Boundary_Detection
-
Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition
2023-03-20 05:11:22Ying Mo, Hongyin Tang, Jiahao Liu, Qifan Wang, Zenglin Xu, Jingang Wang, Wei Wu, Zhoujun LiAbstract
Named entity recognition (NER) is an important research problem in natural language processing. There are three types of NER tasks, including flat, nested and discontinuous entity recognition. Most previous sequential labeling models are task-specific, while recent years have witnessed the rising of generative models due to the advantage of unifying all NER tasks into the seq2seq model framework. Although achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. This paper proposes a multi-task Transformer, which incorporates an entity boundary detection task into the named entity recognition task. More concretely, we achieve entity boundary detection by classifying the relations between tokens within the sentence. To improve the accuracy of entity-type mapping during decoding, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information into the model via the self and cross-attention mechanisms. We perform experiments on an extensive set of NER benchmarks, including two flat, three nested, and three discontinuous NER datasets. Experimental results show that our approach considerably improves the generative NER model's performance.
Abstract (translated)
命名实体识别(NER)是自然语言处理中一个重要的研究问题。NER任务有三种类型,包括平面实体识别、嵌套实体识别和离散实体识别。过去,大多数顺序标记模型都是任务特定的,而近年来由于将所有NER任务统一到序列到序列模型框架的优势,生成模型的数量不断增加。尽管取得了良好的性能,但我们的初步研究表明,现有的生成模型在实体边界估计和实体类型估计方面无效。本文提出了多任务Transformer模型,将实体边界检测任务融入到命名实体识别任务中。更具体地说,我们通过分类句子中 tokens之间的关系来实现实体边界检测。为了改善解码时实体类型映射的准确性,我们采用了外部知识库来计算先前实体类型分布,然后通过自我和交叉注意力机制将信息注入到模型中。我们进行了广泛的NER基准测试集实验,包括两个平面、三个嵌套和三个离散实体识别数据集。实验结果显示,我们的方法显著提高了生成NER模型的性能。
URL
https://arxiv.org/abs/2303.10870
PDF
-
Generic Event Boundary Detection in Video with Pyramid Features
2023-01-11 03:29:27Van Thong Huynh, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung KimAbstract
Generic event boundary detection (GEBD) aims to split video into chunks at a broad and diverse set of actions as humans naturally perceive event boundaries. In this study, we present an approach that considers the correlation between neighbor frames with pyramid feature maps in both spatial and temporal dimensions to construct a framework for localizing generic events in video. The features at multiple spatial dimensions of a pre-trained ResNet-50 are exploited with different views in the temporal dimension to form a temporal pyramid feature map. Based on that, the similarity between neighbor frames is calculated and projected to build a temporal pyramid similarity feature vector. A decoder with 1D convolution operations is used to decode these similarities to a new representation that incorporates their temporal relationship for later boundary score estimation. Extensive experiments conducted on the GEBD benchmark dataset show the effectiveness of our system and its variations, in which we outperformed the state-of-the-art approaches. Additional experiments on TAPOS dataset, which contains long-form videos with Olympic sport actions, demonstrated the effectiveness of our study compared to others.
Abstract (translated)
URL
https://arxiv.org/abs/2301.04288
PDF
-
Learning to Detect Semantic Boundaries with Image-level Class Labels
2022-12-15 01:56:22Namyup Kim, Sehyun Hwang, Suha KwakAbstract
This paper presents the first attempt to learn semantic boundary detection using image-level class labels as supervision. Our method starts by estimating coarse areas of object classes through attentions drawn by an image classification network. Since boundaries will locate somewhere between such areas of different classes, our task is formulated as a multiple instance learning (MIL) problem, where pixels on a line segment connecting areas of two different classes are regarded as a bag of boundary candidates. Moreover, we design a new neural network architecture that can learn to estimate semantic boundaries reliably even with uncertain supervision given by the MIL strategy. Our network is used to generate pseudo semantic boundary labels of training images, which are in turn used to train fully supervised models. The final model trained with our pseudo labels achieves an outstanding performance on the SBD dataset, where it is as competitive as some of previous arts trained with stronger supervision.
Abstract (translated)
URL
https://arxiv.org/abs/2212.07579
PDF
-
Towards trustworthy phoneme boundary detection with autoregressive model and improved evaluation metric
2022-12-13 05:56:57Hyeongju Kim, Hyeong-Seok ChoiAbstract
Phoneme boundary detection has been studied due to its central role in various speech applications. In this work, we point out that this task needs to be addressed not only by algorithmic way, but also by evaluation metric. To this end, we first propose a state-of-the-art phoneme boundary detector that operates in an autoregressive manner, dubbed SuperSeg. Experiments on the TIMIT and Buckeye corpora demonstrates that SuperSeg identifies phoneme boundaries with significant margin compared to existing models. Furthermore, we note that there is a limitation on the popular evaluation metric, R-value, and propose new evaluation metrics that prevent each boundary from contributing to evaluation multiple times. The proposed metrics reveal the weaknesses of non-autoregressive baselines and establishes a reliable criterion that suits for evaluating phoneme boundary detection.
