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2cab964e-118c-4b14-8e0d-2ebbd497f782 | sql-to-text-generation-with-graph-to-sequence | 1809.05255 | null | http://arxiv.org/abs/1809.05255v2 | http://arxiv.org/pdf/1809.05255v2.pdf | SQL-to-Text Generation with Graph-to-Sequence Model | Previous work approaches the SQL-to-text generation task using vanilla
Seq2Seq models, which may not fully capture the inherent graph-structured
information in SQL query. In this paper, we first introduce a strategy to
represent the SQL query as a directed graph and then employ a graph-to-sequence
model to encode the global structure information into node embeddings. This
model can effectively learn the correlation between the SQL query pattern and
its interpretation. Experimental results on the WikiSQL dataset and
Stackoverflow dataset show that our model significantly outperforms the Seq2Seq
and Tree2Seq baselines, achieving the state-of-the-art performance. | ['Lingfei Wu', 'Kun Xu', 'Vadim Sheinin', 'Zhiguo Wang', 'Yansong Feng'] | 2018-09-14 | sql-to-text-generation-with-graph-to-sequence-1 | https://aclanthology.org/D18-1112 | https://aclanthology.org/D18-1112.pdf | emnlp-2018-10 | ['sql-to-text', 'graph-to-sequence'] | ['computer-code', 'natural-language-processing'] | [ 1.91254765e-01 3.66225541e-01 -3.56878906e-01 -7.43091881e-01
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834e2e36-81ea-4f65-998a-6ab48551540a | stagewise-unsupervised-domain-adaptation-with | 2108.12611 | null | https://arxiv.org/abs/2108.12611v1 | https://arxiv.org/pdf/2108.12611v1.pdf | Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images | Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. Deep neural networks have advanced this field by leveraging the power of large-scale labeled data, which, however, are extremely expensive and time-consuming to acquire. One solution is to use cheap available data to train a model and deploy it to directly process the data from a specific application domain. Nevertheless, the well-known domain shift (DS) issue prevents the trained model from generalizing well on the target domain. In this paper, we propose a novel stagewise domain adaptation model called RoadDA to address the DS issue in this field. In the first stage, RoadDA adapts the target domain features to align with the source ones via generative adversarial networks (GAN) based inter-domain adaptation. Specifically, a feature pyramid fusion module is devised to avoid information loss of long and thin roads and learn discriminative and robust features. Besides, to address the intra-domain discrepancy in the target domain, in the second stage, we propose an adversarial self-training method. We generate the pseudo labels of target domain using the trained generator and divide it to labeled easy split and unlabeled hard split based on the road confidence scores. The features of hard split are adapted to align with the easy ones using adversarial learning and the intra-domain adaptation process is repeated to progressively improve the segmentation performance. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods. | ['DaCheng Tao', 'Jing Zhang', 'Meng Lan', 'Lefei Zhang'] | 2021-08-28 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 5.13307989e-01 6.29297122e-02 -7.65534416e-02 -4.28366303e-01
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966203d6-3cd7-4a6c-94fe-03043a01f58f | random-embeddings-and-linear-regression-can | 2104.14661 | null | https://arxiv.org/abs/2104.14661v1 | https://arxiv.org/pdf/2104.14661v1.pdf | Random Embeddings and Linear Regression can Predict Protein Function | Large self-supervised models pretrained on millions of protein sequences have recently gained popularity in generating embeddings of protein sequences for protein function prediction. However, the absence of random baselines makes it difficult to conclude whether pretraining has learned useful information for protein function prediction. Here we show that one-hot encoding and random embeddings, both of which do not require any pretraining, are strong baselines for protein function prediction across 14 diverse sequence-to-function tasks. | ['Alan M. Moses', 'Alex X. Lu', 'Tianyu Lu'] | 2021-04-25 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 4.50013697e-01 3.45750570e-01 -2.22079709e-01 -6.39104843e-01
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e5cc0efc-dfd2-4f41-a523-92ed3a97ffd5 | multi-subregion-based-correlation-filter-bank | 1603.07604 | null | http://arxiv.org/abs/1603.07604v1 | http://arxiv.org/pdf/1603.07604v1.pdf | Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition | In this paper, we propose an effective feature extraction algorithm, called
Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face
recognition. MS-CFB combines the benefits of global-based and local-based
feature extraction algorithms, where multiple correlation filters correspond-
ing to different face subregions are jointly designed to optimize the overall
correlation outputs. Furthermore, we reduce the computational complexi- ty of
MS-CFB by designing the correlation filter bank in the spatial domain and
improve its generalization capability by capitalizing on the unconstrained form
during the filter bank design process. MS-CFB not only takes the d- ifferences
among face subregions into account, but also effectively exploits the
discriminative information in face subregions. Experimental results on various
public face databases demonstrate that the proposed algorithm pro- vides a
better feature representation for classification and achieves higher
recognition rates compared with several state-of-the-art algorithms. | ['David Suter', 'Yan Yan', 'Hanzi Wang'] | 2016-03-24 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [-2.99457759e-01 -7.60593176e-01 -2.40344226e-01 -4.56015229e-01
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d60aeec4-8435-4a34-bdac-9890af32c8f2 | inducing-positive-perspectives-with-text | 2204.02952 | null | https://arxiv.org/abs/2204.02952v1 | https://arxiv.org/pdf/2204.02952v1.pdf | Inducing Positive Perspectives with Text Reframing | Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a reversal in meaning. We introduce a different but related task called positive reframing in which we neutralize a negative point of view and generate a more positive perspective for the author without contradicting the original meaning. Our insistence on meaning preservation makes positive reframing a challenging and semantically rich task. To facilitate rapid progress, we introduce a large-scale benchmark, Positive Psychology Frames, with 8,349 sentence pairs and 12,755 structured annotations to explain positive reframing in terms of six theoretically-motivated reframing strategies. Then we evaluate a set of state-of-the-art text style transfer models, and conclude by discussing key challenges and directions for future work. | ['Diyi Yang', 'Anthony Zhang', 'Minzhi Li', 'Caleb Ziems'] | 2022-04-06 | null | https://aclanthology.org/2022.acl-long.257 | https://aclanthology.org/2022.acl-long.257.pdf | acl-2022-5 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 6.68409348e-01 4.67273533e-01 -2.67857879e-01 -8.68923604e-01
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b358c1d9-1ac8-4d6a-afda-e75ec17e7310 | yolino-generic-single-shot-polyline-detection | 2103.14420 | null | https://arxiv.org/abs/2103.14420v2 | https://arxiv.org/pdf/2103.14420v2.pdf | YOLinO: Generic Single Shot Polyline Detection in Real Time | The detection of polylines is usually either bound to branchless polylines or formulated in a recurrent way, prohibiting their use in real-time systems. We propose an approach that builds upon the idea of single shot object detection. Reformulating the problem of polyline detection as a bottom-up composition of small line segments allows to detect bounded, dashed and continuous polylines with a single head. This has several major advantages over previous methods. Not only is the method at 187 fps more than suited for real-time applications with virtually any restriction on the shapes of the detected polylines. By predicting multiple line segments for each cell, even branching or crossing polylines can be detected. We evaluate our approach on three different applications for road marking, lane border and center line detection. Hereby, we demonstrate the ability to generalize to different domains as well as both implicit and explicit polyline detection tasks. | ['Christoph Stiller', 'Jan-Hendrik Pauls', 'Philipp Skudlik', 'Annika Meyer'] | 2021-03-26 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.13651711e-01 5.12338663e-03 1.82743162e-01 -9.74280573e-03
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46989914-19a6-4dc4-91c0-9d7d86051abc | hilo-exploiting-high-low-frequency-relations | 2303.15994 | null | https://arxiv.org/abs/2303.15994v1 | https://arxiv.org/pdf/2303.15994v1.pdf | HiLo: Exploiting High Low Frequency Relations for Unbiased Panoptic Scene Graph Generation | Panoptic Scene Graph generation (PSG) is a recently proposed task in image scene understanding that aims to segment the image and extract triplets of subjects, objects and their relations to build a scene graph. This task is particularly challenging for two reasons. First, it suffers from a long-tail problem in its relation categories, making naive biased methods more inclined to high-frequency relations. Existing unbiased methods tackle the long-tail problem by data/loss rebalancing to favor low-frequency relations. Second, a subject-object pair can have two or more semantically overlapping relations. While existing methods favor one over the other, our proposed HiLo framework lets different network branches specialize on low and high frequency relations, enforce their consistency and fuse the results. To the best of our knowledge we are the first to propose an explicitly unbiased PSG method. In extensive experiments we show that our HiLo framework achieves state-of-the-art results on the PSG task. We also apply our method to the Scene Graph Generation task that predicts boxes instead of masks and see improvements over all baseline methods. | ['Holger Caesar', 'Miaojing Shi', 'Zijian Zhou'] | 2023-03-28 | null | null | null | null | ['scene-graph-generation', 'panoptic-scene-graph-generation'] | ['computer-vision', 'computer-vision'] | [ 5.13140500e-01 3.52180660e-01 -2.14358672e-01 -5.38556695e-01
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b6161aa3-41ef-4857-b86c-6ef8442997c8 | inducing-alignment-structure-with-gated-graph | 2010.07668 | null | https://arxiv.org/abs/2010.07668v2 | https://arxiv.org/pdf/2010.07668v2.pdf | Inducing Alignment Structure with Gated Graph Attention Networks for Sentence Matching | Sentence matching is a fundamental task of natural language processing with various applications. Most recent approaches adopt attention-based neural models to build word- or phrase-level alignment between two sentences. However, these models usually ignore the inherent structure within the sentences and fail to consider various dependency relationships among text units. To address these issues, this paper proposes a graph-based approach for sentence matching. First, we represent a sentence pair as a graph with several carefully design strategies. We then employ a novel gated graph attention network to encode the constructed graph for sentence matching. Experimental results demonstrate that our method substantially achieves state-of-the-art performance on two datasets across tasks of natural language and paraphrase identification. Further discussions show that our model can learn meaningful graph structure, indicating its superiority on improved interpretability. | ['Yuanchao Liu', 'Le Hu', 'Peng Cui'] | 2020-10-15 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 3.46517533e-01 1.28582001e-01 -3.06099355e-01 -6.66583180e-01
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44ad5df0-4d59-4bc1-8add-b97d2e16f8e1 | adversarially-guided-subgoal-generation-for | 2201.09635 | null | https://arxiv.org/abs/2201.09635v4 | https://arxiv.org/pdf/2201.09635v4.pdf | State-Conditioned Adversarial Subgoal Generation | Hierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy HRL often suffers from the problem of a non-stationary high-level policy since the low-level policy is constantly changing. In this paper, we propose a novel HRL approach for mitigating the non-stationarity by adversarially enforcing the high-level policy to generate subgoals compatible with the current instantiation of the low-level policy. In practice, the adversarial learning is implemented by training a simple state-conditioned discriminator network concurrently with the high-level policy which determines the compatibility level of subgoals. Comparison to state-of-the-art algorithms shows that our approach improves both learning efficiency and performance in challenging continuous control tasks. | ['Joni-Kristian Kämäräinen', 'Tinghuai Wang', 'Joni Pajarinen', 'Vivienne Huiling Wang'] | 2022-01-24 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 2.02990249e-01 3.15957129e-01 -4.01372433e-01 2.16305256e-01
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0f6859eb-016c-4d65-8cd3-fec0732b9e94 | estimation-of-bivariate-structural-causal | 2109.02521 | null | https://arxiv.org/abs/2109.02521v1 | https://arxiv.org/pdf/2109.02521v1.pdf | Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows | One major drawback of state-of-the-art artificial intelligence is its lack of explainability. One approach to solve the problem is taking causality into account. Causal mechanisms can be described by structural causal models. In this work, we propose a method for estimating bivariate structural causal models using a combination of normalising flows applied to density estimation and variational Gaussian process regression for post-nonlinear models. It facilitates causal discovery, i.e. distinguishing cause and effect, by either the independence of cause and residual or a likelihood ratio test. Our method which estimates post-nonlinear models can better explain a variety of real-world cause-effect pairs than a simple additive noise model. Though it remains difficult to exploit this benefit regarding all pairs from the T\"ubingen benchmark database, we demonstrate that combining the additive noise model approach with our method significantly enhances causal discovery. | ['Bin Yang', 'Alexander Bartler', 'Felix Wiewel', 'Nico Reick'] | 2021-09-06 | null | null | null | null | ['normalising-flows'] | ['methodology'] | [ 2.28180319e-01 3.15571219e-01 -4.08637613e-01 -2.11084321e-01
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847f4d65-24a2-432b-a7f9-dac56ff6ce07 | a-quantitative-review-on-language-model | 2306.01768 | null | https://arxiv.org/abs/2306.01768v1 | https://arxiv.org/pdf/2306.01768v1.pdf | A Quantitative Review on Language Model Efficiency Research | Language models (LMs) are being scaled and becoming powerful. Improving their efficiency is one of the core research topics in neural information processing systems. Tay et al. (2022) provided a comprehensive overview of efficient Transformers that have become an indispensable staple in the field of NLP. However, in the section of "On Evaluation", they left an open question "which fundamental efficient Transformer one should consider," answered by "still a mystery" because "many research papers select their own benchmarks." Unfortunately, there was not quantitative analysis about the performances of Transformers on any benchmarks. Moreover, state space models (SSMs) have demonstrated their abilities of modeling long-range sequences with non-attention mechanisms, which were not discussed in the prior review. This article makes a meta analysis on the results from a set of papers on efficient Transformers as well as those on SSMs. It provides a quantitative review on LM efficiency research and gives suggestions for future research. | ['Lingbo Tong', 'Hy Dang', 'Meng Jiang'] | 2023-05-28 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 2.82679293e-02 -1.13594504e-02 -3.12045068e-01 -1.77318722e-01
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ff26cc40-191c-4a48-9672-c65ccc793a9b | rdmnet-reliable-dense-matching-based-point | 2303.18084 | null | https://arxiv.org/abs/2303.18084v1 | https://arxiv.org/pdf/2303.18084v1.pdf | RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving | Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability. | ['Bin Dai', 'Junhao Xiao', 'Wenbang Deng', 'Huimin Lu', 'Xieyuanli Chen', 'Chenghao Shi'] | 2023-03-31 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-2.51471043e-01 -3.70680034e-01 -2.42720738e-01 -4.18841630e-01
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398b5abf-8ca3-4e80-b644-eabc7c6f39ec | multimodal-image-registration-using-laplacian | null | null | https://www.sciencedirect.com/science/article/pii/S1566253517305316 | https://www.sciencedirect.com/science/article/pii/S1566253517305316/pdfft?md5=47e182707e90578060ddb97372c97a26&pid=1-s2.0-S1566253517305316-main.pdf | Multimodal image registration using Laplacian commutators | The fusion and combination of images from multiple modalities is important in many applications. Typically,
this process consists of the alignment of the images and the combination of the complementary information.
In this work, we focused on the former part and propose a multimodal image distance measure based on the
commutativity of graph Laplacians. The eigenvectors of the image graph Laplacian, and thus the graph Laplacian
itself, capture the intrinsic structure of the image’s modality. Using Laplacian commutativity as a criterion of
image structure preservation, we adapt the problem of finding the closest commuting operators to multimodal
image registration. Hence, by using the relation between simultaneous diagonalization and commutativity of
matrices, we compare multimodal image structures by means of the commutativity of their graph Laplacians.
