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23bd5133-475f-46a1-83e4-64c6c02036bd | research-project-text-engineering-tool-for | 1601.01887 | null | http://arxiv.org/abs/1601.01887v1 | http://arxiv.org/pdf/1601.01887v1.pdf | Research Project: Text Engineering Tool for Ontological Scientometry | The number of scientific papers grows exponentially in many disciplines. The
share of online available papers grows as well. At the same time, the period of
time for a paper to loose at chance to be cited anymore shortens. The decay of
the citing rate shows similarity to ultradiffusional processes as for other
online contents in social networks. The distribution of papers per author shows
similarity to the distribution of posts per user in social networks. The rate
of uncited papers for online available papers grows while some papers 'go
viral' in terms of being cited. Summarized, the practice of scientific
publishing moves towards the domain of social networks. The goal of this
project is to create a text engineering tool, which can semi-automatically
categorize a paper according to its type of contribution and extract
relationships between them into an ontological database. Semi-automatic
categorization means that the mistakes made by automatic pre-categorization and
relationship-extraction will be corrected through a wikipedia-like front-end by
volunteers from general public. This tool should not only help researchers and
the general public to find relevant supplementary material and peers faster,
but also provide more information for research funding agencies. | ['Rustam Tagiew'] | 2016-01-08 | null | null | null | null | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [-0.6379378 0.52262914 -0.16764157 0.19700348 -0.12048052 -0.94660056
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1a88501e-9387-46c5-bfb9-70cef38d2b34 | estimating-discontinuous-time-varying-risk | 2211.08991 | null | https://arxiv.org/abs/2211.08991v1 | https://arxiv.org/pdf/2211.08991v1.pdf | Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML | Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities and biomarkers also changed. We add to the conversation regarding inflammation, hemostasis and vascular function in COVID-19 by performing a time-varying observational analysis of over 4000 patients hospitalized for COVID-19 in a New York City hospital system from March 2020 to August 2021. To perform this analysis, we apply tree-based generalized additive models with temporal interactions which recover discontinuous risk changes caused by discrete protocols changes. We find that the biomarkers of thrombosis increasingly predicted mortality from March 2020 to August 2021, while the association between biomarkers of inflammation and thrombosis weakened. Beyond COVID-19, this presents a straightforward methodology to estimate unknown and discontinuous time-varying effects. | ['Rich Caruana', 'Yin Aphinyanaphongs', 'Mark E. Nunnally', 'Benjamin Lengerich'] | 2022-11-15 | null | null | null | null | ['additive-models'] | ['methodology'] | [-8.30552727e-02 -2.57077157e-01 -2.77923375e-01 1.29287168e-01
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b26f0fe8-3c8d-430a-bd83-90660aa9ce6c | ceflow-a-robust-and-efficient-counterfactual | 2303.14668 | null | https://arxiv.org/abs/2303.14668v1 | https://arxiv.org/pdf/2303.14668v1.pdf | CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows | Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements, they suffer from two major limitations: 1) the counterfactuals generation is prohibitively slow, which prevents algorithms from being deployed in interactive environments; 2) the counterfactual explanation algorithms produce unstable results due to the randomness in the sampling procedure of variational autoencoder. In this work, to address the above limitations, we design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features. Numerical experiments demonstrate that our technique compares favorably to state-of-the-art methods. We release our source at https://github.com/tridungduong16/fairCE.git for reproducing the results. | ['Guandong Xu', 'Qian Li', 'Tri Dung Duong'] | 2023-03-26 | null | null | null | null | ['interpretable-machine-learning', 'counterfactual-explanation'] | ['methodology', 'miscellaneous'] | [-8.33068416e-02 4.31766927e-01 -3.46442163e-01 -2.87255973e-01
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49f7d74e-6d60-4182-9466-0336b9675ee8 | coresets-for-vector-summarization-with | 1706.05554 | null | http://arxiv.org/abs/1706.05554v1 | http://arxiv.org/pdf/1706.05554v1.pdf | Coresets for Vector Summarization with Applications to Network Graphs | We provide a deterministic data summarization algorithm that approximates the
mean $\bar{p}=\frac{1}{n}\sum_{p\in P} p$ of a set $P$ of $n$ vectors in
$\REAL^d$, by a weighted mean $\tilde{p}$ of a \emph{subset} of $O(1/\eps)$
vectors, i.e., independent of both $n$ and $d$. We prove that the squared
Euclidean distance between $\bar{p}$ and $\tilde{p}$ is at most $\eps$
multiplied by the variance of $P$. We use this algorithm to maintain an
approximated sum of vectors from an unbounded stream, using memory that is
independent of $d$, and logarithmic in the $n$ vectors seen so far. Our main
application is to extract and represent in a compact way friend groups and
activity summaries of users from underlying data exchanges. For example, in the
case of mobile networks, we can use GPS traces to identify meetings, in the
case of social networks, we can use information exchange to identify friend
groups. Our algorithm provably identifies the {\it Heavy Hitter} entries in a
proximity (adjacency) matrix. The Heavy Hitters can be used to extract and
represent in a compact way friend groups and activity summaries of users from
underlying data exchanges. We evaluate the algorithm on several large data
sets. | ['Sedat Ozer', 'Daniela Rus', 'Dan Feldman'] | 2017-06-17 | coresets-for-vector-summarization-with-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=481 | http://proceedings.mlr.press/v70/feldman17a/feldman17a.pdf | icml-2017-8 | ['data-summarization'] | ['miscellaneous'] | [ 2.31473967e-01 3.62706542e-01 1.46889230e-02 -3.35655408e-03
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35bbdfe8-d65c-4353-b7a0-772d65d9658b | offline-versus-online-triplet-mining-based-on | 2007.02200 | null | https://arxiv.org/abs/2007.02200v3 | https://arxiv.org/pdf/2007.02200v3.pdf | Offline versus Online Triplet Mining based on Extreme Distances of Histopathology Patches | We analyze the effect of offline and online triplet mining for colorectal cancer (CRC) histopathology dataset containing 100,000 patches. We consider the extreme, i.e., farthest and nearest patches to a given anchor, both in online and offline mining. While many works focus solely on selecting the triplets online (batch-wise), we also study the effect of extreme distances and neighbor patches before training in an offline fashion. We analyze extreme cases' impacts in terms of embedding distance for offline versus online mining, including easy positive, batch semi-hard, batch hard triplet mining, neighborhood component analysis loss, its proxy version, and distance weighted sampling. We also investigate online approaches based on extreme distance and comprehensively compare offline, and online mining performance based on the data patterns and explain offline mining as a tractable generalization of the online mining with large mini-batch size. As well, we discuss the relations of different colorectal tissue types in terms of extreme distances. We found that offline and online mining approaches have comparable performances for a specific architecture, such as ResNet-18 in this study. Moreover, we found the assorted case, including different extreme distances, is promising, especially in the online approach. | ['Milad Sikaroudi', 'Fakhri Karray', 'Mark Crowley', 'H. R. Tizhoosh', 'Benyamin Ghojogh', 'Amir Safarpoor'] | 2020-07-04 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [-8.93762410e-02 2.80361950e-01 -3.49224359e-01 -2.81800002e-01
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a39c56fb-e3a2-4058-bccf-41b2a66fb5db | is-lip-region-of-interest-sufficient-for | 2205.14295 | null | https://arxiv.org/abs/2205.14295v2 | https://arxiv.org/pdf/2205.14295v2.pdf | Is Lip Region-of-Interest Sufficient for Lipreading? | Lip region-of-interest (ROI) is conventionally used for visual input in the lipreading task. Few works have adopted the entire face as visual input because lip-excluded parts of the face are usually considered to be redundant and irrelevant to visual speech recognition. However, faces contain much more detailed information than lips, such as speakers' head pose, emotion, identity etc. We argue that such information might benefit visual speech recognition if a powerful feature extractor employing the entire face is trained. In this work, we propose to adopt the entire face for lipreading with self-supervised learning. AV-HuBERT, an audio-visual multi-modal self-supervised learning framework, was adopted in our experiments. Our experimental results showed that adopting the entire face achieved 16% relative word error rate (WER) reduction on the lipreading task, compared with the baseline method using lip as visual input. Without self-supervised pretraining, the model with face input achieved a higher WER than that using lip input in the case of limited training data (30 hours), while a slightly lower WER when using large amount of training data (433 hours). | ['Jia Pan', 'Gen-Shun Wan', 'Jing-Xuan Zhang'] | 2022-05-28 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 0.08339173 0.36826316 -0.25181434 -0.464096 -1.0082207 -0.196323
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f29f2d67-3bef-4976-8962-a9c9e4d8a805 | generalization-error-of-first-order-methods | 2307.04679 | null | https://arxiv.org/abs/2307.04679v2 | https://arxiv.org/pdf/2307.04679v2.pdf | Generalization Error of First-Order Methods for Statistical Learning with Generic Oracles | In this paper, we provide a novel framework for the analysis of generalization error of first-order optimization algorithms for statistical learning when the gradient can only be accessed through partial observations given by an oracle. Our analysis relies on the regularity of the gradient w.r.t. the data samples, and allows to derive near matching upper and lower bounds for the generalization error of multiple learning problems, including supervised learning, transfer learning, robust learning, distributed learning and communication efficient learning using gradient quantization. These results hold for smooth and strongly-convex optimization problems, as well as smooth non-convex optimization problems verifying a Polyak-Lojasiewicz assumption. In particular, our upper and lower bounds depend on a novel quantity that extends the notion of conditional standard deviation, and is a measure of the extent to which the gradient can be approximated by having access to the oracle. As a consequence, our analysis provides a precise meaning to the intuition that optimization of the statistical learning objective is as hard as the estimation of its gradient. Finally, we show that, in the case of standard supervised learning, mini-batch gradient descent with increasing batch sizes and a warm start can reach a generalization error that is optimal up to a multiplicative factor, thus motivating the use of this optimization scheme in practical applications. | ['Laurent Massoulié', 'Mathieu Even', 'Kevin Scaman'] | 2023-07-10 | null | null | null | null | ['quantization', 'transfer-learning'] | ['methodology', 'miscellaneous'] | [-5.92999300e-03 2.91938663e-01 -2.73967862e-01 -5.19510746e-01
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255d1eaa-0d3f-41a8-a6ac-a4f3418e3801 | a-structure-guided-diffusion-model-for-large | 2211.10437 | null | https://arxiv.org/abs/2211.10437v1 | https://arxiv.org/pdf/2211.10437v1.pdf | A Structure-Guided Diffusion Model for Large-Hole Diverse Image Completion | Diverse image completion, a problem of generating various ways of filling incomplete regions (i.e. holes) of an image, has made remarkable success. However, managing input images with large holes is still a challenging problem due to the corruption of semantically important structures. In this paper, we tackle this problem by incorporating explicit structural guidance. We propose a structure-guided diffusion model (SGDM) for the large-hole diverse completion problem. Our proposed SGDM consists of a structure generator and a texture generator, which are both diffusion probabilistic models (DMs). The structure generator generates an edge image representing a plausible structure within the holes, which is later used to guide the texture generation process. To jointly train these two generators, we design a strategy that combines optimal Bayesian denoising and a momentum framework. In addition to the quality improvement, auxiliary edge images generated by the structure generator can be manually edited to allow user-guided image editing. Our experiments using datasets of faces (CelebA-HQ) and natural scenes (Places) show that our method achieves a comparable or superior trade-off between visual quality and diversity compared to other state-of-the-art methods. | ['Kiyoharu Aizawa', 'Yuki Koyama', 'Dong Chen', 'Jiaolong Yang', 'Daichi Horita'] | 2022-11-18 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.21100885e-01 2.12643221e-01 5.54199755e-01 -2.21906930e-01
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571c013a-5af0-488d-bc61-51373d0fc517 | viewnerf-unsupervised-viewpoint-estimation | 2212.00436 | null | https://arxiv.org/abs/2212.00436v1 | https://arxiv.org/pdf/2212.00436v1.pdf | ViewNeRF: Unsupervised Viewpoint Estimation Using Category-Level Neural Radiance Fields | We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF is usually trained with ground-truth camera poses, multiple extensions have been proposed to reduce the need for this expensive supervision. Nonetheless, most of these methods still struggle in complex settings with large camera movements, and are restricted to single scenes, i.e. they cannot be trained on a collection of scenes depicting the same object category. To address these issues, our method uses an analysis by synthesis approach, combining a conditional NeRF with a viewpoint predictor and a scene encoder in order to produce self-supervised reconstructions for whole object categories. Rather than focusing on high fidelity reconstruction, we target efficient and accurate viewpoint prediction in complex scenarios, e.g. 360{\deg} rotation on real data. Our model shows competitive results on synthetic and real datasets, both for single scenes and multi-instance collections. | ['Hakan Bilen', 'Oisin Mac Aodha', 'Octave Mariotti'] | 2022-12-01 | null | null | null | null | ['viewpoint-estimation'] | ['computer-vision'] | [ 3.36150587e-01 3.68886511e-03 -1.17442809e-01 -7.09575653e-01
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c4fa5f3a-6d0a-4439-8253-159e6bedf606 | neural-projection-mapping-using-reflectance | 2306.06595 | null | https://arxiv.org/abs/2306.06595v1 | https://arxiv.org/pdf/2306.06595v1.pdf | Neural Projection Mapping Using Reflectance Fields | We introduce a high resolution spatially adaptive light source, or a projector, into a neural reflectance field that allows to both calibrate the projector and photo realistic light editing. The projected texture is fully differentiable with respect to all scene parameters, and can be optimized to yield a desired appearance suitable for applications in augmented reality and projection mapping. Our neural field consists of three neural networks, estimating geometry, material, and transmittance. Using an analytical BRDF model and carefully selected projection patterns, our acquisition process is simple and intuitive, featuring a fixed uncalibrated projected and a handheld camera with a co-located light source. As we demonstrate, the virtual projector incorporated into the pipeline improves scene understanding and enables various projection mapping applications, alleviating the need for time consuming calibration steps performed in a traditional setting per view or projector location. In addition to enabling novel viewpoint synthesis, we demonstrate state-of-the-art performance projector compensation for novel viewpoints, improvement over the baselines in material and scene reconstruction, and three simply implemented scenarios where projection image optimization is performed, including the use of a 2D generative model to consistently dictate scene appearance from multiple viewpoints. We believe that neural projection mapping opens up the door to novel and exciting downstream tasks, through the joint optimization of the scene and projection images. | ['Amit H. Bermano', 'Daisuke Iwai', 'Yotam Erel'] | 2023-06-11 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 6.72240078e-01 -1.34323761e-01 5.33402681e-01 -4.11759466e-01
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94448961-ca11-4eec-a65e-c2e4341dcaa2 | quantum-finance-a-tutorial-on-quantum | 2208.04382 | null | https://arxiv.org/abs/2208.04382v2 | https://arxiv.org/pdf/2208.04382v2.pdf | Quantum Finance: a tutorial on quantum computing applied to the financial market | Previously only considered a frontier area of Physics, nowadays quantum computing is one of the fastest growing research field, precisely because of its technological applications in optimization problems, machine learning, information security and simulations. The goal of this article is to introduce the fundamentals of quantum computing, focusing on a promising quantum algorithm and its application to a financial market problem. More specifically, we discuss the portfolio optimization problem using the \textit{Quantum Approximate Optimization Algorithm} (QAOA). We not only describe the main concepts involved but also consider simple practical examples, involving financial assets available on the Brazilian stock exchange, with codes, both classic and quantum, freely available as a Jupyter Notebook. We also analyze in details the quality of the combinatorial portfolio optimization solutions through QAOA using SENAI/CIMATEC's ATOS QLM quantum simulator. | ['Taysa M. Mendonça', 'Askery Canabarro', 'Rafael Chaves', 'Gleydson F. de Jesus', 'Anton S. Albino', 'George Moreno', 'Ranieri Nery'] | 2022-08-08 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.72049636e-01 -3.15776616e-01 1.26257434e-01 5.41180670e-02
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35488610-4d23-4759-83f5-9712f1f126be | advanced-deep-convolutional-neural-network | 1904.09075 | null | http://arxiv.org/abs/1904.09075v1 | http://arxiv.org/pdf/1904.09075v1.pdf | Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases | Deep Learning (DL) approaches have been providing state-of-the-art
performance in different modalities in the field of medical imagining including
Digital Pathology Image Analysis (DPIA). Out of many different DL approaches,
Deep Convolutional Neural Network (DCNN) technique provides superior
performance for classification, segmentation, and detection tasks. Most of the
task in DPIA problems are somehow possible to solve with classification,
segmentation, and detection approaches. In addition, sometimes pre and
post-processing methods are applied for solving some specific type of problems.
Recently, different DCNN models including Inception residual recurrent CNN
(IRRCNN), Densely Connected Recurrent Convolution Network (DCRCN), Recurrent
Residual U-Net (R2U-Net), and R2U-Net based regression model (UD-Net) have
proposed and provide state-of-the-art performance for different computer vision
and medical image analysis tasks. However, these advanced DCNN models have not
been explored for solving different problems related to DPIA. In this study, we
have applied these DCNN techniques for solving different DPIA problems and
evaluated on different publicly available benchmark datasets for seven
different tasks in digital pathology including lymphoma classification,
Invasive Ductal Carcinoma (IDC) detection, nuclei segmentation, epithelium
segmentation, tubule segmentation, lymphocyte detection, and mitosis detection.
