Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning

Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an economical alternative, with which training phase could hardly generate satisfactory performance unfortunately. In order to generate high-quality annotated data with a low time cost for accurate segmentation, in this paper, we propose a novel annotation enrichment strategy, which expands existing coarse annotations of training data to a finer scale. Extensive experiments on the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural networks trained with the enriched annotations from our framework yield a significant improvement over that trained with the original coarse labels. It is highly competitive to the performance obtained by using human annotated dense annotations. The proposed method also outperforms among other state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge Managemen

Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image

High-resolution hyperspectral images (HSIs) contain the response of each pixel in different spectral bands, which can be used to effectively distinguish various objects in complex scenes. While HSI cameras have become low cost, algorithms based on it have not been well exploited. In this paper, we focus on a novel topic, weakly-supervised semantic segmentation in cityscape via HSIs. It is based on the idea that high-resolution HSIs in city scenes contain rich spectral information, which can be easily associated to semantics without manual labeling. Therefore, it enables low cost, highly reliable semantic segmentation in complex scenes. Specifically, in this paper, we theoretically analyze the HSIs and introduce a weakly-supervised HSI semantic segmentation framework, which utilizes spectral information to improve the coarse labels to a finer degree. The experimental results show that our method can obtain highly competitive labels and even have higher edge fineness than artificial fine labels in some classes. At the same time, the results also show that the refined labels can effectively improve the effect of semantic segmentation. The combination of HSIs and semantic segmentation proves that HSIs have great potential in high-level visual tasks

