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What is the EgoTempo benchmark?
EgoTempo is a free-form video question-answering dataset for evaluating temporal understanding in egocentric, or first-person, video. Its clips show close-up interactions with objects. The authors designed questions to make it harder for a model to rely on general knowledge, wording hints, or a single informative frame instead of following what happens through the clip.
In their 2025 paper, Chiara Plizzari and co-authors report 500 question-answer pairs across 10 temporal reasoning tasks and 40 scenarios. Videos average 45 seconds. The tasks cover action sequences and counts, ordering events, predicting a future action, object attributes and sequences, object counting, spatial reasoning, locating objects, and identifying objects associated with an action. The authors describe a pipeline that combines automated question generation with manual review.
How does EgoTempo test video understanding?
The benchmark asks open-ended questions about video content. That matters because a multiple-choice format can sometimes let a model eliminate implausible answers without correctly understanding the video. EgoTempo’s design instead aims to require evidence from across the clip, such as the order of actions or how an object changes position.
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The paper’s abstract describes the goal this way: “EgoTempo emphasizes tasks that require integrating information across the entire video, ensuring that models would need to rely on temporal patterns rather than static cues or pre-existing knowledge.” This is the authors’ design objective; it does not mean every question can only be solved by watching every moment or that the benchmark captures all forms of video understanding.
What did the EgoTempo paper find?
In the paper’s reported experiments, Gemini Flash scored 10.0% with question text only and 9.1% when given a single central frame on EgoTempo. With video represented by frames sampled at 1 frame per second, it achieved a 39.1% average across the listed tasks. The authors also report that newer models evaluated with access to full video sequences plateaued around 40%; that is their characterization of the models and methods included in the 2025 study, not a 2026 leaderboard.
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The comparison with other benchmarks illustrates why EgoTempo is challenging. In the same paper’s evaluations, Gemini Flash scored 57.8% text-only accuracy on EgoTaskQA, while on EgoSchema it scored 51% using a single frame. Those results differ in benchmark and input condition from EgoTempo’s 10.0% text-only and 9.1% single-frame results. They are study-specific measurements, not universal estimates of what a model can do on every task.
Frame selection changes the result
The authors report that uniformly sampled frames performed better than randomly sampled or chronologically shuffled frames, particularly on tasks where event order matters. More frames generally helped, but counting and sequence questions remained difficult, and improvements began to level off at higher sampling rates. A benchmark score therefore reflects not just a model but also how the video is sampled and presented.
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People also found the tasks difficult
In the participant experiment reported in the paper, human performance was 24% higher than Gemini’s. This supports the authors’ finding that the evaluated model struggled with this test, but it should not be read as a general comparison between people and AI across video tasks.
How reliable is the scoring?
Because answers are open-ended, the authors use an LLM judge to assess answer quality. That avoids requiring every response to match one exact string, but it introduces a scoring limitation of its own. The paper’s sample error analysis found instances in which a more detailed answer or an imprecisely named object was marked wrong. A benchmark score can consequently reflect both a model’s video reasoning and the judge’s interpretation of its wording.
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How should you interpret EgoTempo’s results?
EgoTempo is best understood as a targeted stress test: it probes whether models can follow object interactions and temporal relationships in short first-person clips. Its low text-only and single-frame results are consistent with the benchmark’s effort to reduce shortcuts based on language or static visual cues. The stronger result with sampled video frames shows that temporal visual evidence helps in the tested setup, while the remaining errors show that access to more frames does not automatically solve the tasks.
- It measures a narrow capability: temporal reasoning about interactions in egocentric video, not general intelligence or all-purpose video understanding.
- Scores depend on evaluation choices: model version, prompt, frame-sampling rate, and answer-scoring protocol can affect results.
- Its figures are dated study results: the 2025 paper does not establish how current models released or evaluated afterward perform.
- Open-ended scoring is not infallible: the judge may penalize answers for wording or detail even when they convey relevant evidence.
Where can researchers find the dataset and code?
The authors state that the dataset and code are available through the Google Research Datasets egotempo repository. Check the repository itself for current contents, licensing, and access details before using or redistributing the materials.
Paper and source
Plizzari, Chiara, Alessio Tonioni, Yongqin Xian, Achin Kulshrestha, and Federico Tombari, “Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos,” submitted to arXiv on 17 March 2025. Read the paper on arXiv.
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