In this way, we avoid spectrum reordering schemes or additional manifold alignment steps which are necessary
to ensure the comparability of eigenspaces across modalities. We show on synthetic and real datasets that this
approach is applicable to dense rigid and non-rigid image registration. Results demonstrated that the proposed
measure is able to deal with very challenging multimodal datasets and compares favorably to normalized mutual
information, a de facto similarity measure for multimodal image registration. | ['Gemma Piellaa', 'Miguel Ángel González Ballester a', 'Veronika A. Zimmer a'] | 2019-09-09 | null | null | null | journal-2019-9 | ['image-registration'] | ['computer-vision'] | [ 4.36896473e-01 -1.86577231e-01 2.58640379e-01 -2.31016323e-01
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373aa430-7917-4d2e-9205-ec808a00915e | monolayout-amodal-scene-layout-from-a-single | 2002.08394 | null | https://arxiv.org/abs/2002.08394v1 | https://arxiv.org/pdf/2002.08394v1.pdf | MonoLayout: Amodal scene layout from a single image | In this paper, we address the novel, highly challenging problem of estimating the layout of a complex urban driving scenario. Given a single color image captured from a driving platform, we aim to predict the bird's-eye view layout of the road and other traffic participants. The estimated layout should reason beyond what is visible in the image, and compensate for the loss of 3D information due to projection. We dub this problem amodal scene layout estimation, which involves "hallucinating" scene layout for even parts of the world that are occluded in the image. To this end, we present MonoLayout, a deep neural network for real-time amodal scene layout estimation from a single image. We represent scene layout as a multi-channel semantic occupancy grid, and leverage adversarial feature learning to hallucinate plausible completions for occluded image parts. Due to the lack of fair baseline methods, we extend several state-of-the-art approaches for road-layout estimation and vehicle occupancy estimation in bird's-eye view to the amodal setup for rigorous evaluation. By leveraging temporal sensor fusion to generate training labels, we significantly outperform current art over a number of datasets. On the KITTI and Argoverse datasets, we outperform all baselines by a significant margin. We also make all our annotations, and code publicly available. A video abstract of this paper is available https://www.youtube.com/watch?v=HcroGyo6yRQ . | ['Krishna Murthy Jatavallabhula', 'Shubhika Garg', 'N. Sai Shankar', 'Swapnil Daga', 'K. Madhava Krishna', 'Kaustubh Mani'] | 2020-02-19 | null | null | null | null | ['amodal-layout-estimation'] | ['computer-vision'] | [ 2.23932564e-01 1.25639319e-01 2.53083795e-01 -3.86993587e-01
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32d41eb1-dbce-4bca-b8ea-cda616ea9fc0 | cronos-colorization-and-contrastive-learning | 2211.10354 | null | https://arxiv.org/abs/2211.10354v4 | https://arxiv.org/pdf/2211.10354v4.pdf | CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI | In recent years, the demand for pervasive smart services and applications has increased rapidly. Device-free human detection through sensors or cameras has been widely adopted, but it comes with privacy issues as well as misdetection for motionless people. To address these drawbacks, channel state information (CSI) captured from commercialized Wi-Fi devices provides rich signal features for accurate detection. However, existing systems suffer from inaccurate classification under a non-line-of-sight (NLoS) and stationary scenario, such as when a person is standing still in a room corner. In this work, we propose a system called CRONOS (Colorization and Contrastive Learning Enhanced NLoS Human Presence Detection), which generates dynamic recurrence plots (RPs) and color-coded CSI ratios to distinguish mobile and stationary people from vacancy in a room, respectively. We also incorporate supervised contrastive learning to retrieve substantial representations, where consultation loss is formulated to differentiate the representative distances between dynamic and stationary cases. Furthermore, we propose a self-switched static feature enhanced classifier (S3FEC) to determine the utilization of either RPs or color-coded CSI ratios. Our comprehensive experimental results show that CRONOS outperforms existing systems that either apply machine learning or non-learning based methods, as well as non-CSI based features in open literature. CRONOS achieves the highest human presence detection accuracy in vacancy, mobility, line-of-sight (LoS), and NLoS scenarios. | ['Chia-Che Hsieh', 'Li-Hsiang Shen', 'Kai-Ten Feng', 'An-Hung Hsiao'] | 2022-11-07 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 2.45244324e-01 -7.07135081e-01 -8.33404064e-02 1.17577547e-02
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42d09201-43fa-4adb-9d59-3c74a451446e | vlsp-2021-shared-task-vietnamese-machine | 2203.11400 | null | https://arxiv.org/abs/2203.11400v3 | https://arxiv.org/pdf/2203.11400v3.pdf | VLSP 2021 - ViMRC Challenge: Vietnamese Machine Reading Comprehension | One of the emerging research trends in natural language understanding is machine reading comprehension (MRC) which is the task to find answers to human questions based on textual data. Existing Vietnamese datasets for MRC research concentrate solely on answerable questions. However, in reality, questions can be unanswerable for which the correct answer is not stated in the given textual data. To address the weakness, we provide the research community with a benchmark dataset named UIT-ViQuAD 2.0 for evaluating the MRC task and question answering systems for the Vietnamese language. We use UIT-ViQuAD 2.0 as a benchmark dataset for the challenge on Vietnamese MRC at the Eighth Workshop on Vietnamese Language and Speech Processing (VLSP 2021). This task attracted 77 participant teams from 34 universities and other organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 77.24% in F1-score and 67.43% in Exact Match on the private test set. The Vietnamese MRC systems proposed by the top 3 teams use XLM-RoBERTa, a powerful pre-trained language model based on the transformer architecture. The UIT-ViQuAD 2.0 dataset motivates researchers to further explore the Vietnamese machine reading comprehension task and related tasks such as question answering, question generation, and natural language inference. | ['Ngan Luu-Thuy Nguyen', 'Son T. Luu', 'Tin Van Huynh', 'Luan Thanh Nguyen', 'Son Quoc Tran', 'Kiet Van Nguyen'] | 2022-03-22 | null | null | null | null | ['vietnamese-datasets'] | ['natural-language-processing'] | [ 3.71266127e-01 3.20363581e-01 2.21713871e-01 -4.64978933e-01
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5e5ac9ca-8e59-41ef-b467-b784e831ef57 | istego100k-large-scale-image-steganalysis | 1911.05542 | null | https://arxiv.org/abs/1911.05542v1 | https://arxiv.org/pdf/1911.05542v1.pdf | IStego100K: Large-scale Image Steganalysis Dataset | In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IStego100K. It contains 208,104 images with the same size of 1024*1024. Among them, 200,000 images (100,000 cover-stego image pairs) are divided as the training set and the remaining 8,104 as testing set. In addition, we hope that IStego100K can help researchers further explore the development of universal image steganalysis algorithms, so we try to reduce limits on the images in IStego100K. For each image in IStego100K, the quality factors is randomly set in the range of 75-95, the steganographic algorithm is randomly selected from three well-known steganographic algorithms, which are J-uniward, nsF5 and UERD, and the embedding rate is also randomly set to be a value of 0.1-0.4. In addition, considering the possible mismatch between training samples and test samples in real environment, we add a test set (DS-Test) whose source of samples are different from the training set. We hope that this test set can help to evaluate the robustness of steganalysis algorithms. We tested the performance of some latest steganalysis algorithms on IStego100K, with specific results and analysis details in the experimental part. We hope that the IStego100K dataset will further promote the development of universal image steganalysis technology. The description of IStego100K and instructions for use can be found at https://github.com/YangzlTHU/IStego100K | ['Yongfeng Huang', 'Sai Ma', 'Ke Wang', 'Xianfeng Zhao', 'Zhongliang Yang', 'Xiangui Kang'] | 2019-11-13 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 1.93425670e-01 -2.57333100e-01 3.44250202e-02 2.52738565e-01
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0902aee1-e850-4c5e-aab3-392502b3e572 | value-function-estimation-using-conditional | 2306.07290 | null | https://arxiv.org/abs/2306.07290v1 | https://arxiv.org/pdf/2306.07290v1.pdf | Value function estimation using conditional diffusion models for control | A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As the appetite for large models increases, it is imperative to address, sooner than later, the potential problem of running out of high-quality demonstrations. In this case, instead of collecting only new data via costly human demonstrations or risking a simulation-to-real transfer with uncertain effects, it would be beneficial to leverage vast amounts of readily-available low-quality data. Since classical control algorithms such as behavior cloning or temporal difference learning cannot be used on reward-free or action-free data out-of-the-box, this solution warrants novel training paradigms for continuous control. We propose a simple algorithm called Diffused Value Function (DVF), which learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. This model can be efficiently learned from state sequences (i.e., without access to reward functions nor actions), and subsequently used to estimate the value of each action out-of-the-box. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers, and show promising qualitative and quantitative results on challenging robotics benchmarks. | ['Josh Susskind', 'Alexander Toshev', 'Devon Hjelm', 'Miguel Angel Bautista', 'Walter Talbott', 'Bogdan Mazoure'] | 2023-06-09 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-4.00534682e-02 1.70011386e-01 -1.91858098e-01 -1.17898561e-01
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4d00b3c3-d6df-4ff8-8e88-38a7b102088e | probabilistic-forecast-based-portfolio | 2305.09474 | null | https://arxiv.org/abs/2305.09474v1 | https://arxiv.org/pdf/2305.09474v1.pdf | Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels | In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of both accuracy and computational efficiency. | ['Kwangwon Ahn', 'Hokyun Kim', 'Fotios Petropoulos', 'Ran Li', 'Jooyoung Jeon', 'Estêvão Alvarenga', 'Jungyeon Park'] | 2023-04-18 | null | null | null | null | ['load-forecasting', 'portfolio-optimization'] | ['miscellaneous', 'time-series'] | [-5.99739194e-01 -5.22419363e-02 -3.01977955e-02 -3.85985523e-01
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0c4fc125-e4c4-4581-a48c-53e530676781 | agss-vos-attention-guided-single-shot-video | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Lin_AGSS-VOS_Attention_Guided_Single-Shot_Video_Object_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Lin_AGSS-VOS_Attention_Guided_Single-Shot_Video_Object_Segmentation_ICCV_2019_paper.pdf | AGSS-VOS: Attention Guided Single-Shot Video Object Segmentation | Most video object segmentation approaches process objects separately. This incurs high computational cost when multiple objects exist. In this paper, we propose AGSS-VOS to segment multiple objects in one feed-forward path via instance-agnostic and instance-specific modules. Information from the two modules is fused via an attention-guided decoder to simultaneously segment all object instances in one path. The whole framework is end-to-end trainable with instance IoU loss. Experimental results on Youtube- VOS and DAVIS-2017 dataset demonstrate that AGSS-VOS achieves competitive results in terms of both accuracy and efficiency.
| [' Jiaya Jia', ' Xiaojuan Qi', 'Huaijia Lin'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.40755614e-02 8.42406154e-02 -5.08856535e-01 -5.57204962e-01
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47e29b76-47c3-4aa5-a2b1-439ab6b5ce75 | unsupervised-mandarin-cantonese-machine | 2301.03971 | null | https://arxiv.org/abs/2301.03971v1 | https://arxiv.org/pdf/2301.03971v1.pdf | Unsupervised Mandarin-Cantonese Machine Translation | Advancements in unsupervised machine translation have enabled the development of machine translation systems that can translate between languages for which there is not an abundance of parallel data available. We explored unsupervised machine translation between Mandarin Chinese and Cantonese. Despite the vast number of native speakers of Cantonese, there is still no large-scale corpus for the language, due to the fact that Cantonese is primarily used for oral communication. The key contributions of our project include: 1. The creation of a new corpus containing approximately 1 million Cantonese sentences, and 2. A large-scale comparison across different model architectures, tokenization schemes, and embedding structures. Our best model trained with character-based tokenization and a Transformer architecture achieved a character-level BLEU of 25.1 when translating from Mandarin to Cantonese and of 24.4 when translating from Cantonese to Mandarin. In this paper we discuss our research process, experiments, and results. | ['Shibingfeng Zhang', 'Yifan Wang', 'Averie Ho Zoen So', 'Valentina Fajardo Diaz', 'Megan Dare'] | 2023-01-10 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 8.47341269e-02 -1.72899857e-01 -4.91807997e-01 -3.51401001e-01
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d5dd2e66-4cff-4e5c-9658-cb4131868a3b | solving-inefficiency-of-self-supervised | 2104.08760 | null | https://arxiv.org/abs/2104.08760v3 | https://arxiv.org/pdf/2104.08760v3.pdf | Solving Inefficiency of Self-supervised Representation Learning | Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised manner. Despite the acknowledged successes, existing contrastive learning methods suffer from very low learning efficiency, e.g., taking about ten times more training epochs than supervised learning for comparable recognition accuracy. In this paper, we reveal two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency. Under-clustering means that the model cannot efficiently learn to discover the dissimilarity between inter-class samples when the negative sample pairs for contrastive learning are insufficient to differentiate all the actual object classes. Over-clustering implies that the model cannot efficiently learn features from excessive negative sample pairs, forcing the model to over-cluster samples of the same actual classes into different clusters. To simultaneously overcome these two problems, we propose a novel self-supervised learning framework using a truncated triplet loss. Precisely, we employ a triplet loss tending to maximize the relative distance between the positive pair and negative pairs to address the under-clustering problem; and we construct the negative pair by selecting a negative sample deputy from all negative samples to avoid the over-clustering problem, guaranteed by the Bernoulli Distribution model. We extensively evaluate our framework in several large-scale benchmarks (e.g., ImageNet, SYSU-30k, and COCO). The results demonstrate our model's superiority (e.g., the learning efficiency) over the latest state-of-the-art methods by a clear margin. Codes available at: https://github.com/wanggrun/triplet . | ['Philip H. S. Torr', 'Liang Lin', 'Guangcong Wang', 'Keze Wang', 'Guangrun Wang'] | 2021-04-18 | solving-inefficiency-of-self-supervised-1 | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Solving_Inefficiency_of_Self-Supervised_Representation_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Solving_Inefficiency_of_Self-Supervised_Representation_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification', 'self-supervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 6.05494790e-02 -2.17361987e-01 -4.19114888e-01 -5.31790137e-01
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ad96a55a-7e84-42bb-871f-e935a8990039 | testing-for-coefficient-randomness-in-local | 2301.04853 | null | https://arxiv.org/abs/2301.04853v2 | https://arxiv.org/pdf/2301.04853v2.pdf | Testing for Coefficient Randomness in Local-to-Unity Autoregressions | In this study, we propose a test for the coefficient randomness in autoregressive models where the autoregressive coefficient is local to unity, which is empirically relevant given the results of earlier studies. Under this specification, we theoretically analyze the effect of the correlation between the random coefficient and disturbance on tests' properties, which remains largely unexplored in the literature. Our analysis reveals that the correlation crucially affects the power of tests for coefficient randomness and that tests proposed by earlier studies can perform poorly when the degree of the correlation is moderate to large. The test we propose in this paper is designed to have a power function robust to the correlation. Because the asymptotic null distribution of our test statistic depends on the correlation $\psi$ between the disturbance and its square as earlier tests do, we also propose a modified version of the test statistic such that its asymptotic null distribution is free from the nuisance parameter $\psi$. The modified test is shown to have better power properties than existing ones in large and finite samples. | ['Mikihito Nishi'] | 2023-01-12 | null | null | null | null | ['unity'] | ['computer-vision'] | [-1.29513159e-01 -7.16211721e-02 -3.67387325e-01 -2.07877159e-01
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f5deed9e-34f4-45b2-963e-9b28594f9a58 | jcse-contrastive-learning-of-japanese | 2301.08193 | null | https://arxiv.org/abs/2301.08193v1 | https://arxiv.org/pdf/2301.08193v1.pdf | JCSE: Contrastive Learning of Japanese Sentence Embeddings and Its Applications | Contrastive learning is widely used for sentence representation learning. Despite this prevalence, most studies have focused exclusively on English and few concern domain adaptation for domain-specific downstream tasks, especially for low-resource languages like Japanese, which are characterized by insufficient target domain data and the lack of a proper training strategy. To overcome this, we propose a novel Japanese sentence representation framework, JCSE (derived from ``Contrastive learning of Sentence Embeddings for Japanese''), that creates training data by generating sentences and synthesizing them with sentences available in a target domain. Specifically, a pre-trained data generator is finetuned to a target domain using our collected corpus. It is then used to generate contradictory sentence pairs that are used in contrastive learning for adapting a Japanese language model to a specific task in the target domain. Another problem of Japanese sentence representation learning is the difficulty of evaluating existing embedding methods due to the lack of benchmark datasets. Thus, we establish a comprehensive Japanese Semantic Textual Similarity (STS) benchmark on which various embedding models are evaluated. Based on this benchmark result, multiple embedding methods are chosen and compared with JCSE on two domain-specific tasks, STS in a clinical domain and information retrieval in an educational domain. The results show that JCSE achieves significant performance improvement surpassing direct transfer and other training strategies. This empirically demonstrates JCSE's effectiveness and practicability for downstream tasks of a low-resource language. | ['Kimiaki Shirahama', 'Hisashi Handa', 'Zihao Chen'] | 2023-01-19 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'semantic-textual-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.79046613e-01 8.26485232e-02 -4.62550372e-02 -2.85542846e-01
