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HASSOD: Hierarchical adaptive self-supervised object detection
The human visual perception system demonstrates exceptional capabilities in learning
without explicit supervision and understanding the part-to-whole composition of objects …
without explicit supervision and understanding the part-to-whole composition of objects …
Exploring transformers for open-world instance segmentation
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 …
image by learning from a limited number of base-category objects. This task is challenging …
Eliminating and mining strategies for open-world object proposal
Object proposal serves as a crucial pre-task of many image and video understanding
applications. However, modern approaches for object proposal are typically based on …
applications. However, modern approaches for object proposal are typically based on …
SOS: Segment Object System for Open-World Instance Segmentation with Object Priors
We propose an approach for Open-World Instance Segmentation (OWIS), a task that aims to
segment arbitrary unknown objects in images by generalizing from a limited set of annotated …
segment arbitrary unknown objects in images by generalizing from a limited set of annotated …
Generalized Class Discovery in Instance Segmentation
This work addresses the task of generalized class discovery (GCD) in instance
segmentation. The goal is to discover novel classes and obtain a model capable of …
segmentation. The goal is to discover novel classes and obtain a model capable of …
st-DenseViT: A Weakly Supervised Spatiotemporal Vision Transformer for Dense Prediction of Dynamic Brain Networks
Objective Modeling dynamic neuronal activity within brain networks enables the precise
tracking of rapid temporal fluctuations across different brain regions. However, current …
tracking of rapid temporal fluctuations across different brain regions. However, current …