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63f31454-965a-4e73-ba09-149fa5a387bd | learning-motion-patterns-in-videos | 1612.07217 | null | http://arxiv.org/abs/1612.07217v2 | http://arxiv.org/pdf/1612.07217v2.pdf | Learning Motion Patterns in Videos | The problem of determining whether an object is in motion, irrespective of
camera motion, is far from being solved. We address this challenging task by
learning motion patterns in videos. The core of our approach is a fully
convolutional network, which is learned entirely from synthetic video
sequences, and their ground-truth optical flow and motion segmentation. This
encoder-decoder style architecture first learns a coarse representation of the
optical flow field features, and then refines it iteratively to produce motion
labels at the original high-resolution. We further improve this labeling with
an objectness map and a conditional random field, to account for errors in
optical flow, and also to focus on moving "things" rather than "stuff". The
output label of each pixel denotes whether it has undergone independent motion,
i.e., irrespective of camera motion. We demonstrate the benefits of this
learning framework on the moving object segmentation task, where the goal is to
segment all objects in motion. Our approach outperforms the top method on the
recently released DAVIS benchmark dataset, comprising real-world sequences, by
5.6%. We also evaluate on the Berkeley motion segmentation database, achieving
state-of-the-art results. | ['Pavel Tokmakov', 'Cordelia Schmid', 'Karteek Alahari'] | 2016-12-21 | learning-motion-patterns-in-videos-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Tokmakov_Learning_Motion_Patterns_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tokmakov_Learning_Motion_Patterns_CVPR_2017_paper.pdf | cvpr-2017-7 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 4.24180597e-01 -2.98994184e-01 -4.65824157e-01 -2.71173984e-01
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cc95df57-9c48-4d66-a674-a95e38416cb4 | pose-from-shape-deep-pose-estimation-for | 1906.05105 | null | https://arxiv.org/abs/1906.05105v2 | https://arxiv.org/pdf/1906.05105v2.pdf | Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects | Most deep pose estimation methods need to be trained for specific object instances or categories. In this work we propose a completely generic deep pose estimation approach, which does not require the network to have been trained on relevant categories, nor objects in a category to have a canonical pose. We believe this is a crucial step to design robotic systems that can interact with new objects in the wild not belonging to a predefined category. Our main insight is to dynamically condition pose estimation with a representation of the 3D shape of the target object. More precisely, we train a Convolutional Neural Network that takes as input both a test image and a 3D model, and outputs the relative 3D pose of the object in the input image with respect to the 3D model. We demonstrate that our method boosts performances for supervised category pose estimation on standard benchmarks, namely Pascal3D+, ObjectNet3D and Pix3D, on which we provide results superior to the state of the art. More importantly, we show that our network trained on everyday man-made objects from ShapeNet generalizes without any additional training to completely new types of 3D objects by providing results on the LINEMOD dataset as well as on natural entities such as animals from ImageNet. | ['Mathieu Aubry', 'Pierre-Alain Langlois', 'Xuchong Qiu', 'Yang Xiao', 'Renaud Marlet'] | 2019-06-12 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [-1.22105770e-01 3.42629790e-01 1.71299040e-01 -5.05699694e-01
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f2e440c7-d236-4603-b529-528a16e45212 | derivative-free-weight-space-ensembling | 2307.03506 | null | https://arxiv.org/abs/2307.03506v1 | https://arxiv.org/pdf/2307.03506v1.pdf | Derivative Free Weight-space Ensembling | Recent work suggests that interpolating between the weights of two specialized language models can transfer knowledge between tasks in a way that multi-task learning cannot. However, very few have explored interpolation between more than two models, where each has a distinct knowledge base. In this paper, we introduce Derivative Free Weight-space Ensembling (DFWE), a new few-sample task transfer approach for open-domain dialogue. Our framework creates a set of diverse expert language models trained using a predefined set of source tasks. Next, we finetune each of the expert models on the target task, approaching the target task from several distinct knowledge bases. Finally, we linearly interpolate between the model weights using a gradient-free-optimization algorithm, to efficiently find a good interpolation weighting. We demonstrate the effectiveness of the method on FETA-Friends outperforming the standard pretrain-finetune approach. | ['Dean Ninalga'] | 2023-07-07 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [ 1.54102385e-01 3.09551775e-01 -1.44950122e-01 -5.72412133e-01
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870abe34-5562-40b3-a197-732b065b1b1b | information-theoretic-gan-compression-with | 2303.16050 | null | https://arxiv.org/abs/2303.16050v1 | https://arxiv.org/pdf/2303.16050v1.pdf | Information-Theoretic GAN Compression with Variational Energy-based Model | We propose an information-theoretic knowledge distillation approach for the compression of generative adversarial networks, which aims to maximize the mutual information between teacher and student networks via a variational optimization based on an energy-based model. Because the direct computation of the mutual information in continuous domains is intractable, our approach alternatively optimizes the student network by maximizing the variational lower bound of the mutual information. To achieve a tight lower bound, we introduce an energy-based model relying on a deep neural network to represent a flexible variational distribution that deals with high-dimensional images and consider spatial dependencies between pixels, effectively. Since the proposed method is a generic optimization algorithm, it can be conveniently incorporated into arbitrary generative adversarial networks and even dense prediction networks, e.g., image enhancement models. We demonstrate that the proposed algorithm achieves outstanding performance in model compression of generative adversarial networks consistently when combined with several existing models. | ['Bohyung Han', 'Hyong-Euk Lee', 'Sehwan Ki', 'Eunhee Kang', 'Hyewon Yoo', 'Minsoo Kang'] | 2023-03-28 | null | null | null | null | ['image-enhancement', 'model-compression'] | ['computer-vision', 'methodology'] | [ 3.89715582e-01 5.39506078e-01 6.53213039e-02 -2.72762090e-01
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eee17297-8ec1-4121-9197-a985f227d726 | fully-parameterized-quantile-function-for | 1911.02140 | null | https://arxiv.org/abs/1911.02140v3 | https://arxiv.org/pdf/1911.02140v3.pdf | Fully Parameterized Quantile Function for Distributional Reinforcement Learning | Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games. The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution. Existing distributional RL algorithms parameterize either the probability side or the return value side of the distribution function, leaving the other side uniformly fixed as in C51, QR-DQN or randomly sampled as in IQN. In this paper, we propose fully parameterized quantile function that parameterizes both the quantile fraction axis (i.e., the x-axis) and the value axis (i.e., y-axis) for distributional RL. Our algorithm contains a fraction proposal network that generates a discrete set of quantile fractions and a quantile value network that gives corresponding quantile values. The two networks are jointly trained to find the best approximation of the true distribution. Experiments on 55 Atari Games show that our algorithm significantly outperforms existing distributional RL algorithms and creates a new record for the Atari Learning Environment for non-distributed agents. | ['Tie-Yan Liu', 'Tao Qin', 'Li Zhao', 'Derek Yang', 'Zichuan Lin', 'Jiang Bian'] | 2019-11-05 | fully-parameterized-quantile-function-for-1 | http://papers.nips.cc/paper/8850-fully-parameterized-quantile-function-for-distributional-reinforcement-learning | http://papers.nips.cc/paper/8850-fully-parameterized-quantile-function-for-distributional-reinforcement-learning.pdf | neurips-2019-12 | ['distributional-reinforcement-learning'] | ['methodology'] | [-7.35874891e-01 4.33919765e-02 -4.18307364e-01 -2.61626899e-01
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557ffd6a-6abe-42e0-9b10-634eaa0db00c | rha-net-an-encoder-decoder-network-with | 2207.14166 | null | https://arxiv.org/abs/2207.14166v1 | https://arxiv.org/pdf/2207.14166v1.pdf | RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation | The acquisition and evaluation of pavement surface data play an essential role in pavement condition evaluation. In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid attention blocks into the encoder-decoder architecture. The ResBlocks are used to improve the ability of RHA-Net to extract high-level abstract features. The hybrid attention blocks are designed to fuse both low-level features and high-level features to help the model focus on correct channels and areas of cracks, thereby improving the feature presentation ability of RHA-Net. An image data set containing 789 pavement crack images collected by a self-designed mobile robot is constructed and used for training and evaluating the proposed model. Compared with other state-of-the-art networks, the proposed model achieves better performance and the functionalities of adding residual blocks and hybrid attention mechanisms are validated in a comprehensive ablation study. Additionally, a light-weighted version of the model generated by introducing depthwise separable convolution achieves better a performance and a much faster processing speed with 1/30 of the number of U-Net parameters. The developed system can segment pavement crack in real-time on an embedded device Jetson TX2 (25 FPS). The video taken in real-time experiments is released at https://youtu.be/3XIogk0fiG4. | ['Kelvin C. P. Wang', 'Weihua Sheng', 'Meihua Wang', 'Peili Ma', 'Duan Yuan', 'Jiacheng Liu', 'Zhun Fan', 'Guijie Zhu'] | 2022-07-28 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-3.42889279e-02 6.04788773e-02 3.32624137e-01 -2.35526577e-01
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a7567f1f-fec6-4f1d-b104-fefd1ae1b669 | revisiting-the-minimalist-approach-to-offline | 2305.09836 | null | https://arxiv.org/abs/2305.09836v1 | https://arxiv.org/pdf/2305.09836v1.pdf | Revisiting the Minimalist Approach to Offline Reinforcement Learning | Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity. While these algorithms have led to noteworthy improvements, many incorporate seemingly minor design choices that impact their effectiveness beyond core algorithmic advances. However, the effect of these design choices on established baselines remains understudied. In this work, we aim to bridge this gap by conducting a retrospective analysis of recent works in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such design elements built on top of the TD3+BC method. We evaluate ReBRAC on 51 datasets with both proprioceptive and visual state spaces using D4RL and V-D4RL benchmarks, demonstrating its state-of-the-art performance among ensemble-free methods. To further illustrate the efficacy of these design choices, we perform a large-scale ablation study and hyperparameter sensitivity analysis on the scale of thousands of experiments. | ['Sergey Kolesnikov', 'Alexander Nikulin', 'Vladislav Kurenkov', 'Denis Tarasov'] | 2023-05-16 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [ 1.15149304e-01 -3.73797148e-01 -3.36052954e-01 -1.53688356e-01
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d33bf1f7-d58e-4e01-8970-72386883baa7 | mime-human-aware-3d-scene-generation | 2212.04360 | null | https://arxiv.org/abs/2212.04360v1 | https://arxiv.org/pdf/2212.04360v1.pdf | MIME: Human-Aware 3D Scene Generation | Generating realistic 3D worlds occupied by moving humans has many applications in games, architecture, and synthetic data creation. But generating such scenes is expensive and labor intensive. Recent work generates human poses and motions given a 3D scene. Here, we take the opposite approach and generate 3D indoor scenes given 3D human motion. Such motions can come from archival motion capture or from IMU sensors worn on the body, effectively turning human movement in a "scanner" of the 3D world. Intuitively, human movement indicates the free-space in a room and human contact indicates surfaces or objects that support activities such as sitting, lying or touching. We propose MIME (Mining Interaction and Movement to infer 3D Environments), which is a generative model of indoor scenes that produces furniture layouts that are consistent with the human movement. MIME uses an auto-regressive transformer architecture that takes the already generated objects in the scene as well as the human motion as input, and outputs the next plausible object. To train MIME, we build a dataset by populating the 3D FRONT scene dataset with 3D humans. Our experiments show that MIME produces more diverse and plausible 3D scenes than a recent generative scene method that does not know about human movement. Code and data will be available for research at https://mime.is.tue.mpg.de. | ['Michael J. Black', 'Justus Thies', 'Lea Hering', 'Shashank Tripathi', 'Chun-Hao P. Huang', 'Hongwei Yi'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yi_MIME_Human-Aware_3D_Scene_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yi_MIME_Human-Aware_3D_Scene_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-generation'] | ['computer-vision'] | [ 1.67940706e-01 7.16970637e-02 4.34008688e-01 -2.55696595e-01
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8866d371-a3db-40d3-be0d-065eb7b7cf8f | memory-augmented-attention-modelling-for | 1611.02261 | null | http://arxiv.org/abs/1611.02261v4 | http://arxiv.org/pdf/1611.02261v4.pdf | Memory-augmented Attention Modelling for Videos | We present a method to improve video description generation by modeling
higher-order interactions between video frames and described concepts. By
storing past visual attention in the video associated to previously generated
words, the system is able to decide what to look at and describe in light of
what it has already looked at and described. This enables not only more
effective local attention, but tractable consideration of the video sequence
while generating each word. Evaluation on the challenging and popular MSVD and
Charades datasets demonstrates that the proposed architecture outperforms
previous video description approaches without requiring external temporal video
features. | ['Margaret Mitchell', 'Abdel-rahman Mohamed', 'Rasool Fakoor', 'Sing Bing Kang', 'Pushmeet Kohli'] | 2016-11-07 | null | null | null | null | ['video-description'] | ['computer-vision'] | [-4.47156839e-02 -3.13949049e-01 -3.96570683e-01 -2.42128611e-01
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f4955105-88e2-4278-a905-9d15b94e641b | recognizing-topic-change-in-search-sessions | 1909.10736 | null | https://arxiv.org/abs/1909.10736v1 | https://arxiv.org/pdf/1909.10736v1.pdf | Recognizing Topic Change in Search Sessions of Digital Libraries based on Thesaurus and Classification System | Log analysis in Web search showed that user sessions often contain several different topics. This means sessions need to be segmented into parts which handle the same topic in order to give appropriate user support based on the topic, and not on a mixture of topics. Different methods have been proposed to segment a user session to different topics based on timeouts, lexical analysis, query similarity or external knowledge sources. In this paper, we study the problem in a digital library for the social sciences. We present a method based on a thesaurus and a classification system which are typical knowledge organization systems in digital libraries. Five experts evaluated our approach and rated it as good for the segmentation of search sessions into parts that treat the same topic. | ['Daniel Hienert', 'Dagmar Kern'] | 2019-09-24 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [-5.08647025e-01 1.69696882e-01 -3.03551018e-01 -2.22492322e-01
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b6c442b7-fe96-45bf-b35e-50d71c2c5c33 | a-survey-on-artificial-intelligence-for-music | 2210.13944 | null | https://arxiv.org/abs/2210.13944v2 | https://arxiv.org/pdf/2210.13944v2.pdf | A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives | Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future. | ['Jose R. Beltran', 'Javier Hernandez-Olivan', 'Carlos Hernandez-Olivan'] | 2022-10-25 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 5.52692413e-01 1.76029350e-03 1.92901999e-01 7.96686932e-02
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c6851ebd-e0f4-4a3d-96f4-b5839688ea7c | ct-block-a-novel-local-and-global-features | 2111.15400 | null | https://arxiv.org/abs/2111.15400v1 | https://arxiv.org/pdf/2111.15400v1.pdf | CT-block: a novel local and global features extractor for point cloud | Deep learning on the point cloud is increasingly developing. Grouping the point with its neighbors and conducting convolution-like operation on them can learn the local feature of the point cloud, but this method is weak to extract the long-distance global feature. Performing the attention-based transformer on the whole point cloud can effectively learn the global feature of it, but this method is hardly to extract the local detailed feature. In this paper, we propose a novel module that can simultaneously extract and fuse local and global features, which is named as CT-block. The CT-block is composed of two branches, where the letter C represents the convolution-branch and the letter T represents the transformer-branch. The convolution-branch performs convolution on the grouped neighbor points to extract the local feature. Meanwhile, the transformer-branch performs offset-attention process on the whole point cloud to extract the global feature. Through the bridge constructed by the feature transmission element in the CT-block, the local and global features guide each other during learning and are fused effectively. We apply the CT-block to construct point cloud classification and segmentation networks, and evaluate the performance of them by several public datasets. The experimental results show that, because the features learned by CT-block are much expressive, the performance of the networks constructed by the CT-block on the point cloud classification and segmentation tasks achieve state of the art. | ['Shaokun Han', 'Xiantong Meng', 'Zhengchao Lai', 'Jun Li', 'Shangwei Guo'] | 2021-11-30 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-4.15761262e-01 -2.98208863e-01 -1.59124956e-02 -4.05465782e-01
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a7508bae-6fb2-4394-8917-bdde88a468e9 | large-margin-structured-convolution-operator | 1804.07006 | null | http://arxiv.org/abs/1804.07006v2 | http://arxiv.org/pdf/1804.07006v2.pdf | Large Margin Structured Convolution Operator for Thermal Infrared Object Tracking | Compared with visible object tracking, thermal infrared (TIR) object tracking
can track an arbitrary target in total darkness since it cannot be influenced
by illumination variations. However, there are many unwanted attributes that
constrain the potentials of TIR tracking, such as the absence of visual color
patterns and low resolutions. Recently, structured output support vector
machine (SOSVM) and discriminative correlation filter (DCF) have been
successfully applied to visible object tracking, respectively. Motivated by
these, in this paper, we propose a large margin structured convolution operator
(LMSCO) to achieve efficient TIR object tracking. To improve the tracking
performance, we employ the spatial regularization and implicit interpolation to
obtain continuous deep feature maps, including deep appearance features and
deep motion features, of the TIR targets. Finally, a collaborative optimization
strategy is exploited to significantly update the operators. Our approach not
only inherits the advantage of the strong discriminative capability of SOSVM
but also achieves accurate and robust tracking with higher-dimensional features
and more dense samples. To the best of our knowledge, we are the first to
incorporate the advantages of DCF and SOSVM for TIR object tracking.