The experimental results are evaluated with different performance metrics such
as sensitivity, specificity, accuracy, F1-score, Receiver Operating
Characteristics (ROC) curve, dice coefficient (DC), and Means Squired Errors
(MSE). The results demonstrate superior performance for classification,
segmentation, and detection tasks compared to existing machine learning and
DCNN based approaches. | ['Simon Arkell', 'Vijayan K. Asari', 'Theus Aspiras', 'Tarek M. Taha', 'Md Zahangir Alom', 'Dave Billiter', 'TJ Bowen'] | 2019-04-19 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.67351979e-01 -1.45690963e-01 -1.45970121e-01 -1.01977140e-02
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3.59180182e-01 -2.01443359e-01 -2.58255512e-01 -2.65887320e-01] | [15.236001968383789, -2.9078681468963623] |
b244ef85-869f-42fc-bd6d-fd3fdbd51940 | end-to-end-emotion-cause-pair-extraction-via | 2002.10710 | null | https://arxiv.org/abs/2002.10710v4 | https://arxiv.org/pdf/2002.10710v4.pdf | End-to-end Emotion-Cause Pair Extraction via Learning to Link | Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. Existing approaches to ECPE generally adopt a two-stage method, i.e., (1) emotion and cause detection, and then (2) pairing the detected emotions and causes. Such pipeline method, while intuitive, suffers from two critical issues, including error propagation across stages that may hinder the effectiveness, and high computational cost that would limit the practical application of the method. To tackle these issues, we propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches. | ['Qiuchi Li', 'Haolin Song', 'Chen Zhang', 'Dawei Song'] | 2020-02-25 | null | null | null | null | ['emotion-cause-pair-extraction', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.28015468e-01 7.36696124e-02 -9.05958116e-02 -4.16722536e-01
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52f410c5-ddc1-42b9-b46a-3211708027f3 | towards-lexical-chains-for-knowledge-graph | null | null | https://aclanthology.org/R17-1087 | https://aclanthology.org/R17-1087.pdf | Towards Lexical Chains for Knowledge-Graph-based Word Embeddings | Word vectors with varying dimensionalities and produced by different algorithms have been extensively used in NLP. The corpora that the algorithms are trained on can contain either natural language text (e.g. Wikipedia or newswire articles) or artificially-generated pseudo corpora due to natural data sparseness. We exploit Lexical Chain based templates over Knowledge Graph for generating pseudo-corpora with controlled linguistic value. These corpora are then used for learning word embeddings. A number of experiments have been conducted over the following test sets: WordSim353 Similarity, WordSim353 Relatedness and SimLex-999. The results show that, on the one hand, the incorporation of many-relation lexical chains improves results, but on the other hand, unrestricted-length chains remain difficult to handle with respect to their huge quantity. | ['Petya Osenova', 'Svetla Boytcheva', 'Kiril Simov'] | 2017-09-01 | null | null | null | ranlp-2017-9 | ['learning-word-embeddings'] | ['methodology'] | [-4.10259068e-02 1.07520692e-01 -4.83826071e-01 6.03427924e-03
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9f1d9c5e-7098-414f-aa40-f2d7595116e9 | speech-structured-prediction-with-energy | 2305.13617 | null | https://arxiv.org/abs/2305.13617v2 | https://arxiv.org/pdf/2305.13617v2.pdf | SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres | Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex dependency among event structured components with energy-based modeling, and represents event classes with simple but effective hyperspheres. Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks. | ['Bryan Hooi', 'Ningyu Zhang', 'Shengyu Mao', 'Shumin Deng'] | 2023-05-23 | null | null | null | null | ['event-relation-extraction'] | ['natural-language-processing'] | [-1.30726323e-01 2.33052269e-01 4.23797145e-02 -6.38803303e-01
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81d9312b-40fe-4a1b-a05b-d27c8fa35a02 | denseclip-extract-free-dense-labels-from-clip | 2112.01071 | null | https://arxiv.org/abs/2112.01071v2 | https://arxiv.org/pdf/2112.01071v2.pdf | Extract Free Dense Labels from CLIP | Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition. Many recent studies leverage the pre-trained CLIP models for image-level classification and manipulation. In this paper, we wish examine the intrinsic potential of CLIP for pixel-level dense prediction, specifically in semantic segmentation. To this end, with minimal modification, we show that MaskCLIP yields compelling segmentation results on open concepts across various datasets in the absence of annotations and fine-tuning. By adding pseudo labeling and self-training, MaskCLIP+ surpasses SOTA transductive zero-shot semantic segmentation methods by large margins, e.g., mIoUs of unseen classes on PASCAL VOC/PASCAL Context/COCO Stuff are improved from 35.6/20.7/30.3 to 86.1/66.7/54.7. We also test the robustness of MaskCLIP under input corruption and evaluate its capability in discriminating fine-grained objects and novel concepts. Our finding suggests that MaskCLIP can serve as a new reliable source of supervision for dense prediction tasks to achieve annotation-free segmentation. Source code is available at https://github.com/chongzhou96/MaskCLIP. | ['Bo Dai', 'Chen Change Loy', 'Chong Zhou'] | 2021-12-02 | null | null | null | null | ['unsupervised-semantic-segmentation-with', 'novel-concepts'] | ['computer-vision', 'reasoning'] | [ 4.58614379e-01 -6.74077943e-02 -4.98178393e-01 -4.46074694e-01
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8b0337c2-6ec9-4869-90dd-a0df6b12c633 | iam-a-comprehensive-and-large-scale-dataset | 2203.12257 | null | https://arxiv.org/abs/2203.12257v3 | https://arxiv.org/pdf/2203.12257v3.pdf | IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks | Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking the evidence for the claims, etc. As the AI debate attracts more attention these years, it is worth exploring the methods to automate the tedious process involved in the debating system. In this work, we introduce a comprehensive and large dataset named IAM, which can be applied to a series of argument mining tasks, including claim extraction, stance classification, evidence extraction, etc. Our dataset is collected from over 1k articles related to 123 topics. Near 70k sentences in the dataset are fully annotated based on their argument properties (e.g., claims, stances, evidence, etc.). We further propose two new integrated argument mining tasks associated with the debate preparation process: (1) claim extraction with stance classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a pipeline approach and an end-to-end method for each integrated task separately. Promising experimental results are reported to show the values and challenges of our proposed tasks, and motivate future research on argument mining. | ['Luo Si', 'Yan Zhang', 'Qian Yu', 'Ruidan He', 'Lidong Bing', 'Liying Cheng'] | 2022-03-23 | null | https://aclanthology.org/2022.acl-long.162 | https://aclanthology.org/2022.acl-long.162.pdf | acl-2022-5 | ['claim-extraction-with-stance-classification', 'claim-evidence-pair-extraction-cepe'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.75340098e-01 3.88288319e-01 -6.24968469e-01 -3.78609449e-01
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0f730fe9-facc-433d-b695-087c575c6000 | he-said-she-said-style-transfer-for-shifting | 2210.15462 | null | https://arxiv.org/abs/2210.15462v1 | https://arxiv.org/pdf/2210.15462v1.pdf | He Said, She Said: Style Transfer for Shifting the Perspective of Dialogues | In this work, we define a new style transfer task: perspective shift, which reframes a dialogue from informal first person to a formal third person rephrasing of the text. This task requires challenging coreference resolution, emotion attribution, and interpretation of informal text. We explore several baseline approaches and discuss further directions on this task when applied to short dialogues. As a sample application, we demonstrate that applying perspective shifting to a dialogue summarization dataset (SAMSum) substantially improves the zero-shot performance of extractive news summarization models on this data. Additionally, supervised extractive models perform better when trained on perspective shifted data than on the original dialogues. We release our code publicly. | ['Matthew R. Gormley', 'Graham Neubig', 'Amanda Bertsch'] | 2022-10-27 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 7.32391119e-01 9.51241851e-01 -1.60790086e-01 -5.60324728e-01
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1484b15d-aba5-4686-aa74-dc4a5ca6e7dc | deep-feature-factorization-for-concept | 1806.10206 | null | http://arxiv.org/abs/1806.10206v5 | http://arxiv.org/pdf/1806.10206v5.pdf | Deep Feature Factorization For Concept Discovery | We propose Deep Feature Factorization (DFF), a method capable of localizing
similar semantic concepts within an image or a set of images. We use DFF to
gain insight into a deep convolutional neural network's learned features, where
we detect hierarchical cluster structures in feature space. This is visualized
as heat maps, which highlight semantically matching regions across a set of
images, revealing what the network `perceives' as similar. DFF can also be used
to perform co-segmentation and co-localization, and we report state-of-the-art
results on these tasks. | ['Sabine Süsstrunk', 'Radhakrishna Achanta', 'Edo Collins'] | 2018-06-26 | deep-feature-factorization-for-concept-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Edo_Collins_Deep_Feature_Factorization_ECCV_2018_paper.pdf | eccv-2018-9 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [-1.71045601e-01 -5.72382398e-02 1.28353700e-01 -5.95255733e-01
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c6ce30d8-1dcb-4983-9a55-b0caee123b70 | decentralized-multi-robot-formation-control | 2306.14489 | null | https://arxiv.org/abs/2306.14489v1 | https://arxiv.org/pdf/2306.14489v1.pdf | Decentralized Multi-Robot Formation Control Using Reinforcement Learning | This paper presents a decentralized leader-follower multi-robot formation control based on a reinforcement learning (RL) algorithm applied to a swarm of small educational Sphero robots. Since the basic Q-learning method is known to require large memory resources for Q-tables, this work implements the Double Deep Q-Network (DDQN) algorithm, which has achieved excellent results in many robotic problems. To enhance the system behavior, we trained two different DDQN models, one for reaching the formation and the other for maintaining it. The models use a discrete set of robot motions (actions) to adapt the continuous nonlinear system to the discrete nature of RL. The presented approach has been tested in simulation and real experiments which show that the multi-robot system can achieve and maintain a stable formation without the need for complex mathematical models and nonlinear control laws. | ['Stjepan Bogdan', 'Marko Krizmancic', 'Juraj Obradovic'] | 2023-06-26 | null | null | null | null | ['q-learning'] | ['methodology'] | [-6.12155437e-01 3.21552068e-01 2.14924291e-01 3.25561672e-01
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2b1a83b8-5e35-4a34-bfaa-c304db25c4cf | warteam-at-semeval-2017-task-6-using-neural | null | null | https://aclanthology.org/S17-2068 | https://aclanthology.org/S17-2068.pdf | \#WarTeam at SemEval-2017 Task 6: Using Neural Networks for Discovering Humorous Tweets | This paper presents the participation of {\#}WarTeam in Task 6 of SemEval2017 with a system classifying humor by comparing and ranking tweets. The training data consists of annotated tweets from the @midnight TV show. {\#}WarTeam{'}s system uses a neural network (TensorFlow) having inputs from a Na{\"\i}ve Bayes humor classifier and a sentiment analyzer. | ['Diana ab{\\u{a}}{\\textcommabelow{t}}', 'Tr', 'ra Maria', 'S ei', 'Amar', 'Cristina S{\\^\\i}rbu', 'ra', 'Iuliana Alex Fle{\\textcommabelow{s}}can-Lovin-Arseni', 'Ramona Andreea Turcu', 'Nichita Herciu', 'Adrian Iftene', 'Larisa Alexa', 'Constantin Scutaru'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['humor-detection'] | ['natural-language-processing'] | [-4.32235956e-01 1.12925820e-01 9.79028195e-02 -4.98561442e-01
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c52c2d1c-c5d1-4a4b-958b-8405d091ef1e | learning-pseudo-labels-for-semi-and-weakly | null | null | https://www.sciencedirect.com/science/article/pii/S003132032200406X | https://www.sciencedirect.com/science/article/pii/S003132032200406X | Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic Segmentation | In this paper, we aim to tackle semi-and-weakly supervised semantic segmentation (SWSSS), where many image-level classification labels and a few pixel-level annotations are available. We believe the most crucial point for solving SWSSS is to produce high-quality pseudo labels, and our method deals with it from two perspectives. Firstly, we introduce a class-aware cross entropy (CCE) loss for network training. Compared to conventional cross entropy loss, CCE loss encourages the model to distinguish concurrent classes only and simplifies the learning target of pseudo label generation. Secondly, we propose a progressive cross training (PCT) method to build cross supervision between two networks with a dynamic evaluation mechanism, which progressively introduces high-quality predictions as additional supervision for network training. Our method significantly improves the quality of generated pseudo labels in the regime with extremely limited annotations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods significantly. | ['Shiguang Shan', 'Meina Kan', 'Jie Zhang', 'Yude Wang'] | 2022-08-02 | null | null | null | pattern-recognition-2022-8 | ['semi-supervised-semantic-segmentation'] | ['computer-vision'] | [ 7.86766469e-01 5.37267864e-01 -4.29312378e-01 -5.74210346e-01
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4700fcaa-144e-45f5-a73e-d80e200c7599 | a-food-recommender-system-in-academic | 2306.16528 | null | https://arxiv.org/abs/2306.16528v1 | https://arxiv.org/pdf/2306.16528v1.pdf | A Food Recommender System in Academic Environments Based on Machine Learning Models | Background: People's health depends on the use of proper diet as an important factor. Today, with the increasing mechanization of people's lives, proper eating habits and behaviors are neglected. On the other hand, food recommendations in the field of health have also tried to deal with this issue. But with the introduction of the Western nutrition style and the advancement of Western chemical medicine, many issues have emerged in the field of disease treatment and nutrition. Recent advances in technology and the use of artificial intelligence methods in information systems have led to the creation of recommender systems in order to improve people's health. Methods: A hybrid recommender system including, collaborative filtering, content-based, and knowledge-based models was used. Machine learning models such as Decision Tree, k-Nearest Neighbors (kNN), AdaBoost, and Bagging were investigated in the field of food recommender systems on 2519 students in the nutrition management system of a university. Student information including profile information for basal metabolic rate, student reservation records, and selected diet type is received online. Among the 15 features collected and after consulting nutrition experts, the most effective features are selected through feature engineering. Using machine learning models based on energy indicators and food selection history by students, food from the university menu is recommended to students. Results: The AdaBoost model has the highest performance in terms of accuracy with a rate of 73.70 percent. Conclusion: Considering the importance of diet in people's health, recommender systems are effective in obtaining useful information from a huge amount of data. Keywords: Recommender system, Food behavior and habits, Machine learning, Classification | ['Babak Teimourpour', 'Abolfazl Ajami'] | 2023-06-26 | null | null | null | null | ['feature-engineering', 'collaborative-filtering'] | ['methodology', 'miscellaneous'] | [-4.96822983e-01 -3.51421386e-01 -7.21979201e-01 -4.28711593e-01
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e8f53d3b-b347-4f35-af98-dc72a3929d14 | structured-time-series-prediction-without | 2202.03539 | null | https://arxiv.org/abs/2202.03539v1 | https://arxiv.org/pdf/2202.03539v1.pdf | Structured Time Series Prediction without Structural Prior | Time series prediction is a widespread and well studied problem with applications in many domains (medical, geoscience, network analysis, finance, econometry etc.). In the case of multivariate time series, the key to good performances is to properly capture the dependencies between the variates. Often, these variates are structured, i.e. they are localised in an abstract space, usually representing an aspect of the physical world, and prediction amounts to a form of diffusion of the information across that space over time. Several neural network models of diffusion have been proposed in the literature. However, most of the existing proposals rely on some a priori knowledge on the structure of the space, usually in the form of a graph weighing the pairwise diffusion capacity of its points. We argue that this piece of information can often be dispensed with, since data already contains the diffusion capacity information, and in a more reliable form than that obtained from the usually largely hand-crafted graphs. We propose instead a fully data-driven model which does not rely on such a graph, nor any other prior structural information. We conduct a first set of experiments to measure the impact on performance of a structural prior, as used in baseline models, and show that, except at very low data levels, it remains negligible, and beyond a threshold, it may even become detrimental. We then investigate, through a second set of experiments, the capacity of our model in two respects: treatment of missing data and domain adaptation. | ['Jean-Marc Andreoli', 'Darko Drakulic'] | 2022-02-07 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 8.17873850e-02 -5.60882688e-02 -1.47599876e-01 -9.47167426e-02
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deea7e3f-5ba7-4841-a8c2-4ed0c3b7c708 | an-efficient-statistical-method-for-image | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Chen_An_Efficient_Statistical_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Chen_An_Efficient_Statistical_ICCV_2015_paper.pdf | An Efficient Statistical Method for Image Noise Level Estimation | In this paper, we address the problem of estimating noise level from a single image contaminated by additive zero-mean Gaussian noise. We first provide rigorous analysis on the statistical relationship between the noise variance and the eigenvalues of the covariance matrix of patches within an image, which shows that many state-of-the-art noise estimation methods underestimate the noise level of an image. To this end, we derive a new nonparametric algorithm for efficient noise level estimation based on the observation that patches decomposed from a clean image often lie around a low-dimensional subspace. The performance of our method has been guaranteed both theoretically and empirically. Specifically, our method outperforms existing state-of-the-art algorithms on estimating noise level with the least executing time in our experiments. We further demonstrate that the denoising algorithm BM3D algorithm achieves optimal performance using noise variance estimated by our algorithm. | ['Fengyuan Zhu', 'Pheng Ann Heng', 'Guangyong Chen'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['noise-estimation'] | ['medical'] | [ 2.19419032e-01 -3.93478185e-01 5.51431954e-01 1.26035482e-01
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1998785e-6eb7-40bb-9e1a-a48ef2f0b66c | downstream-task-agnostic-speech-enhancement | 2305.14723 | null | https://arxiv.org/abs/2305.14723v1 | https://arxiv.org/pdf/2305.14723v1.pdf | Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss | Self-supervised learning (SSL) is the latest breakthrough in speech processing, especially for label-scarce downstream tasks by leveraging massive unlabeled audio data. The noise robustness of the SSL is one of the important challenges to expanding its application. We can use speech enhancement (SE) to tackle this issue. However, the mismatch between the SE model and SSL models potentially limits its effect. In this work, we propose a new SE training criterion that minimizes the distance between clean and enhanced signals in the feature representation of the SSL model to alleviate the mismatch. We expect that the loss in the SSL domain could guide SE training to preserve or enhance various levels of characteristics of the speech signals that may be required for high-level downstream tasks. Experiments show that our proposal improves the performance of an SE and SSL pipeline on five downstream tasks with noisy input while maintaining the SE performance. | ['Nobukatsu Hojo', 'Tomohiro Tanaka', 'Mana Ihori', 'Saki Mizuno', 'Kentaro Shinayama', 'Takanori Ashihara', 'Takafumi Moriya', 'Marc Delcroix', 'Tsubasa Ochiai', 'Ryo Masumura', 'Hiroshi Sato'] | 2023-05-24 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 4.66867477e-01 9.44804847e-02 1.16514713e-01 -4.62258458e-01
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58d4a6b9-ebc0-4e3f-9df2-e0bf69c32ee3 | autoencoding-binary-classifiers-for | 1903.10709 | null | http://arxiv.org/abs/1903.10709v1 | http://arxiv.org/pdf/1903.10709v1.pdf | Autoencoding Binary Classifiers for Supervised Anomaly Detection | We propose the Autoencoding Binary Classifiers (ABC), a novel supervised
anomaly detector based on the Autoencoder (AE). There are two main approaches
in anomaly detection: supervised and unsupervised. The supervised approach
accurately detects the known anomalies included in training data, but it cannot
detect the unknown anomalies. Meanwhile, the unsupervised approach can detect
both known and unknown anomalies that are located away from normal data points.
