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5d4f6f61-4e26-48e5-974b-e1a73ce18d18 | feasible-actor-critic-constrained | 2105.10682 | null | https://arxiv.org/abs/2105.10682v3 | https://arxiv.org/pdf/2105.10682v3.pdf | Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety | The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsafe, which is unsatisfying for real-world safety-critical tasks. In this paper, we introduce the feasible actor-critic (FAC) algorithm, which is the first model-free constrained RL method that considers statewise safety, e.g, safety for each initial state. We claim that some states are inherently unsafe no matter what policy we choose, while for other states there exist policies ensuring safety, where we say such states and policies are feasible. By constructing a statewise Lagrange function available on RL sampling and adopting an additional neural network to approximate the statewise Lagrange multiplier, we manage to obtain the optimal feasible policy which ensures safety for each feasible state and the safest possible policy for infeasible states. Furthermore, the trained multiplier net can indicate whether a given state is feasible or not through the statewise complementary slackness condition. We provide theoretical guarantees that FAC outperforms previous expectation-based constrained RL methods in terms of both constraint satisfaction and reward optimization. Experimental results on both robot locomotive tasks and safe exploration tasks verify the safety enhancement and feasibility interpretation of the proposed method. | ['Jianyu Chen', 'Sifa Zheng', 'Xiangteng Zhang', 'Shegnbo Eben Li', 'Yang Guan', 'Haitong Ma'] | 2021-05-22 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 2.03940850e-02 6.10398650e-01 -9.60899591e-01 -1.70245823e-02
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2764890a-7bcf-4c63-b280-d373d514a0f2 | controllable-3d-generative-adversarial-face | 2208.14263 | null | https://arxiv.org/abs/2208.14263v1 | https://arxiv.org/pdf/2208.14263v1.pdf | Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance | 3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation. Existing 3D deep learning generative models (e.g., VAE, GANs) allow generating compact face representations (both shape and texture) that can model non-linearities in the shape and appearance space (e.g., scatter effects, specularities, etc.). However, they lack the capability to control the generation of subtle expressions. This paper proposes a new 3D face generative model that can decouple identity and expression and provides granular control over expressions. In particular, we propose using a pair of supervised auto-encoder and generative adversarial networks to produce high-quality 3D faces, both in terms of appearance and shape. Experimental results in the generation of 3D faces learned with holistic expression labels, or Action Unit labels, show how we can decouple identity and expression; gaining fine-control over expressions while preserving identity. | ['Daeil Kim', 'Aayush Prakash', 'Steven Song', 'Fernando de la Torre', 'Xuanbai Chen', 'Shaunak Srivastava', 'Quankai Gao', 'Aashish Rai', 'Fariborz Taherkhani'] | 2022-08-30 | null | null | null | null | ['3d-face-modeling', 'face-model'] | ['computer-vision', 'computer-vision'] | [ 2.17970312e-01 5.81854999e-01 3.01061749e-01 -5.66517711e-01
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17013b56-98a6-4e72-a04b-33bb1d1a1f4f | aspect-category-based-sentiment-analysis-with | null | null | https://aclanthology.org/2020.coling-main.72 | https://aclanthology.org/2020.coling-main.72.pdf | Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network | Most of the aspect based sentiment analysis research aims at identifying the sentiment polarities toward some explicit aspect terms while ignores implicit aspects in text. To capture both explicit and implicit aspects, we focus on aspect-category based sentiment analysis, which involves joint aspect category detection and category-oriented sentiment classification. However, currently only a few simple studies have focused on this problem. The shortcomings in the way they defined the task make their approaches difficult to effectively learn the inner-relations between categories and the inter-relations between categories and sentiments. In this work, we re-formalize the task as a category-sentiment hierarchy prediction problem, which contains a hierarchy output structure to first identify multiple aspect categories in a piece of text, and then predict the sentiment for each of the identified categories. Specifically, we propose a Hierarchical Graph Convolutional Network (Hier-GCN), where a lower-level GCN is to model the inner-relations among multiple categories, and the higher-level GCN is to capture the inter-relations between aspect categories and sentiments. Extensive evaluations demonstrate that our hierarchy output structure is superior over existing ones, and the Hier-GCN model can consistently achieve the best results on four benchmarks. | ['Rui Xia', 'Jianfei Yu', 'Xiangsheng Zhou', 'Yaofeng Tu', 'Hongjie Cai'] | 2020-12-01 | null | null | null | coling-2020-8 | ['aspect-category-detection'] | ['natural-language-processing'] | [-1.09884776e-01 1.85265422e-01 -4.34571981e-01 -7.05619097e-01
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c329e07c-cfef-4297-84a7-185ffbad8836 | adaptive-reinforcement-learning-of-multi | 2307.00552 | null | https://arxiv.org/abs/2307.00552v1 | https://arxiv.org/pdf/2307.00552v1.pdf | Adaptive reinforcement learning of multi-agent ethically-aligned behaviours: the QSOM and QDSOM algorithms | The numerous deployed Artificial Intelligence systems need to be aligned with our ethical considerations. However, such ethical considerations might change as time passes: our society is not fixed, and our social mores evolve. This makes it difficult for these AI systems; in the Machine Ethics field especially, it has remained an under-studied challenge. In this paper, we present two algorithms, named QSOM and QDSOM, which are able to adapt to changes in the environment, and especially in the reward function, which represents the ethical considerations that we want these systems to be aligned with. They associate the well-known Q-Table to (Dynamic) Self-Organizing Maps to handle the continuous and multi-dimensional state and action spaces. We evaluate them on a use-case of multi-agent energy repartition within a small Smart Grid neighborhood, and prove their ability to adapt, and their higher performance compared to baseline Reinforcement Learning algorithms. | ['Mathieu Guillermin', 'Olivier Boissier', 'Rémy Chaput'] | 2023-07-02 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-1.20773979e-01 4.95959997e-01 -8.16684291e-02 -2.88311988e-01
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aec21fab-28ef-40b8-ae7f-eab272cec14c | feature-control-as-intrinsic-motivation-for | 1705.06769 | null | http://arxiv.org/abs/1705.06769v2 | http://arxiv.org/pdf/1705.06769v2.pdf | Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning | The problem of sparse rewards is one of the hardest challenges in
contemporary reinforcement learning. Hierarchical reinforcement learning (HRL)
tackles this problem by using a set of temporally-extended actions, or options,
each of which has its own subgoal. These subgoals are normally handcrafted for
specific tasks. Here, though, we introduce a generic class of subgoals with
broad applicability in the visual domain. Underlying our approach (in common
with work using "auxiliary tasks") is the hypothesis that the ability to
control aspects of the environment is an inherently useful skill to have. We
incorporate such subgoals in an end-to-end hierarchical reinforcement learning
system and test two variants of our algorithm on a number of games from the
Atari suite. We highlight the advantage of our approach in one of the hardest
games -- Montezuma's revenge -- for which the ability to handle sparse rewards
is key. Our agent learns several times faster than the current state-of-the-art
HRL agent in this game, reaching a similar level of performance. UPDATE
22/11/17: We found that a standard A3C agent with a simple shaped reward, i.e.
extrinsic reward + feature control intrinsic reward, has comparable performance
to our agent in Montezuma Revenge. In light of the new experiments performed,
the advantage of our HRL approach can be attributed more to its ability to
learn useful features from intrinsic rewards rather than its ability to explore
and reuse abstracted skills with hierarchical components. This has led us to a
new conclusion about the result. | ['Nat Dilokthanakul', 'Murray Shanahan', 'Nick Pawlowski', 'Christos Kaplanis'] | 2017-05-18 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-2.43651718e-02 1.32020667e-01 3.57235037e-02 1.25562266e-01
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043aa160-d9bf-402d-95dc-b7e872dd352c | 3dfpn-hs2-3d-feature-pyramid-network-based | 1906.03467 | null | https://arxiv.org/abs/1906.03467v2 | https://arxiv.org/pdf/1906.03467v2.pdf | 3DFPN-HS$^2$: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection | Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS$^2$) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. The proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves $90.4\%$ sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results $15.6\%$. | ['YingLi Tian', 'Oguz Akin', 'Jingya Liu', 'Liangliang Cao'] | 2019-06-08 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 7.39674717e-02 1.23099171e-01 -1.46431729e-01 -1.16224408e-01
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5fc66979-6dd7-4ee7-8566-e33e61fb7650 | a-fault-detection-scheme-utilizing | 2210.09226 | null | https://arxiv.org/abs/2210.09226v1 | https://arxiv.org/pdf/2210.09226v1.pdf | A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy | Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the solar system, this paper proposes a trained convolutional neural network (CNN) based fault detection scheme to divide the images of photovoltaic modules. For binary classification, the algorithm classifies the input images of PV cells into two categories (i.e. faulty or normal). To further assess the network's capability, the defective PV cells are organized into shadowy, cracked, or dusty cells, and the model is utilized for multiple classifications. The success rate for the proposed CNN model is 91.1% for binary classification and 88.6% for multi-classification. Thus, the proposed trained CNN model remarkably outperforms the CNN model presented in a previous study which used the same datasets. The proposed CNN-based fault detection model is straightforward, simple and effective and could be applied in the fault detection of solar panel. | ['Amin Kazemi', 'Mary Pa'] | 2022-10-14 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.66396558e-01 -2.41775036e-01 2.32423559e-01 7.67235309e-02
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939d8ae4-9142-4a6a-b3fd-20a3c7522ec1 | towards-scalable-multi-view-reconstruction-of | 2306.03747 | null | https://arxiv.org/abs/2306.03747v1 | https://arxiv.org/pdf/2306.03747v1.pdf | Towards Scalable Multi-View Reconstruction of Geometry and Materials | In this paper, we propose a novel method for joint recovery of camera pose, object geometry and spatially-varying Bidirectional Reflectance Distribution Function (svBRDF) of 3D scenes that exceed object-scale and hence cannot be captured with stationary light stages. The input are high-resolution RGB-D images captured by a mobile, hand-held capture system with point lights for active illumination. Compared to previous works that jointly estimate geometry and materials from a hand-held scanner, we formulate this problem using a single objective function that can be minimized using off-the-shelf gradient-based solvers. To facilitate scalability to large numbers of observation views and optimization variables, we introduce a distributed optimization algorithm that reconstructs 2.5D keyframe-based representations of the scene. A novel multi-view consistency regularizer effectively synchronizes neighboring keyframes such that the local optimization results allow for seamless integration into a globally consistent 3D model. We provide a study on the importance of each component in our formulation and show that our method compares favorably to baselines. We further demonstrate that our method accurately reconstructs various objects and materials and allows for expansion to spatially larger scenes. We believe that this work represents a significant step towards making geometry and material estimation from hand-held scanners scalable. | ['Andreas Geiger', 'Joo Ho Lee', 'Andrei Neculai', 'Božidar Antić', 'Carolin Schmitt'] | 2023-06-06 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 2.69239217e-01 -3.05915564e-01 1.80639982e-01 -2.34272793e-01
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c3635163-cc5a-40ee-a2a8-5b4ac090d7f8 | data-efficient-image-quality-assessment-with | 2304.04952 | null | https://arxiv.org/abs/2304.04952v1 | https://arxiv.org/pdf/2304.04952v1.pdf | Data-Efficient Image Quality Assessment with Attention-Panel Decoder | Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data. More specifically, we consider the traditional fine-tuning in BIQA as an interpretation of the pre-trained model. In this way, we further introduce a Transformer decoder to refine the perceptual information of the CLS token from different perspectives. This enables our model to establish the quality-aware feature manifold efficiently while attaining a strong generalization capability. Meanwhile, inspired by the subjective evaluation behaviors of human, we introduce a novel attention panel mechanism, which improves the model performance and reduces the prediction uncertainty simultaneously. The proposed BIQA method maintains a lightweight design with only one layer of the decoder, yet extensive experiments on eight standard BIQA datasets (both synthetic and authentic) demonstrate its superior performance to the state-of-the-art BIQA methods, i.e., achieving the SRCC values of 0.875 (vs. 0.859 in LIVEC) and 0.980 (vs. 0.969 in LIVE). | ['Yan Zhang', 'Xiu Li', 'Haotian Liu', 'Xiawu Zheng', 'Yutao Liu', 'Runze Hu', 'Guanyi Qin'] | 2023-04-11 | null | null | null | null | ['blind-image-quality-assessment', 'image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [-3.77478302e-02 -4.10016567e-01 3.24153900e-01 -3.95288974e-01
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ad1c216c-6ce5-40c5-973c-460737f747e0 | experimenting-with-additive-margins-for | 2306.03664 | null | https://arxiv.org/abs/2306.03664v1 | https://arxiv.org/pdf/2306.03664v1.pdf | Experimenting with Additive Margins for Contrastive Self-Supervised Speaker Verification | Most state-of-the-art self-supervised speaker verification systems rely on a contrastive-based objective function to learn speaker representations from unlabeled speech data. We explore different ways to improve the performance of these methods by: (1) revisiting how positive and negative pairs are sampled through a "symmetric" formulation of the contrastive loss; (2) introducing margins similar to AM-Softmax and AAM-Softmax that have been widely adopted in the supervised setting. We demonstrate the effectiveness of the symmetric contrastive loss which provides more supervision for the self-supervised task. Moreover, we show that Additive Margin and Additive Angular Margin allow reducing the overall number of false negatives and false positives by improving speaker separability. Finally, by combining both techniques and training a larger model we achieve 7.50% EER and 0.5804 minDCF on the VoxCeleb1 test set, which outperforms other contrastive self supervised methods on speaker verification. | ['Reda Dehak', 'Theo Lepage'] | 2023-06-06 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 2.70149976e-01 4.28501457e-01 -2.16707155e-01 -9.52844739e-01
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837e88fc-a49d-4916-a471-7bdc77cf9370 | video-and-accelerometer-based-motion-analysis | 1702.07772 | null | http://arxiv.org/abs/1702.07772v1 | http://arxiv.org/pdf/1702.07772v1.pdf | Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment | Purpose: Basic surgical skills of suturing and knot tying are an essential
part of medical training. Having an automated system for surgical skills
assessment could help save experts time and improve training efficiency. There
have been some recent attempts at automated surgical skills assessment using
either video analysis or acceleration data. In this paper, we present a novel
approach for automated assessment of OSATS based surgical skills and provide an
analysis of different features on multi-modal data (video and accelerometer
data). Methods: We conduct the largest study, to the best of our knowledge, for
basic surgical skills assessment on a dataset that contained video and
accelerometer data for suturing and knot-tying tasks. We introduce "entropy
based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy
(XApEn), which quantify the amount of predictability and regularity of
fluctuations in time-series data. The proposed features are compared to
existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform
(DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment.
