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d1810bc0-dcf3-4bad-bad1-b7a8abdbfefb | aspect-based-emotion-analysis-and-multimodal | null | null | https://aclanthology.org/2022.lrec-1.61 | https://aclanthology.org/2022.lrec-1.61.pdf | Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts | While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content. In this paper, we present a multimodal dataset for Aspect-Based Emotion Analysis (ABEA). Additionally, we take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework. The presented dataset consists of 4,900 comments on 175 images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. Our preliminary experiments suggest that ABEA does not benefit from multimodal coreference resolution, and that aspect and emotion classification only requires textual information. However, when more specific information about the aspects is desired, image recognition could be essential. | ['Veronique Hoste', 'Andrea Prati', 'Orphee De Clercq', 'Akbar Karimi', 'Luna De Bruyne'] | null | null | null | null | lrec-2022-6 | ['emotion-classification', 'aspect-based-sentiment-analysis', 'coreference-resolution', 'emotion-classification'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.51944870e-01 2.70101726e-01 -2.98594180e-02 -5.74213743e-01
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ee4088e9-2c21-4553-9a20-5ca27feb272a | vocabulary-free-image-classification | 2306.00917 | null | https://arxiv.org/abs/2306.00917v1 | https://arxiv.org/pdf/2306.00917v1.pdf | Vocabulary-free Image Classification | Recent advances in large vision-language models have revolutionized the image classification paradigm. Despite showing impressive zero-shot capabilities, a pre-defined set of categories, a.k.a. the vocabulary, is assumed at test time for composing the textual prompts. However, such assumption can be impractical when the semantic context is unknown and evolving. We thus formalize a novel task, termed as Vocabulary-free Image Classification (VIC), where we aim to assign to an input image a class that resides in an unconstrained language-induced semantic space, without the prerequisite of a known vocabulary. VIC is a challenging task as the semantic space is extremely large, containing millions of concepts, with hard-to-discriminate fine-grained categories. In this work, we first empirically verify that representing this semantic space by means of an external vision-language database is the most effective way to obtain semantically relevant content for classifying the image. We then propose Category Search from External Databases (CaSED), a method that exploits a pre-trained vision-language model and an external vision-language database to address VIC in a training-free manner. CaSED first extracts a set of candidate categories from captions retrieved from the database based on their semantic similarity to the image, and then assigns to the image the best matching candidate category according to the same vision-language model. Experiments on benchmark datasets validate that CaSED outperforms other complex vision-language frameworks, while being efficient with much fewer parameters, paving the way for future research in this direction. | ['Elisa Ricci', 'Yiming Wang', 'Paolo Rota', 'Massimiliano Mancini', 'Enrico Fini', 'Alessandro Conti'] | 2023-06-01 | null | null | null | null | ['vocabulary-free-image-classification', 'semantic-textual-similarity', 'semantic-similarity'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 5.00916362e-01 -2.36618146e-01 -2.35420987e-01 -3.39292258e-01
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bef9f599-f22a-49ad-bca6-e2a6c0a7e60f | natural-language-processing-and-sentiment | null | null | https://www.worldscientific.com/doi/abs/10.1142/S2196888823500021 | https://www.worldscientific.com/doi/epdf/10.1142/S2196888823500021 | Natural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using Transformers | The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks such as sentiment analysis (SA) in Bangla is relatively low compared to SA in the English language. It is because there are not enough high-quality publically available datasets for training language models for text classification tasks in Bangla. In this paper, we propose a Bangla annotated dataset for sentiment analysis on the ongoing Ukraine–Russia war. The dataset was developed by collecting Bangla comments from various videos of three prominent YouTube TV news channels of Bangladesh covering their report on the ongoing conflict. A total of 10,861 Bangla comments were collected and labeled with three polarity sentiments, namely Neutral, Pro-Ukraine (Positive), and Pro-Russia (Negative). A benchmark classifier was developed by experimenting with several transformer-based language models all pre-trained on unlabeled Bangla corpus. The models were fine-tuned using our procured dataset. Hyperparameter optimization was performed on all 5 transformer language models which include: BanglaBERT, XLM-RoBERTa-base, XLM-RoBERTa-large, Distil-mBERT and mBERT. Each model was evaluated and analyzed using several evaluation metrics which include: F1 score, accuracy, and AIC (Akaike Information Criterion). The best-performing model achieved the highest accuracy of 86% with 0.82 F1 score. Based on accuracy, F1 score and AIC, BanglaBERT outperforms baseline and all the other transformer-based classifiers. | ['Rashedur M. Rahman', 'Sabrina Mannan Meem', 'Ismat Jahan', 'Labiba Islam', 'Mahmud Hasan'] | 2023-05-04 | null | null | null | vietnam-journal-of-computer-science-2023-5 | ['hyperparameter-optimization', 'text-classification', 'sentiment-analysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-4.56989259e-01 -3.98124307e-01 -1.39805436e-01 -6.71793461e-01
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50fc3101-f11f-4c48-8c3a-d5a61dd7d483 | a-simple-and-effective-method-to-improve-zero-1 | 2210.09934 | null | https://arxiv.org/abs/2210.09934v1 | https://arxiv.org/pdf/2210.09934v1.pdf | A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage from these dependencies, researchers have explored training multilingual models on English-only resources and transferring them to low-resource languages. However, its effect is limited by the gap between embedding clusters of different languages. To address this issue, we propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embeddings without semantic loss, thereby improving cross-lingual transferability. Experimental results on mBERT and XLM-R demonstrate that our method significantly outperforms previous works on the zero-shot cross-lingual text classification task and can obtain a better multilingual alignment. | ['Yiren Chen', 'Rong Tian', 'Haoyan Liu', 'Tao Zhu', 'Zhe Zhao', 'Weiquan Mao', 'Yuejian Fang', 'Weijie Liu', 'Kunbo Ding'] | 2022-10-18 | a-simple-and-effective-method-to-improve-zero | https://aclanthology.org/2022.coling-1.385 | https://aclanthology.org/2022.coling-1.385.pdf | coling-2022-10 | ['xlm-r'] | ['natural-language-processing'] | [-4.46152329e-01 -3.66755426e-01 -7.02043593e-01 -3.72253686e-01
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89f13c67-e07f-427f-b3ec-8a8d5b46e90e | cal-ql-calibrated-offline-rl-pre-training-for | 2303.05479 | null | https://arxiv.org/abs/2303.05479v2 | https://arxiv.org/pdf/2303.05479v2.pdf | Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning | A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also ensuring that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that a conservative offline RL algorithm that also learns a calibrated value function leads to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github.io/projects/Cal-QL | ['Sergey Levine', 'Aviral Kumar', 'Chelsea Finn', 'Yi Ma', 'Max Sobol Mark', 'Anikait Singh', 'Yuexiang Zhai', 'Mitsuhiko Nakamoto'] | 2023-03-09 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-4.79824036e-01 2.42342040e-01 -6.04286969e-01 -2.27475062e-01
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470cdd68-adef-4b26-933a-2ce196a97c3e | deep-auxiliary-learning-for-visual-1 | 2107.00222 | null | https://arxiv.org/abs/2107.00222v1 | https://arxiv.org/pdf/2107.00222v1.pdf | Deep auxiliary learning for visual localization using colorization task | Visual localization is one of the most important components for robotics and autonomous driving. Recently, inspiring results have been shown with CNN-based methods which provide a direct formulation to end-to-end regress 6-DoF absolute pose. Additional information like geometric or semantic constraints is generally introduced to improve performance. Especially, the latter can aggregate high-level semantic information into localization task, but it usually requires enormous manual annotations. To this end, we propose a novel auxiliary learning strategy for camera localization by introducing scene-specific high-level semantics from self-supervised representation learning task. Viewed as a powerful proxy task, image colorization task is chosen as complementary task that outputs pixel-wise color version of grayscale photograph without extra annotations. In our work, feature representations from colorization network are embedded into localization network by design to produce discriminative features for pose regression. Meanwhile an attention mechanism is introduced for the benefit of localization performance. Extensive experiments show that our model significantly improve localization accuracy over state-of-the-arts on both indoor and outdoor datasets. | ['Xiahua Xia', 'Hao Shen', 'Qiong Nie', 'Mi Tian'] | 2021-07-01 | null | null | null | null | ['camera-localization', 'auxiliary-learning'] | ['computer-vision', 'methodology'] | [ 9.20739863e-03 -1.42500296e-01 -3.80509228e-01 -8.05650830e-01
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52914536-256c-4212-a857-867a620298c8 | raum-vo-rotational-adjusted-unsupervised | 2203.07162 | null | https://arxiv.org/abs/2203.07162v1 | https://arxiv.org/pdf/2203.07162v1.pdf | RAUM-VO: Rotational Adjusted Unsupervised Monocular Visual Odometry | Unsupervised learning for monocular camera motion and 3D scene understanding has gained popularity over traditional methods, relying on epipolar geometry or non-linear optimization. Notably, deep learning can overcome many issues of monocular vision, such as perceptual aliasing, low-textured areas, scale-drift, and degenerate motions. Also, concerning supervised learning, we can fully leverage video streams data without the need for depth or motion labels. However, in this work, we note that rotational motion can limit the accuracy of the unsupervised pose networks more than the translational component. Therefore, we present RAUM-VO, an approach based on a model-free epipolar constraint for frame-to-frame motion estimation (F2F) to adjust the rotation during training and online inference. To this end, we match 2D keypoints between consecutive frames using pre-trained deep networks, Superpoint and Superglue, while training a network for depth and pose estimation using an unsupervised training protocol. Then, we adjust the predicted rotation with the motion estimated by F2F using the 2D matches and initializing the solver with the pose network prediction. Ultimately, RAUM-VO shows a considerable accuracy improvement compared to other unsupervised pose networks on the KITTI dataset while reducing the complexity of other hybrid or traditional approaches and achieving comparable state-of-the-art results. | ['Holger Voos', 'Jose Luis Sanchez-Lopez', 'Hriday Bavle', 'Claudio Cimarelli'] | 2022-03-14 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-1.20757353e-02 -1.57189116e-01 -2.69432604e-01 -2.54167885e-01
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0f0d131f-3cc9-42c1-918a-aafe43f2a23e | a-novel-framework-for-image-forgery | 1311.6932 | null | http://arxiv.org/abs/1311.6932v1 | http://arxiv.org/pdf/1311.6932v1.pdf | A novel framework for image forgery localization | Image forgery localization is a very active and open research field for the
difficulty to handle the large variety of manipulations a malicious user can
perform by means of more and more sophisticated image editing tools. Here, we
propose a localization framework based on the fusion of three very different
tools, based, respectively, on sensor noise, patch-matching, and machine
learning. The binary masks provided by these tools are finally fused based on
some suitable reliability indexes. According to preliminary experiments on the
training set, the proposed framework provides often a very good localization
accuracy and sometimes valuable clues for visual scrutiny. | ['Luisa Verdoliva', 'Diego Gragnaniello', 'Davide Cozzolino'] | 2013-11-27 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.94149807e-01 -3.92323673e-01 -8.78981277e-02 6.58078194e-02
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e37fb19a-2c3f-47ce-bc83-1f883139b996 | visual-place-recognition | 2211.14533 | null | https://arxiv.org/abs/2211.14533v1 | https://arxiv.org/pdf/2211.14533v1.pdf | Visual Place Recognition | Visual position recognition affects the safety and accuracy of automatic driving. To accurately identify the location, this paper studies a visual place recognition algorithm based on HMM filter and HMM smoother. Firstly, we constructed the traffic situations in Canberra city. Then the mathematical models of the HMM filter and HMM smoother were performed. Finally, the vehicle position was predicted based on the algorithms. Experiment results show that HMM smoother is better than HMM filter in terms of prediction accuracy. | ['Zishun Zhou', 'Boyu Zhao', 'Bailu Guo'] | 2022-11-26 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-4.56060082e-01 -2.68771619e-01 -2.59162992e-01 -3.35832298e-01
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72a253c3-e0a5-4aa8-87ad-eaa6adb346a9 | fast-fourier-convolution-based-remote-sensor | 2209.00551 | null | https://arxiv.org/abs/2209.00551v1 | https://arxiv.org/pdf/2209.00551v1.pdf | Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation | Remote sensor image object detection is an important technology for Earth observation, and is used in various tasks such as forest fire monitoring and ocean monitoring. Image object detection technology, despite the significant developments, is struggling to handle remote sensor images and small-scale objects, due to the limited pixels of small objects. Numerous existing studies have demonstrated that an effective way to promote small object detection is to introduce the spatial context. Meanwhile, recent researches for image classification have shown that spectral convolution operations can perceive long-term spatial dependence more efficiently in the frequency domain than spatial domain. Inspired by this observation, we propose a Frequency-aware Feature Pyramid Framework (FFPF) for remote sensing object detection, which consists of a novel Frequency-aware ResNet (F-ResNet) and a Bilateral Spectral-aware Feature Pyramid Network (BS-FPN). Specifically, the F-ResNet is proposed to perceive the spectral context information by plugging the frequency domain convolution into each stage of the backbone, extracting richer features of small objects. To the best of our knowledge, this is the first work to introduce frequency-domain convolution into remote sensing object detection task. In addition, the BSFPN is designed to use a bilateral sampling strategy and skipping connection to better model the association of object features at different scales, towards unleashing the potential of the spectral context information from F-ResNet. Extensive experiments are conducted for object detection in the optical remote sensing image dataset (DIOR and DOTA). The experimental results demonstrate the excellent performance of our method. It achieves an average accuracy (mAP) without any tricks. | ['Dong Ge', 'Eugene Popov', 'Gu Lingyun'] | 2022-09-01 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 5.17670333e-01 -5.53651750e-01 6.32654876e-02 -3.15214694e-01
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ade49172-c9e7-4563-b21c-fdf60c5511b4 | dynamic-boundary-time-warping-for-sub | 2010.14464 | null | https://arxiv.org/abs/2010.14464v1 | https://arxiv.org/pdf/2010.14464v1.pdf | Dynamic Boundary Time Warping for Sub-sequence Matching with Few Examples | The paper presents a novel method of finding a fragment in a long temporal sequence similar to the set of shorter sequences. We are the first to propose an algorithm for such a search that does not rely on computing the average sequence from query examples. Instead, we use query examples as is, utilizing all of them simultaneously. The introduced method based on the Dynamic Time Warping (DTW) technique is suited explicitly for few-shot query-by-example retrieval tasks. We evaluate it on two different few-shot problems from the field of Natural Language Processing. The results show it either outperforms baselines and previous approaches or achieves comparable results when a low number of examples is available. | ['Tomasz Górecki', 'Filip Graliński', 'Dawid Jurkiewicz', 'Łukasz Borchmann'] | 2020-10-27 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 4.49344426e-01 -5.82724512e-01 -3.71967286e-01 -2.36734435e-01
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30e29e88-a8b0-4557-a904-1d874897569d | relu-neural-networks-polyhedral | 2306.17418 | null | https://arxiv.org/abs/2306.17418v1 | https://arxiv.org/pdf/2306.17418v1.pdf | ReLU Neural Networks, Polyhedral Decompositions, and Persistent Homolog | A ReLU neural network leads to a finite polyhedral decomposition of input space and a corresponding finite dual graph. We show that while this dual graph is a coarse quantization of input space, it is sufficiently robust that it can be combined with persistent homology to detect homological signals of manifolds in the input space from samples. This property holds for a variety of networks trained for a wide range of purposes that have nothing to do with this topological application. We found this feature to be surprising and interesting; we hope it will also be useful. | ['Michael Kirby', 'Chris Peterson', 'Christina M Cole', 'Yajing Liu'] | 2023-06-30 | null | null | null | null | ['quantization'] | ['methodology'] | [ 1.66500464e-01 6.33343875e-01 -3.97727750e-02 -4.14787196e-02
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47f9f5de-38fd-4ba2-94f2-dbe2c9b3ebeb | neuralroom-geometry-constrained-neural | 2210.06853 | null | https://arxiv.org/abs/2210.06853v2 | https://arxiv.org/pdf/2210.06853v2.pdf | NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction | We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality. | ['Chunxia Xiao', 'Yanping Fu', 'Tuo Cao', 'Kaixuan Zhou', 'Yu Jiang', 'Zongcheng Li', 'Yusen Wang'] | 2022-10-13 | null | null | null | null | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 2.75791675e-01 1.11310817e-02 2.13077664e-01 -6.02524877e-01
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efb1f666-2602-4efe-bf5e-b855a0740b58 | voxel-mae-masked-autoencoders-for-pre | 2206.09900 | null | https://arxiv.org/abs/2206.09900v6 | https://arxiv.org/pdf/2206.09900v6.pdf | Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders | Current perception models in autonomous driving rely heavily on large-scale labeled LiDAR data, which is costly and time-consuming to annotate. In this work, we aim to facilitate research on self-supervised masked learning using the vast amount of unlabeled LiDAR data available in autonomous driving. However, existing masked point autoencoding methods only focus on small-scale indoor point clouds and struggle to adapt to outdoor scenes, which usually have a large number of non-evenly distributed LiDAR points. To address these challenges, we propose a new self-supervised masked learning method named Occupancy-MAE, specifically designed for large-scale outdoor LiDAR points. We leverage the gradually sparse occupancy structure of large-scale outdoor LiDAR point clouds and introduce a range-aware random masking strategy and a pretext task of occupancy prediction. Occupancy-MAE randomly masks voxels of LiDAR point clouds based on their distance to LiDAR and predicts the masked occupancy structure of the whole 3D scene. This simple occupancy prediction objective encourages Occupancy-MAE to extract high-level semantic information to recover the masked voxel from only a small amount of visible voxels. Extensive experiments demonstrate the effectiveness of Occupancy-MAE across several downstream tasks. For the 3D object detection task, Occupancy-MAE reduces the labeled data required for car detection on KITTI by half and boosts small object detection by around 2% mAP on Waymo. For the 3D semantic segmentation task, Occupancy-MAE outperforms training from scratch by around 2% mIOU on nuScenes. For the unsupervised domain adaptation task, Occupancy-MAE improves the performance by about 0.5\% ~ 1% mAP. Our results show that it is feasible to pre-train unlabeled large-scale LiDAR point clouds with masked autoencoding to enhance the 3D perception ability of autonomous driving. | ['Bin Dai', 'Xinli Xu', 'Yiming Nie', 'Liang Xiao', 'Dawei Zhao', 'Chen Min'] | 2022-06-20 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 3.21337223e-01 1.52531683e-01 -3.70554626e-01 -6.43379390e-01
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fae811f8-a8cd-4fd5-8cc5-0cf4bbfc14d4 | temporal-action-localization-with-multi | 2208.07493 | null | https://arxiv.org/abs/2208.07493v1 | https://arxiv.org/pdf/2208.07493v1.pdf | Temporal Action Localization with Multi-temporal Scales | Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. The previous methods often predict actions on a feature space of a single-temporal scale. However, the temporal features of a low-level scale lack enough semantics for action classification while a high-level scale cannot provide rich details of the action boundaries. To address this issue, we propose to predict actions on a feature space of multi-temporal scales. Specifically, we use refined feature pyramids of different scales to pass semantics from high-level scales to low-level scales. Besides, to establish the long temporal scale of the entire video, we use a spatial-temporal transformer encoder to capture the long-range dependencies of video frames. Then the refined features with long-range dependencies are fed into a classifier for the coarse action prediction. Finally, to further improve the prediction accuracy, we propose to use a frame-level self attention module to refine the classification and boundaries of each action instance. Extensive experiments show that the proposed method can outperform state-of-the-art approaches on the THUMOS14 dataset and achieves comparable performance on the ActivityNet1.3 dataset. Compared with A2Net (TIP20, Avg\{0.3:0.7\}), Sub-Action (CSVT2022, Avg\{0.1:0.5\}), and AFSD (CVPR21, Avg\{0.3:0.7\}) on the THUMOS14 dataset, the proposed method can achieve improvements of 12.6\%, 17.4\% and 2.2\%, respectively | ['Shenyong Chen', 'Meng Wang', 'An-An Liu', 'Zhiyong Cheng', 'Tao Zhuo', 'Xinglei Cui', 'Zan Gao'] | 2022-08-16 | null | null | null | null | ['action-classification', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 2.92042494e-01 -2.51471609e-01 -4.34012949e-01 -2.36263022e-01
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775a9ea7-8b1d-43c1-821b-4eabb05e1cb8 | transition-based-dependency-parsing-using | 2001.08279 | null | https://arxiv.org/abs/2001.08279v2 | https://arxiv.org/pdf/2001.08279v2.pdf | Transition-Based Dependency Parsing using Perceptron Learner | Syntactic parsing using dependency structures has become a standard technique in natural language processing with many different parsing models, in particular data-driven models that can be trained on syntactically annotated corpora. In this paper, we tackle transition-based dependency parsing using a Perceptron Learner. Our proposed model, which adds more relevant features to the Perceptron Learner, outperforms a baseline arc-standard parser. We beat the UAS of the MALT and LSTM parsers. We also give possible ways to address parsing of non-projective trees. | ['Miguel Ballesteros', 'Chris Dyer', 'Rahul Radhakrishnan Iyer', 'Robert Frederking'] | 2020-01-22 | null | null | null | null | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 1.83955640e-01 6.45428300e-01 -1.39711678e-01 -1.01005828e+00
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35ac8747-c66b-4bbc-aa05-24e56e317bcb | fully-convolutional-recurrent-network-for | 1604.04953 | null | http://arxiv.org/abs/1604.04953v1 | http://arxiv.org/pdf/1604.04953v1.pdf | Fully Convolutional Recurrent Network for Handwritten Chinese Text Recognition | This paper proposes an end-to-end framework, namely fully convolutional
recurrent network (FCRN) for handwritten Chinese text recognition (HCTR).