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8dfdd0ef-7287-45a7-ab83-1f0533579130 | tma-temporal-motion-aggregation-for-event | 2303.11629 | null | https://arxiv.org/abs/2303.11629v1 | https://arxiv.org/pdf/2303.11629v1.pdf | TMA: Temporal Motion Aggregation for Event-based Optical Flow | Event cameras have the ability to record continuous and detailed trajectories of objects with high temporal resolution, thereby providing intuitive motion cues for optical flow estimation. Nevertheless, most existing learning-based approaches for event optical flow estimation directly remould the paradigm of conventional images by representing the consecutive event stream as static frames, ignoring the inherent temporal continuity of event data. In this paper, we argue that temporal continuity is a vital element of event-based optical flow and propose a novel Temporal Motion Aggregation (TMA) approach to unlock its potential. Technically, TMA comprises three components: an event splitting strategy to incorporate intermediate motion information underlying the temporal context, a linear lookup strategy to align temporally continuous motion features and a novel motion pattern aggregation module to emphasize consistent patterns for motion feature enhancement. By incorporating temporally continuous motion information, TMA can derive better flow estimates than existing methods at early stages, which not only enables TMA to obtain more accurate final predictions, but also greatly reduces the demand for a number of refinements. Extensive experiments on DESC-Flow and MVSEC datasets verify the effectiveness and superiority of our TMA. Remarkably, compared to E-RAFT, TMA achieves a 6% improvement in accuracy and a 40% reduction in inference time on DSEC-Flow. | ['Changjun Jiang', 'Alois Knoll', 'Zhijun Li', 'Yanping Zhang', 'Sanqing Qu', 'Guang Chen', 'Haotian Liu'] | 2023-03-21 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 2.22791415e-02 -5.88160217e-01 -2.91603386e-01 -5.73469028e-02
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55e85234-7981-4415-87c3-29a599f787ce | latent-heterogeneous-graph-network-for | 2208.13669 | null | https://arxiv.org/abs/2208.13669v1 | https://arxiv.org/pdf/2208.13669v1.pdf | Latent Heterogeneous Graph Network for Incomplete Multi-View Learning | Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning, which aims to use multiple incomplete views as fully as possible in a flexible manner. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To explore the complex relationship between samples and latent representations, a neighborhood constraint and a view-existence constraint are proposed, for the first time, to construct a heterogeneous graph. Finally, to avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model over existing state-of-the-art approaches. | ['QinGhua Hu', 'Shuai Zhao', 'Binyuan Hui', 'Meng Cao', 'Yu Wang', 'Xinjie Yao', 'Pengfei Zhu'] | 2022-08-29 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [-2.48719957e-02 1.32669464e-01 -5.91700017e-01 -5.56683123e-01
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814ca9ef-a3c1-4466-b1b9-f429f8b9a1a5 | reintel-challenge-2020-a-comparative-study-of | 2109.12777 | null | https://arxiv.org/abs/2109.12777v1 | https://arxiv.org/pdf/2109.12777v1.pdf | ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs | The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial task. After various ablation studies, we propose a multi-input model that can effectively leverage both tabular metadata and post content for the task. Applying state-of-the-art finetuning techniques for the pretrained component and training strategies for our complete model, we have achieved a 0.9462 ROC-score on the VLSP private test set. | ['Ta Minh Thanh', 'Ngoc N. Tran', 'Quang Huu Pham', 'Huy Quang Dao', 'Tam Minh Nguyen', 'Tung Tien Bui', 'Hoang Viet Trinh'] | 2021-09-27 | null | https://aclanthology.org/2020.vlsp-1.2 | https://aclanthology.org/2020.vlsp-1.2.pdf | vlsp-2020-12 | ['reliable-intelligence-identification'] | ['natural-language-processing'] | [ 7.12698177e-02 1.18966185e-01 -5.55549920e-01 -6.81037679e-02
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7c143f33-1f53-46d8-84d7-d833e6e96051 | a-deep-convolutional-network-for-seismic-shot | 1912.01148 | null | https://arxiv.org/abs/1912.01148v1 | https://arxiv.org/pdf/1912.01148v1.pdf | A Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification | Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the early stages of seismic processing, we are required to identify and fix noisy sail lines. In this work, we introduce a real-world seismogram quality classification dataset based on 6,613 examples, manually labeled by human experts as good, bad or ugly, according to their noise intensity. This dataset is used to train a CNN classifier for seismic shot-gathers quality prediction. In our empirical evaluation, we observe an F1-score of 93.56% in the test set. | ['André Bulcão', 'Sérgio Colcher', 'Ruy Luiz Milidiú', 'Antonio José Grandson Busson', 'Eduardo Betine Bucker', 'Bruno Pereira Dias'] | 2019-12-03 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-2.67450005e-01 -2.48553738e-01 5.05433083e-01 -4.50889140e-01
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56da9c99-f9a4-4ec8-92c7-d0cc86cddec8 | dynamic-routing-on-deep-neural-network-for | 1808.05744 | null | http://arxiv.org/abs/1808.05744v1 | http://arxiv.org/pdf/1808.05744v1.pdf | Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization | We present and evaluate a new deep neural network architecture for automatic
thoracic disease detection on chest X-rays. Deep neural networks have shown
great success in a plethora of visual recognition tasks such as image
classification and object detection by stacking multiple layers of
convolutional neural networks (CNN) in a feed-forward manner. However, the
performance gain by going deeper has reached bottlenecks as a result of the
trade-off between model complexity and discrimination power. We address this
problem by utilizing the recently developed routing-by agreement mechanism in
our architecture. A novel characteristic of our network structure is that it
extends routing to two types of layer connections (1) connection between
feature maps in dense layers, (2) connection between primary capsules and
prediction capsules in final classification layer. We show that our networks
achieve comparable results with much fewer layers in the measurement of AUC
score. We further show the combined benefits of model interpretability by
generating Gradient-weighted Class Activation Mapping (Grad-CAM) for
localization. We demonstrate our results on the NIH chestX-ray14 dataset that
consists of 112,120 images on 30,805 unique patients including 14 kinds of lung
diseases. | ['Mingchen Gao', 'Yan Shen'] | 2018-08-17 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 2.17907250e-01 3.72080743e-01 -2.36493886e-01 -5.63929081e-01
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dfa87af5-7489-43bf-bf59-3b1fe8a5f41e | rcp-recurrent-closest-point-for-scene-flow | 2205.11028 | null | https://arxiv.org/abs/2205.11028v2 | https://arxiv.org/pdf/2205.11028v2.pdf | RCP: Recurrent Closest Point for Scene Flow Estimation on 3D Point Clouds | 3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to construct the cost volume for estimating accurate 3D flow. However, these methods are limited by the fact that it is difficult to define a search window on point clouds because of the irregular data structure. In this paper, we avoid this irregularity by a simple yet effective method.We decompose the problem into two interlaced stages, where the 3D flows are optimized point-wisely at the first stage and then globally regularized in a recurrent network at the second stage. Therefore, the recurrent network only receives the regular point-wise information as the input. In the experiments, we evaluate the proposed method on both the 3D scene flow estimation and the point cloud registration task. For 3D scene flow estimation, we make comparisons on the widely used FlyingThings3D and KITTIdatasets. For point cloud registration, we follow previous works and evaluate the data pairs with large pose and partially overlapping from ModelNet40. The results show that our method outperforms the previous method and achieves a new state-of-the-art performance on both 3D scene flow estimation and point cloud registration, which demonstrates the superiority of the proposed zero-order method on irregular point cloud data. | ['Ping Tan', 'Siyu Zhu', 'Zuozhuo Dai', 'Weihao Yuan', 'Chengzhou Tang', 'Xiaodong Gu'] | 2022-05-23 | null | null | null | null | ['point-cloud-registration', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-4.41894740e-01 -7.37801135e-01 7.88096152e-03 -1.27153188e-01
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fd86f73c-6d43-47d5-b88d-f712b6c3c922 | distinguishing-healthy-ageing-from-dementia-a | 2108.08214 | null | https://arxiv.org/abs/2108.08214v1 | https://arxiv.org/pdf/2108.08214v1.pdf | Distinguishing Healthy Ageing from Dementia: a Biomechanical Simulation of Brain Atrophy using Deep Networks | Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework can estimate realistic deformations, following the known course of Alzheimer's disease, that clearly differentiate between healthy and demented patterns of ageing. This suggests the framework has potential to be incorporated into explainable models of disease, for the exploration of interventions and counterfactual examples. | ['Emma C. Robinson', 'M. Jorge Cardoso', 'Cher Bass', 'Kara Garcia', 'Carole H. Sudre', 'Mariana da Silva'] | 2021-08-18 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.40681397e-02 4.20867741e-01 1.35096967e-01 -5.24219036e-01
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591e120a-21fe-4fe7-ac34-21569745a9f1 | contrast-with-reconstruct-contrastive-3d | 2302.02318 | null | https://arxiv.org/abs/2302.02318v2 | https://arxiv.org/pdf/2302.02318v2.pdf | Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining | Mainstream 3D representation learning approaches are built upon contrastive or generative modeling pretext tasks, where great improvements in performance on various downstream tasks have been achieved. However, we find these two paradigms have different characteristics: (i) contrastive models are data-hungry that suffer from a representation over-fitting issue; (ii) generative models have a data filling issue that shows inferior data scaling capacity compared to contrastive models. This motivates us to learn 3D representations by sharing the merits of both paradigms, which is non-trivial due to the pattern difference between the two paradigms. In this paper, we propose Contrast with Reconstruct (ReCon) that unifies these two paradigms. ReCon is trained to learn from both generative modeling teachers and single/cross-modal contrastive teachers through ensemble distillation, where the generative student guides the contrastive student. An encoder-decoder style ReCon-block is proposed that transfers knowledge through cross attention with stop-gradient, which avoids pretraining over-fitting and pattern difference issues. ReCon achieves a new state-of-the-art in 3D representation learning, e.g., 91.26% accuracy on ScanObjectNN. Codes have been released at https://github.com/qizekun/ReCon. | ['Li Yi', 'Kaisheng Ma', 'Xiangyu Zhang', 'Zheng Ge', 'Guofan Fan', 'Runpei Dong', 'Zekun Qi'] | 2023-02-05 | null | null | null | null | ['3d-point-cloud-classification', '3d-point-cloud-linear-classification', 'zero-shot-transfer-3d-point-cloud', 'few-shot-3d-point-cloud-classification'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.40254572e-02 1.85666531e-01 -1.18048191e-01 -4.84549701e-01
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1c1ea75d-b7cd-4d3f-9d1d-fc9ff3ab3096 | learned-dual-view-reflection-removal | 2010.00702 | null | https://arxiv.org/abs/2010.00702v1 | https://arxiv.org/pdf/2010.00702v1.pdf | Learned Dual-View Reflection Removal | Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because no dataset for dual-view reflection removal exists, we render a synthetic dataset of dual-views with and without reflections for use in training. Our evaluation on an additional real-world dataset of stereo pairs shows that our algorithm outperforms existing single-image and multi-image dereflection approaches. | ['Tianfan Xue', 'Feng Liu', 'Rahul Garg', 'Neal Wadhwa', 'Jonathan T. Barron', 'Xuaner Cecilia Zhang', 'Simon Niklaus'] | 2020-10-01 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 4.47336674e-01 -1.89458966e-01 1.53657109e-01 -3.17781150e-01
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ee943aef-18ea-4fbf-be51-9d9ff944e76c | mvfst-rl-an-asynchronous-rl-framework-for | 1910.04054 | null | https://arxiv.org/abs/1910.04054v4 | https://arxiv.org/pdf/1910.04054v4.pdf | MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions | Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control strategies. Research so far has primarily considered RL interfaces which block the sender while an agent considers its next action. This is largely an artifact of building on top of frameworks designed for RL in games (e.g. OpenAI Gym). However, this does not translate to real-world networking environments, where a network sender waiting on a policy without sending data leads to under-utilization of bandwidth. We instead propose to formulate congestion control with an asynchronous RL agent that handles delayed actions. We present MVFST-RL, a scalable framework for congestion control in the QUIC transport protocol that leverages state-of-the-art in asynchronous RL training with off-policy correction. We analyze modeling improvements to mitigate the deviation from Markovian dynamics, and evaluate our method on emulated networks from the Pantheon benchmark platform. The source code is publicly available at https://github.com/facebookresearch/mvfst-rl. | ['Sebastian Riedel', 'Joelle Pineau', 'Mike Rabbat', 'Heinrich Küttler', 'Alexander H. Miller', 'Tim Rocktäschel', 'Olivier Delalleau', 'Nantas Nardelli', 'Viswanath Sivakumar'] | 2019-10-09 | null | null | null | null | ['network-congestion-control'] | ['miscellaneous'] | [-2.01619402e-01 1.65522084e-01 -7.73099482e-01 -1.08571738e-01
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4af87302-1546-4433-a982-d1a5e3743b72 | robust-category-level-3d-pose-estimation-from | 2305.16124 | null | https://arxiv.org/abs/2305.16124v1 | https://arxiv.org/pdf/2305.16124v1.pdf | Robust Category-Level 3D Pose Estimation from Synthetic Data | Obtaining accurate 3D object poses is vital for numerous computer vision applications, such as 3D reconstruction and scene understanding. However, annotating real-world objects is time-consuming and challenging. While synthetically generated training data is a viable alternative, the domain shift between real and synthetic data is a significant challenge. In this work, we aim to narrow the performance gap between models trained on synthetic data and few real images and fully supervised models trained on large-scale data. We achieve this by approaching the problem from two perspectives: 1) We introduce SyntheticP3D, a new synthetic dataset for object pose estimation generated from CAD models and enhanced with a novel algorithm. 2) We propose a novel approach (CC3D) for training neural mesh models that perform pose estimation via inverse rendering. In particular, we exploit the spatial relationships between features on the mesh surface and a contrastive learning scheme to guide the domain adaptation process. Combined, these two approaches enable our models to perform competitively with state-of-the-art models using only 10% of the respective real training images, while outperforming the SOTA model by 10.4% with a threshold of pi/18 using only 50% of the real training data. Our trained model further demonstrates robust generalization to out-of-distribution scenarios despite being trained with minimal real data. | ['Adam Kortylewski', 'Alan Yuille', 'Xiaoding Yuan', 'Angtian Wang', 'Wufei Ma', 'Jiahao Yang'] | 2023-05-25 | null | null | null | null | ['3d-pose-estimation', '3d-reconstruction', 'inverse-rendering', 'scene-understanding'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.70991850e-01 2.97308356e-01 7.50436038e-02 -2.64205933e-01
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1d059602-aae4-4683-bd9e-947e82fec12b | improving-few-shot-image-classification-using | 2207.03133 | null | https://arxiv.org/abs/2207.03133v1 | https://arxiv.org/pdf/2207.03133v1.pdf | Improving Few-Shot Image Classification Using Machine- and User-Generated Natural Language Descriptions | Humans can obtain the knowledge of novel visual concepts from language descriptions, and we thus use the few-shot image classification task to investigate whether a machine learning model can have this capability. Our proposed model, LIDE (Learning from Image and DEscription), has a text decoder to generate the descriptions and a text encoder to obtain the text representations of machine- or user-generated descriptions. We confirmed that LIDE with machine-generated descriptions outperformed baseline models. Moreover, the performance was improved further with high-quality user-generated descriptions. The generated descriptions can be viewed as the explanations of the model's predictions, and we observed that such explanations were consistent with prediction results. We also investigated why the language description improved the few-shot image classification performance by comparing the image representations and the text representations in the feature spaces. | ['Shuichi Nishioka', 'Kyosuke Nishida', 'Kosuke Nishida'] | 2022-07-07 | null | https://aclanthology.org/2022.findings-naacl.106 | https://aclanthology.org/2022.findings-naacl.106.pdf | findings-naacl-2022-7 | ['few-shot-image-classification'] | ['computer-vision'] | [ 1.82948232e-01 2.32691690e-01 -3.84146601e-01 -6.57674611e-01
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96f565e3-c4f4-427c-89e9-8acebf000832 | an-open-source-tool-for-longitudinal-whole | 2207.04534 | null | https://arxiv.org/abs/2207.04534v2 | https://arxiv.org/pdf/2207.04534v2.pdf | An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation | In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans. It builds upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results, enabling it to better track subtle morphological changes in dozens of neuroanatomical structures and white matter lesions. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis, and compare its results against those obtained with its original cross-sectional formulation and two benchmark longitudinal methods. The results indicate that the method attains a higher test-retest reliability, while being more sensitive to longitudinal disease effect differences between patient groups. An implementation is publicly available as part of the open-source neuroimaging package FreeSurfer. | ['Koen van Leemput', 'Mark Mühlau', 'Hartwig R. Siebner', 'Henrik Lundell', 'Andrew Hoopes', 'Douglas N. Greve', 'Stefano Cerri'] | 2022-07-10 | null | null | null | null | ['3d-medical-imaging-segmentation', 'brain-image-segmentation', 'brain-segmentation', 'brain-lesion-segmentation-from-mri'] | ['medical', 'medical', 'medical', 'medical'] | [ 1.81200746e-02 -1.06987812e-01 -1.07310101e-01 -6.78546607e-01
