ImplicitQA: Going beyond frames towards Implicit Video Reasoning

TL;DR

ImplicitQA addresses a critical gap in VideoQA by evaluating implicit, cross-frame reasoning in cinematic content, where meaning hinges on unstated motives and narrative context rather than explicit visuals. The authors build a manually curated benchmark of 1K QA pairs from 1K clips across diverse genres and decades, organized into nine implicit-reasoning categories, and supply an annotation tool and evaluation protocol. Across extensive benchmarking of 11 model families, even state-of-the-art GPT-based systems show pronounced declines relative to human performance, though reasoning-focused prompts and greater temporal context yield measurable gains. The work demonstrates that current VideoQA approaches struggle with implicit, long-horizon narrative understanding, motivating future directions in model architectures and training strategies to move toward human-like video comprehension.

Abstract

Video Question Answering (VideoQA) has made significant strides by leveraging multimodal learning to align visual and textual modalities. However, current benchmarks overwhelmingly focus on questions answerable through explicit visual content - actions, objects, and events directly observable within individual frames or short clips. In contrast, creative and cinematic videos - such as movies, TV shows, and narrative-driven content - employ storytelling techniques that deliberately omit certain depictions, requiring viewers to infer motives, relationships across discontinuous frames with disjoint visual contexts. Humans naturally excel at such implicit reasoning, seamlessly integrating information across time and context to construct coherent narratives. Yet current benchmarks fail to capture this essential dimension of human-like understanding. To bridge this gap, we present ImplicitQA, a novel benchmark specifically designed to test VideoQA models on human-like implicit reasoning. ImplicitQA comprises 1K meticulously annotated QA pairs drawn from 1K high-quality creative video clips covering 15 genres across 7 decades of content. Questions are systematically categorized into nine key reasoning dimensions: lateral and vertical spatial reasoning, depth and proximity, viewpoint and visibility, motion and trajectory, causal and motivational reasoning, social interactions, physical context, and inferred counting. These annotations are deliberately challenging, crafted by authors, validated through multiple annotators, and benchmarked against human performance to ensure high quality. Our extensive evaluations on 11 leading VideoQA models reveals consistent and significant performance degradation, underscoring their reliance on surface-level visual cues and highlighting the difficulty of implicit reasoning. https://huggingface.co/datasets/ucf-crcv/ImplicitQA.

Figures (25)

• Figure 1: ImplicitQA examples, each targeting a distinct implicit‐reasoning dimension. (a) Lateral spatial reasoning-identifying the toy opposite the wizard clock by mentally mapping objects across the scene. (b) Motion and trajectory dynamics-inferring that black bullets move away from Mario by integrating actions and character positions. (c) Inferred counting-determining which animal is the third to leave a bridge by tracking sequential departures that are never fully visible onscreen. Models that excel at explicit perception often fail on these tasks, highlighting the need for benchmarks that probe deeper narrative understanding.
• Figure 2: ImplicitQA Curation Pipeline. We begin by selecting creative video clips and download them. An expert‐annotator pool then uses our FrameQuiz Annotation Tool to (1) mark temporal segments, (2) add a multiple‑choice question and its correct answer for the segment, and (3) craft plausible distractor options. These annotated clips form the raw ImplicitQA Dataset. Next, a non‑expert annotator pool employs the ImplicitEval Annotation Tool to answer each question, yielding a human baseline accuracy score. We run GPT‑4.1openai2024gpt41 on the dataset to automatically assign initial category tags, which are then relabeled by the expert annotators.
• Figure 3: Visualization of ImplicitQA statistics.
• Figure 4: Question durations for each category.
• Figure 5: Distribution across categories