Unlike traditional methods that rely heavily on segmentation, our FCRN is
trained with online text data directly and learns to associate the pen-tip
trajectory with a sequence of characters. FCRN consists of four parts: a
path-signature layer to extract signature features from the input pen-tip
trajectory, a fully convolutional network to learn informative representation,
a sequence modeling layer to make per-frame predictions on the input sequence
and a transcription layer to translate the predictions into a label sequence.
The FCRN is end-to-end trainable in contrast to conventional methods whose
components are separately trained and tuned. We also present a refined beam
search method that efficiently integrates the language model to decode the FCRN
and significantly improve the recognition results.
We evaluate the performance of the proposed method on the test sets from the
databases CASIA-OLHWDB and ICDAR 2013 Chinese handwriting recognition
competition, and both achieve state-of-the-art performance with correct rates
of 96.40% and 95.00%, respectively. | ['Lianwen Jin', 'Ziyong Feng', 'Zenghui Sun', 'Zecheng Xie', 'Shuye Zhang'] | 2016-04-18 | null | null | null | null | ['handwritten-chinese-text-recognition', 'handwritten-chinese-text-recognition'] | ['computer-vision', 'natural-language-processing'] | [ 4.97641861e-01 -2.92502433e-01 -1.86292216e-01 -7.24126399e-01
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00d86297-8273-4b2a-a779-de1780fadb3a | cure-code-aware-neural-machine-translation | 2103.00073 | null | https://arxiv.org/abs/2103.00073v4 | https://arxiv.org/pdf/2103.00073v4.pdf | CURE: Code-Aware Neural Machine Translation for Automatic Program Repair | Automatic program repair (APR) is crucial to improve software reliability. Recently, neural machine translation (NMT) techniques have been used to fix software bugs automatically. While promising, these approaches have two major limitations. Their search space often does not contain the correct fix, and their search strategy ignores software knowledge such as strict code syntax. Due to these limitations, existing NMT-based techniques underperform the best template-based approaches. We propose CURE, a new NMT-based APR technique with three major novelties. First, CURE pre-trains a programming language (PL) model on a large software codebase to learn developer-like source code before the APR task. Second, CURE designs a new code-aware search strategy that finds more correct fixes by focusing on compilable patches and patches that are close in length to the buggy code. Finally, CURE uses a subword tokenization technique to generate a smaller search space that contains more correct fixes. Our evaluation on two widely-used benchmarks shows that CURE correctly fixes 57 Defects4J bugs and 26 QuixBugs bugs, outperforming all existing APR techniques on both benchmarks. | ['Lin Tan', 'Thibaud Lutellier', 'Nan Jiang'] | 2021-02-26 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 5.97255789e-02 -5.50601743e-02 -6.14962876e-01 -7.28149563e-02
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b5a93333-2ebf-43c9-ad97-e30ab279af99 | embedding-based-silhouette-community | 1908.02556 | null | https://arxiv.org/abs/1908.02556v1 | https://arxiv.org/pdf/1908.02556v1.pdf | Embedding-based Silhouette Community Detection | Mining complex data in the form of networks is of increasing interest in many scientific disciplines. Network communities correspond to densely connected subnetworks, and often represent key functional parts of real-world systems. In this work, we propose Silhouette Community Detection (SCD), an approach for detecting communities, based on clustering of network node embeddings, i.e. real valued representations of nodes derived from their neighborhoods. We investigate the performance of the proposed SCD approach on 234 synthetic networks, as well as on a real-life social network. Even though SCD is not based on any form of modularity optimization, it performs comparably or better than state-of-the-art community detection algorithms, such as the InfoMap and Louvain algorithms. Further, we demonstrate how SCD's outputs can be used along with domain ontologies in semantic subgroup discovery, yielding human-understandable explanations of communities detected in a real-life protein interaction network. Being embedding-based, SCD is widely applicable and can be tested out-of-the-box as part of many existing network learning and exploration pipelines. | ['Nada Lavrač', 'Blaž Škrlj', 'Jan Kralj'] | 2019-07-17 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 1.64117113e-01 4.89436358e-01 6.54116645e-02 -2.76807621e-02
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24aeee7b-3f6e-4d05-b0f5-203e89526cd4 | evalne-a-framework-for-evaluating-network-1 | null | null | https://openreview.net/forum?id=H1eJH3IaLN | https://openreview.net/pdf?id=H1eJH3IaLN | EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction | Network embedding (NE) methods aim to learn low-dimensional representations of network nodes as vectors, typically in Euclidean space. These representations are then used for a variety of downstream prediction tasks. Link prediction is one of the most popular choices for assessing the performance of NE methods. However, the complexity of link prediction requires a carefully designed evaluation pipeline to provide consistent, reproducible and comparable results. We argue this has not been considered sufficiently in recent works. The main goal of this paper is to overcome difficulties associated with evaluation pipelines and reproducibility of results. We introduce EvalNE, an evaluation framework to transparently assess and compare the performance of NE methods on link prediction. EvalNE provides automation and abstraction for tasks such as hyper-parameter tuning, model validation, edge sampling, computation of edge embeddings and model validation. The framework integrates efficient procedures for edge and non-edge sampling and can be used to easily evaluate any off-the-shelf embedding method. The framework is freely available as a Python toolbox. Finally, demonstrating the usefulness of EvalNE in practice, we conduct an empirical study in which we try to replicate and analyse experimental sections of several influential papers. | ['Anonymous'] | 2019-03-06 | null | null | null | iclr-workshop-rml-2019-5 | ['network-embedding'] | ['methodology'] | [-4.95626293e-02 7.36895502e-02 -2.73522437e-01 -6.37077726e-03
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04f36980-177a-4f10-97f8-66c7aa6c6992 | tandem-multitask-training-of-speaker | 2207.03852 | null | https://arxiv.org/abs/2207.03852v1 | https://arxiv.org/pdf/2207.03852v1.pdf | Tandem Multitask Training of Speaker Diarisation and Speech Recognition for Meeting Transcription | Self-supervised-learning-based pre-trained models for speech data, such as Wav2Vec 2.0 (W2V2), have become the backbone of many speech tasks. In this paper, to achieve speaker diarisation and speech recognition using a single model, a tandem multitask training (TMT) method is proposed to fine-tune W2V2. For speaker diarisation, the tasks of voice activity detection (VAD) and speaker classification (SC) are required, and connectionist temporal classification (CTC) is used for ASR. The multitask framework implements VAD, SC, and ASR using an early layer, middle layer, and late layer of W2V2, which coincides with the order of segmenting the audio with VAD, clustering the segments based on speaker embeddings, and transcribing each segment with ASR. Experimental results on the augmented multi-party (AMI) dataset showed that using different W2V2 layers for VAD, SC, and ASR from the earlier to later layers for TMT not only saves computational cost, but also reduces diarisation error rates (DERs). Joint fine-tuning of VAD, SC, and ASR yielded 16%/17% relative reductions of DER with manual/automatic segmentation respectively, and consistent reductions in speaker attributed word error rate, compared to the baseline with separately fine-tuned models. | ['Philip C. Woodland', 'Chao Zhang', 'Xianrui Zheng'] | 2022-07-08 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-6.55360240e-03 1.28848538e-01 7.58696571e-02 -3.87515008e-01
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9f722fe5-e4c5-498a-9cca-7bbd99e818df | deception-detection-in-videos | 1712.04415 | null | http://arxiv.org/abs/1712.04415v1 | http://arxiv.org/pdf/1712.04415v1.pdf | Deception Detection in Videos | We present a system for covert automated deception detection in real-life
courtroom trial videos. We study the importance of different modalities like
vision, audio and text for this task. On the vision side, our system uses
classifiers trained on low level video features which predict human
micro-expressions. We show that predictions of high-level micro-expressions can
be used as features for deception prediction. Surprisingly, IDT (Improved Dense
Trajectory) features which have been widely used for action recognition, are
also very good at predicting deception in videos. We fuse the score of
classifiers trained on IDT features and high-level micro-expressions to improve
performance. MFCC (Mel-frequency Cepstral Coefficients) features from the audio
domain also provide a significant boost in performance, while information from
transcripts is not very beneficial for our system. Using various classifiers,
our automated system obtains an AUC of 0.877 (10-fold cross-validation) when
evaluated on subjects which were not part of the training set. Even though
state-of-the-art methods use human annotations of micro-expressions for
deception detection, our fully automated approach outperforms them by 5%. When
combined with human annotations of micro-expressions, our AUC improves to
0.922. We also present results of a user-study to analyze how well do average
humans perform on this task, what modalities they use for deception detection
and how they perform if only one modality is accessible. Our project page can
be found at \url{https://doubaibai.github.io/DARE/}. | ['Zhe Wu', 'V. S. Subrahmanian', 'Larry S. Davis', 'Bharat Singh'] | 2017-12-12 | null | null | null | null | ['deception-detection-in-videos', 'deception-detection-in-videos', 'deception-detection'] | ['computer-vision', 'miscellaneous', 'miscellaneous'] | [ 9.15075094e-03 -1.18902571e-01 -9.16305259e-02 -4.19586450e-01
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de5e343c-19b2-42b1-b7b6-74303bbb504a | effectively-modeling-time-series-with-simple | 2303.09489 | null | https://arxiv.org/abs/2303.09489v1 | https://arxiv.org/pdf/2303.09489v1.pdf | Effectively Modeling Time Series with Simple Discrete State Spaces | Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence modeling. However, we find fundamental limitations with these prior approaches, proving their SSM representations cannot express autoregressive time series processes. We thus introduce SpaceTime, a new state-space time series architecture that improves all three criteria. For expressivity, we propose a new SSM parameterization based on the companion matrix -- a canonical representation for discrete-time processes -- which enables SpaceTime's SSM layers to learn desirable autoregressive processes. For long horizon forecasting, we introduce a "closed-loop" variation of the companion SSM, which enables SpaceTime to predict many future time-steps by generating its own layer-wise inputs. For efficient training and inference, we introduce an algorithm that reduces the memory and compute of a forward pass with the companion matrix. With sequence length $\ell$ and state-space size $d$, we go from $\tilde{O}(d \ell)$ na\"ively to $\tilde{O}(d + \ell)$. In experiments, our contributions lead to state-of-the-art results on extensive and diverse benchmarks, with best or second-best AUROC on 6 / 7 ECG and speech time series classification, and best MSE on 14 / 16 Informer forecasting tasks. Furthermore, we find SpaceTime (1) fits AR($p$) processes that prior deep SSMs fail on, (2) forecasts notably more accurately on longer horizons than prior state-of-the-art, and (3) speeds up training on real-world ETTh1 data by 73% and 80% relative wall-clock time over Transformers and LSTMs. | ['Christopher Ré', 'Karan Goel', 'Tri Dao', 'Michael Poli', 'Khaled K. Saab', 'Michael Zhang'] | 2023-03-16 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 3.38032037e-01 -2.85520554e-01 2.18385682e-02 -2.45760426e-01
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667312ff-7cf1-4765-b142-9792b0f7fda2 | learning-fast-and-slow-a-goal-directed-memory | 2301.13758 | null | https://arxiv.org/abs/2301.13758v2 | https://arxiv.org/pdf/2301.13758v2.pdf | Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic Environments | Model-based next state prediction and state value prediction are slow to converge. To address these challenges, we do the following: i) Instead of a neural network, we do model-based planning using a parallel memory retrieval system (which we term the slow mechanism); ii) Instead of learning state values, we guide the agent's actions using goal-directed exploration, by using a neural network to choose the next action given the current state and the goal state (which we term the fast mechanism). The goal-directed exploration is trained online using hippocampal replay of visited states and future imagined states every single time step, leading to fast and efficient training. Empirical studies show that our proposed method has a 92% solve rate across 100 episodes in a dynamically changing grid world, significantly outperforming state-of-the-art actor critic mechanisms such as PPO (54%), TRPO (50%) and A2C (24%). Ablation studies demonstrate that both mechanisms are crucial. We posit that the future of Reinforcement Learning (RL) will be to model goals and sub-goals for various tasks, and plan it out in a goal-directed memory-based approach. | ['John Chong Min Tan', 'Mehul Motani'] | 2023-01-31 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-3.62468744e-03 5.09885550e-01 -3.89818609e-01 1.27457455e-01
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ed4171d8-165a-4fe0-b13e-8f8fa88c8379 | pie-net-photometric-invariant-edge-guided | 2203.16670 | null | https://arxiv.org/abs/2203.16670v2 | https://arxiv.org/pdf/2203.16670v2.pdf | PIE-Net: Photometric Invariant Edge Guided Network for Intrinsic Image Decomposition | Intrinsic image decomposition is the process of recovering the image formation components (reflectance and shading) from an image. Previous methods employ either explicit priors to constrain the problem or implicit constraints as formulated by their losses (deep learning). These methods can be negatively influenced by strong illumination conditions causing shading-reflectance leakages. Therefore, in this paper, an end-to-end edge-driven hybrid CNN approach is proposed for intrinsic image decomposition. Edges correspond to illumination invariant gradients. To handle hard negative illumination transitions, a hierarchical approach is taken including global and local refinement layers. We make use of attention layers to further strengthen the learning process. An extensive ablation study and large scale experiments are conducted showing that it is beneficial for edge-driven hybrid IID networks to make use of illumination invariant descriptors and that separating global and local cues helps in improving the performance of the network. Finally, it is shown that the proposed method obtains state of the art performance and is able to generalise well to real world images. The project page with pretrained models, finetuned models and network code can be found at https://ivi.fnwi.uva.nl/cv/pienet/. | ['Theo Gevers', 'Sezer Karaoglu', 'Partha Das'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Das_PIE-Net_Photometric_Invariant_Edge_Guided_Network_for_Intrinsic_Image_Decomposition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 4.40298408e-01 7.30881542e-02 1.53840810e-01 -3.44192564e-01
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d150c834-2a52-4d48-89ea-029a7d9f8748 | joint-estimation-of-age-and-gender-from | 1806.02023 | null | http://arxiv.org/abs/1806.02023v1 | http://arxiv.org/pdf/1806.02023v1.pdf | Joint Estimation of Age and Gender from Unconstrained Face Images using Lightweight Multi-task CNN for Mobile Applications | Automatic age and gender classification based on unconstrained images has
become essential techniques on mobile devices. With limited computing power,
how to develop a robust system becomes a challenging task. In this paper, we
present an efficient convolutional neural network (CNN) called lightweight
multi-task CNN for simultaneous age and gender classification. Lightweight
multi-task CNN uses depthwise separable convolution to reduce the model size
and save the inference time. On the public challenging Adience dataset, the
accuracy of age and gender classification is better than baseline multi-task
CNN methods. | ['Chu-Song Chen', 'Ting-Yen Chen', 'Yi-Ming Chan', 'Jia-Hong Lee'] | 2018-06-06 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-2.60136843e-01 -6.99094608e-02 -2.90201247e-01 -7.94635832e-01
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fbe545c1-5f54-4dbc-9573-2a7bf1e21d79 | convolutional-neural-network-with-median | 1908.06452 | null | https://arxiv.org/abs/1908.06452v1 | https://arxiv.org/pdf/1908.06452v1.pdf | Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations | We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise. A median layer simply performs median filtering on all feature channels. By adding this kind of layer into some widely used fully convolutional deep neural networks, we develop an end-to-end network that removes the extremely high-level s&p noise without performing any non-trivial preprocessing tasks, which is different from all the existing literature in s&p noise removal. Experiments show that inserting median layers into a simple fully-convolutional network with the L2 loss significantly boosts the signal-to-noise ratio. Quantitative comparisons testify that our network outperforms the state-of-the-art methods with a limited amount of training data. The source code has been released for public evaluation and use (https://github.com/llmpass/medianDenoise). | ['Jing Qin', 'Luming Liang', 'Xinming Wu', 'Sen Deng', 'Mingqiang Wei', 'Lionel Gueguen'] | 2019-08-18 | null | null | null | null | ['salt-and-pepper-noise-removal'] | ['computer-vision'] | [ 1.17388263e-01 -2.90121526e-01 3.21186423e-01 -4.99049664e-01
