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6c782a41-13ad-44ec-b29e-1d0b0a4f43e1 | hl-dataset-grounding-high-level-linguistic | 2302.12189 | null | https://arxiv.org/abs/2302.12189v1 | https://arxiv.org/pdf/2302.12189v1.pdf | HL Dataset: Grounding High-Level Linguistic Concepts in Vision | Current captioning datasets, focus on object-centric captions, describing the visible objects in the image, often ending up stating the obvious (for humans), e.g. "people eating food in a park". Although these datasets are useful to evaluate the ability of Vision & Language models to recognize the visual content, they lack in expressing trivial abstract concepts, e.g. "people having a picnic". Such concepts are licensed by human's personal experience and contribute to forming common sense assumptions. We present the High-Level Dataset; a dataset extending 14997 images of the COCO dataset with 134973 human-annotated (high-level) abstract captions collected along three axes: scenes, actions and rationales. We describe and release such dataset and we show how it can be used to assess models' multimodal grounding of abstract concepts and enrich models' visio-lingusitic representations. Moreover, we describe potential tasks enabled by this dataset involving high- and low-level concepts interactions. | ['Albert Gatt', 'Kees Van Deemter', 'Michele Cafagna'] | 2023-02-23 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.67836690e-01 5.93854666e-01 -1.18947200e-01 -5.34337699e-01
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8a5c9c99-f4c7-44a9-b9d2-410d38831103 | proactive-query-expansion-for-streaming-data | 2201.06592 | null | https://arxiv.org/abs/2201.06592v1 | https://arxiv.org/pdf/2201.06592v1.pdf | Proactive Query Expansion for Streaming Data Using External Source | Query expansion is the process of reformulating the original query by adding relevant words. Choosing which terms to add in order to improve the performance of the query expansion methods or to enhance the quality of the retrieved results is an important aspect of any information retrieval system. Adding words that can positively impact the quality of the search query or are informative enough play an important role in returning or gathering relevant documents that cover a certain topic can result in improving the efficiency of the information retrieval system. Typically, query expansion techniques are used to add or substitute words to a given search query to collect relevant data. In this paper, we design and implement a pipeline of automated query expansion. We outline several tools using different methods to expand the query. Our methods depend on targeting emergent events in streaming data over time and finding the hidden topics from targeted documents using probabilistic topic models. We employ Dynamic Eigenvector Centrality to trigger the emergent events, and the Latent Dirichlet Allocation to discover the topics. Also, we use an external data source as a secondary stream to supplement the primary stream with relevant words and expand the query using the words from both primary and secondary streams. An experimental study is performed on Twitter data (primary stream) related to the events that happened during protests in Baltimore in 2015. The quality of the retrieved results was measured using a quality indicator of the streaming data: tweets count, hashtag count, and hashtag clustering. | ['Ilya Safro', 'Alexander Herzog', 'Yuheng Du', 'Amy Apon', 'Farah Alshanik'] | 2022-01-17 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-1.91560071e-02 -1.61328748e-01 -1.95635334e-01 3.35585028e-02
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4d517158-d550-4d69-a074-16d59c0b87d6 | crackseg9k-a-collection-and-benchmark-for | 2208.13054 | null | https://arxiv.org/abs/2208.13054v1 | https://arxiv.org/pdf/2208.13054v1.pdf | CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks | The detection of cracks is a crucial task in monitoring structural health and ensuring structural safety. The manual process of crack detection is time-consuming and subjective to the inspectors. Several researchers have tried tackling this problem using traditional Image Processing or learning-based techniques. However, their scope of work is limited to detecting cracks on a single type of surface (walls, pavements, glass, etc.). The metrics used to evaluate these methods are also varied across the literature, making it challenging to compare techniques. This paper addresses these problems by combining previously available datasets and unifying the annotations by tackling the inherent problems within each dataset, such as noise and distortions. We also present a pipeline that combines Image Processing and Deep Learning models. Finally, we benchmark the results of proposed models on these metrics on our new dataset and compare them with state-of-the-art models in the literature. | ['Sai Chowdeswara Rao Korlapati', 'Saipraneeth Devunuri', 'Siddharth Sharma', 'Dhananjay Balakrishnan', 'Shreyas Singh', 'Shreyas Kulkarni'] | 2022-08-27 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [ 4.33969229e-01 -1.51247755e-01 3.26715022e-01 -1.02955282e-01
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b0bf25c4-13a5-4cb1-959c-44c6d382c95b | dacs-domain-adaptation-via-cross-domain-mixed | 2007.08702 | null | https://arxiv.org/abs/2007.08702v2 | https://arxiv.org/pdf/2007.08702v2.pdf | DACS: Domain Adaptation via Cross-domain Mixed Sampling | Semantic segmentation models based on convolutional neural networks have recently displayed remarkable performance for a multitude of applications. However, these models typically do not generalize well when applied on new domains, especially when going from synthetic to real data. In this paper we address the problem of unsupervised domain adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of interest (target domain). Existing methods have seen success by training on pseudo-labels for these unlabelled images. Multiple techniques have been proposed to mitigate low-quality pseudo-labels arising from the domain shift, with varying degrees of success. We propose DACS: Domain Adaptation via Cross-domain mixed Sampling, which mixes images from the two domains along with the corresponding labels and pseudo-labels. These mixed samples are then trained on, in addition to the labelled data itself. We demonstrate the effectiveness of our solution by achieving state-of-the-art results for GTA5 to Cityscapes, a common synthetic-to-real semantic segmentation benchmark for UDA. | ['Wilhelm Tranheden', 'Viktor Olsson', 'Lennart Svensson', 'Juliano Pinto'] | 2020-07-17 | null | null | null | null | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.73173070e-01 1.90918818e-01 -2.69371811e-02 -7.11811423e-01
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4fa26c72-5877-4855-83de-aa2ea8a6a7ca | matching-feature-sets-for-few-shot-image | 2204.00949 | null | https://arxiv.org/abs/2204.00949v1 | https://arxiv.org/pdf/2204.00949v1.pdf | Matching Feature Sets for Few-Shot Image Classification | In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction and instead propose to extract sets of feature vectors for each image. We argue that a set-based representation intrinsically builds a richer representation of images from the base classes, which can subsequently better transfer to the few-shot classes. To do so, we propose to adapt existing feature extractors to instead produce sets of feature vectors from images. Our approach, dubbed SetFeat, embeds shallow self-attention mechanisms inside existing encoder architectures. The attention modules are lightweight, and as such our method results in encoders that have approximately the same number of parameters as their original versions. During training and inference, a set-to-set matching metric is used to perform image classification. The effectiveness of our proposed architecture and metrics is demonstrated via thorough experiments on standard few-shot datasets -- namely miniImageNet, tieredImageNet, and CUB -- in both the 1- and 5-shot scenarios. In all cases but one, our method outperforms the state-of-the-art. | ['Christian Gagné', 'Jean-François Lalonde', 'Hugo Larochelle', 'Arman Afrasiyabi'] | 2022-04-02 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Afrasiyabi_Matching_Feature_Sets_for_Few-Shot_Image_Classification_CVPR_2022_paper.pdf | cvpr-2022-1 | ['set-matching', 'few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.48832160e-01 -8.49549994e-02 -2.46211275e-01 -7.69696116e-01
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93ad11bd-f411-4d7b-b146-477de5261738 | real-time-3d-tracking-of-articulated-tools | 1605.03483 | null | http://arxiv.org/abs/1605.03483v3 | http://arxiv.org/pdf/1605.03483v3.pdf | Real-time 3D Tracking of Articulated Tools for Robotic Surgery | In robotic surgery, tool tracking is important for providing safe tool-tissue
interaction and facilitating surgical skills assessment. Despite recent
advances in tool tracking, existing approaches are faced with major
difficulties in real-time tracking of articulated tools. Most algorithms are
tailored for offline processing with pre-recorded videos. In this paper, we
propose a real-time 3D tracking method for articulated tools in robotic
surgery. The proposed method is based on the CAD model of the tools as well as
robot kinematics to generate online part-based templates for efficient 2D
matching and 3D pose estimation. A robust verification approach is incorporated
to reject outliers in 2D detections, which is then followed by fusing inliers
with robot kinematic readings for 3D pose estimation of the tool. The proposed
method has been validated with phantom data, as well as ex vivo and in vivo
experiments. The results derived clearly demonstrate the performance advantage
of the proposed method when compared to the state-of-the-art. | ['Guang-Zhong Yang', 'Menglong Ye', 'Stamatia Giannarou', 'Lin Zhang'] | 2016-05-11 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [ 7.86306038e-02 1.08165979e-01 8.76319483e-02 2.26119801e-01
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9858b599-fd08-4f31-bb86-ba4f5781b732 | softmatch-distance-a-novel-distance-for | 2303.04737 | null | https://arxiv.org/abs/2303.04737v1 | https://arxiv.org/pdf/2303.04737v1.pdf | SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images | General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models is very expensive. Therefore, there is a novel solution that intuitively dividing changes into three trends (``appear'', ``disappear'' and ``transform'') instead of semantic categories, named it trend change detection (TCD) in this paper. It offers more detailed change information than GCD, while requiring less manual annotation cost than SCD. However, there are limited public data sets with specific trend labels to support TCD application. To address this issue, we propose a softmatch distance which is used to construct a weakly-supervised TCD branch in a simple GCD model, using GCD labels instead of TCD label for training. Furthermore, a strategic approach is presented to successfully explore and extract background information, which is crucial for the weakly-supervised TCD task. The experiment results on four public data sets are highly encouraging, which demonstrates the effectiveness of our proposed model. | ['Licheng Jiao', 'Jingjing Ma', 'Xiangrong Zhang', 'Xu Tang', 'Yuqun Yang'] | 2023-03-08 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.28079334e-01 -2.58698970e-01 -2.14422405e-01 -5.22262931e-01
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d6a40d81-9c82-44f1-9765-5f35fadfb70a | patchwork-a-patch-wise-attention-network-for | 1904.01784 | null | https://arxiv.org/abs/1904.01784v2 | https://arxiv.org/pdf/1904.01784v2.pdf | Patchwork: A Patch-wise Attention Network for Efficient Object Detection and Segmentation in Video Streams | Recent advances in single-frame object detection and segmentation techniques have motivated a wide range of works to extend these methods to process video streams. In this paper, we explore the idea of hard attention aimed for latency-sensitive applications. Instead of reasoning about every frame separately, our method selects and only processes a small sub-window of the frame. Our technique then makes predictions for the full frame based on the sub-windows from previous frames and the update from the current sub-window. The latency reduction by this hard attention mechanism comes at the cost of degraded accuracy. We made two contributions to address this. First, we propose a specialized memory cell that recovers lost context when processing sub-windows. Secondly, we adopt a Q-learning-based policy training strategy that enables our approach to intelligently select the sub-windows such that the staleness in the memory hurts the performance the least. Our experiments suggest that our approach reduces the latency by approximately four times without significantly sacrificing the accuracy on the ImageNet VID video object detection dataset and the DAVIS video object segmentation dataset. We further demonstrate that we can reinvest the saved computation into other parts of the network, and thus resulting in an accuracy increase at a comparable computational cost as the original system and beating other recently proposed state-of-the-art methods in the low latency range. | ['Yuning Chai'] | 2019-04-03 | patchwork-a-patch-wise-attention-network-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Chai_Patchwork_A_Patch-Wise_Attention_Network_for_Efficient_Object_Detection_and_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Chai_Patchwork_A_Patch-Wise_Attention_Network_for_Efficient_Object_Detection_and_ICCV_2019_paper.pdf | iccv-2019-10 | ['hard-attention'] | ['methodology'] | [ 4.32154417e-01 2.02985965e-02 -2.47276366e-01 -1.86568961e-01
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942f741b-78e6-4516-b316-7337f575d00f | s4nd-modeling-images-and-videos-as | 2210.06583 | null | https://arxiv.org/abs/2210.06583v2 | https://arxiv.org/pdf/2210.06583v2.pdf | S4ND: Modeling Images and Videos as Multidimensional Signals Using State Spaces | Visual data such as images and videos are typically modeled as discretizations of inherently continuous, multidimensional signals. Existing continuous-signal models attempt to exploit this fact by modeling the underlying signals of visual (e.g., image) data directly. However, these models have not yet been able to achieve competitive performance on practical vision tasks such as large-scale image and video classification. Building on a recent line of work on deep state space models (SSMs), we propose S4ND, a new multidimensional SSM layer that extends the continuous-signal modeling ability of SSMs to multidimensional data including images and videos. We show that S4ND can model large-scale visual data in $1$D, $2$D, and $3$D as continuous multidimensional signals and demonstrates strong performance by simply swapping Conv2D and self-attention layers with S4ND layers in existing state-of-the-art models. On ImageNet-1k, S4ND exceeds the performance of a Vision Transformer baseline by $1.5\%$ when training with a $1$D sequence of patches, and matches ConvNeXt when modeling images in $2$D. For videos, S4ND improves on an inflated $3$D ConvNeXt in activity classification on HMDB-51 by $4\%$. S4ND implicitly learns global, continuous convolutional kernels that are resolution invariant by construction, providing an inductive bias that enables generalization across multiple resolutions. By developing a simple bandlimiting modification to S4 to overcome aliasing, S4ND achieves strong zero-shot (unseen at training time) resolution performance, outperforming a baseline Conv2D by $40\%$ on CIFAR-10 when trained on $8 \times 8$ and tested on $32 \times 32$ images. When trained with progressive resizing, S4ND comes within $\sim 1\%$ of a high-resolution model while training $22\%$ faster. | ['Christopher Ré', 'Stephen A. Baccus', 'Tri Dao', 'Preey Shah', 'Gordon W. Downs', 'Albert Gu', 'Karan Goel', 'Eric Nguyen'] | 2022-10-12 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 1.47041649e-01 2.85269674e-02 -2.25244328e-01 -1.69050351e-01
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0aff286b-8410-4019-a9cd-090553950577 | detecting-pulmonary-embolism-from-computed | 2206.01344 | null | https://arxiv.org/abs/2206.01344v1 | https://arxiv.org/pdf/2206.01344v1.pdf | Detecting Pulmonary Embolism from Computed Tomography Using Convolutional Neural Network | The clinical symptoms of pulmonary embolism (PE) are very diverse and non-specific, which makes it difficult to diagnose. In addition, pulmonary embolism has multiple triggers and is one of the major causes of vascular death. Therefore, if it can be detected and treated quickly, it can significantly reduce the risk of death in hospitalized patients. In the detection process, the cost of computed tomography pulmonary angiography (CTPA) is high, and angiography requires the injection of contrast agents, which increase the risk of damage to the patient. Therefore, this study will use a deep learning approach to detect pulmonary embolism in all patients who take a CT image of the chest using a convolutional neural network. With the proposed pulmonary embolism detection system, we can detect the possibility of pulmonary embolism at the same time as the patient's first CT image, and schedule the CTPA test immediately, saving more than a week of CT image screening time and providing timely diagnosis and treatment to the patient. | ['Chin Kuo', 'Yun-Chien Cheng', 'Chia-Hung Yang'] | 2022-06-03 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [ 1.62313227e-02 -2.69302219e-01 -3.05508040e-02 1.05765015e-01
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aa4e1b38-e78e-465d-beae-d878d97d4a5d | why-deep-learning-generalizes | 2211.09639 | null | https://arxiv.org/abs/2211.09639v2 | https://arxiv.org/pdf/2211.09639v2.pdf | Why Deep Learning Generalizes | Very large deep learning models trained using gradient descent are remarkably resistant to memorization given their huge capacity, but are at the same time capable of fitting large datasets of pure noise. Here methods are introduced by which models may be trained to memorize datasets that normally are generalized. We find that memorization is difficult relative to generalization, but that adding noise makes memorization easier. Increasing the dataset size exaggerates the characteristics of that dataset: model access to more training samples makes overfitting easier for random data, but somewhat harder for natural images. The bias of deep learning towards generalization is explored theoretically, and we show that generalization results from a model's parameters being attracted to points of maximal stability with respect to that model's inputs during gradient descent. | ['Benjamin L. Badger'] | 2022-11-17 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-9.44624469e-02 2.68058181e-01 -1.24710336e-01 -3.47710580e-01
