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766ce80f-70c4-400e-8fa7-a48a88e5e2af | towards-unbiased-training-in-federated-open | 2305.00771 | null | https://arxiv.org/abs/2305.00771v1 | https://arxiv.org/pdf/2305.00771v1.pdf | Towards Unbiased Training in Federated Open-world Semi-supervised Learning | Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm for allowing distributed clients to collaboratively train a machine learning model over scarce labeled data and abundant unlabeled data. However, existing works for FedSSL rely on a closed-world assumption that all local training data and global testing data are from seen classes observed in the labeled dataset. It is crucial to go one step further: adapting FL models to an open-world setting, where unseen classes exist in the unlabeled data. In this paper, we propose a novel Federatedopen-world Semi-Supervised Learning (FedoSSL) framework, which can solve the key challenge in distributed and open-world settings, i.e., the biased training process for heterogeneously distributed unseen classes. Specifically, since the advent of a certain unseen class depends on a client basis, the locally unseen classes (exist in multiple clients) are likely to receive differentiated superior aggregation effects than the globally unseen classes (exist only in one client). We adopt an uncertainty-aware suppressed loss to alleviate the biased training between locally unseen and globally unseen classes. Besides, we enable a calibration module supplementary to the global aggregation to avoid potential conflicting knowledge transfer caused by inconsistent data distribution among different clients. The proposed FedoSSL can be easily adapted to state-of-the-art FL methods, which is also validated via extensive experiments on benchmarks and real-world datasets (CIFAR-10, CIFAR-100 and CINIC-10). | ['Wenchao Xu', 'Song Guo', 'Xiaosong Ma', 'Jie Zhang'] | 2023-05-01 | null | null | null | null | ['open-world-semi-supervised-learning'] | ['computer-vision'] | [-2.23523811e-01 1.29796207e-01 -4.14602429e-01 -6.84919953e-01
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9655b080-7d04-4354-b05d-ffe7add71268 | towards-coherent-and-engaging-spoken-dialog | 1904.13015 | null | https://arxiv.org/abs/1904.13015v4 | https://arxiv.org/pdf/1904.13015v4.pdf | Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators | Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood~(MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation. | ['Dilek Hakkani-Tur', 'Behnam Hedayatnia', 'Chandra Khatri', 'Tagyoung Chung', 'Raefer Gabriel', 'Anu Venkatesh', 'Alessandra Cervone', 'Rahul Goel', 'Sanghyun Yi'] | 2019-04-30 | towards-coherent-and-engaging-spoken-dialog-1 | https://aclanthology.org/W19-8608 | https://aclanthology.org/W19-8608.pdf | ws-2019-10 | ['open-domain-dialog'] | ['natural-language-processing'] | [-8.25183839e-02 4.64804918e-01 8.65773484e-02 -9.89979744e-01
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340c45ad-f317-4602-9c28-a8b48dbaf37a | enriching-the-webnlg-corpus | null | null | https://aclanthology.org/W18-6521 | https://aclanthology.org/W18-6521.pdf | Enriching the WebNLG corpus | This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a,b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation. We also produce a silver-standard German translation of the corpus to enable the exploitation of NLG approaches to other languages than English. The enriched corpus is publicly available. | ['er', 'Thiago Castro Ferreira', 'Emiel Krahmer', 'S Wubben', 'Diego Moussallem'] | 2018-11-01 | null | null | null | ws-2018-11 | ['referring-expression-generation'] | ['computer-vision'] | [ 2.34922186e-01 9.23277140e-01 -3.57152104e-01 -1.62870273e-01
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a46f7afe-d017-421b-8d71-4530f751671e | vulnerability-detection-using-two-stage-deep | 2305.09673 | null | https://arxiv.org/abs/2305.09673v1 | https://arxiv.org/pdf/2305.09673v1.pdf | Vulnerability Detection Using Two-Stage Deep Learning Models | Application security is an essential part of developing modern software, as lots of attacks depend on vulnerabilities in software. The number of attacks is increasing globally due to technological advancements. Companies must include security in every stage of developing, testing, and deploying their software in order to prevent data breaches. There are several methods to detect software vulnerability Non-AI-based such as Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST). However, these approaches have substantial false-positive and false-negative rates. On the other side, researchers have been interested in developing an AI-based vulnerability detection system employing deep learning models like BERT, BLSTM, etc. In this paper, we proposed a two-stage solution, two deep learning models were proposed for vulnerability detection in C/C++ source codes, the first stage is CNN which detects if the source code contains any vulnerability (binary classification model) and the second stage is CNN-LTSM that classifies this vulnerability into a class of 50 different types of vulnerabilities (multiclass classification model). Experiments were done on SySeVR dataset. Results show an accuracy of 99% for the first and 98% for the second stage. | ['Khloud Al Jallad', 'Mohammad Hammade', 'Mohamed Mjd Alhafi'] | 2023-05-08 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.23054467e-01 -2.66508073e-01 5.29102236e-02 -2.18017429e-01
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250fa77e-76cf-44a9-be30-83151fcd2dc8 | unsupervised-speech-representation-pooling | 2304.03940 | null | https://arxiv.org/abs/2304.03940v1 | https://arxiv.org/pdf/2304.03940v1.pdf | Unsupervised Speech Representation Pooling Using Vector Quantization | With the advent of general-purpose speech representations from large-scale self-supervised models, applying a single model to multiple downstream tasks is becoming a de-facto approach. However, the pooling problem remains; the length of speech representations is inherently variable. The naive average pooling is often used, even though it ignores the characteristics of speech, such as differently lengthed phonemes. Hence, we design a novel pooling method to squash acoustically similar representations via vector quantization, which does not require additional training, unlike attention-based pooling. Further, we evaluate various unsupervised pooling methods on various self-supervised models. We gather diverse methods scattered around speech and text to evaluate on various tasks: keyword spotting, speaker identification, intent classification, and emotion recognition. Finally, we quantitatively and qualitatively analyze our method, comparing it with supervised pooling methods. | ['Hyung-Min Park', 'Hyunjun Heo', 'Kwanghee Choi', 'Jeongkyun Park'] | 2023-04-08 | null | null | null | null | ['intent-classification', 'keyword-spotting', 'speaker-identification'] | ['natural-language-processing', 'speech', 'speech'] | [ 2.34290704e-01 1.00816973e-01 -3.17026943e-01 -5.30325532e-01
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70afb66f-376e-475e-8152-30308af8874b | cooperative-exploration-for-multi-agent-deep | 2107.11444 | null | https://arxiv.org/abs/2107.11444v1 | https://arxiv.org/pdf/2107.11444v1.pdf | Cooperative Exploration for Multi-Agent Deep Reinforcement Learning | Exploration is critical for good results in deep reinforcement learning and has attracted much attention. However, existing multi-agent deep reinforcement learning algorithms still use mostly noise-based techniques. Very recently, exploration methods that consider cooperation among multiple agents have been developed. However, existing methods suffer from a common challenge: agents struggle to identify states that are worth exploring, and hardly coordinate exploration efforts toward those states. To address this shortcoming, in this paper, we propose cooperative multi-agent exploration (CMAE): agents share a common goal while exploring. The goal is selected from multiple projected state spaces via a normalized entropy-based technique. Then, agents are trained to reach this goal in a coordinated manner. We demonstrate that CMAE consistently outperforms baselines on various tasks, including a sparse-reward version of the multiple-particle environment (MPE) and the Starcraft multi-agent challenge (SMAC). | ['Alexander G. Schwing', 'Raymond A. Yeh', 'Unnat Jain', 'Iou-Jen Liu'] | 2021-07-23 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-4.21818376e-01 -2.70710170e-01 -3.02822769e-01 1.89063832e-01
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e682c084-6f06-4cc9-8ee9-fbe3ddfa2c07 | weakly-supervised-dense-video-captioning | 1704.01502 | null | http://arxiv.org/abs/1704.01502v1 | http://arxiv.org/pdf/1704.01502v1.pdf | Weakly Supervised Dense Video Captioning | This paper focuses on a novel and challenging vision task, dense video
captioning, which aims to automatically describe a video clip with multiple
informative and diverse caption sentences. The proposed method is trained
without explicit annotation of fine-grained sentence to video region-sequence
correspondence, but is only based on weak video-level sentence annotations. It
differs from existing video captioning systems in three technical aspects.
First, we propose lexical fully convolutional neural networks (Lexical-FCN)
with weakly supervised multi-instance multi-label learning to weakly link video
regions with lexical labels. Second, we introduce a novel submodular
maximization scheme to generate multiple informative and diverse
region-sequences based on the Lexical-FCN outputs. A winner-takes-all scheme is
adopted to weakly associate sentences to region-sequences in the training
phase. Third, a sequence-to-sequence learning based language model is trained
with the weakly supervised information obtained through the association
process. We show that the proposed method can not only produce informative and
diverse dense captions, but also outperform state-of-the-art single video
captioning methods by a large margin. | ['xiangyang xue', 'Yu-Gang Jiang', 'Zhou Su', 'Zhiqiang Shen', 'Jianguo Li', 'Yurong Chen', 'Minjun Li'] | 2017-04-05 | weakly-supervised-dense-video-captioning-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Shen_Weakly_Supervised_Dense_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Shen_Weakly_Supervised_Dense_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-video-captioning'] | ['computer-vision'] | [ 7.26640224e-01 1.13881744e-01 -5.22217572e-01 -6.21966898e-01
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c3cc5c95-43f9-49ad-8683-cae3458b5278 | modeling-t1-resting-state-mri-variants-using | 2306.12435 | null | https://arxiv.org/abs/2306.12435v1 | https://arxiv.org/pdf/2306.12435v1.pdf | Modeling T1 Resting-State MRI Variants Using Convolutional Neural Networks in Diagnosis of OCD | Obsessive-compulsive disorder (OCD) presents itself as a highly debilitating disorder. The disorder has common associations with the prefrontal cortex and the glutamate receptor known as Metabotropic Glutamate Receptor 5 (mGluR5). This receptor has been observed to demonstrate higher levels of signaling from positron emission tomography scans measured by its distribution volume ratios in mice. Despite this evidence, studies are unable to fully verify the involvement of mGluR5 as more empirical data is needed. Computational modeling methods were used as a means of validation for previous hypotheses involving mGluR5. The inadequacies in relation to the causal factor of OCD were answered by utilizing T1 resting-state magnetic resonance imaging (TRS-MRI) scans of patients suffering from schizophrenia, major depressive disorder, and obsessive-compulsive disorder. Because comorbid cases often occur within these disorders, cross-comparative abilities become necessary to find distinctive characteristics. Two-dimensional convolutional neural networks alongside ResNet50 and MobileNet models were constructed and evaluated for efficiency. Activation heatmaps of TRS-MRI scans were outputted, allowing for transcriptomics analysis. Though, a lack of ability to predict OCD cases prevented gene expression analysis. Across all models, there was an 88.75\% validation accuracy for MDD, and 82.08\% validation accuracy for SZD under the framework of ResNet50 as well as novel computation. OCD yielded an accuracy rate of ~54.4\%. These results provided further evidence for the p factor theorem regarding mental disorders. Future work involves the application of transfer learning to bolster accuracy rates. | ['Tarun Eswar'] | 2023-06-15 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 3.19826938e-02 -1.90496013e-01 -1.05189621e-01 -2.80364096e-01
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8938d360-3f76-41c8-91d9-80ee296a8dad | manifold-regularized-slow-feature-analysis | 1706.03015 | null | http://arxiv.org/abs/1706.03015v1 | http://arxiv.org/pdf/1706.03015v1.pdf | Manifold Regularized Slow Feature Analysis for Dynamic Texture Recognition | Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams,
but recognizing dynamic texture is challenging due to the complex temporal
variations. In this paper, we present a novel approach stemmed from slow
feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly
varying features from fast varying signals. Fortunately, SFA is capable to
leach invariant representations from dynamic textures. However, complex
temporal variations require high-level semantic representations to fully
achieve temporal slowness, and thus it is impractical to learn a high-level
representation from dynamic textures directly by SFA. In order to learn a
robust low-level feature to resolve the complexity of dynamic textures, we
propose manifold regularized SFA (MR-SFA) by exploring the neighbor
relationship of the initial state of each temporal transition and retaining the
locality of their variations. Therefore, the learned features are not only
slowly varying, but also partly predictable. MR-SFA for dynamic texture
recognition is proposed in the following steps: 1) learning feature extraction
functions as convolution filters by MR-SFA, 2) extracting local features by
convolution and pooling, and 3) employing Fisher vectors to form a video-level
representation for classification. Experimental results on dynamic texture and
dynamic scene recognition datasets validate the effectiveness of the proposed
approach. | ['DaCheng Tao', 'Xiangmin Xu', 'Xiaofen Xing', 'Jie Miao'] | 2017-06-09 | null | null | null | null | ['dynamic-texture-recognition'] | ['computer-vision'] | [ 3.64474088e-01 -8.97101760e-01 -2.50359386e-01 -3.60626310e-01
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cf4910c5-9adf-49d6-a465-39e25f931f79 | vehicle-re-identification-exploring-feature | 1911.05541 | null | https://arxiv.org/abs/1911.05541v3 | https://arxiv.org/pdf/1911.05541v3.pdf | Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks | This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear. | ['Keiko V. O. Fonseca', 'Rayson Laroca', 'Rodrigo Minetto', 'David Menotti', 'Icaro O. de Oliveira'] | 2019-11-13 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-9.86560900e-03 -7.22163856e-01 -6.47962540e-02 -3.47789675e-01
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c6d881c6-8566-4873-ad58-02a0e4e1408b | referential-communication-in-heterogeneous | 2302.08913 | null | https://arxiv.org/abs/2302.08913v2 | https://arxiv.org/pdf/2302.08913v2.pdf | Referential communication in heterogeneous communities of pre-trained visual deep networks | As large pre-trained image-processing neural networks are being embedded in autonomous agents such as self-driving cars or robots, the question arises of how such systems can communicate with each other about the surrounding world, despite their different architectures and training regimes. As a first step in this direction, we systematically explore the task of referential communication in a community of state-of-the-art pre-trained visual networks, showing that they can develop a shared protocol to refer to a target image among a set of candidates. Such shared protocol, induced in a self-supervised way, can to some extent be used to communicate about previously unseen object categories, as well as to make more granular distinctions compared to the categories taught to the original networks. Contradicting a common view in multi-agent emergent communication research, we find that imposing a discrete bottleneck on communication hampers the emergence of a general code. Moreover, we show that a new neural network can learn the shared protocol developed in a community with remarkable ease, and the process of integrating a new agent into a community more stably succeeds when the original community includes a larger set of heterogeneous networks. Finally, we illustrate the independent benefits of developing a shared communication layer by using it to directly transfer an object classifier from a network to another, and we qualitatively and quantitatively study its emergent properties. | ['Marco Baroni', 'Roberto Dessì', 'Francesca Franzon', 'Matéo Mahaut'] | 2023-02-04 | null | null | null | null | ['self-driving-cars'] | ['computer-vision'] | [ 3.40692282e-01 4.44130272e-01 2.07093701e-01 -5.05569540e-02
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a7749f9f-e0f1-4cc5-ab0c-1488b1803eba | x-model-improving-data-efficiency-in-deep-1 | 2110.04572 | null | https://arxiv.org/abs/2110.04572v1 | https://arxiv.org/pdf/2110.04572v1.pdf | X-model: Improving Data Efficiency in Deep Learning with A Minimax Model | To mitigate the burden of data labeling, we aim at improving data efficiency for both classification and regression setups in deep learning. However, the current focus is on classification problems while rare attention has been paid to deep regression, which usually requires more human effort to labeling. Further, due to the intrinsic difference between categorical and continuous label space, the common intuitions for classification, e.g., cluster assumptions or pseudo labeling strategies, cannot be naturally adapted into deep regression. To this end, we first delved into the existing data-efficient methods in deep learning and found that they either encourage invariance to data stochasticity (e.g., consistency regularization under different augmentations) or model stochasticity (e.g., difference penalty for predictions of models with different dropout). To take the power of both worlds, we propose a novel X-model by simultaneously encouraging the invariance to {data stochasticity} and {model stochasticity}. Further, the X-model plays a minimax game between the feature extractor and task-specific heads to further enhance the invariance to model stochasticity. Extensive experiments verify the superiority of the X-model among various tasks, from a single-value prediction task of age estimation to a dense-value prediction task of keypoint localization, a 2D synthetic, and a 3D realistic dataset, as well as a multi-category object recognition task. | ['Mingsheng Long', 'Jianmin Wang', 'Xinyang Chen', 'Ximei Wang'] | 2021-10-09 | x-model-improving-data-efficiency-in-deep | https://openreview.net/forum?id=P3Bh01hBYTH | https://openreview.net/pdf?id=P3Bh01hBYTH | iclr-2022-4 | ['value-prediction', 'age-estimation', 'age-estimation'] | ['computer-code', 'computer-vision', 'miscellaneous'] | [ 1.03485659e-01 4.56439070e-02 -4.43613112e-01 -7.67135501e-01
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b7d50e79-4228-445e-830e-d99338ed4ca8 | compositionality-and-capacity-in-emergent | null | null | https://aclanthology.org/2020.repl4nlp-1.5 | https://aclanthology.org/2020.repl4nlp-1.5.pdf | Compositionality and Capacity in Emergent Languages | Recent works have discussed the extent to which emergent languages can exhibit properties of natural languages particularly learning compositionality. In this paper, we investigate the learning biases that affect the efficacy and compositionality in multi-agent communication in addition to the communicative bandwidth. Our foremost contribution is to explore how the capacity of a neural network impacts its ability to learn a compositional language. We additionally introduce a set of evaluation metrics with which we analyze the learned languages. Our hypothesis is that there should be a specific range of model capacity and channel bandwidth that induces compositional structure in the resulting language and consequently encourages systematic generalization. While we empirically see evidence for the bottom of this range, we curiously do not find evidence for the top part of the range and believe that this is an open question for the community. | ['Kyunghyun Cho', 'Abhinav Gupta', 'Jakob Foerster', 'Andrew Dai', 'Cinjon Resnick'] | 2020-07-01 | null | null | null | ws-2020-7 | ['systematic-generalization'] | ['reasoning'] | [ 1.03317656e-01 3.72325718e-01 -2.91979045e-01 -6.63556233e-02