Comprehensive evaluations on two thermal infrared tracking benchmarks, i.e.
VOT-TIR2015 and VOT-TIR2016, clearly demonstrate that our LMSCO tracker
achieves impressive results and outperforms most state-of-the-art trackers in
terms of accuracy and robustness with sufficient frame rate. | ['Yipeng Ma', 'Peng Gao', 'Liyi Xiao', 'Ke Song', 'Fei Wang', 'Chao Li'] | 2018-04-19 | null | null | null | null | ['thermal-infrared-object-tracking'] | ['computer-vision'] | [ 4.62371297e-02 -7.32599616e-01 -1.91777319e-01 4.03081551e-02
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d814333b-717e-47c1-9cf7-d90bee749744 | dmrst-a-joint-framework-for-document-level | 2110.04518 | null | https://arxiv.org/abs/2110.04518v1 | https://arxiv.org/pdf/2110.04518v1.pdf | DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing | Text discourse parsing weighs importantly in understanding information flow and argumentative structure in natural language, making it beneficial for downstream tasks. While previous work significantly improves the performance of RST discourse parsing, they are not readily applicable to practical use cases: (1) EDU segmentation is not integrated into most existing tree parsing frameworks, thus it is not straightforward to apply such models on newly-coming data. (2) Most parsers cannot be used in multilingual scenarios, because they are developed only in English. (3) Parsers trained from single-domain treebanks do not generalize well on out-of-domain inputs. In this work, we propose a document-level multilingual RST discourse parsing framework, which conducts EDU segmentation and discourse tree parsing jointly. Moreover, we propose a cross-translation augmentation strategy to enable the framework to support multilingual parsing and improve its domain generality. Experimental results show that our model achieves state-of-the-art performance on document-level multilingual RST parsing in all sub-tasks. | ['Nancy F. Chen', 'Ke Shi', 'Zhengyuan Liu'] | 2021-10-09 | null | https://aclanthology.org/2021.codi-main.15 | https://aclanthology.org/2021.codi-main.15.pdf | codi-2021-11 | ['discourse-segmentation', 'discourse-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.34982333e-01 5.41367173e-01 -5.68964005e-01 -3.56983334e-01
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0beea58b-2357-4a3c-bf18-2e4d26cdc69b | deep-neural-networks-based-meta-learning-for | 2302.09394 | null | https://arxiv.org/abs/2302.09394v1 | https://arxiv.org/pdf/2302.09394v1.pdf | Deep Neural Networks based Meta-Learning for Network Intrusion Detection | Designing an intrusion detection system is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenge of dataset shift, we introduce the INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. This approach further improves its predictive power by employing a deep neural network-based Meta-Learner on top of INFUSE. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the proposed technique has strong generalization capability and the potential to detect network attacks. | ['Asifullah Khan', 'Muhammad Mohsin Zafar', 'Irfan Hameed', 'Bibi Ayisha', 'Anabia Sohail'] | 2023-02-18 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 4.15641032e-02 -6.45313442e-01 -1.35749295e-01 -3.08743775e-01
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6a9e6b86-7738-46fb-8efa-f721911b37ac | one-shot-talking-face-generation-from-single | 2112.02749 | null | https://arxiv.org/abs/2112.02749v1 | https://arxiv.org/pdf/2112.02749v1.pdf | One-shot Talking Face Generation from Single-speaker Audio-Visual Correlation Learning | Audio-driven one-shot talking face generation methods are usually trained on video resources of various persons. However, their created videos often suffer unnatural mouth shapes and asynchronous lips because those methods struggle to learn a consistent speech style from different speakers. We observe that it would be much easier to learn a consistent speech style from a specific speaker, which leads to authentic mouth movements. Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and then transferring audio-driven motion fields to a reference image. Specifically, we develop an Audio-Visual Correlation Transformer (AVCT) that aims to infer talking motions represented by keypoint based dense motion fields from an input audio. In particular, considering audio may come from different identities in deployment, we incorporate phonemes to represent audio signals. In this manner, our AVCT can inherently generalize to audio spoken by other identities. Moreover, as face keypoints are used to represent speakers, AVCT is agnostic against appearances of the training speaker, and thus allows us to manipulate face images of different identities readily. Considering different face shapes lead to different motions, a motion field transfer module is exploited to reduce the audio-driven dense motion field gap between the training identity and the one-shot reference. Once we obtained the dense motion field of the reference image, we employ an image renderer to generate its talking face videos from an audio clip. Thanks to our learned consistent speaking style, our method generates authentic mouth shapes and vivid movements. Extensive experiments demonstrate that our synthesized videos outperform the state-of-the-art in terms of visual quality and lip-sync. | ['Xin Yu', 'Yu Ding', 'Lincheng Li', 'Suzhen Wang'] | 2021-12-06 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 8.82539377e-02 -4.81243804e-02 -3.56001370e-02 -2.30239868e-01
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53ce37e8-bc1a-40b4-bde9-972fa785c08e | markov-decision-processes-under-model | 2206.06109 | null | https://arxiv.org/abs/2206.06109v2 | https://arxiv.org/pdf/2206.06109v2.pdf | Markov Decision Processes under Model Uncertainty | We introduce a general framework for Markov decision problems under model uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle we obtain a local-to-global paradigm, namely solving a local, i.e., a one time-step robust optimization problem leads to an optimizer of the global (i.e. infinite time-steps) robust stochastic optimal control problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio optimization involving data of the S&P 500. We present two different types of ambiguity sets; one is fully data-driven given by a Wasserstein-ball around the empirical measure, the second one is described by a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of the parameters are estimated from the data. It turns out that in scenarios where the market is volatile or bearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms the ones without model uncertainty, showcasing the importance of taking model uncertainty into account. | ['Mario Šikić', 'Julian Sester', 'Ariel Neufeld'] | 2022-06-13 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [ 1.17059641e-01 5.10562658e-01 -4.94943634e-02 -1.12633839e-01
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f0053ba5-b22f-42ba-918c-46dd1d8ac3a3 | spatial-likelihood-voting-with-self-knowledge | 2204.06899 | null | https://arxiv.org/abs/2204.06899v1 | https://arxiv.org/pdf/2204.06899v1.pdf | Spatial Likelihood Voting with Self-Knowledge Distillation for Weakly Supervised Object Detection | Weakly supervised object detection (WSOD), which is an effective way to train an object detection model using only image-level annotations, has attracted considerable attention from researchers. However, most of the existing methods, which are based on multiple instance learning (MIL), tend to localize instances to the discriminative parts of salient objects instead of the entire content of all objects. In this paper, we propose a WSOD framework called the Spatial Likelihood Voting with Self-knowledge Distillation Network (SLV-SD Net). In this framework, we introduce a spatial likelihood voting (SLV) module to converge region proposal localization without bounding box annotations. Specifically, in every iteration during training, all the region proposals in a given image act as voters voting for the likelihood of each category in the spatial dimensions. After dilating the alignment on the area with large likelihood values, the voting results are regularized as bounding boxes, which are then used for the final classification and localization. Based on SLV, we further propose a self-knowledge distillation (SD) module to refine the feature representations of the given image. The likelihood maps generated by the SLV module are used to supervise the feature learning of the backbone network, encouraging the network to attend to wider and more diverse areas of the image. Extensive experiments on the PASCAL VOC 2007/2012 and MS-COCO datasets demonstrate the excellent performance of SLV-SD Net. In addition, SLV-SD Net produces new state-of-the-art results on these benchmarks. | ['Xian-Sheng Hua', 'Yaowu Chen', 'Xiang Tian', 'Rongxin Jiang', 'Mingyuan Tao', 'Jianqiang Huang', 'Zhihang Fu', 'Ze Chen'] | 2022-04-14 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-2.10395991e-03 5.95839135e-02 -3.78495157e-01 -5.17495573e-01
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5fa6459e-1fd5-49e0-b742-7b7a7e96405d | similarity-comparisons-for-interactive-fine | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Wah_Similarity_Comparisons_for_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Wah_Similarity_Comparisons_for_2014_CVPR_paper.pdf | Similarity Comparisons for Interactive Fine-Grained Categorization | Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts. In this work, we move away from that expert-driven and attribute-centric paradigm and present a novel interactive classification system that incorporates computer vision and perceptual similarity metrics in a unified framework. At test time, users are asked to judge relative similarity between a query image and various sets of images; these general queries do not require expert-defined terminology and are applicable to other domains and basic-level categories, enabling a flexible, efficient, and scalable system for fine-grained categorization with humans in the loop. Our system outperforms existing state-of-the-art systems for relevance feedback-based image retrieval as well as interactive classification, resulting in a reduction of up to 43% in the average number of questions needed to correctly classify an image. | ['Serge Belongie', 'Grant van Horn', 'Subhransu Maji', 'Steve Branson', 'Catherine Wah', 'Pietro Perona'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.12510294e-01 -3.90461028e-01 -2.89643276e-02 -6.82967544e-01
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6bbcd282-df59-4a7c-b3ac-cb64ca6adad5 | vlbinet-radio-interferometry-data | 2110.07185 | null | https://arxiv.org/abs/2110.07185v1 | https://arxiv.org/pdf/2110.07185v1.pdf | VLBInet: Radio Interferometry Data Classification for EHT with Neural Networks | The Event Horizon Telescope (EHT) recently released the first horizon-scale images of the black hole in M87. Combined with other astronomical data, these images constrain the mass and spin of the hole as well as the accretion rate and magnetic flux trapped on the hole. An important question for the EHT is how well key parameters, such as trapped magnetic flux and the associated disk models, can be extracted from present and future EHT VLBI data products. The process of modeling visibilities and analyzing them is complicated by the fact that the data are sparsely sampled in the Fourier domain while most of the theory/simulation is constructed in the image domain. Here we propose a data-driven approach to analyze complex visibilities and closure quantities for radio interferometric data with neural networks. Using mock interferometric data, we show that our neural networks are able to infer the accretion state as either high magnetic flux (MAD) or low magnetic flux (SANE), suggesting that it is possible to perform parameter extraction directly in the visibility domain without image reconstruction. We have applied VLBInet to real M87 EHT data taken on four different days in 2017 (April 5, 6, 10, 11), and our neural networks give a score prediction 0.52, 0.4, 0.43, 0.76 for each day, with an average score 0.53, which shows no significant indication for the data to lean toward either the MAD or SANE state. | ['Charles F. Gammie', 'Ben S. Prather', 'Ajay Uppili Arasanipalai', 'George N. Wong', 'Dominic W. Pesce', 'Joshua Yao-Yu Lin'] | 2021-10-14 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [-1.65550187e-01 -5.00302948e-02 2.63191640e-01 5.64729720e-02
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021f4b7e-464d-400a-aca7-0b0f66c3e9d5 | exploring-domain-shift-in-extractive-text | 1908.11664 | null | https://arxiv.org/abs/1908.11664v1 | https://arxiv.org/pdf/1908.11664v1.pdf | Exploring Domain Shift in Extractive Text Summarization | Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization. As a result, the model is under-utilizing the nature of the training data due to ignoring the difference in the distribution of training sets and shows poor generalization on the unseen domain. With the above limitation in mind, in this paper, we first extend the conventional definition of the domain from categories into data sources for the text summarization task. Then we re-purpose a multi-domain summarization dataset and verify how the gap between different domains influences the performance of neural summarization models. Furthermore, we investigate four learning strategies and examine their abilities to deal with the domain shift problem. Experimental results on three different settings show their different characteristics in our new testbed. Our source code including \textit{BERT-based}, \textit{meta-learning} methods for multi-domain summarization learning and the re-purposed dataset \textsc{Multi-SUM} will be available on our project: \url{http://pfliu.com/TransferSum/}. | ['PengFei Liu', 'Xuanjing Huang', 'Xipeng Qiu', 'Jie Fu', 'Danqing Wang', 'Ming Zhong'] | 2019-08-30 | null | null | null | null | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.95188177e-01 3.03321481e-01 -4.02903259e-01 -2.59395063e-01
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3ec2ce00-1704-494a-a2c0-893504453983 | zero-shot-reinforcement-learning-with-deep | 2001.00605 | null | https://arxiv.org/abs/2001.00605v1 | https://arxiv.org/pdf/2001.00605v1.pdf | Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks | Simulation-to-simulation and simulation-to-real world transfer of neural network models have been a difficult problem. To close the reality gap, prior methods to simulation-to-real world transfer focused on domain adaptation, decoupling perception and dynamics and solving each problem separately, and randomization of agent parameters and environment conditions to expose the learning agent to a variety of conditions. While these methods provide acceptable performance, the computational complexity required to capture a large variation of parameters for comprehensive scenarios on a given task such as autonomous driving or robotic manipulation is high. Our key contribution is to theoretically prove and empirically demonstrate that a deep attention convolutional neural network (DACNN) with specific visual sensor configuration performs as well as training on a dataset with high domain and parameter variation at lower computational complexity. Specifically, the attention network weights are learned through policy optimization to focus on local dependencies that lead to optimal actions, and does not require tuning in real-world for generalization. Our new architecture adapts perception with respect to the control objective, resulting in zero-shot learning without pre-training a perception network. To measure the impact of our new deep network architecture on domain adaptation, we consider autonomous driving as a use case. We perform an extensive set of experiments in simulation-to-simulation and simulation-to-real scenarios to compare our approach to several baselines including the current state-of-art models. | ['Sunil Mallya', 'Yunzhe Tao', 'Sravan Bodapati', 'Tao Sun', 'Sahika Genc'] | 2020-01-02 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 3.47839929e-02 -7.47061744e-02 2.12931875e-02 -3.49714220e-01
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eb7809ab-45ba-45bc-89eb-f3f39d97edec | unfolding-selection-to-infer-individual-risk | 2009.01354 | null | https://arxiv.org/abs/2009.01354v2 | https://arxiv.org/pdf/2009.01354v2.pdf | Unfolding selection to infer individual risk heterogeneity for optimising disease forecasts and policy development | Mathematical models are increasing adopted for setting targets for disease prevention and control. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and overestimation of intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning single factors for infection and their interactions determine individual propensities to acquire disease. These are potentially so numerous that to attain a full mechanistic description may be unfeasible. To contribute constructively to the development of health policies, model developers either leave factors out (reductionism) or adopt a broader but coarse description (holism). In our view, predictive capacity requires holistic descriptions of heterogeneity which are currently underutilised in infectious disease epidemiology but common in other disciplines. | ['Stephen B. Gordon', 'Tom Wingfield', 'Rachel Tolhurst', 'Dianne J. Terlouw', 'Miriam Taegtmeyer', 'J. Russell Stothard', 'Jamie Rylance', 'Beate Ringwald', 'Lisa Reimer', 'Kate E. Langwig', 'E. James LaCourse', 'Marcelo U. Ferreira', 'Nicholas A. Feasey', 'M. Gabriela M. Gomes'] | 2020-09-02 | null | null | null | null | ['epidemiology'] | ['medical'] | [ 5.25396228e-01 -1.03780642e-01 -3.42302978e-01 1.43879473e-01
