Webthe dataset TDIUC (Kafle and Kanan,2024). We show overall comparable performance with state-of-the-art models and improvements for specific question types that require object attribute informa-tion to be answered correctly. 2 Methodology Our proposed transfer/fine-tuning procedure re-quires a training set of guessing games D g from WebWe validate the relevance of our approach with various ablation studies, and show its superiority to attention-based methods on three datasets: VQA 2.0, VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms state-of-the-art results in this challenging context.

Cadene/murel.bootstrap.pytorch - Github

WebDownload Table The number of questions per type in TDIUC. from publication: An Analysis of Visual Question Answering Algorithms In visual question answering (VQA), an … WebDepending on the question category predicted by QC, only one of the classifiers of AP remains active. The loss functions of QC and AP are aggregated together to make it an end-to-end model. The proposed model (CQ-VQA) is evaluated on the TDIUC dataset and is benchmarked against state-of-the-art approaches. circles activity

An Empirical Study on the Generalization Power of Neural ...

WebTask Directed Image Understanding Challenge (TDIUC) is a new dataset that divides VQA into 12 constituent tasks that makes it easier to measure and compare the performance of VQA algorithms. TDIUC allows us to perform a more nuanced analysis and comparison of VQA algorithms through extensive experimentation. Citation: WebApr 6, 2024 · We experiment with multiple VQA architectures with extensive input ablation studies over the TDIUC dataset and show that QTA systematically improves the … diamondbacks gear shop