Summary of Scenellm: Implicit Language Reasoning in Llm For Dynamic Scene Graph Generation, by Hang Zhang et al.
SceneLLM: Implicit Language Reasoning in LLM for Dynamic Scene Graph Generation
by Hang Zhang, Zhuoling Li, Jun Liu
First submitted to arxiv on: 15 Dec 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 proposes a novel framework called SceneLLM for parsing dynamic scenes into semantic triplets for Scene Graph Generation. Inspired by Large Language Models (LLMs), SceneLLM leverages LLMs as powerful scene analyzers, introducing a Video-to-Language (V2L) mapping module to transform video frames into linguistic signals. The framework also devises a Spatial Information Aggregation (SIA) scheme and uses Optimal Transport (OT) to generate an implicit language signal capturing the video’s spatio-temporal information. To fine-tune the LLM, Low-Rank Adaptation (LoRA) is applied, followed by a transformer-based SGG predictor to decode the LLM’s reasoning and predict semantic triplets. The method achieves state-of-the-art results on the Action Genome (AG) benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SceneLLM is a new way for computers to understand dynamic scenes, like what’s happening in a video of a soccer game or a car accident. It uses special language models that are good at understanding words and sentences, but has never been used before for this task. The system breaks down the scene into smaller parts called “semantic triplets” that describe what’s happening in each part. It does this by first converting the video frames into something the computer can understand better, then using a special method to combine all the information together. This method is really good at understanding dynamic scenes and it even beats other systems that were made for this task. |
Keywords
» Artificial intelligence » Lora » Low rank adaptation » Parsing » Transformer