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99fd0d53-3112-45c2-aa09-972231f3525a | mantis-at-tsar-2022-shared-task-improved | 2212.09855 | null | https://arxiv.org/abs/2212.09855v1 | https://arxiv.org/pdf/2212.09855v1.pdf | MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders | In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system in the following ways: For the subtask of simplification candidate selection, it utilizes a RoBERTa transformer language model and expands the size of the generated candidate list. For subsequent substitution ranking, it introduces a new feature weighting scheme and adopts a candidate filtering method based on textual entailment to maximize semantic similarity between the target word and its simplification. Our best-performing system improves LSBert by 5.9% accuracy and achieves second place out of 33 ranked solutions. | ['Elma Kerz', 'Yu Qiao', 'Daniel Wiechmann', 'Xiaofei Li'] | 2022-12-19 | null | null | null | null | ['lexical-simplification'] | ['natural-language-processing'] | [ 2.53484905e-01 6.93667054e-01 -1.16294228e-01 -1.63986564e-01
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34c05465-12fd-4c03-b73f-eb29dd950f46 | a-survey-on-intrinsic-images-delving-deep | 2112.03842 | null | https://arxiv.org/abs/2112.03842v1 | https://arxiv.org/pdf/2112.03842v1.pdf | A Survey on Intrinsic Images: Delving Deep Into Lambert and Beyond | Intrinsic imaging or intrinsic image decomposition has traditionally been described as the problem of decomposing an image into two layers: a reflectance, the albedo invariant color of the material; and a shading, produced by the interaction between light and geometry. Deep learning techniques have been broadly applied in recent years to increase the accuracy of those separations. In this survey, we overview those results in context of well-known intrinsic image data sets and relevant metrics used in the literature, discussing their suitability to predict a desirable intrinsic image decomposition. Although the Lambertian assumption is still a foundational basis for many methods, we show that there is increasing awareness on the potential of more sophisticated physically-principled components of the image formation process, that is, optically accurate material models and geometry, and more complete inverse light transport estimations. We classify these methods in terms of the type of decomposition, considering the priors and models used, as well as the learning architecture and methodology driving the decomposition process. We also provide insights about future directions for research, given the recent advances in neural, inverse and differentiable rendering techniques. | ['Jorge Lopez-Moreno', 'Dan Casas', 'Carlos Rodriguez-Pardo', 'Elena Garces'] | 2021-12-07 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 7.97442675e-01 -7.92236403e-02 1.95125267e-01 -3.29487324e-01
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25da69f5-9d7f-41ff-bb86-010e83157823 | open-set-semi-supervised-learning-for-3d | 2205.01006 | null | https://arxiv.org/abs/2205.01006v1 | https://arxiv.org/pdf/2205.01006v1.pdf | Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding | Semantic understanding of 3D point cloud relies on learning models with massively annotated data, which, in many cases, are expensive or difficult to collect. This has led to an emerging research interest in semi-supervised learning (SSL) for 3D point cloud. It is commonly assumed in SSL that the unlabeled data are drawn from the same distribution as that of the labeled ones; This assumption, however, rarely holds true in realistic environments. Blindly using out-of-distribution (OOD) unlabeled data could harm SSL performance. In this work, we propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized. To estimate the weights, we adopt a bi-level optimization framework which iteratively optimizes a metaobjective on a held-out validation set and a task-objective on a training set. Faced with the instability of efficient bi-level optimizers, we further propose three regularization techniques to enhance the training stability. Extensive experiments on 3D point cloud classification and segmentation tasks verify the effectiveness of our proposed method. We also demonstrate the feasibility of a more efficient training strategy. | ['Kui Jia', 'Chuan Sheng Foo', 'Xiatian Zhu', 'Wanyue Zhang', 'Xun Xu', 'Xian Shi'] | 2022-05-02 | null | null | null | null | ['3d-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [ 8.05928484e-02 -2.07948666e-02 -2.68313229e-01 -5.92746913e-01
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848acf54-e64f-4a12-9c44-761761d4df35 | crosshuman-learning-cross-guidance-from-multi | 2207.09735 | null | https://arxiv.org/abs/2207.09735v1 | https://arxiv.org/pdf/2207.09735v1.pdf | CrossHuman: Learning Cross-Guidance from Multi-Frame Images for Human Reconstruction | We propose CrossHuman, a novel method that learns cross-guidance from parametric human model and multi-frame RGB images to achieve high-quality 3D human reconstruction. To recover geometry details and texture even in invisible regions, we design a reconstruction pipeline combined with tracking-based methods and tracking-free methods. Given a monocular RGB sequence, we track the parametric human model in the whole sequence, the points (voxels) corresponding to the target frame are warped to reference frames by the parametric body motion. Guided by the geometry priors of the parametric body and spatially aligned features from RGB sequence, the robust implicit surface is fused. Moreover, a multi-frame transformer (MFT) and a self-supervised warp refinement module are integrated to the framework to relax the requirements of parametric body and help to deal with very loose cloth. Compared with previous works, our CrossHuman enables high-fidelity geometry details and texture in both visible and invisible regions and improves the accuracy of the human reconstruction even under estimated inaccurate parametric human models. The experiments demonstrate that our method achieves state-of-the-art (SOTA) performance. | ['Yandong Guo', 'Han Huang', 'Jiaqi Li', 'Liliang Chen'] | 2022-07-20 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 7.20877498e-02 3.74718904e-02 2.54814237e-01 -2.73053963e-02
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81664b58-5a70-4890-9fc9-2718c64ce4ff | weakly-supervised-instance-segmentation-using-1 | 1804.00880 | null | http://arxiv.org/abs/1804.00880v1 | http://arxiv.org/pdf/1804.00880v1.pdf | Weakly Supervised Instance Segmentation using Class Peak Response | Weakly supervised instance segmentation with image-level labels, instead of
expensive pixel-level masks, remains unexplored. In this paper, we tackle this
challenging problem by exploiting class peak responses to enable a
classification network for instance mask extraction. With image labels
supervision only, CNN classifiers in a fully convolutional manner can produce
class response maps, which specify classification confidence at each image
location. We observed that local maximums, i.e., peaks, in a class response map
typically correspond to strong visual cues residing inside each instance.
Motivated by this, we first design a process to stimulate peaks to emerge from
a class response map. The emerged peaks are then back-propagated and
effectively mapped to highly informative regions of each object instance, such
as instance boundaries. We refer to the above maps generated from class peak
responses as Peak Response Maps (PRMs). PRMs provide a fine-detailed
instance-level representation, which allows instance masks to be extracted even
with some off-the-shelf methods. To the best of our knowledge, we for the first
time report results for the challenging image-level supervised instance
segmentation task. Extensive experiments show that our method also boosts
weakly supervised pointwise localization as well as semantic segmentation
performance, and reports state-of-the-art results on popular benchmarks,
including PASCAL VOC 2012 and MS COCO. | ['Yi Zhu', 'Jianbin Jiao', 'Qixiang Ye', 'Qiang Qiu', 'Yanzhao Zhou'] | 2018-04-03 | weakly-supervised-instance-segmentation-using-2 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhou_Weakly_Supervised_Instance_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Weakly_Supervised_Instance_CVPR_2018_paper.pdf | cvpr-2018-6 | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 8.10453415e-01 4.46868122e-01 -4.02585059e-01 -5.92958152e-01
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c21369e1-3f00-4ec2-854b-02e594ced59a | robust-subgroup-discovery | 2103.13686 | null | https://arxiv.org/abs/2103.13686v4 | https://arxiv.org/pdf/2103.13686v4.pdf | Robust subgroup discovery | We introduce the problem of robust subgroup discovery, i.e., finding a set of interpretable descriptions of subsets that 1) stand out with respect to one or more target attributes, 2) are statistically robust, and 3) non-redundant. Many attempts have been made to mine either locally robust subgroups or to tackle the pattern explosion, but we are the first to address both challenges at the same time from a global modelling perspective. First, we formulate the broad model class of subgroup lists, i.e., ordered sets of subgroups, for univariate and multivariate targets that can consist of nominal or numeric variables, including traditional top-1 subgroup discovery in its definition. This novel model class allows us to formalise the problem of optimal robust subgroup discovery using the Minimum Description Length (MDL) principle, where we resort to optimal Normalised Maximum Likelihood and Bayesian encodings for nominal and numeric targets, respectively. Second, finding optimal subgroup lists is NP-hard. Therefore, we propose SSD++, a greedy heuristic that finds good subgroup lists and guarantees that the most significant subgroup found according to the MDL criterion is added in each iteration. In fact, the greedy gain is shown to be equivalent to a Bayesian one-sample proportion, multinomial, or t-test between the subgroup and dataset marginal target distributions plus a multiple hypothesis testing penalty. Furthermore, we empirically show on 54 datasets that SSD++ outperforms previous subgroup discovery methods in terms of quality, generalisation on unseen data, and subgroup list size. | ['Peter Grünwald', 'Thomas Bäck', 'Matthijs van Leeuwen', 'Hugo Manuel Proença'] | 2021-03-25 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [ 6.24759436e-01 4.47461873e-01 -5.01015484e-01 -4.97446626e-01
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82c655d5-cbf9-44e8-9cd0-f1501d4edcc2 | expert-guided-symmetry-detection-in-markov | 2111.10297 | null | https://arxiv.org/abs/2111.10297v1 | https://arxiv.org/pdf/2111.10297v1.pdf | Expert-Guided Symmetry Detection in Markov Decision Processes | Learning a Markov Decision Process (MDP) from a fixed batch of trajectories is a non-trivial task whose outcome's quality depends on both the amount and the diversity of the sampled regions of the state-action space. Yet, many MDPs are endowed with invariant reward and transition functions with respect to some transformations of the current state and action. Being able to detect and exploit these structures could benefit not only the learning of the MDP but also the computation of its subsequent optimal control policy. In this work we propose a paradigm, based on Density Estimation methods, that aims to detect the presence of some already supposed transformations of the state-action space for which the MDP dynamics is invariant. We tested the proposed approach in a discrete toroidal grid environment and in two notorious environments of OpenAI's Gym Learning Suite. The results demonstrate that the model distributional shift is reduced when the dataset is augmented with the data obtained by using the detected symmetries, allowing for a more thorough and data-efficient learning of the transition functions. | ['Caroline P. C. Chanel', 'Nicolas Drougard', 'Giorgio Angelotti'] | 2021-11-19 | null | null | null | null | ['symmetry-detection'] | ['computer-vision'] | [-2.59388592e-02 1.16447687e-01 -1.21563412e-01 -8.68249312e-02
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737f3510-661f-412a-bc32-8088bca355af | uncertain-facial-expression-recognition-via | 2212.07144 | null | https://arxiv.org/abs/2212.07144v1 | https://arxiv.org/pdf/2212.07144v1.pdf | Uncertain Facial Expression Recognition via Multi-task Assisted Correction | Deep models for facial expression recognition achieve high performance by training on large-scale labeled data. However, publicly available datasets contain uncertain facial expressions caused by ambiguous annotations or confusing emotions, which could severely decline the robustness. Previous studies usually follow the bias elimination method in general tasks without considering the uncertainty problem from the perspective of different corresponding sources. In this paper, we propose a novel method of multi-task assisted correction in addressing uncertain facial expression recognition called MTAC. Specifically, a confidence estimation block and a weighted regularization module are applied to highlight solid samples and suppress uncertain samples in every batch. In addition, two auxiliary tasks, i.e., action unit detection and valence-arousal measurement, are introduced to learn semantic distributions from a data-driven AU graph and mitigate category imbalance based on latent dependencies between discrete and continuous emotions, respectively. Moreover, a re-labeling strategy guided by feature-level similarity constraint further generates new labels for identified uncertain samples to promote model learning. The proposed method can flexibly combine with existing frameworks in a fully-supervised or weakly-supervised manner. Experiments on RAF-DB, AffectNet, and AffWild2 datasets demonstrate that the MTAC obtains substantial improvements over baselines when facing synthetic and real uncertainties and outperforms the state-of-the-art methods. | ['Guoying Zhao', 'Janne Kauttonen', 'Xingming Zhang', 'Yang Liu'] | 2022-12-14 | null | null | null | null | ['facial-expression-recognition', 'action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.25185984e-01 1.29223153e-01 -1.99148655e-01 -1.17745757e+00
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057827f2-b4ff-437f-9f19-b5927a35be77 | dense-hybrid-recurrent-multi-view-stereo-net | 2007.10872 | null | https://arxiv.org/abs/2007.10872v1 | https://arxiv.org/pdf/2007.10872v1.pdf | Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking | In this paper, we propose an efficient and effective dense hybrid recurrent multi-view stereo net with dynamic consistency checking, namely $D^{2}$HC-RMVSNet, for accurate dense point cloud reconstruction. Our novel hybrid recurrent multi-view stereo net consists of two core modules: 1) a light DRENet (Dense Reception Expanded) module to extract dense feature maps of original size with multi-scale context information, 2) a HU-LSTM (Hybrid U-LSTM) to regularize 3D matching volume into predicted depth map, which efficiently aggregates different scale information by coupling LSTM and U-Net architecture. To further improve the accuracy and completeness of reconstructed point clouds, we leverage a dynamic consistency checking strategy instead of prefixed parameters and strategies widely adopted in existing methods for dense point cloud reconstruction. In doing so, we dynamically aggregate geometric consistency matching error among all the views. Our method ranks \textbf{$1^{st}$} on the complex outdoor \textsl{Tanks and Temples} benchmark over all the methods. Extensive experiments on the in-door DTU dataset show our method exhibits competitive performance to the state-of-the-art method while dramatically reduces memory consumption, which costs only $19.4\%$ of R-MVSNet memory consumption. The codebase is available at \hyperlink{https://github.com/yhw-yhw/D2HC-RMVSNet}{https://github.com/yhw-yhw/D2HC-RMVSNet}. | ['Jian-Feng Yan', 'Yu-Wing Tai', 'Yisong Chen', 'Zizhuang Wei', 'Runze Zhang', 'Mingyu Ding', 'Hongwei Yi', 'Guoping Wang'] | 2020-07-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2666_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490647.pdf | eccv-2020-8 | ['point-cloud-reconstruction'] | ['computer-vision'] | [-2.66376436e-01 -1.92744583e-01 3.89108837e-01 -3.08960617e-01
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2282fe0d-00de-4251-907f-cdca293ca3ac | weakly-supervised-visualbert-pre-training | 2010.12831 | null | https://arxiv.org/abs/2010.12831v2 | https://arxiv.org/pdf/2010.12831v2.pdf | Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions | Pre-trained contextual vision-and-language (V&L) models have achieved impressive performance on various benchmarks. However, existing models require a large amount of parallel image-caption data for pre-training. Such data are costly to collect and require cumbersome curation. Inspired by unsupervised machine translation, we investigate if a strong V&L representation model can be learned through unsupervised pre-training without image-caption corpora. In particular, we propose to conduct ``mask-and-predict'' pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. We find that such a simple approach achieves performance close to a model pre-trained with aligned data, on four English V&L benchmarks. Our work challenges the widely held notion that aligned data is necessary for V&L pre-training, while significantly reducing the amount of supervision needed for V&L models. | ['Kai-Wei Chang', 'Shih-Fu Chang', 'Alireza Zareian', 'Zhecan Wang', 'Haoxuan You', 'Liunian Harold Li'] | 2020-10-24 | null | https://aclanthology.org/2021.naacl-main.420 | https://aclanthology.org/2021.naacl-main.420.pdf | naacl-2021-4 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 6.82502925e-01 3.22991222e-01 -4.47989851e-01 -5.97326398e-01
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11998215-04f5-49c5-a4e0-40ded8a6b38c | anomalous-subdiffusion-in-living-cells | 2004.01114 | null | https://arxiv.org/abs/2004.01114v1 | https://arxiv.org/pdf/2004.01114v1.pdf | Anomalous subdiffusion in living cells: bridging the gap between experiments and realistic models through collaborative challenges | The life of a cell is governed by highly dynamical microscopic processes. Two notable examples are the diffusion of membrane receptors and the kinetics of transcription factors governing the rates of gene expression. Different fluorescence imaging techniques have emerged to study molecular dynamics. Among them, fluorescence correlation spectroscopy (FCS) and single-particle tracking (SPT) have proven to be instrumental to our understanding of cell dynamics and function. The analysis of SPT and FCS is an ongoing effort, and despite decades of work, much progress remains to be done. In this paper, we give a quick overview of the existing techniques used to analyze anomalous diffusion in cells and propose a collaborative challenge to foster the development of state-of-the-art analysis algorithms. We propose to provide labelled (training) and unlabelled (evaluation) simulated data to competitors all over the world in an open and fair challenge. The goal is to offer unified data benchmarks based on biologically-relevant metrics in order to compare the diffusion analysis software available for the community. | ['Hugues Berry', 'Cyril Favard', 'Ignacio Izeddin', 'Maxime Woringer'] | 2020-04-02 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 2.54196882e-01 -5.76489508e-01 1.84513420e-01 -1.86492622e-01