Results: We report average performance of different features across all
applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows
that the proposed entropy based features out-perform previous state-of-the-art
methods using video data. For accelerometer data, our method performs better
for suturing only. We also show that fusion of video and acceleration features
can improve overall performance with the proposed entropy features achieving
highest accuracy. Conclusions: Automated surgical skills assessment can be
achieved with high accuracy using the proposed entropy features. Such a system
can significantly improve the efficiency of surgical training in medical
schools and teaching hospitals. | ['Yachna Sharma', 'Aneeq Zia', 'Vinay Bettadapura', 'Eric L. Sarin', 'Irfan Essa'] | 2017-02-24 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [ 1.10904433e-01 -1.69865176e-01 -1.06461838e-01 2.51963418e-02
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aa387a2e-0dea-42a9-9311-ef082d4d2186 | democratizing-llms-for-low-resource-languages | 2306.11372 | null | https://arxiv.org/abs/2306.11372v1 | https://arxiv.org/pdf/2306.11372v1.pdf | Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts | Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. However, in low-resource languages, obtaining such hand-picked exemplars can still be challenging, where unsupervised techniques may be necessary. Moreover, competent generative capabilities of LLMs are observed only in high-resource languages, while their performances among under-represented languages fall behind due to pre-training data imbalance. To elicit LLMs' ability onto low-resource languages without any supervised data, we propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. These prompts are then used to create intra-lingual exemplars to perform tasks in the target languages. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages. We also show that fine-tuning a 7B model on data generated from our method helps it perform competitively with a 175B model. In non-English translation tasks, our method even outperforms supervised prompting by up to 3 chrF++ in many low-resource languages. When evaluated on zero-shot multilingual summarization, our method surpasses other English-pivoting baselines by up to 4 ROUGE-L and is also favored by GPT-4. | ['Lidong Bing', 'Shafiq Joty', 'Sharifah Mahani Aljunied', 'Xuan-Phi Nguyen'] | 2023-06-20 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [ 3.66640300e-01 1.59458712e-01 -6.62600100e-01 -2.23593518e-01
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cc56a7f4-bb04-40c5-aac6-d8340966900f | atrial-fibrillation-recurrence-risk | 2208.10550 | null | https://arxiv.org/abs/2208.10550v1 | https://arxiv.org/pdf/2208.10550v1.pdf | Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure | Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial fibrillation (AF) catheter ablation procedure (CAP). It is not easy to determine if CAP was successful without a long follow-up assessing for AF recurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a better management of CAP patients. In this research, we extracted features from 12-lead ECG recorded before and after CAP and train an AFR risk prediction machine learning model. Methods: Pre- and post-CAP segments were extracted from 112 patients. The analysis included a signal quality criterion, heart rate variability and morphological biomarkers engineered from the 12-lead ECG (804 features overall). 43 out of the 112 patients (n) had AFR clinical endpoint available. These were utilized to assess the feasibility of AFR risk prediction, using either pre or post CAP features. A random forest classifier was trained within a nested cross validation framework. Results: 36 features were found statistically significant for distinguishing between the pre and post surgery states (n=112). For the classification, an area under the receiver operating characteristic (AUROC) curve was reported with AUROC_pre=0.64 and AUROC_post=0.74 (n=43). Discussion and conclusions: This preliminary analysis showed the feasibility of AFR risk prediction. Such a model could be used to improve CAP management. | ['Joachim A. Behar', 'Andrea Natale', 'Jason Lewen', 'Sanghamitra Mohanty', 'Sheina Gendelman', 'Eran Zvuloni'] | 2022-08-22 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.35118711e-01 -2.52201647e-01 -4.39986259e-01 -1.37548998e-01
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462cbfe1-aca1-4284-9622-2e6fa4905fc5 | spatio-temporal-multi-task-learning | 2106.11401 | null | https://arxiv.org/abs/2106.11401v1 | https://arxiv.org/pdf/2106.11401v1.pdf | Spatio-Temporal Multi-Task Learning Transformer for Joint Moving Object Detection and Segmentation | Moving objects have special importance for Autonomous Driving tasks. Detecting moving objects can be posed as Moving Object Segmentation, by segmenting the object pixels, or Moving Object Detection, by generating a bounding box for the moving targets. In this paper, we present a Multi-Task Learning architecture, based on Transformers, to jointly perform both tasks through one network. Due to the importance of the motion features to the task, the whole setup is based on a Spatio-Temporal aggregation. We evaluate the performance of the individual tasks architecture versus the MTL setup, both with early shared encoders, and late shared encoder-decoder transformers. For the latter, we present a novel joint tasks query decoder transformer, that enables us to have tasks dedicated heads out of the shared model. To evaluate our approach, we use the KITTI MOD [29] data set. Results show1.5% mAP improvement for Moving Object Detection, and 2%IoU improvement for Moving Object Segmentation, over the individual tasks networks. | ['Ahmed El-Sallab', 'Eslam Mohamed'] | 2021-06-21 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 3.94682914e-01 9.14164484e-02 2.67402898e-03 -4.10187066e-01
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c10348d2-db3d-4fb2-8713-1ccbaa0a872e | doc-nad-a-hybrid-deep-one-class-classifier | 2212.07558 | null | https://arxiv.org/abs/2212.07558v1 | https://arxiv.org/pdf/2212.07558v1.pdf | DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection | Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved near-perfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malicious) network data classes during the training phase. However, attack data samples are often challenging to collect in most organisations due to security controls preventing the penetration of known malicious traffic to their networks. Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples. The novel one-class classification architecture consists of a histogram-based deep feed-forward classifier to extract useful network data features and use efficient outlier detection. The DOC classifier has been extensively evaluated using two benchmark NIDS datasets. The results demonstrate its superiority over current state-of-the-art one-class classifiers in terms of detection and false positive rates. | ['Marius Portmann', 'Siamak Layeghy', 'Wai Weng Lo', 'Gayan Kulatilleke', 'Mohanad Sarhan'] | 2022-12-15 | null | null | null | null | ['one-class-classifier', 'one-class-classification', 'network-intrusion-detection'] | ['methodology', 'miscellaneous', 'miscellaneous'] | [ 2.76006103e-01 -2.58653075e-01 1.08411033e-02 -5.94232559e-01
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88bcc639-0304-4c42-89ab-09a220ecd956 | harim-evaluating-summary-quality-with | 2211.12118 | null | https://arxiv.org/abs/2211.12118v2 | https://arxiv.org/pdf/2211.12118v2.pdf | HaRiM$^+$: Evaluating Summary Quality with Hallucination Risk | One of the challenges of developing a summarization model arises from the difficulty in measuring the factual inconsistency of the generated text. In this study, we reinterpret the decoder overconfidence-regularizing objective suggested in (Miao et al., 2021) as a hallucination risk measurement to better estimate the quality of generated summaries. We propose a reference-free metric, HaRiM+, which only requires an off-the-shelf summarization model to compute the hallucination risk based on token likelihoods. Deploying it requires no additional training of models or ad-hoc modules, which usually need alignment to human judgments. For summary-quality estimation, HaRiM+ records state-of-the-art correlation to human judgment on three summary-quality annotation sets: FRANK, QAGS, and SummEval. We hope that our work, which merits the use of summarization models, facilitates the progress of both automated evaluation and generation of summary. | ['Yeonsoo Lee', 'Hyungjong Noh', 'Junghwa Lee', 'Jeong-in Hwang', 'Junsoo Park', 'Seonil Son'] | 2022-11-22 | null | null | null | null | ['automated-writing-evaluation'] | ['natural-language-processing'] | [ 4.38372754e-02 7.07418025e-01 -7.28959143e-02 -4.65688437e-01
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dc32c0b2-c9b9-48d7-82ab-2d116ca02a0e | learning-spatio-temporal-specifications-for | 2112.10714 | null | https://arxiv.org/abs/2112.10714v1 | https://arxiv.org/pdf/2112.10714v1.pdf | Learning Spatio-Temporal Specifications for Dynamical Systems | Learning dynamical systems properties from data provides important insights that help us understand such systems and mitigate undesired outcomes. In this work, we propose a framework for learning spatio-temporal (ST) properties as formal logic specifications from data. We introduce SVM-STL, an extension of Signal Signal Temporal Logic (STL), capable of specifying spatial and temporal properties of a wide range of dynamical systems that exhibit time-varying spatial patterns. Our framework utilizes machine learning techniques to learn SVM-STL specifications from system executions given by sequences of spatial patterns. We present methods to deal with both labeled and unlabeled data. In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications. Our learning framework and parameter synthesis approach are showcased in an example of a reaction-diffusion system. | ['Calin Belta', 'Ron Weiss', 'Erfan Aasi', 'Suhail Alsalehi'] | 2021-12-20 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 3.73596549e-01 -4.96431217e-02 -4.37835991e-01 -4.24534261e-01
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9fe588fd-9770-4a05-8b92-cc1a76fe9d11 | bkd-fedgnn-a-benchmark-for-classification | 2306.10351 | null | https://arxiv.org/abs/2306.10351v1 | https://arxiv.org/pdf/2306.10351v1.pdf | Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network | Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly growing research topic, as it integrates the strengths of graph neural networks and federated learning to enable advanced machine learning applications without direct access to sensitive data. Despite its advantages, the distributed nature of FedGNN introduces additional vulnerabilities, particularly backdoor attacks stemming from malicious participants. Although graph backdoor attacks have been explored, the compounded complexity introduced by the combination of GNNs and federated learning has hindered a comprehensive understanding of these attacks, as existing research lacks extensive benchmark coverage and in-depth analysis of critical factors. To address these limitations, we propose Bkd-FedGNN, a benchmark for backdoor attacks on FedGNN. Specifically, Bkd-FedGNN decomposes the graph backdoor attack into trigger generation and injection steps, and extending the attack to the node-level federated setting, resulting in a unified framework that covers both node-level and graph-level classification tasks. Moreover, we thoroughly investigate the impact of multiple critical factors in backdoor attacks on FedGNN. These factors are categorized into global-level and local-level factors, including data distribution, the number of malicious attackers, attack time, overlapping rate, trigger size, trigger type, trigger position, and poisoning rate. Finally, we conduct comprehensive evaluations on 13 benchmark datasets and 13 critical factors, comprising 1,725 experimental configurations for node-level and graph-level tasks from six domains. These experiments encompass over 8,000 individual tests, allowing us to provide a thorough evaluation and insightful observations that advance our understanding of backdoor attacks on FedGNN.The Bkd-FedGNN benchmark is publicly available at https://github.com/usail-hkust/BkdFedGCN. | ['Hao liu', 'Yansong Ning', 'Siqi Lai', 'Fan Liu'] | 2023-06-17 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [-1.84950963e-01 -2.64248252e-01 -5.42201281e-01 1.14492655e-01
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7849d8a1-de06-4a69-8326-6714b8d0b38f | comparison-of-deep-object-detectors-on-a-new | 2212.06218 | null | https://arxiv.org/abs/2212.06218v1 | https://arxiv.org/pdf/2212.06218v1.pdf | Comparison Of Deep Object Detectors On A New Vulnerable Pedestrian Dataset | Pedestrian safety is one primary concern in autonomous driving. The under-representation of vulnerable groups in today's pedestrian datasets points to an urgent need for a dataset of vulnerable road users. In this paper, we first introduce a new vulnerable pedestrian detection dataset, BG Vulnerable Pedestrian (BGVP) dataset to help train well-rounded models and thus induce research to increase the efficacy of vulnerable pedestrian detection. The dataset includes four classes, i.e., Children Without Disability, Elderly without Disability, With Disability, and Non-Vulnerable. This dataset consists of images collected from the public domain and manually-annotated bounding boxes. In addition, on the proposed dataset, we have trained and tested five state-of-the-art object detection models, i.e., YOLOv4, YOLOv5, YOLOX, Faster R-CNN, and EfficientDet. Our results indicate that YOLOX and YOLOv4 perform the best on our dataset, YOLOv4 scoring 0.7999 and YOLOX scoring 0.7779 on the mAP 0.5 metric, while YOLOX outperforms YOLOv4 by 3.8 percent on the mAP 0.5:0.95 metric. Generally speaking, all five detectors do well predicting the With Disability class and perform poorly in the Elderly Without Disability class. YOLOX consistently outperforms all other detectors on the mAP (0.5:0.95) per class metric, obtaining 0.5644, 0.5242, 0.4781, and 0.6796 for Children Without Disability, Elderly Without Disability, Non-vulnerable, and With Disability, respectively. Our dataset and codes are available at https://github.com/devvansh1997/BGVP. | ['Qing Tian', 'Tihitina Hade', 'Devansh Sharma'] | 2022-12-12 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-6.22459829e-01 -4.64520305e-02 -2.54572541e-01 -1.56437829e-01
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4fd1245f-f70f-4246-b740-b1b9e25a6367 | adaptive-name-entity-recognition-under-highly | 2003.10296 | null | https://arxiv.org/abs/2003.10296v1 | https://arxiv.org/pdf/2003.10296v1.pdf | Adaptive Name Entity Recognition under Highly Unbalanced Data | For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc. In this report, we present our experiments on a neural architecture composed of a Conditional Random Field (CRF) layer stacked on top of a Bi-directional LSTM (BI-LSTM) layer for solving NER tasks. Besides, we also employ a fusion input of embedding vectors (Glove, BERT), which are pre-trained on the huge corpus to boost the generalization capacity of the model. Unfortunately, due to the heavy unbalanced distribution cross-training data, both approaches just attained a bad performance on less training samples classes. To overcome this challenge, we introduce an add-on classification model to split sentences into two different sets: Weak and Strong classes and then designing a couple of Bi-LSTM-CRF models properly to optimize performance on each set. We evaluated our models on the test set and discovered that our method can improve performance for Weak classes significantly by using a very small data set (approximately 0.45\%) compared to the rest classes. | ['Pramod Rao', 'Duy Nguyen', 'Thong Nguyen'] | 2020-03-10 | null | null | null | null | ['small-data'] | ['computer-vision'] | [-2.33357087e-01 2.31420875e-01 -6.76556444e-03 -6.60197020e-01
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7ca19af4-65ff-4103-a331-ee740ee7c5b4 | few-could-be-better-than-all-feature-sampling | 2203.15221 | null | https://arxiv.org/abs/2203.15221v2 | https://arxiv.org/pdf/2203.15221v2.pdf | Few Could Be Better Than All: Feature Sampling and Grouping for Scene Text Detection | Recently, transformer-based methods have achieved promising progresses in object detection, as they can eliminate the post-processes like NMS and enrich the deep representations. However, these methods cannot well cope with scene text due to its extreme variance of scales and aspect ratios. In this paper, we present a simple yet effective transformer-based architecture for scene text detection. Different from previous approaches that learn robust deep representations of scene text in a holistic manner, our method performs scene text detection based on a few representative features, which avoids the disturbance by background and reduces the computational cost. Specifically, we first select a few representative features at all scales that are highly relevant to foreground text. Then, we adopt a transformer for modeling the relationship of the sampled features, which effectively divides them into reasonable groups. As each feature group corresponds to a text instance, its bounding box can be easily obtained without any post-processing operation. Using the basic feature pyramid network for feature extraction, our method consistently achieves state-of-the-art results on several popular datasets for scene text detection. | ['Xiang Bai', 'Guanglong Hu', 'Bo Jiang', 'Mingkun Yang', 'Hongye Liu', 'Wenqing Zhang', 'Jingqun Tang'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tang_Few_Could_Be_Better_Than_All_Feature_Sampling_and_Grouping_CVPR_2022_paper.pdf | cvpr-2022-1 | ['scene-text-detection', 'object-detection-in-aerial-images'] | ['computer-vision', 'computer-vision'] | [ 3.42899948e-01 -6.42366529e-01 2.41340801e-01 -2.95317709e-01
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dae4b97a-4f46-49d7-961e-a9808bebeb6b | plant-species-richness-prediction-from-desis | 2301.01918 | null | https://arxiv.org/abs/2301.01918v1 | https://arxiv.org/pdf/2301.01918v1.pdf | Plant species richness prediction from DESIS hyperspectral data: A comparison study on feature extraction procedures and regression models | The diversity of terrestrial vascular plants plays a key role in maintaining the stability and productivity of ecosystems. Monitoring species compositional diversity across large spatial scales is challenging and time consuming. The advanced spectral and spatial specification of the recently launched DESIS (the DLR Earth Sensing Imaging Spectrometer) instrument provides a unique opportunity to test the potential for monitoring plant species diversity with spaceborne hyperspectral data. This study provides a quantitative assessment on the ability of DESIS hyperspectral data for predicting plant species richness in two different habitat types in southeast Australia. Spectral features were first extracted from the DESIS spectra, then regressed against on-ground estimates of plant species richness, with a two-fold cross validation scheme to assess the predictive performance. We tested and compared the effectiveness of Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Partial Least Squares analysis (PLS) for feature extraction, and Kernel Ridge Regression (KRR), Gaussian Process Regression (GPR), Random Forest Regression (RFR) for species richness prediction. The best prediction results were r=0.76 and RMSE=5.89 for the Southern Tablelands region, and r=0.68 and RMSE=5.95 for the Snowy Mountains region. Relative importance analysis for the DESIS spectral bands showed that the red-edge, red, and blue spectral regions were more important for predicting plant species richness than the green bands and the near-infrared bands beyond red-edge. We also found that the DESIS hyperspectral data performed better than Sentinel-2 multispectral data in the prediction of plant species richness. Our results provide a quantitative reference for future studies exploring the potential of spaceborne hyperspectral data for plant biodiversity mapping. | ['Shaun R. Levick', 'Simon Ferrier', 'Peyman Moghadam', 'Cindy Ong', 'Karel Mokany', 'Yiqing Guo'] | 2023-01-05 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 5.34572601e-01 -5.78075767e-01 -2.09582731e-01 1.64381191e-01
