowd simulation models. We show that $(i)$ classical navigation performs $44\%$ better than the state-of-the-art learning-based social navigation algorithms, $(ii)$ without a scheduling protocol, our approach results in collisions in social mini-games $(iii)$ our approach yields $2\times$ and $5\times$ fewer velocity changes than CADRL in doorways and intersections, and finally $(iv)$ bi-level navigation in doorways at a flow rate of $2.8 - 3.3$ (ms)$^{-1}$ is comparable to flow rate in human navigation at a flow rate of $4$ (ms)$^{-1}$. | ['Joydeep Biswas', 'Arya Anantula', 'Zayne Sprague', 'Rahul Menon', 'Rohan Chandra'] | 2023-06-15 | null | null | null | null | ['multi-agent-reinforcement-learning', 'navigate', 'social-navigation', 'robot-navigation'] | ['methodology', 'reasoning', 'robots', 'robots'] | [-3.09595555e-01 2.98970371e-01 3.82430494e-01 2.33446173e-02
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25506482-36e9-46f6-97af-5a94d56258ec | modeling-the-mitral-valve | 1902.00018 | null | http://arxiv.org/abs/1902.00018v1 | http://arxiv.org/pdf/1902.00018v1.pdf | Modeling the Mitral Valve | This work is concerned with modeling and simulation of the mitral valve, one
of the four valves in the human heart. The valve is composed of leaflets, the
free edges of which are supported by a system of chordae, which themselves are
anchored to the papillary muscles inside the left ventricle. First, we examine
valve anatomy and present the results of original dissections. These display
the gross anatomy and information on fiber structure of the mitral valve. Next,
we build a model valve following a design-based methodology, meaning that we
derive the model geometry and the forces that are needed to support a given
load, and construct the model accordingly. We incorporate information from the
dissections to specify the fiber topology of this model. We assume the valve
achieves mechanical equilibrium while supporting a static pressure load. The
solution to the resulting differential equations determines the pressurized
configuration of the valve model. To complete the model we then specify a
constitutive law based on a stress-strain relation consistent with experimental
data that achieves the necessary forces computed in previous steps. Finally,
using the immersed boundary method, we simulate the model valve in fluid in a
computer test chamber. The model opens easily and closes without leak when
driven by physiological pressures over multiple beats. Further, its closure is
robust to driving pressures that lack atrial systole or are much lower or
higher than normal. | ['David M. McQueen', 'Charles S. Peskin', 'Alexander D. Kaiser'] | 2019-01-31 | null | null | null | null | ['stress-strain-relation'] | ['miscellaneous'] | [-1.45808935e-01 4.29816455e-01 1.49285868e-01 5.80632806e-01
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1d7eb214-3396-4ab2-9f62-e922fced647a | a-novel-counterfactual-method-for-aspect | 2306.11260 | null | https://arxiv.org/abs/2306.11260v2 | https://arxiv.org/pdf/2306.11260v2.pdf | A novel Counterfactual method for aspect-based sentiment analysis | Aspect-based-sentiment-analysis (ABSA) is a fine-grained sentiment evaluation task, which analyze the emotional polarity of the evaluation aspects. However, previous works only focus on the identification of opinion expressions, forget that the diversity of opinion expressions also has great impacts on the ABSA task. To mitigate this problem, we propose a novel counterfactual data augmentation method to generate opinion expression with reversed sentiment polarity. Specially, the integrated gradients are calculated to identify and mask the opinion expression. Then, a prompt with the reverse label is combined to the original text, and a pre-trained language model (PLM), T5, is finally employed to retrieve the masks. The experimental results show the proposed counterfactual data augmentation method perform better than current augmentation methods on three ABSA datasets, i.e. Laptop, Restaurant and MAMS. | ['Zhaoshu Shi', 'Chao Chen', 'Lulu Wen', 'Dongming Wu'] | 2023-06-20 | null | null | null | null | ['sentiment-analysis', 'aspect-based-sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.21021104e-01 -1.57065347e-01 -1.78896144e-01 -6.05375111e-01
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6f6fa5cf-dfbb-4f5b-94e2-7ed3064d1dbc | self-supervised-video-representation-learning-1 | 1611.06646 | null | http://arxiv.org/abs/1611.06646v4 | http://arxiv.org/pdf/1611.06646v4.pdf | Self-Supervised Video Representation Learning With Odd-One-Out Networks | We propose a new self-supervised CNN pre-training technique based on a novel
auxiliary task called "odd-one-out learning". In this task, the machine is
asked to identify the unrelated or odd element from a set of otherwise related
elements. We apply this technique to self-supervised video representation
learning where we sample subsequences from videos and ask the network to learn
to predict the odd video subsequence. The odd video subsequence is sampled such
that it has wrong temporal order of frames while the even ones have the correct
temporal order. Therefore, to generate a odd-one-out question no manual
annotation is required. Our learning machine is implemented as multi-stream
convolutional neural network, which is learned end-to-end. Using odd-one-out
networks, we learn temporal representations for videos that generalizes to
other related tasks such as action recognition.
