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4f80ee38-d390-4d90-bcf4-dddaebd05e2a | deep-koopman-operator-based-model-predictive | 2103.14321 | null | https://arxiv.org/abs/2103.14321v5 | https://arxiv.org/pdf/2103.14321v5.pdf | Online Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy | Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability of self-adaptively adjusting stimulation inputs. In this work, rooted in optimal control theory, we propose a Koopman-MPC framework for real-time closed-loop electrical neuromodulation in epilepsy, which integrates i) a deep Koopman operator based dynamical model to predict the temporal evolution of epileptic EEG with an approximate finite-dimensional linear dynamics and ii) a model predictive control (MPC) module to design optimal seizure suppression strategies. The Koopman operator based linear dynamical model is embedded in the latent state space of the autoencoder neural network, in which we can approximate and update the Koopman operator online. The linear dynamical property of the Koopman operator ensures the convexity of the optimization problem for subsequent MPC control. The proposed deep Koopman operator model shows greater predictive capability than the baseline models (e.g., vector autoregressive model, kernel based method and recurrent neural network (RNN)) in both synthetic and real epileptic EEG data. Moreover, compared with the RNN-MPC framework, our Koopman-MPC framework can suppress seizure dynamics with better computational efficiency in both the Jansen-Rit model and the Epileptor model. Koopman-MPC framework opens a new window for model-based closed-loop neuromodulation and sheds light on nonlinear neurodynamics and feedback control policies. | ['Quanying Liu', 'Jingwei Qiu', 'Keyin Liu', 'Zixiang Luo', 'Zhichao Liang'] | 2021-03-26 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 4.76117395e-02 3.18592668e-01 -8.47577453e-02 4.88503605e-01
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76203f42-f646-4077-a348-b22bfd54f116 | using-generalized-additive-models-to | null | null | https://link.springer.com/article/10.1007/s10877-022-00873-7 | https://link.springer.com/content/pdf/10.1007/s10877-022-00873-7.pdf | Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiology | Common physiological time series and waveforms are composed of repeating cardiac and respiratory cycles. Often, the cardiac effect is the primary interest, but for, e.g., fluid responsiveness prediction, the respiratory effect on arterial blood pressure also convey important information. In either case, it is relevant to disentangle the two effects. Generalized additive models (GAMs) allow estimating the effect of predictors as nonlinear, smooth functions. These smooth functions can represent the cardiac and respiratory cycles' effects on a physiological signal. We demonstrate how GAMs allow a decomposition of physiological signals from mechanically ventilated subjects into separate effects of the cardiac and respiratory cycles. Two examples are presented. The first is a model of the respiratory variation in pulse pressure. The second demonstrates how a central venous pressure waveform can be decomposed into a cardiac effect, a respiratory effect and the interaction between the two cycles. Generalized additive models provide an intuitive and flexible approach to modelling the repeating, smooth, patterns common in medical monitoring data. | ['Simon T Vistisen', 'Gavin L Simpson', 'Johannes Enevoldsen'] | 2022-06-13 | null | null | null | journal-of-clinical-monitoring-and-computing | ['medical-waveform-analysis', 'additive-models'] | ['medical', 'methodology'] | [ 2.06387550e-01 -1.48540452e-01 1.34421960e-01 -2.20715478e-01
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13768a75-0354-4ef5-a9ff-8ed4126ab7d4 | dual-distribution-alignment-network-for | 2007.13249 | null | https://arxiv.org/abs/2007.13249v1 | https://arxiv.org/pdf/2007.13249v1.pdf | Dual Distribution Alignment Network for Generalizable Person Re-Identification | Domain generalization (DG) serves as a promising solution to handle person Re-Identification (Re-ID), which trains the model using labels from the source domain alone, and then directly adopts the trained model to the target domain without model updating. However, existing DG approaches are usually disturbed by serious domain variations due to significant dataset variations. Subsequently, DG highly relies on designing domain-invariant features, which is however not well exploited, since most existing approaches directly mix multiple datasets to train DG based models without considering the local dataset similarities, i.e., examples that are very similar but from different domains. In this paper, we present a Dual Distribution Alignment Network (DDAN), which handles this challenge by mapping images into a domain-invariant feature space by selectively aligning distributions of multiple source domains. Such an alignment is conducted by dual-level constraints, i.e., the domain-wise adversarial feature learning and the identity-wise similarity enhancement. We evaluate our DDAN on a large-scale Domain Generalization Re-ID (DG Re-ID) benchmark. Quantitative results demonstrate that the proposed DDAN can well align the distributions of various source domains, and significantly outperforms all existing domain generalization approaches. | ['Peixian Chen', 'Jianzhuang Liu', 'Feng Zheng', 'Qi Tian', 'Rongrong Ji', 'Pingyang Dai'] | 2020-07-27 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 2.18824074e-01 -4.31036592e-01 -2.25294217e-01 -5.18194616e-01
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688231f7-0c71-4e48-a935-f53218e1a531 | assessing-the-eligibility-of-backtranslated | null | null | https://aclanthology.org/2021.ranlp-main.35 | https://aclanthology.org/2021.ranlp-main.35.pdf | Assessing the Eligibility of Backtranslated Samples Based on Semantic Similarity for the Paraphrase Identification Task | In the domain of natural language augmentation, the eligibility of generated samples remains not well understood. To gather insights around this eligibility issue, we apply a transformer-based similarity calculation within the BET framework based on backtranslation, in the context of automated paraphrase detection. While providing a rigorous statistical foundation to BET, we push their results by analyzing statistically the impacts of the level of qualification, and several sample sizes. We conducted a vast amount of experiments on the MRPC corpus using six pre-trained models: BERT, XLNet, Albert, RoBERTa, Electra, and DeBerta. We show that our method improves significantly these “base” models while using only a fraction of the corpus. Our results suggest that using some of those smaller pre-trained models, namely RoBERTa base and Electra base, helps us reach F1 scores very close to their large counterparts, as reported on the GLUE benchmark. On top of acting as a regularizer, the proposed method is efficient in dealing with data scarcity with improvements of around 3% in F1 score for most pre-trained models, and more than 7.5% in the case of Electra. | ['Hadi Abdi Ghavidel', 'Jean-Philippe Corbeil'] | null | null | https://aclanthology.org/2021.ranlp-1.35 | https://aclanthology.org/2021.ranlp-1.35.pdf | ranlp-2021-9 | ['paraphrase-identification'] | ['natural-language-processing'] | [ 4.40746725e-01 4.41620171e-01 -2.64403254e-01 -1.29929394e-01
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1b4857bf-55ea-43f7-a764-e8d48732f5fb | bieru-bidirectional-emotional-recurrent-unit | 2006.00492 | null | https://arxiv.org/abs/2006.00492v3 | https://arxiv.org/pdf/2006.00492v3.pdf | BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis | Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information which may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases. | ['Wei Shao', 'Shaoxiong Ji', 'Erik Cambria', 'Wei Li'] | 2020-05-31 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 2.00606257e-01 -1.45149782e-01 -1.98171213e-01 -8.28390360e-01
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7420f263-e0c4-402e-b41a-299d84f67184 | do-neural-language-representations-learn | 1908.02899 | null | https://arxiv.org/abs/1908.02899v1 | https://arxiv.org/pdf/1908.02899v1.pdf | Do Neural Language Representations Learn Physical Commonsense? | Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language. In addition to the properties of objects (e.g., boats require fuel) and their affordances, i.e., the actions that are applicable to them (e.g., boats can be driven), we can also reason about if-then inferences between what properties of objects imply the kind of actions that are applicable to them (e.g., that if we can drive something then it likely requires fuel). In this paper, we investigate the extent to which state-of-the-art neural language representations, trained on a vast amount of natural language text, demonstrate physical commonsense reasoning. While recent advancements of neural language models have demonstrated strong performance on various types of natural language inference tasks, our study based on a dataset of over 200k newly collected annotations suggests that neural language representations still only learn associations that are explicitly written down. | ['Ari Holtzman', 'Yejin Choi', 'Maxwell Forbes'] | 2019-08-08 | null | null | null | null | ['physical-commonsense-reasoning'] | ['reasoning'] | [ 6.92566112e-02 2.90998310e-01 -3.86466801e-01 -4.68734443e-01
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627567ed-9675-4354-960e-1f19ffbca710 | active-inference-for-autonomous-decision | 2209.09185 | null | https://arxiv.org/abs/2209.09185v2 | https://arxiv.org/pdf/2209.09185v2.pdf | Active Inference for Autonomous Decision-Making with Contextual Multi-Armed Bandits | In autonomous robotic decision-making under uncertainty, the tradeoff between exploitation and exploration of available options must be considered. If secondary information associated with options can be utilized, such decision-making problems can often be formulated as contextual multi-armed bandits (CMABs). In this study, we apply active inference, which has been actively studied in the field of neuroscience in recent years, as an alternative action selection strategy for CMABs. Unlike conventional action selection strategies, it is possible to rigorously evaluate the uncertainty of each option when calculating the expected free energy (EFE) associated with the decision agent's probabilistic model, as derived from the free-energy principle. We specifically address the case where a categorical observation likelihood function is used, such that EFE values are analytically intractable. We introduce new approximation methods for computing the EFE based on variational and Laplace approximations. Extensive simulation study results demonstrate that, compared to other strategies, active inference generally requires far fewer iterations to identify optimal options and generally achieves superior cumulative regret, for relatively low extra computational cost. | ['Nisar Ahmed', 'Shohei Wakayama'] | 2022-09-19 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 3.45388174e-01 4.09253150e-01 -5.51877022e-01 -3.01059663e-01
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91ea7e91-104f-4e05-acb3-3bc45597a46a | toward-contextual-valence-shifters-in | null | null | https://aclanthology.org/O17-1016 | https://aclanthology.org/O17-1016.pdf | Toward Contextual Valence Shifters in Vietnamese Reviews | null | ['Tuoi Thi Phan', 'Thien Khai Tran'] | 2017-11-01 | toward-contextual-valence-shifters-in-1 | https://aclanthology.org/O17-1016 | https://aclanthology.org/O17-1016.pdf | roclingijclclp-2017-11 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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d89512a4-4538-4fed-aabc-3118faea7db5 | generator-knows-what-discriminator-should | 2207.13320 | null | https://arxiv.org/abs/2207.13320v1 | https://arxiv.org/pdf/2207.13320v1.pdf | Generator Knows What Discriminator Should Learn in Unconditional GANs | Recent methods for conditional image generation benefit from dense supervision such as segmentation label maps to achieve high-fidelity. However, it is rarely explored to employ dense supervision for unconditional image generation. Here we explore the efficacy of dense supervision in unconditional generation and find generator feature maps can be an alternative of cost-expensive semantic label maps. From our empirical evidences, we propose a new generator-guided discriminator regularization(GGDR) in which the generator feature maps supervise the discriminator to have rich semantic representations in unconditional generation. In specific, we employ an U-Net architecture for discriminator, which is trained to predict the generator feature maps given fake images as inputs. Extensive experiments on mulitple datasets show that our GGDR consistently improves the performance of baseline methods in terms of quantitative and qualitative aspects. Code is available at https://github.com/naver-ai/GGDR | ['Yunjey Choi', 'Jung-Woo Ha', 'Seonghyeon Kim', 'Junho Kim', 'Hyunsu Kim', 'Gayoung Lee'] | 2022-07-27 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.13686776e-01 5.05318582e-01 -3.69258285e-01 -4.03469741e-01
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1d98a0d7-e7df-4d5e-bc40-5006ee94aefd | lightweight-and-unobtrusive-privacy | 1912.09859 | null | https://arxiv.org/abs/1912.09859v3 | https://arxiv.org/pdf/1912.09859v3.pdf | Lightweight and Unobtrusive Data Obfuscation at IoT Edge for Remote Inference | Executing deep neural networks for inference on the server-class or cloud backend based on data generated at the edge of Internet of Things is desirable due primarily to the limited compute power of edge devices and the need to protect the confidentiality of the inference neural networks. However, such a remote inference scheme incurs concerns regarding the privacy of the inference data transmitted by the edge devices to the curious backend. This paper presents a lightweight and unobtrusive approach to obfuscate the inference data at the edge devices. It is lightweight in that the edge device only needs to execute a small-scale neural network; it is unobtrusive in that the edge device does not need to indicate whether obfuscation is applied. Extensive evaluation by three case studies of free spoken digit recognition, handwritten digit recognition, and American sign language recognition shows that our approach effectively protects the confidentiality of the raw forms of the inference data while effectively preserving the backend's inference accuracy. | ['Peng Cheng', 'Rui Tan', 'Linshan Jiang', 'Mengyao Zheng', 'Dixing Xu', 'Chaojie Gu'] | 2019-12-20 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-1.31188273e-01 1.91616565e-01 5.33946939e-02 -4.78595465e-01
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7628660c-f869-4680-9e78-190a4429c3c2 | multi-modality-in-music-predicting-emotion-in | 2302.13321 | null | https://arxiv.org/abs/2302.13321v1 | https://arxiv.org/pdf/2302.13321v1.pdf | Multi-Modality in Music: Predicting Emotion in Music from High-Level Audio Features and Lyrics | This paper aims to test whether a multi-modal approach for music emotion recognition (MER) performs better than a uni-modal one on high-level song features and lyrics. We use 11 song features retrieved from the Spotify API, combined lyrics features including sentiment, TF-IDF, and Anew to predict valence and arousal (Russell, 1980) scores on the Deezer Mood Detection Dataset (DMDD) (Delbouys et al., 2018) with 4 different regression models. We find that out of the 11 high-level song features, mainly 5 contribute to the performance, multi-modal features do better than audio alone when predicting valence. We made our code publically available. | ['Ninell Oldenburg', 'Yana Nikolova', 'Tibor Krols'] | 2023-02-26 | null | null | null | null | ['music-emotion-recognition'] | ['music'] | [-3.36000890e-01 -4.41651523e-01 -3.07589352e-01 -1.89348027e-01
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72d3b87a-f639-472c-be32-fcbe213e1a89 | multi-lingual-discourse-segmentation-and | null | null | https://aclanthology.org/2021.disrpt-1.3 | https://aclanthology.org/2021.disrpt-1.3.pdf | Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021 | We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the Disrpt 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0.85%. Within the Disrpt 21 campaign the system ranks 3rd overall, very close to the 2nd system, but with a significant gap with respect to the best system, which uses a rich set of additional features. The system is nonetheless the best on languages that benefited from crosslingual training on sentence internal segmentation (German and Spanish). | ['Chloé Braud', 'Philippe Muller', 'Morteza Kamaladdini Ezzabady'] | null | null | null | null | emnlp-disrpt-2021-11 | ['discourse-segmentation', 'discourse-parsing', 'connective-detection'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.23133087e-01 5.56283653e-01 -2.43508324e-01 -2.75949448e-01
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843c813c-970b-4d55-bebe-ceea75b0f16b | leveraging-schema-labels-to-enhance-dataset | 2001.10112 | null | https://arxiv.org/abs/2001.10112v1 | https://arxiv.org/pdf/2001.10112v1.pdf | Leveraging Schema Labels to Enhance Dataset Search | A search engine's ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text.We propose a novel schema label generation model which generates possible schema labels based on dataset table content. We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels. To evaluate our method on real-world datasets, we create a new benchmark specifically for the dataset retrieval task. Experiments show that our approach can effectively improve the precision and NDCG scores of the dataset retrieval task compared with baseline methods. We also test on a collection of Wikipedia tables to show that the features generated from schema labels can improve the unsupervised and supervised web table retrieval task as well. | ['Zhiyu Chen', 'Jeff Heflin', 'Brian D. Davison', 'Haiyan Jia'] | 2020-01-27 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 2.68652886e-01 -4.79473397e-02 -7.03749776e-01 -6.91664815e-01
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a102c6fa-f2b8-49f1-a43a-b19713b85b54 | multi-scale-contrastive-co-training-for-event | 2209.00568 | null | https://arxiv.org/abs/2209.00568v1 | https://arxiv.org/pdf/2209.00568v1.pdf | Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction | Extracting temporal relationships between pairs of events in texts is a crucial yet challenging problem for natural language understanding. Depending on the distance between the events, models must learn to differently balance information from local and global contexts surrounding the event pair for temporal relation prediction. Learning how to fuse this information has proved challenging for transformer-based language models. Therefore, we present MulCo: Multi-Scale Contrastive Co-Training, a technique for the better fusion of local and global contextualized features. Our model uses a BERT-based language model to encode local context and a Graph Neural Network (GNN) to represent global document-level syntactic and temporal characteristics. Unlike previous state-of-the-art methods, which use simple concatenation on multi-view features or select optimal sentences using sophisticated reinforcement learning approaches, our model co-trains GNN and BERT modules using a multi-scale contrastive learning objective. The GNN and BERT modules learn a synergistic parameterization by contrasting GNN multi-layer multi-hop subgraphs (i.e., global context embeddings) and BERT outputs (i.e., local context embeddings) through end-to-end back-propagation. We empirically demonstrate that MulCo provides improved ability to fuse local and global contexts encoded using BERT and GNN compared to the current state-of-the-art. Our experimental results show that MulCo achieves new state-of-the-art results on several temporal relation extraction datasets. | ['Carolyn Rose', 'Chunxiao Zhou', 'Aakanksha Naik', 'Luke Breitfeller', 'Hao-Ren Yao'] | 2022-09-01 | null | null | null | null | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-3.58792841e-02 1.36499897e-01 -3.09061348e-01 -5.48546553e-01