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ad7fc92b-f3f3-4ed4-9f21-10e4ed219fd5 | uncovering-the-missing-pattern-unified | 2303.16005 | null | https://arxiv.org/abs/2303.16005v1 | https://arxiv.org/pdf/2303.16005v1.pdf | Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction | Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. This limitation inevitably hinders the accuracy of trajectory prediction. To address this issue, our paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously. Specifically, we introduce a novel Multi-Space Graph Neural Network (MS-GNN) that can extract spatial features from incomplete observations and leverage missing patterns. Additionally, we employ a Conditional VRNN with a specifically designed Temporal Decay (TD) module to capture temporal dependencies and temporal missing patterns in incomplete trajectories. The inclusion of the TD module allows for valuable information to be conveyed through the temporal flow. We also curate and benchmark three practical datasets for the joint problem of trajectory imputation and prediction. Extensive experiments verify the exceptional performance of our proposed method. As far as we know, this is the first work to address the lack of benchmarks and techniques for trajectory imputation and prediction in a unified manner. | ['Yun Fu', 'Chiho Choi', 'Hyung-gun Chi', 'Armin Bazarjani', 'Yi Xu'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['trajectory-prediction'] | ['computer-vision'] | [ 2.08239689e-01 -1.97416514e-01 -5.45672417e-01 -3.42770606e-01
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d67eb62f-aa4f-4d6a-8bea-0354410c6b89 | clip-td-clip-targeted-distillation-for-vision | 2201.05729 | null | https://arxiv.org/abs/2201.05729v3 | https://arxiv.org/pdf/2201.05729v3.pdf | CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks | Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks. While early concurrent works have begun to study this potential on a subset of tasks, important questions remain: 1) What is the benefit of CLIP on unstudied VL tasks? 2) Does CLIP provide benefit in low-shot or domain-shifted scenarios? 3) Can CLIP improve existing approaches without impacting inference or pretraining complexity? In this work, we seek to answer these questions through two key contributions. First, we introduce an evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of data availability constraints and conditions of domain shift. Second, we propose an approach, named CLIP Targeted Distillation (CLIP-TD), to intelligently distill knowledge from CLIP into existing architectures using a dynamically weighted objective applied to adaptively selected tokens per instance. Experiments demonstrate that our proposed CLIP-TD leads to exceptional gains in the low-shot (up to 51.9%) and domain-shifted (up to 71.3%) conditions of VCR, while simultaneously improving performance under standard fully-supervised conditions (up to 2%), achieving state-of-art performance on VCR compared to other single models that are pretrained with image-text data only. On SNLI-VE, CLIP-TD produces significant gains in low-shot conditions (up to 6.6%) as well as fully supervised (up to 3%). On VQA, CLIP-TD provides improvement in low-shot (up to 9%), and in fully-supervised (up to 1.3%). Finally, CLIP-TD outperforms concurrent works utilizing CLIP for finetuning, as well as baseline naive distillation approaches. Code will be made available. | ['Lu Yuan', 'Shih-Fu Chang', 'Haoxuan You', 'Bin Xiao', 'Xiyang Dai', 'Jianwei Yang', 'Luowei Zhou', 'Yen-Chun Chen', 'Noel Codella', 'Zhecan Wang'] | 2022-01-15 | null | null | null | null | ['visual-commonsense-reasoning', 'visual-entailment'] | ['reasoning', 'reasoning'] | [ 3.97842407e-01 -4.74073216e-02 -2.25793570e-01 -2.40434512e-01
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c7d7bf19-5b4b-4d0a-95b5-6f349278403b | dublin-document-understanding-by-language | 2305.14218 | null | https://arxiv.org/abs/2305.14218v3 | https://arxiv.org/pdf/2305.14218v3.pdf | DUBLIN -- Document Understanding By Language-Image Network | Visual document understanding is a complex task that involves analyzing both the text and the visual elements in document images. Existing models often rely on manual feature engineering or domain-specific pipelines, which limit their generalization ability across different document types and languages. In this paper, we propose DUBLIN, which is pretrained on web pages using three novel objectives: Masked Document Text Generation Task, Bounding Box Task, and Rendered Question Answering Task, that leverage both the spatial and semantic information in the document images. Our model achieves competitive or state-of-the-art results on several benchmarks, such as Web-Based Structural Reading Comprehension, Document Visual Question Answering, Key Information Extraction, Diagram Understanding, and Table Question Answering. In particular, we show that DUBLIN is the first pixel-based model to achieve an EM of 77.75 and F1 of 84.25 on the WebSRC dataset. We also show that our model outperforms the current pixel-based SOTA models on DocVQA, InfographicsVQA, OCR-VQA and AI2D datasets by 4.6%, 6.5%, 2.6% and 21%, respectively. We also achieve competitive performance on RVL-CDIP document classification. Moreover, we create new baselines for text-based datasets by rendering them as document images to promote research in this direction. | ['Saurabh Tiwary', 'Subhojit Som', 'Hardik Hansrajbhai Chauhan', 'Vishrav Chaudhary', 'Monojit Choudhury', 'Qiang Liu', 'Owais Mohammed Khan', 'Kumar Tanmay', 'Aditi Khandelwal', 'Kriti Aggarwal'] | 2023-05-23 | null | null | null | null | ['optical-character-recognition', 'feature-engineering', 'document-classification', 'key-information-extraction', 'reading-comprehension'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 3.92540962e-01 -2.24432230e-01 3.19591835e-02 -2.82558352e-01
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a0bb9e1e-672a-48a9-ac21-edb2a939a5c5 | sketch2saliency-learning-to-detect-salient | 2303.11502 | null | https://arxiv.org/abs/2303.11502v3 | https://arxiv.org/pdf/2303.11502v3.pdf | Sketch2Saliency: Learning to Detect Salient Objects from Human Drawings | Human sketch has already proved its worth in various visual understanding tasks (e.g., retrieval, segmentation, image-captioning, etc). In this paper, we reveal a new trait of sketches - that they are also salient. This is intuitive as sketching is a natural attentive process at its core. More specifically, we aim to study how sketches can be used as a weak label to detect salient objects present in an image. To this end, we propose a novel method that emphasises on how "salient object" could be explained by hand-drawn sketches. To accomplish this, we introduce a photo-to-sketch generation model that aims to generate sequential sketch coordinates corresponding to a given visual photo through a 2D attention mechanism. Attention maps accumulated across the time steps give rise to salient regions in the process. Extensive quantitative and qualitative experiments prove our hypothesis and delineate how our sketch-based saliency detection model gives a competitive performance compared to the state-of-the-art. | ['Yi-Zhe Song', 'Tao Xiang', 'Pinaki Nath Chowdhury', 'Aneeshan Sain', 'Amandeep Kumar', 'Subhadeep Koley', 'Ayan Kumar Bhunia'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bhunia_Sketch2Saliency_Learning_To_Detect_Salient_Objects_From_Human_Drawings_CVPR_2023_paper.pdf | cvpr-2023-1 | ['saliency-detection'] | ['computer-vision'] | [ 3.37032557e-01 2.66125798e-01 -1.82686508e-01 -1.88362718e-01
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8f14f6dd-3830-44c9-ac30-04031e88f07a | dvi-depth-guided-video-inpainting-for | 2007.08854 | null | https://arxiv.org/abs/2007.08854v1 | https://arxiv.org/pdf/2007.08854v1.pdf | DVI: Depth Guided Video Inpainting for Autonomous Driving | To get clear street-view and photo-realistic simulation in autonomous driving, we present an automatic video inpainting algorithm that can remove traffic agents from videos and synthesize missing regions with the guidance of depth/point cloud. By building a dense 3D map from stitched point clouds, frames within a video are geometrically correlated via this common 3D map. In order to fill a target inpainting area in a frame, it is straightforward to transform pixels from other frames into the current one with correct occlusion. Furthermore, we are able to fuse multiple videos through 3D point cloud registration, making it possible to inpaint a target video with multiple source videos. The motivation is to solve the long-time occlusion problem where an occluded area has never been visible in the entire video. To our knowledge, we are the first to fuse multiple videos for video inpainting. To verify the effectiveness of our approach, we build a large inpainting dataset in the real urban road environment with synchronized images and Lidar data including many challenge scenes, e.g., long time occlusion. The experimental results show that the proposed approach outperforms the state-of-the-art approaches for all the criteria, especially the RMSE (Root Mean Squared Error) has been reduced by about 13%. | ['Sibo Zhang', 'Ruigang Yang', 'Feixiang Lu', 'Wei Li', 'Miao Liao', 'Dingfu Zhou'] | 2020-07-17 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3620_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660001.pdf | eccv-2020-8 | ['video-inpainting'] | ['computer-vision'] | [ 1.45194829e-01 -1.11852080e-01 1.95086494e-01 -1.72481239e-01
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908e8381-99f5-4c0c-8b58-1f0c18d6862a | multi-person-extreme-motion-prediction-with | 2105.08825 | null | https://arxiv.org/abs/2105.08825v7 | https://arxiv.org/pdf/2105.08825v7.pdf | Multi-Person Extreme Motion Prediction | Human motion prediction aims to forecast future poses given a sequence of past 3D skeletons. While this problem has recently received increasing attention, it has mostly been tackled for single humans in isolation. In this paper, we explore this problem when dealing with humans performing collaborative tasks, we seek to predict the future motion of two interacted persons given two sequences of their past skeletons. We propose a novel cross interaction attention mechanism that exploits historical information of both persons, and learns to predict cross dependencies between the two pose sequences. Since no dataset to train such interactive situations is available, we collected ExPI (Extreme Pose Interaction), a new lab-based person interaction dataset of professional dancers performing Lindy-hop dancing actions, which contains 115 sequences with 30K frames annotated with 3D body poses and shapes. We thoroughly evaluate our cross interaction network on ExPI and show that both in short- and long-term predictions, it consistently outperforms state-of-the-art methods for single-person motion prediction. | ['Francesc Moreno-Noguer', 'Xavier Alameda-Pineda', 'Xiaoyu Bie', 'Wen Guo'] | 2021-05-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Guo_Multi-Person_Extreme_Motion_Prediction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['multi-person-pose-forecasting'] | ['computer-vision'] | [ 1.65552631e-01 1.18505850e-01 -7.13260993e-02 -3.77240002e-01
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3f2a41b3-fc6a-4b5b-b624-177a9afd2ea2 | an-energy-and-gpu-computation-efficient | 1904.09730 | null | http://arxiv.org/abs/1904.09730v1 | http://arxiv.org/pdf/1904.09730v1.pdf | An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection | As DenseNet conserves intermediate features with diverse receptive fields by
aggregating them with dense connection, it shows good performance on the object
detection task. Although feature reuse enables DenseNet to produce strong
features with a small number of model parameters and FLOPs, the detector with
DenseNet backbone shows rather slow speed and low energy efficiency. We find
the linearly increasing input channel by dense connection leads to heavy memory
access cost, which causes computation overhead and more energy consumption. To
solve the inefficiency of DenseNet, we propose an energy and computation
efficient architecture called VoVNet comprised of One-Shot Aggregation (OSA).
The OSA not only adopts the strength of DenseNet that represents diversified
features with multi receptive fields but also overcomes the inefficiency of
dense connection by aggregating all features only once in the last feature
maps. To validate the effectiveness of VoVNet as a backbone network, we design
both lightweight and large-scale VoVNet and apply them to one-stage and
two-stage object detectors. Our VoVNet based detectors outperform DenseNet
based ones with 2x faster speed and the energy consumptions are reduced by 1.6x
- 4.1x. In addition to DenseNet, VoVNet also outperforms widely used ResNet
backbone with faster speed and better energy efficiency. In particular, the
small object detection performance has been significantly improved over
DenseNet and ResNet. | ['Joong-won Hwang', 'Sangrok Lee', 'Jongyoul Park', 'Yuseok Bae', 'Youngwan Lee'] | 2019-04-22 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-4.52931046e-01 -1.66932687e-01 -9.66790915e-02 -1.50600806e-01
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5cab777d-b90e-4f6a-8e18-9163f00738f7 | intermittent-upwelling-events-trigger-delayed | null | null | https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022GL102651 | https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2022GL102651 | Intermittent Upwelling Events Trigger Delayed, Major, and Reproducible Pico-Nanophytoplankton Responses in Coastal Oligotrophic Waters | Pico-nanophytoplankton organisms are dominant in oceanic oligotrophic areas but their adaptive growth rates make their contribution to the carbon cycle difficult to estimate. Here we address their response capacities after sporadic wind gusts causing upwelling events in a coastal Mediterranean station. When the water column is stratified, corresponding to oligotrophic conditions, these events generate intense short-lived nutrient pulses and seawater temperature drops lasting 6 days on average with decreases up to 10°C. Using an automated flow cytometer and statistical rupture-detection methods, we characterize the responses of five pico-nanophytoplankton functional groups at a two-hour frequency from September 2019 to November 2021. These events trigger delayed increases in both abundances and biomasses following similar patterns for most groups that can overpass spring bloom values, and are immediately followed by an overall decrease, suggesting a clear physical driver. These submesoscale events, due to their short duration, are poorly represented in coastal carbon budgets. | ['M. Thyssen', 'O. Grosso', 'C. Pinazo', 'N. Bensoussan', 'C. Caille', 'V. Rossi', 'R. Fuchs'] | 2023-03-02 | null | null | null | geophysical-research-letters-2023-3 | ['pico'] | ['natural-language-processing'] | [ 1.02824710e-01 -5.10869801e-01 5.02162635e-01 3.25628698e-01
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36935611-db02-43f1-89f7-269385c8b853 | jct-at-semeval-2021-task-1-context-aware | null | null | https://aclanthology.org/2021.semeval-1.13 | https://aclanthology.org/2021.semeval-1.13.pdf | JCT at SemEval-2021 Task 1: Context-aware Representation for Lexical Complexity Prediction | In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words | ['Shmuel Liebeskind', 'Otniel Elkayam', 'Chaya Liebeskind'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-6.03979789e-02 2.48075593e-02 -2.51441330e-01 -3.88756603e-01
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15952cd0-b882-4b12-810d-1483c69c2b08 | zoom-vqa-patches-frames-and-clips-integration | 2304.06440 | null | https://arxiv.org/abs/2304.06440v1 | https://arxiv.org/pdf/2304.06440v1.pdf | Zoom-VQA: Patches, Frames and Clips Integration for Video Quality Assessment | Video quality assessment (VQA) aims to simulate the human perception of video quality, which is influenced by factors ranging from low-level color and texture details to high-level semantic content. To effectively model these complicated quality-related factors, in this paper, we decompose video into three levels (\ie, patch level, frame level, and clip level), and propose a novel Zoom-VQA architecture to perceive spatio-temporal features at different levels. It integrates three components: patch attention module, frame pyramid alignment, and clip ensemble strategy, respectively for capturing region-of-interest in the spatial dimension, multi-level information at different feature levels, and distortions distributed over the temporal dimension. Owing to the comprehensive design, Zoom-VQA obtains state-of-the-art results on four VQA benchmarks and achieves 2nd place in the NTIRE 2023 VQA challenge. Notably, Zoom-VQA has outperformed the previous best results on two subsets of LSVQ, achieving 0.8860 (+1.0%) and 0.7985 (+1.9%) of SRCC on the respective subsets. Adequate ablation studies further verify the effectiveness of each component. Codes and models are released in https://github.com/k-zha14/Zoom-VQA. | ['Xing Wen', 'Ming Sun', 'Kun Yuan', 'Kai Zhao'] | 2023-04-13 | null | null | null | null | ['video-quality-assessment', 'video-quality-assessment'] | ['computer-vision', 'time-series'] | [-2.50049084e-01 -7.14964092e-01 -1.51622251e-01 -3.34311873e-01
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aae75246-0afa-42e5-8281-7c19f5601900 | gkd-a-general-knowledge-distillation | 2306.06629 | null | https://arxiv.org/abs/2306.06629v1 | https://arxiv.org/pdf/2306.06629v1.pdf | GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model | Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices. However, the deployment of knowledge distillation systems faces great challenges in real-world industrial-strength applications, which require the use of complex distillation methods on even larger-scale PLMs (over 10B), limited by memory on GPUs and the switching of methods. To overcome these challenges, we propose GKD, a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. With GKD, developers can build larger distillation models on memory-limited GPUs and easily switch and combine different distillation methods within a single framework. Experimental results show that GKD can support the distillation of at least 100B-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs. | ['Jie Tang', 'Peng Zhang', 'Shu Zhao', 'Jingang Wang', 'Jiahao Liu', 'Keqing He', 'Hongyin Tang', 'Yang Yang', 'Wenwen Gong', 'Yuanchun Wang', 'Weng Lam Tam', 'Shicheng Tan'] | 2023-06-11 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [-3.32340002e-01 -1.56788483e-01 -4.33678001e-01 4.67732176e-02
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1743635a-7300-4f66-88a9-a650f2d95964 | traffic-sign-detection-under-challenging | 1908.11262 | null | https://arxiv.org/abs/1908.11262v1 | https://arxiv.org/pdf/1908.11262v1.pdf | Traffic Sign Detection under Challenging Conditions: A Deeper Look Into Performance Variations and Spectral Characteristics | Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. Existing traffic sign datasets are limited in terms of type and severity of challenging conditions. Metadata corresponding to these conditions are unavailable and it is not possible to investigate the effect of a single factor because of simultaneous changes in numerous conditions. To overcome the shortcomings in existing datasets, we introduced the CURE-TSD-Real dataset, which is based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. We test the performance of two benchmark algorithms and show that severe conditions can result in an average performance degradation of 29% in precision and 68% in recall. We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics. Moreover, we show that mean magnitude spectrum of changes in video sequences under challenging conditions can be an indicator of detection performance. CURE-TSD-Real dataset is available online at https://github.com/olivesgatech/CURE-TSD. | ['Min-Hung Chen', 'Ghassan AlRegib', 'Dogancan Temel'] | 2019-08-29 | null | null | null | null | ['traffic-sign-recognition', 'traffic-sign-detection'] | ['computer-vision', 'computer-vision'] | [ 1.86050102e-01 -7.55868852e-01 -1.06868207e-01 -7.35189840e-02
