Publications (28)
Learning Spectral-Decomposed Tokens for Domain Generalized Semantic Segmentation
Jingjun Yi, Qi Bi, Hao Zheng +5
The rapid development of Vision Foundation Model (VFM) brings inherent out-domain generalization for a variety of down-stream tasks. Among them, domain generalized semantic segment…
SAM3-I: Segment Anything with Instructions
Jingjing Li, Yue Feng, Yuchen Guo +10
Segment Anything Model 3 (SAM3) advances open-vocabulary segmentation through promptable concept segmentation, enabling users to segment all instances associated with a given conce…
Spectral-Progressive Thought Flow for Lightweight Multimodal Reasoning
Yixian Shen, Zhiheng Yang, Qi Bi +6
Multimodal spatial reasoning often relies on long chains of intermediate textual and visual thoughts, where accumulating visual tokens and dense cross-modal attention incur substan…
MaCP: Minimal yet Mighty Adaptation via Hierarchical Cosine Projection
Yixian Shen, Qi Bi, Jia-Hong Huang +3
We present a new adaptation method MaCP, Minimal yet Mighty adaptive Cosine Projection, that achieves exceptional performance while requiring minimal parameters and memory for fine…
Multiple instance dense connected convolution neural network for aerial image scene classification
Qi Bi, Kun Qin, Zhili Li +2
With the development of deep learning, many state-of-the-art natural image scene classification methods have demonstrated impressive performance. While the current convolution neur…
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Wei Ji, Jingjing Li, Qi Bi +3
Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the…