Summary of Task Me Anything, by Jieyu Zhang et al.
Task Me Anything
by Jieyu Zhang, Weikai Huang, Zixian Ma, Oscar Michel, Dong He, Tanmay Gupta, Wei-Chiu Ma, Ali Farhadi, Aniruddha Kembhavi, Ranjay Krishna
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Task-Me-Anything, a benchmark generation engine that produces tailored benchmarks for large multimodal language models (MLMs). The engine can programmatically generate a vast number of task instances from an extendable taxonomy of visual assets. This allows developers to identify the most suitable MLMs for their specific use case by evaluating performance on relevant tasks. Task-Me-Anything reveals critical insights into open-source MLMs, including strengths and weaknesses in object and attribute recognition, spatial and temporal understanding, and color distinction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Task-Me-Anything is a new way to test how well language models can understand images, videos, and 3D objects. It helps us figure out which model is best for our specific job by creating custom tests based on what we need. This tool shows that bigger models are generally better, but some smaller models have special skills. One big model, GPT4o, has trouble recognizing moving objects or telling colors apart. |