However, it does not detect known anomalies as accurately as the supervised
approach. Furthermore, even if we have labeled normal data points and
anomalies, the unsupervised approach cannot utilize these labels. The ABC is a
probabilistic binary classifier that effectively exploits the label
information, where normal data points are modeled using the AE as a component.
By maximizing the likelihood, the AE in the proposed ABC is trained to minimize
the reconstruction error for normal data points, and to maximize it for known
anomalies. Since our approach becomes able to reconstruct the normal data
points accurately and fails to reconstruct the known and unknown anomalies, it
can accurately discriminate both known and unknown anomalies from normal data
points. Experimental results show that the ABC achieves higher detection
performance than existing supervised and unsupervised methods. | ['Tomoharu Iwata', 'Sekitoshi Kanai', 'Masanori Yamada', 'Hiroshi Takahashi', 'Yuki Yamanaka'] | 2019-03-26 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-1.08046070e-01 -9.27226059e-03 2.65614152e-01 -4.13795531e-01
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cf089d6f-a70f-4863-b069-00252c27e0de | right-for-the-wrong-scientific-reasons | 2001.05371 | null | https://arxiv.org/abs/2001.05371v3 | https://arxiv.org/pdf/2001.05371v3.pdf | Making deep neural networks right for the right scientific reasons by interacting with their explanations | Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show "Clever Hans"-like behavior---making use of confounding factors within datasets---to achieve high performance. In this work, we introduce the novel learning setting of "explanatory interactive learning" (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust into the underlying model. | ['Anne-Katrin Mahlein', 'Hans-Georg Luigs', 'Patrick Schramowski', 'Anna Brugger', 'Xiaoting Shao', 'Stefano Teso', 'Wolfgang Stammer', 'Kristian Kersting'] | 2020-01-15 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.33992355e-02 7.28342772e-01 -3.84969205e-01 -7.54611373e-01
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a8e9db5d-77a0-4112-88cb-880045371355 | deep-spectral-correspondence-for-matching | 1809.04642 | null | http://arxiv.org/abs/1809.04642v1 | http://arxiv.org/pdf/1809.04642v1.pdf | Deep Spectral Correspondence for Matching Disparate Image Pairs | A novel, non-learning-based, saliency-aware, shape-cognizant correspondence
determination technique is proposed for matching image pairs that are
significantly disparate in nature. Images in the real world often exhibit high
degrees of variation in scale, orientation, viewpoint, illumination and affine
projection parameters, and are often accompanied by the presence of textureless
regions and complete or partial occlusion of scene objects. The above
conditions confound most correspondence determination techniques by rendering
impractical the use of global contour-based descriptors or local pixel-level
features for establishing correspondence. The proposed deep spectral
correspondence (DSC) determination scheme harnesses the representational power
of local feature descriptors to derive a complex high-level global shape
representation for matching disparate images. The proposed scheme reasons about
correspondence between disparate images using high-level global shape cues
derived from low-level local feature descriptors. Consequently, the proposed
scheme enjoys the best of both worlds, i.e., a high degree of invariance to
affine parameters such as scale, orientation, viewpoint, illumination afforded
by the global shape cues and robustness to occlusion provided by the low-level
feature descriptors. While the shape-based component within the proposed scheme
infers what to look for, an additional saliency-based component dictates where
to look at thereby tackling the noisy correspondences arising from the presence
of textureless regions and complex backgrounds. In the proposed scheme, a joint
image graph is constructed using distances computed between interest points in
the appearance (i.e., image) space. Eigenspectral decomposition of the joint
image graph allows for reasoning about shape similarity to be performed
jointly, in the appearance space and eigenspace. | ['Suchendra M. Bhandarkar', 'Arun CS Kumar', 'Shefali Srivastava', 'Anirban Mukhopadhyay'] | 2018-09-12 | null | null | null | null | ['matching-disparate-images'] | ['computer-vision'] | [ 4.76483315e-01 -4.10995990e-01 -1.56764969e-01 -3.35596204e-01
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930f055f-a5b9-48ea-871d-fa112e4af324 | machine-learning-algorithms-for-b-jet-tagging | 1711.08811 | null | http://arxiv.org/abs/1711.08811v1 | http://arxiv.org/pdf/1711.08811v1.pdf | Machine Learning Algorithms for $b$-Jet Tagging at the ATLAS Experiment | The separation of $b$-quark initiated jets from those coming from lighter
quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program
at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms
combine information from low-level taggers, exploiting reconstructed track and
vertex information, into machine learning classifiers. The potential of modern
deep learning techniques is explored using simulated events, and compared to
that achievable from more traditional classifiers such as boosted decision
trees. | ['Michela Paganini'] | 2017-11-23 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-6.35527551e-01 -1.55849680e-01 -7.59161353e-01 -6.01397276e-01
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35f54fec-a630-4085-841b-2080ef53060c | ldso-direct-sparse-odometry-with-loop-closure | 1808.01111 | null | http://arxiv.org/abs/1808.01111v1 | http://arxiv.org/pdf/1808.01111v1.pdf | LDSO: Direct Sparse Odometry with Loop Closure | In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment. | ['Xiang Gao', 'Rui Wang', 'Daniel Cremers', 'Nikolaus Demmel'] | 2018-08-03 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-8.09160247e-02 -1.08352304e-01 -1.29641563e-01 -1.29792839e-01
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78268844-7a63-4155-b26f-601221248960 | enhancing-generalizable-6d-pose-tracking-of | 2210.04026 | null | https://arxiv.org/abs/2210.04026v1 | https://arxiv.org/pdf/2210.04026v1.pdf | Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing | While holding and manipulating an object, humans track the object states through vision and touch so as to achieve complex tasks. However, nowadays the majority of robot research perceives object states just from visual signals, hugely limiting the robotic manipulation abilities. This work presents a tactile-enhanced generalizable 6D pose tracking design named TEG-Track to track previously unseen in-hand objects. TEG-Track extracts tactile kinematic cues of an in-hand object from consecutive tactile sensing signals. Such cues are incorporated into a geometric-kinematic optimization scheme to enhance existing generalizable visual trackers. To test our method in real scenarios and enable future studies on generalizable visual-tactile tracking, we collect a real visual-tactile in-hand object pose tracking dataset. Experiments show that TEG-Track significantly improves state-of-the-art generalizable 6D pose trackers in both synthetic and real cases. | ['Li Yi', 'Rui Chen', 'Jing Xu', 'He Wang', 'Haocheng Yuan', 'Weihang Chen', 'Yun Liu', 'Xiaomeng Xu'] | 2022-10-08 | null | null | null | null | ['pose-tracking', 'hand-object-pose'] | ['computer-vision', 'computer-vision'] | [-2.56526284e-02 -2.84598768e-01 -2.39800692e-01 2.04999790e-01
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a762de1d-a244-40c5-a9cc-b65c3cdfad9a | matchzoo-a-toolkit-for-deep-text-matching | 1707.07270 | null | http://arxiv.org/abs/1707.07270v1 | http://arxiv.org/pdf/1707.07270v1.pdf | MatchZoo: A Toolkit for Deep Text Matching | In recent years, deep neural models have been widely adopted for text
matching tasks, such as question answering and information retrieval, showing
improved performance as compared with previous methods. In this paper, we
introduce the MatchZoo toolkit that aims to facilitate the designing, comparing
and sharing of deep text matching models. Specifically, the toolkit provides a
unified data preparation module for different text matching problems, a
flexible layer-based model construction process, and a variety of training
objectives and evaluation metrics. In addition, the toolkit has implemented two
schools of representative deep text matching models, namely
representation-focused models and interaction-focused models. Finally, users
can easily modify existing models, create and share their own models for text
matching in MatchZoo. | ['Xue-Qi Cheng', 'Jianpeng Hou', 'Yixing Fan', 'Jiafeng Guo', 'Yanyan Lan', 'Liang Pang'] | 2017-07-23 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-8.05696994e-02 -3.58275384e-01 3.13213579e-02 -7.15856016e-01
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9bfa4965-8151-4dec-845c-33fa8b6d1cdb | a-neural-network-based-convex-regularizer-for | 2211.12461 | null | https://arxiv.org/abs/2211.12461v1 | https://arxiv.org/pdf/2211.12461v1.pdf | A Neural-Network-Based Convex Regularizer for Image Reconstruction | The emergence of deep-learning-based methods for solving inverse problems has enabled a significant increase in reconstruction quality. Unfortunately, these new methods often lack reliability and explainability, and there is a growing interest to address these shortcomings while retaining the performance. In this work, this problem is tackled by revisiting regularizers that are the sum of convex-ridge functions. The gradient of such regularizers is parametrized by a neural network that has a single hidden layer with increasing and learnable activation functions. This neural network is trained within a few minutes as a multi-step Gaussian denoiser. The numerical experiments for denoising, CT, and MRI reconstruction show improvements over methods that offer similar reliability guarantees. | ['Michael Unser', 'Stanislas Ducotterd', 'Pakshal Bohra', 'Sebastian Neumayer', 'Alexis Goujon'] | 2022-11-22 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 2.99406052e-02 4.79420930e-01 6.87158853e-02 -4.25069869e-01
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bc9d5060-8b20-41c0-ae4e-3e7b2fad3028 | building-neural-networks-on-matrix-manifolds | 2305.04560 | null | https://arxiv.org/abs/2305.04560v3 | https://arxiv.org/pdf/2305.04560v3.pdf | Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach | Matrix manifolds, such as manifolds of Symmetric Positive Definite (SPD) matrices and Grassmann manifolds, appear in many applications. Recently, by applying the theory of gyrogroups and gyrovector spaces that is a powerful framework for studying hyperbolic geometry, some works have attempted to build principled generalizations of Euclidean neural networks on matrix manifolds. However, due to the lack of many concepts in gyrovector spaces for the considered manifolds, e.g., the inner product and gyroangles, techniques and mathematical tools provided by these works are still limited compared to those developed for studying hyperbolic geometry. In this paper, we generalize some notions in gyrovector spaces for SPD and Grassmann manifolds, and propose new models and layers for building neural networks on these manifolds. We show the effectiveness of our approach in two applications, i.e., human action recognition and knowledge graph completion. | ['Shuo Yang', 'Xuan Son Nguyen'] | 2023-05-08 | null | null | null | null | ['action-recognition-in-videos', 'action-recognition', 'knowledge-graph-completion'] | ['computer-vision', 'computer-vision', 'knowledge-base'] | [-1.52475163e-01 3.49328995e-01 -1.49499014e-01 2.49008019e-03
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d336d4e9-711e-4367-aa9b-6e5134857f40 | dr-wlc-dimensionality-reduction-cognition-for | 2301.06944 | null | https://arxiv.org/abs/2301.06944v1 | https://arxiv.org/pdf/2301.06944v1.pdf | DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking | Object detection and pose estimation are difficult tasks in robotics and autonomous driving. Existing object detection and pose estimation methods mostly adopt the same-dimensional data for training. For example, 2D object detection usually requires a large amount of 2D annotation data with high cost. Using high-dimensional information to supervise lower-dimensional tasks is a feasible way to reduce datasets size. In this work, the DR-WLC, a dimensionality reduction cognitive model, which can perform both object detection and pose estimation tasks at the same time is proposed. The model only requires 3D model of objects and unlabeled environment images (with or without objects) to finish the training. In addition, a bounding boxes generation strategy is also proposed to build the relationship between 3D model and 2D object detection task. Experiments show that our method can qualify the work without any manual annotations and it is easy to deploy for practical applications. Source code is at https://github.com/IN2-ViAUn/DR-WLC. | ['Mengyin Fu', 'Yufeng Yue', 'Yi Yang', 'Siyuan Chen', 'Tianji Jiang', 'Xi Xu', 'Yu Gao'] | 2023-01-17 | null | null | null | null | ['2d-object-detection'] | ['computer-vision'] | [-2.91153163e-01 6.19344860e-02 -3.09542622e-02 -5.63255847e-01
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5f5bb4fa-f0d4-4f63-b1fe-87f17b61881a | hardware-acceleration-of-neural-graphics | 2303.05735 | null | https://arxiv.org/abs/2303.05735v6 | https://arxiv.org/pdf/2303.05735v6.pdf | Hardware Acceleration of Neural Graphics | Rendering and inverse-rendering algorithms that drive conventional computer graphics have recently been superseded by neural representations (NR). NRs have recently been used to learn the geometric and the material properties of the scenes and use the information to synthesize photorealistic imagery, thereby promising a replacement for traditional rendering algorithms with scalable quality and predictable performance. In this work we ask the question: Does neural graphics (NG) need hardware support? We studied representative NG applications showing that, if we want to render 4k res. at 60FPS there is a gap of 1.5X-55X in the desired performance on current GPUs. For AR/VR applications, there is an even larger gap of 2-4 OOM between the desired performance and the required system power. We identify that the input encoding and the MLP kernels are the performance bottlenecks, consuming 72%,60% and 59% of application time for multi res. hashgrid, multi res. densegrid and low res. densegrid encodings, respectively. We propose a NG processing cluster, a scalable and flexible hardware architecture that directly accelerates the input encoding and MLP kernels through dedicated engines and supports a wide range of NG applications. We also accelerate the rest of the kernels by fusing them together in Vulkan, which leads to 9.94X kernel-level performance improvement compared to un-fused implementation of the pre-processing and the post-processing kernels. Our results show that, NGPC gives up to 58X end-to-end application-level performance improvement, for multi res. hashgrid encoding on average across the four NG applications, the performance benefits are 12X,20X,33X and 39X for the scaling factor of 8,16,32 and 64, respectively. Our results show that with multi res. hashgrid encoding, NGPC enables the rendering of 4k res. at 30FPS for NeRF and 8k res. at 120FPS for all our other NG applications. | ['Rakesh Kumar', 'Tobias Zirr', 'Ramakrishna Kanungo', 'Muhammad Husnain Mubarik'] | 2023-03-10 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 2.00317934e-01 -3.39984484e-02 3.20199817e-01 -1.52557954e-01
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249c6809-b9a7-4106-b9f8-cd85f7dc80b3 | non-uniform-blind-deblurring-by-reblurring | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Bahat_Non-Uniform_Blind_Deblurring_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Bahat_Non-Uniform_Blind_Deblurring_ICCV_2017_paper.pdf | Non-Uniform Blind Deblurring by Reblurring | We present an approach for blind image deblurring, which handles non-uniform blurs. Our algorithm has two main components: (i) A new method for recovering the unknown blur-field directly from the blurry image, and (ii) A method for deblurring the image given the recovered nonuniform blur-field. Our blur-field estimation is based on analyzing the spectral content of blurry image patches by Re-blurring them. Being unrestricted by any training data, it can handle a large variety of blur sizes, yielding superior blur-field estimation results compared to training based deep-learning methods. Our non-uniform deblurring algorithm is based on the internal image-specific patch recurrence prior. It attempts to recover a sharp image which, on one hand - results in the blurry image under our estimated blur-field, and on the other hand - maximizes the internal recurrence of patches within and across scales of the recovered sharp image. The combination of these two components gives rise to a blind-deblurring algorithm, which exceeds the performance of state-of-the-art CNN-based blind-deblurring by a significant margin, without the need for any training data.