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230a405f-35db-4c19-871e-53d1d111d3d4 | handling-class-imbalance-in-low-resource | 2010.15090 | null | https://arxiv.org/abs/2010.15090v1 | https://arxiv.org/pdf/2010.15090v1.pdf | Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation | Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels. We present a new end-to-end pairwise learning framework that is designed specifically to tackle this phenomenon by inducing a few-shot classification capability in the utterance representations and augmenting data through an interpolation of utterance representations. Our approach is a general purpose training methodology, agnostic to the neural architecture used for encoding utterances. We show significant improvements in macro-F1 score over standard cross-entropy training for three different neural architectures, demonstrating improvements on a Virtual Patient dialogue dataset as well as a low-resourced emulation of the Switchboard dialogue act classification dataset. | ['Eric Fosler-Lussier', 'Vishal Sunder'] | 2020-10-28 | null | null | null | null | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.08128285e-01 1.00053596e+00 4.40934785e-02 -9.94242430e-01
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e1ea0ccc-50d2-42e7-81ae-6a935a019ca9 | learning-common-rationale-to-improve-self | 2303.01669 | null | https://arxiv.org/abs/2303.01669v1 | https://arxiv.org/pdf/2303.01669v1.pdf | Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems | Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for optimizing SSL objectives are not suitable for characterizing the subtle differences in FGVR. To overcome this issue, we propose learning an additional screening mechanism to identify discriminative clues commonly seen across instances and classes, dubbed as common rationales in this paper. Intuitively, common rationales tend to correspond to the discriminative patterns from the key parts of foreground objects. We show that a common rationale detector can be learned by simply exploiting the GradCAM induced from the SSL objective without using any pre-trained object parts or saliency detectors, making it seamlessly to be integrated with the existing SSL process. Specifically, we fit the GradCAM with a branch with limited fitting capacity, which allows the branch to capture the common rationales and discard the less common discriminative patterns. At the test stage, the branch generates a set of spatial weights to selectively aggregate features representing an instance. Extensive experimental results on four visual tasks demonstrate that the proposed method can lead to a significant improvement in different evaluation settings. | ['Lingqiao Liu', 'Anton Van Den Hengel', 'Yangyang Shu'] | 2023-03-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.pdf | cvpr-2023-1 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 2.93242961e-01 -1.24988012e-01 -2.33484656e-01 -4.62279052e-01
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54fec154-b431-4826-8da9-70337f9a21e4 | improving-human-ai-collaboration-with | 2301.06937 | null | https://arxiv.org/abs/2301.06937v1 | https://arxiv.org/pdf/2301.06937v1.pdf | Improving Human-AI Collaboration With Descriptions of AI Behavior | People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted. To help people appropriately rely on AI aids, we propose showing them behavior descriptions, details of how AI systems perform on subgroups of instances. We tested the efficacy of behavior descriptions through user studies with 225 participants in three distinct domains: fake review detection, satellite image classification, and bird classification. We found that behavior descriptions can increase human-AI accuracy through two mechanisms: helping people identify AI failures and increasing people's reliance on the AI when it is more accurate. These findings highlight the importance of people's mental models in human-AI collaboration and show that informing people of high-level AI behaviors can significantly improve AI-assisted decision making. | ['Jason I. Hong', 'Adam Perer', 'Ángel Alexander Cabrera'] | 2023-01-06 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [-1.01419717e-01 3.48760515e-01 -1.46146446e-01 -4.87287939e-01
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2272bc05-0a6a-45af-acca-d1c1ada6b715 | gans-for-semi-supervised-opinion-spam | 1903.08289 | null | https://arxiv.org/abs/1903.08289v2 | https://arxiv.org/pdf/1903.08289v2.pdf | GANs for Semi-Supervised Opinion Spam Detection | Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews online, only a few of them have been labeled spam or non-spam. In this paper, we propose spamGAN, a generative adversarial network which relies on limited set of labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor dataset show that spamGAN outperforms existing spam detection techniques when limited labeled data is used. Apart from detecting spam reviews, spamGAN can also generate reviews with reasonable perplexity. | ['Gray Stanton', 'Athirai A. Irissappane'] | 2019-03-19 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.94037640e-01 2.68093318e-01 -1.56869233e-01 -5.47610641e-01
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096e4c4f-a6cd-48ff-91c0-2de638f60b93 | cogtree-cognition-tree-loss-for-unbiased | 2009.07526 | null | https://arxiv.org/abs/2009.07526v2 | https://arxiv.org/pdf/2009.07526v2.pdf | CogTree: Cognition Tree Loss for Unbiased Scene Graph Generation | Scene graphs are semantic abstraction of images that encourage visual understanding and reasoning. However, the performance of Scene Graph Generation (SGG) is unsatisfactory when faced with biased data in real-world scenarios. Conventional debiasing research mainly studies from the view of balancing data distribution or learning unbiased models and representations, ignoring the correlations among the biased classes. In this work, we analyze this problem from a novel cognition perspective: automatically building a hierarchical cognitive structure from the biased predictions and navigating that hierarchy to locate the relationships, making the tail relationships receive more attention in a coarse-to-fine mode. To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree to organize the relationships based on the prediction of a biased SGG model. The CogTree distinguishes remarkably different relationships at first and then focuses on a small portion of easily confused ones. Then, we propose a debiasing loss specially for this cognitive structure, which supports coarse-to-fine distinction for the correct relationships. The loss is model-agnostic and consistently boosting the performance of several state-of-the-art models. The code is available at: https://github.com/CYVincent/Scene-Graph-Transformer-CogTree. | ['Qi Wu', 'Yujing Wang', 'Yuan Chai', 'Yue Hu', 'Jing Yu'] | 2020-09-16 | null | null | null | null | ['unbiased-scene-graph-generation'] | ['computer-vision'] | [ 7.04898909e-02 3.83885801e-01 -9.88095105e-02 -4.92116898e-01
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d12eeb77-3463-4ab4-acf3-bf99f3dccf1f | decop-a-multilingual-and-multi-domain-corpus | null | null | https://aclanthology.org/2020.lrec-1.178 | https://aclanthology.org/2020.lrec-1.178.pdf | DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text | In recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these issues is challenging since labelled corpora involving multiple domains and compiled in more than one language are few in the scientific literature. For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios. DecOp is composed of 5000 examples of both truthful and deceitful first-person opinions balanced both across five different domains and two languages and, to the best of our knowledge, is the largest corpus allowing cross-domain and cross-language comparisons in deceit detection tasks. In this paper, we describe the collection procedure of the DecOp corpus and his main characteristics. Moreover, the human performance on the DecOp test-set and preliminary experiments by means of machine learning models based on Transformer architecture are shown. | ['Carlo Strapparava', 'Giuseppe Sartori', 'Ivano Lauriola', 'Fabio Aiolli', 'Pasquale Capuozzo'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['deception-detection'] | ['miscellaneous'] | [-1.40635163e-01 -2.71394759e-01 -1.75610900e-01 -5.09809375e-01
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1f864052-f7df-4a53-8ccf-3003ba65503a | a-systematic-study-reveals-unexpected | null | null | https://aclanthology.org/2022.lrec-1.154 | https://aclanthology.org/2022.lrec-1.154.pdf | A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation | A significant challenge in developing translation systems for the world’s ∼7,000 languages is that very few have sufficient data for state-of-the-art techniques. Transfer learning is a promising direction for low-resource neural machine translation (NMT), but introduces many new variables which are often selected through ablation studies, costly trial-and-error, or niche expertise. When pre-training an NMT system for low-resource translation, the pre-training task is often chosen based on data abundance and similarity to the main task. Factors such as dataset sizes and similarity have typically been analysed independently in previous studies, due to the computational cost associated with systematic studies. However, these factors are not independent. We conducted a three-factor experiment to examine how language similarity, pre-training dataset size and main dataset size interacted in their effect on performance in pre-trained transformer-based low-resource NMT. We replicated the common finding that more data was beneficial in bilingual systems, but also found a statistically significant interaction between the three factors, which reduced the effectiveness of large pre-training datasets for some main task dataset sizes (p-value < 0.0018). The surprising trends identified in these interactions indicate that systematic studies of interactions may be a promising long-term direction for guiding research in low-resource neural methods. | ['Janet Wiles', 'Ashleigh Richardson'] | null | null | null | null | lrec-2022-6 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 9.58102196e-02 -1.73797175e-01 -4.77457345e-01 -2.44750753e-01
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fdf31892-6279-4da4-9807-ff35a2825829 | fever-basketball-a-complex-flexible-and | 2012.03204 | null | https://arxiv.org/abs/2012.03204v1 | https://arxiv.org/pdf/2012.03204v1.pdf | Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning | The development of deep reinforcement learning (DRL) has benefited from the emergency of a variety type of game environments where new challenging problems are proposed and new algorithms can be tested safely and quickly, such as Board games, RTS, FPS, and MOBA games. However, many existing environments lack complexity and flexibility and assume the actions are synchronously executed in multi-agent settings, which become less valuable. We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game. It is a complex and challenging environment that supports multiple characters, multiple positions, and both the single-agent and multi-agent player control modes. In addition, to better simulate real-world basketball games, the execution time of actions differs among players, which makes Fever Basketball a novel asynchronized environment. We evaluate commonly used multi-agent algorithms of both independent learners and joint-action learners in three game scenarios with varying difficulties, and heuristically propose two baseline methods to diminish the extra non-stationarity brought by asynchronism in Fever Basketball Benchmarks. Besides, we propose an integrated curricula training (ICT) framework to better handle Fever Basketball problems, which includes several game-rule based cascading curricula learners and a coordination curricula switcher focusing on enhancing coordination within the team. The results show that the game remains challenging and can be used as a benchmark environment for studies like long-time horizon, sparse rewards, credit assignment, and non-stationarity, etc. in multi-agent settings. | ['Chongjie Zhang', 'Changjie Fan', 'Tangjie Lv', 'Chunxu Ren', 'Yingfeng Chen', 'Yujing Hu', 'Hangtian Jia'] | 2020-12-06 | null | null | null | null | ['board-games'] | ['playing-games'] | [-5.50353229e-01 -3.46707016e-01 -6.76462948e-02 2.42200106e-01
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c0250008-31c7-4fbe-bec1-e1564051a31b | making-small-language-models-better-few-shot | null | null | https://openreview.net/forum?id=ryDLEZuACp | https://openreview.net/pdf?id=ryDLEZuACp | Making Small Language Models Better Few-Shot Learners | Large-scale language models coupled with prompts have shown remarkable performance on few-shot learning. However, through systematic experiments, we find that the few-shot performance of small language models is poor, and using prompts on them brings fewer improvements than on larger ones. In this paper, we propose \textbf{SMASH}, an approach to improve \textbf{SMA}ll language models' few-\textbf{SH}ot ability by training on intermediate tasks before prompt-based fine-tuning on downstream tasks. We design intermediate tasks for sentence-pair tasks and single-sentence classification tasks by creating training examples with prompt templates similar to downstream tasks using sentences sampled from a large-scale unsupervised corpus, and apply knowledge distillation to distill from outputs of larger pre-trained models as training objective. We conduct extensive experiments and show that SMASH can make a 6-layer DistilRoBRETa-base achieve comparable performance on few-shot datasets to a 12-layer RoBERTa-base at a low cost. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.20836511e-01 3.35945845e-01 -2.24547878e-01 -5.92620790e-01
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ffd3ff10-4338-43c9-a5a5-3e619628067d | is-reinforcement-learning-not-for-natural | 2210.01241 | null | https://arxiv.org/abs/2210.01241v3 | https://arxiv.org/pdf/2210.01241v3.pdf | Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization | We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including training instability due to the combinatorial action space, as well as a lack of open-source libraries and benchmarks customized for LM alignment. Thus, a question rises in the research community: is RL a practical paradigm for NLP? To help answer this, we first introduce an open-source modular library, RL4LMs (Reinforcement Learning for Language Models), for optimizing language generators with RL. The library consists of on-policy RL algorithms that can be used to train any encoder or encoder-decoder LM in the HuggingFace library (Wolf et al. 2020) with an arbitrary reward function. Next, we present the GRUE (General Reinforced-language Understanding Evaluation) benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions which capture automated measures of human preference.GRUE is the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally, we introduce an easy-to-use, performant RL algorithm, NLPO (Natural Language Policy Optimization)} that learns to effectively reduce the combinatorial action space in language generation. We show 1) that RL techniques are generally better than supervised methods at aligning LMs to human preferences; and 2) that NLPO exhibits greater stability and performance than previous policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic and human evaluations. | ['Yejin Choi', 'Hannaneh Hajishirzi', 'Christian Bauckhage', 'Rafet Sifa', 'Jack Hessel', 'Kianté Brantley', 'Prithviraj Ammanabrolu', 'Rajkumar Ramamurthy'] | 2022-10-03 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 3.77768010e-01 4.94126350e-01 -4.56108928e-01 1.58852562e-02
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41299c26-53a0-4b81-bdd9-4d21a8058a59 | less-than-few-self-shot-video-instance | 2204.08874 | null | https://arxiv.org/abs/2204.08874v1 | https://arxiv.org/pdf/2204.08874v1.pdf | Less than Few: Self-Shot Video Instance Segmentation | The goal of this paper is to bypass the need for labelled examples in few-shot video understanding at run time. While proven effective, in many practical video settings even labelling a few examples appears unrealistic. This is especially true as the level of details in spatio-temporal video understanding and with it, the complexity of annotations continues to increase. Rather than performing few-shot learning with a human oracle to provide a few densely labelled support videos, we propose to automatically learn to find appropriate support videos given a query. We call this self-shot learning and we outline a simple self-supervised learning method to generate an embedding space well-suited for unsupervised retrieval of relevant samples. To showcase this novel setting, we tackle, for the first time, video instance segmentation in a self-shot (and few-shot) setting, where the goal is to segment instances at the pixel-level across the spatial and temporal domains. We provide strong baseline performances that utilize a novel transformer-based model and show that self-shot learning can even surpass few-shot and can be positively combined for further performance gains. Experiments on new benchmarks show that our approach achieves strong performance, is competitive to oracle support in some settings, scales to large unlabelled video collections, and can be combined in a semi-supervised setting. | ['Cees G. M. Snoek', 'Pascal Mettes', 'Yuki M. Asano', 'Pengwan Yang'] | 2022-04-19 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 5.36689699e-01 3.07117134e-01 -4.89966929e-01 -3.94591600e-01
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efc4d488-1e87-4c0d-9a00-6b011472fdca | lower-bound-on-transmission-using-non-linear | null | null | https://ieeexplore.ieee.org/document/9018379 | https://ieeexplore.ieee.org/document/9018379 | Lower Bound on Transmission Using Non-Linear Bounding Function in Single Image Dehazing | The visibility of an image captured in poor weather (such as haze, fog, mist, smog) degrades due to scattering of light by atmospheric particles. Single image dehazing (SID) methods are used to restore visibility from a single hazy image. The SID is a challenging problem due to its ill-posed nature. Typically, the atmospheric scattering model (ATSM) is used to solve SID problem. The transmission and atmospheric light are two prime parameters of ATSM. The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light. The proposed method translates transmission estimation problem into estimation of the difference between minimum color channel of hazy and haze-free image. The translated problem presents a lower bound on transmission and is used to minimize reconstruction error in dehazing. The lower bound depends upon the bounding function (BF) and a quality control parameter. A non-linear model is then proposed to estimate BF for accurate estimation of transmission. The proposed quality control parameter can be utilized to tune the effect of dehazing. The accuracy obtained by the proposed method for transmission is compared with state of the art dehazing methods. Visual comparison of dehazed images and objective evaluation further validates the effectiveness of the proposed method. | ['Shashikala Tapaswi', 'Suresh Chandra Raikwar'] | 2020-02-28 | null | null | null | ieee-transaction-on-image-processing-2020-2 | ['image-dehazing', 'single-image-haze-removal'] | ['computer-vision', 'computer-vision'] | [ 2.79114306e-01 -5.18743694e-01 7.59672403e-01 -1.32648677e-01
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bb1dce93-d32d-4814-825c-a99686fecdf2 | geometry-based-multiple-camera-head-detection | 1808.00856 | null | http://arxiv.org/abs/1808.00856v1 | http://arxiv.org/pdf/1808.00856v1.pdf | Geometry-Based Multiple Camera Head Detection in Dense Crowds | This paper addresses the problem of head detection in crowded environments.