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3164b07a-1bfb-4438-bf1c-202bc549ce5e | z-lavi-zero-shot-language-solver-fueled-by | 2210.12261 | null | https://arxiv.org/abs/2210.12261v1 | https://arxiv.org/pdf/2210.12261v1.pdf | Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination | Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks. However, they generally suffer from reporting bias, the phenomenon describing the lack of explicit commonsense knowledge in written text, e.g., ''an orange is orange''. To overcome this limitation, we develop a novel approach, Z-LaVI, to endow language models with visual imagination capabilities. Specifically, we leverage two complementary types of ''imaginations'': (i) recalling existing images through retrieval and (ii) synthesizing nonexistent images via text-to-image generation. Jointly exploiting the language inputs and the imagination, a pretrained vision-language model (e.g., CLIP) eventually composes a zero-shot solution to the original language tasks. Notably, fueling language models with imagination can effectively leverage visual knowledge to solve plain language tasks. In consequence, Z-LaVI consistently improves the zero-shot performance of existing language models across a diverse set of language tasks. | ['Jianshu Chen', 'Dong Yu', 'Xiaoyang Wang', 'Hongming Zhang', 'Wenlin Yao', 'Yue Yang'] | 2022-10-21 | null | null | null | null | ['visual-commonsense-tests'] | ['natural-language-processing'] | [ 2.41649389e-01 2.98340380e-01 -1.45736068e-01 -1.77841440e-01
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62963dc7-c29c-43b9-8f50-0b821072807d | coins-dynamically-generating-contextualized | 2106.02497 | null | https://arxiv.org/abs/2106.02497v1 | https://arxiv.org/pdf/2106.02497v1.pdf | COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion | Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present COINS, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply COINS to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of COINS holds the potential for controlled generation of longer sequences. | ['Anette Frank', 'Debjit Paul'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.395 | https://aclanthology.org/2021.acl-long.395.pdf | acl-2021-5 | ['story-completion'] | ['natural-language-processing'] | [ 7.55032599e-01 8.45931113e-01 -8.00702497e-02 -3.96297574e-01
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4541665b-ae36-4ee5-9ff4-c85c68b3d434 | dhivya-hope-detection-lt-edi-eacl2021 | null | null | https://aclanthology.org/2021.ltedi-1.9 | https://aclanthology.org/2021.ltedi-1.9.pdf | dhivya-hope-detection@LT-EDI-EACL2021: Multilingual Hope Speech Detection for Code-mixed and Transliterated Texts | In this paper we work with a hope speech detection corpora that includes English, Tamil, and Malayalam datasets. We present a two phase mechanism to detect hope speech. In the first phase we build a classifier to identify the language of the text. In the second phase, we build a classifier to detect hope speech, non hope speech, or not lang labels. Experimental results show that hope speech detection is challenging and there is scope for improvement. | ['Dhivya Chinnappa'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [ 5.50790727e-02 2.46400356e-01 -2.85360545e-01 -4.16050941e-01
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30f4a6e3-ac58-47b6-8f45-7c216c81df4b | learning-to-tokenize-for-generative-retrieval | 2304.04171 | null | https://arxiv.org/abs/2304.04171v1 | https://arxiv.org/pdf/2304.04171v1.pdf | Learning to Tokenize for Generative Retrieval | Conventional document retrieval techniques are mainly based on the index-retrieve paradigm. It is challenging to optimize pipelines based on this paradigm in an end-to-end manner. As an alternative, generative retrieval represents documents as identifiers (docid) and retrieves documents by generating docids, enabling end-to-end modeling of document retrieval tasks. However, it is an open question how one should define the document identifiers. Current approaches to the task of defining document identifiers rely on fixed rule-based docids, such as the title of a document or the result of clustering BERT embeddings, which often fail to capture the complete semantic information of a document. We propose GenRet, a document tokenization learning method to address the challenge of defining document identifiers for generative retrieval. GenRet learns to tokenize documents into short discrete representations (i.e., docids) via a discrete auto-encoding approach. Three components are included in GenRet: (i) a tokenization model that produces docids for documents; (ii) a reconstruction model that learns to reconstruct a document based on a docid; and (iii) a sequence-to-sequence retrieval model that generates relevant document identifiers directly for a designated query. By using an auto-encoding framework, GenRet learns semantic docids in a fully end-to-end manner. We also develop a progressive training scheme to capture the autoregressive nature of docids and to stabilize training. We conduct experiments on the NQ320K, MS MARCO, and BEIR datasets to assess the effectiveness of GenRet. GenRet establishes the new state-of-the-art on the NQ320K dataset. Especially, compared to generative retrieval baselines, GenRet can achieve significant improvements on the unseen documents. GenRet also outperforms comparable baselines on MS MARCO and BEIR, demonstrating the method's generalizability. | ['Zhaochun Ren', 'Maarten de Rijke', 'Dawei Yin', 'Zhumin Chen', 'Pengjie Ren', 'Haichao Zhu', 'Shuaiqiang Wang', 'Zheng Chen', 'Lingyong Yan', 'Weiwei Sun'] | 2023-04-09 | null | null | null | null | ['open-question'] | ['natural-language-processing'] | [ 8.91724154e-02 -4.40690935e-01 -4.48348895e-02 -4.16585058e-01
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cacfb01d-882d-43d1-a4ce-a1a5e4e541c5 | morphological-disambiguation-of-south-s-ami | null | null | https://aclanthology.org/2020.sltu-1.5 | https://aclanthology.org/2020.sltu-1.5.pdf | Morphological Disambiguation of South S\'ami with FSTs and Neural Networks | We present a method for conducting morphological disambiguation for South S{\'a}mi, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the related North S{\'a}mi UD Treebank and some synthetically generated South S{\'a}mi data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North S{\'a}mi training data for South S{\'a}mi without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South S{\'a}mi, which makes it usable and applicable in the contexts of any other endangered language as well. | ['Mika H{\\"a}m{\\"a}l{\\"a}inen', 'Linda Wiechetek'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 4.64244932e-01 2.71533340e-01 -1.51055399e-03 -3.97147477e-01
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adc3caac-9066-457f-b5c2-ed42b4f2495b | improving-multi-class-classifier-using | 2210.16033 | null | https://arxiv.org/abs/2210.16033v1 | https://arxiv.org/pdf/2210.16033v1.pdf | Improving Multi-class Classifier Using Likelihood Ratio Estimation with Regularization | The universal-set naive Bayes classifier (UNB)~\cite{Komiya:13}, defined using likelihood ratios (LRs), was proposed to address imbalanced classification problems. However, the LR estimator used in the UNB overestimates LRs for low-frequency data, degrading the classification performance. Our previous study~\cite{Kikuchi:19} proposed an effective LR estimator even for low-frequency data. This estimator uses regularization to suppress the overestimation, but we did not consider imbalanced data. In this paper, we integrated the estimator with the UNB. Our experiments with imbalanced data showed that our proposed classifier effectively adjusts the classification scores according to the class balance using regularization parameters and improves the classification performance. | ['Tadachika Ozono', 'Masato Kikuchi'] | 2022-10-28 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-1.35553122e-01 -2.15130314e-01 -6.23534739e-01 -8.95863116e-01
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58844f03-8e43-45bb-9877-d02e9b3bd70b | autoshot-a-short-video-dataset-and-state-of-1 | 2304.06116 | null | https://arxiv.org/abs/2304.06116v1 | https://arxiv.org/pdf/2304.06116v1.pdf | AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection | The short-form videos have explosive popularity and have dominated the new social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou (Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we consume and create content. For video content creation and understanding, the shot boundary detection (SBD) is one of the most essential components in various scenarios. In this work, we release a new public Short video sHot bOundary deTection dataset, named SHOT, consisting of 853 complete short videos and 11,606 shot annotations, with 2,716 high quality shot boundary annotations in 200 test videos. Leveraging this new data wealth, we propose to optimize the model design for video SBD, by conducting neural architecture search in a search space encapsulating various advanced 3D ConvNets and Transformers. Our proposed approach, named AutoShot, achieves higher F1 scores than previous state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being derived and evaluated on our newly constructed SHOT dataset. Moreover, to validate the generalizability of the AutoShot architecture, we directly evaluate it on another three public datasets: ClipShots, BBC and RAI, and the F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%, 0.9% and 1.2%, respectively. The SHOT dataset and code can be found in https://github.com/wentaozhu/AutoShot.git . | ['Ji Liu', 'Zhangyang Wang', 'Debing Zhang', 'Jincan Deng', 'Wenxian Liu', 'Xiufeng Xie', 'Yufang Huang', 'Wentao Zhu'] | 2023-04-12 | autoshot-a-short-video-dataset-and-state-of | https://openreview.net/pdf?id=u89Eq-_3oE4 | https://openreview.net/pdf?id=u89Eq-_3oE4 | submitted-to-iclr-2022-9 | ['boundary-detection', 'architecture-search'] | ['computer-vision', 'methodology'] | [-2.78676242e-01 -3.21220160e-01 -2.70140529e-01 -4.78911996e-02
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1039b2a3-cfe2-4c46-817e-2ae1c8987860 | from-width-based-model-checking-to-width | 2205.10995 | null | https://arxiv.org/abs/2205.10995v2 | https://arxiv.org/pdf/2205.10995v2.pdf | From Width-Based Model Checking to Width-Based Automated Theorem Proving | In the field of parameterized complexity theory, the study of graph width measures has been intimately connected with the development of width-based model checking algorithms for combinatorial properties on graphs. In this work, we introduce a general framework to convert a large class of width-based model-checking algorithms into algorithms that can be used to test the validity of graph-theoretic conjectures on classes of graphs of bounded width. Our framework is modular and can be applied with respect to several well-studied width measures for graphs, including treewidth and cliquewidth. As a quantitative application of our framework, we show that for several long-standing graph-theoretic conjectures, there exists an algorithm that takes a number $k$ as input and correctly determines in time double-exponential in $k^{O(1)}$ whether the conjecture is valid on all graphs of treewidth at most $k$. This improves significantly on upper bounds obtained using previously available techniques. | ['Farhad Vadiee', 'Mateus de Oliveira Oliveira'] | 2022-05-23 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 4.19747144e-01 5.94561815e-01 -3.52648765e-01 -4.60504368e-02
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073ca9e1-b7a7-4e41-afdf-48a484c4a17f | li-yong-attentivelai-gai-shan-duan-dui-duan | null | null | https://aclanthology.org/2019.rocling-1.36 | https://aclanthology.org/2019.rocling-1.36.pdf | 利用Attentive來改善端對端中文語篇剖析遞迴類神經網路系統(Using Attentive to improve Recursive LSTM End-to-End Chinese Discourse Parsing) | null | ['Chia-Hui Chang', 'Yu-Jen Wang'] | null | null | null | null | rocling-2019-10 | ['discourse-parsing'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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ede3f739-1eac-4adf-8475-edfebb5e188b | mumuqa-multimedia-multi-hop-news-question | 2112.10728 | null | https://arxiv.org/abs/2112.10728v2 | https://arxiv.org/pdf/2112.10728v2.pdf | MuMuQA: Multimedia Multi-Hop News Question Answering via Cross-Media Knowledge Extraction and Grounding | Recently, there has been an increasing interest in building question answering (QA) models that reason across multiple modalities, such as text and images. However, QA using images is often limited to just picking the answer from a pre-defined set of options. In addition, images in the real world, especially in news, have objects that are co-referential to the text, with complementary information from both modalities. In this paper, we present a new QA evaluation benchmark with 1,384 questions over news articles that require cross-media grounding of objects in images onto text. Specifically, the task involves multi-hop questions that require reasoning over image-caption pairs to identify the grounded visual object being referred to and then predicting a span from the news body text to answer the question. In addition, we introduce a novel multimedia data augmentation framework, based on cross-media knowledge extraction and synthetic question-answer generation, to automatically augment data that can provide weak supervision for this task. We evaluate both pipeline-based and end-to-end pretraining-based multimedia QA models on our benchmark, and show that they achieve promising performance, while considerably lagging behind human performance hence leaving large room for future work on this challenging new task. | ['Heng Ji', 'Alexander Schwing', 'Shih-Fu Chang', 'Avirup Sil', 'Mohit Bansal', 'Lifu Huang', 'Jaemin Cho', 'Haoyang Wen', 'Xudong Lin', 'Manling Li', 'Xilin Rui', 'Revanth Gangi Reddy'] | 2021-12-20 | null | null | null | null | ['question-answer-generation'] | ['natural-language-processing'] | [ 5.57507694e-01 3.07603538e-01 8.91958475e-02 -5.64181983e-01
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76df5bcc-bccf-4f8d-9b74-4c35a4d1b681 | a-hint-from-arithmetic-on-systematic | 2103.01403 | null | https://arxiv.org/abs/2103.01403v3 | https://arxiv.org/pdf/2103.01403v3.pdf | A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics | Inspired by humans' exceptional ability to master arithmetic and generalize to new problems, we present a new dataset, Handwritten arithmetic with INTegers (HINT), to examine machines' capability of learning generalizable concepts at three levels: perception, syntax, and semantics. In HINT, machines are tasked with learning how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics), all in a weakly supervised manner. Focusing on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts w.r.t. the three levels. Further, we design a few-shot learning split to determine whether or not models can rapidly learn new concepts and generalize them to more complex scenarios. To comprehend existing models' limitations, we undertake extensive experiments with various sequence-to-sequence models, including RNNs, Transformers, and GPT-3 (with the chain of thought prompting). The results indicate that current models struggle to extrapolate to long-range syntactic dependency and semantics. Models exhibit a considerable gap toward human-level generalization when evaluated with new concepts in a few-shot setting. Moreover, we discover that it is infeasible to solve HINT by merely scaling up the dataset and the model size; this strategy contributes little to the extrapolation of syntax and semantics. Finally, in zero-shot GPT-3 experiments, the chain of thought prompting exhibits impressive results and significantly boosts the test accuracy. We believe the HINT dataset and the experimental findings are of great interest to the learning community on systematic generalization. | ['Song-Chun Zhu', 'Ying Nian Wu', 'Yixin Zhu', 'Yining Hong', 'Siyuan Huang', 'Qing Li'] | 2021-03-02 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 4.62389886e-01 -8.32653493e-02 1.62899375e-01 -5.74355841e-01
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4077159d-2f37-4c21-80d5-66dab5826869 | a-survey-on-embedding-dynamic-graphs | 2101.01229 | null | https://arxiv.org/abs/2101.01229v2 | https://arxiv.org/pdf/2101.01229v2.pdf | A Survey on Embedding Dynamic Graphs | Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area. | ['Artur Ziviani', 'Alex B. Vieira', 'Matheus R. F. Mendonça', 'Claudio D. T. Barros'] | 2021-01-04 | null | null | null | null | ['dynamic-link-prediction', 'dynamic-graph-embedding'] | ['graphs', 'graphs'] | [-2.87044615e-01 -7.62289166e-02 -4.18030262e-01 9.18667018e-02
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cc59abc4-70f8-4cb3-8475-23a753712b8d | point-cloud-completion-on-structured-feature | 2202.08583 | null | https://arxiv.org/abs/2202.08583v2 | https://arxiv.org/pdf/2202.08583v2.pdf | Point cloud completion via structured feature maps using a feedback network | In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details, given partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. We accordingly first propose FSNet, a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions. We then integrate FSNet into a coarse-tofine pipeline for point cloud completion. Specifically, a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud. Next, a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output. To efficiently exploit local structures and enhance point distribution uniformity, we propose IFNet, a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud. We have conducted qualitative and quantitative experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our method outperforms state-of-theart point cloud completion approaches. | ['Ruizhen Hu', 'Hui Huang', 'Chongyang Ma', 'Haibin Huang', 'Zejia Su'] | 2022-02-17 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-7.11014867e-02 -5.81413321e-02 1.43890336e-01 -4.92478162e-01