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00185735-5b2f-4f49-b6bc-6ff115b176a6 | tr-misr-multiimage-super-resolution-based-on | null | null | https://ieeexplore.ieee.org/abstract/document/9684717 | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9684717 | TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers | Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with details emerges as a challenging problem. However, most MISR methods based on deep learning cannot make full use of multiple images. Their fusion modules are incapable of adapting to an image sequence with weak temporal correlations well. To cope with these problems, we propose a novel end-to-end framework called TR-MISR. It consists of three parts: An encoder based on residual blocks, a transformer-based fusion module, and a decoder based on subpixel convolution. Specifically, by rearranging multiple feature maps into vectors, the fusion module can assign dynamic attention to the same area of different satellite images simultaneously. In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent.TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches. Extensive experiments performed on the PROBA-V Kelvin dataset demonstrate the superiority of the proposed model that provides an effective method for transformers in other low-level vision tasks. | ['Chunhong Pan', 'Lingfeng Wang', 'Bin Xue', 'Chunlei Huo', 'Xin Zhang', 'Tai An'] | 2022-02-05 | null | null | null | ieee-journal-of-selected-topics-in-applied-3 | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 3.23177040e-01 -4.56078649e-01 3.11771035e-01 -4.03592378e-01
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902bbe8e-a5de-437e-82b9-1d74d62b647b | unsupervised-representation-learning-for-3d | 2110.06632 | null | https://arxiv.org/abs/2110.06632v2 | https://arxiv.org/pdf/2110.06632v2.pdf | Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data | Though a number of point cloud learning methods have been proposed to handle unordered points, most of them are supervised and require labels for training. By contrast, unsupervised learning of point cloud data has received much less attention to date. In this paper, we propose a simple yet effective approach for unsupervised point cloud learning. In particular, we identify a very useful transformation which generates a good contrastive version of an original point cloud. They make up a pair. After going through a shared encoder and a shared head network, the consistency between the output representations are maximized with introducing two variants of contrastive losses to respectively facilitate downstream classification and segmentation. To demonstrate the efficacy of our method, we conduct experiments on three downstream tasks which are 3D object classification (on ModelNet40 and ModelNet10), shape part segmentation (on ShapeNet Part dataset) as well as scene segmentation (on S3DIS). Comprehensive results show that our unsupervised contrastive representation learning enables impressive outcomes in object classification and semantic segmentation. It generally outperforms current unsupervised methods, and even achieves comparable performance to supervised methods. Our source codes will be made publicly available. | ['Meili Wang', 'Wanli Ouyang', 'Xuequan Lu', 'Jincen Jiang'] | 2021-10-13 | null | null | null | null | ['3d-object-classification', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.05810100e-01 1.11512788e-01 -3.04622263e-01 -7.59017348e-01
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7842f3e1-7814-4e2e-8033-a5d579edc656 | automated-stitching-of-coral-reef-images-and | 2006.15478 | null | https://arxiv.org/abs/2006.15478v1 | https://arxiv.org/pdf/2006.15478v1.pdf | Automated Stitching of Coral Reef Images and Extraction of Features for Damselfish Shoaling Behavior Analysis | Behavior analysis of animals involves the observation of intraspecific and interspecific interactions among various organisms in the environment. Collective behavior such as herding in farm animals, flocking of birds, and shoaling and schooling of fish provide information on its benefits on collective survival, fitness, reproductive patterns, group decision-making, and effects in animal epidemiology. In marine ethology, the investigation of behavioral patterns in schooling species can provide supplemental information in the planning and management of marine resources. Currently, damselfish species, although prevalent in tropical waters, have no adequate established base behavior information. This limits reef managers in efficiently planning for stress and disaster responses in protecting the reef. Visual marine data captured in the wild are scarce and prone to multiple scene variations, primarily caused by motion and changes in the natural environment. The gathered videos of damselfish by this research exhibit several scene distortions caused by erratic camera motions during acquisition. To effectively analyze shoaling behavior given the issues posed by capturing data in the wild, we propose a pre-processing system that utilizes color correction and image stitching techniques and extracts behavior features for manual analysis. | ['Kristofer delas Peñas', 'Riza Rae Pineda', 'Dana Manogan'] | 2020-06-28 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 1.58042163e-02 -6.01688087e-01 6.32087469e-01 -3.02180886e-01
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2eba7be4-63e0-427d-9ef3-0492d32e45bf | autoencoder-based-iterative-modeling-and | 2209.04213 | null | https://arxiv.org/abs/2209.04213v2 | https://arxiv.org/pdf/2209.04213v2.pdf | Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm | This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become more and more important due to the increase of availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training condition based maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data which could be used for training a CbM model would emerge. Therefore, we introduce an algorithm which uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems. | ['Clemens Gühmann', 'Lars Henning', 'Jonas Köhne'] | 2022-09-09 | null | null | null | null | ['clustering-algorithms-evaluation', 'clustering-multivariate-time-series'] | ['methodology', 'time-series'] | [ 2.38708243e-01 -5.26986599e-01 2.28384405e-01 -3.06225181e-01
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2ede77d0-f534-408c-a2ec-dbe5e8d266db | computer-vision-with-deep-learning-for-plant | 2006.11391 | null | https://arxiv.org/abs/2006.11391v1 | https://arxiv.org/pdf/2006.11391v1.pdf | Computer Vision with Deep Learning for Plant Phenotyping in Agriculture: A Survey | In light of growing challenges in agriculture with ever growing food demand across the world, efficient crop management techniques are necessary to increase crop yield. Precision agriculture techniques allow the stakeholders to make effective and customized crop management decisions based on data gathered from monitoring crop environments. Plant phenotyping techniques play a major role in accurate crop monitoring. Advancements in deep learning have made previously difficult phenotyping tasks possible. This survey aims to introduce the reader to the state of the art research in deep plant phenotyping. | ['Vineeth N. Balasubramanian', 'Sai Vikas Desai', 'Wei Guo', 'Akshay L Chandra'] | 2020-06-18 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.26999569e-01 -3.49161327e-01 -5.60584843e-01 2.39273198e-02
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3224b739-f8f8-4d55-8d15-a44ed4546a58 | curriculum-guided-abstractive-summarization | 2302.00954 | null | https://arxiv.org/abs/2302.00954v1 | https://arxiv.org/pdf/2302.00954v1.pdf | Curriculum-guided Abstractive Summarization for Mental Health Online Posts | Automatically generating short summaries from users' online mental health posts could save counselors' reading time and reduce their fatigue so that they can provide timely responses to those seeking help for improving their mental state. Recent Transformers-based summarization models have presented a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal with more complicated tasks such as novel word generation and sentence paraphrasing. Nonetheless, these models have a prominent shortcoming; their training strategy is not quite efficient, which restricts the model's performance. In this paper, we include a curriculum learning approach to reweigh the training samples, bringing about an efficient learning procedure. We apply our model on extreme summarization dataset of MentSum posts -- a dataset of mental health related posts from Reddit social media. Compared to the state-of-the-art model, our proposed method makes substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding 3.5% (Rouge-1), 10.4% (Rouge-2), and 4.7% (Rouge-L), 1.5% (Bertscore) relative improvements. | ['Franck Dernoncourt', 'Hanieh Deilamsalehy', 'Nazli Goharian', 'Sajad Sotudeh'] | 2023-02-02 | null | null | null | null | ['abstractive-text-summarization', 'extreme-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.98429883e-01 8.25198233e-01 -4.48605269e-01 -3.96604091e-01
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299ffb8b-6ae3-4605-9a15-ca8567dbc21b | generative-pointnet-energy-based-learning-on | 2004.01301 | null | https://arxiv.org/abs/2004.01301v2 | https://arxiv.org/pdf/2004.01301v2.pdf | Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification | We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network. The energy function learns a coordinate encoding of each point and then aggregates all individual point features into an energy for the whole point cloud. We call our model the Generative PointNet because it can be derived from the discriminative PointNet. Our model can be trained by MCMC-based maximum likelihood learning (as well as its variants), without the help of any assisting networks like those in GANs and VAEs. Unlike most point cloud generators that rely on hand-crafted distance metrics, our model does not require any hand-crafted distance metric for the point cloud generation, because it synthesizes point clouds by matching observed examples in terms of statistical properties defined by the energy function. Furthermore, we can learn a short-run MCMC toward the energy-based model as a flow-like generator for point cloud reconstruction and interpolation. The learned point cloud representation can be useful for point cloud classification. Experiments demonstrate the advantages of the proposed generative model of point clouds. | ['Song-Chun Zhu', 'Zilong Zheng', 'Ying Nian Wu', 'Jianwen Xie', 'Yifei Xu'] | 2020-04-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xie_Generative_PointNet_Deep_Energy-Based_Learning_on_Unordered_Point_Sets_for_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-reconstruction', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [-3.33726220e-02 1.81417286e-01 5.85011691e-02 -4.26507860e-01
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b445cf36-9350-4a79-914b-c2def2b4b6dd | hierarchical-integration-diffusion-model-for | 2305.12966 | null | https://arxiv.org/abs/2305.12966v2 | https://arxiv.org/pdf/2305.12966v2.pdf | Hierarchical Integration Diffusion Model for Realistic Image Deblurring | Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computational resources. Moreover, the distribution synthesized by the diffusion model is often misaligned with the target results, leading to restrictions in distortion-based metrics. To address the above issues, we propose the Hierarchical Integration Diffusion Model (HI-Diff), for realistic image deblurring. Specifically, we perform the DM in a highly compacted latent space to generate the prior feature for the deblurring process. The deblurring process is implemented by a regression-based method to obtain better distortion accuracy. Meanwhile, the highly compact latent space ensures the efficiency of the DM. Furthermore, we design the hierarchical integration module to fuse the prior into the regression-based model from multiple scales, enabling better generalization in complex blurry scenarios. Comprehensive experiments on synthetic and real-world blur datasets demonstrate that our HI-Diff outperforms state-of-the-art methods. Code and trained models are available at https://github.com/zhengchen1999/HI-Diff. | ['Xin Yuan', 'Linghe Kong', 'Jinjin Gu', 'Bin Xia', 'Ding Liu', 'Yulun Zhang', 'Zheng Chen'] | 2023-05-22 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [-1.46222219e-01 -6.44201934e-01 2.24945083e-01 -5.71370982e-02
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834a095f-5618-4347-af2d-c029e785c52e | dlue-benchmarking-document-language | 2305.09520 | null | https://arxiv.org/abs/2305.09520v1 | https://arxiv.org/pdf/2305.09520v1.pdf | DLUE: Benchmarking Document Language Understanding | Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, which significantly hinders the fair comparison and measuring the progress of the field. To benchmark document understanding researches, this paper summarizes four representative abilities, i.e., document classification, document structural analysis, document information extraction, and document transcription. Under the new evaluation framework, we propose \textbf{Document Language Understanding Evaluation} -- \textbf{DLUE}, a new task suite which covers a wide-range of tasks in various forms, domains and document genres. We also systematically evaluate six well-established transformer models on DLUE, and find that due to the lengthy content, complicated underlying structure and dispersed knowledge, document understanding is still far from being solved, and currently there is no neural architecture that dominates all tasks, raising requirements for a universal document understanding architecture. | ['Le Sun', 'Yingfei Sun', 'Xianpei Han', 'Xinyan Guan', 'Hongyu Lin', 'Ruoxi Xu'] | 2023-05-16 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 4.96093273e-01 -1.06823459e-01 -3.06480885e-01 -5.04372001e-01
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b85e7064-a3f8-4fe1-9081-bfab3fec0678 | temporal-relation-extraction-with-a-graph | 2201.06125 | null | https://arxiv.org/abs/2201.06125v1 | https://arxiv.org/pdf/2201.06125v1.pdf | Temporal Relation Extraction with a Graph-Based Deep Biaffine Attention Model | Temporal information extraction plays a critical role in natural language understanding. Previous systems have incorporated advanced neural language models and have successfully enhanced the accuracy of temporal information extraction tasks. However, these systems have two major shortcomings. First, they fail to make use of the two-sided nature of temporal relations in prediction. Second, they involve non-parallelizable pipelines in inference process that bring little performance gain. To this end, we propose a novel temporal information extraction model based on deep biaffine attention to extract temporal relationships between events in unstructured text efficiently and accurately. Our model is performant because we perform relation extraction tasks directly instead of considering event annotation as a prerequisite of relation extraction. Moreover, our architecture uses Multilayer Perceptrons (MLP) with biaffine attention to predict arcs and relation labels separately, improving relation detecting accuracy by exploiting the two-sided nature of temporal relationships. We experimentally demonstrate that our model achieves state-of-the-art performance in temporal relation extraction. | ['Amarnath Gupta', 'Kuan-Yin Lai', 'Shang-Ling Hsu', 'Bo-Ying Su'] | 2022-01-16 | null | null | null | null | ['temporal-relation-extraction', 'temporal-information-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.53875118e-02 3.89352649e-01 -5.30950010e-01 -5.26512980e-01
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e9d8d381-d346-452f-a42a-54f5938870a6 | few-shot-video-to-video-synthesis | 1910.12713 | null | https://arxiv.org/abs/1910.12713v1 | https://arxiv.org/pdf/1910.12713v1.pdf | Few-shot Video-to-Video Synthesis | Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous images of a target human subject or a scene are required for training. Second, a learned model has limited generalization capability. A pose-to-human vid2vid model can only synthesize poses of the single person in the training set. It does not generalize to other humans that are not in the training set. To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time. Our model achieves this few-shot generalization capability via a novel network weight generation module utilizing an attention mechanism. We conduct extensive experimental validations with comparisons to strong baselines using several large-scale video datasets including human-dancing videos, talking-head videos, and street-scene videos. The experimental results verify the effectiveness of the proposed framework in addressing the two limitations of existing vid2vid approaches. | ['Ming-Yu Liu', 'Ting-Chun Wang', 'Jan Kautz', 'Andrew Tao', 'Guilin Liu', 'Bryan Catanzaro'] | 2019-10-28 | few-shot-video-to-video-synthesis-1 | http://papers.nips.cc/paper/8746-few-shot-video-to-video-synthesis | http://papers.nips.cc/paper/8746-few-shot-video-to-video-synthesis.pdf | neurips-2019-12 | ['video-to-video-synthesis'] | ['computer-vision'] | [ 3.38689357e-01 5.03874719e-02 -3.44131291e-02 -3.22905689e-01