On action classification, our method obtains 60.3\% on the UCF101 dataset
using only UCF101 data for training which is approximately 10% better than
current state-of-the-art self-supervised learning methods. Similarly, on HMDB51
dataset we outperform self-supervised state-of-the art methods by 12.7% on
action classification task. | ['Hakan Bilen', 'Efstratios Gavves', 'Stephen Gould', 'Basura Fernando'] | 2016-11-21 | self-supervised-video-representation-learning-2 | http://openaccess.thecvf.com/content_cvpr_2017/html/Fernando_Self-Supervised_Video_Representation_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Fernando_Self-Supervised_Video_Representation_CVPR_2017_paper.pdf | cvpr-2017-7 | ['self-supervised-action-recognition', 'odd-one-out'] | ['computer-vision', 'reasoning'] | [ 6.89154685e-01 8.80475193e-02 -6.97264493e-01 -4.71145660e-01
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93c93845-44df-4719-9114-b56a4c8a60d8 | 3dti-net-learn-inner-transform-invariant-3d | 1812.06254 | null | http://arxiv.org/abs/1812.06254v1 | http://arxiv.org/pdf/1812.06254v1.pdf | 3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN | Deep learning on point clouds has made a lot of progress recently. Many point
cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have
shown advantages in accuracy and speed comparing to those using traditional 3D
convolution algorithms. However, nearly all of these methods face a challenge,
since the coordinates of the point cloud are decided by the coordinate system,
they cannot handle the problem of 3D transform invariance properly. In this
paper, we propose a general framework for point cloud learning. We achieve
transform invariance by learning inner 3D geometry feature based on local graph
representation, and propose a feature extraction network based on graph
convolution network. Through experiments on classification and segmentation
tasks, our method achieves state-of-the-art performance in rotated 3D object
classification, and achieve competitive performance with the state-of-the-art
in classification and segmentation tasks with fixed coordinate value. | ['Rendong Ying', 'Jun Wang', 'Guanghua Pan', 'Peilin Liu'] | 2018-12-15 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-5.38801491e-01 -3.49449873e-01 -9.30866823e-02 -3.87828976e-01
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c58e182d-f95b-44da-aa28-a40bfe58a57d | deep-learning-based-automated-covid-19 | 2111.11191 | null | https://arxiv.org/abs/2111.11191v5 | https://arxiv.org/pdf/2111.11191v5.pdf | Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images | A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. The method aims at increasing the predictive performance for COVID-19 diagnosis while more complex model architecture. Firstly, the CNN model includes four similar convolutional layers followed by a flattening and two dense layers. This work proposes a less complex solution based on simply classifying 2D-slices of Computed Tomography scans. Despite the simplicity in architecture, the proposed CNN model showed improved quantitative results exceeding state-of-the-art when predicting slice cases. The results were achieved on the annotated CT slices of the COV-19-CT-DB dataset. Secondly, the original dataset was processed via anatomy-relevant masking of slice, removing none-representative slices from the CT volume, and hyperparameters tuning. For slice processing, a fixed-sized rectangular area was used for cropping an anatomy-relevant region-of-interest in the images, and a threshold based on the number of white pixels in binarized slices was employed to remove none-representative slices from the 3D-CT scans. The CNN model with a learning rate schedule and an exponential decay and slice flipping techniques was deployed on the processed slices. The proposed method was used to make predictions on the 2D slices and for final diagnosis at patient level, majority voting was applied on the slices of each CT scan to take the diagnosis. The macro F1 score of the proposed method well-exceeded the baseline approach and other alternatives on the validation set as well as on a test partition of previously unseen images from COV-19CT-DB dataset. | ['Devrim Unay', 'Kenan Morani'] | 2021-11-22 | null | null | null | null | ['3d-classification', 'covid-19-detection'] | ['computer-vision', 'medical'] | [ 3.22385550e-01 5.39369643e-01 4.40941192e-02 -5.84072471e-01
