PestVL-Net combines an RWKV visual backbone with saliency-guided window partitioning and MLLM-derived linguistic priors via multimodal chain-of-thought to enable fine-grained multimodal pest recognition on dedicated datasets.
Mamba or rwkv: Exploring high-quality and high-efficiency segment anything model
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.
citing papers explorer
-
PestVL-Net: Enabling Multimodal Pest Learning via Fine-grained Vision-Language Interaction
PestVL-Net combines an RWKV visual backbone with saliency-guided window partitioning and MLLM-derived linguistic priors via multimodal chain-of-thought to enable fine-grained multimodal pest recognition on dedicated datasets.
-
On Efficient Variants of Segment Anything Model: A Survey
A survey that reviews efficient variants of the Segment Anything Model, categorizes acceleration strategies, and provides a unified hardware evaluation on benchmarks.