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2f274a2a-5f9b-447d-adb4-c5eb9f19c64a | reorganizing-educational-institutional-domain | 2306.10300 | null | https://arxiv.org/abs/2306.10300v1 | https://arxiv.org/pdf/2306.10300v1.pdf | Reorganizing Educational Institutional Domain using Faceted Ontological Principles | The purpose of this work is to find out how different library classification systems and linguistic ontologies arrange a particular domain of interest and what are the limitations for information retrieval. We use knowledge representation techniques and languages for construction of a domain specific ontology. This ontology would help not only in problem solving, but it would demonstrate the ease with which complex queries can be handled using principles of domain ontology, thereby facilitating better information retrieval. | ['Sayon Roy', 'Debashis Naskar', 'Subhashis Das'] | 2023-06-17 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-2.74196178e-01 1.95532832e-02 -5.24063647e-01 -2.83062249e-01
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10b7826b-3aa3-4c14-b662-5f3f5bff8919 | dual-attention-network-for-heart-rate-and | 2111.00390 | null | https://arxiv.org/abs/2111.00390v1 | https://arxiv.org/pdf/2111.00390v1.pdf | Dual Attention Network for Heart Rate and Respiratory Rate Estimation | Heart rate and respiratory rate measurement is a vital step for diagnosing many diseases. Non-contact camera based physiological measurement is more accessible and convenient in Telehealth nowadays than contact instruments such as fingertip oximeters since non-contact methods reduce risk of infection. However, remote physiological signal measurement is challenging due to environment illumination variations, head motion, facial expression, etc. It's also desirable to have a unified network which could estimate both heart rate and respiratory rate to reduce system complexity and latency. We propose a convolutional neural network which leverages spatial attention and channel attention, which we call it dual attention network (DAN) to jointly estimate heart rate and respiratory rate with camera video as input. Extensive experiments demonstrate that our proposed system significantly improves heart rate and respiratory rate measurement accuracy. | ['Niranjan Avadhanam', 'Braeden Syrnyk', 'Yuzhuo Ren'] | 2021-10-31 | null | null | null | null | ['respiratory-rate-estimation'] | ['medical'] | [ 8.22053701e-02 -2.20932394e-01 -4.28191014e-02 -3.92781198e-01
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2e21a03d-bb92-4d9d-95cd-ba022363aac4 | comparative-validation-of-ai-and-non-ai | 2207.11534 | null | https://arxiv.org/abs/2207.11534v1 | https://arxiv.org/pdf/2207.11534v1.pdf | Comparative Validation of AI and non-AI Methods in MRI Volumetry to Diagnose Parkinsonian Syndromes | Automated segmentation and volumetry of brain magnetic resonance imaging (MRI) scans are essential for the diagnosis of Parkinson's disease (PD) and Parkinson's plus syndromes (P-plus). To enhance the diagnostic performance, we adopt deep learning (DL) models in brain segmentation and compared their performance with the gold-standard non-DL method. We collected brain MRI scans of healthy controls (n=105) and patients with PD (n=105), multiple systemic atrophy (n=132), and progressive supranuclear palsy (n=69) at Samsung Medical Center from January 2017 to December 2020. Using the gold-standard non-DL model, FreeSurfer (FS), we segmented six brain structures: midbrain, pons, caudate, putamen, pallidum, and third ventricle, and considered them as annotating data for DL models, the representative V-Net and UNETR. The Dice scores and area under the curve (AUC) for differentiating normal, PD, and P-plus cases were calculated. The segmentation times of V-Net and UNETR for the six brain structures per patient were 3.48 +- 0.17 and 48.14 +- 0.97 s, respectively, being at least 300 times faster than FS (15,735 +- 1.07 s). Dice scores of both DL models were sufficiently high (>0.85), and their AUCs for disease classification were superior to that of FS. For classification of normal vs. P-plus and PD vs. multiple systemic atrophy (cerebellar type), the DL models and FS showed AUCs above 0.8. DL significantly reduces the analysis time without compromising the performance of brain segmentation and differential diagnosis. Our findings may contribute to the adoption of DL brain MRI segmentation in clinical settings and advance brain research. | ['Kyung-Su Kim', 'Jong Hyeon Ahn', 'Jin Whan Cho', 'Jinyoung Youn', 'Myung Jin Chung', 'Chae Yeon Lim', 'Jisoo Lee', 'Juyoung Hahm', 'Joomee Song'] | 2022-07-23 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [-2.27314904e-01 2.98700333e-02 -1.00535275e-02 -3.17664713e-01
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9af422e6-db3d-4380-bcb8-e6508dfa35a6 | cross-domain-named-entity-recognition-via-1 | null | null | https://aclanthology.org/2022.findings-acl.210 | https://aclanthology.org/2022.findings-acl.210.pdf | Cross-domain Named Entity Recognition via Graph Matching | Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains. Due to the mismatch problem between entity types across domains, the wide knowledge in the general domain can not effectively transfer to the target domain NER model. To this end, we model the label relationship as a probability distribution and construct label graphs in both source and target label spaces. To enhance the contextual representation with label structures, we fuse the label graph into the word embedding output by BERT. By representing label relationships as graphs, we formulate cross-domain NER as a graph matching problem. Furthermore, the proposed method has good applicability with pre-training methods and is potentially capable of other cross-domain prediction tasks. Empirical results on four datasets show that our method outperforms a series of transfer learning, multi-task learning, and few-shot learning methods. | ['Qianli Ma', 'Haibin Chen', 'Junhao Zheng'] | null | null | null | null | findings-acl-2022-5 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 2.20939949e-01 6.40711561e-02 -3.99724901e-01 -3.52442592e-01
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a74ad78b-3cb4-4915-9a00-4b9ff8210e9a | av-sam-segment-anything-model-meets-audio | 2305.01836 | null | https://arxiv.org/abs/2305.01836v1 | https://arxiv.org/pdf/2305.01836v1.pdf | AV-SAM: Segment Anything Model Meets Audio-Visual Localization and Segmentation | Segment Anything Model (SAM) has recently shown its powerful effectiveness in visual segmentation tasks. However, there is less exploration concerning how SAM works on audio-visual tasks, such as visual sound localization and segmentation. In this work, we propose a simple yet effective audio-visual localization and segmentation framework based on the Segment Anything Model, namely AV-SAM, that can generate sounding object masks corresponding to the audio. Specifically, our AV-SAM simply leverages pixel-wise audio-visual fusion across audio features and visual features from the pre-trained image encoder in SAM to aggregate cross-modal representations. Then, the aggregated cross-modal features are fed into the prompt encoder and mask decoder to generate the final audio-visual segmentation masks. We conduct extensive experiments on Flickr-SoundNet and AVSBench datasets. The results demonstrate that the proposed AV-SAM can achieve competitive performance on sounding object localization and segmentation. | ['Yapeng Tian', 'Shentong Mo'] | 2023-05-03 | null | null | null | null | ['visual-localization', 'object-localization'] | ['computer-vision', 'computer-vision'] | [ 4.13805157e-01 -2.87947953e-01 -3.64962593e-02 -2.68850625e-01
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f40e7159-29e7-494d-bccf-9ec7ef84bbc4 | independent-motion-detection-with-event | 1706.08713 | null | http://arxiv.org/abs/1706.08713v2 | http://arxiv.org/pdf/1706.08713v2.pdf | Independent Motion Detection with Event-driven Cameras | Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for robots. Visual tracking, for example, is easily achieved
even for very fast stimuli, as only moving objects cause brightness changes.
However, cameras mounted on a moving robot are typically non-stationary and the
same tracking problem becomes confounded by background clutter events due to
the robot ego-motion. In this paper, we propose a method for segmenting the
motion of an independently moving object for event-driven cameras. Our method
detects and tracks corners in the event stream and learns the statistics of
their motion as a function of the robot's joint velocities when no
independently moving objects are present. During robot operation, independently
moving objects are identified by discrepancies between the predicted corner
velocities from ego-motion and the measured corner velocities. We validate the
algorithm on data collected from the neuromorphic iCub robot. We achieve a
precision of ~ 90 % and show that the method is robust to changes in speed of
both the head and the target. | ['Elias Mueggler', 'Arren Glover', 'Valentina Vasco', 'Lorenzo Natale', 'Davide Scaramuzza', 'Chiara Bartolozzi'] | 2017-06-27 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 3.44452888e-01 -5.63262880e-01 3.00460398e-01 -2.16380224e-01
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2789a891-e91d-46ea-b52b-8988caa07557 | a-comparison-of-graph-neural-networks-for | 2303.12812 | null | https://arxiv.org/abs/2303.12812v1 | https://arxiv.org/pdf/2303.12812v1.pdf | A Comparison of Graph Neural Networks for Malware Classification | Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant features, which is labor intensive and requires expert knowledge. Function call graphs consist of a set of program functions and their inter-procedural calls, providing a rich source of information that can be leveraged to classify malware without the labor intensive feature extraction step of traditional techniques. In this research, we treat malware classification as a graph classification problem. Based on Local Degree Profile features, we train a wide range of Graph Neural Network (GNN) architectures to generate embeddings which we then classify. We find that our best GNN models outperform previous comparable research involving the well-known MalNet-Tiny Android malware dataset. In addition, our GNN models do not suffer from the overfitting issues that commonly afflict non-GNN techniques, although GNN models require longer training times. | ['Mark Stamp', 'Katerina Potika', 'Vrinda Malhotra'] | 2023-03-22 | null | null | null | null | ['graph-classification', 'malware-classification'] | ['graphs', 'miscellaneous'] | [ 4.76583332e-01 -2.88817197e-01 -6.54170096e-01 -1.99953094e-01
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a24b740b-cf64-4843-9718-bb48b666bdc5 | efficient-and-robust-bayesian-selection-of | 2306.00357 | null | https://arxiv.org/abs/2306.00357v1 | https://arxiv.org/pdf/2306.00357v1.pdf | Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization | We introduce an efficient and robust auto-tuning framework for hyperparameter selection in dimension reduction (DR) algorithms, focusing on large-scale datasets and arbitrary performance metrics. By leveraging Bayesian optimization (BO) with a surrogate model, our approach enables efficient hyperparameter selection with multi-objective trade-offs and allows us to perform data-driven sensitivity analysis. By incorporating normalization and subsampling, the proposed framework demonstrates versatility and efficiency, as shown in applications to visualization techniques such as t-SNE and UMAP. We evaluate our results on various synthetic and real-world datasets using multiple quality metrics, providing a robust and efficient solution for hyperparameter selection in DR algorithms. | ['Anna Ma', 'Hengrui Luo', 'Yin-Ting Liao'] | 2023-06-01 | null | null | null | null | ['dimensionality-reduction', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-1.03675477e-01 -4.20960188e-01 -4.05823022e-01 -1.48995265e-01
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7ad6b644-e228-49c1-b9d2-be51e069dfac | sliderunner-a-tool-for-massive-cell | 1802.02347 | null | http://arxiv.org/abs/1802.02347v1 | http://arxiv.org/pdf/1802.02347v1.pdf | SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images | Large-scale image data such as digital whole-slide histology images pose a
challenging task at annotation software solutions. Today, a number of good
solutions with varying scopes exist. For cell annotation, however, we find that
many do not match the prerequisites for fast annotations. Especially in the
field of mitosis detection, it is assumed that detection accuracy could
significantly benefit from larger annotation databases that are currently
however very troublesome to produce. Further, multiple independent (blind)
expert labels are a big asset for such databases, yet there is currently no
tool for this kind of annotation available.
To ease this tedious process of expert annotation and grading, we introduce
SlideRunner, an open source annotation and visualization tool for digital
histopathology, developed in close cooperation with two pathologists.
SlideRunner is capable of setting annotations like object centers (for e.g.
cells) as well as object boundaries (e.g. for tumor outlines). It provides
single-click annotations as well as a blind mode for multi-annotations, where
the expert is directly shown the microscopy image containing the cells that he
has not yet rated. | ['Christof Bertram', 'Robert Klopfleisch', 'Marc Aubreville', 'Andreas Maier'] | 2018-02-07 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [-3.87134552e-02 2.26303458e-01 1.16443366e-01 -2.58267313e-01
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3922659e-15d6-4a9d-b174-09cf7a891f0b | ebm-life-cycle-mcmc-strategies-for-synthesis-1 | 2205.12243 | null | https://arxiv.org/abs/2205.12243v1 | https://arxiv.org/pdf/2205.12243v1.pdf | EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling | This work presents strategies to learn an Energy-Based Model (EBM) according to the desired length of its MCMC sampling trajectories. MCMC trajectories of different lengths correspond to models with different purposes. Our experiments cover three different trajectory magnitudes and learning outcomes: 1) shortrun sampling for image generation; 2) midrun sampling for classifier-agnostic adversarial defense; and 3) longrun sampling for principled modeling of image probability densities. To achieve these outcomes, we introduce three novel methods of MCMC initialization for negative samples used in Maximum Likelihood (ML) learning. With standard network architectures and an unaltered ML objective, our MCMC initialization methods alone enable significant performance gains across the three applications that we investigate. Our results include state-of-the-art FID scores for unnormalized image densities on the CIFAR-10 and ImageNet datasets; state-of-the-art adversarial defense on CIFAR-10 among purification methods and the first EBM defense on ImageNet; and scalable techniques for learning valid probability densities. Code for this project can be found at https://github.com/point0bar1/ebm-life-cycle. | ['Song-Chun Zhu', 'Mubarak Shah', 'Yuan Du', 'Chu Chen', 'Jonathan Mitchell', 'Mitch Hill'] | 2022-05-24 | ebm-life-cycle-mcmc-strategies-for-synthesis | https://openreview.net/forum?id=psQ6wcNXjS1 | https://openreview.net/pdf?id=psQ6wcNXjS1 | null | ['adversarial-defense'] | ['adversarial'] | [-1.11589760e-01 -1.42854035e-01 -2.74287015e-01 -3.12046647e-01
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063b9d10-fda0-4041-9e39-74400b9037af | image-classification-with-small-datasets | null | null | https://ieeexplore.ieee.org/abstract/document/9770050 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9770050 | Image Classification With Small Datasets: Overview and Benchmark | Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring
reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common
benchmark to allow for objective comparisons between published methods. This article addresses both
issues. First, we systematically organize and connect past studies to consolidate a community that is currently
fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison
of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery,
satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the
standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned
venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a
highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a
single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline
classifier. | ['Joachim Denzler', 'Luca Iocchi', 'Björn Barz', 'Lorenzo Brigato'] | 2022-05-05 | null | null | null | ieee-access-2022-5 | ['small-data'] | ['computer-vision'] | [ 4.63918537e-01 -2.94768065e-01 -4.75600988e-01 -4.46370661e-01
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ed78c40f-c8d4-4452-866d-8d714d2ecee3 | residual-sparsity-connection-learning-for | 2206.07687 | null | https://arxiv.org/abs/2206.07687v3 | https://arxiv.org/pdf/2206.07687v3.pdf | Structured Sparsity Learning for Efficient Video Super-Resolution | The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., smartphones and drones). Existing VSR models contain considerable redundant filters, which drag down the inference efficiency. To prune these unimportant filters, we develop a structured pruning scheme called Structured Sparsity Learning (SSL) according to the properties of VSR. In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, recurrent networks, and upsampling networks. Specifically, we develop a Residual Sparsity Connection (RSC) scheme for residual blocks of recurrent networks to liberate pruning restrictions and preserve the restoration information. For upsampling networks, we design a pixel-shuffle pruning scheme to guarantee the accuracy of feature channel-space conversion. In addition, we observe that pruning error would be amplified as the hidden states propagate along with recurrent networks. To alleviate the issue, we design Temporal Finetuning (TF). Extensive experiments show that SSL can significantly outperform recent methods quantitatively and qualitatively. | ['Luc van Gool', 'Wenming Yang', 'Yapeng Tian', 'Yitong Wang', 'Yulun Zhang', 'Jingwen He', 'Bin Xia'] | 2022-06-15 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xia_Structured_Sparsity_Learning_for_Efficient_Video_Super-Resolution_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-super-resolution'] | ['computer-vision'] | [ 4.68707651e-01 -6.04390427e-02 -5.18026233e-01 -2.56797373e-01
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97cc5dbb-5303-442a-8bd5-0e7227d6f2d9 | rodnet-a-real-time-radar-object-detection | 2102.05150 | null | https://arxiv.org/abs/2102.05150v1 | https://arxiv.org/pdf/2102.05150v1.pdf | RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization | Various autonomous or assisted driving strategies have been facilitated through the accurate and reliable perception of the environment around a vehicle. Among the commonly used sensors, radar has usually been considered as a robust and cost-effective solution even in adverse driving scenarios, e.g., weak/strong lighting or bad weather. Instead of considering to fuse the unreliable information from all available sensors, perception from pure radar data becomes a valuable alternative that is worth exploring. In this paper, we propose a deep radar object detection network, named RODNet, which is cross-supervised by a camera-radar fused algorithm without laborious annotation efforts, to effectively detect objects from the radio frequency (RF) images in real-time. First, the raw signals captured by millimeter-wave radars are transformed to RF images in range-azimuth coordinates. Second, our proposed RODNet takes a sequence of RF images as the input to predict the likelihood of objects in the radar field of view (FoV). Two customized modules are also added to handle multi-chirp information and object relative motion. Instead of using human-labeled ground truth for training, the proposed RODNet is cross-supervised by a novel 3D localization of detected objects using a camera-radar fusion (CRF) strategy in the training stage. Finally, we propose a method to evaluate the object detection performance of the RODNet. Due to no existing public dataset available for our task, we create a new dataset, named CRUW, which contains synchronized RGB and RF image sequences in various driving scenarios. With intensive experiments, our proposed cross-supervised RODNet achieves 86% average precision and 88% average recall of object detection performance, which shows the robustness to noisy scenarios in various driving conditions. | ['Hui Liu', 'Guanbin Xing', 'Jenq-Neng Hwang', 'Yudong Li', 'Zhongyu Jiang', 'Yizhou Wang'] | 2021-02-09 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 2.38252819e-01 -2.56560475e-01 1.62384614e-01 -7.06695795e-01
