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0b0f7126-e41a-490c-8997-51766e5686ae | escaping-the-big-data-paradigm-with-compact | 2104.05704 | null | https://arxiv.org/abs/2104.05704v4 | https://arxiv.org/pdf/2104.05704v4.pdf | Escaping the Big Data Paradigm with Compact Transformers | With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amounts of training data. Many have come to believe that because of this, transformers are not suitable for small sets of data. This trend leads to concerns such as: limited availability of data in certain scientific domains and the exclusion of those with limited resource from research in the field. In this paper, we aim to present an approach for small-scale learning by introducing Compact Transformers. We show for the first time that with the right size, convolutional tokenization, transformers can avoid overfitting and outperform state-of-the-art CNNs on small datasets. Our models are flexible in terms of model size, and can have as little as 0.28M parameters while achieving competitive results. Our best model can reach 98% accuracy when training from scratch on CIFAR-10 with only 3.7M parameters, which is a significant improvement in data-efficiency over previous Transformer based models being over 10x smaller than other transformers and is 15% the size of ResNet50 while achieving similar performance. CCT also outperforms many modern CNN based approaches, and even some recent NAS-based approaches. Additionally, we obtain a new SOTA result on Flowers-102 with 99.76% top-1 accuracy, and improve upon the existing baseline on ImageNet (82.71% accuracy with 29% as many parameters as ViT), as well as NLP tasks. Our simple and compact design for transformers makes them more feasible to study for those with limited computing resources and/or dealing with small datasets, while extending existing research efforts in data efficient transformers. Our code and pre-trained models are publicly available at https://github.com/SHI-Labs/Compact-Transformers. | ['Humphrey Shi', 'Jiachen Li', 'Abulikemu Abuduweili', 'Nikhil Shah', 'Steven Walton', 'Ali Hassani'] | 2021-04-12 | null | null | null | null | ['superpixel-image-classification'] | ['computer-vision'] | [-9.49911550e-02 -2.32494473e-01 -2.68264562e-01 -1.96583480e-01
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1dcd195c-f5dc-4e6a-982a-705b132734ad | contrastive-learning-approach-for-semi | 2210.04776 | null | https://arxiv.org/abs/2210.04776v3 | https://arxiv.org/pdf/2210.04776v3.pdf | CONSS: Contrastive Learning Approach for Semi-Supervised Seismic Facies Classification | Recently, seismic facies classification based on convolutional neural networks (CNN) has garnered significant research interest. However, existing CNN-based supervised learning approaches necessitate massive labeled data. Labeling is laborious and time-consuming, particularly for 3D seismic data volumes. To overcome this challenge, we propose a semi-supervised method based on pixel-level contrastive learning, termed CONSS, which can efficiently identify seismic facies using only 1% of the original annotations. Furthermore, the absence of a unified data division and standardized metrics hinders the fair comparison of various facies classification approaches. To this end, we develop an objective benchmark for the evaluation of semi-supervised methods, including self-training, consistency regularization, and the proposed CONSS. Our benchmark is publicly available to enable researchers to objectively compare different approaches. Experimental results demonstrate that our approach achieves state-of-the-art performance on the F3 survey. | ['Ruilin Jing', 'Hongjie Duan', 'Zhifeng Xu', 'YiMin Dou', 'Wenlong Liu', 'Kewen Li'] | 2022-10-10 | null | null | null | null | ['facies-classification'] | ['miscellaneous'] | [-7.24115744e-02 -2.21643656e-01 -1.59044117e-01 -6.86649084e-01
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3c554dfb-7056-4399-8f8d-5afc4ffcb4f8 | anisotropic-convolutional-networks-for-3d | 2004.02122 | null | https://arxiv.org/abs/2004.02122v1 | https://arxiv.org/pdf/2004.02122v1.pdf | Anisotropic Convolutional Networks for 3D Semantic Scene Completion | As a voxel-wise labeling task, semantic scene completion (SSC) tries to simultaneously infer the occupancy and semantic labels for a scene from a single depth and/or RGB image. The key challenge for SSC is how to effectively take advantage of the 3D context to model various objects or stuffs with severe variations in shapes, layouts and visibility. To handle such variations, we propose a novel module called anisotropic convolution, which properties with flexibility and power impossible for the competing methods such as standard 3D convolution and some of its variations. In contrast to the standard 3D convolution that is limited to a fixed 3D receptive field, our module is capable of modeling the dimensional anisotropy voxel-wisely. The basic idea is to enable anisotropic 3D receptive field by decomposing a 3D convolution into three consecutive 1D convolutions, and the kernel size for each such 1D convolution is adaptively determined on the fly. By stacking multiple such anisotropic convolution modules, the voxel-wise modeling capability can be further enhanced while maintaining a controllable amount of model parameters. Extensive experiments on two SSC benchmarks, NYU-Depth-v2 and NYUCAD, show the superior performance of the proposed method. Our code is available at https://waterljwant.github.io/SSC/ | ['Xia Yuan', 'Yu Liu', 'Kai Han', 'Peng Wang', 'Jie Li'] | 2020-04-05 | anisotropic-convolutional-networks-for-3d-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Anisotropic_Convolutional_Networks_for_3D_Semantic_Scene_Completion_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-semantic-scene-completion', '3d-semantic-scene-completion-from-a-single'] | ['computer-vision', 'computer-vision'] | [ 2.61496305e-01 -6.64990321e-02 7.01493323e-02 -5.06345332e-01
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7f86660e-af5f-4a28-bf02-79ad8bae0cdc | data-efficient-learning-for-3d-mirror | 2112.12579 | null | https://arxiv.org/abs/2112.12579v2 | https://arxiv.org/pdf/2112.12579v2.pdf | NeRD++: Improved 3D-mirror symmetry learning from a single image | Many objects are naturally symmetric, and this symmetry can be exploited to infer unseen 3D properties from a single 2D image. Recently, NeRD is proposed for accurate 3D mirror plane estimation from a single image. Despite the unprecedented accuracy, it relies on large annotated datasets for training and suffers from slow inference. Here we aim to improve its data and compute efficiency. We do away with the computationally expensive 4D feature volumes and instead explicitly compute the feature correlation of the pixel correspondences across depth, thus creating a compact 3D volume. We also design multi-stage spherical convolutions to identify the optimal mirror plane on the hemisphere, whose inductive bias offers gains in data-efficiency. Experiments on both synthetic and real-world datasets show the benefit of our proposed changes for improved data efficiency and inference speed. | ['Jan van Gemert', 'Silvia-Laura Pintea', 'Yancong Lin'] | 2021-12-23 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [ 4.92831767e-02 1.90457955e-01 6.74026534e-02 -5.32012463e-01
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9bd05c17-01f9-4cac-b05f-02faf91fd829 | recommender-systems-a-primer | 2302.02579 | null | https://arxiv.org/abs/2302.02579v1 | https://arxiv.org/pdf/2302.02579v1.pdf | Recommender Systems: A Primer | Personalized recommendations have become a common feature of modern online services, including most major e-commerce sites, media platforms and social networks. Today, due to their high practical relevance, research in the area of recommender systems is flourishing more than ever. However, with the new application scenarios of recommender systems that we observe today, constantly new challenges arise as well, both in terms of algorithmic requirements and with respect to the evaluation of such systems. In this paper, we first provide an overview of the traditional formulation of the recommendation problem. We then review the classical algorithmic paradigms for item retrieval and ranking and elaborate how such systems can be evaluated. Afterwards, we discuss a number of recent developments in recommender systems research, including research on session-based recommendation, biases in recommender systems, and questions regarding the impact and value of recommender systems in practice. | ['Dietmar Jannach', 'Pablo Castells'] | 2023-02-06 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [ 2.46873610e-02 -2.09781960e-01 -2.44614258e-01 -5.21144211e-01
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a32780e1-967c-43af-9a71-502c911be720 | contextual-explainable-video-representation | 2212.06206 | null | https://arxiv.org/abs/2212.06206v2 | https://arxiv.org/pdf/2212.06206v2.pdf | Contextual Explainable Video Representation: Human Perception-based Understanding | Video understanding is a growing field and a subject of intense research, which includes many interesting tasks to understanding both spatial and temporal information, e.g., action detection, action recognition, video captioning, video retrieval. One of the most challenging problems in video understanding is dealing with feature extraction, i.e. extract contextual visual representation from given untrimmed video due to the long and complicated temporal structure of unconstrained videos. Different from existing approaches, which apply a pre-trained backbone network as a black-box to extract visual representation, our approach aims to extract the most contextual information with an explainable mechanism. As we observed, humans typically perceive a video through the interactions between three main factors, i.e., the actors, the relevant objects, and the surrounding environment. Therefore, it is very crucial to design a contextual explainable video representation extraction that can capture each of such factors and model the relationships between them. In this paper, we discuss approaches, that incorporate the human perception process into modeling actors, objects, and the environment. We choose video paragraph captioning and temporal action detection to illustrate the effectiveness of human perception based-contextual representation in video understanding. Source code is publicly available at https://github.com/UARK-AICV/Video_Representation. | ['Ngan Le', 'Khoa Luu', 'Phat Nguyen', 'Phong X. Nguyen', 'Kashu Yamazaki', 'Khoa Vo'] | 2022-12-12 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 3.71379524e-01 -1.41451225e-01 -4.31145191e-01 -3.30162376e-01
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3f5b2ce3-b467-48df-a1a7-9143822dd7c4 | natural-language-detectors-emerge-in | null | null | https://openreview.net/forum?id=BJec2Mfosm | https://openreview.net/pdf?id=BJec2Mfosm | Natural Language Detectors Emerge in Individual Neurons | Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language. | ['Anonymous'] | 2018-10-22 | null | null | null | null | ['concept-alignment'] | ['computer-vision'] | [ 4.08363491e-01 8.34364444e-03 -2.03272805e-01 -5.75897813e-01
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3f6ca3ed-0534-4971-b907-b15e0c1f5f0f | meta-repository-of-screening-mammography | 2108.04800 | null | https://arxiv.org/abs/2108.04800v3 | https://arxiv.org/pdf/2108.04800v3.pdf | Meta-repository of screening mammography classifiers | Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To enable reproducibility of research and to enable comparison between different methods, we release a meta-repository containing models for classification of screening mammograms. This meta-repository creates a framework that enables the evaluation of AI models on any screening mammography data set. At its inception, our meta-repository contains five state-of-the-art models with open-source implementations and cross-platform compatibility. We compare their performance on seven international data sets. Our framework has a flexible design that can be generalized to other medical image analysis tasks. The meta-repository is available at https://www.github.com/nyukat/mammography_metarepository. | ['Krzysztof J. Geras', 'Kyunghyun Cho', 'Farah E. Shamout', 'Jakub Chłędowski', 'Vishwaesh Rajiv', 'Jan Witowski', 'Benjamin Stadnick'] | 2021-08-10 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection', 'breast-tumour-classification'] | ['knowledge-base', 'medical', 'medical'] | [ 3.10249835e-01 3.24834496e-01 -7.95208573e-01 -5.83286166e-01
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9e7790af-8e8a-4ffa-88e5-6f2134005997 | an-examination-of-the-robustness-of-reference | 2305.14998 | null | https://arxiv.org/abs/2305.14998v1 | https://arxiv.org/pdf/2305.14998v1.pdf | An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics | Recently, reference-free metrics such as CLIPScore (Hessel et al., 2021) and UMIC (Lee et al., 2021) have been proposed for automatic evaluation of image captions, demonstrating a high correlation with human judgment. In this work, our focus lies in evaluating the robustness of these metrics in scenarios that require distinguishing between two captions with high lexical overlap but very different meanings. Our findings reveal that despite their high correlation with human judgment, both CLIPScore and UMIC struggle to identify fine-grained errors in captions. However, when comparing different types of fine-grained errors, both metrics exhibit limited sensitivity to implausibility of captions and strong sensitivity to lack of sufficient visual grounding. Probing further into the visual grounding aspect, we found that both CLIPScore and UMIC are impacted by the size of image-relevant objects mentioned in the caption, and that CLIPScore is also sensitive to the number of mentions of image-relevant objects in the caption. In terms of linguistic aspects of a caption, we found that both metrics lack the ability to comprehend negation, UMIC is sensitive to caption lengths, and CLIPScore is insensitive to the structure of the sentence. We hope our findings will serve as a valuable guide towards improving reference-free evaluation in image captioning. | ['Aishwarya Agrawal', 'Saba Ahmadi'] | 2023-05-24 | null | null | null | null | ['visual-grounding', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 3.40361714e-01 1.07358724e-01 -6.67474419e-02 -3.85943562e-01
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55229a9e-4432-49f5-b197-8e0ed09e7f1a | lvos-a-benchmark-for-long-term-video-object | 2211.10181 | null | https://arxiv.org/abs/2211.10181v1 | https://arxiv.org/pdf/2211.10181v1.pdf | LVOS: A Benchmark for Long-term Video Object Segmentation | Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the application in realistic scenarios. So, in this paper, we present a new benchmark dataset and evaluation methodology named LVOS, which consists of 220 videos with a total duration of 421 minutes. To the best of our knowledge, LVOS is the first densely annotated long-term VOS dataset. The videos in our LVOS last 1.59 minutes on average, which is 20 times longer than videos in existing VOS datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts. Moreover, we provide additional language descriptions to encourage the exploration of integrating linguistic and visual features for video object segmentation. Based on LVOS, we assess existing video object segmentation algorithms and propose a Diverse Dynamic Memory network (DDMemory) that consists of three complementary memory banks to exploit temporal information adequately. The experiment results demonstrate the strength and weaknesses of prior methods, pointing promising directions for further study. Our objective is to provide the community with a large and varied benchmark to boost the advancement of long-term VOS. Data and code are available at \url{https://lingyihongfd.github.io/lvos.github.io/}. | ['Wenqiang Zhang', 'Zhaoyu Chen', 'Pinxue Guo', 'Wei zhang', 'Zhongying Liu', 'Wenchao Chen', 'Lingyi Hong'] | 2022-11-18 | null | null | null | null | ['video-object-segmentation'] | ['computer-vision'] | [-1.45757481e-01 -4.31883156e-01 -6.75785184e-01 -2.44226500e-01
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72e1a24a-0a29-4d1b-9e78-47a5f54b01d1 | ranking-loss-and-sequestering-learning-for | 2304.08498 | null | https://arxiv.org/abs/2304.08498v1 | https://arxiv.org/pdf/2304.08498v1.pdf | Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology | Recently, deep learning has started to play an essential role in healthcare applications, including image search in digital pathology. Despite the recent progress in computer vision, significant issues remain for image searching in histopathology archives. A well-known problem is AI bias and lack of generalization. A more particular shortcoming of deep models is the ignorance toward search functionality. The former affects every model, the latter only search and matching. Due to the lack of ranking-based learning, researchers must train models based on the classification error and then use the resultant embedding for image search purposes. Moreover, deep models appear to be prone to internal bias even if using a large image repository of various hospitals. This paper proposes two novel ideas to improve image search performance. First, we use a ranking loss function to guide feature extraction toward the matching-oriented nature of the search. By forcing the model to learn the ranking of matched outputs, the representation learning is customized toward image search instead of learning a class label. Second, we introduce the concept of sequestering learning to enhance the generalization of feature extraction. By excluding the images of the input hospital from the matched outputs, i.e., sequestering the input domain, the institutional bias is reduced. The proposed ideas are implemented and validated through the largest public dataset of whole slide images. The experiments demonstrate superior results compare to the-state-of-art. | ['H. R. Tizhoosh', 'Shahryar Rahnamayan', 'Azam Asilian Bidgoli', 'Pooria Mazaheri'] | 2023-04-15 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 3.43179435e-01 1.21321887e-01 -4.19434160e-01 -3.47368151e-01
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906ad8d1-e949-475e-b4c9-585d1d5dd282 | causal-inference-for-the-expected-number-of | 2306.16571 | null | https://arxiv.org/abs/2306.16571v1 | https://arxiv.org/pdf/2306.16571v1.pdf | Causal inference for the expected number of recurrent events in the presence of a terminal event | We study causal inference and efficient estimation for the expected number of recurrent events in the presence of a terminal event. We define our estimand as the vector comprising both the expected number of recurrent events and the failure survival function evaluated along a sequence of landmark times. We identify the estimand in the presence of right-censoring and causal selection as an observed data functional under coarsening at random, derive the nonparametric efficiency bound, and propose a multiply-robust estimator that achieves the bound and permits nonparametric estimation of nuisance parameters. Throughout, no absolute continuity assumption is made on the underlying probability distributions of failure, censoring, or the observed data. Additionally, we derive the class of influence functions when the coarsening distribution is known and review how published estimators may belong to the class. Along the way, we highlight some interesting inconsistencies in the causal lifetime analysis literature. | ['Ashkan Ertefaie', 'Robert L. Strawderman', 'Benjamin R. Baer'] | 2023-06-28 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 3.38404357e-01 1.66474938e-01 -8.08231294e-01 -5.25845587e-01
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d3d9cd06-8834-4197-86d5-2be42b63cd82 | forgetting-exceptions-is-harmful-in-language | cs/9812021 | null | https://arxiv.org/abs/cs/9812021v1 | https://arxiv.org/pdf/cs/9812021v1.pdf | Forgetting Exceptions is Harmful in Language Learning | We show that in language learning, contrary to received wisdom, keeping exceptional training instances in memory can be beneficial for generalization accuracy. We investigate this phenomenon empirically on a selection of benchmark natural language processing tasks: grapheme-to-phoneme conversion, part-of-speech tagging, prepositional-phrase attachment, and base noun phrase chunking. In a first series of experiments we combine memory-based learning with training set editing techniques, in which instances are edited based on their typicality and class prediction strength. Results show that editing exceptional instances (with low typicality or low class prediction strength) tends to harm generalization accuracy. In a second series of experiments we compare memory-based learning and decision-tree learning methods on the same selection of tasks, and find that decision-tree learning often performs worse than memory-based learning. Moreover, the decrease in performance can be linked to the degree of abstraction from exceptions (i.e., pruning or eagerness). We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms. | ['Antal Van den Bosch', 'Walter Daelemans', 'Jakub Zavrel'] | 1998-12-22 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 4.25061047e-01 3.23184758e-01 -4.39318299e-01 -3.37607563e-01
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a31b06ec-11ca-45cd-b170-7e5f15b4b0f3 | masked-event-modeling-self-supervised | 2212.10368 | null | https://arxiv.org/abs/2212.10368v1 | https://arxiv.org/pdf/2212.10368v1.pdf | Masked Event Modeling: Self-Supervised Pretraining for Event Cameras | Event cameras offer the capacity to asynchronously capture brightness changes with low latency, high temporal resolution, and high dynamic range. Deploying deep learning methods for classification or other tasks to these sensors typically requires large labeled datasets. Since the amount of labeled event data is tiny compared to the bulk of labeled RGB imagery, the progress of event-based vision has remained limited. To reduce the dependency on labeled event data, we introduce Masked Event Modeling (MEM), a self-supervised pretraining framework for events. Our method pretrains a neural network on unlabeled events, which can originate from any event camera recording. Subsequently, the pretrained model is finetuned on a downstream task leading to an overall better performance while requiring fewer labels. Our method outperforms the state-of-the-art on N-ImageNet, N-Cars, and N-Caltech101, increasing the object classification accuracy on N-ImageNet by 7.96%. We demonstrate that Masked Event Modeling is superior to RGB-based pretraining on a real world dataset. | ['Daniel Cremers', 'Lukas Koestler', 'David Bonello', 'Simon Klenk'] | 2022-12-20 | null | null | null | null | ['event-based-vision'] | ['computer-vision'] | [ 3.8702530e-01 -6.4895540e-02 -3.0559201e-02 -7.1129161e-01