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44c9566a-b267-4665-9ba1-7e43bc47150b | connecting-metrics-for-shape-texture | 2301.10608 | null | https://arxiv.org/abs/2301.10608v1 | https://arxiv.org/pdf/2301.10608v1.pdf | Connecting metrics for shape-texture knowledge in computer vision | Modern artificial neural networks, including convolutional neural networks and vision transformers, have mastered several computer vision tasks, including object recognition. However, there are many significant differences between the behavior and robustness of these systems and of the human visual system. Deep neural networks remain brittle and susceptible to many changes in the image that do not cause humans to misclassify images. Part of this different behavior may be explained by the type of features humans and deep neural networks use in vision tasks. Humans tend to classify objects according to their shape while deep neural networks seem to rely mostly on texture. Exploring this question is relevant, since it may lead to better performing neural network architectures and to a better understanding of the workings of the vision system of primates. In this work, we advance the state of the art in our understanding of this phenomenon, by extending previous analyses to a much larger set of deep neural network architectures. We found that the performance of models in image classification tasks is highly correlated with their shape bias measured at the output and penultimate layer. Furthermore, our results showed that the number of neurons that represent shape and texture are strongly anti-correlated, thus providing evidence that there is competition between these two types of features. Finally, we observed that while in general there is a correlation between performance and shape bias, there are significant variations between architecture families. | ['Arlindo L. Oliveira', 'Tiago Marques', 'Tiago Oliveira'] | 2023-01-25 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 7.33603463e-02 -1.36557490e-01 1.92609355e-01 -4.98230040e-01
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0ee93638-81df-4d23-9791-4b2d76c470df | improving-frame-level-classifier-for-word | 2306.07949 | null | https://arxiv.org/abs/2306.07949v1 | https://arxiv.org/pdf/2306.07949v1.pdf | Improving Frame-level Classifier for Word Timings with Non-peaky CTC in End-to-End Automatic Speech Recognition | End-to-end (E2E) systems have shown comparable performance to hybrid systems for automatic speech recognition (ASR). Word timings, as a by-product of ASR, are essential in many applications, especially for subtitling and computer-aided pronunciation training. In this paper, we improve the frame-level classifier for word timings in E2E system by introducing label priors in connectionist temporal classification (CTC) loss, which is adopted from prior works, and combining low-level Mel-scale filter banks with high-level ASR encoder output as input feature. On the internal Chinese corpus, the proposed method achieves 95.68%/94.18% compared to the hybrid system 93.0%/90.22% on the word timing accuracy metrics. It also surpass a previous E2E approach with an absolute increase of 4.80%/8.02% on the metrics on 7 languages. In addition, we further improve word timing accuracy by delaying CTC peaks with frame-wise knowledge distillation, though only experimenting on LibriSpeech. | ['Zejun Ma', 'Yi He', 'Kang Wang', 'Yist Y. Lin', 'Xianzhao Chen'] | 2023-06-09 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 2.60699280e-02 -1.65451556e-01 -3.25698182e-02 -3.04890931e-01
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595b60f6-e860-4b27-9d5a-ac5bad444b47 | wizard-of-tasks-a-novel-conversational | null | null | https://aclanthology.org/2022.coling-1.310 | https://aclanthology.org/2022.coling-1.310.pdf | Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings | Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such conversations in two domains: Cooking and Home Improvement. We crowd-sourced a total of 549 conversations (18,077 utterances) with an asynchronous Wizard-of-Oz setup, relying on recipes from WholeFoods Market for the cooking domain, and WikiHow articles for the home improvement domain. We present a detailed data analysis and show that the collected data can be a valuable and challenging resource for CTAs in two tasks: Intent Classification (IC) and Abstractive Question Answering (AQA). While on IC we acquired a high performing model (>85% F1), on AQA the performance is far from being satisfactory (~27% BertScore-F1), suggesting that more work is needed to solve the task of low-resource AQA. | ['Eugene Agichtein', 'Oleg Rokhlenko', 'Shervin Malmasi', 'Marcus Collins', 'Giuseppe Castellucci', 'Jie Zhao', 'Nikhita Vedula', 'Saar Kuzi', 'Jason Ingyu Choi'] | null | null | null | null | coling-2022-10 | ['intent-classification'] | ['natural-language-processing'] | [ 7.52028599e-02 4.91679937e-01 2.82769650e-01 -6.21903181e-01
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1f8b34a2-d54d-4cc1-9cf4-7f59662fe946 | image-stylization-for-robust-features | 2008.06959 | null | https://arxiv.org/abs/2008.06959v1 | https://arxiv.org/pdf/2008.06959v1.pdf | Image Stylization for Robust Features | Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks. In this work we investigate if photorealistic image stylization improves robustness of local features to not only day-night, but also weather and season variations. We show that image stylization in addition to color augmentation is a powerful method of learning robust features. We evaluate learned features on visual localization benchmarks, outperforming state of the art baseline models despite training without ground-truth 3D correspondences using synthetic homographies only. We use trained feature networks to compete in Long-Term Visual Localization and Map-based Localization for Autonomous Driving challenges achieving competitive scores. | ['Juho Kannala', 'Iaroslav Melekhov', 'Daniyar Turmukhambetov', 'Gabriel J. Brostow'] | 2020-08-16 | null | null | null | null | ['image-stylization'] | ['computer-vision'] | [-2.12290704e-01 -3.54852140e-01 -1.80260345e-01 -4.54656363e-01
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de9cfe3e-8e86-4c8e-9a12-cf49a7bee6e2 | avoid-overthinking-in-self-supervised-models | 2211.08989 | null | https://arxiv.org/abs/2211.08989v1 | https://arxiv.org/pdf/2211.08989v1.pdf | Avoid Overthinking in Self-Supervised Models for Speech Recognition | Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result in slow inference and network overthinking, where predictions made from the last layer of large models is worse than those made from intermediate layers. Early exit (EE) strategies can solve both issues by dynamically reducing computations at inference time for certain samples. Although popular for classification tasks in vision and language, EE has seen less use for sequence-to-sequence speech recognition (ASR) tasks where outputs from early layers are often degenerate. This challenge is further compounded when speech SSL models are applied on out-of-distribution (OOD) data. This paper first shows that SSL models do overthinking in ASR. We then motivate further research in EE by computing an optimal bound for performance versus speed trade-offs. To approach this bound we propose two new strategies for ASR: (1) we adapt the recently proposed patience strategy to ASR; and (2) we design a new EE strategy specific to ASR that performs better than all strategies previously introduced. | ['Shinji Watanabe', 'Brian Yan', 'Dan Berrebbi'] | 2022-11-01 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 3.63499910e-01 3.52399051e-01 4.19529490e-02 -4.59364355e-01
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67bc4996-e9cf-4ada-a350-de757ba5cecf | causality-aided-trade-off-analysis-for | 2305.13057 | null | https://arxiv.org/abs/2305.13057v1 | https://arxiv.org/pdf/2305.13057v1.pdf | Causality-Aided Trade-off Analysis for Machine Learning Fairness | There has been an increasing interest in enhancing the fairness of machine learning (ML). Despite the growing number of fairness-improving methods, we lack a systematic understanding of the trade-offs among factors considered in the ML pipeline when fairness-improving methods are applied. This understanding is essential for developers to make informed decisions regarding the provision of fair ML services. Nonetheless, it is extremely difficult to analyze the trade-offs when there are multiple fairness parameters and other crucial metrics involved, coupled, and even in conflict with one another. This paper uses causality analysis as a principled method for analyzing trade-offs between fairness parameters and other crucial metrics in ML pipelines. To ractically and effectively conduct causality analysis, we propose a set of domain-specific optimizations to facilitate accurate causal discovery and a unified, novel interface for trade-off analysis based on well-established causal inference methods. We conduct a comprehensive empirical study using three real-world datasets on a collection of widelyused fairness-improving techniques. Our study obtains actionable suggestions for users and developers of fair ML. We further demonstrate the versatile usage of our approach in selecting the optimal fairness-improving method, paving the way for more ethical and socially responsible AI technologies. | ['Yanhui Li', 'Shuai Wang', 'Pingchuan Ma', 'Zhenlan Ji'] | 2023-05-22 | null | null | null | null | ['causal-inference', 'causal-discovery', 'causal-inference'] | ['knowledge-base', 'knowledge-base', 'miscellaneous'] | [-2.66162734e-02 4.64481562e-02 -5.30635297e-01 -7.41348505e-01
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8ee14943-7e1e-41bd-ad0c-ae43b38ff6a2 | semeval-2017-task-6-hashtagwars-learning-a | null | null | https://aclanthology.org/S17-2004 | https://aclanthology.org/S17-2004.pdf | SemEval-2017 Task 6: \#HashtagWars: Learning a Sense of Humor | This paper describes a new shared task for humor understanding that attempts to eschew the ubiquitous binary approach to humor detection and focus on comparative humor ranking instead. The task is based on a new dataset of funny tweets posted in response to shared hashtags, collected from the {`}Hashtag Wars{'} segment of the TV show @midnight. The results are evaluated in two subtasks that require the participants to generate either the correct pairwise comparisons of tweets (subtask A), or the correct ranking of the tweets (subtask B) in terms of how funny they are. 7 teams participated in subtask A, and 5 teams participated in subtask B. The best accuracy in subtask A was 0.675. The best (lowest) rank edit distance for subtask B was 0.872. | ['Anna Rumshisky', 'Peter Potash', 'Alexey Romanov'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-1.75445508e-02 -5.38674695e-03 4.32394415e-01 -2.99589664e-01
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b7356580-9abc-4dd6-b816-4a65c3498bd0 | one-model-to-rule-them-all-multitask-and | 1711.01100 | null | http://arxiv.org/abs/1711.01100v1 | http://arxiv.org/pdf/1711.01100v1.pdf | One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis | When learning a new skill, you take advantage of your preexisting skills and
knowledge. For instance, if you are a skilled violinist, you will likely have
an easier time learning to play cello. Similarly, when learning a new language
you take advantage of the languages you already speak. For instance, if your
native language is Norwegian and you decide to learn Dutch, the lexical overlap
between these two languages will likely benefit your rate of language
acquisition. This thesis deals with the intersection of learning multiple tasks
and learning multiple languages in the context of Natural Language Processing
(NLP), which can be defined as the study of computational processing of human
language. Although these two types of learning may seem different on the
surface, we will see that they share many similarities.
The traditional approach in NLP is to consider a single task for a single
language at a time. However, recent advances allow for broadening this
approach, by considering data for multiple tasks and languages simultaneously.
This is an important approach to explore further as the key to improving the
reliability of NLP, especially for low-resource languages, is to take advantage
of all relevant data whenever possible. In doing so, the hope is that in the
long term, low-resource languages can benefit from the advances made in NLP
which are currently to a large extent reserved for high-resource languages.
This, in turn, may then have positive consequences for, e.g., language
preservation, as speakers of minority languages will have a lower degree of
pressure to using high-resource languages. In the short term, answering the
specific research questions posed should be of use to NLP researchers working
towards the same goal. | ['Johannes Bjerva'] | 2017-11-03 | null | null | null | null | ['lexical-analysis'] | ['natural-language-processing'] | [ 2.81533688e-01 2.44084597e-01 -1.90241545e-01 -1.77995756e-01
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429473af-25e6-4935-8213-6fcc0cd9bc86 | real-time-steganalysis-for-stream-media-based | 1902.01286 | null | http://arxiv.org/abs/1902.01286v1 | http://arxiv.org/pdf/1902.01286v1.pdf | Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows | Previous VoIP steganalysis methods face great challenges in detecting speech
signals at low embedding rates, and they are also generally difficult to
perform real-time detection, making them hard to truly maintain cyberspace
security. To solve these two challenges, in this paper, combined with the
sliding window detection algorithm and Convolution Neural Network we propose a
real-time VoIP steganalysis method which based on multi-channel convolution
sliding windows. In order to analyze the correlations between frames and
different neighborhood frames in a VoIP signal, we define multi channel sliding
detection windows. Within each sliding window, we design two feature extraction
channels which contain multiple convolution layers with multiple convolution
kernels each layer to extract correlation features of the input signal. Then
based on these extracted features, we use a forward fully connected network for
feature fusion. Finally, by analyzing the statistical distribution of these
features, the discriminator will determine whether the input speech signal
contains covert information or not.We designed several experiments to test the
proposed model's detection ability under various conditions, including
different embedding rates, different speech length, etc. Experimental results
showed that the proposed model outperforms all the previous methods, especially
in the case of low embedding rate, which showed state-of-the-art performance.
In addition, we also tested the detection efficiency of the proposed model, and
the results showed that it can achieve almost real-time detection of VoIP
speech signals. | ['Yu-Jin Zhang', 'Yuting Hu', 'Yongfeng Huang', 'Hao Yang', 'Zhongliang Yang'] | 2019-02-04 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 2.52748847e-01 -7.40819275e-01 -9.10254568e-02 4.48016316e-01
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f6db9621-1a01-4d63-896e-0582b75162ae | human-motion-detection-based-on-dual-graph | 2304.04879 | null | https://arxiv.org/abs/2304.04879v1 | https://arxiv.org/pdf/2304.04879v1.pdf | Human Motion Detection Based on Dual-Graph and Weighted Nuclear Norm Regularizations | Motion detection has been widely used in many applications, such as surveillance and robotics. Due to the presence of the static background, a motion video can be decomposed into a low-rank background and a sparse foreground. Many regularization techniques that preserve low-rankness of matrices can therefore be imposed on the background. In the meanwhile, geometry-based regularizations, such as graph regularizations, can be imposed on the foreground. Recently, weighted regularization techniques including the weighted nuclear norm regularization have been proposed in the image processing community to promote adaptive sparsity while achieving efficient performance. In this paper, we propose a robust dual graph regularized moving object detection model based on a novel weighted nuclear norm regularization and spatiotemporal graph Laplacians. Numerical experiments on realistic human motion data sets have demonstrated the effectiveness and robustness of this approach in separating moving objects from background, and the enormous potential in robotic applications. | ['Biyun Xie', 'Jing Qin'] | 2023-04-10 | null | null | null | null | ['motion-detection', 'moving-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.83398062e-01 -2.63361722e-01 -1.13102242e-01 1.69115942e-02
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a9999dad-5aac-43aa-9f0f-ebca689eabea | transformerg2g-adaptive-time-stepping-for | 2307.02588 | null | https://arxiv.org/abs/2307.02588v1 | https://arxiv.org/pdf/2307.02588v1.pdf | TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers | Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph generation) in various applications. Such temporal graphs exhibit heterogeneous transient dynamics, varying time intervals, and highly evolving node features throughout their evolution. Hence, incorporating long-range dependencies from the historical graph context plays a crucial role in accurately learning their temporal dynamics. In this paper, we develop a graph embedding model with uncertainty quantification, TransformerG2G, by exploiting the advanced transformer encoder to first learn intermediate node representations from its current state ($t$) and previous context (over timestamps [$t-1, t-l$], $l$ is the length of context). Moreover, we employ two projection layers to generate lower-dimensional multivariate Gaussian distributions as each node's latent embedding at timestamp $t$. We consider diverse benchmarks with varying levels of ``novelty" as measured by the TEA plots. Our experiments demonstrate that the proposed TransformerG2G model outperforms conventional multi-step methods and our prior work (DynG2G) in terms of both link prediction accuracy and computational efficiency, especially for high degree of novelty. Furthermore, the learned time-dependent attention weights across multiple graph snapshots reveal the development of an automatic adaptive time stepping enabled by the transformer. Importantly, by examining the attention weights, we can uncover temporal dependencies, identify influential elements, and gain insights into the complex interactions within the graph structure. For example, we identified a strong correlation between attention weights and node degree at the various stages of the graph topology evolution. | ['George Em Karniadakis', 'Mengjia Xu', 'Aniruddha Bora', 'Alan John Varghese'] | 2023-07-05 | null | null | null | null | ['graph-embedding', 'node-classification', 'link-prediction', 'graph-generation', 'dynamic-graph-embedding', 'anomaly-detection', 'recommendation-systems'] | ['graphs', 'graphs', 'graphs', 'graphs', 'graphs', 'methodology', 'miscellaneous'] | [-3.11353058e-01 7.33887181e-02 -2.43923917e-01 5.18550165e-02
