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81b77345-3554-4407-938f-3ffecee70ff6 | comparing-software-developers-with-chatgpt-an | 2305.11837 | null | https://arxiv.org/abs/2305.11837v2 | https://arxiv.org/pdf/2305.11837v2.pdf | Comparing Software Developers with ChatGPT: An Empirical Investigation | The advent of automation in particular Software Engineering (SE) tasks has transitioned from theory to reality. Numerous scholarly articles have documented the successful application of Artificial Intelligence to address issues in areas such as project management, modeling, testing, and development. A recent innovation is the introduction of ChatGPT, an ML-infused chatbot, touted as a resource proficient in generating programming codes and formulating software testing strategies for developers and testers respectively. Although there is speculation that AI-based computation can increase productivity and even substitute software engineers in software development, there is currently a lack of empirical evidence to verify this. Moreover, despite the primary focus on enhancing the accuracy of AI systems, non-functional requirements including energy efficiency, vulnerability, fairness (i.e., human bias), and safety frequently receive insufficient attention. This paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration, enhancing the reliability of AI-based methods, and understanding task suitability for humans or AI. Furthermore, it facilitates the effective implementation of cooperative work structures and human-in-the-loop processes. This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The empirical study includes a case of assessing ChatGPT-generated code versus code produced by developers and uploaded in Leetcode. | ['Donald Cowan', 'Paulo Alencar', 'Nathalia Nascimento'] | 2023-05-19 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-1.20648019e-01 4.09730732e-01 1.05520487e-01 -1.08521357e-01
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b28e5a97-99e8-4edb-8c1b-67b97c13412d | self-supervised-blind-motion-deblurring-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Self-Supervised_Blind_Motion_Deblurring_With_Deep_Expectation_Maximization_CVPR_2023_paper.pdf | Self-Supervised Blind Motion Deblurring With Deep Expectation Maximization | When taking a picture, any camera shake during the shutter time can result in a blurred image. Recovering a sharp image from the one blurred by camera shake is a challenging yet important problem. Most existing deep learning methods use supervised learning to train a deep neural network (DNN) on a dataset of many pairs of blurred/latent images. In contrast, this paper presents a dataset-free deep learning method for removing uniform and non-uniform blur effects from images of static scenes. Our method involves a DNN-based re-parametrization of the latent image, and we propose a Monte Carlo Expectation Maximization (MCEM) approach to train the DNN without requiring any latent images. The Monte Carlo simulation is implemented via Langevin dynamics. Experiments showed that the proposed method outperforms existing methods significantly in removing motion blur from images of static scenes. | ['Hui Ji', 'Yuesong Nan', 'Weixi Wang', 'Ji Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 1.42619893e-01 -5.40495455e-01 2.18611360e-01 -3.66966993e-01
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96644aad-c097-4c27-b1d5-ffaa2f64a554 | cluster-based-feature-importance-learning-for | null | null | https://openreview.net/forum?id=kroqZZb-6s | https://openreview.net/pdf?id=kroqZZb-6s | Cluster-based Feature Importance Learning for Electronic Health Record Time-series | The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression with EHR is challenging since these data are sparse, heterogeneous, multi-dimensional, and multi-modal time-series. As such, clustering is used to identify similar groups within the patient cohort to improve prediction. Current models
have shown some success in obtaining cluster representation of patient trajectories, however, they i) fail to obtain clinical interpretability for each cluster, and ii) struggle to learn meaningful cluster numbers in the context of the imbalanced distribution of disease outcomes. We propose a supervised deep learning model to cluster EHR data based on the identification of clinically understandable phenotypes with regard to both outcome prediction and patient trajectory. We introduce novel loss functions to address the problems of class imbalance and cluster collapse, and furthermore propose a feature-time attention mechanism to identify cluster-based phenotype importance across time and feature dimensions. We tested our model in over 100,000 unique trajectories from hospitalised patients with Type-II respiratory failure to predict five different outcomes. Our model yielded added interpretability to cluster formation and outperformed benchmarks by at least 5% in mean AUROC. | ['Tingting Zhu', 'Peter Watkinson', 'Mauro Santos', 'Henrique Aguiar'] | 2021-09-29 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [-1.05486542e-01 -2.11929917e-01 -7.02462345e-02 -6.43591106e-01
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df9aa202-555b-4b95-b04a-1861938ff969 | environmental-effects-on-emergent-strategy-in | 2307.00994 | null | https://arxiv.org/abs/2307.00994v1 | https://arxiv.org/pdf/2307.00994v1.pdf | Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning | Multi-Agent Reinforcement Learning (MARL) is a promising candidate for realizing efficient control of microscopic particles, of which micro-robots are a subset. However, the microscopic particles' environment presents unique challenges, such as Brownian motion at sufficiently small length-scales. In this work, we explore the role of temperature in the emergence and efficacy of strategies in MARL systems using particle-based Langevin molecular dynamics simulations as a realistic representation of micro-scale environments. To this end, we perform experiments on two different multi-agent tasks in microscopic environments at different temperatures, detecting the source of a concentration gradient and rotation of a rod. We find that at higher temperatures, the RL agents identify new strategies for achieving these tasks, highlighting the importance of understanding this regime and providing insight into optimal training strategies for bridging the generalization gap between simulation and reality. We also introduce a novel Python package for studying microscopic agents using reinforcement learning (RL) to accompany our results. | ['Christian Holm', 'Clemens Bechinger', 'Veit-Lorenz Heuthe', 'Simon Koppenhoefer', 'Tobias Merkt', 'Christoph Lohrmann', 'David Zimmer', 'Samuel Tovey'] | 2023-07-03 | null | null | null | null | ['multi-agent-reinforcement-learning', 'reinforcement-learning-1'] | ['methodology', 'methodology'] | [-2.16793716e-01 -4.34891433e-01 1.81171164e-01 4.83409375e-01
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32286e1a-8bae-4604-ae80-2ce0388eff5c | deep-optimization-model-for-screen-content | 1903.00705 | null | http://arxiv.org/abs/1903.00705v1 | http://arxiv.org/pdf/1903.00705v1.pdf | Deep Optimization model for Screen Content Image Quality Assessment using Neural Networks | In this paper, we propose a novel quadratic optimized model based on the deep
convolutional neural network (QODCNN) for full-reference and no-reference
screen content image (SCI) quality assessment. Unlike traditional CNN methods
taking all image patches as training data and using average quality pooling,
our model is optimized to obtain a more effective model including three steps.
In the first step, an end-to-end deep CNN is trained to preliminarily predict
the image visual quality, and batch normalized (BN) layers and l2
regularization are employed to improve the speed and performance of network
fitting. For second step, the pretrained model is fine-tuned to achieve better
performance under analysis of the raw training data. An adaptive weighting
method is proposed in the third step to fuse local quality inspired by the
perceptual property of the human visual system (HVS) that the HVS is sensitive
to image patches containing texture and edge information. The novelty of our
algorithm can be concluded as follows: 1) with the consideration of correlation
between local quality and subjective differential mean opinion score (DMOS),
the Euclidean distance is utilized to measure effectiveness of image patches,
and the pretrained model is fine-tuned with more effective training data; 2) an
adaptive pooling approach is employed to fuse patch quality of textual and
pictorial regions, whose feature only extracted from distorted images owns
strong noise robust and effects on both FR and NR IQA; 3) Considering the
characteristics of SCIs, a deep and valid network architecture is designed for
both NR and FR visual quality evaluation of SCIs. Experimental results verify
that our model outperforms both current no-reference and full-reference image
quality assessment methods on the benchmark screen content image quality
assessment database (SIQAD). | ['Xuhao Jiang', 'Liangwei Yu', 'Guorui Feng', 'Ping An', 'Liquan Shen'] | 2019-03-02 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.82482347e-01 -5.48798323e-01 -2.44073458e-02 -3.79768431e-01
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7d0e0047-81ce-4ff3-8d5a-e2d5679634ec | spatially-adaptive-filter-units-for-compact | 1902.07474 | null | https://arxiv.org/abs/1902.07474v2 | https://arxiv.org/pdf/1902.07474v2.pdf | Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks | Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require testing a variety of dilation and down/up-sampling factors and result in non-compact representations and excessive number of parameters. We address this issue by proposing a new convolution filter composed of displaced aggregation units (DAU). DAUs learn spatial displacements and adapt the receptive field sizes of individual convolution filters to a given problem, thus eliminating the need for hand-crafted modifications. DAUs provide a seamless substitution of convolutional filters in existing state-of-the-art architectures, which we demonstrate on AlexNet, ResNet50, ResNet101, DeepLab and SRN-DeblurNet. The benefits of this design are demonstrated on a variety of computer vision tasks and datasets, such as image classification (ILSVRC 2012), semantic segmentation (PASCAL VOC 2011, Cityscape) and blind image de-blurring (GOPRO). Results show that DAUs efficiently allocate parameters resulting in up to four times more compact networks at similar or better performance. | ['Aleš Leonardis', 'Matej Kristan', 'Domen Tabernik'] | 2019-02-20 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.71267837e-01 -3.25264871e-01 5.17458916e-01 -4.68734384e-01
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f9854f24-a623-4ae6-8ae0-fc72d9320e23 | music-artist-classification-with | 1901.04555 | null | http://arxiv.org/abs/1901.04555v2 | http://arxiv.org/pdf/1901.04555v2.pdf | Music Artist Classification with Convolutional Recurrent Neural Networks | Previous attempts at music artist classification use frame level audio
features which summarize frequency content within short intervals of time.
Comparatively, more recent music information retrieval tasks take advantage of
temporal structure in audio spectrograms using deep convolutional and recurrent
models. This paper revisits artist classification with this new framework and
empirically explores the impacts of incorporating temporal structure in the
feature representation. To this end, an established classification
architecture, a Convolutional Recurrent Neural Network (CRNN), is applied to
the artist20 music artist identification dataset under a comprehensive set of
conditions. These include audio clip length, which is a novel contribution in
this work, and previously identified considerations such as dataset split and
feature level. Our results improve upon baseline works, verify the influence of
the producer effect on classification performance and demonstrate the
trade-offs between audio length and training set size. The best performing
model achieves an average F1 score of 0.937 across three independent trials
which is a substantial improvement over the corresponding baseline under
similar conditions. Additionally, to showcase the effectiveness of the CRNN's
feature extraction capabilities, we visualize audio samples at the model's
bottleneck layer demonstrating that learned representations segment into
clusters belonging to their respective artists. | ['Zain Nasrullah', 'Yue Zhao'] | 2019-01-14 | null | null | null | null | ['artist-classification'] | ['computer-vision'] | [ 3.67861450e-01 -4.89336282e-01 -5.80763333e-02 -1.01654017e-02
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d30b05b1-9ea9-4bd4-9724-0fb4140f1ff5 | anatomically-parameterized-statistical-shape | 2202.08580 | null | https://arxiv.org/abs/2202.08580v1 | https://arxiv.org/pdf/2202.08580v1.pdf | Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning | Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. The proposed anatomically parameterized SSM (ANAT-SSM) is based on learning a linear mapping between shape coefficients and selected anatomical parameters. This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT-SSM. We further impose orthogonality constraints to the anatomical parameterization to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters. Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. This study demonstrates the use of anatomical representation for creating anatomically parameterized SSM and as a result, removes the limited clinical interpretability of standard SSMs. The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment. | ['Bhushan Borotikar', 'Valérie Burdin', 'Asma Salhi', 'Arnaud Boutillon'] | 2022-02-17 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [ 5.45124598e-02 2.61194557e-01 -1.80607706e-01 -2.51240790e-01
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332b09ee-0768-4dbd-a7ba-24c27d4b3e50 | an-exploration-of-neural-sequence-to-sequence | 1706.04138 | null | http://arxiv.org/abs/1706.04138v2 | http://arxiv.org/pdf/1706.04138v2.pdf | An Exploration of Neural Sequence-to-Sequence Architectures for Automatic Post-Editing | In this work, we explore multiple neural architectures adapted for the task
of automatic post-editing of machine translation output. We focus on neural
end-to-end models that combine both inputs $mt$ (raw MT output) and $src$
(source language input) in a single neural architecture, modeling $\{mt, src\}
\rightarrow pe$ directly. Apart from that, we investigate the influence of
hard-attention models which seem to be well-suited for monolingual tasks, as
well as combinations of both ideas. We report results on data sets provided
during the WMT-2016 shared task on automatic post-editing and can demonstrate
that dual-attention models that incorporate all available data in the APE
scenario in a single model improve on the best shared task system and on all
other published results after the shared task. Dual-attention models that are
combined with hard attention remain competitive despite applying fewer changes
to the input. | ['Roman Grundkiewicz', 'Marcin Junczys-Dowmunt'] | 2017-06-13 | an-exploration-of-neural-sequence-to-sequence-1 | https://aclanthology.org/I17-1013 | https://aclanthology.org/I17-1013.pdf | ijcnlp-2017-11 | ['hard-attention'] | ['methodology'] | [ 4.31289285e-01 3.38560343e-01 1.10407777e-01 -3.67417753e-01
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feed4125-51d3-41af-a6b1-8584a4e69533 | kuielab-mdx-net-a-two-stream-neural-network | 2111.12203 | null | https://arxiv.org/abs/2111.12203v1 | https://arxiv.org/pdf/2111.12203v1.pdf | KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing | Recently, many methods based on deep learning have been proposed for music source separation. Some state-of-the-art methods have shown that stacking many layers with many skip connections improve the SDR performance. Although such a deep and complex architecture shows outstanding performance, it usually requires numerous computing resources and time for training and evaluation. This paper proposes a two-stream neural network for music demixing, called KUIELab-MDX-Net, which shows a good balance of performance and required resources. The proposed model has a time-frequency branch and a time-domain branch, where each branch separates stems, respectively. It blends results from two streams to generate the final estimation. KUIELab-MDX-Net took second place on leaderboard A and third place on leaderboard B in the Music Demixing Challenge at ISMIR 2021. This paper also summarizes experimental results on another benchmark, MUSDB18. Our source code is available online. | ['Soonyoung Jung', 'Daewon Lee', 'Jaehwa Chung', 'Woosung Choi', 'Minseok Kim'] | 2021-11-24 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.7351797e-01 -6.5127414e-01 -2.5180599e-01 -1.8164292e-01
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fa4e7d5a-83dc-48cd-be0f-8642d25cad76 | nexus-sine-qua-non-essentially-connected | 2307.01482 | null | https://arxiv.org/abs/2307.01482v1 | https://arxiv.org/pdf/2307.01482v1.pdf | Nexus sine qua non: Essentially connected neural networks for spatial-temporal forecasting of multivariate time series | Modeling and forecasting multivariate time series not only facilitates the decision making of practitioners, but also deepens our scientific understanding of the underlying dynamical systems. Spatial-temporal graph neural networks (STGNNs) are emerged as powerful predictors and have become the de facto models for learning spatiotemporal representations in recent years. However, existing architectures of STGNNs tend to be complicated by stacking a series of fancy layers. The designed models could be either redundant or enigmatic, which pose great challenges on their complexity and scalability. Such concerns prompt us to re-examine the designs of modern STGNNs and identify core principles that contribute to a powerful and efficient neural predictor. Here we present a compact predictive model that is fully defined by a dense encoder-decoder and a message-passing layer, powered by node identifications, without any complex sequential modules, e.g., TCNs, RNNs, and Transformers. Empirical results demonstrate how a simple and elegant model with proper inductive basis can compare favorably w.r.t. the state of the art with elaborate designs, while being much more interpretable and computationally efficient for spatial-temporal forecasting problem. We hope our findings would open new horizons for future studies to revisit the design of more concise neural forecasting architectures. | ['Jian Sun', 'Yunpeng Wang', 'Guoyang Qin', 'Tong Nie'] | 2023-07-04 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 6.06112704e-02 -2.75859758e-02 -2.14649469e-01 -2.69494236e-01