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1ddbccd2-2390-4171-8ba7-6db0a8559688 | prototype-aware-heterogeneous-task-for-point | 2209.01733 | null | https://arxiv.org/abs/2209.01733v1 | https://arxiv.org/pdf/2209.01733v1.pdf | Prototype-Aware Heterogeneous Task for Point Cloud Completion | Point cloud completion, which aims at recovering original shape information from partial point clouds, has attracted attention on 3D vision community. Existing methods usually succeed in completion for standard shape, while failing to generate local details of point clouds for some non-standard shapes. To achieve desirable local details, guidance from global shape information is of critical importance. In this work, we design an effective way to distinguish standard/non-standard shapes with the help of intra-class shape prototypical representation, which can be calculated by the proposed supervised shape clustering pretext task, resulting in a heterogeneous component w.r.t completion network. The representative prototype, defined as feature centroid of shape categories, can provide global shape guidance, which is referred to as soft-perceptual prior, to inject into downstream completion network by the desired selective perceptual feature fusion module in a multi-scale manner. Moreover, for effective training, we consider difficulty-based sampling strategy to encourage the network to pay more attention to some partial point clouds with fewer geometric information. Experimental results show that our method outperforms other state-of-the-art methods and has strong ability on completing complex geometric shapes. | ['Lizhuang Ma', 'Yuan Xie', 'Haichuan Song', 'Jingyu Gong', 'Jiachen Xu', 'Junshu Tang'] | 2022-09-05 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 1.76445141e-01 4.57589626e-02 1.54497251e-01 -2.85380781e-01
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6e20f5e9-8149-40eb-9315-774d06bc7695 | single-object-tracking-research-a-survey | 2204.11410 | null | https://arxiv.org/abs/2204.11410v1 | https://arxiv.org/pdf/2204.11410v1.pdf | Single Object Tracking Research: A Survey | Visual object tracking is an important task in computer vision, which has many real-world applications, e.g., video surveillance, visual navigation. Visual object tracking also has many challenges, e.g., object occlusion and deformation. To solve above problems and track the target accurately and efficiently, many tracking algorithms have emerged in recent years. This paper presents the rationale and representative works of two most popular tracking frameworks in past ten years, i.e., the corelation filter and Siamese network for object tracking. Then we present some deep learning based tracking methods categorized by different network structures. We also introduce some classical strategies for handling the challenges in tracking problem. Further, this paper detailedly present and compare the benchmarks and challenges for tracking, from which we summarize the development history and development trend of visual tracking. Focusing on the future development of object tracking, which we think would be applied in real-world scenes before some problems to be addressed, such as the problems in long-term tracking, low-power high-speed tracking and attack-robust tracking. In the future, the integration of multimodal data, e.g., the depth image, thermal image with traditional color image, will provide more solutions for visual tracking. Moreover, tracking task will go together with some other tasks, e.g., video object detection and segmentation. | ['QinGhua Hu', 'Qing Guo', 'Wei Feng', 'Ruize Han'] | 2022-04-25 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-2.36913189e-01 -8.74352276e-01 -2.93979019e-01 1.05623223e-01
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2c98f933-1a28-4f4b-acf9-faf1062c15b1 | the-pitfalls-of-using-open-data-to-develop | 2109.08020 | null | https://arxiv.org/abs/2109.08020v1 | https://arxiv.org/pdf/2109.08020v1.pdf | The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays | Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used open-source data and tested on an external test set and a hospital dataset, for the task of classifying chest X-rays into one of three classes: COVID-19, non-COVID pneumonia and no-pneumonia. Classification performance of the models investigated is evaluated through ROC curves, confusion matrices and standard classification metrics. Explainability modules are implemented to explore the image features most important to classification. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem and that results from testing on this are inflated. Dependence on open-source data can leave models vulnerable to bias and confounding variables, requiring careful analysis to develop clinically useful/viable AI tools for COVID-19 detection in chest X-rays. | ['Kieran Zucker', 'Nishant Ravikumar', 'Alejandro F Frangi', 'Geoff Hall', 'Rachael Harkness'] | 2021-09-14 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 1.23772003e-01 -2.48219058e-01 9.66119841e-02 -3.30012411e-01
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57f0fd08-1ab2-4c54-9fcc-59b7683ec39b | clickbait-identification-using-neural | 1710.08721 | null | http://arxiv.org/abs/1710.08721v1 | http://arxiv.org/pdf/1710.08721v1.pdf | Clickbait Identification using Neural Networks | This paper presents the results of our participation in the Clickbait
Detection Challenge 2017. The system relies on a fusion of neural networks,
incorporating different types of available informations. It does not require
any linguistic preprocessing, and hence generalizes more easily to new domains
and languages. The final combined model achieves a mean squared error of
0.0428, an accuracy of 0.826, and a F1 score of 0.564. According to the
official evaluation metric the system ranked 6th of the 13 participating teams. | ['Philippe Thomas'] | 2017-10-24 | null | null | null | null | ['clickbait-detection'] | ['natural-language-processing'] | [-3.30903798e-01 -1.82868674e-01 -4.75482225e-01 -3.71044666e-01
-1.15756941e+00 -6.08085752e-01 8.04030657e-01 4.48066115e-01
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1edc4c8f-a950-4ec3-8dbd-11daeec28814 | domain-adaptation-in-practice-lessons-from-a | null | null | https://aclanthology.org/2021.adaptnlp-1.11 | https://aclanthology.org/2021.adaptnlp-1.11.pdf | Domain adaptation in practice: Lessons from a real-world information extraction pipeline | Advances in transfer learning and domain adaptation have raised hopes that once-challenging NLP tasks are ready to be put to use for sophisticated information extraction needs. In this work, we describe an effort to do just that – combining state-of-the-art neural methods for negation detection, document time relation extraction, and aspectual link prediction, with the eventual goal of extracting drug timelines from electronic health record text. We train on the THYME colon cancer corpus and test on both the THYME brain cancer corpus and an internal corpus, and show that performance of the combined systems is unacceptable despite good performance of individual systems. Although domain adaptation shows improvements on each individual system, the model selection problem is a barrier to improving overall pipeline performance. | ['Steven Bethard', 'Egoitz Laparra', 'Timothy Miller'] | null | null | null | null | eacl-adaptnlp-2021-4 | ['negation-detection'] | ['natural-language-processing'] | [ 3.49139243e-01 3.97614628e-01 -5.85775375e-01 -5.11443973e-01
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863fd42c-fa6e-46b2-8ade-8278347de93f | fast-aid-brain-fast-and-accurate-segmentation | 2208.14360 | null | https://arxiv.org/abs/2208.14360v1 | https://arxiv.org/pdf/2208.14360v1.pdf | FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain | Medical images used in clinical practice are heterogeneous and not the same quality as scans studied in academic research. Preprocessing breaks down in extreme cases when anatomy, artifacts, or imaging parameters are unusual or protocols are different. Methods robust to these variations are most needed. A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations for the fusion of the orthogonal 2D planes and brain labels during the end-to-end training. Weakly supervised learning is deployed to take the advantage of partially labeled data for the whole brain segmentation and estimation of the intracranial volume (ICV). Moreover, data augmentation is used to expand the magnetic resonance imaging (MRI) data by generating realistic brain scans with high variability for robust training of the model while preserving data privacy. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance. Several experiments using different atlases are conducted to evaluate the segmentation performance of the trained model compared to the state-of-the-art, and the results show higher segmentation accuracy and robustness of the proposed model compared to the existing methods across different intra- and inter-domain datasets. | ['Mads Nielsen', 'Mostafa Mehdipour Ghazi'] | 2022-08-30 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [ 1.26218155e-01 2.94442505e-01 1.40650302e-01 -9.33021963e-01
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b1cc916c-704a-4759-8520-1d5ff61216ee | mole-recruitment-poisoning-of-image | 2303.17080 | null | https://arxiv.org/abs/2303.17080v1 | https://arxiv.org/pdf/2303.17080v1.pdf | Mole Recruitment: Poisoning of Image Classifiers via Selective Batch Sampling | In this work, we present a data poisoning attack that confounds machine learning models without any manipulation of the image or label. This is achieved by simply leveraging the most confounding natural samples found within the training data itself, in a new form of a targeted attack coined "Mole Recruitment." We define moles as the training samples of a class that appear most similar to samples of another class, and show that simply restructuring training batches with an optimal number of moles can lead to significant degradation in the performance of the targeted class. We show the efficacy of this novel attack in an offline setting across several standard image classification datasets, and demonstrate the real-world viability of this attack in a continual learning (CL) setting. Our analysis reveals that state-of-the-art models are susceptible to Mole Recruitment, thereby exposing a previously undetected vulnerability of image classifiers. | ['Yezhou Yang', 'Chaowei Xiao', 'Tejas Gokhale', 'Ethan Wisdom'] | 2023-03-30 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 7.40719914e-01 6.28468469e-02 -1.07417040e-01 3.82852294e-02
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6cc6bedf-9605-4d7c-b5d0-f3f64ce80626 | deep-image-retrieval-a-survey | 2101.11282 | null | https://arxiv.org/abs/2101.11282v4 | https://arxiv.org/pdf/2101.11282v4.pdf | Deep Learning for Instance Retrieval: A Survey | In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content-Content Based Image Retrieval (CBIR)-a long-established research area in which improved efficiency and accuracy are needed for real-time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of instance search. In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep network architecture types, deep features, feature embedding and aggregation methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, whereby we identify milestone work, reveal connections among various methods and present the commonly used benchmarks, evaluation results, common challenges, and propose promising future directions. | ['Michael S. Lew', 'Li Liu', 'Paul Fieguth', 'Theodoros Georgiou', 'Erwin Bakker', 'Weiping Wang', 'Yu Liu', 'Wei Chen'] | 2021-01-27 | null | null | null | null | ['instance-search', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [-2.48291548e-02 -5.38035810e-01 -5.15866399e-01 -1.08499244e-01
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83895d6a-3245-4052-a01c-3ba44601fe1a | gift-graph-induced-fine-tuning-for-multi | 2305.09360 | null | https://arxiv.org/abs/2305.09360v2 | https://arxiv.org/pdf/2305.09360v2.pdf | GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding | Addressing the issues of who saying what to whom in multi-party conversations (MPCs) has recently attracted a lot of research attention. However, existing methods on MPC understanding typically embed interlocutors and utterances into sequential information flows, or utilize only the superficial of inherent graph structures in MPCs. To this end, we present a plug-and-play and lightweight method named graph-induced fine-tuning (GIFT) which can adapt various Transformer-based pre-trained language models (PLMs) for universal MPC understanding. In detail, the full and equivalent connections among utterances in regular Transformer ignore the sparse but distinctive dependency of an utterance on another in MPCs. To distinguish different relationships between utterances, four types of edges are designed to integrate graph-induced signals into attention mechanisms to refine PLMs originally designed for processing sequential texts. We evaluate GIFT by implementing it into three PLMs, and test the performance on three downstream tasks including addressee recognition, speaker identification and response selection. Experimental results show that GIFT can significantly improve the performance of three PLMs on three downstream tasks and two benchmarks with only 4 additional parameters per encoding layer, achieving new state-of-the-art performance on MPC understanding. | ['Guoping Hu', 'Cong Liu', 'Quan Liu', 'Zhen-Hua Ling', 'Jia-Chen Gu'] | 2023-05-16 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 2.96336412e-01 4.41970766e-01 -2.55915344e-01 -8.93347442e-01
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b9a34b49-efc4-40f7-92c1-671148a38ee6 | diversifying-sample-generation-for-accurate | 2103.01049 | null | https://arxiv.org/abs/2103.01049v3 | https://arxiv.org/pdf/2103.01049v3.pdf | Diversifying Sample Generation for Accurate Data-Free Quantization | Quantization has emerged as one of the most prevalent approaches to compress and accelerate neural networks. Recently, data-free quantization has been widely studied as a practical and promising solution. It synthesizes data for calibrating the quantized model according to the batch normalization (BN) statistics of FP32 ones and significantly relieves the heavy dependency on real training data in traditional quantization methods. Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model. We propose Diverse Sample Generation (DSG) scheme to mitigate the adverse effects caused by homogenization. Specifically, we slack the alignment of feature statistics in the BN layer to relax the constraint at the distribution level and design a layerwise enhancement to reinforce specific layers for different data samples. Our DSG scheme is versatile and even able to be applied to the state-of-the-art post-training quantization method like AdaRound. We evaluate the DSG scheme on the large-scale image classification task and consistently obtain significant improvements over various network architectures and quantization methods, especially when quantized to lower bits (e.g., up to 22% improvement on W4A4). Moreover, benefiting from the enhanced diversity, models calibrated by synthetic data perform close to those calibrated by real data and even outperform them on W4A4. | ['Xianglong Liu', 'Fengwei Yu', 'Yuhang Li', 'Renshuai Tao', 'Qinghua Yan', 'Ruihao Gong', 'Yifu Ding', 'Haotong Qin', 'Xiangguo Zhang'] | 2021-03-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Diversifying_Sample_Generation_for_Accurate_Data-Free_Quantization_CVPR_2021_paper.pdf | cvpr-2021-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 3.50280911e-01 -2.06516832e-01 -1.23554215e-01 -5.62905073e-01
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7024bc2e-0f4f-4fdb-b9b4-e2ea02cb95a4 | no-reference-stereoscopic-image-quality | null | null | https://doi.org/10.2352/ISSN.2470-1173.2021.9.IQSP-297 | https://doi.org/10.2352/ISSN.2470-1173.2021.9.IQSP-297 | No-reference Stereoscopic Image Quality Predictor using Deep Features from Cyclopean Image | With the expanding use of stereoscopic imaging for 3D applications, no-reference perceptual quality evaluation has become important to provide good viewing experience. The effect of the quality distortion is related to the scene’s spatial details. Taking this into account, this paper introduces a blind stereoscopic image quality measurement using synthesized cyclopean image and deep feature extraction. The proposed method is based on Human Visual System (HVS) modeling and quality-aware indicators. First, the cyclopean image is formed, taking on the existence of binocular rivalry / suppression that includes the asymmetric distortion case. Second, the cyclopean image is decomposed into four equivalent parts. Then, four Convolutional Neural Network (CNN) models are deployed to automatically extract quality feature sets. Finally, a feature bank is then created from the four patches and mapped to quality score using a Support Vector Regression (SVR) model. The best known 3D LIVE phase I and phase II databases were used to evaluate the efficiency of our technique. Compared with the state-of-the-art stereoscopic image quality measurement metrics, the proposed method has shown competitive outcomes and achieved good performance. | ['Zianou Ahmed seghir', 'Fella Hachouf', 'Aladine Chetouani', 'Oussama Messai'] | 2021-01-01 | null | null | null | electronic-imaging-2021-1 | ['image-quality-estimation', 'blind-image-quality-assessment', 'stereoscopic-image-quality-assessment', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.54756978e-01 -6.21187866e-01 1.86145172e-01 -2.45763749e-01
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a3a1b22d-6a16-4dd5-8842-c406aa40ce72 | text-to-3d-scene-generation-with-rich-lexical | 1505.06289 | null | http://arxiv.org/abs/1505.06289v2 | http://arxiv.org/pdf/1505.06289v2.pdf | Text to 3D Scene Generation with Rich Lexical Grounding | The ability to map descriptions of scenes to 3D geometric representations has
many applications in areas such as art, education, and robotics. However, prior
work on the text to 3D scene generation task has used manually specified object
categories and language that identifies them. We introduce a dataset of 3D
scenes annotated with natural language descriptions and learn from this data
how to ground textual descriptions to physical objects. Our method successfully
grounds a variety of lexical terms to concrete referents, and we show
quantitatively that our method improves 3D scene generation over previous work
using purely rule-based methods. We evaluate the fidelity and plausibility of