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75e951c1-833d-4bd5-b400-758b1ad05717 | supervision-and-source-domain-impact-on | 2005.08629 | null | https://arxiv.org/abs/2005.08629v1 | https://arxiv.org/pdf/2005.08629v1.pdf | Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study | As many algorithms depend on a suitable representation of data, learning unique features is considered a crucial task. Although supervised techniques using deep neural networks have boosted the performance of representation learning, the need for a large set of labeled data limits the application of such methods. As an example, high-quality delineations of regions of interest in the field of pathology is a tedious and time-consuming task due to the large image dimensions. In this work, we explored the performance of a deep neural network and triplet loss in the area of representation learning. We investigated the notion of similarity and dissimilarity in pathology whole-slide images and compared different setups from unsupervised and semi-supervised to supervised learning in our experiments. Additionally, different approaches were tested, applying few-shot learning on two publicly available pathology image datasets. We achieved high accuracy and generalization when the learned representations were applied to two different pathology datasets. | ['Sobhan Shafiei', 'Milad Sikaroudi', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh', 'Amir Safarpoor'] | 2020-05-10 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 5.38474798e-01 -6.30734935e-02 -1.31063201e-02 -6.10699236e-01
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6458f29d-a027-43a1-931e-a50e5bbcca05 | online-map-vectorization-for-autonomous | 2306.10502 | null | https://arxiv.org/abs/2306.10502v1 | https://arxiv.org/pdf/2306.10502v1.pdf | Online Map Vectorization for Autonomous Driving: A Rasterization Perspective | Vectorized high-definition (HD) map is essential for autonomous driving, providing detailed and precise environmental information for advanced perception and planning. However, current map vectorization methods often exhibit deviations, and the existing evaluation metric for map vectorization lacks sufficient sensitivity to detect these deviations. To address these limitations, we propose integrating the philosophy of rasterization into map vectorization. Specifically, we introduce a new rasterization-based evaluation metric, which has superior sensitivity and is better suited to real-world autonomous driving scenarios. Furthermore, we propose MapVR (Map Vectorization via Rasterization), a novel framework that applies differentiable rasterization to vectorized outputs and then performs precise and geometry-aware supervision on rasterized HD maps. Notably, MapVR designs tailored rasterization strategies for various geometric shapes, enabling effective adaptation to a wide range of map elements. Experiments show that incorporating rasterization into map vectorization greatly enhances performance with no extra computational cost during inference, leading to more accurate map perception and ultimately promoting safer autonomous driving. | ['Zuoguan Wang', 'Shijian Lu', 'Yang Xue', 'Zhipeng Luo', 'Yilin Song', 'Shuang Wu', 'Jiahao Lin', 'Gongjie Zhang'] | 2023-06-18 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [ 1.81129515e-01 3.81922908e-02 -2.27138489e-01 -8.42402160e-01
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df3b1602-ca8d-45b0-8ef1-990271b5bfc4 | opi-at-semeval-2022-task-10-transformer-based | null | null | https://aclanthology.org/2022.semeval-1.190 | https://aclanthology.org/2022.semeval-1.190.pdf | OPI at SemEval-2022 Task 10: Transformer-based Sequence Tagging with Relation Classification for Structured Sentiment Analysis | This paper presents our solution for SemEval-2022 Task 10: Structured Sentiment Analysis. The solution consisted of two modules: the first for sequence tagging and the second for relation classification. In both modules we used transformer-based language models. In addition to utilizing language models specific to each of the five competition languages, we also adopted multilingual models. This approach allowed us to apply the solution to both monolingual and cross-lingual sub-tasks, where we obtained average Sentiment Graph F1 of 54.5% and 53.1%, respectively. The source code of the prepared solution is available at https://github.com/rafalposwiata/structured-sentiment-analysis. | ['Rafał Poświata'] | null | null | null | null | semeval-naacl-2022-7 | ['relation-classification'] | ['natural-language-processing'] | [-7.97454417e-02 4.66562808e-01 -1.75058424e-01 -2.82321751e-01
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7e171036-89ad-4f36-b409-f87dd0f6f858 | conditionally-optimistic-exploration-for | 2303.09032 | null | https://arxiv.org/abs/2303.09032v1 | https://arxiv.org/pdf/2303.09032v1.pdf | Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning | Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL). In this paper, we propose an exploration method that efficiently encourages cooperative exploration based on the idea of the theoretically justified tree search algorithm UCT (Upper Confidence bounds applied to Trees). The high-level intuition is that to perform optimism-based exploration, agents would achieve cooperative strategies if each agent's optimism estimate captures a structured dependency relationship with other agents. At each node (i.e., action) of the search tree, UCT performs optimism-based exploration using a bonus derived by conditioning on the visitation count of its parent node. We provide a perspective to view MARL as tree search iterations and develop a method called Conditionally Optimistic Exploration (COE). We assume agents take actions following a sequential order, and consider nodes at the same depth of the search tree as actions of one individual agent. COE computes each agent's state-action value estimate with an optimistic bonus derived from the visitation count of the state and joint actions taken by agents up to the current agent. COE is adaptable to any value decomposition method for centralized training with decentralized execution. Experiments across various cooperative MARL benchmarks show that COE outperforms current state-of-the-art exploration methods on hard-exploration tasks. | ['Janarthanan Rajendran', 'Sarath Chandar', 'Chenjun Xiao', 'Yangchen Pan', 'Xutong Zhao'] | 2023-03-16 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [-3.37846249e-01 6.19681776e-01 -6.16139472e-01 1.14845820e-02
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8c26fbfb-033b-43be-a63d-c51709db94e5 | dialoguetrm-exploring-the-intra-and-inter | 2010.07637 | null | https://arxiv.org/abs/2010.07637v1 | https://arxiv.org/pdf/2010.07637v1.pdf | DialogueTRM: Exploring the Intra- and Inter-Modal Emotional Behaviors in the Conversation | Emotion Recognition in Conversations (ERC) is essential for building empathetic human-machine systems. Existing studies on ERC primarily focus on summarizing the context information in a conversation, however, ignoring the differentiated emotional behaviors within and across different modalities. Designing appropriate strategies that fit the differentiated multi-modal emotional behaviors can produce more accurate emotional predictions. Thus, we propose the DialogueTransformer to explore the differentiated emotional behaviors from the intra- and inter-modal perspectives. For intra-modal, we construct a novel Hierarchical Transformer that can easily switch between sequential and feed-forward structures according to the differentiated context preference within each modality. For inter-modal, we constitute a novel Multi-Grained Interactive Fusion that applies both neuron- and vector-grained feature interactions to learn the differentiated contributions across all modalities. Experimental results show that DialogueTRM outperforms the state-of-the-art by a significant margin on three benchmark datasets. | ['Jianping Shen', 'Xuan Li', 'Weiguo Gao', 'Xiaojie Wang', 'Guang Liu', 'Qi Sun', 'Yuzhao Mao'] | 2020-10-15 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.20448092e-01 -1.65813521e-01 -5.16958907e-02 -7.15200186e-01
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70236dbd-e97c-4505-99d6-6fcc19091326 | unsupervised-conversation-disentanglement | 2109.03199 | null | https://arxiv.org/abs/2109.03199v1 | https://arxiv.org/pdf/2109.03199v1.pdf | Unsupervised Conversation Disentanglement through Co-Training | Conversation disentanglement aims to separate intermingled messages into detached sessions, which is a fundamental task in understanding multi-party conversations. Existing work on conversation disentanglement relies heavily upon human-annotated datasets, which are expensive to obtain in practice. In this work, we explore to train a conversation disentanglement model without referencing any human annotations. Our method is built upon a deep co-training algorithm, which consists of two neural networks: a message-pair classifier and a session classifier. The former is responsible for retrieving local relations between two messages while the latter categorizes a message to a session by capturing context-aware information. Both networks are initialized respectively with pseudo data built from an unannotated corpus. During the deep co-training process, we use the session classifier as a reinforcement learning component to learn a session assigning policy by maximizing the local rewards given by the message-pair classifier. For the message-pair classifier, we enrich its training data by retrieving message pairs with high confidence from the disentangled sessions predicted by the session classifier. Experimental results on the large Movie Dialogue Dataset demonstrate that our proposed approach achieves competitive performance compared to the previous supervised methods. Further experiments show that the predicted disentangled conversations can promote the performance on the downstream task of multi-party response selection. | ['Xiaodan Zhu', 'Zhan Shi', 'Hui Liu'] | 2021-09-07 | null | https://aclanthology.org/2021.emnlp-main.181 | https://aclanthology.org/2021.emnlp-main.181.pdf | emnlp-2021-11 | ['conversation-disentanglement'] | ['natural-language-processing'] | [ 5.16093731e-01 5.74150026e-01 -2.42533579e-01 -6.63347781e-01
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f93924dc-11ab-4754-80d6-72da809d199f | cascadepsp-toward-class-agnostic-and-very | 2005.02551 | null | https://arxiv.org/abs/2005.02551v1 | https://arxiv.org/pdf/2005.02551v1.pdf | CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement | State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation. | ['Chi-Keung Tang', 'Yu-Wing Tai', 'Jihoon Chung', 'Ho Kei Cheng'] | 2020-05-06 | cascadepsp-toward-class-agnostic-and-very-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Cheng_CascadePSP_Toward_Class-Agnostic_and_Very_High-Resolution_Segmentation_via_Global_and_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cheng_CascadePSP_Toward_Class-Agnostic_and_Very_High-Resolution_Segmentation_via_Global_and_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-parsing'] | ['computer-vision'] | [ 6.47649169e-01 3.73053581e-01 -7.29341991e-03 -4.19793665e-01
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3d7c4acf-5a99-4045-b7d7-cd6efd6816b5 | learning-human-compatible-representations-for | 2303.04809 | null | https://arxiv.org/abs/2303.04809v1 | https://arxiv.org/pdf/2303.04809v1.pdf | Learning Human-Compatible Representations for Case-Based Decision Support | Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans. As a result, they have limited effectiveness in case-based decision support. In this work, we incorporate ideas from metric learning with supervised learning to examine the importance of alignment for effective decision support. In addition to instance-level labels, we use human-provided triplet judgments to learn human-compatible decision-focused representations. Using both synthetic data and human subject experiments in multiple classification tasks, we demonstrate that such representation is better aligned with human perception than representation solely optimized for classification. Human-compatible representations identify nearest neighbors that are perceived as more similar by humans and allow humans to make more accurate predictions, leading to substantial improvements in human decision accuracies (17.8% in butterfly vs. moth classification and 13.2% in pneumonia classification). | ['Chenhao Tan', 'Yuxin Chen', 'Shi Feng', 'Chacha Chen', 'Yizhou Tian', 'Han Liu'] | 2023-03-06 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 5.59742808e-01 2.53156513e-01 -3.54669452e-01 -1.13931727e+00
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414fefc9-dfba-4b62-84da-60b9a588f365 | improving-road-segmentation-in-challenging | 2205.14112 | null | https://arxiv.org/abs/2205.14112v1 | https://arxiv.org/pdf/2205.14112v1.pdf | Improving Road Segmentation in Challenging Domains Using Similar Place Priors | Road segmentation in challenging domains, such as night, snow or rain, is a difficult task. Most current approaches boost performance using fine-tuning, domain adaptation, style transfer, or by referencing previously acquired imagery. These approaches share one or more of three significant limitations: a reliance on large amounts of annotated training data that can be costly to obtain, both anticipation of and training data from the type of environmental conditions expected at inference time, and/or imagery captured from a previous visit to the location. In this research, we remove these restrictions by improving road segmentation based on similar places. We use Visual Place Recognition (VPR) to find similar but geographically distinct places, and fuse segmentations for query images and these similar place priors using a Bayesian approach and novel segmentation quality metric. Ablation studies show the need to re-evaluate notions of VPR utility for this task. We demonstrate the system achieving state-of-the-art road segmentation performance across multiple challenging condition scenarios including night time and snow, without requiring any prior training or previous access to the same geographical locations. Furthermore, we show that this method is network agnostic, improves multiple baseline techniques and is competitive against methods specialised for road prediction. | ['Michael Milford', 'Thierry Peynot', 'Ming Xu', 'Sourav Garg', 'Connor Malone'] | 2022-05-27 | null | null | null | null | ['road-segementation', 'visual-place-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.70224106e-01 1.01572294e-02 -7.12198243e-02 -6.60002768e-01
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a8b4a135-8595-4731-81c5-a79deb7cbe69 | unsupervised-noise-adaptation-using-data | 2302.11981 | null | https://arxiv.org/abs/2302.11981v1 | https://arxiv.org/pdf/2302.11981v1.pdf | Unsupervised Noise adaptation using Data Simulation | Deep neural network based speech enhancement approaches aim to learn a noisy-to-clean transformation using a supervised learning paradigm. However, such a trained-well transformation is vulnerable to unseen noises that are not included in training set. In this work, we focus on the unsupervised noise adaptation problem in speech enhancement, where the ground truth of target domain data is completely unavailable. Specifically, we propose a generative adversarial network based method to efficiently learn a converse clean-to-noisy transformation using a few minutes of unpaired target domain data. Then this transformation is utilized to generate sufficient simulated data for domain adaptation of the enhancement model. Experimental results show that our method effectively mitigates the domain mismatch between training and test sets, and surpasses the best baseline by a large margin. | ['Eng Siong Chng', 'Linhui Sun', 'Heqing Zou', 'Yuchen Hu', 'Chen Chen'] | 2023-02-23 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 5.57258666e-01 -2.48987097e-02 3.55565131e-01 -4.70623046e-01
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195853bb-b624-4933-846b-2a8ad07be9d4 | target-based-surrogates-for-stochastic | 2302.02607 | null | https://arxiv.org/abs/2302.02607v2 | https://arxiv.org/pdf/2302.02607v2.pdf | Target-based Surrogates for Stochastic Optimization | We consider minimizing functions for which it is expensive to compute the (possibly stochastic) gradient. Such functions are prevalent in reinforcement learning, imitation learning and adversarial training. Our target optimization framework uses the (expensive) gradient computation to construct surrogate functions in a \emph{target space} (e.g. the logits output by a linear model for classification) that can be minimized efficiently. This allows for multiple parameter updates to the model, amortizing the cost of gradient computation. In the full-batch setting, we prove that our surrogate is a global upper-bound on the loss, and can be (locally) minimized using a black-box optimization algorithm. We prove that the resulting majorization-minimization algorithm ensures convergence to a stationary point of the loss. Next, we instantiate our framework in the stochastic setting and propose the $SSO$ algorithm, which can be viewed as projected stochastic gradient descent in the target space. This connection enables us to prove theoretical guarantees for $SSO$ when minimizing convex functions. Our framework allows the use of standard stochastic optimization algorithms to construct surrogates which can be minimized by any deterministic optimization method. To evaluate our framework, we consider a suite of supervised learning and imitation learning problems. Our experiments indicate the benefits of target optimization and the effectiveness of $SSO$. | ['Nicolas Le Roux', 'Mark Schmidt', 'Reza Babanezhad', 'Sharan Vaswani', 'Jonathan Wilder Lavington'] | 2023-02-06 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 5.94248548e-02 2.67948031e-01 -1.91753373e-01 -3.20942312e-01
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c3fbc28c-56b6-42a9-8984-fd2aadab54cc | a-comparison-of-the-word-similarity | null | null | https://aclanthology.org/2021.triton-1.14 | https://aclanthology.org/2021.triton-1.14.pdf | A Comparison of the Word Similarity Measurement in English-Arabic Translation Memory Segment Retrieval Including an Inflectional Affix Intervention | The aim of this paper is to investigate the similarity measurement approach of translation memory (TM) in five representative computer-aided translation (CAT) tools when retrieving inflectional verb-variation sentences in Arabic to English translation. In English, inflectional affixes in verbs include suffixes only; unlike English, verbs in Arabic derive voice, mood, tense, number and person through various inflectional affixes e.g. pre or post a verb root. The research question focuses on establishing whether the TM similarity algorithm measures a combination of the inflectional affixes as a word or as a character intervention when retrieving a segment. If it is dealt with as a character intervention, are the types of intervention penalized equally or differently? This paper experimentally examines, through a black box testing methodology and a test suite instrument, the penalties that TM systems’ current algorithms impose when input segments and retrieved TM sources are exactly the same, except for a difference in an inflectional affix. It would be expected that, if TM systems had some linguistic knowledge, the penalty would be very light, which would be useful to translators, since a high-scoring match would be presented near the top of the list of proposals. However, analysis of TM systems’ output shows that inflectional affixes are penalized more heavily than expected, and in different ways. They may be treated as an intervention on the whole word, or as a single character change. | ['Khaled Ben Milad'] | null | null | null | null | triton-2021-7 | ['word-similarity'] | ['natural-language-processing'] | [ 4.45435911e-01 -3.18237692e-02 -2.97517985e-01 -2.37653092e-01