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1b2fe7c1-9623-4f89-84e2-bc54d8da093d | little-red-riding-hood-goes-around-the-globe | 2212.10471 | null | https://arxiv.org/abs/2212.10471v2 | https://arxiv.org/pdf/2212.10471v2.pdf | Little Red Riding Hood Goes Around the Globe:Crosslingual Story Planning and Generation with Large Language Models | Previous work has demonstrated the effectiveness of planning for story generation exclusively in a monolingual setting focusing primarily on English. We consider whether planning brings advantages to automatic story generation across languages. We propose a new task of cross-lingual story generation with planning and present a new dataset for this task. We conduct a comprehensive study of different plans and generate stories in several languages, by leveraging the creative and reasoning capabilities of large pre-trained language models. Our results demonstrate that plans which structure stories into three acts lead to more coherent and interesting narratives, while allowing to explicitly control their content and structure. | ['Shashi Narayan', 'Mirella Lapata', 'Annie Louis', 'Joshua Maynez', 'Evgeniia Razumovskaia'] | 2022-12-20 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 1.86929718e-01 6.37923539e-01 -2.69518703e-01 -1.30693704e-01
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104ddfad-a8a1-42b0-ae35-0d30db38afbc | large-scale-visual-font-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Chen_Large-Scale_Visual_Font_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Chen_Large-Scale_Visual_Font_2014_CVPR_paper.pdf | Large-Scale Visual Font Recognition | This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content. Although visual font recognition has many practical applications, it has largely been neglected by the vision community. To address the VFR problem, we construct a large-scale dataset containing 2,420 font classes, which easily exceeds the scale of most image categorization datasets in computer vision. As font recognition is inherently dynamic and open-ended, i.e., new classes and data for existing categories are constantly added to the database over time, we propose a scalable solution based on the nearest class mean classifier (NCM). The core algorithm is built on local feature embedding, local feature metric learning and max-margin template selection, which is naturally amenable to NCM and thus to such open-ended classification problems. The new algorithm can generalize to new classes and new data at little added cost. Extensive experiments demonstrate that our approach is very effective on our synthetic test images, and achieves promising results on real world test images. | ['Eli Shechtman', 'Tony X. Han', 'Aseem Agarwala', 'Jianchao Yang', 'Hailin Jin', 'Guang Chen', 'Jonathan Brandt'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['font-recognition', 'image-categorization'] | ['computer-vision', 'computer-vision'] | [ 3.93919885e-01 -5.33328533e-01 -2.17063397e-01 -4.64061707e-01
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9f4b300a-5ecd-48be-aeef-34bd45838f28 | a-time-series-graph-cut-image-segmentation | 1809.05210 | null | http://arxiv.org/abs/1809.05210v1 | http://arxiv.org/pdf/1809.05210v1.pdf | A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors | Tumor detection in biomedical imaging is a time-consuming process for medical
professionals and is not without errors. Thus in recent decades, researchers
have developed algorithmic techniques for image processing using a wide variety
of mathematical methods, such as statistical modeling, variational techniques,
and machine learning. In this paper, we propose a semi-automatic method for
liver segmentation of 2D CT scans into three labels denoting healthy, vessel,
or tumor tissue based on graph cuts. First, we create a feature vector for each
pixel in a novel way that consists of the 59 intensity values in the time
series data and propose a simplified perimeter cost term in the energy
functional. We normalize the data and perimeter terms in the functional to
expedite the graph cut without having to optimize the scaling parameter
$\lambda$. In place of a training process, predetermined tissue means are
computed based on sample regions identified by expert radiologists. The
proposed method also has the advantage of being relatively simple to implement
computationally. It was evaluated against the ground truth on a clinical CT
dataset of 10 tumors and yielded segmentations with a mean Dice similarity
coefficient (DSC) of .77 and mean volume overlap error (VOE) of 36.7%. The
average processing time was 1.25 minutes per slice. | ['Yu-An Wang', 'Yufeng Cao', 'Brian Hobbs', 'Laramie Paxton', 'Kevin R. Vixie', 'Chaan Ng'] | 2018-09-13 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 2.53773153e-01 1.13437489e-01 1.48103178e-01 -3.78570795e-01
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d6f55c74-4de8-403d-a0a8-b937d80d768d | retrieving-and-highlighting-action-with | 2005.09183 | null | https://arxiv.org/abs/2005.09183v1 | https://arxiv.org/pdf/2005.09183v1.pdf | Retrieving and Highlighting Action with Spatiotemporal Reference | In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlighting, which visualizes where and when actions occur in an untrimmed video setting. Action highlighting is a fine-grained task, compared to conventional action recognition tasks which focus on classification or window-based localization. Leveraging weak supervision from annotated captions, our framework acquires spatiotemporal relevance maps and generates local embeddings which relate to the nouns and verbs in captions. Through experiments, we show that our model generates various maps conditioned on different actions, in which conventional visual reasoning methods only go as far as to show a single deterministic saliency map. Also, our model improves retrieval recall over our baseline without alignment by 2-3% on the MSR-VTT dataset. | ['Seito Kasai', 'Yuchi Ishikawa', 'Masaki Hayashi', 'Yoshimitsu Aoki', 'Hirokatsu Kataoka', 'Kensho Hara'] | 2020-05-19 | null | null | null | null | ['explainable-models', 'video-text-retrieval'] | ['computer-vision', 'computer-vision'] | [ 4.19796765e-01 -2.38154352e-01 -3.69229615e-01 -7.81745836e-02
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50fe402b-765a-48dc-afce-ef0cba0b3da7 | situation-aware-deep-reinforcement-learning | 2301.00124 | null | https://arxiv.org/abs/2301.00124v1 | https://arxiv.org/pdf/2301.00124v1.pdf | Situation-Aware Deep Reinforcement Learning for Autonomous Nonlinear Mobility Control in Cyber-Physical Loitering Munition Systems | According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms. | ['Joongheon Kim', 'Soyi Jung', 'Won Joon Yun', 'Soohyun Park', 'Hyunsoo Lee'] | 2022-12-31 | null | null | null | null | ['unity'] | ['computer-vision'] | [-2.27348953e-01 -3.67419660e-01 5.57147898e-02 1.33077502e-01
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d22d40b1-f555-4b96-91ea-468876ab7931 | full-body-cardiovascular-sensing-with-remote | 2303.09638 | null | https://arxiv.org/abs/2303.09638v1 | https://arxiv.org/pdf/2303.09638v1.pdf | Full-Body Cardiovascular Sensing with Remote Photoplethysmography | Remote photoplethysmography (rPPG) allows for noncontact monitoring of blood volume changes from a camera by detecting minor fluctuations in reflected light. Prior applications of rPPG focused on face videos. In this paper we explored the feasibility of rPPG from non-face body regions such as the arms, legs, and hands. We collected a new dataset titled Multi-Site Physiological Monitoring (MSPM), which will be released with this paper. The dataset consists of 90 frames per second video of exposed arms, legs, and face, along with 10 synchronized PPG recordings. We performed baseline heart rate estimation experiments from non-face regions with several state-of-the-art rPPG approaches, including chrominance-based (CHROM), plane-orthogonal-to-skin (POS) and RemotePulseNet (RPNet). To our knowledge, this is the first evaluation of the fidelity of rPPG signals simultaneously obtained from multiple regions of a human body. Our experiments showed that skin pixels from arms, legs, and hands are all potential sources of the blood volume pulse. The best-performing approach, POS, achieved a mean absolute error peaking at 7.11 beats per minute from non-facial body parts compared to 1.38 beats per minute from the face. Additionally, we performed experiments on pulse transit time (PTT) from both the contact PPG and rPPG signals. We found that remote PTT is possible with moderately high frame rate video when distal locations on the body are visible. These findings and the supporting dataset should facilitate new research on non-face rPPG and monitoring blood flow dynamics over the whole body with a camera. | ['Patrick Flynn', 'Adam Czajka', 'Ben Sporrer', 'Nathan Vance', 'Jeremy Speth', 'Lu Niu'] | 2023-03-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 2.31631264e-01 1.09609313e-01 1.79416627e-01 -9.86350998e-02
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079904c1-01dc-4fdd-8379-51512f078c48 | evaluation-of-spatial-distortion-in | 2306.08053 | null | https://arxiv.org/abs/2306.08053v1 | https://arxiv.org/pdf/2306.08053v1.pdf | Evaluation of Spatial Distortion in Multichannel Audio | Despite the recent proliferation of spatial audio technologies, the evaluation of spatial quality continues to rely on subjective listening tests, often requiring expert listeners. Based on the duplex theory of spatial hearing, it is possible to construct a signal model for frequency-independent spatial distortion by accounting for inter-channel time and level differences relative to a reference signal. By using a combination of least-square optimization and heuristics, we propose a signal decomposition method to isolate the spatial error from a processed signal. This allows the computation of simple energy-ratio metrics, providing objective measures of spatial and non-spatial signal qualities, with minimal assumption and no dataset dependency. Experiments demonstrate robustness of the method against common signal degradation as introduced by, e.g., audio compression and music source separation. Implementation is available at https://github.com/karnwatcharasupat/spauq. | ['Alexander Lerch', 'Karn N. Watcharasupat'] | 2023-06-13 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.01463634e-01 -3.43618333e-01 2.22304255e-01 -7.37314001e-02
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dd572c36-179e-457c-bc81-67fb44ccb791 | dismec-distributed-sparse-machines-for | 1609.02521 | null | http://arxiv.org/abs/1609.02521v1 | http://arxiv.org/pdf/1609.02521v1.pdf | DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification | Extreme multi-label classification refers to supervised multi-label learning
involving hundreds of thousands or even millions of labels. Datasets in extreme
classification exhibit fit to power-law distribution, i.e. a large fraction of
labels have very few positive instances in the data distribution. Most
state-of-the-art approaches for extreme multi-label classification attempt to
capture correlation among labels by embedding the label matrix to a
low-dimensional linear sub-space. However, in the presence of power-law
distributed extremely large and diverse label spaces, structural assumptions
such as low rank can be easily violated.
In this work, we present DiSMEC, which is a large-scale distributed framework
for learning one-versus-rest linear classifiers coupled with explicit capacity
control to control model size. Unlike most state-of-the-art methods, DiSMEC
does not make any low rank assumptions on the label matrix. Using double layer
of parallelization, DiSMEC can learn classifiers for datasets consisting
hundreds of thousands labels within few hours. The explicit capacity control
mechanism filters out spurious parameters which keep the model compact in size,
without losing prediction accuracy. We conduct extensive empirical evaluation
on publicly available real-world datasets consisting upto 670,000 labels. We
compare DiSMEC with recent state-of-the-art approaches, including - SLEEC which
is a leading approach for learning sparse local embeddings, and FastXML which
is a tree-based approach optimizing ranking based loss function. On some of the
datasets, DiSMEC can significantly boost prediction accuracies - 10% better
compared to SLECC and 15% better compared to FastXML, in absolute terms. | ['Bernhard Shoelkopf', 'Rohit Babbar'] | 2016-09-08 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 7.72510692e-02 -2.00121418e-01 -3.02629769e-01 -5.32886326e-01
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c2cd9074-1f69-47d7-bb39-1165ece7c251 | mmfn-multi-modal-fusion-net-for-end-to-end-1 | 2207.00186 | null | https://arxiv.org/abs/2207.00186v2 | https://arxiv.org/pdf/2207.00186v2.pdf | MMFN: Multi-Modal-Fusion-Net for End-to-End Driving | Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks. In addition, we design a new expert to further enhance the model performance by considering multi-road rules. Experimental results prove that both of the proposed improvements enable our agent to achieve superior performance compared with other methods. | ['Lujia Wang', 'Ren Xin', 'Feiyi Chen', 'Ruoyu Geng', 'Mingkai Tang', 'Qingwen Zhang'] | 2022-07-01 | null | null | null | null | ['carla-map-leaderboard'] | ['robots'] | [ 6.41135350e-02 -5.19002788e-02 3.83684002e-02 -8.02900732e-01
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e3f294d8-a1b4-4fdf-a750-c0209a0acaef | an-algorithm-for-the-se-3-transformation-on | 2206.08712 | null | https://arxiv.org/abs/2206.08712v1 | https://arxiv.org/pdf/2206.08712v1.pdf | An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions | Implicit representations are widely used for object reconstruction due to their efficiency and flexibility. In 2021, a novel structure named neural implicit map has been invented for incremental reconstruction. A neural implicit map alleviates the problem of inefficient memory cost of previous online 3D dense reconstruction while producing better quality. % However, the neural implicit map suffers the limitation that it does not support remapping as the frames of scans are encoded into a deep prior after generating the neural implicit map. This means, that neither this generation process is invertible, nor a deep prior is transformable. The non-remappable property makes it not possible to apply loop-closure techniques. % We present a neural implicit map based transformation algorithm to fill this gap. As our neural implicit map is transformable, our model supports remapping for this special map of latent features. % Experiments show that our remapping module is capable to well-transform neural implicit maps to new poses. Embedded into a SLAM framework, our mapping model is able to tackle the remapping of loop closures and demonstrates high-quality surface reconstruction. % Our implementation is available at github\footnote{\url{https://github.com/Jarrome/IMT_Mapping}} for the research community. | ['Andreas Nuechter', 'Yijun Yuan'] | 2022-06-17 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 1.61739528e-01 5.99436283e-01 -4.45308536e-02 -3.60730380e-01
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1c8fc038-a008-47a1-a7ad-03cf0c7d9b47 | folded-graph-signals-sensing-with-unlimited | 1903.03741 | null | http://arxiv.org/abs/1903.03741v2 | http://arxiv.org/pdf/1903.03741v2.pdf | Folded Graph Signals: Sensing with Unlimited Dynamic Range | Self-reset analog-to-digital converters (ADCs) are used to sample high
dynamic range signals resulting in modulo-operation based folded signal
samples. We consider the case where each vertex of a graph (e.g., sensors in a
network) is equipped with a self-reset ADC and senses a time series. Graph
sampling allows the graph time series to be represented by the signals at a
subset of sampled vertices and time instances. We investigate the problem of
recovering bandlimited continuous-time graph signals from folded signal
samples. We derive sufficient conditions to achieve successful recovery of the
graph signal from the folded signal samples, which can be achieved via integer
programming. To resolve the scalability issue of integer programming, we
propose a sparse optimization recovery method for graph signals satisfying
certain technical conditions. Such an approach requires a novel graph sampling
scheme that selects vertices with small signal variation. The proposed
algorithm exploits the inherent relationship among the graph vertices in both
the vertex and time domains to recover the graph signal. Simulations and
experiments on images validate the feasibility of our proposed approach. | [] | 2020-01-20 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 1.00960517e+00 3.76893915e-02 -2.96002001e-01 -2.25818623e-02
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4e169490-a37a-4862-8948-5cfe0f78c99d | reply-to-garcia-et-al-common-mistakes-in | 1505.06750 | null | http://arxiv.org/abs/1505.06750v2 | http://arxiv.org/pdf/1505.06750v2.pdf | Reply to Garcia et al.: Common mistakes in measuring frequency dependent word characteristics | We demonstrate that the concerns expressed by Garcia et al. are misplaced,
due to (1) a misreading of our findings in [1]; (2) a widespread failure to
examine and present words in support of asserted summary quantities based on
word usage frequencies; and (3) a range of misconceptions about word usage
frequency, word rank, and expert-constructed word lists. In particular, we show
that the English component of our study compares well statistically with two
related surveys, that no survey design influence is apparent, and that
estimates of measurement error do not explain the positivity biases reported in
our work and that of others. We further demonstrate that for the frequency
dependence of positivity---of which we explored the nuances in great detail in
[1]---Garcia et al. did not perform a reanalysis of our data---they instead
carried out an analysis of a different, statistically improper data set and
introduced a nonlinearity before performing linear regression. | ['M. R. Frank', 'J. R. Williams', 'I. M. Kloumann', 'P. S. Dodds', 'M. T. McMahon', 'J. P. Bagrow', 'C. M. Danforth', 'L. Mitchell', 'K. D. Harris', 'E. M. Clark', 'S. Desu', 'K. Megerdoomian', 'B. F. Tivnan', 'A. J. Reagan'] | 2015-05-25 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 1.84102401e-01 -4.52913269e-02 -5.98587036e-01 -2.55276322e-01