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d836aa39-4913-4030-aa59-b3f56f25e3e8 | multi-modal-face-stylization-with-a | 2305.18009 | null | https://arxiv.org/abs/2305.18009v1 | https://arxiv.org/pdf/2305.18009v1.pdf | Multi-Modal Face Stylization with a Generative Prior | In this work, we introduce a new approach for artistic face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality stylized faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder-decoder architecture. Specifically, we use the mid-resolution and high-resolution layers of StyleGAN as the decoder to generate high-quality faces, while aligning its low-resolution layer with the encoder to extract and preserve input facial details. We also introduce a two-stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine-tuned with artistic data for stylized face generation. To enable the fine-tuned model to be applied in zero-shot and one-shot stylization tasks, we train an additional mapping network from the large-scale Contrastive-Language-Image-Pre-training (CLIP) space to a latent $w+$ space of fine-tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior face stylization performance in both one-shot and zero-shot stylization tasks, outperforming state-of-the-art methods by a large margin. | ['Chongyang Ma', 'Pengfei Wan', 'Haibin Huang', 'Minxuan Lin', 'Yi Dong', 'Mengtian Li'] | 2023-05-29 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 3.18484128e-01 3.29252839e-01 -4.09577675e-02 -3.57322186e-01
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c01580f6-902c-44b8-bff2-fa6397ebc32c | deep-endovo-a-recurrent-convolutional-neural | 1708.06822 | null | http://arxiv.org/abs/1708.06822v2 | http://arxiv.org/pdf/1708.06822v2.pdf | Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots | Ingestible wireless capsule endoscopy is an emerging minimally invasive
diagnostic technology for inspection of the GI tract and diagnosis of a wide
range of diseases and pathologies. Medical device companies and many research
groups have recently made substantial progresses in converting passive capsule
endoscopes to active capsule robots, enabling more accurate, precise, and
intuitive detection of the location and size of the diseased areas. Since a
reliable real time pose estimation functionality is crucial for actively
controlled endoscopic capsule robots, in this study, we propose a monocular
visual odometry (VO) method for endoscopic capsule robot operations. Our method
lies on the application of the deep Recurrent Convolutional Neural Networks
(RCNNs) for the visual odometry task, where Convolutional Neural Networks
(CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction
and inference of dynamics across the frames, respectively. Detailed analyses
and evaluations made on a real pig stomach dataset proves that our system
achieves high translational and rotational accuracies for different types of
endoscopic capsule robot trajectories. | ['Metin Sitti', 'Mehmet Turan', 'Helder Araujo', 'Yasin Almalioglu', 'Ender Konukoglu'] | 2017-08-22 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-5.25058687e-01 2.12365240e-01 -1.33001715e-01 3.29663396e-01
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64ca3525-864b-4d7a-8dd4-e9a4870bcd93 | multi-content-gan-for-few-shot-font-style | 1712.00516 | null | http://arxiv.org/abs/1712.00516v1 | http://arxiv.org/pdf/1712.00516v1.pdf | Multi-Content GAN for Few-Shot Font Style Transfer | In this work, we focus on the challenge of taking partial observations of
highly-stylized text and generalizing the observations to generate unobserved
glyphs in the ornamented typeface. To generate a set of multi-content images
following a consistent style from very few examples, we propose an end-to-end
stacked conditional GAN model considering content along channels and style
along network layers. Our proposed network transfers the style of given glyphs
to the contents of unseen ones, capturing highly stylized fonts found in the
real-world such as those on movie posters or infographics. We seek to transfer
both the typographic stylization (ex. serifs and ears) as well as the textual
stylization (ex. color gradients and effects.) We base our experiments on our
collected data set including 10,000 fonts with different styles and demonstrate
effective generalization from a very small number of observed glyphs. | ['Zhaowen Wang', 'Vladimir Kim', 'Samaneh Azadi', 'Matthew Fisher', 'Eli Shechtman', 'Trevor Darrell'] | 2017-12-01 | multi-content-gan-for-few-shot-font-style-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Azadi_Multi-Content_GAN_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Azadi_Multi-Content_GAN_for_CVPR_2018_paper.pdf | cvpr-2018-6 | ['font-style-transfer'] | ['computer-vision'] | [ 6.99516654e-01 2.39859093e-02 3.76141906e-01 -3.60507101e-01
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0a043675-53db-4029-81ea-8021d3d351e8 | shape-from-water-reflection | 1906.10284 | null | https://arxiv.org/abs/1906.10284v2 | https://arxiv.org/pdf/1906.10284v2.pdf | Appearance and Shape from Water Reflection | This paper introduces single-image geometric and appearance reconstruction from water reflection photography, i.e., images capturing direct and water-reflected real-world scenes. Water reflection offers an additional viewpoint to the direct sight, collectively forming a stereo pair. The water-reflected scene, however, includes internally scattered and reflected environmental illumination in addition to the scene radiance, which precludes direct stereo matching. We derive a principled iterative method that disentangles this scene radiometry and geometry for reconstructing 3D scene structure as well as its high-dynamic range appearance. In the presence of waves, we simultaneously recover the wave geometry as surface normal perturbations of the water surface. Most important, we show that the water reflection enables calibration of the camera. In other words, for the first time, we show that capturing a direct and water-reflected scene in a single exposure forms a self-calibrating HDR catadioptric stereo camera. We demonstrate our method on a number of images taken in the wild. The results demonstrate a new means for leveraging this accidental catadioptric camera. | ['Meng-Yu Jennifer Kuo', 'Ryo Kawahara', 'Ko Nishino', 'Shohei Nobuhara'] | 2019-06-25 | null | null | null | null | ['stereo-matching', '3d-scene-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 8.31314862e-01 -6.06696047e-02 8.44950795e-01 -2.50025213e-01
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14660b96-8091-4cf4-a5de-535aa57517a9 | cross-image-attention-for-conditional | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kotovenko_Cross-Image-Attention_for_Conditional_Embeddings_in_Deep_Metric_Learning_CVPR_2023_paper.pdf | Cross-Image-Attention for Conditional Embeddings in Deep Metric Learning | Learning compact image embeddings that yield semantic similarities between images and that generalize to unseen test classes, is at the core of deep metric learning (DML). Finding a mapping from a rich, localized image feature map onto a compact embedding vector is challenging: Although similarity emerges between tuples of images, DML approaches marginalize out information in an individual image before considering another image to which similarity is to be computed. Instead, we propose during training to condition the embedding of an image on the image we want to compare it to. Rather than embedding by a simple pooling as in standard DML, we use cross-attention so that one image can identify relevant features in the other image. Consequently, the attention mechanism establishes a hierarchy of conditional embeddings that gradually incorporates information about the tuple to steer the representation of an individual image. The cross-attention layers bridge the gap between the original unconditional embedding and the final similarity and allow backpropagtion to update encodings more directly than through a lossy pooling layer. At test time we use the resulting improved unconditional embeddings, thus requiring no additional parameters or computational overhead. Experiments on established DML benchmarks show that our cross-attention conditional embedding during training improves the underlying standard DML pipeline significantly so that it outperforms the state-of-the-art. | ['Björn Ommer', 'Timo Milbich', 'Pingchuan Ma', 'Dmytro Kotovenko'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 2.47762203e-01 2.68929335e-03 -2.01560497e-01 -6.55683219e-01
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d64b94bc-ebcc-4a6d-ac52-73b550b14ddc | toward-realistic-single-view-3d-object | 2109.02288 | null | https://arxiv.org/abs/2109.02288v2 | https://arxiv.org/pdf/2109.02288v2.pdf | Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images | Recovering the 3D structure of an object from a single image is a challenging task due to its ill-posed nature. One approach is to utilize the plentiful photos of the same object category to learn a strong 3D shape prior for the object. This approach has successfully been demonstrated by a recent work of Wu et al. (2020), which obtained impressive 3D reconstruction networks with unsupervised learning. However, their algorithm is only applicable to symmetric objects. In this paper, we eliminate the symmetry requirement with a novel unsupervised algorithm that can learn a 3D reconstruction network from a multi-image dataset. Our algorithm is more general and covers the symmetry-required scenario as a special case. Besides, we employ a novel albedo loss that improves the reconstructed details and realisticity. Our method surpasses the previous work in both quality and robustness, as shown in experiments on datasets of various structures, including single-view, multi-view, image-collection, and video sets. | ['Minh Hoai', 'Quynh Phung', 'Anh Tuan Tran', 'Long-Nhat Ho'] | 2021-09-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Ho_Toward_Realistic_Single-View_3D_Object_Reconstruction_With_Unsupervised_Learning_From_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Ho_Toward_Realistic_Single-View_3D_Object_Reconstruction_With_Unsupervised_Learning_From_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.70402879e-01 -7.38776848e-02 1.37552455e-01 -3.06558639e-01
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ea065fa7-f984-4cb8-970a-5d21a1c14d46 | towards-fast-and-accurate-neural-chinese-word | null | null | https://aclanthology.org/2020.coling-main.186 | https://aclanthology.org/2020.coling-main.186.pdf | Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning | The ambiguous annotation criteria lead to divergence of Chinese Word Segmentation (CWS) datasets in various granularities. Multi-criteria Chinese word segmentation aims to capture various annotation criteria among datasets and leverage their common underlying knowledge. In this paper, we propose a domain adaptive segmenter to exploit diverse criteria of various datasets. Our model is based on Bidirectional Encoder Representations from Transformers (BERT), which is responsible for introducing open-domain knowledge. Private and shared projection layers are proposed to capture domain-specific knowledge and common knowledge, respectively. We also optimize computational efficiency via distillation, quantization, and compiler optimization. Experiments show that our segmenter outperforms the previous state of the art (SOTA) models on 10 CWS datasets with superior efficiency. | ['Wei Chu', 'Taifeng Wang', 'Kunlong Chen', 'Xingyi Cheng', 'Weipeng Huang'] | 2020-12-01 | null | null | null | coling-2020-8 | ['compiler-optimization', 'chinese-word-segmentation'] | ['computer-code', 'natural-language-processing'] | [ 2.57413507e-01 -6.97161034e-02 -6.43831849e-01 -5.30908287e-01
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e5426b98-b96f-4a4c-9e33-f3ee6ca77b0e | rethinking-context-aggregation-in-natural | 2304.01171 | null | https://arxiv.org/abs/2304.01171v1 | https://arxiv.org/pdf/2304.01171v1.pdf | Rethinking Context Aggregation in Natural Image Matting | For natural image matting, context information plays a crucial role in estimating alpha mattes especially when it is challenging to distinguish foreground from its background. Exiting deep learning-based methods exploit specifically designed context aggregation modules to refine encoder features. However, the effectiveness of these modules has not been thoroughly explored. In this paper, we conduct extensive experiments to reveal that the context aggregation modules are actually not as effective as expected. We also demonstrate that when learned on large image patches, basic encoder-decoder networks with a larger receptive field can effectively aggregate context to achieve better performance.Upon the above findings, we propose a simple yet effective matting network, named AEMatter, which enlarges the receptive field by incorporating an appearance-enhanced axis-wise learning block into the encoder and adopting a hybrid-transformer decoder. Experimental results on four datasets demonstrate that our AEMatter significantly outperforms state-of-the-art matting methods (e.g., on the Adobe Composition-1K dataset, \textbf{25\%} and \textbf{40\%} reduction in terms of SAD and MSE, respectively, compared against MatteFormer). The code and model are available at \url{https://github.com/QLYoo/AEMatter}. | ['Liqiang Nie', 'Bineng Zhong', 'Ru Li', 'Quanling Meng', 'Shengping Zhang', 'Qinglin Liu'] | 2023-04-03 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.58051062e-01 -5.53896688e-02 9.10585076e-02 -4.24158126e-01
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6156ec6e-8608-4dae-890b-1eef00702c23 | distributed-energy-management-and-demand | 2211.15858 | null | https://arxiv.org/abs/2211.15858v1 | https://arxiv.org/pdf/2211.15858v1.pdf | Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework | This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators. | ['Morteza Haashemi', 'Reza Ahmadi', 'Alexandru G. Bardas', 'Yousif Dafalla', 'Kailani Jones', 'Arman Ghasemi', 'Amin Shojaeighadikolaei'] | 2022-11-29 | null | null | null | null | ['energy-management'] | ['time-series'] | [-9.13556814e-01 -2.20179021e-01 -1.75211787e-01 1.15315698e-01
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bb16d651-c1b3-4e32-ae99-3cda5c0cc4f6 | semantic-communication-enabling-robust-edge | 2211.13787 | null | https://arxiv.org/abs/2211.13787v2 | https://arxiv.org/pdf/2211.13787v2.pdf | Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications | This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We systematically analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading by transmitting the most meaningful task data first. This scheme can well utilize all available communication resource and strike a balance between transmission latency and inference accuracy. Then, we design an effectiveness decoding by implementing a novel image augmentation process for convolutional neural network (CNN) training, through which an original CNN model is transformed into a Robust CNN model. We use the proposed training method to generate Robust MobileNet-v2 and Robust ResNet-50. The proposed Edge Intelligence framework consists of the proposed effectiveness encoding and effectiveness decoding. The experimental results show that the effectiveness decoding using the Robust CNN models perform consistently better under various image distortions caused by channel errors or limited communication resource. The proposed Edge Intelligence framework using semantic communication significantly outperforms the conventional approach under latency and data rate constraints, in particular, under ultra stringent deadlines and low data rate. | ['Andrea Cavagna', 'Qi Zhang', 'Alexandros Iosifidis', 'Nan Li'] | 2022-11-24 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 5.05055845e-01 -3.69712450e-02 -2.97331542e-01 -2.91855782e-01
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04f859f7-9235-4d18-b052-7799e79efdfd | unsupervised-video-object-segmentation-with-1 | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Siyang_Li_Unsupervised_Video_Object_ECCV_2018_paper.pdf | Unsupervised Video Object Segmentation with Motion-based Bilateral Networks | In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false positive regions by accurately identifying background objects. Then, we integrate the background estimate from the bilateral network with instance embeddings into a graph, which allows multiple frame reasoning with graph edges linking pixels from different frames. We classify graph nodes by defining and minimizing a cost function, and segment the video frames based on the node labels. The proposed method outperforms previous state-of-the-art unsupervised video object segmentation methods against the DAVIS 2016 and the FBMS-59 datasets. | ['C. -C. Jay Kuo', 'Xuejing Lei', 'Siyang Li', 'Bryan Seybold', 'Alexey Vorobyov'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-salient-object-detection', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.10130945e-01 5.27849123e-02 -4.28112239e-01 -2.22157553e-01
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8cbfea05-34b5-476a-90a8-e7947bf16b7f | curi-a-benchmark-for-productive-concept-1 | 2010.02855 | null | https://arxiv.org/abs/2010.02855v1 | https://arxiv.org/pdf/2010.02855v1.pdf | CURI: A Benchmark for Productive Concept Learning Under Uncertainty | Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same color") and ad-hoc categories defined through goals ("objects that could fall on one's head"). In contrast, standard classification benchmarks: 1) consider only a fixed set of category labels, 2) do not evaluate compositional concept learning and 3) do not explicitly capture a notion of reasoning under uncertainty. We introduce a new few-shot, meta-learning benchmark, Compositional Reasoning Under Uncertainty (CURI) to bridge this gap. CURI evaluates different aspects of productive and systematic generalization, including abstract understandings of disentangling, productive generalization, learning boolean operations, variable binding, etc. Importantly, it also defines a model-independent "compositionality gap" to evaluate the difficulty of generalizing out-of-distribution along each of these axes. Extensive evaluations across a range of modeling choices spanning different modalities (image, schemas, and sounds), splits, privileged auxiliary concept information, and choices of negatives reveal substantial scope for modeling advances on the proposed task. All code and datasets will be available online. | ['Brenden Lake', 'Ari Morcos', 'Maximilian Nickel', 'Arthur Szlam', 'Ramakrishna Vedantam'] | 2020-10-06 | curi-a-benchmark-for-productive-concept | https://openreview.net/forum?id=LuyryrCs6Ez | https://openreview.net/pdf?id=LuyryrCs6Ez | null | ['systematic-generalization'] | ['reasoning'] | [ 3.16263735e-01 2.67664224e-01 -1.79006323e-01 -6.50750339e-01
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1ae94b86-3d03-4024-a7c5-981e4fce0d7b | invariance-aware-randomized-smoothing | 2211.14207 | null | https://arxiv.org/abs/2211.14207v2 | https://arxiv.org/pdf/2211.14207v2.pdf | Invariance-Aware Randomized Smoothing Certificates | Building models that comply with the invariances inherent to different domains, such as invariance under translation or rotation, is a key aspect of applying machine learning to real world problems like molecular property prediction, medical imaging, protein folding or LiDAR classification. For the first time, we study how the invariances of a model can be leveraged to provably guarantee the robustness of its predictions. We propose a gray-box approach, enhancing the powerful black-box randomized smoothing technique with white-box knowledge about invariances. First, we develop gray-box certificates based on group orbits, which can be applied to arbitrary models with invariance under permutation and Euclidean isometries. Then, we derive provably tight gray-box certificates. We experimentally demonstrate that the provably tight certificates can offer much stronger guarantees, but that in practical scenarios the orbit-based method is a good approximation. | ['Stephan Günnemann', 'Jan Schuchardt'] | 2022-11-25 | null | null | null | null | ['molecular-property-prediction', 'protein-folding'] | ['miscellaneous', 'natural-language-processing'] | [ 4.74139959e-01 1.36644304e-01 -4.57896829e-01 -2.50599265e-01
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243fedc0-36b4-46a5-b764-f09da8b77e8c | supervised-dimensionality-reduction-via | 1601.00236 | null | http://arxiv.org/abs/1601.00236v1 | http://arxiv.org/pdf/1601.00236v1.pdf | Supervised Dimensionality Reduction via Distance Correlation Maximization | In our work, we propose a novel formulation for supervised dimensionality
reduction based on a nonlinear dependency criterion called Statistical Distance
Correlation, Szekely et. al. (2007). We propose an objective which is free of
distributional assumptions on regression variables and regression model
assumptions. Our proposed formulation is based on learning a low-dimensional
feature representation $\mathbf{z}$, which maximizes the squared sum of
Distance Correlations between low dimensional features $\mathbf{z}$ and
response $y$, and also between features $\mathbf{z}$ and covariates
$\mathbf{x}$. We propose a novel algorithm to optimize our proposed objective
using the Generalized Minimization Maximizaiton method of \Parizi et. al.
