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Suppressing biased samples for robust vqa

http://export.arxiv.org/abs/2210.04563v1 WebJun 19, 2024 · In return, the performance of these models is further boosted. Extensive ablations have shown the effectiveness of CSS. Particularly, by building on top of the …

Debiased Visual Question Answering from Feature and …

WebMar 14, 2024 · Specifically, for any VQA model, given a training sample (I,Q,a), CSS consists of three main steps: Training VQA model with original sample (I,Q,a); Synthesizing a counterfactual sample (I −,Q,a−) by V-CSS or (I,Q−,a−) by Q-CSS; Training VQA model with the counterfactual sample. WebBased on experimental analysis for existing robust VQA methods, we stress the language bias in VQA that comes from two aspects, i.e., distribution bias and shortcut bias. We … i am resolved youtube https://keonna.net

Transcriptomic Harmonization as the Way for Suppressing Cross …

WebSuppressing Biased Samples for Robust VQA IEEE Transactions on Multimedia 2024 Journal article DOI: 10.1109/TMM.2024.3097502 Contributors : Ninglin Ouyang; Qingbao Huang; Pijian Li; Yi Cai; Bin Liu; Ho-fung Leung; Qing Li Show more detail Source : Crossref WebAs a new way of balancing data to address language bias, SBS overcomes the shortcomings of previous data-balanced methods. Experimental results show that our method can be … WebNov 24, 2024 · CVPR2024 Counterfactual Samples Synthesizing for Robust VQA. This repo contains code for our paper "Counterfactual Samples Synthesizing for Robust Visual Question Answering" This repo contains code modified from here,many thanks!. Prerequisites. Make sure you are on a machine with a NVIDIA GPU and Python 2.7 with … iamresponding cad

VQA-CP Leaderboard - GitHub

Category:Greedy Gradient Ensemble for Robust Visual Question …

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Suppressing biased samples for robust vqa

CVPR2024 Counterfactual Samples Synthesizing for Robust VQA - GitHub

WebNov 21, 2024 · VQA-CP is an out-of-distribution dataset for Visual Question Answering, which is designed to penalize models that rely on question biases to give an answer. You … WebOct 29, 2024 · The counterfactual sample generation mechanism generates numerous counterfactual samples to reduce the learned language bias. A good deal of experimental results shows that this method surpasses most of the advanced models on the VQA-CP v2 dataset and has made significant progress.

Suppressing biased samples for robust vqa

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WebMar 30, 2024 · However, these models reveal a trade-off that the improvements on OOD data severely sacrifice the performance on the in-distribution (ID) data (which is dominated by the biased samples). Therefore, we propose a novel contrastive learning approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. http://export.arxiv.org/abs/2210.04563v1

WebAug 26, 2024 · This type of bias is referred to as Demand Characteristics. When presented with a scale, say a 5-point scale from 1 to 5, people are often biased to only select the … WebAs a new way of balancing data to address language bias, SBS overcomes the shortcomings of previous data-balanced methods. Experimental results show that our method can be …

WebSuppressing Biased Samples for Robust VQA Semantic Scholar. Experimental results show that the proposed model-agnostic training scheme, Suppressing Biased Samples … Webmany VQA models may only capture the biases between questions and answers in a dataset rather than showing real reasoning abilities. For example, given a question, some VQA models tend to output the answer that occurs frequently in the dataset and ignore the images. Toreduce this tendency,existing methods focus on weakening the language bias.

WebSuppressing Biased Samples for Robust VQA. Most existing visual question answering (VQA) models strongly rely on language bias to answer questions, i.e., they always tend to …

WebOct 10, 2024 · approach, MMBS, for building robust VQA models by Making the Most of Biased Samples. Specifically, we construct positive samples for contrastive learning by eliminating the information related to spurious correlation from the original training samples and explore several strategies to use the constructed i am respectedWebApr 12, 2024 · Bias Mimicking: A Simple Sampling Approach for Bias Mitigation Maan Qraitem · Kate Saenko · Bryan Plummer Masked Images Are Counterfactual Samples for Robust Fine-tuning Yao Xiao · Ziyi Tang · Pengxu Wei · Cong Liu · Liang Lin Samples with Low Loss Curvature Improve Data Efficiency Isha Garg · Kaushik Roy iamresponding iarWebTowards Robust Visual Question Answering: Making the Most of Biased Samples via Contrastive Learning - Qingyi Si et al, EMNLP 2024 (Findings). [code] Plug-and-Play VQA: Zero-shot VQA by Conjoining Large Pretrained Models with Zero Training - Anthony Meng Huat Tiong et al, EMNLP 2024 (Findings) . iamresponding for pc