22 citations · 34 across the 4 of their papers we have counts for
8 papers · 1 filter
FractalMamba++: Scaling Vision Mamba Across Resolutions via Hilbert Fractal Geometry
Bo Li, Haoke Xiao, Lv Tang
Vision Mamba offers linear complexity for long visual sequences, yet its performance depends critically on how a two-dimensional patch grid is serialized into a one-dimensional sta…
Evaluating SAM2's Role in Camouflaged Object Detection: From SAM to SAM2
Lv Tang, Bo Li
The Segment Anything Model (SAM), introduced by Meta AI Research as a generic object segmentation model, quickly garnered widespread attention and significantly influenced the acad…
ASAM: Boosting Segment Anything Model with Adversarial Tuning
Bo Li, Haoke Xiao, Lv Tang
In the evolving landscape of computer vision, foundation models have emerged as pivotal tools, exhibiting exceptional adaptability to a myriad of tasks. Among these, the Segment An…
Towards Training-free Open-world Segmentation via Image Prompt Foundation Models
Lv Tang, Peng-Tao Jiang, Hao-Ke Xiao +1
The realm of computer vision has witnessed a paradigm shift with the advent of foundational models, mirroring the transformative influence of large language models in the domain of…
Zero-Shot Co-salient Object Detection Framework
Haoke Xiao, Lv Tang, Bo Li +2
Co-salient Object Detection (CoSOD) endeavors to replicate the human visual system's capacity to recognize common and salient objects within a collection of images. Despite recent…
Can SAM Segment Anything? When SAM Meets Camouflaged Object Detection
Lv Tang, Haoke Xiao, Bo Li
SAM is a segmentation model recently released by Meta AI Research and has been gaining attention quickly due to its impressive performance in generic object segmentation. However,…