(2015). We show superior empirical results on multiple datasets proving the
effectiveness of our proposed approach over several relevant state-of-the-art
supervised dimensionality reduction methods. | ['Ahmed Elgammal', 'Chetan Tonde', 'Praneeth Vepakomma'] | 2016-01-03 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 7.81096965e-02 1.15276143e-01 -1.30868167e-01 -7.14191258e-01
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cd3618bf-86bd-4bd9-9431-1b857c72e3c1 | decomposition-based-domain-adaptation-for | 1412.5758 | null | http://arxiv.org/abs/1412.5758v4 | http://arxiv.org/pdf/1412.5758v4.pdf | Decomposition-Based Domain Adaptation for Real-World Font Recognition | We present a domain adaption framework to address a domain mismatch between
synthetic training and real-world testing data. We demonstrate our method on a
challenging fine-grain classification problem: recognizing a font style from an
image of text. In this task, it is very easy to generate lots of rendered font
examples but very hard to obtain real-world labeled images. This
real-to-synthetic domain gap caused poor generalization to new real data in
previous font recognition methods (Chen et al. (2014)). In this paper, we
introduce a Convolutional Neural Network decomposition approach, leveraging a
large training corpus of synthetic data to obtain effective features for
classification. This is done using an adaptation technique based on a Stacked
Convolutional Auto-Encoder that exploits a large collection of unlabeled
real-world text images combined with synthetic data preprocessed in a specific
way. The proposed DeepFont method achieves an accuracy of higher than 80%
(top-5) on a new large labeled real-world dataset we collected. | ['Aseem Agarwala', 'Zhangyang Wang', 'Jianchao Yang', 'Thomas S. Huang', 'Hailin Jin', 'Eli Shechtman', 'Jonathan Brandt'] | 2014-12-18 | null | null | null | null | ['font-recognition'] | ['computer-vision'] | [ 7.43026674e-01 -2.97215968e-01 2.57912785e-01 -5.50551116e-01
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c3a71189-5cf4-426b-a045-dc2d7474b1a2 | structure-aggregation-for-cross-spectral | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sheng_Structure_Aggregation_for_Cross-Spectral_Stereo_Image_Guided_Denoising_CVPR_2023_paper.pdf | Structure Aggregation for Cross-Spectral Stereo Image Guided Denoising | To obtain clean images with salient structures from noisy observations, a growing trend in current denoising studies is to seek the help of additional guidance images with high signal-to-noise ratios, which are often acquired in different spectral bands such as near infrared. Although previous guided denoising methods basically require the input images to be well-aligned, a more common way to capture the paired noisy target and guidance images is to exploit a stereo camera system. However, current studies on cross-spectral stereo matching cannot fully guarantee the pixel-level registration accuracy, and rarely consider the case of noise contamination. In this work, for the first time, we propose a guided denoising framework for cross-spectral stereo images. Instead of aligning the input images via conventional stereo matching, we aggregate structures from the guidance image to estimate a clean structure map for the noisy target image, which is then used to regress the final denoising result with a spatially variant linear representation model. Based on this, we design a neural network, called as SANet, to complete the entire guided denoising process. Experimental results show that, our SANet can effectively transfer structures from an unaligned guidance image to the restoration result, and outperforms state-of-the-art denoisers on various stereo image datasets. Besides, our structure aggregation strategy also shows its potential to handle other unaligned guided restoration tasks such as super-resolution and deblurring. The source code is available at https://github.com/lustrouselixir/SANet. | ['Huaqi Zhang', 'Hui-Liang Shen', 'Yuqi Liu', 'Si-Yuan Cao', 'Xiongwei Liu', 'Zhu Yu', 'Zehua Sheng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring', 'stereo-matching-1'] | ['computer-vision', 'computer-vision'] | [ 6.87685966e-01 -4.57609057e-01 4.08710152e-01 -3.91155750e-01
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2cccb6e5-6567-452e-907b-198d0e7ffe42 | toward-building-general-foundation-models-for | 2301.05065 | null | https://arxiv.org/abs/2301.05065v1 | https://arxiv.org/pdf/2301.05065v1.pdf | Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks | Foundation models or pre-trained models have substantially improved the performance of various language, vision, and vision-language understanding tasks. However, existing foundation models can only perform the best in one type of tasks, namely language, vision, or vision-language. It is still an open question whether it is possible to construct a foundation model performing the best for all the understanding tasks, which we call a general foundation model. In this paper, we propose a new general foundation model, X-FM (the X-Foundation Model). X-FM has one language encoder, one vision encoder, and one fusion encoder, as well as a new training method. The training method includes two new techniques for learning X-FM from text, image, and image-text pair data. One is to stop gradients from the vision-language training when learning the language encoder. The other is to leverage the vision-language training to guide the learning of the vision encoder. Extensive experiments on benchmark datasets show that X-FM can significantly outperform existing general foundation models and perform better than or comparable to existing foundation models specifically for language, vision, or vision-language understanding. | ['Hang Li', 'Jipeng Zhang', 'Yan Zeng', 'Xinsong Zhang'] | 2023-01-12 | null | null | null | null | ['visual-grounding', 'visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 1.49705887e-01 -1.32596478e-01 -1.47760838e-01 -4.30937052e-01
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5a71e896-8780-4f78-bfe2-fe345cdd3647 | quality-assurance-of-generative-dialog-models | 2203.15414 | null | https://arxiv.org/abs/2203.15414v1 | https://arxiv.org/pdf/2203.15414v1.pdf | Quality Assurance of Generative Dialog Models in an Evolving Conversational Agent Used for Swedish Language Practice | Due to the migration megatrend, efficient and effective second-language acquisition is vital. One proposed solution involves AI-enabled conversational agents for person-centered interactive language practice. We present results from ongoing action research targeting quality assurance of proprietary generative dialog models trained for virtual job interviews. The action team elicited a set of 38 requirements for which we designed corresponding automated test cases for 15 of particular interest to the evolving solution. Our results show that six of the test case designs can detect meaningful differences between candidate models. While quality assurance of natural language processing applications is complex, we provide initial steps toward an automated framework for machine learning model selection in the context of an evolving conversational agent. Future work will focus on model selection in an MLOps setting. | ['Piotr Tomaszewski', 'Isabella Gagner', 'Alexander Hagelborn', 'Harald Österling', 'Johan Bengtsson', 'Markus Borg'] | 2022-03-29 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 3.24879616e-01 9.89046335e-01 -3.32607590e-02 -6.27911687e-01
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c1a61075-8e16-40bd-834c-c0ec2e633701 | degree-decomposition-based-explanation-for-1 | 2305.12895 | null | https://arxiv.org/abs/2305.12895v1 | https://arxiv.org/pdf/2305.12895v1.pdf | DEGREE: Decomposition Based Explanation For Graph Neural Networks | Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximation based and perturbation based approaches with suffer from faithfulness problems and unnatural artifacts, respectively. To tackle these problems, we propose DEGREE \degree to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. Based on this, we further design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods. The efficiency of our algorithm can be further improved by utilizing GNN characteristics. Finally, we conduct quantitative and qualitative experiments on synthetic and real-world datasets to demonstrate the effectiveness of DEGREE on node classification and graph classification tasks. | ['Xia Hu', 'Mengnan Du', 'Ruixiang Tang', 'Fan Yang', 'Ninghao Liu', 'Qizhang Feng'] | 2023-05-22 | degree-decomposition-based-explanation-for | https://openreview.net/forum?id=Ve0Wth3ptT_ | https://openreview.net/pdf?id=Ve0Wth3ptT_ | iclr-2022-4 | ['graph-classification'] | ['graphs'] | [ 2.20771223e-01 6.48249865e-01 -4.55248445e-01 -2.37391055e-01
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8f9091e3-be04-4665-a855-d99d8f72c74b | markov-random-field-model-based-salt-and | 1609.06341 | null | http://arxiv.org/abs/1609.06341v1 | http://arxiv.org/pdf/1609.06341v1.pdf | Markov Random Field Model-Based Salt and Pepper Noise Removal | Problem of impulse noise reduction is a very well studied problem in image
processing community and many different approaches have been proposed to tackle
this problem. In the current work, the problem of fixed value impulse noise
(salt and pepper) removal from images is investigated by use of a Markov Random
Field (MRF) models with smoothness priors. After the formulation of the problem
as an inpainting problem, graph cuts with $\alpha$-expansion moves are
considered for minimization of the energy functional. As for comparisons,
several other minimization techniques that are widely used for MRF models'
optimization are considered and the results are compared using
Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) as
metrics. The investigations show the superiority of graph cuts with
$\alpha$-expansion moves over the other techniques both in terms of PSNR and
also computational times. | ['Ahmadreza Baghaie'] | 2016-09-20 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 5.92660189e-01 -2.93905348e-01 1.67830616e-01 -2.39966765e-01
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f951ac93-6767-4e70-b2bf-7a539dc50db3 | privacy-preserving-remote-heart-rate | 2306.01141 | null | https://arxiv.org/abs/2306.01141v1 | https://arxiv.org/pdf/2306.01141v1.pdf | Privacy-Preserving Remote Heart Rate Estimation from Facial Videos | Remote Photoplethysmography (rPPG) is the process of estimating PPG from facial videos. While this approach benefits from contactless interaction, it is reliant on videos of faces, which often constitutes an important privacy concern. Recent research has revealed that deep learning techniques are vulnerable to attacks, which can result in significant data breaches making deep rPPG estimation even more sensitive. To address this issue, we propose a data perturbation method that involves extraction of certain areas of the face with less identity-related information, followed by pixel shuffling and blurring. Our experiments on two rPPG datasets (PURE and UBFC) show that our approach reduces the accuracy of facial recognition algorithms by over 60%, with minimal impact on rPPG extraction. We also test our method on three facial recognition datasets (LFW, CALFW, and AgeDB), where our approach reduced performance by nearly 50%. Our findings demonstrate the potential of our approach as an effective privacy-preserving solution for rPPG estimation. | ['Ali Etemad', 'Divij Gupta'] | 2023-06-01 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.50915819e-01 3.21141303e-01 6.61295429e-02 -3.79782438e-01
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1ff9c6f8-d729-4c0a-942a-cca57eeb7dde | socs-semantically-aware-object-coordinate | 2303.10346 | null | https://arxiv.org/abs/2303.10346v1 | https://arxiv.org/pdf/2303.10346v1.pdf | SOCS: Semantically-aware Object Coordinate Space for Category-Level 6D Object Pose Estimation under Large Shape Variations | Most learning-based approaches to category-level 6D pose estimation are design around normalized object coordinate space (NOCS). While being successful, NOCS-based methods become inaccurate and less robust when handling objects of a category containing significant intra-category shape variations. This is because the object coordinates induced by global and rigid alignment of objects are semantically incoherent, making the coordinate regression hard to learn and generalize. We propose Semantically-aware Object Coordinate Space (SOCS) built by warping-and-aligning the objects guided by a sparse set of keypoints with semantically meaningful correspondence. SOCS is semantically coherent: Any point on the surface of a object can be mapped to a semantically meaningful location in SOCS, allowing for accurate pose and size estimation under large shape variations. To learn effective coordinate regression to SOCS, we propose a novel multi-scale coordinate-based attention network. Evaluations demonstrate that our method is easy to train, well-generalizing for large intra-category shape variations and robust to inter-object occlusions. | ['Kai Xu', 'Yifei Shi', 'Boyan Wan'] | 2023-03-18 | null | null | null | null | ['6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.21475992e-02 -1.58535987e-01 -1.69764996e-01 -6.20729506e-01
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9183720b-acd5-461d-825b-1f70815bb7cc | resource-efficient-neural-networks-using | 2306.07030 | null | https://arxiv.org/abs/2306.07030v1 | https://arxiv.org/pdf/2306.07030v1.pdf | Resource Efficient Neural Networks Using Hessian Based Pruning | Neural network pruning is a practical way for reducing the size of trained models and the number of floating-point operations. One way of pruning is to use the relative Hessian trace to calculate sensitivity of each channel, as compared to the more common magnitude pruning approach. However, the stochastic approach used to estimate the Hessian trace needs to iterate over many times before it can converge. This can be time-consuming when used for larger models with many millions of parameters. To address this problem, we modify the existing approach by estimating the Hessian trace using FP16 precision instead of FP32. We test the modified approach (EHAP) on ResNet-32/ResNet-56/WideResNet-28-8 trained on CIFAR10/CIFAR100 image classification tasks and achieve faster computation of the Hessian trace. Specifically, our modified approach can achieve speed ups ranging from 17% to as much as 44% during our experiments on different combinations of model architectures and GPU devices. Our modified approach also takes up around 40% less GPU memory when pruning ResNet-32 and ResNet-56 models, which allows for a larger Hessian batch size to be used for estimating the Hessian trace. Meanwhile, we also present the results of pruning using both FP16 and FP32 Hessian trace calculation and show that there are no noticeable accuracy differences between the two. Overall, it is a simple and effective way to compute the relative Hessian trace faster without sacrificing on pruned model performance. We also present a full pipeline using EHAP and quantization aware training (QAT), using INT8 QAT to compress the network further after pruning. In particular, we use symmetric quantization for the weights and asymmetric quantization for the activations. | ['Lihui Chen', 'Manas Gupta', 'Jack Chong'] | 2023-06-12 | null | null | null | null | ['network-pruning', 'quantization'] | ['methodology', 'methodology'] | [-6.63205981e-02 -1.83129773e-01 3.98759842e-01 -4.44890350e-01
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7c1889cc-9f34-45cc-91b5-51f1721b2363 | neural-message-passing-for-visual | 2208.04165 | null | https://arxiv.org/abs/2208.04165v1 | https://arxiv.org/pdf/2208.04165v1.pdf | Neural Message Passing for Visual Relationship Detection | Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions. Since the interactions associated with the same object are dependent, we explore the dependency of interactions to reduce the search space. We explicitly model objects and interactions by an interaction graph and then propose a message-passing-style algorithm to propagate the contextual information. We thus call the proposed method neural message passing (NMP). We further integrate language priors and spatial cues to rule out unrealistic interactions and capture spatial interactions. Experimental results on two benchmark datasets demonstrate the superiority of our proposed method. Our code is available at https://github.com/PhyllisH/NMP. | ['Xiao Gu', 'Ya zhang', 'Xu Chen', 'Siheng Chen', 'Yue Hu'] | 2022-08-08 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.21698305e-01 -1.51907220e-01 -2.71267481e-02 -4.24965709e-01
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a5d96153-c1a1-4d27-811d-ae614ef92180 | distilling-focal-knowledge-from-imperfect | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zeng_Distilling_Focal_Knowledge_From_Imperfect_Expert_for_3D_Object_Detection_CVPR_2023_paper.pdf | Distilling Focal Knowledge From Imperfect Expert for 3D Object Detection | Multi-camera 3D object detection blossoms in recent years and most of state-of-the-art methods are built up on the bird's-eye-view (BEV) representations. Albeit remarkable performance, these works suffer from low efficiency. Typically, knowledge distillation can be used for model compression. However, due to unclear 3D geometry reasoning, expert features usually contain some noisy and confusing areas. In this work, we investigate on how to distill the knowledge from an imperfect expert. We propose FD3D, a Focal Distiller for 3D object detection. Specifically, a set of queries are leveraged to locate the instance-level areas for masked feature generation, to intensify feature representation ability in these areas. Moreover, these queries search out the representative fine-grained positions for refined distillation. We verify the effectiveness of our method by applying it to two popular detection models, BEVFormer and DETR3D. The results demonstrate that our method achieves improvements of 4.07 and 3.17 points respectively in terms of NDS metric on nuScenes benchmark. Code is hosted at https://github.com/OpenPerceptionX/BEVPerception-Survey-Recipe. | ['Hongyang Li', 'Yu Qiao', 'Junchi Yan', 'Lewei Lu', 'Hanming Deng', 'Li Chen', 'Jia Zeng'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['model-compression'] | ['methodology'] | [-1.29819110e-01 -3.45178246e-01 -6.01358153e-02 -2.49145925e-01
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b7e9f59d-0980-4843-9b4a-d83d3fccfc11 | wildqa-in-the-wild-video-question-answering | 2209.06650 | null | https://arxiv.org/abs/2209.06650v1 | https://arxiv.org/pdf/2209.06650v1.pdf | WildQA: In-the-Wild Video Question Answering | Existing video understanding datasets mostly focus on human interactions, with little attention being paid to the "in the wild" settings, where the videos are recorded outdoors. We propose WILDQA, a video understanding dataset of videos recorded in outside settings. In addition to video question answering (Video QA), we also introduce the new task of identifying visual support for a given question and answer (Video Evidence Selection). Through evaluations using a wide range of baseline models, we show that WILDQA poses new challenges to the vision and language research communities. The dataset is available at https://lit.eecs.umich.edu/wildqa/. | ['Rada Mihalcea', 'Mihai Burzo', 'Pingxuan Huang', 'Naihao Deng', 'Santiago Castro'] | 2022-09-14 | null | null | null | null | ['video-question-answering'] | ['computer-vision'] | [ 1.84246480e-01 -1.51297256e-01 -3.19908053e-01 -4.15953487e-01
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7dd6d69d-6652-4aa8-b6c2-c136d8bd9969 | semeval-2017-task-8-rumoureval-determining | 1704.05972 | null | http://arxiv.org/abs/1704.05972v1 | http://arxiv.org/pdf/1704.05972v1.pdf | SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours | Media is full of false claims. Even Oxford Dictionaries named "post-truth" as
the word of 2016. This makes it more important than ever to build systems that
can identify the veracity of a story, and the kind of discourse there is around
it. RumourEval is a SemEval shared task that aims to identify and handle