3D scenes generated with our grounding approach through human judgments. To
ease evaluation on this task, we also introduce an automated metric that
strongly correlates with human judgments. | ['Angel Chang', 'Will Monroe', 'Manolis Savva', 'Christopher Potts', 'Christopher D. Manning'] | 2015-05-23 | text-to-3d-scene-generation-with-rich-lexical-2 | https://aclanthology.org/P15-1006 | https://aclanthology.org/P15-1006.pdf | ijcnlp-2015-7 | ['scene-generation', 'text-to-3d'] | ['computer-vision', 'computer-vision'] | [ 3.43330711e-01 5.58771133e-01 -4.17598197e-03 -7.01826394e-01
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ff55021e-b90c-4a84-8dda-91ad8047b6ff | neural-ode-with-temporal-convolution-and-time | 2008.00209 | null | https://arxiv.org/abs/2008.00209v2 | https://arxiv.org/pdf/2008.00209v2.pdf | Neural ODE with Temporal Convolution and Time Delay Neural Networks for Small-Footprint Keyword Spotting | In this paper, we propose neural network models based on the neural ordinary differential equation (NODE) for small-footprint keyword spotting (KWS). We present techniques to apply NODE to KWS that make it possible to adopt Batch Normalization to NODE-based network and to reduce the number of computations during inference. Finally, we show that the number of model parameters of the proposed model is smaller by 68% than that of the conventional KWS model. | ['Hiroshi Fuketa', 'Yukinori Morita'] | 2020-08-01 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 1.09106660e-01 8.44043717e-02 -3.36359501e-01 -7.13263810e-01
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67571be9-a567-4c95-b1ed-67016a3e2144 | learning-conditional-instrumental-variable | 2306.12453 | null | https://arxiv.org/abs/2306.12453v1 | https://arxiv.org/pdf/2306.12453v1.pdf | Learning Conditional Instrumental Variable Representation for Causal Effect Estimation | One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data. However, causal effect estimation often suffers from the impacts of confounding bias caused by unmeasured confounders that affect both the treatment and the outcome. The instrumental variable (IV) approach is a powerful way to eliminate the confounding bias from latent confounders. However, the existing IV-based estimators require a nominated IV, and for a conditional IV (CIV) the corresponding conditioning set too, for causal effect estimation. This limits the application of IV-based estimators. In this paper, by leveraging the advantage of disentangled representation learning, we propose a novel method, named DVAE.CIV, for learning and disentangling the representations of CIV and the representations of its conditioning set for causal effect estimations from data with latent confounders. Extensive experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed DVAE.CIV method against the existing causal effect estimators. | ['Jixue Liu', 'Thuc Duy Le', 'Lin Liu', 'Jiuyong Li', 'Ziqi Xu', 'Debo Cheng'] | 2023-06-21 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.83427972e-01 -1.37913665e-02 -9.44941640e-01 -3.98276567e-01
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35f9e7af-0909-495a-9292-18aa084f8b5e | job-prediction-from-deep-neural-network | 1912.12214 | null | https://arxiv.org/abs/1912.12214v2 | https://arxiv.org/pdf/1912.12214v2.pdf | Job Prediction: From Deep Neural Network Models to Applications | Determining the job is suitable for a student or a person looking for work based on their job's descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT Job dataset. In addition, we also proposed a simple and effective ensemble model combining different deep neural network models. The experimental results illustrated that our proposed ensemble model achieved the highest result with an F1 score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future. | ['Anh Gia-Tuan Nguyen', 'Ngan Luu-Thuy Nguyen', 'Kiet Van Nguyen', 'Tin Van Huynh'] | 2019-12-27 | null | null | null | null | ['job-classification', 'job-prediction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.34364039e-01 -1.30125448e-01 -2.51253039e-01 -5.58153868e-01
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fb56e662-85ed-4342-ae36-fe3d29907c89 | a-little-fog-for-a-large-turn | 2001.05873 | null | https://arxiv.org/abs/2001.05873v1 | https://arxiv.org/pdf/2001.05873v1.pdf | A Little Fog for a Large Turn | Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found. We turn our attention to the field of Autonomous navigation wherein adverse weather conditions such as fog have a drastic effect on the predictions of these systems. These weather conditions are capable of acting like natural adversaries that can help in testing models. To this end, we introduce a general notion of adversarial perturbations, which can be created using generative models and provide a methodology inspired by Cycle-Consistent Generative Adversarial Networks to generate adversarial weather conditions for a given image. Our formulation and results show that these images provide a suitable testbed for steering models used in Autonomous navigation models. Our work also presents a more natural and general definition of Adversarial perturbations based on Perceptual Similarity. | ['Vineeth N. Balasubramanian', 'Harshitha Machiraju'] | 2020-01-16 | null | null | null | null | ['safety-perception-recognition'] | ['computer-vision'] | [ 2.54900515e-01 3.51040810e-01 6.68908596e-01 -2.80526936e-01
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de060d53-59e2-4c6e-9db4-c8a3028e20d0 | tucker-decomposition-based-temporal-knowledge | 2011.07751 | null | https://arxiv.org/abs/2011.07751v1 | https://arxiv.org/pdf/2011.07751v1.pdf | Tucker decomposition-based Temporal Knowledge Graph Completion | Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years witness that many algorithms for link prediction and knowledge graphs embedding have been designed to infer new facts. But most of these studies focus on the static knowledge graphs and ignore the temporal information that reflects the validity of knowledge. Developing the model for temporal knowledge graphs completion is an increasingly important task. In this paper, we build a new tensor decomposition model for temporal knowledge graphs completion inspired by the Tucker decomposition of order 4 tensor. We demonstrate that the proposed model is fully expressive and report state-of-the-art results for several public benchmarks. Additionally, we present several regularization schemes to improve the strategy and study their impact on the proposed model. Experimental studies on three temporal datasets (i.e. ICEWS2014, ICEWS2005-15, GDELT) justify our design and demonstrate that our model outperforms baselines with an explicit margin on link prediction task. | ['Tong Liu', 'Feihu Che', 'JianHua Tao', 'Dawei Zhang', 'Guohua Yang', 'Pengpeng Shao'] | 2020-11-16 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-3.72013032e-01 8.25359449e-02 -8.23841035e-01 -1.36898383e-01
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c819c7d6-a874-4493-bd45-d5509646f237 | transfer-learning-with-deep-convolutional | 2004.06578 | null | https://arxiv.org/abs/2004.06578v1 | https://arxiv.org/pdf/2004.06578v1.pdf | Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray | Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances made in making accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN)- AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. 5247 Bacterial, viral and normal chest x-rays images underwent preprocessing techniques and the modified images were trained for the transfer learning based classification task. In this work, the authors have reported three schemes of classifications: normal vs pneumonia, bacterial vs viral pneumonia and normal, bacterial and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial and viral pneumonia were 98%, 95%, and 93.3% respectively. This is the highest accuracy in any scheme than the accuracies reported in the literature. Therefore, the proposed study can be useful in faster-diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients. | ['Amith Khandakar', 'Muhammad E. H. Chowdhury', 'Muhammad A. Kadir', 'Zaid B. Mahbub', 'Tawsifur Rahman', 'Khandaker F. Islam', 'Saad Kashem', 'Khandaker R. Islam'] | 2020-04-14 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 1.57457873e-01 -4.63780820e-01 1.24088302e-01 1.47738382e-01
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e2b81c71-469e-48f0-b034-f934cbcd5116 | online-self-supervised-learning-in-machine | 2306.13030 | null | https://arxiv.org/abs/2306.13030v1 | https://arxiv.org/pdf/2306.13030v1.pdf | Online Self-Supervised Learning in Machine Learning Intrusion Detection for the Internet of Things | This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Machine Learning (ML) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known ML models, showing that this SSID framework is very useful and advantageous as an accurate and online learning ML-based IDS for IoT systems. | ['Erol Gelenbe', 'Mert Nakıp'] | 2023-06-22 | null | null | null | null | ['self-supervised-learning', 'intrusion-detection'] | ['computer-vision', 'miscellaneous'] | [-1.07680850e-01 -1.53243020e-01 -2.97219247e-01 -5.83250225e-01
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6e87a512-3e84-4084-8bc5-e03332ef3a8b | text-simplification-of-scientific-texts-for | 2307.03569 | null | https://arxiv.org/abs/2307.03569v1 | https://arxiv.org/pdf/2307.03569v1.pdf | Text Simplification of Scientific Texts for Non-Expert Readers | Reading levels are highly individual and can depend on a text's language, a person's cognitive abilities, or knowledge on a topic. Text simplification is the task of rephrasing a text to better cater to the abilities of a specific target reader group. Simplification of scientific abstracts helps non-experts to access the core information by bypassing formulations that require domain or expert knowledge. This is especially relevant for, e.g., cancer patients reading about novel treatment options. The SimpleText lab hosts the simplification of scientific abstracts for non-experts (Task 3) to advance this field. We contribute three runs employing out-of-the-box summarization models (two based on T5, one based on PEGASUS) and one run using ChatGPT with complex phrase identification. | ['Philipp Schaer', 'Narjes Nikzad Khasmakhi', 'Christin Katharina Kreutz', 'Fabian Haak', 'Björn Engelmann'] | 2023-07-07 | null | null | null | null | ['text-simplification'] | ['natural-language-processing'] | [ 3.39591891e-01 6.56138003e-01 7.21796276e-03 -8.76495987e-02
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01b11ae1-85a4-4157-b94f-e596f114f35f | on-the-limit-of-english-conversational-speech | 2105.00982 | null | https://arxiv.org/abs/2105.00982v1 | https://arxiv.org/pdf/2105.00982v1.pdf | On the limit of English conversational speech recognition | In our previous work we demonstrated that a single headed attention encoder-decoder model is able to reach state-of-the-art results in conversational speech recognition. In this paper, we further improve the results for both Switchboard 300 and 2000. Through use of an improved optimizer, speaker vector embeddings, and alternative speech representations we reduce the recognition errors of our LSTM system on Switchboard-300 by 4% relative. Compensation of the decoder model with the probability ratio approach allows more efficient integration of an external language model, and we report 5.9% and 11.5% WER on the SWB and CHM parts of Hub5'00 with very simple LSTM models. Our study also considers the recently proposed conformer, and more advanced self-attention based language models. Overall, the conformer shows similar performance to the LSTM; nevertheless, their combination and decoding with an improved LM reaches a new record on Switchboard-300, 5.0% and 10.0% WER on SWB and CHM. Our findings are also confirmed on Switchboard-2000, and a new state of the art is reported, practically reaching the limit of the benchmark. | ['Brian Kingsbury', 'George Saon', 'Zoltán Tüske'] | 2021-05-03 | null | null | null | null | ['english-conversational-speech-recognition'] | ['speech'] | [-1.39013514e-01 6.15328193e-01 2.47230623e-02 -3.60639960e-01
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ffed9349-21ae-4a7a-9aca-eadbb603566c | vlocnet-deep-multitask-learning-for-semantic | 1804.08366 | null | http://arxiv.org/abs/1804.08366v6 | http://arxiv.org/pdf/1804.08366v6.pdf | VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry | Semantic understanding and localization are fundamental enablers of robot
autonomy that have for the most part been tackled as disjoint problems. While
deep learning has enabled recent breakthroughs across a wide spectrum of scene
understanding tasks, its applicability to state estimation tasks has been
limited due to the direct formulation that renders it incapable of encoding
scene-specific constrains. In this work, we propose the VLocNet++ architecture
that employs a multitask learning approach to exploit the inter-task
relationship between learning semantics, regressing 6-DoF global pose and
odometry, for the mutual benefit of each of these tasks. Our network overcomes
the aforementioned limitation by simultaneously embedding geometric and
semantic knowledge of the world into the pose regression network. We propose a
novel adaptive weighted fusion layer to aggregate motion-specific temporal
information and to fuse semantic features into the localization stream based on
region activations. Furthermore, we propose a self-supervised warping technique
that uses the relative motion to warp intermediate network representations in
the segmentation stream for learning consistent semantics. Finally, we
introduce a first-of-a-kind urban outdoor localization dataset with pixel-level
semantic labels and multiple loops for training deep networks. Extensive
experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc
dataset demonstrate that our approach exceeds the state-of-the-art
outperforming local feature-based methods while simultaneously performing
multiple tasks and exhibiting substantial robustness in challenging scenarios. | ['Abhinav Valada', 'Noha Radwan', 'Wolfram Burgard'] | 2018-04-23 | null | null | null | null | ['outdoor-localization'] | ['robots'] | [ 1.15748137e-01 -1.87648699e-01 -4.12512749e-01 -5.25072336e-01
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954fe4c6-be75-4072-8519-67e4f546d3a7 | efficient-mixed-transformer-for-single-image | 2305.11403 | null | https://arxiv.org/abs/2305.11403v5 | https://arxiv.org/pdf/2305.11403v5.pdf | Efficient Mixed Transformer for Single Image Super-Resolution | Recently, Transformer-based methods have achieved impressive results in single image super-resolution (SISR). However, the lack of locality mechanism and high complexity limit their application in the field of super-resolution (SR). To solve these problems, we propose a new method, Efficient Mixed Transformer (EMT) in this study. Specifically, we propose the Mixed Transformer Block (MTB), consisting of multiple consecutive transformer layers, in some of which the Pixel Mixer (PM) is used to replace the Self-Attention (SA). PM can enhance the local knowledge aggregation with pixel shifting operations. At the same time, no additional complexity is introduced as PM has no parameters and floating-point operations. Moreover, we employ striped window for SA (SWSA) to gain an efficient global dependency modelling by utilizing image anisotropy. Experimental results show that EMT outperforms the existing methods on benchmark dataset and achieved state-of-the-art performance. The Code is available at https://github.com/Fried-Rice-Lab/FriedRiceLab. | ['Shizhuang Weng', 'Jinpeng Shi', 'Jinchen Zhu', 'Ling Zheng'] | 2023-05-19 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 1.91588163e-01 -3.63166749e-01 4.58792932e-02 -2.31021717e-01
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a8e855d8-9f04-412f-a212-d1d9e9db94de | fast-video-object-segmentation-with-temporal-1 | 2007.05687 | null | https://arxiv.org/abs/2007.05687v1 | https://arxiv.org/pdf/2007.05687v1.pdf | Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching | Significant progress has been made in Video Object Segmentation (VOS), the video object tracking task in its finest level. While the VOS task can be naturally decoupled into image semantic segmentation and video object tracking, significantly much more research effort has been made in segmentation than tracking. In this paper, we introduce "tracking-by-detection" into VOS which can coherently integrate segmentation into tracking, by proposing a new temporal aggregation network and a novel dynamic time-evolving template matching mechanism to achieve significantly improved performance. Notably, our method is entirely online and thus suitable for one-shot learning, and our end-to-end trainable model allows multiple object segmentation in one forward pass. We achieve new state-of-the-art performance on the DAVIS benchmark without complicated bells and whistles in both speed and accuracy, with a speed of 0.14 second per frame and J&F measure of 75.9% respectively. | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Xuhua Huang', 'Jiarui Xu'] | 2020-07-11 | fast-video-object-segmentation-with-temporal | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Fast_Video_Object_Segmentation_With_Temporal_Aggregation_Network_and_Dynamic_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Fast_Video_Object_Segmentation_With_Temporal_Aggregation_Network_and_Dynamic_CVPR_2020_paper.pdf | cvpr-2020-6 | ['template-matching', 'video-object-tracking'] | ['computer-vision', 'computer-vision'] | [ 1.51433095e-01 -1.71711922e-01 -3.21188539e-01 -2.31007829e-01
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62ac6243-44b1-4454-b4e1-b4952420cb57 | fpga-based-implementation-of-deep-neural | 1602.01616 | null | http://arxiv.org/abs/1602.01616v2 | http://arxiv.org/pdf/1602.01616v2.pdf | FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only | Deep neural networks (DNNs) demand a very large amount of computation and
weight storage, and thus efficient implementation using special purpose
hardware is highly desired. In this work, we have developed an FPGA based
fixed-point DNN system using only on-chip memory not to access external DRAM.
