IF-VidCap: Can Video Caption Models Follow Instructions?

The paper introduces IF-VidCap, a benchmark for evaluating instruction-following video captioning, alongside a multi-dimensional evaluation protocol and a fine-tuned IF-Captioner model. It resides in the 'General Video Instruction-Following' leaf under 'Instruction-Tuned Video Understanding Models', which contains six sibling papers. This leaf represents a moderately populated research direction within a broader taxonomy of fifty papers across six major branches, indicating that instruction-following video understanding is an active but not overcrowded area. The work targets a specific gap: assessing whether models generate captions that adhere to user instructions rather than producing exhaustive descriptions.

The taxonomy reveals that IF-VidCap sits adjacent to several related directions. Neighboring leaves include 'Temporal Grounding and Frame Selection' (two papers emphasizing temporal localization) and 'Domain-Specific Instruction-Following' (two papers on specialized domains like remote sensing). The broader 'Evaluation and Benchmarking' branch contains three subcategories, including 'Captioning Quality and Controllability Benchmarks', which directly relates to IF-VidCap's focus. The taxonomy's scope notes clarify that general instruction-following excludes domain-specific models and temporal grounding methods, positioning IF-VidCap as a cross-cutting evaluation resource rather than a model-centric contribution.

Among twenty-seven candidates examined, the benchmark contribution (Contribution A) showed no clear refutation across seven candidates, suggesting relative novelty in its dual-dimensional evaluation framework. However, the multi-dimensional evaluation protocol (Contribution B) encountered four refutable candidates among ten examined, indicating substantial prior work on combining rule-based and LLM-based assessment methods. The training dataset and IF-Captioner model (Contribution C) found no refutation among ten candidates, though this reflects the limited search scope rather than exhaustive coverage. The analysis suggests that while the benchmark design appears distinctive, the evaluation methodology overlaps with existing approaches in the field.

Based on the top-twenty-seven semantic matches examined, the work demonstrates moderate novelty in its benchmark design but less distinctiveness in its evaluation protocol. The limited search scope means that additional relevant work may exist beyond the candidates reviewed, particularly in the broader 'Evaluation and Benchmarking' branch. The contribution-level statistics indicate that the benchmark itself occupies a relatively underexplored niche, whereas the evaluation methodology builds incrementally on established practices in controllable captioning assessment.