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c6a6d4ea-ce15-4ea4-aad1-a18e0a5a985c | link-prediction-on-latent-heterogeneous | 2302.10432 | null | https://arxiv.org/abs/2302.10432v1 | https://arxiv.org/pdf/2302.10432v1.pdf | Link Prediction on Latent Heterogeneous Graphs | On graph data, the multitude of node or edge types gives rise to heterogeneous information networks (HINs). To preserve the heterogeneous semantics on HINs, the rich node/edge types become a cornerstone of HIN representation learning. However, in real-world scenarios, type information is often noisy, missing or inaccessible. Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed. In this paper, we study the challenging and unexplored problem of link prediction on an LHG. As existing approaches depend heavily on type-based information, they are suboptimal or even inapplicable on LHGs. To address the absence of type information, we propose a model named LHGNN, based on the novel idea of semantic embedding at node and path levels, to capture latent seman- tics on and between nodes. We further design a personalization function to modulate the heterogeneous contexts conditioned on their latent semantics w.r.t. the target node, to enable finer-grained aggregation. Finally, we conduct extensive experiments on four benchmark datasets, and demonstrate the superior performance of LHGNN. | ['Yuan Fang', 'Zemin Liu', 'Trung-Kien Nguyen'] | 2023-02-21 | null | null | null | null | ['type'] | ['speech'] | [ 7.95977041e-02 2.21479133e-01 -7.06954837e-01 -2.45955110e-01
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f4ddcb2e-2d04-445b-bd5d-7a48e5102761 | tristereonet-a-trinocular-framework-for-multi | 2111.12502 | null | https://arxiv.org/abs/2111.12502v2 | https://arxiv.org/pdf/2111.12502v2.pdf | TriStereoNet: A Trinocular Framework for Multi-baseline Disparity Estimation | Stereo vision is an effective technique for depth estimation with broad applicability in autonomous urban and highway driving. While various deep learning-based approaches have been developed for stereo, the input data from a binocular setup with a fixed baseline are limited. Addressing such a problem, we present an end-to-end network for processing the data from a trinocular setup, which is a combination of a narrow and a wide stereo pair. In this design, two pairs of binocular data with a common reference image are treated with shared weights of the network and a mid-level fusion. We also propose a Guided Addition method for merging the 4D data of the two baselines. Additionally, an iterative sequential self-supervised and supervised learning on real and synthetic datasets is presented, making the training of the trinocular system practical with no need to ground-truth data of the real dataset. Experimental results demonstrate that the trinocular disparity network surpasses the scenario where individual pairs are fed into a similar architecture. Code and dataset: https://github.com/cogsys-tuebingen/tristereonet. | ['Andreas Zell', 'Faranak Shamsafar'] | 2021-11-24 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [-3.40269208e-02 4.83655743e-02 1.97937250e-01 -7.72581398e-01
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068dc885-eced-44fd-9512-00f6250328d2 | water-surface-patch-classification-using | 2207.06388 | null | https://arxiv.org/abs/2207.06388v4 | https://arxiv.org/pdf/2207.06388v4.pdf | River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index | Urban rivers provide a water environment that influences residential living. River surface monitoring has become crucial for making decisions about where to prioritize cleaning and when to automatically start the cleaning treatment. We focus on the organic mud, or "scum", that accumulates on the river's surface and contributes to the river's odor and has external economic effects on the landscape. Because of its feature of a sparsely distributed and unstable pattern of organic shape, automating the monitoring process has proved difficult. We propose a patch-wise classification pipeline to detect scum features on the river surface using mixture image augmentation to increase the diversity between the scum floating on the river and the entangled background on the river surface reflected by nearby structures like buildings, bridges, poles, and barriers. Furthermore, we propose a scum-index cover on rivers to help monitor worse grade online, collect floating scum, and decide on chemical treatment policies. Finally, we demonstrate the application of our method on a time series dataset with frames every ten minutes recording river scum events over several days. We discuss the significance of our pipeline and its experimental findings. | ['Masazumi Amakata', 'Junichiro Fujii', 'Takato Yasuno'] | 2022-07-13 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.28634888e-01 -1.18616097e-01 3.57010841e-01 3.86275053e-02
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cd79c3a7-cb34-463d-8cbf-0a2de127903a | raw-image-deblurring | 2012.04264 | null | https://arxiv.org/abs/2012.04264v1 | https://arxiv.org/pdf/2012.04264v1.pdf | Raw Image Deblurring | Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results. For this work, in a new aspect, we discover the great opportunity for image enhancement (e.g., deblurring) directly from RAW images and investigate novel neural network structures benefiting RAW-based learning. However, to the best of our knowledge, there is no available RAW image deblurring dataset. Therefore, we built a new dataset containing both RAW images and processed sRGB images and design a new model to utilize the unique characteristics of RAW images. The proposed deblurring model, trained solely from RAW images, achieves the state-of-art performance and outweighs those trained on processed sRGB images. Furthermore, with fine-tuning, the proposed model, trained on our new dataset, can generalize to other sensors. Additionally, by a series of experiments, we demonstrate that existing deblurring models can also be improved by training on the RAW images in our new dataset. Ultimately, we show a new venue for further opportunities based on the devised novel raw-based deblurring method and the brand-new Deblur-RAW dataset. | ['Winston H. Hsu', 'Yueh-Cheng Liu', 'Yu-An Chen', 'Chih-Hung Liang'] | 2020-12-08 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 2.25742087e-01 -5.84644377e-01 1.11339971e-01 -1.68788821e-01
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a43d6b10-7b29-47a2-90e7-274b11dcd77a | feature-adjacent-multi-fidelity-physics | 2303.11577 | null | https://arxiv.org/abs/2303.11577v3 | https://arxiv.org/pdf/2303.11577v3.pdf | Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations | Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate. To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architecture which is based on a feature space shared by the low- and high-fidelity solutions. In the feature space, the projections of the low-fidelity and high-fidelity solutions are adjacent by constraining their relative distance. The feature space is represented with an encoder and its mapping to the original solution space is effected through a decoder. The proposed multi-fidelity approach is validated on forward and inverse problems for steady and unsteady problems described by partial differential equations. | ['Panos Stinis', 'Wenqian Chen'] | 2023-03-21 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.19006895e-01 -1.67785630e-01 3.95883918e-01 -3.36628884e-01
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3fd563dc-0712-48af-930c-cc2f905a603e | booster-shot-boosting-stacked-homography | 2208.09211 | null | https://arxiv.org/abs/2208.09211v1 | https://arxiv.org/pdf/2208.09211v1.pdf | Booster-SHOT: Boosting Stacked Homography Transformations for Multiview Pedestrian Detection with Attention | Improving multi-view aggregation is integral for multi-view pedestrian detection, which aims to obtain a bird's-eye-view pedestrian occupancy map from images captured through a set of calibrated cameras. Inspired by the success of attention modules for deep neural networks, we first propose a Homography Attention Module (HAM) which is shown to boost the performance of existing end-to-end multiview detection approaches by utilizing a novel channel gate and spatial gate. Additionally, we propose Booster-SHOT, an end-to-end convolutional approach to multiview pedestrian detection incorporating our proposed HAM as well as elements from previous approaches such as view-coherent augmentation or stacked homography transformations. Booster-SHOT achieves 92.9% and 94.2% for MODA on Wildtrack and MultiviewX respectively, outperforming the state-of-the-art by 1.4% on Wildtrack and 0.5% on MultiviewX, achieving state-of-the-art performance overall for standard evaluation metrics used in multi-view pedestrian detection. | ['Tae-hoon Kim', 'Philipp Benz', 'Jinwoo Hwang'] | 2022-08-19 | null | null | null | null | ['multiview-detection'] | ['computer-vision'] | [-2.89841831e-01 -2.17768312e-01 2.40009695e-01 -4.25517052e-01
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8e0ebcaa-bba6-42af-8a05-0a7172980c3e | training-effective-neural-sentence-encoders | 2207.12759 | null | https://arxiv.org/abs/2207.12759v1 | https://arxiv.org/pdf/2207.12759v1.pdf | Training Effective Neural Sentence Encoders from Automatically Mined Paraphrases | Sentence embeddings are commonly used in text clustering and semantic retrieval tasks. State-of-the-art sentence representation methods are based on artificial neural networks fine-tuned on large collections of manually labeled sentence pairs. Sufficient amount of annotated data is available for high-resource languages such as English or Chinese. In less popular languages, multilingual models have to be used, which offer lower performance. In this publication, we address this problem by proposing a method for training effective language-specific sentence encoders without manually labeled data. Our approach is to automatically construct a dataset of paraphrase pairs from sentence-aligned bilingual text corpora. We then use the collected data to fine-tune a Transformer language model with an additional recurrent pooling layer. Our sentence encoder can be trained in less than a day on a single graphics card, achieving high performance on a diverse set of sentence-level tasks. We evaluate our method on eight linguistic tasks in Polish, comparing it with the best available multilingual sentence encoders. | ['Sławomir Dadas'] | 2022-07-26 | null | null | null | null | ['text-clustering', 'semantic-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.46253809e-01 -2.14872703e-01 -7.53206015e-02 -6.74120843e-01
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181654a3-16f2-423c-be28-c080ceb274ef | sectioning-of-biomedical-abstracts-a-sequence | 2201.07112 | null | https://arxiv.org/abs/2201.07112v1 | https://arxiv.org/pdf/2201.07112v1.pdf | Sectioning of Biomedical Abstracts: A Sequence of Sequence Classification Task | Rapid growth of the biomedical literature has led to many advances in the biomedical text mining field. Among the vast amount of information, biomedical article abstracts are the easily accessible sources. However, the number of the structured abstracts, describing the rhetorical sections with one of Background, Objective, Method, Result and Conclusion categories is still not considerable. Exploration of valuable information in the biomedical abstracts can be expedited with the improvements in the sequential sentence classification task. Deep learning based models has great performance/potential in achieving significant results in this task. However, they can often be overly complex and overfit to specific data. In this project, we study a state-of-the-art deep learning model, which we called SSN-4 model here. We investigate different components of the SSN-4 model to study the trade-off between the performance and complexity. We explore how well this model generalizes to a new data set beyond Randomized Controlled Trials (RCT) dataset. We address the question that whether word embeddings can be adjusted to the task to improve the performance. Furthermore, we develop a second model that addresses the confusion pairs in the first model. Results show that SSN-4 model does not appear to generalize well beyond RCT dataset. | ['K. Vijay-Shanker', 'Mehmet Efruz Karabulut'] | 2022-01-18 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 1.03088900e-01 2.39024043e-01 -2.53534913e-01 -4.14435655e-01
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c1726ac2-78f8-40d7-bd73-c67d2ca9b801 | fasterrcnn-monitoring-of-road-damages | 2010.11780 | null | https://arxiv.org/abs/2010.11780v1 | https://arxiv.org/pdf/2010.11780v1.pdf | FasterRCNN Monitoring of Road Damages: Competition and Deployment | Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world. An important prerequisite for efficient infrastructure maintenance is to continuously monitor (i.e., quantify the level of safety and reliability) the state of very large structures. Meanwhile, computer vision has made impressive strides in recent years, mainly due to successful applications of deep learning models. These novel progresses are allowing the automation of vision tasks, which were previously impossible to automate, offering promising possibilities to assist administrators in optimizing their infrastructure maintenance operations. In this context, the IEEE 2020 global Road Damage Detection (RDD) Challenge is giving an opportunity for deep learning and computer vision researchers to get involved and help accurately track pavement damages on road networks. This paper proposes two contributions to that topic: In a first part, we detail our solution to the RDD Challenge. In a second part, we present our efforts in deploying our model on a local road network, explaining the proposed methodology and encountered challenges. | ['Yasuo Ariki', 'Tetsuya Takiguchi', 'Ryoichi Takashima', 'Persch Andreas', 'Yihao Zhang', 'Hascoet Tristan'] | 2020-10-22 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [-1.52754232e-01 6.16249405e-02 -5.38323261e-02 -9.93013531e-02
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9a6b1814-c445-46fb-bf4a-3d5ca3882ff2 | white-box-inference-attacks-against | 2301.03595 | null | https://arxiv.org/abs/2301.03595v1 | https://arxiv.org/pdf/2301.03595v1.pdf | White-box Inference Attacks against Centralized Machine Learning and Federated Learning | With the development of information science and technology, various industries have generated massive amounts of data, and machine learning is widely used in the analysis of big data. However, if the privacy of machine learning applications' customers cannot be guaranteed, it will cause security threats and losses to users' personal privacy information and service providers. Therefore, the issue of privacy protection of machine learning has received wide attention. For centralized machine learning models, we evaluate the impact of different neural network layers, gradient, gradient norm, and fine-tuned models on member inference attack performance with prior knowledge; For the federated learning model, we discuss the location of the attacker in the target model and its attack mode. The results show that the centralized machine learning model shows more serious member information leakage in all aspects, and the accuracy of the attacker in the central parameter server is significantly higher than the local Inference attacks as participants. | ['Jingyi Ge'] | 2022-12-15 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [-3.79498005e-01 9.29263458e-02 -5.31535111e-02 -2.69049108e-01
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3fb2f392-6d72-47d9-8ed5-30d6cf23d471 | human-perception-modeling-for-automatic | 2103.17020 | null | https://arxiv.org/abs/2103.17020v3 | https://arxiv.org/pdf/2103.17020v3.pdf | Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention | Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as extra input, while the performance of automatic trimap generation method of dilating foreground segmentation fluctuates with segmentation quality. Therefore, we argue that how to handle trade-off of additional information input is a major issue in automatic matting. This paper presents a semantic-guided automatic natural image matting pipeline with Trimap Generation Network and light-weight non-local attention, which does not need trimap and background as input. Specifically, guided by foreground segmentation, Trimap Generation Network estimates accurate trimap. Then, with estimated trimap as guidance, our light-weight Non-local Matting Network with Refinement produces final alpha matte, whose trimap-guided global aggregation attention block is equipped with stride downsampling convolution, reducing computation complexity and promoting performance. Experimental results show that our matting algorithm has competitive performance with state-of-the-art methods in both trimap-free and trimap-needed aspects. | ['Yangsheng Xu', 'Tin Lun Lam', 'Liguang Zhou', 'Yuhongze Zhou'] | 2021-03-31 | null | null | null | null | ['foreground-segmentation'] | ['computer-vision'] | [ 4.89779085e-01 2.13964172e-02 1.00620754e-01 -2.74683654e-01
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7db573e6-6e1a-4dff-8eb4-4a72f8fc1b59 | online-target-speaker-voice-activity | 2207.05920 | null | https://arxiv.org/abs/2207.05920v1 | https://arxiv.org/pdf/2207.05920v1.pdf | Online Target Speaker Voice Activity Detection for Speaker Diarization | This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. First, we employ a ResNet-based front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. We iteratively extract the results for each block and update the target speaker embedding until reaching the end of the signal. Experimental results show that the proposed method is better than the offline clustering-based diarization system on the AliMeeting dataset. | ['Ming Li', 'Qingjian Lin', 'Weiqing Wang'] | 2022-07-13 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-7.73251131e-02 4.43258742e-03 -2.56519541e-02 -5.87849021e-01
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49a325bf-baa3-46b4-8d5f-a8581c3a3142 | domain-generalization-through-audio-visual | 2110.10101 | null | https://arxiv.org/abs/2110.10101v1 | https://arxiv.org/pdf/2110.10101v1.pdf | Domain Generalization through Audio-Visual Relative Norm Alignment in First Person Action Recognition | First person action recognition is becoming an increasingly researched area thanks to the rising popularity of wearable cameras. This is bringing to light cross-domain issues that are yet to be addressed in this context. Indeed, the information extracted from learned representations suffers from an intrinsic "environmental bias". This strongly affects the ability to generalize to unseen scenarios, limiting the application of current methods to real settings where labeled data are not available during training. In this work, we introduce the first domain generalization approach for egocentric activity recognition, by proposing a new audio-visual loss, called Relative Norm Alignment loss. It re-balances the contributions from the two modalities during training, over different domains, by aligning their feature norm representations. Our approach leads to strong results in domain generalization on both EPIC-Kitchens-55 and EPIC-Kitchens-100, as demonstrated by extensive experiments, and can be extended to work also on domain adaptation settings with competitive results. | ['Barbara Caputo', 'Emanuele Alberti', 'Chiara Plizzari', 'Mirco Planamente'] | 2021-10-19 | null | null | null | null | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 5.02493382e-01 -5.04076891e-02 -2.32850209e-01 -3.89216572e-01
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6a82b23f-ce78-418b-bcbd-5189bf0a83fa | eclipse-disambiguating-illumination-and | 2305.16321 | null | https://arxiv.org/abs/2305.16321v2 | https://arxiv.org/pdf/2305.16321v2.pdf | Eclipse: Disambiguating Illumination and Materials using Unintended Shadows | Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult, even when the object's 3D shape is known beforehand. This problem is ill-conditioned because diffuse materials severely blur incoming light, and is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination -- even from diffuse objects -- by exploiting unintended shadows, like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines, but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it. | ['Pratul P. Srinivasan', 'Todd Zickler', 'Jonathan T. Barron', 'Peter Hedman', 'Ben Mildenhall', 'Dor Verbin'] | 2023-05-25 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 9.13351774e-01 -1.99738935e-01 8.95736933e-01 -1.81262374e-01