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13081e71-f730-4674-ab30-1fa2e48139e1 | temporal-scalability-of-dynamic-volume-data | 2303.00379 | null | https://arxiv.org/abs/2303.00379v1 | https://arxiv.org/pdf/2303.00379v1.pdf | Temporal Scalability of Dynamic Volume Data Using Mesh Compensated Wavelet Lifting | Due to their high resolution, dynamic medical 2D+t and 3D+t volumes from computed tomography (CT) and magnetic resonance tomography (MR) reach a size which makes them very unhandy for teleradiologic applications. A lossless scalable representation offers the advantage of a down-scaled version which can be used for orientation or previewing, while the remaining information for reconstructing the full resolution is transmitted on demand. The wavelet transform offers the desired scalability. A very high quality of the lowpass sub-band is crucial in order to use it as a down-scaled representation. We propose an approach based on compensated wavelet lifting for obtaining a scalable representation of dynamic CT and MR volumes with very high quality. The mesh compensation is feasible to model the displacement in dynamic volumes which is mainly given by expansion and contraction of tissue over time. To achieve this, we propose an optimized estimation of the mesh compensation parameters to optimally fit for dynamic volumes. Within the lifting structure, the inversion of the motion compensation is crucial in the update step. We propose to take this inversion directly into account during the estimation step and can improve the quality of the lowpass sub-band by 0.63 and 0.43 dB on average for our tested dynamic CT and MR volumes at the cost of an increase of the rate by 2.4% and 1.2% on average. | ['André Kaup', 'Thomas Richter', 'Niklas Pallast', 'Wolfgang Schnurrer'] | 2023-03-01 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.70343274e-01 2.55932420e-01 4.80019487e-02 -1.41345151e-02
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ff67e857-c3d6-47d6-a3ef-26b05c73deaf | end-to-end-joint-target-and-non-target | 2306.02273 | null | https://arxiv.org/abs/2306.02273v1 | https://arxiv.org/pdf/2306.02273v1.pdf | End-to-End Joint Target and Non-Target Speakers ASR | This paper proposes a novel automatic speech recognition (ASR) system that can transcribe individual speaker's speech while identifying whether they are target or non-target speakers from multi-talker overlapped speech. Target-speaker ASR systems are a promising way to only transcribe a target speaker's speech by enrolling the target speaker's information. However, in conversational ASR applications, transcribing both the target speaker's speech and non-target speakers' ones is often required to understand interactive information. To naturally consider both target and non-target speakers in a single ASR model, our idea is to extend autoregressive modeling-based multi-talker ASR systems to utilize the enrollment speech of the target speaker. Our proposed ASR is performed by recursively generating both textual tokens and tokens that represent target or non-target speakers. Our experiments demonstrate the effectiveness of our proposed method. | ['Atsushi Ando', 'Nobukatsu Hojo', 'Takafumi Moriya', 'Satoshi Suzuki', 'Akihiko Takashima', 'Tomohiro Tanaka', 'Hiroshi Sato', 'Keita Suzuki', 'Mihiro Uchida', 'Mana Ihori', 'Saki Mizuno', 'Yoshihiko Yamazaki', 'Taiga Yamane', 'Naoki Makishima', 'Ryo Masumura'] | 2023-06-04 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.52095151e-01 2.50379622e-01 -3.96643318e-02 -5.63433647e-01
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032570e1-af52-4a23-97a2-beeb1e689bee | the-power-of-reuse-a-multi-scale-transformer | 2205.08579 | null | https://arxiv.org/abs/2205.08579v2 | https://arxiv.org/pdf/2205.08579v2.pdf | The Power of Fragmentation: A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation | Symbolic Music Generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformer-based model. The learning of musical context is also related to the structural elements in music, i.e. intro, verse, and chorus, which are currently overlooked by the research community. In this paper, we propose a hierarchical Transformer model to learn multi-scale contexts in music. In the encoding phase, we first designed a Fragment Scope Localization layer to syncopate the music into chords and sections. Then, we use a multi-scale attention mechanism to learn note-, chord-, and section-level contexts. In the decoding phase, we proposed a hierarchical Transformer model that uses fine-decoders to generate sections in parallel and a coarse-decoder to decode the combined music. We also designed a Music Style Normalization layer to achieve a consistent music style between the generated sections. Our model is evaluated on two open MIDI datasets, and experiments show that our model outperforms the best contemporary music generative models. More excitingly, the visual evaluation shows that our model is superior in melody reuse, resulting in more realistic music. | ['Xiaoya Fan', 'Shipei Liu', 'Guowei Wu'] | 2022-05-17 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.71115160e-01 -3.29798937e-01 1.12899534e-01 -1.27705425e-01
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1af3fef5-db25-40da-957c-904daa8d2c2d | test-time-training-for-out-of-distribution-1 | 1909.13231 | null | https://arxiv.org/abs/1909.13231v3 | https://arxiv.org/pdf/1909.13231v3.pdf | Test-Time Training with Self-Supervision for Generalization under Distribution Shifts | In this paper, we propose Test-Time Training, a general approach for improving the performance of predictive models when training and test data come from different distributions. We turn a single unlabeled test sample into a self-supervised learning problem, on which we update the model parameters before making a prediction. This also extends naturally to data in an online stream. Our simple approach leads to improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts. | ['Zhuang Liu', 'Yu Sun', 'Xiaolong Wang', 'Alexei A. Efros', 'Moritz Hardt', 'John Miller'] | 2019-09-29 | null | null | null | null | ['supervised-video-summarization', 'building-change-detection-for-remote-sensing', 'carla-map-leaderboard'] | ['computer-vision', 'miscellaneous', 'robots'] | [ 3.29851270e-01 -1.39321581e-01 -6.26071334e-01 -6.96108758e-01
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1e289887-1bca-4cd8-8e8d-8c9ce6fc76f1 | fits-modeling-time-series-with-10k-parameters | 2307.03756 | null | https://arxiv.org/abs/2307.03756v1 | https://arxiv.org/pdf/2307.03756v1.pdf | FITS: Modeling Time Series with $10k$ Parameters | In this paper, we introduce FITS, a lightweight yet powerful model for time series analysis. Unlike existing models that directly process raw time-domain data, FITS operates on the principle that time series can be manipulated through interpolation in the complex frequency domain. By discarding high-frequency components with negligible impact on time series data, FITS achieves performance comparable to state-of-the-art models for time series forecasting and anomaly detection tasks, while having a remarkably compact size of only approximately $10k$ parameters. Such a lightweight model can be easily trained and deployed in edge devices, creating opportunities for various applications. The anonymous code repo is available in: \url{https://anonymous.4open.science/r/FITS} | ['Qiang Xu', 'Ailing Zeng', 'Zhijian Xu'] | 2023-07-06 | null | null | null | null | ['anomaly-detection', 'time-series-forecasting', 'time-series'] | ['methodology', 'time-series', 'time-series'] | [-2.65232146e-01 -2.78132200e-01 -5.97528517e-02 -3.15186262e-01
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cb1ddbf1-ea5c-4101-9db9-6529c27a9ce4 | relational-generalized-few-shot-learning | 1907.09557 | null | https://arxiv.org/abs/1907.09557v2 | https://arxiv.org/pdf/1907.09557v2.pdf | Relational Generalized Few-Shot Learning | Transferring learned models to novel tasks is a challenging problem, particularly if only very few labeled examples are available. Although this few-shot learning setup has received a lot of attention recently, most proposed methods focus on discriminating novel classes only. Instead, we consider the extended setup of generalized few-shot learning (GFSL), where the model is required to perform classification on the joint label space consisting of both previously seen and novel classes. We propose a graph-based framework that explicitly models relationships between all seen and novel classes in the joint label space. Our model Graph-convolutional Global Prototypical Networks (GcGPN) incorporates these inter-class relations using graph-convolution in order to embed novel class representations into the existing space of previously seen classes in a globally consistent manner. Our approach ensures both fast adaptation and global discrimination, which is the major challenge in GFSL. We demonstrate the benefits of our model on two challenging benchmark datasets. | ['Leonard Salewski', 'Xiahan Shi', 'Martin Schiegg', 'Zeynep Akata', 'Max Welling'] | 2019-07-22 | null | null | null | null | ['generalized-few-shot-learning'] | ['methodology'] | [ 5.60017705e-01 1.71654131e-02 -1.77887827e-01 -5.87048292e-01
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ca5379ea-0200-45af-a16f-bc5be33e3d88 | multi-goal-reinforcement-learning | 2106.13687 | null | https://arxiv.org/abs/2106.13687v1 | https://arxiv.org/pdf/2106.13687v1.pdf | Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot | This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. They all follow a Multi-Goal RL framework, allowing to use goal-oriented RL algorithms. To foster open-research, we chose to use the open-source physics engine PyBullet. The implementation chosen for this package allows to define very easily new tasks or new robots. This report also presents a baseline of results obtained with state-of-the-art model-free off-policy algorithms. panda-gym is open-source at https://github.com/qgallouedec/panda-gym. | ['Liming Chen', 'Emmanuel Dellandréa', 'Nicolas Cazin', 'Quentin Gallouédec'] | 2021-06-25 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-7.54250944e-01 1.93450630e-01 -9.40000117e-02 -8.82493183e-02
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bc754494-83f1-4de7-b1f5-25452d1bd58a | multi-class-probabilistic-classification | null | null | https://proceedings.mlr.press/v60/manokhin17a.html | https://proceedings.mlr.press/v60/manokhin17a.html | Multi-class probabilistic classification using inductive and cross Venn-Abers predictors | Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems. We present a new approach to multi-class probability estimation by turning IVAPs and CVAPs into multi-class probabilistic predictors. The proposed multi-class predictors are experimentally more accurate than both uncalibrated predictors and existing calibration methods.
Unlike other methods such as Platt's scaler and Isotonic Regression Venn-ABERS predictors (being a form of conformal prediction) contain inbuilt mathematical guarantees of validity (lack of bias). In addition Venn-ABERS predictors are multi-output predictors that output two probability predictions of class 1. Such two probability prediction of assigning label 1 for each test objectc can be considered prediction interval. The interval width contains valuable information about degree of certainty of prediction, such information is not available when using other calibration methods such as Platt's and isotonic regression. | ['Valery Manokhin'] | 2017-06-16 | null | null | null | proceedings-of-the-sixth-workshop-on | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 5.25515638e-02 3.13998848e-01 -4.47914362e-01 -6.98557317e-01
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b71de1e8-0676-4407-b4a5-61783b3bc1eb | query-resolution-for-conversational-search | 2005.11723 | null | https://arxiv.org/abs/2005.11723v1 | https://arxiv.org/pdf/2005.11723v1.pdf | Query Resolution for Conversational Search with Limited Supervision | In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset. | ['Evangelos Kanoulas', 'Nikos Voskarides', 'Pengjie Ren', 'Maarten de Rijke', 'Dan Li'] | 2020-05-24 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 3.29582274e-01 1.55224800e-01 -5.41592598e-01 -3.68882716e-01
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489d5f2d-2362-4af5-9be3-c4aea935991b | enhancing-underwater-imagery-using-generative | 1801.04011 | null | http://arxiv.org/abs/1801.04011v1 | http://arxiv.org/pdf/1801.04011v1.pdf | Enhancing Underwater Imagery using Generative Adversarial Networks | Autonomous underwater vehicles (AUVs) rely on a variety of sensors -
acoustic, inertial and visual - for intelligent decision making. Due to its
non-intrusive, passive nature, and high information content, vision is an
attractive sensing modality, particularly at shallower depths. However, factors
such as light refraction and absorption, suspended particles in the water, and
color distortion affect the quality of visual data, resulting in noisy and
distorted images. AUVs that rely on visual sensing thus face difficult
challenges, and consequently exhibit poor performance on vision-driven tasks.
This paper proposes a method to improve the quality of visual underwater scenes
using Generative Adversarial Networks (GANs), with the goal of improving input
to vision-driven behaviors further down the autonomy pipeline. Furthermore, we
show how recently proposed methods are able to generate a dataset for the
purpose of such underwater image restoration. For any visually-guided
underwater robots, this improvement can result in increased safety and
reliability through robust visual perception. To that effect, we present
quantitative and qualitative data which demonstrates that images corrected
through the proposed approach generate more visually appealing images, and also
provide increased accuracy for a diver tracking algorithm. | ['Cameron Fabbri', 'Junaed Sattar', 'Md Jahidul Islam'] | 2018-01-11 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 2.51690030e-01 3.99196059e-01 8.94984901e-01 -2.47736022e-01
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3893f8f3-bc68-4cec-87c5-cd077add7b75 | a-fast-network-exploration-strategy-to | 2202.02361 | null | https://arxiv.org/abs/2202.02361v1 | https://arxiv.org/pdf/2202.02361v1.pdf | A Fast Network Exploration Strategy to Profile Low Energy Consumption for Keyword Spotting | Keyword Spotting nowadays is an integral part of speech-oriented user interaction targeted for smart devices. To this extent, neural networks are extensively used for their flexibility and high accuracy. However, coming up with a suitable configuration for both accuracy requirements and hardware deployment is a challenge. We propose a regression-based network exploration technique that considers the scaling of the network filters ($s$) and quantization ($q$) of the network layers, leading to a friendly and energy-efficient configuration for FPGA hardware implementation. We experiment with different combinations of $\mathcal{NN}\scriptstyle\langle q,\,s\rangle \displaystyle$ on the FPGA to profile the energy consumption of the deployed network so that the user can choose the most energy-efficient network configuration promptly. Our accelerator design is deployed on the Xilinx AC 701 platform and has at least 2.1$\times$ and 4$\times$ improvements on energy and energy efficiency results, respectively, compared to recent hardware implementations for keyword spotting. | ['Tinoosh Mohsenin', 'Arnab Neelim Mazumder'] | 2022-02-04 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 4.25895751e-02 -2.34835654e-01 -8.06162804e-02 -3.92001688e-01
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2d06c90a-c5f2-4b0d-be8b-b1095b697b4c | towards-real-time-and-energy-efficient | 2205.10653 | null | https://arxiv.org/abs/2205.10653v1 | https://arxiv.org/pdf/2205.10653v1.pdf | Towards real-time and energy efficient Siamese tracking -- a hardware-software approach | Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPUs. Easy to implement, this approach is energy consuming, and thus cannot be used in many low-power applications. To overcome this, one can use energy-efficient embedded devices, such as heterogeneous platforms joining the ARM processor system with programmable logic (FPGA). In this work, we propose a hardware-software implementation of the well-known fully connected Siamese tracker (SiamFC). We have developed a quantised Siamese network for the FINN accelerator, using algorithm-accelerator co-design, and performed design space exploration to achieve the best efficiency-to-energy ratio (determined by FPS and used resources). For our network, running in the programmable logic part of the Zynq UltraScale+ MPSoC ZCU104, we achieved the processing of almost 50 frames-per-second with tracker accuracy on par with its floating point counterpart, as well as the original SiamFC network. The complete tracking system, implemented in ARM with the network accelerated on FPGA, achieves up to 17 fps. These results bring us towards bridging the gap between the highly accurate but energy-demanding algorithms and energy-efficient solutions ready to be used in low-power, edge systems. | ['Tomasz Kryjak', 'Dominika Przewlocka-Rus'] | 2022-05-21 | null | null | null | null | ['visual-object-tracking'] | ['computer-vision'] | [-5.22428043e-02 -2.60723203e-01 -1.47209868e-01 1.70267299e-01
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62595f52-c730-4497-bb05-1a6f6ce7b30e | loss-guided-activation-for-action-recognition | 1812.04194 | null | http://arxiv.org/abs/1812.04194v1 | http://arxiv.org/pdf/1812.04194v1.pdf | Loss Guided Activation for Action Recognition in Still Images | One significant problem of deep-learning based human action recognition is
that it can be easily misled by the presence of irrelevant objects or
backgrounds. Existing methods commonly address this problem by employing
bounding boxes on the target humans as part of the input, in both training and
testing stages. This requirement of bounding boxes as part of the input is
needed to enable the methods to ignore irrelevant contexts and extract only
human features. However, we consider this solution is inefficient, since the
bounding boxes might not be available. Hence, instead of using a person
bounding box as an input, we introduce a human-mask loss to automatically guide
the activations of the feature maps to the target human who is performing the
action, and hence suppress the activations of misleading contexts. We propose a
multi-task deep learning method that jointly predicts the human action class
and human location heatmap. Extensive experiments demonstrate our approach is
more robust compared to the baseline methods under the presence of irrelevant
misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP)
on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\%
relative improvements over the best results reported in the literature, and
thus set new state-of-the-art results. Additionally, unlike some existing
methods, we eliminate the requirement of using a person bounding box as an
input during testing. | ['ShaoDi You', 'Robby T. Tan', 'Lu Liu'] | 2018-12-11 | null | null | null | null | ['action-recognition-in-still-images'] | ['computer-vision'] | [ 3.97953600e-01 -1.32212758e-01 1.36868760e-01 -3.86248946e-01
