138 research outputs found
Deep Learning for Video Object Segmentation:A Review
As one of the fundamental problems in the field of video understanding, video object segmentation aims at segmenting objects of interest throughout the given video sequence. Recently, with the advancements of deep learning techniques, deep neural networks have shown outstanding performance improvements in many computer vision applications, with video object segmentation being one of the most advocated and intensively investigated. In this paper, we present a systematic review of the deep learning-based video segmentation literature, highlighting the pros and cons of each category of approaches. Concretely, we start by introducing the definition, background concepts and basic ideas of algorithms in this field. Subsequently, we summarise the datasets for training and testing a video object segmentation algorithm, as well as common challenges and evaluation metrics. Next, previous works are grouped and reviewed based on how they extract and use spatial and temporal features, where their architectures, contributions and the differences among each other are elaborated. At last, the quantitative and qualitative results of several representative methods on a dataset with many remaining challenges are provided and analysed, followed by further discussions on future research directions. This article is expected to serve as a tutorial and source of reference for learners intended to quickly grasp the current progress in this research area and practitioners interested in applying the video object segmentation methods to their problems. A public website is built to collect and track the related works in this field: https://github.com/gaomingqi/VOS-Review
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing
unsupervised video object segmentation (UVOS). Most of the previous methods
directly extract and fuse the motion and appearance features for segmenting
target objects in the UVOS setting. However, optical flow is intrinsically an
instantaneous velocity of all pixels among consecutive frames, thus making the
motion features not aligned well with the primary objects among the
corresponding frames. To solve the above challenge, we propose a concise,
practical, and efficient architecture for appearance and motion feature
alignment, dubbed hierarchical feature alignment network (HFAN). Specifically,
the key merits in HFAN are the sequential Feature AlignMent (FAM) module and
the Feature AdaptaTion (FAT) module, which are leveraged for processing the
appearance and motion features hierarchically. FAM is capable of aligning both
appearance and motion features with the primary object semantic
representations, respectively. Further, FAT is explicitly designed for the
adaptive fusion of appearance and motion features to achieve a desirable
trade-off between cross-modal features. Extensive experiments demonstrate the
effectiveness of the proposed HFAN, which reaches a new state-of-the-art
performance on DAVIS-16, achieving 88.7 Mean, i.e.,
a relative improvement of 3.5% over the best published result.Comment: Accepted by ECCV-202
Learning from limited labeled data - Zero-Shot and Few-Shot Learning
Human beings have the remarkable ability to recognize novel visual concepts after observing only few or zero examples of them. Deep learning, however, often requires a large amount of labeled data to achieve a good performance. Labeled instances are expensive, difficult and even infeasible to obtain because the distribution of training instances among labels naturally exhibits a long tail. Therefore, it is of great interest to investigate how to learn efficiently from limited labeled data.
This thesis concerns an important subfield of learning from limited labeled data, namely, low-shot learning. The setting assumes the availability of many labeled examples from known classes and the goal is to learn novel classes from only a few~(few-shot learning) or zero~(zero-shot learning) training examples of them. To this end, we have developed a series of multi-modal learning approaches to facilitate the knowledge transfer from known classes to novel classes for a wide range of visual recognition tasks including image classification, semantic image segmentation and video action recognition. More specifically, this thesis mainly makes the following contributions. First, as there is no agreed upon zero-shot image classification benchmark, we define a new benchmark by unifying both the evaluation protocols and data splits of publicly available datasets. Second, in order to tackle the labeled data scarcity, we propose feature generation frameworks that synthesize data in the visual feature space for novel classes. Third, we extend zero-shot learning and few-shot learning to the semantic segmentation task and propose a challenging benchmark for it. We show that incorporating semantic information into a semantic segmentation network is effective in segmenting novel classes. Finally, we develop better video representation for the few-shot video classification task and leverage weakly-labeled videos by an efficient retrieval method.Menschen haben die bemerkenswerte Fähigkeit, neuartige visuelle Konzepte zu erkennen, nachdem sie nur wenige oder gar keine Beispiele davon beobachtet haben. Tiefes Lernen erfordert jedoch oft eine große Menge an beschrifteten Daten, um eine gute Leistung zu erzielen. Etikettierte Instanzen sind teuer, schwierig und sogar undurchführbar, weil