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47557bce-73d0-49ae-bf37-e427412e469d | when-not-to-trust-language-models | 2212.10511 | null | https://arxiv.org/abs/2212.10511v4 | https://arxiv.org/pdf/2212.10511v4.pdf | When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories | Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation methods on PopQA, our new open-domain QA dataset with 14k questions. We find that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the long tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those findings, we devise a simple, yet effective, method for powerful and efficient retrieval-augmented LMs, which retrieves non-parametric memories only when necessary. Experimental results show that this significantly improves models' performance while reducing the inference costs. | ['Hannaneh Hajishirzi', 'Daniel Khashabi', 'Rajarshi Das', 'Victor Zhong', 'Akari Asai', 'Alex Mallen'] | 2022-12-20 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-5.15691996e-01 3.00634325e-01 -2.95078635e-01 -6.23924844e-02
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56c9a019-7f51-490d-af70-c143443b6669 | empowering-molecule-discovery-for-molecule | 2306.06615 | null | https://arxiv.org/abs/2306.06615v1 | https://arxiv.org/pdf/2306.06615v1.pdf | Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective | Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs. Traditional methods for molecule discovery follow a trial-and-error process, which are both time-consuming and costly, while computational approaches such as artificial intelligence (AI) have emerged as revolutionary tools to expedite various tasks, like molecule-caption translation. Despite the importance of molecule-caption translation for molecule discovery, most of the existing methods heavily rely on domain experts, require excessive computational cost, and suffer from poor performance. On the other hand, Large Language Models (LLMs), like ChatGPT, have shown remarkable performance in various cross-modal tasks due to their great powerful capabilities in natural language understanding, generalization, and reasoning, which provides unprecedented opportunities to advance molecule discovery. To address the above limitations, in this work, we propose a novel LLMs-based framework (\textbf{MolReGPT}) for molecule-caption translation, where a retrieval-based prompt paradigm is introduced to empower molecule discovery with LLMs like ChatGPT without fine-tuning. More specifically, MolReGPT leverages the principle of molecular similarity to retrieve similar molecules and their text descriptions from a local database to ground the generation of LLMs through in-context few-shot molecule learning. We evaluate the effectiveness of MolReGPT via molecule-caption translation, which includes molecule understanding and text-based molecule generation. Experimental results show that MolReGPT outperforms fine-tuned models like MolT5-base without any additional training. To the best of our knowledge, MolReGPT is the first work to leverage LLMs in molecule-caption translation for advancing molecule discovery. | ['Qing Li', 'Jiliang Tang', 'Hui Liu', 'Xiao-Yong Wei', 'Wenqi Fan', 'Yunqing Liu', 'Jiatong Li'] | 2023-06-11 | null | null | null | null | ['molecule-captioning', 'text-based-de-novo-molecule-generation'] | ['medical', 'medical'] | [ 5.36118746e-01 -2.60657072e-01 -7.35663295e-01 -1.99276134e-01
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f264ebbc-5647-4839-b4ef-0de261e5e83b | fingerprint-image-quality-estimation-and-its | 2211.13557 | null | https://arxiv.org/abs/2211.13557v1 | https://arxiv.org/pdf/2211.13557v1.pdf | Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification | Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training). | ['Joaquin Gonzalez-Rodriguez', 'Javier Ortega-Garcia', 'Fernando Alonso-Fernandez', 'Julian Fierrez', 'Josef Bigun', 'Klaus Kollreider', 'Hartwig Fronthaler'] | 2022-11-24 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 5.09824812e-01 -2.50075668e-01 1.28803790e-01 -6.89915299e-01
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00883e42-40d8-4f6f-aebf-92ab491f8fe9 | histalign-improving-context-dependency-in | 2305.04782 | null | https://arxiv.org/abs/2305.04782v1 | https://arxiv.org/pdf/2305.04782v1.pdf | HistAlign: Improving Context Dependency in Language Generation by Aligning with History | Language models (LMs) can generate hallucinations and incoherent outputs, which highlights their weak context dependency. Cache-LMs, which augment LMs with a memory of recent history, can increase context dependency and have shown remarkable performance in diverse language generation tasks. However, we find that even with training, the performance gain stemming from the cache component of current cache-LMs is suboptimal due to the misalignment between the current hidden states and those stored in the memory. In this work, we present HistAlign, a new training approach to ensure good cache alignment such that the model receives useful signals from the history. We first prove our concept on a simple and synthetic task where the memory is essential for correct predictions, and we show that the cache component of HistAlign is better aligned and improves overall performance. Next, we evaluate HistAlign on diverse downstream language generation tasks, including prompt continuation, abstractive summarization, and data-to-text. We demonstrate that HistAlign improves text coherence and faithfulness in open-ended and conditional generation settings respectively. HistAlign is also generalizable across different model families, showcasing its strength in improving context dependency of LMs in diverse scenarios. Our code is publicly available at https://github.com/meetdavidwan/histalign | ['Mohit Bansal', 'Shiyue Zhang', 'David Wan'] | 2023-05-08 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 1.55286103e-01 1.13248721e-01 -2.15247363e-01 -9.73628387e-02
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c0b70381-4ae7-4ae1-abbc-bb8bfde16c17 | graph-sampling-with-determinantal-processes | 1703.01594 | null | http://arxiv.org/abs/1703.01594v1 | http://arxiv.org/pdf/1703.01594v1.pdf | Graph sampling with determinantal processes | We present a new random sampling strategy for k-bandlimited signals defined
on graphs, based on determinantal point processes (DPP). For small graphs, ie,
in cases where the spectrum of the graph is accessible, we exhibit a DPP
sampling scheme that enables perfect recovery of bandlimited signals. For large
graphs, ie, in cases where the graph's spectrum is not accessible, we
investigate, both theoretically and empirically, a sub-optimal but much faster
DPP based on loop-erased random walks on the graph. Preliminary experiments
show promising results especially in cases where the number of measurements
should stay as small as possible and for graphs that have a strong community
structure. Our sampling scheme is efficient and can be applied to graphs with
up to $10^6$ nodes. | ['Simon Barthelmé', 'Pierre-Olivier Amblard', 'Nicolas Tremblay'] | 2017-03-05 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.48527843e-01 4.00144428e-01 2.40111604e-01 3.76999348e-01
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5ff96a8d-2794-454a-8751-a656bd0084b0 | a-survey-on-unsupervised-industrial-anomaly | 2204.11161 | null | https://arxiv.org/abs/2204.11161v4 | https://arxiv.org/pdf/2204.11161v4.pdf | A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images | In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective. | ['Shiguo Lian', 'Zhaoxiang Liu', 'Yajie Cui'] | 2022-04-24 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.66440332e-01 -1.79149777e-01 -2.00830717e-02 -1.32611647e-01
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a2adfcc6-8331-4ec3-9086-7849ed77a9b4 | extracting-victim-counts-from-text | 2302.12367 | null | https://arxiv.org/abs/2302.12367v1 | https://arxiv.org/pdf/2302.12367v1.pdf | Extracting Victim Counts from Text | Decision-makers in the humanitarian sector rely on timely and exact information during crisis events. Knowing how many civilians were injured during an earthquake is vital to allocate aids properly. Information about such victim counts is often only available within full-text event descriptions from newspapers and other reports. Extracting numbers from text is challenging: numbers have different formats and may require numeric reasoning. This renders purely string matching-based approaches insufficient. As a consequence, fine-grained counts of injured, displaced, or abused victims beyond fatalities are often not extracted and remain unseen. We cast victim count extraction as a question answering (QA) task with a regression or classification objective. We compare regex, dependency parsing, semantic role labeling-based approaches, and advanced text-to-text models. Beyond model accuracy, we analyze extraction reliability and robustness which are key for this sensitive task. In particular, we discuss model calibration and investigate few-shot and out-of-distribution performance. Ultimately, we make a comprehensive recommendation on which model to select for different desiderata and data domains. Our work is among the first to apply numeracy-focused large language models in a real-world use case with a positive impact. | ['Niklas Stoehr', 'Shehzaad Dhuliawala', 'Mian Zhong'] | 2023-02-23 | null | null | null | null | ['dependency-parsing', 'semantic-role-labeling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.00690824e-01 -1.52172521e-01 -4.52801287e-01 -2.90772200e-01
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dfdeec3e-8671-47a7-8a82-d4e6f34843e5 | matrix-cofactorization-for-joint-spatial | 1907.08511 | null | https://arxiv.org/abs/1907.08511v2 | https://arxiv.org/pdf/1907.08511v2.pdf | Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images | Hyperspectral unmixing aims at identifying a set of elementary spectra and the corresponding mixture coefficients for each pixel of an image. As the elementary spectra correspond to the reflectance spectra of real materials, they are often very correlated yielding an ill-conditioned problem. To enrich the model and to reduce ambiguity due to the high correlation, it is common to introduce spatial information to complement the spectral information. The most common way to introduce spatial information is to rely on a spatial regularization of the abundance maps. In this paper, instead of considering a simple but limited regularization process, spatial information is directly incorporated through the newly proposed context of spatial unmixing. Contextual features are extracted for each pixel and this additional set of observations is decomposed according to a linear model. Finally the spatial and spectral observations are unmixed jointly through a cofactorization model. In particular, this model introduces a coupling term used to identify clusters of shared spatial and spectral signatures. An evaluation of the proposed method is conducted on synthetic and real data and shows that results are accurate and also very meaningful since they describe both spatially and spectrally the various areas of the scene. | ['Stéphane May', 'Mathieu Fauvel', 'Adrien Lagrange', 'Nicolas Dobigeon'] | 2019-07-19 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 6.91361785e-01 -4.55338150e-01 2.47752026e-01 -1.70525700e-01
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09464276-7944-4de5-aff2-cd1f7f418404 | sat-solvers-and-computer-algebra-systems-a | 1907.04408 | null | https://arxiv.org/abs/1907.04408v2 | https://arxiv.org/pdf/1907.04408v2.pdf | SAT Solvers and Computer Algebra Systems: A Powerful Combination for Mathematics | Over the last few decades, many distinct lines of research aimed at automating mathematics have been developed, including computer algebra systems (CASs) for mathematical modelling, automated theorem provers for first-order logic, SAT/SMT solvers aimed at program verification, and higher-order proof assistants for checking mathematical proofs. More recently, some of these lines of research have started to converge in complementary ways. One success story is the combination of SAT solvers and CASs (SAT+CAS) aimed at resolving mathematical conjectures. Many conjectures in pure and applied mathematics are not amenable to traditional proof methods. Instead, they are best addressed via computational methods that involve very large combinatorial search spaces. SAT solvers are powerful methods to search through such large combinatorial spaces---consequently, many problems from a variety of mathematical domains have been reduced to SAT in an attempt to resolve them. However, solvers traditionally lack deep repositories of mathematical domain knowledge that can be crucial to pruning such large search spaces. By contrast, CASs are deep repositories of mathematical knowledge but lack efficient general search capabilities. By combining the search power of SAT with the deep mathematical knowledge in CASs we can solve many problems in mathematics that no other known methods seem capable of solving. We demonstrate the success of the SAT+CAS paradigm by highlighting many conjectures that have been disproven, verified, or partially verified using our tool MathCheck. These successes indicate that the paradigm is positioned to become a standard method for solving problems requiring both a significant amount of search and deep mathematical reasoning. For example, the SAT+CAS paradigm has recently been used by Heule, Kauers, and Seidl to find many new algorithms for $3\times3$ matrix multiplication. | ['Ilias Kotsireas', 'Vijay Ganesh', 'Curtis Bright'] | 2019-07-09 | null | null | null | null | ['mathematical-proofs', 'mathematical-reasoning'] | ['miscellaneous', 'natural-language-processing'] | [ 1.47392675e-02 3.45305145e-01 -1.86590999e-02 4.12065424e-02
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5bcbd940-61a7-4952-9302-c6f3d0feabb9 | empirical-study-of-text-augmentation-on-1 | null | null | https://aclanthology.org/2020.paclic-1.53 | https://aclanthology.org/2020.paclic-1.53.pdf | Empirical Study of Text Augmentation on Social Media Text in Vietnamese | null | ['Ngan Nguyen', 'Kiet Nguyen', 'Son Luu'] | null | null | null | null | paclic-2020-10 | ['text-augmentation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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438c53dd-e538-482b-8475-af63e5ccde1e | cortical-mirror-system-activation-during-real | 1902.09189 | null | http://arxiv.org/abs/1902.09189v2 | http://arxiv.org/pdf/1902.09189v2.pdf | Cortical Mirror-System Activation During Real-Life Game Playing: An Intracranial Electroencephalography (EEG) Study | Analogous to the mirror neuron system repeatedly described in monkeys as a
possible substrate for imitation learning and/or action understanding, a
neuronal execution/observation matching system (OEMS) is assumed in humans, but
little is known to what extent this system is activated in non-experimental,
real-life conditions. In the present case study, we investigated brain activity
of this system during natural, non-experimental motor behavior as it occurred
during playing of the board game "Malefiz". We compared spectral modulations of
the high-gamma band related to ipsilateral reaching movement execution and
observation of the same kind of movement using electrocorticography (ECoG) in
one participant. Spatially coincident activity during both conditions execution
and observation was recorded at electrode contacts over the premotor/primary
motor cortex. The topography and amplitude of the high-gamma modulations
related to both, movement observation and execution were clearly spatially
correlated over several fronto-parietal brain areas. Thus, our findings
indicate that a network of cortical areas contributes to the human OEMS, beyond
primary/premotor cortex including Brocas area and the temporo-parieto-occipital
junction area, in real-life conditions. | [] | 2019-03-27 | null | null | null | null | ['action-understanding'] | ['computer-vision'] | [ 1.71524912e-01 1.43088564e-01 -9.28942338e-02 3.51685137e-01
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232a1b8c-36d8-41b8-b9b2-f913a1d7f817 | nl-cs-net-deep-learning-with-non-local-prior | 2305.03899 | null | https://arxiv.org/abs/2305.03899v1 | https://arxiv.org/pdf/2305.03899v1.pdf | NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing | Deep learning has been applied to compressive sensing (CS) of images successfully in recent years. However, existing network-based methods are often trained as the black box, in which the lack of prior knowledge is often the bottleneck for further performance improvement. To overcome this drawback, this paper proposes a novel CS method using non-local prior which combines the interpretability of the traditional optimization methods with the speed of network-based methods, called NL-CS Net. We unroll each phase from iteration of the augmented Lagrangian method solving non-local and sparse regularized optimization problem by a network. NL-CS Net is composed of the up-sampling module and the recovery module. In the up-sampling module, we use learnable up-sampling matrix instead of a predefined one. In the recovery module, patch-wise non-local network is employed to capture long-range feature correspondences. Important parameters involved (e.g. sampling matrix, nonlinear transforms, shrinkage thresholds, step size, $etc.$) are learned end-to-end, rather than hand-crafted. Furthermore, to facilitate practical implementation, orthogonal and binary constraints on the sampling matrix are simultaneously adopted. Extensive experiments on natural images and magnetic resonance imaging (MRI) demonstrate that the proposed method outperforms the state-of-the-art methods while maintaining great interpretability and speed. | ['Yueyang Teng', 'YuDong Yao', 'Chen Li', 'Shouliang Qi', 'Shuai Bian'] | 2023-05-06 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.96583819e-01 -1.75435364e-01 -3.50356437e-02 -2.75066137e-01
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aec1b343-40bc-47de-8ee9-ecbe9fc29ba2 | jetseg-efficient-real-time-semantic | 2305.11419 | null | https://arxiv.org/abs/2305.11419v1 | https://arxiv.org/pdf/2305.11419v1.pdf | JetSeg: Efficient Real-Time Semantic Segmentation Model for Low-Power GPU-Embedded Systems | Real-time semantic segmentation is a challenging task that requires high-accuracy models with low-inference times. Implementing these models on embedded systems is limited by hardware capability and memory usage, which produces bottlenecks. We propose an efficient model for real-time semantic segmentation called JetSeg, consisting of an encoder called JetNet, and an improved RegSeg decoder. The JetNet is designed for GPU-Embedded Systems and includes two main components: a new light-weight efficient block called JetBlock, that reduces the number of parameters minimizing memory usage and inference time without sacrificing accuracy; a new strategy that involves the combination of asymmetric and non-asymmetric convolutions with depthwise-dilated convolutions called JetConv, a channel shuffle operation, light-weight activation functions, and a convenient number of group convolutions for embedded systems, and an innovative loss function named JetLoss, which integrates the Precision, Recall, and IoUB losses to improve semantic segmentation and reduce computational complexity. Experiments demonstrate that JetSeg is much faster on workstation devices and more suitable for Low-Power GPU-Embedded Systems than existing state-of-the-art models for real-time semantic segmentation. Our approach outperforms state-of-the-art real-time encoder-decoder models by reducing 46.70M parameters and 5.14% GFLOPs, which makes JetSeg up to 2x faster on the NVIDIA Titan RTX GPU and the Jetson Xavier than other models. The JetSeg code is available at https://github.com/mmontielpz/jetseg. | ['Oscar Montiel', 'Daniel Alejandro Lopez', 'Miguel Lopez-Montiel'] | 2023-05-19 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [-8.64435807e-02 -4.76376154e-02 -2.08846956e-01 -4.38000053e-01