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badb7c6d-1f9f-4c3d-8a4a-0ed4d24c9ef1 | low-light-enhancement-in-the-frequency-domain | 2306.16782 | null | https://arxiv.org/abs/2306.16782v1 | https://arxiv.org/pdf/2306.16782v1.pdf | Low-Light Enhancement in the Frequency Domain | Decreased visibility, intensive noise, and biased color are the common problems existing in low-light images. These visual disturbances further reduce the performance of high-level vision tasks, such as object detection, and tracking. To address this issue, some image enhancement methods have been proposed to increase the image contrast. However, most of them are implemented only in the spatial domain, which can be severely influenced by noise signals while enhancing. Hence, in this work, we propose a novel residual recurrent multi-wavelet convolutional neural network R2-MWCNN learned in the frequency domain that can simultaneously increase the image contrast and reduce noise signals well. This end-to-end trainable network utilizes a multi-level discrete wavelet transform to divide input feature maps into distinct frequencies, resulting in a better denoise impact. A channel-wise loss function is proposed to correct the color distortion for more realistic results. Extensive experiments demonstrate that our proposed R2-MWCNN outperforms the state-of-the-art methods quantitively and qualitatively. | ['Zhi Jin', 'Hao Chen'] | 2023-06-29 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 4.16937977e-01 -5.61853051e-01 2.24726751e-01 -1.57121047e-01
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db379736-9f0c-4fff-a549-599e552be620 | rediscovering-hashed-random-projections-for | 2304.02481 | null | https://arxiv.org/abs/2304.02481v2 | https://arxiv.org/pdf/2304.02481v2.pdf | Rediscovering Hashed Random Projections for Efficient Quantization of Contextualized Sentence Embeddings | Training and inference on edge devices often requires an efficient setup due to computational limitations. While pre-computing data representations and caching them on a server can mitigate extensive edge device computation, this leads to two challenges. First, the amount of storage required on the server that scales linearly with the number of instances. Second, the bandwidth required to send extensively large amounts of data to an edge device. To reduce the memory footprint of pre-computed data representations, we propose a simple, yet effective approach that uses randomly initialized hyperplane projections. To further reduce their size by up to 98.96%, we quantize the resulting floating-point representations into binary vectors. Despite the greatly reduced size, we show that the embeddings remain effective for training models across various English and German sentence classification tasks that retain 94%--99% of their floating-point. | ['Iryna Gurevych', 'Alexander Geyken', 'Ji-Ung Lee', 'Ulf A. Hamster'] | 2023-03-13 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings', 'sentence-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.04568392e-01 -2.53982902e-01 -2.93691218e-01 -2.74054885e-01
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4583f14a-93f7-4c69-afc3-cebbef5414ad | global-visual-localization-in-lidar-maps | 1910.04871 | null | https://arxiv.org/abs/1910.04871v2 | https://arxiv.org/pdf/1910.04871v2.pdf | Global visual localization in LiDAR-maps through shared 2D-3D embedding space | Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonomous driving field. While most vision-based approaches match an image w.r.t. an image database, global visual localization within LiDAR-maps remains fairly unexplored, even though the path toward high definition 3D maps, produced mainly from LiDARs, is clear. In this work we leverage Deep Neural Network (DNN) approaches to create a shared embedding space between images and LiDAR-maps, allowing for image to 3D-LiDAR place recognition. We trained a 2D and a 3D DNN that create embeddings, respectively from images and from point clouds, that are close to each other whether they refer to the same place. An extensive experimental activity is presented to assess the effectiveness of the approach w.r.t. different learning paradigms, network architectures, and loss functions. All the evaluations have been performed using the Oxford Robotcar Dataset, which encompasses a wide range of weather and light conditions. | ['Simone Fontana', 'Matteo Vaghi', 'Daniele Cattaneo', 'Domenico Giorgio Sorrenti', 'Augusto Luis Ballardini'] | 2019-10-02 | null | null | null | null | ['image-to-3d'] | ['computer-vision'] | [-1.95754647e-01 -2.97840655e-01 -1.90969124e-01 -7.07274377e-01
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95f49e66-aa2c-48dc-9dda-98cc65785ad2 | aedfact-scientific-fact-checking-made-easier | 2305.07796 | null | https://arxiv.org/abs/2305.07796v1 | https://arxiv.org/pdf/2305.07796v1.pdf | aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions | In this highly digitised world, fake news is a challenging problem that can cause serious harm to society. Considering how fast fake news can spread, automated methods, tools and services for assisting users to do fact-checking (i.e., fake news detection) become necessary and helpful, for both professionals, such as journalists and researchers, and the general public such as news readers. Experts, especially researchers, play an essential role in informing people about truth and facts, which makes them a good proxy for non-experts to detect fake news by checking relevant expert opinions and comments. Therefore, in this paper, we present aedFaCT, a web browser extension that can help professionals and news readers perform fact-checking via the automatic discovery of expert opinions relevant to the news of concern via shared keywords. Our initial evaluation with three independent testers (who did not participate in the development of the extension) indicated that aedFaCT can provide a faster experience to its users compared with traditional fact-checking practices based on manual online searches, without degrading the quality of retrieved evidence for fact-checking. The source code of aedFaCT is publicly available at https://github.com/altuncu/aedFaCT. | ['Shujun Li', 'Lisa Bonheme', 'Haiyue Yuan', 'Sophie Kaleba', 'Meryem Bagriacik', 'Jason R. C. Nurse', 'Enes Altuncu'] | 2023-05-12 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-5.18580198e-01 2.78907418e-01 -4.76117194e-01 -2.51774415e-02
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33bde67b-8028-4a4f-91d4-e4b10f7d7357 | msfa-frequency-aware-transformer-for | 2303.13404 | null | https://arxiv.org/abs/2303.13404v1 | https://arxiv.org/pdf/2303.13404v1.pdf | MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing | Hyperspectral imaging systems that use multispectral filter arrays (MSFA) capture only one spectral component in each pixel. Hyperspectral demosaicing is used to recover the non-measured components. While deep learning methods have shown promise in this area, they still suffer from several challenges, including limited modeling of non-local dependencies, lack of consideration of the periodic MSFA pattern that could be linked to periodic artifacts, and difficulty in recovering high-frequency details. To address these challenges, this paper proposes a novel de-mosaicing framework, the MSFA-frequency-aware Transformer network (FDM-Net). FDM-Net integrates a novel MSFA-frequency-aware multi-head self-attention mechanism (MaFormer) and a filter-based Fourier zero-padding method to reconstruct high pass components with greater difficulty and low pass components with relative ease, separately. The advantage of Maformer is that it can leverage the MSFA information and non-local dependencies present in the data. Additionally, we introduce a joint spatial and frequency loss to transfer MSFA information and enhance training on frequency components that are hard to recover. Our experimental results demonstrate that FDM-Net outperforms state-of-the-art methods with 6dB PSNR, and reconstructs high-fidelity details successfully. | ['Wilfried Philips', 'Hiep Luong', 'Hongyan zhang', 'Yongyong Chen', 'JieZhang Cao', 'Shaoguang Huang', 'Kai Feng', 'Haijin Zeng'] | 2023-03-23 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 5.63283265e-01 -4.66684341e-01 3.02089781e-01 -2.11269766e-01
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114d9999-2a98-4520-a055-a9f00c37050e | mining-fine-grained-opinions-on-closed | 1708.02420 | null | http://arxiv.org/abs/1708.02420v1 | http://arxiv.org/pdf/1708.02420v1.pdf | Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN | Video reviews are the natural evolution of written product reviews. In this
paper we target this phenomenon and introduce the first dataset created from
closed captions of YouTube product review videos as well as a new attention-RNN
model for aspect extraction and joint aspect extraction and sentiment
classification. Our model provides state-of-the-art performance on aspect
extraction without requiring the usage of hand-crafted features on the SemEval
ABSA corpus, while it outperforms the baseline on the joint task. In our
dataset, the attention-RNN model outperforms the baseline for both tasks, but
we observe important performance drops for all models in comparison to SemEval.
These results, as well as further experiments on domain adaptation for aspect
extraction, suggest that differences between speech and written text, which
have been discussed extensively in the literature, also extend to the domain of
product reviews, where they are relevant for fine-grained opinion mining. | ['Edison Marrese-Taylor', 'Yutaka Matsuo', 'Jorge A. Balazs'] | 2017-08-08 | mining-fine-grained-opinions-on-closed-1 | https://aclanthology.org/W17-5213 | https://aclanthology.org/W17-5213.pdf | ws-2017-9 | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.31588021e-01 3.70620936e-01 -4.63285476e-01 -4.89483118e-01
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8479b528-11e5-4947-8333-634d81a2ae1c | estimating-reflectance-layer-from-a-single | 2211.14751 | null | https://arxiv.org/abs/2211.14751v2 | https://arxiv.org/pdf/2211.14751v2.pdf | Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning | Estimating reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often render an inaccurate estimate of the reflectance layer. Therefore, we propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. In the first stage, an initial reflectance layer free from shadows and specularities is obtained with the constraint of novel losses that are guided by prior-based shadow-free and specular-free images. To further enforce the reflectance layer to be independent from shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image. Our network employs a classifier to categorize shadow/shadow-free, specular/specular-free classes, enabling the activation features to function as attention maps that focus on shadow/specular regions. Our quantitative and qualitative evaluations show that our method outperforms the state-of-the-art methods in the reflectance layer estimation that is free from shadows and specularities. | ['Robby T. Tan', 'Wenhan Yang', 'Ruoteng Li', 'Yeying Jin'] | 2022-11-27 | null | null | null | null | ['shadow-removal', 'intrinsic-image-decomposition', 'highlight-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 8.75058115e-01 3.43008759e-03 1.86012208e-01 -6.00862503e-01
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f1780913-6c1f-42e1-a6dd-30751fa76f3e | weakly-supervised-temporal-article-grounding | 2210.12444 | null | https://arxiv.org/abs/2210.12444v2 | https://arxiv.org/pdf/2210.12444v2.pdf | Weakly-Supervised Temporal Article Grounding | Given a long untrimmed video and natural language queries, video grounding (VG) aims to temporally localize the semantically-aligned video segments. Almost all existing VG work holds two simple but unrealistic assumptions: 1) All query sentences can be grounded in the corresponding video. 2) All query sentences for the same video are always at the same semantic scale. Unfortunately, both assumptions make today's VG models fail to work in practice. For example, in real-world multimodal assets (eg, news articles), most of the sentences in the article can not be grounded in their affiliated videos, and they typically have rich hierarchical relations (ie, at different semantic scales). To this end, we propose a new challenging grounding task: Weakly-Supervised temporal Article Grounding (WSAG). Specifically, given an article and a relevant video, WSAG aims to localize all ``groundable'' sentences to the video, and these sentences are possibly at different semantic scales. Accordingly, we collect the first WSAG dataset to facilitate this task: YouwikiHow, which borrows the inherent multi-scale descriptions in wikiHow articles and plentiful YouTube videos. In addition, we propose a simple but effective method DualMIL for WSAG, which consists of a two-level MIL loss and a single-/cross- sentence constraint loss. These training objectives are carefully designed for these relaxed assumptions. Extensive ablations have verified the effectiveness of DualMIL. | ['Shih-Fu Chang', 'Heng Ji', 'Hammad Ayyubi', 'Christopher Thomas', 'Guangxing Han', 'Xudong Lin', 'Brian Chen', 'Yulei Niu', 'Long Chen'] | 2022-10-22 | null | null | null | null | ['video-grounding'] | ['computer-vision'] | [-6.41512871e-02 9.80342627e-02 -5.68204761e-01 -2.86255836e-01
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5eefeefb-4e61-4b0f-9ecc-a2fd418dc55b | a-method-for-incremental-discovery-of | 2302.08205 | null | https://arxiv.org/abs/2302.08205v1 | https://arxiv.org/pdf/2302.08205v1.pdf | A method for incremental discovery of financial event types based on anomaly detection | Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios. This limitation of a small number of event types does not meet our research needs for more complex tasks such as the prediction of major financial events and the analysis of the ripple effects of financial events. In this paper, a three-stage approach is proposed to accomplish incremental discovery of event types. For an existing annotated financial event dataset, the three-stage approach consists of: for a set of financial event data with a mixture of original and unknown event types, a semi-supervised deep clustering model with anomaly detection is first applied to classify the data into normal and abnormal events, where abnormal events are events that do not belong to known types; then normal events are tagged with appropriate event types and abnormal events are reasonably clustered. Finally, a cluster keyword extraction method is used to recommend the type names of events for the new event clusters, thus incrementally discovering new event types. The proposed method is effective in the incremental discovery of new event types on real data sets. | ['Lan Huang', 'Rui Zhang', 'Zhenhai Guan', 'Zixu Li', 'Dianyue Gu'] | 2023-02-16 | null | null | null | null | ['deep-clustering', 'keyword-extraction', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-4.56180751e-01 -1.57099590e-01 4.39467058e-02 -5.20295382e-01
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97e46c9c-3c71-4b73-aa68-90b8f009368c | a-modular-framework-for-reinforcement | 2208.06244 | null | https://arxiv.org/abs/2208.06244v1 | https://arxiv.org/pdf/2208.06244v1.pdf | A Modular Framework for Reinforcement Learning Optimal Execution | In this article, we develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups. Rather than focusing on agents and optimization methods, we focus on the environment and break down the necessary requirements to simulate an Optimal Trade Execution under a Reinforcement Learning framework such as data pre-processing, construction of observations, action processing, child order execution, simulation of benchmarks, reward calculations etc. We give examples of each component, explore the difficulties their individual implementations \& the interactions between them entail, and discuss the different phenomena that each component induces in the simulation, highlighting the divergences between the simulation and the behavior of a real market. We showcase our modular implementation through a setup that, following a Time-Weighted Average Price (TWAP) order submission schedule, allows the agent to exclusively place limit orders, simulates their execution via iterating over snapshots of the Limit Order Book (LOB), and calculates rewards as the \$ improvement over the price achieved by a TWAP benchmark algorithm following the same schedule. We also develop evaluation procedures that incorporate iterative re-training and evaluation of a given agent over intervals of a training horizon, mimicking how an agent may behave when being continuously retrained as new market data becomes available and emulating the monitoring practices that algorithm providers are bound to perform under current regulatory frameworks. | ['Florin Dascalu', 'Christoph Auth', 'Fernando de Meer Pardo'] | 2022-08-11 | null | null | null | null | ['algorithmic-trading', 'stock-market-prediction'] | ['time-series', 'time-series'] | [-1.25119314e-01 -3.06953024e-02 -1.93985552e-01 -2.74878144e-01
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f094564e-4183-4d9c-881e-585dc114d21a | conceptual-text-region-network-cognition | 2103.09179 | null | https://arxiv.org/abs/2103.09179v1 | https://arxiv.org/pdf/2103.09179v1.pdf | Conceptual Text Region Network: Cognition-Inspired Accurate Scene Text Detection | Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: 1) current label generation techniques are mostly empirical and lack theoretical support, discouraging elaborate label design; 2) as a result, most methods rely heavily on text kernel segmentation which is unstable and requires deliberate tuning. To address these challenges, we propose a human cognition-inspired framework, termed, Conceptual Text Region Network (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which is a class of cognition-based tools inheriting good mathematical properties, allowing for sophisticated label design. Another component of CTRNet is an inference pipeline that, with the help of CTRs, completely omits the need for text kernel segmentation. Compared with previous segmentation-based methods, our approach is not only more interpretable but also more accurate. Experimental results show that CTRNet achieves state-of-the-art performance on benchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding performance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet is among the first detection models to achieve F-measures higher than 85.0% on all four of the benchmarks, with remarkable consistency and stability. | ['Amir Hussain', 'Zhiyuan Tan', 'Liangfu Lu', 'Chenwei Cui'] | 2021-03-16 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 1.63615763e-01 7.26414397e-02 -2.01456413e-01 -1.77041769e-01
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61dfaacf-f824-4cb0-a92a-51e73c10f55a | what-can-we-learn-about-a-generated-image | 2210.06257 | null | https://arxiv.org/abs/2210.06257v1 | https://arxiv.org/pdf/2210.06257v1.pdf | What can we learn about a generated image corrupting its latent representation? | Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can translate images from one imaging modality to another at a low cost. For unpaired datasets, they rely mostly on cycle loss. Despite its effectiveness in learning the underlying data distribution, it can lead to a discrepancy between input and output data. The purpose of this work is to investigate the hypothesis that we can predict image quality based on its latent representation in the GANs bottleneck. We achieve this by corrupting the latent representation with noise and generating multiple outputs. The degree of differences between them is interpreted as the strength of the representation: the more robust the latent representation, the fewer changes in the output image the corruption causes. Our results demonstrate that our proposed method has the ability to i) predict uncertain parts of synthesized images, and ii) identify samples that may not be reliable for downstream tasks, e.g., liver segmentation task. | ['Shadi Albarqouni', 'Nassir Navab', 'Slobodan Ilic', 'Aarushi Gupta', 'Agnieszka Tomczak'] | 2022-10-12 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 8.02310646e-01 4.32494432e-01 -7.03890771e-02 -3.16293210e-01
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f814fa93-ec93-415d-b078-b12db2d2f6e3 | less-is-more-learning-prominent-and-diverse | 1611.09921 | null | http://arxiv.org/abs/1611.09921v2 | http://arxiv.org/pdf/1611.09921v2.pdf | Less is More: Learning Prominent and Diverse Topics for Data Summarization | Statistical topic models efficiently facilitate the exploration of
large-scale data sets. Many models have been developed and broadly used to
summarize the semantic structure in news, science, social media, and digital
