Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Along with the rapid progress of visual tracking, existing benchmarks become less informative due to redundancy of samples and weak discrimination between current trackers, making evaluations on all datasets extremely time-consuming.
In this paper, we propose an active learning method for deep visual tracking, which selects and annotates the unlabeled samples to train the deep CNNs model.
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly.
Ranked #1 on Molecular Graph Generation on ZINC (QED Top-3 metric)
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed.
These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.
These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.
Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction.
The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field.
Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs.
In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task.