Our detection is based entirely on the geometric consistency across cameras
with overlapping fields of view, and no additional learning process is
required. We propose a fully unsupervised method for inferring scene and camera
geometry, in contrast to existing algorithms which require specific calibration
procedures. Moreover, we avoid relying on the presence of body parts other than
heads or on background subtraction, which have limited effectiveness under
heavy clutter. We cast the head detection problem as a stereo MRF-based
optimization of a dense pedestrian height map, and we introduce a constraint
which aligns the height gradient according to the vertical vanishing point
direction. We validate the method in an outdoor setting with varying pedestrian
density levels. With only three views, our approach is able to detect
simultaneously tens of heavily occluded pedestrians across a large, homogeneous
area. | ['Sylvie Le Hégarat-Mascle', 'Emanuel Aldea', 'Nicola Pellicanò'] | 2018-08-02 | null | null | null | null | ['head-detection'] | ['computer-vision'] | [ 6.28366461e-03 -5.82883954e-02 4.07494217e-01 -3.28689635e-01
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693742fb-6ddc-4bb0-83ff-a485431d98c5 | learning-to-select-from-multiple-options | 2212.00301 | null | https://arxiv.org/abs/2212.00301v1 | https://arxiv.org/pdf/2212.00301v1.pdf | Learning to Select from Multiple Options | Many NLP tasks can be regarded as a selection problem from a set of options, such as classification tasks, multi-choice question answering, etc. Textual entailment (TE) has been shown as the state-of-the-art (SOTA) approach to dealing with those selection problems. TE treats input texts as premises (P), options as hypotheses (H), then handles the selection problem by modeling (P, H) pairwise. Two limitations: first, the pairwise modeling is unaware of other options, which is less intuitive since humans often determine the best options by comparing competing candidates; second, the inference process of pairwise TE is time-consuming, especially when the option space is large. To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling. Context-TE is able to learn more reliable decision for the H since it considers various context. Second, we speed up Context-TE by coming up with Parallel-TE, which learns the decisions of multiple options simultaneously. Parallel-TE significantly improves the inference speed while keeping comparable performance with Context-TE. Our methods are evaluated on three tasks (ultra-fine entity typing, intent detection and multi-choice QA) that are typical selection problems with different sizes of options. Experiments show our models set new SOTA performance; particularly, Parallel-TE is faster than the pairwise TE by k times in inference. Our code is publicly available at https://github.com/jiangshdd/LearningToSelect. | ['Philip S. Yu', 'Congying Xia', 'Wenpeng Yin', 'Jiangshu Du'] | 2022-12-01 | null | null | null | null | ['entity-typing', 'intent-detection'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.00998893e-01 -5.71020283e-02 -2.25601479e-01 -4.93275970e-01
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b2b57897-fd16-4be4-92b0-e98744b6d014 | mentos-tracklets-association-with-a-space | 2107.07067 | null | https://arxiv.org/abs/2107.07067v1 | https://arxiv.org/pdf/2107.07067v1.pdf | MeNToS: Tracklets Association with a Space-Time Memory Network | We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection. The proposed method addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, that has a better alignment with the human visual assessment by evenly balancing detections and associations quality, has shown that improvements are still needed for data association. After creating tracklets using instance segmentation and optical flow, the proposed method relies on a space-time memory network (STM) developed for one-shot video object segmentation to improve the association of tracklets with temporal gaps. To the best of our knowledge, our method, named MeNToS, is the first to use the STM network to track object masks for MOTS. We took the 4th place in the RobMOTS challenge. The project page is https://mehdimiah.com/mentos.html. | ['Nicolas Saunier', 'Guillaume-Alexandre Bilodeau', 'Mehdi Miah'] | 2021-07-15 | null | null | null | null | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 5.73238954e-02 -6.51502237e-02 -1.36933789e-01 -9.97543558e-02
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9fc90bdd-b26d-4af9-a1fd-4c6125291ada | cross-modal-subspace-learning-for-fine | 1705.09888 | null | http://arxiv.org/abs/1705.09888v1 | http://arxiv.org/pdf/1705.09888v1.pdf | Cross-modal Subspace Learning for Fine-grained Sketch-based Image Retrieval | Sketch-based image retrieval (SBIR) is challenging due to the inherent
domain-gap between sketch and photo. Compared with pixel-perfect depictions of
photos, sketches are iconic renderings of the real world with highly abstract.
Therefore, matching sketch and photo directly using low-level visual clues are
unsufficient, since a common low-level subspace that traverses semantically
across the two modalities is non-trivial to establish. Most existing SBIR
studies do not directly tackle this cross-modal problem. This naturally
motivates us to explore the effectiveness of cross-modal retrieval methods in
SBIR, which have been applied in the image-text matching successfully. In this
paper, we introduce and compare a series of state-of-the-art cross-modal
subspace learning methods and benchmark them on two recently released
fine-grained SBIR datasets. Through thorough examination of the experimental
results, we have demonstrated that the subspace learning can effectively model
the sketch-photo domain-gap. In addition we draw a few key insights to drive
future research. | ['Yi-Zhe Song', 'Zhanyu Ma', 'Tao Xiang', 'Qiyue Yin', 'Peng Xu', 'Liang Wang', 'Yongye Huang', 'W. Bastiaan Kleijn', 'Jun Guo'] | 2017-05-28 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.88562953e-01 -7.13991404e-01 -4.11975712e-01 -7.78266713e-02
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9c6d7491-e6a5-48c0-b325-4ae6637a4e74 | track-mix-generation-on-music-streaming | 2307.03045 | null | https://arxiv.org/abs/2307.03045v1 | https://arxiv.org/pdf/2307.03045v1.pdf | Track Mix Generation on Music Streaming Services using Transformers | This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer. Track Mix automatically generates "mix" playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider a Transformer model trained on millions of track sequences from user playlists. In light of the growing popularity of Transformers in recent years, we analyze the advantages, drawbacks, and technical challenges of using such a model for mix generation on the service, compared to a more traditional collaborative filtering approach. Since its release, Track Mix has been generating playlists for millions of users daily, enhancing their music discovery experience on Deezer. | ['Guillaume Salha-Galvan', 'Thomas Bouabça', 'Thibault Cador', 'Benjamin Chapus', 'Mathieu Morlon', 'Théo Bontempelli', 'Walid Bendada'] | 2023-07-06 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.75095022e-01 -1.03252664e-01 -2.42369816e-01 -1.45062460e-02
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a726413c-d75a-4df0-be6e-d2a7ae22b8c7 | multisiam-self-supervised-multi-instance | 2108.12178 | null | https://arxiv.org/abs/2108.12178v1 | https://arxiv.org/pdf/2108.12178v1.pdf | MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving | Autonomous driving has attracted much attention over the years but turns out to be harder than expected, probably due to the difficulty of labeled data collection for model training. Self-supervised learning (SSL), which leverages unlabeled data only for representation learning, might be a promising way to improve model performance. Existing SSL methods, however, usually rely on the single-centric-object guarantee, which may not be applicable for multi-instance datasets such as street scenes. To alleviate this limitation, we raise two issues to solve: (1) how to define positive samples for cross-view consistency and (2) how to measure similarity in multi-instance circumstances. We first adopt an IoU threshold during random cropping to transfer global-inconsistency to local-consistency. Then, we propose two feature alignment methods to enable 2D feature maps for multi-instance similarity measurement. Additionally, we adopt intra-image clustering with self-attention for further mining intra-image similarity and translation-invariance. Experiments show that, when pre-trained on Waymo dataset, our method called Multi-instance Siamese Network (MultiSiam) remarkably improves generalization ability and achieves state-of-the-art transfer performance on autonomous driving benchmarks, including Cityscapes and BDD100K, while existing SSL counterparts like MoCo, MoCo-v2, and BYOL show significant performance drop. By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training. Code will be available at https://github.com/KaiChen1998/MultiSiam. | ['Dit-yan Yeung', 'Zhenguo Li', 'Hang Xu', 'Lanqing Hong', 'Kai Chen'] | 2021-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Chen_MultiSiam_Self-Supervised_Multi-Instance_Siamese_Representation_Learning_for_Autonomous_Driving_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Chen_MultiSiam_Self-Supervised_Multi-Instance_Siamese_Representation_Learning_for_Autonomous_Driving_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-clustering'] | ['computer-vision'] | [-3.15812156e-02 -2.18137950e-01 -5.42468727e-01 -5.83839297e-01
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76bd0f18-8948-4f3a-90ba-ef9c3745d590 | supplementing-missing-visions-via-dialog-for | 2204.11143 | null | https://arxiv.org/abs/2204.11143v1 | https://arxiv.org/pdf/2204.11143v1.pdf | Supplementing Missing Visions via Dialog for Scene Graph Generations | Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks. However, the classic task setup rarely considers the challenging, yet common practical situations where the complete visual data may be inaccessible due to various reasons (e.g., restricted view range and occlusions). To this end, we investigate a computer vision task setting with incomplete visual input data. Specifically, we exploit the Scene Graph Generation (SGG) task with various levels of visual data missingness as input. While insufficient visual input intuitively leads to performance drop, we propose to supplement the missing visions via the natural language dialog interactions to better accomplish the task objective. We design a model-agnostic Supplementary Interactive Dialog (SI-Dial) framework that can be jointly learned with most existing models, endowing the current AI systems with the ability of question-answer interactions in natural language. We demonstrate the feasibility of such a task setting with missing visual input and the effectiveness of our proposed dialog module as the supplementary information source through extensive experiments and analysis, by achieving promising performance improvement over multiple baselines. | ['Yan Yan', 'Zhenghao Zhao', 'Yuzhang Shang', 'Xiaoguang Zhu', 'Ye Zhu'] | 2022-04-23 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.54100084e-01 3.42174470e-01 -1.69387851e-02 -3.90684366e-01
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6e529266-988b-44b0-adde-e3db917e0a24 | an-empirical-study-of-leading-measures-of | 1505.02214 | null | http://arxiv.org/abs/1505.02214v2 | http://arxiv.org/pdf/1505.02214v2.pdf | An Empirical Study of Leading Measures of Dependence | In exploratory data analysis, we are often interested in identifying
promising pairwise associations for further analysis while filtering out
weaker, less interesting ones. This can be accomplished by computing a measure
of dependence on all variable pairs and examining the highest-scoring pairs,
provided the measure of dependence used assigns similar scores to equally noisy
relationships of different types. This property, called equitability, is
formalized in Reshef et al. [2015b]. In addition to equitability, measures of
dependence can also be assessed by the power of their corresponding
independence tests as well as their runtime.
Here we present extensive empirical evaluation of the equitability, power
against independence, and runtime of several leading measures of dependence.
These include two statistics introduced in Reshef et al. [2015a]: MICe, which
has equitability as its primary goal, and TICe, which has power against
independence as its goal. Regarding equitability, our analysis finds that MICe
is the most equitable method on functional relationships in most of the
settings we considered, although mutual information estimation proves the most
equitable at large sample sizes in some specific settings. Regarding power
against independence, we find that TICe, along with Heller and Gorfine's S^DDP,
is the state of the art on the relationships we tested. Our analyses also show
a trade-off between power against independence and equitability consistent with
the theory in Reshef et al. [2015b]. In terms of runtime, MICe and TICe are
significantly faster than many other measures of dependence tested, and
computing either one makes computing the other trivial. This suggests that a
fast and useful strategy for achieving a combination of power against
independence and equitability may be to filter relationships by TICe and then
to examine the MICe of only the significant ones. | ['Michael M. Mitzenmacher', 'Yakir A. Reshef', 'Pardis C. Sabeti', 'David N. Reshef'] | 2015-05-09 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [-1.17125720e-01 -7.21440092e-02 -1.01468645e-01 -3.22845161e-01
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8ad62202-f3f3-4dcb-b139-1307a267b284 | a-privacy-preserving-unsupervised-domain | 2201.07317 | null | https://arxiv.org/abs/2201.07317v1 | https://arxiv.org/pdf/2201.07317v1.pdf | A Privacy-Preserving Unsupervised Domain Adaptation Framework for Clinical Text Analysis | Unsupervised domain adaptation (UDA) generally aligns the unlabeled target domain data to the distribution of the source domain to mitigate the distribution shift problem. The standard UDA requires sharing the source data with the target, having potential data privacy leaking risks. To protect the source data's privacy, we first propose to share the source feature distribution instead of the source data. However, sharing only the source feature distribution may still suffer from the membership inference attack who can infer an individual's membership by the black-box access to the source model. To resolve this privacy issue, we further study the under-explored problem of privacy-preserving domain adaptation and propose a method with a novel differential privacy training strategy to protect the source data privacy. We model the source feature distribution by Gaussian Mixture Models (GMMs) under the differential privacy setting and send it to the target client for adaptation. The target client resamples differentially private source features from GMMs and adapts on target data with several state-of-art UDA backbones. With our proposed method, the source data provider could avoid leaking source data privacy during domain adaptation as well as reserve the utility. To evaluate our proposed method's utility and privacy loss, we apply our model on a medical report disease label classification task using two noisy challenging clinical text datasets. The results show that our proposed method can preserve source data's privacy with a minor performance influence on the text classification task. | ['Yingying Zhu', 'Fei Wang', 'Zhiyong Lu', 'Qingyu Chen', 'Hao Zhang', 'Lin Gu', 'Ruijiang Li', 'Qiyuan An'] | 2022-01-18 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 4.60127115e-01 4.26687241e-01 -3.49478394e-01 -7.24165142e-01
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2b1b8da3-c4d4-4105-8545-9af43a497107 | sentence-encoding-with-tree-constrained | 1811.10475 | null | http://arxiv.org/abs/1811.10475v1 | http://arxiv.org/pdf/1811.10475v1.pdf | Sentence Encoding with Tree-constrained Relation Networks | The meaning of a sentence is a function of the relations that hold between
its words. We instantiate this relational view of semantics in a series of
neural models based on variants of relation networks (RNs) which represent a
set of objects (for us, words forming a sentence) in terms of representations
of pairs of objects. We propose two extensions to the basic RN model for
natural language. First, building on the intuition that not all word pairs are
equally informative about the meaning of a sentence, we use constraints based
on both supervised and unsupervised dependency syntax to control which
relations influence the representation. Second, since higher-order relations
are poorly captured by a sum of pairwise relations, we use a recurrent
extension of RNs to propagate information so as to form representations of
higher order relations. Experiments on sentence classification, sentence pair
classification, and machine translation reveal that, while basic RNs are only
modestly effective for sentence representation, recurrent RNs with latent
syntax are a reliably powerful representational device. | ["Cyprien de Masson d'Autume", 'Lingpeng Kong', 'Wang Ling', 'Lei Yu', 'Chris Dyer', 'Phil Blunsom'] | 2018-11-26 | null | https://openreview.net/forum?id=rJxXDsCqYX | https://openreview.net/pdf?id=rJxXDsCqYX | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 5.70267260e-01 5.33906698e-01 -3.27733487e-01 -8.52656126e-01
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c8a5d101-c225-4325-a8d6-aefb7809ddee | divbo-diversity-aware-cash-for-ensemble | 2302.03255 | null | https://arxiv.org/abs/2302.03255v1 | https://arxiv.org/pdf/2302.03255v1.pdf | DivBO: Diversity-aware CASH for Ensemble Learning | The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learner. However, while most CASH methods focus on searching for a single learner with the best performance, they neglect the diversity among base learners (i.e., they may suggest similar configurations to previously evaluated ones), which is also a crucial consideration when building an ensemble. To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems. In the framework, we propose to use a diversity surrogate to predict the pair-wise diversity of two unseen configurations. Furthermore, we introduce a temporary pool and a weighted acquisition function to guide the search of both performance and diversity based on Bayesian optimization. Empirical results on 15 public datasets show that DivBO achieves the best average ranks (1.82 and 1.73) on both validation and test errors among 10 compared methods, including post-hoc designs in recent AutoML systems and state-of-the-art baselines for ensemble learning on CASH problems. | ['Bin Cui', 'Wentao Zhang', 'Yaofeng Tu', 'Yang Li', 'Yupeng Lu', 'Yu Shen'] | 2023-02-07 | null | null | null | null | ['automl'] | ['methodology'] | [-1.50854170e-01 -4.06814992e-01 -7.12412670e-02 -4.81048048e-01
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a93b5f9b-481b-4e36-bdf0-dc2b0f5b2025 | hdrvideo-gan-deep-generative-hdr-video | 2110.11795 | null | https://arxiv.org/abs/2110.11795v2 | https://arxiv.org/pdf/2110.11795v2.pdf | HDRVideo-GAN: Deep Generative HDR Video Reconstruction | High dynamic range (HDR) videos provide a more visually realistic experience than the standard low dynamic range (LDR) videos. Despite having significant progress in HDR imaging, it is still a challenging task to capture high-quality HDR video with a conventional off-the-shelf camera. Existing approaches rely entirely on using dense optical flow between the neighboring LDR sequences to reconstruct an HDR frame. However, they lead to inconsistencies in color and exposure over time when applied to alternating exposures with noisy frames. In this paper, we propose an end-to-end GAN-based framework for HDR video reconstruction from LDR sequences with alternating exposures. We first extract clean LDR frames from noisy LDR video with alternating exposures with a denoising network trained in a self-supervised setting. Using optical flow, we then align the neighboring alternating-exposure frames to a reference frame and then reconstruct high-quality HDR frames in a complete adversarial setting. To further improve the robustness and quality of generated frames, we incorporate temporal stability-based regularization term along with content and style-based losses in the cost function during the training procedure. Experimental results demonstrate that our framework achieves state-of-the-art performance and generates superior quality HDR frames of a video over the existing methods. | ['Shanmuganathan Raman', 'Chandan Kumar', 'Nidhin Harilal', 'Mrinal Anand'] | 2021-10-22 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 2.97614127e-01 -3.53821397e-01 2.28585213e-01 -1.62105635e-01
