arXiv Computer Vision October 9, 2026 By Kun Wang, Yupeng Hu, Ruping Cao, Hao Liu, Zhiran Li, Qianlong Xiang, Harry Cheng

GroundSight at GroundLM 2026 Shared Tasks: GoldenViewVQA

Read the original on arXiv Computer Vision →

GroundSight at GroundLM 2026 Shared Tasks: GoldenViewVQA introduces a new VQA benchmark that requires models to answer driving‑scene questions while also identifying the camera view that supports the answer. The authors present CoVeR‑VQA, a training‑free, multi‑stage verification and correction framework that starts with GPT‑5.6 zero‑shot predictions and applies successive view‑specific, prior‑guided, and cross‑split group‑level verifications using Gemini‑3.6‑Flash, Claude‑Opus‑5, and semantic question grouping. On the official test set, CoVeR‑VQA achieves 84.75 % joint accuracy, surpassing the zero‑shot baseline by 13.56 percentage points, and the final run reaches 88.14 % joint accuracy after evaluator‑informed post‑hoc corrections.

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