Abstract (translated)
URL
https://arxiv.org/abs/2212.06387
PDF
-
Iterative Next Boundary Detection for Instance Segmentation of Tree Rings in Microscopy Images of Shrub Cross Sections
2022-12-06 14:49:41Alexander Gillert, Giulia Resente, Alba Anadon-Rosell, Martin Wilmking, Uwe Freiherr von LukasAbstract
We analyze the problem of detecting tree rings in microscopy images of shrub cross sections. This can be regarded as a special case of the instance segmentation task with several particularities such as the concentric circular ring shape of the objects and high precision requirements due to which existing methods don't perform sufficiently well. We propose a new iterative method which we term Iterative Next Boundary Detection (INBD). It intuitively models the natural growth direction, starting from the center of the shrub cross section and detecting the next ring boundary in each iteration step. In our experiments, INBD shows superior performance to generic instance segmentation methods and is the only one with a built-in notion of chronological order. Our dataset and source code are available at this http URL.
Abstract (translated)
URL
https://arxiv.org/abs/2212.03022
PDF
-
MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data
2022-11-28 21:48:45Ju-Chiang Wang, Jordan B. L. Smith, Yun-Ning HungAbstract
Music structure analysis (MSA) systems aim to segment a song recording into non-overlapping sections with useful labels. Previous MSA systems typically predict abstract labels in a post-processing step and require the full context of the song. By contrast, we recently proposed a supervised framework, called "Music Structural Function Analysis" (MuSFA), that models and predicts meaningful labels like 'verse' and 'chorus' directly from audio, without requiring the full context of a song. However, the performance of this system depends on the amount and quality of training data. In this paper, we propose to repurpose a public dataset, HookTheory Lead Sheet Dataset (HLSD), to improve the performance. HLSD contains over 18K excerpts of music sections originally collected for studying automatic melody harmonization. We treat each excerpt as a partially labeled song and provide a label mapping, so that HLSD can be used together with other public datasets, such as SALAMI, RWC, and Isophonics. In cross-dataset evaluations, we find that including HLSD in training can improve state-of-the-art boundary detection and section labeling scores by ~3% and ~1% respectively.
Abstract (translated)
URL
https://arxiv.org/abs/2211.15787
PDF
-
PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection
2022-11-26 04:27:31Yuquan Lan, Dongxu Li, Hui Zhao, Gang ZhaoAbstract
Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets from unstructured texts, while the relation sets at the training and testing stages are disjoint. Previous state-of-the-art method handles this challenging task by leveraging pretrained language models to generate data as additional training samples, which increases the training cost and severely constrains the model performance. We tackle this task from a new perspective and propose a novel method named PCRED for ZeroRTE with Potential Candidate Relation selection and Entity boundary Detection. The model adopts a relation-first paradigm, which firstly recognizes unseen relations through candidate relation selection. By this approach, the semantics of relations are naturally infused in the context. Entities are extracted based on the context and the semantics of relations subsequently. We evaluate our model on two ZeroRTE datasets. The experiment result shows that our method consistently outperforms previous works. Besides, our model does not rely on any additional data, which boasts the advantages of simplicity and effectiveness. Our code is available at https://anonymous.4open.science/r/PCRED.
Abstract (translated)
URL
https://arxiv.org/abs/2211.14477
PDF
-
Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module
2022-11-17 08:25:04Jiayi Zhang, Xiaoshan Chen, Zhongxi Qiu, Mingming Yang, Yan Hu, Jiang LiuAbstract
Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE segmentation is vital for disease diagnosis and treatment, but automatic segmentation is challenged by its large variation of characteristics including size, shape and position, which makes it difficult to detect tiny lesions and lesion boundaries. Considering the complementary features between segmentation and super-resolution tasks, this paper proposes a novel hard exudates segmentation method named SS-MAF with an auxiliary super-resolution task, which brings in helpful detailed features for tiny lesion and boundaries detection. Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks. MAF first adopts split spatial convolutional (SSC) layer for multi-scale features extraction and then utilize attention mechanism for features fusion of the two tasks. Considering pixel dependency, we introduce region mutual information (RMI) loss to optimize MAF module for tiny lesions and boundary detection. We evaluate our method on two public lesion datasets, IDRiD and E-Ophtha. Our method shows competitive performance with low-resolution inputs, both quantitatively and qualitatively. On E-Ophtha dataset, the method can achieve $\geq3\%$ higher dice and recall compared with the state-of-the-art methods.
