activity
20232026
most citedMobileSAMv2: Faster Segment Anything to Everything

10 citations · 10 across the 6 of their papers we have counts for

collaborators

8 papers

cs.RO2026

InJePA: Intention-First Latent Planning with Coupled JEPA World Models for Visual Navigation

Siming Zhou, Bowen Ma, Ruiran Du +7

Visual navigation requires goal directed planning informed by predictions of environment dynamics. Representative navigation world model methods propose complete action candidates…

cs.CL2026

DASH-KV: Accelerating Long-Context LLM Inference via Asymmetric KV Cache Hashing

Jinyu Guo, Zhihan Zhang, Jiehui Xie +7

The quadratic computational complexity of the standard attention mechanism constitutes a fundamental bottleneck for large language models in long-context inference. While existing…

cs.CL2026

Text summarization via global structure awareness

Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9

Text summarization is a fundamental task in natural language processing (NLP), and the information explosion has made long-document processing increasingly demanding, making summar…

cs.CV2025

Exploring Kernel Transformations for Implicit Neural Representations

Sheng Zheng, Chaoning Zhang, Dongshen Han +4

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant atte…

cs.CV2023★ 10 cited

MobileSAMv2: Faster Segment Anything to Everything

Chaoning Zhang, Dongshen Han, Sheng Zheng +3

Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a certain point to predict the mask for…

cs.CV2023

Single Image Reflection Removal with Patch Reflectance Prior

Dongshen Han, Heechan Yoon, Hyukmin Kwon +4

Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and ref…

cs.CV2023

SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation

Dongshen Han, Chaoning Zhang, Sheng Zheng +3

As Segment Anything Model (SAM) becomes a popular foundation model in computer vision, its adversarial robustness has become a concern that cannot be ignored. This works investigat…

cs.CV2023

Internal-External Boundary Attention Fusion for Glass Surface Segmentation

Dongshen Han, Seungkyu Lee, Chaoning Zhang +4

Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of…