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14e2d021-52c2-4422-afbf-ebeab2f03bd6 | dual-arm-adversarial-robot-learning | 2110.08066 | null | https://arxiv.org/abs/2110.08066v1 | https://arxiv.org/pdf/2110.08066v1.pdf | Dual-Arm Adversarial Robot Learning | Robot learning is a very promising topic for the future of automation and machine intelligence. Future robots should be able to autonomously acquire skills, learn to represent their environment, and interact with it. While these topics have been explored in simulation, real-world robot learning research seems to be still limited. This is due to the additional challenges encountered in the real-world, such as noisy sensors and actuators, safe exploration, non-stationary dynamics, autonomous environment resetting as well as the cost of running experiments for long periods of time. Unless we develop scalable solutions to these problems, learning complex tasks involving hand-eye coordination and rich contacts will remain an untouched vision that is only feasible in controlled lab environments. We propose dual-arm settings as platforms for robot learning. Such settings enable safe data collection for acquiring manipulation skills as well as training perception modules in a robot-supervised manner. They also ease the processes of resetting the environment. Furthermore, adversarial learning could potentially boost the generalization capability of robot learning methods by maximizing the exploration based on game-theoretic objectives while ensuring safety based on collaborative task spaces. In this paper, we will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued. | ['Elie Aljalbout'] | 2021-10-15 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 8.86005610e-02 4.90845174e-01 1.58213466e-01 -4.67858426e-02
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7cf26d3a-342c-4d19-9a3c-9a8f25ece5f6 | glioma-classification-using-multimodal | 2011.05410 | null | https://arxiv.org/abs/2011.05410v1 | https://arxiv.org/pdf/2011.05410v1.pdf | Glioma Classification Using Multimodal Radiology and Histology Data | Gliomas are brain tumours with a high mortality rate. There are various grades and sub-types of this tumour, and the treatment procedure varies accordingly. Clinicians and oncologists diagnose and categorise these tumours based on visual inspection of radiology and histology data. However, this process can be time-consuming and subjective. The computer-assisted methods can help clinicians to make better and faster decisions. In this paper, we propose a pipeline for automatic classification of gliomas into three sub-types: oligodendroglioma, astrocytoma, and glioblastoma, using both radiology and histopathology images. The proposed approach implements distinct classification models for radiographic and histologic modalities and combines them through an ensemble method. The classification algorithm initially carries out tile-level (for histology) and slice-level (for radiology) classification via a deep learning method, then tile/slice-level latent features are combined for a whole-slide and whole-volume sub-type prediction. The classification algorithm was evaluated using the data set provided in the CPM-RadPath 2020 challenge. The proposed pipeline achieved the F1-Score of 0.886, Cohen's Kappa score of 0.811 and Balance accuracy of 0.860. The ability of the proposed model for end-to-end learning of diverse features enables it to give a comparable prediction of glioma tumour sub-types. | ['Yinyin Yuan', 'Otar Akanyeti', 'Maryam Afzali', 'Tomasz Pieciak', 'Azam Hamidinekoo'] | 2020-11-10 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-1.25948608e-01 9.47079062e-02 5.11725098e-02 -4.67021018e-01
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de02d7e7-0ded-430f-93f8-c06685027b0e | ovis-open-vocabulary-visual-instance-search | 2108.03704 | null | https://arxiv.org/abs/2108.03704v1 | https://arxiv.org/pdf/2108.03704v1.pdf | OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning | We introduce the task of open-vocabulary visual instance search (OVIS). Given an arbitrary textual search query, Open-vocabulary Visual Instance Search (OVIS) aims to return a ranked list of visual instances, i.e., image patches, that satisfies the search intent from an image database. The term "open vocabulary" means that there are neither restrictions to the visual instance to be searched nor restrictions to the word that can be used to compose the textual search query. We propose to address such a search challenge via visual-semantic aligned representation learning (ViSA). ViSA leverages massive image-caption pairs as weak image-level (not instance-level) supervision to learn a rich cross-modal semantic space where the representations of visual instances (not images) and those of textual queries are aligned, thus allowing us to measure the similarities between any visual instance and an arbitrary textual query. To evaluate the performance of ViSA, we build two datasets named OVIS40 and OVIS1600 and also introduce a pipeline for error analysis. Through extensive experiments on the two datasets, we demonstrate ViSA's ability to search for visual instances in images not available during training given a wide range of textual queries including those composed of uncommon words. Experimental results show that ViSA achieves an mAP@50 of 21.9% on OVIS40 under the most challenging setting and achieves an mAP@6 of 14.9% on OVIS1600 dataset. | ['Zicheng Liu', 'Junsong Yuan', 'Lijuan Wang', 'Kevin Lin', 'Sheng Liu'] | 2021-08-08 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 2.04224572e-01 -6.88032359e-02 -4.99472052e-01 -2.31579870e-01
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2452cdde-c8f6-4572-a32e-98c740623e37 | investigating-catastrophic-overfitting-in | 2302.11963 | null | https://arxiv.org/abs/2302.11963v2 | https://arxiv.org/pdf/2302.11963v2.pdf | Investigating Catastrophic Overfitting in Fast Adversarial Training: A Self-fitting Perspective | Although fast adversarial training provides an efficient approach for building robust networks, it may suffer from a serious problem known as catastrophic overfitting (CO), where multi-step robust accuracy suddenly collapses to zero. In this paper, we for the first time decouple single-step adversarial examples into data-information and self-information, which reveals an interesting phenomenon called "self-fitting". Self-fitting, i.e., the network learns the self-information embedded in single-step perturbations, naturally leads to the occurrence of CO. When self-fitting occurs, the network experiences an obvious "channel differentiation" phenomenon that some convolution channels accounting for recognizing self-information become dominant, while others for data-information are suppressed. In this way, the network can only recognize images with sufficient self-information and loses generalization ability to other types of data. Based on self-fitting, we provide new insights into the existing methods to mitigate CO and extend CO to multi-step adversarial training. Our findings reveal a self-learning mechanism in adversarial training and open up new perspectives for suppressing different kinds of information to mitigate CO. | ['Xiaolin Huang', 'Sizhe Chen', 'Tao Li', 'Zhengbao He'] | 2023-02-23 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.48467457e-01 9.38072428e-03 2.01496556e-01 1.15716299e-02
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c8fcc563-3d8b-4f4c-8981-33a67c45d1ed | isointense-infant-brain-mri-segmentation-with | 1708.02757 | null | http://arxiv.org/abs/1708.02757v1 | http://arxiv.org/pdf/1708.02757v1.pdf | Isointense infant brain MRI segmentation with a dilated convolutional neural network | Quantitative analysis of brain MRI at the age of 6 months is difficult
because of the limited contrast between white matter and gray matter. In this
study, we use a dilated triplanar convolutional neural network in combination
with a non-dilated 3D convolutional neural network for the segmentation of
white matter, gray matter and cerebrospinal fluid in infant brain MR images, as
provided by the MICCAI grand challenge on 6-month infant brain MRI
segmentation. | ['Josien P. W. Pluim', 'Pim Moeskops'] | 2017-08-09 | null | null | null | null | ['infant-brain-mri-segmentation'] | ['medical'] | [-1.82681337e-01 1.14800215e-01 2.98928082e-01 -5.39955437e-01
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7cc3a439-110e-4c20-8c66-ec01e20cc92b | evaluating-deep-convolutional-neural-networks | 1703.04101 | null | http://arxiv.org/abs/1703.04101v2 | http://arxiv.org/pdf/1703.04101v2.pdf | Evaluating Deep Convolutional Neural Networks for Material Classification | Determining the material category of a surface from an image is a demanding
task in perception that is drawing increasing attention. Following the recent
remarkable results achieved for image classification and object detection
utilising Convolutional Neural Networks (CNNs), we empirically study material
classification of everyday objects employing these techniques. More
specifically, we conduct a rigorous evaluation of how state-of-the art CNN
architectures compare on a common ground over widely used material databases.
Experimental results on three challenging material databases show that the best
performing CNN architectures can achieve up to 94.99\% mean average precision
when classifying materials. | ['Klaus D. McDonald-Maier', 'Grigorios Kalliatakis', 'Shoaib Ehsan', 'Juergen Gall', 'Georgios Stamatiadis', 'Ales Leonardis', 'Anca Sticlaru'] | 2017-03-12 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 6.68703675e-01 -2.43627787e-01 3.54130156e-02 -2.72515357e-01
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22196d79-93a6-4d31-9a33-4d2a3247bec5 | towards-realistic-semi-supervised-learning | 2207.02269 | null | https://arxiv.org/abs/2207.02269v2 | https://arxiv.org/pdf/2207.02269v2.pdf | Towards Realistic Semi-Supervised Learning | Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation cost. The standard SSL approach assumes unlabeled data are from the same distribution as annotated data. Recently, a more realistic SSL problem, called open-world SSL, is introduced, where the unannotated data might contain samples from unknown classes. In this paper, we propose a novel pseudo-label based approach to tackle SSL in open-world setting. At the core of our method, we utilize sample uncertainty and incorporate prior knowledge about class distribution to generate reliable class-distribution-aware pseudo-labels for unlabeled data belonging to both known and unknown classes. Our extensive experimentation showcases the effectiveness of our approach on several benchmark datasets, where it substantially outperforms the existing state-of-the-art on seven diverse datasets including CIFAR-100 (~17%), ImageNet-100 (~5%), and Tiny ImageNet (~9%). We also highlight the flexibility of our approach in solving novel class discovery task, demonstrate its stability in dealing with imbalanced data, and complement our approach with a technique to estimate the number of novel classes | ['Mubarak Shah', 'Navid Kardan', 'Mamshad Nayeem Rizve'] | 2022-07-05 | null | null | null | null | ['novel-class-discovery', 'open-world-semi-supervised-learning', 'novel-class-discovery'] | ['computer-vision', 'computer-vision', 'methodology'] | [ 1.80792779e-01 3.93461347e-01 -4.99464840e-01 -7.69074678e-01
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60f37686-5812-4c90-a09e-cebbf9ec0596 | optical-flow-estimation-using-a-spatial | 1611.00850 | null | http://arxiv.org/abs/1611.00850v2 | http://arxiv.org/pdf/1611.00850v2.pdf | Optical Flow Estimation using a Spatial Pyramid Network | We learn to compute optical flow by combining a classical spatial-pyramid
formulation with deep learning. This estimates large motions in a
coarse-to-fine approach by warping one image of a pair at each pyramid level by
the current flow estimate and computing an update to the flow. Instead of the
standard minimization of an objective function at each pyramid level, we train
one deep network per level to compute the flow update. Unlike the recent
FlowNet approach, the networks do not need to deal with large motions; these
are dealt with by the pyramid. This has several advantages. First, our Spatial
Pyramid Network (SPyNet) is much simpler and 96% smaller than FlowNet in terms
of model parameters. This makes it more efficient and appropriate for embedded
applications. Second, since the flow at each pyramid level is small (< 1
pixel), a convolutional approach applied to pairs of warped images is
appropriate. Third, unlike FlowNet, the learned convolution filters appear
similar to classical spatio-temporal filters, giving insight into the method
and how to improve it. Our results are more accurate than FlowNet on most
standard benchmarks, suggesting a new direction of combining classical flow
methods with deep learning. | ['Michael J. Black', 'Anurag Ranjan'] | 2016-11-03 | optical-flow-estimation-using-a-spatial-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Ranjan_Optical_Flow_Estimation_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Ranjan_Optical_Flow_Estimation_CVPR_2017_paper.pdf | cvpr-2017-7 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [-1.49087161e-01 -3.06756258e-01 -4.76009659e-02 1.62369356e-01
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62afa6cd-21e2-49a0-9275-d4e7d7f38e4e | temporally-smooth-online-action-detection | 2104.08030 | null | https://arxiv.org/abs/2104.08030v1 | https://arxiv.org/pdf/2104.08030v1.pdf | Temporally smooth online action detection using cycle-consistent future anticipation | Many video understanding tasks work in the offline setting by assuming that the input video is given from the start to the end. However, many real-world problems require the online setting, making a decision immediately using only the current and the past frames of videos such as in autonomous driving and surveillance systems. In this paper, we present a novel solution for online action detection by using a simple yet effective RNN-based networks called the Future Anticipation and Temporally Smoothing network (FATSnet). The proposed network consists of a module for anticipating the future that can be trained in an unsupervised manner with the cycle-consistency loss, and another component for aggregating the past and the future for temporally smooth frame-by-frame predictions. We also propose a solution to relieve the performance loss when running RNN-based models on very long sequences. Evaluations on TVSeries, THUMOS14, and BBDB show that our method achieve the state-of-the-art performances compared to the previous works on online action detection. | ['Seon Joo Kim', 'Seonghyeon Nam', 'Young Hwi Kim'] | 2021-04-16 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.24306422e-01 1.05395868e-01 -1.74228922e-01 -5.74419558e-01
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772254a5-5abe-4572-9d03-010f1afd23a6 | rendnet-unified-2d-3d-recognizer-with-latent-1 | 2206.10066 | null | https://arxiv.org/abs/2206.10066v1 | https://arxiv.org/pdf/2206.10066v1.pdf | RendNet: Unified 2D/3D Recognizer With Latent Space Rendering | Vector graphics (VG) have been ubiquitous in our daily life with vast applications in engineering, architecture, designs, etc. The VG recognition process of most existing methods is to first render the VG into raster graphics (RG) and then conduct recognition based on RG formats. However, this procedure discards the structure of geometries and loses the high resolution of VG. Recently, another category of algorithms is proposed to recognize directly from the original VG format. But it is affected by the topological errors that can be filtered out by RG rendering. Instead of looking at one format, it is a good solution to utilize the formats of VG and RG together to avoid these shortcomings. Besides, we argue that the VG-to-RG rendering process is essential to effectively combine VG and RG information. By specifying the rules on how to transfer VG primitives to RG pixels, the rendering process depicts the interaction and correlation between VG and RG. As a result, we propose RendNet, a unified architecture for recognition on both 2D and 3D scenarios, which considers both VG/RG representations and exploits their interaction by incorporating the VG-to-RG rasterization process. Experiments show that RendNet can achieve state-of-the-art performance on 2D and 3D object recognition tasks on various VG datasets. | ['Dongsheng Li', 'Yansen Wang', 'Caihua Shan', 'Xinyang Jiang', 'Ruoxi Shi'] | 2022-06-21 | rendnet-unified-2d-3d-recognizer-with-latent | http://openaccess.thecvf.com//content/CVPR2022/html/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Shi_RendNet_Unified_2D3D_Recognizer_With_Latent_Space_Rendering_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-object-recognition'] | ['computer-vision'] | [ 2.85001010e-01 -3.88680190e-01 3.18237394e-01 -3.48005712e-01
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98d6dd06-bfbc-4f8f-81bf-5ee744ede85a | md-hit-machine-learning-for-materials | 2307.04351 | null | https://arxiv.org/abs/2307.04351v1 | https://arxiv.org/pdf/2307.04351v1.pdf | MD-HIT: Machine learning for materials property prediction with dataset redundancy control | Materials datasets are usually featured by the existence of many redundant (highly similar) materials due to the tinkering material design practice over the history of materials research. For example, the materials project database has many perovskite cubic structure materials similar to SrTiO$_3$. This sample redundancy within the dataset makes the random splitting of machine learning model evaluation to fail so that the ML models tend to achieve over-estimated predictive performance which is misleading for the materials science community. This issue is well known in the field of bioinformatics for protein function prediction, in which a redundancy reduction procedure (CD-Hit) is always applied to reduce the sample redundancy by ensuring no pair of samples has a sequence similarity greater than a given threshold. This paper surveys the overestimated ML performance in the literature for both composition based and structure based material property prediction. We then propose a material dataset redundancy reduction algorithm called MD-HIT and evaluate it with several composition and structure based distance threshold sfor reducing data set sample redundancy. We show that with this control, the predicted performance tends to better reflect their true prediction capability. Our MD-hit code can be freely accessed at https://github.com/usccolumbia/MD-HIT | ['Jianjun Hu', 'Sadman Sadeed Omee', 'Nihang Fu', 'Qin Li'] | 2023-07-10 | null | null | null | null | ['property-prediction', 'protein-function-prediction'] | ['medical', 'medical'] | [ 2.98130274e-01 -1.97549567e-01 -2.05000237e-01 -3.31952542e-01