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cba69810-f187-4949-afc4-1cc8f91feef8 | wechat-ai-s-submission-for-dstc9-interactive | 2101.07947 | null | https://arxiv.org/abs/2101.07947v2 | https://arxiv.org/pdf/2101.07947v2.pdf | WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track | We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2 (Interactive Dialogue). In sub-task 1, we employ a pre-trained language model to generate topic-related responses and propose a response ensemble method for response selection. In sub-task2, we propose a novel Dialogue Planning Model (DPM) to capture conversation flow in the interaction with humans. We also design an integrated open-domain dialogue system containing pre-process, dialogue model, scoring model, and post-process, which can generate fluent, coherent, consistent, and humanlike responses. We tie 1st on human ratings and also get the highest Meteor, and Bert-score in sub-task 1, and rank 3rd on interactive human evaluation in sub-task 2. | ['Jie zhou', 'Yang Feng', 'Jinchao Zhang', 'Zongjia Li', 'Zekang Li'] | 2021-01-20 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 1.44751057e-01 8.47629130e-01 3.85211200e-01 -6.52267158e-01
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5c57addf-a7b1-484f-80f4-feccc941f691 | modern-strategies-for-time-series-regression | 2010.15997 | null | https://arxiv.org/abs/2010.15997v1 | https://arxiv.org/pdf/2010.15997v1.pdf | Modern strategies for time series regression | This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen that there are advantages and disadvantages to most currently available approaches. There is ample room for methodological developments in this area. The work is motivated by an application involving the prediction of water levels as a function of rainfall and other climate variables in an aquifer in eastern Australia. | ['Louise M Ryan', 'Dan Pagendam', 'Rob J Hyndman', 'Stephanie Clark'] | 2020-10-29 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [ 1.32392809e-01 -4.23156321e-01 -2.14943275e-01 -7.34025538e-01
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671c89ac-10de-43f7-98f8-4069a8900732 | an-indexing-scheme-and-descriptor-for-3d | 2008.02916 | null | https://arxiv.org/abs/2008.02916v1 | https://arxiv.org/pdf/2008.02916v1.pdf | An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying | A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment. | ['Theoharis Theoharis', 'Bart Iver van Blokland'] | 2020-08-07 | null | null | null | null | ['3d-object-retrieval'] | ['computer-vision'] | [ 2.06795409e-02 -8.63787055e-01 -9.40555632e-02 -2.97190189e-01
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e0cb06cc-73d4-4b0b-bb6c-22ce6665c1ac | what-is-this-article-about-extreme | 1907.08722 | null | https://arxiv.org/abs/1907.08722v1 | https://arxiv.org/pdf/1907.08722v1.pdf | What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks | We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the article about?''. We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset. | ['Mirella Lapata', 'Shay B. Cohen', 'Shashi Narayan'] | 2019-07-19 | null | null | null | null | ['extreme-summarization'] | ['natural-language-processing'] | [ 6.94633007e-01 7.24461079e-01 -7.63846412e-02 -3.58410805e-01
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5484a068-0c2c-4a7c-a8a8-41bc91f8c44f | story-shaping-teaching-agents-human-like | 2301.10107 | null | https://arxiv.org/abs/2301.10107v1 | https://arxiv.org/pdf/2301.10107v1.pdf | Story Shaping: Teaching Agents Human-like Behavior with Stories | Reward design for reinforcement learning agents can be difficult in situations where one not only wants the agent to achieve some effect in the world but where one also cares about how that effect is achieved. For example, we might wish for an agent to adhere to a tacit understanding of commonsense, align itself to a preference for how to behave for purposes of safety, or taking on a particular role in an interactive game. Storytelling is a mode for communicating tacit procedural knowledge. We introduce a technique, Story Shaping, in which a reinforcement learning agent infers tacit knowledge from an exemplar story of how to accomplish a task and intrinsically rewards itself for performing actions that make its current environment adhere to that of the inferred story world. Specifically, Story Shaping infers a knowledge graph representation of the world state from observations, and also infers a knowledge graph from the exemplar story. An intrinsic reward is generated based on the similarity between the agent's inferred world state graph and the inferred story world graph. We conducted experiments in text-based games requiring commonsense reasoning and shaping the behaviors of agents as virtual game characters. | ['Mark Riedl', 'Renee Jia', 'Wei Zhou', 'Christopher Cui', 'Xiangyu Peng'] | 2023-01-24 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 4.04319793e-01 6.43044710e-01 5.88210337e-02 -2.75165915e-01
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47cffa5e-5ecc-4b5f-9acb-56fdde88e7e2 | mmnet-muscle-motion-guided-network-for-micro | 2201.05297 | null | https://arxiv.org/abs/2201.05297v2 | https://arxiv.org/pdf/2201.05297v2.pdf | MMNet: Muscle motion-guided network for micro-expression recognition | Facial micro-expressions (MEs) are involuntary facial motions revealing peoples real feelings and play an important role in the early intervention of mental illness, the national security, and many human-computer interaction systems. However, existing micro-expression datasets are limited and usually pose some challenges for training good classifiers. To model the subtle facial muscle motions, we propose a robust micro-expression recognition (MER) framework, namely muscle motion-guided network (MMNet). Specifically, a continuous attention (CA) block is introduced to focus on modeling local subtle muscle motion patterns with little identity information, which is different from most previous methods that directly extract features from complete video frames with much identity information. Besides, we design a position calibration (PC) module based on the vision transformer. By adding the position embeddings of the face generated by PC module at the end of the two branches, the PC module can help to add position information to facial muscle motion pattern features for the MER. Extensive experiments on three public micro-expression datasets demonstrate that our approach outperforms state-of-the-art methods by a large margin. | ['Feng Zhao', 'Zhaoqing Zhu', 'Mingzhe Sui', 'Hanting Li'] | 2022-01-14 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [-1.06448665e-01 -8.55014771e-02 -3.98739517e-01 -5.43685794e-01
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cb661fd4-e1cb-431f-b00f-85d35bde4a64 | kernel-density-bayesian-inverse-reinforcement | 2303.06827 | null | https://arxiv.org/abs/2303.06827v1 | https://arxiv.org/pdf/2303.06827v1.pdf | Kernel Density Bayesian Inverse Reinforcement Learning | Inverse reinforcement learning~(IRL) is a powerful framework to infer an agent's reward function by observing its behavior, but IRL algorithms that learn point estimates of the reward function can be misleading because there may be several functions that describe an agent's behavior equally well. A Bayesian approach to IRL models a distribution over candidate reward functions, alleviating the shortcomings of learning a point estimate. However, several Bayesian IRL algorithms use a $Q$-value function in place of the likelihood function. The resulting posterior is computationally intensive to calculate, has few theoretical guarantees, and the $Q$-value function is often a poor approximation for the likelihood. We introduce kernel density Bayesian IRL (KD-BIRL), which uses conditional kernel density estimation to directly approximate the likelihood, providing an efficient framework that, with a modified reward function parameterization, is applicable to environments with complex and infinite state spaces. We demonstrate KD-BIRL's benefits through a series of experiments in Gridworld environments and a simulated sepsis treatment task. | ['Barbara E. Engelhardt', 'Andrew Jones', 'Diana Cai', 'Didong Li', 'Aishwarya Mandyam'] | 2023-03-13 | null | null | null | null | ['birl-cima'] | ['medical'] | [-2.91836709e-01 -2.22380646e-02 -4.53116924e-01 -3.62022370e-01
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67cc2b5c-dccf-4820-a0e2-b7b1f6a713be | on-the-intrinsic-privacy-of-stochastic | 1912.02919 | null | https://arxiv.org/abs/1912.02919v4 | https://arxiv.org/pdf/1912.02919v4.pdf | An Empirical Study on the Intrinsic Privacy of SGD | Introducing noise in the training of machine learning systems is a powerful way to protect individual privacy via differential privacy guarantees, but comes at a cost to utility. This work looks at whether the inherent randomness of stochastic gradient descent (SGD) could contribute to privacy, effectively reducing the amount of \emph{additional} noise required to achieve a given privacy guarantee. We conduct a large-scale empirical study to examine this question. Training a grid of over 120,000 models across four datasets (tabular and images) on convex and non-convex objectives, we demonstrate that the random seed has a larger impact on model weights than any individual training example. We test the distribution over weights induced by the seed, finding that the simple convex case can be modelled with a multivariate Gaussian posterior, while neural networks exhibit multi-modal and non-Gaussian weight distributions. By casting convex SGD as a Gaussian mechanism, we then estimate an `intrinsic' data-dependent $\epsilon_i(\mathcal{D})$, finding values as low as 6.3, dropping to 1.9 using empirical estimates. We use a membership inference attack to estimate $\epsilon$ for non-convex SGD and demonstrate that hiding the random seed from the adversary results in a statistically significant reduction in attack performance, corresponding to a reduction in the effective $\epsilon$. These results provide empirical evidence that SGD exhibits appreciable variability relative to its dataset sensitivity, and this `intrinsic noise' has the potential to be leveraged to improve the utility of privacy-preserving machine learning. | ['Stephanie L. Hyland', 'Shruti Tople'] | 2019-12-05 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.58913279e-01 1.71630651e-01 1.89589225e-02 -4.00712550e-01
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a1453638-267b-4672-b3cf-d7ad33073907 | visual-reasoning-from-state-to-transformation | 2305.01668 | null | https://arxiv.org/abs/2305.01668v1 | https://arxiv.org/pdf/2305.01668v1.pdf | Visual Reasoning: from State to Transformation | Most existing visual reasoning tasks, such as CLEVR in VQA, ignore an important factor, i.e.~transformation. They are solely defined to test how well machines understand concepts and relations within static settings, like one image. Such \textbf{state driven} visual reasoning has limitations in reflecting the ability to infer the dynamics between different states, which has shown to be equally important for human cognition in Piaget's theory. To tackle this problem, we propose a novel \textbf{transformation driven} visual reasoning (TVR) task. Given both the initial and final states, the target becomes to infer the corresponding intermediate transformation. Following this definition, a new synthetic dataset namely TRANCE is first constructed on the basis of CLEVR, including three levels of settings, i.e.~Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Next, we build another real dataset called TRANCO based on COIN, to cover the loss of transformation diversity on TRANCE. Inspired by human reasoning, we propose a three-staged reasoning framework called TranNet, including observing, analyzing, and concluding, to test how recent advanced techniques perform on TVR. Experimental results show that the state-of-the-art visual reasoning models perform well on Basic, but are still far from human-level intelligence on Event, View, and TRANCO. We believe the proposed new paradigm will boost the development of machine visual reasoning. More advanced methods and new problems need to be investigated in this direction. The resource of TVR is available at \url{https://hongxin2019.github.io/TVR/}. | ['Xueqi Cheng', 'Jiafeng Guo', 'Liang Pang', 'Yanyan Lan', 'Xin Hong'] | 2023-05-02 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-2.08829343e-01 -1.64836254e-02 -3.19942832e-02 -2.58677453e-01
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211c63d0-57b1-40c9-9c15-f7355ba5d399 | jointly-dynamic-topic-model-for-recognition | 2111.10846 | null | https://arxiv.org/abs/2111.10846v1 | https://arxiv.org/pdf/2111.10846v1.pdf | Jointly Dynamic Topic Model for Recognition of Lead-lag Relationship in Two Text Corpora | Topic evolution modeling has received significant attentions in recent decades. Although various topic evolution models have been proposed, most studies focus on the single document corpus. However in practice, we can easily access data from multiple sources and also observe relationships between them. Then it is of great interest to recognize the relationship between multiple text corpora and further utilize this relationship to improve topic modeling. In this work, we focus on a special type of relationship between two text corpora, which we define as the "lead-lag relationship". This relationship characterizes the phenomenon that one text corpus would influence the topics to be discussed in the other text corpus in the future. To discover the lead-lag relationship, we propose a jointly dynamic topic model and also develop an embedding extension to address the modeling problem of large-scale text corpus. With the recognized lead-lag relationship, the similarities of the two text corpora can be figured out and the quality of topic learning in both corpora can be improved. We numerically investigate the performance of the jointly dynamic topic modeling approach using synthetic data. Finally, we apply the proposed model on two text corpora consisting of statistical papers and the graduation theses. Results show the proposed model can well recognize the lead-lag relationship between the two corpora, and the specific and shared topic patterns in the two corpora are also discovered. | ['Feifei Wang', 'Jingya Hong', 'Xiaoling Lu', 'Yandi Zhu'] | 2021-11-21 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-8.63082260e-02 -2.41451010e-01 -1.96243867e-01 -1.52652249e-01
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9a375a5e-9c13-4daa-ab17-5ae97d17da60 | pushing-the-envelope-for-depth-based-semi | 2303.15147 | null | https://arxiv.org/abs/2303.15147v1 | https://arxiv.org/pdf/2303.15147v1.pdf | Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training | Despite the significant progress that depth-based 3D hand pose estimation methods have made in recent years, they still require a large amount of labeled training data to achieve high accuracy. However, collecting such data is both costly and time-consuming. To tackle this issue, we propose a semi-supervised method to significantly reduce the dependence on labeled training data. The proposed method consists of two identical networks trained jointly: a teacher network and a student network. The teacher network is trained using both the available labeled and unlabeled samples. It leverages the unlabeled samples via a loss formulation that encourages estimation equivariance under a set of affine transformations. The student network is trained using the unlabeled samples with their pseudo-labels provided by the teacher network. For inference at test time, only the student network is used. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art semi-supervised methods by large margins. | ['Vassilis Athitsos', 'Alex Dillhoff', 'Farnaz Farahanipad', 'Mohammad Rezaei'] | 2023-03-27 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 1.67071179e-01 1.94631621e-01 -5.94896734e-01 -6.49078608e-01
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848d7b7b-d484-439a-9924-6dd5c9683eb9 | all-e-aesthetics-guided-low-light-image | 2304.14610 | null | https://arxiv.org/abs/2304.14610v2 | https://arxiv.org/pdf/2304.14610v2.pdf | ALL-E: Aesthetics-guided Low-light Image Enhancement | Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training enhancement models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions, i.e., its corresponding adjustment curve is estimated sequentially. Extensive experiments show that integrating aesthetic assessment improves both subjective experience and objective evaluation. Our results on various benchmarks demonstrate the superiority of ALL-E over state-of-the-art methods. | ['Songcan Chen', 'Sheng-Jun Huang', 'Yuanhang Gao', 'Dong Liang', 'Ling Li'] | 2023-04-28 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.77473712e-01 -1.14237085e-01 -6.86534941e-02 -4.97873366e-01
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253b3af1-bdb1-4cc3-96da-66f7e3bfb054 | entity-identification-as-multitasking | 1612.02706 | null | http://arxiv.org/abs/1612.02706v2 | http://arxiv.org/pdf/1612.02706v2.pdf | Entity Identification as Multitasking | Standard approaches in entity identification hard-code boundary detection and
type prediction into labels (e.g., John/B-PER Smith/I-PER) and then perform
Viterbi. This has two disadvantages: 1. the runtime complexity grows
quadratically in the number of types, and 2. there is no natural segment-level
representation. In this paper, we propose a novel neural architecture that
addresses these disadvantages. We frame the problem as multitasking, separating
boundary detection and type prediction but optimizing them jointly. Despite its
simplicity, this architecture performs competitively with fully structured
models such as BiLSTM-CRFs while scaling linearly in the number of types.