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45bb9543-0fba-4fb7-ae99-eb83ec1c5a9b | distance-weighted-graph-neural-networks-on | 2008.03601 | null | https://arxiv.org/abs/2008.03601v2 | https://arxiv.org/pdf/2008.03601v2.pdf | Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics | Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than 1$\mu\mathrm{s}$ on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the $\mathtt{hls4ml}$ library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage. | ['Nhan Tran', 'Jennifer Ngadiuba', 'Gerrit Van Onsem', 'Sergo Jindariani', 'Philip Harris', 'Kinga Wozniak', 'Abhijay Gupta', 'Yutaro Iiyama', 'Marcel Rieger', 'Kevin Pedro', 'Jan Kieseler', 'Giuseppe Di Guglielmo', 'Edward Kreinar', 'Zhenbin Wu', 'Vladimir Loncar', 'Shah Rukh Qasim', 'Mia Liu', 'Maurizio Pierini', 'Javier Duarte', 'Sioni Summers', 'Gianluca Cerminara', 'Dylan Rankin'] | 2020-08-08 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [ 1.59898903e-02 1.87868476e-01 -1.57864943e-01 -7.48529911e-01
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e12df3d5-7b43-47e1-934b-37bbaf5de6c6 | gradient-induced-co-saliency-detection | 2004.13364 | null | https://arxiv.org/abs/2004.13364v3 | https://arxiv.org/pdf/2004.13364v3.pdf | Gradient-Induced Co-Saliency Detection | Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images. In this paper, inspired by human behavior, we propose a gradient-induced co-saliency detection (GICD) method. We first abstract a consensus representation for the grouped images in the embedding space; then, by comparing the single image with consensus representation, we utilize the feedback gradient information to induce more attention to the discriminative co-salient features. In addition, due to the lack of Co-SOD training data, we design a jigsaw training strategy, with which Co-SOD networks can be trained on general saliency datasets without extra pixel-level annotations. To evaluate the performance of Co-SOD methods on discovering the co-salient object among multiple foregrounds, we construct a challenging CoCA dataset, where each image contains at least one extraneous foreground along with the co-salient object. Experiments demonstrate that our GICD achieves state-of-the-art performance. Our codes and dataset are available at https://mmcheng.net/gicd/. | ['Ming-Ming Cheng', 'Jun Xu', 'Zhao Zhang', 'Wenda Jin'] | 2020-04-28 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1615_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570443.pdf | eccv-2020-8 | ['co-saliency-detection'] | ['computer-vision'] | [ 3.50177318e-01 8.27094764e-02 -2.62026966e-01 -1.45843923e-01
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ba358bd9-6223-4086-a388-d24ff9884d7e | kert-automatic-extraction-and-ranking-of | 1306.0271 | null | http://arxiv.org/abs/1306.0271v1 | http://arxiv.org/pdf/1306.0271v1.pdf | KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles | We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework
for topical keyphrase generation and ranking. By shifting from the
unigram-centric traditional methods of unsupervised keyphrase extraction to a
phrase-centric approach, we are able to directly compare and rank phrases of
different lengths. We construct a topical keyphrase ranking function which
implements the four criteria that represent high quality topical keyphrases
(coverage, purity, phraseness, and completeness). The effectiveness of our
approach is demonstrated on two collections of content-representative titles in
the domains of Computer Science and Physics. | ['Jiawei Han', 'Marina Danilevsky', 'Nihit Desai', 'Jingyi Guo', 'Chi Wang'] | 2013-06-03 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.40601555e-01 -3.84093001e-02 -5.81678748e-01 3.23650360e-01
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d2bd3404-4143-48d0-b5f3-21113673662c | seismic-inverse-modeling-method-based-on | 2106.04197 | null | https://arxiv.org/abs/2106.04197v1 | https://arxiv.org/pdf/2106.04197v1.pdf | Seismic Inverse Modeling Method based on Generative Adversarial Network | Seismic inverse modeling is a common method in reservoir prediction and it plays a vital role in the exploration and development of oil and gas. Conventional seismic inversion method is difficult to combine with complicated and abstract knowledge on geological mode and its uncertainty is difficult to be assessed. The paper proposes an inversion modeling method based on GAN consistent with geology, well logs, seismic data. GAN is a the most promising generation model algorithm that extracts spatial structure and abstract features of training images. The trained GAN can reproduce the models with specific mode. In our test, 1000 models were generated in 1 second. Based on the trained GAN after assessment, the optimal result of models can be calculated through Bayesian inversion frame. Results show that inversion models conform to observation data and have a low uncertainty under the premise of fast generation. This seismic inverse modeling method increases the efficiency and quality of inversion iteration. It is worthy of studying and applying in fusion of seismic data and geological knowledge. | ['LiXin Wang', 'Mei Chen', 'JiaGen Hou', 'YanShu Yin', 'Pengfei Xie'] | 2021-06-08 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 1.47286281e-01 1.31210431e-01 2.86147654e-01 -2.42743924e-01
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5004a3bd-e339-44eb-90b2-87eb08fb83a6 | exploring-dense-retrieval-for-dialogue | 2110.06612 | null | https://arxiv.org/abs/2110.06612v3 | https://arxiv.org/pdf/2110.06612v3.pdf | Exploring Dense Retrieval for Dialogue Response Selection | Recent progress in deep learning has continuously improved the accuracy of dialogue response selection. In particular, sophisticated neural network architectures are leveraged to capture the rich interactions between dialogue context and response candidates. While remarkably effective, these models also bring in a steep increase in computational cost. Consequently, such models can only be used as a re-rank module in practice. In this study, we present a solution to directly select proper responses from a large corpus or even a nonparallel corpus that only consists of unpaired sentences, using a dense retrieval model. To push the limits of dense retrieval, we design an interaction layer upon the dense retrieval models and apply a set of tailor-designed learning strategies. Our model shows superiority over strong baselines on the conventional re-rank evaluation setting, which is remarkable given its efficiency. To verify the effectiveness of our approach in realistic scenarios, we also conduct full-rank evaluation, where the target is to select proper responses from a full candidate pool that may contain millions of candidates and evaluate them fairly through human annotations. Our proposed model notably outperforms pipeline baselines that integrate fast recall and expressive re-rank modules. Human evaluation results show that enlarging the candidate pool with nonparallel corpora improves response quality further. | ['Xian-Ling Mao', 'Heyan Huang', 'Yixuan Su', 'Yan Wang', 'Deng Cai', 'Tian Lan'] | 2021-10-13 | null | null | null | null | ['conversational-response-selection'] | ['natural-language-processing'] | [ 1.99539542e-01 -9.86445323e-02 -1.83832258e-01 -6.25566661e-01
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c21b91fd-dbe2-4874-813c-1b702e02fe4f | amodal-panoptic-segmentation | 2202.11542 | null | https://arxiv.org/abs/2202.11542v1 | https://arxiv.org/pdf/2202.11542v1.pdf | Amodal Panoptic Segmentation | Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation. The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes. To facilitate research on this new task, we extend two established benchmark datasets with pixel-level amodal panoptic segmentation labels that we make publicly available as KITTI-360-APS and BDD100K-APS. We present several strong baselines, along with the amodal panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify the performance in an interpretable manner. Furthermore, we propose the novel amodal panoptic segmentation network (APSNet), as a first step towards addressing this task by explicitly modeling the complex relationships between the occluders and occludes. Extensive experimental evaluations demonstrate that APSNet achieves state-of-the-art performance on both benchmarks and more importantly exemplifies the utility of amodal recognition. The benchmarks are available at http://amodal-panoptic.cs.uni-freiburg.de. | ['Abhinav Valada', 'Rohit Mohan'] | 2022-02-23 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Mohan_Amodal_Panoptic_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['amodal-panoptic-segmentation'] | ['computer-vision'] | [ 4.57681030e-01 1.38680845e-01 2.23026946e-02 -5.74368358e-01
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4dd1a7cf-80c7-4bad-87bf-650843ad86a9 | optical-flow-based-branch-segmentation-for | 2202.13050 | null | https://arxiv.org/abs/2202.13050v1 | https://arxiv.org/pdf/2202.13050v1.pdf | Optical flow-based branch segmentation for complex orchard environments | Machine vision is a critical subsystem for enabling robots to be able to perform a variety of tasks in orchard environments. However, orchards are highly visually complex environments, and computer vision algorithms operating in them must be able to contend with variable lighting conditions and background noise. Past work on enabling deep learning algorithms to operate in these environments has typically required large amounts of hand-labeled data to train a deep neural network or physically controlling the conditions under which the environment is perceived. In this paper, we train a neural network system in simulation only using simulated RGB data and optical flow. This resulting neural network is able to perform foreground segmentation of branches in a busy orchard environment without additional real-world training or using any special setup or equipment beyond a standard camera. Our results show that our system is highly accurate and, when compared to a network using manually labeled RGBD data, achieves significantly more consistent and robust performance across environments that differ from the training set. | ['Joseph R. Davidson', 'Cindy Grimm', 'Alexander You'] | 2022-02-26 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 2.53891945e-01 -4.21633571e-01 2.12082863e-01 -4.48550224e-01
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292f9642-0ab7-44f1-831a-9330b17e4dc8 | remote-sensing-change-detection-based-on | null | null | https://www.mdpi.com/2072-4292/13/15/3053 | https://www.mdpi.com/2072-4292/13/15/3053 | Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity | Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images. First, we propose an elaborate feature fusion module consisting of a multidirectional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network. The MFP enhances the flexibility and diversity of information paths by creating extra top-down and shortcut-connection paths. The AWF strategy conducts weight recalibration for every fusion node to highlight salient feature maps and overcome semantic gaps between different features. Second, a novel perceptual similarity module is designed to introduce perceptual loss into the RSCD task, which adds perceptual information, such as structure and semantic information, for high-quality change map generation. Extensive experiments on four challenging benchmark datasets demonstrate the superiority of the proposed network compared with eight state-of-the-art methods in terms of F1, Kappa, and visual qualities. | ['Yang Luo', 'Shicai Wei', 'Xinyue Chen', 'Chunbo Luo', 'Jialang Xu'] | 2021-08-03 | null | null | null | remote-sensing-2021-8 | ['change-detection', 'change-detection-for-remote-sensing-images'] | ['computer-vision', 'miscellaneous'] | [ 3.23485613e-01 -5.35961330e-01 4.16941613e-01 -4.87104535e-01
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5012e9d5-572c-416b-91a5-70bee0c727ff | material-recognition-in-the-wild-with-the | 1412.0623 | null | http://arxiv.org/abs/1412.0623v2 | http://arxiv.org/pdf/1412.0623v2.pdf | Material Recognition in the Wild with the Materials in Context Database | Recognizing materials in real-world images is a challenging task. Real-world
materials have rich surface texture, geometry, lighting conditions, and
clutter, which combine to make the problem particularly difficult. In this
paper, we introduce a new, large-scale, open dataset of materials in the wild,
the Materials in Context Database (MINC), and combine this dataset with deep
learning to achieve material recognition and segmentation of images in the
wild.
MINC is an order of magnitude larger than previous material databases, while
being more diverse and well-sampled across its 23 categories. Using MINC, we
train convolutional neural networks (CNNs) for two tasks: classifying materials
from patches, and simultaneous material recognition and segmentation in full
images. For patch-based classification on MINC we found that the best
performing CNN architectures can achieve 85.2% mean class accuracy. We convert
these trained CNN classifiers into an efficient fully convolutional framework
combined with a fully connected conditional random field (CRF) to predict the
material at every pixel in an image, achieving 73.1% mean class accuracy. Our
experiments demonstrate that having a large, well-sampled dataset such as MINC
is crucial for real-world material recognition and segmentation. | ['Paul Upchurch', 'Sean Bell', 'Kavita Bala', 'Noah Snavely'] | 2014-12-01 | material-recognition-in-the-wild-with-the-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Bell_Material_Recognition_in_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Bell_Material_Recognition_in_2015_CVPR_paper.pdf | cvpr-2015-6 | ['material-recognition'] | ['computer-vision'] | [ 7.58011758e-01 -3.26657593e-01 -6.44011982e-03 -2.65800297e-01
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c9d5b565-9d0c-4734-9d85-a1fb576c92a7 | chq-summ-a-dataset-for-consumer-healthcare | 2206.06581 | null | https://arxiv.org/abs/2206.06581v2 | https://arxiv.org/pdf/2206.06581v2.pdf | CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization | The quest for seeking health information has swamped the web with consumers' health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other healthcare needs, contributing to the challenges of natural language understanding. One way to address this challenge is to summarize the questions and distill the key information of the original question. To address this issue, we introduce a new dataset, CHQ-Summ that contains 1507 domain-expert annotated consumer health questions and corresponding summaries. The dataset is derived from the community question-answering forum and therefore provides a valuable resource for understanding consumer health-related posts on social media. We benchmark the dataset on multiple state-of-the-art summarization models to show the effectiveness of the dataset. | ['Dina Demner-Fushman', 'Deepak Gupta', 'Shweta Yadav'] | 2022-06-14 | null | null | null | null | ['community-question-answering', 'community-question-answering'] | ['miscellaneous', 'natural-language-processing'] | [ 2.97872633e-01 6.48126900e-01 -6.33141279e-01 -3.28676820e-01
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c67f5edd-8c80-4257-95a0-9d9c39cb0d0f | codegen2-lessons-for-training-llms-on | 2305.02309 | null | https://arxiv.org/abs/2305.02309v1 | https://arxiv.org/pdf/2305.02309v1.pdf | CodeGen2: Lessons for Training LLMs on Programming and Natural Languages | Large language models (LLMs) have demonstrated remarkable abilities in representation learning for program synthesis and understanding tasks. The quality of the learned representations appears to be dictated by the neural scaling laws as a function of the number of model parameters and observations, while imposing upper bounds on the model performance by the amount of available data and compute, which is costly. In this study, we attempt to render the training of LLMs for program synthesis more efficient by unifying four key components: (1) model architectures, (2) learning methods, (3) infill sampling, and, (4) data distributions. Specifically, for the model architecture, we attempt to unify encoder and decoder-based models into a single prefix-LM. For learning methods, (i) causal language modeling, (ii) span corruption, (iii) infilling are unified into a simple learning algorithm. For infill sampling, we explore the claim of a "free lunch" hypothesis. For data distributions, the effect of a mixture distribution of programming and natural languages on model performance is explored. We conduct a comprehensive series of empirical experiments on 1B LLMs, for which failures and successes of this exploration are distilled into four lessons. We will provide a final recipe for training and release CodeGen2 models in size 1B, 3.7B, 7B, and, 16B parameters, along with the training framework as open-source: https://github.com/salesforce/CodeGen2. | ['Yingbo Zhou', 'Silvio Savarese', 'Caiming Xiong', 'Hiroaki Hayashi', 'Erik Nijkamp'] | 2023-05-03 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-3.65355425e-02 1.05269097e-01 -6.86395407e-01 -3.20845932e-01
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99c3f810-1260-4de5-ad15-f6c47e23ef73 | neurohex-a-deep-q-learning-hex-agent | 1604.07097 | null | http://arxiv.org/abs/1604.07097v2 | http://arxiv.org/pdf/1604.07097v2.pdf | Neurohex: A Deep Q-learning Hex Agent | DeepMind's recent spectacular success in using deep convolutional neural nets
and machine learning to build superhuman level agents --- e.g. for Atari games
via deep Q-learning and for the game of Go via Reinforcement Learning ---
raises many questions, including to what extent these methods will succeed in
other domains. In this paper we consider DQL for the game of Hex: after
supervised initialization, we use selfplay to train NeuroHex, an 11-layer CNN
that plays Hex on the 13x13 board. Hex is the classic two-player alternate-turn
stone placement game played on a rhombus of hexagonal cells in which the winner
is whomever connects their two opposing sides. Despite the large action and
state space, our system trains a Q-network capable of strong play with no
search. After two weeks of Q-learning, NeuroHex achieves win-rates of 20.4% as
first player and 2.1% as second player against a 1-second/move version of
MoHex, the current ICGA Olympiad Hex champion. Our data suggests further
improvement might be possible with more training time. | ['Gautham Vasan', 'Kenny Young', 'Ryan Hayward'] | 2016-04-24 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-5.15465200e-01 3.79129499e-01 -9.90559012e-02 2.04083368e-01
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e93cda20-038a-42bd-bc67-507bda22bf26 | norefer-a-referenceless-quality-metric-for | 2306.12577 | null | https://arxiv.org/abs/2306.12577v1 | https://arxiv.org/pdf/2306.12577v1.pdf | NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning | This paper introduces NoRefER, a novel referenceless quality metric for automatic speech recognition (ASR) systems. Traditional reference-based metrics for evaluating ASR systems require costly ground-truth transcripts. NoRefER overcomes this limitation by fine-tuning a multilingual language model for pair-wise ranking ASR hypotheses using contrastive learning with Siamese network architecture. The self-supervised NoRefER exploits the known quality relationships between hypotheses from multiple compression levels of an ASR for learning to rank intra-sample hypotheses by quality, which is essential for model comparisons. The semi-supervised version also uses a referenced dataset to improve its inter-sample quality ranking, which is crucial for selecting potentially erroneous samples. The results indicate that NoRefER correlates highly with reference-based metrics and their intra-sample ranks, indicating a high potential for referenceless ASR evaluation or a/b testing. | ['Ahmet Gunduz', 'Mohamed El-Badrashiny', 'Golara Javadi', 'Thiago Ferreira', 'Kamer Ali Yuksel'] | 2023-06-21 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'automatic-speech-recognition'] | ['computer-vision', 'methodology', 'speech'] | [ 2.15256035e-01 -1.49263829e-01 -4.06016290e-01 -6.19173765e-01