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2f42ac73-9cd0-41d4-8ffa-4b5e822a973d | self-paced-contrastive-learning-for-semi | 2107.13741 | null | https://arxiv.org/abs/2107.13741v2 | https://arxiv.org/pdf/2107.13741v2.pdf | Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels | Pre-training a recognition model with contrastive learning on a large dataset of unlabeled data has shown great potential to boost the performance of a downstream task, e.g., image classification. However, in domains such as medical imaging, collecting unlabeled data can be challenging and expensive. In this work, we propose to adapt contrastive learning to work with meta-label annotations, for improving the model's performance in medical image segmentation even when no additional unlabeled data is available. Meta-labels such as the location of a 2D slice in a 3D MRI scan or the type of device used, often come for free during the acquisition process. We use the meta-labels for pre-training the image encoder as well as to regularize a semi-supervised training, in which a reduced set of annotated data is used for training. Finally, to fully exploit the weak annotations, a self-paced learning approach is used to help the learning and discriminate useful labels from noise. Results on three different medical image segmentation datasets show that our approach: i) highly boosts the performance of a model trained on a few scans, ii) outperforms previous contrastive and semi-supervised approaches, and iii) reaches close to the performance of a model trained on the full data. | ['Marco Pedersoli', 'Chrisitian Desrosiers', 'Ping Wang', 'Jizong Peng'] | 2021-07-29 | null | http://proceedings.neurips.cc/paper/2021/hash/8b5c8441a8ff8e151b191c53c1842a38-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/8b5c8441a8ff8e151b191c53c1842a38-Paper.pdf | neurips-2021-12 | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 6.54592872e-01 1.94960847e-01 -4.32739913e-01 -7.41041064e-01
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6c6d8c58-faad-4e44-a2ee-47fb735ba961 | ontoed-low-resource-event-detection-with | 2105.10922 | null | https://arxiv.org/abs/2105.10922v4 | https://arxiv.org/pdf/2105.10922v4.pdf | OntoED: Low-resource Event Detection with Ontology Embedding | Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios. | ['Huajun Chen', 'Fei Huang', 'Mosha Chen', 'Huaixiao Tou', 'Hui Chen', 'Luoqiu Li', 'Ningyu Zhang', 'Shumin Deng'] | 2021-05-23 | null | https://aclanthology.org/2021.acl-long.220 | https://aclanthology.org/2021.acl-long.220.pdf | acl-2021-5 | ['ontology-embedding'] | ['knowledge-base'] | [ 5.55384555e-04 1.41351238e-01 -4.13798839e-01 -2.84054786e-01
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e045dd7e-0f14-4356-abed-bec874d972dc | how-human-is-human-evaluation-improving-the | 2205.11930 | null | https://arxiv.org/abs/2205.11930v2 | https://arxiv.org/pdf/2205.11930v2.pdf | The Authenticity Gap in Human Evaluation | Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annotators, and rank NLG systems by their average scores. However, little consideration has been given as to whether this approach faithfully captures human preferences. Analyzing this standard protocol through the lens of utility theory in economics, we identify the implicit assumptions it makes about annotators. These assumptions are often violated in practice, in which case annotator ratings cease to reflect their preferences. The most egregious violations come from using Likert scales, which provably reverse the direction of the true preference in certain cases. We suggest improvements to the standard protocol to make it more theoretically sound, but even in its improved form, it cannot be used to evaluate open-ended tasks like story generation. For the latter, we propose a new human evaluation protocol called $\textit{system-level probabilistic assessment}$ (SPA). When human evaluation of stories is done with SPA, we can recover the ordering of GPT-3 models by size, with statistically significant results. However, when human evaluation is done with the standard protocol, less than half of the expected preferences can be recovered (e.g., there is no significant difference between $\texttt{curie}$ and $\texttt{davinci}$, despite using a highly powered test). | ['Dan Jurafsky', 'Kawin Ethayarajh'] | 2022-05-24 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 8.48670155e-02 4.27968174e-01 -2.81332344e-01 -3.75447571e-01
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16e0d1b8-8e96-47ac-a28b-22005b1810d1 | simple-yet-powerful-an-overlooked-1 | null | null | https://aclanthology.org/2022.coling-1.184 | https://aclanthology.org/2022.coling-1.184.pdf | Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. Traditional NER systems ignore nested entities, which are entities contained in other entity mentions. Although several methods have been proposed to address this case, most of them rely on complex task-specific structures and ignore potentially useful baselines for the task. We argue that this creates an overly optimistic impression of their performance. This paper revisits the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type. Extensive experiments with three nested NER corpora show that, regardless of the simplicity of this model, its performance is better or at least as well as more sophisticated methods. Furthermore, we show that the MLC architecture achieves state-of-the-art results in the Chilean Waiting List corpus by including pre-trained language models. In addition, we implemented an open-source library that computes task-specific metrics for nested NER. The results suggest that metrics used in previous work do not measure well the ability of a model to detect nested entities, while our metrics provide new evidence on how existing approaches handle the task. | ['Jocelyn Dunstan', 'Felipe Bravo-Marquez', 'Matias Rojas'] | null | null | null | null | coling-2022-10 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-2.93461502e-01 2.93828696e-01 -5.66540100e-02 -4.45180923e-01
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500f5cee-85a2-425c-9bc0-96b7b3e314f9 | uninext-exploring-a-unified-architecture-for | 2304.13700 | null | https://arxiv.org/abs/2304.13700v2 | https://arxiv.org/pdf/2304.13700v2.pdf | UniNeXt: Exploring A Unified Architecture for Vision Recognition | Vision Transformers have shown great potential in computer vision tasks. Most recent works have focused on elaborating the spatial token mixer for performance gains. However, we observe that a well-designed general architecture can significantly improve the performance of the entire backbone, regardless of which spatial token mixer is equipped. In this paper, we propose UniNeXt, an improved general architecture for the vision backbone. To verify its effectiveness, we instantiate the spatial token mixer with various typical and modern designs, including both convolution and attention modules. Compared with the architecture in which they are first proposed, our UniNeXt architecture can steadily boost the performance of all the spatial token mixers, and narrows the performance gap among them. Surprisingly, our UniNeXt equipped with naive local window attention even outperforms the previous state-of-the-art. Interestingly, the ranking of these spatial token mixers also changes under our UniNeXt, suggesting that an excellent spatial token mixer may be stifled due to a suboptimal general architecture, which further shows the importance of the study on the general architecture of vision backbone. All models and codes will be publicly available. | ['Zhibin Wang', 'Fan Wang', 'Sitong Wu', 'Jianlong Yuan', 'Fangjian Lin'] | 2023-04-26 | null | null | null | null | ['spatial-token-mixer'] | ['computer-vision'] | [-1.47957252e-02 4.70343195e-02 -6.41011447e-02 1.25790715e-01
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750e3b71-233d-46b0-8a6c-ec31de5cdf8c | machine-learning-and-the-future-of-bayesian | 2304.11251 | null | https://arxiv.org/abs/2304.11251v1 | https://arxiv.org/pdf/2304.11251v1.pdf | Machine Learning and the Future of Bayesian Computation | Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference. | ['David B. Dunson', 'Sanvesh Srivastava', 'Lizhen Lin', 'Trevor Campbell', 'Steven Winter'] | 2023-04-21 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.13783218e-01 2.40930673e-02 -4.15713847e-01 -6.14332318e-01
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32e92953-26f5-4dd3-9619-10aaa762479b | multimodal-intent-discovery-from-livestream | null | null | https://aclanthology.org/2022.findings-naacl.36 | https://aclanthology.org/2022.findings-naacl.36.pdf | Multimodal Intent Discovery from Livestream Videos | Individuals, educational institutions, and businesses are prolific at generating instructional video content such as “how-to” and tutorial guides. While significant progress has been made in basic video understanding tasks, identifying procedural intent within these instructional videos is a challenging and important task that remains unexplored but essential to video summarization, search, and recommendations. This paper introduces the problem of instructional intent identification and extraction from software instructional livestreams. We construct and present a new multimodal dataset consisting of software instructional livestreams and containing manual annotations for both detailed and abstract procedural intent that enable training and evaluation of joint video and text understanding models. We then introduce a multimodal cascaded cross-attention model to efficiently combine the weaker and noisier video signal with the more discriminative text signal. Our experiments show that our proposed model brings significant gains compared to strong baselines, including large-scale pretrained multimodal models. Our analysis further identifies that the task benefits from spatial as well as motion features extracted from videos, and provides insight on how the video signal is preferentially used for intent discovery. We also show that current models struggle to comprehend the nature of abstract intents, revealing important gaps in multimodal understanding and paving the way for future work. | ['Mohit Bansal', 'Walter Chang', 'Trung Bui', 'Seunghyun Yoon', 'Franck Dernoncourt', 'Quan Tran', 'Adyasha Maharana'] | null | null | null | null | findings-naacl-2022-7 | ['intent-discovery'] | ['natural-language-processing'] | [ 3.68240654e-01 -1.83955297e-01 -6.59646988e-01 -3.15267295e-01
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381b1226-0005-41bd-abe8-d39dde357180 | robust-speech-recognition-using-consensus | 1507.06023 | null | http://arxiv.org/abs/1507.06023v1 | http://arxiv.org/pdf/1507.06023v1.pdf | Robust speech recognition using consensus function based on multi-layer networks | The clustering ensembles mingle numerous partitions of a specified data into
a single clustering solution. Clustering ensemble has emerged as a potent
approach for ameliorating both the forcefulness and the stability of
unsupervised classification results. One of the major problems in clustering
ensembles is to find the best consensus function. Finding final partition from
different clustering results requires skillfulness and robustness of the
classification algorithm. In addition, the major problem with the consensus
function is its sensitivity to the used data sets quality. This limitation is
due to the existence of noisy, silence or redundant data. This paper proposes a
novel consensus function of cluster ensembles based on Multilayer networks
technique and a maintenance database method. This maintenance database approach
is used in order to handle any given noisy speech and, thus, to guarantee the
quality of databases. This can generates good results and efficient data
partitions. To show its effectiveness, we support our strategy with empirical
evaluation using distorted speech from Aurora speech databases. | ['Abir Smiti', 'Rimah Amami', 'Ghaith Manita'] | 2015-07-22 | null | null | null | null | ['clustering-ensemble', 'robust-speech-recognition'] | ['graphs', 'speech'] | [-3.63490060e-02 -2.59356976e-01 4.96405125e-01 -4.36079234e-01
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0024eab2-1f35-4da3-aafc-f25567dde7a1 | gest-the-graph-of-events-in-space-and-time-as | 2305.12940 | null | https://arxiv.org/abs/2305.12940v1 | https://arxiv.org/pdf/2305.12940v1.pdf | GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language | One of the essential human skills is the ability to seamlessly build an inner representation of the world. By exploiting this representation, humans are capable of easily finding consensus between visual, auditory and linguistic perspectives. In this work, we set out to understand and emulate this ability through an explicit representation for both vision and language - Graphs of Events in Space and Time (GEST). GEST alows us to measure the similarity between texts and videos in a semantic and fully explainable way, through graph matching. It also allows us to generate text and videos from a common representation that provides a well understood content. In this work we show that the graph matching similarity metrics based on GEST outperform classical text generation metrics and can also boost the performance of state of art, heavily trained metrics. | ['Marius Leordeanu', 'Traian Rebedea', 'Nicolae Cudlenco', 'Mihai Masala'] | 2023-05-22 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 5.87013438e-02 1.36874110e-01 2.17468157e-01 -2.63057858e-01
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f5c98847-b635-4609-b2a1-d17cb6c80c69 | smsmix-sense-maintained-sentence-mixup-for | 2212.07072 | null | https://arxiv.org/abs/2212.07072v2 | https://arxiv.org/pdf/2212.07072v2.pdf | SMSMix: Sense-Maintained Sentence Mixup for Word Sense Disambiguation | Word Sense Disambiguation (WSD) is an NLP task aimed at determining the correct sense of a word in a sentence from discrete sense choices. Although current systems have attained unprecedented performances for such tasks, the nonuniform distribution of word senses during training generally results in systems performing poorly on rare senses. To this end, we consider data augmentation to increase the frequency of these least frequent senses (LFS) to reduce the distributional bias of senses during training. We propose Sense-Maintained Sentence Mixup (SMSMix), a novel word-level mixup method that maintains the sense of a target word. SMSMix smoothly blends two sentences using mask prediction while preserving the relevant span determined by saliency scores to maintain a specific word's sense. To the best of our knowledge, this is the first attempt to apply mixup in NLP while preserving the meaning of a specific word. With extensive experiments, we validate that our augmentation method can effectively give more information about rare senses during training with maintained target sense label. | ['Chang D. Yoo', 'Mark Hasegawa-Johnson', 'Sunjae Yoon', 'John Harvill', 'Eunseop Yoon', 'Hee Suk Yoon'] | 2022-12-14 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 6.12269819e-01 1.16803788e-01 -3.47780824e-01 -3.63854945e-01
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beaebbb0-dde1-44eb-9182-935768137c60 | breaking-the-architecture-barrier-a-method | 2212.13970 | null | https://arxiv.org/abs/2212.13970v1 | https://arxiv.org/pdf/2212.13970v1.pdf | Breaking the Architecture Barrier: A Method for Efficient Knowledge Transfer Across Networks | Transfer learning is a popular technique for improving the performance of neural networks. However, existing methods are limited to transferring parameters between networks with same architectures. We present a method for transferring parameters between neural networks with different architectures. Our method, called DPIAT, uses dynamic programming to match blocks and layers between architectures and transfer parameters efficiently. Compared to existing parameter prediction and random initialization methods, it significantly improves training efficiency and validation accuracy. In experiments on ImageNet, our method improved validation accuracy by an average of 1.6 times after 50 epochs of training. DPIAT allows both researchers and neural architecture search systems to modify trained networks and reuse knowledge, avoiding the need for retraining from scratch. We also introduce a network architecture similarity measure, enabling users to choose the best source network without any training. | ['Kamil Piechowiak', 'Daniel Nowak', 'Maciej A. Czyzewski'] | 2022-12-28 | null | null | null | null | ['parameter-prediction'] | ['miscellaneous'] | [-5.88302724e-02 -1.69212863e-01 -2.45638728e-01 -5.81849694e-01
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0c4ae1e6-cb7b-4c4e-bfe5-f9c9b7356b36 | an-mrc-framework-for-semantic-role-labeling | 2109.06660 | null | https://arxiv.org/abs/2109.06660v2 | https://arxiv.org/pdf/2109.06660v2.pdf | An MRC Framework for Semantic Role Labeling | Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a sentence and can be decomposed into two subtasks: predicate disambiguation and argument labeling. Prior work deals with these two tasks independently, which ignores the semantic connection between the two tasks. In this paper, we propose to use the machine reading comprehension (MRC) framework to bridge this gap. We formalize predicate disambiguation as multiple-choice machine reading comprehension, where the descriptions of candidate senses of a given predicate are used as options to select the correct sense. The chosen predicate sense is then used to determine the semantic roles for that predicate, and these semantic roles are used to construct the query for another MRC model for argument labeling. In this way, we are able to leverage both the predicate semantics and the semantic role semantics for argument labeling. We also propose to select a subset of all the possible semantic roles for computational efficiency. Experiments show that the proposed framework achieves state-of-the-art or comparable results to previous work. Code is available at \url{https://github.com/ShannonAI/MRC-SRL}. | ['Jun He', 'Guoyin Wang', 'Ziyao Wang', 'Han Qiu', 'Xiaofei Sun', 'Yuxian Meng', 'Jiwei Li', 'Nan Wang'] | 2021-09-14 | null | https://aclanthology.org/2022.coling-1.191 | https://aclanthology.org/2022.coling-1.191.pdf | coling-2022-10 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.64446855e-01 5.50447166e-01 -3.36639404e-01 -5.09616554e-01
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03cd1af3-cd71-4dcd-a3b3-8c23b0b23a47 | fair-and-diverse-dpp-based-data-summarization | 1802.04023 | null | http://arxiv.org/abs/1802.04023v1 | http://arxiv.org/pdf/1802.04023v1.pdf | Fair and Diverse DPP-based Data Summarization | Sampling methods that choose a subset of the data proportional to its
diversity in the feature space are popular for data summarization. However,
recent studies have noted the occurrence of bias (under- or over-representation
of a certain gender or race) in such data summarization methods. In this paper
we initiate a study of the problem of outputting a diverse and fair summary of
a given dataset. We work with a well-studied determinantal measure of diversity
and corresponding distributions (DPPs) and present a framework that allows us
to incorporate a general class of fairness constraints into such distributions.