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dda4f32b-690d-43a1-bae5-250ad90177ad | deep-multimodal-guidance-for-medical-image | 2203.05683 | null | https://arxiv.org/abs/2203.05683v2 | https://arxiv.org/pdf/2203.05683v2.pdf | Deep Multimodal Guidance for Medical Image Classification | Medical imaging is a cornerstone of therapy and diagnosis in modern medicine. However, the choice of imaging modality for a particular theranostic task typically involves trade-offs between the feasibility of using a particular modality (e.g., short wait times, low cost, fast acquisition, reduced radiation/invasiveness) and the expected performance on a clinical task (e.g., diagnostic accuracy, efficacy of treatment planning and guidance). In this work, we aim to apply the knowledge learned from the less feasible but better-performing (superior) modality to guide the utilization of the more-feasible yet under-performing (inferior) modality and steer it towards improved performance. We focus on the application of deep learning for image-based diagnosis. We develop a light-weight guidance model that leverages the latent representation learned from the superior modality, when training a model that consumes only the inferior modality. We examine the advantages of our method in the context of two clinical applications: multi-task skin lesion classification from clinical and dermoscopic images and brain tumor classification from multi-sequence magnetic resonance imaging (MRI) and histopathology images. For both these scenarios we show a boost in diagnostic performance of the inferior modality without requiring the superior modality. Furthermore, in the case of brain tumor classification, our method outperforms the model trained on the superior modality while producing comparable results to the model that uses both modalities during inference. | ['Ghassan Hamarneh', 'Mayur Mallya'] | 2022-03-10 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 7.74623930e-01 9.04005915e-02 -3.88826162e-01 -2.23838866e-01
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67db10f2-dab0-4db9-bb52-e037b77ab2ab | low-light-image-enhancement-based-on | null | null | https://www.sciencedirect.com/science/article/pii/S1051200423001495 | https://www.sciencedirect.com/science/article/pii/S1051200423001495 | Low-light image enhancement based on sharpening-smoothing image filter | Low-light images suffer from poor visibility, severe noise, low contrast, and low brightness. To overcome these issues, many image enhancement methods have been proposed. Few techniques solve these problems simultaneously. This paper presents a low-light image enhancement method. The proposed method first applies the HSV (Hue, Saturation, Value) transform to the input image. Here, a multi-scale decomposition of the Sharpening-Smoothing Image Filter (SSIF) is proposed to obtain approximation and detail sub-images of the V component. After the decomposition process, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is applied to the final approximation image to provide higher contrast. The detail sub-images are amplified and added to the enhanced approximation image to reconstruct the enhanced V component. Finally, inverse HSV transform is applied to the enhanced V component and H, S components to obtain the enhanced image. The experimental results show that the proposed method provides better visual quality and more natural colors than the compared state-of-the-art methods. | ['N.H. Kaplan', 'Y. Demir'] | 2023-08-30 | null | null | null | https-www-sciencedirect-com-science-article | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 5.48362255e-01 -7.45081902e-01 2.09787324e-01 -6.96650967e-02
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fb4cce09-16c8-4f7b-bd8f-4ad5cf828ce6 | visual-prompting-via-image-inpainting | 2209.00647 | null | https://arxiv.org/abs/2209.00647v1 | https://arxiv.org/pdf/2209.00647v1.pdf | Visual Prompting via Image Inpainting | How does one adapt a pre-trained visual model to novel downstream tasks without task-specific finetuning or any model modification? Inspired by prompting in NLP, this paper investigates visual prompting: given input-output image example(s) of a new task at test time and a new input image, the goal is to automatically produce the output image, consistent with the given examples. We show that posing this problem as simple image inpainting - literally just filling in a hole in a concatenated visual prompt image - turns out to be surprisingly effective, provided that the inpainting algorithm has been trained on the right data. We train masked auto-encoders on a new dataset that we curated - 88k unlabeled figures from academic papers sources on Arxiv. We apply visual prompting to these pretrained models and demonstrate results on various downstream image-to-image tasks, including foreground segmentation, single object detection, colorization, edge detection, etc. | ['Alexei A. Efros', 'Amir Globerson', 'Trevor Darrell', 'Yossi Gandelsman', 'Amir Bar'] | 2022-09-01 | null | null | null | null | ['colorization', 'visual-prompting', 'personalized-segmentation', 'edge-detection', 'foreground-segmentation', 'image-inpainting'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 9.47295845e-01 4.21986818e-01 1.07963420e-01 -6.07005894e-01
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a64eb0ad-9b4c-4fb7-9ac4-7569876d9b91 | classical-to-quantum-transfer-learning-for | 2110.08689 | null | https://arxiv.org/abs/2110.08689v1 | https://arxiv.org/pdf/2110.08689v1.pdf | Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks | This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters. Since it is inefficient to train the hybrid end-to-end QNN from scratch on a noisy intermediate-scale quantum (NISQ) device, we put forth a hybrid transfer learning algorithm that allows a pre-trained classical network to be transferred to the classical part of the hybrid QNN model. The pre-trained classical network is further modified and augmented through jointly fine-tuning with a variational quantum circuit (VQC). The hybrid transfer learning methodology is particularly attractive for the task of QNN-based SCR because low-dimensional classical features are expected to be encoded into quantum states. We assess the hybrid transfer learning algorithm applied to the hybrid classical-quantum QNN for SCR on the Google speech command dataset, and our classical simulation results suggest that the hybrid transfer learning can boost our baseline performance on the SCR task. | ['Javier Tejedor', 'Jun Qi'] | 2021-10-17 | null | null | null | null | ['spoken-command-recognition'] | ['speech'] | [ 3.68084431e-01 2.19925478e-01 2.94370443e-01 -2.21406221e-01
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6a93f328-1590-47eb-8c67-86e99d4d3f58 | compressive-shack-hartmann-wavefront-sensing | 2011.10241 | null | https://arxiv.org/abs/2011.10241v2 | https://arxiv.org/pdf/2011.10241v2.pdf | Compressive Shack-Hartmann Wavefront Sensor based on Deep Neural Networks | The Shack-Hartmann wavefront sensor is widely used to measure aberrations induced by atmospheric turbulence in adaptive optics systems. However if there exists strong atmospheric turbulence or the brightness of guide stars is low, the accuracy of wavefront measurements will be affected. In this paper, we propose a compressive Shack-Hartmann wavefront sensing method. Instead of reconstructing wavefronts with slope measurements of all sub-apertures, our method reconstructs wavefronts with slope measurements of sub-apertures which have spot images with high signal to noise ratio. Besides, we further propose to use a deep neural network to accelerate wavefront reconstruction speed. During the training stage of the deep neural network, we propose to add a drop-out layer to simulate the compressive sensing process, which could increase development speed of our method. After training, the compressive Shack-Hartmann wavefront sensing method can reconstruct wavefronts in high spatial resolution with slope measurements from only a small amount of sub-apertures. We integrate the straightforward compressive Shack-Hartmann wavefront sensing method with image deconvolution algorithm to develop a high-order image restoration method. We use images restored by the high-order image restoration method to test the performance of our the compressive Shack-Hartmann wavefront sensing method. The results show that our method can improve the accuracy of wavefront measurements and is suitable for real-time applications. | ['Can Li', 'Juanjuan Li', 'Weihua Wang', 'Dongmei Cai', 'Mingyang Ma', 'Peng Jia'] | 2020-11-20 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.09237385e-01 -5.08923352e-01 8.84884238e-01 -2.46066272e-01
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ae9d2c39-a019-4263-9056-cc80f4046908 | perspective-corrected-spatial-referring | 2104.01558 | null | https://arxiv.org/abs/2104.01558v3 | https://arxiv.org/pdf/2104.01558v3.pdf | Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction | Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation, the ambiguity is unavoidable due to the diversity of reference frames, which will lead to an understanding gap between humans and robots. To narrow this gap, in this paper, we propose a novel perspective-corrected spatial referring expression generation (PcSREG) approach for human-robot interaction by considering the selection of reference frames. The task of referring expression generation is simplified into the process of generating diverse spatial relation units. First, we pick out all landmarks in these spatial relation units according to the entropy of preference and allow its updating through a stack model. Then all possible referring expressions are generated according to different reference frame strategies. Finally, we evaluate every expression using a probabilistic referring expression resolution model and find the best expression that satisfies both of the appropriateness and effectiveness. We implement the proposed approach on a robot system and empirical experiments show that our approach can generate more effective spatial referring expressions for practical applications. | ['Chunlin Chen', 'Chengli Xiao', 'Mingjiang Liu'] | 2021-04-04 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 1.33820370e-01 3.27063382e-01 7.27959499e-02 -3.95657748e-01
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45804ccf-30ef-4189-96b8-6a6b84db0833 | multi-label-ecg-classification-using-temporal | 2306.03844 | null | https://arxiv.org/abs/2306.03844v1 | https://arxiv.org/pdf/2306.03844v1.pdf | Multi-Label ECG Classification using Temporal Convolutional Neural Network | Automated analysis of 12-lead electrocardiogram (ECG) plays a crucial role in the early screening and management of cardiovascular diseases (CVDs). In practice, it is common to see multiple co-occurring cardiac disorders, i.e., multi-label or multimorbidity in patients with CVDs, which increases the risk for mortality. Most current research focuses on the single-label ECG classification, i.e., each ECG record corresponds to one cardiac disorder, ignoring ECG records with multi-label phenomenon. In this paper, we propose an ensemble of attention-based temporal convolutional neural network (ATCNN) models for the multi-label classification of 12-lead ECG records. Specifically, a set of ATCNN-based single-lead binary classifiers are trained one for each cardiac disorder, and the predictions from these classifiers with simple thresholding generate the final multi-label decisions. The ATCNN contains a stack of TCNN layers followed by temporal and spatial attention layers. The TCNN layers operate at different dilation rates to represent the multi-scaled pathological ECG features dynamics, and attention layers emphasize/reduce the diagnostically relevant/redundant 12-lead ECG information. The proposed framework is evaluated on the PTBXL-2020 dataset and achieved an average F1-score of 76.51%. Moreover, the spatial and temporal attention weights visualization provides the optimal ECG-lead subset selection for each disease and model interpretability results, respectively. The improved performance and interpretability of the proposed approach demonstrate its ability to screen multimorbidity patients and help clinicians initiate timely treatment. | ['Samarendra Dandapt', 'Eedara Prabhakararao'] | 2023-06-06 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.61830324e-01 -4.38443094e-01 7.64406696e-02 -4.41901654e-01
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0aff88d9-2b1f-44d8-8afd-76ef128903e2 | counterfactual-explanation-algorithms-for | 1912.01819 | null | https://arxiv.org/abs/1912.01819v1 | https://arxiv.org/pdf/1912.01819v1.pdf | Counterfactual Explanation Algorithms for Behavioral and Textual Data | We study the interpretability of predictive systems that use high-dimensonal behavioral and textual data. Examples include predicting product interest based on online browsing data and detecting spam emails or objectionable web content. Recently, counterfactual explanations have been proposed for generating insight into model predictions, which focus on what is relevant to a particular instance. Conducting a complete search to compute counterfactuals is very time-consuming because of the huge dimensionality. To our knowledge, for behavioral and text data, only one model-agnostic heuristic algorithm (SEDC) for finding counterfactual explanations has been proposed in the literature. However, there may be better algorithms for finding counterfactuals quickly. This study aligns the recently proposed Linear Interpretable Model-agnostic Explainer (LIME) and Shapley Additive Explanations (SHAP) with the notion of counterfactual explanations, and empirically benchmarks their effectiveness and efficiency against SEDC using a collection of 13 data sets. Results show that LIME-Counterfactual (LIME-C) and SHAP-Counterfactual (SHAP-C) have low and stable computation times, but mostly, they are less efficient than SEDC. However, for certain instances on certain data sets, SEDC's run time is comparably large. With regard to effectiveness, LIME-C and SHAP-C find reasonable, if not always optimal, counterfactual explanations. SHAP-C, however, seems to have difficulties with highly unbalanced data. Because of its good overall performance, LIME-C seems to be a favorable alternative to SEDC, which failed for some nonlinear models to find counterfactuals because of the particular heuristic search algorithm it uses. A main upshot of this paper is that there is a good deal of room for further research. For example, we propose algorithmic adjustments that are direct upshots of the paper's findings. | ['Theodoros Evgeniou', 'Yanou Ramon', 'David Martens', 'Foster Provost'] | 2019-12-04 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.06915690e-01 4.91121501e-01 -7.17008233e-01 -4.24681067e-01
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36e64028-f7dc-431b-9061-a0cbcdb67cea | construction-of-unbiased-dental-template-and | 2304.03556 | null | https://arxiv.org/abs/2304.03556v1 | https://arxiv.org/pdf/2304.03556v1.pdf | Construction of unbiased dental template and parametric dental model for precision digital dentistry | Dental template and parametric dental models are important tools for various applications in digital dentistry. However, constructing an unbiased dental template and accurate parametric dental models remains a challenging task due to the complex anatomical and morphological dental structures and also low volume ratio of the teeth. In this study, we develop an unbiased dental template by constructing an accurate dental atlas from CBCT images with guidance of teeth segmentation. First, to address the challenges, we propose to enhance the CBCT images and their segmentation images, including image cropping, image masking and segmentation intensity reassigning. Then, we further use the segmentation images to perform co-registration with the CBCT images to generate an accurate dental atlas, from which an unbiased dental template can be generated. By leveraging the unbiased dental template, we construct parametric dental models by estimating point-to-point correspondences between the dental models and employing Principal Component Analysis to determine shape subspaces of the parametric dental models. A total of 159 CBCT images of real subjects are collected to perform the constructions. Experimental results demonstrate effectiveness of our proposed method in constructing unbiased dental template and parametric dental model. The developed dental template and parametric dental models are available at https://github.com/Marvin0724/Teeth_template. | ['Dinggang Shen', 'Zhongxiang Ding', 'Min Zhu', 'Yue Zhao', 'Minhui Tang', 'Yu Fang', 'Zhiming Cui', 'Peng Xue', 'Ke Deng', 'Jingyang Zhang', 'Lei Ma'] | 2023-04-07 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.13199133e-01 3.76061887e-01 -4.60963137e-02 -5.04913747e-01
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c4cfe0cf-ec6e-4334-b6dc-40eb7443687d | blind-image-deblurring-based-on-kernel | 2101.06241 | null | https://arxiv.org/abs/2101.06241v1 | https://arxiv.org/pdf/2101.06241v1.pdf | Blind Image Deblurring based on Kernel Mixture | Blind Image deblurring tries to estimate blurriness and a latent image out of a blurred image. This estimation, as being an ill-posed problem, requires imposing restrictions on the latent image or a blur kernel that represents blurriness. Different from recent studies that impose some priors on the latent image, this paper regulates the structure of the blur kernel. We propose a kernel mixture structure while using the Gaussian kernel as a base kernel. By combining multiple Gaussian kernels structurally enhanced in terms of scales and centers, the kernel mixture becomes capable of modeling nearly non-parametric shape of blurriness. A data-driven decision for the number of base kernels to combine makes the structure even more flexible. We apply this approach to a remote sensing problem to recover images from blurry images of satellite. This case study shows the superiority of the proposed method regulating the blur kernel in comparison with state-of-the-art methods that regulates the latent image. | ['Hoon Hwangbo', 'Sajjad Amrollahi Biyouki'] | 2021-01-15 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.03417814e-01 -2.14542523e-01 3.23045373e-01 -2.10686564e-01
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0006cf0f-a724-4e9e-b3dc-cc182601b8d6 | on-sampling-determinantal-and-pfaffian-point | 2305.15851 | null | https://arxiv.org/abs/2305.15851v1 | https://arxiv.org/pdf/2305.15851v1.pdf | On sampling determinantal and Pfaffian point processes on a quantum computer | DPPs were introduced by Macchi as a model in quantum optics the 1970s. Since then, they have been widely used as models and subsampling tools in statistics and computer science. Most applications require sampling from a DPP, and given their quantum origin, it is natural to wonder whether sampling a DPP on a quantum computer is easier than on a classical one. We focus here on DPPs over a finite state space, which are distributions over the subsets of $\{1,\dots,N\}$ parametrized by an $N\times N$ Hermitian kernel matrix. Vanilla sampling consists in two steps, of respective costs $\mathcal{O}(N^3)$ and $\mathcal{O}(Nr^2)$ operations on a classical computer, where $r$ is the rank of the kernel matrix. A large first part of the current paper consists in explaining why the state-of-the-art in quantum simulation of fermionic systems already yields quantum DPP sampling algorithms. We then modify existing quantum circuits, and discuss their insertion in a full DPP sampling pipeline that starts from practical kernel specifications. The bottom line is that, with $P$ (classical) parallel processors, we can divide the preprocessing cost by $P$ and build a quantum circuit with $\mathcal{O}(Nr)$ gates that sample a given DPP, with depth varying from $\mathcal{O}(N)$ to $\mathcal{O}(r\log N)$ depending on qubit-communication constraints on the target machine. We also connect existing work on the simulation of superconductors to Pfaffian point processes, which generalize DPPs and would be a natural addition to the machine learner's toolbox. Finally, the circuits are empirically validated on a classical simulator and on 5-qubit machines. | ['Alexandre Feller', 'Michaël Fanuel', 'Rémi Bardenet'] | 2023-05-25 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 3.30398083e-01 1.25747576e-01 1.54865056e-01 -1.50145561e-01