The execution time and energy consumption of the developed system is compared
with a GPU based implementation. Since the capacity of memory in FPGA is
limited, only 3-bit weights are used for this implementation, and training
based fixed-point weight optimization is employed. The implementation using
Xilinx XC7Z045 is tested for the MNIST handwritten digit recognition benchmark
and a phoneme recognition task on TIMIT corpus. The obtained speed is about one
quarter of a GPU based implementation and much better than that of a PC based
one. The power consumption is less than 5 Watt at the full speed operation
resulting in much higher efficiency compared to GPU based systems. | ['Wonyong Sung', 'Jinhwan Park'] | 2016-02-04 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-7.29210898e-02 -2.74426877e-01 3.03366661e-01 -3.69647175e-01
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a7407922-ca76-4229-8fc6-03d339e87809 | prune-your-model-before-distill-it | 2109.14960 | null | https://arxiv.org/abs/2109.14960v3 | https://arxiv.org/pdf/2109.14960v3.pdf | Prune Your Model Before Distill It | Knowledge distillation transfers the knowledge from a cumbersome teacher to a small student. Recent results suggest that the student-friendly teacher is more appropriate to distill since it provides more transferable knowledge. In this work, we propose the novel framework, "prune, then distill," that prunes the model first to make it more transferrable and then distill it to the student. We provide several exploratory examples where the pruned teacher teaches better than the original unpruned networks. We further show theoretically that the pruned teacher plays the role of regularizer in distillation, which reduces the generalization error. Based on this result, we propose a novel neural network compression scheme where the student network is formed based on the pruned teacher and then apply the "prune, then distill" strategy. The code is available at https://github.com/ososos888/prune-then-distill | ['Albert No', 'Jinhyuk Park'] | 2021-09-30 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 1.76128715e-01 6.01844251e-01 -2.33679429e-01 -2.38053128e-01
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72006b69-3424-4032-870e-31bcd43b2b78 | onepose-keypoint-free-one-shot-object-pose | 2301.07673 | null | https://arxiv.org/abs/2301.07673v1 | https://arxiv.org/pdf/2301.07673v1.pdf | OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models | We propose a new method for object pose estimation without CAD models. The previous feature-matching-based method OnePose has shown promising results under a one-shot setting which eliminates the need for CAD models or object-specific training. However, OnePose relies on detecting repeatable image keypoints and is thus prone to failure on low-textured objects. We propose a keypoint-free pose estimation pipeline to remove the need for repeatable keypoint detection. Built upon the detector-free feature matching method LoFTR, we devise a new keypoint-free SfM method to reconstruct a semi-dense point-cloud model for the object. Given a query image for object pose estimation, a 2D-3D matching network directly establishes 2D-3D correspondences between the query image and the reconstructed point-cloud model without first detecting keypoints in the image. Experiments show that the proposed pipeline outperforms existing one-shot CAD-model-free methods by a large margin and is comparable to CAD-model-based methods on LINEMOD even for low-textured objects. We also collect a new dataset composed of 80 sequences of 40 low-textured objects to facilitate future research on one-shot object pose estimation. The supplementary material, code and dataset are available on the project page: https://zju3dv.github.io/onepose_plus_plus/. | ['Xiaowei Zhou', 'Hujun Bao', 'Di Huang', 'Yuang Wang', 'Jiaming Sun', 'Xingyi He'] | 2023-01-18 | null | null | null | null | ['keypoint-detection'] | ['computer-vision'] | [ 4.52860259e-03 -2.41045833e-01 -1.35030866e-01 -1.89067066e-01
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0721a708-4d54-4e34-ad7c-a2a16facfab4 | identification-of-bias-against-people-with | 2111.13259 | null | https://arxiv.org/abs/2111.13259v1 | https://arxiv.org/pdf/2111.13259v1.pdf | Identification of Bias Against People with Disabilities in Sentiment Analysis and Toxicity Detection Models | Sociodemographic biases are a common problem for natural language processing, affecting the fairness and integrity of its applications. Within sentiment analysis, these biases may undermine sentiment predictions for texts that mention personal attributes that unbiased human readers would consider neutral. Such discrimination can have great consequences in the applications of sentiment analysis both in the public and private sectors. For example, incorrect inferences in applications like online abuse and opinion analysis in social media platforms can lead to unwanted ramifications, such as wrongful censoring, towards certain populations. In this paper, we address the discrimination against people with disabilities, PWD, done by sentiment analysis and toxicity classification models. We provide an examination of sentiment and toxicity analysis models to understand in detail how they discriminate PWD. We present the Bias Identification Test in Sentiments (BITS), a corpus of 1,126 sentences designed to probe sentiment analysis models for biases in disability. We use this corpus to demonstrate statistically significant biases in four widely used sentiment analysis tools (TextBlob, VADER, Google Cloud Natural Language API and DistilBERT) and two toxicity analysis models trained to predict toxic comments on Jigsaw challenges (Toxic comment classification and Unintended Bias in Toxic comments). The results show that all exhibit strong negative biases on sentences that mention disability. We publicly release BITS Corpus for others to identify potential biases against disability in any sentiment analysis tools and also to update the corpus to be used as a test for other sociodemographic variables as well. | ['Shomir Wilson', 'Pranav Narayanan Venkit'] | 2021-11-25 | null | null | null | null | ['toxic-comment-classification'] | ['natural-language-processing'] | [ 2.88366526e-01 1.04406528e-01 -3.95121634e-01 -8.00750911e-01
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992f8589-bdfa-42dd-89fc-1ee8103742fe | ast-sed-an-effective-sound-event-detection | 2303.03689 | null | https://arxiv.org/abs/2303.03689v1 | https://arxiv.org/pdf/2303.03689v1.pdf | AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer | In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED. Pretrained AST models have recently shown promise on DCASE2022 challenge task4 where they help mitigate a lack of sufficient real annotated data. However, mainly due to differences between the AT and SED tasks, it is suboptimal to directly utilize outputs from a pretrained AST model. Hence the proposed AST-SED adopts an encoder-decoder architecture to enable effective and efficient fine-tuning without needing to redesign or retrain the AST model. Specifically, the Frequency-wise Transformer Encoder (FTE) consists of transformers with self attention along the frequency axis to address multiple overlapped audio events issue in a single clip. The Local Gated Recurrent Units Decoder (LGD) consists of nearest-neighbor interpolation (NNI) and Bidirectional Gated Recurrent Units (Bi-GRU) to compensate for temporal resolution loss in the pretrained AST model output. Experimental results on DCASE2022 task4 development set have demonstrated the superiority of the proposed AST-SED with FTE-LGD architecture. Specifically, the Event-Based F1-score (EB-F1) of 59.60% and Polyphonic Sound detection Score scenario1 (PSDS1) score of 0.5140 significantly outperform CRNN and other pretrained AST-based systems. | ['Lin Liu', 'Xin Fang', 'Ian McLoughlin', 'Li-Rong Dai', 'Yan Song', 'Kang Li'] | 2023-03-07 | null | null | null | null | ['audio-tagging', 'sound-event-detection'] | ['audio', 'audio'] | [ 2.33328551e-01 -4.74407300e-02 3.19416076e-01 -1.33520767e-01
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7b325ba8-f462-4dd1-8ce3-2efaa0db015e | pi-gan-periodic-implicit-generative | 2012.00926 | null | https://arxiv.org/abs/2012.00926v2 | https://arxiv.org/pdf/2012.00926v2.pdf | pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis | We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent; second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks ($\pi$-GAN or pi-GAN), for high-quality 3D-aware image synthesis. $\pi$-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent 3D representations with fine detail. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets. | ['Gordon Wetzstein', 'Jiajun Wu', 'Petr Kellnhofer', 'Marco Monteiro', 'Eric R. Chan'] | 2020-12-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chan_Pi-GAN_Periodic_Implicit_Generative_Adversarial_Networks_for_3D-Aware_Image_Synthesis_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-generation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 3.87798250e-01 2.90671051e-01 1.40258089e-01 -1.75605282e-01
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3dae5469-b8c0-4e1c-878e-855063f19e6c | zero-shot-action-recognition-from-diverse | 2110.13479 | null | https://arxiv.org/abs/2110.13479v1 | https://arxiv.org/pdf/2110.13479v1.pdf | Zero-Shot Action Recognition from Diverse Object-Scene Compositions | This paper investigates the problem of zero-shot action recognition, in the setting where no training videos with seen actions are available. For this challenging scenario, the current leading approach is to transfer knowledge from the image domain by recognizing objects in videos using pre-trained networks, followed by a semantic matching between objects and actions. Where objects provide a local view on the content in videos, in this work we also seek to include a global view of the scene in which actions occur. We find that scenes on their own are also capable of recognizing unseen actions, albeit more marginally than objects, and a direct combination of object-based and scene-based scores degrades the action recognition performance. To get the best out of objects and scenes, we propose to construct them as a Cartesian product of all possible compositions. We outline how to determine the likelihood of object-scene compositions in videos, as well as a semantic matching from object-scene compositions to actions that enforces diversity among the most relevant compositions for each action. While simple, our composition-based approach outperforms object-based approaches and even state-of-the-art zero-shot approaches that rely on large-scale video datasets with hundreds of seen actions for training and knowledge transfer. | ['Pascal Mettes', 'Carlo Bretti'] | 2021-10-26 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 6.05720699e-01 -1.82198226e-01 -2.54020929e-01 -3.11987072e-01
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1814df74-f413-4d7f-a77b-95ab9c7b4292 | post-processing-private-synthetic-data-for | 2305.15538 | null | https://arxiv.org/abs/2305.15538v1 | https://arxiv.org/pdf/2305.15538v1.pdf | Post-processing Private Synthetic Data for Improving Utility on Selected Measures | Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Failure to meet these requirements could significantly reduce the utility of the data for downstream use. We introduce a post-processing technique that improves the utility of the synthetic data with respect to measures selected by the end user, while preserving strong privacy guarantees and dataset quality. Our technique involves resampling from the synthetic data to filter out samples that do not meet the selected utility measures, using an efficient stochastic first-order algorithm to find optimal resampling weights. Through comprehensive numerical experiments, we demonstrate that our approach consistently improves the utility of synthetic data across multiple benchmark datasets and state-of-the-art synthetic data generation algorithms. | ['Akash Srivastava', 'Kristjan Greenewald', 'John Henning', 'Shivchander Sudalairaj', 'Hao Wang'] | 2023-05-24 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 3.54609579e-01 1.78972095e-01 -2.87170112e-01 -4.70065564e-01
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765cfedf-89a5-40c4-993f-6f45c96b86c2 | self-adaptive-hierarchical-sentence-model | 1504.05070 | null | http://arxiv.org/abs/1504.05070v2 | http://arxiv.org/pdf/1504.05070v2.pdf | Self-Adaptive Hierarchical Sentence Model | The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets. | ['Pascal Poupart', 'Zhengdong Lu', 'Han Zhao'] | 2015-04-20 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 4.36376512e-01 -1.44548550e-01 -1.75381780e-01 -5.53916574e-01
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c6458f6f-3772-468d-81c4-587088b3c8ca | nef-neural-edge-fields-for-3d-parametric | 2303.07653 | null | https://arxiv.org/abs/2303.07653v2 | https://arxiv.org/pdf/2303.07653v2.pdf | NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images | We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images. To do so, we learn a neural implicit field representing the density distribution of 3D edges which we refer to as Neural Edge Field (NEF). Inspired by NeRF, NEF is optimized with a view-based rendering loss where a 2D edge map is rendered at a given view and is compared to the ground-truth edge map extracted from the image of that view. The rendering-based differentiable optimization of NEF fully exploits 2D edge detection, without needing a supervision of 3D edges, a 3D geometric operator or cross-view edge correspondence. Several technical designs are devised to ensure learning a range-limited and view-independent NEF for robust edge extraction. The final parametric 3D curves are extracted from NEF with an iterative optimization method. On our benchmark with synthetic data, we demonstrate that NEF outperforms existing state-of-the-art methods on all metrics. Project page: https://yunfan1202.github.io/NEF/. | ['Kai Xu', 'Zhiping Cai', 'Chenyang Zhu', 'Zhirui Gao', 'Renjiao Yi', 'Yunfan Ye'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ye_NEF_Neural_Edge_Fields_for_3D_Parametric_Curve_Reconstruction_From_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ye_NEF_Neural_Edge_Fields_for_3D_Parametric_Curve_Reconstruction_From_CVPR_2023_paper.pdf | cvpr-2023-1 | ['edge-detection'] | ['computer-vision'] | [-9.27356929e-02 -1.41908631e-01 3.22734602e-02 -2.83372909e-01
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2125f5b9-67d6-4d2f-ae83-cd1478f31e38 | neural-head-reenactment-with-latent-pose | 2004.12000 | null | https://arxiv.org/abs/2004.12000v2 | https://arxiv.org/pdf/2004.12000v2.pdf | Neural Head Reenactment with Latent Pose Descriptors | We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval. | ['Igor Pasechnik', 'Artur Grigorev', 'Egor Burkov', 'Victor Lempitsky'] | 2020-04-24 | neural-head-reenactment-with-latent-pose-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Burkov_Neural_Head_Reenactment_with_Latent_Pose_Descriptors_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Burkov_Neural_Head_Reenactment_with_Latent_Pose_Descriptors_CVPR_2020_paper.pdf | cvpr-2020-6 | ['foreground-segmentation'] | ['computer-vision'] | [ 1.55564874e-01 4.32526976e-01 -1.70392349e-01 -3.86867851e-01
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8b6305ab-2522-40d2-bc32-2eeb627bbbbf | accelerating-large-scale-real-time-gnn | 2105.04528 | null | https://arxiv.org/abs/2105.04528v1 | https://arxiv.org/pdf/2105.04528v1.pdf | Accelerating Large Scale Real-Time GNN Inference using Channel Pruning | Graph Neural Networks (GNNs) are proven to be powerful models to generate node embedding for downstream applications. However, due to the high computation complexity of GNN inference, it is hard to deploy GNNs for large-scale or real-time applications. In this paper, we propose to accelerate GNN inference by pruning the dimensions in each layer with negligible accuracy loss. Our pruning framework uses a novel LASSO regression formulation for GNNs to identify feature dimensions (channels) that have high influence on the output activation. We identify two inference scenarios and design pruning schemes based on their computation and memory usage for each. To further reduce the inference complexity, we effectively store and reuse hidden features of visited nodes, which significantly reduces the number of supporting nodes needed to compute the target embedding. We evaluate the proposed method with the node classification problem on five popular datasets and a real-time spam detection application. We demonstrate that the pruned GNN models greatly reduce computation and memory usage with little accuracy loss. For full inference, the proposed method achieves an average of 3.27x speedup with only 0.002 drop in F1-Micro on GPU. For batched inference, the proposed method achieves an average of 6.67x speedup with only 0.003 drop in F1-Micro on CPU. To the best of our knowledge, we are the first to accelerate large scale real-time GNN inference through channel pruning. | ['Viktor Prasanna', 'Rajgopal Kannan', 'Hanqing Zeng', 'Ajitesh Srivastava', 'Hongkuan Zhou'] | 2021-05-10 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.14821382e-01 -3.36422473e-02 -2.01953202e-01 -1.97336614e-01
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d98612b4-8580-4ba0-88c3-815927a8a4c4 | adversarial-attacks-on-image-classification | 2307.02055 | null | https://arxiv.org/abs/2307.02055v1 | https://arxiv.org/pdf/2307.02055v1.pdf | Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impact | This chapter introduces the concept of adversarial attacks on image classification models built on convolutional neural networks (CNN). CNNs are very popular deep-learning models which are used in image classification tasks. However, very powerful and pre-trained CNN models working very accurately on image datasets for image classification tasks may perform disastrously when the networks are under adversarial attacks. In this work, two very well-known adversarial attacks are discussed and their impact on the performance of image classifiers is analyzed. These two adversarial attacks are the fast gradient sign method (FGSM) and adversarial patch attack. These attacks are launched on three powerful pre-trained image classifier architectures, ResNet-34, GoogleNet, and DenseNet-161. The classification accuracy of the models in the absence and presence of the two attacks are computed on images from the publicly accessible ImageNet dataset. The results are analyzed to evaluate the impact of the attacks on the image classification task. | ['Subhasis Dasgupta', 'Jaydip Sen'] | 2023-07-05 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 7.80774653e-02 -2.54695397e-02 3.03214341e-01 -2.95427054e-01
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332a1250-71c9-4eb4-9e1e-2fe188f3f58c | finite-time-regret-of-thompson-sampling | 2206.03520 | null | https://arxiv.org/abs/2206.03520v1 | https://arxiv.org/pdf/2206.03520v1.pdf | Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits | We study the regret of Thompson sampling (TS) algorithms for exponential family bandits, where the reward distribution is from a one-dimensional exponential family, which covers many common reward distributions including Bernoulli, Gaussian, Gamma, Exponential, etc. We propose a Thompson sampling algorithm, termed ExpTS, which uses a novel sampling distribution to avoid the under-estimation of the optimal arm. We provide a tight regret analysis for ExpTS, which simultaneously yields both the finite-time regret bound as well as the asymptotic regret bound. In particular, for a $K$-armed bandit with exponential family rewards, ExpTS over a horizon $T$ is sub-UCB (a strong criterion for the finite-time regret that is problem-dependent), minimax optimal up to a factor $\sqrt{\log K}$, and asymptotically optimal, for exponential family rewards. Moreover, we propose ExpTS$^+$, by adding a greedy exploitation step in addition to the sampling distribution used in ExpTS, to avoid the over-estimation of sub-optimal arms. ExpTS$^+$ is an anytime bandit algorithm and achieves the minimax optimality and asymptotic optimality simultaneously for exponential family reward distributions. Our proof techniques are general and conceptually simple and can be easily applied to analyze standard Thompson sampling with specific reward distributions. | ['Anima Anandkumar', 'Xiaokui Xiao', 'Pan Xu', 'Tianyuan Jin'] | 2022-06-07 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-9.58977342e-02 7.43775954e-03 -6.93921149e-01 -2.29731351e-01
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1cf3448c-b5b7-445a-a232-2f524698e74f | bio-inspired-unsupervised-learning-of-visual | 1504.03871 | null | http://arxiv.org/abs/1504.03871v3 | http://arxiv.org/pdf/1504.03871v3.pdf | Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition | Retinal image of surrounding objects varies tremendously due to the changes
in position, size, pose, illumination condition, background context, occlusion,
noise, and nonrigid deformations. But despite these huge variations, our visual
system is able to invariantly recognize any object in just a fraction of a
second. To date, various computational models have been proposed to mimic the
hierarchical processing of the ventral visual pathway, with limited success.