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7793d443-6d58-49a1-b0f7-903b8959a61f | music-mixing-style-transfer-a-contrastive | 2211.02247 | null | https://arxiv.org/abs/2211.02247v3 | https://arxiv.org/pdf/2211.02247v3.pdf | Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects | We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song. This is achieved with an encoder pre-trained with a contrastive objective to extract only audio effects related information from a reference music recording. All our models are trained in a self-supervised manner from an already-processed wet multitrack dataset with an effective data preprocessing method that alleviates the data scarcity of obtaining unprocessed dry data. We analyze the proposed encoder for the disentanglement capability of audio effects and also validate its performance for mixing style transfer through both objective and subjective evaluations. From the results, we show the proposed system not only converts the mixing style of multitrack audio close to a reference but is also robust with mixture-wise style transfer upon using a music source separation model. | ['Marco A. Martínez-Ramírez', 'Yuki Mitsufuji', 'Kyogu Lee', 'Stefan Uhlich', 'Wei-Hsiang Liao', 'Junghyun Koo'] | 2022-11-04 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 5.46035409e-01 -3.36692303e-01 2.77043670e-01 -2.46530503e-01
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78cbe849-8f2a-464f-ac84-5cedd6dfa75e | id2image-leakage-of-non-id-information-into | 2304.07522 | null | https://arxiv.org/abs/2304.07522v1 | https://arxiv.org/pdf/2304.07522v1.pdf | ID2image: Leakage of non-ID information into face descriptors and inversion from descriptors to images | Embedding a face image to a descriptor vector using a deep CNN is a widely used technique in face recognition. Via several possible training strategies, such embeddings are supposed to capture only identity information. Information about the environment (such as background and lighting) or changeable aspects of the face (such as pose, expression, presence of glasses, hat etc.) should be discarded since they are not useful for recognition. In this paper, we present a surprising result that this is not the case. We show that non-ID attributes, as well as landmark positions and the image histogram can be recovered from the ID embedding of state-of-the-art face embedding networks (VGGFace2 and ArcFace). In fact, these non-ID attributes can be predicted from ID embeddings with similar accuracy to a prediction from the original image. Going further, we present an optimisation strategy that uses a generative model (specifically StyleGAN2 for faces) to recover images from an ID embedding. We show photorealistic inversion from ID embedding to face image in which not only is the ID realistically reconstructed but the pose, lighting and background/apparel to some extent as well. | ['Patrik Huber', 'William A. P. Smith', 'Mingrui Li'] | 2023-04-15 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 1.47769347e-01 2.01824903e-01 2.34993115e-01 -7.01109290e-01
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f58276a1-3e49-4a47-af8c-acb30c28947a | missing-data-reconstruction-in-remote-sensing | 1802.08369 | null | http://arxiv.org/abs/1802.08369v1 | http://arxiv.org/pdf/1802.08369v1.pdf | Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network | Because of the internal malfunction of satellite sensors and poor atmospheric
conditions such as thick cloud, the acquired remote sensing data often suffer
from missing information, i.e., the data usability is greatly reduced. In this
paper, a novel method of missing information reconstruction in remote sensing
images is proposed. The unified spatial-temporal-spectral framework based on a
deep convolutional neural network (STS-CNN) employs a unified deep
convolutional neural network combined with spatial-temporal-spectral
supplementary information. In addition, to address the fact that most methods
can only deal with a single missing information reconstruction task, the
proposed approach can solve three typical missing information reconstruction
tasks: 1) dead lines in Aqua MODIS band 6; 2) the Landsat ETM+ Scan Line
Corrector (SLC)-off problem; and 3) thick cloud removal. It should be noted
