2 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
MSVIT: Improving Spiking Vision Transformer Using Multi-scale Attention Fusion
Wei Hua, Chenlin Zhou, Jibin Wu +2
The combination of Spiking Neural Networks (SNNs) with Vision Transformer architectures has garnered significant attention due to their potential for energy-efficient and high-perf…
CIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation
Jinchao Ge, Bowen Zhang, Akide Liu +4
Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by dist…
Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale
Yangyang Shu, Xiaofeng Cao, Qi Chen +4
Source-Free Unsupervised Domain Adaptation (SFUDA) is a challenging task where a model needs to be adapted to a new domain without access to target domain labels or source domain d…
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
Yangyang Shu, Baosheng Yu, Haiming Xu +1
The challenge of fine-grained visual recognition often lies in discovering the key discriminative regions. While such regions can be automatically identified from a large-scale lab…