EgoMonth: A Month-Level Egocentric Video Benchmark for Long-Term Spatiotemporal Memory
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. However, existing benchmarks rely predominantly on web-sourced videos that lack inter-clip spatiotemporal continuity, making it difficult to assess whether models can maintain consistent memory across days or weeks of real-world experience. We introduce EgoMonth, the first month-level egocentric video understanding benchmark. EgoMonth comprises over 300 hours of first-person daily-life recordings from 20 participants spanning 20 to 120 days, paired with 1,443 human-crafted multiple-choice question-answer pairs. We de...
| Authors | Weitao Chen, Hu Jiaxin, Xie Tianyidan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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