Here, we show that the association of both biologically inspired network
architecture and learning rule significantly improves the models' performance
when facing challenging invariant object recognition problems. Our model is an
asynchronous feedforward spiking neural network. When the network is presented
with natural images, the neurons in the entry layers detect edges, and the most
activated ones fire first, while neurons in higher layers are equipped with
spike timing-dependent plasticity. These neurons progressively become selective
to intermediate complexity visual features appropriate for object
categorization. The model is evaluated on 3D-Object and ETH-80 datasets which
are two benchmarks for invariant object recognition, and is shown to outperform
state-of-the-art models, including DeepConvNet and HMAX. This demonstrates its
ability to accurately recognize different instances of multiple object classes
even under various appearance conditions (different views, scales, tilts, and
backgrounds). Several statistical analysis techniques are used to show that our
model extracts class specific and highly informative features. | ['Timothée Masquelier', 'Saeed Reza Kheradpisheh', 'Mohammad Ganjtabesh'] | 2015-04-15 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 2.77720541e-01 -4.70130056e-01 2.29419500e-01 -1.56357065e-01
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06503229-7894-408d-a6e6-668d8a38ef69 | on-the-bias-variance-characteristics-of-lime | 2206.04784 | null | https://arxiv.org/abs/2206.04784v1 | https://arxiv.org/pdf/2206.04784v1.pdf | On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks | We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set. LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIMB) that is superior to LIME and much faster than SHAP. | ['Ashok Chandrashekar', 'Ehtsham Elahi', 'Claudia V. Roberts'] | 2022-06-09 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.91034302e-01 2.24289805e-01 -5.25881827e-01 -4.43444669e-01
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58e51769-64a4-4b07-9bc9-eb195b08d2e6 | radio-map-estimation-a-data-driven-approach | 2202.03269 | null | https://arxiv.org/abs/2202.03269v2 | https://arxiv.org/pdf/2202.03269v2.pdf | Radio Map Estimation: A Data-Driven Approach to Spectrum Cartography | Radio maps characterize quantities of interest in radio communication environments, such as the received signal strength and channel attenuation, at every point of a geographical region. Radio map estimation typically entails interpolative inference based on spatially distributed measurements. In this tutorial article, after presenting some representative applications of radio maps, the most prominent radio map estimation methods are discussed. Starting from simple regression, the exposition gradually delves into more sophisticated algorithms, eventually touching upon state-of-the-art techniques. To gain insight into this versatile toolkit, illustrative toy examples will also be presented. | ['Seung-Jun Kim', 'Daniel Romero'] | 2022-02-01 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 7.59112462e-02 1.59379199e-01 -1.26489788e-01 -4.03497934e-01
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c826e2fd-53cb-4a9f-8eed-99b52c9dbff1 | learning-a-disentangled-embedding-for | 1812.09899 | null | http://arxiv.org/abs/1812.09899v2 | http://arxiv.org/pdf/1812.09899v2.pdf | Learning a Disentangled Embedding for Monocular 3D Shape Retrieval and Pose Estimation | We propose a novel approach to jointly perform 3D shape retrieval and pose
estimation from monocular images.In order to make the method robust to
real-world image variations, e.g. complex textures and backgrounds, we learn an
embedding space from 3D data that only includes the relevant information,
namely the shape and pose. Our approach explicitly disentangles a shape vector
and a pose vector, which alleviates both pose bias for 3D shape retrieval and
categorical bias for pose estimation. We then train a CNN to map the images to
this embedding space, and then retrieve the closest 3D shape from the database
and estimate the 6D pose of the object. Our method achieves 10.3 median error
for pose estimation and 0.592 top-1-accuracy for category agnostic 3D object
retrieval on the Pascal3D+ dataset, outperforming the previous state-of-the-art
methods on both tasks. | ['Tat-Seng Chua', 'Weipeng Xu', 'Kyaw Zaw Lin', 'Qianru Sun', 'Christian Theobalt'] | 2018-12-24 | null | null | null | null | ['3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision'] | [-1.72396928e-01 -3.09905469e-01 -1.52856559e-01 -4.43880588e-01
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935fd03a-46f9-4d97-9235-71729fd14118 | a-user-study-on-explainable-online | 2307.04098 | null | https://arxiv.org/abs/2307.04098v1 | https://arxiv.org/pdf/2307.04098v1.pdf | A User Study on Explainable Online Reinforcement Learning for Adaptive Systems | Online reinforcement learning (RL) is increasingly used for realizing adaptive systems in the presence of design time uncertainty. Online RL facilitates learning from actual operational data and thereby leverages feedback only available at runtime. However, Online RL requires the definition of an effective and correct reward function, which quantifies the feedback to the RL algorithm and thereby guides learning. With Deep RL gaining interest, the learned knowledge is no longer explicitly represented, but is represented as a neural network. For a human, it becomes practically impossible to relate the parametrization of the neural network to concrete RL decisions. Deep RL thus essentially appears as a black box, which severely limits the debugging of adaptive systems. We previously introduced the explainable RL technique XRL-DINE, which provides visual insights into why certain decisions were made at important time points. Here, we introduce an empirical user study involving 54 software engineers from academia and industry to assess (1) the performance of software engineers when performing different tasks using XRL-DINE and (2) the perceived usefulness and ease of use of XRL-DINE. | ['Klaus Pohl', 'Felix Feit', 'Jan Laufer', 'Andreas Metzger'] | 2023-07-09 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-2.42792115e-01 3.53270710e-01 -2.04654217e-01 -9.50816423e-02
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bc134f2a-2b41-4e17-b185-489fecab097c | an-effective-ahp-based-metaheuristic-approach | 1404.4067 | null | http://arxiv.org/abs/1404.4067v1 | http://arxiv.org/pdf/1404.4067v1.pdf | An effective AHP-based metaheuristic approach to solve supplier selection problem | The supplier selection problem is based on electing the best supplier from a
group of pre-specified candidates, is identified as a Multi Criteria Decision
Making (MCDM), is proportionately significant in terms of qualitative and
quantitative attributes. It is a fundamental issue to achieve a trade-off
between such quantifiable and unquantifiable attributes with an aim to
accomplish the best solution to the abovementioned problem. This article
portrays a metaheuristic based optimization model to solve this NP-Complete
problem. Initially the Analytic Hierarchy Process (AHP) is implemented to
generate an initial feasible solution of the problem. Thereafter a Simulated
Annealing (SA) algorithm is exploited to improve the quality of the obtained
solution. The Taguchi robust design method is exploited to solve the critical
issues on the subject of the parameter selection of the SA technique. In order
to verify the proposed methodology the numerical results are demonstrated based
on tangible industry data. | ['Tanmoy Chakraborty', 'Pranab K Dan', 'Tamal Ghosh'] | 2014-04-15 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 3.43473822e-01 -3.17324758e-01 2.32763335e-01 -4.21569616e-01
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cf2719de-1475-4722-b736-33d485c39f4e | extending-heideltime-for-temporal-expressions | null | null | https://aclanthology.org/L14-1655 | https://aclanthology.org/L14-1655.pdf | Extending HeidelTime for Temporal Expressions Referring to Historic Dates | Research on temporal tagging has achieved a lot of attention during the last years. However, most of the work focuses on processing news-style documents. Thus, references to historic dates are often not well handled by temporal taggers although they frequently occur in narrative-style documents about history, e.g., in many Wikipedia articles. In this paper, we present the AncientTimes corpus containing documents about different historic time periods in eight languages, in which we manually annotated temporal expressions. Based on this corpus, we explain the challenges of temporal tagging documents about history. Furthermore, we use the corpus to extend our multilingual, cross-domain temporal tagger HeidelTime to extract and normalize temporal expressions referring to historic dates, and to demonstrate HeidelTime{'}s new capabilities. Both, the AncientTimes corpus as well as the new HeidelTime version are made publicly available. | ['Thomas B{\\"o}gel', 'Jannik Str{\\"o}tgen', 'Michael Gertz', 'Julian Zell', 'Ayser Armiti', 'Tran Van Canh'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['temporal-information-extraction', 'temporal-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-2.81824410e-01 -1.94794446e-01 -6.76438689e-01 -3.66052657e-01
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58796e45-b36a-46ff-a46d-6714bef55ee1 | authorship-verification-based-on-compression | 1706.00516 | null | https://arxiv.org/abs/1706.00516v1 | https://arxiv.org/pdf/1706.00516v1.pdf | Authorship Verification based on Compression-Models | Compression models represent an interesting approach for different classification tasks and have been used widely across many research fields. We adapt compression models to the field of authorship verification (AV), a branch of digital text forensics. The task in AV is to verify if a questioned document and a reference document of a known author are written by the same person. We propose an intrinsic AV method, which yields competitive results compared to a number of current state-of-the-art approaches, based on support vector machines or neural networks. However, in contrast to these approaches our method does not make use of machine learning algorithms, natural language processing techniques, feature engineering, hyperparameter optimization or external documents (a common strategy to transform AV from a one-class to a multi-class classification problem). Instead, the only three key components of our method are a compressing algorithm, a dissimilarity measure and a threshold, needed to accept or reject the authorship of the questioned document. Due to its compactness, our method performs very fast and can be reimplemented with minimal effort. In addition, the method can handle complicated AV cases where both, the questioned and the reference document, are not related to each other in terms of topic or genre. We evaluated our approach against publicly available datasets, which were used in three international AV competitions. Furthermore, we constructed our own corpora, where we evaluated our method against state-of-the-art approaches and achieved, in both cases, promising results. | ['Lukas Graner', 'Christian Winter', 'Oren Halvani'] | 2017-06-01 | null | null | null | null | ['authorship-verification', 'text-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.64324510e-01 -2.14438871e-01 -2.33827218e-01 -1.47572048e-02
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0c0e598f-5f4d-4b48-9d1e-dafbf9f85e48 | solos-a-dataset-for-audio-visual-music | 2006.07931 | null | https://arxiv.org/abs/2006.07931v1 | https://arxiv.org/pdf/2006.07931v1.pdf | Solos: A Dataset for Audio-Visual Music Analysis | In this paper, we present a new dataset of music performance videos which can be used for training machine learning methods for multiple tasks such as audio-visual blind source separation and localization, cross-modal correspondences, cross-modal generation and, in general, any audio-visual selfsupervised task. These videos, gathered from YouTube, consist of solo musical performances of 13 different instruments. Compared to previously proposed audio-visual datasets, Solos is cleaner since a big amount of its recordings are auditions and manually checked recordings, ensuring there is no background noise nor effects added in the video post-processing. Besides, it is, up to the best of our knowledge, the only dataset that contains the whole set of instruments present in the URMP [1] dataset, a highquality dataset of 44 multi-instrument audio-visual recordings of classical music pieces with individual audio tracks. URMP was intented to be used for source separation, thus, we evaluate the performance on the URMP dataset of two different BSS models trained on Solos | ['Gloria Haro', 'Olga Slizovskaia', 'Juan F. Montesinos'] | 2020-06-14 | null | null | null | null | ['audio-source-separation', 'audio-visual-synchronization', 'audio-visual-synchronization', 'music-source-separation'] | ['audio', 'audio', 'computer-vision', 'music'] | [ 5.00467792e-02 -4.25076693e-01 -1.28837317e-01 4.72873926e-01
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8ad6f106-b330-4031-9297-0456b761ab7d | are-attribute-inference-attacks-just | 2209.01292 | null | https://arxiv.org/abs/2209.01292v1 | https://arxiv.org/pdf/2209.01292v1.pdf | Are Attribute Inference Attacks Just Imputation? | Models can expose sensitive information about their training data. In an attribute inference attack, an adversary has partial knowledge of some training records and access to a model trained on those records, and infers the unknown values of a sensitive feature of those records. We study a fine-grained variant of attribute inference we call \emph{sensitive value inference}, where the adversary's goal is to identify with high confidence some records from a candidate set where the unknown attribute has a particular sensitive value. We explicitly compare attribute inference with data imputation that captures the training distribution statistics, under various assumptions about the training data available to the adversary. Our main conclusions are: (1) previous attribute inference methods do not reveal more about the training data from the model than can be inferred by an adversary without access to the trained model, but with the same knowledge of the underlying distribution as needed to train the attribute inference attack; (2) black-box attribute inference attacks rarely learn anything that cannot be learned without the model; but (3) white-box attacks, which we introduce and evaluate in the paper, can reliably identify some records with the sensitive value attribute that would not be predicted without having access to the model. Furthermore, we show that proposed defenses such as differentially private training and removing vulnerable records from training do not mitigate this privacy risk. The code for our experiments is available at \url{https://github.com/bargavj/EvaluatingDPML}. | ['David Evans', 'Bargav Jayaraman'] | 2022-09-02 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 4.39419687e-01 3.91888678e-01 -2.74564207e-01 -6.69650793e-01
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004d8f1d-a785-4a47-a887-0f3e68ad0549 | nerve-neural-volumetric-edges-for-parametric | 2303.16465 | null | https://arxiv.org/abs/2303.16465v1 | https://arxiv.org/pdf/2303.16465v1.pdf | NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloud | Extracting parametric edge curves from point clouds is a fundamental problem in 3D vision and geometry processing. Existing approaches mainly rely on keypoint detection, a challenging procedure that tends to generate noisy output, making the subsequent edge extraction error-prone. To address this issue, we propose to directly detect structured edges to circumvent the limitations of the previous point-wise methods. We achieve this goal by presenting NerVE, a novel neural volumetric edge representation that can be easily learned through a volumetric learning framework. NerVE can be seamlessly converted to a versatile piece-wise linear (PWL) curve representation, enabling a unified strategy for learning all types of free-form curves. Furthermore, as NerVE encodes rich structural information, we show that edge extraction based on NerVE can be reduced to a simple graph search problem. After converting NerVE to the PWL representation, parametric curves can be obtained via off-the-shelf spline fitting algorithms. We evaluate our method on the challenging ABC dataset. We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin. Project page: https://dongdu3.github.io/projects/2023/NerVE/. | ['Xiaoguang Han', 'Yinyu Nie', 'Zhiyou Zhao', 'Weikai Chen', 'Dong Du', 'Xiangyu Zhu'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhu_NerVE_Neural_Volumetric_Edges_for_Parametric_Curve_Extraction_From_Point_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhu_NerVE_Neural_Volumetric_Edges_for_Parametric_Curve_Extraction_From_Point_CVPR_2023_paper.pdf | cvpr-2023-1 | ['keypoint-detection'] | ['computer-vision'] | [-1.6387054e-01 -1.8889120e-02 -4.2149857e-02 1.2204440e-01
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0895c9f5-cca2-4850-9332-a6919cf80e9e | icdar-2021-competition-on-scene-video-text | 2107.11919 | null | https://arxiv.org/abs/2107.11919v1 | https://arxiv.org/pdf/2107.11919v1.pdf | ICDAR 2021 Competition on Scene Video Text Spotting | Scene video text spotting (SVTS) is a very important research topic because of many real-life applications. However, only a little effort has put to spotting scene video text, in contrast to massive studies of scene text spotting in static images. Due to various environmental interferences like motion blur, spotting scene video text becomes very challenging. To promote this research area, this competition introduces a new challenge dataset containing 129 video clips from 21 natural scenarios in full annotations. The competition containts three tasks, that is, video text detection (Task 1), video text tracking (Task 2) and end-to-end video text spotting (Task3). During the competition period (opened on 1st March, 2021 and closed on 11th April, 2021), a total of 24 teams participated in the three proposed tasks with 46 valid submissions, respectively. This paper includes dataset descriptions, task definitions, evaluation protocols and results summaries of the ICDAR 2021 on SVTS competition. Thanks to the healthy number of teams as well as submissions, we consider that the SVTS competition has been successfully held, drawing much attention from the community and promoting the field research and its development. | ['Fei Wu', 'Shuigeng Zhou', 'Baorui Zou', 'Jing Lu', 'Zhanzhan Cheng'] | 2021-07-26 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 6.64304614e-01 -3.45714718e-01 1.04981028e-01 -1.99617237e-01
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54da00bf-47bf-4291-b2b0-c1de9a3ea749 | visual-pursuit-control-based-on-gaussian | 2208.08645 | null | https://arxiv.org/abs/2208.08645v1 | https://arxiv.org/pdf/2208.08645v1.pdf | Visual Pursuit Control based on Gaussian Processes with Switched Motion Trajectories | This paper considers a scenario of pursuing a moving target that may switch behaviors due to external factors in a dynamic environment by motion estimation using visual sensors. First, we present an improved Visual Motion Observer with switched Gaussian Process models for an extended class of target motion profiles. We then propose a pursuit control law with an online method to estimate the switching behavior of the target by the GP model uncertainty. Next, we prove ultimate boundedness of the control and estimation errors for the switch in target behavior with high probability. Finally, a Digital Twin simulation demonstrates the effectiveness of the proposed switching estimation and control law to prove applicability to real world scenarios. | ['Masayuki Fujita', 'Junya Yamauchi', 'Marco Omainska'] | 2022-08-18 | null | null | null | null | ['3d-pose-estimation', 'real-time-visual-tracking', 'drone-based-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.62420884e-01 1.10798001e-01 -4.19000328e-01 3.86616200e-01
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7768741d-70e9-4e52-b982-524477cbf9f5 | mugen-a-playground-for-video-audio-text | 2204.08058 | null | https://arxiv.org/abs/2204.08058v3 | https://arxiv.org/pdf/2204.08058v3.pdf | MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration | Multimodal video-audio-text understanding and generation can benefit from datasets that are narrow but rich. The narrowness allows bite-sized challenges that the research community can make progress on. The richness ensures we are making progress along the core challenges. To this end, we present a large-scale video-audio-text dataset MUGEN, collected using the open-sourced platform game CoinRun [11]. We made substantial modifications to make the game richer by introducing audio and enabling new interactions. We trained RL agents with different objectives to navigate the game and interact with 13 objects and characters. This allows us to automatically extract a large collection of diverse videos and associated audio. We sample 375K video clips (3.2s each) and collect text descriptions from human annotators. Each video has additional annotations that are extracted automatically from the game engine, such as accurate semantic maps for each frame and templated textual descriptions. Altogether, MUGEN can help progress research in many tasks in multimodal understanding and generation. We benchmark representative approaches on tasks involving video-audio-text retrieval and generation. Our dataset and code are released at: https://mugen-org.github.io/. | ['Qiyuan Hu', 'Devi Parikh', 'Songwei Ge', 'Harry Yang', 'Sasha Sheng', 'Guan Pang', 'Xi Yin', 'Songyang Zhang', 'Thomas Hayes'] | 2022-04-17 | null | null | null | null | ['text-to-video-generation'] | ['natural-language-processing'] | [ 2.07410455e-01 1.51383638e-01 -8.29198733e-02 -1.98927313e-01