Furthermore, by construction, the model induces type-disambiguating embeddings
of predicted mentions. | ['Karl Stratos'] | 2016-12-08 | entity-identification-as-multitasking-1 | https://aclanthology.org/W17-4302 | https://aclanthology.org/W17-4302.pdf | ws-2017-9 | ['type-prediction'] | ['computer-code'] | [ 5.43549769e-02 3.54384631e-01 -4.88096267e-01 -3.60668391e-01
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a18c38c4-e480-4a22-8538-7f629de5cc90 | farsight-a-physics-driven-whole-body | 2306.17206 | null | https://arxiv.org/abs/2306.17206v1 | https://arxiv.org/pdf/2306.17206v1.pdf | FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude | Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. FarSight accepts videos from elevated platforms and drones as input and outputs a candidate list of identities from a gallery. The system is designed to address several challenges, including (i) low-quality imagery, (ii) large yaw and pitch angles, (iii) robust feature extraction to accommodate large intra-person variabilities and large inter-person similarities, and (iv) the large domain gap between training and test sets. FarSight combines the physics of imaging and deep learning models to enhance image restoration and biometric feature encoding. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) dataset. Notably, FarSight demonstrated a substantial performance increase on the BRIAR dataset, with gains of +11.82% Rank-20 identification and +11.3% TAR@1% FAR. | ['Xiaoming Liu', 'Anil Jain', 'Zhangyang Wang', 'Humphrey Shi', 'Arun Ross', 'Stanley Chan', 'Xingguang Zhang', 'Kai Wang', 'Pegah Varghaei', 'Yiyang Su', 'Zhiyuan Ren', 'Christopher Perry', 'Zhiyuan Mao', 'Minchul Kim', 'Ajay Jaiswal', 'Ali Hassani', 'Najmul Hassan', 'Nicholas Chimitt', 'Ryan Ashbaugh', 'Feng Liu'] | 2023-06-29 | null | null | null | null | ['image-restoration'] | ['computer-vision'] | [ 7.78172463e-02 -4.51015919e-01 -3.94531526e-02 -4.16496873e-01
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88048a93-b367-4ed0-bec8-c0b1b4f414f2 | comet-qe-and-active-learning-for-low-resource | 2210.15696 | null | https://arxiv.org/abs/2210.15696v1 | https://arxiv.org/pdf/2210.15696v1.pdf | COMET-QE and Active Learning for Low-Resource Machine Translation | Active learning aims to deliver maximum benefit when resources are scarce. We use COMET-QE, a reference-free evaluation metric, to select sentences for low-resource neural machine translation. Using Swahili, Kinyarwanda and Spanish for our experiments, we show that COMET-QE significantly outperforms two variants of Round Trip Translation Likelihood (RTTL) and random sentence selection by up to 5 BLEU points for 20k sentences selected by Active Learning on a 30k baseline. This suggests that COMET-QE is a powerful tool for sentence selection in the very low-resource limit. | ['Bruce A. Bassett', 'Everlyn Asiko Chimoto'] | 2022-10-27 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.30073982e-01 6.20120652e-02 -6.17242098e-01 -3.58320296e-01
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0dcb6450-5028-44a3-8408-0debb996a527 | reference-based-color-transfer-for-medical | 2210.08083 | null | https://arxiv.org/abs/2210.08083v1 | https://arxiv.org/pdf/2210.08083v1.pdf | Reference Based Color Transfer for Medical Volume Rendering | The benefits of medical imaging are enormous. Medical images provide considerable amounts of anatomical information and this facilitates medical practitioners in performing effective disease diagnosis and deciding upon the best course of medical treatment. A transition from traditional monochromatic medical images like CT scans, X-Rays or MRI images to a colored 3D representation of the anatomical structure further enhances the capabilities of medical professionals in extracting valuable medical information. The proposed framework in our research starts with performing color transfer by finding deep semantic correspondence between two medical images: a colored reference image, and a monochromatic CT scan or an MRI image. We extend this idea of reference-based colorization technique to perform colored volume rendering from a stack of grayscale medical images. Furthermore, we also propose to use an effective reference image recommendation system to aid in the selection of good reference images. With our approach, we successfully perform colored medical volume visualization and essentially eliminate the painstaking process of user interaction with a transfer function to obtain color and opacity parameters for volume rendering. | ['Summanta Pattanaik', 'Sudarshan Devkota'] | 2022-10-14 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.89579386e-01 -2.67951433e-02 2.46705323e-01 -3.12699229e-01
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1faf37ef-53e8-41b3-a171-5c9394a4ea75 | bayesian-predictive-beamforming-for-vehicular | 2005.07698 | null | https://arxiv.org/abs/2005.07698v1 | https://arxiv.org/pdf/2005.07698v1.pdf | Bayesian Predictive Beamforming for Vehicular Networks: A Low-overhead Joint Radar-Communication Approach | The development of dual-functional radar-communication (DFRC) systems, where vehicle localization and tracking can be combined with vehicular communication, will lead to more efficient future vehicular networks. In this paper, we develop a predictive beamforming scheme in the context of DFRC systems. We consider a system model where the road-side units estimates and predicts the motion parameters of vehicles based on the echoes of the DFRC signal. Compared to the conventional feedback-based beam tracking approaches, the proposed method can reduce the signaling overhead and improve the accuracy. To accurately estimate the motion parameters of vehicles in real-time, we propose a novel message passing algorithm based on factor graph, which yields near optimal solution to the maximum a posteriori estimation. The beamformers are then designed based on the predicted angles for establishing the communication links.}With the employment of appropriate approximations, all messages on the factor graph can be derived in a closed-form, thus reduce the complexity. Simulation results show that the proposed DFRC based beamforming scheme is superior to the feedback-based approach in terms of both estimation and communication performance. Moreover, the proposed message passing algorithm achieves a similar performance of the high-complexity particle-based methods. | ['Nuria Gonzalez-Prelcic', 'Derrick Wing Kwan Ng', 'Jinhong Yuan', 'Christos Masouros', 'Fan Liu', 'Weijie Yuan'] | 2020-05-15 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [-5.23397028e-02 -2.44791135e-01 -3.74730378e-02 -2.97940195e-01
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99d55fc4-118f-4019-9089-7e4c03bdbaf2 | autodial-efficient-asynchronous-task-oriented | 2303.06245 | null | https://arxiv.org/abs/2303.06245v3 | https://arxiv.org/pdf/2303.06245v3.pdf | AUTODIAL: Efficient Asynchronous Task-Oriented Dialogue Model | As large dialogue models become commonplace in practice, the problems surrounding high compute requirements for training, inference and larger memory footprint still persists. In this work, we present AUTODIAL, a multi-task dialogue model that addresses the challenges of deploying dialogue model. AUTODIAL utilizes parallel decoders to perform tasks such as dialogue act prediction, domain prediction, intent prediction, and dialogue state tracking. Using classification decoders over generative decoders allows AUTODIAL to significantly reduce memory footprint and achieve faster inference times compared to existing generative approach namely SimpleTOD. We demonstrate that AUTODIAL provides 3-6x speedups during inference while having 11x fewer parameters on three dialogue tasks compared to SimpleTOD. Our results show that extending current dialogue models to have parallel decoders can be a viable alternative for deploying them in resource-constrained environments. | ['Chinnadhurai Sankar', 'Shahin Shayandeh', 'Pooyan Amini', 'Prajjwal Bhargava'] | 2023-03-10 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [-1.40099138e-01 5.32354534e-01 1.66493878e-01 -3.56255442e-01
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c8186fd1-9cf7-4661-8ab1-2ed90d937f53 | denoising-based-turbo-message-passing-for | 2012.05626 | null | https://arxiv.org/abs/2012.05626v1 | https://arxiv.org/pdf/2012.05626v1.pdf | Denoising-based Turbo Message Passing for Compressed Video Background Subtraction | In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements. The background of a video usually lies in a low dimensional space and the foreground is usually sparse. More importantly, each video frame is a natural image that has textural patterns. By exploiting these properties, we develop a message passing algorithm termed offline denoising-based turbo message passing (DTMP). We show that these structural properties can be efficiently handled by the existing denoising techniques under the turbo message passing framework. We further extend the DTMP algorithm to the online scenario where the video data is collected in an online manner. The extension is based on the similarity/continuity between adjacent video frames. We adopt the optical flow method to refine the estimation of the foreground. We also adopt the sliding window based background estimation to reduce complexity. By exploiting the Gaussianity of messages, we develop the state evolution to characterize the per-iteration performance of offline and online DTMP. Comparing to the existing algorithms, DTMP can work at much lower compression rates, and can subtract the background successfully with a lower mean squared error and better visual quality for both offline and online compressed video background subtraction. | ['Yang Yang', 'Xiaojun Yuan', 'Zhipeng Xue'] | 2020-12-10 | null | null | null | null | ['video-background-subtraction'] | ['computer-vision'] | [ 5.09220362e-01 -4.72251892e-01 4.96772230e-02 2.37970781e-02
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5e180f89-be57-46ad-8db5-2ac89f33bbbc | consistency-based-self-supervised-learning | 2208.05251 | null | https://arxiv.org/abs/2208.05251v1 | https://arxiv.org/pdf/2208.05251v1.pdf | Consistency-based Self-supervised Learning for Temporal Anomaly Localization | This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training. In particular, we deal with the localization of anomalous activities within the video stream: this is a very challenging scenario, as training examples come only with video-level annotations (and not frame-level). Several recent works have proposed various regularization terms to address it i.e. by enforcing sparsity and smoothness constraints over the weakly-learned frame-level anomaly scores. In this work, we get inspired by recent advances within the field of self-supervised learning and ask the model to yield the same scores for different augmentations of the same video sequence. We show that enforcing such an alignment improves the performance of the model on XD-Violence. | ['Rita Cucchiara', 'Simone Calderara', 'Angelo Porrello', 'Aniello Panariello'] | 2022-08-10 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'supervised-anomaly-detection', 'weakly-supervised-temporal-action', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 4.52980399e-01 1.26449674e-01 -1.58229217e-01 -5.32966912e-01
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fe29831f-bee3-4e37-b527-2dec348d0aee | experimental-evaluation-of-quantum-bayesian | 2005.12474 | null | https://arxiv.org/abs/2005.12474v1 | https://arxiv.org/pdf/2005.12474v1.pdf | Experimental evaluation of quantum Bayesian networks on IBM QX hardware | Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes the principles of quantum mechanical systems to improve the computational performance of various analyses. In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis. We consider a 4-node BN for stock prediction for our experimental evaluation. We construct a quantum circuit to represent the 4-node BN using Qiskit, and run the circuit on nine IBM quantum devices: Yorktown, Vigo, Ourense, Essex, Burlington, London, Rome, Athens and Melbourne. We will also compare the performance of each device across the four levels of optimization performed by the IBM Transpiler when mapping a given quantum circuit to a given device. We use the root mean square percentage error as the metric for performance comparison of various hardware. | ['Saideep Nannapaneni', 'Sima E. Borujeni', 'Elizabeth C. Behrman', 'Nam H. Nguyen', 'James E. Steck'] | 2020-05-26 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-1.60996243e-01 -1.07694209e-01 8.18802640e-02 -4.64802116e-01
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dadb296a-7b90-42a8-9b9a-ee38da06c027 | detecting-images-generated-by-deep-diffusion | 2307.02347 | null | https://arxiv.org/abs/2307.02347v1 | https://arxiv.org/pdf/2307.02347v1.pdf | Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality | Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that multiLID exhibits superiority in diffusion detection and model identification. Since the empirical evaluations of recent publications on the detection of generated images is often too focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes to evaluate the performance of their multiLID. Code for our experiments is provided at https://github.com/deepfake-study/deepfake_multiLID. | ['Janis Keuper', 'Ricard Durall', 'Peter Lorenz'] | 2023-07-05 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.10699111e-01 -1.91899866e-01 2.10890725e-01 3.37241828e-01
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35e4be72-bd91-4f0e-9857-06e1ecf93d4e | escape-room-a-configurable-testbed-for | 1812.09521 | null | http://arxiv.org/abs/1812.09521v1 | http://arxiv.org/pdf/1812.09521v1.pdf | Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning | Recent successes in Reinforcement Learning have encouraged a fast-growing
network of RL researchers and a number of breakthroughs in RL research. As the
RL community and the body of RL work grows, so does the need for widely
applicable benchmarks that can fairly and effectively evaluate a variety of RL
algorithms.
This need is particularly apparent in the realm of Hierarchical Reinforcement
Learning (HRL). While many existing test domains may exhibit hierarchical
action or state structures, modern RL algorithms still exhibit great difficulty
in solving domains that necessitate hierarchical modeling and action planning,
even when such domains are seemingly trivial. These difficulties highlight both
the need for more focus on HRL algorithms themselves, and the need for new
testbeds that will encourage and validate HRL research.
Existing HRL testbeds exhibit a Goldilocks problem; they are often either too
simple (e.g. Taxi) or too complex (e.g. Montezuma's Revenge from the Arcade
Learning Environment). In this paper we present the Escape Room Domain (ERD), a
new flexible, scalable, and fully implemented testing domain for HRL that
bridges the "moderate complexity" gap left behind by existing alternatives. ERD
is open-source and freely available through GitHub, and conforms to widely-used
public testing interfaces for simple integration and testing with a variety of
public RL agent implementations. We show that the ERD presents a suite of
challenges with scalable difficulty to provide a smooth learning gradient from
Taxi to the Arcade Learning Environment. | ['Jacob Menashe', 'Peter Stone'] | 2018-12-22 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-2.79833257e-01 -2.75308676e-02 -3.08719784e-01 -2.53762484e-01
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cf352ff4-53e0-4542-93e9-89fb904393f7 | iiitd-20k-dense-captioning-for-text-image | 2305.04497 | null | https://arxiv.org/abs/2305.04497v1 | https://arxiv.org/pdf/2305.04497v1.pdf | IIITD-20K: Dense captioning for Text-Image ReID | Text-to-Image (T2I) ReID has attracted a lot of attention in the recent past. CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from MSMT17 but due to limited number of unique persons, the diversity is limited. On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively shorter text description on average. Further, these datasets are captured in a restricted environment with limited number of cameras. In order to further diversify the identities and provide dense captions, we propose a novel dataset called IIITD-20K. IIITD-20K comprises of 20,000 unique identities captured in the wild and provides a rich dataset for text-to-image ReID. With a minimum of 26 words for a description, each image is densely captioned. We further synthetically generate images and fine-grained captions using Stable-diffusion and BLIP models trained on our dataset. We perform elaborate experiments using state-of-art text-to-image ReID models and vision-language pre-trained models and present a comprehensive analysis of the dataset. Our experiments also reveal that synthetically generated data leads to a substantial performance improvement in both same dataset as well as cross dataset settings. Our dataset is available at https://bit.ly/3pkA3Rj. | ['Brejesh lall', 'Vibhu Dubey', 'Niranjan Sundararajan', 'A V Subramanyam'] | 2023-05-08 | null | null | null | null | ['dense-captioning'] | ['computer-vision'] | [ 7.98045546e-02 -2.44739741e-01 4.57281321e-02 -5.10719180e-01
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cffee839-c043-4df4-9327-1ec107f3e8db | rethinking-performance-gains-in-image | 2209.11448 | null | https://arxiv.org/abs/2209.11448v1 | https://arxiv.org/pdf/2209.11448v1.pdf | Rethinking Performance Gains in Image Dehazing Networks | Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning. Although these networks' pipelines work fine, the key mechanism to improving image dehazing performance remains unclear. For this reason, we do not target to propose a dehazing network with fancy modules; rather, we make minimal modifications to popular U-Net to obtain a compact dehazing network. Specifically, we swap out the convolutional blocks in U-Net for residual blocks with the gating mechanism, fuse the feature maps of main paths and skip connections using the selective kernel, and call the resulting U-Net variant gUNet. As a result, with a significantly reduced overhead, gUNet is superior to state-of-the-art methods on multiple image dehazing datasets. Finally, we verify these key designs to the performance gain of image dehazing networks through extensive ablation studies. | ['Xin Du', 'Hui Qian', 'Yang Zhou', 'Yuda Song'] | 2022-09-23 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.99827391e-01 1.69032559e-01 1.40619911e-02 -9.65655670e-02
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2fb2024c-095a-4a81-afa0-60fcdd901003 | end-to-end-automatic-sleep-stage | 2005.05437 | null | https://arxiv.org/abs/2005.05437v1 | https://arxiv.org/pdf/2005.05437v1.pdf | End-to-End Automatic Sleep Stage Classification Using Spectral-Temporal Sleep Features | Sleep disorder is one of many neurological diseases that can affect greatly the quality of daily life. It is very burdensome to manually classify the sleep stages to detect sleep disorders. Therefore, the automatic sleep stage classification techniques are needed. However, the previous automatic sleep scoring methods using raw signals are still low classification performance. In this study, we proposed an end-to-end automatic sleep staging framework based on optimal spectral-temporal sleep features using a sleep-edf dataset. The input data were modified using a bandpass filter and then applied to a convolutional neural network model. For five sleep stage classification, the classification performance 85.6% and 91.1% using the raw input data and the proposed input, respectively. This result also shows the highest performance compared to conventional studies using the same dataset. The proposed framework has shown high performance by using optimal features associated with each sleep stage, which may help to find new features in the automatic sleep stage method. | ['Seong-Whan Lee', 'Minji Lee', 'Hyeong-Jin Kim'] | 2020-05-04 | null | null | null | null | ['sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical'] | [ 1.62580609e-01 -3.67809147e-01 -5.00094779e-02 -5.17239034e-01
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afb29b54-6374-419a-a6a5-d8a0a466ec9a | cross-domain-document-layout-analysis-via | 2201.09407 | null | https://arxiv.org/abs/2201.09407v1 | https://arxiv.org/pdf/2201.09407v1.pdf | Cross-Domain Document Layout Analysis via Unsupervised Document Style Guide | The document layout analysis (DLA) aims to decompose document images into high-level semantic areas (i.e., figures, tables, texts, and background). Creating a DLA framework with strong generalization capabilities is a challenge due to document objects are diversity in layout, size, aspect ratio, texture, etc. Many researchers devoted this challenge by synthesizing data to build large training sets. However, the synthetic training data has different styles and erratic quality. Besides, there is a large gap between the source data and the target data. In this paper, we propose an unsupervised cross-domain DLA framework based on document style guidance. We integrated the document quality assessment and the document cross-domain analysis into a unified framework. Our framework is composed of three components, Document Layout Generator (GLD), Document Elements Decorator(GED), and Document Style Discriminator(DSD). The GLD is used to document layout generates, the GED is used to document layout elements fill, and the DSD is used to document quality assessment and cross-domain guidance. First, we apply GLD to predict the positions of the generated document. Then, we design a novel algorithm based on aesthetic guidance to fill the document positions. Finally, we use contrastive learning to evaluate the quality assessment of the document. Besides, we design a new strategy to change the document quality assessment component into a document cross-domain style guide component. Our framework is an unsupervised document layout analysis framework. We have proved through numerous experiments that our proposed method has achieved remarkable performance. | ['Liang He', 'Tianlong Ma', 'Xin Li', 'Yingbin Zheng', 'Xiangcheng Du', 'Luwei Xiao', 'Xingjiao Wu'] | 2022-01-24 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [ 2.55534232e-01 -5.12691498e-01 1.77955985e-01 -3.21073681e-01
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a2d0dda9-9a1d-48f5-b2a6-d4d5e421b539 | spatio-temporal-parking-behaviour-forecasting | 2108.07731 | null | https://arxiv.org/abs/2108.07731v1 | https://arxiv.org/pdf/2108.07731v1.pdf | Spatio-temporal Parking Behaviour Forecasting and Analysis Before and During COVID-19 | Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. | ['Ruibin Bai', 'Wei Tu', 'Yu Liu', 'Rui Cao', 'Xiaopeng Mo', 'Shuhui Gong'] | 2021-08-15 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-3.84342611e-01 -2.62184292e-01 -4.61935163e-01 -1.74220100e-01
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7f274dbb-e52e-4083-b419-74c03405a80e | english-contrastive-learning-can-learn | 2211.06127 | null | https://arxiv.org/abs/2211.06127v1 | https://arxiv.org/pdf/2211.06127v1.pdf | English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings | Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data. In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS. The performance can be further enhanced when cross-lingual NLI data is available. Our code is publicly available at https://github.com/yaushian/mSimCSE. | ['Graham Neubig', 'Ashley Wu', 'Yau-Shian Wang'] | 2022-11-11 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-3.58289540e-01 -3.59335124e-01 -6.08485281e-01 -3.42025131e-01
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a14557e5-b3b5-42de-a0df-74c4dfa83026 | generalized-multichannel-variational | 1810.00223 | null | http://arxiv.org/abs/1810.00223v1 | http://arxiv.org/pdf/1810.00223v1.pdf | Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation | This paper deals with a multichannel audio source separation problem under
underdetermined conditions. Multichannel Non-negative Matrix Factorization
(MNMF) is one of powerful approaches, which adopts the NMF concept for source
power spectrogram modeling. This concept is also employed in Independent
Low-Rank Matrix Analysis (ILRMA), a special class of the MNMF framework
formulated under determined conditions. While these methods work reasonably
well for particular types of sound sources, one limitation is that they can
fail to work for sources with spectrograms that do not comply with the NMF
model. To address this limitation, an extension of ILRMA called the
Multichannel Variational Autoencoder (MVAE) method was recently proposed, where
a Conditional VAE (CVAE) is used instead of the NMF model for source power
spectrogram modeling. This approach has shown to perform impressively in
determined source separation tasks thanks to the representation power of DNNs.