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fdff8f50-0365-481a-bb56-6b1b8b9adb51 | an-event-based-algorithm-for-simultaneous-6 | 2301.00618 | null | https://arxiv.org/abs/2301.00618v2 | https://arxiv.org/pdf/2301.00618v2.pdf | An Event-based Algorithm for Simultaneous 6-DOF Camera Pose Tracking and Mapping | Compared to regular cameras, Dynamic Vision Sensors or Event Cameras can output compact visual data based on a change in the intensity in each pixel location asynchronously. In this paper, we study the application of current image-based SLAM techniques to these novel sensors. To this end, the information in adaptively selected event windows is processed to form motion-compensated images. These images are then used to reconstruct the scene and estimate the 6-DOF pose of the camera. We also propose an inertial version of the event-only pipeline to assess its capabilities. We compare the results of different configurations of the proposed algorithm against the ground truth for sequences of two publicly available event datasets. We also compare the results of the proposed event-inertial pipeline with the state-of-the-art and show it can produce comparable or more accurate results provided the map estimate is reliable. | ['Mohammad Reza Ahmadzadeh', 'Masoud Dayani Najafabadi'] | 2023-01-02 | null | null | null | null | ['pose-tracking'] | ['computer-vision'] | [ 3.89333516e-01 -3.94004196e-01 3.78658652e-01 -4.18791443e-01
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6d59a1a6-c740-40fb-b050-1d7308646207 | scene-labeling-with-contextual-hierarchical | 1402.0595 | null | http://arxiv.org/abs/1402.0595v1 | http://arxiv.org/pdf/1402.0595v1.pdf | Scene Labeling with Contextual Hierarchical Models | Scene labeling is the problem of assigning an object label to each pixel. It
unifies the image segmentation and object recognition problems. The importance
of using contextual information in scene labeling frameworks has been widely
realized in the field. We propose a contextual framework, called contextual
hierarchical model (CHM), which learns contextual information in a hierarchical
framework for scene labeling. At each level of the hierarchy, a classifier is
trained based on downsampled input images and outputs of previous levels. Our
model then incorporates the resulting multi-resolution contextual information
into a classifier to segment the input image at original resolution. This
training strategy allows for optimization of a joint posterior probability at
multiple resolutions through the hierarchy. Contextual hierarchical model is
purely based on the input image patches and does not make use of any fragments
or shape examples. Hence, it is applicable to a variety of problems such as
object segmentation and edge detection. We demonstrate that CHM outperforms
state-of-the-art on Stanford background and Weizmann horse datasets. It also
outperforms state-of-the-art edge detection methods on NYU depth dataset and
achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500). | ['Tolga Tasdizen', 'Mojtaba Seyedhosseini'] | 2014-02-04 | null | null | null | null | ['scene-labeling'] | ['computer-vision'] | [ 7.05801845e-01 2.40001231e-02 -3.65994573e-01 -4.69061077e-01
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5d8a39b1-7542-47a3-b30e-624bed89e546 | identification-of-truth-and-deception-in-text | null | null | https://aclanthology.org/W12-0415 | https://aclanthology.org/W12-0415.pdf | Identification of Truth and Deception in Text: Application of Vector Space Model to Rhetorical Structure Theory | null | ['Victoria L. Rubin', 'Tatiana Vashchilko'] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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7db87f36-407e-4b89-91ab-8fd2949d7805 | timebalance-temporally-invariant-and | 2303.16268 | null | https://arxiv.org/abs/2303.16268v1 | https://arxiv.org/pdf/2303.16268v1.pdf | TimeBalance: Temporally-Invariant and Temporally-Distinctive Video Representations for Semi-Supervised Action Recognition | Semi-Supervised Learning can be more beneficial for the video domain compared to images because of its higher annotation cost and dimensionality. Besides, any video understanding task requires reasoning over both spatial and temporal dimensions. In order to learn both the static and motion related features for the semi-supervised action recognition task, existing methods rely on hard input inductive biases like using two-modalities (RGB and Optical-flow) or two-stream of different playback rates. Instead of utilizing unlabeled videos through diverse input streams, we rely on self-supervised video representations, particularly, we utilize temporally-invariant and temporally-distinctive representations. We observe that these representations complement each other depending on the nature of the action. Based on this observation, we propose a student-teacher semi-supervised learning framework, TimeBalance, where we distill the knowledge from a temporally-invariant and a temporally-distinctive teacher. Depending on the nature of the unlabeled video, we dynamically combine the knowledge of these two teachers based on a novel temporal similarity-based reweighting scheme. Our method achieves state-of-the-art performance on three action recognition benchmarks: UCF101, HMDB51, and Kinetics400. Code: https://github.com/DAVEISHAN/TimeBalance | ['Mubarak Shah', 'Chen Chen', 'Mamshad Nayeem Rizve', 'Ishan Rajendrakumar Dave'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dave_TimeBalance_Temporally-Invariant_and_Temporally-Distinctive_Video_Representations_for_Semi-Supervised_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-recognition-in-videos', 'video-understanding'] | ['computer-vision', 'computer-vision'] | [ 1.66375875e-01 -3.10048163e-01 -6.44414067e-01 -4.73653972e-01
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95aada56-4c32-46b1-84e5-45ba5dbb16fb | spatial-graph-convolutional-neural-network | 2112.06033 | null | https://arxiv.org/abs/2112.06033v1 | https://arxiv.org/pdf/2112.06033v1.pdf | Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosis | Unsupervised domain adaptation (UDA) has shown remarkable results in bearing fault diagnosis under changing working conditions in recent years. However, most UDA methods do not consider the geometric structure of the data. Furthermore, the global domain adaptation technique is commonly applied, which ignores the relation between subdomains. This paper addresses mentioned challenges by presenting the novel deep subdomain adaptation graph convolution neural network (DSAGCN), which has two key characteristics: First, graph convolution neural network (GCNN) is employed to model the structure of data. Second, adversarial domain adaptation and local maximum mean discrepancy (LMMD) methods are applied concurrently to align the subdomain's distribution and reduce structure discrepancy between relevant subdomains and global domains. CWRU and Paderborn bearing datasets are used to validate the DSAGCN method's efficiency and superiority between comparison models. The experimental results demonstrate the significance of aligning structured subdomains along with domain adaptation methods to obtain an accurate data-driven model in unsupervised fault diagnosis. | ['Amin Ramezani', 'Mohammad TH Beheshti', 'Mohammadreza Kavianpour', 'Mohammadreza Ghorvei'] | 2021-12-11 | null | null | null | null | ['subdomain-adaptation'] | ['methodology'] | [-1.61494702e-01 7.52360746e-02 1.99699104e-01 -9.24170613e-02
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423e6d41-931e-459d-9f00-1b40be6c2386 | evolving-differentiable-gene-regulatory | 1807.05948 | null | http://arxiv.org/abs/1807.05948v1 | http://arxiv.org/pdf/1807.05948v1.pdf | Evolving Differentiable Gene Regulatory Networks | Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have
shown their capacity to solve real-world problems in various domains such as
agent control, signal processing and artificial life experiments. They have
also benefited from new evolutionary approaches and improvements to dynamic
which have increased their optimization efficiency. In this paper, we present
an additional step toward their usability in machine learning applications. We
detail an GPU-based implementation of differentiable GRNs, allowing for local
optimization of GRN architectures with stochastic gradient descent (SGD). Using
a standard machine learning dataset, we evaluate the ways in which evolution
and SGD can be combined to further GRN optimization. We compare these
approaches with neural network models trained by SGD and with support vector
machines. | ['Hervé Luga', 'Sylvain Cussat-Blanc', 'Dennis G Wilson', 'Kyle Harrington'] | 2018-07-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 4.12167132e-01 8.24011117e-03 1.34272248e-01 -2.29834929e-01
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48de0cc7-ec30-4f7e-ae53-3de668963a25 | multilevel-memetic-hypergraph-partitioning | 2204.03730 | null | https://arxiv.org/abs/2204.03730v1 | https://arxiv.org/pdf/2204.03730v1.pdf | Multilevel Memetic Hypergraph Partitioning with Greedy Recombination | The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. The literature on the HGP problem has heavily focused on developing fast heuristic approaches. In several application domains, such as the VLSI design and database migration planning, the quality of the solution is more of a concern than the running time of the algorithm. KaHyPar-E is the first multilevel memetic algorithm designed for the HGP problem and it returns better quality solutions, compared to the heuristic algorithms, if sufficient computation time is given. In this work, we introduce novel problem-specific recombination and mutation operators, and develop a new multilevel memetic algorithm by combining KaHyPar-E with these operators. The performance of our algorithm is compared with the state-of-the-art HGP algorithms on $150$ real-life instances taken from the benchmark datasets used in the literature. In the experiments, which would take $39,000$ hours in a single-core computer, each algorithm is given $2, 4$, and $8$ hours to compute a solution for each instance. Our algorithm outperforms all others and finds the best solutions in $112$, $115$, and $125$ instances in $2, 4$, and $8$ hours, respectively. | ['Bugra Caskurlu', 'Utku Umur Acikalin'] | 2022-04-07 | null | null | null | null | ['hypergraph-partitioning'] | ['graphs'] | [ 1.02848187e-01 1.19972512e-01 -3.61789733e-01 1.17983082e-02
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e288157f-d80a-4f9c-b7cd-3a8a1342b732 | dense-transformer-networks-for-brain-electron | null | null | https://doi.org/10.24963/ijcai.2019/401 | https://www.ijcai.org/proceedings/2019/0401.pdf | Dense Transformer Networks for Brain Electron Microscopy Image Segmentation | The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods. | ['Jun Li', 'Yongjun Chen', 'Lei Cai', 'Shuiwang Ji', 'Ian Davidson'] | 2019-08-10 | null | null | null | twenty-eighth-international-joint-conference | ['electron-microscopy-image-segmentation'] | ['computer-vision'] | [ 4.68715757e-01 5.87510526e-01 -3.07098101e-03 -4.13365960e-01
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4b9ced61-6d57-4714-84df-4e23bd53464b | improving-neural-rst-parsing-model-with | null | null | https://aclanthology.org/2021.naacl-main.127 | https://aclanthology.org/2021.naacl-main.127.pdf | Improving Neural RST Parsing Model with Silver Agreement Subtrees | Most of the previous Rhetorical Structure Theory (RST) parsing methods are based on supervised learning such as neural networks, that require an annotated corpus of sufficient size and quality. However, the RST Discourse Treebank (RST-DT), the benchmark corpus for RST parsing in English, is small due to the costly annotation of RST trees. The lack of large annotated training data causes poor performance especially in relation labeling. Therefore, we propose a method for improving neural RST parsing models by exploiting silver data, i.e., automatically annotated data. We create large-scale silver data from an unlabeled corpus by using a state-of-the-art RST parser. To obtain high-quality silver data, we extract agreement subtrees from RST trees for documents built using the RST parsers. We then pre-train a neural RST parser with the obtained silver data and fine-tune it on the RST-DT. Experimental results show that our method achieved the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2, respectively. Furthermore, we obtained a remarkable gain in the Relation score, 3.0 points, against the previous state-of-the-art parser. | ['Masaaki Nagata', 'Manabu Okumura', 'Hidetaka Kamigaito', 'Tsutomu Hirao', 'Naoki Kobayashi'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['discourse-parsing'] | ['natural-language-processing'] | [ 2.53625035e-01 1.05470634e+00 -4.01980370e-01 -4.19330150e-01
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68091a29-719d-4317-9d43-90578ff92a51 | joint-encoding-of-appearance-and-motion | 2002.03982 | null | https://arxiv.org/abs/2002.03982v2 | https://arxiv.org/pdf/2002.03982v2.pdf | Self-Supervised Joint Encoding of Motion and Appearance for First Person Action Recognition | Wearable cameras are becoming more and more popular in several applications, increasing the interest of the research community in developing approaches for recognizing actions from the first-person point of view. An open challenge in egocentric action recognition is that videos lack detailed information about the main actor's pose and thus tend to record only parts of the movement when focusing on manipulation tasks. Thus, the amount of information about the action itself is limited, making crucial the understanding of the manipulated objects and their context. Many previous works addressed this issue with two-stream architectures, where one stream is dedicated to modeling the appearance of objects involved in the action, and another to extracting motion features from optical flow. In this paper, we argue that learning features jointly from these two information channels is beneficial to capture the spatio-temporal correlations between the two better. To this end, we propose a single stream architecture able to do so, thanks to the addition of a self-supervised block that uses a pretext motion prediction task to intertwine motion and appearance knowledge. Experiments on several publicly available databases show the power of our approach. | ['Barbara Caputo', 'Andrea Bottino', 'Mirco Planamente'] | 2020-02-10 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 1.51792318e-01 -1.17472701e-01 -2.85681188e-01 -2.00781584e-01
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9ea4afb1-b883-4b4a-bd43-9596da5487cb | rumour-detection-via-zero-shot-cross-lingual | 2109.12773 | null | https://arxiv.org/abs/2109.12773v1 | https://arxiv.org/pdf/2109.12773v1.pdf | Rumour Detection via Zero-shot Cross-lingual Transfer Learning | Most rumour detection models for social media are designed for one specific language (mostly English). There are over 40 languages on Twitter and most languages lack annotated resources to build rumour detection models. In this paper we propose a zero-shot cross-lingual transfer learning framework that can adapt a rumour detection model trained for a source language to another target language. Our framework utilises pretrained multilingual language models (e.g.\ multilingual BERT) and a self-training loop to iteratively bootstrap the creation of ''silver labels'' in the target language to adapt the model from the source language to the target language. We evaluate our methodology on English and Chinese rumour datasets and demonstrate that our model substantially outperforms competitive benchmarks in both source and target language rumour detection. | ['Jey Han Lau', 'Xiuzhen Zhang', 'Lin Tian'] | 2021-09-27 | null | null | null | null | ['rumour-detection', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-4.08451855e-01 -1.38773233e-01 -6.25078261e-01 -2.24891141e-01
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ae35293a-e3d3-4f0f-8d6e-9d39d42a8778 | effective-email-spam-detection-system-using | 2012.14430 | null | https://arxiv.org/abs/2012.14430v1 | https://arxiv.org/pdf/2012.14430v1.pdf | Effective Email Spam Detection System using Extreme Gradient Boosting | The popularity, cost-effectiveness and ease of information exchange that electronic mails offer to electronic device users has been plagued with the rising number of unsolicited or spam emails. Driven by the need to protect email users from this growing menace, research in spam email filtering/detection systems has being increasingly active in the last decade. However, the adaptive nature of spam emails has often rendered most of these systems ineffective. While several spam detection models have been reported in literature, the reported performance on an out of sample test data shows the room for more improvement. Presented in this research is an improved spam detection model based on Extreme Gradient Boosting (XGBoost) which to the best of our knowledge has received little attention spam email detection problems. Experimental results show that the proposed model outperforms earlier approaches across a wide range of evaluation metrics. A thorough analysis of the model results in comparison to the results of earlier works is also presented. | ['Afolabi Kazeem', 'Siti Mariyam Shamsuddin', 'Sunday O. Olatunji', 'Shafaatunnur Hasan', 'Ismail B. Mustapha'] | 2020-12-27 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-4.33634743e-02 -3.41550320e-01 -4.46863063e-02 -6.33903563e-01
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747e1946-e6af-47e3-a07a-5c2bb0feae6d | on-the-generalization-of-gan-image-forensics | 1902.11153 | null | https://arxiv.org/abs/1902.11153v2 | https://arxiv.org/pdf/1902.11153v2.pdf | On the generalization of GAN image forensics | Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake training images, the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method. | ['Xinsheng Xuan', 'Jing Dong', 'Bo Peng', 'Wei Wang'] | 2019-02-27 | null | null | null | null | ['gan-image-forensics', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 1.28005728e-01 2.09226355e-01 1.31486043e-01 -1.85539499e-01
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ee8e7418-53b8-41ca-9c51-8cbe570ac6ab | utopia-universally-trainable-optimal | 2306.16549 | null | https://arxiv.org/abs/2306.16549v1 | https://arxiv.org/pdf/2306.16549v1.pdf | UTOPIA: Universally Trainable Optimal Prediction Intervals Aggregation | Uncertainty quantification for prediction is an intriguing problem with significant applications in various fields, such as biomedical science, economic studies, and weather forecasts. Numerous methods are available for constructing prediction intervals, such as quantile regression and conformal predictions, among others. Nevertheless, model misspecification (especially in high-dimension) or sub-optimal constructions can frequently result in biased or unnecessarily-wide prediction intervals. In this paper, we propose a novel and widely applicable technique for aggregating multiple prediction intervals to minimize the average width of the prediction band along with coverage guarantee, called Universally Trainable Optimal Predictive Intervals Aggregation (UTOPIA). The method also allows us to directly construct predictive bands based on elementary basis functions. Our approach is based on linear or convex programming which is easy to implement. All of our proposed methodologies are supported by theoretical guarantees on the coverage probability and optimal average length, which are detailed in this paper. The effectiveness of our approach is convincingly demonstrated by applying it to synthetic data and two real datasets on finance and macroeconomics. | ['Debarghya Mukherjee', 'Jiawei Ge', 'Jianqing Fan'] | 2023-06-28 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 8.89617428e-02 3.48422825e-01 -4.61136460e-01 -4.28122044e-01