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6190ee23-5520-42ad-833c-4a30f18251df | building-hierarchically-disentangled-language | null | null | https://aclanthology.org/2020.coling-main.3 | https://aclanthology.org/2020.coling-main.3.pdf | Building Hierarchically Disentangled Language Models for Text Generation with Named Entities | Named entities pose a unique challenge to traditional methods of language modeling. While several domains are characterised with a high proportion of named entities, the occurrence of specific entities varies widely. Cooking recipes, for example, contain a lot of named entities {---} viz. ingredients, cooking techniques (also called processes), and utensils. However, some ingredients occur frequently within the instructions while most occur rarely. In this paper, we build upon the previous work done on language models developed for text with named entities by introducing a Hierarchically Disentangled Model. Training is divided into multiple branches with each branch producing a model with overlapping subsets of vocabulary. We found the existing datasets insufficient to accurately judge the performance of the model. Hence, we have curated 158,473 cooking recipes from several publicly available online sources. To reliably derive the entities within this corpus, we employ a combination of Named Entity Recognition (NER) as well as an unsupervised method of interpretation using dependency parsing and POS tagging, followed by a further cleaning of the dataset. This unsupervised interpretation models instructions as action graphs and is specific to the corpus of cooking recipes, unlike NER which is a general method applicable to all corpora. To delve into the utility of our language model, we apply it to tasks such as graph-to-text generation and ingredients-to-recipe generation, comparing it to previous state-of-the-art baselines. We make our dataset (including annotations and processed action graphs) available for use, considering their potential use cases for language modeling and text generation research. | ['Ganesh Bagler', 'Devansh Batra', 'Yash Agarwal'] | 2020-12-01 | null | null | null | coling-2020-8 | ['recipe-generation'] | ['miscellaneous'] | [ 3.96598041e-01 5.28219342e-01 -2.51495630e-01 -3.85908604e-01
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0ce9ffe7-3539-4e2e-8501-5230e39d8b6b | joint-discriminative-and-metric-embedding | 2212.14107 | null | https://arxiv.org/abs/2212.14107v1 | https://arxiv.org/pdf/2212.14107v1.pdf | Joint Discriminative and Metric Embedding Learning for Person Re-Identification | Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representations invariant to nuisance factors, by training them with losses that minimize intra-class variance and maximize inter-class separation, without modeling nuisance factors explicitly. The dominant approaches use either a discriminative loss with margin, like the softmax loss with the additive angular margin, or a metric learning loss, like the triplet loss with batch hard mining of triplets. Since the softmax imposes feature normalization, it limits the gradient flow supervising the feature embedding. We address this by joining the losses and leveraging the triplet loss as a proxy for the missing gradients. We further improve invariance to nuisance factors by adding the discriminative task of predicting attributes. Our extensive evaluation highlights that when only a holistic representation is learned, we consistently outperform the state-of-the-art on the three most challenging datasets. Such representations are easier to deploy in practical systems. Finally, we found that joining the losses removes the requirement for having a margin in the softmax loss while increasing performance. | ['Gianfranco Doretto', 'Zaigham Randhawa', 'Sinan Sabri'] | 2022-12-28 | null | null | null | null | ['person-re-identification', 'metric-learning', 'metric-learning'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.74440458e-01 -2.33027205e-01 -8.28744397e-02 -7.55541503e-01
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57bd5dd4-5d90-4e08-8749-e40901ca7599 | single-image-haze-removal-using-conditional | 1903.00395 | null | http://arxiv.org/abs/1903.00395v1 | http://arxiv.org/pdf/1903.00395v1.pdf | Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks | We present a method to restore a clear image from a haze-affected image using
a Wasserstein generative adversarial network. As the problem is
ill-conditioned, previous methods have required a prior on natural images or
multiple images of the same scene. We train a generative adversarial network to
learn the probability distribution of clear images conditioned on the
haze-affected images using the Wasserstein loss function, using a gradient
penalty to enforce the Lipschitz constraint. The method is data-adaptive,
end-to-end, and requires no further processing or tuning of parameters. We also
incorporate the use of a texture-based loss metric and the L1 loss to improve
results, and show that our results are better than the current
state-of-the-art. | ['Joshua Peter Ebenezer', 'Bijaylaxmi Das', 'Sudipta Mukhopadhyay'] | 2019-03-01 | null | null | null | null | ['single-image-haze-removal'] | ['computer-vision'] | [ 4.84476030e-01 1.21835239e-01 6.35285079e-01 -5.89650452e-01
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c7f9531c-9c0a-4ee2-a16b-d2689448e7d1 | fashionformer-a-simple-effective-and-unified | 2204.04654 | null | https://arxiv.org/abs/2204.04654v2 | https://arxiv.org/pdf/2204.04654v2.pdf | Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition | Human fashion understanding is one crucial computer vision task since it has comprehensive information for real-world applications. This focus on joint human fashion segmentation and attribute recognition. Contrary to the previous works that separately model each task as a multi-head prediction problem, our insight is to bridge these two tasks with one unified model via vision transformer modeling to benefit each task. In particular, we introduce the object query for segmentation and the attribute query for attribute prediction. Both queries and their corresponding features can be linked via mask prediction. Then we adopt a two-stream query learning framework to learn the decoupled query representations.We design a novel Multi-Layer Rendering module for attribute stream to explore more fine-grained features. The decoder design shares the same spirit as DETR. Thus we name the proposed method \textit{Fahsionformer}. Extensive experiments on three human fashion datasets illustrate the effectiveness of our approach. In particular, our method with the same backbone achieve \textbf{relative 10\% improvements} than previous works in case of \textit{a joint metric (AP$^{\text{mask}}_{\text{IoU+F}_1}$) for both segmentation and attribute recognition}. To the best of our knowledge, we are the first unified end-to-end vision transformer framework for human fashion analysis. We hope this simple yet effective method can serve as a new flexible baseline for fashion analysis. Code is available at https://github.com/xushilin1/FashionFormer. | ['DaCheng Tao', 'Yunhai Tong', 'Guangliang Cheng', 'Jingbo Wang', 'Xiangtai Li', 'Shilin Xu'] | 2022-04-10 | null | null | null | null | ['fashion-understanding'] | ['computer-vision'] | [ 3.60730201e-01 -3.69497202e-03 -3.54145586e-01 -6.55487716e-01
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f9980f0e-1843-4a26-8143-f3013ed69565 | coarse-to-fine-contrastive-learning-in-image | 2305.13812 | null | https://arxiv.org/abs/2305.13812v1 | https://arxiv.org/pdf/2305.13812v1.pdf | Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality | Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning, leading to state-of-the-art models for various downstream multimodal tasks. However, recent research has highlighted severe limitations of these models in their ability to perform compositional reasoning over objects, attributes, and relations. Scene graphs have emerged as an effective way to understand images compositionally. These are graph-structured semantic representations of images that contain objects, their attributes, and relations with other objects in a scene. In this work, we consider the scene graph parsed from text as a proxy for the image scene graph and propose a graph decomposition and augmentation framework along with a coarse-to-fine contrastive learning objective between images and text that aligns sentences of various complexities to the same image. Along with this, we propose novel negative mining techniques in the scene graph space for improving attribute binding and relation understanding. Through extensive experiments, we demonstrate the effectiveness of our approach that significantly improves attribute binding, relation understanding, systematic generalization, and productivity on multiple recently proposed benchmarks (For example, improvements upto $18\%$ for systematic generalization, $16.5\%$ for relation understanding over a strong baseline), while achieving similar or better performance than CLIP on various general multimodal tasks. | ['Yu Chen', 'Jingfei Du', 'Wenhan Xiong', 'Mengjiao Wang', 'Qifan Wang', 'Pengchuan Zhang', 'Harman Singh'] | 2023-05-23 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 7.24827468e-01 2.79080003e-01 -3.69499803e-01 -7.40817070e-01
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c2e0a517-e15e-431f-a08f-8923d950070f | generative-diffeomorphic-modelling-of-large | null | null | https://doi.org/10.1016/j.neuroimage.2017.10.060 | http://discovery.ucl.ac.uk/10036377/1/Ashburner_1-s2.0-S1053811917308947-main.pdf | Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction | In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies. | ['Claudia Blaiotta', 'John Ashburner', 'Patrick Freund', 'M. Jorge Cardoso'] | 2018-02-01 | null | null | null | neuroimage-2018-2 | ['diffeomorphic-medical-image-registration'] | ['medical'] | [ 6.17211223e-01 1.94905981e-01 2.41619125e-01 -4.34997827e-01
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3fb61e4c-b66f-4e2f-8e21-1813953f56f7 | few-shot-personalized-saliency-prediction | 2307.02799 | null | https://arxiv.org/abs/2307.02799v1 | https://arxiv.org/pdf/2307.02799v1.pdf | Few-Shot Personalized Saliency Prediction Using Tensor Regression for Preserving Structural Global Information | This paper presents a few-shot personalized saliency prediction using tensor-to-matrix regression for preserving the structural global information of personalized saliency maps (PSMs). In contrast to a general saliency map, a PSM has been great potential since its map indicates the person-specific visual attention that is useful for obtaining individual visual preferences from heterogeneity of gazed areas. The PSM prediction is needed for acquiring the PSM for the unseen image, but its prediction is still a challenging task due to the complexity of individual gaze patterns. For recognizing individual gaze patterns from the limited amount of eye-tracking data, the previous methods adopt the similarity of gaze tendency between persons. However, in the previous methods, the PSMs are vectorized for the prediction model. In this way, the structural global information of the PSMs corresponding to the image is ignored. For automatically revealing the relationship between PSMs, we focus on the tensor-based regression model that can preserve the structural information of PSMs, and realize the improvement of the prediction accuracy. In the experimental results, we confirm the proposed method including the tensor-based regression outperforms the comparative methods. | ['Miki Haseyama', 'Takahiro Ogawa', 'Keisuke Maeda', 'Yuya Moroto'] | 2023-07-06 | null | null | null | null | ['saliency-prediction'] | ['computer-vision'] | [ 1.45603567e-01 -3.51524472e-01 -2.19013214e-01 -2.13302806e-01
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9492d4da-3ff4-4f46-92d8-0655b6ede034 | exploring-high-quality-target-domain | 2208.06100 | null | https://arxiv.org/abs/2208.06100v1 | https://arxiv.org/pdf/2208.06100v1.pdf | Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation | In unsupervised domain adaptive (UDA) semantic segmentation, the distillation based methods are currently dominant in performance. However, the distillation technique requires complicate multi-stage process and many training tricks. In this paper, we propose a simple yet effective method that can achieve competitive performance to the advanced distillation methods. Our core idea is to fully explore the target-domain information from the views of boundaries and features. First, we propose a novel mix-up strategy to generate high-quality target-domain boundaries with ground-truth labels. Different from the source-domain boundaries in previous works, we select the high-confidence target-domain areas and then paste them to the source-domain images. Such a strategy can generate the object boundaries in target domain (edge of target-domain object areas) with the correct labels. Consequently, the boundary information of target domain can be effectively captured by learning on the mixed-up samples. Second, we design a multi-level contrastive loss to improve the representation of target-domain data, including pixel-level and prototype-level contrastive learning. By combining two proposed methods, more discriminative features can be extracted and hard object boundaries can be better addressed for the target domain. The experimental results on two commonly adopted benchmarks (\textit{i.e.}, GTA5 $\rightarrow$ Cityscapes and SYNTHIA $\rightarrow$ Cityscapes) show that our method achieves competitive performance to complicated distillation methods. Notably, for the SYNTHIA$\rightarrow$ Cityscapes scenario, our method achieves the state-of-the-art performance with $57.8\%$ mIoU and $64.6\%$ mIoU on 16 classes and 13 classes. Code is available at https://github.com/ljjcoder/EHTDI. | ['Xiaoming Hu', 'Yuan Gao', 'Zilei Wang', 'Junjie Li'] | 2022-08-12 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.55915809e-01 1.53224066e-01 -2.31949702e-01 -3.20997983e-01
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1e6e1ee8-56ba-449a-9a15-a2660a8b65ae | exploring-the-power-of-romanian-bert-for | null | null | https://aclanthology.org/2020.vardial-1.22 | https://aclanthology.org/2020.vardial-1.22.pdf | Exploring the Power of Romanian BERT for Dialect Identification | Dialect identification represents a key aspect for improving a series of tasks, for example, opinion mining, considering that the location of the speaker can greatly influence the attitude towards a subject. In this work, we describe the systems developed by our team for VarDial 2020: Romanian Dialect Identification, a task specifically created for challenging participants to solve the previously mentioned issue. More specifically, we introduce a series of neural systems based on Transformers, that combine a BERT model exclusively pre-trained on the Romanian language with techniques such as adversarial training or character-level embeddings. By using these approaches, we were able to obtain a 0.6475 macro F1 score on the test dataset, thus allowing us to be ranked 5th out of 8 participant teams. | ['Traian Rebedea', 'Dumitru-Clementin Cercel', 'Andrei-Marius Avram', 'George-Eduard Zaharia'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-2.11987376e-01 1.10445797e-01 3.29598874e-01 -3.97466511e-01
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dfe6bd6e-6bd1-4d5d-a41a-1209c6bf0e8e | semantic-sentence-composition-reasoning-for | 2203.00160 | null | https://arxiv.org/abs/2203.00160v1 | https://arxiv.org/pdf/2203.00160v1.pdf | Semantic Sentence Composition Reasoning for Multi-Hop Question Answering | Due to the lack of insufficient data, existing multi-hop open domain question answering systems require to effectively find out relevant supporting facts according to each question. To alleviate the challenges of semantic factual sentences retrieval and multi-hop context expansion, we present a semantic sentence composition reasoning approach for a multi-hop question answering task, which consists of two key modules: a multi-stage semantic matching module (MSSM) and a factual sentence composition module (FSC). With the combination of factual sentences and multi-stage semantic retrieval, our approach can provide more comprehensive contextual information for model training and reasoning. Experimental results demonstrate our model is able to incorporate existing pre-trained language models and outperform the existing SOTA method on the QASC task with an improvement of about 9%. | ['Qianglong Chen'] | 2022-03-01 | null | null | null | null | ['multi-hop-question-answering', 'semantic-retrieval'] | ['knowledge-base', 'natural-language-processing'] | [ 2.85163194e-01 5.53499222e-01 -1.19756617e-01 -3.23579550e-01
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9ed81dc8-0a27-441e-a2af-7e642064da8a | unicoder-a-universal-language-encoder-by-pre | 1909.00964 | null | https://arxiv.org/abs/1909.00964v2 | https://arxiv.org/pdf/1909.00964v2.pdf | Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks | We present Unicoder, a universal language encoder that is insensitive to different languages. Given an arbitrary NLP task, a model can be trained with Unicoder using training data in one language and directly applied to inputs of the same task in other languages. Comparing to similar efforts such as Multilingual BERT and XLM, three new cross-lingual pre-training tasks are proposed, including cross-lingual word recovery, cross-lingual paraphrase classification and cross-lingual masked language model. These tasks help Unicoder learn the mappings among different languages from more perspectives. We also find that doing fine-tuning on multiple languages together can bring further improvement. Experiments are performed on two tasks: cross-lingual natural language inference (XNLI) and cross-lingual question answering (XQA), where XLM is our baseline. On XNLI, 1.8% averaged accuracy improvement (on 15 languages) is obtained. On XQA, which is a new cross-lingual dataset built by us, 5.5% averaged accuracy improvement (on French and German) is obtained. | ['Haoyang Huang', 'Yaobo Liang', 'Nan Duan', 'Ming Zhou', 'Ming Gong', 'Daxin Jiang', 'Linjun Shou'] | 2019-09-03 | unicoder-a-universal-language-encoder-by-pre-1 | https://aclanthology.org/D19-1252 | https://aclanthology.org/D19-1252.pdf | ijcnlp-2019-11 | ['cross-lingual-natural-language-inference', 'cross-lingual-question-answering'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.49453653e-02 -1.21459365e-01 -3.80284935e-01 -5.34753203e-01
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cc216bc0-f8f0-41f5-9a9c-7d3fc480522f | ambulance-demand-prediction-via-convolutional | 2306.04994 | null | https://arxiv.org/abs/2306.04994v1 | https://arxiv.org/pdf/2306.04994v1.pdf | Ambulance Demand Prediction via Convolutional Neural Networks | Minimizing response times is crucial for emergency medical services to reduce patients' waiting times and to increase their survival rates. Many models exist to optimize operational tasks such as ambulance allocation and dispatching. Including accurate demand forecasts in such models can improve operational decision-making. Against this background, we present a novel convolutional neural network (CNN) architecture that transforms time series data into heatmaps to predict ambulance demand. Applying such predictions requires incorporating external features that influence ambulance demands. We contribute to the existing literature by providing a flexible, generic CNN architecture, allowing for the inclusion of external features with varying dimensions. Additionally, we provide a feature selection and hyperparameter optimization framework utilizing Bayesian optimization. We integrate historical ambulance demand and external information such as weather, events, holidays, and time. To show the superiority of the developed CNN architecture over existing approaches, we conduct a case study for Seattle's 911 call data and include external information. We show that the developed CNN architecture outperforms existing state-of-the-art methods and industry practice by more than 9%. | ['Maximilian Schiffer', 'Maximiliane Rautenstrauß'] | 2023-06-08 | null | null | null | null | ['hyperparameter-optimization', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-4.52046752e-01 -3.07699233e-01 -3.76508944e-02 -8.45058918e-01
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47da2092-8a3a-4b75-ae6c-4b04d563f847 | neural-motifs-scene-graph-parsing-with-global | 1711.06640 | null | http://arxiv.org/abs/1711.06640v2 | http://arxiv.org/pdf/1711.06640v2.pdf | Neural Motifs: Scene Graph Parsing with Global Context | We investigate the problem of producing structured graph representations of
visual scenes. Our work analyzes the role of motifs: regularly appearing
substructures in scene graphs. We present new quantitative insights on such
repeated structures in the Visual Genome dataset. Our analysis shows that
object labels are highly predictive of relation labels but not vice-versa. We
also find that there are recurring patterns even in larger subgraphs: more than
50% of graphs contain motifs involving at least two relations. Our analysis
motivates a new baseline: given object detections, predict the most frequent
relation between object pairs with the given labels, as seen in the training