| ['Netalee Efrat', 'Yuval Bahat', 'Michal Irani'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.13237554e-01 -4.90068495e-01 3.96563351e-01 4.85406332e-02
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7e3978ad-9737-450b-90a5-018f0c52f5e8 | taskmatrix-ai-completing-tasks-by-connecting | 2303.16434 | null | https://arxiv.org/abs/2303.16434v1 | https://arxiv.org/pdf/2303.16434v1.pdf | TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs | Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning and code generation abilities on a broad range of open-domain tasks. They can also generate high-level solution outlines for domain-specific tasks based on the common sense knowledge they have acquired. However, they still face difficulties with some specialized tasks because they lack enough domain-specific data during pre-training or they often have errors in their neural network computations on those tasks that need accurate executions. On the other hand, there are also many existing models and systems (symbolic-based or neural-based) that can do some domain-specific tasks very well. However, due to the different implementation or working mechanisms, they are not easily accessible or compatible with foundation models. Therefore, there is a clear and pressing need for a mechanism that can leverage foundation models to propose task solution outlines and then automatically match some of the sub-tasks in the outlines to the off-the-shelf models and systems with special functionalities to complete them. Inspired by this, we introduce TaskMatrix.AI as a new AI ecosystem that connects foundation models with millions of APIs for task completion. Unlike most previous work that aimed to improve a single AI model, TaskMatrix.AI focuses more on using existing foundation models (as a brain-like central system) and APIs of other AI models and systems (as sub-task solvers) to achieve diversified tasks in both digital and physical domains. As a position paper, we will present our vision of how to build such an ecosystem, explain each key component, and use study cases to illustrate both the feasibility of this vision and the main challenges we need to address next. | ['Nan Duan', 'Ming Gong', 'Linjun Shou', 'Yun Wang', 'Shaoguang Mao', 'Lei Ji', 'Shuai Lu', 'Yang Ou', 'Yu Liu', 'Yan Xia', 'Wenshan Wu', 'Ting Song', 'Chenfei Wu', 'Yaobo Liang'] | 2023-03-29 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.87856201e-02 2.92733997e-01 -1.74569711e-02 -1.24820307e-01
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7c69ad60-df37-4b21-b179-dfdf1050380b | visualization-of-decision-trees-based-on | 2205.04035 | null | https://arxiv.org/abs/2205.04035v1 | https://arxiv.org/pdf/2205.04035v1.pdf | Visualization of Decision Trees based on General Line Coordinates to Support Explainable Models | Visualization of Machine Learning (ML) models is an important part of the ML process to enhance the interpretability and prediction accuracy of the ML models. This paper proposes a new method SPC-DT to visualize the Decision Tree (DT) as interpretable models. These methods use a version of General Line Coordinates called Shifted Paired Coordinates (SPC). In SPC, each n-D point is visualized in a set of shifted pairs of 2-D Cartesian coordinates as a directed graph. The new method expands and complements the capabilities of existing methods, to visualize DT models. It shows: (1) relations between attributes, (2) individual cases relative to the DT structure, (3) data flow in the DT, (4) how tight each split is to thresholds in the DT nodes, and (5) the density of cases in parts of the n-D space. This information is important for domain experts for evaluating and improving the DT models, including avoiding overgeneralization and overfitting of models, along with their performance. The benefits of the methods are demonstrated in the case studies, using three real datasets. | ['Boris Kovalerchuk', 'Sridevi Wagle', 'Alex Worland'] | 2022-05-09 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-1.82168841e-01 4.08443600e-01 -2.03089714e-01 -3.18402588e-01
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150762de-d016-42fb-9ccf-c3b66909e019 | self-training-through-classifier-disagreement | 2302.14719 | null | https://arxiv.org/abs/2302.14719v1 | https://arxiv.org/pdf/2302.14719v1.pdf | Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction | Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. | ['Xudong Liu', 'Yongyi Mao', 'Nikolaos Aletras', 'Samuel Mensah', 'Richong Zhang', 'Kai Sun'] | 2023-02-28 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 6.99159801e-01 2.74493158e-01 -3.74044836e-01 -5.70947111e-01
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18c12214-ba53-4067-af43-bd5295a22598 | pose-driven-attention-guided-image-generation | 2104.13773 | null | https://arxiv.org/abs/2104.13773v1 | https://arxiv.org/pdf/2104.13773v1.pdf | Pose-driven Attention-guided Image Generation for Person Re-Identification | Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person re-ID is pose variations across the camera network, which significantly affects the appearance of a person. Existing development data lack adequate pose variations to carry out effective training of person re-ID systems. To solve this issue, in this paper we propose an end-to-end pose-driven attention-guided generative adversarial network, to generate multiple poses of a person. We propose to attentively learn and transfer the subject pose through an attention mechanism. A semantic-consistency loss is proposed to preserve the semantic information of the person during pose transfer. To ensure fine image details are realistic after pose translation, an appearance discriminator is used while a pose discriminator is used to ensure the pose of the transferred images will exactly be the same as the target pose. We show that by incorporating the proposed approach in a person re-identification framework, realistic pose transferred images and state-of-the-art re-identification results can be achieved. | ['Clinton Fookes', 'Sridha Sridharan', 'Simon Denman', 'Amena Khatun'] | 2021-04-28 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 2.66405731e-01 -1.13077931e-01 4.74661142e-01 -5.20322084e-01
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14096e1e-b8a3-43f6-8289-1d5852a0afeb | reflash-dropout-in-image-super-resolution | 2112.12089 | null | https://arxiv.org/abs/2112.12089v3 | https://arxiv.org/pdf/2112.12089v3.pdf | Reflash Dropout in Image Super-Resolution | Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a different behaviour as high-level tasks and is sensitive to the dropout operation. However, in this paper, we show that appropriate usage of dropout benefits SR networks and improves the generalization ability. Specifically, dropout is better embedded at the end of the network and is significantly helpful for the multi-degradation settings. This discovery breaks our common sense and inspires us to explore its working mechanism. We further use two analysis tools -- one is from recent network interpretation works, and the other is specially designed for this task. The analysis results provide side proofs to our experimental findings and show us a new perspective to understand SR networks. | ['Chao Dong', 'Yu Qiao', 'Jinjin Gu', 'Xina Liu', 'Xiangtao Kong'] | 2021-12-22 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kong_Reflash_Dropout_in_Image_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['network-interpretation'] | ['computer-vision'] | [ 5.09705603e-01 2.88340479e-01 -3.33419114e-01 -2.88844377e-01
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b8a8cc52-605b-4041-aa38-68cb3efd56fb | transformers-generalize-deepsets-and-can-be | 2110.14416 | null | https://arxiv.org/abs/2110.14416v2 | https://arxiv.org/pdf/2110.14416v2.pdf | Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs | We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs). We begin by observing that Transformers generalize DeepSets, or first-order (set-input) permutation invariant MLPs. Then, based on recently characterized higher-order invariant MLPs, we extend the concept of self-attention to higher orders and propose higher-order Transformers for order-$k$ data ($k=2$ for graphs and $k>2$ for hypergraphs). Unfortunately, higher-order Transformers turn out to have prohibitive complexity $\mathcal{O}(n^{2k})$ to the number of input nodes $n$. To address this problem, we present sparse higher-order Transformers that have quadratic complexity to the number of input hyperedges, and further adopt the kernel attention approach to reduce the complexity to linear. In particular, we show that the sparse second-order Transformers with kernel attention are theoretically more expressive than message passing operations while having an asymptotically identical complexity. Our models achieve significant performance improvement over invariant MLPs and message-passing graph neural networks in large-scale graph regression and set-to-(hyper)graph prediction tasks. Our implementation is available at https://github.com/jw9730/hot. | ['Seunghoon Hong', 'Saeyoon Oh', 'Jinwoo Kim'] | 2021-10-27 | transformers-generalize-deepsets-and-can-be-1 | http://proceedings.neurips.cc/paper/2021/hash/ec0f40c389aeef789ce03eb814facc6c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/ec0f40c389aeef789ce03eb814facc6c-Paper.pdf | neurips-2021-12 | ['hyperedge-prediction', 'set-to-graph-prediction-1', 'graph-regression'] | ['graphs', 'graphs', 'graphs'] | [ 3.76431555e-01 4.71439391e-01 -1.15219578e-01 -2.17053697e-01
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38665b15-9bf7-4ea8-bcb8-68fa76ab91b3 | videdit-zero-shot-and-spatially-aware-text | 2306.08707 | null | https://arxiv.org/abs/2306.08707v1 | https://arxiv.org/pdf/2306.08707v1.pdf | VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing | Recently, diffusion-based generative models have achieved remarkable success for image generation and edition. However, their use for video editing still faces important limitations. This paper introduces VidEdit, a novel method for zero-shot text-based video editing ensuring strong temporal and spatial consistency. Firstly, we propose to combine atlas-based and pre-trained text-to-image diffusion models to provide a training-free and efficient editing method, which by design fulfills temporal smoothness. Secondly, we leverage off-the-shelf panoptic segmenters along with edge detectors and adapt their use for conditioned diffusion-based atlas editing. This ensures a fine spatial control on targeted regions while strictly preserving the structure of the original video. Quantitative and qualitative experiments show that VidEdit outperforms state-of-the-art methods on DAVIS dataset, regarding semantic faithfulness, image preservation, and temporal consistency metrics. With this framework, processing a single video only takes approximately one minute, and it can generate multiple compatible edits based on a unique text prompt. Project web-page at https://videdit.github.io | ['Nicolas Thome', 'Jean-Emmanuel Haugeard', 'Clément Rambour', 'Paul Couairon'] | 2023-06-14 | null | null | null | null | ['video-editing'] | ['computer-vision'] | [ 6.56491965e-02 -1.08723409e-01 -2.12576672e-01 -1.67986140e-01
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c94f7f33-cc51-4319-9927-85480b2c030e | automatic-generation-of-abstracts-for | null | null | https://aclanthology.org/2022.rocling-1.28 | https://aclanthology.org/2022.rocling-1.28.pdf | Automatic Generation of Abstracts for Research Papers | Summarizing has always been an important utility for reading long documents. Research papers are unique in this regard, as they have a compulsory summary in the form of the abstract in the beginning of the document which gives the gist of the entire study often within a set upper limit for the word count. Writing the abstract to be sufficiently succinct while being descriptive enough is a hard task even for native English speakers. This study is the first step in generating abstracts for research papers in the computational linguistics domain automatically using the domain-specific abstractive summarization power of the GPT-Neo model. | ['Nisansa de Silva', 'Dushan Kumarasinghe'] | null | null | null | null | rocling-2022-11 | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 0.24080944 0.5483719 -0.42481977 -0.17746876 -0.7043371 -0.6667422
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f031235a-d6cb-4afc-925c-64293e3050af | toolqa-a-dataset-for-llm-question-answering | 2306.13304 | null | https://arxiv.org/abs/2306.13304v1 | https://arxiv.org/pdf/2306.13304v1.pdf | ToolQA: A Dataset for LLM Question Answering with External Tools | Large Language Models (LLMs) have demonstrated impressive performance in various NLP tasks, but they still suffer from challenges such as hallucination and weak numerical reasoning. To overcome these challenges, external tools can be used to enhance LLMs' question-answering abilities. However, current evaluation methods do not distinguish between questions that can be answered using LLMs' internal knowledge and those that require external information through tool use. To address this issue, we introduce a new dataset called ToolQA, which is designed to faithfully evaluate LLMs' ability to use external tools for question answering. Our development of ToolQA involved a scalable, automated process for dataset curation, along with 13 specialized tools designed for interaction with external knowledge in order to answer questions. Importantly, we strive to minimize the overlap between our benchmark data and LLMs' pre-training data, enabling a more precise evaluation of LLMs' tool-use reasoning abilities. We conducted an in-depth diagnosis of existing tool-use LLMs to highlight their strengths, weaknesses, and potential improvements. Our findings set a new benchmark for evaluating LLMs and suggest new directions for future advancements. Our data and code are freely available to the broader scientific community on GitHub. | ['Chao Zhang', 'Haotian Sun', 'Kuan Wang', 'Yue Yu', 'Yuchen Zhuang'] | 2023-06-23 | null | null | null | null | ['question-answering'] | ['natural-language-processing'] | [-2.26807460e-01 2.13165611e-01 -1.41856611e-01 -2.39770055e-01
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181d449e-1e37-4598-b909-e85490fa95d1 | pvdd-a-practical-video-denoising-dataset-with | 2207.01356 | null | https://arxiv.org/abs/2207.01356v2 | https://arxiv.org/pdf/2207.01356v2.pdf | Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network | To facilitate video denoising research, we construct a compelling dataset, namely, "Practical Video Denoising Dataset" (PVDD), containing 200 noisy-clean dynamic video pairs in both sRGB and RAW format. Compared with existing datasets consisting of limited motion information, PVDD covers dynamic scenes with varying and natural motion. Different from datasets using primarily Gaussian or Poisson distributions to synthesize noise in the sRGB domain, PVDD synthesizes realistic noise from the RAW domain with a physically meaningful sensor noise model followed by ISP processing. Moreover, we also propose a new video denoising framework, called Recurrent Video Denoising Transformer (RVDT), which can achieve SOTA performance on PVDD and other current video denoising benchmarks. RVDT consists of both spatial and temporal transformer blocks to conduct denoising with long-range operations on the spatial dimension and long-term propagation on the temporal dimension. Especially, RVDT exploits the attention mechanism to implement the bi-directional feature propagation with both implicit and explicit temporal modeling. Extensive experiments demonstrate that 1) models trained on PVDD achieve superior denoising performance on many challenging real-world videos than on models trained on other existing datasets; 2) trained on the same dataset, our proposed RVDT can have better denoising performance than other types of networks. | ['Jiaya Jia', 'Bei Yu', 'Jiangbo Lu', 'Nianjuan Jiang', 'Yitong Yu', 'Xiaogang Xu'] | 2022-07-04 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [-2.06948444e-02 -6.36395693e-01 3.39304835e-01 -2.40674555e-01
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d4a47f68-d391-4941-8f91-4f3894e59efa | a-real-time-fusion-framework-for-long-term | 2210.09757 | null | https://arxiv.org/abs/2210.09757v1 | https://arxiv.org/pdf/2210.09757v1.pdf | A Real-Time Fusion Framework for Long-term Visual Localization | Visual localization is a fundamental task that regresses the 6 Degree Of Freedom (6DoF) poses with image features in order to serve the high precision localization requests in many robotics applications. Degenerate conditions like motion blur, illumination changes and environment variations place great challenges in this task. Fusion with additional information, such as sequential information and Inertial Measurement Unit (IMU) inputs, would greatly assist such problems. In this paper, we present an efficient client-server visual localization architecture that fuses global and local pose estimations to realize promising precision and efficiency. We include additional geometry hints in mapping and global pose regressing modules to improve the measurement quality. A loosely coupled fusion policy is adopted to leverage the computation complexity and accuracy. We conduct the evaluations on two typical open-source benchmarks, 4Seasons and OpenLORIS. Quantitative results prove that our framework has competitive performance with respect to other state-of-the-art visual localization solutions. | ['Yandong Guo', 'Jijunnan Li', 'Yuyue Liu', 'Yishan Ping', 'Jixiang Wan', 'Shuang Gao', 'Xudong Zhang', 'Yuchen Yang'] | 2022-10-18 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [-6.95378542e-01 -6.02928936e-01 -1.82252869e-01 -4.33433861e-01
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5b64d269-cf64-4c6a-bd99-71e036033db0 | exploiting-web-images-for-fine-grained-visual | 2101.09412 | null | https://arxiv.org/abs/2101.09412v1 | https://arxiv.org/pdf/2101.09412v1.pdf | Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones | Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators. As such, learning directly from web images for fine-grained recognition has attracted broad attention. However, the presence of label noise and hard examples in web images are two obstacles for training robust fine-grained recognition models. Therefore, in this paper, we propose a novel approach for removing irrelevant samples from real-world web images during training, while employing useful hard examples to update the network. Thus, our approach can alleviate the harmful effects of irrelevant noisy web images and hard examples to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. | ['Zhenmin Tang', 'Jian Zhang', 'Fumin Shen', 'Xiushen Wei', 'Yazhou Yao', 'Chuanyi Zhang', 'Huafeng Liu'] | 2021-01-23 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 3.13314259e-01 -7.47157335e-02 -3.90077859e-01 -5.69465756e-01
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161e5856-8e81-4044-861a-de0698c80518 | are-explainability-tools-gender-biased-a-case | 2304.13419 | null | https://arxiv.org/abs/2304.13419v2 | https://arxiv.org/pdf/2304.13419v2.pdf | Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection | Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations. | ['Naser Damer', 'Fadi Boutros', 'Meiling Fang', 'Marco Huber'] | 2023-04-26 | null | null | null | null | ['face-presentation-attack-detection', 'face-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.62025031e-02 6.40208185e-01 -1.60684273e-01 -8.59602451e-01
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1e158a48-8342-4bee-b033-0ef6ded02ec4 | second-order-anisotropic-gaussian-directional | 2305.00435 | null | https://arxiv.org/abs/2305.00435v1 | https://arxiv.org/pdf/2305.00435v1.pdf | Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection | Interest point detection methods have received increasing attention and are widely used in computer vision tasks such as image retrieval and 3D reconstruction. In this work, second-order anisotropic Gaussian directional derivative filters with multiple scales are used to smooth the input image and a novel blob detection method is proposed. Extensive experiments demonstrate the superiority of our proposed method over state-of-the-art benchmarks in terms of detection performance and robustness to affine transformations. | ['Changming Sun', 'Weichuan Zhang', 'Jiapan Guo', 'Wenya Yu', 'Jie Ren'] | 2023-04-30 | null | null | null | null | ['interest-point-detection', '3d-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.38556222e-02 -5.97226858e-01 -1.58924654e-01 -1.49307862e-01
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5ee09161-e470-4a10-a50d-8fd15d0f9f77 | cost-sensitive-gnn-based-imbalanced-learning | 2303.17486 | null | https://arxiv.org/abs/2303.17486v1 | https://arxiv.org/pdf/2303.17486v1.pdf | Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection | With the rapid development of mobile networks, the people's social contacts have been considerably facilitated. However, the rise of mobile social network fraud upon those networks, has caused a great deal of distress, in case of depleting personal and social wealth, then potentially doing significant economic harm. To detect fraudulent users, call detail record (CDR) data, which portrays the social behavior of users in mobile networks, has been widely utilized. But the imbalance problem in the aforementioned data, which could severely hinder the effectiveness of fraud detectors based on graph neural networks(GNN), has hardly been addressed in previous work. In this paper, we are going to present a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks. We conduct extensive experiments on two open-source realworld mobile network fraud datasets. The results show that CSGNN can effectively solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms. We believe that our research can be applied to solve the graph imbalance problems in other fields. The CSGNN code and datasets are publicly available at https://github.com/xxhu94/CSGNN. | ['xiangyang xue', 'Yahui Wang', 'Shibo Zhang', 'Xing Li', 'Shuxin Liu', 'Hongchang Chen', 'Haotian Chen', 'Xinxin Hu'] | 2023-03-28 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-8.03623423e-02 -5.11771999e-02 -5.65608144e-01 -1.87324181e-01
