1 citations · 2 across the 3 of their papers we have counts for
7 papers
Understanding top-down attention using task-oriented ablation design
Freddie Bickford Smith, Brett D Roads, Xiaoliang Luo +1
Top-down attention allows neural networks, both artificial and biological, to focus on the information most relevant for a given task. This is known to enhance performance in visua…
A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
Nikolay Dagaev, Brett D. Roads, Xiaoliang Luo +3
Despite their impressive performance in object recognition and other tasks under standard testing conditions, deep networks often fail to generalize to out-of-distribution (o.o.d.)…
Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings
Brett D. Roads, Bradley C. Love
Advances in object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks---such as ILSVRC---are r…
Transforming Neural Network Visual Representations to Predict Human Judgments of Similarity
Maria Attarian, Brett D. Roads, Michael C. Mozer
Deep-learning vision models have shown intriguing similarities and differences with respect to human vision. We investigate how to bring machine visual representations into better…
The perceptual boost of visual attention is task-dependent in naturalistic settings
Freddie Bickford Smith, Xiaoliang Luo, Brett D. Roads +1
Top-down attention allows people to focus on task-relevant visual information. Is the resulting perceptual boost task-dependent in naturalistic settings? We aim to answer this with…
The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks
Xiaoliang Luo, Brett D. Roads, Bradley C. Love
People deploy top-down, goal-directed attention to accomplish tasks, such as finding lost keys. By tuning the visual system to relevant information sources, object recognition can…