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f368a1ba-fb18-4d8b-a195-9cd37a4f0a46 | emergence-of-object-segmentation-in-perturbed | 1905.12663 | null | https://arxiv.org/abs/1905.12663v2 | https://arxiv.org/pdf/1905.12663v2.pdf | Emergence of Object Segmentation in Perturbed Generative Models | We introduce a novel framework to build a model that can learn how to segment objects from a collection of images without any human annotation. Our method builds on the observation that the location of object segments can be perturbed locally relative to a given background without affecting the realism of a scene. Our approach is to first train a generative model of a layered scene. The layered representation consists of a background image, a foreground image and the mask of the foreground. A composite image is then obtained by overlaying the masked foreground image onto the background. The generative model is trained in an adversarial fashion against a discriminator, which forces the generative model to produce realistic composite images. To force the generator to learn a representation where the foreground layer corresponds to an object, we perturb the output of the generative model by introducing a random shift of both the foreground image and mask relative to the background. Because the generator is unaware of the shift before computing its output, it must produce layered representations that are realistic for any such random perturbation. Finally, we learn to segment an image by defining an autoencoder consisting of an encoder, which we train, and the pre-trained generator as the decoder, which we freeze. The encoder maps an image to a feature vector, which is fed as input to the generator to give a composite image matching the original input image. Because the generator outputs an explicit layered representation of the scene, the encoder learns to detect and segment objects. We demonstrate this framework on real images of several object categories. | ['Paolo Favaro', 'Adam Bielski'] | 2019-05-29 | emergence-of-object-segmentation-in-perturbed-1 | http://papers.nips.cc/paper/8946-emergence-of-object-segmentation-in-perturbed-generative-models | http://papers.nips.cc/paper/8946-emergence-of-object-segmentation-in-perturbed-generative-models.pdf | neurips-2019-12 | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 1.06203079e+00 7.42840409e-01 4.27541345e-01 -2.73772061e-01
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993d2815-1924-4933-bea1-19cc89aca0ed | convergence-of-policy-gradient-methods-for | 2211.00617 | null | https://arxiv.org/abs/2211.00617v1 | https://arxiv.org/pdf/2211.00617v1.pdf | Convergence of policy gradient methods for finite-horizon stochastic linear-quadratic control problems | We study the global linear convergence of policy gradient (PG) methods for finite-horizon exploratory linear-quadratic control (LQC) problems. The setting includes stochastic LQC problems with indefinite costs and allows additional entropy regularisers in the objective. We consider a continuous-time Gaussian policy whose mean is linear in the state variable and whose covariance is state-independent. Contrary to discrete-time problems, the cost is noncoercive in the policy and not all descent directions lead to bounded iterates. We propose geometry-aware gradient descents for the mean and covariance of the policy using the Fisher geometry and the Bures-Wasserstein geometry, respectively. The policy iterates are shown to satisfy an a-priori bound, and converge globally to the optimal policy with a linear rate. We further propose a novel PG method with discrete-time policies. The algorithm leverages the continuous-time analysis, and achieves a robust linear convergence across different action frequencies. A numerical experiment confirms the convergence and robustness of the proposed algorithm. | ['Yufei Zhang', 'Christoph Reisinger', 'Michael Giegrich'] | 2022-11-01 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.79097134e-01 3.09695899e-01 -3.63207668e-01 8.43743831e-02
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f8079add-bdbb-40f7-ba6a-3a70e7a7eca2 | attention-based-conditioning-methods-using | 2206.13680 | null | https://arxiv.org/abs/2206.13680v1 | https://arxiv.org/pdf/2206.13680v1.pdf | Attention-based conditioning methods using variable frame rate for style-robust speaker verification | We propose an approach to extract speaker embeddings that are robust to speaking style variations in text-independent speaker verification. Typically, speaker embedding extraction includes training a DNN for speaker classification and using the bottleneck features as speaker representations. Such a network has a pooling layer to transform frame-level to utterance-level features by calculating statistics over all utterance frames, with equal weighting. However, self-attentive embeddings perform weighted pooling such that the weights correspond to the importance of the frames in a speaker classification task. Entropy can capture acoustic variability due to speaking style variations. Hence, an entropy-based variable frame rate vector is proposed as an external conditioning vector for the self-attention layer to provide the network with information that can address style effects. This work explores five different approaches to conditioning. The best conditioning approach, concatenation with gating, provided statistically significant improvements over the x-vector baseline in 12/23 tasks and was the same as the baseline in 11/23 tasks when using the UCLA speaker variability database. It also significantly outperformed self-attention without conditioning in 9/23 tasks and was worse in 1/23. The method also showed significant improvements in multi-speaker scenarios of SITW. | ['Abeer Alwan', 'Amber Afshan'] | 2022-06-28 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 7.87585080e-02 -4.59148660e-02 -4.25330177e-02 -8.53309929e-01
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629fed3a-8942-4f04-914b-508a17d82dd5 | multi-object-tracking-and-segmentation-via | 2207.07454 | null | https://arxiv.org/abs/2207.07454v1 | https://arxiv.org/pdf/2207.07454v1.pdf | Multi-Object Tracking and Segmentation via Neural Message Passing | Graphs offer a natural way to formulate Multiple Object Tracking (MOT) and Multiple Object Tracking and Segmentation (MOTS) within the tracking-by-detection paradigm. However, they also introduce a major challenge for learning methods, as defining a model that can operate on such structured domain is not trivial. In this work, we exploit the classical network flow formulation of MOT to define a fully differentiable framework based on Message Passing Networks (MPNs). By operating directly on the graph domain, our method can reason globally over an entire set of detections and exploit contextual features. It then jointly predicts both final solutions for the data association problem and segmentation masks for all objects in the scene while exploiting synergies between the two tasks. We achieve state-of-the-art results for both tracking and segmentation in several publicly available datasets. Our code is available at github.com/ocetintas/MPNTrackSeg. | ['Laura Leal-Taixe', 'Orcun Cetintas', 'Guillem Braso'] | 2022-07-15 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 1.64373189e-01 -1.01538830e-01 -2.57438928e-01 -1.56475320e-01
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6fa4bb45-4c07-4657-aa90-8846674a0efc | fine-grained-predicates-learning-for-scene | 2204.02597 | null | https://arxiv.org/abs/2204.02597v2 | https://arxiv.org/pdf/2204.02597v2.pdf | Fine-Grained Predicates Learning for Scene Graph Generation | The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e.g., "woman-on/standing on/walking on-beach" or "woman-near/looking at/in front of-child". While general SGG models are prone to predict head predicates and existing re-balancing strategies prefer tail categories, none of them can appropriately handle these hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained image classification, which focuses on differentiating among hard-to-distinguish object classes, we propose a method named Fine-Grained Predicates Learning (FGPL) which aims at differentiating among hard-to-distinguish predicates for Scene Graph Generation task. Specifically, we first introduce a Predicate Lattice that helps SGG models to figure out fine-grained predicate pairs. Then, utilizing the Predicate Lattice, we propose a Category Discriminating Loss and an Entity Discriminating Loss, which both contribute to distinguishing fine-grained predicates while maintaining learned discriminatory power over recognizable ones. The proposed model-agnostic strategy significantly boosts the performances of three benchmark models (Transformer, VCTree, and Motif) by 22.8\%, 24.1\% and 21.7\% of Mean Recall (mR@100) on the Predicate Classification sub-task, respectively. Our model also outperforms state-of-the-art methods by a large margin (i.e., 6.1\%, 4.6\%, and 3.2\% of Mean Recall (mR@100)) on the Visual Genome dataset. | ['Jingkuan Song', 'Heng Tao Shen', 'Hao Huang', 'Zhou Zhao', 'Yuyu Guo', 'Lianli Gao', 'Xinyu Lyu'] | 2022-04-06 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lyu_Fine-Grained_Predicates_Learning_for_Scene_Graph_Generation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lyu_Fine-Grained_Predicates_Learning_for_Scene_Graph_Generation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-graph-generation', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.82558274e-01 2.36521125e-01 -1.63021192e-01 -3.32716644e-01
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658b812a-1c40-4342-ae85-b0c04d74df00 | exploring-the-syntactic-abilities-of-rnns | 1706.03542 | null | http://arxiv.org/abs/1706.03542v1 | http://arxiv.org/pdf/1706.03542v1.pdf | Exploring the Syntactic Abilities of RNNs with Multi-task Learning | Recent work has explored the syntactic abilities of RNNs using the
subject-verb agreement task, which diagnoses sensitivity to sentence structure.
RNNs performed this task well in common cases, but faltered in complex
sentences (Linzen et al., 2016). We test whether these errors are due to
inherent limitations of the architecture or to the relatively indirect
supervision provided by most agreement dependencies in a corpus. We trained a
single RNN to perform both the agreement task and an additional task, either
CCG supertagging or language modeling. Multi-task training led to significantly
lower error rates, in particular on complex sentences, suggesting that RNNs
have the ability to evolve more sophisticated syntactic representations than
shown before. We also show that easily available agreement training data can
improve performance on other syntactic tasks, in particular when only a limited
amount of training data is available for those tasks. The multi-task paradigm
can also be leveraged to inject grammatical knowledge into language models. | ['Tal Linzen', 'Yoav Goldberg', 'Emile Enguehard'] | 2017-06-12 | exploring-the-syntactic-abilities-of-rnns-1 | https://aclanthology.org/K17-1003 | https://aclanthology.org/K17-1003.pdf | conll-2017-8 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 2.34938800e-01 5.90372205e-01 1.87428012e-01 -7.03729033e-01
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12f7114d-c9b7-4eb8-9148-f68b6e3e50a8 | anatomy-aware-3d-human-pose-estimation-in | 2002.10322 | null | https://arxiv.org/abs/2002.10322v5 | https://arxiv.org/pdf/2002.10322v5.pdf | Anatomy-aware 3D Human Pose Estimation with Bone-based Pose Decomposition | In this work, we propose a new solution to 3D human pose estimation in videos. Instead of directly regressing the 3D joint locations, we draw inspiration from the human skeleton anatomy and decompose the task into bone direction prediction and bone length prediction, from which the 3D joint locations can be completely derived. Our motivation is the fact that the bone lengths of a human skeleton remain consistent across time. This promotes us to develop effective techniques to utilize global information across all the frames in a video for high-accuracy bone length prediction. Moreover, for the bone direction prediction network, we propose a fully-convolutional propagating architecture with long skip connections. Essentially, it predicts the directions of different bones hierarchically without using any time-consuming memory units e.g. LSTM). A novel joint shift loss is further introduced to bridge the training of the bone length and bone direction prediction networks. Finally, we employ an implicit attention mechanism to feed the 2D keypoint visibility scores into the model as extra guidance, which significantly mitigates the depth ambiguity in many challenging poses. Our full model outperforms the previous best results on Human3.6M and MPI-INF-3DHP datasets, where comprehensive evaluation validates the effectiveness of our model. | ['Jiebo Luo', 'Chen Fang', 'Zhili Chen', 'Xiaohui Shen', 'Tianlang Chen', 'Yiheng Zhu'] | 2020-02-24 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.05164669e-01 3.10352445e-01 -2.51698822e-01 -4.95192498e-01
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92ce82f5-3dde-4ef6-953e-9d4885b7796f | face-anonymization-by-manipulating-decoupled | 2105.11137 | null | https://arxiv.org/abs/2105.11137v2 | https://arxiv.org/pdf/2105.11137v2.pdf | CFA-Net: Controllable Face Anonymization Network with Identity Representation Manipulation | De-identification of face data has drawn increasing attention in recent years. It is important to protect people's identities meanwhile keeping the utility of the data in many computer vision tasks. We propose a Controllable Face Anonymization Network (CFA-Net), a novel approach that can anonymize the identity of given faces in images and videos, based on a generator that can disentangle face identity from other image contents. We reach the goal of controllable face anonymization through manipulating identity vectors in the generator's identity representation space. Various anonymized faces deriving from an original face can be generated through our method and maintain high similarity to the original image contents. Quantitative and qualitative results demonstrate our method's superiority over literature models on visual quality and anonymization validity. | ['Jing Dong', 'Wei Wang', 'Dongze Li', 'Tianxiang Ma'] | 2021-05-24 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 1.89347431e-01 2.42594704e-01 4.07905057e-02 -5.32289445e-01
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6d38625d-1c26-419b-b461-ea91b50c2918 | on-information-captured-by-neural-networks | 2306.15918 | null | https://arxiv.org/abs/2306.15918v1 | https://arxiv.org/pdf/2306.15918v1.pdf | On information captured by neural networks: connections with memorization and generalization | Despite the popularity and success of deep learning, there is limited understanding of when, how, and why neural networks generalize to unseen examples. Since learning can be seen as extracting information from data, we formally study information captured by neural networks during training. Specifically, we start with viewing learning in presence of noisy labels from an information-theoretic perspective and derive a learning algorithm that limits label noise information in weights. We then define a notion of unique information that an individual sample provides to the training of a deep network, shedding some light on the behavior of neural networks on examples that are atypical, ambiguous, or belong to underrepresented subpopulations. We relate example informativeness to generalization by deriving nonvacuous generalization gap bounds. Finally, by studying knowledge distillation, we highlight the important role of data and label complexity in generalization. Overall, our findings contribute to a deeper understanding of the mechanisms underlying neural network generalization. | ['Hrayr Harutyunyan'] | 2023-06-28 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 6.09572232e-01 1.69059739e-01 -3.38874757e-01 -6.47992313e-01
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bd457037-79bb-40f2-ad8c-314e770cdeac | mconv-an-environment-for-multimodal | null | null | https://dl.acm.org/doi/abs/10.1145/3404835.3462970 | https://dl.acm.org/doi/pdf/10.1145/3404835.3462970 | MConv: An Environment for Multimodal Conversational Search across Multiple Domains | Although conversational search has become a hot topic in both dialogue research and IR community, the real breakthrough has been limited by the scale and quality of datasets available. To address this fundamental obstacle, we introduce the Multimodal Multi-domain Conversational dataset (MMConv), a fully annotated collection of human-to-human role-playing dialogues spanning over multiple domains and tasks. The contribution is two-fold. First, beyond the task-oriented multimodal dialogues among user and agent pairs, dialogues are fully annotated with dialogue belief states and dialogue acts. More importantly, we create a relatively comprehensive environment for conducting multimodal conversational search with real user settings, structured venue database, annotated image repository as well as crowd-sourced knowledge database. A detailed description of the data collection procedure along with a summary of data structure and analysis is provided. Second, a set of benchmark results for dialogue state tracking, conversational recommendation, response generation as well as a unified model for multiple tasks are reported. We adopt the state-of-the-art methods for these tasks respectively to demonstrate the usability of the data, discuss limitations of current methods and set baselines for future studies. | ['Tat-Seng Chua', 'Minlie Huang', 'Zheng Zhang', 'Le Hong Long', 'Lizi Liao'] | 2021-07-11 | null | null | null | sigir-2021-7 | ['response-generation', 'dialogue-state-tracking', 'conversational-search'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.92283571e-01 8.23501423e-02 -3.70064341e-02 -3.94137412e-01
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95478d42-7fb7-4ecd-b9c8-796ff7cc0b9a | grant-free-massive-random-access-with | 2304.05648 | null | https://arxiv.org/abs/2304.05648v1 | https://arxiv.org/pdf/2304.05648v1.pdf | Grant-free Massive Random Access with Retransmission: Receiver Optimization and Performance Analysis | There is an increasing demand of massive machine-type communication (mMTC) to provide scalable access for a large number of devices, which has prompted extensive investigation on grant-free massive random access (RA) in 5G and beyond wireless networks. Although many efficient signal processing algorithms have been developed, the limited radio resource for pilot transmission in grant-free massive RA systems makes accurate user activity detection and channel estimation challenging, which thereby compromises the communication reliability. In this paper, we adopt retransmission as a means to improve the quality of service (QoS) for grant-free massive RA. Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm. Then, we analyze the performance of the proposed receiver, including the user activity detection, channel estimation, and data error, by resorting to the state evolution of the correlated AMP algorithm and the random matrix theory (RMT). Our analysis admits a tight closed-form approximation for frame error rate (FER) evaluation. Simulation results corroborate our theoretical analysis and demonstrate the effectiveness of the proposed receiver for grant-free massive RA with retransmission, compared with a conventional design that disregards the critical user activity correlation. | ['Jun Zhang', 'Yuyi Mao', 'Xinyu Bian'] | 2023-04-12 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.90856802e-01 6.12009354e-02 -2.79726505e-01 1.94760442e-01
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3a8a7b0b-166b-4927-a846-9a259787587b | e-2vts-energy-efficient-video-text-spotting | 2206.02281 | null | https://arxiv.org/abs/2206.02281v1 | https://arxiv.org/pdf/2206.02281v1.pdf | E^2VTS: Energy-Efficient Video Text Spotting from Unmanned Aerial Vehicles | Unmanned Aerial Vehicles (UAVs) based video text spotting has been extensively used in civil and military domains. UAV's limited battery capacity motivates us to develop an energy-efficient video text spotting solution. In this paper, we first revisit RCNN's crop & resize training strategy and empirically find that it outperforms aligned RoI sampling on a real-world video text dataset captured by UAV. To reduce energy consumption, we further propose a multi-stage image processor that takes videos' redundancy, continuity, and mixed degradation into account. Lastly, the model is pruned and quantized before deployed on Raspberry Pi. Our proposed energy-efficient video text spotting solution, dubbed as E^2VTS, outperforms all previous methods by achieving a competitive tradeoff between energy efficiency and performance. All our codes and pre-trained models are available at https://github.com/wuzhenyusjtu/LPCVC20-VideoTextSpotting. | ['Ji Liu', 'Zhangyang Wang', 'Xiangru Lian', 'Jianchao Tan', 'Jiayi Shen', 'Yunhe Xue', 'Pengcheng Pi', 'Zhenyu Wu', 'Zhenyu Hu'] | 2022-06-05 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.88310069e-01 -6.14681721e-01 -4.59660560e-01 1.80020645e-01