Abstract (translated)
URL
https://arxiv.org/abs/2211.09404
PDF
-
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
2022-11-04 12:47:13Yucong Lin, Jinhua Su, Yuhang Li, Yuhao Wei, Hanchao Yan, Saining Zhang, Jiaan Luo, Danni Ai, Hong Song, Jingfan Fan, Tianyu Fu, Deqiang Xiao, Feifei Wang, Jue Hou, Jian YangAbstract
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.
Abstract (translated)
URL
https://arxiv.org/abs/2211.02419
PDF
-
Waveform Boundary Detection for Partially Spoofed Audio
2022-11-01 02:31:54Zexin Cai, Weiqing Wang, Ming LiAbstract
The present paper proposes a waveform boundary detection system for audio spoofing attacks containing partially manipulated segments. Partially spoofed/fake audio, where part of the utterance is replaced, either with synthetic or natural audio clips, has recently been reported as one scenario of audio deepfakes. As deepfakes can be a threat to social security, the detection of such spoofing audio is essential. Accordingly, we propose to address the problem with a deep learning-based frame-level detection system that can detect partially spoofed audio and locate the manipulated pieces. Our proposed method is trained and evaluated on data provided by the ADD2022 Challenge. We evaluate our detection model concerning various acoustic features and network configurations. As a result, our detection system achieves an equal error rate (EER) of 6.58% on the ADD2022 challenge test set, which is the best performance in partially spoofed audio detection systems that can locate manipulated clips.
Abstract (translated)
URL
https://arxiv.org/abs/2211.00226
PDF
-
HiddenGems: Efficient safety boundary detection with active learning
2022-10-25 12:20:53Aleksandar Petrov, Carter Fang, Khang Minh Pham, You Hong Eng, James Guo Ming Fu, Scott Drew PendletonAbstract
Evaluating safety performance in a resource-efficient way is crucial for the development of autonomous systems. Simulation of parameterized scenarios is a popular testing strategy but parameter sweeps can be prohibitively expensive. To address this, we propose HiddenGems: a sample-efficient method for discovering the boundary between compliant and non-compliant behavior via active learning. Given a parameterized scenario, one or more compliance metrics, and a simulation oracle, HiddenGems maps the compliant and non-compliant domains of the scenario. The methodology enables critical test case identification, comparative analysis of different versions of the system under test, as well as verification of design objectives. We evaluate HiddenGems on a scenario with a jaywalker crossing in front of an autonomous vehicle and obtain compliance boundary estimates for collision, lane keep, and acceleration metrics individually and in combination, with 6 times fewer simulations than a parameter sweep. We also show how HiddenGems can be used to detect and rectify a failure mode for an unprotected turn with 86% fewer simulations.
Abstract (translated)
URL
https://arxiv.org/abs/2210.13956
PDF
-
Composite Learning for Robust and Effective Dense Predictions
2022-10-13 17:59:16Menelaos Kanakis, Thomas E. Huang, David Bruggemann, Fisher Yu, Luc Van GoolAbstract
Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. However, the current practice requires additional labeling efforts for the auxiliary task, while not guaranteeing better model performance. In this paper, we find that jointly training a dense prediction (target) task with a self-supervised (auxiliary) task can consistently improve the performance of the target task, while eliminating the need for labeling auxiliary tasks. We refer to this joint training as Composite Learning (CompL). Experiments of CompL on monocular depth estimation, semantic segmentation, and boundary detection show consistent performance improvements in fully and partially labeled datasets. Further analysis on depth estimation reveals that joint training with self-supervision outperforms most labeled auxiliary tasks. We also find that CompL can improve model robustness when the models are evaluated in new domains. These results demonstrate the benefits of self-supervision as an auxiliary task, and establish the design of novel task-specific self-supervised methods as a new axis of investigation for future multi-task learning research.
Abstract (translated)
URL
https://arxiv.org/abs/2210.07239
PDF
-
Motion Aware Self-Supervision for Generic Event Boundary Detection
2022-10-11 16:09:13Ayush K. Rai, Tarun Krishna, Julia Dietlmeier, Kevin McGuinness, Alan F. Smeaton, Noel E. O'ConnorAbstract
The task of Generic Event Boundary Detection (GEBD) aims to detect moments in videos that are naturally perceived by humans as generic and taxonomy-free event boundaries. Modeling the dynamically evolving temporal and spatial changes in a video makes GEBD a difficult problem to solve. Existing approaches involve very complex and sophisticated pipelines in terms of architectural design choices, hence creating a need for more straightforward and simplified approaches. In this work, we address this issue by revisiting a simple and effective self-supervised method and augment it with a differentiable motion feature learning module to tackle the spatial and temporal diversities in the GEBD task. We perform extensive experiments on the challenging Kinetics-GEBD and TAPOS datasets to demonstrate the efficacy of the proposed approach compared to the other self-supervised state-of-the-art methods. We also show that this simple self-supervised approach learns motion features without any explicit motion-specific pretext task.