set. This baseline improves on the previous state-of-the-art by an average of
3.6% relative improvement across evaluation settings. We then introduce Stacked
Motif Networks, a new architecture designed to capture higher order motifs in
scene graphs that further improves over our strong baseline by an average 7.1%
relative gain. Our code is available at github.com/rowanz/neural-motifs. | ['Mark Yatskar', 'Rowan Zellers', 'Yejin Choi', 'Sam Thomson'] | 2017-11-17 | neural-motifs-scene-graph-parsing-with-global-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Zellers_Neural_Motifs_Scene_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Zellers_Neural_Motifs_Scene_CVPR_2018_paper.pdf | cvpr-2018-6 | ['panoptic-scene-graph-generation'] | ['computer-vision'] | [ 6.08926117e-01 4.30068642e-01 -2.60908872e-01 -3.43096346e-01
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dded2683-124f-4572-8320-00d40c6f3b8c | tbpos-dataset-for-large-scale-precision | 2302.09825 | null | https://arxiv.org/abs/2302.09825v1 | https://arxiv.org/pdf/2302.09825v1.pdf | TBPos: Dataset for Large-Scale Precision Visual Localization | Image based localization is a classical computer vision challenge, with several well-known datasets. Generally, datasets consist of a visual 3D database that captures the modeled scenery, as well as query images whose 3D pose is to be discovered. Usually the query images have been acquired with a camera that differs from the imaging hardware used to collect the 3D database; consequently, it is hard to acquire accurate ground truth poses between query images and the 3D database. As the accuracy of visual localization algorithms constantly improves, precise ground truth becomes increasingly important. This paper proposes TBPos, a novel large-scale visual dataset for image based positioning, which provides query images with fully accurate ground truth poses: both the database images and the query images have been derived from the same laser scanner data. In the experimental part of the paper, the proposed dataset is evaluated by means of an image-based localization pipeline. | ['Jani Boutellier', 'Juho Kannala', 'Luca Ferranti', 'Ilona Söchting', 'Masud Fahim'] | 2023-02-20 | null | null | null | null | ['image-based-localization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 4.88642082e-02 -4.37956572e-01 -2.75385410e-01 -3.56524050e-01
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c35571d9-3e30-492f-83d9-77bb91a2fc9c | towards-better-dynamic-graph-learning-new | 2303.13047 | null | https://arxiv.org/abs/2303.13047v1 | https://arxiv.org/pdf/2303.13047v1.pdf | Towards Better Dynamic Graph Learning: New Architecture and Unified Library | We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning that solely learns from the sequences of nodes' historical first-hop interactions. DyGFormer incorporates two distinct designs: a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their sequences; a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing extensive experiments on thirteen datasets from various domains for transductive/inductive dynamic link prediction and dynamic node classification tasks, we observe that: DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating the effectiveness of capturing nodes' correlations and long-term temporal dependencies; the results of baselines vary across different datasets and some findings are inconsistent with previous reports, which may be caused by their diverse pipelines and problematic implementations. We hope our work can provide new insights and facilitate the development of the dynamic graph learning field. All the resources including datasets, data loaders, algorithms, and executing scripts are publicly available at https://github.com/yule-BUAA/DyGLib. | ['Weifeng Lv', 'Bowen Du', 'Leilei Sun', 'Le Yu'] | 2023-03-23 | null | null | null | null | ['dynamic-link-prediction'] | ['graphs'] | [-1.74996093e-01 -6.22314401e-02 -1.07099533e+00 -3.21652263e-01
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d42ae5e3-8939-4bd4-9d35-0b11bf90b67c | blind-image-deblurring-using-row-column | 1712.01937 | null | http://arxiv.org/abs/1712.01937v1 | http://arxiv.org/pdf/1712.01937v1.pdf | Blind Image Deblurring Using Row-Column Sparse Representations | Blind image deblurring is a particularly challenging inverse problem where
the blur kernel is unknown and must be estimated en route to recover the
deblurred image. The problem is of strong practical relevance since many
imaging devices such as cellphone cameras, must rely on deblurring algorithms
to yield satisfactory image quality. Despite significant research effort,
handling large motions remains an open problem. In this paper, we develop a new
method called Blind Image Deblurring using Row-Column Sparsity (BD-RCS) to
address this issue. Specifically, we model the outer product of kernel and
image coefficients in certain transformation domains as a rank-one matrix, and
recover it by solving a rank minimization problem. Our central contribution
then includes solving {\em two new} optimization problems involving row and
column sparsity to automatically determine blur kernel and image support
sequentially. The kernel and image can then be recovered through a singular
value decomposition (SVD). Experimental results on linear motion deblurring
demonstrate that BD-RCS can yield better results than state of the art,
particularly when the blur is caused by large motion. This is confirmed both
visually and through quantitative measures. | ['Vishal Monga', 'Yuelong Li', 'Mohammad Tofighi'] | 2017-12-05 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.98669749e-01 -5.58216274e-01 1.02363899e-01 1.15516633e-01
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9f9c5126-d590-492a-b0e2-c1def39232c3 | controlled-sparsity-kernel-learning | 1401.0116 | null | http://arxiv.org/abs/1401.0116v1 | http://arxiv.org/pdf/1401.0116v1.pdf | Controlled Sparsity Kernel Learning | Multiple Kernel Learning(MKL) on Support Vector Machines(SVMs) has been a
popular front of research in recent times due to its success in application
problems like Object Categorization. This success is due to the fact that MKL
has the ability to choose from a variety of feature kernels to identify the
optimal kernel combination. But the initial formulation of MKL was only able to
select the best of the features and misses out many other informative kernels
presented. To overcome this, the Lp norm based formulation was proposed by
Kloft et. al. This formulation is capable of choosing a non-sparse set of
kernels through a control parameter p. Unfortunately, the parameter p does not
have a direct meaning to the number of kernels selected. We have observed that
stricter control over the number of kernels selected gives us an edge over
these techniques in terms of accuracy of classification and also helps us to
fine tune the algorithms to the time requirements at hand. In this work, we
propose a Controlled Sparsity Kernel Learning (CSKL) formulation that can
strictly control the number of kernels which we wish to select. The CSKL
formulation introduces a parameter t which directly corresponds to the number
of kernels selected. It is important to note that a search in t space is finite
and fast as compared to p. We have also provided an efficient Reduced Gradient
Descent based algorithm to solve the CSKL formulation, which is proven to
converge. Through our experiments on the Caltech101 Object Categorization
dataset, we have also shown that one can achieve better accuracies than the
previous formulations through the right choice of t. | ['Raman Sankaran', 'Sreedal Menon', 'Dinesh Govindaraj', 'Chiranjib Bhattacharyya'] | 2013-12-31 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-9.58039984e-03 -5.14525592e-01 -2.99868733e-01 -2.18405873e-01
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6228161c-54c9-4ada-8eaf-89b3e3376400 | ernie-20-a-continual-pre-training-framework | 1907.12412 | null | https://arxiv.org/abs/1907.12412v2 | https://arxiv.org/pdf/1907.12412v2.pdf | ERNIE 2.0: A Continual Pre-training Framework for Language Understanding | Recently, pre-trained models have achieved state-of-the-art results in various language understanding tasks, which indicates that pre-training on large-scale corpora may play a crucial role in natural language processing. Current pre-training procedures usually focus on training the model with several simple tasks to grasp the co-occurrence of words or sentences. However, besides co-occurring, there exists other valuable lexical, syntactic and semantic information in training corpora, such as named entity, semantic closeness and discourse relations. In order to extract to the fullest extent, the lexical, syntactic and semantic information from training corpora, we propose a continual pre-training framework named ERNIE 2.0 which builds and learns incrementally pre-training tasks through constant multi-task learning. Experimental results demonstrate that ERNIE 2.0 outperforms BERT and XLNet on 16 tasks including English tasks on GLUE benchmarks and several common tasks in Chinese. The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE. | ['Shikun Feng', 'Yu Sun', 'Hua Wu', 'Hao Tian', 'Yukun Li', 'Shuohuan Wang', 'Haifeng Wang'] | 2019-07-29 | null | null | null | null | ['linguistic-acceptability', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.17020734e-01 7.64309764e-02 -2.98908919e-01 -6.52542770e-01
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35d873a3-ce20-462b-8ae3-f69249dd9d04 | discrete-diffusion-probabilistic-models-for | 2305.09489 | null | https://arxiv.org/abs/2305.09489v1 | https://arxiv.org/pdf/2305.09489v1.pdf | Discrete Diffusion Probabilistic Models for Symbolic Music Generation | Denoising Diffusion Probabilistic Models (DDPMs) have made great strides in generating high-quality samples in both discrete and continuous domains. However, Discrete DDPMs (D3PMs) have yet to be applied to the domain of Symbolic Music. This work presents the direct generation of Polyphonic Symbolic Music using D3PMs. Our model exhibits state-of-the-art sample quality, according to current quantitative evaluation metrics, and allows for flexible infilling at the note level. We further show, that our models are accessible to post-hoc classifier guidance, widening the scope of possible applications. However, we also cast a critical view on quantitative evaluation of music sample quality via statistical metrics, and present a simple algorithm that can confound our metrics with completely spurious, non-musical samples. | ['Gerhard Widmer', 'Silvan Peter', 'Matthias Plasser'] | 2023-05-16 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 4.00588721e-01 -2.82350928e-02 3.04796875e-01 2.04848610e-02
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fd38e11a-b967-440b-ba76-9f388e3d1a2a | painsight-an-extendable-opinion-mining | 2306.02043 | null | https://arxiv.org/abs/2306.02043v1 | https://arxiv.org/pdf/2306.02043v1.pdf | Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews | As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospective buyers. Previous studies have endeavored to identify key aspects of customer reviews through the development of sentiment analysis models and topic models. However, extracting specific dissatisfaction factors remains a challenging task. In this study, we delineate the pain point detection problem and propose Painsight, an unsupervised framework for automatically extracting distinct dissatisfaction factors from customer reviews without relying on ground truth labels. Painsight employs pre-trained language models to construct sentiment analysis and topic models, leveraging attribution scores derived from model gradients to extract dissatisfaction factors. Upon application of the proposed methodology to customer review data spanning five product categories, we successfully identified and categorized dissatisfaction factors within each group, as well as isolated factors for each type. Notably, Painsight outperformed benchmark methods, achieving substantial performance enhancements and exceptional results in human evaluations. | ['Pilsung Kang', 'Younsun Kim', 'Yookyung Kho', 'Doyoon Kim', 'Jaehee Kim', 'Yukyung Lee'] | 2023-06-03 | null | null | null | null | ['sentiment-analysis', 'topic-models', 'opinion-mining'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.16318651e-01 -3.87985222e-02 -5.06913126e-01 -7.63954222e-01
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74e91c7d-cc93-437d-87bd-aafd0906aa42 | piecewise-stationary-multi-objective-multi | 2302.05257 | null | https://arxiv.org/abs/2302.05257v2 | https://arxiv.org/pdf/2302.05257v2.pdf | Piecewise-Stationary Multi-Objective Multi-Armed Bandit with Application to Joint Communications and Sensing | We study a multi-objective multi-armed bandit problem in a dynamic environment. The problem portrays a decision-maker that sequentially selects an arm from a given set. If selected, each action produces a reward vector, where every element follows a piecewise-stationary Bernoulli distribution. The agent aims at choosing an arm among the Pareto optimal set of arms to minimize its regret. We propose a Pareto generic upper confidence bound (UCB)-based algorithm with change detection to solve this problem. By developing the essential inequalities for multi-dimensional spaces, we establish that our proposal guarantees a regret bound in the order of $\gamma_T\log(T/{\gamma_T})$ when the number of breakpoints $\gamma_T$ is known. Without this assumption, the regret bound of our algorithm is $\gamma_T\log(T)$. Finally, we formulate an energy-efficient waveform design problem in an integrated communication and sensing system as a toy example. Numerical experiments on the toy example and synthetic and real-world datasets demonstrate the efficiency of our policy compared to the current methods. | ['Setareh Maghsudi', 'Amir Rezaei Balef'] | 2023-02-10 | null | null | null | null | ['change-detection', 'multi-objective-reinforcement-learning', 'multi-armed-bandits'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 3.04518014e-01 3.62895355e-02 -5.05663037e-01 7.83659071e-02
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8576daaa-b140-4f19-b735-95c912aac6ac | exploring-visual-patterns-in-projected-human | 2001.08372 | null | https://arxiv.org/abs/2001.08372v3 | https://arxiv.org/pdf/2001.08372v3.pdf | ProjectionPathExplorer: Exploring Visual Patterns in Projected Decision-Making Paths | In problem-solving, a path towards solutions can be viewed as a sequence of decisions. The decisions, made by humans or computers, describe a trajectory through a high-dimensional representation space of the problem. By means of dimensionality reduction, these trajectories can be visualized in lower-dimensional space. Such embedded trajectories have previously been applied to a wide variety of data, but analysis has focused almost exclusively on the self-similarity of single trajectories. In contrast, we describe patterns emerging from drawing many trajectories -- for different initial conditions, end states, and solution strategies -- in the same embedding space. We argue that general statements about the problem-solving tasks and solving strategies can be made by interpreting these patterns. We explore and characterize such patterns in trajectories resulting from human and machine-made decisions in a variety of application domains: logic puzzles (Rubik's cube), strategy games (chess), and optimization problems (neural network training). We also discuss the importance of suitably chosen representation spaces and similarity metrics for the embedding. | ['Holger Stitz', 'Moritz Schöfl', 'Christian Steinparz', 'Andreas Hinterreiter', 'Marc Streit'] | 2020-01-20 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [ 1.52968302e-01 4.91656996e-02 2.13683248e-01 -2.32767105e-01
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291efab1-6f7c-44d8-9361-7af8aace5bdf | human-vs-computer-go-review-and-prospect | 1606.02032 | null | http://arxiv.org/abs/1606.02032v1 | http://arxiv.org/pdf/1606.02032v1.pdf | Human vs. Computer Go: Review and Prospect | The Google DeepMind challenge match in March 2016 was a historic achievement
for computer Go development. This article discusses the development of
computational intelligence (CI) and its relative strength in comparison with
human intelligence for the game of Go. We first summarize the milestones
achieved for computer Go from 1998 to 2016. Then, the computer Go programs that
have participated in previous IEEE CIS competitions as well as methods and
techniques used in AlphaGo are briefly introduced. Commentaries from three
high-level professional Go players on the five AlphaGo versus Lee Sedol games
are also included. We conclude that AlphaGo beating Lee Sedol is a huge
achievement in artificial intelligence (AI) based largely on CI methods. In the
future, powerful computer Go programs such as AlphaGo are expected to be
instrumental in promoting Go education and AI real-world applications. | ['Tai-Hsiung Yang', 'Ming-Wan Wang', 'Chun-Hsun Chou', 'Ping-Chiang Chou', 'I-Chen Wu', 'Ting-Han Wei', 'Shi-Jim Yen', 'Mei-Hui Wang', 'Chang-Shing Lee'] | 2016-06-07 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-3.51247460e-01 3.19259197e-01 -2.45603040e-01 -7.77157024e-02
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98cd1247-970e-4e5e-a6e0-ec23e2fcf5bd | vid2curve-simultaneously-camera-motion | 2005.03372 | null | https://arxiv.org/abs/2005.03372v3 | https://arxiv.org/pdf/2005.03372v3.pdf | Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video | Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world. It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods because thin structures often lack distinct point features and have severe self-occlusion. We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera. Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on. Enabled by this effective curve-based camera pose estimation strategy, we develop an iterative optimization method with tailored measures on geometry, topology as well as self-occlusion handling for reconstructing 3D thin structures. Extensive validations on a variety of thin structures show that our method achieves accurate camera pose estimation and faithful reconstruction of 3D thin structures with complex shape and topology at a level that has not been attained by other existing reconstruction methods. | ['Wenping Wang', 'Hung-Kuo Chu', 'Lingjie Liu', 'Nenglun Chen', 'Peng Wang', 'Christian Theobalt'] | 2020-05-07 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 2.53967553e-01 -4.33746576e-02 2.45863497e-01 1.53175816e-01
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5c22c739-5fc1-4a8b-ae7c-4f3bc93aa6cb | flexible-compositional-learning-of-structured | 2105.09848 | null | https://arxiv.org/abs/2105.09848v1 | https://arxiv.org/pdf/2105.09848v1.pdf | Flexible Compositional Learning of Structured Visual Concepts | Humans are highly efficient learners, with the ability to grasp the meaning of a new concept from just a few examples. Unlike popular computer vision systems, humans can flexibly leverage the compositional structure of the visual world, understanding new concepts as combinations of existing concepts. In the current paper, we study how people learn different types of visual compositions, using abstract visual forms with rich relational structure. We find that people can make meaningful compositional generalizations from just a few examples in a variety of scenarios, and we develop a Bayesian program induction model that provides a close fit to the behavioral data. Unlike past work examining special cases of compositionality, our work shows how a single computational approach can account for many distinct types of compositional generalization. | ['Brenden M. Lake', 'Yanli Zhou'] | 2021-05-20 | null | null | null | null | ['program-induction'] | ['computer-code'] | [ 1.56365022e-01 1.69821292e-01 -1.47608504e-01 -5.06135225e-01
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1fb8f476-ce4d-41ae-859f-232677b86e79 | heart-rate-variability-and-respiration-signal | 1605.05247 | null | http://arxiv.org/abs/1605.05247v1 | http://arxiv.org/pdf/1605.05247v1.pdf | Heart Rate Variability and Respiration Signal as Diagnostic Tools for Late Onset Sepsis in Neonatal Intensive Care Units | Apnea-bradycardia is one of the major clinical early indicators of late-onset
sepsis occurring in approximately 7% to 10% of all neonates and in more than
25% of very low birth weight infants in NICU. The objective of this paper was
to determine if HRV, respiration and their relationships help to diagnose
infection in premature infants via non-invasive ways in NICU. Therefore, we
implement Mono-Channel (MC) and Bi-Channel (BC) Analysis in two groups: sepsis
(S) vs. non-sepsis (NS). Firstly, we studied RR series not only by linear
methods: time domain and frequency domain, but also by non-linear methods:
chaos theory and information theory. The results show that alpha Slow, alpha
Fast and Sample Entropy are significant parameters to distinguish S from NS.
