@conference {5547,
	title = { Learning to Learn: How to Continuously Teach Humans and Machines },
	booktitle = { International Conference on Computer Vision (ICCV), 2023},
	year = {2023},
	month = {10/2023},
	address = {Paris, France},
	abstract = {<p>Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, which also reinforces our addition and multiplication skills. Designing a curriculum for teaching either a human or a machine shares the underlying goal of maximizing knowledge transfer from earlier to later tasks, while also minimizing forgetting of learned tasks. Prior research on curriculum design for image classification focuses on the ordering of training examples during a single offline task. Here, we investigate the effect of the order in which multiple distinct tasks are learned in a sequence. We focus on the online class-incremental continual learning setting, where algorithms or humans must learn image classes one at a time during a single pass through a dataset. We find that curriculum consistently influences learning outcomes for humans and for multiple continual machine learning algorithms across several benchmark datasets. We introduce a novel-object recognition dataset for human curriculum learning experiments and observe that curricula that are effective for humans are highly correlated with those that are effective for machines. As an initial step towards automated curriculum design for online class-incremental learning, we propose a novel algorithm, dubbed Curriculum Designer (CD), that designs and ranks curricula based on inter-class feature similarities. We find significant overlap between curricula that are empirically highly effective and those that are highly ranked by our CD. Our study establishes a framework for further research on teaching humans and machines to learn continuously using optimized curricula.</p>
},
	url = {https://openaccess.thecvf.com/content/ICCV2023/html/Singh_Learning_to_Learn_How_to_Continuously_Teach_Humans_and_Machines_ICCV_2023_paper.html},
	author = {Parantak Singh and You Li and Ankur Sikarwar and Stan Weixian Lei and Difei Gao and Morgan B. Talbot and Ying Sun and Mike Zheng Shou and Gabriel Kreiman and Mengmi Zhang}
}
@article {5035,
	title = {On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering},
	journal = {arXiv},
	year = {2022},
	month = {01/2022},
	abstract = {<p>In recent years, multi-modal transformers have shown significant progress in Vision-Language tasks, such as Visual Question Answering (VQA), outperforming previous architectures by a considerable margin. This improvement in VQA is often attributed to the rich interactions between vision and language streams. In this work, we investigate the efficacy of co-attention transformer layers in helping the network focus on relevant regions while answering the question. We generate visual attention maps using the question-conditioned image attention scores in these co-attention layers. We evaluate the effect of the following critical components on visual attention of a state-of-the-art VQA model: (i) number of object region proposals, (ii) question part of speech (POS) tags, (iii) question semantics, (iv) number of co-attention layers, and (v) answer accuracy. We compare the neural network attention maps against human attention maps both qualitatively and quantitatively. Our findings indicate that co-attention transformer modules are crucial in attending to relevant regions of the image given a question. Importantly, we observe that the semantic meaning of the question is not what drives visual attention, but specific keywords in the question do. Our work sheds light on the function and interpretation of co-attention transformer layers, highlights gaps in current networks, and can guide the development of future VQA models and networks that simultaneously process visual and language streams.</p>
},
	doi = {10.48550/arXiv.2201.03965},
	url = {https://arxiv.org/abs/2201.03965},
	author = {Ankur Sikarwar and Gabriel Kreiman}
}