humanities. However, a common and practical objective in data exploration tasks
is not to enumerate all existing topics, but to quickly extract representative
ones that broadly cover the content of the corpus, i.e., a few topics that
serve as a good summary of the data. Most existing topic models fit exactly the
same number of topics as a user specifies, which have imposed an unnecessary
burden to the users who have limited prior knowledge. We instead propose new
models that are able to learn fewer but more representative topics for the
purpose of data summarization. We propose a reinforced random walk that allows
prominent topics to absorb tokens from similar and smaller topics, thus
enhances the diversity among the top topics extracted. With this reinforced
random walk as a general process embedded in classical topic models, we obtain
\textit{diverse topic models} that are able to extract the most prominent and
diverse topics from data. The inference procedures of these diverse topic
models remain as simple and efficient as the classical models. Experimental
results demonstrate that the diverse topic models not only discover topics that
better summarize the data, but also require minimal prior knowledge of the
users. | ['Qiaozhu Mei', 'Ming Zhang', 'Jian Tang', 'Cheng Li'] | 2016-11-29 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [-2.25968763e-01 4.81371433e-01 -4.17255700e-01 -1.28139272e-01
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48f5cfd6-6d07-4e5c-b872-05a54398399a | explainable-label-flipping-attacks-on-human | 2302.04109 | null | https://arxiv.org/abs/2302.04109v1 | https://arxiv.org/pdf/2302.04109v1.pdf | Explainable Label-flipping Attacks on Human Emotion Assessment System | This paper's main goal is to provide an attacker's point of view on data poisoning assaults that use label-flipping during the training phase of systems that use electroencephalogram (EEG) signals to evaluate human emotion. To attack different machine learning classifiers such as Adaptive Boosting (AdaBoost) and Random Forest dedicated to the classification of 4 different human emotions using EEG signals, this paper proposes two scenarios of label-flipping methods. The results of the studies show that the proposed data poison attacksm based on label-flipping are successful regardless of the model, but different models show different degrees of resistance to the assaults. In addition, numerous Explainable Artificial Intelligence (XAI) techniques are used to explain the data poison attacks on EEG signal-based human emotion evaluation systems. | ['Chan Yeob Yeun', 'Ernesto Damiani', 'Ahmed Y. Al Hammadi', 'Zhibo Zhang'] | 2023-02-08 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 6.42201528e-02 -1.30382687e-01 1.83509469e-01 -6.68795884e-01
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1a321d4b-e530-4e1a-a378-fe79cbcb94a6 | understanding-neural-architecture-search | 1904.00438 | null | https://arxiv.org/abs/1904.00438v2 | https://arxiv.org/pdf/1904.00438v2.pdf | Understanding Neural Architecture Search Techniques | Automatic methods for generating state-of-the-art neural network architectures without human experts have generated significant attention recently. This is because of the potential to remove human experts from the design loop which can reduce costs and decrease time to model deployment. Neural architecture search (NAS) techniques have improved significantly in their computational efficiency since the original NAS was proposed. This reduction in computation is enabled via weight sharing such as in Efficient Neural Architecture Search (ENAS). However, recently a body of work confirms our discovery that ENAS does not do significantly better than random search with weight sharing, contradicting the initial claims of the authors. We provide an explanation for this phenomenon by investigating the interpretability of the ENAS controller's hidden state. We find models sampled from identical controller hidden states have no correlation with various graph similarity metrics, so no notion of structural similarity is learned. This failure mode implies the RNN controller does not condition on past architecture choices. Lastly, we propose a solution to this failure mode by forcing the controller's hidden state to encode pasts decisions by training it with a memory buffer of previously sampled architectures. Doing this improves hidden state interpretability by increasing the correlation between controller hidden states and graph similarity metrics. | ['Jonathan Lorraine', 'George Adam'] | 2019-03-31 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 3.59730244e-01 6.37173414e-01 -1.34791464e-01 -1.99347273e-01
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e7875d80-6b97-4064-bbcd-8be4e15c0e6a | synthetic-temporal-anomaly-guided-end-to-end | 2110.09768 | null | https://arxiv.org/abs/2110.09768v1 | https://arxiv.org/pdf/2110.09768v1.pdf | Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection | Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data. At test time, the AE is then expected to reconstruct the normal input well while reconstructing the anomalies poorly. However, several studies show that, even with normal data only training, AEs can often start reconstructing anomalies as well which depletes their anomaly detection performance. To mitigate this, we propose a temporal pseudo anomaly synthesizer that generates fake-anomalies using only normal data. An AE is then trained to maximize the reconstruction loss on pseudo anomalies while minimizing this loss on normal data. This way, the AE is encouraged to produce distinguishable reconstructions for normal and anomalous frames. Extensive experiments and analysis on three challenging video anomaly datasets demonstrate the effectiveness of our approach to improve the basic AEs in achieving superiority against several existing state-of-the-art models. | ['Seung-Ik Lee', 'Muhammad Zaigham Zaheer', 'Marcella Astrid'] | 2021-10-19 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 5.52307725e-01 7.81402178e-03 1.52153715e-01 -7.91017115e-02
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69b867c0-0eeb-416d-8394-8ad1d9b34d9d | rxfood-plug-in-rgb-x-fusion-for-object-of | 2306.12621 | null | https://arxiv.org/abs/2306.12621v1 | https://arxiv.org/pdf/2306.12621v1.pdf | RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection | The emergence of different sensors (Near-Infrared, Depth, etc.) is a remedy for the limited application scenarios of traditional RGB camera. The RGB-X tasks, which rely on RGB input and another type of data input to resolve specific problems, have become a popular research topic in multimedia. A crucial part in two-branch RGB-X deep neural networks is how to fuse information across modalities. Given the tremendous information inside RGB-X networks, previous works typically apply naive fusion (e.g., average or max fusion) or only focus on the feature fusion at the same scale(s). While in this paper, we propose a novel method called RXFOOD for the fusion of features across different scales within the same modality branch and from different modality branches simultaneously in a unified attention mechanism. An Energy Exchange Module is designed for the interaction of each feature map's energy matrix, who reflects the inter-relationship of different positions and different channels inside a feature map. The RXFOOD method can be easily incorporated to any dual-branch encoder-decoder network as a plug-in module, and help the original backbone network better focus on important positions and channels for object of interest detection. Experimental results on RGB-NIR salient object detection, RGB-D salient object detection, and RGBFrequency image manipulation detection demonstrate the clear effectiveness of the proposed RXFOOD. | ['Hongkai Yu', 'Yuewei Lin', 'Tianyun Zhang', 'Qing Guo', 'Jinlong Li', 'Jin Ma'] | 2023-06-22 | null | null | null | null | ['image-manipulation-detection', 'rgb-d-salient-object-detection', 'image-manipulation', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.61890173e-01 -1.98429003e-01 -1.09960474e-01 -2.76917428e-01
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86f6bae0-075d-4248-9f86-c29adeb75ea4 | detect-any-shadow-segment-anything-for-video | 2305.16698 | null | https://arxiv.org/abs/2305.16698v1 | https://arxiv.org/pdf/2305.16698v1.pdf | Detect Any Shadow: Segment Anything for Video Shadow Detection | Segment anything model (SAM) has achieved great success in the field of natural image segmentation. Nevertheless, SAM tends to classify shadows as background, resulting in poor segmentation performance for shadow detection task. In this paper, we propose an simple but effective approach for fine tuning SAM to detect shadows. Additionally, we also combine it with long short-term attention mechanism to extend its capabilities to video shadow detection. Specifically, we first fine tune SAM by utilizing shadow data combined with sparse prompts and apply the fine-tuned model to detect a specific frame (e.g., first frame) in the video with a little user assistance. Subsequently, using the detected frame as a reference, we employ a long short-term network to learn spatial correlations between distant frames and temporal consistency between contiguous frames, thereby achieving shadow information propagation across frames. Extensive experimental results demonstrate that our method outperforms the state-of-the-art techniques, with improvements of 17.2% and 3.3% in terms of MAE and IoU, respectively, validating the effectiveness of our method. | ['Houqiang Li', 'Yunyao Mao', 'Wengang Zhou', 'Yonghui Wang'] | 2023-05-26 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.22425240e-01 -1.15139253e-01 -1.49094522e-01 -3.69704247e-01
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2bcdc4bb-bb63-41e0-88ac-b7f069900614 | meteornet-deep-learning-on-dynamic-3d-point | 1910.09165 | null | https://arxiv.org/abs/1910.09165v2 | https://arxiv.org/pdf/1910.09165v2.pdf | MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences | Understanding dynamic 3D environment is crucial for robotic agents and many other applications. We propose a novel neural network architecture called $MeteorNet$ for learning representations for dynamic 3D point cloud sequences. Different from previous work that adopts a grid-based representation and applies 3D or 4D convolutions, our network directly processes point clouds. We propose two ways to construct spatiotemporal neighborhoods for each point in the point cloud sequence. Information from these neighborhoods is aggregated to learn features per point. We benchmark our network on a variety of 3D recognition tasks including action recognition, semantic segmentation and scene flow estimation. MeteorNet shows stronger performance than previous grid-based methods while achieving state-of-the-art performance on Synthia. MeteorNet also outperforms previous baseline methods that are able to process at most two consecutive point clouds. To the best of our knowledge, this is the first work on deep learning for dynamic raw point cloud sequences. | ['Xingyu Liu', 'Mengyuan Yan', 'Jeannette Bohg'] | 2019-10-21 | meteornet-deep-learning-on-dynamic-3d-point-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_MeteorNet_Deep_Learning_on_Dynamic_3D_Point_Cloud_Sequences_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_MeteorNet_Deep_Learning_on_Dynamic_3D_Point_Cloud_Sequences_ICCV_2019_paper.pdf | iccv-2019-10 | ['scene-flow-estimation'] | ['computer-vision'] | [-2.36461475e-01 -4.68741417e-01 -1.01535739e-02 -2.51193464e-01
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e8aa969a-dd1e-496a-9f41-2eb53438315f | mirrorflow-exploiting-symmetries-in-joint | 1708.05355 | null | http://arxiv.org/abs/1708.05355v1 | http://arxiv.org/pdf/1708.05355v1.pdf | MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation | Optical flow estimation is one of the most studied problems in computer
vision, yet recent benchmark datasets continue to reveal problem areas of
today's approaches. Occlusions have remained one of the key challenges. In this
paper, we propose a symmetric optical flow method to address the well-known
chicken-and-egg relation between optical flow and occlusions. In contrast to
many state-of-the-art methods that consider occlusions as outliers, possibly
filtered out during post-processing, we highlight the importance of joint
occlusion reasoning in the optimization and show how to utilize occlusion as an
important cue for estimating optical flow. The key feature of our model is to
fully exploit the symmetry properties that characterize optical flow and
occlusions in the two consecutive images. Specifically through utilizing
forward-backward consistency and occlusion-disocclusion symmetry in the energy,
our model jointly estimates optical flow in both forward and backward
direction, as well as consistent occlusion maps in both views. We demonstrate
significant performance benefits on standard benchmarks, especially from the
occlusion-disocclusion symmetry. On the challenging KITTI dataset we report the
most accurate two-frame results to date. | ['Junhwa Hur', 'Stefan Roth'] | 2017-08-17 | mirrorflow-exploiting-symmetries-in-joint-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hur_MirrorFlow_Exploiting_Symmetries_ICCV_2017_paper.pdf | iccv-2017-10 | ['occlusion-estimation'] | ['computer-vision'] | [-2.27355495e-01 -5.59454322e-01 -2.45783821e-01 -2.74148971e-01
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c5528606-fc8e-47ed-b4b9-eefaedf1c285 | light-field-distortion-feature-for | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Maeno_Light_Field_Distortion_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Maeno_Light_Field_Distortion_2013_CVPR_paper.pdf | Light Field Distortion Feature for Transparent Object Recognition | Current object-recognition algorithms use local features, such as scale-invariant feature transform (SIFT) and speeded-up robust features (SURF), for visually learning to recognize objects. These approaches though cannot apply to transparent objects made of glass or plastic, as such objects take on the visual features of background objects, and the appearance of such objects dramatically varies with changes in scene background. Indeed, in transmitting light, transparent objects have the unique characteristic of distorting the background by refraction. In this paper, we use a single-shot light Aeld image as an input and model the distortion of the light Aeld caused by the refractive property of a transparent object. We propose a new feature, called the light Aeld distortion (LFD) feature, for identifying a transparent object. The proposal incorporates this LFD feature into the bag-of-features approach for recognizing transparent objects. We evaluated its performance in laboratory and real settings. | ['Rin-ichiro Taniguchi', 'Atsushi Shimada', 'Hajime Nagahara', 'Kazuki Maeno'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['transparent-objects'] | ['computer-vision'] | [ 0.36359677 -0.6056713 0.3099673 -0.48933047 -0.08691908 -0.596984
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51441821-df4b-4f3c-adf8-c35622dc5ceb | vowel-based-meeteilon-dialect-identification | 2107.13419 | null | https://arxiv.org/abs/2107.13419v1 | https://arxiv.org/pdf/2107.13419v1.pdf | Vowel-based Meeteilon dialect identification using a Random Forest classifier | This paper presents a vowel-based dialect identification system for Meeteilon. For this work, a vowel dataset is created by using Meeteilon Speech Corpora available at Linguistic Data Consortium for Indian Languages (LDC-IL). Spectral features such as formant frequencies (F1, F1 and F3) and prosodic features such as pitch (F0), energy, intensity and segment duration values are extracted from monophthong vowel sounds. Random forest classifier, a decision tree-based ensemble algorithm is used for classification of three major dialects of Meeteilon namely, Imphal, Kakching and Sekmai. Model has shown an average dialect identification performance in terms of accuracy of around 61.57%. The role of spectral and prosodic features are found to be significant in Meeteilon dialect classification. | ['Kabita Thaoroijam', 'Thangjam Clarinda Devi'] | 2021-07-26 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-5.01434326e-01 -3.55219841e-01 -6.28456399e-02 -4.78069246e-01
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8fc66c65-9c15-4c9f-953d-d61f6c126855 | clustering-based-contrastive-learning-for | 2004.02195 | null | https://arxiv.org/abs/2004.02195v1 | https://arxiv.org/pdf/2004.02195v1.pdf | Clustering based Contrastive Learning for Improving Face Representations | A good clustering algorithm can discover natural groupings in data. These groupings, if used wisely, provide a form of weak supervision for learning representations. In this work, we present Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that uses labels obtained from clustering along with video constraints to learn discriminative face features. We demonstrate our method on the challenging task of learning representations for video face clustering. Through several ablation studies, we analyze the impact of creating pair-wise positive and negative labels from different sources. Experiments on three challenging video face clustering datasets: BBT-0101, BF-0502, and ACCIO show that CCL achieves a new state-of-the-art on all datasets. | ['Rainer Stiefelhagen', 'Vivek Sharma', 'M. Saquib Sarfraz', 'Makarand Tapaswi'] | 2020-04-05 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.14648327e-01 -2.02131182e-01 -3.96070510e-01 -8.85400236e-01
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af3505b3-4f89-4f4d-8fbb-4106d4f0d444 | bimal-bijective-maximum-likelihood-approach | 2108.03267 | null | https://arxiv.org/abs/2108.03267v1 | https://arxiv.org/pdf/2108.03267v1.pdf | BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation | Semantic segmentation aims to predict pixel-level labels. It has become a popular task in various computer vision applications. While fully supervised segmentation methods have achieved high accuracy on large-scale vision datasets, they are unable to generalize on a new test environment or a new domain well. In this work, we first introduce a new Un-aligned Domain Score to measure the efficiency of a learned model on a new target domain in unsupervised manner. Then, we present the new Bijective Maximum Likelihood(BiMaL) loss that is a generalized form of the Adversarial Entropy Minimization without any assumption about pixel independence. We have evaluated the proposed BiMaL on two domains. The proposed BiMaL approach consistently outperforms the SOTA methods on empirical experiments on "SYNTHIA to Cityscapes", "GTA5 to Cityscapes", and "SYNTHIA to Vistas". | ['Khoa Luu', 'Chase Rainwater', 'Son Lam Phung', 'Ngan Le', 'Chi Nhan Duong', 'Thanh-Dat Truong'] | 2021-08-06 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Truong_BiMaL_Bijective_Maximum_Likelihood_Approach_to_Domain_Adaptation_in_Semantic_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Truong_BiMaL_Bijective_Maximum_Likelihood_Approach_to_Domain_Adaptation_in_Semantic_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-segmentation'] | ['computer-vision'] | [ 4.55469340e-01 1.04767151e-01 3.03799156e-02 -5.54510355e-01
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e52272a7-83fa-4564-9dd5-cc1edf0eadca | spotting-rumors-via-novelty-detection | 1611.06322 | null | http://arxiv.org/abs/1611.06322v1 | http://arxiv.org/pdf/1611.06322v1.pdf | Spotting Rumors via Novelty Detection | Rumour detection is hard because the most accurate systems operate
retrospectively, only recognizing rumours once they have collected repeated
signals. By then the rumours might have already spread and caused harm. We
introduce a new category of features based on novelty, tailored to detect
rumours early on. To compensate for the absence of repeated signals, we make
use of news wire as an additional data source. Unconfirmed (novel) information
with respect to the news articles is considered as an indication of rumours.
Additionally we introduce pseudo feedback, which assumes that documents that
are similar to previous rumours, are more likely to also be a rumour.