Secondly, the question about the functional coupling of HRV and nasal
respiration is addressed. Local linear correlation coefficient r2t,f has been
explored, while non-linear regression coefficient h2 was calculated in two
directions. It is obvious that r2t,f within the third frequency band (0.2<f<0.4
Hz) and h2 in two directions were complementary approaches to diagnose sepsis.
Thirdly, feasibility study is carried out on the candidate parameters selected
from MC and BC respectively. We discovered that the proposed test based on
optimal fusion of 6 features shows good performance with the largest AUC and a
reduced probability of false alarm (PFA). | ['Yu-An Wang', 'Guy Carrault', 'Huazhong Shu', 'Nathalie Costet', 'Lotfi Senhadji', 'Alain Beuchee'] | 2016-05-12 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 2.99458094e-02 -1.21086843e-01 2.68445253e-01 -9.05617252e-02
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1c93f14c-5647-4394-bf0a-a12d192afebb | analyzing-inexact-hypergradients-for-bilevel | 2301.04764 | null | https://arxiv.org/abs/2301.04764v1 | https://arxiv.org/pdf/2301.04764v1.pdf | Analyzing Inexact Hypergradients for Bilevel Learning | Estimating hyperparameters has been a long-standing problem in machine learning. We consider the case where the task at hand is modeled as the solution to an optimization problem. Here the exact gradient with respect to the hyperparameters cannot be feasibly computed and approximate strategies are required. We introduce a unified framework for computing hypergradients that generalizes existing methods based on the implicit function theorem and automatic differentiation/backpropagation, showing that these two seemingly disparate approaches are actually tightly connected. Our framework is extremely flexible, allowing its subproblems to be solved with any suitable method, to any degree of accuracy. We derive a priori and computable a posteriori error bounds for all our methods, and numerically show that our a posteriori bounds are usually more accurate. Our numerical results also show that, surprisingly, for efficient bilevel optimization, the choice of hypergradient algorithm is at least as important as the choice of lower-level solver. | ['Lindon Roberts', 'Matthias J. Ehrhardt'] | 2023-01-11 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-2.12613001e-01 2.05504864e-01 -2.04610169e-01 -7.66929761e-02
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2cdc45ba-e705-4cac-85d2-52f4cd1a0712 | weakly-and-partially-supervised-learning | null | null | https://www.researchgate.net/publication/350580836_Weakly_and_Partially_Supervised_Learning_Frameworks_for_Anomaly_Detection | https://www.di.ubi.pt/~hugomcp/doc/bruno_degardin_2019.pdf | Weakly and Partially Supervised Learning Frameworks for Anomaly Detection | The main objective is to provide several solutions to the mentioned problems, by focusing on analyzing previous state-of-the-art methods and presenting an extensive overview to clarify the concepts employed on capturing normal and abnormal patterns. Also, by exploring different strategies, we were able to develop new approaches that consistently advance the state-of-the-art performance. Moreover, we announce the availability of a new large-scale first of its kind dataset fully annotated at the frame level, concerning a specific anomaly detection event with a wide diversity in fighting scenarios, that can be freely used by the research community. Along with this document with the purpose of requiring minimal supervision, two different proposals are described; the first method employs the recent technique of self-supervised learning to avoid the laborious task of annotation, where the training set is autonomously labeled using an iterative learning framework composed of two independent experts that feed data to each other through a Bayesian framework. The second proposal explores a new method to learn an anomaly ranking model in the multiple instance learning paradigm by leveraging weakly labeled videos, where the training labels are done at the video-level. The experiments were conducted in several well-known datasets, and our solutions solidly outperform the state-of-the-art. Additionally, as a proof-of-concept system, we also present the results of collected real-world simulations in different environments to perform a field test of our learned models. | ['Bruno Degardin'] | 2020-07-23 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video', 'anomaly-detection-in-surveillance-videos', 'abnormal-event-detection-in-video'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 2.47404397e-01 6.29536733e-02 -2.17416480e-01 -4.22331631e-01
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e37c5f45-3b13-4bd1-ae99-96f1055a5873 | help-the-blind-see-assistance-for-the | 2303.13536 | null | https://arxiv.org/abs/2303.13536v1 | https://arxiv.org/pdf/2303.13536v1.pdf | Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic Simulation | An estimated 253 million people have visual impairments. These visual impairments affect everyday lives, and limit their understanding of the outside world. This can pose a risk to health from falling or collisions. We propose a solution to this through quick and detailed communication of environmental spatial geometry through sound, providing the blind and visually impaired the ability to understand their spatial environment through sound technology. The model consists of fast object detection and 3D environmental mapping, which is communicated through a series of quick sound notes. These sound notes are at different frequencies, pitches, and arrangements in order to precisely communicate the depth and location of points within the environment. Sounds are communicated in the form of musical notes in order to be easily recognizable and distinguishable. A unique algorithm is used to segment objects, providing minimal accuracy loss and improvement from the normal O(n2 ) to O(n) (which is significant, as N in point clouds can often be in the range of 105 ). In testing, we achieved an R-value of 0.866 on detailed objects and an accuracy of 87.5% on an outdoor scene at night with large amounts of noise. We also provide a supplementary video demo of our system. | ['Ritik Jalisatgi', 'Alexander Mehta'] | 2023-02-09 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.66615355e-02 -3.77145439e-01 7.85895169e-01 -5.99294342e-02
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8da6a2e8-088a-48eb-9f3e-6ee5f4daba0d | automatic-online-detection-of-atrial | null | null | https://doi.org/10.1186/1475-925X-13-18 | https://biomedical-engineering-online.biomedcentral.com/track/pdf/10.1186/1475-925X-13-18 | Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy | Background
Atrial fibrillation (AF) is the most common and debilitating abnormalities of the arrhythmias worldwide, with a major impact on morbidity and mortality. The detection of AF becomes crucial in preventing both acute and chronic cardiac rhythm disorders.
Objective
Our objective is to devise a method for real-time, automated detection of AF episodes in electrocardiograms (ECGs). This method utilizes RR intervals, and it involves several basic operations of nonlinear/linear integer filters, symbolic dynamics and the calculation of Shannon entropy. Using novel recursive algorithms, online analytical processing of this method can be achieved.
Results
Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were selected for investigation. The first database is used as a training set; in accordance with the receiver operating characteristic (ROC) curve, the best performance using this method was achieved at the discrimination threshold of 0.353: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.72%, 95.07%, 96.61% and 96.05%, respectively. The other three databases are used as testing sets. Using the obtained threshold value (i.e., 0.353), for the second set, the obtained parameters were 96.89%, 98.25%, 97.62% and 97.67%, respectively; for the third database, these parameters were 97.33%, 90.78%, 55.29% and 91.46%, respectively; finally, for the fourth set, the Sp was 98.28%. The existing methods were also employed for comparison.
Conclusions
Overall, in contrast to the other available techniques, the test results indicate that the newly developed approach outperforms traditional methods using these databases under assessed various experimental situations, and suggest our technique could be of practical use for clinicians in the future. | ['YuanTing Zhang', 'Emma Pickwell-MacPherson', 'Hongxia Ding', 'Benjamin Ung', 'Xiaolin Zhou'] | 2014-02-17 | null | null | null | biomedical-engineering-online-2014-2 | ['atrial-fibrillation-detection'] | ['medical'] | [ 1.93986192e-01 -3.63845825e-01 5.23379864e-03 1.01149052e-01
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701fb375-b068-4150-988f-10ac66422c6e | fsnet-redesign-self-supervised-monodepth-for | 2304.10719 | null | https://arxiv.org/abs/2304.10719v1 | https://arxiv.org/pdf/2304.10719v1.pdf | FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving | Predicting accurate depth with monocular images is important for low-cost robotic applications and autonomous driving. This study proposes a comprehensive self-supervised framework for accurate scale-aware depth prediction on autonomous driving scenes utilizing inter-frame poses obtained from inertial measurements. In particular, we introduce a Full-Scale depth prediction network named FSNet. FSNet contains four important improvements over existing self-supervised models: (1) a multichannel output representation for stable training of depth prediction in driving scenarios, (2) an optical-flow-based mask designed for dynamic object removal, (3) a self-distillation training strategy to augment the training process, and (4) an optimization-based post-processing algorithm in test time, fusing the results from visual odometry. With this framework, robots and vehicles with only one well-calibrated camera can collect sequences of training image frames and camera poses, and infer accurate 3D depths of the environment without extra labeling work or 3D data. Extensive experiments on the KITTI dataset, KITTI-360 dataset and the nuScenes dataset demonstrate the potential of FSNet. More visualizations are presented in \url{https://sites.google.com/view/fsnet/home} | ['Ming Liu', 'Lujia Wang', 'Huaiyang Huang', 'Zhenhua Xu', 'Yuxuan Liu'] | 2023-04-21 | null | null | null | null | ['visual-odometry'] | ['robots'] | [ 4.08487618e-02 1.68941468e-01 -1.90855786e-01 -7.90921152e-01
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4e6b85c6-15a3-4d47-87a5-1ff96a8b612e | joint-visual-and-audio-learning-for-video | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Badamdorj_Joint_Visual_and_Audio_Learning_for_Video_Highlight_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Badamdorj_Joint_Visual_and_Audio_Learning_for_Video_Highlight_Detection_ICCV_2021_paper.pdf | Joint Visual and Audio Learning for Video Highlight Detection | In video highlight detection, the goal is to identify the interesting moments within an unedited video. Although the audio component of the video provides important cues for highlight detection, the majority of existing efforts focus almost exclusively on the visual component. In this paper, we argue that both audio and visual components of a video should be modeled jointly to retrieve its best moments. To this end, we propose an audio-visual network for video highlight detection. At the core of our approach lies a bimodal attention mechanism, which captures the interaction between the audio and visual components of a video, and produces fused representations to facilitate highlight detection. Furthermore, we introduce a noise sentinel technique to adaptively discount a noisy visual or audio modality. Empirical evaluations on two benchmark datasets demonstrate the superior performance of our approach over the state-of-the-art methods. | ['Li Cheng', 'Yang Wang', 'Mrigank Rochan', 'Taivanbat Badamdorj'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['highlight-detection'] | ['computer-vision'] | [ 6.21481910e-02 -3.98824394e-01 -1.97513714e-01 3.44677418e-02
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7ba5b56b-2155-460d-9617-4c7707e86dff | using-chatgpt-for-entity-matching | 2305.03423 | null | https://arxiv.org/abs/2305.03423v2 | https://arxiv.org/pdf/2305.03423v2.pdf | Using ChatGPT for Entity Matching | Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks of using these models for entity matching are that (i) the models require significant amounts of fine-tuning data for reaching a good performance and (ii) the fine-tuned models are not robust concerning out-of-distribution entities. In this paper, we investigate using ChatGPT for entity matching as a more robust, training data-efficient alternative to traditional Transformer models. We perform experiments along three dimensions: (i) general prompt design, (ii) in-context learning, and (iii) provision of higher-level matching knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model, reaching a zero-shot performance of 82.35% F1 on a challenging matching task on which RoBERTa requires 2000 training examples for reaching a similar performance. Adding in-context demonstrations to the prompts further improves the F1 by up to 7.85% when using similarity-based example selection. Always using the same set of 10 handpicked demonstrations leads to an improvement of 4.92% over the zero-shot performance. Finally, we show that ChatGPT can also be guided by adding higher-level matching knowledge in the form of rules to the prompts. Providing matching rules leads to similar performance gains as providing in-context demonstrations. | ['Christian Bizer', 'Ralph Peeters'] | 2023-05-05 | null | null | null | null | ['data-integration', 'entity-resolution'] | ['knowledge-base', 'natural-language-processing'] | [-1.66001201e-01 -1.60452537e-02 -9.48422477e-02 -4.34243947e-01
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03af5d42-00e7-4981-b618-344ddaf99830 | epro-pnp-generalized-end-to-end-probabilistic-1 | 2303.12787 | null | https://arxiv.org/abs/2303.12787v2 | https://arxiv.org/pdf/2303.12787v2.pdf | EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation | Locating 3D objects from a single RGB image via Perspective-n-Point (PnP) is a long-standing problem in computer vision. Driven by end-to-end deep learning, recent studies suggest interpreting PnP as a differentiable layer, allowing for partial learning of 2D-3D point correspondences by backpropagating the gradients of pose loss. Yet, learning the entire correspondences from scratch is highly challenging, particularly for ambiguous pose solutions, where the globally optimal pose is theoretically non-differentiable w.r.t. the points. In this paper, we propose the EPro-PnP, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold. The 2D-3D coordinates and corresponding weights are treated as intermediate variables learned by minimizing the KL divergence between the predicted and target pose distribution. The underlying principle generalizes previous approaches, and resembles the attention mechanism. EPro-PnP can enhance existing correspondence networks, closing the gap between PnP-based method and the task-specific leaders on the LineMOD 6DoF pose estimation benchmark. Furthermore, EPro-PnP helps to explore new possibilities of network design, as we demonstrate a novel deformable correspondence network with the state-of-the-art pose accuracy on the nuScenes 3D object detection benchmark. Our code is available at https://github.com/tjiiv-cprg/EPro-PnP-v2. | ['Hao Li', 'Lu Xiong', 'Fan Wang', 'Pichao Wang', 'Wei Tian', 'Hansheng Chen'] | 2023-03-22 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [-2.12667510e-01 8.20284933e-02 -2.00018182e-01 -3.69990468e-01
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56b02ade-b458-4afc-afc9-79786ef43f87 | nerf-gaze-a-head-eye-redirection-parametric | 2212.14710 | null | https://arxiv.org/abs/2212.14710v1 | https://arxiv.org/pdf/2212.14710v1.pdf | NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation | Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks. | ['ShiLiang Pu', 'Di Xie', 'Jingjing Wang', 'Jiawu Dai', 'Pengwei Yin'] | 2022-12-30 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-3.04126684e-02 -2.04115249e-02 -2.29228929e-01 -7.78004944e-01
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21fdd58e-b1ed-4552-8b5f-3d5dfdac2982 | efenet-reference-based-video-super-resolution | 2110.07797 | null | https://arxiv.org/abs/2110.07797v2 | https://arxiv.org/pdf/2110.07797v2.pdf | EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation | In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i.e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence. The existing approaches to RefVSR essentially attempt to align the reference and the input sequence, in the presence of resolution gap and long temporal range. However, they either ignore temporal structure within the input sequence, or suffer accumulative alignment errors. To address these issues, we propose EFENet to exploit simultaneously the visual cues contained in the HR reference and the temporal information contained in the LR sequence. EFENet first globally estimates cross-scale flow between the reference and each LR frame. Then our novel flow refinement module of EFENet refines the flow regarding the furthest frame using all the estimated flows, which leverages the global temporal information within the sequence and therefore effectively reduces the alignment errors. We provide comprehensive evaluations to validate the strengths of our approach, and to demonstrate that the proposed framework outperforms the state-of-the-art methods. Code is available at https://github.com/IndigoPurple/EFENet. | ['Shengjin Wang', 'Bin Wang', 'Ruqi Huang', 'Mengqi Ji', 'Yaping Zhao'] | 2021-10-15 | null | null | null | null | ['video-super-resolution', 'reference-based-video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 2.50797927e-01 -4.06618118e-01 -1.29604146e-01 8.58854875e-02
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206cc9fb-99d3-4918-a41b-7429fdc01b4a | zero-shot-detection-of-daily-objects-in-ycb | null | null | https://openreview.net/forum?id=jJWK09skiNl | https://openreview.net/pdf?id=jJWK09skiNl | Zero-shot detection of daily objects in YCB video dataset | To let robots be able to manipulate objects, they have to sense the location of objects. With the development of visual data collecting and processing technology, robots are gradually evolving to localize objects in a greater field of view rather than being limited to a small space where the object could appear. To train such a robot vision system, pictures of all the objects need to be taken under various orientations and illumination. In the traditional manufacturing environment, this is applicable since objects involved in the production process does not change frequently. However, in the vision of smart manufacturing and high-mix-low-volume production, parts and products for robots to handle may change frequently. Thus, it is unrealistic to re-training the vision system for new products and tasks. Under this situation, we discovered the necessity to introduce a hot concept which is zero-shot object detection. Zero-shot object detection is a subset of unsupervised learning, and it aims to detect novel objects in the image with the knowledge learned from and only from seen objects. With zero-shot object detection algorithm, time can be greatly saved from collecting training data and training the vision system. Previous works focus on detecting objects in outdoor scenes, such as bikes, car, people, and dogs. The detection of daily objects is actually more challenging since the knowledge can be learned from each object is very limited. In this work, we explore the zero-shot detection of daily objects in indoor scenes since the objects’ size and environment are closely related to the manufacturing setup. The YCB Video Dataset is used in this work, which contains 21 objects in various categories. To the best of our knowledge, no previous work has explored zero-shot detection in this object size level and on this dataset. | ['Wanqing Xia'] | 2021-09-29 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [ 2.41713554e-01 -2.07971841e-01 1.43494770e-01 -2.53810614e-01