rumours and reactions to them, in text. We present an annotation scheme, a
large dataset covering multiple topics - each having their own families of
claims and replies - and use these to pose two concrete challenges as well as
the results achieved by participants on these challenges. | ['Rob Procter', 'Leon Derczynski', 'Kalina Bontcheva', 'Maria Liakata', 'Geraldine Wong Sak Hoi', 'Arkaitz Zubiaga'] | 2017-04-20 | semeval-2017-task-8-rumoureval-determining-1 | https://aclanthology.org/S17-2006 | https://aclanthology.org/S17-2006.pdf | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-3.03322017e-01 4.26292717e-01 -2.84586519e-01 -6.93528578e-02
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3367a571-f809-4b3a-b508-2d6d212b84ab | panogen-text-conditioned-panoramic | 2305.19195 | null | https://arxiv.org/abs/2305.19195v1 | https://arxiv.org/pdf/2305.19195v1.pdf | PanoGen: Text-Conditioned Panoramic Environment Generation for Vision-and-Language Navigation | Vision-and-Language Navigation (VLN) requires the agent to follow language instructions to navigate through 3D environments. One main challenge in VLN is the limited availability of photorealistic training environments, which makes it hard to generalize to new and unseen environments. To address this problem, we propose PanoGen, a generation method that can potentially create an infinite number of diverse panoramic environments conditioned on text. Specifically, we collect room descriptions by captioning the room images in existing Matterport3D environments, and leverage a state-of-the-art text-to-image diffusion model to generate the new panoramic environments. We use recursive outpainting over the generated images to create consistent 360-degree panorama views. Our new panoramic environments share similar semantic information with the original environments by conditioning on text descriptions, which ensures the co-occurrence of objects in the panorama follows human intuition, and creates enough diversity in room appearance and layout with image outpainting. Lastly, we explore two ways of utilizing PanoGen in VLN pre-training and fine-tuning. We generate instructions for paths in our PanoGen environments with a speaker built on a pre-trained vision-and-language model for VLN pre-training, and augment the visual observation with our panoramic environments during agents' fine-tuning to avoid overfitting to seen environments. Empirically, learning with our PanoGen environments achieves the new state-of-the-art on the Room-to-Room, Room-for-Room, and CVDN datasets. Pre-training with our PanoGen speaker data is especially effective for CVDN, which has under-specified instructions and needs commonsense knowledge. Lastly, we show that the agent can benefit from training with more generated panoramic environments, suggesting promising results for scaling up the PanoGen environments. | ['Mohit Bansal', 'Jialu Li'] | 2023-05-30 | null | null | null | null | ['image-outpainting', 'navigate', 'vision-and-language-navigation'] | ['computer-vision', 'reasoning', 'robots'] | [ 2.40321219e-01 1.38715938e-01 4.86351550e-01 -3.41986597e-01
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b2a91721-e307-461b-b5bf-5e74589a724a | dental-claires-contrastive-language-image | 2306.15651 | null | https://arxiv.org/abs/2306.15651v1 | https://arxiv.org/pdf/2306.15651v1.pdf | Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research | Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework, Contrastive LAnguage Image REtrieval Search for dental research, Dental CLAIRES, utilizes periapical radiographs and associated clinical details such as periodontal diagnosis, demographic information to retrieve the best-matched images based on the text query. We applied a contrastive representation learning method to find images described by the user's text by maximizing the similarity score of positive pairs (true pairs) and minimizing the score of negative pairs (random pairs). Our model achieved a hit@3 ratio of 96% and a Mean Reciprocal Rank (MRR) of 0.82. We also designed a graphical user interface that allows researchers to verify the model's performance with interactions. | ['Shayan Shams', 'Xiaoqian Jiang', 'Luca Giancardo', 'Muhammad F Walji', 'Luyao Chen', 'Tanjida Kabir'] | 2023-06-27 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 1.45819977e-01 -5.26447110e-02 -5.98649800e-01 -5.22832096e-01
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ad495a93-e6af-47e3-b036-dd8d2015673b | how-will-the-internet-of-things-enable | 1801.00356 | null | http://arxiv.org/abs/1801.00356v1 | http://arxiv.org/pdf/1801.00356v1.pdf | How will the Internet of Things enable Augmented Personalized Health? | Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed. | ['Amit Sheth', 'Utkarshani Jaimini', 'Hong Yung Yip'] | 2017-12-31 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.28973794e-01 3.43495876e-01 -7.12342799e-01 -3.82700145e-01
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ae485b61-f8f0-4728-ac5a-b5cf53c7c4b0 | dreamfusion-text-to-3d-using-2d-diffusion | 2209.14988 | null | https://arxiv.org/abs/2209.14988v1 | https://arxiv.org/pdf/2209.14988v1.pdf | DreamFusion: Text-to-3D using 2D Diffusion | Recent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoising 3D data, neither of which currently exist. In this work, we circumvent these limitations by using a pretrained 2D text-to-image diffusion model to perform text-to-3D synthesis. We introduce a loss based on probability density distillation that enables the use of a 2D diffusion model as a prior for optimization of a parametric image generator. Using this loss in a DeepDream-like procedure, we optimize a randomly-initialized 3D model (a Neural Radiance Field, or NeRF) via gradient descent such that its 2D renderings from random angles achieve a low loss. The resulting 3D model of the given text can be viewed from any angle, relit by arbitrary illumination, or composited into any 3D environment. Our approach requires no 3D training data and no modifications to the image diffusion model, demonstrating the effectiveness of pretrained image diffusion models as priors. | ['Ben Mildenhall', 'Jonathan T. Barron', 'Ajay Jain', 'Ben Poole'] | 2022-09-29 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 4.69482929e-01 3.25047970e-01 3.74657273e-01 -3.63107294e-01
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cbd642af-3794-4768-98eb-063afcf1e70c | topomask-instance-mask-based-formulation-for | 2306.05419 | null | https://arxiv.org/abs/2306.05419v1 | https://arxiv.org/pdf/2306.05419v1.pdf | TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture | Driving scene understanding task involves detecting static elements such as lanes, traffic signs, and traffic lights, and their relationships with each other. To facilitate the development of comprehensive scene understanding solutions using multiple camera views, a new dataset called Road Genome (OpenLane-V2) has been released. This dataset allows for the exploration of complex road connections and situations where lane markings may be absent. Instead of using traditional lane markings, the lanes in this dataset are represented by centerlines, which offer a more suitable representation of lanes and their connections. In this study, we have introduced a new approach called TopoMask for predicting centerlines in road topology. Unlike existing approaches in the literature that rely on keypoints or parametric methods, TopoMask utilizes an instance-mask based formulation with a transformer-based architecture and, in order to enrich the mask instances with flow information, a direction label representation is proposed. TopoMask have ranked 4th in the OpenLane-V2 Score (OLS) and ranked 2nd in the F1 score of centerline prediction in OpenLane Topology Challenge 2023. In comparison to the current state-of-the-art method, TopoNet, the proposed method has achieved similar performance in Frechet-based lane detection and outperformed TopoNet in Chamfer-based lane detection without utilizing its scene graph neural network. | ['Alptekin Temizel', 'Ozsel Kilinc', 'Halil Ibrahim Ozturk', 'M. Esat Kalfaoglu'] | 2023-06-08 | null | null | null | null | ['lane-detection', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [-2.16787145e-01 5.94620518e-02 -2.18308970e-01 -5.27758420e-01
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3241b16c-dde0-47c6-bf5e-a3639ef9f06c | lying-aversion-and-vague-communication-an | 2301.00372 | null | https://arxiv.org/abs/2301.00372v1 | https://arxiv.org/pdf/2301.00372v1.pdf | Lying Aversion and Vague Communication: An Experimental Study | An agent may strategically employ a vague message to mislead an audience's belief about the state of the world, but this may cause the agent to feel guilt or negatively impact how the audience perceives the agent. Using a novel experimental design that allows participants to be vague while at the same time isolating the internal cost of lying from the social identity cost of appearing dishonest, we explore the extent to which these two types of lying costs affect communication. We find that participants exploit vagueness to be consistent with the truth, while at the same time leveraging the imprecision to their own benefit. More participants use vague messages in treatments where concern with social identity is relevant. In addition, we find that social identity concerns substantially affect the length and patterns of vague messages used across the treatments. | ['Stella Papadokonstantaki', 'Keh-Kuan Sun'] | 2023-01-01 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-7.36010373e-02 8.07833314e-01 -1.72942102e-01 -4.68924105e-01
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9ec33698-79ac-457f-bc6d-cbf07d96588b | adversarial-learning-for-image-forensics-deep | 1809.02791 | null | http://arxiv.org/abs/1809.02791v1 | http://arxiv.org/pdf/1809.02791v1.pdf | Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution | Constrained image splicing detection and localization (CISDL) is a newly
proposed challenging task for image forensics, which investigates two input
suspected images and identifies whether one image has suspected regions pasted
from the other. In this paper, we propose a novel adversarial learning
framework to train the deep matching network for CISDL. Our framework mainly
consists of three building blocks: 1) the deep matching network based on atrous
convolution (DMAC) aims to generate two high-quality candidate masks which
indicate the suspected regions of the two input images, 2) the detection
network is designed to rectify inconsistencies between the two corresponding
candidate masks, 3) the discriminative network drives the DMAC network to
produce masks that are hard to distinguish from ground-truth ones. In DMAC,
atrous convolution is adopted to extract features with rich spatial
information, the correlation layer based on the skip architecture is proposed
to capture hierarchical features, and atrous spatial pyramid pooling is
constructed to localize tampered regions at multiple scales. The detection
network and the discriminative network act as the losses with auxiliary
parameters to supervise the training of DMAC in an adversarial way. Extensive
experiments, conducted on 21 generated testing sets and two public datasets,
demonstrate the effectiveness of the proposed framework and the superior
performance of DMAC. | ['Xiaobin Zhu', 'Yun Cao', 'Yaqi Liu', 'Xianfeng Zhao'] | 2018-09-08 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 5.65702796e-01 -1.54216737e-01 2.90545315e-01 -1.42404228e-01
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06adbc8a-e2bd-41dc-98c1-8244d363fe3e | localised-generative-flows-1 | 1909.13833 | null | https://arxiv.org/abs/1909.13833v5 | https://arxiv.org/pdf/1909.13833v5.pdf | Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows | We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitrarily numerically noninvertible in order to approximate the target closely. This result has implications for all flow-based models, and especially Residual Flows (ResFlows), which explicitly control the Lipschitz constant of the bijection used. To address this, we propose Continuously Indexed Flows (CIFs), which replace the single bijection used by normalising flows with a continuously indexed family of bijections, and which can intuitively "clean up" mass that would otherwise be misplaced by a single bijection. We show theoretically that CIFs are not subject to the same topological limitations as normalising flows, and obtain better empirical performance on a variety of models and benchmarks. | ['Rob Cornish', 'Anthony L. Caterini', 'George Deligiannidis', 'Arnaud Doucet'] | 2019-09-30 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/4509-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/4509-Paper.pdf | icml-2020-1 | ['normalising-flows'] | ['methodology'] | [ 8.70529786e-02 2.90705681e-01 -4.05738264e-01 1.37217149e-01
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6fafc2d7-c3c6-4d4f-97d7-b5f8d862fe2e | audio-tagging-on-an-embedded-hardware | 2306.09106 | null | https://arxiv.org/abs/2306.09106v1 | https://arxiv.org/pdf/2306.09106v1.pdf | Audio Tagging on an Embedded Hardware Platform | Convolutional neural networks (CNNs) have exhibited state-of-the-art performance in various audio classification tasks. However, their real-time deployment remains a challenge on resource-constrained devices like embedded systems. In this paper, we analyze how the performance of large-scale pretrained audio neural networks designed for audio pattern recognition changes when deployed on a hardware such as Raspberry Pi. We empirically study the role of CPU temperature, microphone quality and audio signal volume on performance. Our experiments reveal that the continuous CPU usage results in an increased temperature that can trigger an automated slowdown mechanism in the Raspberry Pi, impacting inference latency. The quality of a microphone, specifically with affordable devices like the Google AIY Voice Kit, and audio signal volume, all affect the system performance. In the course of our investigation, we encounter substantial complications linked to library compatibility and the unique processor architecture requirements of the Raspberry Pi, making the process less straightforward compared to conventional computers (PCs). Our observations, while presenting challenges, pave the way for future researchers to develop more compact machine learning models, design heat-dissipative hardware, and select appropriate microphones when AI models are deployed for real-time applications on edge devices. All related assets and an interactive demo can be found on GitHub | ['Mark D. Plumbley', 'Arshdeep Singh', 'Gabriel Bibbo'] | 2023-06-15 | null | null | null | null | ['audio-tagging', 'audio-classification'] | ['audio', 'audio'] | [ 3.82471420e-02 -5.76650083e-01 1.13422312e-01 -8.71155336e-02
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5b914aa0-a16c-4427-a039-c376687b65b5 | neural-3d-scene-reconstruction-with-the | 2205.02836 | null | https://arxiv.org/abs/2205.02836v2 | https://arxiv.org/pdf/2205.02836v2.pdf | Neural 3D Scene Reconstruction with the Manhattan-world Assumption | This paper addresses the challenge of reconstructing 3D indoor scenes from multi-view images. Many previous works have shown impressive reconstruction results on textured objects, but they still have difficulty in handling low-textured planar regions, which are common in indoor scenes. An approach to solving this issue is to incorporate planer constraints into the depth map estimation in multi-view stereo-based methods, but the per-view plane estimation and depth optimization lack both efficiency and multi-view consistency. In this work, we show that the planar constraints can be conveniently integrated into the recent implicit neural representation-based reconstruction methods. Specifically, we use an MLP network to represent the signed distance function as the scene geometry. Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network. To resolve the inaccurate segmentation, we encode the semantics of 3D points with another MLP and design a novel loss that jointly optimizes the scene geometry and semantics in 3D space. Experiments on ScanNet and 7-Scenes datasets show that the proposed method outperforms previous methods by a large margin on 3D reconstruction quality. The code is available at https://zju3dv.github.io/manhattan_sdf. | ['Xiaowei Zhou', 'Hujun Bao', 'Guofeng Zhang', 'Qianqian Wang', 'Haotong Lin', 'Sida Peng', 'Haoyu Guo'] | 2022-05-05 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Neural_3D_Scene_Reconstruction_With_the_Manhattan-World_Assumption_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-scene-reconstruction', '2d-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.76470011e-01 -7.74067938e-02 2.76749935e-02 -6.70269132e-01
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efc7e6eb-d301-44d3-9ff2-0b8d7c6195e2 | soft-gazetteers-for-low-resource-named-entity | 2005.01866 | null | https://arxiv.org/abs/2005.01866v1 | https://arxiv.org/pdf/2005.01866v1.pdf | Soft Gazetteers for Low-Resource Named Entity Recognition | Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score. Code and data are available at https://github.com/neulab/soft-gazetteers. | ['Jaime Carbonell', 'Shuyan Zhou', 'Shruti Rijhwani', 'Graham Neubig'] | 2020-05-04 | soft-gazetteers-for-low-resource-named-entity-1 | https://aclanthology.org/2020.acl-main.722 | https://aclanthology.org/2020.acl-main.722.pdf | acl-2020-6 | ['cross-lingual-entity-linking', 'low-resource-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-5.00214696e-01 -5.35326963e-03 -4.28238928e-01 -6.95438802e-01
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6d2bba51-5091-4d3d-a2c3-d8230cc480d9 | scene-retrieval-for-contextual-visual-mapping | 2102.12728 | null | https://arxiv.org/abs/2102.12728v1 | https://arxiv.org/pdf/2102.12728v1.pdf | Scene Retrieval for Contextual Visual Mapping | Visual navigation localizes a query place image against a reference database of place images, also known as a `visual map'. Localization accuracy requirements for specific areas of the visual map, `scene classes', vary according to the context of the environment and task. State-of-the-art visual mapping is unable to reflect these requirements by explicitly targetting scene classes for inclusion in the map. Four different scene classes, including pedestrian crossings and stations, are identified in each of the Nordland and St. Lucia datasets. Instead of re-training separate scene classifiers which struggle with these overlapping scene classes we make our first contribution: defining the problem of `scene retrieval'. Scene retrieval extends image retrieval to classification of scenes defined at test time by associating a single query image to reference images of scene classes. Our second contribution is a triplet-trained convolutional neural network (CNN) to address this problem which increases scene classification accuracy by up to 7% against state-of-the-art networks pre-trained for scene recognition. The second contribution is an algorithm `DMC' that combines our scene classification with distance and memorability for visual mapping. Our analysis shows that DMC includes 64% more images of our chosen scene classes in a visual map than just using distance interval mapping. State-of-the-art visual place descriptors AMOS-Net, Hybrid-Net and NetVLAD are finally used to show that DMC improves scene class localization accuracy by a mean of 3% and localization accuracy of the remaining map images by a mean of 10% across both datasets. | ['Shoaib Ehsan', 'Klaus D. McDonald-Maier', 'Michael Milford', 'William H. B. Smith'] | 2021-02-25 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 2.39557251e-02 -3.18938553e-01 4.87722382e-02 -5.48703551e-01
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bc0d2f11-6326-45b2-8c0c-6a5418e97999 | how-useful-is-photo-realistic-rendering-for | 1603.08152 | null | http://arxiv.org/abs/1603.08152v2 | http://arxiv.org/pdf/1603.08152v2.pdf | How useful is photo-realistic rendering for visual learning? | Data seems cheap to get, and in many ways it is, but the process of creating
a high quality labeled dataset from a mass of data is time-consuming and
expensive.
