SC-ML: Self-supervised Counterfactual Metric Learning for Debiased Visual Question Answering

Visual question answering (VQA) is a critical multimodal task in which an agent must answer questions according to the visual cue. Unfortunately, language bias is a common problem in VQA, which refers to the model generating answers only by associating with the questions while ignoring the visual content, resulting in biased results. We tackle the language bias problem by proposing a self-supervised counterfactual metric learning (SC-ML) method to focus the image features better. SC-ML can adaptively select the question-relevant visual features to answer the question, reducing the negative influence of question-irrelevant visual features on inferring answers. In addition, question-irrelevant visual features can be seamlessly incorporated into counterfactual training schemes to further boost robustness. Extensive experiments have proved the effectiveness of our method with improved results on the VQA-CP dataset. Our code will be made publicly available.

AdaTriplet-RA: Domain Matching via Adaptive Triplet and Reinforced Attention for Unsupervised Domain Adaptation

Unsupervised domain adaption (UDA) is a transfer learning task where the data and annotations of the source domain are available but only have access to the unlabeled target data during training. Most previous methods try to minimise the domain gap by performing distribution alignment between the source and target domains, which has a notable limitation, i.e., operating at the domain level, but neglecting the sample-level differences. To mitigate this weakness, we propose to improve the unsupervised domain adaptation task with an inter-domain sample matching scheme. We apply the widely-used and robust Triplet loss to match the inter-domain samples. To reduce the catastrophic effect of the inaccurate pseudo-labels generated during training, we propose a novel uncertainty measurement method to select reliable pseudo-labels automatically and progressively refine them. We apply the advanced discrete relaxation Gumbel Softmax technique to realise an adaptive Topk scheme to fulfil the functionality. In addition, to enable the global ranking optimisation within one batch for the domain matching, the whole model is optimised via a novel reinforced attention mechanism with supervision from the policy gradient algorithm, using the Average Precision (AP) as the reward. Our model (termed \textbf{\textit{AdaTriplet-RA}}) achieves State-of-the-art results on several public benchmark datasets, and its effectiveness is validated via comprehensive ablation studies. Our method improves the accuracy of the baseline by 9.7\% (ResNet-101) and 6.2\% (ResNet-50) on the VisDa dataset and 4.22\% (ResNet-50) on the Domainnet dataset. {The source code is publicly available at \textit{https://github.com/shuxy0120/AdaTriplet-RA}}.