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c96a21ca-eac9-4273-b070-e308364eadc6 | g-nm-a-group-of-numerical-time-series | 2306.11667 | null | https://arxiv.org/abs/2306.11667v3 | https://arxiv.org/pdf/2306.11667v3.pdf | G-NM: A Group of Numerical Time Series Prediction Models | In this study, we focus on the development and implementation of a comprehensive ensemble of numerical time series forecasting models, collectively referred to as the Group of Numerical Time Series Prediction Model (G-NM). This inclusive set comprises traditional models such as Autoregressive Integrated Moving Average (ARIMA), Holt-Winters' method, and Support Vector Regression (SVR), in addition to modern neural network models including Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). G-NM is explicitly constructed to augment our predictive capabilities related to patterns and trends inherent in complex natural phenomena. By utilizing time series data relevant to these events, G-NM facilitates the prediction of such phenomena over extended periods. The primary objective of this research is to both advance our understanding of such occurrences and to significantly enhance the accuracy of our forecasts. G-NM encapsulates both linear and non-linear dependencies, seasonalities, and trends present in time series data. Each of these models contributes distinct strengths, from ARIMA's resilience in handling linear trends and seasonality, SVR's proficiency in capturing non-linear patterns, to LSTM's adaptability in modeling various components of time series data. Through the exploitation of the G-NM potential, we strive to advance the state-of-the-art in large-scale time series forecasting models. We anticipate that this research will represent a significant stepping stone in our ongoing endeavor to comprehend and forecast the complex events that constitute the natural world. | ['Juyoung Yun'] | 2023-06-20 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [-1.00288324e-01 -5.58760643e-01 -7.57412091e-02 -3.13309819e-01
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49f4c8bf-6ada-4de4-9c62-1a83f1cbee38 | deconstructing-data-reconstruction-multiclass | 2307.01827 | null | https://arxiv.org/abs/2307.01827v1 | https://arxiv.org/pdf/2307.01827v1.pdf | Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses | Memorization of training data is an active research area, yet our understanding of the inner workings of neural networks is still in its infancy. Recently, Haim et al. (2022) proposed a scheme to reconstruct training samples from multilayer perceptron binary classifiers, effectively demonstrating that a large portion of training samples are encoded in the parameters of such networks. In this work, we extend their findings in several directions, including reconstruction from multiclass and convolutional neural networks. We derive a more general reconstruction scheme which is applicable to a wider range of loss functions such as regression losses. Moreover, we study the various factors that contribute to networks' susceptibility to such reconstruction schemes. Intriguingly, we observe that using weight decay during training increases reconstructability both in terms of quantity and quality. Additionally, we examine the influence of the number of neurons relative to the number of training samples on the reconstructability. | ['Michal Irani', 'Yaniv Nikankin', 'Yakir Oz', 'Gal Vardi', 'Gilad Yehudai', 'Niv Haim', 'Gon Buzaglo'] | 2023-07-04 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 4.73854840e-01 1.21121086e-01 -2.08028480e-01 -4.81607437e-01
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fdb7d3d2-a2a2-4ad6-becf-82fa77f79b9f | learning-to-generate-scene-graph-from-head-to | 2206.11653 | null | https://arxiv.org/abs/2206.11653v1 | https://arxiv.org/pdf/2206.11653v1.pdf | Learning To Generate Scene Graph from Head to Tail | Scene Graph Generation (SGG) represents objects and their interactions with a graph structure. Recently, many works are devoted to solving the imbalanced problem in SGG. However, underestimating the head predicates in the whole training process, they wreck the features of head predicates that provide general features for tail ones. Besides, assigning excessive attention to the tail predicates leads to semantic deviation. Based on this, we propose a novel SGG framework, learning to generate scene graphs from Head to Tail (SGG-HT), containing Curriculum Re-weight Mechanism (CRM) and Semantic Context Module (SCM). CRM learns head/easy samples firstly for robust features of head predicates and then gradually focuses on tail/hard ones. SCM is proposed to relieve semantic deviation by ensuring the semantic consistency between the generated scene graph and the ground truth in global and local representations. Experiments show that SGG-HT significantly alleviates the biased problem and chieves state-of-the-art performances on Visual Genome. | ['Lianli Gao', 'Jingkuan Song', 'Pengpeng Zeng', 'Yuyu Guo', 'Xinyu Lyu', 'Chaofan Zheng'] | 2022-06-23 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 2.41771221e-01 5.25062084e-01 -7.80481175e-02 -3.38018507e-01
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11f2c075-fa34-4aeb-a480-c4633cc84e35 | draw-a-recurrent-neural-network-for-image | 1502.04623 | null | http://arxiv.org/abs/1502.04623v2 | http://arxiv.org/pdf/1502.04623v2.pdf | DRAW: A Recurrent Neural Network For Image Generation | This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural
network architecture for image generation. DRAW networks combine a novel
spatial attention mechanism that mimics the foveation of the human eye, with a
sequential variational auto-encoding framework that allows for the iterative
construction of complex images. The system substantially improves on the state
of the art for generative models on MNIST, and, when trained on the Street View
House Numbers dataset, it generates images that cannot be distinguished from
real data with the naked eye. | ['Danilo Jimenez Rezende', 'Ivo Danihelka', 'Daan Wierstra', 'Karol Gregor', 'Alex Graves'] | 2015-02-16 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 1.57224000e-01 4.08718556e-01 4.37371969e-01 1.87933147e-02
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be2b5c18-0034-4cf0-b8e3-ad80fa853377 | efficient-feature-compression-for-edge-cloud | 2211.09897 | null | https://arxiv.org/abs/2211.09897v1 | https://arxiv.org/pdf/2211.09897v1.pdf | Efficient Feature Compression for Edge-Cloud Systems | Optimizing computation in an edge-cloud system is an important yet challenging problem. In this paper, we consider a three-way trade-off between bit rate, classification accuracy, and encoding complexity in an edge-cloud image classification system. Our method includes a new training strategy and an efficient encoder architecture to improve the rate-accuracy performance. Our design can also be easily scaled according to different computation resources on the edge device, taking a step towards achieving a rate-accuracy-complexity (RAC) trade-off. Under various settings, our feature coding system consistently outperforms previous methods in terms of the RAC performance. | ['Fengqing Zhu', 'Zhihao Duan'] | 2022-11-17 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 3.25065583e-01 -6.94247723e-01 -6.03848577e-01 -4.89873976e-01
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8a4bf9b8-bd43-440e-a908-d07c97e4683b | matryoshka-networks-predicting-3d-geometry | 1804.10975 | null | http://arxiv.org/abs/1804.10975v1 | http://arxiv.org/pdf/1804.10975v1.pdf | Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers | In this paper, we develop novel, efficient 2D encodings for 3D geometry,
which enable reconstructing full 3D shapes from a single image at high
resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction
problem. To that end, we first develop a simple baseline network that predicts
entire voxel tubes at each pixel of a reference view. By leveraging well-proven
architectures for 2D pixel-prediction tasks, we attain state-of-the-art
results, clearly outperforming purely voxel-based approaches. We scale this
baseline to higher resolutions by proposing a memory-efficient shape encoding,
which recursively decomposes a 3D shape into nested shape layers, similar to
the pieces of a Matryoshka doll. This allows reconstructing highly detailed
shapes with complex topology, as demonstrated in extensive experiments; we
clearly outperform previous octree-based approaches despite having a much
simpler architecture using standard network components. Our Matryoshka networks
further enable reconstructing shapes from IDs or shape similarity, as well as
shape sampling. | ['Stephan R. Richter', 'Stefan Roth'] | 2018-04-29 | matryoshka-networks-predicting-3d-geometry-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Richter_Matryoshka_Networks_Predicting_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Richter_Matryoshka_Networks_Predicting_CVPR_2018_paper.pdf | cvpr-2018-6 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 2.07868710e-01 4.16583359e-01 2.60964990e-01 -3.26653957e-01
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149d8e55-3999-4d4e-ba38-038f4154d32d | dagan-depth-aware-generative-adversarial | 2305.06225 | null | https://arxiv.org/abs/2305.06225v1 | https://arxiv.org/pdf/2305.06225v1.pdf | DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation | Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial geometry, such as pixel-wise depth, plays a critical role in constructing accurate 3D facial structures and suppressing complex background noises for generation. However, dense 3D annotations for facial videos is prohibitively costly to obtain. In this work, firstly, we present a novel self-supervised method for learning dense 3D facial geometry (ie, depth) from face videos, without requiring camera parameters and 3D geometry annotations in training. We further propose a strategy to learn pixel-level uncertainties to perceive more reliable rigid-motion pixels for geometry learning. Secondly, we design an effective geometry-guided facial keypoint estimation module, providing accurate keypoints for generating motion fields. Lastly, we develop a 3D-aware cross-modal (ie, appearance and depth) attention mechanism, which can be applied to each generation layer, to capture facial geometries in a coarse-to-fine manner. Extensive experiments are conducted on three challenging benchmarks (ie, VoxCeleb1, VoxCeleb2, and HDTF). The results demonstrate that our proposed framework can generate highly realistic-looking reenacted talking videos, with new state-of-the-art performances established on these benchmarks. The codes and trained models are publicly available on the GitHub project page at https://github.com/harlanhong/CVPR2022-DaGAN | ['Dan Xu', 'Li Shen', 'Fa-Ting Hong'] | 2023-05-10 | null | null | null | null | ['talking-head-generation', 'video-generation'] | ['computer-vision', 'computer-vision'] | [-1.58085227e-01 2.16917902e-01 5.68098724e-02 -6.39888942e-01
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86f46857-640c-4c0c-bded-d3d8f352862d | pane-gnn-unifying-positive-and-negative-edges | 2306.04095 | null | https://arxiv.org/abs/2306.04095v2 | https://arxiv.org/pdf/2306.04095v2.pdf | PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation | Recommender systems play a crucial role in addressing the issue of information overload by delivering personalized recommendations to users. In recent years, there has been a growing interest in leveraging graph neural networks (GNNs) for recommender systems, capitalizing on advancements in graph representation learning. These GNN-based models primarily focus on analyzing users' positive feedback while overlooking the valuable insights provided by their negative feedback. In this paper, we propose PANE-GNN, an innovative recommendation model that unifies Positive And Negative Edges in Graph Neural Networks for recommendation. By incorporating user preferences and dispreferences, our approach enhances the capability of recommender systems to offer personalized suggestions. PANE-GNN first partitions the raw rating graph into two distinct bipartite graphs based on positive and negative feedback. Subsequently, we employ two separate embeddings, the interest embedding and the disinterest embedding, to capture users' likes and dislikes, respectively. To facilitate effective information propagation, we design distinct message-passing mechanisms for positive and negative feedback. Furthermore, we introduce a distortion to the negative graph, which exclusively consists of negative feedback edges, for contrastive training. This distortion plays a crucial role in effectively denoising the negative feedback. The experimental results provide compelling evidence that PANE-GNN surpasses the existing state-of-the-art benchmark methods across four real-world datasets. These datasets include three commonly used recommender system datasets and one open-source short video recommendation dataset. | ['Kun Gai', 'Na Mou', 'Yang song', 'Kai Zheng', 'Cheng Wu', 'Jingcao Xu', 'Chaokun Wang', 'Ziyang Liu'] | 2023-06-07 | null | null | null | null | ['graph-representation-learning'] | ['methodology'] | [-2.54024584e-02 -1.56239625e-02 -5.53112566e-01 -3.38927239e-01
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0ff1f5b4-41c2-4fca-809d-f3614b68f9a0 | a-better-choice-entire-space-datasets-for | 2212.09052 | null | https://arxiv.org/abs/2212.09052v1 | https://arxiv.org/pdf/2212.09052v1.pdf | A Better Choice: Entire-space Datasets for Aspect Sentiment Triplet Extraction | Aspect sentiment triplet extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences. Since the initial datasets used to evaluate models on ASTE had flaws, several studies later corrected the initial datasets and released new versions of the datasets independently. As a result, different studies select different versions of datasets to evaluate their methods, which makes ASTE-related works hard to follow. In this paper, we analyze the relation between different versions of datasets and suggest that the entire-space version should be used for ASTE. Besides the sentences containing triplets and the triplets in the sentences, the entire-space version additionally includes the sentences without triplets and the aspect terms which do not belong to any triplets. Hence, the entire-space version is consistent with real-world scenarios and evaluating models on the entire-space version can better reflect the models' performance in real-world scenarios. In addition, experimental results show that evaluating models on non-entire-space datasets inflates the performance of existing models and models trained on the entire-space version can obtain better performance. | ['Sheng-hua Zhong', 'Fang Wang', 'Yuncong Li'] | 2022-12-18 | null | null | null | null | ['extract-aspect', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.02229454e-01 -1.93223283e-01 -2.69742310e-01 -6.53538883e-01
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3e41ecfc-004c-4f81-b8cc-0928efd94fcb | victr-video-conditioned-text-representations | 2304.02560 | null | https://arxiv.org/abs/2304.02560v1 | https://arxiv.org/pdf/2304.02560v1.pdf | VicTR: Video-conditioned Text Representations for Activity Recognition | Vision-Language models have shown strong performance in the image-domain -- even in zero-shot settings, thanks to the availability of large amount of pretraining data (i.e., paired image-text examples). However for videos, such paired data is not as abundant. Thus, video-text models are usually designed by adapting pretrained image-text models to video-domain, instead of training from scratch. All such recipes rely on augmenting visual embeddings with temporal information (i.e., image -> video), often keeping text embeddings unchanged or even being discarded. In this paper, we argue that such adapted video-text models can benefit more by augmenting text rather than visual information. We propose VicTR, which jointly-optimizes text and video tokens, generating 'Video-conditioned Text' embeddings. Our method can further make use of freely-available semantic information, in the form of visually-grounded auxiliary text (e.g., object or scene information). We conduct experiments on multiple benchmarks including supervised (Kinetics-400, Charades), zero-shot and few-shot (HMDB-51, UCF-101) settings, showing competitive performance on activity recognition based on video-text models. | ['Michael S. Ryoo', 'Arsha Nagrani', 'Anurag Arnab', 'Kumara Kahatapitiya'] | 2023-04-05 | null | null | null | null | ['zero-shot-action-recognition', 'action-classification'] | ['computer-vision', 'computer-vision'] | [ 2.25560471e-01 -3.09138656e-01 -3.39422554e-01 -3.04219157e-01
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691c6b12-ae65-4843-a3ab-c381d14f7295 | ccs-explorer-relevance-prediction-extractive | 2211.00201 | null | https://arxiv.org/abs/2211.00201v2 | https://arxiv.org/pdf/2211.00201v2.pdf | CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies | Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through the long list of these articles faster and document the crucial takeaways from each of these articles? In this work, we propose CCS Explorer, an end-to-end system for relevance prediction of sentences, extractive summarization, and patient, outcome, and intervention entity detection from CCS. CCS Explorer is packaged in a web-based graphical user interface where the user can provide any disease name. CCS Explorer then extracts and aggregates all relevant information from articles on PubMed based on the results of an automatically generated query produced on the back-end. For each task, CCS Explorer fine-tunes pre-trained language representation models based on transformers with additional layers. The models are evaluated using two publicly available datasets. CCS Explorer obtains a recall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence relevance prediction using BioBERT and achieves an average Micro F1-Score of 77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus, CCS Explorer can reliably extract relevant information to summarize articles, saving time by $\sim \text{660}\times$. | ['Cassie S. Mitchell', 'Sarah Bi', 'Albert J. Lee', 'Davi Nakajima An', 'Irfan Al-Hussaini'] | 2022-11-01 | null | null | null | null | ['extractive-summarization'] | ['natural-language-processing'] | [ 1.99349463e-01 -1.38051892e-02 -6.08032942e-01 -1.60469785e-01
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1443b6d2-7f5c-45b7-aba6-8e3680d7fe83 | privacy-preserving-visual-localization-with | 2212.03177 | null | https://arxiv.org/abs/2212.03177v2 | https://arxiv.org/pdf/2212.03177v2.pdf | Privacy-Preserving Visual Localization with Event Cameras | We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further enhance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user's view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link: https://github.com/82magnolia/event_localization. | ['Jian Wang', 'Sizhuo Ma', 'Gurunandan Krishnan', 'Weston A. Welge', 'Ramzi Zahreddine', 'Yicheng Wu', 'Young Min Kim', 'Junho Kim'] | 2022-12-04 | null | null | null | null | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 3.71998906e-01 -1.34207204e-01 -1.51841894e-01 -4.98699278e-01
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5779ca74-a085-47cb-87df-08797e4ac6e4 | adaptive-data-free-quantization | 2303.06869 | null | https://arxiv.org/abs/2303.06869v3 | https://arxiv.org/pdf/2303.06869v3.pdf | Adaptive Data-Free Quantization | Data-free quantization (DFQ) recovers the performance of quantized network (Q) without the original data, but generates the fake sample via a generator (G) by learning from full-precision network (P), which, however, is totally independent of Q, overlooking the adaptability of the knowledge from generated samples, i.e., informative or not to the learning process of Q, resulting into the overflow of generalization error. Building on this, several critical questions -- how to measure the sample adaptability to Q under varied bit-width scenarios? whether the largest adaptability is the best? how to generate the samples with adaptive adaptability to improve Q's generalization? To answer the above questions, in this paper, we propose an Adaptive Data-Free Quantization (AdaDFQ) method, which revisits DFQ from a zero-sum game perspective upon the sample adaptability between two players -- a generator and a quantized network. Following this viewpoint, we further define the disagreement and agreement samples to form two boundaries, where the margin is optimized to adaptively regulate the adaptability of generated samples to Q, so as to address the over-and-under fitting issues. Our AdaDFQ reveals: 1) the largest adaptability is NOT the best for sample generation to benefit Q's generalization; 2) the knowledge of the generated sample should not be informative to Q only, but also related to the category and distribution information of the training data for P. The theoretical and empirical analysis validate the advantages of AdaDFQ over the state-of-the-arts. Our code is available at https://github.com/hfutqian/AdaDFQ. | ['Meng Wang', 'Richang Hong', 'Yang Wang', 'Biao Qian'] | 2023-03-13 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qian_Adaptive_Data-Free_Quantization_CVPR_2023_paper.pdf | cvpr-2023-1 | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 1.14430778e-03 4.96240795e-01 -3.49678874e-01 -3.67580093e-02
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15894e26-3790-461d-812d-432b4edaf949 | efficient-dialog-policy-learning-via-positive | 1810.01371 | null | https://arxiv.org/abs/1810.01371v3 | https://arxiv.org/pdf/1810.01371v3.pdf | Efficient Dialog Policy Learning via Positive Memory Retention | This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning. Training such agents with policy gradients typically requires a large amount of samples. However, the collection of the required data in form of conversations between chat-bots and human agents is time-consuming and expensive. To mitigate this problem, we describe an efficient policy gradient method using positive memory retention, which significantly increases the sample-efficiency. We show that our method is 10 times more sample-efficient than policy gradients in extensive experiments on a new synthetic number guessing game. Moreover, in a real-word visual object discovery game, the proposed method is twice as sample-efficient as policy gradients and shows state-of-the-art performance. | ['Rui Zhao', 'Volker Tresp'] | 2018-10-02 | null | null | null | null | ['goal-oriented-dialog'] | ['natural-language-processing'] | [-2.20558822e-01 1.85906500e-01 -2.26350158e-01 -1.21092321e-02