While the original MVAE method was formulated under determined mixing
conditions, this paper generalizes it so that it can also deal with
underdetermined cases. We call the proposed framework the Generalized MVAE
(GMVAE). The proposed method was evaluated on a underdetermined source
separation task of separating out three sources from two microphone inputs.
Experimental results revealed that the GMVAE method achieved better performance
than the MNMF method. | ['Tomoki Toda', 'Shogo Seki', 'Li Li', 'Kazuya Takeda', 'Hirokazu Kameoka'] | 2018-09-29 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 2.75278658e-01 -4.32207525e-01 2.40773648e-01 1.42946303e-01
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6088f6ca-dd76-439e-be6b-10473c344b9c | multifix-learning-to-repair-multiple-errors | null | null | https://aclanthology.org/2021.findings-emnlp.417 | https://aclanthology.org/2021.findings-emnlp.417.pdf | MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning | We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs. Since the previous approaches fix a single error in a line, it is inevitable to iterate the fixing process until no errors remain. In this work, we propose a novel sequence-to-sequence learning framework for fixing multiple program errors at a time. We introduce the edit-distance-based data labeling approach for program error correction. Instead of labeling a program repair example by pairing an erroneous program with a line fix, we label the example by paring an erroneous program with an optimal alignment to the corresponding correct program produced by the edit-distance computation. We evaluate our proposed approach on a publicly available dataset (DeepFix dataset) that consists of erroneous C programs submitted by novice programming students. On a set of 6,975 erroneous C programs from the DeepFix dataset, our approach achieves the state-of-the-art result in terms of full repair rate on the DeepFix dataset (without extra data such as compiler error message or additional source codes for pre-training). | ['Sang-Ki Ko', 'Yo-Sub Han', 'HyeonTae Seo'] | null | null | null | null | findings-emnlp-2021-11 | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 3.69265109e-01 -1.59215406e-01 -2.59669334e-01 -6.95372581e-01
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3a05e39b-2e47-451b-bf1d-f0afa4ed98c6 | estimating-task-completion-times-for-network | 2211.10866 | null | https://arxiv.org/abs/2211.10866v2 | https://arxiv.org/pdf/2211.10866v2.pdf | Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods | This paper proposes a data and Machine Learning-based forecasting solution for the Telecommunications network-rollout planning problem. Milestone completion-time estimation is crucial to network-rollout planning; accurate estimates enable better crew utilisation and optimised cost of materials and logistics. Using historical data of milestone completion times, a model needs to incorporate domain knowledge, handle noise and yet be interpretable to project managers. This paper proposes partition-based regression models that incorporate data-driven statistical models within each partition, as a solution to the problem. Benchmarking experiments demonstrate that the proposed approach obtains competitive to better performance, at a small fraction of the model complexity of the best alternative approach based on Gradient Boosting. Experiments also demonstrate that the proposed approach is effective for both short and long-range forecasts. The proposed idea is applicable in any context requiring time-series regression with noisy and attributed data. | ['Thalanayar Muthukumar', 'Shrihari Vasudevan', 'Venkatachalam Natchiappan'] | 2022-11-20 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-1.62456017e-02 -2.64874279e-01 -1.69647232e-01 -8.01279902e-01
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6cd4d5f5-ae94-4972-9ab6-a7d7a4f8bedf | dlpalign-a-deep-learning-based-progressive | null | null | https://dl.acm.org/doi/10.1145/3429210.3429221 | https://dl.acm.org/doi/pdf/10.1145/3429210.3429221 | DLPAlign: A Deep Learning based Progressive Alignment Method for Multiple Protein Sequences | This paper proposed a novel and straightforward approach to improve the accuracy of progressive multiple protein sequence alignment method. We trained a decision-making model based on the convolutional neural networks and bi-directional long short term memory networks, and progressively aligned the input protein sequences by calculating different posterior probability matrices.
To evaluate this method, we have implemented a multiple sequence alignment tool called DLPAlign and compared its performance with eleven leading alignment methods on three empirical alignment benchmarks (BAliBASE, OXBench and SABMark). Our results show that DLPAlign can get the best total-column scores on the three benchmarks. When evaluated against the 711 low similarity families with average PID ≤ 30%, DLPAlign improved about 2.8% over the second-best MSA software. Besides, we compared the performance of DLPAlign and other alignment tools on a real-life application, namely protein secondary structure prediction on four protein sequences related to SARS-COV-2, and DLPAlign provides the best result in all cases. | ['Lufei Gao', 'Yong liu', 'Mengmeng Kuang'] | 2020-11-21 | null | null | null | null | ['protein-secondary-structure-prediction', 'multiple-sequence-alignment'] | ['medical', 'medical'] | [ 4.29847091e-01 -3.21805865e-01 -9.16881040e-02 -4.04626489e-01
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02da21ee-bbb7-4436-9ecc-81d4e0e0c150 | transppg-two-stream-transformer-for-remote | 2201.10873 | null | https://arxiv.org/abs/2201.10873v1 | https://arxiv.org/pdf/2201.10873v1.pdf | TransPPG: Two-stream Transformer for Remote Heart Rate Estimate | Non-contact facial video-based heart rate estimation using remote photoplethysmography (rPPG) has shown great potential in many applications (e.g., remote health care) and achieved creditable results in constrained scenarios. However, practical applications require results to be accurate even under complex environment with head movement and unstable illumination. Therefore, improving the performance of rPPG in complex environment has become a key challenge. In this paper, we propose a novel video embedding method that embeds each facial video sequence into a feature map referred to as Multi-scale Adaptive Spatial and Temporal Map with Overlap (MAST_Mop), which contains not only vital information but also surrounding information as reference, which acts as the mirror to figure out the homogeneous perturbations imposed on foreground and background simultaneously, such as illumination instability. Correspondingly, we propose a two-stream Transformer model to map the MAST_Mop into heart rate (HR), where one stream follows the pulse signal in the facial area while the other figures out the perturbation signal from the surrounding region such that the difference of the two channels leads to adaptive noise cancellation. Our approach significantly outperforms all current state-of-the-art methods on two public datasets MAHNOB-HCI and VIPL-HR. As far as we know, it is the first work with Transformer as backbone to capture the temporal dependencies in rPPGs and apply the two stream scheme to figure out the interference from backgrounds as mirror of the corresponding perturbation on foreground signals for noise tolerating. | ['Weishan Zhang', 'Su Yang', 'Jiaqi Kang'] | 2022-01-26 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.31858397e-01 -1.63628295e-01 3.06687266e-01 -1.36808634e-01
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906dabf6-8985-41a1-8736-da5629c4d7cc | mmgp-a-mesh-morphing-gaussian-process-based | 2305.12871 | null | https://arxiv.org/abs/2305.12871v1 | https://arxiv.org/pdf/2305.12871v1.pdf | MMGP: a Mesh Morphing Gaussian Process-based machine learning method for regression of physical problems under non-parameterized geometrical variability | When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. For parameterized geometries, classical regression techniques can be successfully employed. However, in practice, the shape parametrization is generally not available in the inference stage and we only have access to a mesh discretization. Learning mesh-based simulations is challenging and most of the recent advances have been relying on deep graph neural networks in order to overcome the limitations of standard machine learning approaches. While graph neural networks have shown promising performances, they still suffer from a few shortcomings, such as the need of large datasets or their inability to provide predictive uncertainties out of the shelf. In this work, we propose a machine learning method that do not rely on graph neural networks. Complex geometrical shapes and variations with fixed topology are dealt with using well-known mesh morphing onto a common support, combined with classical dimensionality reduction techniques and Gaussian processes. The proposed methodology can easily deal with large meshes, without knowing any parametrization describing the shape, and provide predictive uncertainties, which are of primary importance for decision-making. In the considered numerical experiments, the proposed method is competitive with respect to our implementation of graph neural networks, regarding either efficiency of the training and accuracy of the predictions. | ['Xavier Roynard', 'Brian Staber', 'Fabien Casenave'] | 2023-05-22 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-8.00285563e-02 1.37211621e-01 1.11613669e-01 -5.33397449e-03
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760cf513-755e-4048-af6f-052dd72969d8 | is-attention-better-than-matrix-decomposition-1 | 2109.04553 | null | https://arxiv.org/abs/2109.04553v2 | https://arxiv.org/pdf/2109.04553v2.pdf | Is Attention Better Than Matrix Decomposition? | As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted attention irreplaceable when modeling the global context? Our intriguing finding is that self-attention is not better than the matrix decomposition (MD) model developed 20 years ago regarding the performance and computational cost for encoding the long-distance dependencies. We model the global context issue as a low-rank recovery problem and show that its optimization algorithms can help design global information blocks. This paper then proposes a series of Hamburgers, in which we employ the optimization algorithms for solving MDs to factorize the input representations into sub-matrices and reconstruct a low-rank embedding. Hamburgers with different MDs can perform favorably against the popular global context module self-attention when carefully coping with gradients back-propagated through MDs. Comprehensive experiments are conducted in the vision tasks where it is crucial to learn the global context, including semantic segmentation and image generation, demonstrating significant improvements over self-attention and its variants. | ['Zhouchen Lin', 'Ke Wei', 'Xia Li', 'Hongxu Chen', 'Meng-Hao Guo', 'Zhengyang Geng'] | 2021-09-09 | is-attention-better-than-matrix-decomposition | https://openreview.net/forum?id=1FvkSpWosOl | https://openreview.net/pdf?id=1FvkSpWosOl | iclr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 8.42181519e-02 5.34381829e-02 -1.57624319e-01 -4.85849291e-01
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26ccf15d-6a0d-48c6-ad7b-ee23b13f1561 | an-animated-picture-says-at-least-a-thousand | 2109.12212 | null | https://arxiv.org/abs/2109.12212v2 | https://arxiv.org/pdf/2109.12212v2.pdf | An animated picture says at least a thousand words: Selecting Gif-based Replies in Multimodal Dialog | Online conversations include more than just text. Increasingly, image-based responses such as memes and animated gifs serve as culturally recognized and often humorous responses in conversation. However, while NLP has broadened to multimodal models, conversational dialog systems have largely focused only on generating text replies. Here, we introduce a new dataset of 1.56M text-gif conversation turns and introduce a new multimodal conversational model Pepe the King Prawn for selecting gif-based replies. We demonstrate that our model produces relevant and high-quality gif responses and, in a large randomized control trial of multiple models replying to real users, we show that our model replies with gifs that are significantly better received by the community. | ['David Jurgens', 'Xingyao Wang'] | 2021-09-24 | null | https://aclanthology.org/2021.findings-emnlp.276 | https://aclanthology.org/2021.findings-emnlp.276.pdf | findings-emnlp-2021-11 | ['multimodal-gif-dialog'] | ['natural-language-processing'] | [ 8.80625471e-02 3.15127015e-01 1.35988012e-01 -4.93902355e-01
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ab42fbe1-c389-45ad-8946-ec1d84605f97 | learning-to-refactor-action-and-co-occurrence-1 | 2206.11493 | null | https://arxiv.org/abs/2206.11493v1 | https://arxiv.org/pdf/2206.11493v1.pdf | Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization | The main challenge of Temporal Action Localization is to retrieve subtle human actions from various co-occurring ingredients, e.g., context and background, in an untrimmed video. While prior approaches have achieved substantial progress through devising advanced action detectors, they still suffer from these co-occurring ingredients which often dominate the actual action content in videos. In this paper, we explore two orthogonal but complementary aspects of a video snippet, i.e., the action features and the co-occurrence features. Especially, we develop a novel auxiliary task by decoupling these two types of features within a video snippet and recombining them to generate a new feature representation with more salient action information for accurate action localization. We term our method RefactorNet, which first explicitly factorizes the action content and regularizes its co-occurrence features, and then synthesizes a new action-dominated video representation. Extensive experimental results and ablation studies on THUMOS14 and ActivityNet v1.3 demonstrate that our new representation, combined with a simple action detector, can significantly improve the action localization performance. | ['Wei Tang', 'Nanning Zheng', 'Sanping Zhou', 'Le Wang', 'Kun Xia'] | 2022-06-23 | learning-to-refactor-action-and-co-occurrence | http://openaccess.thecvf.com//content/CVPR2022/html/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xia_Learning_To_Refactor_Action_and_Co-Occurrence_Features_for_Temporal_Action_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-localization'] | ['computer-vision'] | [ 6.21753931e-01 -4.27567333e-01 -4.95220274e-01 -1.31286308e-01
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4520e430-d3f6-447c-a75e-1963c9fe068a | augmenting-training-data-for-massive-semantic | null | null | https://aclanthology.org/2022.naacl-industry.19 | https://aclanthology.org/2022.naacl-industry.19.pdf | Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores | Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amounts of customer behavior data (e.g. queries, clicks, purchases) for training. However, behavioral data is limited in low-traffic e-commerce stores, impacting performance of these systems. In this paper, we present a technique that augments behavioral training data via query reformulation. We use the Aggregated Label eXtreme Multi-label Classification (AL-XMC) system (Shen et al., 2020) as an example semantic matching model and show via crowd-sourced human judgments that, when the training data is augmented through query reformulations, the quality of AL-XMC improves over a baseline that does not use query reformulation. We also show in online A/B tests that our method significantly improves business metrics for the AL-XMC model. | ['Jonathan May', 'Rahul Bhagat', 'Vishy Vishwanathan', 'Vaclav Petricek', 'Choon Teo', 'Shankar Vishwanath', 'Ashutosh Joshi'] | null | null | null | null | naacl-acl-2022-7 | ['extreme-multi-label-classification'] | ['methodology'] | [-5.21901138e-02 -3.47809821e-01 -3.73742938e-01 -9.40745056e-01