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7d41d643-3941-4548-9d2e-44c39e05e302 | semantic-code-classification-for-automated | 2201.11252 | null | https://arxiv.org/abs/2201.11252v1 | https://arxiv.org/pdf/2201.11252v1.pdf | Semantic Code Classification for Automated Machine Learning | A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we present a semantic code classification task and discuss methods for solving this problem on the Natural Language to Machine Learning (NL2ML) dataset. | ['Andrey Ustuzhanin', 'Anna Scherbakova', 'Ivan Pyaternev', 'Daria Sapozhnikova', 'Natalia Denisenko', 'Anastasia Drozdova', 'Polina Guseva'] | 2022-01-25 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 4.14015442e-01 6.59231067e-01 -2.31927916e-01 -7.71708906e-01
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abdc1fef-c356-4727-b31a-c8cfd2f6becc | masked-student-dataset-of-expressions | 2304.03867 | null | https://arxiv.org/abs/2304.03867v1 | https://arxiv.org/pdf/2304.03867v1.pdf | Masked Student Dataset of Expressions | Facial expression recognition (FER) algorithms work well in constrained environments with little or no occlusion of the face. However, real-world face occlusion is prevalent, most notably with the need to use a face mask in the current Covid-19 scenario. While there are works on the problem of occlusion in FER, little has been done before on the particular face mask scenario. Moreover, the few works in this area largely use synthetically created masked FER datasets. Motivated by these challenges posed by the pandemic to FER, we present a novel dataset, the Masked Student Dataset of Expressions or MSD-E, consisting of 1,960 real-world non-masked and masked facial expression images collected from 142 individuals. Along with the issue of obfuscated facial features, we illustrate how other subtler issues in masked FER are represented in our dataset. We then provide baseline results using ResNet-18, finding that its performance dips in the non-masked case when trained for FER in the presence of masks. To tackle this, we test two training paradigms: contrastive learning and knowledge distillation, and find that they increase the model's performance in the masked scenario while maintaining its non-masked performance. We further visualise our results using t-SNE plots and Grad-CAM, demonstrating that these paradigms capitalise on the limited features available in the masked scenario. Finally, we benchmark SOTA methods on MSD-E. | ['Darshan Gera', 'Sridhar Sola'] | 2023-04-07 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.62705702e-01 1.72473326e-01 1.95453852e-01 -6.83193088e-01
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300d80d9-98a8-418e-9142-c2bf2aee462c | modelling-multi-agent-epistemic-planning-in | 2008.03007 | null | https://arxiv.org/abs/2008.03007v1 | https://arxiv.org/pdf/2008.03007v1.pdf | Modelling Multi-Agent Epistemic Planning in ASP. Theory and Practice of Logic Programming | Designing agents that reason and act upon the world has always been one of the main objectives of the Artificial Intelligence community. While for planning in "simple" domains the agents can solely rely on facts about the world, in several contexts, e.g., economy, security, justice and politics, the mere knowledge of the world could be insufficient to reach a desired goal. In these scenarios, epistemic reasoning, i.e., reasoning about agents' beliefs about themselves and about other agents' beliefs, is essential to design winning strategies. This paper addresses the problem of reasoning in multi-agent epistemic settings exploiting declarative programming techniques. In particular, the paper presents an actual implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings, called PLATO (ePistemic muLti-agent Answer seT programming sOlver). The ASP paradigm enables a concise and elegant design of the planner, w.r.t. other imperative implementations, facilitating the development of formal verification of correctness. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature. It is under consideration for acceptance in TPLP. | ['Enrico Pontelli', 'Francesco Fabiano', 'Alessandro Burigana', 'Agostino Dovier'] | 2020-08-07 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-1.32995591e-01 8.79036069e-01 -6.20661937e-02 -2.64620662e-01
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4bcd5374-8665-4a13-8fb0-99c43f29998c | dependency-decomposition-and-a-reject-option | 2012.06523 | null | https://arxiv.org/abs/2012.06523v1 | https://arxiv.org/pdf/2012.06523v1.pdf | Dependency Decomposition and a Reject Option for Explainable Models | Deploying machine learning models in safety-related do-mains (e.g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial attacks and aware of the model uncertainty. Recent deep learning models perform extremely well in various inference tasks, but the black-box nature of these approaches leads to a weakness regarding the three requirements mentioned above. Recent advances offer methods to visualize features, describe attribution of the input (e.g.heatmaps), provide textual explanations or reduce dimensionality. However,are explanations for classification tasks dependent or are they independent of each other? For in-stance, is the shape of an object dependent on the color? What is the effect of using the predicted class for generating explanations and vice versa? In the context of explainable deep learning models, we present the first analysis of dependencies regarding the probability distribution over the desired image classification outputs and the explaining variables (e.g. attributes, texts, heatmaps). Therefore, we perform an Explanation Dependency Decomposition (EDD). We analyze the implications of the different dependencies and propose two ways of generating the explanation. Finally, we use the explanation to verify (accept or reject) the prediction | ['Anselm Haselhoff', 'Jan Kronenberger'] | 2020-12-11 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.67332792e-01 8.70333552e-01 -3.91869023e-02 -6.98505700e-01
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7ed3cfd5-7f93-472e-aca3-3e5583ed5fdb | deep-survival-machines-fully-parametric | 2003.01176 | null | https://arxiv.org/abs/2003.01176v3 | https://arxiv.org/pdf/2003.01176v3.pdf | Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks | We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring. | ['Xinyu Rachel Li', 'Chirag Nagpal', 'Artur Dubrawski'] | 2020-03-02 | null | null | null | null | ['time-to-event-prediction'] | ['time-series'] | [-8.92056450e-02 9.55727976e-03 -5.45126855e-01 -8.68520916e-01
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6d295c18-9a9e-41ef-9796-d08637f66c79 | leveraging-synthetic-data-to-learn-video | 2208.12763 | null | https://arxiv.org/abs/2208.12763v1 | https://arxiv.org/pdf/2208.12763v1.pdf | Leveraging Synthetic Data to Learn Video Stabilization Under Adverse Conditions | Video stabilization plays a central role to improve videos quality. However, despite the substantial progress made by these methods, they were, mainly, tested under standard weather and lighting conditions, and may perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather robust algorithm for video stabilization that does not require real data and can be trained only on synthetic data. We also present Silver, a novel rendering engine to generate the required training data with an automatic ground-truth extraction procedure. Our approach uses our specially generated synthetic data for training an affine transformation matrix estimator avoiding the feature extraction issues faced by current methods. Additionally, since no video stabilization datasets under adverse conditions are available, we propose the novel VSAC105Real dataset for evaluation. We compare our method to five state-of-the-art video stabilization algorithms using two benchmarks. Our results show that current approaches perform poorly in at least one weather condition, and that, even training in a small dataset with synthetic data only, we achieve the best performance in terms of stability average score, distortion score, success rate, and average cropping ratio when considering all weather conditions. Hence, our video stabilization model generalizes well on real-world videos and does not require large-scale synthetic training data to converge. | ['Richard Jiang', 'Erickson R. Nascimento', 'Leandro Soriano Marcolino', 'Washington L. S. Ramos', 'Abdulrahman Kerim'] | 2022-08-26 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 1.65338278e-01 -5.73798776e-01 2.00324617e-02 1.93166919e-02
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4fd8af12-10dd-459a-953a-a874ce78a20b | ocbev-object-centric-bev-transformer-for | 2306.01738 | null | https://arxiv.org/abs/2306.01738v1 | https://arxiv.org/pdf/2306.01738v1.pdf | OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection | Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query-based bird's-eye-view (BEV) paradigm, which benefits from both BEV's strong perception power and end-to-end pipeline. Despite achieving substantial progress, existing works model objects via globally leveraging temporal and spatial information of BEV features, resulting in problems when handling the challenging complex and dynamic autonomous driving scenarios. In this paper, we proposed an Object-Centric query-BEV detector OCBEV, which can carve the temporal and spatial cues of moving targets more effectively. OCBEV comprises three designs: Object Aligned Temporal Fusion aligns the BEV feature based on ego-motion and estimated current locations of moving objects, leading to a precise instance-level feature fusion. Object Focused Multi-View Sampling samples more 3D features from an adaptive local height ranges of objects for each scene to enrich foreground information. Object Informed Query Enhancement replaces part of pre-defined decoder queries in common DETR-style decoders with positional features of objects on high-confidence locations, introducing more direct object positional priors. Extensive experimental evaluations are conducted on the challenging nuScenes dataset. Our approach achieves a state-of-the-art result, surpassing the traditional BEVFormer by 1.5 NDS points. Moreover, we have a faster convergence speed and only need half of the training iterations to get comparable performance, which further demonstrates its effectiveness. | ['Hengshuang Zhao', 'Xiaoyang Wu', 'Jiaqi Wang', 'Zhangyang Qi'] | 2023-06-02 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [-9.04394388e-02 -4.00575846e-01 -2.55268067e-01 -5.96957743e-01
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80585d4c-4495-46a7-b37f-d23fe04e9aa2 | 190807919 | 1908.07919 | null | https://arxiv.org/abs/1908.07919v2 | https://arxiv.org/pdf/1908.07919v2.pdf | Deep High-Resolution Representation Learning for Visual Recognition | High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions \emph{in series} (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams \emph{in parallel}; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at~{\url{https://github.com/HRNet}}. | ['Bin Xiao', 'Chaorui Deng', 'Ke Sun', 'Wenyu Liu', 'Yadong Mu', 'Xinggang Wang', 'Tianheng Cheng', 'Mingkui Tan', 'Jingdong Wang', 'Yang Zhao', 'Dong Liu', 'Borui Jiang'] | 2019-08-20 | deep-high-resolution-representation-learning-2 | null | null | null | ['thermal-image-segmentation', 'dichotomous-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.36474380e-01 2.61071716e-02 -3.99625711e-02 -2.82291651e-01
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e609fda6-b44b-465f-8579-d4be26189f17 | learning-to-mine-aligned-code-and-natural | 1805.08949 | null | http://arxiv.org/abs/1805.08949v1 | http://arxiv.org/pdf/1805.08949v1.pdf | Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow | For tasks like code synthesis from natural language, code retrieval, and code
summarization, data-driven models have shown great promise. However, creating
these models require parallel data between natural language (NL) and code with
fine-grained alignments. Stack Overflow (SO) is a promising source to create
such a data set: the questions are diverse and most of them have corresponding
answers with high-quality code snippets. However, existing heuristic methods
(e.g., pairing the title of a post with the code in the accepted answer) are
limited both in their coverage and the correctness of the NL-code pairs
obtained. In this paper, we propose a novel method to mine high-quality aligned
data from SO using two sets of features: hand-crafted features considering the
structure of the extracted snippets, and correspondence features obtained by
training a probabilistic model to capture the correlation between NL and code
using neural networks. These features are fed into a classifier that determines
the quality of mined NL-code pairs. Experiments using Python and Java as test
beds show that the proposed method greatly expands coverage and accuracy over
existing mining methods, even when using only a small number of labeled
examples. Further, we find that reasonable results are achieved even when
training the classifier on one language and testing on another, showing promise
for scaling NL-code mining to a wide variety of programming languages beyond
those for which we are able to annotate data. | ['Pengcheng Yin', 'Bogdan Vasilescu', 'Edgar Chen', 'Bowen Deng', 'Graham Neubig'] | 2018-05-23 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 8.81223977e-02 -6.21542260e-02 -4.81633395e-01 -4.25024658e-01
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cf7a819f-0eff-4b9b-89da-f354efcc9bf6 | learning-a-deep-embedding-model-for-zero-shot | 1611.05088 | null | https://arxiv.org/abs/1611.05088v4 | https://arxiv.org/pdf/1611.05088v4.pdf | Learning a Deep Embedding Model for Zero-Shot Learning | Zero-shot learning (ZSL) models rely on learning a joint embedding space where both textual/semantic description of object classes and visual representation of object images can be projected to for nearest neighbour search. Despite the success of deep neural networks that learn an end-to-end model between text and images in other vision problems such as image captioning, very few deep ZSL model exists and they show little advantage over ZSL models that utilise deep feature representations but do not learn an end-to-end embedding. In this paper we argue that the key to make deep ZSL models succeed is to choose the right embedding space. Instead of embedding into a semantic space or an intermediate space, we propose to use the visual space as the embedding space. This is because that in this space, the subsequent nearest neighbour search would suffer much less from the hubness problem and thus become more effective. This model design also provides a natural mechanism for multiple semantic modalities (e.g., attributes and sentence descriptions) to be fused and optimised jointly in an end-to-end manner. Extensive experiments on four benchmarks show that our model significantly outperforms the existing models. Code is available at https://github.com/lzrobots/DeepEmbeddingModel_ZSL | ['Li Zhang', 'Tao Xiang', 'Shaogang Gong'] | 2016-11-15 | learning-a-deep-embedding-model-for-zero-shot-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhang_Learning_a_Deep_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_Learning_a_Deep_CVPR_2017_paper.pdf | cvpr-2017-7 | ['zero-shot-action-recognition'] | ['computer-vision'] | [-6.41978113e-03 3.42202842e-01 -2.77458042e-01 -6.37024939e-01
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eef26543-b3c4-4275-88b2-36d9b35d85d7 | semi-on-policy-training-for-sample-efficient | 2104.13446 | null | https://arxiv.org/abs/2104.13446v2 | https://arxiv.org/pdf/2104.13446v2.pdf | Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients | Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenarios. However, there is a significant performance gap between state-of-the-art policy gradient and value-based methods on the popular StarCraft Multi-Agent Challenge (SMAC) benchmark. In this paper, we introduce semi-on-policy (SOP) training as an effective and computationally efficient way to address the sample inefficiency of on-policy policy gradient methods. We enhance two state-of-the-art policy gradient algorithms with SOP training, demonstrating significant performance improvements. Furthermore, we show that our methods perform as well or better than state-of-the-art value-based methods on a variety of SMAC tasks. | ['Shimon Whiteson', 'Bei Peng', 'Tarun Gupta', 'Bozhidar Vasilev'] | 2021-04-27 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.58766490e-01 -3.51856321e-01 -8.86138141e-01 1.94682404e-01
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97643acf-3e5d-4416-af4f-b5c173149b8f | flex-convolution-million-scale-point-cloud | 1803.07289 | null | https://arxiv.org/abs/1803.07289v4 | https://arxiv.org/pdf/1803.07289v4.pdf | Flex-Convolution (Million-Scale Point-Cloud Learning Beyond Grid-Worlds) | Traditional convolution layers are specifically designed to exploit the natural data representation of images -- a fixed and regular grid. However, unstructured data like 3D point clouds containing irregular neighborhoods constantly breaks the grid-based data assumption. Therefore applying best-practices and design choices from 2D-image learning methods towards processing point clouds are not readily possible. In this work, we introduce a natural generalization flex-convolution of the conventional convolution layer along with an efficient GPU implementation. We demonstrate competitive performance on rather small benchmark sets using fewer parameters and lower memory consumption and obtain significant improvements on a million-scale real-world dataset. Ours is the first which allows to efficiently process 7 million points concurrently. | ['Hendrik P. A. Lensch', 'Patrick Wieschollek', 'Fabian Groh'] | 2018-03-20 | null | null | null | null | ['classify-3d-point-clouds'] | ['computer-vision'] | [-1.74540326e-01 -3.59409243e-01 7.72797763e-02 -2.94060498e-01
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50e42922-cb7f-4e45-81c1-5fe88f71f156 | towards-realistic-generative-3d-face-models | 2304.12483 | null | https://arxiv.org/abs/2304.12483v1 | https://arxiv.org/pdf/2304.12483v1.pdf | Towards Realistic Generative 3D Face Models | In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle to accurately disentangle facial attributes like pose, expression, and illumination, limiting their editing capabilities. To address this limitation, this paper proposes a 3D controllable generative face model to produce high-quality albedo and precise 3D shape leveraging existing 2D generative models. By combining 2D face generative models with semantic face manipulation, this method enables editing of detailed 3D rendered faces. The proposed framework utilizes an alternating descent optimization approach over shape and albedo. Differentiable rendering is used to train high-quality shapes and albedo without 3D supervision. Moreover, this approach outperforms the state-of-the-art (SOTA) methods in the well-known NoW benchmark for shape reconstruction. It also outperforms the SOTA reconstruction models in recovering rendered faces' identities across novel poses by an average of 10%. Additionally, the paper demonstrates direct control of expressions in 3D faces by exploiting latent space leading to text-based editing of 3D faces. | ['Fernando de la Torre', 'Aayush Prakash', 'Daeil Kim', 'Amaury Aubel', 'Shingo Jason Takagi', 'Francisco Vicente Carrasco', 'Ayush Pandey', 'Hiresh Gupta', 'Aashish Rai'] | 2023-04-24 | null | null | null | null | ['3d-face-reconstruction', 'face-model', 'synthetic-data-generation', 'synthetic-data-generation'] | ['computer-vision', 'computer-vision', 'medical', 'miscellaneous'] | [ 1.95696950e-01 3.47583681e-01 4.54546958e-01 -5.32035053e-01
