activity
20242026
most citedAutoTrust: Benchmarking Trustworthiness in Large Vision Language Models for Autonomous Driving

2 citations · 6 across the 21 of their papers we have counts for

collaborators

24 papers

cs.CV2026

CinematicVQA: Benchmarking Film-Grammar Reasoning in Large Vision-Language Models

Shuo Xing, Pooja Verlani, Balu Adsumilli +1

Cinematography, the craft of visual storytelling through framing, lighting, and camera operation, fundamentally shapes how audiences perceive and emotionally engage with video cont…

cs.CV2026

SUMO: Segment and Track Any Motion with Nonlinear State Space Models

Kexin Tian, Sixu Li, Keshu Wu +2

Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics. Existing meth…

cs.CL2026

Knowledge Is Not Static: Order-Aware Hypergraph RAG for Language Models

Keshu Wu, Chenchen Kuai, Zihao Li +6

Retrieval-augmented generation (RAG) enhances large language models by grounding outputs in retrieved knowledge. However, existing RAG methods including graph- and hypergraph-based…

cs.CV2025

Knowing the Answer Isn't Enough: Fixing Reasoning Path Failures in LVLMs

Chaoyang Wang, Yangfan He, Yiyang Zhou +6

We reveal a critical yet underexplored flaw in Large Vision-Language Models (LVLMs): even when these models know the correct answer, they frequently arrive there through incorrect…

cs.CV2025

NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks

Fangzhou Lin, Yuping Wang, Yuliang Guo +7

Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the…

cs.LG2025

KANMixer: a minimal KAN-centered mixer for long-term time series forecasting

Lingyu Jiang, Dengzhe Hou, Yuping Wang +9

Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-step-ahead accuracy remains chal…