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36d21c35-bf94-40f6-a517-8432b41e9351 | phocnet-a-deep-convolutional-neural-network | 1604.00187 | null | http://arxiv.org/abs/1604.00187v3 | http://arxiv.org/pdf/1604.00187v3.pdf | PHOCNet: A Deep Convolutional Neural Network for Word Spotting in Handwritten Documents | In recent years, deep convolutional neural networks have achieved state of
the art performance in various computer vision task such as classification,
detection or segmentation. Due to their outstanding performance, CNNs are more
and more used in the field of document image analysis as well. In this work, we
present a CNN architecture that is trained with the recently proposed PHOC
representation. We show empirically that our CNN architecture is able to
outperform state of the art results for various word spotting benchmarks while
exhibiting short training and test times. | ['Sebastian Sudholt', 'Gernot A. Fink'] | 2016-04-01 | null | null | null | null | ['word-spotting-in-handwritten-documents'] | ['computer-vision'] | [ 3.07471603e-01 -3.46254528e-01 -2.62862056e-01 -2.13830560e-01
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7f9dcc03-35c7-4408-9037-d7decbf9a513 | sentiment-analysis-of-arabic-tweets-using | null | null | http://www.ijcis.info/Vol13N1/Vol13N1PP9-14.pdf | http://www.ijcis.info/Vol13N1/Vol13N1PP9-14.pdf | Sentiment Analysis of Arabic Tweets Using Semantic Resources | Sentiment analysis has grown to be one of the most active research areas in natural language processing
and text mining. Many researchers have investigated sentiment analysis and opinion mining from different
classification approaches. However, limited research is conducted on Arabic sentiment analysis as compared to the
English language. In this paper, we have proposed and implemented a technique for Twitter Arabic sentiment
analysis consisting of a semantic approach and Arabic linguistic features. Hence, we introduced a mechanism for
preprocessing Arabic tweets, and for the methodology of sentiment classification we used a semantic approach.
Also, we proposed a technique of classification which uses both Arabic and English sentiment lexicons to classify
the Arabic tweets into three sentiment categories (positive or negative or neutral). Our experiments show that
many issues were encountered when we used the Arabic SentiWordNet facility to classify Arabic tweets directly;
these issues are basically related to Arabic text processing. The Arabic lexicons and Arabic tools must be improved
or built from scratch in order to improve Arabic sentiment analysis using the semantic approach. The improvement
in results, which are due to our contribution in the form of enhanced Arabic lexicons and amended Arabic tools,
demonstrate this need. | ['Lamia Al-Horaibi', 'Muhammad Badruddin Khan'] | 2017-01-30 | null | null | null | international-journal-of-computing | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.38358911e-02 -5.92853576e-02 1.71973005e-01 -7.18853951e-01
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48759338-99e6-46b4-89fa-8a643bc57cae | exact-set-valued-estimation-using-constrained | 2304.04826 | null | https://arxiv.org/abs/2304.04826v1 | https://arxiv.org/pdf/2304.04826v1.pdf | Exact Set-valued Estimation using Constrained Convex Generators for uncertain Linear Systems | Set-valued state estimation when in the presence of uncertainties in the model have been addressed in the literature essentially following three main approaches: i) interval arithmetic of the uncertain dynamics with the estimates; ii) factorizing the uncertainty into matrices with unity rank; and, iii) performing the convex hull for the vertices of the uncertainty space. Approach i) and ii) introduce a lot of conservatism because both disregard the relationship of the parameters with the entries of the dynamics matrix. On the other hand, approach iii) has a large growth on the number of variables required to represent the set or is approximated losing its main advantage in comparison with i) and ii). In this paper, with the application of autonomous vehicles in GPS-denied areas that resort to beacon signals for localization, we develop an exact (meaning no added conservatism) and optimal (smallest growth in the number of variables) closed-form definition for the convex hull of Convex Constrained Generators (CCGs). This results in a more efficient method to represent the minimum volume convex set corresponding to the state estimation. Given that reductions methods are still lacking in the literature for CCGs, we employ an approximation using ray-shooting that is comparable in terms of accuracy with methods for Constrained Zonotopes as the ones implemented in CORA. Simulations illustrate the greater accuracy of CCGs with the proposed convex hull operation in comparison to Constrained Zonotopes. | ['Daniel Silvestre'] | 2023-04-10 | null | null | null | null | ['unity'] | ['computer-vision'] | [-7.50415623e-02 5.84480584e-01 2.22148195e-01 3.57099473e-02
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8b54a991-3790-4d35-ab54-9949e5ca83ba | toward-asymptotic-optimality-sequential | 2302.09810 | null | https://arxiv.org/abs/2302.09810v1 | https://arxiv.org/pdf/2302.09810v1.pdf | Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early Classification | Theoretically-inspired sequential density ratio estimation (SDRE) algorithms are proposed for the early classification of time series. Conventional SDRE algorithms can fail to estimate DRs precisely due to the internal overnormalization problem, which prevents the DR-based sequential algorithm, Sequential Probability Ratio Test (SPRT), from reaching its asymptotic Bayes optimality. Two novel SPRT-based algorithms, B2Bsqrt-TANDEM and TANDEMformer, are designed to avoid the overnormalization problem for precise unsupervised regression of SDRs. The two algorithms statistically significantly reduce DR estimation errors and classification errors on an artificial sequential Gaussian dataset and real datasets (SiW, UCF101, and HMDB51), respectively. The code is available at: https://github.com/Akinori-F-Ebihara/LLR_saturation_problem. | ['Hitoshi Imaoka', 'Kazuyuki Sakurai', 'Taiki Miyagawa', 'Akinori F. Ebihara'] | 2023-02-20 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [-2.89464384e-01 -4.25898850e-01 -3.52412879e-01 -4.36558425e-01
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12309e86-bfb8-4245-8ae7-6be595b0bd94 | texture-generation-using-dual-domain-feature | 2203.06901 | null | https://arxiv.org/abs/2203.06901v1 | https://arxiv.org/pdf/2203.06901v1.pdf | Texture Generation Using Dual-Domain Feature Flow with Multi-View Hallucinations | We propose a dual-domain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide sufficient information for estimating a complete texture map, the proposed model simultaneously generates multi-view hallucinations in the image domain and an estimated texture map in the texture domain. During the generating process, each domain generator exchanges features to the other by a flow-based local attention mechanism. In this manner, the proposed model can estimate a texture map utilizing abundant multi-view image features from which multiview hallucinations are generated. As a result, the estimated texture map contains consistent colors and patterns over the entire region. Experiments show the superiority of our model for estimating a directly render-able texture map, which is applicable to 3D animation rendering. Furthermore, our model also improves an overall generation quality in the image domain for pose and viewpoint transfer tasks. | ['Songhwai Oh', 'Jungchan Cho', 'Seunggyu Chang'] | 2022-03-14 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 2.40564290e-02 1.02760084e-01 1.78066298e-01 -1.96844146e-01
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51665a30-3cdb-4b44-89a0-c4450ad912fd | diplomat-a-dialogue-dataset-for-situated | 2306.09030 | null | https://arxiv.org/abs/2306.09030v2 | https://arxiv.org/pdf/2306.09030v2.pdf | DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning | Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations and is essential for the development of communicative social agents. In this paper, we introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding. Compared with previous works that treat different figurative expressions (e.g. metaphor, sarcasm) as individual tasks, DiPlomat provides a cohesive framework towards general pragmatic understanding. Our dataset is created through the utilization of Amazon Mechanical Turk ( AMT ), resulting in a total of 4, 177 multi-turn dialogues. In conjunction with the dataset, we propose two tasks, Pragmatic Identification and Reasoning (PIR) and Conversational Question Answering (CQA). Experimental results with state-of-the-art (SOTA) neural architectures reveal several significant findings: 1) large language models ( LLMs) exhibit poor performance in tackling this subjective domain; 2) comprehensive comprehension of context emerges as a critical factor for establishing benign human-machine interactions; 3) current models defect in the application of pragmatic reasoning. As a result, we call on more attention to improve the ability of context understanding, reasoning, and implied meaning modeling. | ['Song-Chun Zhu', 'Zilong Zheng', 'Hengli Li'] | 2023-06-15 | null | null | null | null | ['conversational-question-answering', 'question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.13007173e-01 6.78247690e-01 8.89603794e-02 -4.02220160e-01
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c3144124-2f1c-499e-b55e-a27757746f50 | modelling-context-and-syntactical-features | null | null | https://aclanthology.org/2020.acl-main.293 | https://aclanthology.org/2020.acl-main.293.pdf | Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis | The aspect-based sentiment analysis (ABSA) consists of two conceptual tasks, namely an aspect extraction and an aspect sentiment classification. Rather than considering the tasks separately, we build an end-to-end ABSA solution. Previous works in ABSA tasks did not fully leverage the importance of syntactical information. Hence, the aspect extraction model often failed to detect the boundaries of multi-word aspect terms. On the other hand, the aspect sentiment classifier was unable to account for the syntactical correlation between aspect terms and the context words. This paper explores the grammatical aspect of the sentence and employs the self-attention mechanism for syntactical learning. We combine part-of-speech embeddings, dependency-based embeddings and contextualized embeddings (e.g. BERT, RoBERTa) to enhance the performance of the aspect extractor. We also propose the syntactic relative distance to de-emphasize the adverse effects of unrelated words, having weak syntactic connection with the aspect terms. This increases the accuracy of the aspect sentiment classifier. Our solutions outperform the state-of-the-art models on SemEval-2014 dataset in both two subtasks. | ['Philip O. Ogunbona', 'Minh Hieu Phan'] | 2020-07-01 | null | null | null | acl-2020-6 | ['aspect-extraction'] | ['natural-language-processing'] | [-3.88657711e-02 3.62053752e-01 -9.24779698e-02 -7.49990225e-01
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51fc57ef-90c2-42bb-970b-93b13ae946e3 | yes-this-way-learning-to-ground-referring | 2305.12880 | null | https://arxiv.org/abs/2305.12880v1 | https://arxiv.org/pdf/2305.12880v1.pdf | Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers | The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally. In this paper, we present an initial study that evaluates intra-episodic feedback given in a collaborative setting. We use a referential language game as a controllable example of a task-oriented collaborative joint activity. A teacher utters a referring expression generated by a well-known symbolic algorithm (the "Incremental Algorithm") as an initial instruction and then monitors the follower's actions to possibly intervene with intra-episodic feedback (which does not explicitly have to be requested). We frame this task as a reinforcement learning problem with sparse rewards and learn a follower policy for a heuristic teacher. Our results show that intra-episodic feedback allows the follower to generalize on aspects of scene complexity and performs better than providing only the initial statement. | ['David Schlangen', 'Sherzod Hakimov', 'Philipp Sadler'] | 2023-05-22 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 4.40009713e-01 7.07953990e-01 -6.92387596e-02 -3.66344959e-01
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def7f9f8-eb2e-4026-bac5-6a8b5c12f951 | self-knowledge-distillation-via-dropout | 2208.05642 | null | https://arxiv.org/abs/2208.05642v1 | https://arxiv.org/pdf/2208.05642v1.pdf | Self-Knowledge Distillation via Dropout | To boost the performance, deep neural networks require deeper or wider network structures that involve massive computational and memory costs. To alleviate this issue, the self-knowledge distillation method regularizes the model by distilling the internal knowledge of the model itself. Conventional self-knowledge distillation methods require additional trainable parameters or are dependent on the data. In this paper, we propose a simple and effective self-knowledge distillation method using a dropout (SD-Dropout). SD-Dropout distills the posterior distributions of multiple models through a dropout sampling. Our method does not require any additional trainable modules, does not rely on data, and requires only simple operations. Furthermore, this simple method can be easily combined with various self-knowledge distillation approaches. We provide a theoretical and experimental analysis of the effect of forward and reverse KL-divergences in our work. Extensive experiments on various vision tasks, i.e., image classification, object detection, and distribution shift, demonstrate that the proposed method can effectively improve the generalization of a single network. Further experiments show that the proposed method also improves calibration performance, adversarial robustness, and out-of-distribution detection ability. | ['Myungjoo Kang', 'Hyun Seo', 'Yeachan Park', 'Hyoje Lee'] | 2022-08-11 | null | null | null | null | ['self-knowledge-distillation'] | ['computer-vision'] | [-1.23971559e-01 -7.20577091e-02 -1.52156036e-02 -4.87018973e-01
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a9f5ee33-2cb6-4c87-a9ab-ffc9f8f635db | inter-subject-deep-transfer-learning-for | 2103.05351 | null | https://arxiv.org/abs/2103.05351v1 | https://arxiv.org/pdf/2103.05351v1.pdf | Inter-subject Deep Transfer Learning for Motor Imagery EEG Decoding | Convolutional neural networks (CNNs) have become a powerful technique to decode EEG and have become the benchmark for motor imagery EEG Brain-Computer-Interface (BCI) decoding. However, it is still challenging to train CNNs on multiple subjects' EEG without decreasing individual performance. This is known as the negative transfer problem, i.e. learning from dissimilar distributions causes CNNs to misrepresent each of them instead of learning a richer representation. As a result, CNNs cannot directly use multiple subjects' EEG to enhance model performance directly. To address this problem, we extend deep transfer learning techniques to the EEG multi-subject training case. We propose a multi-branch deep transfer network, the Separate-Common-Separate Network (SCSN) based on splitting the network's feature extractors for individual subjects. We also explore the possibility of applying Maximum-mean discrepancy (MMD) to the SCSN (SCSN-MMD) to better align distributions of features from individual feature extractors. The proposed network is evaluated on the BCI Competition IV 2a dataset (BCICIV2a dataset) and our online recorded dataset. Results show that the proposed SCSN (81.8%, 53.2%) and SCSN-MMD (81.8%, 54.8%) outperformed the benchmark CNN (73.4%, 48.8%) on both datasets using multiple subjects. Our proposed networks show the potential to utilise larger multi-subject datasets to train an EEG decoder without being influenced by negative transfer. | ['A. Aldo Faisal', 'Pablo Ortega', 'Xiaoxi Wei'] | 2021-03-09 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 3.00528109e-01 1.88340386e-03 3.10751706e-01 -4.10140216e-01
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2fd37f6f-4e1d-46ec-9a84-fa33d8edf457 | rankcse-unsupervised-representation-learning | null | null | https://openreview.net/forum?id=y_sZyxuuFh3 | https://openreview.net/pdf?id=y_sZyxuuFh3 | RankCSE: Unsupervised Representation Learning via Learning to Rank | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing with various downstream applications. Recently, contrastive learning has been widely adopted which derives high-quality sentence representations by pulling similar semantics closer and pushing dissimilar ones away. However, these methods fail to capture the fine-grained ranking information among the sentences, where each sentence is only treated as either positive or negative. In many real-world scenarios, one needs to distinguish and rank the sentences based on their similarities to a query sentence, e.g., very relevant, moderate relevant, less relevant, irrelevant, etc. In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework. In particular, we learn semantically discriminative sentence representations by simultaneously ensuring ranking consistency between two representations with different dropout masks, and distilling listwise ranking knowledge from the teacher. An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks. Experimental results demonstrate the superior performance of our approach over several state-of-the-art baselines. | ['Anonymous'] | 2022-11-14 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 6.57461107e-01 -2.22953379e-01 -1.08911484e-01 -8.71165931e-01
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d82a5e03-224d-4b91-ad5e-a84301ac4e53 | weakly-supervised-anomaly-detection-a-survey | 2302.04549 | null | https://arxiv.org/abs/2302.04549v1 | https://arxiv.org/pdf/2302.04549v1.pdf | Weakly Supervised Anomaly Detection: A Survey | Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive survey of WSAD methods by categorizing them into the above three weak supervision settings across four data modalities (i.e., tabular, graph, time-series, and image/video data). For each setting, we provide formal definitions, key algorithms, and potential future directions. To support future research, we conduct experiments on a selected setting and release the source code, along with a collection of WSAD methods and data. | ['Yue Zhao', 'Philip S. Yu', 'Xiangnan He', 'Hailiang Huang', 'Songqiao Han', 'Xiyang Hu', 'Ao Zheng', 'Chaochuan Hou', 'Minqi Jiang'] | 2023-02-09 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.21687219e-01 -3.62779796e-02 -4.97274607e-01 -4.23990041e-01