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521ac416-0b1a-4269-9db0-115e7af43686 | c2kd-cross-lingual-cross-modal-knowledge | 2210.03625 | null | https://arxiv.org/abs/2210.03625v2 | https://arxiv.org/pdf/2210.03625v2.pdf | C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval | Multilingual text-video retrieval methods have improved significantly in recent years, but the performance for other languages lags behind English. We propose a Cross-Lingual Cross-Modal Knowledge Distillation method to improve multilingual text-video retrieval. Inspired by the fact that English text-video retrieval outperforms other languages, we train a student model using input text in different languages to match the cross-modal predictions from teacher models using input text in English. We propose a cross entropy based objective which forces the distribution over the student's text-video similarity scores to be similar to those of the teacher models. We introduce a new multilingual video dataset, Multi-YouCook2, by translating the English captions in the YouCook2 video dataset to 8 other languages. Our method improves multilingual text-video retrieval performance on Multi-YouCook2 and several other datasets such as Multi-MSRVTT and VATEX. We also conducted an analysis on the effectiveness of different multilingual text models as teachers. The code, models, and dataset are available at https://github.com/roudimit/c2kd. | ['James Glass', 'Hilde Kuehne', 'David Harwath', 'Leonid Karlinsky', 'Brian Kingsbury', 'Rogerio Feris', 'Samuel Thomas', 'Nina Shvetsova', 'Yung-Sung Chuang', 'Andrew Rouditchenko'] | 2022-10-07 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [-5.69968879e-01 -4.51038718e-01 -6.13196552e-01 -3.31305534e-01
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00ec8578-2f4f-49cb-80ac-a200aeea7a4e | dual-glance-model-for-deciphering-social | 1708.00634 | null | http://arxiv.org/abs/1708.00634v1 | http://arxiv.org/pdf/1708.00634v1.pdf | Dual-Glance Model for Deciphering Social Relationships | Since the beginning of early civilizations, social relationships derived from
each individual fundamentally form the basis of social structure in our daily
life. In the computer vision literature, much progress has been made in scene
understanding, such as object detection and scene parsing. Recent research
focuses on the relationship between objects based on its functionality and
geometrical relations. In this work, we aim to study the problem of social
relationship recognition, in still images. We have proposed a dual-glance model
for social relationship recognition, where the first glance fixates at the
individual pair of interest and the second glance deploys attention mechanism
to explore contextual cues. We have also collected a new large scale People in
Social Context (PISC) dataset, which comprises of 22,670 images and 76,568
annotated samples from 9 types of social relationship. We provide benchmark
results on the PISC dataset, and qualitatively demonstrate the efficacy of the
proposed model. | ['Mohan S. Kankanhalli', 'Yongkang Wong', 'Junnan Li', 'Qi Zhao'] | 2017-08-02 | dual-glance-model-for-deciphering-social-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Li_Dual-Glance_Model_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Dual-Glance_Model_for_ICCV_2017_paper.pdf | iccv-2017-10 | ['visual-social-relationship-recognition'] | ['computer-vision'] | [ 1.64101854e-01 1.13664679e-01 2.32882112e-01 -7.54199445e-01
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0e8def57-209b-45bd-bbdc-0a24d36755e5 | dialgraph-sparse-graph-learning-networks-for | 2004.06698 | null | https://arxiv.org/abs/2004.06698v2 | https://arxiv.org/pdf/2004.06698v2.pdf | Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer | Visual dialog is a task of answering a sequence of questions grounded in an image using the previous dialog history as context. In this paper, we study how to address two fundamental challenges for this task: (1) reasoning over underlying semantic structures among dialog rounds and (2) identifying several appropriate answers to the given question. To address these challenges, we propose a Sparse Graph Learning (SGL) method to formulate visual dialog as a graph structure learning task. SGL infers inherently sparse dialog structures by incorporating binary and score edges and leveraging a new structural loss function. Next, we introduce a Knowledge Transfer (KT) method that extracts the answer predictions from the teacher model and uses them as pseudo labels. We propose KT to remedy the shortcomings of single ground-truth labels, which severely limit the ability of a model to obtain multiple reasonable answers. As a result, our proposed model significantly improves reasoning capability compared to baseline methods and outperforms the state-of-the-art approaches on the VisDial v1.0 dataset. The source code is available at https://github.com/gicheonkang/SGLKT-VisDial. | ['Jin-Hwa Kim', 'Byoung-Tak Zhang', 'Gi-Cheon Kang', 'Junseok Park', 'Hwaran Lee'] | 2020-04-14 | null | https://aclanthology.org/2021.findings-emnlp.31 | https://aclanthology.org/2021.findings-emnlp.31.pdf | findings-emnlp-2021-11 | ['graph-structure-learning'] | ['graphs'] | [-6.80793077e-03 5.73948622e-01 -3.20127517e-01 -5.08055925e-01
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89d484f0-2c1b-49d2-b85f-f4e984391c42 | learning-to-recover-spectral-reflectance-from | 2304.02162 | null | https://arxiv.org/abs/2304.02162v1 | https://arxiv.org/pdf/2304.02162v1.pdf | Learning to Recover Spectral Reflectance from RGB Images | This paper tackles spectral reflectance recovery (SRR) from RGB images. Since capturing ground-truth spectral reflectance and camera spectral sensitivity are challenging and costly, most existing approaches are trained on synthetic images and utilize the same parameters for all unseen testing images, which are suboptimal especially when the trained models are tested on real images because they never exploit the internal information of the testing images. To address this issue, we adopt a self-supervised meta-auxiliary learning (MAXL) strategy that fine-tunes the well-trained network parameters with each testing image to combine external with internal information. To the best of our knowledge, this is the first work that successfully adapts the MAXL strategy to this problem. Instead of relying on naive end-to-end training, we also propose a novel architecture that integrates the physical relationship between the spectral reflectance and the corresponding RGB images into the network based on our mathematical analysis. Besides, since the spectral reflectance of a scene is independent to its illumination while the corresponding RGB images are not, we recover the spectral reflectance of a scene from its RGB images captured under multiple illuminations to further reduce the unknown. Qualitative and quantitative evaluations demonstrate the effectiveness of our proposed network and of the MAXL. Our code and data are available at https://github.com/Dong-Huo/SRR-MAXL. | ['Yee-Hong Yang', 'Yiming Qian', 'Jian Wang', 'Dong Huo'] | 2023-04-04 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 6.54737234e-01 -2.34107837e-01 1.04648106e-01 -3.75465304e-01
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733da6b7-4445-45e8-a2b3-cb76ce65f827 | effective-decision-boundary-learning-for | 2301.05180 | null | https://arxiv.org/abs/2301.05180v1 | https://arxiv.org/pdf/2301.05180v1.pdf | Effective Decision Boundary Learning for Class Incremental Learning | Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory. In this work, we present a simple but effective approach to tackle these two factors. First, we employ a re-sampling strategy and Mixup K}nowledge D}istillation (Re-MKD) to improve the performances of KD, which would greatly alleviate the overfitting problem. Specifically, we combine mixup and re-sampling strategies to synthesize adequate data used in KD training that are more consistent with the latent distribution between the learned and new classes. Second, we propose a novel incremental influence balance (IIB) method for CIL to tackle the classification of imbalanced data by extending the influence balance method into the CIL setting, which re-weights samples by their influences to create a proper decision boundary. With these two improvements, we present the effective decision boundary learning algorithm (EDBL) which improves the performance of KD and deals with the imbalanced data learning simultaneously. Experiments show that the proposed EDBL achieves state-of-the-art performances on several CIL benchmarks. | ['Shan Yu', 'Jun Wan', 'Kunchi Li'] | 2023-01-12 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-7.64856488e-02 -1.38765603e-01 -5.05871534e-01 -3.27606410e-01
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643cb5e9-779c-4d0b-b6dd-c4d9e007f356 | towards-making-the-most-of-cross-lingual | null | null | https://aclanthology.org/2022.acl-long.12 | https://aclanthology.org/2022.acl-long.12.pdf | Towards Making the Most of Cross-Lingual Transfer for Zero-Shot Neural Machine Translation | This paper demonstrates that multilingual pretraining and multilingual fine-tuning are both critical for facilitating cross-lingual transfer in zero-shot translation, where the neural machine translation (NMT) model is tested on source languages unseen during supervised training. Following this idea, we present SixT+, a strong many-to-English NMT model that supports 100 source languages but is trained with a parallel dataset in only six source languages. SixT+ initializes the decoder embedding and the full encoder with XLM-R large and then trains the encoder and decoder layers with a simple two-stage training strategy. SixT+ achieves impressive performance on many-to-English translation. It significantly outperforms CRISS and m2m-100, two strong multilingual NMT systems, with an average gain of 7.2 and 5.0 BLEU respectively. Additionally, SixT+ offers a set of model parameters that can be further fine-tuned to other unsupervised tasks. We demonstrate that adding SixT+ initialization outperforms state-of-the-art explicitly designed unsupervised NMT models on Si<->En and Ne<->En by over 1.2 average BLEU. When applied to zero-shot cross-lingual abstractive summarization, it produces an average performance gain of 12.3 ROUGE-L over mBART-ft. We conduct detailed analyses to understand the key ingredients of SixT+, including multilinguality of the auxiliary parallel data, positional disentangled encoder, and the cross-lingual transferability of its encoder. | ['Furu Wei', 'Wenping Wang', 'Jia Pan', 'Dongdong Zhang', 'Yun Chen', 'Shuming Ma', 'Guanhua Chen'] | null | null | null | null | acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [ 1.53379366e-01 2.06325904e-01 -6.55553281e-01 -3.27320576e-01
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8cb665b4-9199-4ba2-b7f0-5417e1062b59 | 190412665 | 1904.12665 | null | http://arxiv.org/abs/1904.12665v1 | http://arxiv.org/pdf/1904.12665v1.pdf | PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras | We present the first purely event-based, energy-efficient approach for object
detection and categorization using an event camera. Compared to traditional
frame-based cameras, choosing event cameras results in high temporal resolution
(order of microseconds), low power consumption (few hundred mW) and wide
dynamic range (120 dB) as attractive properties. However, event-based object
recognition systems are far behind their frame-based counterparts in terms of
accuracy. To this end, this paper presents an event-based feature extraction
method devised by accumulating local activity across the image frame and then
applying principal component analysis (PCA) to the normalized neighborhood
region. Subsequently, we propose a backtracking-free k-d tree mechanism for
efficient feature matching by taking advantage of the low-dimensionality of the
feature representation. Additionally, the proposed k-d tree mechanism allows
for feature selection to obtain a lower-dimensional dictionary representation
when hardware resources are limited to implement dimensionality reduction.
Consequently, the proposed system can be realized on a field-programmable gate
array (FPGA) device leading to high performance over resource ratio. The
proposed system is tested on real-world event-based datasets for object
categorization, showing superior classification performance and relevance to
state-of-the-art algorithms. Additionally, we verified the object detection
method and real-time FPGA performance in lab settings under non-controlled
illumination conditions with limited training data and ground truth
annotations. | ['Bharath Ramesh', 'Garrick Orchard', 'Andres Ussa', 'Luca Della Vedova', 'Hong Yang'] | 2019-04-24 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 4.57123399e-01 -7.60482073e-01 -1.53376848e-01 -2.27079391e-01
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914088d1-2db8-4de6-96f2-fb64d9446287 | dltta-dynamic-learning-rate-for-test-time | 2205.13723 | null | https://arxiv.org/abs/2205.13723v1 | https://arxiv.org/pdf/2205.13723v1.pdf | DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images | Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions. Previous TTA methods have a common limitation of using a fixed learning rate for all the test samples. Such a practice would be sub-optimal for TTA, because test data may arrive sequentially therefore the scale of distribution shift would change frequently. To address this problem, we propose a novel dynamic learning rate adjustment method for test-time adaptation, called DLTTA, which dynamically modulates the amount of weights update for each test image to account for the differences in their distribution shift. Specifically, our DLTTA is equipped with a memory bank based estimation scheme to effectively measure the discrepancy of a given test sample. Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample. The effectiveness and general applicability of our DLTTA is extensively demonstrated on three tasks including retinal optical coherence tomography (OCT) segmentation, histopathological image classification, and prostate 3D MRI segmentation. Our method achieves effective and fast test-time adaptation with consistent performance improvement over current state-of-the-art test-time adaptation methods. Code is available at: https://github.com/med-air/DLTTA. | ['Qi Dou', 'Pheng Ann Heng', 'Jianfeng Cao', 'Quande Liu', 'Meirui Jiang', 'Cheng Chen', 'Hongzheng Yang'] | 2022-05-27 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 1.11341268e-01 -4.00911897e-01 -3.27506304e-01 -4.43316996e-01
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68021813-1258-45e6-95b0-9b7f30be2c36 | material-classification-using-frequency-and | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Tanaka_Material_Classification_Using_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Tanaka_Material_Classification_Using_CVPR_2017_paper.pdf | Material Classification Using Frequency- and Depth-Dependent Time-Of-Flight Distortion | This paper presents a material classification method using an off-the-shelf Time-of-Flight (ToF) camera. We use a key observation that the depth measurement by a ToF camera is distorted in objects with certain materials, especially with translucent materials. We show that this distortion is caused by the variations of time domain impulse responses across materials and also by the measurement mechanism of the existing ToF cameras. Specifically, we reveal that the amount of distortion varies according to the modulation frequency of the ToF camera, the material of the object, and the distance between the camera and object. Our method uses the depth distortion of ToF measurements as features and achieves material classification of a scene. Effectiveness of the proposed method is demonstrated by numerical evaluation and real-world experiments, showing its capability of even classifying visually similar objects.
| ['Yasuyuki Matsushita', 'Takuya Funatomi', 'Kenichiro Tanaka', 'Hiroyuki Kubo', 'Yasushi Yagi', 'Yasuhiro Mukaigawa'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['material-classification'] | ['computer-vision'] | [ 5.98019838e-01 -5.51604986e-01 2.97020555e-01 4.92818877e-02
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803d7c89-83d2-432c-9e02-e71a8ee5f46d | learning-hierarchical-sparse-representations | 1106.0357 | null | https://arxiv.org/abs/1106.0357v1 | https://arxiv.org/pdf/1106.0357v1.pdf | Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction | This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierarchical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models showing the generality of our simple prescription. We then perform preliminary experiments using this framework, illustrating with the example of an object recognition task using standard datasets. This work introduces the very first steps towards an integrated framework for designing and analyzing various computational tasks from learning to attention to action. The ultimate goal is building a mathematically rigorous, integrated theory of intelligence. | ['Jeffery Ho', 'Meera Sitharam', 'Mohamad Tarifi'] | 2011-06-02 | null | null | null | null | ['object-recognition', 'dimensionality-reduction', 'dictionary-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 3.95412952e-01 4.45384309e-02 -1.53224289e-01 -2.95226187e-01
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f8781f59-4bb4-4382-a247-c7bc3a0bf319 | joint-transition-based-dependency-parsing-and | null | null | https://aclanthology.org/D16-1109 | https://aclanthology.org/D16-1109.pdf | Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts | null | ['Masashi Yoshikawa', 'Yuji Matsumoto', 'Hiroyuki Shindo'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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e437766e-9e2e-456d-bedc-b8113af0f994 | learning-deep-representations-by-mutual | 1808.06670 | null | http://arxiv.org/abs/1808.06670v5 | http://arxiv.org/pdf/1808.06670v5.pdf | Learning deep representations by mutual information estimation and maximization | In this work, we perform unsupervised learning of representations by
maximizing mutual information between an input and the output of a deep neural
network encoder. Importantly, we show that structure matters: incorporating
knowledge about locality of the input to the objective can greatly influence a
representation's suitability for downstream tasks. We further control
characteristics of the representation by matching to a prior distribution
adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a
number of popular unsupervised learning methods and competes with
fully-supervised learning on several classification tasks. DIM opens new
avenues for unsupervised learning of representations and is an important step
towards flexible formulations of representation-learning objectives for
specific end-goals. | ['R. Devon Hjelm', 'Yoshua Bengio', 'Samuel Lavoie-Marchildon', 'Alex Fedorov', 'Karan Grewal', 'Adam Trischler', 'Phil Bachman'] | 2018-08-20 | learning-deep-representations-by-mutual-1 | https://openreview.net/forum?id=Bklr3j0cKX | https://openreview.net/pdf?id=Bklr3j0cKX | iclr-2019-5 | ['mutual-information-estimation'] | ['methodology'] | [ 6.18045151e-01 5.70341051e-01 -3.74871701e-01 -5.44525981e-01
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b0ead87a-7e40-4f16-bae2-a442afd5b18e | dartsrenet-exploring-new-rnn-cells-in-renet | 2304.05838 | null | https://arxiv.org/abs/2304.05838v1 | https://arxiv.org/pdf/2304.05838v1.pdf | DartsReNet: Exploring new RNN cells in ReNet architectures | We present new Recurrent Neural Network (RNN) cells for image classification using a Neural Architecture Search (NAS) approach called DARTS. We are interested in the ReNet architecture, which is a RNN based approach presented as an alternative for convolutional and pooling steps. ReNet can be defined using any standard RNN cells, such as LSTM and GRU. One limitation is that standard RNN cells were designed for one dimensional sequential data and not for two dimensions like it is the case for image classification. We overcome this limitation by using DARTS to find new cell designs. We compare our results with ReNet that uses GRU and LSTM cells. Our found cells outperform the standard RNN cells on CIFAR-10 and SVHN. The improvements on SVHN indicate generalizability, as we derived the RNN cell designs from CIFAR-10 without performing a new cell search for SVHN. | ['Andreas Dengel', 'Jörn Hees', 'Federico Raue', 'Brian Moser'] | 2023-04-11 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-3.30130756e-03 5.63975610e-02 8.39714557e-02 1.06690396e-02
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-2.53558159e-01 2.77236700e-01 3.64998847e-01 7.32385576e-01
2.60748535e-01 7.63550222e-01 1.65927052e-01 -9.22287226e-01
2.37539381e-01 -9.54237580e-01 -6.05584979e-01 -4.92350549e-01
6.74974620e-01 -2.21506387e-01 -5.75129017e-02 -3.89742643e-01] | [10.844740867614746, 6.346129417419434] |
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