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fd9b19b7-72e5-43c9-8758-525104118ccc | progressive-domain-independent-feature | 2003.09869 | null | https://arxiv.org/abs/2003.09869v2 | https://arxiv.org/pdf/2003.09869v2.pdf | Progressive Domain-Independent Feature Decomposition Network for Zero-Shot Sketch-Based Image Retrieval | Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for searching natural images given free-hand sketches under the zero-shot scenario. Most existing methods solve this problem by simultaneously projecting visual features and semantic supervision into a low-dimensional common space for efficient retrieval. However, such low-dimensional projection destroys the completeness of semantic knowledge in original semantic space, so that it is unable to transfer useful knowledge well when learning semantic from different modalities. Moreover, the domain information and semantic information are entangled in visual features, which is not conducive for cross-modal matching since it will hinder the reduction of domain gap between sketch and image. In this paper, we propose a Progressive Domain-independent Feature Decomposition (PDFD) network for ZS-SBIR. Specifically, with the supervision of original semantic knowledge, PDFD decomposes visual features into domain features and semantic ones, and then the semantic features are projected into common space as retrieval features for ZS-SBIR. The progressive projection strategy maintains strong semantic supervision. Besides, to guarantee the retrieval features to capture clean and complete semantic information, the cross-reconstruction loss is introduced to encourage that any combinations of retrieval features and domain features can reconstruct the visual features. Extensive experiments demonstrate the superiority of our PDFD over state-of-the-art competitors. | ['Yanhua Yang', 'Muli Yang', 'Hao Wang', 'Xinxun Xu'] | 2020-03-22 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.31595242e-02 -4.41743940e-01 -5.43018103e-01 -2.05272764e-01
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8affc434-3446-43b4-b2cf-788458116d6d | 190807849 | 1908.07849 | null | https://arxiv.org/abs/1908.07849v1 | https://arxiv.org/pdf/1908.07849v1.pdf | Semi-supervised Sequence Modeling for Elastic Impedance Inversion | Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints - the lack of which might lead to undesirable results. To overcome this issue, we have developed a semi-supervised sequence modeling framework based on recurrent neural networks for elastic impedance inversion from multi-angle seismic data. Specifically, seismic traces and elastic impedance (EI) traces are modeled as a time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for EI. The proposed workflow uses well-log data to guide the inversion. In addition, it uses seismic forward modeling to regularize the training and to serve as a geophysical constraint for the inversion. The proposed workflow achieves an average correlation of 98% between the estimated and target EI using 10 well logs for training on a synthetic data set. | ['Ghassan AlRegib', 'Motaz Alfarraj'] | 2019-08-19 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [ 3.53121281e-01 8.90718848e-02 3.49415421e-01 -3.53733003e-01
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48d0a8b0-1660-481e-be72-74429722acbd | when-source-free-domain-adaptation-meets | 2301.08413 | null | https://arxiv.org/abs/2301.08413v2 | https://arxiv.org/pdf/2301.08413v2.pdf | Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation | Source-free domain adaptation (SFDA), where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation in the real world. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. By analyzing the clustering performance of the target features, we show that they still contain core features related to discriminative attributes but lack the collation of semantic information. Inspired by this insight, we present Chaos to Order (CtO), a novel approach for SFDA that strives to constrain semantic credibility and propagate label information among target subpopulations. CtO divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, customizing the learning strategy to fit the data properties best. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In CtO, by employing different learning strategies to propagate the labels from the inner local to outlier instances, it clusters the global samples from chaos to order. We further adaptively regulate the neighborhood affinity of the inner samples to constrain the local semantic credibility. In theoretical and empirical analyses, we demonstrate that our algorithm not only propagates from inner to outlier but also prevents local clustering from forming spurious clusters. Empirical evidence demonstrates that CtO outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA. | ['Hong Wang', 'Wenming Cao', 'Xidong Xi', 'Yan Li', 'Guitao Cao', 'Chunwei Wu'] | 2023-01-20 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-1.52430877e-01 -5.10046817e-02 -5.55849850e-01 -3.38585705e-01
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c23b1ada-a4f8-4465-8bc1-1aa285a07648 | inspired-by-norbert-wiener-feedback-loop | 2212.02096 | null | https://arxiv.org/abs/2212.02096v1 | https://arxiv.org/pdf/2212.02096v1.pdf | Inspired by Norbert Wiener: FeedBack Loop Network Learning Incremental Knowledge for Driver Attention Prediction and Beyond | The problem of predicting driver attention from the driving perspective is gaining the increasing research focuses due to its remarkable significance for autonomous driving and assisted driving systems. Driving experience is extremely important for driver attention prediction, a skilled driver is able to effortlessly predict oncoming danger (before it becomes salient) based on driving experience and quickly pay attention on the corresponding zones. However, the nonobjective driving experience is difficult to model, so a mechanism simulating driver experience accumulation procedure is absent in existing methods, and the existing methods usually follow the technique line of saliency prediction methods to predict driver attention. In this paper, we propose a FeedBack Loop Network (FBLNet), which attempts to model the driving experience accumulation procedure. By over-and-over iterations, FBLNet generates the incremental knowledge that carries rich historically-accumulative long-term temporal information. The incremental knowledge to our model is like the driving experience to humans. Under the guidance of the incremental knowledge, our model fuses the CNN feature and Transformer feature that are extracted from the input image to predict driver attention. Our model exhibits solid advantage over existing methods, achieving an average 10.3% performance improvement on three public datasets. | ['Zhixiong Nan', 'Yilong Chen'] | 2022-12-05 | null | null | null | null | ['saliency-prediction', 'driver-attention-monitoring'] | ['computer-vision', 'computer-vision'] | [ 1.41133845e-01 1.53465450e-01 -4.31081891e-01 -3.82730037e-01
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85d21be2-40a9-4934-b062-c40e1a834eec | lyapunov-driven-deep-reinforcement-learning | 2305.10931 | null | https://arxiv.org/abs/2305.10931v1 | https://arxiv.org/pdf/2305.10931v1.pdf | Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces | In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system. Building on the marriage between Lyapunov stochastic optimization and deep reinforcement learning (DRL), we devise a dynamic learning algorithm that jointly optimizes the data compression scheme, the allocation of radio resources (i.e., power, transmission precoding), the computation resources (i.e., CPU cycles), and the RIS reflectivity parameters (i.e., phase shifts), with the aim of performing energy-efficient edge classification with end-to-end (E2E) delay and inference accuracy constraints. The proposed strategy enables dynamic control of the system and of the wireless propagation environment, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. Numerical results assess the performance of the proposed RIS-empowered edge inference strategy in terms of trade-off between energy, delay, and accuracy of a classification task. | ['George C. Alexandropoulos', 'Paolo Di Lorenzo', 'Mattia Merluzzi', 'Kyriakos Stylianopoulos'] | 2023-05-18 | null | null | null | null | ['stochastic-optimization', 'data-compression'] | ['methodology', 'time-series'] | [ 3.03158581e-01 3.04770581e-02 -1.43405408e-01 3.35296802e-02
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6df5696a-a56f-4697-8117-76fa0435f03f | filterbank-learning-for-small-footprint | 2211.10565 | null | https://arxiv.org/abs/2211.10565v2 | https://arxiv.org/pdf/2211.10565v2.pdf | Filterbank Learning for Noise-Robust Small-Footprint Keyword Spotting | In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstrate that filterbank learning outperforms handcrafted speech features for KWS whenever the number of filterbank channels is severely decreased. Reducing the number of channels might yield certain KWS performance drop, but also a substantial energy consumption reduction, which is key when deploying common always-on KWS on low-resource devices. Experimental results on a noisy version of the Google Speech Commands Dataset show that filterbank learning adapts to noise characteristics to provide a higher degree of robustness to noise, especially when dropout is integrated. Thus, switching from typically used 40-channel log-Mel features to 8-channel learned features leads to a relative KWS accuracy loss of only 3.5% while simultaneously achieving a 6.3x energy consumption reduction. | ['John H. L. Hansen', 'Jesper Jensen', 'Zheng-Hua Tan', 'Ram C. M. C. Shekar', 'Iván López-Espejo'] | 2022-11-19 | null | null | null | null | ['small-footprint-keyword-spotting', 'keyword-spotting'] | ['speech', 'speech'] | [ 1.82896271e-01 -1.01478435e-01 -3.17745388e-01 -3.07755709e-01
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9221b009-a82e-4d4b-aad6-20902efe5220 | legal-tech-open-diaries-lesson-learned-on-how | 2210.13086 | null | https://arxiv.org/abs/2210.13086v1 | https://arxiv.org/pdf/2210.13086v1.pdf | Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models | In the era of billion-parameter-sized Language Models (LMs), start-ups have to follow trends and adapt their technology accordingly. Nonetheless, there are open challenges since the development and deployment of large models comes with a need for high computational resources and has economical consequences. In this work, we follow the steps of the R&D group of a modern legal-tech start-up and present important insights on model development and deployment. We start from ground zero by pre-training multiple domain-specific multi-lingual LMs which are a better fit to contractual and regulatory text compared to the available alternatives (XLM-R). We present benchmark results of such models in a half-public half-private legal benchmark comprising 5 downstream tasks showing the impact of larger model size. Lastly, we examine the impact of a full-scale pipeline for model compression which includes: a) Parameter Pruning, b) Knowledge Distillation, and c) Quantization: The resulting models are much more efficient without sacrificing performance at large. | ['Ilias Chalkidis', 'Prodromos Malakasiotis', 'Sotiris Legkas', 'Stelios Maroudas'] | 2022-10-24 | null | null | null | null | ['xlm-r'] | ['natural-language-processing'] | [-9.78062302e-03 4.41886395e-01 -4.28323478e-01 -3.65957409e-01
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d2f9d40f-9286-4d23-8a9c-0e32c852e15a | protein-language-model-rescue-mutations | 2211.10000 | null | https://arxiv.org/abs/2211.10000v1 | https://arxiv.org/pdf/2211.10000v1.pdf | Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes | Despite being self-supervised, protein language models have shown remarkable performance in fundamental biological tasks such as predicting impact of genetic variation on protein structure and function. The effectiveness of these models on diverse set of tasks suggests that they learn meaningful representations of fitness landscape that can be useful for downstream clinical applications. Here, we interrogate the use of these language models in characterizing known pathogenic mutations in curated, medically actionable genes through an exhaustive search of putative compensatory mutations on each variant's genetic background. Systematic analysis of the predicted effects of these compensatory mutations reveal unappreciated structural features of proteins that are missed by other structure predictors like AlphaFold. While deep mutational scan experiments provide an unbiased estimate of the mutational landscape, we encourage the community to generate and curate rescue mutation experiments to inform the design of more sophisticated co-masking strategies and leverage large language models more effectively for downstream clinical prediction tasks. | ['Pablo Cordero', 'Onuralp Soylemez'] | 2022-11-18 | null | null | null | null | ['protein-language-model'] | ['medical'] | [ 6.26508772e-01 1.75795555e-01 -1.49127990e-01 -5.58148682e-01
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4bc13f46-3679-4e5c-b0bb-eadef94e2eb2 | sparse-instance-activation-for-real-time | 2203.12827 | null | https://arxiv.org/abs/2203.12827v1 | https://arxiv.org/pdf/2203.12827v1.pdf | Sparse Instance Activation for Real-Time Instance Segmentation | In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to highlight informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly outperforms the counterparts in terms of speed and accuracy. Code and models are available at https://github.com/hustvl/SparseInst. | ['Wenyu Liu', 'Zhaoxiang Zhang', 'Chang Huang', 'Qian Zhang', 'Wenqiang Zhang', 'Shaoyu Chen', 'Xinggang Wang', 'Tianheng Cheng'] | 2022-03-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Cheng_Sparse_Instance_Activation_for_Real-Time_Instance_Segmentation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 3.78869683e-01 2.09787294e-01 -1.75477043e-01 -3.98866504e-01
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0e097b97-62da-467f-8714-43b3ac118302 | a-simple-baseline-for-multi-camera-3d-object | 2208.10035 | null | https://arxiv.org/abs/2208.10035v1 | https://arxiv.org/pdf/2208.10035v1.pdf | A Simple Baseline for Multi-Camera 3D Object Detection | 3D object detection with surrounding cameras has been a promising direction for autonomous driving. In this paper, we present SimMOD, a Simple baseline for Multi-camera Object Detection, to solve the problem. To incorporate multi-view information as well as build upon previous efforts on monocular 3D object detection, the framework is built on sample-wise object proposals and designed to work in a two-stage manner. First, we extract multi-scale features and generate the perspective object proposals on each monocular image. Second, the multi-view proposals are aggregated and then iteratively refined with multi-view and multi-scale visual features in the DETR3D-style. The refined proposals are end-to-end decoded into the detection results. To further boost the performance, we incorporate the auxiliary branches alongside the proposal generation to enhance the feature learning. Also, we design the methods of target filtering and teacher forcing to promote the consistency of two-stage training. We conduct extensive experiments on the 3D object detection benchmark of nuScenes to demonstrate the effectiveness of SimMOD and achieve new state-of-the-art performance. Code will be available at https://github.com/zhangyp15/SimMOD. | ['Jiwen Lu', 'Jie zhou', 'Guan Huang', 'Zheng Zhu', 'Wenzhao Zheng', 'Yunpeng Zhang'] | 2022-08-22 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.56344082e-02 -2.35325903e-01 -2.74407893e-01 -4.34586823e-01
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5bfa900c-9ef8-4ca8-a87b-dc78cf703059 | camera-calibration-without-camera-access-a | 2302.06949 | null | https://arxiv.org/abs/2302.06949v1 | https://arxiv.org/pdf/2302.06949v1.pdf | Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods | A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models' residual variances - however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth. | ['Johan Edstedt', 'Per-Erik Forssén', 'Emil Brissman'] | 2023-02-14 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [ 2.42810532e-01 -1.49861351e-01 2.58673906e-01 -2.96869904e-01
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688f34bc-2c82-490d-82f7-41048836d409 | pillarsegnet-pillar-based-semantic-grid-map | 2105.04169 | null | https://arxiv.org/abs/2105.04169v2 | https://arxiv.org/pdf/2105.04169v2.pdf | PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data | Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios. While most existing approaches predict sparse pointwise semantic classes for the sparse input LiDAR scan, we propose PillarSegNet to be able to output a dense semantic grid map. In contrast to a previously proposed grid map method, PillarSegNet uses PointNet to learn features directly from the 3D point cloud and then conducts 2D semantic segmentation in the top view. To train and evaluate our approach, we use both sparse and dense ground truth, where the dense ground truth is obtained from multiple superimposed scans. Experimental results on the SemanticKITTI dataset show that PillarSegNet achieves a performance gain of about 10% mIoU over the state-of-the-art grid map method. | ['Christoph Stiller', 'Frank Bieder', 'Philipp Heidenreich', 'Kunyu Peng', 'Juncong Fei'] | 2021-05-10 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.00696325e-01 3.02916318e-01 -5.94262481e-02 -7.63232291e-01
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612da542-ef2f-4f2d-9e42-9cfa3406b2e5 | hospital-readmission-prediction-applying | 1804.01188 | null | http://arxiv.org/abs/1804.01188v1 | http://arxiv.org/pdf/1804.01188v1.pdf | Hospital Readmission Prediction - Applying Hierarchical Sparsity Norms for Interpretable Models | Hospital readmissions have become one of the key measures of healthcare
quality. Preventable readmissions have been identified as one of the primary
targets for reducing costs and improving healthcare delivery. However, most
data driven studies for understanding readmissions have produced black box
classification and predictive models with moderate performance, which precludes
them from being used effectively within the decision support systems in the
hospitals. In this paper we present an application of structured
sparsity-inducing norms for predicting readmission risk for patients based on
their disease history and demographics. Most existing studies have focused on
hospital utilization, test results, etc., to assign a readmission label to each
episode of hospitalization. However, we focus on assigning a readmission risk
label to a patient based on their disease history. Our emphasis is on
interpreting the models to improve the understanding of the readmission
problem. To achieve this, we exploit the domain induced hierarchical structure
available for the disease codes which are the features for the classification
algorithm. We use a tree based sparsity-inducing regularization strategy that
explicitly uses the domain hierarchy. The resulting model not only outperforms
standard regularization procedures but is also highly sparse and interpretable.
We analyze the model and identify several significant factors that have an
effect on readmission risk. Some of these factors conform to existing beliefs,
e.g., impact of surgical complications and infections during hospital stay.