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5f7d8d4c-a842-4b35-a1f5-1ea26c07a21f | the-carbon-footprint-of-machine-learning | 2204.05149 | null | https://arxiv.org/abs/2204.05149v1 | https://arxiv.org/pdf/2204.05149v1.pdf | The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink | Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 emissions up to 1000x. By following best practices, overall ML energy use (across research, development, and production) held steady at <15% of Google's total energy use for the past three years. If the whole ML field were to adopt best practices, total carbon emissions from training would reduce. Hence, we recommend that ML papers include emissions explicitly to foster competition on more than just model quality. Estimates of emissions in papers that omitted them have been off 100x-100,000x, so publishing emissions has the added benefit of ensuring accurate accounting. Given the importance of climate change, we must get the numbers right to make certain that we work on its biggest challenges. | ['Jeff Dean', 'Maud Texier', 'David So', 'Daniel Rothchild', 'Lluis-Miquel Munguia', 'Chen Liang', 'Quoc Le', 'Urs Hölzle', 'Joseph Gonzalez', 'David Patterson'] | 2022-04-11 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-4.60745841e-02 1.69224724e-01 -6.35383666e-01 -3.63350809e-02
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42833e0d-29f0-49f0-b5be-41a610833d33 | practical-real-video-denoising-with-realistic | 2208.11803 | null | https://arxiv.org/abs/2208.11803v3 | https://arxiv.org/pdf/2208.11803v3.pdf | Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising | Video denoising aims at removing noise from videos to recover clean ones. Some existing works show that optical flow can help the denoising by exploiting the additional spatial-temporal clues from nearby frames. However, the flow estimation itself is also sensitive to noise, and can be unusable under large noise levels. To this end, we propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels. Our method mainly consists of a denoising-oriented flow refinement (DFR) module and a flow-guided mutual denoising propagation (FMDP) module. Unlike previous works that directly use off-the-shelf flow solutions, DFR first learns robust multi-scale optical flows, and FMDP makes use of the flow guidance by progressively introducing and refining more flow information from low resolution to high resolution. Together with real noise degradation synthesis, the proposed multi-scale flow-guided denoising network achieves state-of-the-art performance on both synthetic Gaussian denoising and real video denoising. The codes will be made publicly available. | ['Luc van Gool', 'Radu Timofte', 'Kai Zhang', 'Yulun Zhang', 'Jingyun Liang', 'Qin Wang', 'JieZhang Cao'] | 2022-08-25 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 3.92710492e-02 -4.59169209e-01 2.30040282e-01 -1.68211594e-01
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0540809a-3632-4e90-87c1-9a0b2c009d67 | state-of-the-art-in-open-set-iris | 2208.10564 | null | https://arxiv.org/abs/2208.10564v1 | https://arxiv.org/pdf/2208.10564v1.pdf | State Of The Art In Open-Set Iris Presentation Attack Detection | Research in presentation attack detection (PAD) for iris recognition has largely moved beyond evaluation in "closed-set" scenarios, to emphasize ability to generalize to presentation attack types not present in the training data. This paper offers several contributions to understand and extend the state-of-the-art in open-set iris PAD. First, it describes the most authoritative evaluation to date of iris PAD. We have curated the largest publicly-available image dataset for this problem, drawing from 26 benchmarks previously released by various groups, and adding 150,000 images being released with the journal version of this paper, to create a set of 450,000 images representing authentic iris and seven types of presentation attack instrument (PAI). We formulate a leave-one-PAI-out evaluation protocol, and show that even the best algorithms in the closed-set evaluations exhibit catastrophic failures on multiple attack types in the open-set scenario. This includes algorithms performing well in the most recent LivDet-Iris 2020 competition, which may come from the fact that the LivDet-Iris protocol emphasizes sequestered images rather than unseen attack types. Second, we evaluate the accuracy of five open-source iris presentation attack algorithms available today, one of which is newly-proposed in this paper, and build an ensemble method that beats the winner of the LivDet-Iris 2020 by a substantial margin. This paper demonstrates that closed-set iris PAD, when all PAIs are known during training, is a solved problem, with multiple algorithms showing very high accuracy, while open-set iris PAD, when evaluated correctly, is far from being solved. The newly-created dataset, new open-source algorithms, and evaluation protocol, made publicly available with the journal version of this paper, provide the experimental artifacts that researchers can use to measure progress on this important problem. | ['Adam Czajka', 'Kevin Bowyer', 'Lucas Parzianello', 'Jeremy Speth', 'Aidan Boyd'] | 2022-08-22 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 4.61166412e-01 -3.91970098e-01 -3.14069688e-01 1.51968701e-02
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70cc7a53-0548-4b33-947c-9bb85e8aa01e | can-sam-count-anything-an-empirical-study-on | 2304.10817 | null | https://arxiv.org/abs/2304.10817v1 | https://arxiv.org/pdf/2304.10817v1.pdf | Can SAM Count Anything? An Empirical Study on SAM Counting | Meta AI recently released the Segment Anything model (SAM), which has garnered attention due to its impressive performance in class-agnostic segmenting. In this study, we explore the use of SAM for the challenging task of few-shot object counting, which involves counting objects of an unseen category by providing a few bounding boxes of examples. We compare SAM's performance with other few-shot counting methods and find that it is currently unsatisfactory without further fine-tuning, particularly for small and crowded objects. Code can be found at \url{https://github.com/Vision-Intelligence-and-Robots-Group/count-anything}. | ['Qinnan Shangguan', 'Xiaopeng Hong', 'Zhiheng Ma'] | 2023-04-21 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.27630159e-01 -1.10280298e-01 -2.15323299e-01 -4.05077457e-01
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a2549aa1-3753-445c-b4e8-34878dbe715b | analyzing-different-expert-opined-strategies | 2307.02254 | null | https://arxiv.org/abs/2307.02254v1 | https://arxiv.org/pdf/2307.02254v1.pdf | Analyzing Different Expert-Opined Strategies to Enhance the Effect on the Goal of a Multi-Attribute Decision-Making System Using a Concept of Effort Propagation and Application in Enhancement of High School Students' Performance | In many real-world multi-attribute decision-making (MADM) problems, mining the inter-relationships and possible hierarchical structures among the factors are considered to be one of the primary tasks. But, besides that, one major task is to determine an optimal strategy to work on the factors to enhance the effect on the goal attribute. This paper proposes two such strategies, namely parallel and hierarchical effort assignment, and propagation strategies. The concept of effort propagation through a strategy is formally defined and described in the paper. Both the parallel and hierarchical strategies are divided into sub-strategies based on whether the assignment of efforts to the factors is uniform or depends upon some appropriate heuristics related to the factors in the system. The adapted and discussed heuristics are the relative significance and effort propagability of the factors. The strategies are analyzed for a real-life case study regarding Indian high school administrative factors that play an important role in enhancing students' performance. Total effort propagation of around 7%-15% to the goal is seen across the proposed strategies given a total of 1 unit of effort to the directly accessible factors of the system. A comparative analysis is adapted to determine the optimal strategy among the proposed ones to enhance student performance most effectively. The highest effort propagation achieved in the work is approximately 14.4348%. The analysis in the paper establishes the necessity of research towards the direction of effort propagation analysis in case of decision-making problems. | ['Adrijit Goswami', 'Suvojit Dhara'] | 2023-07-05 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.33988762e-01 3.40879798e-01 -1.93800539e-01 -3.71453553e-01
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7f0816f5-571b-4d05-b48f-72177bc74a42 | semlinker-a-modular-and-open-source-framework | null | null | https://aclanthology.org/L16-1085 | https://aclanthology.org/L16-1085.pdf | SemLinker, a Modular and Open Source Framework for Named Entity Discovery and Linking | This paper presents SemLinker, an open source system that discovers named entities, connects them to a reference knowledge base, and clusters them semantically. SemLinker relies on several modules that perform surface form generation, mutual disambiguation, entity clustering, and make use of two annotation engines. SemLinker was evaluated in the English Entity Discovery and Linking track of the Text Analysis Conference on Knowledge Base Population, organized by the US National Institute of Standards and Technology. Along with the SemLinker source code, we release our annotation files containing the discovered named entities, their types, and position across processed documents. | ['Ludovic Jean-Louis', 'Marie-Jean Meurs', 'Eric Charton', 'Hayda Almeida'] | 2016-05-01 | semlinker-a-modular-and-open-source-framework-1 | https://aclanthology.org/L16-1085 | https://aclanthology.org/L16-1085.pdf | lrec-2016-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.64785486e-01 7.76544392e-01 -3.59260917e-01 -3.24380118e-03
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2e0e4088-4a82-4bbf-a864-4ad28218aa07 | systematic-generalization-on-gscan-what-is | 2109.12243 | null | https://arxiv.org/abs/2109.12243v1 | https://arxiv.org/pdf/2109.12243v1.pdf | Systematic Generalization on gSCAN: What is Nearly Solved and What is Next? | We analyze the grounded SCAN (gSCAN) benchmark, which was recently proposed to study systematic generalization for grounded language understanding. First, we study which aspects of the original benchmark can be solved by commonly used methods in multi-modal research. We find that a general-purpose Transformer-based model with cross-modal attention achieves strong performance on a majority of the gSCAN splits, surprisingly outperforming more specialized approaches from prior work. Furthermore, our analysis suggests that many of the remaining errors reveal the same fundamental challenge in systematic generalization of linguistic constructs regardless of visual context. Second, inspired by this finding, we propose challenging new tasks for gSCAN by generating data to incorporate relations between objects in the visual environment. Finally, we find that current models are surprisingly data inefficient given the narrow scope of commands in gSCAN, suggesting another challenge for future work. | ['Fei Sha', 'Peter Shaw', 'BoWen Zhang', 'Hexiang Hu', 'Linlu Qiu'] | 2021-09-25 | null | https://aclanthology.org/2021.emnlp-main.166 | https://aclanthology.org/2021.emnlp-main.166.pdf | emnlp-2021-11 | ['systematic-generalization'] | ['reasoning'] | [ 2.02930406e-01 2.65786797e-01 -5.93372062e-02 -4.37613100e-01
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27bc4b52-094c-4c9a-b01d-c16ba7394a4c | out-of-the-box-reasoning-with-graph | 1811.00538 | null | http://arxiv.org/abs/1811.00538v1 | http://arxiv.org/pdf/1811.00538v1.pdf | Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering | Accurately answering a question about a given image requires combining
observations with general knowledge. While this is effortless for humans,
reasoning with general knowledge remains an algorithmic challenge. To advance
research in this direction a novel `fact-based' visual question answering
(FVQA) task has been introduced recently along with a large set of curated
facts which link two entities, i.e., two possible answers, via a relation.
Given a question-image pair, deep network techniques have been employed to
successively reduce the large set of facts until one of the two entities of the
final remaining fact is predicted as the answer. We observe that a successive
process which considers one fact at a time to form a local decision is
sub-optimal. Instead, we develop an entity graph and use a graph convolutional
network to `reason' about the correct answer by jointly considering all
entities. We show on the challenging FVQA dataset that this leads to an
improvement in accuracy of around 7% compared to the state of the art. | ['Alexander G. Schwing', 'Svetlana Lazebnik', 'Medhini Narasimhan'] | 2018-11-01 | out-of-the-box-reasoning-with-graph-1 | http://papers.nips.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering | http://papers.nips.cc/paper/7531-out-of-the-box-reasoning-with-graph-convolution-nets-for-factual-visual-question-answering.pdf | neurips-2018-12 | ['factual-visual-question-answering'] | ['computer-vision'] | [ 2.58887231e-01 6.57417119e-01 1.53664008e-01 -4.73538011e-01
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db64597d-1d7b-4d09-9966-0ad25ad5f536 | workshop-on-autonomous-driving-at-cvpr-2021 | 2108.04230 | null | https://arxiv.org/abs/2108.04230v1 | https://arxiv.org/pdf/2108.04230v1.pdf | Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge | In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YOLOX. On the Argoverse-HD dataset, our system achieves 41.0 streaming AP, which surpassed second place by 7.8/6.1 on detection-only track/fully track, respectively. Moreover, equipped with TensorRT, our model achieves the 30FPS inference speed with a high-resolution input size (e.g., 1440-2304). Code and models will be available at https://github.com/Megvii-BaseDetection/YOLOX | ['Jian Sun', 'Xuming He', 'Zeming Li', 'Zheng Ge', 'Songtao Liu', 'Lin Song', 'Songyang Zhang'] | 2021-07-27 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-7.39379048e-01 -1.05414279e-01 -1.85615569e-01 -3.76750827e-01
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2600da23-f268-493e-9646-d84e853d8d4a | learning-deep-graph-matching-with-channel | null | null | https://openreview.net/forum?id=rJgBd2NYPH | https://openreview.net/pdf?id=rJgBd2NYPH | Learning deep graph matching with channel-independent embedding and Hungarian attention | Graph matching aims to establishing node-wise correspondence between two graphs, which is a classic combinatorial problem and in general NP-complete. Until very recently, deep graph matching methods start to resort to deep networks to achieve unprecedented matching accuracy. Along this direction, this paper makes two complementary contributions which can also be reused as plugin in existing works: i) a novel node and edge embedding strategy which stimulates the multi-head strategy in attention models and allows the information in each channel to be merged independently. In contrast, only node embedding is accounted in previous works; ii) a general masking mechanism over the loss function is devised to improve the smoothness of objective learning for graph matching. Using Hungarian algorithm, it dynamically constructs a structured and sparsely connected layer, taking into account the most contributing matching pairs as hard attention. Our approach performs competitively, and can also improve state-of-the-art methods as plugin, regarding with matching accuracy on three public benchmarks. | ['Runzhong Wang', 'Junchi Yan', 'Baoxin Li', 'Tianshu Yu'] | 2020-01-01 | null | null | null | iclr-2020-1 | ['hard-attention'] | ['methodology'] | [ 1.34969428e-01 5.23909807e-01 -4.02290314e-01 -2.40623206e-01
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51f9265a-4eab-4db9-bc34-cb49bdb486bb | flow-guided-recurrent-neural-encoder-for | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Flow_Guided_Recurrent_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Flow_Guided_Recurrent_CVPR_2018_paper.pdf | Flow Guided Recurrent Neural Encoder for Video Salient Object Detection | Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks. However, extending state-of-the-art saliency detectors from image to video is challenging. The performance of salient object detection suffers from object or camera motion and the dramatic change of the appearance contrast in videos. In this paper, we present flow guided recurrent neural encoder(FGRNE), an accurate and end-to-end learning framework for video salient object detection. It works by enhancing the temporal coherence of the per-frame feature by exploiting both motion information in terms of optical flow and sequential feature evolution encoding in terms of LSTM networks. It can be considered as a universal framework to extend any FCN based static saliency detector to video salient object detection. Intensive experimental results verify the effectiveness of each part of FGRNE and confirm that our proposed method significantly outperforms state-of-the-art methods on the public benchmarks of DAVIS and FBMS. | ['Liang Lin', 'Keze Wang', 'Tianhao Wei', 'Guanbin Li', 'Yuan Xie'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-salient-object-detection'] | ['computer-vision'] | [ 4.68958288e-01 -2.58577853e-01 -3.42692852e-01 -8.31588954e-02
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fdcd56d9-133f-4d77-a546-87316e188e6c | on-the-role-of-morphological-information-for | 2302.00407 | null | https://arxiv.org/abs/2302.00407v1 | https://arxiv.org/pdf/2302.00407v1.pdf | On the Role of Morphological Information for Contextual Lemmatization | Lemmatization is a Natural Language Processing (NLP) task which consists of producing, from a given inflected word, its canonical form or lemma. Lemmatization is one of the basic tasks that facilitate downstream NLP applications, and is of particular importance for high-inflected languages. Given that the process to obtain a lemma from an inflected word can be explained by looking at its morphosyntactic category, including fine-grained morphosyntactic information to train contextual lemmatizers has become common practice, without analyzing whether that is the optimum in terms of downstream performance. Thus, in this paper we empirically investigate the role of morphological information to develop contextual lemmatizers in six languages within a varied spectrum of morphological complexity: Basque, Turkish, Russian, Czech, Spanish and English. Furthermore, and unlike the vast majority of previous work, we also evaluate lemmatizers in out-of-domain settings, which constitutes, after all, their most common application use. The results of our study are rather surprising: (i) providing lemmatizers with fine-grained morphological features during training is not that beneficial, not even for agglutinative languages; (ii) in fact, modern contextual word representations seem to implicitly encode enough morphological information to obtain good contextual lemmatizers without seeing any explicit morphological signal; (iii) the best lemmatizers out-of-domain are those using simple UPOS tags or those trained without morphology; (iv) current evaluation practices for lemmatization are not adequate to clearly discriminate between models. | ['Rodrigo Agerri', 'Olia Toporkov'] | 2023-02-01 | null | null | null | null | ['lemmatization'] | ['natural-language-processing'] | [ 1.34981006e-01 6.58881143e-02 -6.10987544e-02 -2.40141705e-01