Comparison with other real-time approaches shows that novelty based features in
conjunction with pseudo feedback perform significantly better, when detecting
rumours instantly after their publication. | ['Yumeng Qin', 'Dominik Wurzer', 'Cunchen Tang', 'Victor Lavrenko'] | 2016-11-19 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-1.86528698e-01 1.05327845e-01 -3.77807915e-01 -1.29244551e-01
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0ca41a58-74e6-4660-a8d2-fbbbe71ef7dd | attention-is-all-you-need-for-videos-self | 1906.02792 | null | https://arxiv.org/abs/1906.02792v1 | https://arxiv.org/pdf/1906.02792v1.pdf | Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers | Video Captioning and Summarization have become very popular in the recent years due to advancements in Sequence Modelling, with the resurgence of Long-Short Term Memory networks (LSTMs) and introduction of Gated Recurrent Units (GRUs). Existing architectures extract spatio-temporal features using CNNs and utilize either GRUs or LSTMs to model dependencies with soft attention layers. These attention layers do help in attending to the most prominent features and improve upon the recurrent units, however, these models suffer from the inherent drawbacks of the recurrent units themselves. The introduction of the Transformer model has driven the Sequence Modelling field into a new direction. In this project, we implement a Transformer-based model for Video captioning, utilizing 3D CNN architectures like C3D and Two-stream I3D for video extraction. We also apply certain dimensionality reduction techniques so as to keep the overall size of the model within limits. We finally present our results on the MSVD and ActivityNet datasets for Single and Dense video captioning tasks respectively. | ['Tushar Dobhal', 'Siyang Wang', 'Manjot Bilkhu'] | 2019-06-06 | null | null | null | null | ['dense-video-captioning'] | ['computer-vision'] | [ 2.58496046e-01 6.30032271e-02 -8.57613906e-02 -2.19836310e-01
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8d063305-33c3-4ff2-a480-f494700cc93a | deploying-machine-learning-models-to-ahead-of | 2304.04842 | null | https://arxiv.org/abs/2304.04842v2 | https://arxiv.org/pdf/2304.04842v2.pdf | Deploying Machine Learning Models to Ahead-of-Time Runtime on Edge Using MicroTVM | In the past few years, more and more AI applications have been applied to edge devices. However, models trained by data scientists with machine learning frameworks, such as PyTorch or TensorFlow, can not be seamlessly executed on edge. In this paper, we develop an end-to-end code generator parsing a pre-trained model to C source libraries for the backend using MicroTVM, a machine learning compiler framework extension addressing inference on bare metal devices. An analysis shows that specific compute-intensive operators can be easily offloaded to the dedicated accelerator with a Universal Modular Accelerator (UMA) interface, while others are processed in the CPU cores. By using the automatically generated ahead-of-time C runtime, we conduct a hand gesture recognition experiment on an ARM Cortex M4F core. | ['Christian Mayr', 'Johannes Partzsch', 'Xinyue Shi', 'Liyuan Guo', 'Matthias Jobst', 'Chen Liu'] | 2023-04-10 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.37620613e-02 -1.15928859e-01 -4.46523935e-01 -4.87985909e-01
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a86547c3-bea8-425f-a6fc-dc43ff27861c | building-extraction-from-remote-sensing | null | null | https://www.mdpi.com/2072-4292/13/21/4441 | https://www.mdpi.com/2072-4292/13/21/4441 | Building Extraction from Remote Sensing Images with Sparse Token Transformers | Deep learning methods have achieved considerable progress in remote sensing image building extraction. Most building extraction methods are based on Convolutional Neural Networks (CNN). Recently, vision transformers have provided a better perspective for modeling long-range context in images, but usually suffer from high computational complexity and memory usage. In this paper, we explored the potential of using transformers for efficient building extraction. We design an efficient dual-pathway transformer structure that learns the long-term dependency of tokens in both their spatial and channel dimensions and achieves state-of-the-art accuracy on benchmark building extraction datasets. Since single buildings in remote sensing images usually only occupy a very small part of the image pixels, we represent buildings as a set of “sparse” feature vectors in their feature space by introducing a new module called “sparse token sampler”. With such a design, the computational complexity in transformers can be greatly reduced over an order of magnitude. We refer to our method as Sparse Token Transformers (STT). Experiments conducted on the Wuhan University Aerial Building Dataset (WHU) and the Inria Aerial Image Labeling Dataset (INRIA) suggest the effectiveness and efficiency of our method. Compared with some widely used segmentation methods and some state-of-the-art building extraction methods, STT has achieved the best performance with low time cost. | ['Zhenwei Shi', 'Zhengxia Zou', 'Keyan Chen'] | 2021-11-05 | null | null | null | remote-sens-2021-11-1 | ['building-change-detection-for-remote-sensing', 'extracting-buildings-in-remote-sensing-images'] | ['miscellaneous', 'miscellaneous'] | [ 2.80356020e-01 -4.33186501e-01 1.54529646e-01 -2.50675261e-01
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c7430bab-2bd5-4e16-8672-e18277ab9a1b | visual-realism-assessment-for-face-swap | 2302.00918 | null | https://arxiv.org/abs/2302.00918v1 | https://arxiv.org/pdf/2302.00918v1.pdf | Visual Realism Assessment for Face-swap Videos | Deep-learning based face-swap videos, also known as deep fakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the as sessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also important as a quality assessment metric to compare different face-swap methods. In this paper, we make a small step to wards this new VRA direction by building a benchmark for evaluating the effectiveness of different automatic VRA models, which range from using traditional hand-crafted features to different kinds of deep-learning features. The evaluations are based on a recent competition dataset named as DFGC 2022, which contains 1400 diverse face-swap videos that are annotated with Mean Opinion Scores (MOS) on visual realism. Comprehensive experiment results using 11 models and 3 protocols are shown and discussed. We demonstrate the feasibility of devising effective VRA models for assessing face-swap videos and methods. The particular usefulness of existing deepfake detection features for VRA is also noted. The code and benchmark will be made publicly available. | ['Jing Dong', 'Bo Peng', 'Caiyong Wang', 'Beibei Dong', 'Xianyun Sun'] | 2023-02-02 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [-2.99709469e-01 -1.09217681e-01 -7.67059550e-02 -4.12329167e-01
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16f13a35-bad2-4320-993c-3903b46821c4 | bayesian-sparsification-methods-for-deep | 2003.11413 | null | https://arxiv.org/abs/2003.11413v2 | https://arxiv.org/pdf/2003.11413v2.pdf | Bayesian Sparsification Methods for Deep Complex-valued Networks | With continual miniaturization ever more applications of deep learning can be found in embedded systems, where it is common to encounter data with natural complex domain representation. To this end we extend Sparse Variational Dropout to complex-valued neural networks and verify the proposed Bayesian technique by conducting a large numerical study of the performance-compression trade-off of C-valued networks on two tasks: image recognition on MNIST-like and CIFAR10 datasets and music transcription on MusicNet. We replicate the state-of-the-art result by Trabelsi et al. [2018] on MusicNet with a complex-valued network compressed by 50-100x at a small performance penalty. | ['Ivan Nazarov', 'Evgeny Burnaev'] | 2020-03-25 | null | null | null | null | ['music-transcription'] | ['music'] | [ 1.01587050e-01 2.76290417e-01 -2.52193481e-01 -4.84439164e-01
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34be37ca-59d3-4806-8cf7-be78e390e2d3 | the-xpress-challenge-xray-projectomic | 2302.03819 | null | https://arxiv.org/abs/2302.03819v2 | https://arxiv.org/pdf/2302.03819v2.pdf | The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons | The wiring and connectivity of neurons form a structural basis for the function of the nervous system. Advances in volume electron microscopy (EM) and image segmentation have enabled mapping of circuit diagrams (connectomics) within local regions of the mouse brain. However, applying volume EM over the whole brain is not currently feasible due to technological challenges. As a result, comprehensive maps of long-range connections between brain regions are lacking. Recently, we demonstrated that X-ray holographic nanotomography (XNH) can provide high-resolution images of brain tissue at a much larger scale than EM. In particular, XNH is wellsuited to resolve large, myelinated axon tracts (white matter) that make up the bulk of long-range connections (projections) and are critical for inter-region communication. Thus, XNH provides an imaging solution for brain-wide projectomics. However, because XNH data is typically collected at lower resolutions and larger fields-of-view than EM, accurate segmentation of XNH images remains an important challenge that we present here. In this task, we provide volumetric XNH images of cortical white matter axons from the mouse brain along with ground truth annotations for axon trajectories. Manual voxel-wise annotation of ground truth is a time-consuming bottleneck for training segmentation networks. On the other hand, skeleton-based ground truth is much faster to annotate, and sufficient to determine connectivity. Therefore, we encourage participants to develop methods to leverage skeleton-based training. To this end, we provide two types of ground-truth annotations: a small volume of voxel-wise annotations and a larger volume with skeleton-based annotations. Entries will be evaluated on how accurately the submitted segmentations agree with the ground-truth skeleton annotations. | ['Aaron T. Kuan', 'Wei-Chung Lee', 'Alexandra Pacureanu', 'Lu Mi', 'Nir Shavit', 'Donglai Wei', 'Hanspeter Pfister', 'Shuhan Xie', 'Yicong Li', 'Mark Larson', 'Mukul Narwani', 'Tri Nguyen'] | 2023-02-08 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.65972799e-01 5.26551344e-02 2.26445958e-01 -3.50064278e-01
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b33bead6-f4c3-49bf-85f6-184e29cd21d7 | sentence-level-feedback-generation-for | 2212.08999 | null | https://arxiv.org/abs/2212.08999v1 | https://arxiv.org/pdf/2212.08999v1.pdf | Sentence-level Feedback Generation for English Language Learners: Does Data Augmentation Help? | In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo datasets for the task, investigating how it affects the performance of our system. We present our results for the task along with extensive analysis of the generated comments with the aim of aiding future studies in feedback comment generation for English language learners. | ['Nathan Schneider', 'Amir Zeldes', 'Shabnam Behzad'] | 2022-12-18 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 3.94648939e-01 5.40408790e-01 -5.66362366e-02 -5.00417709e-01
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2f9dd818-20f4-4055-afb0-d547735c0185 | twibot-22-towards-graph-based-twitter-bot | 2206.04564 | null | https://arxiv.org/abs/2206.04564v6 | https://arxiv.org/pdf/2206.04564v6.pdf | TwiBot-22: Towards Graph-Based Twitter Bot Detection | Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse. State-of-the-art bot detection methods generally leverage the graph structure of the Twitter network, and they exhibit promising performance when confronting novel Twitter bots that traditional methods fail to detect. However, very few of the existing Twitter bot detection datasets are graph-based, and even these few graph-based datasets suffer from limited dataset scale, incomplete graph structure, as well as low annotation quality. In fact, the lack of a large-scale graph-based Twitter bot detection benchmark that addresses these issues has seriously hindered the development and evaluation of novel graph-based bot detection approaches. In this paper, we propose TwiBot-22, a comprehensive graph-based Twitter bot detection benchmark that presents the largest dataset to date, provides diversified entities and relations on the Twitter network, and has considerably better annotation quality than existing datasets. In addition, we re-implement 35 representative Twitter bot detection baselines and evaluate them on 9 datasets, including TwiBot-22, to promote a fair comparison of model performance and a holistic understanding of research progress. To facilitate further research, we consolidate all implemented codes and datasets into the TwiBot-22 evaluation framework, where researchers could consistently evaluate new models and datasets. The TwiBot-22 Twitter bot detection benchmark and evaluation framework are publicly available at https://twibot22.github.io/ | ['Minnan Luo', 'Jundong Li', 'Zihan Ma', 'Lijing Zheng', 'Yanbo Wang', 'Zijian Cai', 'Heng Wang', 'Yuyang Bai', 'YuHan Liu', 'Hongrui Wang', 'Qingyue Zhang', 'Xinshun Feng', 'Shujie Yang', 'Zhenyu Lei', 'Wenqian Zhang', 'Qinghua Zheng', 'Binchi Zhang', 'Zilong Chen', 'Ningnan Wang', 'Herun Wan', 'Zhaoxuan Tan', 'Shangbin Feng'] | 2022-06-09 | null | null | null | null | ['twitter-bot-detection'] | ['miscellaneous'] | [-2.72700727e-01 -2.07399607e-01 -7.07113743e-01 4.81859982e-01
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88d46d58-d1bb-4a38-87a2-43f58ff0bce4 | mcgkt-net-multi-level-context-gating | 2010.09241 | null | https://arxiv.org/abs/2010.09241v1 | https://arxiv.org/pdf/2010.09241v1.pdf | MCGKT-Net: Multi-level Context Gating Knowledge Transfer Network for Single Image Deraining | Rain streak removal in a single image is a very challenging task due to its ill-posed nature in essence. Recently, the end-to-end learning techniques with deep convolutional neural networks (DCNN) have made great progress in this task. However, the conventional DCNN-based deraining methods have struggled to exploit deeper and more complex network architectures for pursuing better performance. This study proposes a novel MCGKT-Net for boosting deraining performance, which is a naturally multi-scale learning framework being capable of exploring multi-scale attributes of rain streaks and different semantic structures of the clear images. In order to obtain high representative features inside MCGKT-Net, we explore internal knowledge transfer module using ConvLSTM unit for conducting interaction learning between different layers and investigate external knowledge transfer module for leveraging the knowledge already learned in other task domains. Furthermore, to dynamically select useful features in learning procedure, we propose a multi-scale context gating module in the MCGKT-Net using squeeze-and-excitation block. Experiments on three benchmark datasets: Rain100H, Rain100L, and Rain800, manifest impressive performance compared with state-of-the-art methods. | ['Xian-Hua Han', 'Kohei Yamamichi'] | 2020-10-19 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 3.63157541e-02 -4.14998680e-01 5.33174455e-01 -7.23712742e-01
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cba51c41-dd42-4dc2-be3c-74c41f5b57a5 | anyteleop-a-general-vision-based-dexterous | 2307.04577 | null | https://arxiv.org/abs/2307.04577v1 | https://arxiv.org/pdf/2307.04577v1.pdf | AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System | Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: http://anyteleop.com/. | ['Dietor Fox', 'Yu-Wei Chao', 'Xiaolong Wang', 'Hao Su', 'Karl Van Wyk', 'Binghao Huang', 'Wei Yang', 'Yuzhe Qin'] | 2023-07-10 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-4.07428026e-01 1.75044119e-01 1.49706230e-01 5.35906330e-02
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16a2a97f-c058-41e4-af14-4f4892a585ee | qampari-an-open-domain-question-answering | 2205.12665 | null | https://arxiv.org/abs/2205.12665v4 | https://arxiv.org/pdf/2205.12665v4.pdf | QAMPARI: An Open-domain Question Answering Benchmark for Questions with Many Answers from Multiple Paragraphs | Existing benchmarks for open-domain question answering (ODQA) typically focus on questions whose answers can be extracted from a single paragraph. By contrast, many natural questions, such as "What players were drafted by the Brooklyn Nets?" have a list of answers. Answering such questions requires retrieving and reading from many passages, in a large corpus. We introduce QAMPARI, an ODQA benchmark, where question answers are lists of entities, spread across many paragraphs. We created QAMPARI by (a) generating questions with multiple answers from Wikipedia's knowledge graph and tables, (b) automatically pairing answers with supporting evidence in Wikipedia paragraphs, and (c) manually paraphrasing questions and validating each answer. We train ODQA models from the retrieve-and-read family and find that QAMPARI is challenging in terms of both passage retrieval and answer generation, reaching an F1 score of 32.8 at best. Our results highlight the need for developing ODQA models that handle a broad range of question types, including single and multi-answer questions. | ['Ohad Rubin', 'Jonathan Berant', 'Jonathan Herzig', 'Samuel Joseph Amouyal', 'Tomer Wolfson', 'Ori Yoran'] | 2022-05-25 | null | null | null | null | ['natural-questions', 'passage-retrieval'] | ['miscellaneous', 'natural-language-processing'] | [-3.15956622e-01 5.11138976e-01 1.92975998e-01 -3.52686718e-02
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a26c7162-2ce1-4f00-83c5-daaf42c0ffac | reliableswap-boosting-general-face-swapping | 2306.05356 | null | https://arxiv.org/abs/2306.05356v1 | https://arxiv.org/pdf/2306.05356v1.pdf | ReliableSwap: Boosting General Face Swapping Via Reliable Supervision | Almost all advanced face swapping approaches use reconstruction as the proxy task, i.e., supervision only exists when the target and source belong to the same person. Otherwise, lacking pixel-level supervision, these methods struggle for source identity preservation. This paper proposes to construct reliable supervision, dubbed cycle triplets, which serves as the image-level guidance when the source identity differs from the target one during training. Specifically, we use face reenactment and blending techniques to synthesize the swapped face from real images in advance, where the synthetic face preserves source identity and target attributes. However, there may be some artifacts in such a synthetic face. To avoid the potential artifacts and drive the distribution of the network output close to the natural one, we reversely take synthetic images as input while the real face as reliable supervision during the training stage of face swapping. Besides, we empirically find that the existing methods tend to lose lower-face details like face shape and mouth from the source. This paper additionally designs a FixerNet, providing discriminative embeddings of lower faces as an enhancement. Our face swapping framework, named ReliableSwap, can boost the performance of any existing face swapping network with negligible overhead. Extensive experiments demonstrate the efficacy of our ReliableSwap, especially in identity preservation. The project page is https://reliable-swap.github.io/. | ['Huicheng Zheng', 'Yong Zhang', 'Maomao Li', 'Ge Yuan'] | 2023-06-08 | null | null | null | null | ['face-swapping', 'face-reenactment'] | ['computer-vision', 'computer-vision'] | [ 1.93921953e-01 2.52610922e-01 -1.84516102e-01 -5.32284558e-01
