A comprehensive survey on segment anything model for vision and beyond
Artificial intelligence (AI) is evolving towards artificial general intelligence, which refers to the
ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence …
ability of an AI system to perform a wide range of tasks and exhibit a level of intelligence …
Llama-adapter: Efficient fine-tuning of language models with zero-init attention
We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …
Multimodal foundation models: From specialists to general-purpose assistants
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …
methods to data compression. Recent advances in statistical machine learning have opened …
RSPrompter: Learning to prompt for remote sensing instance segmentation based on visual foundation model
Leveraging the extensive training data from SA-1B, the segment anything model (SAM)
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …
demonstrates remarkable generalization and zero-shot capabilities. However, as a category …
[HTML][HTML] The segment anything model (sam) for remote sensing applications: From zero to one shot
Segmentation is an essential step for remote sensing image processing. This study aims to
advance the application of the Segment Anything Model (SAM), an innovative image …
advance the application of the Segment Anything Model (SAM), an innovative image …
Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning
Large-scale pre-trained models have shown promising open-world performance for both
vision and language tasks. However, their transferred capacity on 3D point clouds is still …
vision and language tasks. However, their transferred capacity on 3D point clouds is still …
Not all features matter: Enhancing few-shot clip with adaptive prior refinement
Abstract The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its
application to diverse downstream vision tasks. To improve its capacity on downstream …
application to diverse downstream vision tasks. To improve its capacity on downstream …
Samrs: Scaling-up remote sensing segmentation dataset with segment anything model
The success of the Segment Anything Model (SAM) demonstrates the significance of data-
centric machine learning. However, due to the difficulties and high costs associated with …
centric machine learning. However, due to the difficulties and high costs associated with …
Sam-6d: Segment anything model meets zero-shot 6d object pose estimation
Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D
poses in cluttered scenes presenting significant challenges for model generalizability …
poses in cluttered scenes presenting significant challenges for model generalizability …