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f91c5ced-95ce-4345-bf97-bfa58f4827dd | on-the-transferability-of-whisper-based | 2305.14546 | null | https://arxiv.org/abs/2305.14546v1 | https://arxiv.org/pdf/2305.14546v1.pdf | On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applications | Large self-supervised pre-trained speech models have achieved remarkable success across various speech-processing tasks. The self-supervised training of these models leads to universal speech representations that can be used for different downstream tasks, ranging from automatic speech recognition (ASR) to speaker identification. Recently, Whisper, a transformer-based model was proposed and trained on large amount of weakly supervised data for ASR; it outperformed several state-of-the-art self-supervised models. Given the superiority of Whisper for ASR, in this paper we explore the transferability of the representation for four other speech tasks in SUPERB benchmark. Moreover, we explore the robustness of Whisper representation for ``in the wild'' tasks where speech is corrupted by environment noise and room reverberation. Experimental results show Whisper achieves promising results across tasks and environmental conditions, thus showing potential for cross-task real-world deployment. | ['Tiago Falk', 'Boxing Chen', 'Mehdi Rezagholizadeh', 'Anderson Avila', 'Arthur Pimentel', 'Heitor Guimaraes', 'Marzieh Tahaei', 'Vamsikrishna Chemudupati'] | 2023-05-23 | null | null | null | null | ['automatic-speech-recognition', 'speaker-identification'] | ['speech', 'speech'] | [ 2.66355157e-01 7.83560798e-02 1.53329775e-01 -6.12946272e-01
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68f3d854-6dea-412f-aa49-dc7d7ca838d0 | focusing-on-targets-for-improving-weakly | 2302.11252 | null | https://arxiv.org/abs/2302.11252v1 | https://arxiv.org/pdf/2302.11252v1.pdf | Focusing On Targets For Improving Weakly Supervised Visual Grounding | Weakly supervised visual grounding aims to predict the region in an image that corresponds to a specific linguistic query, where the mapping between the target object and query is unknown in the training stage. The state-of-the-art method uses a vision language pre-training model to acquire heatmaps from Grad-CAM, which matches every query word with an image region, and uses the combined heatmap to rank the region proposals. In this paper, we propose two simple but efficient methods for improving this approach. First, we propose a target-aware cropping approach to encourage the model to learn both object and scene level semantic representations. Second, we apply dependency parsing to extract words related to the target object, and then put emphasis on these words in the heatmap combination. Our method surpasses the previous SOTA methods on RefCOCO, RefCOCO+, and RefCOCOg by a notable margin. | ['Nao Mishima', 'Viet-Quoc Pham'] | 2023-02-22 | null | null | null | null | ['visual-grounding', 'dependency-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 3.17024320e-01 2.40654215e-01 -5.64274192e-01 -3.99355143e-01
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806d97de-b80d-4fcd-be9e-2d45b20d0ba6 | how-to-choose-pretrained-handwriting | 2305.02593 | null | https://arxiv.org/abs/2305.02593v1 | https://arxiv.org/pdf/2305.02593v1.pdf | How to Choose Pretrained Handwriting Recognition Models for Single Writer Fine-Tuning | Recent advancements in Deep Learning-based Handwritten Text Recognition (HTR) have led to models with remarkable performance on both modern and historical manuscripts in large benchmark datasets. Nonetheless, those models struggle to obtain the same performance when applied to manuscripts with peculiar characteristics, such as language, paper support, ink, and author handwriting. This issue is very relevant for valuable but small collections of documents preserved in historical archives, for which obtaining sufficient annotated training data is costly or, in some cases, unfeasible. To overcome this challenge, a possible solution is to pretrain HTR models on large datasets and then fine-tune them on small single-author collections. In this paper, we take into account large, real benchmark datasets and synthetic ones obtained with a styled Handwritten Text Generation model. Through extensive experimental analysis, also considering the amount of fine-tuning lines, we give a quantitative indication of the most relevant characteristics of such data for obtaining an HTR model able to effectively transcribe manuscripts in small collections with as little as five real fine-tuning lines. | ['Rita Cucchiara', 'Christopher Kermorvant', 'Silvia Cascianelli', 'Vittorio Pippi'] | 2023-05-04 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 1.36070237e-01 -1.51352957e-01 1.74785331e-01 -3.30565214e-01
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ab087558-b461-48d9-b2f6-3d793630c0ff | comparing-heterogeneous-visual-gestures-for | 1805.02948 | null | http://arxiv.org/abs/1805.02948v1 | http://arxiv.org/pdf/1805.02948v1.pdf | Comparing heterogeneous visual gestures for measuring the diversity of visual speech signals | Visual lip gestures observed whilst lipreading have a few working
definitions, the most common two are; `the visual equivalent of a phoneme' and
`phonemes which are indistinguishable on the lips'. To date there is no formal
definition, in part because to date we have not established a two-way
relationship or mapping between visemes and phonemes. Some evidence suggests
that visual speech is highly dependent upon the speaker. So here, we use a
phoneme-clustering method to form new phoneme-to-viseme maps for both
individual and multiple speakers. We test these phoneme to viseme maps to
examine how similarly speakers talk visually and we use signed rank tests to
measure the distance between individuals. We conclude that broadly speaking,
speakers have the same repertoire of mouth gestures, where they differ is in
the use of the gestures. | ['Helen L. Bear', 'Richard Harvey'] | 2018-05-08 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.97034851e-02 -1.36750728e-01 -2.94281393e-01 -3.37488472e-01
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a145d974-12c9-43f3-afb9-cd25cc4cb6a9 | rethinking-embedding-coupling-in-pre-trained-1 | 2010.12821 | null | https://arxiv.org/abs/2010.12821v1 | https://arxiv.org/pdf/2010.12821v1.pdf | Rethinking embedding coupling in pre-trained language models | We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that allocating additional capacity to the output embedding provides benefits to the model that persist through the fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the number of parameters at the fine-tuning stage. | ['Sebastian Ruder', 'Melvin Johnson', 'Henry Tsai', 'Thibault Févry', 'Hyung Won Chung'] | 2020-10-24 | rethinking-embedding-coupling-in-pre-trained | https://openreview.net/forum?id=xpFFI_NtgpW | https://openreview.net/pdf?id=xpFFI_NtgpW | iclr-2021-1 | ['cross-lingual-natural-language-inference', 'cross-lingual-question-answering', 'cross-lingual-ner'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.36397511e-01 3.89848024e-01 -1.65689379e-01 -4.59059238e-01
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a61cbed3-6534-47da-8480-f789878542c2 | revisiting-quantization-error-in-face | null | null | https://openaccess.thecvf.com/content/ICCV2021W/MFR/html/Lan_Revisting_Quantization_Error_in_Face_Alignment_ICCVW_2021_paper.html | https://openaccess.thecvf.com/content/ICCV2021W/MFR/papers/Lan_Revisting_Quantization_Error_in_Face_Alignment_ICCVW_2021_paper.pdf | Revisiting Quantization Error in Face Alignment | Recently, heatmap regression models have become the mainstream in locating facial landmarks. To keep com- putation affordable and reduce memory usage, the whole procedure involves downsampling from the raw image to the output heatmap. However, how much impact will the quantization error introduced by downsampling bring? The problem is hardly systematically investigated among previ- ous works. This work fills the blank and we are the first to quantitatively analyze the negative gain. The statistical re- sults show the NME generated by quantization error is even larger than 1/3 of the SOTA item, which is a serious obsta- cle for making a new breakthrough in face alignment. To compensate for the impact of quantization effect, we pro- pose a novel method, called Heatmap In Heatmap (HIH), which leverages two categories of heatmaps as label repre- sentation to encode the coordinate. And in HIH, the range of one heatmap represents a pixel of the other category of heatmap. Also, we even combine the face alignment with solutions of other fields to make a comparison. Extensive experiments on various benchmarks show the feasibility of HIH and superior performance than other solutions. More- over, the mean error reaches to 4.18 on WFLW, which ex- ceeds SOTA a lot. | ['Jian Cheng', 'Qinghao Hu', 'Xing Lan'] | 2021-09-13 | null | null | null | iccv-workshop-2021-9 | ['face-alignment'] | ['computer-vision'] | [ 7.41231740e-02 9.53859463e-02 -2.87691027e-01 -5.32597065e-01
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a9614d4d-f330-4eca-82df-992fcebc7e87 | eddynet-a-deep-neural-network-for-pixel-wise | 1711.03954 | null | http://arxiv.org/abs/1711.03954v1 | http://arxiv.org/pdf/1711.03954v1.pdf | EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies | This work presents EddyNet, a deep learning based architecture for automated
eddy detection and classification from Sea Surface Height (SSH) maps provided
by the Copernicus Marine and Environment Monitoring Service (CMEMS). EddyNet is
a U-Net like network that consists of a convolutional encoder-decoder followed
by a pixel-wise classification layer. The output is a map with the same size of
the input where pixels have the following labels \{'0': Non eddy, '1':
anticyclonic eddy, '2': cyclonic eddy\}. We investigate the use of SELU
activation function instead of the classical ReLU+BN and we use an overlap
based loss function instead of the cross entropy loss. Keras Python code, the
training datasets and EddyNet weights files are open-source and freely
available on https://github.com/redouanelg/EddyNet. | ['Pierre Tandeo', 'Ronan Fablet', 'Redouane Lguensat', 'Ge Chen', 'Evan Mason', 'Miao Sun'] | 2017-11-10 | null | null | null | null | ['oceanic-eddy-classification'] | ['miscellaneous'] | [ 7.81212226e-02 3.45842983e-03 4.72806484e-01 -7.70817876e-01
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84ebe409-47d5-4448-bf2f-2db2292ac4d2 | learning-by-distilling-context | 2209.15189 | null | https://arxiv.org/abs/2209.15189v1 | https://arxiv.org/pdf/2209.15189v1.pdf | Learning by Distilling Context | Language models significantly benefit from context tokens, such as prompts or scratchpads. They perform better when prompted with informative instructions, and they acquire new reasoning capabilities by generating a scratch-pad before predicting the final answers. However, they do not \textit{internalize} these performance gains, which disappear when the context tokens are gone. Our work proposes to apply context distillation so that a language model can improve itself by internalizing these gains. Concretely, given a synthetic unlabeled input for the target task, we condition the model on ``[instructions] + [task-input]'' to predict ``[scratch-pad] + [final answer]''; then we fine-tune the same model to predict its own ``[final answer]'' conditioned on the ``[task-input]'', without seeing the ``[instructions]'' or using the ``[scratch-pad]''. We show that context distillation is a general method to train language models, and it can effectively internalize 3 types of training signals. First, it can internalize abstract task instructions and explanations, so we can iteratively update the model parameters with new instructions and overwrite old ones. Second, it can internalize step-by-step reasoning for complex tasks (e.g., 8-digit addition), and such a newly acquired capability proves to be useful for other downstream tasks. Finally, it can internalize concrete training examples, and it outperforms directly learning with gradient descent by 9\% on the SPIDER Text-to-SQL dataset; furthermore, combining context distillation operations can internalize more training examples than the context window size allows. | ['Ruiqi Zhong', 'Dan Klein', 'Charlie Snell'] | 2022-09-30 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 5.59258819e-01 3.56708288e-01 -2.74339110e-01 -4.69425827e-01
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7d0fda95-6175-43e3-8c63-b9dbffaa85d3 | multiple-input-multiple-output-fusion-network | null | null | https://ieeexplore.ieee.org/document/9413509 | https://ieeexplore.ieee.org/document/9413509 | Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning | Generalized zero-shot learning (GZSL) has attracted consid-
erable attention recently, which trains models with data from
seen classes and tests on data from both seen and unseen
classes. Most of the existing methods attempt to find a map-
ping from visual space to semantic space, such mapping can
easily result in the domain shift problem. To address this
issue, we propose a Multiple-Input Multiple-Output Fusion
Network to GZSL. It can generate similar common seman-
tic representation to paired inputs even with only the class
semantic embeddings. This makes it possible to synthesize
pseudo samples from attributes of unseen classes. Extensive
experiments carried out on three benchmark datasets show the
effectiveness of the proposed model. | ['Feng Xia', 'Xu Yuan', 'Zhikui Chen', 'Guangze Wang', 'Fangming Zhong∗'] | 2021-05-13 | null | null | null | ieee-2021-5 | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 2.86968470e-01 -5.95185235e-02 -5.03223091e-02 -6.15695238e-01
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8291dacd-452b-43e8-bcf6-be0ba49eea95 | cvxnets-learnable-convex-decomposition | 1909.05736 | null | https://arxiv.org/abs/1909.05736v4 | https://arxiv.org/pdf/1909.05736v4.pdf | CvxNet: Learnable Convex Decomposition | Any solid object can be decomposed into a collection of convex polytopes (in short, convexes). When a small number of convexes are used, such a decomposition can be thought of as a piece-wise approximation of the geometry. This decomposition is fundamental in computer graphics, where it provides one of the most common ways to approximate geometry, for example, in real-time physics simulation. A convex object also has the property of being simultaneously an explicit and implicit representation: one can interpret it explicitly as a mesh derived by computing the vertices of a convex hull, or implicitly as the collection of half-space constraints or support functions. Their implicit representation makes them particularly well suited for neural network training, as they abstract away from the topology of the geometry they need to represent. However, at testing time, convexes can also generate explicit representations -- polygonal meshes -- which can then be used in any downstream application. We introduce a network architecture to represent a low dimensional family of convexes. This family is automatically derived via an auto-encoding process. We investigate the applications of this architecture including automatic convex decomposition, image to 3D reconstruction, and part-based shape retrieval. | ['Soroosh Yazdani', 'Sofien Bouaziz', 'Andrea Tagliasacchi', 'Kyle Genova', 'Geoffrey Hinton', 'Boyang Deng'] | 2019-09-12 | cvxnet-learnable-convex-decomposition | http://openaccess.thecvf.com/content_CVPR_2020/html/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Deng_CvxNet_Learnable_Convex_Decomposition_CVPR_2020_paper.pdf | cvpr-2020-6 | ['image-to-3d'] | ['computer-vision'] | [ 1.98145472e-02 4.18123931e-01 2.03808248e-01 -3.55076730e-01
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7d53a619-0fd0-40ef-94fd-cd02a808abf4 | bygpt5-end-to-end-style-conditioned-poetry | 2212.10474 | null | https://arxiv.org/abs/2212.10474v2 | https://arxiv.org/pdf/2212.10474v2.pdf | ByGPT5: End-to-End Style-conditioned Poetry Generation with Token-free Language Models | State-of-the-art poetry generation systems are often complex. They either consist of task-specific model pipelines, incorporate prior knowledge in the form of manually created constraints, or both. In contrast, end-to-end models would not suffer from the overhead of having to model prior knowledge and could learn the nuances of poetry from data alone, reducing the degree of human supervision required. In this work, we investigate end-to-end poetry generation conditioned on styles such as rhyme, meter, and alliteration. We identify and address lack of training data and mismatching tokenization algorithms as possible limitations of past attempts. In particular, we successfully pre-train ByGPT5, a new token-free decoder-only language model, and fine-tune it on a large custom corpus of English and German quatrains annotated with our styles. We show that ByGPT5 outperforms other models such as mT5, ByT5, GPT-2 and ChatGPT, while also being more parameter efficient and performing favorably compared to humans. In addition, we analyze its runtime performance and demonstrate that it is not prone to memorization. We make our code, models, and datasets publicly available. | ['Steffen Eger', 'Jonas Belouadi'] | 2022-12-20 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.83026850e-01 3.59567046e-01 2.08887279e-01 -2.43161529e-01
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50ff4a95-bead-499f-93b1-192e77cc55de | one-shot-video-object-segmentation | 1611.05198 | null | http://arxiv.org/abs/1611.05198v4 | http://arxiv.org/pdf/1611.05198v4.pdf | One-Shot Video Object Segmentation | This paper tackles the task of semi-supervised video object segmentation,
i.e., the separation of an object from the background in a video, given the
mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS),
based on a fully-convolutional neural network architecture that is able to
successively transfer generic semantic information, learned on ImageNet, to the
task of foreground segmentation, and finally to learning the appearance of a
single annotated object of the test sequence (hence one-shot). Although all
frames are processed independently, the results are temporally coherent and
stable. We perform experiments on two annotated video segmentation databases,
which show that OSVOS is fast and improves the state of the art by a
significant margin (79.8% vs 68.0%). | ['Luc van Gool', 'Laura Leal-Taixé', 'Jordi Pont-Tuset', 'Kevis-Kokitsi Maninis', 'Sergi Caelles', 'Daniel Cremers'] | 2016-11-16 | one-shot-video-object-segmentation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Caelles_One-Shot_Video_Object_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Caelles_One-Shot_Video_Object_CVPR_2017_paper.pdf | cvpr-2017-7 | ['foreground-segmentation'] | ['computer-vision'] | [ 5.71118534e-01 1.90265536e-01 -7.92051032e-02 -3.58635962e-01
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27155db6-6843-4bb7-847d-ca3cb78cd81d | 2x-faster-language-model-pre-training-via | 2305.02869 | null | https://arxiv.org/abs/2305.02869v1 | https://arxiv.org/pdf/2305.02869v1.pdf | 2x Faster Language Model Pre-training via Masked Structural Growth | Acceleration of large language model pre-training is a critical issue in present NLP research. In this paper, we focus on speeding up pre-training by progressively growing from a small Transformer structure to a large one. There are two main research problems related to progressive growth: growth schedule and growth operator. For growth schedule, existing work has explored multi-stage expansion of depth and feedforward layers. However, the impact of each dimension on the schedule's efficiency is still an open question. For growth operator, existing work relies on the initialization of new weights to inherit knowledge, and achieve only non-strict function preservation, limiting further optimization of training dynamics. To address these issues, we propose Masked Structural Growth (MSG), including growth schedules involving all possible dimensions and strictly function-preserving growth operators that is independent of the initialization of new weights. Experiments show that MSG is significantly faster than related work: we achieve a speed-up of 80% for Bert-base and 120% for Bert-large pre-training. Moreover, MSG is able to improve fine-tuning performances at the same time. | ['Yequan Wang', 'Jing Li', 'Zheng Zhang', 'Yiqun Yao'] | 2023-05-04 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 3.28200869e-02 1.08691089e-01 -3.05992186e-01 -2.04241455e-01