that the proposed model can use multi-source data (spatial, spectral, and
temporal) as the input of the unified framework. The results of both simulated
and real-data experiments demonstrate that the proposed model exhibits high
effectiveness in the three missing information reconstruction tasks listed
above. | ['Xinghua Li', 'Yancong Wei', 'Chao Zeng', 'Qiang Zhang', 'Qiangqiang Yuan'] | 2018-02-23 | null | null | null | null | ['cloud-removal'] | ['computer-vision'] | [ 3.97133797e-01 -6.84529543e-01 -8.49672407e-02 -5.93779922e-01
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83903a0a-f1d9-443c-8c51-13afd775adaa | weighted-structure-tensor-total-variation-for | 2306.10482 | null | https://arxiv.org/abs/2306.10482v1 | https://arxiv.org/pdf/2306.10482v1.pdf | Weighted structure tensor total variation for image denoising | Based on the variational framework of the image denoising problem, we introduce a novel image denoising regularizer that combines anisotropic total variation model (ATV) and structure tensor total variation model (STV) in this paper. The model can effectively capture the first-order information of the image and maintain local features during the denoising process by applying the matrix weighting operator proposed in the ATV model to the patch-based Jacobian matrix in the STV model. Denoising experiments on grayscale and RGB color images demonstrate that the suggested model can produce better restoration quality in comparison to other well-known methods based on total-variation-based models and the STV model. | ['Jingya Changa', 'Xiuhan Sheng'] | 2023-06-18 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 2.33645499e-01 -5.14950156e-01 4.57420290e-01 -1.50727760e-02
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ef20dc30-2905-46dd-b1e1-88c01fdf3f3f | translating-implicit-discourse-connectives | null | null | https://aclanthology.org/W17-4812 | https://aclanthology.org/W17-4812.pdf | Translating Implicit Discourse Connectives Based on Cross-lingual Annotation and Alignment | Implicit discourse connectives and relations are distributed more widely in Chinese texts, when translating into English, such connectives are usually translated explicitly. Towards Chinese-English MT, in this paper we describe cross-lingual annotation and alignment of dis-course connectives in a parallel corpus, describing related surveys and findings. We then conduct some evaluation experiments to testify the translation of implicit connectives and whether representing implicit connectives explicitly in source language can improve the final translation performance significantly. Preliminary results show it has little improvement by just inserting explicit connectives for implicit relations. | ['Philippe Langlais', 'Hongzheng Li', 'Yaohong Jin'] | 2017-09-01 | null | null | null | ws-2017-9 | ['implicit-relations'] | ['natural-language-processing'] | [ 2.65887994e-02 7.74105430e-01 -7.18778670e-01 -5.07790089e-01
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133d20ae-29b4-4258-bf26-fe27fdf92cf7 | towards-nir-vis-masked-face-recognition | 2104.06761 | null | https://arxiv.org/abs/2104.06761v1 | https://arxiv.org/pdf/2104.06761v1.pdf | Towards NIR-VIS Masked Face Recognition | Near-infrared to visible (NIR-VIS) face recognition is the most common case in heterogeneous face recognition, which aims to match a pair of face images captured from two different modalities. Existing deep learning based methods have made remarkable progress in NIR-VIS face recognition, while it encounters certain newly-emerged difficulties during the pandemic of COVID-19, since people are supposed to wear facial masks to cut off the spread of the virus. We define this task as NIR-VIS masked face recognition, and find it problematic with the masked face in the NIR probe image. First, the lack of masked face data is a challenging issue for the network training. Second, most of the facial parts (cheeks, mouth, nose etc.) are fully occluded by the mask, which leads to a large amount of loss of information. Third, the domain gap still exists in the remaining facial parts. In such scenario, the existing methods suffer from significant performance degradation caused by the above issues. In this paper, we aim to address the challenge of NIR-VIS masked face recognition from the perspectives of training data and