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0ea70d73-3922-43bd-a258-62dd1d43ca61 | augnet-end-to-end-unsupervised-visual | 2106.06250 | null | https://arxiv.org/abs/2106.06250v1 | https://arxiv.org/pdf/2106.06250v1.pdf | AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation | Most of the achievements in artificial intelligence so far were accomplished by supervised learning which requires numerous annotated training data and thus costs innumerable manpower for labeling. Unsupervised learning is one of the effective solutions to overcome such difficulties. In our work, we propose AugNet, a new deep learning training paradigm to learn image features from a collection of unlabeled pictures. We develop a method to construct the similarities between pictures as distance metrics in the embedding space by leveraging the inter-correlation between augmented versions of samples. Our experiments demonstrate that the method is able to represent the image in low dimensional space and performs competitively in downstream tasks such as image classification and image similarity comparison. Specifically, we achieved over 60% and 27% accuracy on the STL10 and CIFAR100 datasets with unsupervised clustering, respectively. Moreover, unlike many deep-learning-based image retrieval algorithms, our approach does not require access to external annotated datasets to train the feature extractor, but still shows comparable or even better feature representation ability and easy-to-use characteristics. In our evaluations, the method outperforms all the state-of-the-art image retrieval algorithms on some out-of-domain image datasets. The code for the model implementation is available at https://github.com/chenmingxiang110/AugNet. | ['Zhecheng Wang', 'Liufang Guo', 'Zhuang Li', 'Bitao Yang', 'Haonan Lu', 'Zhanguo Chang', 'Mingxiang Chen'] | 2021-06-11 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-1.21826030e-01 -5.14514804e-01 -2.56746590e-01 -7.74429202e-01
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9b65f53a-85b6-428a-94a3-7a672d7424d0 | pedestrian-detection-with-high-resolution | 2305.18008 | null | https://arxiv.org/abs/2305.18008v1 | https://arxiv.org/pdf/2305.18008v1.pdf | Pedestrian detection with high-resolution event camera | Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by traditional cameras is often prone to problems such as motion blur or degraded image quality due to challenging lighting conditions. In addition, the frame rate - typically 30 or 60 frames per second - can be a limiting factor in certain scenarios. Event cameras (DVS -- Dynamic Vision Sensor) are a potentially interesting technology to address the above mentioned problems. In this paper, we compare two methods of processing event data by means of deep learning for the task of pedestrian detection. We used a representation in the form of video frames, convolutional neural networks and asynchronous sparse convolutional neural networks. The results obtained illustrate the potential of event cameras and allow the evaluation of the accuracy and efficiency of the methods used for high-resolution (1280 x 720 pixels) footage. | ['Tomasz Kryjak', 'Piotr Wzorek'] | 2023-05-29 | null | null | null | null | ['self-driving-cars', 'pedestrian-detection', 'autonomous-vehicles'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.75776356e-01 -4.76023853e-01 1.53197318e-01 -2.40799770e-01
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52cf6298-2092-42d4-8f1a-32325970b207 | projection-domain-self-supervision-for | 2212.07431 | null | https://arxiv.org/abs/2212.07431v2 | https://arxiv.org/pdf/2212.07431v2.pdf | Simulator-Based Self-Supervision for Learned 3D Tomography Reconstruction | We propose a deep learning method for 3D volumetric reconstruction in low-dose helical cone-beam computed tomography. Prior machine learning approaches require reference reconstructions computed by another algorithm for training. In contrast, we train our model in a fully self-supervised manner using only noisy 2D X-ray data. This is enabled by incorporating a fast differentiable CT simulator in the training loop. As we do not rely on reference reconstructions, the fidelity of our results is not limited by their potential shortcomings. We evaluate our method on real helical cone-beam projections and simulated phantoms. Our results show significantly higher visual fidelity and better PSNR over techniques that rely on existing reconstructions. When applied to full-dose data, our method produces high-quality results orders of magnitude faster than iterative techniques. | ['Jaakko Lehtinen', 'Miika Aittala', 'Tero Karras', 'Samuli Laine', 'Onni Kosomaa'] | 2022-12-14 | null | null | null | null | ['3d-volumetric-reconstruction'] | ['computer-vision'] | [ 1.76282391e-01 1.99290320e-01 -4.37362790e-02 -5.94233751e-01
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795bbcdf-03a7-4353-855e-82f199c5b932 | a-fast-topological-approach-for-predicting | 2305.06523 | null | https://arxiv.org/abs/2305.06523v1 | https://arxiv.org/pdf/2305.06523v1.pdf | A fast topological approach for predicting anomalies in time-varying graphs | Large time-varying graphs are increasingly common in financial, social and biological settings. Feature extraction that efficiently encodes the complex structure of sparse, multi-layered, dynamic graphs presents computational and methodological challenges. In the past decade, a persistence diagram (PD) from topological data analysis (TDA) has become a popular descriptor of shape of data with a well-defined distance between points. However, applications of TDA to graphs, where there is no intrinsic concept of distance between the nodes, remain largely unexplored. This paper addresses this gap in the literature by introducing a computationally efficient framework to extract shape information from graph data. Our framework has two main steps: first, we compute a PD using the so-called lower-star filtration which utilizes quantitative node attributes, and then vectorize it by averaging the associated Betti function over successive scale values on a one-dimensional grid. Our approach avoids embedding a graph into a metric space and has stability properties against input noise. In simulation studies, we show that the proposed vector summary leads to improved change point detection rate in time-varying graphs. In a real data application, our approach provides up to 22% gain in anomalous price prediction for the Ethereum cryptocurrency transaction networks. | ['Ekaterina Smirnova', 'Cuneyt Akcora', 'Omid Khormali', 'Hasani Pathirana', 'Umar Islambekov'] | 2023-05-11 | null | null | null | null | ['topological-data-analysis', 'change-point-detection'] | ['graphs', 'time-series'] | [ 8.17043483e-02 -3.12311053e-01 -1.61118098e-02 6.84618950e-02
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5cb13570-34a9-4a7b-a03f-dc2d67e153e0 | learning-monocular-depth-estimation-infusing | 1904.04144 | null | http://arxiv.org/abs/1904.04144v1 | http://arxiv.org/pdf/1904.04144v1.pdf | Learning monocular depth estimation infusing traditional stereo knowledge | Depth estimation from a single image represents a fascinating, yet
challenging problem with countless applications. Recent works proved that this
task could be learned without direct supervision from ground truth labels
leveraging image synthesis on sequences or stereo pairs. Focusing on this
second case, in this paper we leverage stereo matching in order to improve
monocular depth estimation. To this aim we propose monoResMatch, a novel deep
architecture designed to infer depth from a single input image by synthesizing
features from a different point of view, horizontally aligned with the input
image, performing stereo matching between the two cues. In contrast to previous
works sharing this rationale, our network is the first trained end-to-end from
scratch. Moreover, we show how obtaining proxy ground truth annotation through
traditional stereo algorithms, such as Semi-Global Matching, enables more
accurate monocular depth estimation still countering the need for expensive
depth labels by keeping a self-supervised approach. Exhaustive experimental
results prove how the synergy between i) the proposed monoResMatch architecture
and ii) proxy-supervision attains state-of-the-art for self-supervised
monocular depth estimation. The code is publicly available at
https://github.com/fabiotosi92/monoResMatch-Tensorflow. | ['Filippo Aleotti', 'Stefano Mattoccia', 'Matteo Poggi', 'Fabio Tosi'] | 2019-04-08 | learning-monocular-depth-estimation-infusing-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Tosi_Learning_Monocular_Depth_Estimation_Infusing_Traditional_Stereo_Knowledge_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Tosi_Learning_Monocular_Depth_Estimation_Infusing_Traditional_Stereo_Knowledge_CVPR_2019_paper.pdf | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [ 3.06945384e-01 3.53159696e-01 -1.03415839e-01 -3.74632299e-01
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d91f9d23-11ff-4488-b310-ad3c68bfd159 | a-synthesis-based-approach-for-thermal-to | 2108.09558 | null | https://arxiv.org/abs/2108.09558v3 | https://arxiv.org/pdf/2108.09558v3.pdf | A Synthesis-Based Approach for Thermal-to-Visible Face Verification | In recent years, visible-spectrum face verification systems have been shown to match the performance of experienced forensic examiners. However, such systems are ineffective in low-light and nighttime conditions. Thermal face imagery, which captures body heat emissions, effectively augments the visible spectrum, capturing discriminative facial features in scenes with limited illumination. Due to the increased cost and difficulty of obtaining diverse, paired thermal and visible spectrum datasets, not many algorithms and large-scale benchmarks for low-light recognition are available. This paper presents an algorithm that achieves state-of-the-art performance on both the ARL-VTF and TUFTS multi-spectral face datasets. Importantly, we study the impact of face alignment, pixel-level correspondence, and identity classification with label smoothing for multi-spectral face synthesis and verification. We show that our proposed method is widely applicable, robust, and highly effective. In addition, we show that the proposed method significantly outperforms face frontalization methods on profile-to-frontal verification. Finally, we present MILAB-VTF(B), a challenging multi-spectral face dataset that is composed of paired thermal and visible videos. To the best of our knowledge, with face data from 400 subjects, this dataset represents the most extensive collection of indoor and long-range outdoor thermal-visible face imagery. Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset. | ['Rama Chellappa', 'Vishal M. Patel', 'Thirimachos Bourlai', 'Carlos D. Castillo', 'Joshua Gleason', 'Neehar Peri'] | 2021-08-21 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 4.55150545e-01 -6.45599782e-01 1.39552772e-01 -6.32916987e-01
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fe5ab75e-46ce-45a0-8b31-75b1b7616942 | quality-and-cost-trade-offs-in-passage-re | 2111.09927 | null | https://arxiv.org/abs/2111.09927v1 | https://arxiv.org/pdf/2111.09927v1.pdf | Quality and Cost Trade-offs in Passage Re-ranking Task | Deep learning models named transformers achieved state-of-the-art results in a vast majority of NLP tasks at the cost of increased computational complexity and high memory consumption. Using the transformer model in real-time inference becomes a major challenge when implemented in production, because it requires expensive computational resources. The more executions of a transformer are needed the lower the overall throughput is, and switching to the smaller encoders leads to the decrease of accuracy. Our paper is devoted to the problem of how to choose the right architecture for the ranking step of the information retrieval pipeline, so that the number of required calls of transformer encoder is minimal with the maximum achievable quality of ranking. We investigated several late-interaction models such as Colbert and Poly-encoder architectures along with their modifications. Also, we took care of the memory footprint of the search index and tried to apply the learning-to-hash method to binarize the output vectors from the transformer encoders. The results of the evaluation are provided using TREC 2019-2021 and MS Marco dev datasets. | ['Marina Chernyshevich', 'Nikolay Bushkov', 'Andrei Khobnia', 'Pavel Goncharov', 'Raman Makouski', 'Vsevolod Mitskevich', 'Pavel Podberezko'] | 2021-11-18 | null | null | null | null | ['passage-re-ranking'] | ['natural-language-processing'] | [ 6.42118379e-02 7.92969167e-02 1.54913336e-01 -3.88015330e-01
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075c7d81-f7c1-4093-9bd4-ffca840cfc83 | fault-detection-scheme-for-grid-forming | 2209.12457 | null | https://arxiv.org/abs/2209.12457v3 | https://arxiv.org/pdf/2209.12457v3.pdf | Fault Detection for Grid-Forming Inverters in Islanded Droop-Controlled AC Microgrids | In this paper, we develop an observer-based fault detection mechanism for grid-forming inverters operating in islanded droop-controlled AC microgrids. The detection scheme uses linear matrix inequalities as constraints with $\mathcal{H}_{-}/\mathcal{H}_{\infty}$ optimization to achieve sensitivity to faults and robustness against disturbances or parametric uncertainties. We explore a nonlinear inverter model formulation based on the less-restrictive one-sided Lipschitz and quadratic inner-boundedness conditions instead of the state-of-the-art formulation based on the Lipschitz condition. In this sense, we aim to overcome the sensitivity of observer-based schemes to the Lipschitz constant. The relation between these two formulations for fault detection is analyzed theoretically. We find the deterministic matrix expressions of different internal faults, including busbar, actuator, and inverter bridge faults. The performance of the proposed detection method is tested on an islanded AC microgrid with four grid-forming inverters and compared against the state-of-the-art nonlinear detection based on the Lipschitz condition. Most importantly, this method requires no additional sensors, a crucial advantage over many proposed solutions in the literature. | ['Charalambos Konstantinou', 'Yu Zhang', 'Andres Intriago', 'Gabriel Intriago'] | 2022-09-26 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [-0.05266977 0.09146027 0.13207304 0.02838328 -0.30355775 -0.72432107
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70200d6b-7cd2-43b1-a0d2-906cce9ac204 | mimicking-a-pathologist-dual-attention-model | 2302.09682 | null | https://arxiv.org/abs/2302.09682v1 | https://arxiv.org/pdf/2302.09682v1.pdf | Mimicking a Pathologist: Dual Attention Model for Scoring of Gigapixel Histology Images | Some major challenges associated with the automated processing of whole slide images (WSIs) includes their sheer size, different magnification levels and high resolution. Utilizing these images directly in AI frameworks is computationally expensive due to memory constraints, while downsampling WSIs incurs information loss and splitting WSIs into tiles and patches results in loss of important contextual information. We propose a novel dual attention approach, consisting of two main components, to mimic visual examination by a pathologist. The first component is a soft attention model which takes as input a high-level view of the WSI to determine various regions of interest. We employ a custom sampling method to extract diverse and spatially distinct image tiles from selected high attention areas. The second component is a hard attention classification model, which further extracts a sequence of multi-resolution glimpses from each tile for classification. Since hard attention is non-differentiable, we train this component using reinforcement learning and predict the location of glimpses without processing all patches of a given tile, thereby aligning with pathologist's way of diagnosis. We train our components both separately and in an end-to-end fashion using a joint loss function to demonstrate the efficacy of our proposed model. We employ our proposed model on two different IHC use cases: HER2 prediction on breast cancer and prediction of Intact/Loss status of two MMR biomarkers, for colorectal cancer. We show that the proposed model achieves accuracy comparable to state-of-the-art methods while only processing a small fraction of the WSI at highest magnification. | ['Nasir M. Rajpoot', 'Talha Qaiser', 'Raja Muhammad Saad Bashir', 'Ruqayya Awan', 'Manahil Raza'] | 2023-02-19 | null | null | null | null | ['whole-slide-images', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 8.30066502e-01 4.85299319e-01 4.51282412e-02 -5.72059341e-02
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ea318b4f-62d0-490a-8fb2-61257bf36918 | speaker-specific-thresholding-for-robust | 2306.00952 | null | https://arxiv.org/abs/2306.00952v1 | https://arxiv.org/pdf/2306.00952v1.pdf | Speaker-specific Thresholding for Robust Imposter Identification in Unseen Speaker Recognition | Speaker identification systems are deployed in diverse environments, often different from the lab conditions on which they are trained and tested. In this paper, first, we show the problem of generalization using fixed thresholds computed using the equal error rate metric. Secondly, we introduce a novel and generalizable speaker-specific thresholding technique for robust imposter identification in unseen speaker identification. We propose a speaker-specific adaptive threshold, which can be computed using the enrollment audio samples, for identifying imposters in unseen speaker identification. Furthermore, we show the efficacy of the proposed technique on VoxCeleb1, VCTK and the FFSVC 2022 datasets, beating the baseline fixed thresholding by up to 25%. Finally, we exhibit that the proposed algorithm is also generalizable, demonstrating its performance on ResNet50, ECAPA-TDNN and RawNet3 speaker encoders. | ['Susmita Ghose', 'Sparsh Sinha', 'Ashutosh Chaubey'] | 2023-06-01 | null | null | null | null | ['speaker-recognition', 'speaker-identification'] | ['speech', 'speech'] | [ 3.15462917e-01 -2.26739287e-01 2.60099620e-01 -5.75426817e-01
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235d148d-7a52-46e9-93e8-b8edac34d549 | improved-conformalized-quantile-regression | 2207.02808 | null | https://arxiv.org/abs/2207.02808v8 | https://arxiv.org/pdf/2207.02808v8.pdf | Improved conformalized quantile regression | Conformalized quantile regression is a procedure that inherits the advantages of conformal prediction and quantile regression. That is, we use quantile regression to estimate the true conditional quantile and then apply a conformal step on a calibration set to ensure marginal coverage. In this way, we get adaptive prediction intervals that account for heteroscedasticity. However, the aforementioned conformal step lacks adaptiveness as described in (Romano et al., 2019). To overcome this limitation, instead of applying a single conformal step after estimating conditional quantiles with quantile regression, we propose to cluster the explanatory variables weighted by their permutation importance with an optimized k-means and apply k conformal steps. To show that this improved version outperforms the classic version of conformalized quantile regression and is more adaptive to heteroscedasticity, we extensively compare the prediction intervals of both in open datasets. | ['José Moreira', 'Ana Maria Tomé', 'Martim Sousa'] | 2022-07-06 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 5.12350872e-02 7.23451227e-02 -3.32857698e-01 -6.95724785e-01
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f4e1e4e5-7ad7-4dfc-8668-c2e6245f45d3 | aestheticnet-reducing-bias-in-facial-data | null | null | https://openreview.net/forum?id=Eot1M5o2Zy | https://openreview.net/pdf?id=Eot1M5o2Zy | AestheticNet: Reducing bias in facial data sets under ethical considerations | Facial Beauty Prediction (FBP) aims to develop a machine that can automatically evaluate facial attractiveness. Usually, these results were highly correlated with human ratings, and therefore also reflected human bias in annotations. Everyone will have biases that are usually subconscious and not easy to notice. Unconscious bias deserves more attention than explicit discrimination. It affects moral judgement and can evade moral responsibility, and we cannot eliminate it completely. A new challenge for scientists is to provide training data and AI algorithms that can withstand distorted information. Our experiments prove that human aesthetic judgements are usually biased. In this work, we introduce AestheticNet, the most advanced attractiveness prediction network, with a Pearson correlation coefficient of 0.9601, which is significantly better than the competition. This network is then used to enrich the training data with synthetic images in order to overwrite the ground truth values with fair assessments.
