Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDo Different Tracking Tasks Require Different Appearance Models?
178
0
0.0
(
0
)
Added by
Zhongdao Wang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too, and now the novel approaches proposed by the community are usually specialised to fit only one specific setup. To understand to what extent this specialisation is actually necessary, in this work we present UniTrack, a unified tracking solution to address five different tasks within the same framework. UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple heads to address individual tasks and that do not require training. We show how most tracking tasks can be solved within this framework, and that the same appearance model can be used to obtain performance that is competitive against specialised methods for all the five tasks considered. The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus significantly extending their evaluation and comparison to a larger variety of important problems. Code available at https://github.com/Zhongdao/UniTrack.
rate research
Read More
Radiomics is an active area of research focusing on high throughput feature extraction from medical images with a wide array of applications in clinical practice, such as clinical decision support in oncology. However, noise in low dose computed tomography (CT) scans can impair the accurate extraction of radiomic features. In this article, we investigate the possibility of using deep learning generative models to improve the performance of radiomics from low dose CTs. We used two datasets of low dose CT scans -NSCLC Radiogenomics and LIDC-IDRI - as test datasets for two tasks - pre-treatment survival prediction and lung cancer diagnosis. We used encoder-decoder networks and conditional generative adversarial networks (CGANs) trained in a previous study as generative models to transform low dose CT images into full dose CT images. Radiomic features extracted from the original and improved CT scans were used to build two classifiers - a support vector machine (SVM) and a deep attention based multiple instance learning model - for survival prediction and lung cancer diagnosis respectively. Finally, we compared the performance of the models derived from the original and improved CT scans. Encoder-decoder networks and CGANs improved the area under the curve (AUC) of survival prediction from 0.52 to 0.57 (p-value<0.01). On the other hand, Encoder-decoder network and CGAN can improve the AUC of lung cancer diagnosis from 0.84 to 0.88 and 0.89 respectively (p-value<0.01). Moreover, there are no statistically significant differences in improving AUC by using encoder-decoder network and CGAN (p-value=0.34) when networks trained at 75 and 100 epochs. Generative models can improve the performance of low dose CT-based radiomics in different tasks. Hence, denoising using generative models seems to be a necessary pre-processing step for calculating radiomic features from low dose CTs.
We study a new challenging problem of efficient deployment for diverse tasks with different resources, where the resource constraint and task of interest corresponding to a group of classes are dynamically specified at testing time. Previous NAS approaches seek to design architectures for all classes simultaneously, which may not be optimal for some individual tasks. A straightforward solution is to search an architecture from scratch for each deployment scenario, which however is computation-intensive and impractical. To address this, we present a novel and general framework, called Elastic Architecture Search (EAS), permitting instant specializations at runtime for diverse tasks with various resource constraints. To this end, we first propose to effectively train the over-parameterized network via a task dropout strategy to disentangle the tasks during training. In this way, the resulting model is robust to the subsequent task dropping at inference time. Based on the well-trained over-parameterized network, we then propose an efficient architecture generator to obtain optimal architectures within a single forward pass. Experiments on two image classification datasets show that EAS is able to find more compact networks with better performance while remarkably being orders of magnitude faster than state-of-the-art NAS methods. For example, our proposed EAS finds compact architectures within 0.1 second for 50 deployment scenarios.
Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism. The query vector of each tasks attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture.
The nature of galactic spiral arms in disc galaxies remains elusive. Regardless of the spiral model, arms are expected to play a role in sculpting the star-forming interstellar medium. As such, different arm models may result in differences in the structure of the interstellar medium and molecular cloud properties. In this study we present simulations of galactic discs subject to spiral arm perturbations of different natures. We find very little difference in how the cloud population or gas kinematics vary between the different grand-design spirals, indicting that the interstellar medium on cloud scales cares little about where spiral arms come from. We do, however, see a difference in the interarm/arm mass spectra, {and minor differences in tails of the distributions of cloud properties} (as well as radial variations in the stellar/gaseous velocity dispersions). These features can be attributed to differences in the radial dependence of the pattern speeds between the different spiral models, and could act as a metric of the nature of spiral structure in observational studies.
Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing. Previous investigations prove that introducing a further pre-training phase between pre-training and fine-tuning phases to adapt the model on the domain-specific unlabeled data can bring positive effects. However, most of these further pre-training works just keep running the conventional pre-training task, e.g., masked language model, which can be regarded as the domain adaptation to bridge the data distribution gap. After observing diverse downstream tasks, we suggest that different tasks may also need a further pre-training phase with appropriate training tasks to bridge the task formulation gap. To investigate this, we carry out a study for improving multiple task-oriented dialogue downstream tasks through designing various tasks at the further pre-training phase. The experiment shows that different downstream tasks prefer different further pre-training tasks, which have intrinsic correlation and most further pre-training tasks significantly improve certain target tasks rather than all. Our investigation indicates that it is of great importance and effectiveness to design appropriate further pre-training tasks modeling specific information that benefit downstream tasks. Besides, we present multiple constructive empirical conclusions for enhancing task-oriented dialogues.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