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105202df-55c5-4659-a917-00934e84bfa8 | survshap-t-time-dependent-explanations-of | 2208.11080 | null | https://arxiv.org/abs/2208.11080v2 | https://arxiv.org/pdf/2208.11080v2.pdf | SurvSHAP(t): Time-dependent explanations of machine learning survival models | Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanations are available to overcome this issue; however, none directly explain the survival function prediction. In this paper, we introduce SurvSHAP(t), the first time-dependent explanation that allows for interpreting survival black-box models. It is based on SHapley Additive exPlanations with solid theoretical foundations and a broad adoption among machine learning practitioners. The proposed methods aim to enhance precision diagnostics and support domain experts in making decisions. Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME. SurvSHAP(t) is model-agnostic and can be applied to all models with functional output. We provide an accessible implementation of time-dependent explanations in Python at http://github.com/MI2DataLab/survshap. | ['Przemysław Biecek', 'Hubert Baniecki', 'Mikołaj Spytek', 'Mateusz Krzyziński'] | 2022-08-23 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-2.08871454e-01 3.25661123e-01 -5.74505031e-01 -7.15480566e-01
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341a3b62-81ad-4cc9-9364-54b9b21a9bb9 | future-sight-dynamic-story-generation-with | 2212.09947 | null | https://arxiv.org/abs/2212.09947v1 | https://arxiv.org/pdf/2212.09947v1.pdf | Future Sight: Dynamic Story Generation with Large Pretrained Language Models | Recent advances in deep learning research, such as transformers, have bolstered the ability for automated agents to generate creative texts similar to those that a human would write. By default, transformer decoders can only generate new text with respect to previously generated text. The output distribution of candidate tokens at any position is conditioned on previously selected tokens using a self-attention mechanism to emulate the property of autoregression. This is inherently limiting for tasks such as controllable story generation where it may be necessary to condition on future plot events when writing a story. In this work, we propose Future Sight, a method for finetuning a pretrained generative transformer on the task of future conditioning. Transformer decoders are typically pretrained on the task of completing a context, one token at a time, by means of self-attention. Future Sight additionally enables a decoder to attend to an encoded future plot event. This motivates the decoder to expand on the context in a way that logically concludes with the provided future. During inference, the future plot event can be written by a human author to steer the narrative being generated in a certain direction. We evaluate the efficacy of our approach on a story generation task with human evaluators. | ['Olga Vechtomova', 'Gaurav Sahu', 'Brian D. Zimmerman'] | 2022-12-20 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 6.63070440e-01 6.19894087e-01 6.00752458e-02 -4.96022701e-01
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8c09f967-ddba-43a1-b9ef-5864de930fd5 | addressing-optimism-bias-in-sequence-modeling | 2207.10295 | null | https://arxiv.org/abs/2207.10295v1 | https://arxiv.org/pdf/2207.10295v1.pdf | Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning | Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem. Recent works based on this paradigm have achieved state-of-the-art results in several of the mostly deterministic offline Atari and D4RL benchmarks. However, because these methods jointly model the states and actions as a single sequencing problem, they struggle to disentangle the effects of the policy and world dynamics on the return. Thus, in adversarial or stochastic environments, these methods lead to overly optimistic behavior that can be dangerous in safety-critical systems like autonomous driving. In this work, we propose a method that addresses this optimism bias by explicitly disentangling the policy and world models, which allows us at test time to search for policies that are robust to multiple possible futures in the environment. We demonstrate our method's superior performance on a variety of autonomous driving tasks in simulation. | ['Jeff Schneider', 'John Dolan', 'Swapnil Pande', 'Zhe Huang', 'Adam Villaflor'] | 2022-07-21 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.61934361e-01 3.78563767e-03 -3.90078455e-01 -2.91755944e-01
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808ffd83-2884-4de6-b68f-c862a17f93a1 | 3d-sic-3d-semantic-instance-completion-for | 1904.12012 | null | https://arxiv.org/abs/1904.12012v3 | https://arxiv.org/pdf/1904.12012v3.pdf | RevealNet: Seeing Behind Objects in RGB-D Scans | During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geometry can be fundamentally limiting for many applications, e.g., a robot needs to know the unseen geometry to perform a precise grasp on an object. Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry. This will open up new possibilities for interactions with objects in a scene, for instance for virtual or robotic agents. We tackle this problem by introducing RevealNet, a new data-driven approach that jointly detects object instances and predicts their complete geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts. RevealNet is an end-to-end 3D neural network architecture that leverages joint color and geometry feature learning. The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass. We show that predicting complete object geometry improves both 3D detection and instance segmentation performance. We evaluate on both real and synthetic scan benchmark data for the new task, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG. | ['Matthias Nießner', 'Angela Dai', 'Ji Hou'] | 2019-04-26 | revealnet-seeing-behind-objects-in-rgb-d | http://openaccess.thecvf.com/content_CVPR_2020/html/Hou_RevealNet_Seeing_Behind_Objects_in_RGB-D_Scans_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hou_RevealNet_Seeing_Behind_Objects_in_RGB-D_Scans_CVPR_2020_paper.pdf | cvpr-2020-6 | ['3d-semantic-instance-segmentation'] | ['computer-vision'] | [ 2.28603989e-01 3.76052618e-01 2.74534911e-01 -5.78104973e-01
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ddbb4969-be78-4ba2-9b7c-1ed99479ee72 | 3dinvnet-a-deep-learning-based-3d-ground | 2305.05425 | null | https://arxiv.org/abs/2305.05425v1 | https://arxiv.org/pdf/2305.05425v1.pdf | 3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion | The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity. Traditional iterative 3D reconstruction algorithms suffer from strong non-linearity, ill-posedness, and high computational cost. To tackle these issues, a 3D deep learning scheme, called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR C-scans. The proposed scheme leverages a prior 3D convolutional neural network with a feature attention mechanism to suppress the noise in the C-scans due to subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder network with multi-scale feature aggregation modules is designed to establish the optimal inverse mapping from the denoised C-scans to 3D permittivity maps. Furthermore, a three-step separate learning strategy is employed to pre-train and fine-tune the networks. The proposed scheme is applied to numerical simulation as well as real measurement data. The quantitative and qualitative results show the network capability, generalizability, and robustness in denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface objects. | ['Abdulkadir C. Yucel', 'Mohamed Lokman Mohd Yusof', 'Genevieve Ow', 'Hai-Han Sun', 'Yee Hui Lee', 'Qiqi Dai'] | 2023-05-09 | null | null | null | null | ['3d-reconstruction', 'gpr', 'gpr'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 3.22779775e-01 9.89405885e-02 7.33585417e-01 -7.10934460e-01
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602a9eae-5112-4ab4-9a58-a207f6345946 | gap-a-graph-aware-language-model-framework | 2204.06674 | null | https://arxiv.org/abs/2204.06674v4 | https://arxiv.org/pdf/2204.06674v4.pdf | GAP: A Graph-aware Language Model Framework for Knowledge Graph-to-Text Generation | Recent improvements in KG-to-text generation are due to additional auxiliary pre-training tasks designed to give the fine-tune task a boost in performance. These tasks require extensive computational resources while only suggesting marginal improvements. Here, we demonstrate that by fusing graph-aware elements into existing pre-trained language models, we are able to outperform state-of-the-art models and close the gap imposed by additional pre-training tasks. We do so by proposing a mask structure to capture neighborhood information and a novel type encoder that adds a bias to the graph-attention weights depending on the connection type. Experiments on two KG-to-text benchmark datasets show our models are competitive while involving fewer parameters and no additional pre-training tasks. By formulating the problem as a framework, we can interchange the various proposed components and begin interpreting KG-to-text generative models based on the topological and type information found in a graph. | ['Daisy Zhe Wang', 'Mehrdad Alvandipour', 'Anthony Colas'] | 2022-04-13 | null | https://aclanthology.org/2022.coling-1.506 | https://aclanthology.org/2022.coling-1.506.pdf | coling-2022-10 | ['data-to-text-generation', 'kg-to-text'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.66903359e-01 7.39902079e-01 -1.27366871e-01 -3.96170884e-01
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9c4b73e6-8f2a-431f-b927-5217ccfb977a | point-cloud-generation-with-continuous | 2202.08526 | null | https://arxiv.org/abs/2202.08526v1 | https://arxiv.org/pdf/2202.08526v1.pdf | Point Cloud Generation with Continuous Conditioning | Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper proposes a novel generative adversarial network (GAN) setup that generates 3D point cloud shapes conditioned on a continuous parameter. In an exemplary application, we use this to guide the generative process to create a 3D object with a custom-fit shape. We formulate this generation process in a multi-task setting by using the concept of auxiliary classifier GANs. Further, we propose to sample the generator label input for training from a kernel density estimation (KDE) of the dataset. Our ablations show that this leads to significant performance increase in regions with few samples. Extensive quantitative and qualitative experiments show that we gain explicit control over the object dimensions while maintaining good generation quality and diversity. | ['J. Marius Zöllner', 'Fabian B. Flohr', 'David Peter', 'Andre Bühler', 'Larissa T. Triess'] | 2022-02-17 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 3.58529121e-01 4.28151786e-01 2.26568133e-01 -6.78247353e-03
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442c8021-0308-4014-95b7-61aa6662ddef | convolutional-set-matching-for-graph | 1810.10866 | null | http://arxiv.org/abs/1810.10866v3 | http://arxiv.org/pdf/1810.10866v3.pdf | Convolutional Set Matching for Graph Similarity | We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural
Networks) for predicting the similarity score between two graphs. As the core
operation of graph similarity search, pairwise graph similarity computation is
a challenging problem due to the NP-hard nature of computing many graph
distance/similarity metrics. We demonstrate our model using the Graph Edit
Distance (GED) as the example metric. Experiments on three real graph datasets
demonstrate that our model achieves the state-of-the-art performance on graph
similarity search. | ['Yunsheng Bai', 'Hao Ding', 'Yizhou Sun', 'Wei Wang'] | 2018-10-23 | null | null | null | null | ['set-matching', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 8.42827559e-02 7.49439225e-02 -1.63567662e-01 -4.47986484e-01
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ffc2e971-c4c1-482e-b9a9-f30bd7cccd4d | feed-forward-and-noise-tolerant-detection-of | 1806.03881 | null | https://arxiv.org/abs/1806.03881v4 | https://arxiv.org/pdf/1806.03881v4.pdf | Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code | In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired. | ['Marc-Oliver Gewaltig', 'Thomas Wachtler', 'Ad Aertsen', 'Rüdiger Kupper', 'Michael Schmuker'] | 2018-06-11 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 4.16106492e-01 -6.73639536e-01 4.89283711e-01 -1.19587176e-01
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47676f65-c68a-4a64-9982-08a6b46f3b13 | diversity-induced-multi-view-subspace | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Cao_Diversity-Induced_Multi-View_Subspace_2015_CVPR_paper.pdf | Diversity-Induced Multi-View Subspace Clustering | In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering into the multi-view domain, and utilize the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term to explore the complementarity of multi-view representations, which could be solved efficiently by using the alternating minimizing optimization. Compared to other multi-view clustering methods, the enhanced complementarity reduces the redundancy between the multi-view features, and improves the accuracy of the clustering results. Experiments on both image and video face clustering well demonstrate that the proposed method outperforms the state-of-the-art methods. | ['Hua Zhang', 'Huazhu Fu', 'Xiaochun Cao', 'Si Liu', 'Changqing Zhang'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['multi-view-subspace-clustering', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [-3.23289812e-01 -6.34995997e-01 -8.36557820e-02 -2.18551457e-01
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a98f0626-6a9c-4af1-9004-1237aef5214b | few-shot-subgoal-planning-with-language | 2205.14288 | null | https://arxiv.org/abs/2205.14288v1 | https://arxiv.org/pdf/2205.14288v1.pdf | Few-shot Subgoal Planning with Language Models | Pre-trained large language models have shown successful progress in many language understanding benchmarks. This work explores the capability of these models to predict actionable plans in real-world environments. Given a text instruction, we show that language priors encoded in pre-trained language models allow us to infer fine-grained subgoal sequences. In contrast to recent methods which make strong assumptions about subgoal supervision, our experiments show that language models can infer detailed subgoal sequences from few training sequences without any fine-tuning. We further propose a simple strategy to re-rank language model predictions based on interaction and feedback from the environment. Combined with pre-trained navigation and visual reasoning components, our approach demonstrates competitive performance on subgoal prediction and task completion in the ALFRED benchmark compared to prior methods that assume more subgoal supervision. | ['Honglak Lee', 'Moontae Lee', 'Yao Fu', 'Lajanugen Logeswaran'] | 2022-05-28 | null | https://aclanthology.org/2022.naacl-main.402 | https://aclanthology.org/2022.naacl-main.402.pdf | naacl-2022-7 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 3.25388491e-01 7.67759323e-01 -3.77861053e-01 -6.87575519e-01
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335f5d4d-7771-4dd7-9109-d45714a818e2 | learning-topology-specific-experts-for | 2302.13693 | null | https://arxiv.org/abs/2302.13693v3 | https://arxiv.org/pdf/2302.13693v3.pdf | Learning Topology-Specific Experts for Molecular Property Prediction | Recently, graph neural networks (GNNs) have been successfully applied to predicting molecular properties, which is one of the most classical cheminformatics tasks with various applications. Despite their effectiveness, we empirically observe that training a single GNN model for diverse molecules with distinct structural patterns limits its prediction performance. In this paper, motivated by this observation, we propose TopExpert to leverage topology-specific prediction models (referred to as experts), each of which is responsible for each molecular group sharing similar topological semantics. That is, each expert learns topology-specific discriminative features while being trained with its corresponding topological group. To tackle the key challenge of grouping molecules by their topological patterns, we introduce a clustering-based gating module that assigns an input molecule into one of the clusters and further optimizes the gating module with two different types of self-supervision: topological semantics induced by GNNs and molecular scaffolds, respectively. Extensive experiments demonstrate that TopExpert has boosted the performance for molecular property prediction and also achieved better generalization for new molecules with unseen scaffolds than baselines. The code is available at https://github.com/kimsu55/ToxExpert. | ['Hwanjo Yu', 'Seonghyeon Lee', 'SeongKu Kang', 'Dongha Lee', 'Su Kim'] | 2023-02-27 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 2.91473776e-01 -5.59412688e-03 -6.78201675e-01 -4.33285713e-01
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668dee0a-ce37-47c1-bfc3-20479c2030dd | vl-bert-detecting-protected-groups-in-hateful | null | null | https://aclanthology.org/2021.woah-1.22 | https://aclanthology.org/2021.woah-1.22.pdf | VL-BERT+: Detecting Protected Groups in Hateful Multimodal Memes | This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags such as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set. | ['Torsten Zesch', 'Darina Gold', 'Michelle Espranita Liman', 'Piush Aggarwal'] | null | null | null | null | acl-woah-2021-8 | ['meme-classification'] | ['natural-language-processing'] | [-3.23059857e-01 5.87155893e-02 -2.81065136e-01 -2.24520922e-01
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6aed6682-dba8-4d1d-a458-8e461975c3ac | variational-information-pursuit-for | 2302.02876 | null | https://arxiv.org/abs/2302.02876v2 | https://arxiv.org/pdf/2302.02876v2.pdf | Variational Information Pursuit for Interpretable Predictions | There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make interpretable decisions by sequentially asking interpretable queries about data until a prediction can be made with high confidence based on the answers obtained (the history). To promote short query-answer chains, a greedy procedure called Information Pursuit (IP) is used, which adaptively chooses queries in order of information gain. Generative models are employed to learn the distribution of query-answers and labels, which is in turn used to estimate the most informative query. However, learning and inference with a full generative model of the data is often intractable for complex tasks. In this work, we propose Variational Information Pursuit (V-IP), a variational characterization of IP which bypasses the need for learning generative models. V-IP is based on finding a query selection strategy and a classifier that minimizes the expected cross-entropy between true and predicted labels. We then demonstrate that the IP strategy is the optimal solution to this problem. Therefore, instead of learning generative models, we can use our optimal strategy to directly pick the most informative query given any history. We then develop a practical algorithm by defining a finite-dimensional parameterization of our strategy and classifier using deep networks and train them end-to-end using our objective. Empirically, V-IP is 10-100x faster than IP on different Vision and NLP tasks with competitive performance. Moreover, V-IP finds much shorter query chains when compared to reinforcement learning which is typically used in sequential-decision-making problems. Finally, we demonstrate the utility of V-IP on challenging tasks like medical diagnosis where the performance is far superior to the generative modelling approach. | ['René Vidal', 'Donald Geman', 'Benjamin D. Haeffele', 'Kwan Ho Ryan Chan', 'Aditya Chattopadhyay'] | 2023-02-06 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 4.05979753e-01 4.56457645e-01 -3.16702574e-01 -4.84864891e-01
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f0b6d220-d3d4-4670-89bd-757cbe32db09 | learning-visibility-field-for-detailed-3d | 2304.11900 | null | https://arxiv.org/abs/2304.11900v1 | https://arxiv.org/pdf/2304.11900v1.pdf | Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting | Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field--it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth. | ['Tao Yu', 'Haoqian Wang', 'Peng Li', 'Ruichen Zheng'] | 2023-04-24 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zheng_Learning_Visibility_Field_for_Detailed_3D_Human_Reconstruction_and_Relighting_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-reconstruction', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.14394748e-01 -1.14042833e-01 2.10273549e-01 -5.24297178e-01