With the advent of rich 3D repositories, photo-realistic rendering systems
offer the opportunity to provide nearly limitless data. Yet, their primary
value for visual learning may be the quality of the data they can provide
rather than the quantity. Rendering engines offer the promise of perfect labels
in addition to the data: what the precise camera pose is; what the precise
lighting location, temperature, and distribution is; what the geometry of the
object is.
In this work we focus on semi-automating dataset creation through use of
synthetic data and apply this method to an important task -- object viewpoint
estimation. Using state-of-the-art rendering software we generate a large
labeled dataset of cars rendered densely in viewpoint space. We investigate the
effect of rendering parameters on estimation performance and show realism is
important. We show that generalizing from synthetic data is not harder than the
domain adaptation required between two real-image datasets and that combining
synthetic images with a small amount of real data improves estimation accuracy. | ['Yaser Sheikh', 'Yair Movshovitz-Attias', 'Takeo Kanade'] | 2016-03-26 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 2.12750584e-02 -6.41848892e-02 1.86059773e-01 -7.32742071e-01
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6786db97-2b71-45f3-848c-40fd9f22adc8 | multilingual-named-entity-recognition-on | null | null | https://aclanthology.org/W18-3218 | https://aclanthology.org/W18-3218.pdf | Multilingual Named Entity Recognition on Spanish-English Code-switched Tweets using Support Vector Machines | This paper describes our system submission for the ACL 2018 shared task on named entity recognition (NER) in code-switched Twitter data. Our best result (F1 = 53.65) was obtained using a Support Vector Machine (SVM) with 14 features combined with rule-based post processing. | ['Dennis Felske', 'Daniel Claeser', 'Samantha Kent'] | 2018-07-01 | null | null | null | ws-2018-7 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-2.37673894e-01 5.01727201e-02 -2.12566882e-01 -6.07888460e-01
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c49f65bf-9021-45f1-8cc9-1ce069259626 | a-framework-for-fast-image-deconvolution-with | 1602.01410 | null | http://arxiv.org/abs/1602.01410v2 | http://arxiv.org/pdf/1602.01410v2.pdf | A Framework for Fast Image Deconvolution with Incomplete Observations | In image deconvolution problems, the diagonalization of the underlying
operators by means of the FFT usually yields very large speedups. When there
are incomplete observations (e.g., in the case of unknown boundaries), standard
deconvolution techniques normally involve non-diagonalizable operators,
resulting in rather slow methods, or, otherwise, use inexact convolution
models, resulting in the occurrence of artifacts in the enhanced images. In
this paper, we propose a new deconvolution framework for images with incomplete
observations that allows us to work with diagonalized convolution operators,
and therefore is very fast. We iteratively alternate the estimation of the
unknown pixels and of the deconvolved image, using, e.g., an FFT-based
deconvolution method. This framework is an efficient, high-quality alternative
to existing methods of dealing with the image boundaries, such as edge
tapering. It can be used with any fast deconvolution method. We give an example
in which a state-of-the-art method that assumes periodic boundary conditions is
extended, through the use of this framework, to unknown boundary conditions.
Furthermore, we propose a specific implementation of this framework, based on
the alternating direction method of multipliers (ADMM). We provide a proof of
convergence for the resulting algorithm, which can be seen as a "partial" ADMM,
in which not all variables are dualized. We report experimental comparisons
with other primal-dual methods, where the proposed one performed at the level
of the state of the art. Four different kinds of applications were tested in
the experiments: deconvolution, deconvolution with inpainting, superresolution,
and demosaicing, all with unknown boundaries. | ['Jocelyn Chanussot', 'José Bioucas-Dias', 'Miguel Simões', 'Luis B. Almeida'] | 2016-02-03 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 2.56110698e-01 5.45961149e-02 6.25688732e-01 5.85711263e-02
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7962a4aa-104c-4a32-bae9-18f98fa4cd7b | crpn-sfnet-a-high-performance-object-detector | null | null | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241817 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9241817 | CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images | Limited by the GPU memory, the current
mainstream detectors fail to directly apply to large-scale remote
sensing images for object detection. Moreover, the scale range
of objects in remote sensing images is much wider than that
of general images, which also greatly hinders the existing
methods to effectively detect geospatial objects of various scales.
For achieving high-performance object detection on large-scale
remote sensing images, this article proposes a much faster
and more accurate detecting framework, called cropping region
proposal network-based scale folding network (CRPN-SFNet).
In our framework, the CRPN includes a weak semantic RPN for
quickly locating interesting regions and a strategy of generating
cropping regions to effectively filter out meaningless regions,
which can greatly reduce the computation and storage burden.
Meanwhile, the proposed SFNet leverages the scale folding-based
training and testing methods to extend the valid detection range
of existing detectors, which is beneficial for detecting remote
sensing objects of various scales, including very small and very
large geospatial objects. Extensive experiments on the public
Dataset for Object deTection in Aerial images data set indicate
that our CRPN can help our detector deal the larger image
faster with the limited GPU memory; meanwhile, the SFNet is
beneficial to achieve more accurate detection of geospatial objects
with wide-scale range. For large-scale remote sensing images, the
proposed detection framework outperforms the state-of-the-art
object detection methods in terms of accuracy and speed | ['IEEE', 'Member', 'and Zhiyong Yuan', 'Gang Fu', 'Jianhui Zhao', 'QiFeng Lin'] | 2020-10-28 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.72281051e-01 -5.83848357e-01 -7.94236660e-02 9.58793685e-02
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a9ad8abd-d0d2-424d-be89-d19dfb665add | tpsnet-thin-plate-spline-representation-for | 2110.12826 | null | https://arxiv.org/abs/2110.12826v2 | https://arxiv.org/pdf/2110.12826v2.pdf | TPSNet: Reverse Thinking of Thin Plate Splines for Arbitrary Shape Scene Text Representation | The research focus of scene text detection and recognition has shifted to arbitrary shape text in recent years, where the text shape representation is a fundamental problem. An ideal representation should be compact, complete, efficient, and reusable for subsequent recognition in our opinion. However, previous representations have flaws in one or more aspects. Thin-Plate-Spline (TPS) transformation has achieved great success in scene text recognition. Inspired by this, we reversely think of its usage and sophisticatedly take TPS as an exquisite representation for arbitrary shape text representation. The TPS representation is compact, complete, and efficient. With the predicted TPS parameters, the detected text region can be directly rectified to a near-horizontal one to assist the subsequent recognition. To further exploit the potential of the TPS representation, the Border Alignment Loss is proposed. Based on these designs, we implement the text detector TPSNet, which can be extended to a text spotter conveniently. Extensive evaluation and ablation of several public benchmarks demonstrate the effectiveness and superiority of the proposed method for text representation and spotting. Particularly, TPSNet achieves the detection F-Measure improvement of 4.4\% (78.4\% vs. 74.0\%) on Art dataset and the end-to-end spotting F-Measure improvement of 5.0\% (78.5\% vs. 73.5\%) on Total-Text, which are large margins with no bells and whistles. | ['Weiping Wang', 'Ning Jiang', 'Guoqing Zhao', 'Dayan Wu', 'Jiahao Lv', 'Yu Zhou', 'Wei Wang'] | 2021-10-25 | null | null | null | null | ['text-spotting', 'scene-text-recognition', 'scene-text-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.97197956e-01 -2.53011048e-01 9.59417969e-02 -1.76080436e-01
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6bcb9e20-f731-4023-85b4-ceeba1a8fd44 | cd-tta-compound-domain-test-time-adaptation | 2212.08356 | null | https://arxiv.org/abs/2212.08356v3 | https://arxiv.org/pdf/2212.08356v3.pdf | Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management | Prior to the deployment of robotic systems, pre-training the deep-recognition models on all potential visual cases is infeasible in practice. Hence, test-time adaptation (TTA) allows the model to adapt itself to novel environments and improve its performance during test time (i.e., lifelong adaptation). Several works for TTA have shown promising adaptation performances in continuously changing environments. However, our investigation reveals that existing methods are vulnerable to dynamic distributional changes and often lead to overfitting of TTA models. To address this problem, this paper first presents a robust TTA framework with compound domain knowledge management. Our framework helps the TTA model to harvest the knowledge of multiple representative domains (i.e., compound domain) and conduct the TTA based on the compound domain knowledge. In addition, to prevent overfitting of the TTA model, we devise novel regularization which modulates the adaptation rates using domain-similarity between the source and the current target domain. With the synergy of the proposed framework and regularization, we achieve consistent performance improvements in diverse TTA scenarios, especially on dynamic domain shifts. We demonstrate the generality of proposals via extensive experiments including image classification on ImageNet-C and semantic segmentation on GTA5, C-driving, and corrupted Cityscapes datasets. | ['Chaoning Zhang', 'In So Kweon', 'Sanghyun Woo', 'Inkyu Shin', 'KwanYong Park', 'Junha Song'] | 2022-12-16 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 7.72956908e-02 -2.92817682e-01 -2.14820001e-02 -5.80653369e-01
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37eb65e4-4c8f-486d-8159-91b2d01c1471 | revisiting-non-english-text-simplification-a | 2305.15678 | null | https://arxiv.org/abs/2305.15678v1 | https://arxiv.org/pdf/2305.15678v1.pdf | Revisiting non-English Text Simplification: A Unified Multilingual Benchmark | Recent advancements in high-quality, large-scale English resources have pushed the frontier of English Automatic Text Simplification (ATS) research. However, less work has been done on multilingual text simplification due to the lack of a diverse evaluation benchmark that covers complex-simple sentence pairs in many languages. This paper introduces the MultiSim benchmark, a collection of 27 resources in 12 distinct languages containing over 1.7 million complex-simple sentence pairs. This benchmark will encourage research in developing more effective multilingual text simplification models and evaluation metrics. Our experiments using MultiSim with pre-trained multilingual language models reveal exciting performance improvements from multilingual training in non-English settings. We observe strong performance from Russian in zero-shot cross-lingual transfer to low-resource languages. We further show that few-shot prompting with BLOOM-176b achieves comparable quality to reference simplifications outperforming fine-tuned models in most languages. We validate these findings through human evaluation. | ['Wei Xu', 'Tarek Naous', 'Michael J. Ryan'] | 2023-05-25 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-1.78992644e-01 -1.73807219e-01 -1.72303349e-01 -4.63329762e-01
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75856817-5a11-4b4b-af02-fc291d7259b8 | occlusion-robust-online-multi-object-visual | 1912.05949 | null | https://arxiv.org/abs/1912.05949v6 | https://arxiv.org/pdf/1912.05949v6.pdf | Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification | We propose a novel online multi-object visual tracker using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has a linear complexity with the number of objects and observations while estimating the states and cardinality of time-varying number of objects, however, it is susceptible to miss-detections and does not include the identity of objects. We use visual-spatio-temporal information obtained from object bounding boxes and deeply learned appearance representations to perform estimates-to-tracks data association for target labeling as well as formulate an augmented likelihood and then integrate into the update step of the GM-PHD filter. We also employ additional unassigned tracks prediction after the data association step to overcome the susceptibility of the GM-PHD filter towards miss-detections caused by occlusion. Extensive evaluations on MOT16, MOT17 and HiEve benchmark datasets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy and identification. | ['Nathanael L. Baisa'] | 2019-12-10 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-2.88915515e-01 -3.18673968e-01 -2.09488526e-01 -1.80645123e-01
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c9197547-dbfd-4c82-aad6-a30a0e6cb500 | collective-mind-cleaning-up-the-research-and | 1308.2410 | null | http://arxiv.org/abs/1308.2410v1 | http://arxiv.org/pdf/1308.2410v1.pdf | Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning | Software and hardware co-design and optimization of HPC systems has become
intolerably complex, ad-hoc, time consuming and error prone due to enormous
number of available design and optimization choices, complex interactions
between all software and hardware components, and multiple strict requirements
placed on performance, power consumption, size, reliability and cost. We
present our novel long-term holistic and practical solution to this problem
based on customizable, plugin-based, schema-free, heterogeneous, open-source
Collective Mind repository and infrastructure with unified web interfaces and
on-line advise system. This collaborative framework distributes analysis and
multi-objective off-line and on-line auto-tuning of computer systems among many
participants while utilizing any available smart phone, tablet, laptop, cluster
or data center, and continuously observing, classifying and modeling their
realistic behavior. Any unexpected behavior is analyzed using shared data
mining and predictive modeling plugins or exposed to the community at
cTuning.org for collaborative explanation, top-down complexity reduction,
incremental problem decomposition and detection of correlating program,
architecture or run-time properties (features). Gradually increasing
optimization knowledge helps to continuously improve optimization heuristics of
any compiler, predict optimizations for new programs or suggest efficient
run-time (online) tuning and adaptation strategies depending on end-user
requirements. We decided to share all our past research artifacts including
hundreds of codelets, numerical applications, data sets, models, universal
experimental analysis and auto-tuning pipelines, self-tuning machine learning
based meta compiler, and unified statistical analysis and machine learning
plugins in a public repository to initiate systematic, reproducible and
collaborative research, development and experimentation with a new publication
model where experiments and techniques are validated, ranked and improved by
the community. | ['Grigori Fursin'] | 2013-08-11 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [-5.38938165e-01 -6.03310645e-01 9.61501822e-02 -2.93862611e-01
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2c2f0d03-7159-48bc-ac8a-f948b2328f25 | pansharpening-via-detail-injection-based | 1806.08898 | null | http://arxiv.org/abs/1806.08898v1 | http://arxiv.org/pdf/1806.08898v1.pdf | Pansharpening via Detail Injection Based Convolutional Neural Networks | Pansharpening aims to fuse a multispectral (MS) image with an associated
panchromatic (PAN) image, producing a composite image with the spectral
resolution of the former and the spatial resolution of the latter. Traditional
pansharpening methods can be ascribed to a unified detail injection context,
which views the injected MS details as the integration of PAN details and
band-wise injection gains. In this work, we design a detail injection based CNN
(DiCNN) framework for pansharpening, with the MS details being directly
formulated in end-to-end manners, where the first detail injection based CNN
(DiCNN1) mines MS details through the PAN image and the MS image, and the
second one (DiCNN2) utilizes only the PAN image. The main advantage of the
proposed DiCNNs is that they provide explicit physical interpretations and can
achieve fast convergence while achieving high pansharpening quality.