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance

강인한 대화형 영상 분할 알고리즘을 위한 시드 정보 확장 기법에 대한 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 이경무.Segmentation of an area corresponding to a desired object in an image is essential to computer vision problems. This is because most algorithms are performed in semantic units when interpreting or analyzing images. However, segmenting the desired object from a given image is an ambiguous issue. The target object varies depending on user and purpose. To solve this problem, an interactive segmentation technique has been proposed. In this approach, segmentation was performed in the desired direction according to interaction with the user. In this case, seed information provided by the user plays an important role. If the seed provided by a user contain abundant information, the accuracy of segmentation increases. However, providing rich seed information places much burden on the users. Therefore, the main goal of the present study was to obtain satisfactory segmentation results using simple seed information. We primarily focused on converting the provided sparse seed information to a rich state so that accurate segmentation results can be derived. To this end, a minimum user input was taken and enriched it through various seed enrichment techniques. A total of three interactive segmentation techniques was proposed based on: (1) Seed Expansion, (2) Seed Generation, (3) Seed Attention. Our seed enriching type comprised expansion of area around a seed, generation of new seed in a new position, and attention to semantic information. First, in seed expansion, we expanded the scope of the seed. We integrated reliable pixels around the initial seed into the seed set through an expansion step composed of two stages. Through the extended seed covering a wider area than the initial seed, the seed's scarcity and imbalance problems was resolved. Next, in seed generation, we created a seed at a new point, but not around the seed. We trained the system by imitating the user behavior through providing a new seed point in the erroneous region. By learning the user's intention, our model could e ciently create a new seed point. The generated seed helped segmentation and could be used as additional information for weakly supervised learning. Finally, through seed attention, we put semantic information in the seed. Unlike the previous models, we integrated both the segmentation process and seed enrichment process. We reinforced the seed information by adding semantic information to the seed instead of spatial expansion. The seed information was enriched through mutual attention with feature maps generated during the segmentation process. The proposed models show superiority compared to the existing techniques through various experiments. To note, even with sparse seed information, our proposed seed enrichment technique gave by far more accurate segmentation results than the other existing methods.영상에서 원하는 물체 영역을 잘라내는 것은 컴퓨터 비전 문제에서 필수적인 요소이다. 영상을 해석하거나 분석할 때, 대부분의 알고리즘들이 의미론적인 단위 기반으로 동작하기 때문이다. 그러나 영상에서 물체 영역을 분할하는 것은 모호한 문제이다. 사용자와 목적에 따라 원하는 물체 영역이 달라지기 때문이다. 이를 해결하기 위해 사용자와의 교류를 통해 원하는 방향으로 영상 분할을 진행하는 대화형 영상 분할 기법이 사용된다. 여기서 사용자가 제공하는 시드 정보가 중요한 역할을 한다. 사용자의 의도를 담고 있는 시드 정보가 정확할수록 영상 분할의 정확도도 증가하게 된다. 그러나 풍부한 시드 정보를 제공하는 것은 사용자에게 많은 부담을 주게 된다. 그러므로 간단한 시드 정보를 사용하여 만족할만한 분할 결과를 얻는 것이 주요 목적이 된다. 우리는 제공된 희소한 시드 정보를 변환하는 작업에 초점을 두었다. 만약 시드 정보가 풍부하게 변환된다면 정확한 영상 분할 결과를 얻을 수 있기 때문이다. 그러므로 본 학위 논문에서는 시드 정보를 풍부하게 하는 기법들을 제안한다. 최소한의 사용자 입력을 가정하고 이를 다양한 시드 확장 기법을 통해 변환한다. 우리는 시드 확대, 시드 생성, 시드 주의 집중에 기반한 총 세 가지의 대화형 영상 분할 기법을 제안한다. 각각 시드 주변으로의 영역 확대, 새로운 지점에 시드 생성, 의미론적 정보에 주목하는 형태의 시드 확장 기법을 사용한다. 먼저 시드 확대에 기반한 기법에서 우리는 시드의 영역 확장을 목표로 한다. 두 단계로 구성된 확대 과정을 통해 처음 시드 주변의 비슷한 픽셀들을 시드 영역으로 편입한다. 이렇게 확장된 시드를 사용함으로써 시드의 희소함과 불균형으로 인한 문제를 해결할 수 있다. 다음으로 시드 생성에 기반한 기법에서 우리는 시드 주변이 아닌 새로운 지점에 시드를 생성한다. 우리는 오차가 발생한 영역에 사용자가 새로운 시드를 제공하는 동작을 모방하여 시스템을 학습하였다. 사용자의 의도를 학습함으로써 효과적으로 시드를 생성할 수 있다. 생성된 시드는 영상 분할의 정확도를 높일 뿐만 아니라 약지도학습을 위한 데이터로써 활용될 수 있다. 마지막으로 시드 주의 집중을 활용한 기법에서 우리는 의미론적 정보를 시드에 담는다. 기존에 제안한 기법들과 달리 영상 분할 동작과 시드 확장 동작이 통합된 모델을 제안한다. 시드 정보는 영상 분할 네트워크의 특징맵과 상호 교류하며 그 정보가 풍부해진다. 제안한 모델들은 다양한 실험을 통해 기존 기법 대비 우수한 성능을 기록하였다. 특히 시드가 부족한 상황에서 시드 확장 기법들은 훌륭한 대화형 영상 분할 성능을 보였다.1 Introduction 1 1.1 Previous Works 2 1.2 Proposed Methods 4 2 Interactive Segmentation with Seed Expansion 9 2.1 Introduction 9 2.2 Proposed Method 12 2.2.1 Background 13 2.2.2 Pyramidal RWR 16 2.2.3 Seed Expansion 19 2.2.4 Re nement with Global Information 24 2.3 Experiments 27 2.3.1 Dataset 27 2.3.2 Implement Details 28 2.3.3 Performance 29 2.3.4 Contribution of Each Part 30 2.3.5 Seed Consistency 31 2.3.6 Running Time 33 2.4 Summary 34 3 Interactive Segmentation with Seed Generation 37 3.1 Introduction 37 3.2 Related Works 40 3.3 Proposed Method 41 3.3.1 System Overview 41 3.3.2 Markov Decision Process 42 3.3.3 Deep Q-Network 46 3.3.4 Model Architecture 47 3.4 Experiments 48 3.4.1 Implement Details 48 3.4.2 Performance 49 3.4.3 Ablation Study 53 3.4.4 Other Datasets 55 3.5 Summary 58 4 Interactive Segmentation with Seed Attention 61 4.1 Introduction 61 4.2 Related Works 64 4.3 Proposed Method 65 4.3.1 Interactive Segmentation Network 65 4.3.2 Bi-directional Seed Attention Module 67 4.4 Experiments 70 4.4.1 Datasets 70 4.4.2 Metrics 70 4.4.3 Implement Details 71 4.4.4 Performance 71 4.4.5 Ablation Study 76 4.4.6 Seed enrichment methods 79 4.5 Summary 82 5 Conclusions 87 5.1 Summary 89 Bibliography 90 국문초록 103Docto

Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application

Iterative Few-shot Semantic Segmentation from Image Label Text

Few-shot semantic segmentation aims to learn to segment unseen class objects with the guidance of only a few support images. Most previous methods rely on the pixel-level label of support images. In this paper, we focus on a more challenging setting, in which only the image-level labels are available. We propose a general framework to firstly generate coarse masks with the help of the powerful vision-language model CLIP, and then iteratively and mutually refine the mask predictions of support and query images. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method not only outperforms the state-of-the-art weakly supervised approaches by a significant margin, but also achieves comparable or better results to recent supervised methods. Moreover, our method owns an excellent generalization ability for the images in the wild and uncommon classes. Code will be available at https://github.com/Whileherham/IMR-HSNet.Comment: ijcai 202