training method. Specifically, we propose a novel heterogeneous training method to maximize the mutual information shared by the face representation of two domains with the help of semi-siamese networks. In addition, a 3D face reconstruction based approach is employed to synthesize masked face from the existing NIR image. Resorting to these practices, our solution provides the domain-invariant face representation which is also robust to the mask occlusion. Extensive experiments on three NIR-VIS face datasets demonstrate the effectiveness and cross-dataset-generalization capacity of our method. | ['Tao Mei', 'Dan Zeng', 'Yinglu Liu', 'Hailin Shi', 'Hang Du'] | 2021-04-14 | null | null | null | null | ['heterogeneous-face-recognition', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.57762665e-01 -1.24584131e-01 9.39582586e-02 -2.63261676e-01
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e17d4f84-ad89-47b2-9285-e53fc00e775f | a-general-class-of-transfer-learning | 2006.13228 | null | https://arxiv.org/abs/2006.13228v2 | https://arxiv.org/pdf/2006.13228v2.pdf | A General Class of Transfer Learning Regression without Implementation Cost | We propose a novel framework that unifies and extends existing methods of transfer learning (TL) for regression. To bridge a pretrained source model to the model on a target task, we introduce a density-ratio reweighting function, which is estimated through the Bayesian framework with a specific prior distribution. By changing two intrinsic hyperparameters and the choice of the density-ratio model, the proposed method can integrate three popular methods of TL: TL based on cross-domain similarity regularization, a probabilistic TL using the density-ratio estimation, and fine-tuning of pretrained neural networks. Moreover, the proposed method can benefit from its simple implementation without any additional cost; the regression model can be fully trained using off-the-shelf libraries for supervised learning in which the original output variable is simply transformed to a new output variable. We demonstrate its simplicity, generality, and applicability using various real data applications. | ['Shunya Minami', 'Stephen Wu', 'Kenji Fukumizu', 'Song Liu', 'Ryo Yoshida'] | 2020-06-23 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.33776575e-01 -6.08060956e-02 -4.47429448e-01 -5.37980855e-01
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1ec894d1-7c43-4714-8d88-cf221a32790c | empirical-assessment-of-end-to-end-iris | 2303.12742 | null | https://arxiv.org/abs/2303.12742v1 | https://arxiv.org/pdf/2303.12742v1.pdf | Empirical Assessment of End-to-End Iris Recognition System Capacity | Iris is an established modality in biometric recognition applications including consumer electronics, e-commerce, border security, forensics, and de-duplication of identity at a national scale. In light of the expanding usage of biometric recognition, identity clash (when templates from two different people match) is an imperative factor of consideration for a system's deployment. This study explores system capacity estimation by empirically estimating the constrained capacity of an end-to-end iris recognition system (NIR systems with Daugman-based feature extraction) operating at an acceptable error rate i.e. the number of subjects a system can resolve before encountering an error. We study the impact of six system parameters on an iris recognition system's constrained capacity -- number of enrolled identities, image quality, template dimension, random feature elimination, filter resolution, and system operating point. In our assessment, we analyzed 13.2 million comparisons from 5158 unique identities for each of 24 different system configurations. This work provides a framework to better understand iris recognition system capacity as a function of biometric system configurations beyond the operating point, for large-scale applications. | ['Stephanie Schuckers', 'Joseph Skufca', 'Matthew Valenti', 'Natalia Schmid', 'Veeru Talreja', 'Richard Plesh', 'Priyanka Das'] | 2023-03-20 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 3.77961755e-01 -3.54654819e-01 -8.02007020e-02 -2.27910534e-01