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30664257-3da6-48fc-8f47-2d8a02b6f199 | group-activity-prediction-with-sequential | 2008.02441 | null | https://arxiv.org/abs/2008.02441v1 | https://arxiv.org/pdf/2008.02441v1.pdf | Group Activity Prediction with Sequential Relational Anticipation Model | In this paper, we propose a novel approach to predict group activities given the beginning frames with incomplete activity executions. Existing action prediction approaches learn to enhance the representation power of the partial observation. However, for group activity prediction, the relation evolution of people's activity and their positions over time is an important cue for predicting group activity. To this end, we propose a sequential relational anticipation model (SRAM) that summarizes the relational dynamics in the partial observation and progressively anticipates the group representations with rich discriminative information. Our model explicitly anticipates both activity features and positions by two graph auto-encoders, aiming to learn a discriminative group representation for group activity prediction. Experimental results on two popularly used datasets demonstrate that our approach significantly outperforms the state-of-the-art activity prediction methods. | ['Yu Kong', 'Wentao Bao', 'Junwen Chen'] | 2020-08-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3803_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660579.pdf | eccv-2020-8 | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.14732373e-01 2.66798586e-01 -8.59574437e-01 -4.04686540e-01
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02e30c37-caf1-4fa4-88cc-e2b8ac974562 | skill-decision-transformer | 2301.13573 | null | https://arxiv.org/abs/2301.13573v1 | https://arxiv.org/pdf/2301.13573v1.pdf | Skill Decision Transformer | Recent work has shown that Large Language Models (LLMs) can be incredibly effective for offline reinforcement learning (RL) by representing the traditional RL problem as a sequence modelling problem (Chen et al., 2021; Janner et al., 2021). However many of these methods only optimize for high returns, and may not extract much information from a diverse dataset of trajectories. Generalized Decision Transformers (GDTs) (Furuta et al., 2021) have shown that utilizing future trajectory information, in the form of information statistics, can help extract more information from offline trajectory data. Building upon this, we propose Skill Decision Transformer (Skill DT). Skill DT draws inspiration from hindsight relabelling (Andrychowicz et al., 2017) and skill discovery methods to discover a diverse set of primitive behaviors, or skills. We show that Skill DT can not only perform offline state-marginal matching (SMM), but can discovery descriptive behaviors that can be easily sampled. Furthermore, we show that through purely reward-free optimization, Skill DT is still competitive with supervised offline RL approaches on the D4RL benchmark. The code and videos can be found on our project page: https://github.com/shyamsn97/skill-dt | ['Sebastian Risi', 'Shyam Sudhakaran'] | 2023-01-31 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.85899988e-01 -1.65787395e-02 -6.23130739e-01 -4.91379797e-02
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c01ec839-9e8d-45cb-9d1b-c6b7fac1d68e | accurate-2d-soft-segmentation-of-medical | 2007.14556 | null | https://arxiv.org/abs/2007.14556v3 | https://arxiv.org/pdf/2007.14556v3.pdf | Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision | Accurate lung lesion segmentation from Computed Tomography (CT) images is crucial to the analysis and diagnosis of lung diseases such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named Soft Mask which has richer and more accurate edge details description and better visualization and develop a universal automatic Soft Mask annotation pipeline to deal with different datasets correspondingly. Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special Detail Representation Transfer Module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images, and an adversarial training framework with Soft Mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies. | ['Xiaopeng Zhang', 'Jun Xiao', 'Weiliang Meng', 'Rongtao Xu', 'Changwei Wang', 'Shibiao Xu'] | 2020-07-29 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [ 3.22868794e-01 2.54736960e-01 -3.83136779e-01 -3.24662149e-01
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c206ea4a-70bb-486b-bfb7-4c27a39fa367 | multi-view-photometric-stereo-revisited | 2210.07670 | null | https://arxiv.org/abs/2210.07670v1 | https://arxiv.org/pdf/2210.07670v1.pdf | Multi-View Photometric Stereo Revisited | Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D acquisition of an object from images. Although popular methods for MVPS can provide outstanding results, they are often complex to execute and limited to isotropic material objects. To address such limitations, we present a simple, practical approach to MVPS, which works well for isotropic as well as other object material types such as anisotropic and glossy. The proposed approach in this paper exploits the benefit of uncertainty modeling in a deep neural network for a reliable fusion of photometric stereo (PS) and multi-view stereo (MVS) network predictions. Yet, contrary to the recently proposed state-of-the-art, we introduce neural volume rendering methodology for a trustworthy fusion of MVS and PS measurements. The advantage of introducing neural volume rendering is that it helps in the reliable modeling of objects with diverse material types, where existing MVS methods, PS methods, or both may fail. Furthermore, it allows us to work on neural 3D shape representation, which has recently shown outstanding results for many geometric processing tasks. Our suggested new loss function aims to fits the zero level set of the implicit neural function using the most certain MVS and PS network predictions coupled with weighted neural volume rendering cost. The proposed approach shows state-of-the-art results when tested extensively on several benchmark datasets. | ['Luc van Gool', 'Vittorio Ferrari', 'Carlos Oliveira', 'Suryansh Kumar', 'Berk Kaya'] | 2022-10-14 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 2.91555643e-01 -4.05287892e-02 5.38430095e-01 -4.20846105e-01
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584f3d33-9317-4349-a2ae-d83139a46e7c | transparent-interpretation-with-knockouts | 2011.00639 | null | https://arxiv.org/abs/2011.00639v3 | https://arxiv.org/pdf/2011.00639v3.pdf | Model-Agnostic Explanations using Minimal Forcing Subsets | How can we find a subset of training samples that are most responsible for a specific prediction made by a complex black-box machine learning model? More generally, how can we explain the model's decisions to end-users in a transparent way? We propose a new model-agnostic algorithm to identify a minimal set of training samples that are indispensable for a given model's decision at a particular test point, i.e., the model's decision would have changed upon the removal of this subset from the training dataset. Our algorithm identifies such a set of "indispensable" samples iteratively by solving a constrained optimization problem. Further, we speed up the algorithm through efficient approximations and provide theoretical justification for its performance. To demonstrate the applicability and effectiveness of our approach, we apply it to a variety of tasks including data poisoning detection, training set debugging and understanding loan decisions. The results show that our algorithm is an effective and easy-to-comprehend tool that helps to better understand local model behavior, and therefore facilitates the adoption of machine learning in domains where such understanding is a requisite. | ['Joydeep Ghosh', 'Xing Han'] | 2020-11-01 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.37821525e-01 7.34703913e-02 -5.08626342e-01 -6.28326893e-01
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6f47603f-10e9-4453-a2d7-844e83196628 | fitness-for-duty-classification-using | 2304.11858 | null | https://arxiv.org/abs/2304.11858v1 | https://arxiv.org/pdf/2304.11858v1.pdf | Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images | Fitness for Duty (FFD) techniques detects whether a subject is Fit to perform their work safely, which means no reduced alertness condition and security, or if they are Unfit, which means alertness condition reduced by sleepiness or consumption of alcohol and drugs. Human iris behaviour provides valuable information to predict FFD since pupil and iris movements are controlled by the central nervous system and are influenced by illumination, fatigue, alcohol, and drugs. This work aims to classify FFD using sequences of 8 iris images and to extract spatial and temporal information using Convolutional Neural Networks (CNN) and Long Short Term Memory Networks (LSTM). Our results achieved a precision of 81.4\% and 96.9\% for the prediction of Fit and Unfit subjects, respectively. The results also show that it is possible to determine if a subject is under alcohol, drug, and sleepiness conditions. Sleepiness can be identified as the most difficult condition to be determined. This system opens a different insight into iris biometric applications. | ['Juan E. Tapia', 'Daniel P. Benalcazar', 'Pamela C. Zurita'] | 2023-04-24 | null | null | null | null | ['temporal-sequences'] | ['reasoning'] | [ 1.37267590e-01 -2.83898145e-01 -3.63925666e-01 -3.09223592e-01
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11042fae-80d4-4863-8345-5574e91b4240 | nonlinear-unmixing-of-hyperspectral-images | 1310.8612 | null | http://arxiv.org/abs/1310.8612v1 | http://arxiv.org/pdf/1310.8612v1.pdf | Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization | Incorporating spatial information into hyperspectral unmixing procedures has
been shown to have positive effects, due to the inherent spatial-spectral
duality in hyperspectral scenes. Current research works that consider spatial
information are mainly focused on the linear mixing model. In this paper, we
investigate a variational approach to incorporating spatial correlation into a
nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing
kernel Hilbert spaces, associated with an $\ell_1$ local variation norm as the
spatial regularizer, is derived. Experimental results, with both synthetic and
real data, illustrate the effectiveness of the proposed scheme. | ['Alfred O. Hero III', 'Cédric Richard', 'Jie Chen'] | 2013-10-31 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.32267570e-01 -4.91748720e-01 -1.11647040e-01 -1.64303526e-01
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37a706ba-9e90-48da-b238-a310ba5add63 | focused-meeting-summarization-via | 1606.07849 | null | http://arxiv.org/abs/1606.07849v1 | http://arxiv.org/pdf/1606.07849v1.pdf | Focused Meeting Summarization via Unsupervised Relation Extraction | We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarization task and show that it outperforms unsupervised utterance-level
extractive summarization baselines as well as an existing generic
relation-extraction-based summarization method. Moreover, our approach produces
summaries competitive with those generated by supervised methods in terms of
the standard ROUGE score. | ['Lu Wang', 'Claire Cardie'] | 2016-06-24 | focused-meeting-summarization-via-1 | https://aclanthology.org/W12-1642 | https://aclanthology.org/W12-1642.pdf | ws-2012-7 | ['meeting-summarization'] | ['natural-language-processing'] | [ 8.19777429e-01 1.04782486e+00 -6.05078697e-01 -3.13112140e-01
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9941b45c-3a54-475e-94fe-4707fbf14fc2 | selective-sparse-sampling-for-fine-grained | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Ding_Selective_Sparse_Sampling_for_Fine-Grained_Image_Recognition_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ding_Selective_Sparse_Sampling_for_Fine-Grained_Image_Recognition_ICCV_2019_paper.pdf | Selective Sparse Sampling for Fine-Grained Image Recognition | Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e.g., beaks for bird species). Conventional approaches crop local regions and learn detailed representation from those regions, but suffer from the fixed number of parts and missing of surrounding context. In this paper, we propose a simple yet effective framework, called Selective Sparse Sampling, to capture diverse and fine-grained details. The framework is implemented using Convolutional Neural Networks, referred to as Selective Sparse Sampling Networks (S3Ns). With image-level supervision, S3Ns collect peaks, i.e., local maximums, from class response maps to estimate informative, receptive fields and learn a set of sparse attention for capturing fine-detailed visual evidence as well as preserving context. The evidence is selectively sampled to extract discriminative and complementary features, which significantly enrich the learned representation and guide the network to discover more subtle cues. Extensive experiments and ablation studies show that the proposed method consistently outperforms the state-of-the-art methods on challenging benchmarks including CUB-200-2011, FGVC-Aircraft, and Stanford Cars.
| [' Jianbin Jiao', ' Qixiang Ye', ' Yi Zhu', ' Yanzhao Zhou', 'Yao Ding'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 3.56600732e-01 -3.57907712e-01 -5.29363692e-01 -6.18955135e-01
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48c089f9-0b77-46fb-a419-d792e6f3ec39 | generalizable-synthetic-image-detection-via | 2305.13800 | null | https://arxiv.org/abs/2305.13800v1 | https://arxiv.org/pdf/2305.13800v1.pdf | Generalizable Synthetic Image Detection via Language-guided Contrastive Learning | The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises significant concerns regarding the authenticity of images. Though many forensic algorithms have been developed for detecting synthetic images, their performance, especially the generalization capability, is still far from being adequate to cope with the increasing number of synthetic models. In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem. We first augment the training images with carefully-designed textual labels, enabling us to use a joint image-text contrastive learning for the forensic feature extraction. In addition, we formulate the synthetic image detection as an identification problem, which is vastly different from the traditional classification-based approaches. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models and delivers promising performance that far exceeds state-of-the-art competitors by +22.66% accuracy and +15.24% AUC. The code is available at https://github.com/HighwayWu/LASTED. | ['Shile Zhang', 'Jiantao Zhou', 'Haiwei Wu'] | 2023-05-23 | null | null | null | null | ['synthetic-image-detection'] | ['computer-vision'] | [ 4.98068094e-01 -6.56482130e-02 -4.42753509e-02 -6.45944178e-02
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fa1184a1-0a9e-4e81-8f7d-3d59adc17b62 | autoencoders-as-cross-modal-teachers-can | 2212.08320 | null | https://arxiv.org/abs/2212.08320v2 | https://arxiv.org/pdf/2212.08320v2.pdf | Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning? | The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages. This promotes the potential of utilizing models pretrained with data more than 3D as teachers for cross-modal knowledge transferring. In this paper, we revisit masked modeling in a unified fashion of knowledge distillation, and we show that foundational Transformers pretrained with 2D images or natural languages can help self-supervised 3D representation learning through training Autoencoders as Cross-Modal Teachers (ACT). The pretrained Transformers are transferred as cross-modal 3D teachers using discrete variational autoencoding self-supervision, during which the Transformers are frozen with prompt tuning for better knowledge inheritance. The latent features encoded by the 3D teachers are used as the target of masked point modeling, wherein the dark knowledge is distilled to the 3D Transformer students as foundational geometry understanding. Our ACT pretrained 3D learner achieves state-of-the-art generalization capacity across various downstream benchmarks, e.g., 88.21% overall accuracy on ScanObjectNN. Codes have been released at https://github.com/RunpeiDong/ACT. | ['Kaisheng Ma', 'Li Yi', 'Zheng Ge', 'Jianjian Sun', 'Junbo Zhang', 'Linfeng Zhang', 'Zekun Qi', 'Runpei Dong'] | 2022-12-16 | null | null | null | null | ['3d-point-cloud-classification', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-2.06263870e-01 5.42877197e-01 -3.03800195e-01 -4.34047937e-01
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5a539e04-abf2-489f-bb1a-406961216c9b | generating-informative-and-diverse | 1809.05972 | null | http://arxiv.org/abs/1809.05972v5 | http://arxiv.org/pdf/1809.05972v5.pdf | Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization | Responses generated by neural conversational models tend to lack
informativeness and diversity. We present Adversarial Information Maximization
(AIM), an adversarial learning strategy that addresses these two related but
distinct problems. To foster response diversity, we leverage adversarial
training that allows distributional matching of synthetic and real responses.
To improve informativeness, our framework explicitly optimizes a variational
lower bound on pairwise mutual information between query and response.