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a2f691c5-261f-44b8-b7b0-641569d8cbfd | adaptive-spikenet-event-based-optical-flow | 2209.11741 | null | https://arxiv.org/abs/2209.11741v2 | https://arxiv.org/pdf/2209.11741v2.pdf | Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics | Event-based cameras have recently shown great potential for high-speed motion estimation owing to their ability to capture temporally rich information asynchronously. Spiking Neural Networks (SNNs), with their neuro-inspired event-driven processing can efficiently handle such asynchronous data, while neuron models such as the leaky-integrate and fire (LIF) can keep track of the quintessential timing information contained in the inputs. SNNs achieve this by maintaining a dynamic state in the neuron memory, retaining important information while forgetting redundant data over time. Thus, we posit that SNNs would allow for better performance on sequential regression tasks compared to similarly sized Analog Neural Networks (ANNs). However, deep SNNs are difficult to train due to vanishing spikes at later layers. To that effect, we propose an adaptive fully-spiking framework with learnable neuronal dynamics to alleviate the spike vanishing problem. We utilize surrogate gradient-based backpropagation through time (BPTT) to train our deep SNNs from scratch. We validate our approach for the task of optical flow estimation on the Multi-Vehicle Stereo Event-Camera (MVSEC) dataset and the DSEC-Flow dataset. Our experiments on these datasets show an average reduction of 13% in average endpoint error (AEE) compared to state-of-the-art ANNs. We also explore several down-scaled models and observe that our SNN models consistently outperform similarly sized ANNs offering 10%-16% lower AEE. These results demonstrate the importance of SNNs for smaller models and their suitability at the edge. In terms of efficiency, our SNNs offer substantial savings in network parameters (48.3x) and computational energy (10.2x) while attaining ~10% lower EPE compared to the state-of-the-art ANN implementations. | ['Kaushik Roy', 'Adarsh Kumar Kosta'] | 2022-09-21 | null | null | null | null | ['event-based-optical-flow'] | ['computer-vision'] | [ 2.37092435e-01 -4.58817810e-01 2.86560923e-01 1.17253780e-01
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69cb9999-787d-4af7-a76c-1d57fca62fb3 | is-an-affine-constraint-needed-for-affine-1 | 2005.03888 | null | https://arxiv.org/abs/2005.03888v1 | https://arxiv.org/pdf/2005.03888v1.pdf | Is an Affine Constraint Needed for Affine Subspace Clustering? | Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as motion segmentation, face and digit clustering. In face clustering, the subspaces are linear and subspace clustering methods can be applied directly. In motion segmentation, the subspaces are affine and an additional affine constraint on the coefficients is often enforced. However, since affine subspaces can always be embedded into linear subspaces of one extra dimension, it is unclear if the affine constraint is really necessary. This paper shows, both theoretically and empirically, that when the dimension of the ambient space is high relative to the sum of the dimensions of the affine subspaces, the affine constraint has a negligible effect on clustering performance. Specifically, our analysis provides conditions that guarantee the correctness of affine subspace clustering methods both with and without the affine constraint, and shows that these conditions are satisfied for high-dimensional data. Underlying our analysis is the notion of affinely independent subspaces, which not only provides geometrically interpretable correctness conditions, but also clarifies the relationships between existing results for affine subspace clustering. | ['Rene Vidal', 'Chun-Guang Li', 'Chong You', 'Daniel P. Robinson'] | 2020-05-08 | is-an-affine-constraint-needed-for-affine | http://openaccess.thecvf.com/content_ICCV_2019/html/You_Is_an_Affine_Constraint_Needed_for_Affine_Subspace_Clustering_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/You_Is_an_Affine_Constraint_Needed_for_Affine_Subspace_Clustering_ICCV_2019_paper.pdf | iccv-2019-10 | ['motion-segmentation', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [-8.52854364e-03 -6.97145537e-02 -2.41468266e-01 -1.39551461e-01
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054e08df-83ac-4874-9f44-d71c3d0d93e9 | a-domain-generalization-approach-for-out-of | 2208.09656 | null | https://arxiv.org/abs/2208.09656v2 | https://arxiv.org/pdf/2208.09656v2.pdf | A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks | Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases. As of late, they have also established themselves in the biomedical and healthcare domains, where they have shown a lot of promise, but have not yet achieved widespread adoption. This is in part due to the fact that most methods fail to maintain their performance when they are called to make decisions on data that originate from a different distribution than the one they were trained on, namely Out-Of-Distribution (OOD) data. For example, in the case of biosignal classification, models often fail to generalize well on datasets from different hospitals, due to the distribution discrepancy amongst different sources of data. Our goal is to demonstrate the Domain Generalization problem present between distinct hospital databases and propose a method that classifies abnormalities on 12-lead Electrocardiograms (ECGs), by leveraging information extracted across the architecture of a Deep Neural Network, and capturing the underlying structure of the signal. To this end, we adopt a ResNet-18 as the backbone model and extract features from several intermediate convolutional layers of the network. To evaluate our method, we adopt publicly available ECG datasets from four sources and handle them as separate domains. To simulate the distributional shift present in real-world settings, we train our model on a subset of the domains and leave-out the remaining ones. We then evaluate our model both on the data present at training time (intra-distribution) and the held-out data (out-of-distribution), achieving promising results and surpassing the baseline of a vanilla Residual Network in most of the cases. | ['Christos Diou', 'Aristotelis Ballas'] | 2022-08-20 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 3.30881685e-01 -1.55086949e-01 1.46372437e-01 -6.26636744e-01
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8e9610d6-b5db-4871-aa38-867ae584c03f | accurate-3d-body-shape-regression-using-1 | 2206.07036 | null | https://arxiv.org/abs/2206.07036v1 | https://arxiv.org/pdf/2206.07036v1.pdf | Accurate 3D Body Shape Regression using Metric and Semantic Attributes | While methods that regress 3D human meshes from images have progressed rapidly, the estimated body shapes often do not capture the true human shape. This is problematic since, for many applications, accurate body shape is as important as pose. The key reason that body shape accuracy lags pose accuracy is the lack of data. While humans can label 2D joints, and these constrain 3D pose, it is not so easy to "label" 3D body shape. Since paired data with images and 3D body shape are rare, we exploit two sources of information: (1) we collect internet images of diverse "fashion" models together with a small set of anthropometric measurements; (2) we collect linguistic shape attributes for a wide range of 3D body meshes and the model images. Taken together, these datasets provide sufficient constraints to infer dense 3D shape. We exploit the anthropometric measurements and linguistic shape attributes in several novel ways to train a neural network, called SHAPY, that regresses 3D human pose and shape from an RGB image. We evaluate SHAPY on public benchmarks, but note that they either lack significant body shape variation, ground-truth shape, or clothing variation. Thus, we collect a new dataset for evaluating 3D human shape estimation, called HBW, containing photos of "Human Bodies in the Wild" for which we have ground-truth 3D body scans. On this new benchmark, SHAPY significantly outperforms state-of-the-art methods on the task of 3D body shape estimation. This is the first demonstration that 3D body shape regression from images can be trained from easy-to-obtain anthropometric measurements and linguistic shape attributes. Our model and data are available at: shapy.is.tue.mpg.de | ['Michael J. Black', 'Dimitrios Tzionas', 'Siyu Tang', 'Chun-Hao P. Huang', 'Lea Muller', 'Vasileios Choutas'] | 2022-06-14 | accurate-3d-body-shape-regression-using | http://openaccess.thecvf.com//content/CVPR2022/html/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Choutas_Accurate_3D_Body_Shape_Regression_Using_Metric_and_Semantic_Attributes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-human-reconstruction'] | ['computer-vision'] | [-1.98941022e-01 3.82100716e-02 -2.66760916e-01 -6.10875785e-01
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47bc1094-7cbf-4afb-a806-eb73827eab5d | beyond-a-video-frame-interpolator-a-space | 2203.09771 | null | https://arxiv.org/abs/2203.09771v1 | https://arxiv.org/pdf/2203.09771v1.pdf | Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image Transition | Video frame interpolation (VFI) aims to improve the temporal resolution of a video sequence. Most of the existing deep learning based VFI methods adopt off-the-shelf optical flow algorithms to estimate the bidirectional flows and interpolate the missing frames accordingly. Though having achieved a great success, these methods require much human experience to tune the bidirectional flows and often generate unpleasant results when the estimated flows are not accurate. In this work, we rethink the VFI problem and formulate it as a continuous image transition (CIT) task, whose key issue is to transition an image from one space to another space continuously. More specifically, we learn to implicitly decouple the images into a translatable flow space and a non-translatable feature space. The former depicts the translatable states between the given images, while the later aims to reconstruct the intermediate features that cannot be directly translated. In this way, we can easily perform image interpolation in the flow space and intermediate image synthesis in the feature space, obtaining a CIT model. The proposed space decoupled learning (SDL) approach is simple to implement, while it provides an effective framework to a variety of CIT problems beyond VFI, such as style transfer and image morphing. Our extensive experiments on a variety of CIT tasks demonstrate the superiority of SDL to existing methods. The source code and models can be found at \url{https://github.com/yangxy/SDL}. | ['Lei Zhang', 'Xiansheng Hua', 'Xuansong Xie', 'Peiran Ren', 'Tao Yang'] | 2022-03-18 | null | null | null | null | ['image-morphing'] | ['computer-vision'] | [ 2.1206641e-01 -3.1658822e-01 -2.7041703e-01 -2.1281865e-01
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263fe3c2-e7f3-461d-a29a-cb394840f45e | causal-feature-engineering-of-price | 2306.08157 | null | https://arxiv.org/abs/2306.08157v1 | https://arxiv.org/pdf/2306.08157v1.pdf | Causal Feature Engineering of Price Directions of Cryptocurrencies using Dynamic Bayesian Networks | Cryptocurrencies have gained popularity across various sectors, especially in finance and investment. The popularity is partly due to their unique specifications originating from blockchain-related characteristics such as privacy, decentralisation, and untraceability. Despite their growing popularity, cryptocurrencies remain a high-risk investment due to their price volatility and uncertainty. The inherent volatility in cryptocurrency prices, coupled with internal cryptocurrency-related factors and external influential global economic factors makes predicting their prices and price movement directions challenging. Nevertheless, the knowledge obtained from predicting the direction of cryptocurrency prices can provide valuable guidance for investors in making informed investment decisions. To address this issue, this paper proposes a dynamic Bayesian network (DBN) approach, which can model complex systems in multivariate settings, to predict the price movement direction of five popular altcoins (cryptocurrencies other than Bitcoin) in the next trading day. The efficacy of the proposed model in predicting cryptocurrency price directions is evaluated from two perspectives. Firstly, our proposed approach is compared to two baseline models, namely an auto-regressive integrated moving average and support vector regression. Secondly, from a feature engineering point of view, the impact of twenty-three different features, grouped into four categories, on the DBN's prediction performance is investigated. The experimental results demonstrate that the DBN significantly outperforms the baseline models. In addition, among the groups of features, technical indicators are found to be the most effective predictors of cryptocurrency price directions. | ['Mong Shan Ee', 'Dhananjay Thiruvady', 'Asef Nazari', 'Rasoul Amirzadeh'] | 2023-06-13 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-6.83996081e-01 -3.36091936e-01 -4.81873631e-01 -2.55243410e-03
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855883a1-8abe-4d27-ba2d-1b317203fb8b | multi-label-learning-to-rank-through-multi | 2207.03060 | null | https://arxiv.org/abs/2207.03060v2 | https://arxiv.org/pdf/2207.03060v2.pdf | Multi-Label Learning to Rank through Multi-Objective Optimization | Learning to Rank (LTR) technique is ubiquitous in the Information Retrieval system nowadays, especially in the Search Ranking application. The query-item relevance labels typically used to train the ranking model are often noisy measurements of human behavior, e.g., product rating for product search. The coarse measurements make the ground truth ranking non-unique with respect to a single relevance criterion. To resolve ambiguity, it is desirable to train a model using many relevance criteria, giving rise to Multi-Label LTR (MLLTR). Moreover, it formulates multiple goals that may be conflicting yet important to optimize for simultaneously, e.g., in product search, a ranking model can be trained based on product quality and purchase likelihood to increase revenue. In this research, we leverage the Multi-Objective Optimization (MOO) aspect of the MLLTR problem and employ recently developed MOO algorithms to solve it. Specifically, we propose a general framework where the information from labels can be combined in a variety of ways to meaningfully characterize the trade-off among the goals. Our framework allows for any gradient based MOO algorithm to be used for solving the MLLTR problem. We test the proposed framework on two publicly available LTR datasets and one e-commerce dataset to show its efficacy. | ['Michinari Momma', 'Deqiang Meng', 'Yetian Chen', 'Chaosheng Dong', 'Debabrata Mahapatra'] | 2022-07-07 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 2.49109641e-01 -4.03359503e-01 -8.02272797e-01 -6.93423927e-01
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8cefe614-d18a-41f1-9455-c8372a7d07fe | lingyi-medical-conversational-question | 2204.09220 | null | https://arxiv.org/abs/2204.09220v1 | https://arxiv.org/pdf/2204.09220v1.pdf | LingYi: Medical Conversational Question Answering System based on Multi-modal Knowledge Graphs | The medical conversational system can relieve the burden of doctors and improve the efficiency of healthcare, especially during the pandemic. This paper presents a medical conversational question answering (CQA) system based on the multi-modal knowledge graph, namely "LingYi", which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures including medical triage, consultation, image-text drug recommendation and record. To conduct knowledge-grounded dialogues with patients, we first construct a Chinese Medical Multi-Modal Knowledge Graph (CM3KG) and collect a large-scale Chinese Medical CQA (CMCQA) dataset. Compared with the other existing medical question-answering systems, our system adopts several state-of-the-art technologies including medical entity disambiguation and medical dialogue generation, which is more friendly to provide medical services to patients. In addition, we have open-sourced our codes which contain back-end models and front-end web pages at https://github.com/WENGSYX/LingYi. The datasets including CM3KG at https://github.com/WENGSYX/CM3KG and CMCQA at https://github.com/WENGSYX/CMCQA are also released to further promote future research. | ['Jun Zhao', 'Shutao Li', 'Bin Sun', 'Kang Liu', 'Shizhu He', 'Yixuan Weng', 'Bin Li', 'Fei Xia'] | 2022-04-20 | null | null | null | null | ['multi-modal-knowledge-graph', 'entity-disambiguation'] | ['knowledge-base', 'natural-language-processing'] | [-3.88928175e-01 5.42428970e-01 -1.29898533e-01 -1.22501120e-01
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328f5c26-28db-4f5f-9370-d0560bd0e279 | dssl-deep-surroundings-person-separation | 2109.05534 | null | https://arxiv.org/abs/2109.05534v1 | https://arxiv.org/pdf/2109.05534v1.pdf | DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval | Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality. Nevertheless, due to the complexity of high-dimensional data, the unconstrained mapping paradigms are not able to properly catch discriminative clues about the corresponding person while drop the misaligned information. Intuitively, the information contained in visual data can be divided into person information (PI) and surroundings information (SI), which are mutually exclusive from each other. To this end, we propose a novel Deep Surroundings-person Separation Learning (DSSL) model in this paper to effectively extract and match person information, and hence achieve a superior retrieval accuracy. A surroundings-person separation and fusion mechanism plays the key role to realize an accurate and effective surroundings-person separation under a mutually exclusion constraint. In order to adequately utilize multi-modal and multi-granular information for a higher retrieval accuracy, five diverse alignment paradigms are adopted. Extensive experiments are carried out to evaluate the proposed DSSL on CUHK-PEDES, which is currently the only accessible dataset for text-base person retrieval task. DSSL achieves the state-of-the-art performance on CUHK-PEDES. To properly evaluate our proposed DSSL in the real scenarios, a Real Scenarios Text-based Person Reidentification (RSTPReid) dataset is constructed to benefit future research on text-based person retrieval, which will be publicly available. | ['Gang Hua', 'Fangqiang Hu', 'Tian Wang', 'Jing Jin', 'Xili Wan', 'Yifeng Li', 'Zijie Wang', 'Aichun Zhu'] | 2021-09-12 | null | null | null | null | ['person-retrieval', 'nlp-based-person-retrival'] | ['computer-vision', 'computer-vision'] | [-1.92113072e-01 -7.61568666e-01 -8.44381079e-02 -3.67574960e-01
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d76d4da8-2ca2-41fb-977a-c48f45139007 | a-bayesian-traction-force-microscopy-method | 2005.01377 | null | https://arxiv.org/abs/2005.01377v1 | https://arxiv.org/pdf/2005.01377v1.pdf | A Bayesian traction force microscopy method with automated denoising in a user-friendly software package | Adherent biological cells generate traction forces on a substrate that play a central role for migration, mechanosensing, differentiation, and collective behavior. The established method for quantifying this cell-substrate interaction is traction force microscopy (TFM). In spite of recent advancements, inference of the traction forces from measurements remains very sensitive to noise. However, suppression of the noise reduces the measurement accuracy and the spatial resolution, which makes it crucial to select an optimal level of noise reduction. Here, we present a fully automated method for noise reduction and robust, standardized traction-force reconstruction. The method, termed Bayesian Fourier transform traction cytometry, combines the robustness of Bayesian L2 regularization with the computation speed of Fourier transform traction cytometry. We validate the performance of the method with synthetic and real data. The method is made freely available as a software package with a graphical user-interface for intuitive usage. | ['Benedikt Sabass', 'Gerhard Gompper', 'Yunfei Huang'] | 2020-05-04 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 4.87081140e-01 -5.17260730e-01 6.75621778e-02 -1.10212630e-02
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6dddd182-fa7d-4c47-b788-542d603a1e4e | meg-decoding-across-subjects | 1404.4175 | null | http://arxiv.org/abs/1404.4175v1 | http://arxiv.org/pdf/1404.4175v1.pdf | MEG Decoding Across Subjects | Brain decoding is a data analysis paradigm for neuroimaging experiments that
is based on predicting the stimulus presented to the subject from the
concurrent brain activity. In order to make inference at the group level, a
straightforward but sometimes unsuccessful approach is to train a classifier on
the trials of a group of subjects and then to test it on unseen trials from new
subjects. The extreme difficulty is related to the structural and functional
variability across the subjects. We call this approach "decoding across
subjects". In this work, we address the problem of decoding across subjects for
magnetoencephalographic (MEG) experiments and we provide the following
contributions: first, we formally describe the problem and show that it belongs
to a machine learning sub-field called transductive transfer learning (TTL).