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fd67a9ea-25e3-4e21-8602-e973247a91cb | expectation-maximization-for-learning | 1411.1088 | null | http://arxiv.org/abs/1411.1088v1 | http://arxiv.org/pdf/1411.1088v1.pdf | Expectation-Maximization for Learning Determinantal Point Processes | A determinantal point process (DPP) is a probabilistic model of set diversity
compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP
to a given task, we would like to learn the entries of its kernel matrix by
maximizing the log-likelihood of the available data. However, log-likelihood is
non-convex in the entries of the kernel matrix, and this learning problem is
conjectured to be NP-hard. Thus, previous work has instead focused on more
restricted convex learning settings: learning only a single weight for each row
of the kernel matrix, or learning weights for a linear combination of DPPs with
fixed kernel matrices. In this work we propose a novel algorithm for learning
the full kernel matrix. By changing the kernel parameterization from matrix
entries to eigenvalues and eigenvectors, and then lower-bounding the likelihood
in the manner of expectation-maximization algorithms, we obtain an effective
optimization procedure. We test our method on a real-world product
recommendation task, and achieve relative gains of up to 16.5% in test
log-likelihood compared to the naive approach of maximizing likelihood by
projected gradient ascent on the entries of the kernel matrix. | ['Ben Taskar', 'Emily Fox', 'Jennifer Gillenwater', 'Alex Kulesza'] | 2014-11-04 | expectation-maximization-for-learning-1 | http://papers.nips.cc/paper/5564-expectation-maximization-for-learning-determinantal-point-processes | http://papers.nips.cc/paper/5564-expectation-maximization-for-learning-determinantal-point-processes.pdf | neurips-2014-12 | ['product-recommendation'] | ['miscellaneous'] | [ 1.14132695e-01 -4.73904423e-02 -3.55513811e-01 -2.94837862e-01
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06fb3263-e8cd-4aaa-bd99-6a2a6be97b40 | importance-of-methodological-choices-in-data | 2302.10672 | null | https://arxiv.org/abs/2302.10672v1 | https://arxiv.org/pdf/2302.10672v1.pdf | Importance of methodological choices in data manipulation for validating epileptic seizure detection models | Epilepsy is a chronic neurological disorder that affects a significant portion of the human population and imposes serious risks in the daily life of patients. Despite advances in machine learning and IoT, small, nonstigmatizing wearable devices for continuous monitoring and detection in outpatient environments are not yet available. Part of the reason is the complexity of epilepsy itself, including highly imbalanced data, multimodal nature, and very subject-specific signatures. However, another problem is the heterogeneity of methodological approaches in research, leading to slower progress, difficulty comparing results, and low reproducibility. Therefore, this article identifies a wide range of methodological decisions that must be made and reported when training and evaluating the performance of epilepsy detection systems. We characterize the influence of individual choices using a typical ensemble random-forest model and the publicly available CHB-MIT database, providing a broader picture of each decision and giving good-practice recommendations, based on our experience, where possible. | ['David Atienza', 'Tomas Teijeiro', 'Una Pale'] | 2023-02-21 | null | null | null | null | ['seizure-detection'] | ['medical'] | [ 3.76297742e-01 -3.69925290e-01 -3.81149262e-01 -4.86759156e-01
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ee956122-441a-42c9-850f-5d290a63e870 | graph-neural-networks-in-tensorflow-and-keras | 2006.12138 | null | https://arxiv.org/abs/2006.12138v1 | https://arxiv.org/pdf/2006.12138v1.pdf | Graph Neural Networks in TensorFlow and Keras with Spektral | In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression. | ['Cesare Alippi', 'Daniele Grattarola'] | 2020-06-22 | null | null | null | null | ['graph-regression'] | ['graphs'] | [-7.20103383e-01 4.54313159e-02 -1.06280863e-01 -4.71402168e-01
