Webachieves competitive results on VQA-CP v2 test set, and outperforms RandImge on in-domain settings by over 3%. These results demonstrate that CF-VQA not only effectively reduces language bias, but also performs robustly. Table 2 shows the ablation study on VQA-CP v1 test split. As shown in Table 2, CF-VQA is general to both base- Special thanks to the authors of RUBi, BLOCK, and bootstrap.pytorch, and the datasets used in this research project. See more

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WebTable 2. Accuracies (%) on VQA-CP v2 and VQA v2 of SOTA models. “DA” denotes the data augmentation methods. \(^*\) indicates the results from our reimplementation. “MUTANT \(^\dagger \) ” denotes MUTANT only trained with XE loss. From: Rethinking Data Augmentation for Robust Visual Question Answering WebMay 13, 2024 · Concepts related to “cooking and food” (CF), “plants and animals” (PA) and “science and technology” (ST) correspond to a superior performance in the OK-VQA dataset. This phenomenon likely occurs because the answers to such questions are usually entities different than the main entity in the question and visual features in the image. fees and commission analyst

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WebCounterfactual VQA (CF-VQA) This repository is the Pytorch implementation of our paper "Counterfactual VQA: A Cause-Effect Look at Language Bias" in CVPR 2024. This code … Webachieves competitive results on VQA-CP v2 test set, and outperforms RandImge on in-domain settings by over 3%. These results demonstrate that CF-VQA not only effectively … WebMay 24, 2024 · VQA. To better understand the underlying causes of poor generalization, we comprehensively investigate performance of two pretrained V L models under different settings (i.e. classification and open-ended text generation) by conducting cross-dataset evaluations. We find that these models tend to learn fees and charges shopee