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3cea09f4-a62b-4dff-86db-e18cb0c8d172 | representation-matters-the-game-of-chess | 2304.14918 | null | https://arxiv.org/abs/2304.14918v1 | https://arxiv.org/pdf/2304.14918v1.pdf | Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers | While transformers have gained the reputation as the "Swiss army knife of AI", no one has challenged them to master the game of chess, one of the classical AI benchmarks. Simply using vision transformers (ViTs) within AlphaZero does not master the game of chess, mainly because ViTs are too slow. Even making them more efficient using a combination of MobileNet and NextViT does not beat what actually matters: a simple change of the input representation and value loss, resulting in a greater boost of up to 180 Elo points over AlphaZero. | ['Kristian Kersting', 'Jannis Blüml', 'Johannes Czech'] | 2023-04-28 | null | null | null | null | ['game-of-chess'] | ['playing-games'] | [-2.31320754e-01 3.38038169e-02 2.66180843e-01 2.32900903e-02
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45ec418a-266e-4a51-989f-382c70cbdb36 | few-shot-keypoint-detection-with-uncertainty | 2112.06183 | null | https://arxiv.org/abs/2112.06183v3 | https://arxiv.org/pdf/2112.06183v3.pdf | Few-shot Keypoint Detection with Uncertainty Learning for Unseen Species | Current non-rigid object keypoint detectors perform well on a chosen kind of species and body parts, and require a large amount of labelled keypoints for training. Moreover, their heatmaps, tailored to specific body parts, cannot recognize novel keypoints (keypoints not labelled for training) on unseen species. We raise an interesting yet challenging question: how to detect both base (annotated for training) and novel keypoints for unseen species given a few annotated samples? Thus, we propose a versatile Few-shot Keypoint Detection (FSKD) pipeline, which can detect a varying number of keypoints of different kinds. Our FSKD provides the uncertainty estimation of predicted keypoints. Specifically, FSKD involves main and auxiliary keypoint representation learning, similarity learning, and keypoint localization with uncertainty modeling to tackle the localization noise. Moreover, we model the uncertainty across groups of keypoints by multivariate Gaussian distribution to exploit implicit correlations between neighboring keypoints. We show the effectiveness of our FSKD on (i) novel keypoint detection for unseen species, and (ii) few-shot Fine-Grained Visual Recognition (FGVR) and (iii) Semantic Alignment (SA) downstream tasks. For FGVR, detected keypoints improve the classification accuracy. For SA, we showcase a novel thin-plate-spline warping that uses estimated keypoint uncertainty under imperfect keypoint corespondences. | ['Piotr Koniusz', 'Changsheng Lu'] | 2021-12-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lu_Few-Shot_Keypoint_Detection_With_Uncertainty_Learning_for_Unseen_Species_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-4.30705771e-03 -1.43963113e-01 -2.83174157e-01 -8.38093907e-02
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2da6765b-d547-400f-ae86-1345bbeec6f4 | effective-parallel-corpus-mining-using | 1807.11906 | null | http://arxiv.org/abs/1807.11906v2 | http://arxiv.org/pdf/1807.11906v2.pdf | Effective Parallel Corpus Mining using Bilingual Sentence Embeddings | This paper presents an effective approach for parallel corpus mining using
bilingual sentence embeddings. Our embedding models are trained to produce
similar representations exclusively for bilingual sentence pairs that are
translations of each other. This is achieved using a novel training method that
introduces hard negatives consisting of sentences that are not translations but
that have some degree of semantic similarity. The quality of the resulting
embeddings are evaluated on parallel corpus reconstruction and by assessing
machine translation systems trained on gold vs. mined sentence pairs. We find
that the sentence embeddings can be used to reconstruct the United Nations
Parallel Corpus at the sentence level with a precision of 48.9% for en-fr and
54.9% for en-es. When adapted to document level matching, we achieve a parallel
document matching accuracy that is comparable to the significantly more
computationally intensive approach of [Jakob 2010]. Using reconstructed
parallel data, we are able to train NMT models that perform nearly as well as
models trained on the original data (within 1-2 BLEU). | ['Yun-Hsuan Sung', 'Keith Stevens', 'Yinfei Yang', 'Qinlan Shen', 'Brian Strope', 'Daniel Cer', 'Mandy Guo', 'Heming Ge', 'Ray Kurzweil', 'Noah Constant', 'Gustavo Hernandez Abrego'] | 2018-07-31 | effective-parallel-corpus-mining-using-1 | https://aclanthology.org/W18-6317 | https://aclanthology.org/W18-6317.pdf | ws-2018-10 | ['parallel-corpus-mining'] | ['natural-language-processing'] | [ 2.49892309e-01 3.53144407e-01 -2.65875936e-01 -2.53910094e-01
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fa8679d0-d7f8-4497-9a6f-44500c70947f | deep-sequence-learning-for-video-anticipation | 2010.04368 | null | https://arxiv.org/abs/2010.04368v1 | https://arxiv.org/pdf/2010.04368v1.pdf | Deep Sequence Learning for Video Anticipation: From Discrete and Deterministic to Continuous and Stochastic | Video anticipation is the task of predicting one/multiple future representation(s) given limited, partial observation. This is a challenging task due to the fact that given limited observation, the future representation can be highly ambiguous. Based on the nature of the task, video anticipation can be considered from two viewpoints: the level of details and the level of determinism in the predicted future. In this research, we start from anticipating a coarse representation of a deterministic future and then move towards predicting continuous and fine-grained future representations of a stochastic process. The example of the former is video action anticipation in which we are interested in predicting one action label given a partially observed video and the example of the latter is forecasting multiple diverse continuations of human motion given partially observed one. In particular, in this thesis, we make several contributions to the literature of video anticipation... | ['Sadegh Aliakbarian'] | 2020-10-09 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 7.72935987e-01 2.51907617e-01 -1.29373997e-01 -3.36876094e-01
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3d56acee-8f69-4c09-a47c-2c6a0846293e | symmetric-shape-preserving-autoencoder-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ma_Symmetric_Shape-Preserving_Autoencoder_for_Unsupervised_Real_Scene_Point_Cloud_Completion_CVPR_2023_paper.pdf | Symmetric Shape-Preserving Autoencoder for Unsupervised Real Scene Point Cloud Completion | Unsupervised completion of real scene objects is of vital importance but still remains extremely challenging in preserving input shapes, predicting accurate results, and adapting to multi-category data. To solve these problems, we propose in this paper an Unsupervised Symmetric Shape-Preserving Autoencoding Network, termed USSPA, to predict complete point clouds of objects from real scenes. One of our main observations is that many natural and man-made objects exhibit significant symmetries. To accommodate this, we devise a symmetry learning module to learn from those objects and to preserve structural symmetries. Starting from an initial coarse predictor, our autoencoder refines the complete shape with a carefully designed upsampling refinement module. Besides the discriminative process on the latent space, the discriminators of our USSPA also take predicted point clouds as direct guidance, enabling more detailed shape prediction. Clearly different from previous methods which train each category separately, our USSPA can be adapted to the training of multi-category data in one pass through a classifier-guided discriminator, with consistent performance on single category. For more accurate evaluation, we contribute to the community a real scene dataset with paired CAD models as ground truth. Extensive experiments and comparisons demonstrate our superiority and generalization and show that our method achieves state-of-the-art performance on unsupervised completion of real scene objects. | ['Yanwen Guo', 'Chongjun Wang', 'Jie Guo', 'Pengxiao Guo', 'Yinuo Chen', 'Changfeng Ma'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 2.23082289e-01 1.46500006e-01 1.58222198e-01 -5.31604886e-01
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b4821170-2966-482d-b6e1-68b5d8c7178a | multilingual-text-classification-for | 2112.01705 | null | https://arxiv.org/abs/2112.01705v1 | https://arxiv.org/pdf/2112.01705v1.pdf | Multilingual Text Classification for Dravidian Languages | As the fourth largest language family in the world, the Dravidian languages have become a research hotspot in natural language processing (NLP). Although the Dravidian languages contain a large number of languages, there are relatively few public available resources. Besides, text classification task, as a basic task of natural language processing, how to combine it to multiple languages in the Dravidian languages, is still a major difficulty in Dravidian Natural Language Processing. Hence, to address these problems, we proposed a multilingual text classification framework for the Dravidian languages. On the one hand, the framework used the LaBSE pre-trained model as the base model. Aiming at the problem of text information bias in multi-task learning, we propose to use the MLM strategy to select language-specific words, and used adversarial training to perturb them. On the other hand, in view of the problem that the model cannot well recognize and utilize the correlation among languages, we further proposed a language-specific representation module to enrich semantic information for the model. The experimental results demonstrated that the framework we proposed has a significant performance in multilingual text classification tasks with each strategy achieving certain improvements. | ['Lianxi Wang', 'Shengyi Jiang', 'Kanoksak Wattanachote', 'Nankai Lin', 'Xiaotian Lin'] | 2021-12-03 | null | null | null | null | ['multilingual-text-classification'] | ['miscellaneous'] | [-1.72513232e-01 -2.39678085e-01 -1.75680354e-01 -1.72438905e-01
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c414d32d-f9d7-4e4e-804f-c27ace6d8456 | statistical-qos-provisioning-analysis-and | 2302.10092 | null | https://arxiv.org/abs/2302.10092v4 | https://arxiv.org/pdf/2302.10092v4.pdf | Statistical QoS Provisioning Analysis and Performance Optimization in xURLLC-enabled Massive MU-MIMO Networks: A Stochastic Network Calculus Perspective | In this paper, fundamentals and performance tradeoffs of the neXt-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem that is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE) have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems and prove that the EP-EC maximization problem is equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent inner-search collaborative algorithm. Extensive simulations demonstrate that the proposed framework in reducing computational complexity compared to reference schemes, and in providing various tradeoffs and optimization performance of xURLLC concerning UB-SDVP, EP, EP-EC, and EP-EE. | ['Chenwu Zhang', 'Langtian Qin', 'Chang Wen Chen', 'Hancheng Lu', 'Yuang Chen'] | 2023-02-20 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-3.84754948e-02 8.02139789e-02 -4.91694301e-01 6.11187741e-02
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7d0de619-07a0-41df-b4a1-d0b48be7710e | potential-convolution-embedding-point-clouds | 2104.01754 | null | https://arxiv.org/abs/2104.01754v1 | https://arxiv.org/pdf/2104.01754v1.pdf | Potential Convolution: Embedding Point Clouds into Potential Fields | Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications, such as shape classification, scene segmentation and so on. However, they still suffer from some drawbacks. For continuous kernels, the inaccurate estimation of the kernel weights constitutes a bottleneck for further improving the performance; while for discrete ones, the kernels represented as the points located in the 3D space are lack of rich geometry information. In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution. It is convenient for us to define various potential functions for potential convolution which can generalize well to a wide range of tasks. Specifically, we provide two simple yet effective potential functions via point-wise convolution operations. Comprehensive experiments demonstrate the effectiveness of our method, which achieves superior performance on the popular 3D shape classification and scene segmentation benchmarks compared with other state-of-the-art point convolution methods. | ['Kai Xu', 'Yao Duan', 'Duo Li', 'Jun Li', 'Haowen Deng', 'Dengsheng Chen'] | 2021-04-05 | null | null | null | null | ['3d-shape-retrieval', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [-7.99161419e-02 -4.51103896e-01 4.87369709e-02 -4.65429306e-01
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409c3dae-1297-488b-b613-4b338d22b3d4 | end-to-end-multihop-retrieval-for | 2106.00200 | null | https://arxiv.org/abs/2106.00200v2 | https://arxiv.org/pdf/2106.00200v2.pdf | Iterative Hierarchical Attention for Answering Complex Questions over Long Documents | We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop question-answering (QA) systems, at each step, DocHopper uses a query $q$ to attend to information from a document, combines this ``retrieved'' information with $q$ to produce the next query. However, in contrast to most previous multi-hop QA systems, DocHopper is able to ``retrieve'' either short passages or long sections of the document, thus emulating a multi-step process of ``navigating'' through a long document to answer a question. To enable this novel behavior, DocHopper does not combine document information with $q$ by concatenating text to the text of $q$, but by combining a compact neural representation of $q$ with a compact neural representation of a hierarchical part of the document, which can potentially be quite large. We experiment with DocHopper on four different QA tasks that require reading long and complex documents to answer multi-hop questions, and show that DocHopper achieves state-of-the-art results on three of the datasets. Additionally, DocHopper is efficient at inference time, being 3--10 times faster than the baselines. | ['Ruslan Salakhutdinov', 'William W. Cohen', 'Haitian Sun'] | 2021-06-01 | iterative-hierarchical-attention-for | https://openreview.net/forum?id=EVqFdCB5PfV | https://openreview.net/pdf?id=EVqFdCB5PfV | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 7.36687332e-02 3.72768104e-01 2.58900404e-01 -2.43241146e-01
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3683418c-bacc-4a8e-94cc-34e2c16ed463 | micron-bert-bert-based-facial-micro | 2304.03195 | null | https://arxiv.org/abs/2304.03195v1 | https://arxiv.org/pdf/2304.03195v1.pdf | Micron-BERT: BERT-based Facial Micro-Expression Recognition | Micro-expression recognition is one of the most challenging topics in affective computing. It aims to recognize tiny facial movements difficult for humans to perceive in a brief period, i.e., 0.25 to 0.5 seconds. Recent advances in pre-training deep Bidirectional Transformers (BERT) have significantly improved self-supervised learning tasks in computer vision. However, the standard BERT in vision problems is designed to learn only from full images or videos, and the architecture cannot accurately detect details of facial micro-expressions. This paper presents Micron-BERT ($\mu$-BERT), a novel approach to facial micro-expression recognition. The proposed method can automatically capture these movements in an unsupervised manner based on two key ideas. First, we employ Diagonal Micro-Attention (DMA) to detect tiny differences between two frames. Second, we introduce a new Patch of Interest (PoI) module to localize and highlight micro-expression interest regions and simultaneously reduce noisy backgrounds and distractions. By incorporating these components into an end-to-end deep network, the proposed $\mu$-BERT significantly outperforms all previous work in various micro-expression tasks. $\mu$-BERT can be trained on a large-scale unlabeled dataset, i.e., up to 8 million images, and achieves high accuracy on new unseen facial micro-expression datasets. Empirical experiments show $\mu$-BERT consistently outperforms state-of-the-art performance on four micro-expression benchmarks, including SAMM, CASME II, SMIC, and CASME3, by significant margins. Code will be available at \url{https://github.com/uark-cviu/Micron-BERT} | ['Khoa Luu', 'Han-Seok Seo', 'Susan Gauch', 'Xin Li', 'Chi Nhan Duong', 'Xuan-Bac Nguyen'] | 2023-04-06 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Nguyen_Micron-BERT_BERT-Based_Facial_Micro-Expression_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['micro-expression-recognition'] | ['computer-vision'] | [-8.04113373e-02 -1.96074829e-01 -9.92534161e-02 -6.41068578e-01
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efeb9085-a65e-4ab9-a372-f424f9d20f17 | wide-and-deep-volumetric-residual-networks | 1710.01217 | null | http://arxiv.org/abs/1710.01217v1 | http://arxiv.org/pdf/1710.01217v1.pdf | Wide and deep volumetric residual networks for volumetric image classification | 3D shape models that directly classify objects from 3D information have
become more widely implementable. Current state of the art models rely on deep
convolutional and inception models that are resource intensive. Residual neural
networks have been demonstrated to be easier to optimize and do not suffer from
vanishing/exploding gradients observed in deep networks. Here we implement a
residual neural network for 3D object classification of the 3D Princeton
ModelNet dataset. Further, we show that widening network layers dramatically
improves accuracy in shallow residual nets, and residual neural networks
perform comparable to state-of-the-art 3D shape net models, and we show that
widening network layers improves classification accuracy. We provide extensive
training and architecture parameters providing a better understanding of
available network architectures for use in 3D object classification. | ['Eric Oermann', 'Samuel Cho', 'Anthony Costa', 'Marcus Badgeley', 'Varun Arvind'] | 2017-09-18 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-2.88749874e-01 8.02241713e-02 -1.25149578e-01 -5.25367260e-01
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bb1e0b88-e01c-4410-8616-0581f495d2a7 | structural-rnn-deep-learning-on-spatio | 1511.05298 | null | http://arxiv.org/abs/1511.05298v3 | http://arxiv.org/pdf/1511.05298v3.pdf | Structural-RNN: Deep Learning on Spatio-Temporal Graphs | Deep Recurrent Neural Network architectures, though remarkably capable at
modeling sequences, lack an intuitive high-level spatio-temporal structure.
