31 citations · 44 across the 9 of their papers we have counts for
11 papers
Context Sensitivity Improves Human-Machine Visual Alignment
Frieda Born, Tom Neuhäuser, Lukas Muttenthaler +6
Modern machine learning models typically represent inputs as fixed points in a high-dimensional embedding space. While this approach has been proven powerful for a wide range of do…
Benchmarking Egocentric Multimodal Goal Inference for Assistive Wearable Agents
Vijay Veerabadran, Fanyi Xiao, Nitin Kamra +10
There has been a surge of interest in assistive wearable agents: agents embodied in wearable form factors (e.g., smart glasses) who take assistive actions toward a user's goal/quer…
EgoToM: Benchmarking Theory of Mind Reasoning from Egocentric Videos
Yuxuan Li, Vijay Veerabadran, Michael L. Iuzzolino +3
We introduce EgoToM, a new video question-answering benchmark that extends Theory-of-Mind (ToM) evaluation to egocentric domains. Using a causal ToM model, we generate multi-choice…
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…
Learning as the Unsupervised Alignment of Conceptual Systems
Brett D. Roads, Bradley C. Love
Concept induction requires the extraction and naming of concepts from noisy perceptual experience. For supervised approaches, as the number of concepts grows, so does the number of…
Learning to Generate Images with Perceptual Similarity Metrics
Jake Snell, Karl Ridgeway, Renjie Liao +3
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compa…