Furthermore, the effectiveness of the proposed approaches is also analyzed from
a relatively theoretical point of view. Our methods are evaluated via
experiments on real-world MS image datasets, achieving excellent performance
when compared to other state-of-the-art methods. | [] | 2018-06-23 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 6.14946663e-01 -2.18416199e-01 -1.38550460e-01 4.67949770e-02
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eb622446-1931-4af6-b22d-a3c067d37f76 | scs-co-self-consistent-style-contrastive | 2204.13962 | null | https://arxiv.org/abs/2204.13962v1 | https://arxiv.org/pdf/2204.13962v1.pdf | SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization | Image harmonization aims to achieve visual consistency in composite images by adapting a foreground to make it compatible with a background. However, existing methods always only use the real image as the positive sample to guide the training, and at most introduce the corresponding composite image as a single negative sample for an auxiliary constraint, which leads to limited distortion knowledge, and further causes a too large solution space, making the generated harmonized image distorted. Besides, none of them jointly constrain from the foreground self-style and foreground-background style consistency, which exacerbates this problem. Moreover, recent region-aware adaptive instance normalization achieves great success but only considers the global background feature distribution, making the aligned foreground feature distribution biased. To address these issues, we propose a self-consistent style contrastive learning scheme (SCS-Co). By dynamically generating multiple negative samples, our SCS-Co can learn more distortion knowledge and well regularize the generated harmonized image in the style representation space from two aspects of the foreground self-style and foreground-background style consistency, leading to a more photorealistic visual result. In addition, we propose a background-attentional adaptive instance normalization (BAIN) to achieve an attention-weighted background feature distribution according to the foreground-background feature similarity. Experiments demonstrate the superiority of our method over other state-of-the-art methods in both quantitative comparison and visual analysis. | ['Qingmin Liao', 'Wenming Yang', 'Bin Xia', 'Yucheng Hang'] | 2022-04-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hang_SCS-Co_Self-Consistent_Style_Contrastive_Learning_for_Image_Harmonization_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-harmonization'] | ['computer-vision'] | [ 4.40658450e-01 -3.05028468e-01 -2.73598105e-01 -2.79098302e-01
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6a3c4ac5-2e16-4061-8f45-4a966f9c77fb | supervised-exponential-family-principal | null | null | http://papers.nips.cc/paper/3442-supervised-exponential-family-principal-component-analysis-via-convex-optimization | http://papers.nips.cc/paper/3442-supervised-exponential-family-principal-component-analysis-via-convex-optimization.pdf | Supervised Exponential Family Principal Component Analysis via Convex Optimization | Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and strongly suggest important underlying structures in the data. In this paper, we present a novel convex supervised dimensionality reduction approach based on exponential family PCA and provide a simple but novel form to project new testing data into the embedded space. This convex approach successfully avoids the local optima of the EM learning. Moreover, by introducing a sample-based multinomial approximation to exponential family models, it avoids the limitation of the prevailing Gaussian assumptions of standard PCA, and produces a kernelized formulation for nonlinear supervised dimensionality reduction. A training algorithm is then devised based on a subgradient bundle method, whose scalability can be gained through a coordinate descent procedure. The advantage of our global optimization approach is demonstrated by empirical results over both synthetic and real data. | ['Yuhong Guo'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 1.44639969e-01 -9.84939747e-03 1.10500101e-02 -4.11065578e-01
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95fdc57d-0ff5-4dff-aa6d-d3aed1338a32 | application-of-deep-learning-for-predictive | 2306.11040 | null | https://arxiv.org/abs/2306.11040v1 | https://arxiv.org/pdf/2306.11040v1.pdf | Application of Deep Learning for Predictive Maintenance of Oilfield Equipment | This thesis explored applications of the new emerging techniques of artificial intelligence and deep learning (neural networks in particular) for predictive maintenance, diagnostics and prognostics. Many neural architectures such as fully-connected, convolutional and recurrent neural networks were developed and tested on public datasets such as NASA C-MAPSS, Case Western Reserve University Bearings and FEMTO Bearings datasets to diagnose equipment health state and/or predict the remaining useful life (RUL) before breakdown. Many data processing and feature extraction procedures were used in combination with deep learning techniques such as dimensionality reduction (Principal Component Analysis) and signal processing (Fourier and Wavelet analyses) in order to create more meaningful and robust features to use as an input for neural networks architectures. This thesis also explored the potential use of these techniques in predictive maintenance within oil rigs for monitoring oilfield critical equipment in order to reduce unpredicted downtime and maintenance costs. | ['Abdeldjalil Latrach'] | 2023-06-19 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-4.97197993e-02 -9.27783698e-02 4.20088798e-01 -1.60490900e-01
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6dcbb003-08bc-469d-a457-3a5064a0652b | language-models-use-monotonicity-to-assess | 2105.13818 | null | https://arxiv.org/abs/2105.13818v1 | https://arxiv.org/pdf/2105.13818v1.pdf | Language Models Use Monotonicity to Assess NPI Licensing | We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item licensing as a case study. We introduce a series of experiments consisting of probing with diagnostic classifiers (DCs), linguistic acceptability tasks, as well as a novel DC ranking method that tightly connects the probing results to the inner workings of the LM. By applying our experimental pipeline to LMs trained on various filtered corpora, we are able to gain stronger insights into the semantic generalizations that are acquired by these models. | ['Shane Steinert-Threlkeld', 'Dieuwke Hupkes', 'Jakub Szymanik', 'Milica Denić', 'Jaap Jumelet'] | 2021-05-28 | null | https://aclanthology.org/2021.findings-acl.439 | https://aclanthology.org/2021.findings-acl.439.pdf | findings-acl-2021-8 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 6.53710961e-02 7.55661666e-01 -3.84454489e-01 -6.17146373e-01
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8e9eebe1-db72-491d-9de2-045dcb4eef74 | graph-sampling-based-deep-metric-learning-for | 2104.01546 | null | https://arxiv.org/abs/2104.01546v4 | https://arxiv.org/pdf/2104.01546v4.pdf | Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification | Recent studies show that, both explicit deep feature matching as well as large-scale and diverse training data can significantly improve the generalization of person re-identification. However, the efficiency of learning deep matchers on large-scale data has not yet been adequately studied. Though learning with classification parameters or class memory is a popular way, it incurs large memory and computational costs. In contrast, pairwise deep metric learning within mini batches would be a better choice. However, the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. Though online hard example mining has improved the learning efficiency to some extent, the mining in mini batches after random sampling is still limited. This inspires us to explore the use of hard example mining earlier, in the data sampling stage. To do so, in this paper, we propose an efficient mini-batch sampling method, called graph sampling (GS), for large-scale deep metric learning. The basic idea is to build a nearest neighbor relationship graph for all classes at the beginning of each epoch. Then, each mini batch is composed of a randomly selected class and its nearest neighboring classes so as to provide informative and challenging examples for learning. Together with an adapted competitive baseline, we improve the state of the art in generalizable person re-identification significantly, by 25.1% in Rank-1 on MSMT17 when trained on RandPerson. Besides, the proposed method also outperforms the competitive baseline, by 6.8% in Rank-1 on CUHK03-NP when trained on MSMT17. Meanwhile, the training time is significantly reduced, from 25.4 hours to 2 hours when trained on RandPerson with 8,000 identities. Code is available at https://github.com/ShengcaiLiao/QAConv. | ['Ling Shao', 'Shengcai Liao'] | 2021-04-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liao_Graph_Sampling_Based_Deep_Metric_Learning_for_Generalizable_Person_Re-Identification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['generalizable-person-re-identification', 'graph-sampling'] | ['computer-vision', 'graphs'] | [-0.28658387 -0.29275465 -0.18615885 -0.6455792 -0.6464612 -0.2860453
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f974ebf7-a006-428f-9056-11c0ea75cfce | deep-attentional-guided-image-filtering | 2112.06401 | null | https://arxiv.org/abs/2112.06401v2 | https://arxiv.org/pdf/2112.06401v2.pdf | Deep Attentional Guided Image Filtering | Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods construct filter kernels from the guidance itself without considering the mutual dependency between the guidance and the target. However, since there typically exist significantly different edges in the two images, simply transferring all structural information of the guidance to the target would result in various artifacts. To cope with this problem, we propose an effective framework named deep attentional guided image filtering, the filtering process of which can fully integrate the complementary information contained in both images. Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images. Meanwhile, we propose a multi-scale guided image filtering module to progressively generate the filtering result with the constructed kernels in a coarse-to-fine manner. Correspondingly, a multi-scale fusion strategy is introduced to reuse the intermediate results in the coarse-to-fine process. Extensive experiments show that the proposed framework compares favorably with the state-of-the-art methods in a wide range of guided image filtering applications, such as guided super-resolution, cross-modality restoration, texture removal, and semantic segmentation. | ['Xiangyang Ji', 'Debin Zhao', 'Junjun Jiang', 'Xianming Liu', 'Zhiwei Zhong'] | 2021-12-13 | null | null | null | null | ['depth-image-upsampling', 'depth-map-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 6.77760541e-01 -4.13079262e-01 2.78681427e-01 -3.36405516e-01
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56fd5d54-22cc-4f7e-8083-dc1934aa323b | dent-ddsp-data-efficient-noisy-speech | 2208.00987 | null | https://arxiv.org/abs/2208.00987v1 | https://arxiv.org/pdf/2208.00987v1.pdf | DENT-DDSP: Data-efficient noisy speech generator using differentiable digital signal processors for explicit distortion modelling and noise-robust speech recognition | The performances of automatic speech recognition (ASR) systems degrade drastically under noisy conditions. Explicit distortion modelling (EDM), as a feature compensation step, is able to enhance ASR systems under such conditions by simulating the in-domain noisy speeches from the clean counterparts. Yet, existing distortion models are either non-trainable or unexplainable and often lack controllability and generalization ability. In this paper, we propose a fully explainable and controllable model: DENT-DDSP to achieve EDM. DENT-DDSP utilizes novel differentiable digital signal processing (DDSP) components and requires only 10 seconds of training data to achieve high fidelity. The experiment shows that the simulated noisy data from DENT-DDSP achieves the highest simulation fidelity compared to other baseline models in terms of multi-scale spectral loss (MSSL). Moreover, to validate whether the data simulated by DENT-DDSP are able to replace the scarce in-domain noisy data in the noise-robust ASR tasks, several downstream ASR models with the same architecture are trained using the simulated data and the real data. The experiment shows that the model trained with the simulated noisy data from DENT-DDSP achieves similar performances to the benchmark with a 2.7\% difference in terms of word error rate (WER). The code of the model is released online. | ['E. S. Chng', 'C. Chen', 'Z. Guo'] | 2022-08-01 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.31711707e-01 1.65177852e-01 4.79320019e-01 -2.38105237e-01
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9670cb70-4540-47b0-b223-193efcc10007 | a-dataset-for-hyper-relational-extraction-and | 2211.10018 | null | https://arxiv.org/abs/2211.10018v1 | https://arxiv.org/pdf/2211.10018v1.pdf | A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach | Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as time, quantity or location. The qualifiers form hyper-relational facts which better capture the rich and complex knowledge graph structure. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967). Hence, we propose the task of hyper-relational extraction to extract more specific and complete facts from text. To support the task, we construct HyperRED, a large-scale and general-purpose dataset. Existing models cannot perform hyper-relational extraction as it requires a model to consider the interaction between three entities. Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. To improve model scalability and reduce negative class imbalance, we further propose a cube-pruning method. Our experiments show that CubeRE outperforms strong baselines and reveal possible directions for future research. Our code and data are available at github.com/declare-lab/HyperRED. | ['Soujanya Poria', 'Luo Si', 'Sharifah Mahani Aljunied', 'Lidong Bing', 'Yew Ken Chia'] | 2022-11-18 | null | null | null | null | ['hyper-relational-extraction'] | ['natural-language-processing'] | [-2.28599489e-01 4.53610003e-01 -8.74268830e-01 -3.40250671e-01
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eb7e3363-d55c-47f1-9103-7d82a93d8f72 | elsr-extreme-low-power-super-resolution | 2208.14600 | null | https://arxiv.org/abs/2208.14600v1 | https://arxiv.org/pdf/2208.14600v1.pdf | ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices | With the popularity of mobile devices, e.g., smartphone and wearable devices, lighter and faster model is crucial for the application of video super resolution. However, most previous lightweight models tend to concentrate on reducing lantency of model inference on desktop GPU, which may be not energy efficient in current mobile devices. In this paper, we proposed Extreme Low-Power Super Resolution (ELSR) network which only consumes a small amount of energy in mobile devices. Pretraining and finetuning methods are applied to boost the performance of the extremely tiny model. Extensive experiments show that our method achieves a excellent balance between restoration quality and power consumption. Finally, we achieve a competitive score of 90.9 with PSNR 27.34 dB and power 0.09 W/30FPS on the target MediaTek Dimensity 9000 plantform, ranking 1st place in the Mobile AI & AIM 2022 Real-Time Video Super-Resolution Challenge. | ['Heng Sun', 'Long Bao', 'Yijian Zhang', 'Zhuang Jia', 'Tianyu Xu'] | 2022-08-31 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 0.40907922 -0.29057923 -0.329253 -0.06703544 -0.5934206 -0.15315437
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bb12ca69-11b1-48a0-b7b8-ad41eb66bd70 | select-extract-and-generate-neural-keyphrase | 2008.01739 | null | https://arxiv.org/abs/2008.01739v2 | https://arxiv.org/pdf/2008.01739v2.pdf | Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention | Natural language processing techniques have demonstrated promising results in keyphrase generation. However, one of the major challenges in \emph{neural} keyphrase generation is processing long documents using deep neural networks. Generally, documents are truncated before given as inputs to neural networks. Consequently, the models may miss essential points conveyed in the target document. To overcome this limitation, we propose \emph{SEG-Net}, a neural keyphrase generation model that is composed of two major components, (1) a selector that selects the salient sentences in a document and (2) an extractor-generator that jointly extracts and generates keyphrases from the selected sentences. SEG-Net uses Transformer, a self-attentive architecture, as the basic building block with a novel \emph{layer-wise} coverage attention to summarize most of the points discussed in the document. The experimental results on seven keyphrase generation benchmarks from scientific and web documents demonstrate that SEG-Net outperforms the state-of-the-art neural generative methods by a large margin. | ['Kai-Wei Chang', 'Wasi Uddin Ahmad', 'Soomin Lee', 'Xiao Bai'] | 2020-08-04 | null | https://aclanthology.org/2021.acl-long.111 | https://aclanthology.org/2021.acl-long.111.pdf | acl-2021-5 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 3.58761638e-01 3.12334567e-01 1.21711874e-02 -4.97217886e-02
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bb83df84-096c-4f6d-b2d8-ba8cce6a5a2c | disentangled-motif-aware-graph-learning-for | 2104.06008 | null | https://arxiv.org/abs/2104.06008v1 | https://arxiv.org/pdf/2104.06008v1.pdf | Disentangled Motif-aware Graph Learning for Phrase Grounding | In this paper, we propose a novel graph learning framework for phrase grounding in the image. Developing from the sequential to the dense graph model, existing works capture coarse-grained context but fail to distinguish the diversity of context among phrases and image regions. In contrast, we pay special attention to different motifs implied in the context of the scene graph and devise the disentangled graph network to integrate the motif-aware contextual information into representations. Besides, we adopt interventional strategies at the feature and the structure levels to consolidate and generalize representations. Finally, the cross-modal attention network is utilized to fuse intra-modal features, where each phrase can be computed similarity with regions to select the best-grounded one. We validate the efficiency of disentangled and interventional graph network (DIGN) through a series of ablation studies, and our model achieves state-of-the-art performance on Flickr30K Entities and ReferIt Game benchmarks. | ['Yueting Zhuang', 'Qiang Yu', 'Jie Tan', 'Siliang Tang', 'Zongshen Mu'] | 2021-04-13 | null | null | null | null | ['phrase-grounding'] | ['natural-language-processing'] | [ 1.21708252e-01 -9.91524160e-02 -3.09016854e-01 -1.90048561e-01