That is while many problems in computer vision inherently have an underlying
high-level structure and can benefit from it. Spatio-temporal graphs are a
popular tool for imposing such high-level intuitions in the formulation of real
world problems. In this paper, we propose an approach for combining the power
of high-level spatio-temporal graphs and sequence learning success of Recurrent
Neural Networks~(RNNs). We develop a scalable method for casting an arbitrary
spatio-temporal graph as a rich RNN mixture that is feedforward, fully
differentiable, and jointly trainable. The proposed method is generic and
principled as it can be used for transforming any spatio-temporal graph through
employing a certain set of well defined steps. The evaluations of the proposed
approach on a diverse set of problems, ranging from modeling human motion to
object interactions, shows improvement over the state-of-the-art with a large
margin. We expect this method to empower new approaches to problem formulation
through high-level spatio-temporal graphs and Recurrent Neural Networks. | ['Ashutosh Saxena', 'Silvio Savarese', 'Amir R. Zamir', 'Ashesh Jain'] | 2015-11-17 | structural-rnn-deep-learning-on-spatio-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Jain_Structural-RNN_Deep_Learning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Jain_Structural-RNN_Deep_Learning_CVPR_2016_paper.pdf | cvpr-2016-6 | ['human-pose-forecasting'] | ['computer-vision'] | [ 3.88127744e-01 7.69136772e-02 -1.83659762e-01 -3.61693144e-01
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9c663ded-310b-4599-be62-a2c8a8ad0705 | un-reasonable-allure-of-ante-hoc | 2306.02312 | null | https://arxiv.org/abs/2306.02312v2 | https://arxiv.org/pdf/2306.02312v2.pdf | (Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility | Ante-hoc interpretability has become the holy grail of explainable artificial intelligence for high-stakes domains such as healthcare; however, this notion is elusive, lacks a widely-accepted definition and depends on the operational context. It can refer to predictive models whose structure adheres to domain-specific constraints, or ones that are inherently transparent. The latter conceptualisation assumes observers who judge this quality, whereas the former presupposes them to have technical and domain expertise (thus alienating other groups of explainees). Additionally, the distinction between ante-hoc interpretability and the less desirable post-hoc explainability, which refers to methods that construct a separate explanatory model, is vague given that transparent predictive models may still require (post-)processing to yield suitable explanatory insights. Ante-hoc interpretability is thus an overloaded concept that comprises a range of implicit properties, which we unpack in this paper to better understand what is needed for its safe adoption across high-stakes domains. To this end, we outline modelling and explaining desiderata that allow us to navigate its distinct realisations in view of the envisaged application and audience. | ['Julia E. Vogt', 'Kacper Sokol'] | 2023-06-04 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 6.31734133e-01 1.22078812e+00 -3.88432443e-01 -6.86538100e-01
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99801714-71c0-419b-a702-002d92b433e7 | facial-movement-synergies-and-action-unit | 2008.08791 | null | https://arxiv.org/abs/2008.08791v1 | https://arxiv.org/pdf/2008.08791v1.pdf | Facial movement synergies and Action Unit detection from distal wearable Electromyography and Computer Vision | Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalizes on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction of the facial expression production, whilst allowing EMG measurements. However, measuring EMG distally entails that the exact source of the facial movement is unknown. We propose a novel method to estimate specific Facial Action Units (AUs) from distal facial EMG and Computer Vision (CV). This method is based on Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NNMF), and sorting of the resulting components to determine which is the most likely to correspond to each CV-labeled action unit (AU). Performance on the detection of AU06 (Orbicularis Oculi) and AU12 (Zygomaticus Major) was estimated by calculating the agreement with Human Coders. The results of our proposed algorithm showed an accuracy of 81% and a Cohen's Kappa of 0.49 for AU6; and accuracy of 82% and a Cohen's Kappa of 0.53 for AU12. This demonstrates the potential of distal EMG to detect individual facial movements. Using this multimodal method, several AU synergies were identified. We quantified the co-occurrence and timing of AU6 and AU12 in posed and spontaneous smiles using the human-coded labels, and for comparison, using the continuous CV-labels. The co-occurrence analysis was also performed on the EMG-based labels to uncover the relationship between muscle synergies and the kinematics of visible facial movement. | ['Saho Ayabe-Kanamura', 'Monica Perusquia-Hernandez', 'Felix Dollack', 'Kenji Suzuki', 'Chun Kwang Tan', 'Shushi Namba'] | 2020-08-20 | null | null | null | null | ['action-unit-detection', 'electromyography-emg'] | ['computer-vision', 'medical'] | [ 2.43343472e-01 2.10736364e-01 -2.56553918e-01 1.04552798e-01
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2aa4b68f-300d-4a45-8653-31dcb4403749 | poem-polarization-of-embeddings-for-domain | 2305.13046 | null | https://arxiv.org/abs/2305.13046v1 | https://arxiv.org/pdf/2305.13046v1.pdf | POEM: Polarization of Embeddings for Domain-Invariant Representations | Handling out-of-distribution samples is a long-lasting challenge for deep visual models. In particular, domain generalization (DG) is one of the most relevant tasks that aims to train a model with a generalization capability on novel domains. Most existing DG approaches share the same philosophy to minimize the discrepancy between domains by finding the domain-invariant representations. On the contrary, our proposed method called POEM acquires a strong DG capability by learning domain-invariant and domain-specific representations and polarizing them. Specifically, POEM cotrains category-classifying and domain-classifying embeddings while regularizing them to be orthogonal via minimizing the cosine-similarity between their features, i.e., the polarization of embeddings. The clear separation of embeddings suppresses domain-specific features in the domain-invariant embeddings. The concept of POEM shows a unique direction to enhance the domain robustness of representations that brings considerable and consistent performance gains when combined with existing DG methods. Extensive simulation results in popular DG benchmarks with the PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet datasets show that POEM indeed facilitates the category-classifying embedding to be more domain-invariant. | ['Sung Whan Yoon', 'Sang-Yeong Jo'] | 2023-05-22 | null | null | null | null | ['philosophy'] | ['miscellaneous'] | [-1.79166034e-01 -1.69994414e-01 -4.38276589e-01 -5.44829011e-01
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780810c6-cf8c-4512-9328-4af7f866b944 | generalizable-low-resource-activity | 2306.04641 | null | https://arxiv.org/abs/2306.04641v2 | https://arxiv.org/pdf/2306.04641v2.pdf | Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning | Human activity recognition (HAR) is a time series classification task that focuses on identifying the motion patterns from human sensor readings. Adequate data is essential but a major bottleneck for training a generalizable HAR model, which assists customization and optimization of online web applications. However, it is costly in time and economy to collect large-scale labeled data in reality, i.e., the low-resource challenge. Meanwhile, data collected from different persons have distribution shifts due to different living habits, body shapes, age groups, etc. The low-resource and distribution shift challenges are detrimental to HAR when applying the trained model to new unseen subjects. In this paper, we propose a novel approach called Diverse and Discriminative representation Learning (DDLearn) for generalizable low-resource HAR. DDLearn simultaneously considers diversity and discrimination learning. With the constructed self-supervised learning task, DDLearn enlarges the data diversity and explores the latent activity properties. Then, we propose a diversity preservation module to preserve the diversity of learned features by enlarging the distribution divergence between the original and augmented domains. Meanwhile, DDLearn also enhances semantic discrimination by learning discriminative representations with supervised contrastive learning. Extensive experiments on three public HAR datasets demonstrate that our method significantly outperforms state-of-art methods by an average accuracy improvement of 9.5% under the low-resource distribution shift scenarios, while being a generic, explainable, and flexible framework. Code is available at: https://github.com/microsoft/robustlearn. | ['Yiqiang Chen', 'Xing Xie', 'Yongchun Zhu', 'Wang Lu', 'Shuo Ma', 'Jindong Wang', 'Xin Qin'] | 2023-05-25 | null | null | null | null | ['human-activity-recognition', 'time-series-classification', 'human-activity-recognition'] | ['computer-vision', 'time-series', 'time-series'] | [-8.48033279e-02 -5.66210806e-01 -3.32858801e-01 -2.64858216e-01
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ee25c0b6-d79c-4815-ac2c-461c44b1d209 | multispectral-fusion-for-object-detection | 2009.12664 | null | https://arxiv.org/abs/2009.12664v1 | https://arxiv.org/pdf/2009.12664v1.pdf | Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks | Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods. | ['Sébastien Lefevre', 'Heng Zhang', 'Elisa Fromont', 'Bruno Avignon'] | 2020-09-26 | null | null | null | null | ['multispectral-object-detection'] | ['computer-vision'] | [ 4.88003731e-01 -8.84326100e-01 4.20894653e-01 -3.00855070e-01
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6b7b99d7-48d5-4544-a5a4-c7111730bb2a | deepedge-a-multi-scale-bifurcated-deep | 1412.1123 | null | http://arxiv.org/abs/1412.1123v3 | http://arxiv.org/pdf/1412.1123v3.pdf | DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection | Contour detection has been a fundamental component in many image segmentation
and object detection systems. Most previous work utilizes low-level features
such as texture or saliency to detect contours and then use them as cues for a
higher-level task such as object detection. However, we claim that recognizing
objects and predicting contours are two mutually related tasks. Contrary to
traditional approaches, we show that we can invert the commonly established
pipeline: instead of detecting contours with low-level cues for a higher-level
recognition task, we exploit object-related features as high-level cues for
contour detection.
We achieve this goal by means of a multi-scale deep network that consists of
five convolutional layers and a bifurcated fully-connected sub-network. The
section from the input layer to the fifth convolutional layer is fixed and
directly lifted from a pre-trained network optimized over a large-scale object
classification task. This section of the network is applied to four different
scales of the image input. These four parallel and identical streams are then
attached to a bifurcated sub-network consisting of two independently-trained
branches. One branch learns to predict the contour likelihood (with a
classification objective) whereas the other branch is trained to learn the
fraction of human labelers agreeing about the contour presence at a given point
(with a regression criterion).
We show that without any feature engineering our multi-scale deep learning
approach achieves state-of-the-art results in contour detection. | ['Lorenzo Torresani', 'Gedas Bertasius', 'Jianbo Shi'] | 2014-12-02 | deepedge-a-multi-scale-bifurcated-deep-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Bertasius_DeepEdge_A_Multi-Scale_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Bertasius_DeepEdge_A_Multi-Scale_2015_CVPR_paper.pdf | cvpr-2015-6 | ['contour-detection'] | ['computer-vision'] | [ 5.98988652e-01 2.85077184e-01 -1.70535848e-01 -4.23504919e-01
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fdea7e24-915a-4bed-a657-c8c9b073d7aa | finetuning-for-sarcasm-detection-with-a | 2212.12213 | null | https://arxiv.org/abs/2212.12213v1 | https://arxiv.org/pdf/2212.12213v1.pdf | Finetuning for Sarcasm Detection with a Pruned Dataset | Sarcasm is a form of irony that involves saying or writing something that is opposite or opposite to what one really means, often in a humorous or mocking way. It is often used to mock or mock someone or something, or to be humorous or amusing. Sarcasm is usually conveyed through tone of voice, facial expressions, or other forms of nonverbal communication, but it can also be indicated by the use of certain words or phrases that are typically associated with irony or humor. Sarcasm detection is difficult because it relies on context and non-verbal cues. It can also be culturally specific, subjective and ambiguous. In this work, we fine-tune the RoBERTa based sarcasm detection model presented in Abaskohi et al. [2022] to get to within 0.02 F1 of the state-of-the-art (Hercog et al. [2022]) on the iSarcasm dataset (Oprea and Magdy [2019]). This performance is achieved by augmenting iSarcasm with a pruned version of the Self Annotated Reddit Corpus (SARC) (Khodak et al. [2017]). Our pruned version is 100 times smaller than the subset of SARC used to train the state-of-the-art model. | ['Sanjana Dulam', 'Priyank Bhandia', 'Ishita Goyal'] | 2022-12-23 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [ 3.69906798e-02 2.82471120e-01 -1.82151452e-01 -3.23327422e-01
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6f2f8cfa-9ebc-4a48-a3a0-5ab112cd966f | free-lunch-for-surgical-video-understanding | 2205.09292 | null | https://arxiv.org/abs/2205.09292v2 | https://arxiv.org/pdf/2205.09292v2.pdf | Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions | Self-supervised learning has witnessed great progress in vision and NLP; recently, it also attracted much attention to various medical imaging modalities such as X-ray, CT, and MRI. Existing methods mostly focus on building new pretext self-supervision tasks such as reconstruction, orientation, and masking identification according to the properties of medical images. However, the publicly available self-supervision models are not fully exploited. In this paper, we present a powerful yet efficient self-supervision framework for surgical video understanding. Our key insight is to distill knowledge from publicly available models trained on large generic datasets4 to facilitate the self-supervised learning of surgical videos. To this end, we first introduce a semantic-preserving training scheme to obtain our teacher model, which not only contains semantics from the publicly available models, but also can produce accurate knowledge for surgical data. Besides training with only contrastive learning, we also introduce a distillation objective to transfer the rich learned information from the teacher model to self-supervised learning on surgical data. Extensive experiments on two surgical phase recognition benchmarks show that our framework can significantly improve the performance of existing self-supervised learning methods. Notably, our framework demonstrates a compelling advantage under a low-data regime. Our code is available at https://github.com/xmed-lab/DistillingSelf. | ['Xiaomeng Li', 'Ziwei Liu', 'Xinpeng Ding'] | 2022-05-19 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 3.61860752e-01 2.45099530e-01 -8.34041297e-01 -5.23515284e-01
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89e84eed-354f-4724-9d49-ed32c0cbd540 | content-explorer-recommending-novel-entities | null | null | https://aclanthology.org/D18-1374 | https://aclanthology.org/D18-1374.pdf | Content Explorer: Recommending Novel Entities for a Document Writer | Background research is an essential part of document writing. Search engines are great for retrieving information once we know what to look for. However, the bigger challenge is often identifying topics for further research. Automated tools could help significantly in this discovery process and increase the productivity of the writer. In this paper, we formulate the problem of recommending topics to a writer. We consider this as a supervised learning problem and run a user study to validate this approach. We propose an evaluation metric and perform an empirical comparison of state-of-the-art models for extreme multi-label classification on a large data set. We demonstrate how a simple modification of the cross-entropy loss function leads to improved results of the deep learning models. | ['Richard Zens', 'Michal Lukasik'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['extreme-multi-label-classification'] | ['methodology'] | [ 9.77198854e-02 -1.25653565e-01 -4.75452453e-01 -5.25358081e-01
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b05445fa-6bf2-4238-afd0-b6cee75711ce | leveraging-advantages-of-interactive-and-non | 2111.01992 | null | https://arxiv.org/abs/2111.01992v1 | https://arxiv.org/pdf/2111.01992v1.pdf | Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval | Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively. From the retrieval accuracy and computational efficiency perspectives, each model has its own superiority and shortcoming. In this paper, we propose a novel framework to leverage the advantages of these two paradigms. Concretely, we introduce semi-interactive mechanism, which builds our model upon non-interactive architecture but encodes each document together with its associated multilingual queries. Accordingly, cross-lingual features can be better learned like an interactive model. Besides, we further transfer knowledge from a well-trained interactive model to ours by reusing its word embeddings and adopting knowledge distillation. Our model is initialized from a multilingual pre-trained language model M-BERT, and evaluated on two open-resource CLIR datasets derived from Wikipedia and an in-house dataset collected from a real-world search engine. Extensive analyses reveal that our methods significantly boost the retrieval accuracy while maintaining the computational efficiency. | ['Haibo Zhang', 'Dayiheng Liu', 'Tianchi Bi', 'Xiaoyu Lv', 'Baosong Yang', 'Linlong Xu'] | 2021-11-03 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.87711942e-01 -4.41718578e-01 -6.47140622e-01 -5.92905320e-02
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e20a8673-62e1-43ad-a4f3-32c9d8b18f9d | neurologic-decoding-un-supervised-neural-text | 2010.12884 | null | https://arxiv.org/abs/2010.12884v2 | https://arxiv.org/pdf/2010.12884v2.pdf | NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints | Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. While the dominant recipe for conditional text generation has been large-scale pretrained language models that are finetuned on the task-specific training data, such models do not learn to follow the underlying constraints reliably, even when supervised with large amounts of task-specific examples. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models -- supervised or not -- to generate fluent text while satisfying complex lexical constraints. Our approach is powerful yet efficient. It handles any set of lexical constraints that is expressible under predicate logic, while its asymptotic runtime is equivalent to conventional beam search. Empirical results on four benchmarks show that NeuroLogic Decoding outperforms previous approaches, including algorithms that handle a subset of our constraints. Moreover, we find that unsupervised models with NeuroLogic Decoding often outperform supervised models with conventional decoding, even when the latter is based on considerably larger networks. Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms. | ['Yejin Choi', 'Chandra Bhagavatula', 'Ronan Le Bras', 'Rowan Zellers', 'Peter West', 'Ximing Lu'] | 2020-10-24 | null | https://aclanthology.org/2021.naacl-main.339 | https://aclanthology.org/2021.naacl-main.339.pdf | naacl-2021-4 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 6.03078485e-01 5.66038609e-01 -5.41150451e-01 -4.71590817e-01