Empirical results from automatic and human evaluations demonstrate that our
methods significantly boost informativeness and diversity. | ['Xiujun Li', 'Chris Brockett', 'Yizhe Zhang', 'Jianfeng Gao', 'Zhe Gan', 'Michel Galley', 'Bill Dolan'] | 2018-09-16 | generating-informative-and-diverse-1 | http://papers.nips.cc/paper/7452-generating-informative-and-diverse-conversational-responses-via-adversarial-information-maximization | http://papers.nips.cc/paper/7452-generating-informative-and-diverse-conversational-responses-via-adversarial-information-maximization.pdf | neurips-2018-12 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 3.06746989e-01 2.65209526e-01 -1.29668266e-02 -8.23479354e-01
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4344736f-0d7a-4130-8e8b-0f3cfd2efc8c | video-highlights-detection-and-summarization | null | null | https://aclanthology.org/W17-4501 | https://aclanthology.org/W17-4501.pdf | Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments | With the prevalence of video sharing, there are increasing demands for automatic video digestion such as highlight detection. Recently, platforms with crowdsourced time-sync video comments have emerged worldwide, providing a good opportunity for highlight detection. However, this task is non-trivial: (1) time-sync comments often lag behind their corresponding shot; (2) time-sync comments are semantically sparse and noisy; (3) to determine which shots are highlights is highly subjective. The present paper aims to tackle these challenges by proposing a framework that (1) uses concept-mapped lexical-chains for lag-calibration; (2) models video highlights based on comment intensity and combination of emotion and concept concentration of each shot; (3) summarize each detected highlight using improved SumBasic with emotion and concept mapping. Experiments on large real-world datasets show that our highlight detection method and summarization method both outperform other benchmarks with considerable margins. | ['Chaomei Chen', 'Qing Ping'] | 2017-09-01 | null | null | null | ws-2017-9 | ['highlight-detection'] | ['computer-vision'] | [ 3.14665250e-02 -5.13218224e-01 -4.10650790e-01 1.27346188e-01
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f86237a8-c98e-4def-9b35-b39efefea691 | fine-grained-hand-gesture-recognition-in | 2109.02917 | null | https://arxiv.org/abs/2109.02917v1 | https://arxiv.org/pdf/2109.02917v1.pdf | Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene | This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH". Generally, current datasets are not focused on: (i) fine-grained actions; and (ii) data mismatch between different viewpoints, which are available under realistic settings. To address the aforementioned issues, the MFH dataset is proposed to contain a total of 731147 samples obtained by different camera views in 6 non-overlapping locations. Additionally, each sample belongs to one of seven steps introduced by the World Health Organization (WHO). As a minor contribution, inspired by advances in fine-grained image recognition and distribution adaptation, this paper recommends using the self-supervised learning method to handle these preceding problems. The extensive experiments on the benchmarking MFH dataset show that the introduced method yields competitive performance in both the Accuracy and the Macro F1-score. The code and the MFH dataset are available at https://github.com/willogy-team/hand-gesture-recognition-smc2021. | ['Quang D. Tran', 'An T. Duong', 'Duy Nguyen', 'Vi C. Pham', 'Tuong Do', 'Huy Q. Vo'] | 2021-09-07 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 1.48424149e-01 -4.79804099e-01 -5.26017308e-01 -2.88879663e-01
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dde5c71b-a8ec-4c23-a8f5-ec3bdc0ea4f5 | data-fusion-on-motion-and-magnetic-sensors | 1711.07328 | null | http://arxiv.org/abs/1711.07328v1 | http://arxiv.org/pdf/1711.07328v1.pdf | Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living | Several types of sensors have been available in off-the-shelf mobile devices,
including motion, magnetic, vision, acoustic, and location sensors. This paper
focuses on the fusion of the data acquired from motion and magnetic sensors,
i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of
Activities of Daily Living (ADL) using pattern recognition techniques. The
system developed in this study includes data acquisition, data processing, data
fusion, and artificial intelligence methods. Artificial Neural Networks (ANN)
are included in artificial intelligence methods, which are used in this study
for the recognition of ADL. The purpose of this study is the creation of a new
method using ANN for the identification of ADL, comparing three types of ANN,
in order to achieve results with a reliable accuracy. The best accuracy was
obtained with Deep Learning, which, after the application of the L2
regularization and normalization techniques on the sensors data, reports an
accuracy of 89.51%. | ['Francisco Flórez-Revuelta', 'Ivan Miguel Pires', 'Susanna Spinsante', 'Nuno Pombo', 'Nuno M. Garcia'] | 2017-10-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.03241645e-01 -3.13758999e-01 -2.05981925e-01 -2.32823014e-01
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85d0b1a7-37dd-4c17-8341-bb49a1b71559 | is-multi-modal-vision-supervision-beneficial | 2302.05016 | null | https://arxiv.org/abs/2302.05016v2 | https://arxiv.org/pdf/2302.05016v2.pdf | Is Multimodal Vision Supervision Beneficial to Language? | Vision (image and video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality supervision. In this paper, we explore if the language representations trained using vision supervision perform better than vanilla language representations on Natural Language Understanding and commonsense reasoning benchmarks. We experiment with a diverse set of image-text models such as ALBEF, BLIP, METER and video-text models like ALPRO, Frozen-in-Time (FiT), VIOLET. We compare the performance of language representations of stand-alone text encoders of these models to the language representations of text encoders learnt through vision supervision. Our experiments suggest that vanilla language representations show superior performance on most of the tasks. These results shed light on the current drawbacks of the vision-language models. | ['Vasudev Lal', 'Avinash Madasu'] | 2023-02-10 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 4.04739715e-02 4.14712839e-02 -2.80270368e-01 -4.82740372e-01
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787198ee-8420-4cdc-80c5-e9d2682bfa6f | relate-physically-plausible-multi-object | 2007.01272 | null | https://arxiv.org/abs/2007.01272v2 | https://arxiv.org/pdf/2007.01272v2.pdf | RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces | We present RELATE, a model that learns to generate physically plausible scenes and videos of multiple interacting objects. Similar to other generative approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE combines an object-centric GAN formulation with a model that explicitly accounts for correlations between individual objects. This allows the model to generate realistic scenes and videos from a physically-interpretable parameterization. Furthermore, we show that modeling the object correlation is necessary to learn to disentangle object positions and identity. We find that RELATE is also amenable to physically realistic scene editing and that it significantly outperforms prior art in object-centric scene generation in both synthetic (CLEVR, ShapeStacks) and real-world data (cars). In addition, in contrast to state-of-the-art methods in object-centric generative modeling, RELATE also extends naturally to dynamic scenes and generates videos of high visual fidelity. Source code, datasets and more results are available at http://geometry.cs.ucl.ac.uk/projects/2020/relate/. | ['Aron Monszpart', 'Sebastien Ehrhardt', 'Niloy Mitra', 'Martin Engelcke', 'Ingmar Posner', 'Andrea Vedaldi', 'Oliver Groth'] | 2020-07-02 | null | http://proceedings.neurips.cc/paper/2020/hash/806beafe154032a5b818e97b4420ad98-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/806beafe154032a5b818e97b4420ad98-Paper.pdf | neurips-2020-12 | ['scene-generation'] | ['computer-vision'] | [ 1.62914142e-01 2.37580433e-01 3.62718225e-01 -2.89867222e-01
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40d44154-049c-4361-81ec-fa080bd2fca7 | robust-probabilistic-time-series-forecasting | 2202.11910 | null | https://arxiv.org/abs/2202.11910v1 | https://arxiv.org/pdf/2202.11910v1.pdf | Robust Probabilistic Time Series Forecasting | Probabilistic time series forecasting has played critical role in decision-making processes due to its capability to quantify uncertainties. Deep forecasting models, however, could be prone to input perturbations, and the notion of such perturbations, together with that of robustness, has not even been completely established in the regime of probabilistic forecasting. In this work, we propose a framework for robust probabilistic time series forecasting. First, we generalize the concept of adversarial input perturbations, based on which we formulate the concept of robustness in terms of bounded Wasserstein deviation. Then we extend the randomized smoothing technique to attain robust probabilistic forecasters with theoretical robustness certificates against certain classes of adversarial perturbations. Lastly, extensive experiments demonstrate that our methods are empirically effective in enhancing the forecast quality under additive adversarial attacks and forecast consistency under supplement of noisy observations. | ['Yuyang Wang', 'Ernest K. Ryu', 'Youngsuk Park', 'Taeho Yoon'] | 2022-02-24 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [ 1.23467371e-01 6.59950599e-02 4.66548741e-01 -2.91415304e-01
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c2266741-0eb2-4ac0-8c63-80124da13ad5 | adaptive-quantum-state-tomography-with-neural | 1812.06693 | null | http://arxiv.org/abs/1812.06693v1 | http://arxiv.org/pdf/1812.06693v1.pdf | Adaptive Quantum State Tomography with Neural Networks | Quantum State Tomography is the task of determining an unknown quantum state
by making measurements on identical copies of the state. Current algorithms are
costly both on the experimental front -- requiring vast numbers of measurements
-- as well as in terms of the computational time to analyze those measurements.
In this paper, we address the problem of analysis speed and flexibility,
introducing \textit{Neural Adaptive Quantum State Tomography} (NA-QST), a
machine learning based algorithm for quantum state tomography that adapts
measurements and provides orders of magnitude faster processing while retaining
state-of-the-art reconstruction accuracy. Our algorithm is inspired by particle
swarm optimization and Bayesian particle-filter based adaptive methods, which
we extend and enhance using neural networks. The resampling step, in which a
bank of candidate solutions -- particles -- is refined, is in our case learned
directly from data, removing the computational bottleneck of standard methods.
We successfully replace the Bayesian calculation that requires computational
time of $O(\mathrm{poly}(n))$ with a learned heuristic whose time complexity
empirically scales as $O(\log(n))$ with the number of copies measured $n$,
while retaining the same reconstruction accuracy. This corresponds to a factor
of a million speedup for $10^7$ copies measured. We demonstrate that our
algorithm learns to work with basis, symmetric informationally complete (SIC),
as well as other types of POVMs. We discuss the value of measurement adaptivity
for each POVM type, demonstrating that its effect is significant only for basis
POVMs. Our algorithm can be retrained within hours on a single laptop for a
two-qubit situation, which suggests a feasible time-cost when extended to
larger systems. It can also adapt to a subset of possible states, a choice of
the type of measurement, and other experimental details. | ['Yihui Quek', 'Stanislav Fort', 'Hui Khoon Ng'] | 2018-12-17 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 5.30850828e-01 3.38685326e-02 2.60505259e-01 -2.19192713e-01
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4ba94737-b6d8-4ee7-acbe-d3fa74b81b3d | efficient-convolutional-neural-networks-for-4 | 2001.04537 | null | https://arxiv.org/abs/2001.04537v3 | https://arxiv.org/pdf/2001.04537v3.pdf | Deep convolutional neural networks for multi-planar lung nodule detection: improvement in small nodule identification | Objective: In clinical practice, small lung nodules can be easily overlooked by radiologists. The paper aims to provide an efficient and accurate detection system for small lung nodules while keeping good performance for large nodules. Methods: We propose a multi-planar detection system using convolutional neural networks. The 2-D convolutional neural network model, U-net++, was trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are combined. For false positive reduction, we apply 3-D multi-scale dense convolutional neural networks to efficiently remove false positive candidates. We use the public LIDC-IDRI dataset which includes 888 CT scans with 1186 nodules annotated by four radiologists. Results: After ten-fold cross-validation, our proposed system achieves a sensitivity of 94.2% with 1.0 false positive/scan and a sensitivity of 96.0% with 2.0 false positives/scan. Although it is difficult to detect small nodules (i.e. < 6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall false positive rate of 1.0 (2.0) false positives/scan. At the nodule candidate detection stage, results show that a multi-planar method is capable to detect more nodules compared to using a single plane. Conclusion: Our approach achieves good performance not only for small nodules, but also for large lesions on this dataset. This demonstrates the effectiveness and efficiency of our developed CAD system for lung nodule detection. Significance: The proposed system could provide support for radiologists on early detection of lung cancer. | ['Raymond N. J. Veldhuis', 'Xiaonan Cui', 'Xueping Jing', 'Sunyi Zheng', 'Peter M. A. van Ooijen', 'Matthijs Oudkerk', 'Ludo J. Cornelissen'] | 2020-01-13 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.96624953e-02 4.61457282e-01 -3.12944263e-01 2.10742354e-01
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e246eeaf-e3e3-421e-b748-1767f9b398c1 | space-invariant-projection-in-streaming | 2303.06293 | null | https://arxiv.org/abs/2303.06293v1 | https://arxiv.org/pdf/2303.06293v1.pdf | Space-Invariant Projection in Streaming Network Embedding | Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable. An exact threshold size of these new nodes, below which the node embedding space will be predicatively maintained, however, is rarely considered in either theory or experiment. From the view of matrix perturbation theory, a threshold of the maximum number of new nodes that keep the node embedding space approximately equivalent is analytically provided and empirically validated. It is therefore theoretically guaranteed that as the size of newly arriving nodes is below this threshold, embeddings of these new nodes can be quickly derived from embeddings of original nodes. A generation framework, Space-Invariant Projection (SIP), is accordingly proposed to enables arbitrary static MF-based embedding schemes to embed new nodes in dynamics networks fast. The time complexity of SIP is linear with the network size. By combining SIP with four state-of-the-art MF-based schemes, we show that SIP exhibits not only wide adaptability but also strong empirical performance in terms of efficiency and efficacy on the node classification task in three real datasets. | ['Jichang Zhao', 'Huiwen Wang', 'Yanwen Zhang'] | 2023-03-11 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-4.80143055e-02 3.83370101e-01 -1.63612336e-01 5.52481301e-02
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c65a5f3d-a92e-4643-9c34-c4d0cfb167fb | deepfh-segmentations-for-superpixel-based | 2108.03503 | null | https://arxiv.org/abs/2108.03503v1 | https://arxiv.org/pdf/2108.03503v1.pdf | DeepFH Segmentations for Superpixel-based Object Proposal Refinement | Class-agnostic object proposal generation is an important first step in many object detection pipelines. However, object proposals of modern systems are rather inaccurate in terms of segmentation and only roughly adhere to object boundaries. Since typical refinement steps are usually not applicable to thousands of proposals, we propose a superpixel-based refinement system for object proposal generation systems. Utilizing precise superpixels and superpixel pooling on deep features, we refine initial coarse proposals in an end-to-end learned system. Furthermore, we propose a novel DeepFH segmentation, which enriches the classic Felzenszwalb and Huttenlocher (FH) segmentation with deep features leading to improved segmentation results and better object proposal refinements. On the COCO dataset with LVIS annotations, we show that our refinement based on DeepFH superpixels outperforms state-of-the-art methods and leads to more precise object proposals. | ['Simone Frintrop', 'Christian Wilms'] | 2021-08-07 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 5.38927242e-02 3.78878206e-01 -5.94146885e-02 -6.57719076e-01
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ee42c4f5-1ae7-400e-8fa8-da2ecc8a0cf6 | efficient-speech-translation-with-dynamic | 2210.16264 | null | https://arxiv.org/abs/2210.16264v2 | https://arxiv.org/pdf/2210.16264v2.pdf | Efficient Speech Translation with Dynamic Latent Perceivers | Transformers have been the dominant architecture for Speech Translation in recent years, achieving significant improvements in translation quality. Since speech signals are longer than their textual counterparts, and due to the quadratic complexity of the Transformer, a down-sampling step is essential for its adoption in Speech Translation. Instead, in this research, we propose to ease the complexity by using a Perceiver encoder to map the speech inputs to a fixed-length latent representation. Furthermore, we introduce a novel way of training Perceivers, with Dynamic Latent Access (DLA), unlocking larger latent spaces without any additional computational overhead. Speech-to-Text Perceivers with DLA can match the performance of Transformer baselines across three language pairs in MuST-C. Finally, a DLA-trained model is easily adaptable to DLA at inference, and can be flexibly deployed with various computational budgets, without significant drops in translation quality. | ['Marta R. Costa-jussà', 'José A. R. Fonollosa', 'Gerard I. Gállego', 'Ioannis Tsiamas'] | 2022-10-28 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.96080202e-01 3.67540658e-01 -1.90801948e-01 -4.28129733e-01
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e75da0e5-a3d0-4f50-bffb-c7798e246526 | sepit-approaching-a-single-channel-speech | 2205.11801 | null | https://arxiv.org/abs/2205.11801v4 | https://arxiv.org/pdf/2205.11801v4.pdf | SepIt: Approaching a Single Channel Speech Separation Bound | We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech. Using the bound, we are able to show that while the recent methods have made significant progress for a few speakers, there is room for improvement for five and ten speakers. We then introduce a Deep neural network, SepIt, that iteratively improves the different speakers' estimation. At test time, SpeIt has a varying number of iterations per test sample, based on a mutual information criterion that arises from our analysis. In an extensive set of experiments, SepIt outperforms the state-of-the-art neural networks for 2, 3, 5, and 10 speakers. | ['Lior Wolf', 'Eliya Nachmani', 'Shahar Lutati'] | 2022-05-24 | null | null | null | null | ['audio-source-separation', 'speech-separation', 'multi-speaker-source-separation'] | ['audio', 'speech', 'speech'] | [ 1.51777223e-01 2.75203526e-01 -1.60541430e-01 -4.02749658e-01
