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20232026
most citedState Space Model for New-Generation Network Alternative to Transformers: A Survey

19 citations · 36 across the 26 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.AI2026

MAC-RRG: Iterative Multi-Agent Collaboration for X-ray Radiology Report Generation

Futian Wang, Yuhan Qiao, Xiao Wang +5

Despite the remarkable progress of LLM-based and knowledge graph-augmented Radiology Report Generation (RRG) methods, existing techniques still suffer from inherent defects. Conven…

cs.CV2026

Dynamic Parsing and Updating Natural Language Specification using VLMs for Robust Vision-Language Tracking

Xiao Wang, Liye Jin, Dan Xu +5

Vision-language tracking guided by natural language specifications leverages high-level semantic cues of target objects to substantially boost tracking accuracy and robustness. Exi…

cs.CV2026

Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking

Xiao Wang, Xufeng Lou, Zikang Yan +5

RGB-Event tracking improves localization robustness by fusing RGB appearance textures and dense temporal motion cues from event sensors. While this multi-modal scheme broadens trac…

cs.CV2026

FEMOT: Multi-Object Tracking using Frame and Event Cameras

Shiao Wang, Xiao Wang, Chao Wang +6

Conventional RGB cameras have been widely used in multi-object tracking due to their ability to capture rich appearance and semantic information. However, their performance is ofte…

cs.CV2026

Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization

Yumiao Zhao, Bo Jiang, Beibei Wang +3

Visual Prompting (VP) has emerged as an efficient paradigm for adapting large-scale pre-trained vision models to downstream tasks by incorporating learnable prompts at the input le…

cs.CV2026

Decoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking

Shiao Wang, Xiao Wang, Haonan Zhao +6

Existing RGB-Event visual object tracking approaches primarily rely on conventional feature-level fusion, failing to fully exploit the unique advantages of event cameras. In partic…