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9d234ba5-1ecd-4b06-8534-2508233e9381 | towards-active-learning-for-action-spotting | 2304.04220 | null | https://arxiv.org/abs/2304.04220v1 | https://arxiv.org/pdf/2304.04220v1.pdf | Towards Active Learning for Action Spotting in Association Football Videos | Association football is a complex and dynamic sport, with numerous actions occurring simultaneously in each game. Analyzing football videos is challenging and requires identifying subtle and diverse spatio-temporal patterns. Despite recent advances in computer vision, current algorithms still face significant challenges when learning from limited annotated data, lowering their performance in detecting these patterns. In this paper, we propose an active learning framework that selects the most informative video samples to be annotated next, thus drastically reducing the annotation effort and accelerating the training of action spotting models to reach the highest accuracy at a faster pace. Our approach leverages the notion of uncertainty sampling to select the most challenging video clips to train on next, hastening the learning process of the algorithm. We demonstrate that our proposed active learning framework effectively reduces the required training data for accurate action spotting in football videos. We achieve similar performances for action spotting with NetVLAD++ on SoccerNet-v2, using only one-third of the dataset, indicating significant capabilities for reducing annotation time and improving data efficiency. We further validate our approach on two new datasets that focus on temporally localizing actions of headers and passes, proving its effectiveness across different action semantics in football. We believe our active learning framework for action spotting would support further applications of action spotting algorithms and accelerate annotation campaigns in the sports domain. | ['Marc Van Droogenbroeck', 'Bernard Ghanem', 'Kerry Peek', 'Andreas Serner', 'Johsan Billingham', 'Julia Georgieva', 'Anthony Cioppa', 'Silvio Giancola'] | 2023-04-09 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 3.48308891e-01 -1.87668800e-01 -7.93477237e-01 -1.93762705e-01
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6affd99d-aabe-40e0-a3ad-1e93892a252b | a-paired-sparse-representation-model-for | 1910.02192 | null | https://arxiv.org/abs/1910.02192v1 | https://arxiv.org/pdf/1910.02192v1.pdf | A Paired Sparse Representation Model for Robust Face Recognition from a Single Sample | Sparse representation-based classification (SRC) has been shown to achieve a high level of accuracy in face recognition (FR). However, matching faces captured in unconstrained video against a gallery with a single reference facial still per individual typically yields low accuracy. For improved robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary dictionary. Despite their success in handling linear variations, non-linear variations (e.g., pose and expressions) between probe and reference facial images cannot be accurately reconstructed with a linear combination of images in the gallery and auxiliary dictionaries because they do not share the same type of variations. In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images. The proposed model, called synthetic plus variational model, reconstructs a probe image by jointly using (1) a variational dictionary and (2) a gallery dictionary augmented with a set of synthetic images generated over a wide diversity of pose angles. The augmented gallery dictionary is then encouraged to pair the same sparsity pattern with the variational dictionary for similar pose angles by solving a newly formulated simultaneous sparsity-based optimization problem. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a single sample per person. | ['Fania Mokhayeri', 'Eric Granger'] | 2019-10-05 | null | null | null | null | ['robust-face-recognition', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 4.31103587e-01 -1.81829274e-01 -2.70961616e-02 -5.26764512e-01
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25cc882c-67f9-4f03-a682-7cb2640ee7b6 | polar-shapelets | astro-ph/0408445 | null | https://arxiv.org/abs/astro-ph/0408445v3 | https://arxiv.org/pdf/astro-ph/0408445v3.pdf | Polar Shapelets | The shapelets method for image analysis is based upon the decomposition of localised objects into a series of orthogonal components with convenient mathematical properties. We extend the "Cartesian shapelet" formalism from earlier work, and construct "polar shapelet" basis functions that separate an image into components with explicit rotational symmetries. These frequently provide a more compact parameterisation, and can be interpreted in an intuitive way. Image manipulation in shapelet space is simplified by the concise expressions for linear coordinate transformations; and shape measures (including object photometry, astrometry and galaxy morphology estimators) take a naturally elegant form. Particular attention is paid to the analysis of astronomical survey images, and we test shapelet techniques upon real data from the Hubble Space Telescope. We present a practical method to automatically optimise the quality of an arbitrary shapelet decomposition in the presence of observational noise, pixellisation and a Point-Spread Function. A central component of this procedure is the adaptive choice of the shapelet expansion's scale size and truncation order. A complete software package to perform shapelet image analysis is made available on the world-wide web at http://www.astro.caltech.edu/~rjm/shapelets/ . | ['Alexandre Refregier', 'Richard Massey'] | 2004-08-24 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 2.78305332e-03 -3.43840241e-01 2.80926049e-01 -9.88184884e-02
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8b06e557-5a8a-4461-a4b8-93eb1fdc3a37 | complementing-gpt-3-with-few-shot-sequence-to | 2305.14202 | null | https://arxiv.org/abs/2305.14202v1 | https://arxiv.org/pdf/2305.14202v1.pdf | Complementing GPT-3 with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata | As the largest knowledge base, Wikidata is a massive source of knowledge, complementing large language models with well-structured data. In this paper, we present WikiWebQuestions, a high-quality knowledge base question answering benchmark for Wikidata. This new benchmark uses real-world human data with SPARQL annotation to facilitate a more accurate comparison with large language models utilizing the up-to-date answers from Wikidata. Additionally, a baseline for this benchmark is established with an effective training data synthesis methodology and WikiSP, a Seq2Seq semantic parser, that handles large noisy knowledge graphs. Experimental results illustrate the effectiveness of this methodology, achieving 69% and 59% answer accuracy in the dev set and test set, respectively. We showed that we can pair semantic parsers with GPT-3 to provide a combination of verifiable results and qualified guesses that can provide useful answers to 97% of the questions in the dev set of our benchmark. | ['Monica S. Lam', 'Sina J. Semnani', 'Meng-Hsi Wu', 'Theo Culhane', 'Silei Xu'] | 2023-05-23 | null | null | null | null | ['knowledge-base-question-answering', 'semantic-parsing'] | ['natural-language-processing', 'natural-language-processing'] | [-3.33928347e-01 6.15844190e-01 -8.79173577e-02 -2.53286600e-01
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808e20fd-e2bf-421c-9c28-6ac581bc22f2 | deep-learning-meets-liveness-detection-recent | 2112.14796 | null | https://arxiv.org/abs/2112.14796v1 | https://arxiv.org/pdf/2112.14796v1.pdf | Deep Learning meets Liveness Detection: Recent Advancements and Challenges | Facial biometrics has been recently received tremendous attention as a convenient replacement for traditional authentication systems. Consequently, detecting malicious attempts has found great significance, leading to extensive studies in face anti-spoofing~(FAS),i.e., face presentation attack detection. Deep feature learning and techniques, as opposed to hand-crafted features, have promised a dramatic increase in the FAS systems' accuracy, tackling the key challenges of materializing the real-world application of such systems. Hence, a new research area dealing with the development of more generalized as well as accurate models is increasingly attracting the attention of the research community and industry. In this paper, we present a comprehensive survey on the literature related to deep-feature-based FAS methods since 2017. To shed light on this topic, a semantic taxonomy based on various features and learning methodologies is represented. Further, we cover predominant public datasets for FAS in chronological order, their evolutional progress, and the evaluation criteria (both intra-dataset and inter-dataset). Finally, we discuss the open research challenges and future directions. | ['Mohammad Akbari', 'Kooshan Hashemifard', 'Marzieh Oghbaie', 'Arian Sabaghi'] | 2021-12-29 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 1.15100265e-01 -5.49970210e-01 -2.80283362e-01 -3.74847651e-01
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1d24d511-84b0-46ea-8abe-dc41ead0515a | cross-domain-data-integration-for-named | 2110.08228 | null | https://arxiv.org/abs/2110.08228v1 | https://arxiv.org/pdf/2110.08228v1.pdf | Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text | Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points. | ['Christopher Ré', 'Xiao Ling', 'Megan Leszczynski', 'Sen Wu', 'Laurel Orr', 'Maya Varma'] | 2021-10-15 | null | https://aclanthology.org/2021.findings-emnlp.388 | https://aclanthology.org/2021.findings-emnlp.388.pdf | findings-emnlp-2021-11 | ['data-integration', 'entity-disambiguation'] | ['knowledge-base', 'natural-language-processing'] | [ 6.47096410e-02 4.49357331e-01 -5.42354882e-01 -2.47783318e-01
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9325ea8e-d29a-4cd4-bf7a-b6121402cc76 | heterogeneous-directed-hypergraph-neural | 2305.04228 | null | https://arxiv.org/abs/2305.04228v2 | https://arxiv.org/pdf/2305.04228v2.pdf | Heterogeneous Directed Hypergraph Neural Network over abstract syntax tree (AST) for Code Classification | Code classification is a difficult issue in program understanding and automatic coding. Due to the elusive syntax and complicated semantics in programs, most existing studies use techniques based on abstract syntax tree (AST) and graph neural network (GNN) to create code representations for code classification. These techniques utilize the structure and semantic information of the code, but they only take into account pairwise associations and neglect the high-order correlations that already exist between nodes in the AST, which may result in the loss of code structural information. On the other hand, while a general hypergraph can encode high-order data correlations, it is homogeneous and undirected which will result in a lack of semantic and structural information such as node types, edge types, and directions between child nodes and parent nodes when modeling AST. In this study, we propose to represent AST as a heterogeneous directed hypergraph (HDHG) and process the graph by heterogeneous directed hypergraph neural network (HDHGN) for code classification. Our method improves code understanding and can represent high-order data correlations beyond paired interactions. We assess heterogeneous directed hypergraph neural network (HDHGN) on public datasets of Python and Java programs. Our method outperforms previous AST-based and GNN-based methods, which demonstrates the capability of our model. | ['Liang Dou', 'Tiancheng Jin', 'Guang Yang'] | 2023-05-07 | null | null | null | null | ['code-classification'] | ['computer-code'] | [-3.13503265e-01 2.41378307e-01 -3.38524401e-01 -3.96932751e-01
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fb33758d-bae4-4f53-9f51-c3bfcd4f40e4 | the-benefits-of-being-distributional-small | 2305.15703 | null | https://arxiv.org/abs/2305.15703v2 | https://arxiv.org/pdf/2305.15703v2.pdf | The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning | While distributional reinforcement learning (RL) has demonstrated empirical success, the question of when and why it is beneficial has remained unanswered. In this work, we provide one explanation for the benefits of distributional RL through the lens of small-loss bounds, which scale with the instance-dependent optimal cost. If the optimal cost is small, our bounds are stronger than those from non-distributional approaches. As warmup, we show that learning the cost distribution leads to small-loss regret bounds in contextual bandits (CB), and we find that distributional CB empirically outperforms the state-of-the-art on three challenging tasks. For online RL, we propose a distributional version-space algorithm that constructs confidence sets using maximum likelihood estimation, and we prove that it achieves small-loss regret in the tabular MDPs and enjoys small-loss PAC bounds in latent variable models. Building on similar insights, we propose a distributional offline RL algorithm based on the pessimism principle and prove that it enjoys small-loss PAC bounds, which exhibit a novel robustness property. For both online and offline RL, our results provide the first theoretical benefits of learning distributions even when we only need the mean for making decisions. | ['Wen Sun', 'Nathan Kallus', 'Runzhe Wu', 'Kevin Zhou', 'Kaiwen Wang'] | 2023-05-25 | null | null | null | null | ['distributional-reinforcement-learning', 'multi-armed-bandits', 'offline-rl'] | ['methodology', 'miscellaneous', 'playing-games'] | [-1.15223840e-01 3.34077418e-01 -9.03558791e-01 -2.87022263e-01
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b668d612-f7f5-4425-ad02-a9619311b866 | robust-3d-shape-classification-via-non-local | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.pdf | Robust 3D Shape Classification via Non-Local Graph Attention Network | We introduce a non-local graph attention network (NLGAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In the second sub-network, we enhance the local features with a geometric shape attention map obtained from a global structure network (GSN). To keep rotation invariant and extract more information from sparse point clouds, all sub-networks use the Gram matrices with different dimensions as input for working with robust classification. Additionally, GRN effectively preserves the low-frequency features and improves the classification results. Experimental results on various datasets exhibit that the classification effect of the NLGAT model is better than other state-of-the-art models. Especially, in the case of sparse point clouds (64 points) with noise under arbitrary SO(3) rotation, the classification result (85.4%) of NLGAT is improved by 39.4% compared with the best development of other methods. | ['Ligang Liu', 'Zhong Li', 'Shengwei Qin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-shape-retrieval'] | ['computer-vision'] | [-2.47782767e-01 -1.99963525e-02 -1.66504025e-01 -1.15713306e-01
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b9df6778-ba10-4c1c-8279-9954aa4a9a45 | object-to-scene-learning-to-transfer-object | 2108.00399 | null | https://arxiv.org/abs/2108.00399v1 | https://arxiv.org/pdf/2108.00399v1.pdf | Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition | Accurate perception of the surrounding scene is helpful for robots to make reasonable judgments and behaviours. Therefore, developing effective scene representation and recognition methods are of significant importance in robotics. Currently, a large body of research focuses on developing novel auxiliary features and networks to improve indoor scene recognition ability. However, few of them focus on directly constructing object features and relations for indoor scene recognition. In this paper, we analyze the weaknesses of current methods and propose an Object-to-Scene (OTS) method, which extracts object features and learns object relations to recognize indoor scenes. The proposed OTS first extracts object features based on the segmentation network and the proposed object feature aggregation module (OFAM). Afterwards, the object relations are calculated and the scene representation is constructed based on the proposed object attention module (OAM) and global relation aggregation module (GRAM). The final results in this work show that OTS successfully extracts object features and learns object relations from the segmentation network. Moreover, OTS outperforms the state-of-the-art methods by more than 2\% on indoor scene recognition without using any additional streams. Code is publicly available at: https://github.com/FreeformRobotics/OTS. | ['Yangsheng Xu', 'Tin Lun Lam', 'Ajmal Mian', 'Liguang Zhou', 'Bo Miao'] | 2021-08-01 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 3.42288524e-01 -6.35171905e-02 7.78782442e-02 -6.90984666e-01
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a73f223b-864a-4517-9b2d-e0e98ff88a65 | a-survey-on-deep-learning-based-architectures | 1912.10230 | null | https://arxiv.org/abs/1912.10230v5 | https://arxiv.org/pdf/1912.10230v5.pdf | A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D images | Semantic segmentation is the pixel-wise labelling of an image. Since the problem is defined at the pixel level, determining image class labels only is not acceptable, but localising them at the original image pixel resolution is necessary. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high level and hierarchical image features; several deep learning-based 2D semantic segmentation approaches have been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorised the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analysed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localisation and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they have been solved. | ['Irem Ulku', 'Erdem Akagunduz'] | 2019-12-21 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 5.91665566e-01 4.66807157e-01 -3.78107168e-02 -4.70415175e-01
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7d64dcc7-d73a-4157-a340-8837e0060653 | emergence-of-maps-in-the-memories-of-blind | 2301.13261 | null | https://arxiv.org/abs/2301.13261v1 | https://arxiv.org/pdf/2301.13261v1.pdf | Emergence of Maps in the Memories of Blind Navigation Agents | Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial intelligence (AI) navigation agents -- also build implicit (or 'mental') maps. A positive answer to this question would (a) explain the surprising phenomenon in recent literature of ostensibly map-free neural-networks achieving strong performance, and (b) strengthen the evidence of mapping as a fundamental mechanism for navigation by intelligent embodied agents, whether they be biological or artificial. Unlike animal navigation, we can judiciously design the agent's perceptual system and control the learning paradigm to nullify alternative navigation mechanisms. Specifically, we train 'blind' agents -- with sensing limited to only egomotion and no other sensing of any kind -- to perform PointGoal navigation ('go to $\Delta$ x, $\Delta$ y') via reinforcement learning. Our agents are composed of navigation-agnostic components (fully-connected and recurrent neural networks), and our experimental setup provides no inductive bias towards mapping. Despite these harsh conditions, we find that blind agents are (1) surprisingly effective navigators in new environments (~95% success); (2) they utilize memory over long horizons (remembering ~1,000 steps of past experience in an episode); (3) this memory enables them to exhibit intelligent behavior (following walls, detecting collisions, taking shortcuts); (4) there is emergence of maps and collision detection neurons in the representations of the environment built by a blind agent as it navigates; and (5) the emergent maps are selective and task dependent (e.g. the agent 'forgets' exploratory detours). Overall, this paper presents no new techniques for the AI audience, but a surprising finding, an insight, and an explanation. | ['Dhruv Batra', 'Ari S. Morcos', 'Stefan Lee', 'Irfan Essa', 'Manolis Savva', 'Erik Wijmans'] | 2023-01-30 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [ 1.49293497e-01 4.00449276e-01 3.99621725e-01 2.00889513e-01