We propose a new method to generate an unbiased CNN to improve the fairness of machine learning. Prediction and recommender systems based on Artificial Intelligence (AI) technology are widely used in various sectors of industry, such as intelligent recruitment, security, etc. Therefore, their fairness is very important. Our research provides a practical example of how to build a fair and trustable AI. | ['Josef Kittler', 'Matthias Rätsch', 'Patrik Huber', 'Tobias Gerlach', 'Thomas Weber', 'Muhammad Awais Tanvir Rana', 'Michael Danner'] | 2021-09-29 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [-3.55373509e-02 5.51996112e-01 1.09101757e-01 -8.20860624e-01
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79e39d3f-519c-497e-9446-e73179da5960 | accurate-and-diverse-sampling-of-sequences | 1806.07772 | null | http://arxiv.org/abs/1806.07772v2 | http://arxiv.org/pdf/1806.07772v2.pdf | Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective | For autonomous agents to successfully operate in the real world, anticipation
of future events and states of their environment is a key competence. This
problem has been formalized as a sequence extrapolation problem, where a number
of observations are used to predict the sequence into the future. Real-world
scenarios demand a model of uncertainty of such predictions, as predictions
become increasingly uncertain -- in particular on long time horizons. While
impressive results have been shown on point estimates, scenarios that induce
multi-modal distributions over future sequences remain challenging. Our work
addresses these challenges in a Gaussian Latent Variable model for sequence
prediction. Our core contribution is a "Best of Many" sample objective that
leads to more accurate and more diverse predictions that better capture the
true variations in real-world sequence data. Beyond our analysis of improved
model fit, our models also empirically outperform prior work on three diverse
tasks ranging from traffic scenes to weather data. | ['Apratim Bhattacharyya', 'Mario Fritz', 'Bernt Schiele'] | 2018-06-20 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 3.44117850e-01 -5.21110334e-02 -1.94575816e-01 -6.26769066e-01
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53fd9a94-70bf-458c-929b-f81481286d35 | attentive-multi-view-deep-subspace-clustering | 2112.12506 | null | https://arxiv.org/abs/2112.12506v1 | https://arxiv.org/pdf/2112.12506v1.pdf | Attentive Multi-View Deep Subspace Clustering Net | In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained by attention mechanism. Unlike most multi-view subspace learning methods that they directly reconstruct data points on raw data or only consider consistency or complementarity when learning representation in deep or shallow space, our proposed method seeks to find a joint latent representation that explicitly considers both consensus and view-specific information among multiple views, and then performs subspace clustering on learned joint latent representation.Besides, different views contribute differently to representation learning, we therefore introduce attention mechanism to derive dynamic weight for each view, which performs much better than previous fusion methods in the field of multi-view subspace clustering. The proposed algorithm is intuitive and can be easily optimized just by using Stochastic Gradient Descent (SGD) because of the neural network framework, which also provides strong non-linear characterization capability compared with traditional subspace clustering approaches. The experimental results on seven real-world data sets have demonstrated the effectiveness of our proposed algorithm against some state-of-the-art subspace learning approaches. | ['Xin Zuo', 'Jian-wei Liu', 'Run-kun Lu'] | 2021-12-23 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.38702643e-01 -5.94862163e-01 -3.14332157e-01 -2.93947995e-01
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9441f8fb-5e40-41ea-ae42-0be2859d9682 | desam-decoupling-segment-anything-model-for | 2306.00499 | null | https://arxiv.org/abs/2306.00499v1 | https://arxiv.org/pdf/2306.00499v1.pdf | DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation | Deep learning based automatic medical image segmentation models often suffer from domain shift, where the models trained on a source domain do not generalize well to other unseen domains. As a vision foundation model with powerful generalization capabilities, Segment Anything Model (SAM) shows potential for improving the cross-domain robustness of medical image segmentation. However, SAM and its fine-tuned models performed significantly worse in fully automatic mode compared to when given manual prompts. Upon further investigation, we discovered that the degradation in performance was related to the coupling effect of poor prompts and mask segmentation. In fully automatic mode, the presence of inevitable poor prompts (such as points outside the mask or boxes significantly larger than the mask) can significantly mislead mask generation. To address the coupling effect, we propose the decoupling SAM (DeSAM). DeSAM modifies SAM's mask decoder to decouple mask generation and prompt embeddings while leveraging pre-trained weights. We conducted experiments on publicly available prostate cross-site datasets. The results show that DeSAM improves dice score by an average of 8.96% (from 70.06% to 79.02%) compared to previous state-of-the-art domain generalization method. Moreover, DeSAM can be trained on personal devices with entry-level GPU since our approach does not rely on tuning the heavyweight image encoder. The code is publicly available at https://github.com/yifangao112/DeSAM. | ['Xin Gao', 'Dingdu Hu', 'Wei Xia', 'Yifan Gao'] | 2023-06-01 | null | null | null | null | ['domain-generalization'] | ['methodology'] | [ 5.65285563e-01 3.43847305e-01 -2.55131155e-01 -5.30566871e-01
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c78befc3-320e-4f17-939a-51f64497e8cc | highres-net-recursive-fusion-for-multi-frame | 2002.06460 | null | https://arxiv.org/abs/2002.06460v1 | https://arxiv.org/pdf/2002.06460v1.pdf | HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery | Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery. | ['Vincent Michalski', 'Julien Cornebise', 'Israel Goytom', 'Yoshua Bengio', 'Samira E. Kahou', 'Michel Deudon', 'Kris Sankaran', 'Zhichao Lin', 'Md Rifat Arefin', 'Alfredo Kalaitzis'] | 2020-02-15 | null | null | null | null | ['de-aliasing', 'multi-frame-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 0.6323837 0.1988844 0.20600392 -0.49250332 -1.5286572 -0.41399863
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11ee0f62-d55a-4af9-9249-3e5776d86a78 | encoding-high-level-visual-attributes-in | 1909.05926 | null | https://arxiv.org/abs/1909.05926v5 | https://arxiv.org/pdf/1909.05926v5.pdf | Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses | Convolutional neural network based systems have largely failed to be adopted in many high-risk application areas, including healthcare, military, security, transportation, finance, and legal, due to their highly uninterpretable "black-box" nature. Towards solving this deficiency, we teach a novel multi-task capsule network to improve the explainability of predictions by embodying the same high-level language used by human-experts. Our explainable capsule network, X-Caps, encodes high-level visual object attributes within the vectors of its capsules, then forms predictions based solely on these human-interpretable features. To encode attributes, X-Caps utilizes a new routing sigmoid function to independently route information from child capsules to parents. Further, to provide radiologists with an estimate of model confidence, we train our network on a distribution of expert labels, modeling inter-observer agreement and punishing over/under confidence during training, supervised by human-experts' agreement. X-Caps simultaneously learns attribute and malignancy scores from a multi-center dataset of over 1000 CT scans of lung cancer screening patients. We demonstrate a simple 2D capsule network can outperform a state-of-the-art deep dense dual-path 3D CNN at capturing visually-interpretable high-level attributes and malignancy prediction, while providing malignancy prediction scores approaching that of non-explainable 3D CNNs. To the best of our knowledge, this is the first study to investigate capsule networks for making predictions based on radiologist-level interpretable attributes and its applications to medical image diagnosis. Code is publicly available at https://github.com/lalonderodney/X-Caps . | ['Ulas Bagci', 'Drew Torigian', 'Rodney LaLonde'] | 2019-09-12 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [-9.86038372e-02 9.66703951e-01 -5.78788400e-01 -7.63088703e-01
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7e02985f-e9fc-43ee-bc0b-76b4b00badce | photometric-lidar-and-rgb-d-bundle-adjustment | 2303.16878 | null | https://arxiv.org/abs/2303.16878v1 | https://arxiv.org/pdf/2303.16878v1.pdf | Photometric LiDAR and RGB-D Bundle Adjustment | The joint optimization of the sensor trajectory and 3D map is a crucial characteristic of Simultaneous Localization and Mapping (SLAM) systems. To achieve this, the gold standard is Bundle Adjustment (BA). Modern 3D LiDARs now retain higher resolutions that enable the creation of point cloud images resembling those taken by conventional cameras. Nevertheless, the typical effective global refinement techniques employed for RGB-D sensors are not widely applied to LiDARs. This paper presents a novel BA photometric strategy that accounts for both RGB-D and LiDAR in the same way. Our work can be used on top of any SLAM/GNSS estimate to improve and refine the initial trajectory. We conducted different experiments using these two depth sensors on public benchmarks. Our results show that our system performs on par or better compared to other state-of-the-art ad-hoc SLAM/BA strategies, free from data association and without making assumptions about the environment. In addition, we present the benefit of jointly using RGB-D and LiDAR within our unified method. We finally release an open-source CUDA/C++ implementation. | ['Giorgio Grisetti', 'Omar Salem', 'Leonardo Brizi', 'Emanuele Giacomini', 'Luca Di Giammarino'] | 2023-03-29 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-3.98436524e-02 -4.62215394e-01 1.74063370e-01 -5.62282979e-01
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36a186b9-c577-438f-b4ab-ab22388feda8 | deep-reinforcement-learning-based-text | null | null | https://aclanthology.org/D19-1240 | https://aclanthology.org/D19-1240.pdf | Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference | User-generated textual data is rich in content and has been used in many user behavioral modeling tasks. However, it could also leak user private-attribute information that they may not want to disclose such as age and location. User{'}s privacy concerns mandate data publishers to protect privacy. One effective way is to anonymize the textual data. In this paper, we study the problem of textual data anonymization and propose a novel Reinforcement Learning-based Text Anonymizor, RLTA, which addresses the problem of private-attribute leakage while preserving the utility of textual data. Our approach first extracts a latent representation of the original text w.r.t. a given task, then leverages deep reinforcement learning to automatically learn an optimal strategy for manipulating text representations w.r.t. the received privacy and utility feedback. Experiments show the effectiveness of this approach in terms of preserving both privacy and utility. | ['Huan Liu', 'Ghazaleh Beigi', 'Ahmadreza Mosallanezhad'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['text-anonymization'] | ['natural-language-processing'] | [ 3.36268663e-01 3.40476185e-01 -5.03230274e-01 -7.05634594e-01
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0102446c-47dc-45e1-9761-9eba8f937cc6 | automatic-sentence-classifier-using-sentence | null | null | https://aclanthology.org/U12-1018 | https://aclanthology.org/U12-1018.pdf | Automatic sentence classifier using sentence ordering features for Event Based Medicine: Shared task system description | null | ['ana', 'Sp Gella', 'Long Duong Thanh'] | 2012-12-01 | automatic-sentence-classifier-using-sentence-1 | https://aclanthology.org/U12-1018 | https://aclanthology.org/U12-1018.pdf | alta-2012-12 | ['sentence-ordering'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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c5aedf4e-6613-45b3-a16e-a07a06e01447 | learnable-skeleton-aware-3d-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wen_Learnable_Skeleton-Aware_3D_Point_Cloud_Sampling_CVPR_2023_paper.pdf | Learnable Skeleton-Aware 3D Point Cloud Sampling | Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-to-sample methods have recently received increasing attention from the community for jointly training with downstream tasks. However, the above-mentioned task-specific sampling methods usually fail to explore the geometries of objects in an explicit manner. In this paper, we introduce a new skeleton-aware learning-to-sample method by learning object skeletons as the prior knowledge to preserve the object geometry and topology information during sampling. Specifically, without labor-intensive annotations per object category, we first learn category-agnostic object skeletons via the medial axis transform definition in an unsupervised manner. With object skeleton, we then evaluate the histogram of the local feature size as the prior knowledge to formulate skeleton-aware sampling from a probabilistic perspective. Additionally, the proposed skeleton-aware sampling pipeline with the task network is thus end-to-end trainable by exploring the reparameterization trick. Extensive experiments on three popular downstream tasks, point cloud classification, retrieval, and reconstruction, demonstrate the effectiveness of the proposed method for efficient point cloud analysis. | ['DaCheng Tao', 'Baosheng Yu', 'Cheng Wen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-classification'] | ['computer-vision'] | [ 8.34892318e-02 -1.52306288e-01 -1.99731439e-01 -6.04011357e-01
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db0f60a4-ac63-4ee7-98a4-9744e5743ba6 | simpson-simplifying-photo-cleanup-with-single-1 | 2305.17624 | null | https://arxiv.org/abs/2305.17624v1 | https://arxiv.org/pdf/2305.17624v1.pdf | SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network | In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject. However, manually selecting and removing these small and dense distracting regions can be a laborious and time-consuming task. In this paper, we propose an interactive distractor selection method that is optimized to achieve the task with just a single click. Our method surpasses the precision and recall achieved by the traditional method of running panoptic segmentation and then selecting the segments containing the clicks. We also showcase how a transformer-based module can be used to identify more distracting regions similar to the user's click position. Our experiments demonstrate that the model can effectively and accurately segment unknown distracting objects interactively and in groups. By significantly simplifying the photo cleaning and retouching process, our proposed model provides inspiration for exploring rare object segmentation and group selection with a single click. | ['Abhinav Shrivastava', 'Sohrab Amirghodsi', 'Eli Shechtman', 'Connelly Barnes', 'Zhe Lin', 'Yuqian Zhou', 'Chuong Huynh'] | 2023-05-28 | simpson-simplifying-photo-cleanup-with-single | http://openaccess.thecvf.com//content/CVPR2023/html/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huynh_SimpSON_Simplifying_Photo_Cleanup_With_Single-Click_Distracting_Object_Segmentation_Network_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.30144173e-01 -1.26646832e-01 9.08969417e-02 -7.10094646e-02
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b80ba5db-3246-4f16-9d3c-a5524fbcd6a9 | the-compositional-structure-of-bayesian | 2305.06112 | null | https://arxiv.org/abs/2305.06112v1 | https://arxiv.org/pdf/2305.06112v1.pdf | The Compositional Structure of Bayesian Inference | Bayes' rule tells us how to invert a causal process in order to update our beliefs in light of new evidence. If the process is believed to have a complex compositional structure, we may observe that the inversion of the whole can be computed piecewise in terms of the component processes. We study the structure of this compositional rule, noting that it relates to the lens pattern in functional programming. Working in a suitably general axiomatic presentation of a category of Markov kernels, we see how we can think of Bayesian inversion as a particular instance of a state-dependent morphism in a fibred category. We discuss the compositional nature of this, formulated as a functor on the underlying category and explore how this can used for a more type-driven approach to statistical inference. | ['Toby St Clere Smithe', 'Jules Hedges', 'Dylan Braithwaite'] | 2023-05-10 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 4.47010636e-01 5.38070798e-01 7.57513866e-02 -5.24613380e-01
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69a02cfa-eb90-4e3e-954c-99ddd848f290 | towards-realistic-face-photo-sketch-synthesis | 1712.00899 | null | https://arxiv.org/abs/1712.00899v4 | https://arxiv.org/pdf/1712.00899v4.pdf | Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs | Face photo-sketch synthesis aims at generating a facial sketch/photo conditioned on a given photo/sketch. It is of wide applications including digital entertainment and law enforcement. Precisely depicting face photos/sketches remains challenging due to the restrictions on structural realism and textural consistency. While existing methods achieve compelling results, they mostly yield blurred effects and great deformation over various facial components, leading to the unrealistic feeling of synthesized images. To tackle this challenge, in this work, we propose to use the facial composition information to help the synthesis of face sketch/photo. Specially, we propose a novel composition-aided generative adversarial network (CA-GAN) for face photo-sketch synthesis. In CA-GAN, we utilize paired inputs including a face photo/sketch and the corresponding pixel-wise face labels for generating a sketch/photo. In addition, to focus training on hard-generated components and delicate facial structures, we propose a compositional reconstruction loss. Finally, we use stacked CA-GANs (SCA-GAN) to further rectify defects and add compelling details. Experimental results show that our method is capable of generating both visually comfortable and identity-preserving face sketches/photos over a wide range of challenging data. Our method achieves the state-of-the-art quality, reducing best previous Frechet Inception distance (FID) by a large margin. Besides, we demonstrate that the proposed method is of considerable generalization ability. We have made our code and results publicly available: https://fei-hdu.github.io/ca-gan/. | ['DaCheng Tao', 'Xingxin Xu', 'Meng Wang', 'Fei Gao', 'Qingming Huang', 'Jun Yu', 'Shengjie Shi'] | 2017-12-04 | null | null | null | null | ['face-sketch-synthesis'] | ['computer-vision'] | [ 4.69991565e-01 1.20046653e-01 1.37102008e-01 -3.06515962e-01
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b99bc83f-2997-42d7-8e2c-9a4c47491918 | video-semantic-segmentation-with-inter-frame | 2301.03832 | null | https://arxiv.org/abs/2301.03832v1 | https://arxiv.org/pdf/2301.03832v1.pdf | Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature Refinement | Video semantic segmentation aims to generate accurate semantic maps for each video frame. To this end, many works dedicate to integrate diverse information from consecutive frames to enhance the features for prediction, where a feature alignment procedure via estimated optical flow is usually required. However, the optical flow would inevitably suffer from inaccuracy, and then introduce noises in feature fusion and further result in unsatisfactory segmentation results. In this paper, to tackle the misalignment issue, we propose a spatial-temporal fusion (STF) module to model dense pairwise relationships among multi-frame features. Different from previous methods, STF uniformly and adaptively fuses features at different spatial and temporal positions, and avoids error-prone optical flow estimation. Besides, we further exploit feature refinement within a single frame and propose a novel memory-augmented refinement (MAR) module to tackle difficult predictions among semantic boundaries. Specifically, MAR can store the boundary features and prototypes extracted from the training samples, which together form the task-specific memory, and then use them to refine the features during inference. Essentially, MAR can move the hard features closer to the most likely category and thus make them more discriminative. We conduct extensive experiments on Cityscapes and CamVid, and the results show that our proposed methods significantly outperform previous methods and achieves the state-of-the-art performance. Code and pretrained models are available at https://github.com/jfzhuang/ST_Memory. | ['Junjie Li', 'Zilei Wang', 'Jiafan Zhuang'] | 2023-01-10 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [-8.18246827e-02 -3.90181601e-01 -2.02188671e-01 -3.38942230e-01
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049668de-4644-4964-a809-df062e453463 | shoe-supervised-hashing-with-output | 1502.00030 | null | http://arxiv.org/abs/1502.00030v1 | http://arxiv.org/pdf/1502.00030v1.pdf | SHOE: Supervised Hashing with Output Embeddings | We present a supervised binary encoding scheme for image retrieval that
learns projections by taking into account similarity between classes obtained
from output embeddings. Our motivation is that binary hash codes learned in
this way improve both the visual quality of retrieval results and existing
supervised hashing schemes. We employ a sequential greedy optimization that
learns relationship aware projections by minimizing the difference between
inner products of binary codes and output embedding vectors. We develop a joint
optimization framework to learn projections which improve the accuracy of
supervised hashing over the current state of the art with respect to standard
and sibling evaluation metrics. We further boost performance by applying the
supervised dimensionality reduction technique on kernelized input CNN features.