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102cc6c4-21a1-4a22-9d4a-cc5f31d7d7f5 | length-controllable-image-captioning | 2007.09580 | null | https://arxiv.org/abs/2007.09580v1 | https://arxiv.org/pdf/2007.09580v1.pdf | Length-Controllable Image Captioning | The last decade has witnessed remarkable progress in the image captioning task; however, most existing methods cannot control their captions, \emph{e.g.}, choosing to describe the image either roughly or in detail. In this paper, we propose to use a simple length level embedding to endow them with this ability. Moreover, due to their autoregressive nature, the computational complexity of existing models increases linearly as the length of the generated captions grows. Thus, we further devise a non-autoregressive image captioning approach that can generate captions in a length-irrelevant complexity. We verify the merit of the proposed length level embedding on three models: two state-of-the-art (SOTA) autoregressive models with different types of decoder, as well as our proposed non-autoregressive model, to show its generalization ability. In the experiments, our length-controllable image captioning models not only achieve SOTA performance on the challenging MS COCO dataset but also generate length-controllable and diverse image captions. Specifically, our non-autoregressive model outperforms the autoregressive baselines in terms of controllability and diversity, and also significantly improves the decoding efficiency for long captions. Our code and models are released at \textcolor{magenta}{\texttt{https://github.com/bearcatt/LaBERT}}. | ['Qi Wu', 'Mingkui Tan', 'Ning Ding', 'Chaorui Deng'] | 2020-07-19 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2035_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580698.pdf | eccv-2020-8 | ['controllable-image-captioning'] | ['computer-vision'] | [ 2.75793344e-01 1.13808371e-01 -1.28907993e-01 -3.90915751e-01
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126cb41c-8e7a-4d88-af61-e8e2be09f2ba | depth-aware-image-compositing-model-for | 2303.09334 | null | https://arxiv.org/abs/2303.09334v2 | https://arxiv.org/pdf/2303.09334v2.pdf | Depth-Aware Image Compositing Model for Parallax Camera Motion Blur | Camera motion introduces spatially varying blur due to the depth changes in the 3D world. This work investigates scene configurations where such blur is produced under parallax camera motion. We present a simple, yet accurate, Image Compositing Blur (ICB) model for depth-dependent spatially varying blur. The (forward) model produces realistic motion blur from a single image, depth map, and camera trajectory. Furthermore, we utilize the ICB model, combined with a coordinate-based MLP, to learn a sharp neural representation from the blurred input. Experimental results are reported for synthetic and real examples. The results verify that the ICB forward model is computationally efficient and produces realistic blur, despite the lack of occlusion information. Additionally, our method for restoring a sharp representation proves to be a competitive approach for the deblurring task. | ['Joni-Kristian Kämäräinen', 'German F. Torres'] | 2023-03-16 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.02624187e-01 -5.59467554e-01 3.04002374e-01 -1.81393266e-01
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a884f08e-d80b-4c29-b5ea-f06e1baa7c43 | shuttleset22-benchmarking-stroke-forecasting | 2306.15664 | null | https://arxiv.org/abs/2306.15664v2 | https://arxiv.org/pdf/2306.15664v2.pdf | ShuttleSet22: Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset | In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used for researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we test the state-of-the-art stroke forecasting approach, ShuttleNet, with the corresponding stroke forecasting task, i.e., predict the future strokes based on the given strokes of each rally. We also hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge 2023 to boost researchers to tackle this problem. The baseline codes and the dataset will be made available on https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023. | ['Wen-Chih Peng', 'Wei-Wei Du', 'Wei-Yao Wang'] | 2023-06-27 | null | null | null | null | ['benchmarking', 'benchmarking'] | ['miscellaneous', 'robots'] | [-5.45933783e-01 -4.79507178e-01 -5.14043212e-01 2.31676386e-04
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dba3d4a1-dd31-4606-86eb-96b4bf6c8359 | simulated-chats-for-task-oriented-dialog | 2010.10216 | null | https://arxiv.org/abs/2010.10216v4 | https://arxiv.org/pdf/2010.10216v4.pdf | Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions | Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - the MultiWOZ dataset and the Persona chat dataset. | ['Sachindra Joshi', 'Danish Contractor', 'Gaurav Pandey', 'Biswesh Mohapatra'] | 2020-10-20 | null | https://aclanthology.org/2021.findings-emnlp.103 | https://aclanthology.org/2021.findings-emnlp.103.pdf | findings-emnlp-2021-11 | ['dialog-learning'] | ['natural-language-processing'] | [-3.11406672e-01 5.22247195e-01 5.32013535e-01 -4.24489558e-01
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ac944085-d9a5-4473-975e-53ce2538cdca | measuring-interlanguage-native-language | null | null | https://aclanthology.org/L12-1016 | https://aclanthology.org/L12-1016.pdf | Measuring Interlanguage: Native Language Identification with L1-influence Metrics | The task of native language (L1) identification suffers from a relative paucity of useful training corpora, and standard within-corpus evaluation is often problematic due to topic bias. In this paper, we introduce a method for L1 identification in second language (L2) texts that relies only on much more plentiful L1 data, rather than the L2 texts that are traditionally used for training. In particular, we do word-by-word translation of large L1 blog corpora to create a mapping to L2 forms that are a possible result of language transfer, and then use that information for unsupervised classification. We show this method is effective in several different learner corpora, with bigram features being particularly useful. | ['Graeme Hirst', 'Julian Brooke'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['native-language-identification'] | ['natural-language-processing'] | [ 3.07943702e-01 -2.51436532e-02 -6.24230683e-01 -3.44751745e-01
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fc1e0e29-e79e-464d-8306-a03263126a6a | a-mathematical-abstraction-for-balancing-the | 2306.02295 | null | https://arxiv.org/abs/2306.02295v1 | https://arxiv.org/pdf/2306.02295v1.pdf | A Mathematical Abstraction for Balancing the Trade-off Between Creativity and Reality in Large Language Models | Large Language Models have become popular for their remarkable capabilities in human-oriented tasks and traditional natural language processing tasks. Its efficient functioning is attributed to the attention mechanism in the Transformer architecture, enabling it to concentrate on particular aspects of the input. LLMs are increasingly being used in domains such as generating prose, poetry or art, which require the model to be creative (e.g. Adobe firefly). LLMs possess advanced language generation abilities that enable them to generate distinctive and captivating content. This utilization of LLMs in generating narratives shows their flexibility and potential for use in domains that extend beyond conventional natural language processing duties. In different contexts, we may expect the LLM to generate factually correct answers, that match reality; e.g., question-answering systems or online assistants. In such situations, being correct is critical to LLMs being trusted in practice. The Bing Chatbot provides its users with the flexibility to select one of the three output modes: creative, balanced, and precise. Each mode emphasizes creativity and factual accuracy differently. In this work, we provide a mathematical abstraction to describe creativity and reality based on certain losses. A model trained on these losses balances the trade-off between the creativity and reality of the model. | ['Tianyi Zhou', 'Zhao Song', 'Ritwik Sinha'] | 2023-06-04 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-7.44760782e-02 4.08317775e-01 1.55072451e-01 -1.33661404e-01
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c94a1144-d8eb-4bbd-945d-edc2394d57ac | pretraining-approaches-for-spoken-language | 2205.07083 | null | https://arxiv.org/abs/2205.07083v1 | https://arxiv.org/pdf/2205.07083v1.pdf | Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge | This paper investigates different pretraining approaches to spoken language identification. The paper is based on our submission to the Oriental Language Recognition 2021 Challenge. We participated in two tracks of the challenge: constrained and unconstrained language recognition. For the constrained track, we first trained a Conformer-based encoder-decoder model for multilingual automatic speech recognition (ASR), using the provided training data that had transcripts available. The shared encoder of the multilingual ASR model was then finetuned for the language identification task. For the unconstrained task, we relied on both externally available pretrained models as well as external data: the multilingual XLSR-53 wav2vec2.0 model was finetuned on the VoxLingua107 corpus for the language recognition task, and finally finetuned on the provided target language training data, augmented with CommonVoice data. Our primary metric $C_{\rm avg}$ values on the Test set are 0.0079 for the constrained task and 0.0119 for the unconstrained task which resulted in the second place in both rankings. In post-evaluation experiments, we study the amount of target language data needed for training an accurate backend model, the importance of multilingual pretraining data, and compare different models as finetuning starting points. | ['Kunnar Kukk', 'Tanel Alumäe'] | 2022-05-14 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 5.74894175e-02 1.88068002e-01 -5.51193394e-02 -7.19267130e-01
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40f00477-9c26-405e-8570-cd7927ce4e71 | optical-character-recognition-and | 2303.13549 | null | https://arxiv.org/abs/2303.13549v1 | https://arxiv.org/pdf/2303.13549v1.pdf | Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh | The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lacks adequate representation in education, research, web applications etc. For instance, there is no option of translation to or from Amazigh / Berber on Google Translate, which hosts over 100 languages today. Consequently, we do not find specialized educational apps, L2 (2nd language learner) acquisition, automated language translation, and remote-access facilities enabled in Berber. Motivated by this background, we propose a supervised approach called DaToBS for Detection and Transcription of Berber Signs. The DaToBS approach entails the automatic recognition and transcription of Tifinagh characters from signs in photographs of natural environments. This is achieved by self-creating a corpus of 1862 pre-processed character images; curating the corpus with human-guided annotation; and feeding it into an OCR model via the deployment of CNN for deep learning based on computer vision models. We deploy computer vision modeling (rather than language models) because there are pictorial symbols in this alphabet, this deployment being a novel aspect of our work. The DaToBS experimentation and analyses yield over 92 percent accuracy in our research. To the best of our knowledge, ours is among the first few works in the automated transcription of Berber signs from roadside images with deep learning, yielding high accuracy. This can pave the way for developing pedagogical applications in the Berber language, thereby addressing an important goal of outreach to underrepresented communities via AI in education. | ['Aparna S. Varde', 'Levi Corallo'] | 2023-03-21 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [-2.38446355e-01 -5.74567355e-02 -1.37699068e-01 -1.26866087e-01
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1cfef972-5267-4a91-abf3-f5a175b1cd9e | portfolio-optimization-using-predictive | 2304.11856 | null | https://arxiv.org/abs/2304.11856v1 | https://arxiv.org/pdf/2304.11856v1.pdf | Portfolio Optimization using Predictive Auxiliary Classifier Generative Adversarial Networks with Measuring Uncertainty | In financial engineering, portfolio optimization has been of consistent interest. Portfolio optimization is a process of modulating asset distributions to maximize expected returns and minimize risks. To obtain the expected returns, deep learning models have been explored in recent years. However, due to the deterministic nature of the models, it is difficult to consider the risk of portfolios because conventional deep learning models do not know how reliable their predictions can be. To address this limitation, this paper proposes a probabilistic model, namely predictive auxiliary classifier generative adversarial networks (PredACGAN). The proposed PredACGAN utilizes the characteristic of the ACGAN framework in which the output of the generator forms a distribution. While ACGAN has not been employed for predictive models and is generally utilized for image sample generation, this paper proposes a method to use the ACGAN structure for a probabilistic and predictive model. Additionally, an algorithm to use the risk measurement obtained by PredACGAN is proposed. In the algorithm, the assets that are predicted to be at high risk are eliminated from the investment universe at the rebalancing moment. Therefore, PredACGAN considers both return and risk to optimize portfolios. The proposed algorithm and PredACGAN have been evaluated with daily close price data of S&P 500 from 1990 to 2020. Experimental scenarios are assumed to rebalance the portfolios monthly according to predictions and risk measures with PredACGAN. As a result, a portfolio using PredACGAN exhibits 9.123% yearly returns and a Sharpe ratio of 1.054, while a portfolio without considering risk measures shows 1.024% yearly returns and a Sharpe ratio of 0.236 in the same scenario. Also, the maximum drawdown of the proposed portfolio is lower than the portfolio without PredACGAN. | ['Minhyeok Lee', 'Jiwook Kim'] | 2023-04-24 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.37800539e-01 1.67995110e-01 1.58248141e-01 -9.79818255e-02
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52fb5e6d-3857-4b07-b3b4-ad258bdf34f6 | a-fine-tuned-wav2vec-2-0-hubert-benchmark-for | 2111.02735 | null | https://arxiv.org/abs/2111.02735v3 | https://arxiv.org/pdf/2111.02735v3.pdf | A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding | Speech self-supervised models such as wav2vec 2.0 and HuBERT are making revolutionary progress in Automatic Speech Recognition (ASR). However, they have not been totally proven to produce better performance on tasks other than ASR. In this work, we explored partial fine-tuning and entire fine-tuning on wav2vec 2.0 and HuBERT pre-trained models for three non-ASR speech tasks: Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding. With simple proposed downstream frameworks, the best scores reached 79.58% weighted accuracy on speaker-dependent setting and 73.01% weighted accuracy on speaker-independent setting for Speech Emotion Recognition on IEMOCAP, 2.36% equal error rate for Speaker Verification on VoxCeleb1, 89.38% accuracy for Intent Classification and 78.92% F1 for Slot Filling on SLURP, showing the strength of fine-tuned wav2vec 2.0 and HuBERT on learning prosodic, voice-print and semantic representations. | ['Abdelwahab Heba', 'Abdelmoumene Boumadane', 'Yingzhi Wang'] | 2021-11-04 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [-2.36955613e-01 5.79294622e-01 -9.61985365e-02 -7.75242567e-01
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2fa21923-1f6f-4840-9d54-dd875a1e9708 | adaptive-mutual-supervision-for-weakly | 2104.02357 | null | https://arxiv.org/abs/2104.02357v1 | https://arxiv.org/pdf/2104.02357v1.pdf | Adaptive Mutual Supervision for Weakly-Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level action category labels. Most of previous methods ignore the incompleteness issue of Class Activation Sequences (CAS), suffering from trivial localization results. To solve this issue, we introduce an adaptive mutual supervision framework (AMS) with two branches, where the base branch adopts CAS to localize the most discriminative action regions, while the supplementary branch localizes the less discriminative action regions through a novel adaptive sampler. The adaptive sampler dynamically updates the input of the supplementary branch with a sampling weight sequence negatively correlated with the CAS from the base branch, thereby prompting the supplementary branch to localize the action regions underestimated by the base branch. To promote mutual enhancement between these two branches, we construct mutual location supervision. Each branch leverages location pseudo-labels generated from the other branch as localization supervision. By alternately optimizing the two branches in multiple iterations, we progressively complete action regions. Extensive experiments on THUMOS14 and ActivityNet1.2 demonstrate that the proposed AMS method significantly outperforms the state-of-the-art methods. | ['Qi Tian', 'Xiaoyun Zhang', 'Ya zhang', 'Siheng Chen', 'Peisen Zhao', 'Chen Ju'] | 2021-04-06 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.51820642e-01 5.00766225e-02 -7.23331511e-01 -1.03530526e-01
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a86654d2-a2de-4382-b2ca-396fa58dbeea | a-self-training-approach-for-short-text | null | null | https://aclanthology.org/W19-4322 | https://aclanthology.org/W19-4322.pdf | A Self-Training Approach for Short Text Clustering | Short text clustering is a challenging problem when adopting traditional bag-of-words or TF-IDF representations, since these lead to sparse vector representations of the short texts. Low-dimensional continuous representations or embeddings can counter that sparseness problem: their high representational power is exploited in deep clustering algorithms. While deep clustering has been studied extensively in computer vision, relatively little work has focused on NLP. The method we propose, learns discriminative features from both an autoencoder and a sentence embedding, then uses assignments from a clustering algorithm as supervision to update weights of the encoder network. Experiments on three short text datasets empirically validate the effectiveness of our method. | ['Chris Develder', 'Thomas Demeester', 'Lucas Sterckx', 'Amir Hadifar'] | 2019-08-01 | null | null | null | ws-2019-8 | ['text-clustering', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing'] | [-1.40761897e-01 -1.34192064e-01 -3.90930951e-01 -4.53962475e-01
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468d2f31-e538-42c0-a4c9-c23c9f295251 | how-an-electrical-engineer-became-an | 1803.11261 | null | http://arxiv.org/abs/1803.11261v1 | http://arxiv.org/pdf/1803.11261v1.pdf | How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis | This essay examines how what is considered to be artificial intelligence (AI)
has changed over time and come to intersect with the expertise of the author.