Coming up with efficient algorithms to sample from these constrained
determinantal distributions, however, suffers from a complexity barrier and we
present a fast sampler that is provably good when the input vectors satisfy a
natural property. Our experimental results on a real-world and an image dataset
show that the diversity of the samples produced by adding fairness constraints
is not too far from the unconstrained case, and we also provide a theoretical
explanation of it. | ['Tarun Kathuria', 'Amit Deshpande', 'Vijay Keswani', 'L. Elisa Celis', 'Damian Straszak', 'Nisheeth K. Vishnoi'] | 2018-02-12 | fair-and-diverse-dpp-based-data-summarization-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2176 | http://proceedings.mlr.press/v80/celis18a/celis18a.pdf | icml-2018-7 | ['data-summarization'] | ['miscellaneous'] | [ 5.28728902e-01 2.96761870e-01 -5.36310792e-01 -6.93460763e-01
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5d453971-9abd-4a9b-bfb5-d9f23c4d7854 | hyp-2-loss-beyond-hypersphere-metric-space | 2208.06866 | null | https://arxiv.org/abs/2208.06866v1 | https://arxiv.org/pdf/2208.06866v1.pdf | HyP$^2$ Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval | Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and efficient retrieval. In this paper, we carried out in-depth investigations on metric learning in deep hashing for establishing a powerful metric space in multi-label scenarios, where the pair loss suffers high computational overhead and converge difficulty, while the proxy loss is theoretically incapable of expressing the profound label dependencies and exhibits conflicts in the constructed hypersphere space. To address the problems, we propose a novel metric learning framework with Hybrid Proxy-Pair Loss (HyP$^2$ Loss) that constructs an expressive metric space with efficient training complexity w.r.t. the whole dataset. The proposed HyP$^2$ Loss focuses on optimizing the hypersphere space by learnable proxies and excavating data-to-data correlations of irrelevant pairs, which integrates sufficient data correspondence of pair-based methods and high-efficiency of proxy-based methods. Extensive experiments on four standard multi-label benchmarks justify the proposed method outperforms the state-of-the-art, is robust among different hash bits and achieves significant performance gains with a faster, more stable convergence speed. Our code is available at https://github.com/JerryXu0129/HyP2-Loss. | ['Jue Wang', 'Yanbo Fan', 'Chun Yuan', 'Zhengzhuo Xu', 'Zenghao Chai', 'Chengyin Xu'] | 2022-08-14 | null | null | null | null | ['multi-label-image-retrieval'] | ['computer-vision'] | [-2.00757295e-01 -4.35680449e-01 -2.43328586e-01 -5.08315563e-01
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edd1b409-4c51-4c94-aab4-07acad3fa016 | uncovering-political-hate-speech-during | 2306.14764 | null | https://arxiv.org/abs/2306.14764v2 | https://arxiv.org/pdf/2306.14764v2.pdf | Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines | The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages. To address this issue, we introduce a new dataset named IEHate, which contains 11,457 manually annotated Hindi tweets related to the Indian Assembly Election Campaign from November 1, 2021, to March 9, 2022. We performed a detailed analysis of the dataset, focusing on the prevalence of hate speech in political communication and the different forms of hateful language used. Additionally, we benchmark the dataset using a range of machine learning, deep learning, and transformer-based algorithms. Our experiments reveal that the performance of these models can be further improved, highlighting the need for more advanced techniques for hate speech detection in low-resource languages. In particular, the relatively higher score of human evaluation over algorithms emphasizes the importance of utilizing both human and automated approaches for effective hate speech moderation. Our IEHate dataset can serve as a valuable resource for researchers and practitioners working on developing and evaluating hate speech detection techniques in low-resource languages. Overall, our work underscores the importance of addressing the challenges of identifying and mitigating hate speech in political discourse, particularly in the context of low-resource languages. The dataset and resources for this work are made available at https://github.com/Farhan-jafri/Indian-Election. | ['Imran Razzak', 'Usman Naseem', 'Kritesh Rauniyar', 'Surendrabikram Thapa', 'Mohammad Aman Siddiqui', 'Farhan Ahmad Jafri'] | 2023-06-26 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-1.83796108e-01 4.92761433e-02 -1.87390506e-01 -7.00652748e-02
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eb258463-36fa-42fb-9fbb-556a97dc6abb | collaborative-representation-based | 1204.2358 | null | https://arxiv.org/abs/1204.2358v2 | https://arxiv.org/pdf/1204.2358v2.pdf | Collaborative Representation based Classification for Face Recognition | By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations. | ['David Zhang', 'Xiangchu Feng', 'Yi Ma', 'Lei Zhang', 'Meng Yang'] | 2012-04-11 | null | null | null | null | ['robust-face-recognition', 'sparse-representation-based-classification'] | ['computer-vision', 'computer-vision'] | [ 2.92250544e-01 -5.80440350e-02 -3.11446667e-01 -5.19458294e-01
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e27ccf21-e2e9-469e-8b7c-9d842995f4a6 | colada-a-collaborative-label-denoising | 2305.14913 | null | https://arxiv.org/abs/2305.14913v1 | https://arxiv.org/pdf/2305.14913v1.pdf | CoLaDa: A Collaborative Label Denoising Framework for Cross-lingual Named Entity Recognition | Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-language labeled data or performing knowledge distillation on target-language unlabeled data. However, these methods may suffer from label noise due to the automatic labeling process. In this paper, we propose CoLaDa, a Collaborative Label Denoising Framework, to address this problem. Specifically, we first explore a model-collaboration-based denoising scheme that enables models trained on different data sources to collaboratively denoise pseudo labels used by each other. We then present an instance-collaboration-based strategy that considers the label consistency of each token's neighborhood in the representation space for denoising. Experiments on different benchmark datasets show that the proposed CoLaDa achieves superior results compared to previous methods, especially when generalizing to distant languages. | ['Chin-Yew Lin', 'Tiejun Zhao', 'Börje F. Karlsson', 'Huiqiang Jiang', 'Qianhui Wu', 'Tingting Ma'] | 2023-05-24 | null | null | null | null | ['named-entity-recognition-ner'] | ['natural-language-processing'] | [ 2.27228906e-02 -1.72545135e-01 -1.74980015e-02 -6.63747013e-01
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4ea4c80f-fc93-4cce-8a7a-acc142695d31 | how-far-are-we-from-robust-voice-conversion-a | 2011.12063 | null | https://arxiv.org/abs/2011.12063v3 | https://arxiv.org/pdf/2011.12063v3.pdf | How Far Are We from Robust Voice Conversion: A Survey | Voice conversion technologies have been greatly improved in recent years with the help of deep learning, but their capabilities of producing natural sounding utterances in different conditions remain unclear. In this paper, we gave a thorough study of the robustness of known VC models. We also modified these models, such as the replacement of speaker embeddings, to further improve their performances. We found that the sampling rate and audio duration greatly influence voice conversion. All the VC models suffer from unseen data, but AdaIN-VC is relatively more robust. Also, the speaker embedding jointly trained is more suitable for voice conversion than those trained on speaker identification. | ['Hung-Yi Lee', 'Chien-yu Huang', 'Jheng-Hao Lin', 'Tzu-Hsien Huang'] | 2020-11-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-3.11072797e-01 -2.64867038e-01 2.34275199e-02 -2.64979333e-01
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4ed67227-9fc1-4ac8-9c95-d0528fd76800 | llm-empowered-chatbots-for-psychiatrist-and | 2305.13614 | null | https://arxiv.org/abs/2305.13614v1 | https://arxiv.org/pdf/2305.13614v1.pdf | LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation | Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiatric outpatient scenarios. In this work, we focus on exploring the potential of ChatGPT in powering chatbots for psychiatrist and patient simulation. We collaborate with psychiatrists to identify objectives and iteratively develop the dialogue system to closely align with real-world scenarios. In the evaluation experiments, we recruit real psychiatrists and patients to engage in diagnostic conversations with the chatbots, collecting their ratings for assessment. Our findings demonstrate the feasibility of using ChatGPT-powered chatbots in psychiatric scenarios and explore the impact of prompt designs on chatbot behavior and user experience. | ['Lyuchun Cui', 'Zhiling Zhang', 'Kunyao Lan', 'Kenny Q. Zhu', 'Mengyue Wu', 'Siyuan Chen'] | 2023-05-23 | null | null | null | null | ['chatbot', 'chatbot'] | ['methodology', 'natural-language-processing'] | [-2.31017500e-01 1.05630517e+00 1.08242393e-01 -5.64159811e-01
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2a634609-e5e0-4737-ba1d-4f22393376c0 | findings-of-the-2016-conference-on-machine | null | null | https://aclanthology.org/W16-2301 | https://aclanthology.org/W16-2301.pdf | Findings of the 2016 Conference on Machine Translation | null | ['Lucia Specia', "Aur{\\'e}lie N{\\'e}v{\\'e}ol", 'Philipp Koehn', 'Rajen Chatterjee', 'Ond{\\v{r}}ej Bojar', 'Varvara Logacheva', 'Raphael Rubino', 'Marco Turchi', 'Marcos Zampieri', 'Matt Post', 'Christof Monz', 'Carolina Scarton', 'Barry Haddow', 'Martin Popel', 'Antonio Jimeno Yepes', 'Yvette Graham', 'Matthias Huck', 'Matteo Negri', 'Mariana Neves', 'Karin Verspoor', 'Christian Federmann'] | 2016-08-01 | null | null | null | ws-2016-8 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
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401a3bae-593e-4abb-b4a7-f88b9a5b227e | an-unsupervised-extractive-summarization | 2112.03203 | null | https://arxiv.org/abs/2112.03203v4 | https://arxiv.org/pdf/2112.03203v4.pdf | An unsupervised extractive summarization method based on multi-round computation | Text summarization methods have attracted much attention all the time. In recent years, deep learning has been applied to text summarization, and it turned out to be pretty effective. Most of the current text summarization methods based on deep learning are supervised methods which need large-scale datasets. However, large-scale datasets are difficult to obtain in practical applications. In this paper, an unsupervised extractive text summarization method based on multi-round calculation is proposed. Based on the directed graph algorithm, we change the common method which calculates the sentence ranking at one time to multi-round calculation, and we dynamically optimize the relation of sentences after each round of calculation to reduce the redundancy of summarization. Experiments are carried out on four data sets, each separately containing Chinese, English, long and short texts. The experiment results show that our method has better performance than other unsupervised methods. | ['Kevin Song', 'Jin He', 'Yongfeng Huang', 'Zhongliang Yang', 'Yingzhu Xiong', 'Dehao Tao'] | 2021-12-06 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.37687400e-01 -1.60965979e-01 -1.02655165e-01 -2.41853029e-01
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6f9e7378-eb31-43ba-a399-f1439c0c0b8b | learning-dynamic-guidance-for-depth-image | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Gu_Learning_Dynamic_Guidance_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Gu_Learning_Dynamic_Guidance_CVPR_2017_paper.pdf | Learning Dynamic Guidance for Depth Image Enhancement | The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.
| ['WangMeng Zuo', 'Shuhang Gu', 'Yunjin Chen', 'Shi Guo', 'Chongyu Chen', 'Lei Zhang'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['depth-image-upsampling'] | ['computer-vision'] | [ 4.86604869e-01 -1.40020475e-01 -1.74455851e-01 -7.20415175e-01
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7c660457-1531-4440-9bf0-095d0c4a7e0a | efficiently-aligned-cross-lingual-transfer | 2304.01295 | null | https://arxiv.org/abs/2304.01295v2 | https://arxiv.org/pdf/2304.01295v2.pdf | Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning | Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rather limited. This is partly due to the high cost of obtaining non-English conversational data, which results in limited coverage. In this work, we introduce XSGD, a parallel and large-scale multilingual conversation dataset that we created by translating the English-only Schema-Guided Dialogue (SGD) dataset (Rastogi et al., 2020) into 105 other languages. XSGD contains approximately 330k utterances per language. To facilitate aligned cross-lingual representations, we develop an efficient prompt-tuning-based method for learning alignment prompts. We also investigate two different classifiers: NLI-based and vanilla classifiers, and test cross-lingual capability enabled by the aligned prompts. We evaluate our model's cross-lingual generalization capabilities on two conversation tasks: slot-filling and intent classification. Our results demonstrate the strong and efficient modeling ability of NLI-based classifiers and the large cross-lingual transfer improvements achieved by our aligned prompts, particularly in few-shot settings. In addition, we highlight the nice results of our approach compared to LLMs such as text-davinci-003 and ChatGPT in both zero-shot and few-shot settings. While LLMs exhibit impressive performance in English, their cross-lingual capabilities in other languages, particularly low-resource languages, are limited. | ['Yingbo Zhou', 'Caiming Xiong', 'Wenhao Liu', 'Shafiq Joty', 'Semih Yavuz', 'Jin Qu', 'Lifu Tu'] | 2023-04-03 | null | null | null | null | ['cross-lingual-transfer', 'intent-classification', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.11944519e-01 1.85243860e-01 -5.30020952e-01 -5.00128746e-01
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c959520b-b6fa-4e2a-8114-83dd6a4661ce | flow-lenia-mass-conservation-for-the-study-of | 2212.07906 | null | https://arxiv.org/abs/2212.07906v2 | https://arxiv.org/pdf/2212.07906v2.pdf | Flow-Lenia: Towards open-ended evolution in cellular automata through mass conservation and parameter localization | The design of complex self-organising systems producing life-like phenomena, such as the open-ended evolution of virtual creatures, is one of the main goals of artificial life. Lenia, a family of cellular automata (CA) generalizing Conway's Game of Life to continuous space, time and states, has attracted a lot of attention because of the wide diversity of self-organizing patterns it can generate. Among those, some spatially localized patterns (SLPs) resemble life-like artificial creatures and display complex behaviors. However, those creatures are found in only a small subspace of the Lenia parameter space and are not trivial to discover, necessitating advanced search algorithms. Furthermore, each of these creatures exist only in worlds governed by specific update rules and thus cannot interact in the same one. This paper proposes as mass-conservative extension of Lenia, called Flow Lenia, that solve both of these issues. We present experiments demonstrating its effectiveness in generating SLPs with complex behaviors and show that the update rule parameters can be optimized to generate SLPs showing behaviors of interest. Finally, we show that Flow Lenia enables the integration of the parameters of the CA update rules within the CA dynamics, making them dynamic and localized, allowing for multi-species simulations, with locally coherent update rules that define properties of the emerging creatures, and that can be mixed with neighbouring rules. We argue that this paves the way for the intrinsic evolution of self-organized artificial life forms within continuous CAs. | ['Bert Wang-Chak Chan', 'Clément Moulin-Frier', 'Pierre-Yves Oudeyer', 'Mayalen Etcheverry', 'Gautier Hamon', 'Erwan Plantec'] | 2022-12-14 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.20313950e-01 5.07820174e-02 3.87773365e-01 6.65801108e-01
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d5249218-d0fc-4c56-87f7-ba2594c9d3d0 | an-overview-of-ai-and-blockchain-integration | 2305.03928 | null | https://arxiv.org/abs/2305.03928v1 | https://arxiv.org/pdf/2305.03928v1.pdf | An Overview of AI and Blockchain Integration for Privacy-Preserving | With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy. | ['Wenkai Li', 'Xiaoqi Li', 'Hongli Peng', 'Yuanzheng Niu', 'Dechao Kong', 'Zongwei Li'] | 2023-05-06 | null | null | null | null | ['de-identification'] | ['natural-language-processing'] | [ 1.01657473e-01 1.02723040e-01 -7.43038297e-01 -4.18358654e-01
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c32834a9-a081-42c1-b7e2-51e25b04f25d | calico-self-supervised-camera-lidar | 2306.00349 | null | https://arxiv.org/abs/2306.00349v1 | https://arxiv.org/pdf/2306.00349v1.pdf | CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception | Perception is crucial in the realm of autonomous driving systems, where bird's eye view (BEV)-based architectures have recently reached state-of-the-art performance. The desirability of self-supervised representation learning stems from the expensive and laborious process of annotating 2D and 3D data. Although previous research has investigated pretraining methods for both LiDAR and camera-based 3D object detection, a unified pretraining framework for multimodal BEV perception is missing. In this study, we introduce CALICO, a novel framework that applies contrastive objectives to both LiDAR and camera backbones. Specifically, CALICO incorporates two stages: point-region contrast (PRC) and region-aware distillation (RAD). PRC better balances the region- and scene-level representation learning on the LiDAR modality and offers significant performance improvement compared to existing methods. RAD effectively achieves contrastive distillation on our self-trained teacher model. CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements. Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection against adversarial attacks and corruption. Additionally, our framework can be tailored to different backbones and heads, positioning it as a promising approach for multimodal BEV perception. | ['Chaowei Xiao', 'Z. Morley Mao', 'Atul Prakash', 'Qingzhao Zhang', 'Haizhong Zheng', 'Jiachen Sun'] | 2023-06-01 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [ 7.80990273e-02 2.43220665e-02 -2.87840962e-01 -4.22537953e-01
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d3df7760-63a6-4824-a7dd-c3604bd29994 | naturalinversion-data-free-image-synthesis | 2306.16661 | null | https://arxiv.org/abs/2306.16661v1 | https://arxiv.org/pdf/2306.16661v1.pdf | NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency | We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature maps extracted from the pre-trained classifier, (2) a one-to-one approach generative model where only one batch of images are synthesized by one generator to bring the non-linearity to optimization and to ease the overall optimizing process, (3) learnable Adaptive Channel Scaling parameters which are end-to-end trained to scale the output image channel to utilize the original image prior further. With our NaturalInversion, we synthesize images from classifiers trained on CIFAR-10/100 and show that our images are more consistent with original data distribution than prior works by visualization and additional analysis. Furthermore, our synthesized images outperform prior works on various applications such as knowledge distillation and pruning, demonstrating the effectiveness of our proposed method. | ['Suhyun Kim', 'Dohee Kim', 'Dogyun Park', 'Yujin Kim'] | 2023-06-29 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 4.50742006e-01 1.32680997e-01 2.40027145e-01 -5.23450851e-01
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2334dff5-c3f6-468e-86f6-87d172d9e32b | high-order-tensor-formulation-for | null | null | http://openaccess.thecvf.com/content_iccv_2017/html/Bibi_High_Order_Tensor_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Bibi_High_Order_Tensor_ICCV_2017_paper.pdf | High Order Tensor Formulation for Convolutional Sparse Coding | Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct single-feature 2D images independently. However, learning multi-dimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.