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a0908b59-fd03-49b0-8e5e-9438d2fe8295 | stacked-dense-u-nets-with-dual-transformers | 1812.01936 | null | http://arxiv.org/abs/1812.01936v1 | http://arxiv.org/pdf/1812.01936v1.pdf | Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment | Face Analysis Project on MXNet | ['Stefanos Zafeiriou', 'Niannan Xue', 'Jia Guo', 'Jiankang Deng'] | 2018-12-05 | null | null | null | null | ['robust-face-alignment', 'robust-face-recognition'] | ['computer-vision', 'computer-vision'] | [-3.57896090e-01 2.76142210e-02 6.74940765e-01 -7.16409683e-01
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e2588e2b-1407-480e-977f-859828e25329 | joint-face-completion-and-super-resolution | 2003.00255 | null | https://arxiv.org/abs/2003.00255v2 | https://arxiv.org/pdf/2003.00255v2.pdf | Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning | Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned forms of image degradation often coexist. Therefore, it is important to design a model that can repair LR occluded images simultaneously. This paper proposes a multi-scale feature graph generative adversarial network (MFG-GAN) to implement the face restoration of images in which both degradation modes coexist, and also to repair images with a single type of degradation. Based on the GAN, the MFG-GAN integrates the graph convolution and feature pyramid network to restore occluded low-resolution face images to non-occluded high-resolution face images. The MFG-GAN uses a set of customized losses to ensure that high-quality images are generated. In addition, we designed the network in an end-to-end format. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms state-of-the-art methods in performing face super-resolution (up to 4x or 8x) and face completion simultaneously. Cross-database testing also revealed that the proposed approach has good generalizability. | ['Zhilei Liu', 'Baoyuan Wu', 'Le Li', 'Cuicui Zhang', 'Yunpeng Wu'] | 2020-02-29 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 2.91496247e-01 1.04515053e-01 3.53684157e-01 -3.92416090e-01
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18d34dc9-390c-4cce-9686-825d28838b08 | global-inference-for-bridging-anaphora | null | null | https://aclanthology.org/N13-1111 | https://aclanthology.org/N13-1111.pdf | Global Inference for Bridging Anaphora Resolution | null | ['Yufang Hou', 'Katja Markert', 'Michael Strube'] | 2013-06-01 | null | null | null | naacl-2013-6 | ['bridging-anaphora-resolution'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c81267aa-efb4-4a50-b095-9ce2d97faee1 | infinitenature-zero-learning-perpetual-view | 2207.11148 | null | https://arxiv.org/abs/2207.11148v1 | https://arxiv.org/pdf/2207.11148v1.pdf | InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images | We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality. | ['Angjoo Kanazawa', 'Noah Snavely', 'Qianqian Wang', 'Zhengqi Li'] | 2022-07-22 | null | null | null | null | ['perpetual-view-generation'] | ['computer-vision'] | [ 4.41942424e-01 1.99591517e-01 1.68298259e-01 -1.47104368e-01
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aa445478-749b-41ea-b83b-7e8bdbdf4ac5 | shadownav-crater-based-localization-for | 2301.04630 | null | https://arxiv.org/abs/2301.04630v1 | https://arxiv.org/pdf/2301.04630v1.pdf | ShadowNav: Crater-Based Localization for Nighttime and Permanently Shadowed Region Lunar Navigation | There has been an increase in interest in missions that drive significantly longer distances per day than what has currently been performed. Further, some of these proposed missions require autonomous driving and absolute localization in darkness. For example, the Endurance A mission proposes to drive 1200km of its total traverse at night. The lack of natural light available during such missions limits what can be used as visual landmarks and the range at which landmarks can be observed. In order for planetary rovers to traverse long ranges, onboard absolute localization is critical to the ability of the rover to maintain its planned trajectory and avoid known hazardous regions. Currently, to accomplish absolute localization, a ground in the loop (GITL) operation is performed wherein a human operator matches local maps or images from onboard with orbital images and maps. This GITL operation limits the distance that can be driven in a day to a few hundred meters, which is the distance that the rover can maintain acceptable localization error via relative methods. Previous work has shown that using craters as landmarks is a promising approach for performing absolute localization on the moon during the day. In this work we present a method of absolute localization that utilizes craters as landmarks and matches detected crater edges on the surface with known craters in orbital maps. We focus on a localization method based on a perception system which has an external illuminator and a stereo camera. We evaluate (1) both monocular and stereo based surface crater edge detection techniques, (2) methods of scoring the crater edge matches for optimal localization, and (3) localization performance on simulated Lunar surface imagery at night. We demonstrate that this technique shows promise for maintaining absolute localization error of less than 10m required for most planetary rover missions. | ['Deegan Atha', 'Larry Matthies', 'John Elliott', 'Shreyansh Daftry', 'Hiro Ono', 'R. Michael Swan', 'Abhishek Cauligi'] | 2023-01-11 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [-5.57703227e-02 -2.84666151e-01 1.52862687e-02 -3.81550133e-01
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75fc5fa2-1624-4269-bc94-85402f73f76a | stranding-risk-for-underactuated-vessels-in | 2307.01917 | null | https://arxiv.org/abs/2307.01917v1 | https://arxiv.org/pdf/2307.01917v1.pdf | Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers | Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with high probability despite forecast errors. However, these results do not consider the critical aspect of safety of such vessels: because of their low propulsion which is much smaller than the magnitude of currents, they might end up in currents that inevitably push them into unsafe areas such as shallow areas, garbage patches, and shipping lanes. In this work, we first investigate the risk of stranding for free-floating vessels in the Northeast Pacific. We find that at least 5.04% would strand within 90 days. Next, we encode the unsafe sets as hard constraints into Hamilton-Jacobi Multi-Time Reachability (HJ-MTR) to synthesize a feedback policy that is equivalent to re-planning at each time step at low computational cost. While applying this policy closed-loop guarantees safe operation when the currents are known, in realistic situations only imperfect forecasts are available. We demonstrate the safety of our approach in such realistic situations empirically with large-scale simulations of a vessel navigating in high-risk regions in the Northeast Pacific. We find that applying our policy closed-loop with daily re-planning on new forecasts can ensure safety with high probability even under forecast errors that exceed the maximal propulsion. Our method significantly improves safety over the baselines and still achieves a timely arrival of the vessel at the destination. | ['Claire J. Tomlin', 'Pierre F. J. Lermusiaux', 'Manan Doshi', 'Hanna Krasowski', 'Marius Wiggert', 'Andreas Doering'] | 2023-07-04 | null | null | null | null | ['navigate'] | ['reasoning'] | [-3.52977961e-01 3.46678376e-01 2.19153240e-01 1.13967188e-01
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480f5e4d-d98a-4b96-9b75-8d03aaf281dd | lic-gan-language-information-conditioned | 2306.01937 | null | https://arxiv.org/abs/2306.01937v1 | https://arxiv.org/pdf/2306.01937v1.pdf | LIC-GAN: Language Information Conditioned Graph Generative GAN Model | Deep generative models for Natural Language data offer a new angle on the problem of graph synthesis: by optimizing differentiable models that directly generate graphs, it is possible to side-step expensive search procedures in the discrete and vast space of possible graphs. We introduce LIC-GAN, an implicit, likelihood-free generative model for small graphs that circumvents the need for expensive graph matching procedures. Our method takes as input a natural language query and using a combination of language modelling and Generative Adversarial Networks (GANs) and returns a graph that closely matches the description of the query. We combine our approach with a reward network to further enhance the graph generation with desired properties. Our experiments, show that LIC-GAN does well on metrics such as PropMatch and Closeness getting scores of 0.36 and 0.48. We also show that LIC-GAN performs as good as ChatGPT, with ChatGPT getting scores of 0.40 and 0.42. We also conduct a few experiments to demonstrate the robustness of our method, while also highlighting a few interesting caveats of the model. | ['Abishek Sridhar', 'Arnhav Datar', 'Robert Lo'] | 2023-06-02 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 7.99940005e-02 7.05617905e-01 4.12976630e-02 -2.20574901e-01
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23fb9427-93a1-4498-942b-7e77d747e197 | 3ddepthnet-point-cloud-guided-depth | 2003.09175 | null | https://arxiv.org/abs/2003.09175v1 | https://arxiv.org/pdf/2003.09175v1.pdf | 3dDepthNet: Point Cloud Guided Depth Completion Network for Sparse Depth and Single Color Image | In this paper, we propose an end-to-end deep learning network named 3dDepthNet, which produces an accurate dense depth image from a single pair of sparse LiDAR depth and color image for robotics and autonomous driving tasks. Based on the dimensional nature of depth images, our network offers a novel 3D-to-2D coarse-to-fine dual densification design that is both accurate and lightweight. Depth densification is first performed in 3D space via point cloud completion, followed by a specially designed encoder-decoder structure that utilizes the projected dense depth from 3D completion and the original RGB-D images to perform 2D image completion. Experiments on the KITTI dataset show our network achieves state-of-art accuracy while being more efficient. Ablation and generalization tests prove that each module in our network has positive influences on the final results, and furthermore, our network is resilient to even sparser depth. | ['Zhe Zhang', 'Feng Zheng', 'Rui Xiang', 'Huapeng Su'] | 2020-03-20 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-1.36931539e-01 3.04798275e-01 -4.12173457e-02 -5.74159741e-01
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536f5def-ff8b-473d-be67-9623266ceaf1 | hierarchical-imitation-and-reinforcement | 1803.00590 | null | http://arxiv.org/abs/1803.00590v2 | http://arxiv.org/pdf/1803.00590v2.pdf | Hierarchical Imitation and Reinforcement Learning | We study how to effectively leverage expert feedback to learn sequential
decision-making policies. We focus on problems with sparse rewards and long
time horizons, which typically pose significant challenges in reinforcement
learning. We propose an algorithmic framework, called hierarchical guidance,
that leverages the hierarchical structure of the underlying problem to
integrate different modes of expert interaction. Our framework can incorporate
different combinations of imitation learning (IL) and reinforcement learning
(RL) at different levels, leading to dramatic reductions in both expert effort
and cost of exploration. Using long-horizon benchmarks, including Montezuma's
Revenge, we demonstrate that our approach can learn significantly faster than
hierarchical RL, and be significantly more label-efficient than standard IL. We
also theoretically analyze labeling cost for certain instantiations of our
framework. | ['Hal Daumé III', 'Miroslav Dudík', 'Alekh Agarwal', 'Yisong Yue', 'Nan Jiang', 'Hoang M. Le'] | 2018-03-01 | hierarchical-imitation-and-reinforcement-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2290 | http://proceedings.mlr.press/v80/le18a/le18a.pdf | icml-2018-7 | ['montezumas-revenge'] | ['playing-games'] | [ 8.76116231e-02 2.25946128e-01 -7.22474396e-01 -3.89470495e-02
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c29041b9-c65d-4ca1-9f88-77c4a1ca625a | gans-and-closures-micro-macro-consistency-in | 2208.10715 | null | https://arxiv.org/abs/2208.10715v3 | https://arxiv.org/pdf/2208.10715v3.pdf | GANs and Closures: Micro-Macro Consistency in Multiscale Modeling | Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in many fields, from protein folding to materials discovery. These problems are often multiscale in nature: they can be described in terms of low-dimensional effective free energy surfaces parametrized by a small number of "slow" reaction coordinates; the remaining "fast" degrees of freedom populate an equilibrium measure on the reaction coordinate values. Sampling procedures for such problems are used to estimate effective free energy differences as well as ensemble averages with respect to the conditional equilibrium distributions; these latter averages lead to closures for effective reduced dynamic models. Over the years, enhanced sampling techniques coupled with molecular simulation have been developed. An intriguing analogy arises with the field of Machine Learning (ML), where Generative Adversarial Networks can produce high dimensional samples from low dimensional probability distributions. This sample generation returns plausible high dimensional space realizations of a model state, from information about its low-dimensional representation. In this work, we present an approach that couples physics-based simulations and biasing methods for sampling conditional distributions with ML-based conditional generative adversarial networks for the same task. The "coarse descriptors" on which we condition the fine scale realizations can either be known a priori, or learned through nonlinear dimensionality reduction. We suggest that this may bring out the best features of both approaches: we demonstrate that a framework that couples cGANs with physics-based enhanced sampling techniques can improve multiscale SDE dynamical systems sampling, and even shows promise for systems of increasing complexity. | ['Ioannis G. Kevrekidis', 'Andrew L. Ferguson', 'Juan M. Bello-Rivas', 'Ellis R. Crabtree'] | 2022-08-23 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 3.42999905e-01 1.31639794e-01 -1.22665659e-01 -1.50652915e-01
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f41125fb-1350-4897-b83d-bc0aa4111bcd | human-activity-recognition-in-an-open-world | 2212.12141 | null | https://arxiv.org/abs/2212.12141v1 | https://arxiv.org/pdf/2212.12141v1.pdf | Human Activity Recognition in an Open World | Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current state-of-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The protocol as an algorithm for reproducing experiments using the KOWL-718 benchmark will be publicly released with code and containers at https://github.com/prijatelj/human-activity-recognition-in-an-open-world. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released. | ['Walter J. Scheirer', 'Eric Robertson', 'Adam Kaufman', 'Christopher Funk', 'Ameya Shringi', 'Dawei Du', 'Jin Huang', 'Samuel Grieggs', 'Derek S. Prijatelj'] | 2022-12-23 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 1.33234322e-01 -3.43939066e-01 4.03126180e-01 -1.88497126e-01
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819b1fda-7342-40ca-8ccd-ae23e29c8f8a | multiple-instance-based-video-anomaly | 2007.01548 | null | https://arxiv.org/abs/2007.01548v2 | https://arxiv.org/pdf/2007.01548v2.pdf | Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding | In this paper, we propose a weakly supervised deep temporal encoding-decoding solution for anomaly detection in surveillance videos using multiple instance learning. The proposed approach uses both abnormal and normal video clips during the training phase which is developed in the multiple instance framework where we treat video as a bag and video clips as instances in the bag. Our main contribution lies in the proposed novel approach to consider temporal relations between video instances. We deal with video instances (clips) as a sequential visual data rather than independent instances. We employ a deep temporal and encoder network that is designed to capture spatial-temporal evolution of video instances over time. We also propose a new loss function that is smoother than similar loss functions recently presented in the computer vision literature, and therefore; enjoys faster convergence and improved tolerance to local minima during the training phase. The proposed temporal encoding-decoding approach with modified loss is benchmarked against the state-of-the-art in simulation studies. The results show that the proposed method performs similar to or better than the state-of-the-art solutions for anomaly detection in video surveillance applications. | ['Alireza Bab-Hadiashar', 'Ammar Mansoor Kamoona', 'Reza Hoseinnezhad', 'Amirali Khodadadian Gosta'] | 2020-07-03 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 2.46581405e-01 -9.31959301e-02 2.09644586e-02 -3.98998052e-01
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0b5a4f48-98d4-4000-ac9e-1b526f01b270 | metaformer-a-unified-meta-framework-for-fine | 2203.02751 | null | https://arxiv.org/abs/2203.02751v1 | https://arxiv.org/pdf/2203.02751v1.pdf | MetaFormer: A Unified Meta Framework for Fine-Grained Recognition | Fine-Grained Visual Classification(FGVC) is the task that requires recognizing the objects belonging to multiple subordinate categories of a super-category. Recent state-of-the-art methods usually design sophisticated learning pipelines to tackle this task. However, visual information alone is often not sufficient to accurately differentiate between fine-grained visual categories. Nowadays, the meta-information (e.g., spatio-temporal prior, attribute, and text description) usually appears along with the images. This inspires us to ask the question: Is it possible to use a unified and simple framework to utilize various meta-information to assist in fine-grained identification? To answer this problem, we explore a unified and strong meta-framework(MetaFormer) for fine-grained visual classification. In practice, MetaFormer provides a simple yet effective approach to address the joint learning of vision and various meta-information. Moreover, MetaFormer also provides a strong baseline for FGVC without bells and whistles. Extensive experiments demonstrate that MetaFormer can effectively use various meta-information to improve the performance of fine-grained recognition. In a fair comparison, MetaFormer can outperform the current SotA approaches with only vision information on the iNaturalist2017 and iNaturalist2018 datasets. Adding meta-information, MetaFormer can exceed the current SotA approaches by 5.9% and 5.3%, respectively. Moreover, MetaFormer can achieve 92.3% and 92.7% on CUB-200-2011 and NABirds, which significantly outperforms the SotA approaches. The source code and pre-trained models are released athttps://github.com/dqshuai/MetaFormer. | ['Zehuan Yuan', 'Jia Sun', 'Bin Wen', 'Yi Jiang', 'Qishuai Diao'] | 2022-03-05 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [-2.30113447e-01 -5.39223373e-01 -1.53716087e-01 -3.86469394e-01