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aeddd706-842a-43f3-a14f-df98a14d04e0 | importance-filtering-with-risk-models-for | 2303.06935 | null | https://arxiv.org/abs/2303.06935v1 | https://arxiv.org/pdf/2303.06935v1.pdf | Importance Filtering with Risk Models for Complex Driving Situations | Self-driving cars face complex driving situations with a large amount of agents when moving in crowded cities. However, some of the agents are actually not influencing the behavior of the self-driving car. Filtering out unimportant agents would inherently simplify the behavior or motion planning task for the system. The planning system can then focus on fewer agents to find optimal behavior solutions for the ego~agent. This is helpful especially in terms of computational efficiency. In this paper, therefore, the research topic of importance filtering with driving risk models is introduced. We give an overview of state-of-the-art risk models and present newly adapted risk models for filtering. Their capability to filter out surrounding unimportant agents is compared in a large-scale experiment. As it turns out, the novel trajectory distance balances performance, robustness and efficiency well. Based on the results, we can further derive a novel filter architecture with multiple filter steps, for which risk models are recommended for each step, to further improve the robustness. We are confident that this will enable current behavior planning systems to better solve complex situations in everyday driving. | ['Julian Eggert', 'Malte Probst', 'Benedict Flade', 'Raphael Wenzel', 'Tim Puphal'] | 2023-03-13 | null | null | null | null | ['self-driving-cars', 'motion-planning'] | ['computer-vision', 'robots'] | [-4.29188579e-01 1.59460664e-01 -1.21068314e-01 -4.67993140e-01
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2190aad3-da5b-470f-826d-896be5291a86 | flow-plugin-network-for-conditional | 2110.04081 | null | https://arxiv.org/abs/2110.04081v1 | https://arxiv.org/pdf/2110.04081v1.pdf | Flow Plugin Network for conditional generation | Generative models have gained many researchers' attention in the last years resulting in models such as StyleGAN for human face generation or PointFlow for the 3D point cloud generation. However, by default, we cannot control its sampling process, i.e., we cannot generate a sample with a specific set of attributes. The current approach is model retraining with additional inputs and different architecture, which requires time and computational resources. We propose a novel approach that enables to a generation of objects with a given set of attributes without retraining the base model. For this purpose, we utilize the normalizing flow models - Conditional Masked Autoregressive Flow and Conditional Real NVP, as a Flow Plugin Network (FPN). | ['Maciej Zięba', 'Michał Koperski', 'Patryk Wielopolski'] | 2021-10-07 | null | null | null | null | ['conditional-image-generation', 'point-cloud-generation'] | ['computer-vision', 'computer-vision'] | [ 7.57651106e-02 3.05481493e-01 -1.86484959e-02 -3.44520569e-01
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846e1225-4fa7-4b39-bf40-852acfb1bd0b | multi-step-retriever-reader-interaction-for-1 | 1905.05733 | null | https://arxiv.org/abs/1905.05733v1 | https://arxiv.org/pdf/1905.05733v1.pdf | Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering | This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only requiring access to the token-level hidden representations of the reader. The retriever uses fast nearest neighbor search to scale to corpora containing millions of paragraphs. A gated recurrent unit updates the query at each step conditioned on the state of the reader and the reformulated query is used to re-rank the paragraphs by the retriever. We conduct analysis and show that iterative interaction helps in retrieving informative paragraphs from the corpus. Finally, we show that our multi-step-reasoning framework brings consistent improvement when applied to two widely used reader architectures DrQA and BiDAF on various large open-domain datasets --- TriviaQA-unfiltered, QuasarT, SearchQA, and SQuAD-Open. | ['Manzil Zaheer', 'Shehzaad Dhuliawala', 'Andrew McCallum', 'Rajarshi Das'] | 2019-05-14 | multi-step-retriever-reader-interaction-for | https://openreview.net/forum?id=HkfPSh05K7 | https://openreview.net/pdf?id=HkfPSh05K7 | iclr-2019-5 | ['triviaqa'] | ['miscellaneous'] | [-1.04996204e-01 6.99866533e-01 -7.34459981e-02 -3.62253666e-01
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34da9303-81a7-4f9b-a5a4-ff0fefbf7847 | the-multi-modal-universe-of-fast-fashion-the | 2204.06972 | null | https://arxiv.org/abs/2204.06972v2 | https://arxiv.org/pdf/2204.06972v2.pdf | The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark | We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE and MAE by up to 5.48% and 7% respectively compared to competitive baseline methods. The dataset is available at https://humaticslab.github.io/forecasting/visuelle | ['Marco Cristani', 'Berniero Scarpa', 'Matteo Denitto', 'Christian Joppi', 'Geri Skenderi'] | 2022-04-14 | null | null | null | null | ['short-observation-new-product-sales'] | ['time-series'] | [-1.93422794e-01 -2.82596022e-01 -2.12374583e-01 -4.39722359e-01
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bcf4f771-e4f4-405d-9c64-e26d51d4c6a1 | n-ary-constituent-tree-parsing-with-recursive | null | null | https://aclanthology.org/2021.acl-long.205/ | https://aclanthology.org/2021.acl-long.205.pdf | N-ary Constituent Tree Parsing with Recursive Semi-Markov Model | In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children. Previous graph-based methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction. The limitation is that the hidden nodes break the sibling relations of the n-ary node’s children. Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored. To solve this limitation, we propose a novel graph-based framework, which is called “recursive semi-Markov model”. The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent. In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation. Through experiments, the proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0.3-1.1 points in PTB and 2.3-6.8 points in CTB 5.1. | ['Zeqi Tan', 'Jinlong Li', 'Xin Xin'] | 2021-07-26 | null | https://aclanthology.org/2021.acl-long.205 | https://aclanthology.org/2021.acl-long.205.pdf | acl-2021-5 | ['constituency-parsing'] | ['natural-language-processing'] | [ 2.70053923e-01 5.04350364e-01 -3.65124047e-01 -4.20705706e-01
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85b7eca6-0079-407c-bf99-0c596008b7dd | machine-learning-based-source-code | 1703.07638 | null | http://arxiv.org/abs/1703.07638v1 | http://arxiv.org/pdf/1703.07638v1.pdf | Machine Learning Based Source Code Classification Using Syntax Oriented Features | As of today the programming language of the vast majority of the published
source code is manually specified or programmatically assigned based on the
sole file extension. In this paper we show that the source code programming
language identification task can be fully automated using machine learning
techniques. We first define the criteria that a production-level automatic
programming language identification solution should meet. Our criteria include
accuracy, programming language coverage, extensibility and performance. We then
describe our approach: How training files are preprocessed for extracting
features that mimic grammar productions, and then how these extracted grammar
productions are used for the training and testing of our classifier. We achieve
a 99 percent accuracy rate while classifying 29 of the most popular programming
languages with a Maximum Entropy classifier. | ['Shaul Zevin', 'Catherine Holzem'] | 2017-03-04 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 2.30224401e-01 -3.48507501e-02 -5.57270586e-01 -4.85463291e-01
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9179cf8c-df00-4cce-a8a1-52d2da643435 | on-the-applicability-of-synthetic-data-for | 2104.02815 | null | https://arxiv.org/abs/2104.02815v1 | https://arxiv.org/pdf/2104.02815v1.pdf | On the Applicability of Synthetic Data for Face Recognition | Face verification has come into increasing focus in various applications including the European Entry/Exit System, which integrates face recognition mechanisms. At the same time, the rapid advancement of biometric authentication requires extensive performance tests in order to inhibit the discriminatory treatment of travellers due to their demographic background. However, the use of face images collected as part of border controls is restricted by the European General Data Protection Law to be processed for no other reason than its original purpose. Therefore, this paper investigates the suitability of synthetic face images generated with StyleGAN and StyleGAN2 to compensate for the urgent lack of publicly available large-scale test data. Specifically, two deep learning-based (SER-FIQ, FaceQnet v1) and one standard-based (ISO/IEC TR 29794-5) face image quality assessment algorithm is utilized to compare the applicability of synthetic face images compared to real face images extracted from the FRGC dataset. Finally, based on the analysis of impostor score distributions and utility score distributions, our experiments reveal negligible differences between StyleGAN vs. StyleGAN2, and further also minor discrepancies compared to real face images. | ['Christoph Busch', 'Kiran Raja', 'Raghavendra Ramachandra', 'Marcel Grimmer', 'Haoyu Zhang'] | 2021-04-06 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.04599702e-01 -6.96366951e-02 3.69512767e-01 -4.42409188e-01
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bffb1966-f426-4e71-9372-629cb3066128 | mando-multi-level-heterogeneous-graph | 2208.13252 | null | https://arxiv.org/abs/2208.13252v2 | https://arxiv.org/pdf/2208.13252v2.pdf | MANDO: Multi-Level Heterogeneous Graph Embeddings for Fine-Grained Detection of Smart Contract Vulnerabilities | Learning heterogeneous graphs consisting of different types of nodes and edges enhances the results of homogeneous graph techniques. An interesting example of such graphs is control-flow graphs representing possible software code execution flows. As such graphs represent more semantic information of code, developing techniques and tools for such graphs can be highly beneficial for detecting vulnerabilities in software for its reliability. However, existing heterogeneous graph techniques are still insufficient in handling complex graphs where the number of different types of nodes and edges is large and variable. This paper concentrates on the Ethereum smart contracts as a sample of software codes represented by heterogeneous contract graphs built upon both control-flow graphs and call graphs containing different types of nodes and links. We propose MANDO, a new heterogeneous graph representation to learn such heterogeneous contract graphs' structures. MANDO extracts customized metapaths, which compose relational connections between different types of nodes and their neighbors. Moreover, it develops a multi-metapath heterogeneous graph attention network to learn multi-level embeddings of different types of nodes and their metapaths in the heterogeneous contract graphs, which can capture the code semantics of smart contracts more accurately and facilitate both fine-grained line-level and coarse-grained contract-level vulnerability detection. Our extensive evaluation of large smart contract datasets shows that MANDO improves the vulnerability detection results of other techniques at the coarse-grained contract level. More importantly, it is the first learning-based approach capable of identifying vulnerabilities at the fine-grained line-level, and significantly improves the traditional code analysis-based vulnerability detection approaches by 11.35% to 70.81% in terms of F1-score. | ['Lingxiao Jiang', 'Thanh-Nam Doan', 'Daniel Kudendo', 'Zahra Ahmadi', 'Chunyao Xie', 'Nhat-Minh Nguyen', 'Hoang H. Nguyen'] | 2022-08-28 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-3.52063298e-01 3.45236510e-01 -5.26381135e-01 -8.05464238e-02
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87f4c1d2-ec4e-421e-9aee-b04d95907c80 | exponentially-weighted-l-2-regularization | 2007.01208 | null | https://arxiv.org/abs/2007.01208v1 | https://arxiv.org/pdf/2007.01208v1.pdf | Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model | In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant or linear functions are usually utilized as the consequent parts of the fuzzy rules, but they cannot effectively describe the behavior within local regions defined by the antecedent parts. In this article, a theoretical and practical design methodology is developed to address this problem. First, the information granulation (Fuzzy C-Means) method is applied to capture the structure in the data and split the input space into subspaces, as well as form the antecedent parts. Second, the quadratic polynomials (QPs) are employed as the consequent parts. Compared with constant and linear functions, QPs can describe the input-output behavior within the local regions (subspaces) by refining the relationship between input and output variables. However, although QP can improve the approximation ability of the model, it could lead to the deterioration of the prediction ability of the model (e.g., overfitting). To handle this issue, we introduce an exponential weight approach inspired by the weight function theory encountered in harmonic analysis. More specifically, we adopt the exponential functions as the targeted penalty terms, which are equipped with l2 regularization (l2) (i.e., exponential weighted l2, ewl_2) to match the proposed reinforced second-order fuzzy rule-based model (RSFRM) properly. The advantage of el 2 compared to ordinary l2 lies in separately identifying and penalizing different types of polynomial terms in the coefficient estimation, and its results not only alleviate the overfitting and prevent the deterioration of generalization ability but also effectively release the prediction potential of the model. | ['Shanzhen Lu', 'Sung-Kwun Oh', 'Congcong Zhang', 'Witold Pedrycz', 'Zunwei Fu'] | 2020-07-02 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.48507535e-01 -1.02071956e-01 -2.17754751e-01 -8.10876638e-02
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aa27d72b-1a42-4ca3-b098-5a25f7ad4433 | forecasting-action-through-contact | 2102.00649 | null | https://arxiv.org/abs/2102.00649v1 | https://arxiv.org/pdf/2102.00649v1.pdf | Forecasting Action through Contact Representations from First Person Video | Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We annotate a subset of the EPIC Kitchens dataset to include time-to-contact between hands and objects, as well as segmentations of hands and objects. Using these annotations we train the Anticipation Module, a module producing Contact Anticipation Maps and Next Active Object Segmentations - novel low-level representations providing temporal and spatial characteristics of anticipated near future action. On top of the Anticipation Module we apply Egocentric Object Manipulation Graphs (Ego-OMG), a framework for action anticipation and prediction. Ego-OMG models longer term temporal semantic relations through the use of a graph modeling transitions between contact delineated action states. Use of the Anticipation Module within Ego-OMG produces state-of-the-art results, achieving 1st and 2nd place on the unseen and seen test sets, respectively, of the EPIC Kitchens Action Anticipation Challenge, and achieving state-of-the-art results on the tasks of action anticipation and action prediction over EPIC Kitchens. We perform ablation studies over characteristics of the Anticipation Module to evaluate their utility. | ['Yiannis Aloimonos', 'Cornelia Fermuller', 'Michael Maynord', 'Chinmaya Devaraj', 'Eadom Dessalene'] | 2021-02-01 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [ 5.30266404e-01 4.17634517e-01 -1.65143922e-01 -3.44067514e-01
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2b5acfaf-e3c3-43e5-9337-9870e4e1662e | multi-task-neural-processes-1 | 2111.05820 | null | https://arxiv.org/abs/2111.05820v2 | https://arxiv.org/pdf/2111.05820v2.pdf | Multi-Task Neural Processes | Neural processes have recently emerged as a class of powerful neural latent variable models that combine the strengths of neural networks and stochastic processes. As they can encode contextual data in the network's function space, they offer a new way to model task relatedness in multi-task learning. To study its potential, we develop multi-task neural processes, a new variant of neural processes for multi-task learning. In particular, we propose to explore transferable knowledge from related tasks in the function space to provide inductive bias for improving each individual task. To do so, we derive the function priors in a hierarchical Bayesian inference framework, which enables each task to incorporate the shared knowledge provided by related tasks into its context of the prediction function. Our multi-task neural processes methodologically expand the scope of vanilla neural processes and provide a new way of exploring task relatedness in function spaces for multi-task learning. The proposed multi-task neural processes are capable of learning multiple tasks with limited labeled data and in the presence of domain shift. We perform extensive experimental evaluations on several benchmarks for the multi-task regression and classification tasks. The results demonstrate the effectiveness of multi-task neural processes in transferring useful knowledge among tasks for multi-task learning and superior performance in multi-task classification and brain image segmentation. | ['Ling Shao', 'Marcel Worring', 'XianTong Zhen', 'Jiayi Shen'] | 2021-11-10 | multi-task-neural-processes | https://openreview.net/forum?id=wfRZkDvxOqj | https://openreview.net/pdf?id=wfRZkDvxOqj | null | ['brain-image-segmentation'] | ['medical'] | [ 6.80897892e-01 -4.54665758e-02 -1.64150670e-01 -4.51234907e-01
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150e7d16-087b-4406-918a-e5cb29025fb9 | synthetic-speech-detection-using-meta | 2201.09470 | null | https://arxiv.org/abs/2201.09470v1 | https://arxiv.org/pdf/2201.09470v1.pdf | Synthetic speech detection using meta-learning with prototypical loss | Recent works on speech spoofing countermeasures still lack generalization ability to unseen spoofing attacks. This is one of the key issues of ASVspoof challenges especially with the rapid development of diverse and high-quality spoofing algorithms. In this work, we address the generalizability of spoofing detection by proposing prototypical loss under the meta-learning paradigm to mimic the unseen test scenario during training. Prototypical loss with metric-learning objectives can learn the embedding space directly and emerges as a strong alternative to prevailing classification loss functions. We propose an anti-spoofing system based on squeeze-excitation Residual network (SE-ResNet) architecture with prototypical loss. We demonstrate that the proposed single system without any data augmentation can achieve competitive performance to the recent best anti-spoofing systems on ASVspoof 2019 logical access (LA) task. Furthermore, the proposed system with data augmentation outperforms the ASVspoof 2021 challenge best baseline both in the progress and evaluation phase of the LA task. On ASVspoof 2019 and 2021 evaluation set LA scenario, we attain a relative 68.4% and 3.6% improvement in min-tDCF compared to the challenge best baselines, respectively. | ['Sunil Kumar Kopparapu', 'Ashish Panda', 'Aditya Raikar', 'Monisankha Pal'] | 2022-01-24 | null | null | null | null | ['synthetic-speech-detection'] | ['audio'] | [ 3.07710350e-01 -3.07136238e-01 -4.14483905e-01 7.69120008e-02
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73fb6f6f-c74a-44a5-b7cf-c972c51a7864 | bridging-few-shot-learning-and-adaptation-new | 2105.11804 | null | https://arxiv.org/abs/2105.11804v2 | https://arxiv.org/pdf/2105.11804v2.pdf | Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift | Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes encountered at test-time, they only require a support set composed of a few labelled samples. FSL benchmarks commonly assume that those queries come from the same distribution as instances in the support set. However, in a realistic set-ting, data distribution is plausibly subject to change, a situation referred to as Distribution Shift (DS). The present work addresses the new and challenging problem of Few-Shot Learning under Support/Query Shift (FSQS) i.e., when support and query instances are sampled from related but different distributions. Our contributions are the following. First, we release a testbed for FSQS, including datasets, relevant baselines and a protocol for a rigorous and reproducible evaluation. Second, we observe that well-established FSL algorithms unsurprisingly suffer from a considerable drop in accuracy when facing FSQS, stressing the significance of our study. Finally, we show that transductive algorithms can limit the inopportune effect of DS. In particular, we study both the role of Batch-Normalization and Optimal Transport (OT) in aligning distributions, bridging Unsupervised Domain Adaptation with FSL. This results in a new method that efficiently combines OT with the celebrated Prototypical Networks. We bring compelling experiments demonstrating the advantage of our method. Our work opens an exciting line of research by providing a testbed and strong baselines. Our code is available at https://github.com/ebennequin/meta-domain-shift. | ['Céline Hudelot', 'Antoine Toubhans', 'Myriam Tami', 'Victor Bouvier', 'Etienne Bennequin'] | 2021-05-25 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 4.49747145e-01 -1.81310043e-01 -4.66623813e-01 -6.45110428e-01