Second, we propose to use a simple TTL technique that accounts for the
differences between train data and test data. Third, we propose the use of
ensemble learning, and specifically of stacked generalization, to address the
variability across subjects within train data, with the aim of producing more
stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we
compare the standard approach of not modelling the differences across subjects,
to the proposed one of combining TTL and ensemble learning. We show that the
proposed approach is consistently more accurate than the standard one. | ['Emanuele Olivetti', 'Seyed Mostafa Kia', 'Paolo Avesani'] | 2014-04-16 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.35508478e-01 -1.57945037e-01 5.43246746e-01 -6.15877628e-01
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e3b15347-fae1-4742-bdf8-57bee902fde5 | an-algorithm-for-the-visualization-of | 1903.03254 | null | http://arxiv.org/abs/1903.03254v1 | http://arxiv.org/pdf/1903.03254v1.pdf | An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves | Within the last years, the classification of variable stars with Machine
Learning has become a mainstream area of research. Recently, visualization of
time series is attracting more attention in data science as a tool to visually
help scientists to recognize significant patterns in complex dynamics. Within
the Machine Learning literature, dictionary-based methods have been widely used
to encode relevant parts of image data. These methods intrinsically assign a
degree of importance to patches in pictures, according to their contribution in
the image reconstruction. Inspired by dictionary-based techniques, we present
an approach that naturally provides the visualization of salient parts in
astronomical light curves, making the analogy between image patches and
relevant pieces in time series. Our approach encodes the most meaningful
patterns such that we can approximately reconstruct light curves by just using
the encoded information. We test our method in light curves from the OGLE-III
and StarLight databases. Our results show that the proposed model delivers an
automatic and intuitive visualization of relevant light curve parts, such as
local peaks and drops in magnitude. | ['Márcio Catelán', 'Christian Pieringer', 'Karim Pichara', 'Pavlos Protopapas'] | 2019-03-08 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [-3.12890679e-01 -4.75758702e-01 -3.54244933e-02 -7.55994692e-02
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71f87e17-f25d-4eb7-9abd-dcd63bae82e6 | the-devil-is-in-the-wrongly-classified | 2302.04002 | null | https://arxiv.org/abs/2302.04002v1 | https://arxiv.org/pdf/2302.04002v1.pdf | The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition | Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. The UOSR draws little attention since it is proposed, but we find sometimes it is even more practical than OSR in the real world applications, as evaluation results of known but wrongly classified samples are also wrong like unknown samples. In this paper, we deeply analyze the UOSR task under different training and evaluation settings to shed light on this promising research direction. For this purpose, we first evaluate the UOSR performance of several OSR methods and show a significant finding that the UOSR performance consistently surpasses the OSR performance by a large margin for the same method. We show that the reason lies in the known but wrongly classified samples, as their uncertainty distribution is extremely close to unknown samples rather than known and correctly classified samples. Second, we analyze how the two training settings of OSR (i.e., pre-training and outlier exposure) influence the UOSR. We find although they are both beneficial for distinguishing known and correctly classified samples from unknown samples, pre-training is also helpful for identifying known but wrongly classified samples while outlier exposure is not. In addition to different training settings, we also formulate a new evaluation setting for UOSR which is called few-shot UOSR, where only one or five samples per unknown class are available during evaluation to help identify unknown samples. We propose FS-KNNS for the few-shot UOSR to achieve state-of-the-art performance under all settings. | ['Qifeng Chen', 'Shaojie Shen', 'Deli Zhao', 'Yingya Zhang', 'Yixuan Pei', 'Shiwei Zhang', 'Di Luan', 'Jun Cen'] | 2023-02-08 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.90523759e-02 -2.11598426e-01 -3.68709147e-01 -4.46476132e-01
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291c1466-2330-42ed-8b99-f83c1f383692 | sequence-to-sequence-singing-voice-synthesis | 2010.12024 | null | https://arxiv.org/abs/2010.12024v2 | https://arxiv.org/pdf/2010.12024v2.pdf | Sequence-to-sequence Singing Voice Synthesis with Perceptual Entropy Loss | The neural network (NN) based singing voice synthesis (SVS) systems require sufficient data to train well and are prone to over-fitting due to data scarcity. However, we often encounter data limitation problem in building SVS systems because of high data acquisition and annotation costs. In this work, we propose a Perceptual Entropy (PE) loss derived from a psycho-acoustic hearing model to regularize the network. With a one-hour open-source singing voice database, we explore the impact of the PE loss on various mainstream sequence-to-sequence models, including the RNN-based, transformer-based, and conformer-based models. Our experiments show that the PE loss can mitigate the over-fitting problem and significantly improve the synthesized singing quality reflected in objective and subjective evaluations. | ['Qin Jin', 'Yuekai Zhang', 'Nan Huo', 'Shuai Guo', 'Jiatong Shi'] | 2020-10-22 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [ 1.67699873e-01 -2.86179394e-01 1.89947486e-01 -1.57127514e-01
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c9a584a1-cf64-4d0e-9e9a-f3255d998050 | towards-flexible-blind-jpeg-artifacts-removal | 2109.14573 | null | https://arxiv.org/abs/2109.14573v1 | https://arxiv.org/pdf/2109.14573v1.pdf | Towards Flexible Blind JPEG Artifacts Removal | Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage. However, existing deep blind methods usually directly reconstruct the image without predicting the quality factor, thus lacking the flexibility to control the output as the non-blind methods. To remedy this problem, in this paper, we propose a flexible blind convolutional neural network, namely FBCNN, that can predict the adjustable quality factor to control the trade-off between artifacts removal and details preservation. Specifically, FBCNN decouples the quality factor from the JPEG image via a decoupler module and then embeds the predicted quality factor into the subsequent reconstructor module through a quality factor attention block for flexible control. Besides, we find existing methods are prone to fail on non-aligned double JPEG images even with only a one-pixel shift, and we thus propose a double JPEG degradation model to augment the training data. Extensive experiments on single JPEG images, more general double JPEG images, and real-world JPEG images demonstrate that our proposed FBCNN achieves favorable performance against state-of-the-art methods in terms of both quantitative metrics and visual quality. | ['Radu Timofte', 'Kai Zhang', 'Jiaxi Jiang'] | 2021-09-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jiang_Towards_Flexible_Blind_JPEG_Artifacts_Removal_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-deblocking', 'jpeg-artifact-correction', 'image-compression-artifact-reduction', 'image-forensics', 'jpeg-artifact-removal'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.42857531e-01 -4.09872621e-01 -7.19987275e-03 -3.58508080e-01
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cb7f9780-083c-4b74-9bb2-bb287a1cd031 | efficient-failure-pattern-identification-of | 2306.00760 | null | https://arxiv.org/abs/2306.00760v1 | https://arxiv.org/pdf/2306.00760v1.pdf | Efficient Failure Pattern Identification of Predictive Algorithms | Given a (machine learning) classifier and a collection of unlabeled data, how can we efficiently identify misclassification patterns presented in this dataset? To address this problem, we propose a human-machine collaborative framework that consists of a team of human annotators and a sequential recommendation algorithm. The recommendation algorithm is conceptualized as a stochastic sampler that, in each round, queries the annotators a subset of samples for their true labels and obtains the feedback information on whether the samples are misclassified. The sampling mechanism needs to balance between discovering new patterns of misclassification (exploration) and confirming the potential patterns of classification (exploitation). We construct a determinantal point process, whose intensity balances the exploration-exploitation trade-off through the weighted update of the posterior at each round to form the generator of the stochastic sampler. The numerical results empirically demonstrate the competitive performance of our framework on multiple datasets at various signal-to-noise ratios. | ['Viet Anh Nguyen', 'Bao Nguyen'] | 2023-06-01 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [ 4.17985082e-01 3.46164793e-01 -4.35636699e-01 -5.46678901e-01
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3074d8bf-8a15-415b-8350-70e9e74864a6 | actions-speak-louder-than-listening | 2110.12855 | null | https://arxiv.org/abs/2110.12855v1 | https://arxiv.org/pdf/2110.12855v1.pdf | Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience | The subjective evaluation of music generation techniques has been mostly done with questionnaire-based listening tests while ignoring the perspectives from music composition, arrangement, and soundtrack editing. In this paper, we propose an editing test to evaluate users' editing experience of music generation models in a systematic way. To do this, we design a new music style transfer model combining the non-chronological inference architecture, autoregressive models and the Transformer, which serves as an improvement from the baseline model on the same style transfer task. Then, we compare the performance of the two models with a conventional listening test and the proposed editing test, in which the quality of generated samples is assessed by the amount of effort (e.g., the number of required keyboard and mouse actions) spent by users to polish a music clip. Results on two target styles indicate that the improvement over the baseline model can be reflected by the editing test quantitatively. Also, the editing test provides profound insights which are not accessible from usual listening tests. The major contribution of this paper is the systematic presentation of the editing test and the corresponding insights, while the proposed music style transfer model based on state-of-the-art neural networks represents another contribution. | ['Li Su', 'Yuh-Ming Chiu', 'Meng-Hsuan Wu', 'Wei-Tsung Lu'] | 2021-10-25 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.47118646e-01 -7.58338422e-02 1.67290136e-01 -3.16609405e-02
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22f32bba-ea72-48bb-9cb8-72d6649ffbc0 | rendezvous-in-time-an-attention-based | 2211.16963 | null | https://arxiv.org/abs/2211.16963v2 | https://arxiv.org/pdf/2211.16963v2.pdf | Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition | One of the recent advances in surgical AI is the recognition of surgical activities as triplets of (instrument, verb, target). Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single frame features. Exploiting the temporal cues from earlier frames would improve the recognition of surgical action triplets from videos. In this paper, we propose Rendezvous in Time (RiT) - a deep learning model that extends the state-of-the-art model, Rendezvous, with temporal modeling. Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition. We validate our proposal on the challenging surgical triplet dataset, CholecT45, demonstrating an improved recognition of the verb and triplet along with other interactions involving the verb such as (instrument, verb). Qualitative results show that the RiT produces smoother predictions for most triplet instances than the state-of-the-arts. We present a novel attention-based approach that leverages the temporal fusion of video frames to model the evolution of surgical actions and exploit their benefits for surgical triplet recognition. | ['Nicolas Padoy', 'Didier Mutter', 'Chinedu Innocent Nwoye', 'Saurav Sharma'] | 2022-11-30 | null | null | null | null | ['action-triplet-recognition'] | ['computer-vision'] | [ 3.42761904e-01 1.55242622e-01 -6.35095596e-01 -1.02231748e-01
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084ac661-477c-45f7-9f5e-5b5527f67214 | aging-with-grace-lifelong-model-editing-with | 2211.11031 | null | https://arxiv.org/abs/2211.11031v4 | https://arxiv.org/pdf/2211.11031v4.pdf | Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adapters | Deployed models decay over time due to shifting inputs, changing user needs, or emergent knowledge gaps. When harmful behaviors are identified, targeted edits are required. However, current model editors, which adjust specific behaviors of pre-trained models, degrade model performance over multiple edits. We propose GRACE, a Lifelong Model Editing method, which implements spot-fixes on streaming errors of a deployed model, ensuring minimal impact on unrelated inputs. GRACE writes new mappings into a pre-trained model's latent space, creating a discrete, local codebook of edits without altering model weights. This is the first method enabling thousands of sequential edits using only streaming errors. Our experiments on T5, BERT, and GPT models show GRACE's state-of-the-art performance in making and retaining edits, while generalizing to unseen inputs. Our code is available at \href{https://www.github.com/thartvigsen/grace}{github.com/thartvigsen/grace}. | ['Marzyeh Ghassemi', 'Yoon Kim', 'Hamid Palangi', 'Swami Sankaranarayanan', 'Thomas Hartvigsen'] | 2022-11-20 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 1.87846825e-01 2.60284364e-01 -6.91711083e-02 -3.32034588e-01
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47ad007d-a11e-45cc-b51a-64c243438483 | automatic-procurement-fraud-detection-with | 2304.10105 | null | https://arxiv.org/abs/2304.10105v1 | https://arxiv.org/pdf/2304.10105v1.pdf | Automatic Procurement Fraud Detection with Machine Learning | Although procurement fraud is always a critical problem in almost every free market, audit departments still have a strong reliance on reporting from informed sources when detecting them. With our generous cooperator, SF Express, sharing the access to the database related with procurements took place from 2015 to 2017 in their company, our team studies how machine learning techniques could help with the audition of one of the most profound crime among current chinese market, namely procurement frauds. By representing each procurement event as 9 specific features, we construct neural network models to identify suspicious procurements and classify their fraud types. Through testing our models over 50000 samples collected from the procurement database, we have proven that such models -- despite having space for improvements -- are useful in detecting procurement frauds. | ['Tong Qiu', 'Jin Bai'] | 2023-04-20 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-4.32352781e-01 -1.61719859e-01 -2.66799033e-01 -4.22051221e-01
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a59ebb50-f8ad-4c01-bcfd-d7235ae47347 | natural-language-descriptions-for-human | null | null | https://aclanthology.org/W17-3512 | https://aclanthology.org/W17-3512.pdf | Natural Language Descriptions for Human Activities in Video Streams | There has been continuous growth in the volume and ubiquity of video material. It has become essential to define video semantics in order to aid the searchability and retrieval of this data. We present a framework that produces textual descriptions of video, based on the visual semantic content. Detected action classes rendered as verbs, participant objects converted to noun phrases, visual properties of detected objects rendered as adjectives and spatial relations between objects rendered as prepositions. Further, in cases of zero-shot action recognition, a language model is used to infer a missing verb, aided by the detection of objects and scene settings. These extracted features are converted into textual descriptions using a template-based approach. The proposed video descriptions framework evaluated on the NLDHA dataset using ROUGE scores and human judgment evaluation. | ['Nouf Alharbi', 'Yoshihiko Gotoh'] | 2017-09-01 | null | null | null | ws-2017-9 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.86889964e-01 -1.54647514e-01 3.52288485e-02 -3.77062172e-01
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1b261519-cd7f-4a1e-9715-c67799507c2b | different-games-in-dialogue-combining | 2307.02087 | null | https://arxiv.org/abs/2307.02087v1 | https://arxiv.org/pdf/2307.02087v1.pdf | Different Games in Dialogue: Combining character and conversational types in strategic choice | In this paper, we show that investigating the interaction of conversational type (often known as language game or speech genre) with the character types of the interlocutors is worthwhile. We present a method of calculating the decision making process for selecting dialogue moves that combines character type and conversational type. We also present a mathematical model that illustrate these factors' interactions in a quantitative way. | ['Alafate Abulimiti'] | 2023-07-05 | null | null | null | null | ['decision-making'] | ['reasoning'] | [-3.44774812e-01 2.61309355e-01 -1.08851627e-01 -1.80173755e-01
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df0956bd-1bb7-4da1-bee9-ec3bb5bf793b | learning-to-detect-and-segment-for-open | 2212.12130 | null | https://arxiv.org/abs/2212.12130v5 | https://arxiv.org/pdf/2212.12130v5.pdf | Learning to Detect and Segment for Open Vocabulary Object Detection | Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and the dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only 1.1% more computation. | ['Nan Li', 'Tao Wang'] | 2022-12-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Learning_To_Detect_and_Segment_for_Open_Vocabulary_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.37549162e-01 3.56685340e-01 -1.32292256e-01 -3.61545324e-01