Experiments are performed on three datasets: CUB-2011, SUN-Attribute and
ImageNet ILSVRC 2010. As a by-product of our method, we show that using a
simple k-nn pooling classifier with our discriminative codes improves over the
complex classification models on fine grained datasets like CUB and offer an
impressive compression ratio of 1024 on CNN features. | ['Larry S. Davis', 'David Doermann', 'Varun Manjunatha', 'Sravanthi Bondugula'] | 2015-01-30 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [ 7.93535542e-03 -2.92162418e-01 -4.08008635e-01 -7.47578979e-01
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b6ffd95a-4e9b-4722-95e9-5706540bf9bf | out-of-distribution-detection-for-long-tailed | 2206.15186 | null | https://arxiv.org/abs/2206.15186v1 | https://arxiv.org/pdf/2206.15186v1.pdf | Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images | Recent years have witnessed a rapid development of automated methods for skin lesion diagnosis and classification. Due to an increasing deployment of such systems in clinics, it has become important to develop a more robust system towards various Out-of-Distribution(OOD) samples (unknown skin lesions and conditions). However, the current deep learning models trained for skin lesion classification tend to classify these OOD samples incorrectly into one of their learned skin lesion categories. To address this issue, we propose a simple yet strategic approach that improves the OOD detection performance while maintaining the multi-class classification accuracy for the known categories of skin lesion. To specify, this approach is built upon a realistic scenario of a long-tailed and fine-grained OOD detection task for skin lesion images. Through this approach, 1) First, we target the mixup amongst middle and tail classes to address the long-tail problem. 2) Later, we combine the above mixup strategy with prototype learning to address the fine-grained nature of the dataset. The unique contribution of this paper is two-fold, justified by extensive experiments. First, we present a realistic problem setting of OOD task for skin lesion. Second, we propose an approach to target the long-tailed and fine-grained aspects of the problem setting simultaneously to increase the OOD performance. | ['ZongYuan Ge', 'Paul Bonnington', 'Adrian Bowling', 'Yaniv Gal', 'Deval Mehta'] | 2022-06-30 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.35275936e-01 9.60360244e-02 -2.80612648e-01 -3.52967344e-02
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4dbbb53c-eb2e-4a00-98bd-5341217242f3 | toward-pareto-efficient-fairness-utility | 2201.00140 | null | https://arxiv.org/abs/2201.00140v1 | https://arxiv.org/pdf/2201.00140v1.pdf | Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning | The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems touch and influence more and more people in their daily lives. In fairness-aware recommendation, most of the existing algorithmic approaches mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizing the main recommendation objective, e.g., CTR. While this alleviates the impact of unfair recommendations, the expected return of an approach may significantly compromise the recommendation accuracy due to the inherent trade-off between fairness and utility. This motivates us to deal with these conflicting objectives and explore the optimal trade-off between them in recommendation. One conspicuous approach is to seek a Pareto efficient solution to guarantee optimal compromises between utility and fairness. Moreover, considering the needs of real-world e-commerce platforms, it would be more desirable if we can generalize the whole Pareto Frontier, so that the decision-makers can specify any preference of one objective over another based on their current business needs. Therefore, in this work, we propose a fairness-aware recommendation framework using multi-objective reinforcement learning, called MoFIR, which is able to learn a single parametric representation for optimal recommendation policies over the space of all possible preferences. Specially, we modify traditional DDPG by introducing conditioned network into it, which conditions the networks directly on these preferences and outputs Q-value-vectors. Experiments on several real-world recommendation datasets verify the superiority of our framework on both fairness metrics and recommendation measures when compared with all other baselines. We also extract the approximate Pareto Frontier on real-world datasets generated by MoFIR and compare to state-of-the-art fairness methods. | ['Yongfeng Zhang', 'Chu-Cheng Hsieh', 'Diane Hu', 'Saurabh Paul', 'Lucia Yu', 'Xiaoting Zhao', 'Yingqiang Ge'] | 2022-01-01 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-3.03497463e-01 -1.28554747e-01 -7.31492817e-01 -4.82485712e-01
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137ad251-84a1-4492-b186-7b2d4a107b0a | segnet-a-deep-convolutional-encoder-decoder | 1511.00561 | null | http://arxiv.org/abs/1511.00561v3 | http://arxiv.org/pdf/1511.00561v3.pdf | SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation | We present a novel and practical deep fully convolutional neural network
architecture for semantic pixel-wise segmentation termed SegNet. This core
trainable segmentation engine consists of an encoder network, a corresponding
decoder network followed by a pixel-wise classification layer. The architecture
of the encoder network is topologically identical to the 13 convolutional
layers in the VGG16 network. The role of the decoder network is to map the low
resolution encoder feature maps to full input resolution feature maps for
pixel-wise classification. The novelty of SegNet lies is in the manner in which
the decoder upsamples its lower resolution input feature map(s). Specifically,
the decoder uses pooling indices computed in the max-pooling step of the
corresponding encoder to perform non-linear upsampling. This eliminates the
need for learning to upsample. The upsampled maps are sparse and are then
convolved with trainable filters to produce dense feature maps. We compare our
proposed architecture with the widely adopted FCN and also with the well known
DeepLab-LargeFOV, DeconvNet architectures. This comparison reveals the memory
versus accuracy trade-off involved in achieving good segmentation performance.
SegNet was primarily motivated by scene understanding applications. Hence, it
is designed to be efficient both in terms of memory and computational time
during inference. It is also significantly smaller in the number of trainable
parameters than other competing architectures. We also performed a controlled
benchmark of SegNet and other architectures on both road scenes and SUN RGB-D
indoor scene segmentation tasks. We show that SegNet provides good performance
with competitive inference time and more efficient inference memory-wise as
compared to other architectures. We also provide a Caffe implementation of
SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/. | ['Vijay Badrinarayanan', 'Alex Kendall', 'Roberto Cipolla'] | 2015-11-02 | null | null | null | null | ['thermal-image-segmentation'] | ['computer-vision'] | [ 4.38547105e-01 2.40882382e-01 1.11435875e-01 -5.38078487e-01
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72b8585c-604c-4402-88b4-c356619c5b83 | evaluating-link-prediction-accuracy-on | 1607.07330 | null | http://arxiv.org/abs/1607.07330v1 | http://arxiv.org/pdf/1607.07330v1.pdf | Evaluating Link Prediction Accuracy on Dynamic Networks with Added and Removed Edges | The task of predicting future relationships in a social network, known as
link prediction, has been studied extensively in the literature. Many link
prediction methods have been proposed, ranging from common neighbors to
probabilistic models. Recent work by Yang et al. has highlighted several
challenges in evaluating link prediction accuracy. In dynamic networks where
edges are both added and removed over time, the link prediction problem is more
complex and involves predicting both newly added and newly removed edges. This
results in new challenges in the evaluation of dynamic link prediction methods,
and the recommendations provided by Yang et al. are no longer applicable,
because they do not address edge removal. In this paper, we investigate several
metrics currently used for evaluating accuracies of dynamic link prediction
methods and demonstrate why they can be misleading in many cases. We provide
several recommendations on evaluating dynamic link prediction accuracy,
including separation into two categories of evaluation. Finally we propose a
unified metric to characterize link prediction accuracy effectively using a
single number. | ['Vijay K. Devabhaktuni', 'Ruthwik R. Junuthula', 'Kevin S. Xu'] | 2016-07-25 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [ 6.49285167e-02 3.23373377e-01 -8.78816605e-01 -2.23320439e-01
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1a63f018-6956-4017-b377-2360ce0f4ce6 | exact-hard-monotonic-attention-for-character | 1905.06319 | null | https://arxiv.org/abs/1905.06319v2 | https://arxiv.org/pdf/1905.06319v2.pdf | Exact Hard Monotonic Attention for Character-Level Transduction | Many common character-level, string-to-string transduction tasks, e.g. graphemeto-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, which are non-monotonic, often outperform popular monotonic models. In this work, we ask the following question: Is monotonicity really a helpful inductive bias in these tasks? We develop a hard attention sequence-to-sequence model that enforces strict monotonicity and learns a latent alignment jointly while learning to transduce. With the help of dynamic programming, we are able to compute the exact marginalization over all monotonic alignments. Our models achieve state-of-the-art performance on morphological inflection. Furthermore, we find strong performance on two other character-level transduction tasks. Code is available at https://github.com/shijie-wu/neural-transducer. | ['Ryan Cotterell', 'Shijie Wu'] | 2019-05-15 | exact-hard-monotonic-attention-for-character-1 | https://aclanthology.org/P19-1148 | https://aclanthology.org/P19-1148.pdf | acl-2019-7 | ['hard-attention'] | ['methodology'] | [ 7.30469465e-01 -3.43980119e-02 -3.95113289e-01 -4.13940847e-01
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bd278683-9565-4b27-92a9-c084eab9aa53 | hysia-serving-dnn-based-video-to-retail | 2006.05117 | null | https://arxiv.org/abs/2006.05117v1 | https://arxiv.org/pdf/2006.05117v1.pdf | Hysia: Serving DNN-Based Video-to-Retail Applications in Cloud | Combining \underline{v}ideo streaming and online \underline{r}etailing (V2R) has been a growing trend recently. In this paper, we provide practitioners and researchers in multimedia with a cloud-based platform named Hysia for easy development and deployment of V2R applications. The system consists of: 1) a back-end infrastructure providing optimized V2R related services including data engine, model repository, model serving and content matching; and 2) an application layer which enables rapid V2R application prototyping. Hysia addresses industry and academic needs in large-scale multimedia by: 1) seamlessly integrating state-of-the-art libraries including NVIDIA video SDK, Facebook faiss, and gRPC; 2) efficiently utilizing GPU computation; and 3) allowing developers to bind new models easily to meet the rapidly changing deep learning (DL) techniques. On top of that, we implement an orchestrator for further optimizing DL model serving performance. Hysia has been released as an open source project on GitHub, and attracted considerable attention. We have published Hysia to DockerHub as an official image for seamless integration and deployment in current cloud environments. | ['Yichao Jin', 'Yong Luo', 'Nguyen Binh Duong Ta', 'Yuanming Li', 'Qiming Ai', 'Huaizheng Zhang', 'Yonggang Wen'] | 2020-06-09 | null | null | null | null | ['video-to-shop'] | ['computer-vision'] | [-6.86127484e-01 -6.99112773e-01 -2.14319751e-01 -1.72195524e-01
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ea5333be-b149-4d2d-b0c8-fc089bfa810c | selftext-beyond-polygon-unconstrained-text | 2011.13307 | null | https://arxiv.org/abs/2011.13307v3 | https://arxiv.org/pdf/2011.13307v3.pdf | Polygon-free: Unconstrained Scene Text Detection with Box Annotations | Although a polygon is a more accurate representation than an upright bounding box for text detection, the annotations of polygons are extremely expensive and challenging. Unlike existing works that employ fully-supervised training with polygon annotations, this study proposes an unconstrained text detection system termed Polygon-free (PF), in which most existing polygon-based text detectors (e.g., PSENet [33],DB [16]) are trained with only upright bounding box annotations. Our core idea is to transfer knowledge from synthetic data to real data to enhance the supervision information of upright bounding boxes. This is made possible with a simple segmentation network, namely Skeleton Attention Segmentation Network (SASN), that includes three vital components (i.e., channel attention, spatial attention and skeleton attention map) and one soft cross-entropy loss. Experiments demonstrate that the proposed Polygonfree system can combine general detectors (e.g., EAST, PSENet, DB) to yield surprisingly high-quality pixel-level results with only upright bounding box annotations on a variety of datasets (e.g., ICDAR2019-Art, TotalText, ICDAR2015). For example, without using polygon annotations, PSENet achieves an 80.5% F-score on TotalText [3] (vs. 80.9% of fully supervised counterpart), 31.1% better than training directly with upright bounding box annotations, and saves 80%+ labeling costs. We hope that PF can provide a new perspective for text detection to reduce the labeling costs. The code can be found at https://github.com/weijiawu/Unconstrained-Text-Detection-with-Box-Supervisionand-Dynamic-Self-Training. | ['Hong Zhou', 'Ping Luo', 'Wenhai Wang', 'Ruimao Zhang', 'Enze Xie', 'Weijia Wu'] | 2020-11-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.50823128e-01 3.04343432e-01 -2.04576254e-01 -7.21424669e-02
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ffc0afe3-6702-48c9-89d8-2788db94df78 | performance-analysis-of-free-space | 2211.14771 | null | https://arxiv.org/abs/2211.14771v1 | https://arxiv.org/pdf/2211.14771v1.pdf | Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications | In next-generation Internet services, such as Metaverse, the mixed reality (MR) technique plays a vital role. Yet the limited computing capacity of the user-side MR headset-mounted device (HMD) prevents its further application, especially in scenarios that require a lot of computation. One way out of this dilemma is to design an efficient information sharing scheme among users to replace the heavy and repetitive computation. In this paper, we propose a free-space information sharing mechanism based on full-duplex device-to-device (D2D) semantic communications. Specifically, the view images of MR users in the same real-world scenario may be analogous. Therefore, when one user (i.e., a device) completes some computation tasks, the user can send his own calculation results and the semantic features extracted from the user's own view image to nearby users (i.e., other devices). On this basis, other users can use the received semantic features to obtain the spatial matching of the computational results under their own view images without repeating the computation. Using generalized small-scale fading models, we analyze the key performance indicators of full-duplex D2D communications, including channel capacity and bit error probability, which directly affect the transmission of semantic information. Finally, the numerical analysis experiment proves the effectiveness of our proposed methods. | ['Boon Hee Soong', 'Dong In Kim', 'Zehui Xiong', 'Jiawen Kang', 'Dusit Niyato', 'Jiacheng Wang', 'Hongyang Du'] | 2022-11-27 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-7.69003034e-02 -2.75630150e-02 -9.15338397e-02 -2.22474560e-01
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92f9495b-9fd6-414d-9ddd-3794c1d255dd | exploiting-optical-flow-guidance-for | 2301.10048 | null | https://arxiv.org/abs/2301.10048v1 | https://arxiv.org/pdf/2301.10048v1.pdf | Exploiting Optical Flow Guidance for Transformer-Based Video Inpainting | Transformers have been widely used for video processing owing to the multi-head self attention (MHSA) mechanism. However, the MHSA mechanism encounters an intrinsic difficulty for video inpainting, since the features associated with the corrupted regions are degraded and incur inaccurate self attention. This problem, termed query degradation, may be mitigated by first completing optical flows and then using the flows to guide the self attention, which was verified in our previous work - flow-guided transformer (FGT). We further exploit the flow guidance and propose FGT++ to pursue more effective and efficient video inpainting. First, we design a lightweight flow completion network by using local aggregation and edge loss. Second, to address the query degradation, we propose a flow guidance feature integration module, which uses the motion discrepancy to enhance the features, together with a flow-guided feature propagation module that warps the features according to the flows. Third, we decouple the transformer along the temporal and spatial dimensions, where flows are used to select the tokens through a temporally deformable MHSA mechanism, and global tokens are combined with the inner-window local tokens through a dual perspective MHSA mechanism. FGT++ is experimentally evaluated to be outperforming the existing video inpainting networks qualitatively and quantitatively. | ['Dong Liu', 'Jingjing Fu', 'Jialun Peng', 'Kaidong Zhang'] | 2023-01-24 | null | null | null | null | ['video-inpainting'] | ['computer-vision'] | [-0.04445363 -0.38034216 -0.09133589 -0.04327165 -0.47532895 -0.24358751
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90babc86-0b54-484f-8070-393c0c1c4225 | spatio-temporal-instance-learning-action | 1807.02800 | null | http://arxiv.org/abs/1807.02800v2 | http://arxiv.org/pdf/1807.02800v2.pdf | Spatio-Temporal Instance Learning: Action Tubes from Class Supervision | The goal of this work is spatio-temporal action localization in videos, using
only the supervision from video-level class labels. The state-of-the-art casts
this weakly-supervised action localization regime as a Multiple Instance
Learning problem, where instances are a priori computed spatio-temporal
proposals. Rather than disconnecting the spatio-temporal learning from the
training, we propose Spatio-Temporal Instance Learning, which enables action
localization directly from box proposals in video frames. We outline the
assumptions of our model and propose a max-margin objective and optimization
with latent variables that enable spatio-temporal learning of actions from
video labels. We also provide an efficient linking algorithm and two reranking
strategies to facilitate and further improve the action localization.
