toplogo
Đăng nhập

CounterCurate: Enhancing Visio-Linguistic Reasoning with Counterfactual Examples


Khái niệm cốt lõi
CounterCurate enhances visio-linguistic reasoning by addressing physically grounded reasoning and leveraging text and image generation models for semantic counterfactual fine-tuning.
Tóm tắt

Abstract:

  • CounterCurate proposes a framework to improve visio-linguistic compositional reasoning.
  • Identifies under-explored problems in physically grounded reasoning and semantic counterfactual fine-tuning.
  • Demonstrates significant performance improvements on benchmarks.

Introduction:

  • Large language models show remarkable knowledge but lack in compositional reasoning.
  • Multimodal models leverage image-text pairs but struggle with compositional reasoning.
  • Current research focuses on evaluation benchmarks or rule-based counterfactual fine-tuning.

Data Extraction:

  • "Our approach shows significant improvements such as 33% for CLIP and 37% for LLaVA on our Flickr30k-Positions benchmark."
  • "Our method empirically demonstrates a significant performance boost by fine-tuning CLIP and LLaVA using our data generation pipeline on benchmarks such as SugarCrepe."
edit_icon

Tùy Chỉnh Tóm Tắt

edit_icon

Viết Lại Với AI

edit_icon

Tạo Trích Dẫn

translate_icon

Dịch Nguồn

visual_icon

Tạo sơ đồ tư duy

visit_icon

Xem Nguồn

Thống kê
私たちのアプローチは、Flickr30k-PositionsベンチマークでCLIPに33%、LLaVAに37%という大幅な性能向上を示しています。 私たちの方法は、SugarCrepeなどのベンチマークでCLIPとLLaVAをファインチューニングすることで、明らかなパフォーマンス向上を実証しています。
Trích dẫn
"Our approach shows significant improvements such as 33% for CLIP and 37% for LLaVA on our Flickr30k-Positions benchmark." "Our method empirically demonstrates a significant performance boost by fine-tuning CLIP and LLaVA using our data generation pipeline on benchmarks such as SugarCrepe."

Thông tin chi tiết chính được chắt lọc từ

by Jianrui Zhan... lúc arxiv.org 03-13-2024

https://arxiv.org/pdf/2402.13254.pdf
CounterCurate

Yêu cầu sâu hơn

他のデータセットでもCounterCurateの効果が同じように現れる可能性はありますか?

CounterCurateのアプローチは、物理的な根拠を持つ推論や意味論的なカウンターファクトを生成する能力を向上させることに焦点を当てています。このアプローチは、異なるデータセットでも同様に効果的である可能性があります。他のデータセットでも同様の問題や課題が存在し、CounterCurateがこれらの問題に対処する方法が適用できるかもしれません。
0
star