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30784565-e58a-4e54-8fa1-9a9844e62f64 | learning-to-detect-good-keypoints-to-match | 2212.09589 | null | https://arxiv.org/abs/2212.09589v1 | https://arxiv.org/pdf/2212.09589v1.pdf | Learning to Detect Good Keypoints to Match Non-Rigid Objects in RGB Images | We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor, as a ground-truth to train a convolutional neural network (CNN). We optimize the model architecture by applying known geometric transformations to images as the supervisory signal. Experiments show that our method outperforms the state-of-the-art keypoint detector on real images of non-rigid objects by 20 p.p. on Mean Matching Accuracy and also improves the matching performance of several descriptors when coupled with our detection method. We also employ the proposed method in one challenging realworld application: object retrieval, where our detector exhibits performance on par with the best available keypoint detectors. The source code and trained model are publicly available at https://github.com/verlab/LearningToDetect SIBGRAPI 2022 | ['Erickson R. Nascimento', 'Renato Martins', 'Felipe Cadar', 'Guilherme Potje', 'Welerson Melo'] | 2022-12-13 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [-5.70260808e-02 4.80738208e-02 -2.58803606e-01 -2.43057683e-01
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69338d51-fc2d-4335-a804-9c2aa2028214 | din-sql-decomposed-in-context-learning-of | 2304.11015 | null | https://arxiv.org/abs/2304.11015v2 | https://arxiv.org/pdf/2304.11015v2.pdf | DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction | We study the problem of decomposing a complex text-to-sql task into smaller sub-tasks and how such a decomposition can significantly improve the performance of Large Language Models (LLMs) in the reasoning process. There is currently a significant gap between the performance of fine-tuned models and prompting approaches using LLMs on challenging text-to-sql datasets such as Spider. We show that SQL queries, despite their declarative structure, can be broken down into sub-problems and the solutions of those sub-problems can be fed into LLMs to significantly improve their performance. Our experiments with three LLMs show that this approach consistently improves their performance by roughly 10%, pushing the accuracy of LLMs towards state-of-the-art, and even beating large fine-tuned models on the holdout Spider dataset. | ['Davood Rafiei', 'Mohammadreza Pourreza'] | 2023-04-21 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [-1.80201873e-01 3.99137765e-01 -3.63661110e-01 -5.08794248e-01
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f136b69c-291c-4d11-a6bb-205f60cde737 | evolution-leads-to-a-diversity-of-motion | 1804.02508 | null | http://arxiv.org/abs/1804.02508v2 | http://arxiv.org/pdf/1804.02508v2.pdf | Evolution leads to a diversity of motion-detection neuronal circuits | A central goal of evolutionary biology is to explain the origins and
distribution of diversity across life. Beyond species or genetic diversity, we
also observe diversity in the circuits (genetic or otherwise) underlying
complex functional traits. However, while the theory behind the origins and
maintenance of genetic and species diversity has been studied for decades,
theory concerning the origin of diverse functional circuits is still in its
infancy. It is not known how many different circuit structures can implement
any given function, which evolutionary factors lead to different circuits, and
whether the evolution of a particular circuit was due to adaptive or
non-adaptive processes. Here, we use digital experimental evolution to study
the diversity of neural circuits that encode motion detection in digital
(artificial) brains. We find that evolution leads to an enormous diversity of
potential neural architectures encoding motion detection circuits, even for
circuits encoding the exact same function. Evolved circuits vary in both
redundancy and complexity (as previously found in genetic circuits) suggesting
that similar evolutionary principles underlie circuit formation using any
substrate. We also show that a simple (designed) motion detection circuit that
is optimally-adapted gains in complexity when evolved further, and that
selection for mutational robustness led this gain in complexity. | ['Christoph Adami', 'Ali Tehrani-Saleh', 'Thomas LaBar'] | 2018-04-07 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.75452220e-01 -1.65736362e-01 2.63596237e-01 -1.63102567e-01
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3.64531040e-01 -2.50546843e-01 -4.16823119e-01 2.35853344e-01] | [5.665654182434082, 4.112533092498779] |
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