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74ce0d9e-6872-4335-854d-271b4fb44ae8 | hierarchical-multi-grained-generative-model | 2009.08474 | null | https://arxiv.org/abs/2009.08474v2 | https://arxiv.org/pdf/2009.08474v2.pdf | Hierarchical Multi-Grained Generative Model for Expressive Speech Synthesis | This paper proposes a hierarchical generative model with a multi-grained latent variable to synthesize expressive speech. In recent years, fine-grained latent variables are introduced into the text-to-speech synthesis that enable the fine control of the prosody and speaking styles of synthesized speech. However, the naturalness of speech degrades when these latent variables are obtained by sampling from the standard Gaussian prior. To solve this problem, we propose a novel framework for modeling the fine-grained latent variables, considering the dependence on an input text, a hierarchical linguistic structure, and a temporal structure of latent variables. This framework consists of a multi-grained variational autoencoder, a conditional prior, and a multi-level auto-regressive latent converter to obtain the different time-resolution latent variables and sample the finer-level latent variables from the coarser-level ones by taking into account the input text. Experimental results indicate an appropriate method of sampling fine-grained latent variables without the reference signal at the synthesis stage. Our proposed framework also provides the controllability of speaking style in an entire utterance. | ['Yoshihiko Nankaku', 'Keiichiro Oura', 'Yukiya Hono', 'Kei Hashimoto', 'Kei Sawada', 'Keiichi Tokuda', 'Kazuna Tsuboi'] | 2020-09-17 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-7.33371601e-02 6.59571439e-02 -2.55000710e-01 -4.23971951e-01
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6c7892dc-e24b-4d93-8f3e-c0e930182172 | edinburgh-at-semeval-2022-task-1-jointly | null | null | https://aclanthology.org/2022.semeval-1.8 | https://aclanthology.org/2022.semeval-1.8.pdf | Edinburgh at SemEval-2022 Task 1: Jointly Fishing for Word Embeddings and Definitions | This paper presents a winning submission to the SemEval 2022 Task 1 on two sub-tasks: reverse dictionary and definition modelling. We leverage a recently proposed unified model with multi-task training. It utilizes data symmetrically and learns to tackle both tracks concurrently. Analysis shows that our system performs consistently on diverse languages, and works the best with sgns embeddings. Yet, char and electra carry intriguing properties. The two tracks’ best results are always in differing subsets grouped by linguistic annotations. In this task, the quality of definition generation lags behind, and BLEU scores might be misleading. | ['Zheng Zhao', 'Pinzhen Chen'] | null | null | null | null | semeval-naacl-2022-7 | ['definition-extraction', 'definition-modelling', 'reverse-dictionary'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 8.60837027e-02 1.30778048e-02 -5.70014536e-01 -1.55938491e-01
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f6ba2ddb-5bf0-40c8-bdc6-b139c4be801f | localized-sparse-incomplete-multi-view | 2208.02998 | null | https://arxiv.org/abs/2208.02998v3 | https://arxiv.org/pdf/2208.02998v3.pdf | Localized Sparse Incomplete Multi-view Clustering | Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a l1 norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC. | ['Yong Xu', 'Chao Huang', 'Jie Wen', 'Zhihao Wu', 'Chengliang Liu'] | 2022-08-05 | null | null | null | null | ['incomplete-multi-view-clustering', 'multi-view-learning'] | ['computer-vision', 'computer-vision'] | [-2.68596917e-01 -2.55739033e-01 -3.49258393e-01 -2.25716129e-01
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0558c59d-4cb2-4b7d-83a8-ca6359e4efb0 | zooming-slowmo-an-efficient-one-stage | 2104.07473 | null | https://arxiv.org/abs/2104.07473v1 | https://arxiv.org/pdf/2104.07473v1.pdf | Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution | In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LFR) video sequence. A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). Nevertheless, temporal interpolation and spatial upscaling are intra-related in this problem. Two-stage approaches cannot fully make use of this natural property. Besides, state-of-the-art VFI or VSR deep networks usually have a large frame reconstruction module in order to obtain high-quality photo-realistic video frames, which makes the two-stage approaches have large models and thus be relatively time-consuming. To overcome the issues, we present a one-stage space-time video super-resolution framework, which can directly reconstruct an HR slow-motion video sequence from an input LR and LFR video. Instead of reconstructing missing LR intermediate frames as VFI models do, we temporally interpolate LR frame features of the missing LR frames capturing local temporal contexts by a feature temporal interpolation module. Extensive experiments on widely used benchmarks demonstrate that the proposed framework not only achieves better qualitative and quantitative performance on both clean and noisy LR frames but also is several times faster than recent state-of-the-art two-stage networks. The source code is released in https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020 . | ['Chenliang Xu', 'Jan P. Allebach', 'Yun Fu', 'Yulun Zhang', 'Yapeng Tian', 'Xiaoyu Xiang'] | 2021-04-15 | null | null | null | null | ['space-time-video-super-resolution'] | ['computer-vision'] | [ 3.07917476e-01 -2.93398827e-01 -2.38463998e-01 -3.11016589e-01
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2c0a2ed7-1601-4ebb-bfd3-fdf60a943d4b | target-aware-dual-adversarial-learning-and-a | 2203.16220 | null | https://arxiv.org/abs/2203.16220v1 | https://arxiv.org/pdf/2203.16220v1.pdf | Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection | This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. These approaches neglect that modality differences implying the complementary information are extremely important for both fusion and subsequent detection task. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network. The fusion network with one generator and dual discriminators seeks commons while learning from differences, which preserves structural information of targets from the infrared and textural details from the visible. Furthermore, we build a synchronized imaging system with calibrated infrared and optical sensors, and collect currently the most comprehensive benchmark covering a wide range of scenarios. Extensive experiments on several public datasets and our benchmark demonstrate that our method outputs not only visually appealing fusion but also higher detection mAP than the state-of-the-art approaches. | ['Zhongxuan Luo', 'Wei Zhong', 'Risheng Liu', 'Guanyao Wu', 'Zhanbo Huang', 'Xin Fan', 'JinYuan Liu'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liu_Target-Aware_Dual_Adversarial_Learning_and_a_Multi-Scenario_Multi-Modality_Benchmark_To_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liu_Target-Aware_Dual_Adversarial_Learning_and_a_Multi-Scenario_Multi-Modality_Benchmark_To_CVPR_2022_paper.pdf | cvpr-2022-1 | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 6.77923977e-01 -3.07897598e-01 7.83783570e-03 2.34300286e-01
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49eb9566-a8dd-4b47-9d8e-b51a1050a745 | a-comprehensive-review-of-state-of-the-art | 2306.06371 | null | https://arxiv.org/abs/2306.06371v1 | https://arxiv.org/pdf/2306.06371v1.pdf | A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text | Java Code Generation consists in generating automatically Java code from a Natural Language Text. This NLP task helps in increasing programmers' productivity by providing them with immediate solutions to the simplest and most repetitive tasks. Code generation is a challenging task because of the hard syntactic rules and the necessity of a deep understanding of the semantic aspect of the programming language. Many works tried to tackle this task using either RNN-based, or Transformer-based models. The latter achieved remarkable advancement in the domain and they can be divided into three groups: (1) encoder-only models, (2) decoder-only models, and (3) encoder-decoder models. In this paper, we provide a comprehensive review of the evolution and progress of deep learning models in Java code generation task. We focus on the most important methods and present their merits and limitations, as well as the objective functions used by the community. In addition, we provide a detailed description of datasets and evaluation metrics used in the literature. Finally, we discuss results of different models on CONCODE dataset, then propose some future directions. | ['El Hassane Ettifouri', 'Walid Dahhane', 'El Mehdi Chouham', 'Mahaman Sanoussi Yahaya Alassan', 'Jessica López Espejel'] | 2023-06-10 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 0.27807578 0.23901276 -0.19062023 -0.28764376 -0.6810898 -0.4714248
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b3a8a164-26f8-40d9-a952-c4b2ff08779f | the-second-cross-lingual-challenge-on | null | null | https://aclanthology.org/W19-3709 | https://aclanthology.org/W19-3709.pdf | The Second Cross-Lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages | We describe the Second Multilingual Named Entity Challenge in Slavic languages. The task is recognizing mentions of named entities in Web documents, their normalization, and cross-lingual linking. The Challenge was organized as part of the 7th Balto-Slavic Natural Language Processing Workshop, co-located with the ACL-2019 conference. Eight teams participated in the competition, which covered four languages and five entity types. Performance for the named entity recognition task reached 90{\%} F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all four languages, and five teams participated in the cross-lingual entity linking task. Detailed evaluation information is available on the shared task web page. | ['Josef Steinberger', "Pavel P{\\v{r}}ib{\\'a}{\\v{n}}", "Micha{\\l} Marci{\\'n}czuk", 'Lidia Pivovarova', 'Laska Laskova', 'Jakub Piskorski', 'Roman Yangarber'] | 2019-08-01 | null | null | null | ws-2019-8 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-4.23244327e-01 1.38738111e-01 -4.55849946e-01 -4.83268321e-01
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c18f0034-10f0-43cf-ab02-add24b1bcf89 | sqa3d-situated-question-answering-in-3d | 2210.07474 | null | https://arxiv.org/abs/2210.07474v5 | https://arxiv.org/pdf/2210.07474v5.pdf | SQA3D: Situated Question Answering in 3D Scenes | We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of 47.20%, while amateur human participants can reach 90.06%. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capability. | ['Siyuan Huang', 'Song-Chun Zhu', 'Yitao Liang', 'Qing Li', 'Zilong Zheng', 'Silong Yong', 'Xiaojian Ma'] | 2022-10-14 | null | null | null | null | ['referring-expression'] | ['computer-vision'] | [ 4.02654111e-02 2.74145603e-01 4.46961612e-01 -5.12497663e-01
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a5f5f3a0-5365-4413-9f07-7d00f38f5d10 | safety-enhanced-uav-path-planning-with | 2104.10033 | null | https://arxiv.org/abs/2104.10033v1 | https://arxiv.org/pdf/2104.10033v1.pdf | Safety-enhanced UAV Path Planning with Spherical Vector-based Particle Swarm Optimization | This paper presents a new algorithm named spherical vector-based particle swarm optimization (SPSO) to deal with the problem of path planning for unmanned aerial vehicles (UAVs) in complicated environments subjected to multiple threats. A cost function is first formulated to convert the path planning into an optimization problem that incorporates requirements and constraints for the feasible and safe operation of the UAV. SPSO is then used to find the optimal path that minimizes the cost function by efficiently searching the configuration space of the UAV via the correspondence between the particle position and the speed, turn angle and climb/dive angle of the UAV. To evaluate the performance of SPSO, eight benchmarking scenarios have been generated from real digital elevation model maps. The results show that the proposed SPSO outperforms not only other particle swarm optimization (PSO) variants including the classic PSO, phase angle-encoded PSO and quantum-behave PSO but also other state-of-the-art metaheuristic optimization algorithms including the genetic algorithm (GA), artificial bee colony (ABC), and differential evolution (DE) in most scenarios. In addition, experiments have been conducted to demonstrate the validity of the generated paths for real UAV operations. Source code of the algorithm can be found at https://github.com/duongpm/SPSO. | ['Quang Phuc Ha', 'Manh Duong Phung'] | 2021-04-13 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-2.66219825e-02 -4.30357248e-01 3.10729057e-01 3.66102487e-01
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78a2b98b-f024-4afc-855a-9b42553e3c41 | ppmf-a-patient-based-predictive-modeling | 1704.07499 | null | http://arxiv.org/abs/1704.07499v1 | http://arxiv.org/pdf/1704.07499v1.pdf | PPMF: A Patient-based Predictive Modeling Framework for Early ICU Mortality Prediction | To date, developing a good model for early intensive care unit (ICU)
mortality prediction is still challenging. This paper presents a patient based
predictive modeling framework (PPMF) to improve the performance of ICU
mortality prediction using data collected during the first 48 hours of ICU
admission. PPMF consists of three main components verifying three related
research hypotheses. The first component captures dynamic changes of patients
status in the ICU using their time series data (e.g., vital signs and
laboratory tests). The second component is a local approximation algorithm that
classifies patients based on their similarities. The third component is a
Gradient Decent wrapper that updates feature weights according to the
classification feedback. Experiments using data from MIMICIII show that PPMF
significantly outperforms: (1) the severity score systems, namely SASP III,
APACHE IV, and MPM0III, (2) the aggregation based classifiers that utilize
summarized time series, and (3) baseline feature selection methods. | ['Samir AbdelRahman', 'Olivia R. Liu Sheng', 'Mohammad Amin Morid'] | 2017-04-25 | null | null | null | null | ['icu-mortality'] | ['medical'] | [-2.84137636e-01 -7.22597718e-01 -8.64415988e-02 -3.72691900e-01
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388f6281-2182-44aa-a816-39d4656246b9 | habitat-synthetic-scenes-dataset-hssd-200-an | 2306.11290 | null | https://arxiv.org/abs/2306.11290v2 | https://arxiv.org/pdf/2306.11290v2.pdf | Habitat Synthetic Scenes Dataset (HSSD-200): An Analysis of 3D Scene Scale and Realism Tradeoffs for ObjectGoal Navigation | We contribute the Habitat Synthetic Scene Dataset, a dataset of 211 high-quality 3D scenes, and use it to test navigation agent generalization to realistic 3D environments. Our dataset represents real interiors and contains a diverse set of 18,656 models of real-world objects. We investigate the impact of synthetic 3D scene dataset scale and realism on the task of training embodied agents to find and navigate to objects (ObjectGoal navigation). By comparing to synthetic 3D scene datasets from prior work, we find that scale helps in generalization, but the benefits quickly saturate, making visual fidelity and correlation to real-world scenes more important. Our experiments show that agents trained on our smaller-scale dataset can match or outperform agents trained on much larger datasets. Surprisingly, we observe that agents trained on just 122 scenes from our dataset outperform agents trained on 10,000 scenes from the ProcTHOR-10K dataset in terms of zero-shot generalization in real-world scanned environments. | ['Brennan Shacklett', 'Manolis Savva', 'Angel X. Chang', 'Eric Undersander', 'Alexander Clegg', 'Dhruv Batra', 'Sanjay Haresh', 'Hanxiao Jiang', 'Yongsen Mao', 'Mukul Khanna'] | 2023-06-20 | null | null | null | null | ['navigate'] | ['reasoning'] | [-5.37751205e-02 -2.32634485e-01 3.50060016e-01 -3.93156886e-01
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a47c3723-2627-4222-8ee4-481c548f9a08 | constraining-latent-space-to-improve-deep | null | null | https://openreview.net/forum?id=PcBVjfeLODY | https://openreview.net/pdf?id=PcBVjfeLODY | Constraining Latent Space to Improve Deep Self-Supervised e-Commerce Products Embeddings for Downstream Tasks | The representation of products in a e-commerce marketplace is a key aspect to be exploited when trying to improve the user experience on the site. A well known example of the importance of a good product representation are tasks such as product search or product recommendation. There is however a multitude of lesser known tasks relevant to the business, examples are the detection of counterfeit items, the estimation of package sizes or the categorization of products, among others. It is in this setting that good vector representations of products that can be reused on different tasks are very valuable. Past years have seen a major increase in research in the area of latent representations for products in e-Commerce. Examples of this are models like Prod2Vec or Meta-Prod2Vec which leverage from the information of a user session in order to generate vectors of the products that can be used in product recommendations. This work proposes a novel deep encoder model for learning product embeddings to be applied in several downstream tasks. The model uses pairs of products that appear together in a browsing session of the users and adds a proximity constraint to the final latent space in order to project the embeddings of similar products close to each other. This has a regularization effect which gives better features representations to use across multiple downstream tasks, we explore such effect in our experimentation by assessing its impact on the performance of the tasks. Our experiments show effectiveness in transfer learning scenarios comparable to several industrial baselines. | ['Rafael Carrascosa', 'Cristian Cardellino'] | 2021-01-01 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-1.51858419e-01 -2.65483633e-02 -2.44818494e-01 -6.46970749e-01