die Verteilung der Trainingsinstanzen auf die Etiketten naturgemäß einen langen Schwanz aufweist. Daher ist es von großem Interesse zu untersuchen, wie man effizient aus begrenzten gelabelten Daten lernen kann. Diese These betrifft einen wichtigen Teilbereich des Lernens aus begrenzt gelabelten Daten, nämlich das Low-Shot-Lernen. Das Setting setzt die Verfügbarkeit vieler gelabelter Beispiele aus bekannten Klassen voraus, und das Ziel ist es, neuartige Klassen aus nur wenigen (few-shot learning) oder null (zero-shot learning) Trainingsbeispielen davon zu lernen. Zu diesem Zweck haben wir eine Reihe von multimodalen Lernansätzen entwickelt, um den Wissenstransfer von bekannten Klassen zu neuartigen Klassen für ein breites Spektrum von visuellen Erkennungsaufgaben zu erleichtern, darunter Bildklassifizierung, semantische Bildsegmentierung und Videoaktionserkennung. Genauer gesagt, leistet diese Arbeit hauptsächlich die folgenden Beiträge. Da es keinen vereinbarten Benchmark für die Zero-Shot- Bildklassifikation gibt, definieren wir zunächst einen neuen Benchmark, indem wir sowohl die Evaluierungsprotokolle als auch die Datensplits öffentlich zugänglicher Datensätze vereinheitlichen. Zweitens schlagen wir zur Bewältigung der etikettierten Datenknappheit einen Rahmen für die Generierung von Merkmalen vor, der Daten im visuellen Merkmalsraum für neuartige Klassen synthetisiert. Drittens dehnen wir das Zero-Shot-Lernen und das few-Shot-Lernen auf die semantische Segmentierungsaufgabe aus und schlagen dafür einen anspruchsvollen Benchmark vor. Wir zeigen, dass die Einbindung semantischer Informationen in ein semantisches Segmentierungsnetz bei der Segmentierung neuartiger Klassen effektiv ist. Schließlich entwickeln wir eine bessere Videodarstellung für die Klassifizierungsaufgabe ”few-shot video” und nutzen schwach markierte Videos durch eine effiziente Abrufmethode.Max Planck Institute Informatic
Language Semantic Graph Guided Data-Efficient Learning
Developing generalizable models that can effectively learn from limited data
and with minimal reliance on human supervision is a significant objective
within the machine learning community, particularly in the era of deep neural
networks. Therefore, to achieve data-efficient learning, researchers typically
explore approaches that can leverage more related or unlabeled data without
necessitating additional manual labeling efforts, such as Semi-Supervised
Learning (SSL), Transfer Learning (TL), and Data Augmentation (DA). SSL
leverages unlabeled data in the training process, while TL enables the transfer
of expertise from related data distributions. DA broadens the dataset by
synthesizing new data from existing examples. However, the significance of
additional knowledge contained within labels has been largely overlooked in
research. In this paper, we propose a novel perspective on data efficiency that
involves exploiting the semantic information contained in the labels of the
available data. Specifically, we introduce a Language Semantic Graph (LSG)
which is constructed from labels manifest as natural language descriptions.
Upon this graph, an auxiliary graph neural network is trained to extract
high-level semantic relations and then used to guide the training of the
primary model, enabling more adequate utilization of label knowledge. Across
image, video, and audio modalities, we utilize the LSG method in both TL and
SSL scenarios and illustrate its versatility in significantly enhancing
performance compared to other data-efficient learning approaches. Additionally,
our in-depth analysis shows that the LSG method also expedites the training
process.Comment: Accepted by NeurIPS 202
A multi-resolution multi-source and multi-modal (M3) transductive framework for concept detection in news video
Ph.DDOCTOR OF PHILOSOPH
Learning Transferable Representations for Hierarchical Relationship Exploration
The visual scenes are composed of basic elements, such as objects, parts, and other semantic regions. It is well-acknowledged that humans perceive the world in a compositional and hierarchical way in which visual scenes are treated as a layout of distinct semantic objects/attributes/parts. Those separated objects/attributes/parts are linked together via different relationships, including visual relationships and semantic relationships. Particularly, the shared parts/attributes/objects of the visual concepts (object, visual relationships), are shared and thus transferable among different visual concepts. Humans can easily imagine a new composite concept from the shared parts of different concepts, while one of the important shortcomings of current deep neural networks is the compositional perception ability and thus it requires a large scale of data to optimize the deep neural networks. From the perspective of compositional perception, this thesis thinks one of the limitations of typical neural networks is that the factor representations of deep neural networks are not sharable and transferable among different concepts. Therefore, the thesis introduces various techniques, including compositional learning framework, compositional invariant learning, and BatchFormer module, to enable the factor representations of deep neural networks sharable and transferable among different concepts for hierarchical relationship exploration, involving human-object interaction, 3D human-object interaction and sample relationships
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