Recently, segmentation-based scene text detection methods have drawn extensive attention
in the scene text detection field, because of their superiority in detecting the text instances of …

The emergence of large models, also known as foundation models, has brought significant
advancements to AI research. One such model is Segment Anything (SAM), which is …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Humans are able to recognize structured relations in observation, allowing us to decompose
complex scenes into simpler parts and abstract the visual world in multiple levels. However …

Current semantic segmentation methods focus only on mining" local" context, ie,
dependencies between pixels within individual images, by context-aggregation modules …

The emergence of large models, also known as foundation models, has brought significant
advancements to AI research. One such model is Segment Anything (SAM), which is …

Prototype learning is extensively used for few-shot segmentation. Typically, a single
prototype is obtained from the support feature by averaging the global object information …

Existing few-shot segmentation methods have achieved great progress based on the
support-query matching framework. But they still heavily suffer from the limited coverage of …

Referring image segmentation is an advanced semantic segmentation task where target is
not a predefined class but is described in natural language. Most of existing methods for this …

Monocular 3D object detection is an important yet challenging task in autonomous driving.
Some existing methods leverage depth information from an off-the-shelf depth estimator to …