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7f221c8b-c626-48b4-b885-179c112e8046 | offline-prioritized-experience-replay | 2306.05412 | null | https://arxiv.org/abs/2306.05412v2 | https://arxiv.org/pdf/2306.05412v2.pdf | Offline Prioritized Experience Replay | Offline reinforcement learning (RL) is challenged by the distributional shift problem. To address this problem, existing works mainly focus on designing sophisticated policy constraints between the learned policy and the behavior policy. However, these constraints are applied equally to well-performing and inferior actions through uniform sampling, which might negatively affect the learned policy. To alleviate this issue, we propose Offline Prioritized Experience Replay (OPER), featuring a class of priority functions designed to prioritize highly-rewarding transitions, making them more frequently visited during training. Through theoretical analysis, we show that this class of priority functions induce an improved behavior policy, and when constrained to this improved policy, a policy-constrained offline RL algorithm is likely to yield a better solution. We develop two practical strategies to obtain priority weights by estimating advantages based on a fitted value network (OPER-A) or utilizing trajectory returns (OPER-R) for quick computation. OPER is a plug-and-play component for offline RL algorithms. As case studies, we evaluate OPER on five different algorithms, including BC, TD3+BC, Onestep RL, CQL, and IQL. Extensive experiments demonstrate that both OPER-A and OPER-R significantly improve the performance for all baseline methods. Codes and priority weights are availiable at https://github.com/sail-sg/OPER. | ['Shuicheng Yan', 'Shiji Song', 'Gao Huang', 'Xiao Ma', 'Bingyi Kang', 'Yang Yue'] | 2023-06-08 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.11530155e-01 -8.42691734e-02 -6.42225206e-01 -1.40456870e-01
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fffdb3bc-608c-4240-93ec-6fd39a249a29 | toward-efficient-language-model-pretraining | 2212.01853 | null | https://arxiv.org/abs/2212.01853v1 | https://arxiv.org/pdf/2212.01853v1.pdf | Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE | This technical report briefly describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language understanding evaluation (GLUE) benchmark, containing eight difficult language understanding tasks, including question answering, natural language inference, word sense disambiguation, coreference resolution, and reasoning. [Method] Instead of arbitrarily increasing the size of a pretrained language model (PLM), our aim is to 1) fully extract knowledge from the input pretraining data given a certain parameter budget, e.g., 6B, and 2) effectively transfer this knowledge to downstream tasks. To achieve goal 1), we propose self-evolution learning for PLMs to wisely predict the informative tokens that should be masked, and supervise the masked language modeling (MLM) process with rectified smooth labels. For goal 2), we leverage the prompt transfer technique to improve the low-resource tasks by transferring the knowledge from the foundation model and related downstream tasks to the target task. [Results] According to our submission record (Oct. 2022), with our optimized pretraining and fine-tuning strategies, our 6B Vega method achieved new state-of-the-art performance on 4/8 tasks, sitting atop the SuperGLUE leaderboard on Oct. 8, 2022, with an average score of 91.3. | ['DaCheng Tao', 'Xiaoou Tang', 'Chunyan Miao', 'Xinbo Gao', 'Yixin Chen', 'Bo Du', 'Baosheng Yu', 'Juhua Liu', 'Li Shen', 'Yonggang Wen', 'Yu Qiao', 'Yibing Zhan', 'Liang Ding', 'Qihuang Zhong'] | 2022-12-04 | null | null | null | null | ['word-sense-disambiguation', 'coreference-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.93261638e-01 5.04514396e-01 -2.47875541e-01 -5.12325525e-01
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9775a205-84af-4c8b-bfef-ac0c73292694 | kpconv-flexible-and-deformable-convolution | 1904.08889 | null | https://arxiv.org/abs/1904.08889v2 | https://arxiv.org/pdf/1904.08889v2.pdf | KPConv: Flexible and Deformable Convolution for Point Clouds | We present Kernel Point Convolution (KPConv), a new design of point convolution, i.e. that operates on point clouds without any intermediate representation. The convolution weights of KPConv are located in Euclidean space by kernel points, and applied to the input points close to them. Its capacity to use any number of kernel points gives KPConv more flexibility than fixed grid convolutions. Furthermore, these locations are continuous in space and can be learned by the network. Therefore, KPConv can be extended to deformable convolutions that learn to adapt kernel points to local geometry. Thanks to a regular subsampling strategy, KPConv is also efficient and robust to varying densities. Whether they use deformable KPConv for complex tasks, or rigid KPconv for simpler tasks, our networks outperform state-of-the-art classification and segmentation approaches on several datasets. We also offer ablation studies and visualizations to provide understanding of what has been learned by KPConv and to validate the descriptive power of deformable KPConv. | ['François Goulette', 'Jean-Emmanuel Deschaud', 'Leonidas J. Guibas', 'Beatriz Marcotegui', 'Hugues Thomas', 'Charles R. Qi'] | 2019-04-18 | kpconv-flexible-and-deformable-convolution-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Thomas_KPConv_Flexible_and_Deformable_Convolution_for_Point_Clouds_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Thomas_KPConv_Flexible_and_Deformable_Convolution_for_Point_Clouds_ICCV_2019_paper.pdf | iccv-2019-10 | ['robust-3d-semantic-segmentation', '3d-part-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.17585337e-01 8.84837061e-02 7.91845173e-02 -2.47348815e-01
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33eef2c9-a0ca-46e7-9165-7bd97cbdd1c7 | active-policy-improvement-from-multiple-black | 2306.10259 | null | https://arxiv.org/abs/2306.10259v2 | https://arxiv.org/pdf/2306.10259v2.pdf | Active Policy Improvement from Multiple Black-box Oracles | Reinforcement learning (RL) has made significant strides in various complex domains. However, identifying an effective policy via RL often necessitates extensive exploration. Imitation learning aims to mitigate this issue by using expert demonstrations to guide exploration. In real-world scenarios, one often has access to multiple suboptimal black-box experts, rather than a single optimal oracle. These experts do not universally outperform each other across all states, presenting a challenge in actively deciding which oracle to use and in which state. We introduce MAPS and MAPS-SE, a class of policy improvement algorithms that perform imitation learning from multiple suboptimal oracles. In particular, MAPS actively selects which of the oracles to imitate and improve their value function estimates, and MAPS-SE additionally leverages an active state exploration criterion to determine which states one should explore. We provide a comprehensive theoretical analysis and demonstrate that MAPS and MAPS-SE enjoy sample efficiency advantage over the state-of-the-art policy improvement algorithms. Empirical results show that MAPS-SE significantly accelerates policy optimization via state-wise imitation learning from multiple oracles across a broad spectrum of control tasks in the DeepMind Control Suite. Our code is publicly available at: https://github.com/ripl/maps. | ['Yuxin Chen', 'Matthew R. Walter', 'Chaoqi Wang', 'Takuma Yoneda', 'Xuefeng Liu'] | 2023-06-17 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-8.81026685e-02 -1.23781443e-01 -8.01577985e-01 1.19502649e-01
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35707241-6dcd-4868-827f-d2bf8f3e0dbc | robot-structure-prior-guided-temporal | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tian_Robot_Structure_Prior_Guided_Temporal_Attention_for_Camera-to-Robot_Pose_Estimation_CVPR_2023_paper.pdf | Robot Structure Prior Guided Temporal Attention for Camera-to-Robot Pose Estimation From Image Sequence | In this work, we tackle the problem of online camera-to-robot pose estimation from single-view successive frames of an image sequence, a crucial task for robots to interact with the world. The primary obstacles of this task are the robot's self-occlusions and the ambiguity of single-view images. This work demonstrates, for the first time, the effectiveness of temporal information and the robot structure prior in addressing these challenges. Given the successive frames and the robot joint configuration, our method learns to accurately regress the 2D coordinates of the predefined robot's keypoints (e.g., joints). With the camera intrinsic and robotic joints status known, we get the camera-to-robot pose using a Perspective-n-point (PnP) solver. We further improve the camera-to-robot pose iteratively using the robot structure prior. To train the whole pipeline, we build a large-scale synthetic dataset generated with domain randomisation to bridge the sim-to-real gap. The extensive experiments on synthetic and real-world datasets and the downstream robotic grasping task demonstrate that our method achieves new state-of-the-art performances and outperforms traditional hand-eye calibration algorithms in real-time (36 FPS). Code and data are available at the project page: https://sites.google.com/view/sgtapose. | ['Hao Dong', 'Zekai Yin', 'Jiyao Zhang', 'Yang Tian'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['robotic-grasping'] | ['robots'] | [-1.33300856e-01 1.28703490e-01 1.66284382e-01 -2.62824714e-01
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06b416a3-ab0a-4850-b957-9b8cfb1bf9ca | webly-supervised-fine-grained-recognition-1 | null | null | https://www.ijcai.org/proceedings/2022/209 | https://www.ijcai.org/proceedings/2022/0209.pdf | Webly-Supervised Fine-Grained Recognition with Partial Label Learning | The task of webly-supervised fne-grained recognition is to boost recognition accuracy of classifying subordinate categories (e.g., different bird species)by utilizing freely available but noisy web data.As the label noises signifcantly hurt the network
training, it is desirable to distinguish and eliminate noisy images. In this paper, we propose two strategies, i.e., open-set noise removal and closed-set noise correction, to both remove such two kinds of web noises w.r.t. fne-grained recognition. Specifically, for open-set noise removal, we utilize a pre-trained deep model to perform deep descriptor transformation to estimate the positive correlation between these web images, and detect the open-set noises based on the correlation values. Regarding closed-set noise correction, we develop a top-k recall optimization loss for frstly assigning a label set towards each web image to reduce the impact of hard label assignment for closed-set noises. Then,we further propose to correct the sample with itslabel set as the true single label from a partial label learning perspective. Experiments on several webly-supervised fne-grained benchmark datasets show that our method obviously outperforms other existing state-of-the-art methods. | ['Jian Yang', 'Xiu-Shen Wei', 'Yang shen', 'Yu-Yan Xu'] | 2022-02-09 | null | null | null | ijcai-2022-2 | ['partial-label-learning'] | ['methodology'] | [ 2.97345996e-01 -4.68791574e-01 2.40342077e-02 -7.51588106e-01
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cecea4eb-4785-4418-b53f-2b9169c7e985 | x-modaler-a-versatile-and-high-performance | 2108.08217 | null | https://arxiv.org/abs/2108.08217v1 | https://arxiv.org/pdf/2108.08217v1.pdf | X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics | With the rise and development of deep learning over the past decade, there has been a steady momentum of innovation and breakthroughs that convincingly push the state-of-the-art of cross-modal analytics between vision and language in multimedia field. Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion. In this work, we propose X-modaler -- a versatile and high-performance codebase that encapsulates the state-of-the-art cross-modal analytics into several general-purpose stages (e.g., pre-processing, encoder, cross-modal interaction, decoder, and decode strategy). Each stage is empowered with the functionality that covers a series of modules widely adopted in state-of-the-arts and allows seamless switching in between. This way naturally enables a flexible implementation of state-of-the-art algorithms for image captioning, video captioning, and vision-language pre-training, aiming to facilitate the rapid development of research community. Meanwhile, since the effective modular designs in several stages (e.g., cross-modal interaction) are shared across different vision-language tasks, X-modaler can be simply extended to power startup prototypes for other tasks in cross-modal analytics, including visual question answering, visual commonsense reasoning, and cross-modal retrieval. X-modaler is an Apache-licensed codebase, and its source codes, sample projects and pre-trained models are available on-line: https://github.com/YehLi/xmodaler. | ['Tao Mei', 'Ting Yao', 'Jingwen Chen', 'Yingwei Pan', 'Yehao Li'] | 2021-08-18 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [-1.07944541e-01 2.53375024e-02 -2.34957412e-01 -2.25870445e-01
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1aaee015-c642-4ba8-ae73-856dad6ef5c3 | a-generalized-framework-for-edge-preserving | 1907.09642 | null | https://arxiv.org/abs/1907.09642v4 | https://arxiv.org/pdf/1907.09642v4.pdf | A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing | Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. To this end, we first introduce the truncated Huber penalty function which has seldom been used in image smoothing. A robust framework is then proposed. When combined with the strong flexibility of the truncated Huber penalty function, our framework is capable of a range of applications and can outperform the state-of-the-art approaches in several tasks. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. The effectiveness and superior performance of our approach are validated through comprehensive experimental results in a range of applications. | ['Pingping Zhang', 'Yinjie Lei', 'Wei Liu', 'Xiaolin Huang', 'Jie Yang', 'Ian Reid'] | 2019-07-23 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 9.51497406e-02 -1.33462504e-01 -1.92302081e-03 -2.26115540e-01
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2d6d9a0d-732a-4c54-a4fd-28b7e26fa327 | memexqa-visual-memex-question-answering | 1708.01336 | null | http://arxiv.org/abs/1708.01336v1 | http://arxiv.org/pdf/1708.01336v1.pdf | MemexQA: Visual Memex Question Answering | This paper proposes a new task, MemexQA: given a collection of photos or
videos from a user, the goal is to automatically answer questions that help
users recover their memory about events captured in the collection. Towards
solving the task, we 1) present the MemexQA dataset, a large, realistic
multimodal dataset consisting of real personal photos and crowd-sourced
questions/answers, 2) propose MemexNet, a unified, end-to-end trainable network
architecture for image, text and video question answering. Experimental results
on the MemexQA dataset demonstrate that MemexNet outperforms strong baselines
and yields the state-of-the-art on this novel and challenging task. The
promising results on TextQA and VideoQA suggest MemexNet's efficacy and
scalability across various QA tasks. | ['Yannis Kalantidis', 'Junwei Liang', 'Alexander Hauptmann', 'Sachin Farfade', 'Lu Jiang', 'Liangliang Cao'] | 2017-08-04 | null | null | null | null | ['memex-question-answering'] | ['natural-language-processing'] | [-2.79712558e-01 -1.93333507e-01 1.94317743e-01 -4.18699533e-01
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4a8987de-5d09-4482-beca-3c48a2b87e1f | amd-severity-prediction-and-explainability | 1907.03075 | null | https://arxiv.org/abs/1907.03075v1 | https://arxiv.org/pdf/1907.03075v1.pdf | AMD Severity Prediction And Explainability Using Image Registration And Deep Embedded Clustering | We propose a method to predict severity of age related macular degeneration (AMD) from input optical coherence tomography (OCT) images. Although there is no standard clinical severity scale for AMD, we leverage deep learning (DL) based image registration and clustering methods to identify diseased cases and predict their severity. Experiments demonstrate our approach's disease classification performance matches state of the art methods. The predicted disease severity performs well on previously unseen data. Registration output provides better explainability than class activation maps regarding label and severity decisions | ['Dwarikanath Mahapatra'] | 2019-07-06 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 8.29769894e-02 -2.70484276e-02 -3.41619283e-01 -4.73434329e-01
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add092f7-4697-40e2-8f98-590e1f802f25 | editing-large-language-models-problems | 2305.13172 | null | https://arxiv.org/abs/2305.13172v1 | https://arxiv.org/pdf/2305.13172v1.pdf | Editing Large Language Models: Problems, Methods, and Opportunities | Recent advancements in deep learning have precipitated the emergence of large language models (LLMs) which exhibit an impressive aptitude for understanding and producing text akin to human language. Despite the ability to train highly capable LLMs, the methodology for maintaining their relevancy and rectifying errors remains elusive. To that end, the past few years have witnessed a surge in techniques for editing LLMs, the objective of which is to alter the behavior of LLMs within a specific domain without negatively impacting performance across other inputs. This paper embarks on a deep exploration of the problems, methods, and opportunities relating to model editing for LLMs. In particular, we provide an exhaustive overview of the task definition and challenges associated with model editing, along with an in-depth empirical analysis of the most progressive methods currently at our disposal. We also build a new benchmark dataset to facilitate a more robust evaluation and pinpoint enduring issues intrinsic to existing techniques. Our objective is to provide valuable insights into the effectiveness and feasibility of each model editing technique, thereby assisting the research community in making informed decisions when choosing the most appropriate method for a specific task or context. Code and datasets will be available at https://github.com/zjunlp/EasyEdit. | ['Ningyu Zhang', 'Huajun Chen', 'Shumin Deng', 'Zhoubo Li', 'Siyuan Cheng', 'Bozhong Tian', 'Peng Wang', 'Yunzhi Yao'] | 2023-05-22 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 5.08195817e-01 8.40136036e-02 -5.75999543e-02 -4.07987088e-01
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-5.30516744e-01 -3.17163378e-01 2.79698491e-01 -3.23824614e-01
3.17320153e-02 -6.74756646e-01 -1.10817623e+00 -6.17505789e-01
2.98466627e-03 2.80813109e-02 4.91281211e-01 9.66144681e-01
6.16437495e-01 4.34528798e-01 1.28320739e-01 -6.66276515e-01
-7.52665401e-01 -9.62106943e-01 -2.12816805e-01 3.01420867e-01
2.85575598e-01 -4.02827978e-01 -2.02659801e-01 2.38129273e-01] | [10.888508796691895, 8.768978118896484] |
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