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4dd296a4-be3d-4013-a42e-fed70ea88104 | separable-batch-normalization-for-robust | 2101.06663 | null | https://arxiv.org/abs/2101.06663v1 | https://arxiv.org/pdf/2101.06663v1.pdf | Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training | A big, diverse and balanced training data is the key to the success of deep neural network training. However, existing publicly available datasets used in facial landmark localization are usually much smaller than those for other computer vision tasks. A small dataset without diverse and balanced training samples cannot support the training of a deep network effectively. To address the above issues, this paper presents a novel Separable Batch Normalization (SepBN) module with a Cross-protocol Network Training (CNT) strategy for robust facial landmark localization. Different from the standard BN layer that uses all the training data to calculate a single set of parameters, SepBN considers that the samples of a training dataset may belong to different sub-domains. Accordingly, the proposed SepBN module uses multiple sets of parameters, each corresponding to a specific sub-domain. However, the selection of an appropriate branch in the inference stage remains a challenging task because the sub-domain of a test sample is unknown. To mitigate this difficulty, we propose a novel attention mechanism that assigns different weights to each branch for automatic selection in an effective style. As a further innovation, the proposed CNT strategy trains a network using multiple datasets having different facial landmark annotation systems, boosting the performance and enhancing the generalization capacity of the trained network. The experimental results obtained on several well-known datasets demonstrate the effectiveness of the proposed method. | ['Josef Kittler', 'Wankou Yang', 'ZhenHua Feng', 'Shuangping Jin'] | 2021-01-17 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 1.65585503e-01 -2.03647092e-01 -3.30039382e-01 -7.90978611e-01
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c4423501-3bed-4529-8c2a-dfe51dbadb89 | mitigating-severe-over-parameterization-in | 2106.14190 | null | https://arxiv.org/abs/2106.14190v1 | https://arxiv.org/pdf/2106.14190v1.pdf | Mitigating severe over-parameterization in deep convolutional neural networks through forced feature abstraction and compression with an entropy-based heuristic | Convolutional Neural Networks (CNNs) such as ResNet-50, DenseNet-40 and ResNeXt-56 are severely over-parameterized, necessitating a consequent increase in the computational resources required for model training which scales exponentially for increments in model depth. In this paper, we propose an Entropy-Based Convolutional Layer Estimation (EBCLE) heuristic which is robust and simple, yet effective in resolving the problem of over-parameterization with regards to network depth of CNN model. The EBCLE heuristic employs a priori knowledge of the entropic data distribution of input datasets to determine an upper bound for convolutional network depth, beyond which identity transformations are prevalent offering insignificant contributions for enhancing model performance. Restricting depth redundancies by forcing feature compression and abstraction restricts over-parameterization while decreasing training time by 24.99% - 78.59% without degradation in model performance. We present empirical evidence to emphasize the relative effectiveness of broader, yet shallower models trained using the EBCLE heuristic, which maintains or outperforms baseline classification accuracies of narrower yet deeper models. The EBCLE heuristic is architecturally agnostic and EBCLE based CNN models restrict depth redundancies resulting in enhanced utilization of the available computational resources. The proposed EBCLE heuristic is a compelling technique for researchers to analytically justify their HyperParameter (HP) choices for CNNs. Empirical validation of the EBCLE heuristic in training CNN models was established on five benchmarking datasets (ImageNet32, CIFAR-10/100, STL-10, MNIST) and four network architectures (DenseNet, ResNet, ResNeXt and EfficientNet B0-B2) with appropriate statistical tests employed to infer any conclusive claims presented in this paper. | ['Wei Qi Yan', 'Stephen MacDonell', 'Roopak Sinha', 'Nidhi Gowdra'] | 2021-06-27 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 6.03591725e-02 3.79446387e-01 -1.58908933e-01 -2.16677636e-01
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981ef9de-7ef2-4dee-985f-37180ae26446 | teaching-machines-to-understand-baseball | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Minho_Shim_Teaching_Machines_to_ECCV_2018_paper.pdf | Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks | A major obstacle in teaching machines to understand videos is the lack of training data, as creating temporal annotations for long videos requires a huge amount of human effort. To this end, we introduce a new large-scale baseball video dataset called the BBDB, which is produced semi-automatically by using play-by-play texts available online. The BBDB contains 4200 hours of baseball game videos with 400k temporally annotated activity segments. The new dataset has several major challenging factors compared to other datasets: 1) the dataset contains a large number of visually similar segments with different labels. 2) It can be used for many video understanding tasks including video recognition, localization, text-video alignment, video highlight generation, and data imbalance problem. To observe the potential of the BBDB, we conducted extensive experiments by running many different types of video understanding algorithms on our new dataset. The database is available at https://sites.google.com/site/eccv2018bbdb/ | ['Kyung-Min Kim', 'Young Hwi Kim', 'Minho Shim', 'Seon Joo Kim'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['video-alignment'] | ['computer-vision'] | [ 6.86047971e-02 -4.03697580e-01 -5.76520562e-01 -2.44579822e-01
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97c28323-777f-425d-834a-b1aed64d328e | automated-3d-recovery-from-very-high | 1905.07475 | null | https://arxiv.org/abs/1905.07475v2 | https://arxiv.org/pdf/1905.07475v2.pdf | Automated 3D recovery from very high resolution multi-view satellite images | This paper presents an automated pipeline for processing multi-view satellite images to 3D digital surface models (DSM). The proposed pipeline performs automated geo-referencing and generates high-quality densely matched point clouds. In particular, a novel approach is developed that fuses multiple depth maps derived by stereo matching to generate high-quality 3D maps. By learning critical configurations of stereo pairs from sample LiDAR data, we rank the image pairs based on the proximity of the results to the sample data. Multiple depth maps derived from individual image pairs are fused with an adaptive 3D median filter that considers the image spectral similarities. We demonstrate that the proposed adaptive median filter generally delivers better results in general as compared to normal median filter, and achieved an accuracy of improvement of 0.36 meters RMSE in the best case. Results and analysis are introduced in detail. | ['Rongjun Qin'] | 2019-05-17 | null | null | null | null | ['stereo-matching'] | ['computer-vision'] | [ 3.90536845e-01 -3.37091506e-01 4.52477008e-01 -5.16071200e-01
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c6ac4301-52d0-49ca-88a0-5f4e3e83c38d | collision-avoidance-detour-for-multi-agent | 2306.11638 | null | https://arxiv.org/abs/2306.11638v1 | https://arxiv.org/pdf/2306.11638v1.pdf | Collision Avoidance Detour for Multi-Agent Trajectory Forecasting | We present our approach, Collision Avoidance Detour (CAD), which won the 3rd place award in the 2023 Waymo Open Dataset Challenge - Sim Agents, held at the 2023 CVPR Workshop on Autonomous Driving. To satisfy the motion prediction factorization requirement, we partition all the valid objects into three mutually exclusive sets: Autonomous Driving Vehicle (ADV), World-tracks-to-predict, and World-others. We use different motion models to forecast their future trajectories independently. Furthermore, we also apply collision avoidance detour resampling, additive Gaussian noise, and velocity-based heading estimation to improve the realism of our simulation result. | ['Stephen F. Smith', 'Hsu-kuang Chiu'] | 2023-06-20 | null | null | null | null | ['motion-prediction', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision'] | [-7.28963792e-01 2.22495481e-01 -1.73183441e-01 -3.83293033e-01
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d62c65e0-eb7f-4bd5-95ce-c88fd0de4fbd | towards-interactive-language-modeling | 2112.11911 | null | https://arxiv.org/abs/2112.11911v2 | https://arxiv.org/pdf/2112.11911v2.pdf | Towards Interactive Language Modeling | Interaction between caregivers and children plays a critical role in human language acquisition and development. Given this observation, it is remarkable that explicit interaction plays little to no role in artificial language modeling -- which also targets the acquisition of human language, yet by artificial models. Moreover, an interactive approach to language modeling has the potential to make language models substantially more versatile and to considerably impact downstream applications. Motivated by these considerations, we pioneer the space of interactive language modeling. As a first contribution we present a road map in which we detail the steps that need to be taken towards interactive language modeling. We then lead by example and take the first steps on this road map, showing the initial feasibility of our approach. As such, this work aims to be the start of a larger research agenda on interactive language modeling. | ['Emmanuel Dupoux', 'Dieuwke Hupkes', 'Evgeny Kharitonov', 'Maartje ter Hoeve'] | 2021-12-14 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 9.08903405e-02 1.07951379e+00 -4.07083593e-02 -5.44908047e-01
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fbd1cfcb-e584-4f07-8fb4-eb7424f0495e | densely-connected-convolutional-networks | 1608.06993 | null | http://arxiv.org/abs/1608.06993v5 | http://arxiv.org/pdf/1608.06993v5.pdf | Densely Connected Convolutional Networks | Recent work has shown that convolutional networks can be substantially
deeper, more accurate, and efficient to train if they contain shorter
connections between layers close to the input and those close to the output. In
this paper, we embrace this observation and introduce the Dense Convolutional
Network (DenseNet), which connects each layer to every other layer in a
feed-forward fashion. Whereas traditional convolutional networks with L layers
have L connections - one between each layer and its subsequent layer - our
network has L(L+1)/2 direct connections. For each layer, the feature-maps of
all preceding layers are used as inputs, and its own feature-maps are used as
inputs into all subsequent layers. DenseNets have several compelling
advantages: they alleviate the vanishing-gradient problem, strengthen feature
propagation, encourage feature reuse, and substantially reduce the number of
parameters. We evaluate our proposed architecture on four highly competitive
object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet).
DenseNets obtain significant improvements over the state-of-the-art on most of
them, whilst requiring less computation to achieve high performance. Code and
pre-trained models are available at https://github.com/liuzhuang13/DenseNet . | ['Zhuang Liu', 'Kilian Q. Weinberger', 'Gao Huang', 'Laurens van der Maaten'] | 2016-08-25 | densely-connected-convolutional-networks-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.pdf | cvpr-2017-7 | ['pedestrian-attribute-recognition', 'speaker-specific-lip-to-speech-synthesis', 'breast-tumour-classification'] | ['computer-vision', 'computer-vision', 'medical'] | [-1.70031920e-01 -1.06625058e-01 -2.03545559e-02 -5.46848893e-01
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e0ed58ac-0c32-4bcd-9a0a-968a3909ff55 | mpc-protocol-for-g-module-and-its-application | 2007.03975 | null | https://arxiv.org/abs/2007.03975v3 | https://arxiv.org/pdf/2007.03975v3.pdf | MPC Protocol for G-module and its Application in Secure Compare and ReLU | Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU computation in privacy preserving deep learning. In this paper, we introduce G-module, a mathematics tool, to re-design such protocols. In mathematics, given a group G, a G-module is an abelian group M on which G acts compatibly with the abelian group structure on M. We design three secure protocols for three G-module operations. i.e. "G-module action", "Cross G-module action" and "G-module recover". As far as we know, this is the first work on secure G-module operations. Based on them, we design secure comparison, selection, ReLU and DReLU protocols, which improve communication efficiency by 2X to 10X compared with state of arts. Our protocols are very computation efficient too. They do not require public key operations or any other expensive operations. | ['Lichun Li', 'Qizhi Zhang', 'Juanjuan Sun', 'Shan Yin'] | 2020-07-08 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-7.60520622e-02 4.71765064e-02 2.26496682e-01 -3.81488949e-01
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fd973f6d-0d88-409b-97d3-36a1bbb2726a | learned-distributed-image-compression-with | 2209.02514 | null | https://arxiv.org/abs/2209.02514v2 | https://arxiv.org/pdf/2209.02514v2.pdf | Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain | Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed compression scenario, the existing method (Ayzik and Avidan 2020) only implements patch matching at the image domain to solve the parallax problem caused by the difference in viewing points. However, the patch matching at the image domain is not robust to the variance of scale, shape, and illumination caused by the different viewing angles, and can not make full use of the rich texture information of the side information image. To resolve this issue, we propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes side information at the decoder of the distributed image compression model. Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network. Furthermore, we reuse inter-patch correlation from the shallow layer to accelerate the patch matching of the deep layer. Finally, we nd that our patch matching in a multi-scale feature domain further improves compression rate by about 20% compared with the patch matching method at image domain (Ayzik and Avidan 2020). | ['Shu-Tao Xia', 'Tao Dai', 'YaoWei Wang', 'Jiawei Li', 'Shiyu Qin', 'Bin Chen', 'Yujun Huang'] | 2022-09-06 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 1.95341066e-01 -2.68491089e-01 -4.74782735e-02 -1.21076465e-01
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938bb2cf-442d-4c7e-877a-930cadb9b4a5 | aerial-image-object-detection-with-vision | 2301.12058 | null | https://arxiv.org/abs/2301.12058v2 | https://arxiv.org/pdf/2301.12058v2.pdf | Aerial Image Object Detection With Vision Transformer Detector (ViTDet) | The past few years have seen an increased interest in aerial image object detection due to its critical value to large-scale geo-scientific research like environmental studies, urban planning, and intelligence monitoring. However, the task is very challenging due to the birds-eye view perspective, complex backgrounds, large and various image sizes, different appearances of objects, and the scarcity of well-annotated datasets. Recent advances in computer vision have shown promise tackling the challenge. Specifically, Vision Transformer Detector (ViTDet) was proposed to extract multi-scale features for object detection. The empirical study shows that ViTDet's simple design achieves good performance on natural scene images and can be easily embedded into any detector architecture. To date, ViTDet's potential benefit to challenging aerial image object detection has not been explored. Therefore, in our study, 25 experiments were carried out to evaluate the effectiveness of ViTDet for aerial image object detection on three well-known datasets: Airbus Aircraft, RarePlanes, and Dataset of Object DeTection in Aerial images (DOTA). Our results show that ViTDet can consistently outperform its convolutional neural network counterparts on horizontal bounding box (HBB) object detection by a large margin (up to 17% on average precision) and that it achieves the competitive performance for oriented bounding box (OBB) object detection. Our results also establish a baseline for future research. | ['Alex Tien', 'Liya Wang'] | 2023-01-28 | null | null | null | null | ['object-detection-in-aerial-images'] | ['computer-vision'] | [ 2.40262076e-01 -5.00350237e-01 6.37896881e-02 -1.68943718e-01
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8504865d-8e0d-47a8-b53b-69abe2511baf | tasked-transformer-based-adversarial-learning | 2209.09092 | null | https://arxiv.org/abs/2209.09092v2 | https://arxiv.org/pdf/2209.09092v2.pdf | TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation | Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are limited to operating in a local neighborhood in the process of a standard convolution neural network, and correlations between different sensors on body positions are ignored. In addition, they still face significant challenging problems with performance degradation due to large gaps in the distribution of training and test data, and behavioral differences between subjects. In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features. The proposed method is capable of learning cross-domain embedding feature representations from multiple subjects datasets using adversarial learning and the maximum mean discrepancy (MMD) regularization to align the data distribution over multiple domains. In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition. Experimental results show that TASKED not only outperforms state-of-the-art methods on the four real-world public HAR datasets (alone or combined) but also improves the subject generalization effectively. | ['Paul Lukowicz', 'Vitor Fortes Rey', 'Sungho Suh'] | 2022-09-14 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [ 3.38123918e-01 -3.91643882e-01 -7.24583045e-02 -3.65125388e-01
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27410ece-2221-452a-84c2-849d4fdb2841 | interactive-audio-text-representation-for | 2203.15526 | null | https://arxiv.org/abs/2203.15526v2 | https://arxiv.org/pdf/2203.15526v2.pdf | Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning | Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio. Most prior works usually extract single-modality acoustic features and are therefore sub-optimal for the cross-modal decoding task. In this work, we propose a novel AAC system called CLIP-AAC to learn interactive cross-modality representation with both acoustic and textual information. Specifically, the proposed CLIP-AAC introduces an audio-head and a text-head in the pre-trained encoder to extract audio-text information. Furthermore, we also apply contrastive learning to narrow the domain difference by learning the correspondence between the audio signal and its paired captions. Experimental results show that the proposed CLIP-AAC approach surpasses the best baseline by a significant margin on the Clotho dataset in terms of NLP evaluation metrics. The ablation study indicates that both the pre-trained model and contrastive learning contribute to the performance gain of the AAC model. | ['Eng Siong Chng', 'Xiaofeng Qi', 'Heqing Zou', 'Yuchen Hu', 'Nana Hou', 'Chen Chen'] | 2022-03-29 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.41801274e-01 -9.38126724e-03 1.37282148e-01 -2.85144269e-01
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5144f47f-9136-43e7-ba2a-ba46ca222e92 | adatriplet-adaptive-gradient-triplet-loss | 2205.02849 | null | https://arxiv.org/abs/2205.02849v2 | https://arxiv.org/pdf/2205.02849v2.pdf | AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching | This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss -- an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method -- a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. The codes allowing replication of this study have been made publicly available at \url{https://github.com/Oulu-IMEDS/AdaTriplet}. | ['Aleksei Tiulpin', 'Huy Hoang Nguyen', 'Khanh Nguyen'] | 2022-05-05 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 2.65447289e-01 -3.25012296e-01 -2.68260926e-01 -3.44967097e-01
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d21dd9e5-07f2-403b-8c92-60f3e2aebc53 | rgb-d-salient-object-detection-based-on | 1703.00122 | null | http://arxiv.org/abs/1703.00122v2 | http://arxiv.org/pdf/1703.00122v2.pdf | RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning | In this work, we propose to utilize Convolutional Neural Networks to boost
the performance of depth-induced salient object detection by capturing the
high-level representative features for depth modality. We formulate the
depth-induced saliency detection as a CNN-based cross-modal transfer problem to
bridge the gap between the "data-hungry" nature of CNNs and the unavailability
of sufficient labeled training data in depth modality. In the proposed
approach, we leverage the auxiliary data from the source modality effectively
by training the RGB saliency detection network to obtain the task-specific
pre-understanding layers for the target modality. Meanwhile, we exploit the
depth-specific information by pre-training a modality classification network
that encourages modal-specific representations during the optimizing course.