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22085970-e63c-45bb-89bb-9abc2b0d0d9c | unpaired-learning-for-high-dynamic-range-1 | 2111.00219 | null | https://arxiv.org/abs/2111.00219v1 | https://arxiv.org/pdf/2111.00219v1.pdf | Unpaired Learning for High Dynamic Range Image Tone Mapping | High dynamic range (HDR) photography is becoming increasingly popular and available by DSLR and mobile-phone cameras. While deep neural networks (DNN) have greatly impacted other domains of image manipulation, their use for HDR tone-mapping is limited due to the lack of a definite notion of ground-truth solution, which is needed for producing training data. In this paper we describe a new tone-mapping approach guided by the distinct goal of producing low dynamic range (LDR) renditions that best reproduce the visual characteristics of native LDR images. This goal enables the use of an unpaired adversarial training based on unrelated sets of HDR and LDR images, both of which are widely available and easy to acquire. In order to achieve an effective training under this minimal requirements, we introduce the following new steps and components: (i) a range-normalizing pre-process which estimates and applies a different level of curve-based compression, (ii) a loss that preserves the input content while allowing the network to achieve its goal, and (iii) the use of a more concise discriminator network, designed to promote the reproduction of low-level attributes native LDR possess. Evaluation of the resulting network demonstrates its ability to produce photo-realistic artifact-free tone-mapped images, and state-of-the-art performance on different image fidelity indices and visual distances. | ['Raanan Fattal', 'Inbar Huberman-Spiegelglas', 'Yael Vinker'] | 2021-10-30 | unpaired-learning-for-high-dynamic-range | http://openaccess.thecvf.com//content/ICCV2021/html/Vinker_Unpaired_Learning_for_High_Dynamic_Range_Image_Tone_Mapping_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Vinker_Unpaired_Learning_for_High_Dynamic_Range_Image_Tone_Mapping_ICCV_2021_paper.pdf | iccv-2021-1 | ['tone-mapping'] | ['computer-vision'] | [ 6.92141593e-01 -2.29195610e-01 8.61299634e-02 -5.75937256e-02
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c9684c86-f54e-4d89-bc83-2afc861fbb04 | can-action-be-imitated-learn-to-reconstruct | 2107.11756 | null | https://arxiv.org/abs/2107.11756v1 | https://arxiv.org/pdf/2107.11756v1.pdf | Can Action be Imitated? Learn to Reconstruct and Transfer Human Dynamics from Videos | Given a video demonstration, can we imitate the action contained in this video? In this paper, we introduce a novel task, dubbed mesh-based action imitation. The goal of this task is to enable an arbitrary target human mesh to perform the same action shown on the video demonstration. To achieve this, a novel Mesh-based Video Action Imitation (M-VAI) method is proposed by us. M-VAI first learns to reconstruct the meshes from the given source image frames, then the initial recovered mesh sequence is fed into mesh2mesh, a mesh sequence smooth module proposed by us, to improve the temporal consistency. Finally, we imitate the actions by transferring the pose from the constructed human body to our target identity mesh. High-quality and detailed human body meshes can be generated by using our M-VAI. Extensive experiments demonstrate the feasibility of our task and the effectiveness of our proposed method. | ['Yu-Gang Jiang', 'Yanwei Fu', 'Yuqian Fu'] | 2021-07-25 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [ 2.26810947e-01 3.44401121e-01 1.92097053e-01 7.98420161e-02
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8d561bd9-296e-49ad-a0f0-0a19557644ca | a-unified-framework-for-multi-intent-spoken | 2210.03337 | null | https://arxiv.org/abs/2210.03337v1 | https://arxiv.org/pdf/2210.03337v1.pdf | A Unified Framework for Multi-intent Spoken Language Understanding with prompting | Multi-intent Spoken Language Understanding has great potential for widespread implementation. Jointly modeling Intent Detection and Slot Filling in it provides a channel to exploit the correlation between intents and slots. However, current approaches are apt to formulate these two sub-tasks differently, which leads to two issues: 1) It hinders models from effective extraction of shared features. 2) Pretty complicated structures are involved to enhance expression ability while causing damage to the interpretability of frameworks. In this work, we describe a Prompt-based Spoken Language Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into the same form by offering a common pre-trained Seq2Seq model. In detail, ID and SF are completed by concisely filling the utterance into task-specific prompt templates as input, and sharing output formats of key-value pairs sequence. Furthermore, variable intents are predicted first, then naturally embedded into prompts to guide slot-value pairs inference from a semantic perspective. Finally, we are inspired by prevalent multi-task learning to introduce an auxiliary sub-task, which helps to learn relationships among provided labels. Experiment results show that our framework outperforms several state-of-the-art baselines on two public datasets. | ['Houfeng Wang', 'Lianzhe Huang', 'Feifan Song'] | 2022-10-07 | null | null | null | null | ['spoken-language-understanding', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [ 4.21001494e-01 3.63881052e-01 -3.13468516e-01 -9.33357835e-01
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bf150b3b-72d1-4a02-813a-475bb8d2f6d2 | revisiting-class-imbalance-for-end-to-end | 2306.02268 | null | https://arxiv.org/abs/2306.02268v1 | https://arxiv.org/pdf/2306.02268v1.pdf | Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection | Semi-supervised object detection (SSOD) has made significant progress with the development of pseudo-label-based end-to-end methods. However, many of these methods face challenges due to class imbalance, which hinders the effectiveness of the pseudo-label generator. Furthermore, in the literature, it has been observed that low-quality pseudo-labels severely limit the performance of SSOD. In this paper, we examine the root causes of low-quality pseudo-labels and present novel learning mechanisms to improve the label generation quality. To cope with high false-negative and low precision rates, we introduce an adaptive thresholding mechanism that helps the proposed network to filter out optimal bounding boxes. We further introduce a Jitter-Bagging module to provide accurate information on localization to help refine the bounding boxes. Additionally, two new losses are introduced using the background and foreground scores predicted by the teacher and student networks to improvise the pseudo-label recall rate. Furthermore, our method applies strict supervision to the teacher network by feeding strong & weak augmented data to generate robust pseudo-labels so that it can detect small and complex objects. Finally, the extensive experiments show that the proposed network outperforms state-of-the-art methods on MS-COCO and Pascal VOC datasets and allows the baseline network to achieve 100% supervised performance with much less (i.e., 20%) labeled data. | ['Pankaj Wasnik', 'Naoyuki Onoe', 'Vishal Chudasama', 'Purbayan Kar'] | 2023-06-04 | null | null | null | null | ['semi-supervised-object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 2.57281363e-01 1.31894529e-01 -1.61051095e-01 -6.87404752e-01
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7b692f34-07c4-4423-be61-d6705d3140ac | improve-cross-lingual-voice-cloning-using-low | 2110.07210 | null | https://arxiv.org/abs/2110.07210v2 | https://arxiv.org/pdf/2110.07210v2.pdf | Improve Cross-lingual Voice Cloning Using Low-quality Code-switched Data | Recently, sequence-to-sequence (seq-to-seq) models have been successfully applied in text-to-speech (TTS) to synthesize speech for single-language text. To synthesize speech for multiple languages usually requires multi-lingual speech from the target speaker. However, it is both laborious and expensive to collect high-quality multi-lingual TTS data for the target speakers. In this paper, we proposed to use low-quality code-switched found data from the non-target speakers to achieve cross-lingual voice cloning for the target speakers. Experiments show that our proposed method can generate high-quality code-switched speech in the target voices in terms of both naturalness and speaker consistency. More importantly, we find that our method can achieve a comparable result to the state-of-the-art (SOTA) performance in cross-lingual voice cloning. | ['Yue Lin', 'Haitong Zhang'] | 2021-10-14 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 7.60968402e-02 -2.35745192e-01 -1.53084786e-03 -5.08449256e-01
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8f56df27-b4f2-43db-b551-a5fde80a04bd | semi-supervised-endmember-identification-in | 1701.00804 | null | http://arxiv.org/abs/1701.00804v1 | http://arxiv.org/pdf/1701.00804v1.pdf | Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation | This paper proposes a new hyperspectral unmixing method for nonlinearly mixed
hyperspectral data using a semantic representation in a semi-supervised
fashion, assuming the availability of a spectral reference library. Existing
semi-supervised unmixing algorithms select members from an endmember library
that are present at each of the pixels; most such methods assume a linear
mixing model. However, those methods will fail in the presence of nonlinear
mixing among the observed spectra. To address this issue, we develop an
endmember selection method using a recently proposed semantic spectral
representation obtained via non-homogeneous hidden Markov chain (NHMC) model
for a wavelet transform of the spectra. The semantic representation can encode
spectrally discriminative features for any observed spectrum and, therefore,
our proposed method can perform endmember selection without any assumption on
the mixing model. Experimental results show that in the presence of
sufficiently nonlinear mixing our proposed method outperforms dictionary-based
sparse unmixing approaches based on linear models. | ['Marco F. Duarte', 'Yuki Itoh', 'Mario Parente', 'Siwei Feng'] | 2017-01-03 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 9.07538831e-01 -5.19515336e-01 -2.14461491e-01 -1.43936962e-01
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47d52e64-b3a6-4020-acce-0176380cc81d | action-state-update-approach-to-dialogue | 2011.04637 | null | https://arxiv.org/abs/2011.04637v2 | https://arxiv.org/pdf/2011.04637v2.pdf | Action State Update Approach to Dialogue Management | Utterance interpretation is one of the main functions of a dialogue manager, which is the key component of a dialogue system. We propose the action state update approach (ASU) for utterance interpretation, featuring a statistically trained binary classifier used to detect dialogue state update actions in the text of a user utterance. Our goal is to interpret referring expressions in user input without a domain-specific natural language understanding component. For training the model, we use active learning to automatically select simulated training examples. With both user-simulated and interactive human evaluations, we show that the ASU approach successfully interprets user utterances in a dialogue system, including those with referring expressions. | ['Rama Doddipatla', 'Simon Keizer', 'Svetlana Stoyanchev'] | 2020-11-09 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [ 5.26148200e-01 8.87140155e-01 -2.59229928e-01 -8.07127833e-01
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63ba0003-4e1b-4afc-84b3-2c740bbb4191 | single-image-depth-estimation-trained-via-1 | 2001.05036 | null | https://arxiv.org/abs/2001.05036v1 | https://arxiv.org/pdf/2001.05036v1.pdf | Single Image Depth Estimation Trained via Depth from Defocus Cues | Estimating depth from a single RGB images is a fundamental task in computer vision, which is most directly solved using supervised deep learning. In the field of unsupervised learning of depth from a single RGB image, depth is not given explicitly. Existing work in the field receives either a stereo pair, a monocular video, or multiple views, and, using losses that are based on structure-from-motion, trains a depth estimation network. In this work, we rely, instead of different views, on depth from focus cues. Learning is based on a novel Point Spread Function convolutional layer, which applies location specific kernels that arise from the Circle-Of-Confusion in each image location. We evaluate our method on data derived from five common datasets for depth estimation and lightfield images, and present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches. Since the phenomenon of depth from defocus is not dataset specific, we hypothesize that learning based on it would overfit less to the specific content in each dataset. Our experiments show that this is indeed the case, and an estimator learned on one dataset using our method provides better results on other datasets, than the directly supervised methods. | ['Lior Wolf', 'Shir Gur'] | 2020-01-14 | single-image-depth-estimation-trained-via | http://openaccess.thecvf.com/content_CVPR_2019/html/Gur_Single_Image_Depth_Estimation_Trained_via_Depth_From_Defocus_Cues_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gur_Single_Image_Depth_Estimation_Trained_via_Depth_From_Defocus_Cues_CVPR_2019_paper.pdf | cvpr-2019-6 | ['lightfield'] | ['computer-vision'] | [ 4.54544872e-01 7.54446760e-02 3.94136310e-02 -6.93888664e-01
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25f10d59-36f8-4620-8994-25be9de649e4 | standing-between-past-and-future-spatio | 2302.03802 | null | https://arxiv.org/abs/2302.03802v2 | https://arxiv.org/pdf/2302.03802v2.pdf | Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking | This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It emphasizes spatio-temporal continuity and integrates both past and future reasoning for tracked objects. Thus, we name it "Past-and-Future reasoning for Tracking" (PF-Track). Specifically, our method adapts the "tracking by attention" framework and represents tracked instances coherently over time with object queries. To explicitly use historical cues, our "Past Reasoning" module learns to refine the tracks and enhance the object features by cross-attending to queries from previous frames and other objects. The "Future Reasoning" module digests historical information and predicts robust future trajectories. In the case of long-term occlusions, our method maintains the object positions and enables re-association by integrating motion predictions. On the nuScenes dataset, our method improves AMOTA by a large margin and remarkably reduces ID-Switches by 90% compared to prior approaches, which is an order of magnitude less. The code and models are made available at https://github.com/TRI-ML/PF-Track. | ['Yu-Xiong Wang', 'Sergey Zagoruyko', 'Dian Chen', 'Pavel Tokmakov', 'Jie Li', 'Ziqi Pang'] | 2023-02-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Pang_Standing_Between_Past_and_Future_Spatio-Temporal_Modeling_for_Multi-Camera_3D_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-multi-object-tracking'] | ['computer-vision'] | [-3.32406431e-01 -3.93119514e-01 -4.20679808e-01 -3.00458491e-01
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cdafecf5-304f-4739-b87d-5e5697b5291f | fast-classification-with-sequential-feature | 2306.14347 | null | https://arxiv.org/abs/2306.14347v1 | https://arxiv.org/pdf/2306.14347v1.pdf | Fast Classification with Sequential Feature Selection in Test Phase | This paper introduces a novel approach to active feature acquisition for classification, which is the task of sequentially selecting the most informative subset of features to achieve optimal prediction performance during testing while minimizing cost. The proposed approach involves a new lazy model that is significantly faster and more efficient compared to existing methods, while still producing comparable accuracy results. During the test phase, the proposed approach utilizes Fisher scores for feature ranking to identify the most important feature at each step. In the next step the training dataset is filtered based on the observed value of the selected feature and then we continue this process to reach to acceptable accuracy or limit of the budget for feature acquisition. The performance of the proposed approach was evaluated on synthetic and real datasets, including our new synthetic dataset, CUBE dataset and also real dataset Forest. The experimental results demonstrate that our approach achieves competitive accuracy results compared to existing methods, while significantly outperforming them in terms of speed. The source code of the algorithm is released at github with this link: https://github.com/alimirzaei/FCwSFS. | ['Alireza Abdollahpourrostam', 'Hamid Sheikhzadeh', 'Vahid Pourahmadi', 'Ali Mirzaei'] | 2023-06-25 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 3.51544470e-01 -2.97383755e-01 -2.75488824e-01 -4.72997963e-01
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18bca29e-c23c-41f3-b177-d25014d25d67 | pgb-a-pubmed-graph-benchmark-for | 2305.02691 | null | https://arxiv.org/abs/2305.02691v2 | https://arxiv.org/pdf/2305.02691v2.pdf | PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning | There has been a rapid growth in biomedical literature, yet capturing the heterogeneity of the bibliographic information of these articles remains relatively understudied. Although graph mining research via heterogeneous graph neural networks has taken center stage, it remains unclear whether these approaches capture the heterogeneity of the PubMed database, a vast digital repository containing over 33 million articles. We introduce PubMed Graph Benchmark (PGB), a new benchmark dataset for evaluating heterogeneous graph embeddings for biomedical literature. PGB is one of the largest heterogeneous networks to date and consists of 30 million English articles. The benchmark contains rich metadata including abstract, authors, citations, MeSH terms, MeSH hierarchy, and some other information. The benchmark contains three different evaluation tasks encompassing systematic reviews, node classification, and node clustering. In PGB, we aggregate the metadata associated with the biomedical articles from PubMed into a unified source and make the benchmark publicly available for any future works. | ['Joyce C Ho', 'Eric W Lee'] | 2023-05-04 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-2.60814637e-01 3.51124674e-01 -1.00381541e+00 2.08710775e-01
