Datasets:
Add task categories (#2)
Browse files- Add task categories (befc10b16c4f403bbcb969e66c9734a3345c89a7)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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@@ -1,5 +1,7 @@
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---
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license: apache-2.0
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dataset_info:
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- config_name: temporal_order
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features:
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path: timelapse_estimation/test-*
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---
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-
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### **Dataset Description**
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The Temporal-VQA dataset is a challenging benchmark designed to evaluate the temporal reasoning capabilities of Multimodal Large Language Models (MLLMs) in tasks requiring visual temporal understanding. It emphasizes real-world temporal dynamics through two core evaluation tasks:-
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- **Temporal Order Understanding:** This task presents MLLMs with temporally consecutive frames from video sequences. The models must analyze and determine the correct sequence of events, assessing their ability to comprehend event progression over time.
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import os
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from io import BytesIO
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-
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def encode_image(image):
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buffer = BytesIO()
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print(response)
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```
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-
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-
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### **Cite Us**
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```
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@misc{imam2025multimodalllmsvisualtemporal,
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---
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license: apache-2.0
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task_categories:
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- image-text-to-text
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dataset_info:
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- config_name: temporal_order
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features:
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path: timelapse_estimation/test-*
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---
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### **Dataset Description**
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The Temporal-VQA dataset is a challenging benchmark designed to evaluate the temporal reasoning capabilities of Multimodal Large Language Models (MLLMs) in tasks requiring visual temporal understanding. It emphasizes real-world temporal dynamics through two core evaluation tasks:-
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- **Temporal Order Understanding:** This task presents MLLMs with temporally consecutive frames from video sequences. The models must analyze and determine the correct sequence of events, assessing their ability to comprehend event progression over time.
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import os
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from io import BytesIO
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# Replace with your OpenAI API key
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API_KEY = "YOUR_API_KEY"
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def encode_image(image):
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buffer = BytesIO()
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print(response)
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```
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### **Cite Us**
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```
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@misc{imam2025multimodalllmsvisualtemporal,
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