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a54eeab2-566c-4ae4-99eb-815a94698a71 | new-benchmarks-for-accountable-text-based | 2303.05983 | null | https://arxiv.org/abs/2303.05983v2 | https://arxiv.org/pdf/2303.05983v2.pdf | Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation | The recent success of ChatGPT and GPT-4 has drawn widespread attention to multimodal dialogue systems. However, the academia community lacks a dataset that can validate the multimodal generation capabilities of Visual Language Models (VLMs) in textual-visual chat tasks. In this paper, we construct two new multimodal datasets: the synthetic CLEVR-ATVC dataset (620K) and the manually pictured Fruit-ATVC dataset (50K), both featuring visual and text-based inputs and outputs. Additionally, to enable the multimodal system to reject human requests (i.e., demonstrate accountability), as in language-based ChatGPT conversations, we develop and incorporate specific rules into the datasets as supervisory signals. This allows the trained VLM to provide a yes or no answer after visual and textual reasoning, accompanied by a language explanation as to why the human instruction cannot be excuted. In our method, we propose a two-state training procedure to train the image auto-encoder and auto-regressive transformer from scratch. The first state involves a discrete variational autoencoder (dVAE) to compress each image into short tokens, which are then concatenated with text tokens as a single data stream to be fed into the decoder-based transformer for generating visual re-creation and textual feedback in the second state. We provide comprehensive analyses of experimental results in terms of re-created image quality, answer accuracy, and the model behavior when faced with uncertainty and imperfect user queries. We hope our explorations and findings contribute valuable insights regarding the accountability of textual-visual generative models. | ['Yuliang Liu', 'Zhiwei Zhang'] | 2023-03-10 | null | null | null | null | ['visual-reasoning', 'multimodal-generation', 'visual-reasoning'] | ['computer-vision', 'natural-language-processing', 'reasoning'] | [ 1.59570038e-01 4.47284102e-01 1.80610061e-01 -4.22827750e-01
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9210cda8-ae3d-4cc3-86f4-7caed2651c26 | scalable-neural-contextual-bandit-for | 2306.14834 | null | https://arxiv.org/abs/2306.14834v1 | https://arxiv.org/pdf/2306.14834v1.pdf | Scalable Neural Contextual Bandit for Recommender Systems | High-quality recommender systems ought to deliver both innovative and relevant content through effective and exploratory interactions with users. Yet, supervised learning-based neural networks, which form the backbone of many existing recommender systems, only leverage recognized user interests, falling short when it comes to efficiently uncovering unknown user preferences. While there has been some progress with neural contextual bandit algorithms towards enabling online exploration through neural networks, their onerous computational demands hinder widespread adoption in real-world recommender systems. In this work, we propose a scalable sample-efficient neural contextual bandit algorithm for recommender systems. To do this, we design an epistemic neural network architecture, Epistemic Neural Recommendation (ENR), that enables Thompson sampling at a large scale. In two distinct large-scale experiments with real-world tasks, ENR significantly boosts click-through rates and user ratings by at least 9% and 6% respectively compared to state-of-the-art neural contextual bandit algorithms. Furthermore, it achieves equivalent performance with at least 29% fewer user interactions compared to the best-performing baseline algorithm. Remarkably, while accomplishing these improvements, ENR demands orders of magnitude fewer computational resources than neural contextual bandit baseline algorithms. | ['Benjamin Van Roy', 'Zheqing Zhu'] | 2023-06-26 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.29980680e-02 2.18654543e-01 -8.67884874e-01 -3.90007138e-01
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286d725b-0a09-4b1d-834e-4df96fa20db2 | 3dmodt-attention-guided-affinities-for-joint | 2211.00746 | null | https://arxiv.org/abs/2211.00746v1 | https://arxiv.org/pdf/2211.00746v1.pdf | 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds | We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followed by data association. Our method embeds both steps into a single end-to-end trainable network eliminating the dependency on external object detectors. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network, thereby making it a utilitarian formulation for real-world scenarios. Computing affinity matrix by employing features similarity across consecutive point cloud scans forms an integral part of visual tracking. We propose an attention-based refinement module to refine the affinity matrix by suppressing erroneous correspondences. The module is designed to capture the global context in affinity matrix by employing self-attention within each affinity matrix and cross-attention across a pair of affinity matrices. Unlike competing approaches, our network does not require complex post-processing algorithms, and processes raw LiDAR frames to directly output tracking results. We demonstrate the effectiveness of our method on the three tracking benchmarks: JRDB, Waymo, and KITTI. Experimental evaluations indicate the ability of our model to generalize well across datasets. | ['Mubarak Shah', 'Ajmal Mian', 'Jyoti Kini'] | 2022-11-01 | null | null | null | null | ['visual-tracking'] | ['computer-vision'] | [ 7.42392689e-02 -2.13626295e-01 -3.82192247e-03 -3.30484688e-01
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26995fc9-a6cf-4161-aba3-da0c80aa1236 | multi-channel-vision-transformer-for | null | null | https://doi.org/10.3390/biomedicines10071551 | https://doi.org/10.3390/biomedicines10071551 | Multi-Channel Vision Transformer for Epileptic Seizure Prediction | Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication. The ability to predict the future occurrence of seizures would enable the patients to take precautions against probable injuries and administer timely treatment to abort or control impending seizures. In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel EEG data. Continuous wavelet transform, a simple yet efficient pre-processing approach, is first used for turning the time-series EEG signals into image-like time-frequency representations named Scalograms. Each scalogram is split into a sequence of fixed-size non-overlapping patches, which are then fed as inputs to the MViT for EEG classification. Extensive experiments on three benchmark EEG datasets demonstrate the superiority of the proposed MViT algorithm over the state-of-the-art seizure prediction methods, achieving an average prediction sensitivity of 99.80% for surface EEG and 90.28–91.15% for invasive EEG data. | ['Rabab Ward', 'Soojin Lee', 'Ramy Hussein'] | 2022-06-29 | null | null | null | biomedicines-2022-6 | ['seizure-prediction'] | ['medical'] | [ 2.57090658e-01 -5.44972777e-01 4.97283965e-01 -2.17551768e-01
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180f3a47-f3fa-463b-a481-86c4f73f5338 | same-author-or-just-same-topic-towards | 2204.04907 | null | https://arxiv.org/abs/2204.04907v1 | https://arxiv.org/pdf/2204.04907v1.pdf | Same Author or Just Same Topic? Towards Content-Independent Style Representations | Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training. However, a good performance on the AV task does not ensure good "general-purpose" style representations. For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone. We introduce a variation of the AV training task that controls for content using conversation or domain labels. We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework. We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content. | ['Dong Nguyen', 'Marijn Schraagen', 'Anna Wegmann'] | 2022-04-11 | null | https://aclanthology.org/2022.repl4nlp-1.26 | https://aclanthology.org/2022.repl4nlp-1.26.pdf | repl4nlp-acl-2022-5 | ['authorship-verification'] | ['natural-language-processing'] | [ 1.31755188e-01 1.53053403e-01 -1.91399246e-01 -8.07137012e-01
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4ea49bb0-eeac-4ef7-a5f2-de9e4ced0fca | satellite-image-classification-and | 1401.2416 | null | http://arxiv.org/abs/1401.2416v1 | http://arxiv.org/pdf/1401.2416v1.pdf | Satellite image classification and segmentation using non-additive entropy | Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis
entropy on the pattern recognition and segmentation of coloured images obtained
by satellites, via "Google Earth". By segmentation we mean split an image to
locate regions of interest. Here, we discriminate and define an image partition
classes according to a training basis. This training basis consists of three
pattern classes: aquatic, urban and vegetation regions. Our numerical
experiments demonstrate that the Tsallis entropy, used as a feature vector
composed of distinct entropic indexes $q$ outperforms the standard entropy.
There are several applications of our proposed methodology, once satellite
images can be used to monitor migration form rural to urban regions,
agricultural activities, oil spreading on the ocean etc. | ['Odemir Martinez Bruno', 'Lucas Assirati', 'Alexandre Souto Martinez'] | 2014-01-10 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 3.17344934e-01 -1.84582680e-01 5.76865636e-02 -1.05763339e-01
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91cbff5e-e81a-4b1b-ba85-dc0a3fc44fcd | intrinsic-autoencoders-for-joint-neural | 2006.16011 | null | https://arxiv.org/abs/2006.16011v3 | https://arxiv.org/pdf/2006.16011v3.pdf | Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition | Neural rendering techniques promise efficient photo-realistic image synthesis while at the same time providing rich control over scene parameters by learning the physical image formation process. While several supervised methods have been proposed for this task, acquiring a dataset of images with accurately aligned 3D models is very difficult. The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data. More specifically, we propose an autoencoder for joint generation of realistic images from synthetic 3D models while simultaneously decomposing real images into their intrinsic shape and appearance properties. In contrast to a traditional graphics pipeline, our approach does not require to specify all scene properties, such as material parameters and lighting by hand. Instead, we learn photo-realistic deferred rendering from a small set of 3D models and a larger set of unaligned real images, both of which are easy to acquire in practice. Simultaneously, we obtain accurate intrinsic decompositions of real images while not requiring paired ground truth. Our experiments confirm that a joint treatment of rendering and decomposition is indeed beneficial and that our approach outperforms state-of-the-art image-to-image translation baselines both qualitatively and quantitatively. | ['Matthias Nießner', 'Varun Jampani', 'Siva Karthik Mustikovela', 'Justus Thies', 'Carsten Rother', 'Hassan Abu Alhaija', 'Andreas Geiger'] | 2020-06-29 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.08654535e-01 2.46327028e-01 3.02998275e-01 -2.87869483e-01
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f4a66c05-ea2c-4a9c-b234-89fd2d873648 | securing-the-classification-of-covid-19-in | 2203.07728 | null | https://arxiv.org/abs/2203.07728v1 | https://arxiv.org/pdf/2203.07728v1.pdf | Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach | Deep learning (DL) is being increasingly utilized in healthcare-related fields due to its outstanding efficiency. However, we have to keep the individual health data used by DL models private and secure. Protecting data and preserving the privacy of individuals has become an increasingly prevalent issue. The gap between the DL and privacy communities must be bridged. In this paper, we propose privacy-preserving deep learning (PPDL)-based approach to secure the classification of Chest X-ray images. This study aims to use Chest X-ray images to their fullest potential without compromising the privacy of the data that it contains. The proposed approach is based on two steps: encrypting the dataset using partially homomorphic encryption and training/testing the DL algorithm over the encrypted images. Experimental results on the COVID-19 Radiography database show that the MobileNetV2 model achieves an accuracy of 94.2% over the plain data and 93.3% over the encrypted data. | ['Anis Koubaa', 'Bilel Benjdira', 'Adel Ammar', 'Wadii Boulila'] | 2022-03-15 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 6.89980090e-02 -8.41348059e-03 -1.93466797e-01 -5.39344072e-01
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ddec9ff9-71df-4417-a980-c3c1b843bab4 | dart-a-lightweight-quality-suggestive-data-to | 2010.04141 | null | https://arxiv.org/abs/2010.04141v2 | https://arxiv.org/pdf/2010.04141v2.pdf | DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool | We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree structure. By using a backend sequence-to-sequence model, our system iteratively analyzes the annotated labels in order to better sample unlabeled data. In a simulation experiment performed on annotating large quantities of structured data, DART has been shown to reduce the total number of annotations needed with active learning and automatically suggesting relevant labels. | ['Vera Demberg', 'Xiaoyu Shen', 'Alex Marin', 'Jeriah Caplinger', 'Ernie Chang'] | 2020-10-08 | null | https://aclanthology.org/2020.coling-demos.3 | https://aclanthology.org/2020.coling-demos.3.pdf | coling-2020-8 | ['text-annotation'] | ['natural-language-processing'] | [ 4.48945701e-01 9.26854551e-01 -2.96706736e-01 -6.59077644e-01
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bf4891a4-5561-4fbf-ac49-07f52f06527b | tough-tables-carefully-evaluating-entity | null | null | https://doi.org/10.1007/978-3-030-62466-8_21 | https://openaccess.city.ac.uk/id/eprint/24776/1/Tough_Tables_Carefully_Evaluating_Entity_Linking_for_Tabular_Data.pdf | Tough Tables: Carefully Evaluating Entity Linking for Tabular Data | Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate table annotation algorithms by providing a common interface and several general-purpose datasets as a ground truth. The SemTab dataset is useful to have a general understanding of how these algorithms work, and the organizers of the challenge included some artificial noise to the data to make the annotation trickier. However, it is hard to analyze specific aspects in an automatic way. For example, the ambiguity of names at the entity-level can largely affect the quality of the annotation. In this paper, we propose a novel dataset to complement the datasets proposed by SemTab. The dataset consists of a set of highquality manually-curated tables with non-obviously linkable cells, i.e., where values are ambiguous names, typos, and misspelled entity names not appearing in the current version of the SemTab dataset. These challenges are particularly relevant for the ingestion of structured legacy sources into existing knowledge graphs. Evaluations run on this dataset show that ambiguity is a key problem for entity linking algorithms and encourage a promising direction for future work in the field. | ['Matteo Palmonari', 'Ernesto Jimenez-Ruiz', 'Federico Bianchi', 'Vincenzo Cutrona'] | 2020-11-01 | null | null | null | international-semantic-web-conference-iswc | ['table-annotation', 'table-annotation', 'column-type-annotation', 'cell-entity-annotation'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-3.82098466e-01 3.43269020e-01 -4.05611068e-01 -1.43153235e-01
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91b3bd22-bc3d-405d-8515-0adcd50d2033 | modeling-diagnostic-label-correlation-for-1 | 2106.12800 | null | https://arxiv.org/abs/2106.12800v1 | https://arxiv.org/pdf/2106.12800v1.pdf | Modeling Diagnostic Label Correlation for Automatic ICD Coding | Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced data make this prediction task extremely hard. Most existing work built a binary prediction for each label independently, ignoring the dependencies between labels. To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate generated by a base predictor. This paper is the first attempt at learning the label set distribution as a reranking module for medical code prediction. In the experiments, our proposed framework is able to improve upon best-performing predictors on the benchmark MIMIC datasets. The source code of this project is available at https://github.com/MiuLab/ICD-Correlation. | ['Yun-Nung Chen', 'Chao-Wei Huang', 'Shang-Chi Tsai'] | 2021-06-24 | modeling-diagnostic-label-correlation-for | https://aclanthology.org/2021.naacl-main.318 | https://aclanthology.org/2021.naacl-main.318.pdf | naacl-2021-4 | ['medical-code-prediction'] | ['medical'] | [ 3.92750382e-01 1.71253964e-01 -5.57643473e-01 -7.70177543e-01
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5ed969c4-d0f1-4215-a8f2-bf6bc334c6ab | analysis-of-social-robotic-navigation | 2008.07965 | null | https://arxiv.org/abs/2008.07965v2 | https://arxiv.org/pdf/2008.07965v2.pdf | Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning | Dealing with social tasks in robotic scenarios is difficult, as having humans in the learning loop is incompatible with most of the state-of-the-art machine learning algorithms. This is the case when exploring Incremental learning models, in particular the ones involving reinforcement learning. In this work, we discuss this problem and possible solutions by analysing a previous study on adaptive convolutional encoders for a social navigation task. | ['Yves M. Galvão', 'Letícia Castro', 'Agostinho A. F. Júnior', 'Pablo Barros', 'Bruno J. T. Fernandes', 'Janderson Ferreira'] | 2020-08-18 | null | null | null | null | ['social-navigation'] | ['robots'] | [ 2.58894026e-01 9.70526040e-01 1.13607913e-01 -2.38591582e-01
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6aaa23dd-f06a-4d86-9280-f24e43fee2b0 | a-dataset-for-benchmarking-image-based | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Sun_A_Dataset_for_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Sun_A_Dataset_for_CVPR_2017_paper.pdf | A Dataset for Benchmarking Image-Based Localization | A novel dataset for benchmarking image-based localization is presented. With increasing research interests in visual place recognition and localization, several datasets have been published in the past few years. One of the evident limitations of existing datasets is that precise ground truth camera poses of query images are not available in a meaningful 3D metric system. This is in part due to the underlying 3D models of these datasets are reconstructed from Structure from Motion methods. So far little attention has been paid to metric evaluations of localization accuracy. In this paper we address the problem of whether state-of-the-art visual localization techniques can be applied to tasks with demanding accuracy requirements. We acquired training data for a large indoor environment with cameras and a LiDAR scanner. In addition, we collected over 2000 query images with cell phone cameras. Using LiDAR point clouds as a reference, we employed a semi-automatic approach to estimate the 6 degrees of freedom camera poses precisely in the world coordinate system. The proposed dataset enables us to quantitatively assess the performance of various algorithms using a fair and intuitive metric.
| ['Yuanfan Xie', 'Xun Sun', 'Pei Luo', 'Liang Wang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['image-based-localization'] | ['computer-vision'] | [-1.14870630e-01 -6.54859364e-01 -1.77913070e-01 -5.56818008e-01
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3.91632885e-01 -7.92342782e-01 -2.63010114e-01 5.20757809e-02] | [7.548708438873291, -2.0956573486328125] |
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