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798686f7-d06f-4b80-bd3e-c62129da008f | the-multivariate-community-hawkes-model-for | 2205.00639 | null | https://arxiv.org/abs/2205.00639v2 | https://arxiv.org/pdf/2205.00639v2.pdf | The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks | The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks. | ['Kevin S. Xu', 'Subhadeep Paul', 'Zhipeng Huang', 'Lingfei Zhao', 'Hadeel Soliman'] | 2022-05-02 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-1.50305508e-02 1.51254330e-02 -9.18279961e-02 -8.55358392e-02
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8a52c398-1f70-4d6c-b92e-09a280fbe4e4 | binary-segmentation-of-seismic-facies-using | 2012.03675 | null | https://arxiv.org/abs/2012.03675v1 | https://arxiv.org/pdf/2012.03675v1.pdf | Binary Segmentation of Seismic Facies Using Encoder-Decoder Neural Networks | The interpretation of seismic data is vital for characterizing sediments' shape in areas of geological study. In seismic interpretation, deep learning becomes useful for reducing the dependence on handcrafted facies segmentation geometry and the time required to study geological areas. This work presents a Deep Neural Network for Facies Segmentation (DNFS) to obtain state-of-the-art results for seismic facies segmentation. DNFS is trained using a combination of cross-entropy and Jaccard loss functions. Our results show that DNFS obtains highly detailed predictions for seismic facies segmentation using fewer parameters than StNet and U-Net. | ['Ariane da Silveira', 'Felipe Zeiser', 'Sandro Rigo', 'Gabriel Ramos', 'Gefersom Lima'] | 2020-11-15 | null | null | null | null | ['seismic-interpretation'] | ['miscellaneous'] | [-2.64633209e-01 2.21757308e-01 2.90903598e-01 -6.14753544e-01
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06b9d68f-39c0-46ec-8676-baebd9ce1abf | rethinking-complex-valued-deep-neural | 2301.04320 | null | https://arxiv.org/abs/2301.04320v1 | https://arxiv.org/pdf/2301.04320v1.pdf | Rethinking complex-valued deep neural networks for monaural speech enhancement | Despite multiple efforts made towards adopting complex-valued deep neural networks (DNNs), it remains an open question whether complex-valued DNNs are generally more effective than real-valued DNNs for monaural speech enhancement. This work is devoted to presenting a critical assessment by systematically examining complex-valued DNNs against their real-valued counterparts. Specifically, we investigate complex-valued DNN atomic units, including linear layers, convolutional layers, long short-term memory (LSTM), and gated linear units. By comparing complex- and real-valued versions of fundamental building blocks in the recently developed gated convolutional recurrent network (GCRN), we show how different mechanisms for basic blocks affect the performance. We also find that the use of complex-valued operations hinders the model capacity when the model size is small. In addition, we examine two recent complex-valued DNNs, i.e. deep complex convolutional recurrent network (DCCRN) and deep complex U-Net (DCUNET). Evaluation results show that both DNNs produce identical performance to their real-valued counterparts while requiring much more computation. Based on these comprehensive comparisons, we conclude that complex-valued DNNs do not provide a performance gain over their real-valued counterparts for monaural speech enhancement, and thus are less desirable due to their higher computational costs. | ['Daniel Wong', 'Anurag Kumar', 'Buye Xu', 'Ke Tan', 'Haibin Wu'] | 2023-01-11 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 3.88317406e-01 1.98007300e-01 2.20071435e-01 -2.47363985e-01
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d69c20c2-d527-4afd-8491-1969ef6d73b8 | modeformer-modality-preserving-embedding-for | 2303.11551 | null | https://arxiv.org/abs/2303.11551v1 | https://arxiv.org/pdf/2303.11551v1.pdf | ModEFormer: Modality-Preserving Embedding for Audio-Video Synchronization using Transformers | Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience. A widely accepted paradigm is to create an error detection mechanism that identifies the cases when audio is leading or lagging. We propose ModEFormer, which independently extracts audio and video embeddings using modality-specific transformers. Different from the other transformer-based approaches, ModEFormer preserves the modality of the input streams which allows us to use a larger batch size with more negative audio samples for contrastive learning. Further, we propose a trade-off between the number of negative samples and number of unique samples in a batch to significantly exceed the performance of previous methods. Experimental results show that ModEFormer achieves state-of-the-art performance, 94.5% for LRS2 and 90.9% for LRS3. Finally, we demonstrate how ModEFormer can be used for offset detection for test clips. | ['WonDong Jang', 'Rohun Tripathi', 'Akash Gupta'] | 2023-03-21 | null | null | null | null | ['video-synchronization'] | ['computer-vision'] | [ 1.12908237e-01 -3.31027150e-01 -4.49198075e-02 -3.03373307e-01
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80a72632-47f0-4302-8850-e6fc1ade47b2 | domain-randomization-for-object-counting | 2202.08670 | null | https://arxiv.org/abs/2202.08670v1 | https://arxiv.org/pdf/2202.08670v1.pdf | Domain Randomization for Object Counting | Recently, the use of synthetic datasets based on game engines has been shown to improve the performance of several tasks in computer vision. However, these datasets are typically only appropriate for the specific domains depicted in computer games, such as urban scenes involving vehicles and people. In this paper, we present an approach to generate synthetic datasets for object counting for any domain without the need for photo-realistic techniques manually generated by expensive teams of 3D artists. We introduce a domain randomization approach for object counting based on synthetic datasets that are quick and inexpensive to generate. We deliberately avoid photorealism and drastically increase the variability of the dataset, producing images with random textures and 3D transformations, which improves generalization. Experiments show that our method facilitates good performance on various real word object counting datasets for multiple domains: people, vehicles, penguins, and fruit. The source code is available at: https://github.com/enric1994/dr4oc | ["Noel E. O'Connor", 'Diego Ortego', 'Kevin McGuinness', 'Enric Moreu'] | 2022-02-17 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 1.18111409e-01 -1.63429111e-01 3.28054756e-01 -1.42765343e-01
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169692e6-6af1-48e2-9d7e-2922b7acb557 | sl-cyclegan-blind-motion-deblurring-in-cycles | 2111.04026 | null | https://arxiv.org/abs/2111.04026v1 | https://arxiv.org/pdf/2111.04026v1.pdf | SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning | In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN. For the first time in blind motion deblurring, we propose a sparse ResNet-block as a combination of sparse convolution layers and a trainable spatial pooler k-winner based on HTM (Hierarchical Temporal Memory) to replace non-linearity such as ReLU in the ResNet-block of SL-CycleGAN generators. Furthermore, unlike many state-of-the-art GAN-based motion deblurring methods that treat motion deblurring as a linear end-to-end process, we take our inspiration from the domain-to-domain translation ability of CycleGAN, and we show that image deblurring can be cycle-consistent while achieving the best qualitative results. Finally, we perform extensive experiments on popular image benchmarks both qualitatively and quantitatively and achieve the record-breaking PSNR of 38.087 dB on GoPro dataset, which is 5.377 dB better than the most recent deblurring method. | ['Fang Fang', 'Li-Yun Wang', 'Ali Syed Saqlain'] | 2021-11-07 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 3.63003939e-01 -1.83245718e-01 -2.92495131e-01 1.78383335e-01
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bb4b00a2-3151-4b55-9e95-e94a108f5cff | jointly-extracting-explicit-and-implicit | null | null | https://aclanthology.org/2021.naacl-main.453 | https://aclanthology.org/2021.naacl-main.453.pdf | Jointly Extracting Explicit and Implicit Relational Triples with Reasoning Pattern Enhanced Binary Pointer Network | Relational triple extraction is a crucial task for knowledge graph construction. Existing methods mainly focused on explicit relational triples that are directly expressed, but usually suffer from ignoring implicit triples that lack explicit expressions. This will lead to serious incompleteness of the constructed knowledge graphs. Fortunately, other triples in the sentence provide supplementary information for discovering entity pairs that may have implicit relations. Also, the relation types between the implicitly connected entity pairs can be identified with relational reasoning patterns in the real world. In this paper, we propose a unified framework to jointly extract explicit and implicit relational triples. To explore entity pairs that may be implicitly connected by relations, we propose a binary pointer network to extract overlapping relational triples relevant to each word sequentially and retain the information of previously extracted triples in an external memory. To infer the relation types of implicit relational triples, we propose to introduce real-world relational reasoning patterns in our model and capture these patterns with a relation network. We conduct experiments on several benchmark datasets, and the results prove the validity of our method. | ['Yongfeng Huang', 'Changran Hu', 'Yunqi Zhang', 'Yubo Chen'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['implicit-relations'] | ['natural-language-processing'] | [-1.14383869e-01 5.22573233e-01 -7.17674732e-01 -3.73819083e-01
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960332b3-ac7c-445a-a9f8-5abbaa7a6e99 | on-hyperparameter-search-in-cluster-ensembles | 1803.11008 | null | http://arxiv.org/abs/1803.11008v1 | http://arxiv.org/pdf/1803.11008v1.pdf | On Hyperparameter Search in Cluster Ensembles | Quality assessments of models in unsupervised learning and clustering
verification in particular have been a long-standing problem in the machine
learning research. The lack of robust and universally applicable cluster
validity scores often makes the algorithm selection and hyperparameter
evaluation a tough guess. In this paper, we show that cluster ensemble
aggregation techniques such as consensus clustering may be used to evaluate
clusterings and their hyperparameter configurations. We use normalized mutual
information to compare individual objects of a clustering ensemble to the
constructed consensus of the whole ensemble and show, that the resulting score
can serve as an overall quality measure for clustering problems. This method is
capable of highlighting the standout clustering and hyperparameter
configuration in the ensemble even in the case of a distorted consensus. We
apply this very general framework to various data sets and give possible
directions for future research. | ['Ralf Banisch', 'Luzie Helfmann', 'Mattes Mollenhauer', 'Johannes von Lindheim'] | 2018-03-29 | null | null | null | null | ['clustering-ensemble'] | ['graphs'] | [ 5.93726849e-03 -1.22548193e-01 4.06492919e-01 -4.76548463e-01
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b0e46f4c-afe6-4800-9d4a-8b35fb32f2b4 | star-ris-assisted-privacy-protection-in | 2306.12675 | null | https://arxiv.org/abs/2306.12675v1 | https://arxiv.org/pdf/2306.12675v1.pdf | STAR-RIS-Assisted Privacy Protection in Semantic Communication System | Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances communication efficiency greatly. However, the open nature of wireless transmission and the presence of homogeneous KBs among subscribers of identical data type pose a risk of privacy leakage in SemCom. To address this challenge, we propose to leverage the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to achieve privacy protection in a SemCom system. In this system, the STAR-RIS is utilized to enhance the signal transmission of the SemCom between a base station and a destination user, as well as to covert the signal to interference specifically for the eavesdropper (Eve). Simulation results demonstrate that our generated task-level disturbance outperforms other benchmarks in protecting SemCom privacy, as evidenced by the significantly lower task success rate achieved by Eve. | ['Zehui Xiong', 'Yuping Zhao', 'Pengxin Guan', 'Wanting Yang', 'Yiru Wang'] | 2023-06-22 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [ 5.78417599e-01 6.72580063e-01 6.95020556e-01 -2.12664112e-01
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0c258566-ec8e-4dbc-808a-eff7f01ee95f | big-data-and-cross-document-coreference | 1311.3987 | null | http://arxiv.org/abs/1311.3987v1 | http://arxiv.org/pdf/1311.3987v1.pdf | Big Data and Cross-Document Coreference Resolution: Current State and Future Opportunities | Information Extraction (IE) is the task of automatically extracting
structured information from unstructured/semi-structured machine-readable
documents. Among various IE tasks, extracting actionable intelligence from
ever-increasing amount of data depends critically upon Cross-Document
Coreference Resolution (CDCR) - the task of identifying entity mentions across
multiple documents that refer to the same underlying entity. Recently, document
datasets of the order of peta-/tera-bytes has raised many challenges for
performing effective CDCR such as scaling to large numbers of mentions and
limited representational power. The problem of analysing such datasets is
called "big data". The aim of this paper is to provide readers with an
understanding of the central concepts, subtasks, and the current
state-of-the-art in CDCR process. We provide assessment of existing
tools/techniques for CDCR subtasks and highlight big data challenges in each of
them to help readers identify important and outstanding issues for further
investigation. Finally, we provide concluding remarks and discuss possible
directions for future work. | ['Seyed-Mehdi-Reza Beheshti', 'Seung Hwan Ryu', 'Boualem Benatallah', 'Wei Wang', 'Srikumar Venugopal'] | 2013-11-14 | null | null | null | null | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 3.22931468e-01 6.96002185e-01 -1.93618491e-01 -2.71994382e-01
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dde0829f-ebf9-4f16-ba51-a38557255f26 | mo-padgan-generating-diverse-designs-with | 2007.04790 | null | https://arxiv.org/abs/2007.04790v1 | https://arxiv.org/pdf/2007.04790v1.pdf | MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement | Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing models generally do not generate high-performance novel designs, outside the domain of the training data. To address these challenges, we propose MO-PaDGAN, which contains a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances. Through a real-world airfoil design example, we demonstrate that MO-PaDGAN expands the existing boundary of the design space towards high-performance regions and generates new designs with high diversity and performances exceeding training data. | ['Wei Chen', 'Faez Ahmed'] | 2020-07-07 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-1.84592739e-01 -4.66954038e-02 -2.88774610e-01 -1.45992488e-02
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7c662050-7ea2-4942-b737-2dfa0e244ce6 | music-instrument-classification-reprogrammed | 2211.08379 | null | https://arxiv.org/abs/2211.08379v1 | https://arxiv.org/pdf/2211.08379v1.pdf | Music Instrument Classification Reprogrammed | The performance of approaches to Music Instrument Classification, a popular task in Music Information Retrieval, is often impacted and limited by the lack of availability of annotated data for training. We propose to address this issue with "reprogramming," a technique that utilizes pre-trained deep and complex neural networks originally targeting a different task by modifying and mapping both the input and output of the pre-trained model. We demonstrate that reprogramming can effectively leverage the power of the representation learned for a different task and that the resulting reprogrammed system can perform on par or even outperform state-of-the-art systems at a fraction of training parameters. Our results, therefore, indicate that reprogramming is a promising technique potentially applicable to other tasks impeded by data scarcity. | ['Alexander Lerch', 'Hsin-Hung Chen'] | 2022-11-15 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 5.16824901e-01 -1.36757150e-01 -1.40209883e-01 -2.56108772e-02
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bbb98908-b4c9-496d-b28b-f23518f5f835 | comparative-benchmarking-of-causal-discovery | 1708.06246 | null | http://arxiv.org/abs/1708.06246v2 | http://arxiv.org/pdf/1708.06246v2.pdf | Comparative Benchmarking of Causal Discovery Techniques | In this paper we present a comprehensive view of prominent causal discovery
algorithms, categorized into two main categories (1) assuming acyclic and no
latent variables, and (2) allowing both cycles and latent variables, along with
experimental results comparing them from three perspectives: (a) structural
accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual
inference. For (b) and (c) we train causal Bayesian networks with structures as
predicted by each causal discovery technique to carry out counterfactual or
standard predictive inference. We compare causal algorithms on two pub- licly
available and one simulated datasets having different sample sizes: small,
medium and large. Experiments show that structural accuracy of a technique does
not necessarily correlate with higher accuracy of inferencing tasks. Fur- ther,
surveyed structure learning algorithms do not perform well in terms of
structural accuracy in case of datasets having large number of variables. | ['Garima Gupta', 'Vartika Tewari', 'Gautam Shroff', 'Karamjit Singh'] | 2017-08-18 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 3.85362148e-01 4.19615537e-01 -7.14357793e-01 -4.27115500e-01
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9db523b5-5d51-4b74-b766-b7ce8f6782e8 | high-frequency-residual-learning-for-multi | 1905.02649 | null | https://arxiv.org/abs/1905.02649v1 | https://arxiv.org/pdf/1905.02649v1.pdf | High Frequency Residual Learning for Multi-Scale Image Classification | We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. The architecture utilizes two networks: a low resolution network to efficiently approximate low frequency components and a high resolution network to learn high frequency residuals by reusing the upsampled low resolution features. With a classifier calibration module, MSNet can dynamically allocate computation resources during inference to achieve a better speed and accuracy trade-off. We evaluate our methods on the challenging ImageNet-1k dataset and observe consistent improvements over different base networks. On ResNet-18 and MobileNet with alpha=1.0, MSNet gains 1.5% accuracy over both architectures without increasing computations. On the more efficient MobileNet with alpha=0.25, our method gains 3.8% accuracy with the same amount of computations. | ['Jian-Feng Wang', 'Bowen Cheng', 'Rong Xiao', 'Lei Zhang', 'Thomas Huang'] | 2019-05-07 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 1.94140822e-01 3.25491391e-02 -1.83584824e-01 -9.87028256e-02
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3635cf96-9147-43cf-8ba7-904534ac68ce | multi-task-pre-training-for-plug-and-play-1 | null | null | https://openreview.net/forum?id=46-q5-S-mEF | https://openreview.net/pdf?id=46-q5-S-mEF | Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System | Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified plug-and-play model for task-oriented dialogue. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. Experimental results show that PPTOD achieves new state of the art on all evaluated tasks in both high-resource and low-resource scenarios. Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators. | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-1.93645041e-02 5.58311641e-01 6.63457513e-02 -6.20223820e-01