Experimental evaluation on four action datasets demonstrate the effectiveness
of our approach for localization from weak supervision. Moreover, we show how
to incorporate other supervision levels and mixtures, as a step towards
determining optimal supervision strategies for action localization. | ['Pascal Mettes', 'Cees G. M. Snoek'] | 2018-07-08 | null | null | null | null | ['weakly-supervised-action-localization', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision'] | [ 3.47268403e-01 1.54639587e-01 -1.04468584e+00 -2.69848943e-01
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ad6f0f2c-28ca-4b7c-a578-7f0c6e373f84 | sta-spatial-temporal-attention-for-large | 1811.04129 | null | http://arxiv.org/abs/1811.04129v1 | http://arxiv.org/pdf/1811.04129v1.pdf | STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification | In this work, we propose a novel Spatial-Temporal Attention (STA) approach to
tackle the large-scale person re-identification task in videos. Different from
the most existing methods, which simply compute representations of video clips
using frame-level aggregation (e.g. average pooling), the proposed STA adopts a
more effective way for producing robust clip-level feature representation.
Concretely, our STA fully exploits those discriminative parts of one target
person in both spatial and temporal dimensions, which results in a 2-D
attention score matrix via inter-frame regularization to measure the
importances of spatial parts across different frames. Thus, a more robust
clip-level feature representation can be generated according to a weighted sum
operation guided by the mined 2-D attention score matrix. In this way, the
challenging cases for video-based person re-identification such as pose
variation and partial occlusion can be well tackled by the STA. We conduct
extensive experiments on two large-scale benchmarks, i.e. MARS and
DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which
significantly outperforms the state-of-the-arts with a large margin of more
than 11.6%. | ['Yunchao Wei', 'Yang Fu', 'Xiaoyang Wang', 'Thomas Huang'] | 2018-11-09 | null | null | null | null | ['large-scale-person-re-identification'] | ['computer-vision'] | [-6.97646141e-02 -5.14658153e-01 6.50356263e-02 -3.56041312e-01
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f57a8f0d-790b-4ea1-a4d0-37a931bc91dc | vision-lanauge-pre-training-by-contrastive | 2305.04474 | null | https://arxiv.org/abs/2305.04474v3 | https://arxiv.org/pdf/2305.04474v3.pdf | Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation | Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance. | ['Fei Huang', 'Jie Zhang', 'Shikun Zhang', 'Miang yan', 'Haiyang Xu', 'Wei Ye', 'Chaoya Jiang'] | 2023-05-08 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 4.43277985e-01 1.04926057e-01 -1.20099835e-01 -4.47880089e-01
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af3cd297-d150-4b4f-99a0-06b2faca48a8 | hyperspectral-unmixing-ground-truth-labeling | 1708.05125 | null | http://arxiv.org/abs/1708.05125v2 | http://arxiv.org/pdf/1708.05125v2.pdf | Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey | Hyperspectral unmixing (HU) is a very useful and increasingly popular
preprocessing step for a wide range of hyperspectral applications. However, the
HU research has been constrained a lot by three factors: (a) the number of
hyperspectral images (especially the ones with ground truths) are very limited;
(b) the ground truths of most hyperspectral images are not shared on the web,
which may cause lots of unnecessary troubles for researchers to evaluate their
algorithms; (c) the codes of most state-of-the-art methods are not shared,
which may also delay the testing of new methods.
Accordingly, this paper deals with the above issues from the following three
perspectives: (1) as a profound contribution, we provide a general labeling
method for the HU. With it, we labeled up to 15 hyperspectral images, providing
18 versions of ground truths. To the best of our knowledge, this is the first
paper to summarize and share up to 15 hyperspectral images and their 18
versions of ground truths for the HU. Observing that the hyperspectral
classification (HyC) has much more standard datasets (whose ground truths are
generally publicly shared) than the HU, we propose an interesting method to
transform the HyC datasets for the HU research. (2) To further facilitate the
evaluation of HU methods under different conditions, we reviewed and
implemented the algorithm to generate a complex synthetic hyperspectral image.
By tuning the hyper-parameters in the code, we may verify the HU methods from
four perspectives. The code would also be shared on the web. (3) To provide a
standard comparison, we reviewed up to 10 state-of-the-art HU algorithms, then
selected the 5 most benchmark HU algorithms, and compared them on the 15 real
hyperspectral datasets. The experiment results are surely reproducible; the
implemented codes would be shared on the web. | ['Feiyun Zhu'] | 2017-08-17 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 4.78233814e-01 -4.37697828e-01 -2.68615838e-02 -1.19258203e-01
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5d55b4ff-44fa-4022-9551-7b9d340fb324 | multi-graph-based-multi-scenario | 2205.02446 | null | https://arxiv.org/abs/2205.02446v1 | https://arxiv.org/pdf/2205.02446v1.pdf | Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services | Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circulations of machine learning training on human interaction history leads algorithms to repeatedly suggest exposed items while ignoring less-active ones. Additional problems exist in multi-scenario platforms, e.g. appropriate data fusion from subsidiary scenarios, which we observe could be alleviated through graph structured data integration via message passing. In this paper, we present a multi-graph structured multi-scenario recommendation solution, which encapsulates interaction data across scenarios with multi-graph and obtains representation via graph learning. Extensive offline and online experiments on real-world datasets are conducted where the proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video Views per capita on new users over deployed set of baselines and outperforms regular method in increasing the number of outer-scenario videos by 25% and video watches by 116%, validating its superiority in activating cold videos and enriching target recommendation. | ['Yue Qi', 'Da Lin', 'Rong Zeng', 'Zheng Pan', 'Yulin Wu', 'Qiuying Peng', 'Fan Zhang'] | 2022-05-05 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-5.12989834e-02 -9.72324517e-03 -6.20833337e-01 -3.18929493e-01
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86504c34-30e4-4050-8852-8e6224c5ee91 | effects-of-a-mesoporous-bioactive-glass-on | 2103.08552 | null | https://arxiv.org/abs/2103.08552v1 | https://arxiv.org/pdf/2103.08552v1.pdf | Effects of a mesoporous bioactive glass on osteoblasts, osteoclasts and macrophages | A mesoporous bioactive glass (MBG) of molar composition 75SiO2-20CaO-5P2O5 (MBG-75S) has been synthetized as a potential bioceramic for bone regeneration purposes. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), nitrogen adsorption studies and transmission electron microscopy (TEM) demonstrated that MBG-75Spossess a highly ordered mesoporous structure with high surface area and porosity, which would explain the high ionic exchange rate (mainly calcium and silicon soluble species) with the surrounded media. MBG-75S showed high biocompatibility in contact with Saos-2 osteoblast-like cells. Concentrations up to 1 mg/ml did not lead to significant alterations on either morphology or cell cycle. Regarding the effects on osteoclasts, MBG-75S allowed the differentiation of RAW-264.7 macrophages into osteoclast-like cells but exhibiting a decreased resorptive activity. These results point out that MBG-75S does not inhibit osteoclastogenesis but reduces the osteoclast boneresorbing capability. Finally, in vitro studies focused on the innate immune response, evidenced that MBG-75S allows the proliferation of macrophages without inducing their polarization towards the M1 pro-inflammatory phenotype. This in vitro behavior is indicative that MBG-75S would just induce the required innate immune response without further inflammatory complications under in vivo conditions. The overall behavior respect to osteoblasts, osteoclasts and macrophages, makes this MBG a very interesting candidate for bone grafting applications in osteoporotic patients. | ['M. T. Portoles', 'D. Arcos', 'M. Vallet-Regi', 'M. J. Feito', 'I. Morales', 'L. Casarrubios', 'N. Gomez-Cerezo'] | 2021-03-15 | null | null | null | null | ['x-ray-diffraction'] | ['miscellaneous'] | [-7.49827325e-02 2.65652776e-01 -4.63142157e-01 5.71000516e-01
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0d93bd2c-4a23-48e7-904c-026a5c406d0a | robust-zero-shot-cross-domain-slot-filling | 1906.06870 | null | https://arxiv.org/abs/1906.06870v1 | https://arxiv.org/pdf/1906.06870v1.pdf | Robust Zero-Shot Cross-Domain Slot Filling with Example Values | Task-oriented dialog systems increasingly rely on deep learning-based slot filling models, usually needing extensive labeled training data for target domains. Often, however, little to no target domain training data may be available, or the training and target domain schemas may be misaligned, as is common for web forms on similar websites. Prior zero-shot slot filling models use slot descriptions to learn concepts, but are not robust to misaligned schemas. We propose utilizing both the slot description and a small number of examples of slot values, which may be easily available, to learn semantic representations of slots which are transferable across domains and robust to misaligned schemas. Our approach outperforms state-of-the-art models on two multi-domain datasets, especially in the low-data setting. | ['Dilek Hakkani-Tur', 'Raghav Gupta', 'Darsh J Shah', 'Amir A Fayazi'] | 2019-06-17 | robust-zero-shot-cross-domain-slot-filling-1 | https://aclanthology.org/P19-1547 | https://aclanthology.org/P19-1547.pdf | acl-2019-7 | ['zero-shot-slot-filling'] | ['natural-language-processing'] | [-7.74306385e-03 5.12671173e-01 -6.69593155e-01 -6.89381599e-01
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87f10336-e15a-437c-9297-206c26d96aec | pypop7-a-pure-python-library-for-population | 2212.05652 | null | https://arxiv.org/abs/2212.05652v2 | https://arxiv.org/pdf/2212.05652v2.pdf | PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization | In this paper, we present a pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods are becoming increasingly popular for BBO, our design goal is to provide a unified API and elegant implementations for them, particularly for high-dimensional cases. Since population-based methods suffer easily from the curse of dimenisoanlity owing to their random sampling nature, various improvements have been proposed to alleviate this issue via exploiting possible problem structures: such as space decomposition, low-memory approximation, low-rank metric learning, variance reduction, ensemble of random subspaces, model self-adaptation, and smoothing. Now PyPop7 has covered these advances with $>72$ versions and variants of 13 BBO algorithm families from different research communities. Its open-source code and full-fledged documents are available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io, respectively. | ['Yuhui Shi', 'Qi Zhao', 'Yijun Yang', 'Mingyang Feng', 'Zhuowei Wang', 'Chang Shao', 'Guochen Zhou', 'Qiqi Duan'] | 2022-12-12 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-5.06277621e-01 -2.62051314e-01 -9.88385528e-02 -6.90298527e-02
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2306b0ef-d6af-43b1-b95b-24093157a2fa | unsupervised-deep-keyphrase-generation | 2104.08729 | null | https://arxiv.org/abs/2104.08729v1 | https://arxiv.org/pdf/2104.08729v1.pdf | Unsupervised Deep Keyphrase Generation | Keyphrase generation aims to summarize long documents with a collection of salient phrases. Deep neural models have demonstrated a remarkable success in this task, capable of predicting keyphrases that are even absent from a document. However, such abstractiveness is acquired at the expense of a substantial amount of annotated data. In this paper, we present a novel method for keyphrase generation, AutoKeyGen, without the supervision of any human annotation. Motivated by the observation that an absent keyphrase in one document can appear in other places, in whole or in part, we first construct a phrase bank by pooling all phrases in a corpus. With this phrase bank, we then draw candidate absent keyphrases for each document through a partial matching process. To rank both types of candidates, we combine their lexical- and semantic-level similarities to the input document. Moreover, we utilize these top-ranked candidates as to train a deep generative model for more absent keyphrases. Extensive experiments demonstrate that AutoKeyGen outperforms all unsupervised baselines and can even beat strong supervised methods in certain cases. | ['Jingbo Shang', 'Rui Meng', 'Yinghan Wang', 'Xianjie Shen'] | 2021-04-18 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 2.15536460e-01 1.98759452e-01 -3.16205442e-01 7.97464848e-02
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0b275fb5-a41e-4cf7-800b-75f92c305af7 | faf-a-novel-multimodal-emotion-recognition | 2211.15425 | null | https://arxiv.org/abs/2211.15425v1 | https://arxiv.org/pdf/2211.15425v1.pdf | FAF: A novel multimodal emotion recognition approach integrating face, body and text | Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information from the aligned face, body and text samples. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our method. The results show that the five classification accuracy of the proposed multimodal fusion method is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62% respectively compared with that of individual modalities. The complementarity between each channel is effectively used to improve the performance of emotion recognition. We had also established a multimodal online emotion prediction platform, aiming to provide free emotion prediction to more users. | ['Lei Ma', 'Tong Zhang', 'Weiping Ding', 'Baopeng Gao', 'Qihui Yu', 'Aoyun He', 'Zhongyu Fang'] | 2022-11-20 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-4.78357300e-02 -3.03778380e-01 8.75161365e-02 -3.45247775e-01
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0552b66a-98de-4a59-88c6-d502f472c821 | learn-what-is-possible-then-choose-what-is | 2304.07258 | null | https://arxiv.org/abs/2304.07258v2 | https://arxiv.org/pdf/2304.07258v2.pdf | Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games | Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP. These pre-training datasets often have a one-to-many structure--e.g. in dialogue there are many valid responses for a given context. However, only some of these responses will be desirable in our downstream task. This raises the question of how we should train the model such that it can emulate the desirable behaviours, but not the undesirable ones. Current approaches train in a one-to-one setup--only a single target response is given for a single dialogue context--leading to models only learning to predict the average response, while ignoring the full range of possible responses. Using text-based games as a testbed, our approach, PASA, uses discrete latent variables to capture the range of different behaviours represented in our larger pre-training dataset. We then use knowledge distillation to distil the posterior probability distribution into a student model. This probability distribution is far richer than learning from only the hard targets of the dataset, and thus allows the student model to benefit from the richer range of actions the teacher model has learned. Results show up to 49% empirical improvement over the previous state-of-the-art model on the Jericho Walkthroughs dataset. | ['Ke Zhou', 'Benjamin Towle'] | 2023-04-14 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [ 2.11612001e-01 6.06136262e-01 -3.30804199e-01 -6.06790900e-01
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31cfe6e7-14da-4992-9a20-5128d69325df | indudonet-a-model-driven-interpretable-dual | 2112.12660 | null | https://arxiv.org/abs/2112.12660v2 | https://arxiv.org/pdf/2112.12660v2.pdf | InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images | During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the specific MAR task; hence, the role of each network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet+ with clear interpretability. Furthermore, we analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. . Code is available at \url{https://github.com/hongwang01/InDuDoNet_plus}. | ['Yefeng Zheng', 'Deyu Meng', 'Haimiao Zhang', 'Yuexiang Li', 'Hong Wang'] | 2021-12-23 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 6.29097447e-02 1.09193400e-01 5.94229437e-02 -1.20896794e-01
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83aaf916-5b91-4f6c-92ee-ca8c89bd2b70 | enhancing-dynamic-image-advertising-with | 2306.14112 | null | https://arxiv.org/abs/2306.14112v1 | https://arxiv.org/pdf/2306.14112v1.pdf | Enhancing Dynamic Image Advertising with Vision-Language Pre-training | In the multimedia era, image is an effective medium in search advertising. Dynamic Image Advertising (DIA), a system that matches queries with ad images and generates multimodal ads, is introduced to improve user experience and ad revenue. The core of DIA is a query-image matching module performing ad image retrieval and relevance modeling. Current query-image matching suffers from limited and inconsistent data, and insufficient cross-modal interaction. Also, the separate optimization of retrieval and relevance models affects overall performance. To address this issue, we propose a vision-language framework consisting of two parts. First, we train a base model on large-scale image-text pairs to learn general multimodal representation. Then, we fine-tune the base model on advertising business data, unifying relevance modeling and retrieval through multi-objective learning. Our framework has been implemented in Baidu search advertising system "Phoneix Nest". Online evaluation shows that it improves cost per mille (CPM) and click-through rate (CTR) by 1.04% and 1.865%. | ['Lin Liu', 'Shuanglong Li', 'Xiaodong Chen', 'Wei Jia', 'Yi Yang', 'Zhipeng Jin', 'Xinyu Zhao', 'Zhoufutu Wen'] | 2023-06-25 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 2.79695950e-02 -2.45605081e-01 -5.69947839e-01 -5.46103060e-01