| ['Adel Bibi', 'Bernard Ghanem'] | 2017-10-01 | null | null | null | iccv-2017-10 | ['video-reconstruction'] | ['computer-vision'] | [-3.17025324e-03 -4.33403671e-01 4.37385105e-02 1.43310996e-02
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31cba3ab-e0af-4546-84d4-8376714e5b40 | revisiting-discriminative-entropy-clustering | 2301.11405 | null | https://arxiv.org/abs/2301.11405v2 | https://arxiv.org/pdf/2301.11405v2.pdf | Discriminative Entropy Clustering and its Relation to K-means and SVM | Maximization of mutual information between the model's input and output is formally related to "decisiveness" and "fairness" of the softmax predictions, motivating such unsupervised entropy-based losses for discriminative models. Recent self-labeling methods based on such losses represent the state of the art in deep clustering. First, we discuss a number of general properties of such entropy clustering methods, including their relation to K-means and unsupervised SVM-based techniques. Disproving some earlier published claims, we point out fundamental differences with K-means. On the other hand, we show similarity with SVM-based clustering allowing us to link explicit margin maximization to entropy clustering. Finally, we observe that the common form of cross-entropy is not robust to pseudo-label errors. Our new loss addresses the problem and leads to a new EM algorithm improving the state of the art on many standard benchmarks. | ['Yuri Boykov', 'Zhongwen Zhang'] | 2023-01-26 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [ 1.52808562e-01 6.05586052e-01 -4.57884550e-01 -8.41452301e-01
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49002979-ac55-4b8b-a52f-4f26f77e4b94 | stylemelgan-an-efficient-high-fidelity | 2011.01557 | null | https://arxiv.org/abs/2011.01557v2 | https://arxiv.org/pdf/2011.01557v2.pdf | StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization | In recent years, neural vocoders have surpassed classical speech generation approaches in naturalness and perceptual quality of the synthesized speech. Computationally heavy models like WaveNet and WaveGlow achieve best results, while lightweight GAN models, e.g. MelGAN and Parallel WaveGAN, remain inferior in terms of perceptual quality. We therefore propose StyleMelGAN, a lightweight neural vocoder allowing synthesis of high-fidelity speech with low computational complexity. StyleMelGAN employs temporal adaptive normalization to style a low-dimensional noise vector with the acoustic features of the target speech. For efficient training, multiple random-window discriminators adversarially evaluate the speech signal analyzed by a filter bank, with regularization provided by a multi-scale spectral reconstruction loss. The highly parallelizable speech generation is several times faster than real-time on CPUs and GPUs. MUSHRA and P.800 listening tests show that StyleMelGAN outperforms prior neural vocoders in copy-synthesis and Text-to-Speech scenarios. | ['Guillaume Fuchs', 'Nicola Pia', 'Ahmed Mustafa'] | 2020-11-03 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 3.87696356e-01 3.56807172e-01 3.90313745e-01 9.62408707e-02
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c8e192d5-b51b-4ead-84d6-01e8c487b0b1 | reinforcement-learning-in-robotic-motion | 2306.06754 | null | https://arxiv.org/abs/2306.06754v1 | https://arxiv.org/pdf/2306.06754v1.pdf | Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning | High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks. For real robots, it is challenging to collect enough qualified data either as demonstrations for IL or experiences for RL due to safety considerations in environments with obstacles. We target this challenge by proposing the self-imitation learning by planning plus (SILP+) algorithm, which efficiently embeds experience-based planning into the learning architecture to mitigate the data-collection problem. The planner generates demonstrations based on successfully visited states from the current RL policy, and the policy improves by learning from these demonstrations. In this way, we relieve the demand for human expert operators to collect demonstrations required by IL and improve the RL performance as well. Various experimental results show that SILP+ achieves better training efficiency higher and more stable success rate in complex motion planning tasks compared to several other methods. Extensive tests on physical robots illustrate the effectiveness of SILP+ in a physical setting. | ['Lambert Schomaker', 'Sha Luo'] | 2023-06-11 | null | null | null | null | ['imitation-learning', 'motion-planning'] | ['methodology', 'robots'] | [-1.05679028e-01 2.09082991e-01 -3.76636386e-01 -4.18348722e-02
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0cf36688-65cf-44e8-9e0b-38d13aef6be1 | transfer-learning-across-several-centuries | 2306.14592 | null | https://arxiv.org/abs/2306.14592v1 | https://arxiv.org/pdf/2306.14592v1.pdf | Transfer Learning across Several Centuries: Machine and Historian Integrated Method to Decipher Royal Secretary's Diary | A named entity recognition and classification plays the first and foremost important role in capturing semantics in data and anchoring in translation as well as downstream study for history. However, NER in historical text has faced challenges such as scarcity of annotated corpus, multilanguage variety, various noise, and different convention far different from the contemporary language model. This paper introduces Korean historical corpus (Diary of Royal secretary which is named SeungJeongWon) recorded over several centuries and recently added with named entity information as well as phrase markers which historians carefully annotated. We fined-tuned the language model on history corpus, conducted extensive comparative experiments using our language model and pretrained muti-language models. We set up the hypothesis of combination of time and annotation information and tested it based on statistical t test. Our finding shows that phrase markers clearly improve the performance of NER model in predicting unseen entity in documents written far different time period. It also shows that each of phrase marker and corpus-specific trained model does not improve the performance. We discuss the future research directions and practical strategies to decipher the history document. | ['Jaehyuk Lee', 'Hyungil Lee', 'Joonmo Ahn', 'Taehong Jang', 'Sojung Lucia Kim'] | 2023-06-26 | null | null | null | null | ['transfer-learning', 'cg'] | ['miscellaneous', 'natural-language-processing'] | [-3.20299774e-01 -2.51662880e-01 -4.68997151e-01 -2.83995450e-01
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e94ad713-3a08-414b-8f00-4a3b91be0687 | high-fidelity-pseudo-labels-for-boosting | 2304.02621 | null | https://arxiv.org/abs/2304.02621v1 | https://arxiv.org/pdf/2304.02621v1.pdf | High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation | The task of image-level weakly-supervised semantic segmentation (WSSS) has gained popularity in recent years, as it reduces the vast data annotation cost for training segmentation models. The typical approach for WSSS involves training an image classification network using global average pooling (GAP) on convolutional feature maps. This enables the estimation of object locations based on class activation maps (CAMs), which identify the importance of image regions. The CAMs are then used to generate pseudo-labels, in the form of segmentation masks, to supervise a segmentation model in the absence of pixel-level ground truth. In case of the SEAM baseline, a previous work proposed to improve CAM learning in two ways: (1) Importance sampling, which is a substitute for GAP, and (2) the feature similarity loss, which utilizes a heuristic that object contours almost exclusively align with color edges in images. In this work, we propose a different probabilistic interpretation of CAMs for these techniques, rendering the likelihood more appropriate than the multinomial posterior. As a result, we propose an add-on method that can boost essentially any previous WSSS method, improving both the region similarity and contour quality of all implemented state-of-the-art baselines. This is demonstrated on a wide variety of baselines on the PASCAL VOC dataset. Experiments on the MS COCO dataset show that performance gains can also be achieved in a large-scale setting. Our code is available at https://github.com/arvijj/hfpl. | ['Michael Felsberg', 'Yushan Zhang', 'Arvi Jonnarth'] | 2023-04-05 | null | null | null | null | ['weakly-supervised-segmentation'] | ['computer-vision'] | [ 5.35346448e-01 2.98324078e-01 -2.67721564e-01 -5.12658775e-01
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26001202-5786-4149-8f5d-f99f90ecd365 | design-and-implementation-of-image-processing | 1607.04760 | null | http://arxiv.org/abs/1607.04760v1 | http://arxiv.org/pdf/1607.04760v1.pdf | Design and implementation of image processing system for Lumen social robot-humanoid as an exhibition guide for Electrical Engineering Days 2015 | Lumen Social Robot is a humanoid robot development with the purpose that it
could be a good friend to all people. In this year, the Lumen Social Robot is
being developed into a guide in the exhibition and in the seminar of the Final
Exam of undergraduate and graduate students in Electrical Engineering ITB,
named Electrical Engineering Days 2015. In order to be the guide in that
occasion, Lumen is supported by several things. They are Nao robot components,
servers, and multiple processor systems. The image processing system is a
processing application system that allows Lumen to recognize and determine an
object from the image taken from the camera eye. The image processing system is
provided with four modules. They are face detection module to detect a person's
face, face recognition module to recognize a person's face, face tracking
module to follow a person's face, and human detection module to detect humans
based on the upper parts of person's body. Face detection module and human
detection module are implemented by using the library harcascade.xml on EMGU
CV. Face recognition module is implemented by adding the database for the face
that has been detected and store it in that database. Face tracking module is
implemented by using the Smooth Gaussian filter to the image.
-----
Lumen Sosial Robot merupakan sebuah pengembangan robot humanoid agar dapat
menjadi teman bagi banyak orang. Sistem pengolahan citra merupakan sistem
aplikasi pengolah yang bertujuan Lumen dapat mengenali dan mengetahui suatu
objek pada citra yang diambil dari camera mata Lumen. System pengolahan citra
dilengkapi dengan empat buah modul, yaitu modul face detection untuk mendeteksi
wajah seseorang, modul face recognition untuk mengenali wajah orang tersebut,
modul face tracking untuk mengikuti wajah seseorang, dan modul human detection
untuk mendeteksi manusia berdasarkan bagian tubuh atas orang | ['Setyaki Sholata Sya', 'Ary Setijadi Prihatmanto'] | 2016-07-16 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [-1.74387515e-01 3.12580645e-01 3.41893166e-01 -5.83455712e-02
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9940c1b8-3538-4114-8592-ff5e0fe81677 | entity-linking-meets-deep-learning-techniques | 2109.12520 | null | https://arxiv.org/abs/2109.12520v1 | https://arxiv.org/pdf/2109.12520v1.pdf | Entity Linking Meets Deep Learning: Techniques and Solutions | Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base. EL plays an important role in the fields of knowledge engineering and data mining, underlying a variety of downstream applications such as knowledge base population, content analysis, relation extraction, and question answering. In recent years, deep learning (DL), which has achieved tremendous success in various domains, has also been leveraged in EL methods to surpass traditional machine learning based methods and yield the state-of-the-art performance. In this survey, we present a comprehensive review and analysis of existing DL based EL methods. First of all, we propose a new taxonomy, which organizes existing DL based EL methods using three axes: embedding, feature, and algorithm. Then we systematically survey the representative EL methods along the three axes of the taxonomy. Later, we introduce ten commonly used EL data sets and give a quantitative performance analysis of DL based EL methods over these data sets. Finally, we discuss the remaining limitations of existing methods and highlight some promising future directions. | ['Xiaojie Yuan', 'Jianyong Wang', 'Jiawei Han', 'Yinan Liu', 'Yuhan Li', 'Wei Shen'] | 2021-09-26 | null | null | null | null | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.46150690e-01 1.78628594e-01 -8.91261101e-01 -2.36488394e-02
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972398ed-24df-4486-b862-5bfbd6bcaf09 | u-net-vs-transformer-is-u-net-outdated-in | 2208.04939 | null | https://arxiv.org/abs/2208.04939v2 | https://arxiv.org/pdf/2208.04939v2.pdf | U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration? | Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to capture accurate deformations without needing long-range dependencies. The purpose of this study is therefore to investigate whether U-Net-based methods are outdated compared to modern transformer-based approaches when applied to medical image registration. For this, we propose a large kernel U-Net (LKU-Net) by embedding a parallel convolutional block to a vanilla U-Net in order to enhance the effective receptive field. On the public 3D IXI brain dataset for atlas-based registration, we show that the performance of the vanilla U-Net is already comparable with that of state-of-the-art transformer-based networks (such as TransMorph), and that the proposed LKU-Net outperforms TransMorph by using only 1.12% of its parameters and 10.8% of its mult-adds operations. We further evaluate LKU-Net on a MICCAI Learn2Reg 2021 challenge dataset for inter-subject registration, our LKU-Net also outperforms TransMorph on this dataset and ranks first on the public leaderboard as of the submission of this work. With only modest modifications to the vanilla U-Net, we show that U-Net can outperform transformer-based architectures on inter-subject and atlas-based 3D medical image registration. Code is available at https://github.com/xi-jia/LKU-Net. | ['Jinming Duan', 'Zhaowen Qiu', 'Wenqi Lu', 'Tianyang Zhang', 'Joseph Bartlett', 'Xi Jia'] | 2022-08-07 | null | null | null | null | ['medical-image-registration', 'long-range-modeling'] | ['medical', 'natural-language-processing'] | [-1.30858585e-01 2.89070487e-01 5.80109470e-02 -4.42105949e-01
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f1b0b92d-40c2-4083-81f2-7cacf3e4769f | the-evolution-of-sentiment-analysis-a-review | 1612.01556 | null | http://arxiv.org/abs/1612.01556v4 | http://arxiv.org/pdf/1612.01556v4.pdf | The Evolution of Sentiment Analysis - A Review of Research Topics, Venues, and Top Cited Papers | Sentiment analysis is one of the fastest growing research areas in computer
science, making it challenging to keep track of all the activities in the area.
We present a computer-assisted literature review, where we utilize both text
mining and qualitative coding, and analyze 6,996 papers from Scopus. We find
that the roots of sentiment analysis are in the studies on public opinion
analysis at the beginning of 20th century and in the text subjectivity analysis
performed by the computational linguistics community in 1990's. However, the
outbreak of computer-based sentiment analysis only occurred with the
availability of subjective texts on the Web. Consequently, 99% of the papers
have been published after 2004. Sentiment analysis papers are scattered to
multiple publication venues, and the combined number of papers in the top-15
venues only represent ca. 30% of the papers in total. We present the top-20
cited papers from Google Scholar and Scopus and a taxonomy of research topics.
In recent years, sentiment analysis has shifted from analyzing online product
reviews to social media texts from Twitter and Facebook. Many topics beyond
product reviews like stock markets, elections, disasters, medicine, software
engineering and cyberbullying extend the utilization of sentiment analysis | ['Daniel Graziotin', 'Mika Viking Mäntylä', 'Miikka Kuutila'] | 2016-12-05 | null | null | null | null | ['subjectivity-analysis'] | ['natural-language-processing'] | [-3.68284643e-01 -4.39693853e-02 -8.15380812e-01 2.37777680e-01
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c20164d5-4dc9-487a-9fb4-4ff1b580c9f3 | asymmetric-cross-guided-attention-network-for | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Asymmetric_Cross-Guided_Attention_Network_for_Actor_and_Action_Video_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Asymmetric_Cross-Guided_Attention_Network_for_Actor_and_Action_Video_Segmentation_ICCV_2019_paper.pdf | Asymmetric Cross-Guided Attention Network for Actor and Action Video Segmentation From Natural Language Query | Actor and action video segmentation from natural language query aims to selectively segment the actor and its action in a video based on an input textual description. Previous works mostly focus on learning simple correlation between two heterogeneous features of vision and language via dynamic convolution or fully convolutional classification. However, they ignore the linguistic variation of natural language query and have difficulty in modeling global visual context, which leads to unsatisfactory segmentation performance. To address these issues, we propose an asymmetric cross-guided attention network for actor and action video segmentation from natural language query. Specifically, we frame an asymmetric cross-guided attention network, which consists of vision guided language attention to reduce the linguistic variation of input query and language guided vision attention to incorporate query-focused global visual context simultaneously. Moreover, we adopt multi-resolution fusion scheme and weighted loss for foreground and background pixels to obtain further performance improvement. Extensive experiments on Actor-Action Dataset Sentences and J-HMDB Sentences show that our proposed approach notably outperforms state-of-the-art methods.
| [' Dacheng Tao', ' Junchi Yan', ' Cheng Deng', 'Hao Wang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['referring-expression-segmentation'] | ['computer-vision'] | [ 2.50732034e-01 -1.79193050e-01 -4.06286269e-01 -5.29405057e-01
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cf1fdaa0-d4b4-41cf-a11b-2042bd919b86 | efficient-dynamic-filter-for-robust-and-low | 2205.01304 | null | https://arxiv.org/abs/2205.01304v2 | https://arxiv.org/pdf/2205.01304v2.pdf | Efficient dynamic filter for robust and low computational feature extraction | Unseen noise signal which is not considered in a model training process is difficult to anticipate and would lead to performance degradation. Various methods have been investigated to mitigate unseen noise. In our previous work, an Instance-level Dynamic Filter (IDF) and a Pixel Dynamic Filter (PDF) were proposed to extract noise-robust features. However, the performance of the dynamic filter might be degraded since simple feature pooling is used to reduce the computational resource in the IDF part. In this paper, we propose an efficient dynamic filter to enhance the performance of the dynamic filter. Instead of utilizing the simple feature mean, we separate Time-Frequency (T-F) features as non-overlapping chunks, and separable convolutions are carried out for each feature direction (inter chunks and intra chunks). Additionally, we propose Dynamic Attention Pooling that maps high dimensional features as low dimensional feature embeddings. These methods are applied to the IDF for keyword spotting and speaker verification tasks. We confirm that our proposed method performs better in unseen environments (unseen noise and unseen speakers) than state-of-the-art models. | ['Hanseok Ko', 'David K. Han', 'Jeong-gi Kwak', 'Bokyeung Lee', 'Gwantae Kim', 'Donghyeon Kim'] | 2022-05-03 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.01055929e-01 -3.23689312e-01 3.36078078e-01 -5.06497025e-01
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a442ce0b-bac4-4881-91b9-8e90bf0810a6 | a-survey-on-compiler-autotuning-using-machine | 1801.04405 | null | http://arxiv.org/abs/1801.04405v5 | http://arxiv.org/pdf/1801.04405v5.pdf | A Survey on Compiler Autotuning using Machine Learning | Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field. | ['John Cavazos', 'Amir H. Ashouri', 'Cristina Silvano', 'Gianluca Palermo', 'William Killian'] | 2018-01-13 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-2.48252526e-02 -3.82849246e-01 -9.16992784e-01 -3.30250502e-01
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2.53596187e-01 -6.79911554e-01 -2.82478750e-01 7.68168941e-02] | [7.779078960418701, 7.478159427642822] |
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