Other factors, such as the impact of mental disorder and substance abuse on
readmission, provide empirical evidence for several pre-existing but unverified
hypotheses. The analysis also reveals previously undiscovered connections such
as the influence of socioeconomic factors like lack of housing and
malnutrition. | ['Jialiang Jiang', 'Varun Chandola', 'Sharon Hewner'] | 2018-04-03 | null | null | null | null | ['readmission-prediction'] | ['medical'] | [ 2.32375432e-02 1.16175734e-01 -8.91262829e-01 -3.13898325e-01
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3b3943c1-7ffd-485a-9061-15ce16f9e212 | making-a-long-story-short-a-multi-importance | 1711.03473 | null | http://arxiv.org/abs/1711.03473v3 | http://arxiv.org/pdf/1711.03473v3.pdf | Making a long story short: A Multi-Importance fast-forwarding egocentric videos with the emphasis on relevant objects | The emergence of low-cost high-quality personal wearable cameras combined
with the increasing storage capacity of video-sharing websites have evoked a
growing interest in first-person videos, since most videos are composed of
long-running unedited streams which are usually tedious and unpleasant to
watch. State-of-the-art semantic fast-forward methods currently face the
challenge of providing an adequate balance between smoothness in visual flow
and the emphasis on the relevant parts. In this work, we present the
Multi-Importance Fast-Forward (MIFF), a fully automatic methodology to
fast-forward egocentric videos facing these challenges. The dilemma of defining
what is the semantic information of a video is addressed by a learning process
based on the preferences of the user. Results show that the proposed method
keeps over $3$ times more semantic content than the state-of-the-art
fast-forward. Finally, we discuss the need of a particular video stabilization
technique for fast-forward egocentric videos. | ['Erickson Rangel Nascimento', 'João Pedro Klock Ferreira', 'Michel Melo Silva', 'Felipe Cadar Chamone', 'Washington Luis Souza Ramos', 'Mario Fernando Montenegro Campos'] | 2017-11-09 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [-1.69800501e-02 -3.13779026e-01 -1.75985917e-01 -4.60801601e-01
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d57c4677-3d20-44bd-a183-943d15050ddb | permuteattack-counterfactual-explanation-of | 2008.10138 | null | https://arxiv.org/abs/2008.10138v2 | https://arxiv.org/pdf/2008.10138v2.pdf | PermuteAttack: Counterfactual Explanation of Machine Learning Credit Scorecards | This paper is a note on new directions and methodologies for validation and explanation of Machine Learning (ML) models employed for retail credit scoring in finance. Our proposed framework draws motivation from the field of Artificial Intelligence (AI) security and adversarial ML where the need for certifying the performance of the ML algorithms in the face of their overwhelming complexity poses a need for rethinking the traditional notions of model architecture selection, sensitivity analysis and stress testing. Our point of view is that the phenomenon of adversarial perturbations when detached from the AI security domain, has purely algorithmic roots and fall within the scope of model risk assessment. We propose a model criticism and explanation framework based on adversarially generated counterfactual examples for tabular data. A counterfactual example to a given instance in this context is defined as a synthetically generated data point sampled from the estimated data distribution which is treated differently by a model. The counterfactual examples can be used to provide a black-box instance-level explanation of the model behaviour as well as studying the regions in the input space where the model performance deteriorates. Adversarial example generating algorithms are extensively studied in the image and natural language processing (NLP) domains. However, most financial data come in tabular format and naive application of the existing techniques on this class of datasets generates unrealistic samples. In this paper, we propose a counterfactual example generation method capable of handling tabular data including discrete and categorical variables. Our proposed algorithm uses a gradient-free optimization based on genetic algorithms and therefore is applicable to any classification model. | ['Masoud Hashemi', 'Ali Fathi'] | 2020-08-24 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.81352437e-01 5.32796502e-01 8.79761428e-02 -2.48345584e-01
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74a58195-0081-48c4-a9c0-8cd3040ef2f0 | object-landmark-discovery-through | 1910.09469 | null | https://arxiv.org/abs/1910.09469v1 | https://arxiv.org/pdf/1910.09469v1.pdf | Object landmark discovery through unsupervised adaptation | This paper proposes a method to ease the unsupervised learning of object landmark detectors. Similarly to previous methods, our approach is fully unsupervised in a sense that it does not require or make any use of annotated landmarks for the target object category. Contrary to previous works, we do however assume that a landmark detector, which has already learned a structured representation for a given object category in a fully supervised manner, is available. Under this setting, our main idea boils down to adapting the given pre-trained network to the target object categories in a fully unsupervised manner. To this end, our method uses the pre-trained network as a core which remains frozen and does not get updated during training, and learns, in an unsupervised manner, only a projection matrix to perform the adaptation to the target categories. By building upon an existing structured representation learned in a supervised manner, the optimization problem solved by our method is much more constrained with significantly less parameters to learn which seems to be important for the case of unsupervised learning. We show that our method surpasses fully unsupervised techniques trained from scratch as well as a strong baseline based on fine-tuning, and produces state-of-the-art results on several datasets. Code can be found at https://github.com/ESanchezLozano/SAIC-Unsupervised-landmark-detection-NeurIPS2019 . | ['Enrique Sanchez', 'Georgios Tzimiropoulos'] | 2019-10-21 | object-landmark-discovery-through-1 | http://papers.nips.cc/paper/9505-object-landmark-discovery-through-unsupervised-adaptation | http://papers.nips.cc/paper/9505-object-landmark-discovery-through-unsupervised-adaptation.pdf | neurips-2019-12 | ['unsupervised-landmark-detection'] | ['computer-vision'] | [ 4.87725213e-02 5.10532022e-01 -4.39859070e-02 -4.09345597e-01
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beee3f8a-9ff1-4c58-82b0-5bfa7dd74f86 | toward-stance-classification-based-on-claim | null | null | https://aclanthology.org/W17-5210 | https://aclanthology.org/W17-5210.pdf | Toward Stance Classification Based on Claim Microstructures | Claims are the building blocks of arguments and the reasons underpinning opinions, thus analyzing claims is important for both argumentation mining and opinion mining. We propose a framework for representing claims as microstructures, which express the beliefs, judgments, and policies about the relations between domain-specific concepts. In a proof-of-concept study, we manually build microstructures for over 800 claims extracted from an online debate. We test the so-obtained microstructures on the task of claim stance classification, achieving considerable improvements over text-based baselines. | ['Jan {\\v{S}}najder', "Filip Boltu{\\v{z}}i{\\'c}"] | 2017-09-01 | null | null | null | ws-2017-9 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 3.74254167e-01 8.99688005e-01 -1.07174981e+00 -3.22422296e-01
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84829b94-80f4-4607-a864-d1b371c3e54c | home-action-genome-cooperative-compositional | 2105.05226 | null | https://arxiv.org/abs/2105.05226v1 | https://arxiv.org/pdf/2105.05226v1.pdf | Home Action Genome: Cooperative Compositional Action Understanding | Existing research on action recognition treats activities as monolithic events occurring in videos. Recently, the benefits of formulating actions as a combination of atomic-actions have shown promise in improving action understanding with the emergence of datasets containing such annotations, allowing us to learn representations capturing this information. However, there remains a lack of studies that extend action composition and leverage multiple viewpoints and multiple modalities of data for representation learning. To promote research in this direction, we introduce Home Action Genome (HOMAGE): a multi-view action dataset with multiple modalities and view-points supplemented with hierarchical activity and atomic action labels together with dense scene composition labels. Leveraging rich multi-modal and multi-view settings, we propose Cooperative Compositional Action Understanding (CCAU), a cooperative learning framework for hierarchical action recognition that is aware of compositional action elements. CCAU shows consistent performance improvements across all modalities. Furthermore, we demonstrate the utility of co-learning compositions in few-shot action recognition by achieving 28.6% mAP with just a single sample. | ['Juan Carlos Niebles', 'Ehsan Adeli', 'Shun Ishizaka', 'Kazuki Kozuka', 'Rishi Desai', 'Jingwei Ji', 'Haofeng Chen', 'Nishant Rai'] | 2021-05-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Rai_Home_Action_Genome_Cooperative_Compositional_Action_Understanding_CVPR_2021_paper.pdf | cvpr-2021-1 | ['few-shot-action-recognition', 'action-understanding'] | ['computer-vision', 'computer-vision'] | [ 6.22559905e-01 2.12687198e-02 -7.45497704e-01 -4.51340616e-01
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73978870-37fe-4415-84e2-d37f32d9e4fc | deepi2p-image-to-point-cloud-registration-via | 2104.03501 | null | https://arxiv.org/abs/2104.03501v1 | https://arxiv.org/pdf/2104.03501v1.pdf | DeepI2P: Image-to-Point Cloud Registration via Deep Classification | This paper presents DeepI2P: a novel approach for cross-modality registration between an image and a point cloud. Given an image (e.g. from a rgb-camera) and a general point cloud (e.g. from a 3D Lidar scanner) captured at different locations in the same scene, our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar. Learning common feature descriptors to establish correspondences for the registration is inherently challenging due to the lack of appearance and geometric correlations across the two modalities. We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem. A classification neural network is designed to label whether the projection of each point in the point cloud is within or beyond the camera frustum. These labeled points are subsequently passed into a novel inverse camera projection solver to estimate the relative pose. Extensive experimental results on Oxford Robotcar and KITTI datasets demonstrate the feasibility of our approach. Our source code is available at https://github.com/lijx10/DeepI2P | ['Gim Hee Lee', 'Jiaxin Li'] | 2021-04-08 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_DeepI2P_Image-to-Point_Cloud_Registration_via_Deep_Classification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['image-to-point-cloud-registration'] | ['computer-vision'] | [ 2.91254222e-01 -1.67264178e-01 -2.16642041e-02 -4.50478345e-01
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f5930472-da86-4e90-9837-72c5f70a947c | context-aware-taxi-dispatching-at-city-scale | null | null | https://ieeexplore.ieee.org/abstract/document/9247444 | https://ieeexplore.ieee.org/abstract/document/9247444 | Context-aware taxi dispatching at city-scale using deep reinforcement learning | Abstract— Proactive taxi dispatching is of great importance to
balance taxi demand-supply gaps among different locations in
a city. Recent advances primarily rely on deep reinforcement
learning (DRL) to directly learn the optimal dispatching policy.
These works, however, are still not sufficiently efficient because
they overlook several pieces of valuable context information. As a
result, they may generate quite a few improper actions and
introduce unnecessary coordination costs. To improve existing
works, we present COX – a context-aware taxi dispatching
approach that incorporates rich contexts into DRL modeling for
more efficient taxi reallocations. Specifically, rather than simply
dividing the service area into grids, COX proposes a road connectivity aware clustering algorithm to divide the road network
graph into zones for practical taxi dispatching. In addition, COX
comprehensively analyzes zone-level taxi demands and supplies
through accurate taxi demand prediction and timely updates of
taxi statuses. COX improves the DRL modeling by integrating
these derived contexts, e.g., state representation with complete
demand/supply data and sequential action generation with full
coordination among idle taxis. In particular, we implement an
environment simulator to train and evaluate COX using a large
real-world taxi dataset. Extensive experiments show that COX
outperforms state-of-the-art approaches on various performance
metrics, e.g., on average improving the total order values by
6.74%, while reducing the number of unserved taxi orders and
passengers’ waiting time by 4.92% and 44.84%, respectively. | ['and Kaishun Wu', 'Jiangzhou Li', 'IEEE', 'Member', 'Zhidan Liu'] | 2021-05-26 | null | null | null | ieee-transactions-on-intelligent-9 | ['action-generation'] | ['computer-vision'] | [-3.99177641e-01 -1.55036941e-01 -7.81845868e-01 -3.70504647e-01
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cb9199e3-cb05-43ed-9371-e36c59f2e4f8 | biomedical-chinese-english-clir-using-an | null | null | https://aclanthology.org/L12-1149 | https://aclanthology.org/L12-1149.pdf | Biomedical Chinese-English CLIR Using an Extended CMeSH Resource to Expand Queries | Cross-lingual information retrieval (CLIR) involving the Chinese language has been thoroughly studied in the general language domain, but rarely in the biomedical domain, due to the lack of suitable linguistic resources and parsing tools. In this paper, we describe a Chinese-English CLIR system for biomedical literature, which exploits a bilingual ontology, the ``eCMeSH Tree''''''''. This is an extension of the Chinese Medical Subject Headings (CMeSH) Tree, based on Medical Subject Headings (MeSH). Using the 2006 and 2007 TREC Genomics track data, we have evaluated the performance of the eCMeSH Tree in expanding queries. We have compared our results to those obtained using two other approaches, i.e. pseudo-relevance feedback (PRF) and document translation (DT). Subsequently, we evaluate the performance of different combinations of these three retrieval methods. Our results show that our method of expanding queries using the eCMeSH Tree can outperform the PRF method. Furthermore, combining this method with PRF and DT helps to smooth the differences in query expansion, and consequently results in the best performance amongst all experiments reported. All experiments compare the use of two different retrieval models, i.e. Okapi BM25 and a query likelihood language model. In general, the former performs slightly better. | ["Jun{'}ichi Tsujii", 'Xinkai Wang', 'Sophia Ananiadou', 'Paul Thompson'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 1.06879093e-01 -2.01305240e-01 -1.94121093e-01 5.24433516e-02
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e3c07b88-dc82-4621-be56-03320c06c85b | looking-outside-the-window-wider-context | 2106.15754 | null | https://arxiv.org/abs/2106.15754v6 | https://arxiv.org/pdf/2106.15754v6.pdf | Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images | Long-range contextual information is crucial for the semantic segmentation of High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs. Apart from extracting local features with a conventional CNN, the WiCoNet has an extra context branch to aggregate information from a larger image area. Moreover, we introduce a Context Transformer to embed contextual information from the context branch and selectively project it onto the local features. The Context Transformer extends the Vision Transformer, an emerging kind of neural network, to model the dual-branch semantic correlations. It overcomes the locality limitation of CNNs and enables the WiCoNet to see the bigger picture before segmenting the land-cover/land-use (LCLU) classes. Ablation studies and comparative experiments conducted on several benchmark datasets demonstrate the effectiveness of the proposed method. In addition, we present a new Beijing Land-Use (BLU) dataset. This is a large-scale HR satellite dataset with high-quality and fine-grained reference labels, which can facilitate future studies in this field. | ['Lorenzo Bruzzone', 'Hao Tang', 'Yuebin Wang', 'Xiaojie Cui', 'Jing Zhang', 'Shaofu Lin', 'Dong Lin', 'Lei Ding'] | 2021-06-29 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 5.13823748e-01 -1.11906156e-01 -1.39386415e-01 -5.23403883e-01
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81b35af9-d9bb-45a8-ad4b-a7a00e360361 | unified-vision-language-representation | 2302.05093 | null | https://arxiv.org/abs/2302.05093v2 | https://arxiv.org/pdf/2302.05093v2.pdf | Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval | Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images. It can be used for similar products retrieval from different suppliers or duplicate products detection of one supplier. Common methods use the image as the detected object, but they only consider the visual features and overlook the attribute information contained in the textual descriptions, and perform weakly for products in image less important industries like machinery, hardware tools and electronic component, even if an additional text matching module is added. In this paper, we propose a unified vision-language modeling method for e-commerce same-style products retrieval, which is designed to represent one product with its textual descriptions and visual contents. It contains one sampling skill to collect positive pairs from user click log with category and relevance constrained, and a novel contrastive loss unit to model the image, text, and image+text representations into one joint embedding space. It is capable of cross-modal product-to-product retrieval, as well as style transfer and user-interactive search. Offline evaluations on annotated data demonstrate its superior retrieval performance, and online testings show it can attract more clicks and conversions. Moreover, this model has already been deployed online for similar products retrieval in alibaba.com, the largest B2B e-commerce platform in the world. | ['Wei Ning', 'Wen Jiang', 'Dehong Gao', 'Xinxin Wang', 'Linbo Jin', 'Ben Chen'] | 2023-02-10 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [ 1.01021945e-01 -6.35470986e-01 -3.15241754e-01 -2.24050075e-01
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f2aa5013-4b78-41ce-8af6-9b2f4fc84955 | a-light-weight-model-for-active-speaker | 2303.04439 | null | https://arxiv.org/abs/2303.04439v1 | https://arxiv.org/pdf/2303.04439v1.pdf | A Light Weight Model for Active Speaker Detection | Active speaker detection is a challenging task in audio-visual scenario understanding, which aims to detect who is speaking in one or more speakers scenarios. This task has received extensive attention as it is crucial in applications such as speaker diarization, speaker tracking, and automatic video editing. The existing studies try to improve performance by inputting multiple candidate information and designing complex models. Although these methods achieved outstanding performance, their high consumption of memory and computational power make them difficult to be applied in resource-limited scenarios. Therefore, we construct a lightweight active speaker detection architecture by reducing input candidates, splitting 2D and 3D convolutions for audio-visual feature extraction, and applying gated recurrent unit (GRU) with low computational complexity for cross-modal modeling. Experimental results on the AVA-ActiveSpeaker dataset show that our framework achieves competitive mAP performance (94.1% vs. 94.2%), while the resource costs are significantly lower than the state-of-the-art method, especially in model parameters (1.0M vs. 22.5M, about 23x) and FLOPs (0.6G vs. 2.6G, about 4x). In addition, our framework also performs well on the Columbia dataset showing good robustness. The code and model weights are available at https://github.com/Junhua-Liao/Light-ASD. | ['Liangyin Chen', 'Yanbing Yang', 'Wanbing Zhao', 'Kanghui Feng', 'Haihan Duan', 'Junhua Liao'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['audio-visual-active-speaker-detection'] | ['computer-vision'] | [ 1.29860118e-01 -2.56268531e-01 -6.33742064e-02 -3.41153532e-01
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810d450e-c693-496f-ad47-415bea4a4ccf | character-level-representations-improve-drs | 2011.04308 | null | https://arxiv.org/abs/2011.04308v1 | https://arxiv.org/pdf/2011.04308v1.pdf | Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT | We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena. | ['Johan Bos', 'Antonio Toral', 'Rik van Noord'] | 2020-11-09 | null | https://aclanthology.org/2020.emnlp-main.371 | https://aclanthology.org/2020.emnlp-main.371.pdf | emnlp-2020-11 | ['drs-parsing'] | ['natural-language-processing'] | [ 4.53330874e-01 4.36841249e-01 -7.16376007e-01 -4.98357207e-01
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204d112b-76b2-467e-b914-b4c0e63882a2 | probabilistic-neural-programmed-networks-for | null | null | http://papers.nips.cc/paper/7658-probabilistic-neural-programmed-networks-for-scene-generation | http://papers.nips.cc/paper/7658-probabilistic-neural-programmed-networks-for-scene-generation.pdf | Probabilistic Neural Programmed Networks for Scene Generation | In this paper we address the text to scene image generation problem. Generative models that capture the variability in complicated scenes containing rich semantics is a grand goal of image generation. Complicated scene images contain rich visual elements, compositional visual concepts, and complicated relations between objects. Generative models, as an analysis-by-synthesis process, should encompass the following three core components: 1) the generation process that composes the scene; 2) what are the primitive visual elements and how are they composed; 3) the rendering of abstract concepts into their pixel-level realizations. We propose PNP-Net, a variational auto-encoder framework that addresses these three challenges: it flexibly composes images with a dynamic network structure, learns a set of distribution transformers that can compose distributions based on semantics, and decodes samples from these distributions into realistic images. | ['Jiacheng Chen', 'Zhiwei Deng', 'Yifang Fu', 'Greg Mori'] | 2018-12-01 | null | null | null | neurips-2018-12 | ['scene-generation'] | ['computer-vision'] | [ 3.60118508e-01 3.22040841e-02 2.77490616e-01 -4.15507734e-01
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9dd3fc19-9a3a-404c-9aff-b10cf33af3b7 | what-can-unsupervised-machine-translation | 2106.15818 | null | https://arxiv.org/abs/2106.15818v2 | https://arxiv.org/pdf/2106.15818v2.pdf | On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation | Modern unsupervised machine translation (MT) systems reach reasonable translation quality under clean and controlled data conditions. As the performance gap between supervised and unsupervised MT narrows, it is interesting to ask whether the different training methods result in systematically different output beyond what is visible via quality metrics like adequacy or BLEU. We compare translations from supervised and unsupervised MT systems of similar quality, finding that unsupervised output is more fluent and more structurally different in comparison to human translation than is supervised MT. We then demonstrate a way to combine the benefits of both methods into a single system which results in improved adequacy and fluency as rated by human evaluators. Our results open the door to interesting discussions about how supervised and unsupervised MT might be different yet mutually-beneficial. | ['David Grangier', 'Markus Freitag', 'Kelly Marchisio'] | 2021-06-30 | null | https://aclanthology.org/2022.naacl-main.161 | https://aclanthology.org/2022.naacl-main.161.pdf | naacl-2022-7 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 4.15772736e-01 4.52893227e-01 -4.54611897e-01 -5.08262277e-01
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d591d272-0dd8-4ad0-b6e7-923447cd6b56 | ultra-light-ocr-competition-technical-report | 2110.12623 | null | https://arxiv.org/abs/2110.12623v1 | https://arxiv.org/pdf/2110.12623v1.pdf | Ultra Light OCR Competition Technical Report | Ultra Light OCR Competition is a Chinese scene text recognition competition jointly organized by CSIG (China Society of Image and Graphics) and Baidu, Inc. In addition to focusing on common problems in Chinese scene text recognition, such as long text length and massive characters, we need to balance the trade-off of model scale and accuracy since the model size limitation in the competition is 10M. From experiments in aspects of data, model, training, etc, we proposed a general and effective method for Chinese scene text recognition, which got us second place among over 100 teams with accuracy 0.817 in TestB dataset. The code is available at https://aistudio.baidu.com/aistudio/projectdetail/2159102. | ['Yichao Xiong', 'Tianhe Wang', 'Yuxin Zou', 'Shuhan Zhang'] | 2021-10-25 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [-1.14279106e-01 -8.62209976e-01 7.87675008e-02 -1.28585830e-01
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-2.60547340e-01 9.61168185e-02 -1.14928387e-01 2.30125666e-01] | [11.957849502563477, 2.261159896850586] |
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