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40d0917b-ee23-429d-898f-03695f5ef942 | deep-fitting-degree-scoring-network-for | 1904.12681 | null | https://arxiv.org/abs/1904.12681v2 | https://arxiv.org/pdf/1904.12681v2.pdf | Deep Fitting Degree Scoring Network for Monocular 3D Object Detection | In this paper, we propose to learn a deep fitting degree scoring network for monocular 3D object detection, which aims to score fitting degree between proposals and object conclusively. Different from most existing monocular frameworks which use tight constraint to get 3D location, our approach achieves high-precision localization through measuring the visual fitting degree between the projected 3D proposals and the object. We first regress the dimension and orientation of the object using an anchor-based method so that a suitable 3D proposal can be constructed. We propose FQNet, which can infer the 3D IoU between the 3D proposals and the object solely based on 2D cues. Therefore, during the detection process, we sample a large number of candidates in the 3D space and project these 3D bounding boxes on 2D image individually. The best candidate can be picked out by simply exploring the spatial overlap between proposals and the object, in the form of the output 3D IoU score of FQNet. Experiments on the KITTI dataset demonstrate the effectiveness of our framework. | ['Jie zhou', 'Lijie Liu', 'Jiwen Lu', 'Qi Tian', 'Chunjing Xu'] | 2019-04-26 | deep-fitting-degree-scoring-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Deep_Fitting_Degree_Scoring_Network_for_Monocular_3D_Object_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Deep_Fitting_Degree_Scoring_Network_for_Monocular_3D_Object_Detection_CVPR_2019_paper.pdf | cvpr-2019-6 | ['vehicle-pose-estimation'] | ['computer-vision'] | [-4.26120609e-01 -4.33328003e-02 -4.59893048e-02 -3.42886358e-01
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08b0bd93-69a9-4ba9-be7e-983775bf327c | using-integer-linear-programming-in-concept | null | null | https://aclanthology.org/P13-2100 | https://aclanthology.org/P13-2100.pdf | Using Integer Linear Programming in Concept-to-Text Generation to Produce More Compact Texts | null | ['Ion Androutsopoulos', 'Gerasimos Lampouras'] | 2013-08-01 | null | null | null | acl-2013-8 | ['concept-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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01947d6e-33e0-4950-8b66-95f67f4b77bd | evaluation-of-post-processing-algorithms-for | 1906.06909 | null | https://arxiv.org/abs/1906.06909v2 | https://arxiv.org/pdf/1906.06909v2.pdf | Evaluation of post-processing algorithms for polyphonic sound event detection | Sound event detection (SED) aims at identifying audio events (audio tagging task) in recordings and then locating them temporally (localization task). This last task ends with the segmentation of the frame-level class predictions, that determines the onsets and offsets of the audio events. Yet, this step is often overlooked in scientific publications. In this paper, we focus on the post-processing algorithms used to identify the audio event boundaries. Different post-processing steps are investigated, through smoothing, thresholding, and optimization. In particular, we evaluate different approaches for temporal segmentation, namely statistic-based and parametric methods. Experiments are carried out on the DCASE 2018 challenge task 4 data. We compared post-processing algorithms on the temporal prediction curves of two models: one based on the challenge's baseline and a Multiple Instance Learning (MIL) model. Results show the crucial impact of the post-processing methods on the final detection score. Statistic-based methods yield a 22.9% event-based F-score on the evaluation set with our MIL model. Moreover, the best results were obtained using class-dependent parametric methods with 32.0% F-score. | ['Thomas Pellegrini', 'Leo Cances', 'Patrice Guyot'] | 2019-06-17 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.94576585e-01 -1.97536200e-01 2.01548189e-01 -1.08842343e-01
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13c45be3-b08e-4f8b-920e-3ac9689a9870 | audio-driven-talking-face-video-generation | 2002.10137 | null | https://arxiv.org/abs/2002.10137v2 | https://arxiv.org/pdf/2002.10137v2.pdf | Audio-driven Talking Face Video Generation with Learning-based Personalized Head Pose | Real-world talking faces often accompany with natural head movement. However, most existing talking face video generation methods only consider facial animation with fixed head pose. In this paper, we address this problem by proposing a deep neural network model that takes an audio signal A of a source person and a very short video V of a target person as input, and outputs a synthesized high-quality talking face video with personalized head pose (making use of the visual information in V), expression and lip synchronization (by considering both A and V). The most challenging issue in our work is that natural poses often cause in-plane and out-of-plane head rotations, which makes synthesized talking face video far from realistic. To address this challenge, we reconstruct 3D face animation and re-render it into synthesized frames. To fine tune these frames into realistic ones with smooth background transition, we propose a novel memory-augmented GAN module. By first training a general mapping based on a publicly available dataset and fine-tuning the mapping using the input short video of target person, we develop an effective strategy that only requires a small number of frames (about 300 frames) to learn personalized talking behavior including head pose. Extensive experiments and two user studies show that our method can generate high-quality (i.e., personalized head movements, expressions and good lip synchronization) talking face videos, which are naturally looking with more distinguishing head movement effects than the state-of-the-art methods. | ['Yong-Jin Liu', 'Hujun Bao', 'Ran Yi', 'Juyong Zhang', 'Zipeng Ye'] | 2020-02-24 | null | null | null | null | ['3d-face-animation'] | ['computer-vision'] | [ 3.47104226e-03 1.49182513e-01 1.18521973e-01 -4.97891307e-01
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70f7d2dd-df6c-45db-9728-b8f400b23b06 | city-scale-scene-change-detection-using-point | 2103.14314 | null | https://arxiv.org/abs/2103.14314v1 | https://arxiv.org/pdf/2103.14314v1.pdf | City-scale Scene Change Detection using Point Clouds | We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times. We first generate 3D point clouds for each traversal from the images and approximate GNSS/INS readings using Structure-from-Motion (SfM). A direct comparison of the two point clouds for change detection is not ideal due to inaccurate geo-location information and possible drifts in the SfM. To circumvent this problem, we propose a deep learning-based non-rigid registration on the point clouds which allows us to compare the point clouds for structural change detection in the scene. Furthermore, we introduce a dual thresholding check and post-processing step to enhance the robustness of our method. We collect two datasets for the evaluation of our approach. Experiments show that our method is able to detect scene changes effectively, even in the presence of viewpoint and illumination differences. | ['Gim Hee Lee', 'Zi Jian Yew'] | 2021-03-26 | null | null | null | null | ['scene-change-detection'] | ['computer-vision'] | [-1.04359994e-02 -5.07642031e-01 3.97488654e-01 -5.12394369e-01
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8e6952b2-93cb-45e6-91fa-587b9faaf84b | at-the-crossroads-of-epidemiology-and-biology | 2301.12975 | null | https://arxiv.org/abs/2301.12975v1 | https://arxiv.org/pdf/2301.12975v1.pdf | At the crossroads of epidemiology and biology: bridging the gap between SARS-CoV-2 viral strain properties and epidemic wave characteristics | The COVID-19 pandemic has given rise to numerous articles from different scientific fields (epidemiology, virology, immunology, airflow physics...) without any effort to link these different insights. In this review, we aim to establish relationships between epidemiological data and the characteristics of the virus strain responsible for the epidemic wave concerned. We have carried out this study on the Wuhan, Alpha, Delta and Omicron strains allowing us to illustrate the evolution of the relationships we have highlighted according to these different viral strains. We addressed the following questions: 1) How can the mean infectious dose (one quantum, by definition in epidemiology) be measured and expressed as an amount of viral RNA molecules (in genome units, GU) or as a number of replicative viral particles (in plaque-forming units, PFU)? 2) How many infectious quanta are exhaled by an infected person per unit of time? 3) How many infectious quanta are exhaled, on average, integrated over the whole contagious period? 4) How do these quantities relate to the epidemic reproduction rate R as measured in epidemiology, and to the viral load, as measured by molecular biological methods? 5) How has the infectious dose evolved with the different strains of SARS-CoV-2? We make use of state-of-the-art modelling, reviewed and explained in the appendix of the article (Supplemental Information, SI), to answer these questions using data from the literature in both epidemiology and virology. We have considered the modification of these relationships according to the vaccination status of the population. We hope that this work will allow a better integration of data from different fields (virology, epidemiology, and immunology) to anticipate the evolution of the epidemic in the case of COVID-19, but also in respiratory pathologies transmissible in an airborne manner. | ['Bruno Andreotti', 'Jacques Haiech', 'Alice Lebreton', 'Florian Poydenot'] | 2023-01-30 | null | null | null | null | ['epidemiology', 'virology'] | ['medical', 'miscellaneous'] | [ 2.37873301e-01 -4.21697557e-01 3.86268735e-01 3.95707339e-01
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385193be-db67-48b0-8a99-f2801fe78d16 | retrieving-signals-with-deep-complex | null | null | https://openreview.net/forum?id=H1x22Xn5Ur | https://openreview.net/pdf?id=H1x22Xn5Ur | Retrieving Signals with Deep Complex Extractors | Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations. Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain. As a case study, we perform audio source separation in the Fourier domain. Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms. Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method. We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram. Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss. | ['Christopher J Pal', 'Negar Rostamzadeh', 'Jonathan Binas', 'Mirco Ravanelli', 'Ying Zhang', 'Ousmane Dia', 'Olexa Bilaniuk', 'Chiheb Trabelsi'] | 2019-09-14 | null | null | null | neurips-workshop-deep-invers-2019-12 | ['audio-source-separation'] | ['audio'] | [ 6.09352231e-01 -2.79826134e-01 2.82114625e-01 -2.64160693e-01
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0b9f6197-168b-4ee6-9c0f-1e0f779dc5b3 | a-unified-multimodal-de-and-re-coupling | 2211.09146 | null | https://arxiv.org/abs/2211.09146v2 | https://arxiv.org/pdf/2211.09146v2.pdf | A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion Recognition | Motion recognition is a promising direction in computer vision, but the training of video classification models is much harder than images due to insufficient data and considerable parameters. To get around this, some works strive to explore multimodal cues from RGB-D data. Although improving motion recognition to some extent, these methods still face sub-optimal situations in the following aspects: (i) Data augmentation, i.e., the scale of the RGB-D datasets is still limited, and few efforts have been made to explore novel data augmentation strategies for videos; (ii) Optimization mechanism, i.e., the tightly space-time-entangled network structure brings more challenges to spatiotemporal information modeling; And (iii) cross-modal knowledge fusion, i.e., the high similarity between multimodal representations caused to insufficient late fusion. To alleviate these drawbacks, we propose to improve RGB-D-based motion recognition both from data and algorithm perspectives in this paper. In more detail, firstly, we introduce a novel video data augmentation method dubbed ShuffleMix, which acts as a supplement to MixUp, to provide additional temporal regularization for motion recognition. Secondly, a Unified Multimodal De-coupling and multi-stage Re-coupling framework, termed UMDR, is proposed for video representation learning. Finally, a novel cross-modal Complement Feature Catcher (CFCer) is explored to mine potential commonalities features in multimodal information as the auxiliary fusion stream, to improve the late fusion results. The seamless combination of these novel designs forms a robust spatiotemporal representation and achieves better performance than state-of-the-art methods on four public motion datasets. Specifically, UMDR achieves unprecedented improvements of +4.5% on the Chalearn IsoGD dataset. Our code is available at https://github.com/zhoubenjia/MotionRGBD-PAMI. | ['Fan Wang', 'Yanyan Liang', 'Jun Wan', 'Pichao Wang', 'Benjia Zhou'] | 2022-11-16 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 9.46251526e-02 -6.04318082e-01 -5.61092734e-01 -3.25202420e-02
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9c427187-eda9-4bb4-8d43-57bf1a594233 | steex-steering-counterfactual-explanations | 2111.09094 | null | https://arxiv.org/abs/2111.09094v3 | https://arxiv.org/pdf/2111.09094v3.pdf | STEEX: Steering Counterfactual Explanations with Semantics | As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decision mechanisms of a trained classification model. In this work, we address the problem of producing counterfactual explanations for high-quality images and complex scenes. Leveraging recent semantic-to-image models, we propose a new generative counterfactual explanation framework that produces plausible and sparse modifications which preserve the overall scene structure. Furthermore, we introduce the concept of "region-targeted counterfactual explanations", and a corresponding framework, where users can guide the generation of counterfactuals by specifying a set of semantic regions of the query image the explanation must be about. Extensive experiments are conducted on challenging datasets including high-quality portraits (CelebAMask-HQ) and driving scenes (BDD100k). Code is available at https://github.com/valeoai/STEEX | ['Matthieu Cord', 'Patrick Pérez', 'Mickaël Chen', 'Hédi Ben-Younes', 'Éloi Zablocki', 'Paul Jacob'] | 2021-11-17 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.24602240e-01 6.54116392e-01 -3.87349755e-01 -5.97812772e-01
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b688fc4f-9f7a-4cf4-ab0b-c398561b6016 | heuristic-search-for-structural-constraints | 1711.02823 | null | http://arxiv.org/abs/1711.02823v1 | http://arxiv.org/pdf/1711.02823v1.pdf | Heuristic Search for Structural Constraints in Data Association | The research on multi-object tracking (MOT) is essentially to solve for the
data association assignment, the core of which is to design the association
cost as discriminative as possible. Generally speaking, the match ambiguities
caused by similar appearances of objects and the moving cameras make the data
association perplexing and challenging. In this paper, we propose a new
heuristic method to search for structural constraints (HSSC) of multiple
targets when solving the problem of online multi-object tracking. We believe
that the internal structure among multiple targets in the adjacent frames could
remain constant and stable even though the video sequences are captured by a
moving camera. As a result, the structural constraints are able to cut down the
match ambiguities caused by the moving cameras as well as similar appearances
of the tracked objects. The proposed heuristic method aims to obtain a maximum
match set under the minimum structural cost for each available match pair,
which can be integrated with the raw association costs and make them more
elaborate and discriminative compared with other approaches. In addition, this
paper presents a new method to recover missing targets by minimizing the cost
function generated from both motion and structure cues. Our online multi-object
tracking (MOT) algorithm based on HSSC has achieved the multi-object tracking
accuracy (MOTA) of 25.0 on the public dataset 2DMOT2015[1]. | ['Xiao Zhou', 'Fei Wang', 'Peilin Jiang'] | 2017-11-08 | null | null | null | null | ['online-multi-object-tracking'] | ['computer-vision'] | [-5.78136109e-02 -5.79025269e-01 -8.50367546e-02 -1.44231677e-01
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2e9b23ab-bb89-45cf-9563-bb4c8931b89e | when-does-aggregating-multiple-skills-with | 2305.14007 | null | https://arxiv.org/abs/2305.14007v1 | https://arxiv.org/pdf/2305.14007v1.pdf | When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLP | Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work -- sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by algorithmic tricks. However, what tasks and skills should be included is less well explored. In this work, we conduct a case study in Financial NLP where multiple datasets exist for skills relevant to the domain, such as numeric reasoning and sentiment analysis. Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work. Our findings suggest that the key to MTL success lies in skill diversity, relatedness between tasks, and choice of aggregation size and shared capacity. Specifically, MTL works well when tasks are diverse but related, and when the size of the task aggregation and the shared capacity of the model are balanced to avoid overwhelming certain tasks. | ['Markus Leippold', 'Mrinmaya Sachan', 'Qian Wang', 'Zhijing Jin', 'Jingwei Ni'] | 2023-05-23 | null | null | null | null | ['sentiment-analysis', 'open-question'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.81994495e-02 -2.53056102e-02 -1.38905019e-01 -2.01216325e-01
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1.65406585e-01 -9.47200656e-01 -4.31008011e-01 -2.07617030e-01] | [9.919711112976074, 7.477308750152588] |
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