Thus, it could make the feature representations of the RGB and depth modalities
as discriminative as possible. These two modules are pre-trained independently
and then stitched to initialize and optimize the eventual depth-induced
saliency detection model. Experiments demonstrate the effectiveness of the
proposed novel pre-training strategy as well as the significant and consistent
improvements of the proposed approach over other state-of-the-art methods. | ['Hao Chen', 'Dan Su', 'Y. F. Li'] | 2017-03-01 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.24511999e-01 2.05710441e-01 -2.31347620e-01 -4.45794612e-01
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699b97c2-d20d-46d6-b69c-60a98fdd167f | a-competitive-analysis-of-online-multi-agent | 2106.11454 | null | https://arxiv.org/abs/2106.11454v1 | https://arxiv.org/pdf/2106.11454v1.pdf | A Competitive Analysis of Online Multi-Agent Path Finding | We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based on (1) controllability (the set of agents that they can plan paths for at each time) and (2) rationality (the quality of paths they plan) and study the relationships between them. We perform a competitive analysis for each category of online MAPF algorithms with respect to commonly-used objective functions. We show that a naive algorithm that routes newly-revealed agents one at a time in sequence achieves a competitive ratio that is asymptotically bounded from both below and above by the number of agents with respect to flowtime and makespan. We then show a counter-intuitive result that, if rerouting of previously-revealed agents is not allowed, any rational online MAPF algorithms, including ones that plan optimal paths for all newly-revealed agents, have the same asymptotic competitive ratio as the naive algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on the competitive ratio of any rational online MAPF algorithms that allow rerouting. The results thus provide theoretical insights into the effectiveness of using MAPF algorithms in an online setting for the first time. | ['Hang Ma'] | 2021-06-22 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-3.04492265e-01 5.18966973e-01 -2.25701109e-01 3.61569338e-02
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6c4b3027-9cfc-430e-8120-f159a16f0740 | motif-guided-time-series-counterfactual | 2211.04411 | null | https://arxiv.org/abs/2211.04411v2 | https://arxiv.org/pdf/2211.04411v2.pdf | Motif-guided Time Series Counterfactual Explanations | With the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. To improve the trust and transparency of AI-based systems, the EXplainable Artificial Intelligence (XAI) field has emerged. The XAI paradigm is bifurcated into two main categories: feature attribution and counterfactual explanation methods. While feature attribution methods are based on explaining the reason behind a model decision, counterfactual explanation methods discover the smallest input changes that will result in a different decision. In this paper, we aim at building trust and transparency in time series models by using motifs to generate counterfactual explanations. We propose Motif-Guided Counterfactual Explanation (MG-CF), a novel model that generates intuitive post-hoc counterfactual explanations that make full use of important motifs to provide interpretive information in decision-making processes. To the best of our knowledge, this is the first effort that leverages motifs to guide the counterfactual explanation generation. We validated our model using five real-world time-series datasets from the UCR repository. Our experimental results show the superiority of MG-CF in balancing all the desirable counterfactual explanations properties in comparison with other competing state-of-the-art baselines. | ['Shah Muhammad Hamdi', 'Soukaina Filali Boubrahimi', 'Peiyu Li'] | 2022-11-08 | null | null | null | null | ['interpretable-machine-learning', 'counterfactual-explanation', 'explanation-generation'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 4.61980373e-01 7.27150023e-01 -5.02269447e-01 -5.74628830e-01
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2a69c19a-8799-4074-9332-29829b5e3aea | learning-data-driven-vector-quantized | 2303.09826 | null | https://arxiv.org/abs/2303.09826v1 | https://arxiv.org/pdf/2303.09826v1.pdf | Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution | Existing real-world video super-resolution (VSR) methods focus on designing a general degradation pipeline for open-domain videos while ignoring data intrinsic characteristics which strongly limit their performance when applying to some specific domains (e.g. animation videos). In this paper, we thoroughly explore the characteristics of animation videos and leverage the rich priors in real-world animation data for a more practical animation VSR model. In particular, we propose a multi-scale Vector-Quantized Degradation model for animation video Super-Resolution (VQD-SR) to decompose the local details from global structures and transfer the degradation priors in real-world animation videos to a learned vector-quantized codebook for degradation modeling. A rich-content Real Animation Low-quality (RAL) video dataset is collected for extracting the priors. We further propose a data enhancement strategy for high-resolution (HR) training videos based on our observation that existing HR videos are mostly collected from the Web which contains conspicuous compression artifacts. The proposed strategy is valid to lift the upper bound of animation VSR performance, regardless of the specific VSR model. Experimental results demonstrate the superiority of the proposed VQD-SR over state-of-the-art methods, through extensive quantitative and qualitative evaluations of the latest animation video super-resolution benchmark. | ['Xueming Qian', 'Yujie Dun', 'Jianlong Fu', 'Huan Yang', 'Zixi Tuo'] | 2023-03-17 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 3.26957226e-01 -3.80914003e-01 -2.94734895e-01 1.18933767e-01
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d915ceda-1ee6-477f-b3a9-697a72f4516e | robust-online-video-instance-segmentation | 2211.09108 | null | https://arxiv.org/abs/2211.09108v1 | https://arxiv.org/pdf/2211.09108v1.pdf | Robust Online Video Instance Segmentation with Track Queries | Recently, transformer-based methods have achieved impressive results on Video Instance Segmentation (VIS). However, most of these top-performing methods run in an offline manner by processing the entire video clip at once to predict instance mask volumes. This makes them incapable of handling the long videos that appear in challenging new video instance segmentation datasets like UVO and OVIS. We propose a fully online transformer-based video instance segmentation model that performs comparably to top offline methods on the YouTube-VIS 2019 benchmark and considerably outperforms them on UVO and OVIS. This method, called Robust Online Video Segmentation (ROVIS), augments the Mask2Former image instance segmentation model with track queries, a lightweight mechanism for carrying track information from frame to frame, originally introduced by the TrackFormer method for multi-object tracking. We show that, when combined with a strong enough image segmentation architecture, track queries can exhibit impressive accuracy while not being constrained to short videos. | ['Svetlana Lazebnik', 'Daniel McKee', 'Zitong Zhan'] | 2022-11-16 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 1.42319903e-01 -2.36798137e-01 -5.63576102e-01 -1.42709374e-01
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58bd2ec9-3519-4598-ab2c-1f9a64485922 | evaluating-the-impact-of-source-code-parsers | 2206.08713 | null | https://arxiv.org/abs/2206.08713v1 | https://arxiv.org/pdf/2206.08713v1.pdf | Evaluating the Impact of Source Code Parsers on ML4SE Models | As researchers and practitioners apply Machine Learning to increasingly more software engineering problems, the approaches they use become more sophisticated. A lot of modern approaches utilize internal code structure in the form of an abstract syntax tree (AST) or its extensions: path-based representation, complex graph combining AST with additional edges. Even though the process of extracting ASTs from code can be done with different parsers, the impact of choosing a parser on the final model quality remains unstudied. Moreover, researchers often omit the exact details of extracting particular code representations. In this work, we evaluate two models, namely Code2Seq and TreeLSTM, in the method name prediction task backed by eight different parsers for the Java language. To unify the process of data preparation with different parsers, we develop SuperParser, a multi-language parser-agnostic library based on PathMiner. SuperParser facilitates the end-to-end creation of datasets suitable for training and evaluation of ML models that work with structural information from source code. Our results demonstrate that trees built by different parsers vary in their structure and content. We then analyze how this diversity affects the models' quality and show that the quality gap between the most and least suitable parsers for both models turns out to be significant. Finally, we discuss other features of the parsers that researchers and practitioners should take into account when selecting a parser along with the impact on the models' quality. The code of SuperParser is publicly available at https://doi.org/10.5281/zenodo.6366591. We also publish Java-norm, the dataset we use to evaluate the models: https://doi.org/10.5281/zenodo.6366599. | ['Timofey Bryksin', 'Egor Bogomolov', 'Egor Spirin', 'Ilya Utkin'] | 2022-06-17 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-1.05478473e-01 3.58020850e-02 -1.86171979e-01 -4.42171246e-01
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4c674197-9265-4472-b617-92428ba910f9 | an-embarrassingly-simple-consistency | 2202.00677 | null | https://arxiv.org/abs/2202.00677v2 | https://arxiv.org/pdf/2202.00677v2.pdf | An Embarrassingly Simple Consistency Regularization Method for Semi-Supervised Medical Image Segmentation | The scarcity of pixel-level annotation is a prevalent problem in medical image segmentation tasks. In this paper, we introduce a novel regularization strategy involving interpolation-based mixing for semi-supervised medical image segmentation. The proposed method is a new consistency regularization strategy that encourages segmentation of interpolation of two unlabelled data to be consistent with the interpolation of segmentation maps of those data. This method represents a specific type of data-adaptive regularization paradigm which aids to minimize the overfitting of labelled data under high confidence values. The proposed method is advantageous over adversarial and generative models as it requires no additional computation. Upon evaluation on two publicly available MRI datasets: ACDC and MMWHS, experimental results demonstrate the superiority of the proposed method in comparison to existing semi-supervised models. Code is available at: https://github.com/hritam-98/ICT-MedSeg | ['Agniv Chatterjee', 'Rukhshanda Hussain', 'Rajarshi Bhattacharya', 'Hritam Basak'] | 2022-02-01 | null | null | null | null | ['semi-supervised-medical-image-segmentation', '3d-medical-imaging-segmentation'] | ['computer-vision', 'medical'] | [ 4.62512612e-01 5.40313780e-01 -1.20114677e-01 -5.58303535e-01
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9.33486521e-02 -5.07674932e-01 -3.47055554e-01 5.92206001e-01] | [14.532055854797363, -2.1734018325805664] |
8cd9fb5d-480d-40a7-a0f7-b92d4131f531 | from-product-recommendation-to-cyber-attack | 1804.10276 | null | http://arxiv.org/abs/1804.10276v1 | http://arxiv.org/pdf/1804.10276v1.pdf | From product recommendation to cyber-attack prediction: Generating attack graphs and predicting future attacks | Modern information society depends on reliable functionality of information
systems infrastructure, while at the same time the number of cyber-attacks has
been increasing over the years and damages have been caused. Furthermore,
graphs can be used to show paths than can be exploited by attackers to intrude
into systems and gain unauthorized access through vulnerability exploitation.
This paper presents a method that builds attack graphs using data supplied from
the maritime supply chain infrastructure. The method delivers all possible
paths that can be exploited to gain access. Then, a recommendation system is
utilized to make predictions about future attack steps within the network. We
show that recommender systems can be used in cyber defense by predicting
attacks. The goal of this paper is to identify attack paths and show how a
recommendation method can be used to classify future cyber-attacks in terms of
risk management. The proposed method has been experimentally evaluated and
validated, with the results showing that it is both practical and effective. | ['Mouratidis Haralambos', 'Papastergiou Spyridon', 'Pavlidis Michalis', 'Pimenidis Elias', 'Polatidis Nikolaos'] | 2018-04-26 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.03121735e-01 1.62335768e-01 -1.26726991e-02 9.47162684e-04
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f9895674-0c29-435e-b5d9-ee50a2c77d7d | heat-holistic-edge-attention-transformer-for | 2111.15143 | null | https://arxiv.org/abs/2111.15143v3 | https://arxiv.org/pdf/2111.15143v3.pdf | HEAT: Holistic Edge Attention Transformer for Structured Reconstruction | This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure. The approach detects corners and classifies edge candidates between corners in an end-to-end manner. Our contribution is a holistic edge classification architecture, which 1) initializes the feature of an edge candidate by a trigonometric positional encoding of its end-points; 2) fuses image feature to each edge candidate by deformable attention; 3) employs two weight-sharing Transformer decoders to learn holistic structural patterns over the graph edge candidates; and 4) is trained with a masked learning strategy. The corner detector is a variant of the edge classification architecture, adapted to operate on pixels as corner candidates. We conduct experiments on two structured reconstruction tasks: outdoor building architecture and indoor floorplan planar graph reconstruction. Extensive qualitative and quantitative evaluations demonstrate the superiority of our approach over the state of the art. Code and pre-trained models are available at https://heat-structured-reconstruction.github.io. | ['Yasutaka Furukawa', 'Yiming Qian', 'Jiacheng Chen'] | 2021-11-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_HEAT_Holistic_Edge_Attention_Transformer_for_Structured_Reconstruction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['graph-reconstruction', 'extracting-buildings-in-remote-sensing-images'] | ['graphs', 'miscellaneous'] | [ 5.06612301e-01 4.47710931e-01 -3.57923582e-02 -3.92481387e-01
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9d7b26fb-85a0-4411-bacc-b5198a8378c3 | algorithms-of-real-time-navigation-and | 2208.10172 | null | https://arxiv.org/abs/2208.10172v1 | https://arxiv.org/pdf/2208.10172v1.pdf | Algorithms of Real-Time Navigation and Control of Autonomous Unmanned Vehicles | The rapid development of robotics has benefited by more and more people putting their attention to it. With the demand for robots is growing for the purpose of fulfilling tasks instead of humans, how to control the robot better is becoming a hot topic. For obstacle avoidance, we proposed algorithms for both 2D planar environments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. In addition, we put efforts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. For future expectations, there are some possible directions. How to combine traditional navigation algorithms and high-tech algorithms together so as to fulfill the tasks perfectly while the computational efficiency is not too high is a worthy topic. In addition, extending the obstacle avoidance algorithms to more competitive situations. Moreover, cooperation among multi robots are worth attention by researchers. All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots. | ['Yang Zhang'] | 2022-08-22 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 5.36404885e-02 1.22233197e-01 1.90025359e-01 -4.79029715e-01
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6fdb3985-c329-44b7-b78b-91f4b9c32933 | referring-image-segmentation-via-cross-modal-1 | 2010.00514 | null | https://arxiv.org/abs/2010.00514v1 | https://arxiv.org/pdf/2010.00514v1.pdf | Referring Image Segmentation via Cross-Modal Progressive Comprehension | Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities, but usually fail to explore informative words of the expression to well align features from the two modalities for accurately identifying the referred entity. In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task. Concretely, the CMPC module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the correct entity as well as suppress other irrelevant ones by multimodal graph reasoning. In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information. In this way, features from multi-levels could communicate with each other and be refined based on the textual context. We conduct extensive experiments on four popular referring segmentation benchmarks and achieve new state-of-the-art performances. | ['Guanbin Li', 'Si Liu', 'Shaofei Huang', 'Luoqi Liu', 'Bo Li', 'Yunchao Wei', 'Tianrui Hui', 'Jizhong Han'] | 2020-10-01 | referring-image-segmentation-via-cross-modal | http://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huang_Referring_Image_Segmentation_via_Cross-Modal_Progressive_Comprehension_CVPR_2020_paper.pdf | cvpr-2020-6 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 3.51383448e-01 2.66819239e-01 -1.28819287e-01 -4.54336435e-01
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e51eb5a7-4b6b-41b1-8ccd-e690f970a841 | privacy-against-inference-attacks-in-vertical | 2207.11788 | null | https://arxiv.org/abs/2207.11788v3 | https://arxiv.org/pdf/2207.11788v3.pdf | Privacy Against Inference Attacks in Vertical Federated Learning | Vertical federated learning is considered, where an active party, having access to true class labels, wishes to build a classification model by utilizing more features from a passive party, which has no access to the labels, to improve the model accuracy. In the prediction phase, with logistic regression as the classification model, several inference attack techniques are proposed that the adversary, i.e., the active party, can employ to reconstruct the passive party's features, regarded as sensitive information. These attacks, which are mainly based on a classical notion of the center of a set, i.e., the Chebyshev center, are shown to be superior to those proposed in the literature. Moreover, several theoretical performance guarantees are provided for the aforementioned attacks. Subsequently, we consider the minimum amount of information that the adversary needs to fully reconstruct the passive party's features. In particular, it is shown that when the passive party holds one feature, and the adversary is only aware of the signs of the parameters involved, it can perfectly reconstruct that feature when the number of predictions is large enough. Next, as a defense mechanism, a privacy-preserving scheme is proposed that worsen the adversary's reconstruction attacks, while preserving the full benefits that VFL brings to the active party. Finally, experimental results demonstrate the effectiveness of the proposed attacks and the privacy-preserving scheme. | ['Deniz Gunduz', 'Morteza Varasteh', 'Borzoo Rassouli'] | 2022-07-24 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 2.00204909e-01 3.15638542e-01 -2.57595479e-01 -2.51786172e-01
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8dd74582-d6d9-439d-b423-8217fb2741b8 | reproducing-personalised-session-search-over | 2201.08622 | null | https://arxiv.org/abs/2201.08622v1 | https://arxiv.org/pdf/2201.08622v1.pdf | Reproducing Personalised Session Search over the AOL Query Log | Despite its troubled past, the AOL Query Log continues to be an important resource to the research community -- particularly for tasks like search personalisation. When using the query log these ranking experiments, little attention is usually paid to the document corpus. Recent work typically uses a corpus containing versions of the documents collected long after the log was produced. Given that web documents are prone to change over time, we study the differences present between a version of the corpus containing documents as they appeared in 2017 (which has been used by several recent works) and a new version we construct that includes documents close to as they appeared at the time the query log was produced (2006). We demonstrate that this new version of the corpus has a far higher coverage of documents present in the original log (93%) than the 2017 version (55%). Among the overlapping documents, the content often differs substantially. Given these differences, we re-conduct session search experiments that originally used the 2017 corpus and find that when using our corpus for training or evaluation, system performance improves. We place the results in context by introducing recent adhoc ranking baselines. We also confirm the navigational nature of the queries in the AOL corpus by showing that including the URL substantially improves performance across a variety of models. Our version of the corpus can be easily reconstructed by other researchers and is included in the ir-datasets package. | ['Iadh Ounis', 'Craig Macdonald', 'Sean MacAvaney'] | 2022-01-21 | null | null | null | null | ['session-search'] | ['natural-language-processing'] | [ 5.21886759e-02 -1.28262252e-01 -3.00255597e-01 -1.55249655e-01