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b63d23b1-01c3-426e-8ac9-de720d9497ff | adaptive-sequence-submodularity | 1902.05981 | null | https://arxiv.org/abs/1902.05981v2 | https://arxiv.org/pdf/1902.05981v2.pdf | Adaptive Sequence Submodularity | In many machine learning applications, one needs to interactively select a sequence of items (e.g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e.g., guiding an agent through a series of states). Not only do sequences already pose a dauntingly large search space, but we must also take into account past observations, as well as the uncertainty of future outcomes. Without further structure, finding an optimal sequence is notoriously challenging, if not completely intractable. In this paper, we view the problem of adaptive and sequential decision making through the lens of submodularity and propose an adaptive greedy policy with strong theoretical guarantees. Additionally, to demonstrate the practical utility of our results, we run experiments on Amazon product recommendation and Wikipedia link prediction tasks. | ['Andreas Krause', 'Amin Karbasi', 'Moran Feldman', 'Marko Mitrovic', 'Ehsan Kazemi'] | 2019-02-15 | adaptive-sequence-submodularity-1 | http://papers.nips.cc/paper/8776-adaptive-sequence-submodularity | http://papers.nips.cc/paper/8776-adaptive-sequence-submodularity.pdf | neurips-2019-12 | ['product-recommendation'] | ['miscellaneous'] | [ 1.71882316e-01 4.35872003e-02 -5.70080876e-01 -2.43434638e-01
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20065493-6f52-4803-81fe-901ea56820f8 | integrating-whole-context-to-sequence-to | 1912.01777 | null | https://arxiv.org/abs/1912.01777v2 | https://arxiv.org/pdf/1912.01777v2.pdf | Integrating Knowledge into End-to-End Speech Recognition from External Text-Only Data | Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained with speech-text paired data. It is challenging to incorporate external text-only data into AED models. Another issue of the AED model is that it does not use the right context of a text token while predicting the token. To alleviate the above two issues, we propose a unified method called LST (Learn Spelling from Teachers) to integrate knowledge into an AED model from the external text-only data and leverage the whole context in a sentence. The method is divided into two stages. First, in the representation stage, a language model is trained on the text. It can be seen as that the knowledge in the text is compressed into the LM. Then, at the transferring stage, the knowledge is transferred to the AED model via teacher-student learning. To further use the whole context of the text sentence, we propose an LM called causal cloze completer (COR), which estimates the probability of a token, given both the left context and the right context of it. Therefore, with LST training, the AED model can leverage the whole context in the sentence. Different from fusion based methods, which use LM during decoding, the proposed method does not increase any extra complexity at the inference stage. We conduct experiments on two scales of public Chinese datasets AISHELL-1 and AISHELL-2. The experimental results demonstrate the effectiveness of leveraging external text-only data and the whole context in a sentence with our proposed method, compared with baseline hybrid systems and AED model based systems. | ['Jian-Hua Tao', 'Zhengkun Tian', 'Zhengqi Wen', 'Jiangyan Yi', 'Ye Bai', 'Shuai Zhang'] | 2019-12-04 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 1.85093299e-01 1.52164519e-01 -2.23714352e-01 -4.70038086e-01
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f3805c50-7b5d-469c-ba0c-0b8e65902fa8 | local-class-specific-and-global-image-level | 1912.12215 | null | https://arxiv.org/abs/1912.12215v3 | https://arxiv.org/pdf/1912.12215v3.pdf | Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation | In this paper, we address the task of semantic-guided scene generation. One open challenge in scene generation is the difficulty of the generation of small objects and detailed local texture, which has been widely observed in global image-level generation methods. To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details. To learn more discriminative class-specific feature representations for the local generation, a novel classification module is also proposed. To combine the advantage of both the global image-level and the local class-specific generation, a joint generation network is designed with an attention fusion module and a dual-discriminator structure embedded. Extensive experiments on two scene image generation tasks show superior generation performance of the proposed model. The state-of-the-art results are established by large margins on both tasks and on challenging public benchmarks. The source code and trained models are available at https://github.com/Ha0Tang/LGGAN. | ['Nicu Sebe', 'Philip H. S. Torr', 'Yan Yan', 'Hao Tang', 'Dan Xu'] | 2019-12-27 | local-class-specific-and-global-image-level-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Tang_Local_Class-Specific_and_Global_Image-Level_Generative_Adversarial_Networks_for_Semantic-Guided_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-generation'] | ['computer-vision'] | [ 5.39243937e-01 1.87682018e-01 6.30938709e-02 -4.02596742e-01
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23118726-97df-417c-9f78-d5586a66c3e5 | transformers-and-ensemble-methods-a-solution | 2303.09823 | null | https://arxiv.org/abs/2303.09823v1 | https://arxiv.org/pdf/2303.09823v1.pdf | Transformers and Ensemble methods: A solution for Hate Speech Detection in Arabic languages | This paper describes our participation in the shared task of hate speech detection, which is one of the subtasks of the CERIST NLP Challenge 2022. Our experiments evaluate the performance of six transformer models and their combination using 2 ensemble approaches. The best results on the training set, in a five-fold cross validation scenario, were obtained by using the ensemble approach based on the majority vote. The evaluation of this approach on the test set resulted in an F1-score of 0.60 and an Accuracy of 0.86. | ['Wajdi Zaghouani', 'Paolo Rosso', 'Imene Bensalem', 'Angel Felipe Magnossão de Paula'] | 2023-03-17 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.00484261e-01 2.79468417e-01 3.13335776e-01 -1.92537084e-01
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a1be54df-b6d0-49d1-973d-d46c062bb77b | satellite-image-semantic-segmentation | 2110.05812 | null | https://arxiv.org/abs/2110.05812v1 | https://arxiv.org/pdf/2110.05812v1.pdf | Satellite Image Semantic Segmentation | In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building, and road). We rely on Swin Transformer architecture and build the dataset from IGN open data. We report quantitative and qualitative segmentation results on this dataset and discuss strengths and limitations. The dataset and the trained model are made publicly available. | ['Benoît Martinez', 'Christian Wolf', 'Killian Oechslin', 'Eric Guérin'] | 2021-10-12 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 4.51554239e-01 2.06268877e-01 -1.55685753e-01 -5.32773137e-01
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af82f0ad-d97e-4abf-aff4-bd12a9f3a6f8 | tvr-a-large-scale-dataset-for-video-subtitle | 2001.09099 | null | https://arxiv.org/abs/2001.09099v2 | https://arxiv.org/pdf/2001.09099v2.pdf | TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval | We introduce TV show Retrieval (TVR), a new multimodal retrieval dataset. TVR requires systems to understand both videos and their associated subtitle (dialogue) texts, making it more realistic. The dataset contains 109K queries collected on 21.8K videos from 6 TV shows of diverse genres, where each query is associated with a tight temporal window. The queries are also labeled with query types that indicate whether each of them is more related to video or subtitle or both, allowing for in-depth analysis of the dataset and the methods that built on top of it. Strict qualification and post-annotation verification tests are applied to ensure the quality of the collected data. Further, we present several baselines and a novel Cross-modal Moment Localization (XML ) network for multimodal moment retrieval tasks. The proposed XML model uses a late fusion design with a novel Convolutional Start-End detector (ConvSE), surpassing baselines by a large margin and with better efficiency, providing a strong starting point for future work. We have also collected additional descriptions for each annotated moment in TVR to form a new multimodal captioning dataset with 262K captions, named TV show Caption (TVC). Both datasets are publicly available. TVR: https://tvr.cs.unc.edu, TVC: https://tvr.cs.unc.edu/tvc.html. | ['Tamara L. Berg', 'Licheng Yu', 'Mohit Bansal', 'Jie Lei'] | 2020-01-24 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3768_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660443.pdf | eccv-2020-8 | ['moment-retrieval'] | ['computer-vision'] | [ 1.36498675e-01 -3.52002978e-01 -3.27207386e-01 -5.41734278e-01
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b9e13f1a-ae05-439e-bca9-12a3aef28036 | compressive-sensing-with-tensorized | 2303.06235 | null | https://arxiv.org/abs/2303.06235v1 | https://arxiv.org/pdf/2303.06235v1.pdf | Compressive Sensing with Tensorized Autoencoder | Deep networks can be trained to map images into a low-dimensional latent space. In many cases, different images in a collection are articulated versions of one another; for example, same object with different lighting, background, or pose. Furthermore, in many cases, parts of images can be corrupted by noise or missing entries. In this paper, our goal is to recover images without access to the ground-truth (clean) images using the articulations as structural prior of the data. Such recovery problems fall under the domain of compressive sensing. We propose to learn autoencoder with tensor ring factorization on the the embedding space to impose structural constraints on the data. In particular, we use a tensor ring structure in the bottleneck layer of the autoencoder that utilizes the soft labels of the structured dataset. We empirically demonstrate the effectiveness of the proposed approach for inpainting and denoising applications. The resulting method achieves better reconstruction quality compared to other generative prior-based self-supervised recovery approaches for compressive sensing. | ['M. Salman Asif', 'Rakib Hyder'] | 2023-03-10 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 3.03791106e-01 -9.29660723e-03 1.34351268e-01 -2.13060439e-01
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e3a82885-41f5-49c1-aa93-0c00307c31f5 | generating-questions-from-wikidata-triples | null | null | https://aclanthology.org/2022.lrec-1.29 | https://aclanthology.org/2022.lrec-1.29.pdf | Generating Questions from Wikidata Triples | Question generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To handle rare entities and generalize to unseen properties, previous work on KBQG resorted to extensive, often ad-hoc pre- and post-processing of the input triple. We revisit KBQG – using pre training, a new (triple, question) dataset and taking question type into account – and show that our approach outperforms previous work both in a standard and in a zero-shot setting. We also show that the extended KBQG dataset (also helpful for knowledge base question answering) we provide allows not only for better coverage in terms of knowledge base (KB) properties but also for increased output variability in that it permits the generation of multiple questions from the same KB triple. | ['Claire Gardent', 'Thiago castro Ferreira', 'Kelvin Han'] | null | null | null | null | lrec-2022-6 | ['knowledge-base-question-answering', 'question-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.18288526e-02 9.57428038e-01 1.67808443e-01 -9.47158486e-02
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ae3bfac0-7f8c-48ab-89dd-0f6fafb917a1 | extreme-multi-label-classification-with-label | null | null | https://aclanthology.org/2022.ecnlp-1.16 | https://aclanthology.org/2022.ecnlp-1.16.pdf | Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction | Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicalization of extracted values. Furthermore, since values needed for actual services is static in many attributes, extraction of new values is not always necessary. Given the above, we formalize AVE as extreme multi-label classification (XMC). A major problem in solving AVE as XMC is that the distribution between positive and negative labels for products is heavily imbalanced. To mitigate the negative impact derived from such biased distribution, we propose label masking, a simple and effective method to reduce the number of negative labels in training. We exploit attribute taxonomy designed for e-commerce platforms to determine which labels are negative for products. Experimental results using a dataset collected from a Japanese e-commerce platform demonstrate that the label masking improves micro and macro F_1 scores by 3.38 and 23.20 points, respectively. | ['Keiji Shinzato', 'Yandi Xia', 'Wei-Te Chen'] | null | null | null | null | ecnlp-acl-2022-5 | ['extreme-multi-label-classification', 'attribute-value-extraction'] | ['methodology', 'natural-language-processing'] | [ 3.85639048e-03 -1.86355591e-01 -6.63347244e-01 -7.65239596e-01
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23dce76e-6bce-4502-a312-79ea10d9f315 | robust-face-recognition-by-constrained-part | 1501.04717 | null | http://arxiv.org/abs/1501.04717v1 | http://arxiv.org/pdf/1501.04717v1.pdf | Robust Face Recognition by Constrained Part-based Alignment | Developing a reliable and practical face recognition system is a
long-standing goal in computer vision research. Existing literature suggests
that pixel-wise face alignment is the key to achieve high-accuracy face
recognition. By assuming a human face as piece-wise planar surfaces, where each
surface corresponds to a facial part, we develop in this paper a Constrained
Part-based Alignment (CPA) algorithm for face recognition across pose and/or
expression. Our proposed algorithm is based on a trainable CPA model, which
learns appearance evidence of individual parts and a tree-structured shape
configuration among different parts. Given a probe face, CPA simultaneously
aligns all its parts by fitting them to the appearance evidence with
consideration of the constraint from the tree-structured shape configuration.
This objective is formulated as a norm minimization problem regularized by
graph likelihoods. CPA can be easily integrated with many existing classifiers
to perform part-based face recognition. Extensive experiments on benchmark face
datasets show that CPA outperforms or is on par with existing methods for
robust face recognition across pose, expression, and/or illumination changes. | ['Tsung-Han Chan', 'Yueming Wang', 'Kui Jia', 'Gang Pan', 'Yi Ma', 'Yuting Zhang'] | 2015-01-20 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.43321347e-01 -1.20781362e-01 -1.48236742e-02 -8.00947905e-01
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88277b2c-2be8-4a7d-b062-48747d20fc2c | understanding-art-through-multi-modal | 1904.10615 | null | http://arxiv.org/abs/1904.10615v1 | http://arxiv.org/pdf/1904.10615v1.pdf | Understanding Art through Multi-Modal Retrieval in Paintings | In computer vision, visual arts are often studied from a purely aesthetics
perspective, mostly by analysing the visual appearance of an artistic
reproduction to infer its style, its author, or its representative features. In
this work, however, we explore art from both a visual and a language
perspective. Our aim is to bridge the gap between the visual appearance of an
artwork and its underlying meaning, by jointly analysing its aesthetics and its
semantics. We introduce the use of multi-modal techniques in the field of
automatic art analysis by 1) collecting a multi-modal dataset with fine-art
paintings and comments, and 2) exploring robust visual and textual
representations in artistic images. | ['Yuta Nakashima', 'Noa Garcia', 'Benjamin Renoust'] | 2019-04-24 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 4.17171210e-01 -9.48835835e-02 3.06423232e-02 -7.97843412e-02
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ba51d198-0808-4f30-96c1-475ea2a59a4c | lower-bounds-and-accelerated-algorithms-in | 2305.07612 | null | https://arxiv.org/abs/2305.07612v1 | https://arxiv.org/pdf/2305.07612v1.pdf | Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression | Communication compression is an essential strategy for alleviating communication overhead by reducing the volume of information exchanged between computing nodes in large-scale distributed stochastic optimization. Although numerous algorithms with convergence guarantees have been obtained, the optimal performance limit under communication compression remains unclear. In this paper, we investigate the performance limit of distributed stochastic optimization algorithms employing communication compression. We focus on two main types of compressors, unbiased and contractive, and address the best-possible convergence rates one can obtain with these compressors. We establish the lower bounds for the convergence rates of distributed stochastic optimization in six different settings, combining strongly-convex, generally-convex, or non-convex functions with unbiased or contractive compressor types. To bridge the gap between lower bounds and existing algorithms' rates, we propose NEOLITHIC, a nearly optimal algorithm with compression that achieves the established lower bounds up to logarithmic factors under mild conditions. Extensive experimental results support our theoretical findings. This work provides insights into the theoretical limitations of existing compressors and motivates further research into fundamentally new compressor properties. | ['Kun Yuan', 'Wotao Yin', 'Yiming Chen', 'Xinmeng Huang', 'Yutong He'] | 2023-05-12 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [ 1.25776321e-01 -8.29237625e-02 -2.45313793e-01 -2.76227891e-01
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1b1922fb-7762-4132-b6a8-1d412453e17a | stepnet-spatial-temporal-part-aware-network | 2212.12857 | null | https://arxiv.org/abs/2212.12857v1 | https://arxiv.org/pdf/2212.12857v1.pdf | StepNet: Spatial-temporal Part-aware Network for Sign Language Recognition | Sign language recognition (SLR) aims to overcome the communication barrier for the people with deafness or the people with hard hearing. Most existing approaches can be typically divided into two lines, i.e., Skeleton-based and RGB-based methods, but both the two lines of methods have their limitations. RGB-based approaches usually overlook the fine-grained hand structure, while Skeleton-based methods do not take the facial expression into account. In attempts to address both limitations, we propose a new framework named Spatial-temporal Part-aware network (StepNet), based on RGB parts. As the name implies, StepNet consists of two modules: Part-level Spatial Modeling and Part-level Temporal Modeling. Particularly, without using any keypoint-level annotations, Part-level Spatial Modeling implicitly captures the appearance-based properties, such as hands and faces, in the feature space. On the other hand, Part-level Temporal Modeling captures the pertinent properties over time by implicitly mining the long-short term context. Extensive experiments show that our StepNet, thanks to Spatial-temporal modules, achieves competitive Top-1 Per-instance accuracy on three widely-used SLR benchmarks, i.e., 56.89% on WLASL, 77.2% on NMFs-CSL, and 77.1% on BOBSL. Moreover, the proposed method is compatible with the optical flow input, and can yield higher performance if fused. We hope that this work can serve as a preliminary step for the people with deafness. | ['Yi Yang', 'Zhedong Zheng', 'Xiaolong Shen'] | 2022-12-25 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 4.89170551e-02 -3.25910181e-01 -4.28266287e-01 -4.50721115e-01
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6.04618371e-01 -7.62023509e-01 -3.23648602e-01 -1.76943019e-01] | [9.195001602172852, -6.476389408111572] |
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