Initially, AI developed on a separate trajectory, both topically and
institutionally, from pattern recognition, neural information processing,
decision and control systems, and allied topics by focusing on symbolic systems
within computer science departments rather than on continuous systems in
electrical engineering departments. The separate evolutions continued
throughout the author's lifetime, with some crossover in reinforcement learning
and graphical models, but were shocked into converging by the virality of deep
learning, thus making an electrical engineer into an AI researcher. Now that
this convergence has happened, opportunity exists to pursue an agenda that
combines learning and reasoning bridged by interpretable machine learning
models. | ['Kush R. Varshney'] | 2018-03-29 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.32754871e-01 5.80100894e-01 -3.35059345e-01 -3.50599766e-01
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9f3f31e9-85bc-4fd8-897f-84898d2c4ced | self-supervised-surgical-instrument-3d | 2211.14467 | null | https://arxiv.org/abs/2211.14467v1 | https://arxiv.org/pdf/2211.14467v1.pdf | Self-Supervised Surgical Instrument 3D Reconstruction from a Single Camera Image | Surgical instrument tracking is an active research area that can provide surgeons feedback about the location of their tools relative to anatomy. Recent tracking methods are mainly divided into two parts: segmentation and object detection. However, both can only predict 2D information, which is limiting for application to real-world surgery. An accurate 3D surgical instrument model is a prerequisite for precise predictions of the pose and depth of the instrument. Recent single-view 3D reconstruction methods are only used in natural object reconstruction and do not achieve satisfying reconstruction accuracy without 3D attribute-level supervision. Further, those methods are not suitable for the surgical instruments because of their elongated shapes. In this paper, we firstly propose an end-to-end surgical instrument reconstruction system -- Self-supervised Surgical Instrument Reconstruction (SSIR). With SSIR, we propose a multi-cycle-consistency strategy to help capture the texture information from a slim instrument while only requiring a binary instrument label map. Experiments demonstrate that our approach improves the reconstruction quality of surgical instruments compared to other self-supervised methods and achieves promising results. | ['Jack Noble', 'Jintong Han', 'Ziteng Liu', 'Xing Yao', 'Ange Lou'] | 2022-11-26 | null | null | null | null | ['single-view-3d-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [-4.00047691e-04 1.26143754e-01 -8.08497548e-01 1.28605384e-02
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8853625e-f3c0-4ac1-8d73-0d6157f1a2f0 | label-enhanced-prototypical-network-with | 2206.13980 | null | https://arxiv.org/abs/2206.13980v1 | https://arxiv.org/pdf/2206.13980v1.pdf | Label-enhanced Prototypical Network with Contrastive Learning for Multi-label Few-shot Aspect Category Detection | Multi-label aspect category detection allows a given review sentence to contain multiple aspect categories, which is shown to be more practical in sentiment analysis and attracting increasing attention. As annotating large amounts of data is time-consuming and labor-intensive, data scarcity occurs frequently in real-world scenarios, which motivates multi-label few-shot aspect category detection. However, research on this problem is still in infancy and few methods are available. In this paper, we propose a novel label-enhanced prototypical network (LPN) for multi-label few-shot aspect category detection. The highlights of LPN can be summarized as follows. First, it leverages label description as auxiliary knowledge to learn more discriminative prototypes, which can retain aspect-relevant information while eliminating the harmful effect caused by irrelevant aspects. Second, it integrates with contrastive learning, which encourages that the sentences with the same aspect label are pulled together in embedding space while simultaneously pushing apart the sentences with different aspect labels. In addition, it introduces an adaptive multi-label inference module to predict the aspect count in the sentence, which is simple yet effective. Extensive experimental results on three datasets demonstrate that our proposed model LPN can consistently achieve state-of-the-art performance. | ['Xianchao Zhang', 'Hong Yu', 'Junjie Sun', 'Siyang Zhao', 'Xiaotong Zhang', 'Feng Zhang', 'Han Liu'] | 2022-06-14 | null | null | null | null | ['aspect-category-detection'] | ['natural-language-processing'] | [ 1.90051690e-01 2.24016868e-02 -5.95083237e-01 -5.49366057e-01
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7ec0f71f-3d08-4cca-a7a0-09dfa0a7e6d1 | globally-optimal-contrast-maximisation-for | 2002.10686 | null | https://arxiv.org/abs/2002.10686v3 | https://arxiv.org/pdf/2002.10686v3.pdf | Globally Optimal Contrast Maximisation for Event-based Motion Estimation | Contrast maximisation estimates the motion captured in an event stream by maximising the sharpness of the motion compensated event image. To carry out contrast maximisation, many previous works employ iterative optimisation algorithms, such as conjugate gradient, which require good initialisation to avoid converging to bad local minima. To alleviate this weakness, we propose a new globally optimal event-based motion estimation algorithm. Based on branch-and-bound (BnB), our method solves rotational (3DoF) motion estimation on event streams, which supports practical applications such as video stabilisation and attitude estimation. Underpinning our method are novel bounding functions for contrast maximisation, whose theoretical validity is rigorously established. We show concrete examples from public datasets where globally optimal solutions are vital to the success of contrast maximisation. Despite its exact nature, our algorithm is currently able to process a 50,000 event input in 300 seconds (a locally optimal solver takes 30 seconds on the same input), and has the potential to be further speeded-up using GPUs. | ['Tat-Jun Chin', 'Álvaro Parra', 'Daqi Liu'] | 2020-02-25 | globally-optimal-contrast-maximisation-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Liu_Globally_Optimal_Contrast_Maximisation_for_Event-Based_Motion_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Globally_Optimal_Contrast_Maximisation_for_Event-Based_Motion_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['event-based-motion-estimation'] | ['computer-vision'] | [ 4.68591243e-01 7.05826581e-02 1.65771961e-01 -1.22488409e-01
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e924b052-0d76-47ce-ad9e-f4764c3572dd | small-total-cost-constraints-in-contextual | 2305.15807 | null | https://arxiv.org/abs/2305.15807v1 | https://arxiv.org/pdf/2305.15807v1.pdf | Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness | We consider contextual bandit problems with knapsacks [CBwK], a problem where at each round, a scalar reward is obtained and vector-valued costs are suffered. The learner aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constraints. We assume that contexts come from a continuous set, that costs can be signed, and that the expected reward and cost functions, while unknown, may be uniformly estimated -- a typical assumption in the literature. In this setting, total cost constraints had so far to be at least of order $T^{3/4}$, where $T$ is the number of rounds, and were even typically assumed to depend linearly on $T$. We are however motivated to use CBwK to impose a fairness constraint of equalized average costs between groups: the budget associated with the corresponding cost constraints should be as close as possible to the natural deviations, of order $\sqrt{T}$. To that end, we introduce a dual strategy based on projected-gradient-descent updates, that is able to deal with total-cost constraints of the order of $\sqrt{T}$ up to poly-logarithmic terms. This strategy is more direct and simpler than existing strategies in the literature. It relies on a careful, adaptive, tuning of the step size. | ['Gilles Stoltz', 'Zhen Li', 'Christophe Giraud', 'Evgenii Chzhen'] | 2023-05-25 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 4.61859517e-02 3.53993356e-01 -5.30160427e-01 -2.70980269e-01
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fa065fcf-acad-4fb5-9d0a-d2022ba991a7 | graph-reasoning-for-question-answering-with | 2305.18742 | null | https://arxiv.org/abs/2305.18742v1 | https://arxiv.org/pdf/2305.18742v1.pdf | Graph Reasoning for Question Answering with Triplet Retrieval | Answering complex questions often requires reasoning over knowledge graphs (KGs). State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which are then fed into KG encoder, e.g. graph neural networks (GNNs), to model their local structures and integrated into language models for question answering. However, this paradigm constrains retrieved knowledge in local subgraphs and discards more diverse triplets buried in KGs that are disconnected but useful for question answering. In this paper, we propose a simple yet effective method to first retrieve the most relevant triplets from KGs and then rerank them, which are then concatenated with questions to be fed into language models. Extensive results on both CommonsenseQA and OpenbookQA datasets show that our method can outperform state-of-the-art up to 4.6% absolute accuracy. | ['Bing Yin', 'Chao Zhang', 'Xifeng Yan', 'Zheng Li', 'Qingyu Yin', 'Haoming Jiang', 'Yifan Gao', 'Shiyang Li'] | 2023-05-30 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-2.25452796e-01 5.24345756e-01 -1.65030926e-01 -2.59011984e-01
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0b71a7d6-f6cd-47d5-bd51-d716b120fd04 | revisiting-the-spatial-and-temporal-modeling | 2301.07944 | null | https://arxiv.org/abs/2301.07944v2 | https://arxiv.org/pdf/2301.07944v2.pdf | Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition | Spatial and temporal modeling is one of the most core aspects of few-shot action recognition. Most previous works mainly focus on long-term temporal relation modeling based on high-level spatial representations, without considering the crucial low-level spatial features and short-term temporal relations. Actually, the former feature could bring rich local semantic information, and the latter feature could represent motion characteristics of adjacent frames, respectively. In this paper, we propose SloshNet, a new framework that revisits the spatial and temporal modeling for few-shot action recognition in a finer manner. First, to exploit the low-level spatial features, we design a feature fusion architecture search module to automatically search for the best combination of the low-level and high-level spatial features. Next, inspired by the recent transformer, we introduce a long-term temporal modeling module to model the global temporal relations based on the extracted spatial appearance features. Meanwhile, we design another short-term temporal modeling module to encode the motion characteristics between adjacent frame representations. After that, the final predictions can be obtained by feeding the embedded rich spatial-temporal features to a common frame-level class prototype matcher. We extensively validate the proposed SloshNet on four few-shot action recognition datasets, including Something-Something V2, Kinetics, UCF101, and HMDB51. It achieves favorable results against state-of-the-art methods in all datasets. | ['Boyu Mu', 'Yong liu', 'Mengmeng Wang', 'Jiazheng Xing'] | 2023-01-19 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 2.18181070e-02 -6.29534185e-01 -5.07002354e-01 -3.37321758e-01
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3f9e0486-ecda-4902-bb22-fdab8263746a | vector-space-is-not-the-final-frontier | 2304.11473 | null | https://arxiv.org/abs/2304.11473v2 | https://arxiv.org/pdf/2304.11473v2.pdf | (Vector) Space is Not the Final Frontier: Product Search as Program Synthesis | As ecommerce continues growing, huge investments in ML and NLP for Information Retrieval are following. While the vector space model dominated retrieval modelling in product search - even as vectorization itself greatly changed with the advent of deep learning -, our position paper argues in a contrarian fashion that program synthesis provides significant advantages for many queries and a significant number of players in the market. We detail the industry significance of the proposed approach, sketch implementation details, and address common objections drawing from our experience building a similar system at Tooso. | ['Ciro Greco', 'Jacopo Tagliabue'] | 2023-04-22 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-4.20866907e-01 -8.90445039e-02 -5.98074734e-01 -1.05926052e-01
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9e074988-b655-41f1-92b4-6745adfc9cef | effects-of-image-compression-on-face-image | 2103.03654 | null | https://arxiv.org/abs/2103.03654v1 | https://arxiv.org/pdf/2103.03654v1.pdf | Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching | In the past years, numerous methods have been introduced to reliably detect digital face image manipulations. Lately, the generalizability of these schemes has been questioned in particular with respect to image post-processing. Image compression represents a post-processing which is frequently applied in diverse biometric application scenarios. Severe compression might erase digital traces of face image manipulation and hence hamper a reliable detection thereof. In this work, the effects of image compression on face image manipulation detection are analyzed. In particular, a case study on facial retouching detection under the influence of image compression is presented. To this end, ICAO-compliant subsets of two public face databases are used to automatically create a database containing more than 9,000 retouched reference images together with unconstrained probe images. Subsequently, reference images are compressed applying JPEG and JPEG 2000 at compression levels recommended for face image storage in electronic travel documents. Novel detection algorithms utilizing texture descriptors and deep face representations are proposed and evaluated in a single image and differential scenario. Results obtained from challenging cross-database experiments in which the analyzed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilizing deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance. | ['Christoph Busch', 'Nathania E. Haryanto', 'Kevin Bernardo', 'Christian Rathgeb'] | 2021-03-05 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 8.89082789e-01 -2.31235519e-01 1.51716873e-01 -2.62896657e-01
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58d7971e-27bf-4047-bd4d-8a7dcb5c7a9e | with-a-little-help-from-my-friends-nearest | 2104.14548 | null | https://arxiv.org/abs/2104.14548v2 | https://arxiv.org/pdf/2104.14548v2.pdf | With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations | Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as positives for a contrastive loss, we are interested in using positives from other instances in the dataset. Our method, Nearest-Neighbor Contrastive Learning of visual Representations (NNCLR), samples the nearest neighbors from the dataset in the latent space, and treats them as positives. This provides more semantic variations than pre-defined transformations. We find that using the nearest-neighbor as positive in contrastive losses improves performance significantly on ImageNet classification, from 71.7% to 75.6%, outperforming previous state-of-the-art methods. On semi-supervised learning benchmarks we improve performance significantly when only 1% ImageNet labels are available, from 53.8% to 56.5%. On transfer learning benchmarks our method outperforms state-of-the-art methods (including supervised learning with ImageNet) on 8 out of 12 downstream datasets. Furthermore, we demonstrate empirically that our method is less reliant on complex data augmentations. We see a relative reduction of only 2.1% ImageNet Top-1 accuracy when we train using only random crops. | ['Andrew Zisserman', 'Pierre Sermanet', 'Jonathan Tompson', 'Yusuf Aytar', 'Debidatta Dwibedi'] | 2021-04-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Dwibedi_With_a_Little_Help_From_My_Friends_Nearest-Neighbor_Contrastive_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.68812490e-01 1.03731528e-01 -6.92327142e-01 -6.04529858e-01
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2ea30d61-b78e-48f7-b4eb-43dd28f80c32 | a-dual-benchmarking-study-of-facial-forgery | 2111.12912 | null | https://arxiv.org/abs/2111.12912v2 | https://arxiv.org/pdf/2111.12912v2.pdf | A War Beyond Deepfake: Benchmarking Facial Counterfeits and Countermeasures | In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. In this paper, we present a benchmark that provides in-depth insights into visual forgery and visual forensics, using a comprehensive and empirical approach. More specifically, we develop an independent framework that integrates state-of-the-arts counterfeit generators and detectors, and measure the performance of these techniques using various criteria. We also perform an exhaustive analysis of the benchmarking results, to determine the characteristics of the methods that serve as a comparative reference in this never-ending war between measures and countermeasures. | ['Quoc Viet Hung Nguyen', 'Hongzhi Yin', 'Thanh Thi Nguyen', 'Thanh Tam Nguyen', 'Van Vinh Tong', 'Thanh Trung Huynh', 'Minh Tam Pham'] | 2021-11-25 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 3.48251499e-02 -2.49781385e-01 -1.86360657e-01 8.85983184e-02
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a756e182-be97-45ee-847b-3c89f0908e42 | saliency-detection-in-360a-videos | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Ziheng_Zhang_Saliency_Detection_in_ECCV_2018_paper.pdf | Saliency Detection in 360° Videos | This paper presents a novel spherical convolutional neural network based scheme for saliency detection for 360° videos. Specifically, in our spherical convolution neural network definition, kernel is defined on a spherical crown, and the convolution involves the rotation of the kernel along the sphere. Considering that the 360° videos are usually stored with equirectangular panorama, we propose to implement the spherical convolution on panorama by stretching and rotating the kernel based on the location of patch to be convolved. Compared with existing spherical convolution, our definition has the parameter sharing property, which would greatly reduce the parameters to be learned. We further take the temporal coherence of the viewing process into consideration, and propose a sequential saliency detection by leveraging a spherical U-Net. To validate our approach, we construct a large-scale 360° videos saliency detection benchmark that consists of 104 360° videos viewed by 20+ human subjects. Comprehensive experiments validate the effectiveness of our spherical U-net for 360° video saliency detection. | ['Shenghua Gao ', 'Ziheng Zhang', 'Jingyi Yu', 'Yanyu Xu'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-saliency-detection'] | ['computer-vision'] | [ 1.34805784e-01 -1.37486234e-01 -1.67845353e-01 -3.33103895e-01
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bb80beb6-3c68-4c25-9877-70e12eac6b1e | hooknet-multi-resolution-convolutional-neural | 2006.12230 | null | https://arxiv.org/abs/2006.12230v1 | https://arxiv.org/pdf/2006.12230v1.pdf | HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images | We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentricpatches at multiple resolutions with different fields of view are used to feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. Weshow the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation | ['Karina Siliņa', 'Maschenka Balkenhol', 'Mart van Rijthoven', 'Jeroen van der Laak', 'Francesco Ciompi'] | 2020-06-22 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [ 4.58924860e-01 5.10232687e-01 -3.82457525e-01 -3.62709701e-01
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61dbfbef-2de3-4bc3-af17-f553a3cca941 | semi-supervised-medical-image-segmentation-3 | 2202.06104 | null | https://arxiv.org/abs/2202.06104v1 | https://arxiv.org/pdf/2202.06104v1.pdf | Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training | The performance of supervised deep learning methods for medical image segmentation is often limited by the scarcity of labeled data. As a promising research direction, semi-supervised learning addresses this dilemma by leveraging unlabeled data information to assist the learning process. In this paper, a novel geometry-aware semi-supervised learning framework is proposed for medical image segmentation, which is a consistency-based method. Considering that the hard-to-segment regions are mainly located around the object boundary, we introduce an auxiliary prediction task to learn the global geometric information. Based on the geometric constraint, the ambiguous boundary regions are emphasized through an exponentially weighted strategy for the model training to better exploit both labeled and unlabeled data. In addition, a dual-view network is designed to perform segmentation from different perspectives and reduce the prediction uncertainty. The proposed method is evaluated on the public left atrium benchmark dataset and improves fully supervised method by 8.7% in Dice with 10% labeled images, while 4.3% with 20% labeled images. Meanwhile, our framework outperforms six state-of-the-art semi-supervised segmentation methods. | ['Chunhui Zhao', 'Zihang Liu'] | 2022-02-12 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 2.18030423e-01 5.87965786e-01 -5.12665570e-01 -7.12440491e-01
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6.29075244e-02 -6.38394833e-01 -2.74241090e-01 7.50188977e-02] | [14.655975341796875, -2.0751683712005615] |
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