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414c938f-a607-4ed3-8e5c-436ce8b17ee6 | self-explainable-graph-neural-networks-for | 2305.12578 | null | https://arxiv.org/abs/2305.12578v1 | https://arxiv.org/pdf/2305.12578v1.pdf | Self-Explainable Graph Neural Networks for Link Prediction | Graph Neural Networks (GNNs) have achieved state-of-the-art performance for link prediction. However, GNNs suffer from poor interpretability, which limits their adoptions in critical scenarios that require knowing why certain links are predicted. Despite various methods proposed for the explainability of GNNs, most of them are post-hoc explainers developed for explaining node classification. Directly adopting existing post-hoc explainers for explaining link prediction is sub-optimal because: (i) post-hoc explainers usually adopt another strategy or model to explain a target model, which could misinterpret the target model; and (ii) GNN explainers for node classification identify crucial subgraphs around each node for the explanation; while for link prediction, one needs to explain the prediction for each pair of nodes based on graph structure and node attributes. Therefore, in this paper, we study a novel problem of self-explainable GNNs for link prediction, which can simultaneously give accurate predictions and explanations. Concretely, we propose a new framework and it can find various $K$ important neighbors of one node to learn pair-specific representations for links from this node to other nodes. These $K$ different neighbors represent important characteristics of the node and model various factors for links from it. Thus, $K$ neighbors can provide explanations for the existence of links. Experiments on both synthetic and real-world datasets verify the effectiveness of the proposed framework for link prediction and explanation. | ['Suhang Wang', 'Hui Liu', 'Junjie Xu', 'Xianfeng Tang', 'Dongsheng Luo', 'Huaisheng Zhu'] | 2023-05-21 | null | null | null | null | ['link-prediction'] | ['graphs'] | [ 4.67625931e-02 8.78301084e-01 -6.24021471e-01 -4.51288015e-01
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0b1e9764-921b-46ac-99de-273e4f7eaf14 | towards-modeling-human-attention-from-eye | 2305.09773 | null | https://arxiv.org/abs/2305.09773v1 | https://arxiv.org/pdf/2305.09773v1.pdf | Towards Modeling Human Attention from Eye Movements for Neural Source Code Summarization | Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions. Humans also pay attention to some features in code more than others. This human attention reflects experience and high-level cognition well beyond the capability of any current neural model. In this paper, we use data from published eye-tracking experiments to create a model of this human attention. The model predicts which words in source code are the most important for code summarization. Next, we augment a baseline neural code summarization approach using our model of human attention. We observe an improvement in prediction performance of the augmented approach in line with other bio-inspired neural models. | ['Collin McMillan', 'Bonita Sharif', 'Aakash Bansal'] | 2023-05-16 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.01797658e-01 6.61355734e-01 -1.18525565e-01 -2.21311644e-01
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e6598323-6d7a-4349-b2ab-27be175f6d59 | msgdd-cgan-multi-scale-gradients-dual | 2109.05614 | null | https://arxiv.org/abs/2109.05614v1 | https://arxiv.org/pdf/2109.05614v1.pdf | MSGDD-cGAN: Multi-Scale Gradients Dual Discriminator Conditional Generative Adversarial Network | Conditional Generative Adversarial Networks (cGANs) have been used in many image processing tasks. However, they still have serious problems maintaining the balance between conditioning the output on the input and creating the output with the desired distribution based on the corresponding ground truth. The traditional cGANs, similar to most conventional GANs, suffer from vanishing gradients, which backpropagate from the discriminator to the generator. Moreover, the traditional cGANs are sensitive to architectural changes due to previously mentioned gradient problems. Therefore, balancing the architecture of the cGANs is almost impossible. Recently MSG-GAN has been proposed to stabilize the performance of the GANs by applying multiple connections between the generator and discriminator. In this work, we propose a method called MSGDD-cGAN, which first stabilizes the performance of the cGANs using multi-connections gradients flow. Secondly, the proposed network architecture balances the correlation of the output to input and the fitness of the output on the target distribution. This balance is generated by using the proposed dual discrimination procedure. We tested our model by segmentation of fetal ultrasound images. Our model shows a 3.18% increase in the F1 score comparing to the pix2pix version of cGANs. | ['Shadrokh Samavi', 'Shahram Shirani', 'Nader Karimi', 'Zahra Nabizadeh', 'Mohammadreza Naderi'] | 2021-09-12 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 3.76360148e-01 3.76928866e-01 3.82195234e-01 -1.70388460e-01
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d5333a42-1499-4973-8a3a-90be40fa7fc5 | data-driven-chance-constrained-multiple | 2306.14690 | null | https://arxiv.org/abs/2306.14690v1 | https://arxiv.org/pdf/2306.14690v1.pdf | Data-Driven Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications | The multiple-choice knapsack problem (MCKP) is a classic NP-hard combinatorial optimization problem. Motivated by several significant practical applications, this work investigates a novel variant of MCKP called data-driven chance-constrained multiple-choice knapsack problem (DDCCMCKP), where the item weight is a random variable with unknown probability distribution. We first present the problem formulation of DDCCMCKP, and then establish two benchmark sets. The first set contains synthetic instances, and the second set is devised to simulate a real-world application scenario of a certain telecommunication company. To solve DDCCMCKP, we propose a data-driven adaptive local search (DDALS) algorithm. The main merit of DDALS lies in evaluating solutions with chance constraints by data-driven methods, under the condition of unknown distributions and only historical sample data being available. The experimental results demonstrate the effectiveness of the proposed algorithm and show that it is superior to other baselines. Additionally, ablation experiments confirm the necessity of each component in the algorithm. Our proposed algorithm can serve as the baseline for future research, and the code and benchmark sets will be open-sourced to further promote research on this challenging problem. | ['Ke Tang', 'Yew-Soon Ong', 'Xiao Chen', 'Jin Wang', 'Shengcai Liu', 'Xuanfeng Li'] | 2023-06-26 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 6.25904575e-02 -3.85996103e-01 -6.05999768e-01 -1.45463124e-01
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1bc43f87-a2c3-4c8a-9e4c-3ac892b4639a | nuclei-detection-using-mixture-density | 1808.08279 | null | http://arxiv.org/abs/1808.08279v1 | http://arxiv.org/pdf/1808.08279v1.pdf | Nuclei Detection Using Mixture Density Networks | Nuclei detection is an important task in the histology domain as it is a main
step toward further analysis such as cell counting, cell segmentation, study of
cell connections, etc. This is a challenging task due to the complex texture of
histology image, variation in shape, and touching cells. To tackle these
hurdles, many approaches have been proposed in the literature where deep
learning methods stand on top in terms of performance. Hence, in this paper, we
propose a novel framework for nuclei detection based on Mixture Density
Networks (MDNs). These networks are suitable to map a single input to several
possible outputs and we utilize this property to detect multiple seeds in a
single image patch. A new modified form of a cost function is proposed for
training and handling patches with missing nuclei. The probability maps of the
nuclei in the individual patches are next combined to generate the final
image-wide result. The experimental results show the state-of-the-art
performance on complex colorectal adenocarcinoma dataset. | ['Ali Gooya', 'Navid Alemi Koohababni', 'Mostafa Jahanifar', 'Nasir Rajpoot'] | 2018-08-22 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [ 2.65063763e-01 -4.39649150e-02 5.01616448e-02 -1.32249296e-01
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64417ddf-88ec-4fc6-8d14-b2a96aa8a887 | unsupervised-person-re-identification-by | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Unsupervised_Person_Re-Identification_by_Camera-Aware_Similarity_Consistency_Learning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wu_Unsupervised_Person_Re-Identification_by_Camera-Aware_Similarity_Consistency_Learning_ICCV_2019_paper.pdf | Unsupervised Person Re-Identification by Camera-Aware Similarity Consistency Learning | For matching pedestrians across disjoint camera views in surveillance, person re-identification (Re-ID) has made great progress in supervised learning. However, it is infeasible to label data in a number of new scenes when extending a Re-ID system. Thus, studying unsupervised learning for Re-ID is important for saving labelling cost. Yet, cross-camera scene variation is a key challenge for unsupervised Re-ID, such as illumination, background and viewpoint variations, which cause domain shift in the feature space and result in inconsistent pairwise similarity distributions that degrade matching performance. To alleviate the effect of cross-camera scene variation, we propose a Camera-Aware Similarity Consistency Loss to learn consistent pairwise similarity distributions for intra-camera matching and cross-camera matching. To avoid learning ineffective knowledge in consistency learning, we preserve the prior common knowledge of intra-camera matching in the pretrained model as reliable guiding information, which does not suffer from cross-camera scene variation as cross-camera matching. To learn similarity consistency more effectively, we further develop a coarse-to-fine consistency learning scheme to learn consistency globally and locally in two steps. Experiments show that our method outperformed the state-of-the-art unsupervised Re-ID methods.
| [' Jian-Huang Lai', ' Wei-Shi Zheng', 'Ancong Wu'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.96040481e-01 -5.61775029e-01 -9.16847810e-02 -7.16349125e-01
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790b3af3-929c-48c4-a1e5-e4403faaa604 | age-and-gender-prediction-from-face-images | 2010.03791 | null | https://arxiv.org/abs/2010.03791v2 | https://arxiv.org/pdf/2010.03791v2.pdf | Age and Gender Prediction From Face Images Using Attentional Convolutional Network | Automatic prediction of age and gender from face images has drawn a lot of attention recently, due it is wide applications in various facial analysis problems. However, due to the large intra-class variation of face images (such as variation in lighting, pose, scale, occlusion), the existing models are still behind the desired accuracy level, which is necessary for the use of these models in real-world applications. In this work, we propose a deep learning framework, based on the ensemble of attentional and residual convolutional networks, to predict gender and age group of facial images with high accuracy rate. Using attention mechanism enables our model to focus on the important and informative parts of the face, which can help it to make a more accurate prediction. We train our model in a multi-task learning fashion, and augment the feature embedding of the age classifier, with the predicted gender, and show that doing so can further increase the accuracy of age prediction. Our model is trained on a popular face age and gender dataset, and achieved promising results. Through visualization of the attention maps of the train model, we show that our model has learned to become sensitive to the right regions of the face. | ['Shervin Minaee', 'Elham Azimi', 'Mehdi Minaei', 'Amirali Abdolrashidi'] | 2020-10-08 | null | null | null | null | ['gender-prediction'] | ['computer-vision'] | [-1.16336912e-01 1.44884303e-01 -4.80502658e-02 -7.58911550e-01
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af026a78-d213-4d6e-9ccc-3e3f88001e3b | compression-of-dynamic-medical-ct-data-using | 2302.01014 | null | https://arxiv.org/abs/2302.01014v1 | https://arxiv.org/pdf/2302.01014v1.pdf | Compression of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Update | For the lossless compression of dynamic 3-D+t volumes as produced by medical devices like Computed Tomography, various coding schemes can be applied. This paper shows that 3-D subband coding outperforms lossless HEVC coding and additionally provides a scalable representation, which is often required in telemedicine applications. However, the resulting lowpass subband, which shall be used as a downscaled representative of the whole original sequence, contains a lot of ghosting artifacts. This can be alleviated by incorporating motion compensation methods into the subband coder. This results in a high quality lowpass subband but also leads to a lower compression ratio. In order to cope with this, we introduce a new approach for improving the compression efficiency of compensated 3-D wavelet lifting by performing denoising in the update step. We are able to reduce the file size of the lowpass subband by up to 1.64\%, while the lowpass subband is still applicable for being used as a downscaled representative of the whole original sequence. | ['André Kaup', 'Karina Jaskolka', 'Jürgen Seiler', 'Daniela Lanz'] | 2023-02-02 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.54094374e-01 1.15199545e-02 6.62938431e-02 7.35798199e-03
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73bdb342-4e81-492d-aaf6-7f608a96797b | accessing-higher-dimensions-for-unsupervised | 2305.14200 | null | https://arxiv.org/abs/2305.14200v1 | https://arxiv.org/pdf/2305.14200v1.pdf | Accessing Higher Dimensions for Unsupervised Word Translation | The striking ability of unsupervised word translation has been demonstrated with the help of word vectors / pretraining; however, they require large amounts of data and usually fails if the data come from different domains. We propose coocmap, a method that can use either high-dimensional co-occurrence counts or their lower-dimensional approximations. Freed from the limits of low dimensions, we show that relying on low-dimensional vectors and their incidental properties miss out on better denoising methods and useful world knowledge in high dimensions, thus stunting the potential of the data. Our results show that unsupervised translation can be achieved more easily and robustly than previously thought -- less than 80MB and minutes of CPU time is required to achieve over 50\% accuracy for English to Finnish, Hungarian, and Chinese translations when trained on similar data; even under domain mismatch, we show coocmap still works fully unsupervised on English NewsCrawl to Chinese Wikipedia and English Europarl to Spanish Wikipedia, among others. These results challenge prevailing assumptions on the necessity and superiority of low-dimensional vectors, and suggest that similarly processed co-occurrences can outperform dense vectors on other tasks too. | ['Sida I. Wang'] | 2023-05-23 | null | null | null | null | ['word-translation'] | ['natural-language-processing'] | [ 3.79767679e-02 -1.40332314e-03 -3.69540095e-01 -1.61042079e-01
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b5d4b308-cafb-47bc-94d2-9671866f08f2 | provably-powerful-graph-networks | 1905.11136 | null | https://arxiv.org/abs/1905.11136v4 | https://arxiv.org/pdf/1905.11136v4.pdf | Provably Powerful Graph Networks | Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to measure the expressive power of graph neural networks (GNN). It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019). Unfortunately, many simple instances of graphs are indistinguishable by the 1-WL test. In search for more expressive graph learning models we build upon the recent k-order invariant and equivariant graph neural networks (Maron et al. 2019a,b) and present two results: First, we show that such k-order networks can distinguish between non-isomorphic graphs as good as the k-WL tests, which are provably stronger than the 1-WL test for k>2. This makes these models strictly stronger than message passing models. Unfortunately, the higher expressiveness of these models comes with a computational cost of processing high order tensors. Second, setting our goal at building a provably stronger, simple and scalable model we show that a reduced 2-order network containing just scaled identity operator, augmented with a single quadratic operation (matrix multiplication) has a provable 3-WL expressive power. Differently put, we suggest a simple model that interleaves applications of standard Multilayer-Perceptron (MLP) applied to the feature dimension and matrix multiplication. We validate this model by presenting state of the art results on popular graph classification and regression tasks. To the best of our knowledge, this is the first practical invariant/equivariant model with guaranteed 3-WL expressiveness, strictly stronger than message passing models. | ['Heli Ben-Hamu', 'Hadar Serviansky', 'Yaron Lipman', 'Haggai Maron'] | 2019-05-27 | provably-powerful-graph-networks-1 | http://papers.nips.cc/paper/8488-provably-powerful-graph-networks | http://papers.nips.cc/paper/8488-provably-powerful-graph-networks.pdf | neurips-2019-12 | ['graph-regression'] | ['graphs'] | [ 2.18510568e-01 4.53288853e-01 -9.91163701e-02 -1.12192772e-01
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ff7a9700-fb35-47bf-9cb6-4fc37d74f248 | spherical-formulation-of-moving-object | 2003.03262 | null | https://arxiv.org/abs/2003.03262v1 | https://arxiv.org/pdf/2003.03262v1.pdf | Spherical formulation of moving object geometric constraints for monocular fisheye cameras | In this paper, we introduce a moving object detection algorithm for fisheye cameras used in autonomous driving. We reformulate the three commonly used constraints in rectilinear images (epipolar, positive depth and positive height constraints) to spherical coordinates which is invariant to specific camera configuration once the calibration is known. One of the main challenging use case in autonomous driving is to detect parallel moving objects which suffer from motion-parallax ambiguity. To alleviate this, we formulate an additional fourth constraint, called the anti-parallel constraint, which aids the detection of objects with motion that mirrors the ego-vehicle possible. We analyze the proposed algorithm in different scenarios and demonstrate that it works effectively operating directly on fisheye images. | ['Ciaran Hughes', 'Letizia Mariotti'] | 2020-03-06 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [ 1.81328747e-02 -1.43407704e-02 -1.87935364e-02 -3.36535424e-01
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e48e6649-d2f1-4786-8409-ead481a9bb23 | clipup-a-simple-and-powerful-optimizer-for | 2008.02387 | null | https://arxiv.org/abs/2008.02387v3 | https://arxiv.org/pdf/2008.02387v3.pdf | ClipUp: A Simple and Powerful Optimizer for Distribution-based Policy Evolution | Distribution-based search algorithms are an effective approach for evolutionary reinforcement learning of neural network controllers. In these algorithms, gradients of the total reward with respect to the policy parameters are estimated using a population of solutions drawn from a search distribution, and then used for policy optimization with stochastic gradient ascent. A common choice in the community is to use the Adam optimization algorithm for obtaining an adaptive behavior during gradient ascent, due to its success in a variety of supervised learning settings. As an alternative to Adam, we propose to enhance classical momentum-based gradient ascent with two simple techniques: gradient normalization and update clipping. We argue that the resulting optimizer called ClipUp (short for "clipped updates") is a better choice for distribution-based policy evolution because its working principles are simple and easy to understand and its hyperparameters can be tuned more intuitively in practice. Moreover, it removes the need to re-tune hyperparameters if the reward scale changes. Experiments show that ClipUp is competitive with Adam despite its simplicity and is effective on challenging continuous control benchmarks, including the Humanoid control task based on the Bullet physics simulator. | ['Rupesh Kumar Srivastava', 'Paweł Liskowski', 'Nihat Engin Toklu'] | 2020-08-05 | null | null | null | null | ['humanoid-control'] | ['robots'] | [-1.92811191e-01 -2.88393468e-01 -4.15367067e-01 3.71007062e-02
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2.57031024e-01 -1.09526598e+00 -2.80617714e-01 -4.86207396e-01] | [4.265748023986816, 2.2496871948242188] |
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