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7fab28a8-0fed-4638-8976-0d161f2068d7 | abstracting-sketches-through-simple | 2207.13543 | null | https://arxiv.org/abs/2207.13543v1 | https://arxiv.org/pdf/2207.13543v1.pdf | Abstracting Sketches through Simple Primitives | Humans show high-level of abstraction capabilities in games that require quickly communicating object information. They decompose the message content into multiple parts and communicate them in an interpretable protocol. Toward equipping machines with such capabilities, we propose the Primitive-based Sketch Abstraction task where the goal is to represent sketches using a fixed set of drawing primitives under the influence of a budget. To solve this task, our Primitive-Matching Network (PMN), learns interpretable abstractions of a sketch in a self supervised manner. Specifically, PMN maps each stroke of a sketch to its most similar primitive in a given set, predicting an affine transformation that aligns the selected primitive to the target stroke. We learn this stroke-to-primitive mapping end-to-end with a distance-transform loss that is minimal when the original sketch is precisely reconstructed with the predicted primitives. Our PMN abstraction empirically achieves the highest performance on sketch recognition and sketch-based image retrieval given a communication budget, while at the same time being highly interpretable. This opens up new possibilities for sketch analysis, such as comparing sketches by extracting the most relevant primitives that define an object category. Code is available at https://github.com/ExplainableML/sketch-primitives. | ['Zeynep Akata', 'Diego Marcos', 'Anjan Dutta', 'Massimiliano Mancini', 'Stephan Alaniz'] | 2022-07-27 | null | null | null | null | ['sketch-based-image-retrieval', 'sketch-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.71053636e-01 1.97178349e-01 -3.30593586e-01 -4.43300933e-01
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995b562d-bf0e-4c6e-a725-d2f9735c40d7 | molecular-graph-enhanced-transformer-for | null | null | https://openreview.net/forum?id=S1e__ANKvB | https://openreview.net/pdf?id=S1e__ANKvB | Molecular Graph Enhanced Transformer for Retrosynthesis Prediction | With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of synthesis is analogized to a process of language translation from reactants to products. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. In this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embedding learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the Transformer model in test accuracy. | ['Junzhou Huang', 'Xi Xiao', 'Yu Rong', 'Tingyang Xu', 'Peilin Zhao', 'Kelong Mao'] | 2019-09-25 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 5.78694582e-01 4.58890535e-02 -5.77469051e-01 -6.46514967e-02
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fec2f6e2-3f9a-44f9-a880-7ad26233030d | respiratory-sound-classification-using-long | 2008.02900 | null | https://arxiv.org/abs/2008.02900v1 | https://arxiv.org/pdf/2008.02900v1.pdf | Respiratory Sound Classification Using Long-Short Term Memory | Developing a reliable sound detection and recognition system offers many benefits and has many useful applications in different industries. This paper examines the difficulties that exist when attempting to perform sound classification as it relates to respiratory disease classification. Some methods which have been employed such as independent component analysis and blind source separation are examined. Finally, an examination on the use of deep learning and long short-term memory networks is performed in order to identify how such a task can be implemented. | ['Mohammad-Parsa Hosseini', 'Chelsea Villanueva', 'Alexander Slowinski', 'Joshua Vincent'] | 2020-08-06 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.04008043e-01 -5.30340075e-01 2.38675609e-01 -1.68586090e-01
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2cdd11ce-890c-4505-8688-241ba2d49a46 | fednoil-a-simple-two-level-sampling-method | 2205.10110 | null | https://arxiv.org/abs/2205.10110v1 | https://arxiv.org/pdf/2205.10110v1.pdf | FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels | Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varying expertise or criteria and thus contain different amounts of noises. Local training on noisy labels can easily result in overfitting to noisy labels, which is devastating to the global model through aggregation. Although recent robust FL methods take malicious clients into account, they have not addressed local noisy labels on each device and the impact to the global model. In this paper, we develop a simple two-level sampling method "FedNoiL" that (1) selects clients for more robust global aggregation on the server; and (2) selects clean labels and correct pseudo-labels at the client end for more robust local training. The sampling probabilities are built upon clean label detection by the global model. Moreover, we investigate different schedules changing the local epochs between aggregations over the course of FL, which notably improves the communication and computation efficiency in noisy label setting. In experiments with homogeneous/heterogeneous data distributions and noise ratios, we observed that direct combinations of SOTA FL methods with SOTA noisy-label learning methods can easily fail but our method consistently achieves better and robust performance. | ['Jing Jiang', 'Bo Han', 'Guodong Long', 'Tianyi Zhou', 'Zhuowei Wang'] | 2022-05-20 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 1.72616899e-01 -2.06953570e-01 -1.26570806e-01 -3.87729257e-01
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26afeab2-2fb8-4bee-879d-215dccb8f3bc | that-sounds-right-auditory-self-supervision | 2210.01116 | null | https://arxiv.org/abs/2210.01116v1 | https://arxiv.org/pdf/2210.01116v1.pdf | That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation | Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual or tactile sensing, where unfortunately one fails to capture high-frequency interaction, while the other can be too delicate for large-scale data collection. In this work, we propose a data-centric approach to dynamic manipulation that uses an often ignored source of information: sound. We first collect a dataset of 25k interaction-sound pairs across five dynamic tasks using commodity contact microphones. Then, given this data, we leverage self-supervised learning to accelerate behavior prediction from sound. Our experiments indicate that this self-supervised 'pretraining' is crucial to achieving high performance, with a 34.5% lower MSE than plain supervised learning and a 54.3% lower MSE over visual training. Importantly, we find that when asked to generate desired sound profiles, online rollouts of our models on a UR10 robot can produce dynamic behavior that achieves an average of 11.5% improvement over supervised learning on audio similarity metrics. | ['Lerrel Pinto', 'Abitha Thankaraj'] | 2022-10-03 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.27333361e-01 -9.35153291e-02 1.10656053e-01 -1.22352727e-01
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8c70d458-e0c9-4bbd-a031-63a016cec2ea | a-marker-free-head-tracker-using-vision-based | 2103.13006 | null | https://arxiv.org/abs/2103.13006v1 | https://arxiv.org/pdf/2103.13006v1.pdf | A Marker-free Head Tracker Using Vision-based Head Pose Estimation with Adaptive Kalman Filter | The immersion and the interaction are the important features of the driving simulator. To improve these characteristics, this paper proposes a low-cost and mark-less driver head tracking framework based on the head pose estimation model, which makes the view of the simulator can automatically align with the driver's head pose. The proposed method only uses the RGB camera without the other hardware or marker. To handle the error of the head pose estimation model, this paper proposes an adaptive Kalman Filter. By analyzing the error distribution of the estimation model and user experience, the proposed Kalman Filter includes the adaptive observation noise coefficient and loop closure module, which can adaptive moderate the smoothness of the curve and keep the curve stable near the initial position. The experiments show that the proposed method is feasible, and it can be used with different head pose estimation models. | ['Wenhui Huang', 'Yiran Zhang', 'Yanxin Zhou', 'Chen Lv', 'Zhongxu Hu'] | 2021-03-24 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-7.10709572e-01 -6.08467273e-02 1.07291520e-01 -3.02607298e-01
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9bd8d85d-85af-440b-95cc-ae90e91772e6 | triplet-entropy-loss-improving-the | 2012.03775 | null | https://arxiv.org/abs/2012.03775v1 | https://arxiv.org/pdf/2012.03775v1.pdf | Triplet Entropy Loss: Improving The Generalisation of Short Speech Language Identification Systems | We present several methods to improve the generalisation of language identification (LID) systems to new speakers and to new domains. These methods involve Spectral augmentation, where spectrograms are masked in the frequency or time bands during training and CNN architectures that are pre-trained on the Imagenet dataset. The paper also introduces the novel Triplet Entropy Loss training method, which involves training a network simultaneously using Cross Entropy and Triplet loss. It was found that all three methods improved the generalisation of the models, though not significantly. Even though the models trained using Triplet Entropy Loss showed a better understanding of the languages and higher accuracies, it appears as though the models still memorise word patterns present in the spectrograms rather than learning the finer nuances of a language. The research shows that Triplet Entropy Loss has great potential and should be investigated further, not only in language identification tasks but any classification task. | ['Ruan van der Merwe'] | 2020-12-03 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 2.52768576e-01 3.93040068e-02 -1.00300848e-01 -3.92733008e-01
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c72a539a-39ff-4892-b8dc-8cdd6d6a5e6e | stylegene-crossover-and-mutation-of-region | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_StyleGene_Crossover_and_Mutation_of_Region-Level_Facial_Genes_for_Kinship_CVPR_2023_paper.pdf | StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face Synthesis | High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of StyleGAN2. As cycle-like losses are designed to measure the L_2 distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods. | ['Linlin Shen', 'Zepeng Huang', 'Xianxu Hou', 'Hao Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['kinship-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 2.51556069e-01 4.46847379e-01 6.73339143e-02 -7.73335755e-01
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8223c5e9-d678-4d23-b68a-ca7d343a3b5c | spatial-temporal-concept-based-explanation-of | 2206.05275 | null | https://arxiv.org/abs/2206.05275v1 | https://arxiv.org/pdf/2206.05275v1.pdf | Spatial-temporal Concept based Explanation of 3D ConvNets | Recent studies have achieved outstanding success in explaining 2D image recognition ConvNets. On the other hand, due to the computation cost and complexity of video data, the explanation of 3D video recognition ConvNets is relatively less studied. In this paper, we present a 3D ACE (Automatic Concept-based Explanation) framework for interpreting 3D ConvNets. In our approach: (1) videos are represented using high-level supervoxels, which is straightforward for human to understand; and (2) the interpreting framework estimates a score for each voxel, which reflects its importance in the decision procedure. Experiments show that our method can discover spatial-temporal concepts of different importance-levels, and thus can explore the influence of the concepts on a target task, such as action classification, in-depth. The codes are publicly available. | ['Jien Kato', 'Kensaku MORI', 'Yu Wang', 'Ying Ji'] | 2022-06-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ji_Spatial-Temporal_Concept_Based_Explanation_of_3D_ConvNets_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-classification'] | ['computer-vision'] | [ 2.57537872e-01 2.32771546e-01 -3.73480976e-01 -4.48873818e-01
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275d8e41-2399-4968-af53-428edf1f2a31 | msaf-multimodal-split-attention-fusion | 2012.07175 | null | https://arxiv.org/abs/2012.07175v2 | https://arxiv.org/pdf/2012.07175v2.pdf | MSAF: Multimodal Split Attention Fusion | Multimodal learning mimics the reasoning process of the human multi-sensory system, which is used to perceive the surrounding world. While making a prediction, the human brain tends to relate crucial cues from multiple sources of information. In this work, we propose a novel multimodal fusion module that learns to emphasize more contributive features across all modalities. Specifically, the proposed Multimodal Split Attention Fusion (MSAF) module splits each modality into channel-wise equal feature blocks and creates a joint representation that is used to generate soft attention for each channel across the feature blocks. Further, the MSAF module is designed to be compatible with features of various spatial dimensions and sequence lengths, suitable for both CNNs and RNNs. Thus, MSAF can be easily added to fuse features of any unimodal networks and utilize existing pretrained unimodal model weights. To demonstrate the effectiveness of our fusion module, we design three multimodal networks with MSAF for emotion recognition, sentiment analysis, and action recognition tasks. Our approach achieves competitive results in each task and outperforms other application-specific networks and multimodal fusion benchmarks. | ['Dongpu Cao', 'Guofa Li', 'Chuqing Hu', 'Lang Su'] | 2020-12-13 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.33552241e-01 -2.57821918e-01 -3.65978554e-02 -4.95090276e-01
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508dce17-7e8b-44af-b1ad-a7b2a1d2cd15 | twice-binnable-color-filter-arrays | 2306.17078 | null | https://arxiv.org/abs/2306.17078v1 | https://arxiv.org/pdf/2306.17078v1.pdf | Twice Binnable Color Filter Arrays | Pixel binning enables high speed, low power readout in low resolution modes, and more importantly, a reduction of read noise via floating diffusion binning. New, high resolution CMOS image sensors for mobile phones have moved beyond the once-binnable Quad Bayer and RGBW-Kodak patterns to the twice binnable Hexadeca Bayer pattern featuring 4x4 tiles of like colored pixels.Pixel binning enables high speed, low power readout in low resolution modes, and more importantly, a reduction of read noise via floating diffusion binning. New, high resolution CMOS image sensors for mobile phones have moved beyond the once-binnable Quad Bayer and RGBW-Kodak patterns to the twice binnable Hexadeca Bayer pattern featuring 4x4 tiles of like colored pixels. In this paper we present the non-intuitive result that Nona and Hexadeca Bayer can be superior to Quad Bayer in demosaicking quality due to degeneracies in the latter's spectrum. Hexadeca Bayer, nevertheless, suffers from the weakness of generating Quad Bayer after one round of binning. We present a novel twice binnable RGBW CFA, composed of 2x2 tiles capable of 4:1 floating diffusion binning, that is free from spectral degeneracies and thus demosaick well in full resolution and both binned modes. It also has a 4 dB low light SNR advantage over Quad and Hexadeca Bayer in the full resolution mode, and 6 dB SNR advantage in both the binned modes. | ['Tripurari Singh', 'Mritunjay Singh'] | 2023-06-29 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 6.18124664e-01 -5.60469747e-01 4.16918039e-01 1.13781698e-01
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7f01c548-88c3-479b-93fb-789c8150c6e6 | diffusion-models-as-masked-autoencoders | 2304.03283 | null | https://arxiv.org/abs/2304.03283v1 | https://arxiv.org/pdf/2304.03283v1.pdf | Diffusion Models as Masked Autoencoders | There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representations in light of recent interest in denoising diffusion models. While directly pre-training with diffusion models does not produce strong representations, we condition diffusion models on masked input and formulate diffusion models as masked autoencoders (DiffMAE). Our approach is capable of (i) serving as a strong initialization for downstream recognition tasks, (ii) conducting high-quality image inpainting, and (iii) being effortlessly extended to video where it produces state-of-the-art classification accuracy. We further perform a comprehensive study on the pros and cons of design choices and build connections between diffusion models and masked autoencoders. | ['Christoph Feichtenhofer', 'Alan Yuille', 'Cihang Xie', 'Huiyu Wang', 'Hu Xu', 'Haoqi Fan', 'Yanghao Li', 'Po-Yao Huang', 'Karttikeya Mangalam', 'Chen Wei'] | 2023-04-06 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 3.34356189e-01 2.55279362e-01 8.30450580e-02 -2.29420200e-01
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5c375a4d-cb82-4ce8-98a7-e3b5d2c4bcd2 | language-anisotropic-cross-lingual-model | 2205.12677 | null | https://arxiv.org/abs/2205.12677v2 | https://arxiv.org/pdf/2205.12677v2.pdf | Language Anisotropic Cross-Lingual Model Editing | Multilingual pre-trained language models can learn task-specific abilities or memorize facts across multiple languages but inevitably make undesired predictions with specific inputs. Under similar observation, model editing aims to post-hoc calibrate a model targeted to specific inputs with keeping the model's raw behavior. However, existing work only studies the monolingual scenario, which lacks the cross-lingual transferability to perform editing simultaneously across languages. In this work, we focus on cross-lingual model editing. Firstly, we define the cross-lingual model editing task and corresponding metrics, where an edit in one language propagates to the others. Next, we propose a framework to naturally adapt monolingual model editing approaches to the cross-lingual scenario using parallel corpus. Further, we propose language anisotropic editing to improve cross-lingual editing by amplifying different subsets of parameters for each language. On the newly defined cross-lingual model editing task, we empirically demonstrate the failure of monolingual baselines in propagating the edit to multiple languages and the effectiveness of the proposed language anisotropic model editing. Our code is publicly available at https://github.com/franklear/LiME. | ['Min Zhang', 'Wanxiang Che', 'Yutai Hou', 'Yang Xu'] | 2022-05-25 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 7.15924427e-02 2.92635951e-02 -1.74150601e-01 -5.44684231e-01
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fca42905-d378-4b0a-80f2-6515f3f4d754 | bilingual-lexicon-induction-for-low-resource-1 | 2210.14378 | null | https://arxiv.org/abs/2210.14378v1 | https://arxiv.org/pdf/2210.14378v1.pdf | Bilingual Lexicon Induction for Low-Resource Languages using Graph Matching via Optimal Transport | Bilingual lexicons form a critical component of various natural language processing applications, including unsupervised and semisupervised machine translation and crosslingual information retrieval. We improve bilingual lexicon induction performance across 40 language pairs with a graph-matching method based on optimal transport. The method is especially strong with low amounts of supervision. | ['Philipp Koehn', 'Carey Priebe', 'Kevin Duh', 'Ali Saad-Eldin', 'Kelly Marchisio'] | 2022-10-25 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 4.12856899e-02 -5.87890483e-02 -1.00052333e+00 -3.76254290e-01