Abstract (translated)
URL
https://arxiv.org/abs/2210.05574
PDF
-
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
2022-09-01 21:10:00Saad Wazir, Muhammad Moazam FrazAbstract
Medical image segmentation assists in computer-aided diagnosis, surgeries, and treatment. Digitize tissue slide images are used to analyze and segment glands, nuclei, and other biomarkers which are further used in computer-aided medical applications. To this end, many researchers developed different neural networks to perform segmentation on histological images, mostly these networks are based on encoder-decoder architecture and also utilize complex attention modules or transformers. However, these networks are less accurate to capture relevant local and global features with accurate boundary detection at multiple scales, therefore, we proposed an Encoder-Decoder Network, Quick Attention Module and a Multi Loss Function (combination of Binary Cross Entropy (BCE) Loss, Focal Loss & Dice Loss). We evaluate the generalization capability of our proposed network on two publicly available datasets for medical image segmentation MoNuSeg and GlaS and outperform the state-of-the-art networks with 1.99% improvement on the MoNuSeg dataset and 7.15% improvement on the GlaS dataset. Implementation Code is available at this link: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2209.00729
PDF
-
Flat Multi-modal Interaction Transformer for Named Entity Recognition
2022-08-23 15:25:44Junyu Lu, Dixiang Zhang, Pingjian ZhangAbstract
Multi-modal named entity recognition (MNER) aims at identifying entity spans and recognizing their categories in social media posts with the aid of images. However, in dominant MNER approaches, the interaction of different modalities is usually carried out through the alternation of self-attention and cross-attention or over-reliance on the gating machine, which results in imprecise and biased correspondence between fine-grained semantic units of text and image. To address this issue, we propose a Flat Multi-modal Interaction Transformer (FMIT) for MNER. Specifically, we first utilize noun phrases in sentences and general domain words to obtain visual cues. Then, we transform the fine-grained semantic representation of the vision and text into a unified lattice structure and design a novel relative position encoding to match different modalities in Transformer. Meanwhile, we propose to leverage entity boundary detection as an auxiliary task to alleviate visual bias. Experiments show that our methods achieve the new state-of-the-art performance on two benchmark datasets.
Abstract (translated)
URL
https://arxiv.org/abs/2208.11039
PDF
-
Traditional methods in Edge, Corner and Boundary detection
2022-08-12 22:26:05Sai Pavan TademAbstract
This is a review paper of traditional approaches for edge, corner, and boundary detection methods. There are many real-world applications of edge, corner, and boundary detection methods. For instance, in medical image analysis, edge detectors are used to extract the features from the given image. In modern innovations like autonomous vehicles, edge detection and segmentation are the most crucial things. If we want to detect motion or track video, corner detectors help. I tried to compare the results of detectors stage-wise wherever it is possible and also discussed the importance of image prepossessing to minimise the noise. Real-world images are used to validate detector performance and limitations.
Abstract (translated)
URL
https://arxiv.org/abs/2208.07714
PDF
-
A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences
2022-07-15 17:28:52Razieh Rastgoo, Kourosh Kiani, Sergio EscaleraAbstract
Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences
Abstract (translated)
URL
https://arxiv.org/abs/2207.07619
PDF
-
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
2022-07-06 16:45:25Qianglong Chen, Xiangji Zeng, Jiangang Zhu, Yin Zhang, Bojia Lin, Yang Yang, Daxin JiangAbstract
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analysis to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer has improved the most situations where the dataset is difficult to learn well for the conventional NER model. (2) the performance of model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set.
Abstract (translated)
URL
https://arxiv.org/abs/2207.02802
PDF
-
Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach
2022-06-30 13:19:53Jiaqi Tang, Zhaoyang Liu, Jing Tan, Chen Qian, Wayne Wu, Limin WangAbstract
Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries. In this paper, we present a local context modeling and global boundary decoding approach for GEBD task. Local context modeling sub-network is proposed to perceive diverse patterns of generic event boundaries, and it generates powerful video representations and reliable boundary confidence. Based on them, global boundary decoding sub-network is exploited to decode event boundaries from a global view. Our proposed method achieves 85.13% F1-score on Kinetics-GEBD testing set, which achieves a more than 22% F1-score boost compared to the baseline method. The code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2206.15268
PDF
-
SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
2022-06-25 12:27:13Dexiang Hong, Xiaoqi Ma, Xinyao Wang, Congcong Li, Yufei Wang, Longyin WenAbstract
This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is used to further boost performance. The proposed method achieves 86.49% F1 score on Kinetics-GEBD test set. which improves 2.86% F1 score compared to the previous SOTA method.
Abstract (translated)
URL
https://arxiv.org/abs/2206.12634
PDF
