Summary of Countercurate: Enhancing Physical and Semantic Visio-linguistic Compositional Reasoning Via Counterfactual Examples, by Jianrui Zhang et al.
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples
by Jianrui Zhang, Mu Cai, Tengyang Xie, Yong Jae Lee
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 CounterCurate is a framework designed to enhance the visio-linguistic compositional reasoning capabilities of both contrastive and generative multimodal models. The framework addresses two critical under-explored problems: physically grounded reasoning (counting and position understanding) and the potential use of text and image generation models for semantic counterfactual fine-tuning. The researchers first highlight the poor performance of multimodal models like CLIP and LLaVA in physically grounded compositional reasoning, then apply simple data augmentation using a grounded image generation model to generate fine-tuning data. This results in significant performance improvements: +33% and +37% for CLIP and LLaVA, respectively, on the Flickr30k-Positions benchmark. Additionally, the researchers exploit high-performing text generation and image generation models (GPT-4V and DALLE-3) to curate challenging semantic counterfactuals, leading to enhanced compositional reasoning capabilities on benchmarks like SugarCrepe. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CounterCurate is a new way for computers to understand connections between images and words. Right now, these machines are not very good at this. The people who made CounterCurate found out why: they’re missing some important skills, like being able to count and tell where things are in an image. They also discovered that using super-powerful text and image generators can help improve how well the models understand language. To test their idea, they tried adding some simple tricks to the data used for training these models. This made a big difference: it helped the models get 33% better at understanding images and words on one important benchmark. They also used these powerful generators to create new, challenging examples that helped the models learn even more. |
Keywords
* Artificial intelligence * Data augmentation * Fine tuning * Gpt * Image generation * Text generation