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c10bbb5f-7123-4103-b68f-78730617fb19 | feature-selection-based-intrusion-detection | 2201.00584 | null | https://arxiv.org/abs/2201.00584v1 | https://arxiv.org/pdf/2201.00584v1.pdf | Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification | Preventing and detecting intrusions and attacks on wireless networks has become an important and serious challenge. On the other hand, due to the limited resources of wireless nodes, the use of monitoring nodes for permanent monitoring in wireless sensor networks in order to prevent and detect intrusion and attacks in this type of network is practically non-existent. Therefore, the solution to overcome this problem today is the discussion of remote-control systems and has become one of the topics of interest in various fields. Remote monitoring of node performance and behavior in wireless sensor networks, in addition to detecting malicious nodes within the network, can also predict malicious node behavior in future. In present research, a network intrusion detection system using feature selection based on a combination of Whale optimization algorithm (WOA) and genetic algorithm (GA) and sample-based classification is proposed. In this research, the standard data set KDDCUP1999 has been used in which the characteristics related to healthy nodes and types of malicious nodes are stored based on the type of attacks in the network. The proposed method is based on the combination of feature selection based on Whale optimization algorithm and genetic algorithm with KNN classification in terms of accuracy criteria, has better results than other previous methods. Based on this, it can be said that the Whale optimization algorithm and the genetic algorithm have extracted the features related to the class label well, and the KNN method has been able to well detect the misconduct nodes in the intrusion detection data set in wireless networks. | ['Saeedeh Shafaei Mehr', 'Aidin Molazadeh', 'Alireza Najafi Souha', 'Farid Sorouri', 'Amir Mojtahedi'] | 2022-01-03 | null | null | null | null | ['network-intrusion-detection'] | ['miscellaneous'] | [ 1.20467342e-01 -1.25369325e-01 1.62671074e-01 -1.57054558e-01
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83c57708-d5c9-466d-ad36-4630b184ad26 | which-style-makes-me-attractive-interpretable | 2201.09689 | null | https://arxiv.org/abs/2201.09689v1 | https://arxiv.org/pdf/2201.09689v1.pdf | Which Style Makes Me Attractive? Interpretable Control Discovery and Counterfactual Explanation on StyleGAN | The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e.g., face parsers and face landmark detectors. These models provide the flexibility to construct various criterions with very concrete and interpretable semantic meanings (e.g., change face shape or change skin color) to restrict latent subspace disentanglement. Rich latent space controls unknown previously can be discovered using the constructed criterions. Second, we propose a new perspective to explain the behavior of a CNN classifier by generating counterfactuals in the interpretable latent subspaces we discovered. This explanation helps reveal whether the classifier learns semantics as intended. Experiments on various disentanglement criterions demonstrate the effectiveness of our approach. We believe this approach contributes to both areas of image manipulation and counterfactual explainability of CNNs. The code is available at \url{https://github.com/prclibo/ice}. | ['Xiangyang Ji', 'Yu Yang', 'JiQuan Pei', 'Qiulin Wang', 'Bo Li'] | 2022-01-24 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.14379901e-01 7.77054727e-01 -4.30247635e-01 -4.55752641e-01
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3ba5f9ad-2121-4030-bcc5-29d6b811e1c9 | rinq-fingerprinting-recurrence-informed | 1907.05277 | null | https://arxiv.org/abs/1907.05277v2 | https://arxiv.org/pdf/1907.05277v2.pdf | RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting | Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times $T_1$ and $T_2$. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network. In this work, we propose a Recurrent Neural Network (RNN) architecture in combination with a novel quantile layer. RNNs are well suited for the processing of time-dependent signals and the quantile layer helps to overcome the noisy outliers by considering the spatial neighbors of the signal. We evaluate our approach using in-vivo data from multiple brain slices and several volunteers, running various experiments. We show that the RNN approach with small patches of complex-valued input signals in combination with a quantile layer outperforms other architectures, e.g. previously proposed CNNs for the MRF reconstruction reducing the error in $T_1$ and $T_2$ by more than 80%. | ['Gregor Körzdörfer', 'Heiko Meyer', 'Franziska Schirrmacher', 'Elisabeth Hoppe', 'Mathias Nittka', 'Florian Thamm', 'Christopher Syben', 'Andreas Maier', 'Josef Pfeuffer'] | 2019-07-09 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.46285880e-01 -1.51938975e-01 2.62734592e-01 -5.54933190e-01
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3f997295-b34a-47d8-961d-676631d5b5d5 | np-match-when-neural-processes-meet-semi | 2207.01066 | null | https://arxiv.org/abs/2207.01066v1 | https://arxiv.org/pdf/2207.01066v1.pdf | NP-Match: When Neural Processes meet Semi-Supervised Learning | Semi-supervised learning (SSL) has been widely explored in recent years, and it is an effective way of leveraging unlabeled data to reduce the reliance on labeled data. In this work, we adjust neural processes (NPs) to the semi-supervised image classification task, resulting in a new method named NP-Match. NP-Match is suited to this task for two reasons. Firstly, NP-Match implicitly compares data points when making predictions, and as a result, the prediction of each unlabeled data point is affected by the labeled data points that are similar to it, which improves the quality of pseudo-labels. Secondly, NP-Match is able to estimate uncertainty that can be used as a tool for selecting unlabeled samples with reliable pseudo-labels. Compared with uncertainty-based SSL methods implemented with Monte Carlo (MC) dropout, NP-Match estimates uncertainty with much less computational overhead, which can save time at both the training and the testing phases. We conducted extensive experiments on four public datasets, and NP-Match outperforms state-of-the-art (SOTA) results or achieves competitive results on them, which shows the effectiveness of NP-Match and its potential for SSL. | ['Alexandros Neophytou', 'Vladimir Pavlovic', 'Xiaolin Hu', 'Daniela Massiceti', 'Thomas Lukasiewicz', 'JianFeng Wang'] | 2022-07-03 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 2.94010453e-02 1.95091873e-01 -5.60871601e-01 -8.68455887e-01
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dba14620-0713-4bed-b704-0fcaf9fa5ea0 | semantic-communication-an-information | 2204.13366 | null | https://arxiv.org/abs/2204.13366v4 | https://arxiv.org/pdf/2204.13366v4.pdf | Semantic Information Recovery in Wireless Networks | Motivated by the recent success of Machine Learning (ML) tools in wireless communications, the idea of semantic communication by Weaver from 1949 has gained attention. It breaks with Shannon's classic design paradigm by aiming to transmit the meaning of a message, i.e., semantics, rather than its exact version and thus allows for savings in information rate. In this work, we extend the fundamental approach from Basu et al. for modeling semantics to the complete communications Markov chain. Thus, we model semantics by means of hidden random variables and define the semantic communication task as the data-reduced and reliable transmission of messages over a communication channel such that semantics is best preserved. We cast this task as an end-to-end Information Bottleneck problem, allowing for compression while preserving relevant information most. As a solution approach, we propose the ML-based semantic communication system SINFONY and use it for a distributed multipoint scenario: SINFONY communicates the meaning behind multiple messages that are observed at different senders to a single receiver for semantic recovery. We analyze SINFONY by processing images as message examples. Numerical results reveal a tremendous rate-normalized SNR shift up to 20 dB compared to classically designed communication systems. | ['Armin Dekorsy', 'Carsten Bockelmann', 'Edgar Beck'] | 2022-04-28 | null | null | null | null | ['semantic-retrieval'] | ['natural-language-processing'] | [ 7.45763481e-01 5.90990841e-01 -3.22750002e-01 -1.58933103e-01
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bcb5cd68-7994-4f99-921d-dea2739a27db | bertifying-sinhala-a-comprehensive-analysis-1 | null | null | https://aclanthology.org/2022.lrec-1.803 | https://aclanthology.org/2022.lrec-1.803.pdf | BERTifying Sinhala - A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification | This research provides the first comprehensive analysis of the performance of pre-trained language models for Sinhala text classification. We test on a set of different Sinhala text classification tasks and our analysis shows that out of the pre-trained multilingual models that include Sinhala (XLM-R, LaBSE, and LASER), XLM-R is the best model by far for Sinhala text classification. We also pre-train two RoBERTa-based monolingual Sinhala models, which are far superior to the existing pre-trained language models for Sinhala. We show that when fine-tuned, these pre-trained language models set a very strong baseline for Sinhala text classification and are robust in situations where labeled data is insufficient for fine-tuning. We further provide a set of recommendations for using pre-trained models for Sinhala text classification. We also introduce new annotated datasets useful for future research in Sinhala text classification and publicly release our pre-trained models. | ['Sanath Jayasena', 'Surangika Ranathunga', 'Piyumal Demotte', 'Vinura Dhananjaya'] | null | null | null | null | lrec-2022-6 | ['xlm-r'] | ['natural-language-processing'] | [-3.10414016e-01 -9.20301154e-02 -2.88718879e-01 -8.48261178e-01
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41fe5a14-d913-480e-ac57-9ae26db941d4 | stackelberg-games-for-learning-emergent | 2305.03735 | null | https://arxiv.org/abs/2305.03735v1 | https://arxiv.org/pdf/2305.03735v1.pdf | Stackelberg Games for Learning Emergent Behaviors During Competitive Autocurricula | Autocurricular training is an important sub-area of multi-agent reinforcement learning~(MARL) that allows multiple agents to learn emergent skills in an unsupervised co-evolving scheme. The robotics community has experimented autocurricular training with physically grounded problems, such as robust control and interactive manipulation tasks. However, the asymmetric nature of these tasks makes the generation of sophisticated policies challenging. Indeed, the asymmetry in the environment may implicitly or explicitly provide an advantage to a subset of agents which could, in turn, lead to a low-quality equilibrium. This paper proposes a novel game-theoretic algorithm, Stackelberg Multi-Agent Deep Deterministic Policy Gradient (ST-MADDPG), which formulates a two-player MARL problem as a Stackelberg game with one player as the `leader' and the other as the `follower' in a hierarchical interaction structure wherein the leader has an advantage. We first demonstrate that the leader's advantage from ST-MADDPG can be used to alleviate the inherent asymmetry in the environment. By exploiting the leader's advantage, ST-MADDPG improves the quality of a co-evolution process and results in more sophisticated and complex strategies that work well even against an unseen strong opponent. | ['Joshua R. Smith', 'Byron Boots', 'Lillian J. Ratliff', 'Liyuan Zheng', 'Boling Yang'] | 2023-05-04 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.51807576e-01 3.69899869e-01 1.66445553e-01 3.74627978e-01
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60cedf18-41ff-48f7-a513-fee1ae499917 | decoding-finger-flexion-from-band-specific | null | null | https://www.frontiersin.org/articles/10.3389/fnins.2012.00091/full | https://www.frontiersin.org/articles/10.3389/fnins.2012.00091/pdf | Decoding finger flexion from band-specific ECoG signals in humans | This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV. | ['Laurent Bougrain', 'Nanying Liang'] | 2012-06-28 | null | null | null | frontiers-in-neuroscience-section | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.65743351e-01 -2.11561382e-01 2.00893879e-01 -2.41633952e-01
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afdcbe86-bc61-46b8-a889-2353c59c5e17 | exposure-fusion-for-hand-held-camera-inputs | 2304.04464 | null | https://arxiv.org/abs/2304.04464v1 | https://arxiv.org/pdf/2304.04464v1.pdf | Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch | This paper proposes a hybrid synthesis method for multi-exposure image fusion taken by hand-held cameras. Motions either due to the shaky camera or caused by dynamic scenes should be compensated before any content fusion. Any misalignment can easily cause blurring/ghosting artifacts in the fused result. Our hybrid method can deal with such motions and maintain the exposure information of each input effectively. In particular, the proposed method first applies optical flow for a coarse registration, which performs well with complex non-rigid motion but produces deformations at regions with missing correspondences. The absence of correspondences is due to the occlusions of scene parallax or the moving contents. To correct such error registration, we segment images into superpixels and identify problematic alignments based on each superpixel, which is further aligned by PatchMatch. The method combines the efficiency of optical flow and the accuracy of PatchMatch. After PatchMatch correction, we obtain a fully aligned image stack that facilitates a high-quality fusion that is free from blurring/ghosting artifacts. We compare our method with existing fusion algorithms on various challenging examples, including the static/dynamic, the indoor/outdoor and the daytime/nighttime scenes. Experiment results demonstrate the effectiveness and robustness of our method. | ['Shuaicheng Liu', 'Bing Zeng', 'Guanghui Liu', 'Ru Li'] | 2023-04-10 | null | null | null | null | ['multi-exposure-image-fusion', 'superpixels'] | ['computer-vision', 'computer-vision'] | [ 3.46015543e-01 -5.63480854e-01 2.80607462e-01 -1.44088836e-02
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693dcb27-654a-4274-9853-4599b54f7f97 | sma-stn-segmented-movement-attending | 2010.09342 | null | https://arxiv.org/abs/2010.09342v1 | https://arxiv.org/pdf/2010.09342v1.pdf | SMA-STN: Segmented Movement-Attending Spatiotemporal Network forMicro-Expression Recognition | Correctly perceiving micro-expression is difficult since micro-expression is an involuntary, repressed, and subtle facial expression, and efficiently revealing the subtle movement changes and capturing the significant segments in a micro-expression sequence is the key to micro-expression recognition (MER). To handle the crucial issue, in this paper, we firstly propose a dynamic segmented sparse imaging module (DSSI) to compute dynamic images as local-global spatiotemporal descriptors under a unique sampling protocol, which reveals the subtle movement changes visually in an efficient way. Secondly, a segmented movement-attending spatiotemporal network (SMA-STN) is proposed to further unveil imperceptible small movement changes, which utilizes a spatiotemporal movement-attending module (STMA) to capture long-distance spatial relation for facial expression and weigh temporal segments. Besides, a deviation enhancement loss (DE-Loss) is embedded in the SMA-STN to enhance the robustness of SMA-STN to subtle movement changes in feature level. Extensive experiments on three widely used benchmarks, i.e., CASME II, SAMM, and SHIC, show that the proposed SMA-STN achieves better MER performance than other state-of-the-art methods, which proves that the proposed method is effective to handle the challenging MER problem. | ['Yuan Zong', 'Wenming Zheng', 'Jiateng Liu'] | 2020-10-19 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 2.95432657e-01 -7.20884144e-01 -1.09987691e-01 -3.32596540e-01
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fd97636b-d9d0-4426-958c-0ab5b93dcb15 | gray-level-co-occurrence-matrices | 1205.4831 | null | https://arxiv.org/abs/1205.4831v1 | https://arxiv.org/pdf/1205.4831v1.pdf | Gray Level Co-Occurrence Matrices: Generalisation and Some New Features | Gray Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR. | ['Kannan Balakrishnan', 'A. Unnikrishnan', 'Bino Sebastian V'] | 2012-05-22 | null | null | null | null | ['texture-classification', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 4.77720857e-01 -7.68867254e-01 -4.65966724e-02 -1.49635136e-01
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2dc8c776-5862-46ef-aa95-0acf1a213efa | towards-accurate-open-set-recognition-via | 2207.10287 | null | https://arxiv.org/abs/2207.10287v1 | https://arxiv.org/pdf/2207.10287v1.pdf | Towards Accurate Open-Set Recognition via Background-Class Regularization | In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g., distance-based feature analyses, or complicated network architectures. To conduct OSR via a simple inference process (without offline analyses) in standard classifier architectures, we use distance-based classifiers instead of conventional Softmax classifiers. Afterwards, we design a background-class regularization strategy, which uses background-class data as surrogates of unknown-class ones during training phase. Specifically, we formulate a novel regularization loss suitable for distance-based classifiers, which reserves sufficiently large class-wise latent feature spaces for known classes and forces background-class samples to be located far away from the limited spaces. Through our extensive experiments, we show that the proposed method provides robust OSR results, while maintaining high closed-set classification accuracy. | ['Jaegul Choo', 'Wonwoo Cho'] | 2022-07-21 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.40571278e-01 9.34746191e-02 -4.73585695e-01 -5.68591893e-01
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-1.28277826e+00 6.77327275e-01 3.57024342e-01 1.42550811e-01
-6.99820876e-01 9.23081338e-01 9.24592465e-02 -8.49865675e-01
6.00745022e-01 -4.66729105e-01 -2.09875271e-01 1.46903679e-01
4.40794170e-01 3.54578584e-01 2.34712020e-01 -2.72666126e-01
-5.10805249e-01 2.78643966e-01 -3.77737172e-02 4.96613711e-01
1.10520828e+00 -1.27060369e-01 1.70557812e-01 6.09565854e-01
1.21170831e+00 -3.53831463e-02 -1.34066832e+00 -2.26179913e-01
2.79032402e-02 -7.99477816e-01 2.80334294e-01 -6.45562172e-01
-6.41890943e-01 6.70920730e-01 8.56491506e-01 2.10838601e-01
1.11788225e+00 -1.83840781e-01 3.78212482e-01 5.37746370e-01
8.85160714e-02 -1.26749957e+00 -1.58941701e-01 3.92730504e-01
6.85579956e-01 -1.36746085e+00 -5.45744337e-02 -5.91407359e-01
-1.15563966e-01 1.24915278e+00 7.64575839e-01 -1.70516118e-01
8.32969069e-01 1.34171695e-01 -7.42033422e-02 2.38907456e-01
-5.52917182e-01 1.30651340e-01 5.36296904e-01 5.26596367e-01
1.24946795e-01 2.23748684e-01 6.46682270e-03 1.18661821e+00
1.61489844e-01 -7.65733793e-03 2.44804591e-01 9.31244016e-01
-4.37368810e-01 -7.73909867e-01 -8.67599189e-01 7.62528658e-01
-3.02656502e-01 1.11441322e-01 -7.37672150e-02 5.32745361e-01
1.24130011e-01 9.04095709e-01 3.16064328e-01 -4.34582472e-01
2.29776397e-01 2.08151221e-01 4.08899814e-01 -5.15956521e-01
-1.08082920e-01 1.69469833e-01 2.21436005e-02 -2.73209900e-01
-8.65408033e-03 -3.47093165e-01 -1.10222435e+00 1.46660373e-01
-1.24449146e+00 4.50119888e-03 5.16272902e-01 8.82901847e-01
1.40399545e-01 4.95053530e-01 8.84876013e-01 -5.98442912e-01
-1.23427272e+00 -7.92036355e-01 -8.42079103e-01 6.78159595e-02
5.05904436e-01 -8.70141923e-01 -8.00296843e-01 -1.26094639e-01] | [9.633349418640137, 3.1200311183929443] |
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