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d707f94c-0b20-481d-9167-e26ec06b0501 | laplace-redux-effortless-bayesian-deep-1 | null | null | https://openreview.net/forum?id=gDcaUj4Myhn | https://openreview.net/pdf?id=gDcaUj4Myhn | Laplace Redux - Effortless Bayesian Deep Learning | Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection. The Laplace approximation (LA) is a classic, and arguably the simplest family of approximations for the intractable posteriors of deep neural networks. Yet, despite its simplicity, the LA is not as popular as alternatives like variational Bayes or deep ensembles. This may be due to assumptions that the LA is expensive due to the involved Hessian computation, that it is difficult to implement, or that it yields inferior results. In this work we show that these are misconceptions: we (i) review the range of variants of the LA including versions with minimal cost overhead; (ii) introduce "laplace", an easy-to-use software library for PyTorch offering user-friendly access to all major flavors of the LA; and (iii) demonstrate through extensive experiments that the LA is competitive with more popular alternatives in terms of performance, while excelling in terms of computational cost. We hope that this work will serve as a catalyst to a wider adoption of the LA in practical deep learning, including in domains where Bayesian approaches are not typically considered at the moment. | ['Philipp Hennig', 'Matthias Bauer', 'Runa Eschenhagen', 'Alexander Immer', 'Agustinus Kristiadi', 'Erik Daxberger'] | 2021-05-21 | null | null | null | neurips-2021-12 | ['misconceptions'] | ['miscellaneous'] | [-1.95319295e-01 5.36134802e-02 1.77595124e-01 -5.21418393e-01
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4879de81-b1c7-4911-8974-799f323ad35b | de-fake-detection-and-attribution-of-fake | 2210.06998 | null | https://arxiv.org/abs/2210.06998v2 | https://arxiv.org/pdf/2210.06998v2.pdf | DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models | Text-to-image generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns about the misuse of their generated fake images. To tackle this problem, we pioneer a systematic study on the detection and attribution of fake images generated by text-to-image generation models. Concretely, we first build a machine learning classifier to detect the fake images generated by various text-to-image generation models. We then attribute these fake images to their source models, such that model owners can be held responsible for their models' misuse. We further investigate how prompts that generate fake images affect detection and attribution. We conduct extensive experiments on four popular text-to-image generation models, including DALL$\cdot$E 2, Stable Diffusion, GLIDE, and Latent Diffusion, and two benchmark prompt-image datasets. Empirical results show that (1) fake images generated by various models can be distinguished from real ones, as there exists a common artifact shared by fake images from different models; (2) fake images can be effectively attributed to their source models, as different models leave unique fingerprints in their generated images; (3) prompts with the ``person'' topic or a length between 25 and 75 enable models to generate fake images with higher authenticity. All findings contribute to the community's insight into the threats caused by text-to-image generation models. We appeal to the community's consideration of the counterpart solutions, like ours, against the rapidly-evolving fake image generation. | ['Yang Zhang', 'Ning Yu', 'Zheng Li', 'Zeyang Sha'] | 2022-10-13 | null | null | null | null | ['fake-image-detection'] | ['computer-vision'] | [ 3.37356508e-01 2.00893372e-01 -1.48998603e-01 -1.29192099e-01
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c34c83ec-554e-4768-a287-4e3ff1030e9f | empirical-analysis-of-noising-scheme-based | null | null | https://aclanthology.org/2022.lrec-1.93 | https://aclanthology.org/2022.lrec-1.93.pdf | Empirical Analysis of Noising Scheme based Synthetic Data Generation for Automatic Post-editing | Automatic post-editing (APE) refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system. This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs. Moreover, the amount of data is restricted even for language pairs in which official data has been released, such as WMT. To solve this problem and promote universal APE research regardless of APE data existence, this study proposes a method for automatically generating APE data based on a noising scheme from a parallel corpus. Particularly, we propose a human mimicking errors-based noising scheme that considers a practical correction process at the human level. We propose a precise inspection to attain high performance, and we derived the optimal noising schemes that show substantial effectiveness. Through these, we also demonstrate that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance. In addition, we propose a dynamic noise injection strategy that enables the acquisition of a robust error correction capability and demonstrated its effectiveness by comparative analysis. This study enables obtaining a high performance APE model without human-generated data and can promote universal APE research for all language pairs targeting English. | ['Heuiseok Lim', 'Sugyeong Eo', 'Jungseob Lee', 'Jaehyung Seo', 'Seolhwa Lee', 'Chanjun Park', 'Hyeonseok Moon'] | null | null | null | null | lrec-2022-6 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 3.36168438e-01 -1.05550975e-01 1.51388466e-01 -6.25298172e-02
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b0782767-a1d9-40e7-83ab-69a6cd06d711 | deformable-kernel-expansion-model-for | 2303.15737 | null | https://arxiv.org/abs/2303.15737v1 | https://arxiv.org/pdf/2303.15737v1.pdf | Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection | Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kernel Expansion (DKE), which incorporates the merits of both segmentation and contour-based detectors. DKE employs a segmentation module to segment the shrunken text region as the text kernel, then expands the text kernel contour to obtain text boundary by regressing the vertex-wise offsets. Generating the text kernel by segmentation enables DKE to inherit the arbitrary-shaped text region modeling capability of segmentation-based detectors. Regressing the kernel contour with some sampled vertices enables DKE to avoid the complicated pixel-level post-processing and better learn contour deformation as the contour-based detectors. Moreover, we propose an Optimal Bipartite Graph Matching Loss (OBGML) that measures the matching error between the predicted contour and the ground truth, which efficiently minimizes the global contour matching distance. Extensive experiments on CTW1500, Total-Text, MSRA-TD500, and ICDAR2015 demonstrate that DKE achieves a good tradeoff between accuracy and efficiency in scene text detection. | ['Bo Liu', 'Wenhao Tang', 'Sheng Huang', 'Tao He'] | 2023-03-28 | null | null | null | null | ['scene-text-detection', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 9.14054364e-02 -7.26545900e-02 1.05397115e-02 -1.15085803e-01
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0aafe691-c429-4f92-b402-0cef4b23906f | continual-vision-language-representaion | 2305.07437 | null | https://arxiv.org/abs/2305.07437v5 | https://arxiv.org/pdf/2305.07437v5.pdf | Continual Vision-Language Representation Learning with Off-Diagonal Information | Large-scale multi-modal contrastive learning frameworks like CLIP typically require a large amount of image-text samples for training. However, these samples are always collected continuously in real scenarios. This paper discusses the feasibility of continual CLIP training using streaming data. Unlike continual learning based on self-supervised learning methods for pure images, which is empirically robust against catastrophic forgetting, CLIP's performance degeneration in the continual setting is significant and non-neglectable. By analyzing the changes in the model's representation space during continual CLIP training from a spatial geometry perspective, we explore and summarize these spatial variations as Spatial Disorder (SD), which can be divided into Intra-modal Rotation and Inter-modal Deviation. Moreover, we empirically and theoretically demonstrate how SD leads to a performance decline for CLIP on cross-modal retrieval tasks. To alleviate SD, we propose a new continual vision-language representation learning framework Mod-X: Maintain off-diagonal information-matriX. By selectively aligning the off-diagonal information distribution of contrastive matrices, the Mod-X improves the capability of the multi-modal model by maintaining the multi-modal representation space alignment on the old data domain during continuously fitting the new training data domain. Experiments on commonly used datasets with different scales and scopes have demonstrated the effectiveness of our method. | ['Qi Tian', 'Yueting Zhuang', 'Siliang Tang', 'Longhui Wei', 'Zixuan Ni'] | 2023-05-11 | null | null | null | null | ['cross-modal-retrieval'] | ['miscellaneous'] | [ 1.44280732e-01 -3.88730735e-01 -1.39673918e-01 -3.09437769e-03
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6117648a-7dbf-4685-94d7-c60dcf798bba | eliminating-mole-size-in-melanoma | 2212.05116 | null | https://arxiv.org/abs/2212.05116v1 | https://arxiv.org/pdf/2212.05116v1.pdf | Eliminating Mole Size in Melanoma Classification | While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on which they are trained. This paper combats this notion, arguing that mole size, a vital attribute in professional dermatology, is a red herring in automated melanoma detection. Although malignant melanomas are consistently larger than benign melanomas, this distinction proves unreliable and harmful when images cannot be contextually scaled. This implementation builds a custom model that eliminates size as a training feature to prevent overfitting to incorrect parameters. Additionally, random rotation and contrast augmentations are performed to simulate the real-world use of melanoma detection applications. Several custom models with varying forms of data augmentation are implemented to demonstrate the most significant features of the generalization abilities of mole classifiers. These implementations show that user unpredictability is crucial when utilizing such applications. The caution required when manually modifying data is acknowledged, as data loss and biased conclusions are necessary considerations in this process. Additionally, mole size inconsistency and its significance are discussed in both the dermatology and deep learning communities. | ['Benjamin Sanders', 'Ethan Martinez', 'Gavin Harding', 'Nick DiSanto'] | 2022-12-09 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 6.55213237e-01 -2.49076523e-02 -2.21816562e-02 -6.66092038e-01
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dbeb8e1c-b21b-4417-9b89-2d3237af2420 | dynamic-graph-embedding-via-lstm-history | 1911.01551 | null | https://arxiv.org/abs/1911.01551v1 | https://arxiv.org/pdf/1911.01551v1.pdf | Dynamic Graph Embedding via LSTM History Tracking | Many real world networks are very large and constantly change over time. These dynamic networks exist in various domains such as social networks, traffic networks and biological interactions. To handle large dynamic networks in downstream applications such as link prediction and anomaly detection, it is essential for such networks to be transferred into a low dimensional space. Recently, network embedding, a technique that converts a large graph into a low-dimensional representation, has become increasingly popular due to its strength in preserving the structure of a network. Efficient dynamic network embedding, however, has not yet been fully explored. In this paper, we present a dynamic network embedding method that integrates the history of nodes over time into the current state of nodes. The key contribution of our work is 1) generating dynamic network embedding by combining both dynamic and static node information 2) tracking history of neighbors of nodes using LSTM 3) significantly decreasing the time and memory by training an autoencoder LSTM model using temporal walks rather than adjacency matrices of graphs which are the common practice. We evaluate our method in multiple applications such as anomaly detection, link prediction and node classification in datasets from various domains. | ['Yonggang Hu', 'Shima Khoshraftar', 'Sedigheh Mahdavi', 'Junfeng Liu', 'Aijun An'] | 2019-11-05 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-6.23728521e-02 2.17526659e-01 -1.15900278e-01 -1.61889642e-01
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f069446b-9e79-447d-b731-3e30e7d17509 | rb-dust-a-reference-based-dataset-for-vision | 2306.07244 | null | https://arxiv.org/abs/2306.07244v1 | https://arxiv.org/pdf/2306.07244v1.pdf | RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal | Dust in the agricultural landscape is a significant challenge and influences, for example, the environmental perception of autonomous agricultural machines. Image enhancement algorithms can be used to reduce dust. However, these require dusty and dust-free images of the same environment for validation. In fact, to date, there is no dataset that we are aware of that addresses this issue. Therefore, we present the agriscapes RB-Dust dataset, which is named after its purpose of reference-based dust removal. It is not possible to take pictures from the cabin during tillage, as this would cause shifts in the images. Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor. The test setup was based on a half-sided gate through which the tractor could drive. The field tests were carried out on a farm in Bavaria, Germany, during tillage. During the field tests, other parameters such as soil moisture and wind speed were controlled, as these significantly affect dust development. We validated our dataset with contrast enhancement and image dehazing algorithms and analyzed the generalizability from recordings from the moving tractor. Finally, we demonstrate the application of dust removal based on a high-level vision task, such as person classification. Our empirical study confirms the validity of RB-Dust for vision-based dust removal in agriculture. | ['Thomas Dietmueller', 'Timo Oksanen', 'Peter Buckel'] | 2023-06-12 | null | null | null | null | ['image-dehazing', 'image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 2.47812748e-01 -1.86410367e-01 7.75369048e-01 -3.40260565e-02
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43335516-d850-41f9-a784-08ca54337006 | faq-based-question-answering-via-word | 1507.02628 | null | http://arxiv.org/abs/1507.02628v1 | http://arxiv.org/pdf/1507.02628v1.pdf | FAQ-based Question Answering via Word Alignment | In this paper, we propose a novel word-alignment-based method to solve the
FAQ-based question answering task. First, we employ a neural network model to
calculate question similarity, where the word alignment between two questions
is used for extracting features. Second, we design a bootstrap-based feature
extraction method to extract a small set of effective lexical features. Third,
we propose a learning-to-rank algorithm to train parameters more suitable for
the ranking tasks. Experimental results, conducted on three languages (English,
Spanish and Japanese), demonstrate that the question similarity model is more
effective than baseline systems, the sparse features bring 5% improvements on
top-1 accuracy, and the learning-to-rank algorithm works significantly better
than the traditional method. We further evaluate our method on the answer
sentence selection task. Our method outperforms all the previous systems on the
standard TREC data set. | ['Abraham Ittycheriah', 'Zhiguo Wang'] | 2015-07-09 | null | null | null | null | ['question-similarity'] | ['natural-language-processing'] | [ 1.17643088e-01 -4.63770598e-01 -6.69834316e-02 -6.83572948e-01
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c5a1a948-c22a-425a-bde3-33ecd52b4ed4 | inertial-navigation-using-an-inertial-sensor | 2201.11983 | null | https://arxiv.org/abs/2201.11983v1 | https://arxiv.org/pdf/2201.11983v1.pdf | Inertial Navigation Using an Inertial Sensor Array | We present a comprehensive framework for fusing measurements from multiple and generally placed accelerometers and gyroscopes to perform inertial navigation. Using the angular acceleration provided by the accelerometer array, we show that the numerical integration of the orientation can be done with second-order accuracy, which is more accurate compared to the traditional first-order accuracy that can be achieved when only using the gyroscopes. Since orientation errors are the most significant error source in inertial navigation, improving the orientation estimation reduces the overall navigation error. The practical performance benefit depends on prior knowledge of the inertial sensor array, and therefore we present four different state-space models using different underlying assumptions regarding the orientation modeling. The models are evaluated using a Lie Group Extended Kalman filter through simulations and real-world experiments. We also show how individual accelerometer biases are unobservable and can be replaced by a six-dimensional bias term whose dimension is fixed and independent of the number of accelerometers. | ['Joakim Jaldén', 'Gustaf Hendeby', 'Isaac Skog', 'Håkan Carlsson'] | 2022-01-28 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 4.76401895e-02 -2.68744081e-02 -2.29353756e-01 -4.77219187e-02
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7cf56270-87e1-45b4-87ea-62ab0a00da32 | learnable-human-mesh-triangulation-for-3d | 2208.11251 | null | https://arxiv.org/abs/2208.11251v1 | https://arxiv.org/pdf/2208.11251v1.pdf | Learnable human mesh triangulation for 3D human pose and shape estimation | Compared to joint position, the accuracy of joint rotation and shape estimation has received relatively little attention in the skinned multi-person linear model (SMPL)-based human mesh reconstruction from multi-view images. The work in this field is broadly classified into two categories. The first approach performs joint estimation and then produces SMPL parameters by fitting SMPL to resultant joints. The second approach regresses SMPL parameters directly from the input images through a convolutional neural network (CNN)-based model. However, these approaches suffer from the lack of information for resolving the ambiguity of joint rotation and shape reconstruction and the difficulty of network learning. To solve the aforementioned problems, we propose a two-stage method. The proposed method first estimates the coordinates of mesh vertices through a CNN-based model from input images, and acquires SMPL parameters by fitting the SMPL model to the estimated vertices. Estimated mesh vertices provide sufficient information for determining joint rotation and shape, and are easier to learn than SMPL parameters. According to experiments using Human3.6M and MPI-INF-3DHP datasets, the proposed method significantly outperforms the previous works in terms of joint rotation and shape estimation, and achieves competitive performance in terms of joint location estimation. | ['Ju Yong Chang', 'Sungbum Park', 'Sungho Chun'] | 2022-08-24 | null | null | null | null | ['3d-human-pose-estimation', '3d-human-pose-and-shape-estimation'] | ['computer-vision', 'computer-vision'] | [-3.47532779e-01 -5.54875992e-02 -2.97415018e-01 -2.67918017e-02
