Poster Sat, Apr 25, 2026 • 11:15 AM – 1:45 PM PDT Pavilion 4 P4-#4807

IF-VidCap: Can Video Caption Models Follow Instructions?

Shihao Li ⋅ Yuanxing Zhang ⋅ Jiangtao Wu ⋅ Zhide Lei ⋅ Yiwen He ⋅ Runzhe Wen ⋅ Chenxi Liao ⋅ Chengkang Jiang ⋅ An Ping ⋅ Shuo Gao ⋅ Suhan Wang ⋅ Zhaozhou Bian ⋅ Zijun Zhou ⋅ Jingyi Xie ⋅ Jiayi Zhou ⋅ Jing Wang ⋅ Yifan Yao ⋅ Weihao Xie ⋅ Yingshui Tan ⋅ Yanghai Wang ⋅ Qianqian Xie ⋅ Zhaoxiang Zhang ⋅ JIAHENG LIU

Abstract

Although Multimodal Large Language Models (MLLMs) have demonstrated proficiency in video captioning, practical applications require captions that follow specific user instructions rather than generating exhaustive, unconstrained descriptions. Current benchmarks, however, primarily assess descriptive comprehensiveness while largely overlook instruction-following capabilities. To address this gap, we introduce IF-VidCap, a new benchmark for evaluating controllable video captioning, which contains 1,400 high-quality samples. Distinct from existing video captioning or general instruction-following benchmarks, IF-VidCap incorporates a systematic framework that assesses captions on two dimensions: format correctness and content correctness. Our comprehensive evaluation of 26 prominent models reveals a nuanced landscape: despite the continued dominance of proprietary models, the performance gap is closing, with top-tier open-source solutions now achieving near-parity. Furthermore, we find that models specialized for dense captioning underperform general-purpose MLLMs on complex instructions, indicating that future work should simultaneously advance both descriptive richness and instruction-following fidelity.