OpenHome is a visual-only, multiple-choice benchmark designed to evaluate smart‑home camera video understanding. It includes 2,254 questions across 1,118 real‑world clips, organized into 10 tasks spanning perception, reasoning, and insight levels. The benchmark reveals that closed‑source models outperform open‑weight models, with the best closed‑source model surpassing the best open‑weight model by significant margins on all levels.
Video provides a rich record of human behavior, interaction, and situated contexts, offering important evidence for understanding people and conducting human-centered research. As vision-language mode...
arXiv:2607. 09876v1 Announce Type: cross Abstract: Automatically retrieving videos from large camera-trap datasets remains challenging.
arXiv:2606. 07433v1 Announce Type: cross Abstract: Video understanding is being rapidly transformed by multimodal large language models (MLLMs), as research moves from short clips to long, multimodal, and knowledge-intensive video scenarios.
The paper investigates when vision‑language models (VLMs) can independently analyze human‑centered video and when human oversight is still needed. By reviewing 1,702 CHI 2026 papers, the authors develop a five‑dimensional taxonomy of video annotation tasks and build a benchmark of 15 representative tasks. Experiments show that VLMs alone achieve near‑human accuracy (HNS = 97.0), while human verification of VLM outputs yields the highest accuracy (HNS = 121.5) and significantly reduces annotation time and cost.
arXiv:2606. 01485v1 Announce Type: cross Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the \emph{ImplicitQA} / \emph{VRR-QA} benchmark~\cite{implicitqa}: multiple-choice video question answering in which answers are deliberately \emph{not} observable in any single frame and must be inferred from spatial layout, motion, depth, viewpoint, causality, and social context across discontinuous frames of creative video.
The paper introduces structured video prompting, a training‑free inference‑time technique that augments input videos with lightweight spatial and temporal structure to provide explicit anchors for evidence organization. By applying this method to two video benchmarks and two open video‑language models, the authors demonstrate performance improvements across several tasks, with gains varying by model and task. The study suggests that failures in video‑language models stem not only from reasoning capacity but also from how video evidence is presented during inference.
arXiv:2502.14994v2 Announce Type: replace Abstract: The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on spec...
arXiv:2609.09396v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) advance toward physical deployment, the focus has remained on action-oriented Embodied AI evaluated on subject-centric...
arXiv:2610.33478v1 Announce Type: cross Abstract: Marine video understanding is crucial for a wide range of real-world applications. However, existing marine video datasets suffer from limitations, i...
arXiv:2608. 13113v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks.
The paper introduces the Very Big Video Reasoning (VBVR) Dataset, a large-scale collection of over one million video clips organized into 200 curated reasoning tasks. It also presents VBVR-Bench, a benchmark framework that uses rule-based, human-aligned scorers for reproducible evaluation of video reasoning models. The authors conduct a large-scale scaling study, noting early signs of emergent generalization to unseen reasoning tasks, and make all resources publicly available.
The paper introduces three new vision‑centric evaluation benchmarks—temporal frame retrieval, video future prediction, and causal memory distortion—to assess visual question answering in large video models. Unlike traditional benchmarks that rely on text-based multiple choice questions, these tasks require models to reason directly from visual inputs. The authors find that current state‑of‑the‑art models struggle with visual queries, highlighting a gap in visual understanding that future research should address.