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7665ab3a-97d4-418d-b90e-966a596474cc | multi-objective-coordination-graphs-for-the | 2207.00368 | null | https://arxiv.org/abs/2207.00368v1 | https://arxiv.org/pdf/2207.00368v1.pdf | Multi-Objective Coordination Graphs for the Expected Scalarised Returns with Generative Flow Models | Many real-world problems contain multiple objectives and agents, where a trade-off exists between objectives. Key to solving such problems is to exploit sparse dependency structures that exist between agents. For example, in wind farm control a trade-off exists between maximising power and minimising stress on the systems components. Dependencies between turbines arise due to the wake effect. We model such sparse dependencies between agents as a multi-objective coordination graph (MO-CoG). In multi-objective reinforcement learning a utility function is typically used to model a users preferences over objectives, which may be unknown a priori. In such settings a set of optimal policies must be computed. Which policies are optimal depends on which optimality criterion applies. If the utility function of a user is derived from multiple executions of a policy, the scalarised expected returns (SER) must be optimised. If the utility of a user is derived from a single execution of a policy, the expected scalarised returns (ESR) criterion must be optimised. For example, wind farms are subjected to constraints and regulations that must be adhered to at all times, therefore the ESR criterion must be optimised. For MO-CoGs, the state-of-the-art algorithms can only compute a set of optimal policies for the SER criterion, leaving the ESR criterion understudied. To compute a set of optimal polices under the ESR criterion, also known as the ESR set, distributions over the returns must be maintained. Therefore, to compute a set of optimal policies under the ESR criterion for MO-CoGs, we present a novel distributional multi-objective variable elimination (DMOVE) algorithm. We evaluate DMOVE in realistic wind farm simulations. Given the returns in real-world wind farm settings are continuous, we utilise a model known as real-NVP to learn the continuous return distributions to calculate the ESR set. | ['Patrick Mannion', 'Enda Howley', 'Diederik M. Roijers', 'Timothy Verstraeten', 'Conor F. Hayes'] | 2022-07-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 7.28686824e-02 -5.17646745e-02 -9.11121741e-02 -5.12715466e-02
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e583ddd7-4254-4b02-a10a-01cf9005e4c2 | a-hybrid-parametric-deep-learning-approach | 1908.10133 | null | https://arxiv.org/abs/1908.10133v1 | https://arxiv.org/pdf/1908.10133v1.pdf | A hybrid parametric-deep learning approach for sound event localization and detection | This work describes and discusses an algorithm submitted to the Sound Event Localization and Detection Task of DCASE2019 Challenge. The proposed methodology relies on parametric spatial audio analysis for source localization and detection, combined with a deep learning-based monophonic event classifier. The evaluation of the proposed algorithm yields overall results comparable to the baseline system. The main highlight is a reduction of the localization error on the evaluation dataset by a factor of 2.6, compared with the baseline performance. | ['Xavier Serra', 'Andres Perez-Lopez', 'Eduardo Fonseca'] | 2019-08-27 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [ 1.52093126e-02 -3.11759681e-01 6.02437079e-01 6.27352968e-02
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585f37b6-fce1-4be1-ac18-02033d5c01fa | talking-head-generation-driven-by-speech | 2204.12756 | null | https://arxiv.org/abs/2204.12756v1 | https://arxiv.org/pdf/2204.12756v1.pdf | Talking Head Generation Driven by Speech-Related Facial Action Units and Audio- Based on Multimodal Representation Fusion | Talking head generation is to synthesize a lip-synchronized talking head video by inputting an arbitrary face image and corresponding audio clips. Existing methods ignore not only the interaction and relationship of cross-modal information, but also the local driving information of the mouth muscles. In this study, we propose a novel generative framework that contains a dilated non-causal temporal convolutional self-attention network as a multimodal fusion module to promote the relationship learning of cross-modal features. In addition, our proposed method uses both audio- and speech-related facial action units (AUs) as driving information. Speech-related AU information can guide mouth movements more accurately. Because speech is highly correlated with speech-related AUs, we propose an audio-to-AU module to predict speech-related AU information. We utilize pre-trained AU classifier to ensure that the generated images contain correct AU information. We verify the effectiveness of the proposed model on the GRID and TCD-TIMIT datasets. An ablation study is also conducted to verify the contribution of each component. The results of quantitative and qualitative experiments demonstrate that our method outperforms existing methods in terms of both image quality and lip-sync accuracy. | ['Longbiao Wang', 'Jiaxing Liu', 'Zhilei Liu', 'Sen Chen'] | 2022-04-27 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 1.29468322e-01 5.69624268e-02 -2.29576766e-01 -4.22444224e-01
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85168829-7164-4119-bf07-977ea69ce226 | better-speech-synthesis-through-scaling | 2305.07243 | null | https://arxiv.org/abs/2305.07243v2 | https://arxiv.org/pdf/2305.07243v2.pdf | Better speech synthesis through scaling | In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts. | ['James Betker'] | 2023-05-12 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.58007458e-01 2.59536624e-01 1.35055274e-01 -2.94749349e-01
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0189857e-2e93-4227-bcfd-2a2697015d23 | entropy-based-training-methods-for-scalable | 2306.04952 | null | https://arxiv.org/abs/2306.04952v1 | https://arxiv.org/pdf/2306.04952v1.pdf | Entropy-based Training Methods for Scalable Neural Implicit Sampler | Efficiently sampling from un-normalized target distributions is a fundamental problem in scientific computing and machine learning. Traditional approaches like Markov Chain Monte Carlo (MCMC) guarantee asymptotically unbiased samples from such distributions but suffer from computational inefficiency, particularly when dealing with high-dimensional targets, as they require numerous iterations to generate a batch of samples. In this paper, we propose an efficient and scalable neural implicit sampler that overcomes these limitations. Our sampler can generate large batches of samples with low computational costs by leveraging a neural transformation that directly maps easily sampled latent vectors to target samples without the need for iterative procedures. To train the neural implicit sampler, we introduce two novel methods: the KL training method and the Fisher training method. The former minimizes the Kullback-Leibler divergence, while the latter minimizes the Fisher divergence. By employing these training methods, we effectively optimize the neural implicit sampler to capture the desired target distribution. To demonstrate the effectiveness, efficiency, and scalability of our proposed samplers, we evaluate them on three sampling benchmarks with different scales. These benchmarks include sampling from 2D targets, Bayesian inference, and sampling from high-dimensional energy-based models (EBMs). Notably, in the experiment involving high-dimensional EBMs, our sampler produces samples that are comparable to those generated by MCMC-based methods while being more than 100 times more efficient, showcasing the efficiency of our neural sampler. We believe that the theoretical and empirical contributions presented in this work will stimulate further research on developing efficient samplers for various applications beyond the ones explored in this study. | ['Zhihua Zhang', 'Boya Zhang', 'Weijian Luo'] | 2023-06-08 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.76276231e-01 -3.16408277e-01 -4.21179622e-01 -2.10676357e-01
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4018152b-1709-4ef1-b559-73d783f2a1c8 | towards-automatic-learning-of-procedures-from | 1703.09788 | null | http://arxiv.org/abs/1703.09788v3 | http://arxiv.org/pdf/1703.09788v3.pdf | Towards Automatic Learning of Procedures from Web Instructional Videos | The potential for agents, whether embodied or software, to learn by observing
other agents performing procedures involving objects and actions is rich.
Current research on automatic procedure learning heavily relies on action
labels or video subtitles, even during the evaluation phase, which makes them
infeasible in real-world scenarios. This leads to our question: can the
human-consensus structure of a procedure be learned from a large set of long,
unconstrained videos (e.g., instructional videos from YouTube) with only visual
evidence? To answer this question, we introduce the problem of procedure
segmentation--to segment a video procedure into category-independent procedure
segments. Given that no large-scale dataset is available for this problem, we
collect a large-scale procedure segmentation dataset with procedure segments
temporally localized and described; we use cooking videos and name the dataset
YouCook2. We propose a segment-level recurrent network for generating procedure
segments by modeling the dependencies across segments. The generated segments
can be used as pre-processing for other tasks, such as dense video captioning
and event parsing. We show in our experiments that the proposed model
outperforms competitive baselines in procedure segmentation. | ['Chenliang Xu', 'Luowei Zhou', 'Jason J. Corso'] | 2017-03-28 | null | null | null | null | ['procedure-learning', 'dense-video-captioning'] | ['computer-vision', 'computer-vision'] | [ 6.50981605e-01 4.13355052e-01 -3.92458171e-01 -3.47685575e-01
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711baa82-15af-4a6d-8d5c-7c07e865d5bd | adaptive-dimension-reduction-and-variational | 2209.08527 | null | https://arxiv.org/abs/2209.08527v1 | https://arxiv.org/pdf/2209.08527v1.pdf | Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification | Transductive Few-Shot learning has gained increased attention nowadays considering the cost of data annotations along with the increased accuracy provided by unlabelled samples in the domain of few shot. Especially in Few-Shot Classification (FSC), recent works explore the feature distributions aiming at maximizing likelihoods or posteriors with respect to the unknown parameters. Following this vein, and considering the parallel between FSC and clustering, we seek for better taking into account the uncertainty in estimation due to lack of data, as well as better statistical properties of the clusters associated with each class. Therefore in this paper we propose a new clustering method based on Variational Bayesian inference, further improved by Adaptive Dimension Reduction based on Probabilistic Linear Discriminant Analysis. Our proposed method significantly improves accuracy in the realistic unbalanced transductive setting on various Few-Shot benchmarks when applied to features used in previous studies, with a gain of up to $6\%$ in accuracy. In addition, when applied to balanced setting, we obtain very competitive results without making use of the class-balance artefact which is disputable for practical use cases. We also provide the performance of our method on a high performing pretrained backbone, with the reported results further surpassing the current state-of-the-art accuracy, suggesting the genericity of the proposed method. | ['Vincent Gripon', 'Stéphane Pateux', 'Yuqing Hu'] | 2022-09-18 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.15498978e-01 -5.19714728e-02 -1.62634656e-01 -3.19683313e-01
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69edba9d-9da0-4346-9087-5d88a95851e8 | 190600494 | 1906.00494 | null | https://arxiv.org/abs/1906.00494v2 | https://arxiv.org/pdf/1906.00494v2.pdf | Graphon Estimation from Partially Observed Network Data | We consider estimating the edge-probability matrix of a network generated from a graphon model when the full network is not observed---only some overlapping subgraphs are. We extend the neighbourhood smoothing (NBS) algorithm of Zhang et al. (2017) to this missing-data set-up and show experimentally that, for a wide range of graphons, the extended NBS algorithm achieves significantly smaller error rates than standard graphon estimation algorithms such as vanilla neighbourhood smoothing (NBS), universal singular value thresholding (USVT), blockmodel approximation, matrix completion, etc. We also show that the extended NBS algorithm is much more robust to missing data. | ['Soumendu Sundar Mukherjee', 'Sayak Chakrabarti'] | 2019-06-02 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 3.55847597e-01 5.97805798e-01 -2.27797359e-01 -7.87311718e-02
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13669e26-62e2-4fa4-b8a4-4fba6aafda33 | large-deviation-principles-for-block-and-step | 2101.07025 | null | https://arxiv.org/abs/2101.07025v2 | https://arxiv.org/pdf/2101.07025v2.pdf | Large Deviation Principles for Block and Step Graphon Random Graph Models | Borgs, Chayes, Gaudio, Petti and Sen [arXiv:2007.14508] proved a large deviation principle for block model random graphs with rational block ratios. We strengthen their result by allowing any block ratios (and also establish a simpler formula for the rate function). We apply the new result to derive a large deviation principle for graph sampling from any given step graphon. | ['Oleg Pikhurko', 'Jan Grebík'] | 2021-01-18 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 2.22433463e-01 6.34107411e-01 -5.72707117e-01 6.34851679e-02
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a13c98a7-8512-4b7e-9737-52d27b2c64d4 | fusedream-training-free-text-to-image | 2112.01573 | null | https://arxiv.org/abs/2112.01573v1 | https://arxiv.org/pdf/2112.01573v1.pdf | FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization | Generating images from natural language instructions is an intriguing yet highly challenging task. We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf image generator (GANs), optimizing in the latent space of GAN to find images that achieve maximum CLIP score with the given input text. Compared to traditional methods that train generative models from text to image starting from scratch, the CLIP+GAN approach is training-free, zero shot and can be easily customized with different generators. However, optimizing CLIP score in the GAN space casts a highly challenging optimization problem and off-the-shelf optimizers such as Adam fail to yield satisfying results. In this work, we propose a FuseDream pipeline, which improves the CLIP+GAN approach with three key techniques: 1) an AugCLIP score which robustifies the CLIP objective by introducing random augmentation on image. 2) a novel initialization and over-parameterization strategy for optimization which allows us to efficiently navigate the non-convex landscape in GAN space. 3) a composed generation technique which, by leveraging a novel bi-level optimization formulation, can compose multiple images to extend the GAN space and overcome the data-bias. When promoted by different input text, FuseDream can generate high-quality images with varying objects, backgrounds, artistic styles, even novel counterfactual concepts that do not appear in the training data of the GAN we use. Quantitatively, the images generated by FuseDream yield top-level Inception score and FID score on MS COCO dataset, without additional architecture design or training. Our code is publicly available at \url{https://github.com/gnobitab/FuseDream}. | ['Qiang Liu', 'Hao Su', 'Shujian Zhang', 'Lemeng Wu', 'Chengyue Gong', 'Xingchao Liu'] | 2021-12-02 | null | null | null | null | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 4.97108251e-01 1.96708396e-01 1.05259478e-01 -9.82012898e-02
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576eb5fd-ccc0-4479-a575-b269c4cd8f12 | restricted-black-box-adversarial-attack | 2204.12347 | null | https://arxiv.org/abs/2204.12347v1 | https://arxiv.org/pdf/2204.12347v1.pdf | Restricted Black-box Adversarial Attack Against DeepFake Face Swapping | DeepFake face swapping presents a significant threat to online security and social media, which can replace the source face in an arbitrary photo/video with the target face of an entirely different person. In order to prevent this fraud, some researchers have begun to study the adversarial methods against DeepFake or face manipulation. However, existing works focus on the white-box setting or the black-box setting driven by abundant queries, which severely limits the practical application of these methods. To tackle this problem, we introduce a practical adversarial attack that does not require any queries to the facial image forgery model. Our method is built on a substitute model persuing for face reconstruction and then transfers adversarial examples from the substitute model directly to inaccessible black-box DeepFake models. Specially, we propose the Transferable Cycle Adversary Generative Adversarial Network (TCA-GAN) to construct the adversarial perturbation for disrupting unknown DeepFake systems. We also present a novel post-regularization module for enhancing the transferability of generated adversarial examples. To comprehensively measure the effectiveness of our approaches, we construct a challenging benchmark of DeepFake adversarial attacks for future development. Extensive experiments impressively show that the proposed adversarial attack method makes the visual quality of DeepFake face images plummet so that they are easier to be detected by humans and algorithms. Moreover, we demonstrate that the proposed algorithm can be generalized to offer face image protection against various face translation methods. | ['Xiaohua Xie', 'JianHuang Lai', 'YuAn Wang', 'Junhao Dong'] | 2022-04-26 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.46568525e-01 2.26396292e-01 2.44504049e-01 -2.02422842e-01
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d9c529ee-aedb-428b-b6a9-d8fc8f9f8639 | a-comparison-of-stereo-matching-cost-between | 1905.09147 | null | https://arxiv.org/abs/1905.09147v1 | https://arxiv.org/pdf/1905.09147v1.pdf | A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images | Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics. Census has been proofed to be one of the most efficient low-level feature based matching methods, while fast Convolutional Neural Network (fst-CNN), as a deep feature based method, has small computing time and is robust for satellite images. Thus, a comparison between fst-CNN and census is critical for further studies in stereo dense image matching. This paper used cost function of fst-CNN and census to do stereo matching, then utilized semi-global matching method to obtain optimized disparity images. Those images are used to produce digital surface model to compare with ground truth points. It addresses that fstCNN performs better than census in the aspect of absolute matching accuracy, histogram of error distribution and matching completeness, but these two algorithms still performs in the same order of magnitude. | ['Xu Huang', 'Shuang Song', 'Xiaohu Lu', 'Rongjun Qin', 'Bihe Chen'] | 2019-05-22 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [-1.53065249e-01 -5.20056367e-01 1.33161649e-01 -4.03483003e-01
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34cd7f63-edf1-4e2c-87ae-8d4e699eec33 | related-terms-search-based-on-wordnet | 0907.2209 | null | http://arxiv.org/abs/0907.2209v2 | http://arxiv.org/pdf/0907.2209v2.pdf | Related terms search based on WordNet / Wiktionary and its application in Ontology Matching | A set of ontology matching algorithms (for finding correspondences between
concepts) is based on a thesaurus that provides the source data for the
semantic distance calculations. In this wiki era, new resources may spring up
and improve this kind of semantic search. In the paper a solution of this task
based on Russian Wiktionary is compared to WordNet based algorithms. Metrics
are estimated using the test collection, containing 353 English word pairs with
a relatedness score assigned by human evaluators. The experiment shows that the
proposed method is capable in principle of calculating a semantic distance
between pair of words in any language presented in Russian Wiktionary. The
calculation of Wiktionary based metric had required the development of the
open-source Wiktionary parser software. | ['Feiyu Lin', 'A. A. Krizhanovsky'] | 2009-07-13 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [-2.35928953e-01 3.43941808e-01 4.09719616e-01 -5.12247682e-01
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