Abstract The field of Continual Learning investigates the ability to learn consecutive tasks
without losing performance on those previously learned. The efforts of researchers have …

Deep networks have strong capacities of embedding data into latent representations and
finishing following tasks. However, the capacities largely come from high-quality annotated …

Dataset condensation aims at reducing the network training effort through condensing a
cumbersome training set into a compact synthetic one. State-of-the-art approaches largely …

Abstract Vision-Language Pretraining (VLP) has shown impressive results on diverse
downstream tasks by offline training on large-scale datasets. Regarding the growing nature …

The great success of deep learning heavily relies on increasingly larger training data, which
comes at a price of huge computational and infrastructural costs. This poses crucial …

Graph neural network has shown impressive ability to capture relations among support
(labeled) and query (unlabeled) instances in a few-shot task. It is a feasible way that features …

Open world object detection (OWOD) has attracted extensive attention due to its
practicability in the real world. Previous OWOD works manually designed unknown-discover …

We show that classifiers trained with random region proposals achieve state-of-the-art Open-
world Object Detection (OWOD): they can not only maintain the accuracy of the known …

Open-world instance segmentation is a rising task, which aims to segment all objects in the
image by learning from a limited number of base-category objects. This task is challenging …

Open World Object Detection (OWOD), simulating the real dynamic world where knowledge
grows continuously, attempts to detect both known and unknown classes and incrementally …