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57f5d0d3-4776-46fb-b60a-65d922dcd520 | can-discrete-information-extraction-prompts | 2302.09865 | null | https://arxiv.org/abs/2302.09865v2 | https://arxiv.org/pdf/2302.09865v2.pdf | Can discrete information extraction prompts generalize across language models? | We study whether automatically-induced prompts that effectively extract information from a language model can also be used, out-of-the-box, to probe other language models for the same information. After confirming that discrete prompts induced with the AutoPrompt algorithm outperform manual and semi-manual prompts on the slot-filling task, we demonstrate a drop in performance for AutoPrompt prompts learned on a model and tested on another. We introduce a way to induce prompts by mixing language models at training time that results in prompts that generalize well across models. We conduct an extensive analysis of the induced prompts, finding that the more general prompts include a larger proportion of existing English words and have a less order-dependent and more uniform distribution of information across their component tokens. Our work provides preliminary evidence that it's possible to generate discrete prompts that can be induced once and used with a number of different models, and gives insights on the properties characterizing such prompts. | ['Marco Baroni', 'Sebastian Riedel', 'Fabio Petroni', 'Roberto Dessì', 'Nathanaël Carraz Rakotonirina'] | 2023-02-20 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 3.58210981e-01 5.42508066e-01 -3.19621414e-01 -5.41133940e-01
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eff3f36f-7f6e-4299-8ff2-c15f10fe3c71 | importance-of-copying-mechanism-for-news | 1904.11475 | null | http://arxiv.org/abs/1904.11475v1 | http://arxiv.org/pdf/1904.11475v1.pdf | Importance of Copying Mechanism for News Headline Generation | News headline generation is an essential problem of text summarization
because it is constrained, well-defined, and is still hard to solve. Models
with a limited vocabulary can not solve it well, as new named entities can
appear regularly in the news and these entities often should be in the
headline. News articles in morphologically rich languages such as Russian
require model modifications due to a large number of possible word forms. This
study aims to validate that models with a possibility of copying words from the
original article performs better than models without such an option. The
proposed model achieves a mean ROUGE score of 23 on the provided test dataset,
which is 8 points greater than the result of a similar model without a copying
mechanism. Moreover, the resulting model performs better than any known model
on the new dataset of Russian news. | ['Ilya Gusev'] | 2019-04-25 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [-3.97742763e-02 4.63198692e-01 -3.79112512e-01 6.87939227e-02
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5d39637c-e397-4f92-be06-9d8752c8fd66 | attention-based-end-to-end-models-for-small | 1803.10916 | null | http://arxiv.org/abs/1803.10916v1 | http://arxiv.org/pdf/1803.10916v1.pdf | Attention-based End-to-End Models for Small-Footprint Keyword Spotting | In this paper, we propose an attention-based end-to-end neural approach for
small-footprint keyword spotting (KWS), which aims to simplify the pipelines of
building a production-quality KWS system. Our model consists of an encoder and
an attention mechanism. The encoder transforms the input signal into a high
level representation using RNNs. Then the attention mechanism weights the
encoder features and generates a fixed-length vector. Finally, by linear
transformation and softmax function, the vector becomes a score used for
keyword detection. We also evaluate the performance of different encoder
architectures, including LSTM, GRU and CRNN. Experiments on real-world wake-up
data show that our approach outperforms the recent Deep KWS approach by a large
margin and the best performance is achieved by CRNN. To be more specific, with
~84K parameters, our attention-based model achieves 1.02% false rejection rate
(FRR) at 1.0 false alarm (FA) per hour. | ['Yujun Wang', 'Junbo Zhang', 'Changhao Shan', 'Lei Xie'] | 2018-03-29 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 7.12205376e-03 -1.14313722e-01 -1.91136956e-01 -4.66436833e-01
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713f5f25-4e1b-4157-a301-69f229d4557d | attention-aware-face-hallucination-via-deep | 1708.03132 | null | http://arxiv.org/abs/1708.03132v1 | http://arxiv.org/pdf/1708.03132v1.pdf | Attention-Aware Face Hallucination via Deep Reinforcement Learning | Face hallucination is a domain-specific super-resolution problem with the
goal to generate high-resolution (HR) faces from low-resolution (LR) input
images. In contrast to existing methods that often learn a single
patch-to-patch mapping from LR to HR images and are regardless of the
contextual interdependency between patches, we propose a novel Attention-aware
Face Hallucination (Attention-FH) framework which resorts to deep reinforcement
learning for sequentially discovering attended patches and then performing the
facial part enhancement by fully exploiting the global interdependency of the
image. Specifically, in each time step, the recurrent policy network is
proposed to dynamically specify a new attended region by incorporating what
happened in the past. The state (i.e., face hallucination result for the whole
image) can thus be exploited and updated by the local enhancement network on
the selected region. The Attention-FH approach jointly learns the recurrent
policy network and local enhancement network through maximizing the long-term
reward that reflects the hallucination performance over the whole image.
Therefore, our proposed Attention-FH is capable of adaptively personalizing an
optimal searching path for each face image according to its own characteristic.
Extensive experiments show our approach significantly surpasses the
state-of-the-arts on in-the-wild faces with large pose and illumination
variations. | ['Qingxing Cao', 'Liang Lin', 'Xiaodan Liang', 'Yukai Shi', 'Guanbin Li'] | 2017-08-10 | attention-aware-face-hallucination-via-deep-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Cao_Attention-Aware_Face_Hallucination_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Cao_Attention-Aware_Face_Hallucination_CVPR_2017_paper.pdf | cvpr-2017-7 | ['face-hallucination'] | ['computer-vision'] | [ 2.60036051e-01 3.92319709e-01 7.64947757e-02 -3.46179217e-01
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0f5dcec5-4c34-4768-a2ed-09ac05428748 | show-and-tell-a-neural-image-caption | 1411.4555 | null | http://arxiv.org/abs/1411.4555v2 | http://arxiv.org/pdf/1411.4555v2.pdf | Show and Tell: A Neural Image Caption Generator | Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. For instance, while the
current state-of-the-art BLEU-1 score (the higher the better) on the Pascal
dataset is 25, our approach yields 59, to be compared to human performance
around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66,
and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we
achieve a BLEU-4 of 27.7, which is the current state-of-the-art. | ['Samy Bengio', 'Alexander Toshev', 'Dumitru Erhan', 'Oriol Vinyals'] | 2014-11-17 | show-and-tell-a-neural-image-caption-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Vinyals_Show_and_Tell_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf | cvpr-2015-6 | ['multi-modal'] | ['miscellaneous'] | [ 3.85772765e-01 2.97358006e-01 -1.63762391e-01 -4.56343293e-01
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4dcb0d7e-a0a0-4cb6-9957-fc847c0d42fc | bsn-complementary-boundary-regressor-with | 2009.07641 | null | https://arxiv.org/abs/2009.07641v5 | https://arxiv.org/pdf/2009.07641v5.pdf | BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation | Generating human action proposals in untrimmed videos is an important yet challenging task with wide applications. Current methods often suffer from the noisy boundary locations and the inferior quality of confidence scores used for proposal retrieving. In this paper, we present BSN++, a new framework which exploits complementary boundary regressor and relation modeling for temporal proposal generation. First, we propose a novel boundary regressor based on the complementary characteristics of both starting and ending boundary classifiers. Specifically, we utilize the U-shaped architecture with nested skip connections to capture rich contexts and introduce bi-directional boundary matching mechanism to improve boundary precision. Second, to account for the proposal-proposal relations ignored in previous methods, we devise a proposal relation block to which includes two self-attention modules from the aspects of position and channel. Furthermore, we find that there inevitably exists data imbalanced problems in the positive/negative proposals and temporal durations, which harm the model performance on tail distributions. To relieve this issue, we introduce the scale-balanced re-sampling strategy. Extensive experiments are conducted on two popular benchmarks: ActivityNet-1.3 and THUMOS14, which demonstrate that BSN++ achieves the state-of-the-art performance. Not surprisingly, the proposed BSN++ ranked 1st place in the CVPR19 - ActivityNet challenge leaderboard on temporal action localization task. | ['Haisheng Su', 'Yu Qiao', 'Weihao Gan', 'Wei Wu', 'Junjie Yan'] | 2020-09-15 | null | null | null | null | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 3.21531177e-01 -1.92497283e-01 -6.95007980e-01 -2.35847637e-01
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e3cac619-c248-461b-9a38-8fc1f31de282 | diga-distil-to-generalize-and-then-adapt-for | 2304.02222 | null | https://arxiv.org/abs/2304.02222v1 | https://arxiv.org/pdf/2304.02222v1.pdf | DiGA: Distil to Generalize and then Adapt for Domain Adaptive Semantic Segmentation | Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces several challenges in each stage: for warm-up, the widely adopted adversarial training often results in limited performance gain, due to blind feature alignment; for self-training, finding proper categorical thresholds is very tricky. To alleviate these issues, we first propose to replace the adversarial training in the warm-up stage by a novel symmetric knowledge distillation module that only accesses the source domain data and makes the model domain generalizable. Surprisingly, this domain generalizable warm-up model brings substantial performance improvement, which can be further amplified via our proposed cross-domain mixture data augmentation technique. Then, for the self-training stage, we propose a threshold-free dynamic pseudo-label selection mechanism to ease the aforementioned threshold problem and make the model better adapted to the target domain. Extensive experiments demonstrate that our framework achieves remarkable and consistent improvements compared to the prior arts on popular benchmarks. Codes and models are available at https://github.com/fy-vision/DiGA | ['Alois Knoll', 'He Wang', 'Ziyuan Liu', 'Akhil Gurram', 'Fengyi Shen'] | 2023-04-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Shen_DiGA_Distil_To_Generalize_and_Then_Adapt_for_Domain_Adaptive_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pseudo-label'] | ['miscellaneous'] | [ 2.21348137e-01 -3.76904570e-02 -4.11170304e-01 -5.19410670e-01
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542f3ed8-5bf2-4bc6-bf14-4c05d701c55a | sf-dst-few-shot-self-feeding-reading | 2209.07742 | null | https://arxiv.org/abs/2209.07742v1 | https://arxiv.org/pdf/2209.07742v1.pdf | SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task | Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data. In this paper, we introduce an ontology-free few-shot DST with self-feeding belief state input. The self-feeding belief state input increases the accuracy in multi-turn dialogue by summarizing previous dialogue. Also, we newly developed a slot-gate auxiliary task. This new auxiliary task helps classify whether a slot is mentioned in the dialogue. Our model achieved the best score in a few-shot setting for four domains on multiWOZ 2.0. | ['Gary Geunbae Lee', 'Jihyun Lee'] | 2022-09-16 | null | null | null | null | ['dialogue-state-tracking'] | ['natural-language-processing'] | [-1.82253107e-01 4.76361394e-01 -3.74177814e-01 -6.64647460e-01
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912dfdda-c018-4076-a30b-a6e91a11421c | learning-to-generate-reviews-and-discovering | 1704.01444 | null | http://arxiv.org/abs/1704.01444v2 | http://arxiv.org/pdf/1704.01444v2.pdf | Learning to Generate Reviews and Discovering Sentiment | We explore the properties of byte-level recurrent language models. When given
sufficient amounts of capacity, training data, and compute time, the
representations learned by these models include disentangled features
corresponding to high-level concepts. Specifically, we find a single unit which
performs sentiment analysis. These representations, learned in an unsupervised
manner, achieve state of the art on the binary subset of the Stanford Sentiment
Treebank. They are also very data efficient. When using only a handful of
labeled examples, our approach matches the performance of strong baselines
trained on full datasets. We also demonstrate the sentiment unit has a direct
influence on the generative process of the model. Simply fixing its value to be
positive or negative generates samples with the corresponding positive or
negative sentiment. | ['Rafal Jozefowicz', 'Ilya Sutskever', 'Alec Radford'] | 2017-04-05 | learning-to-generate-reviews-and-discovering-1 | https://openreview.net/forum?id=SJ71VXZAZ | https://openreview.net/pdf?id=SJ71VXZAZ | iclr-2018-1 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 7.13025257e-02 4.99874234e-01 -7.74446249e-01 -5.80890596e-01
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c00e823e-0679-4b2f-8272-3164072cbb9a | reinforced-medical-report-generation-with-x | 2011.07680 | null | https://arxiv.org/abs/2011.07680v1 | https://arxiv.org/pdf/2011.07680v1.pdf | Reinforced Medical Report Generation with X-Linear Attention and Repetition Penalty | To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated with the classic encoder-decoder architecture to enhance the performance of deep models. However, these state-of-the-art solutions mainly suffer from two shortcomings: (i) their attention mechanisms cannot utilize high-order feature interactions, and (ii) due to the use of TF-IDF-based reward functions, these methods are fragile with generating repeated terms. Therefore, in this work, we propose a reinforced medical report generation solution with x-linear attention and repetition penalty mechanisms (ReMRG-XR) to overcome these problems. Specifically, x-linear attention modules are used to explore high-order feature interactions and achieve multi-modal reasoning, while repetition penalty is used to apply penalties to repeated terms during the model's training process. Extensive experimental studies have been conducted on two public datasets, and the results show that ReMRG-XR greatly outperforms the state-of-the-art baselines in terms of all metrics. | ['Thomas Lukasiewicz', 'Zhenghua Xu', 'Chang Qi', 'Wenting Xu'] | 2020-11-16 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.29078825e-02 3.24773788e-01 -2.52138793e-01 -5.24824083e-01
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e4a49487-7d51-4c66-b3b2-997264eb9121 | cross-spatial-pixel-integration-and-cross | 2307.02974 | null | https://arxiv.org/abs/2307.02974v1 | https://arxiv.org/pdf/2307.02974v1.pdf | Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution | Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local window, enhancing its efficiency. Consequently, the receptive fields in a single attention layer are inadequate, leading to insufficient context modeling. Furthermore, while most transform-based approaches reuse shallow features through skip connections, relying solely on these connections treats shallow and deep features equally, impeding the model's ability to characterize them. To address these issues, we propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances global cognition and understanding of the entire image, facilitating efficient integration of features cross-stages. The model incorporates cross-spatial pixel integration attention (CSPIA) to introduce contextual information into a local window, while cross-stage feature fusion attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed SPIFFNet in terms of both quantitative metrics and visual quality when compared to state-of-the-art methods. | ['Teng Long', 'Yongqiang Zhao', 'Xiaoxu Wang', 'Le Zheng', 'Binglu Wang', 'Lingtong Min', 'Yuting Lu'] | 2023-07-06 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.64839494e-01 -4.81163740e-01 6.07249737e-02 -4.61969912e-01
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73f0ca5b-639f-49bb-acb7-f4d5b77b8070 | unsupervised-domain-adaptation-via-1 | 1907.13590 | null | https://arxiv.org/abs/1907.13590v2 | https://arxiv.org/pdf/1907.13590v2.pdf | Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation | A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised domain adaptation methods address this problem by alleviating the domain shift between the labeled source data and the unlabeled target data. In this work, we achieve cross-modality domain adaptation, i.e. between CT and MRI images, via disentangled representations. Compared to learning a one-to-one mapping as the state-of-art CycleGAN, our model recovers a many-to-many mapping between domains to capture the complex cross-domain relations. It preserves semantic feature-level information by finding a shared content space instead of a direct pixelwise style transfer. Domain adaptation is achieved in two steps. First, images from each domain are embedded into two spaces, a shared domain-invariant content space and a domain-specific style space. Next, the representation in the content space is extracted to perform a task. We validated our method on a cross-modality liver segmentation task, to train a liver segmentation model on CT images that also performs well on MRI. Our method achieved Dice Similarity Coefficient (DSC) of 0.81, outperforming a CycleGAN-based method of 0.72. Moreover, our model achieved good generalization to joint-domain learning, in which unpaired data from different modalities are jointly learned to improve the segmentation performance on each individual modality. Lastly, under a multi-modal target domain with significant diversity, our approach exhibited the potential for diverse image generation and remained effective with DSC of 0.74 on multi-phasic MRI while the CycleGAN-based method performed poorly with a DSC of only 0.52. | ['Nicha C. Dvornek', 'Junlin Yang', 'James S. Duncan', 'MingDe Lin', 'Julius Chapiro', 'Fan Zhang'] | 2019-07-31 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 6.18334889e-01 1.99453667e-01 -2.74568200e-01 -4.08765763e-01
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6.24213815e-01 -5.23569107e-01 -1.66234508e-01 2.26368606e-01] | [14.586112022399902, -2.033586263656616] |
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