Search Results for author:
Found 11 papers, 2 papers with code
Counterfactual Vision-and-Language Navigation via Adversarial Path Sampler
In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.
A deep Q-learning method for optimizing visual search strategies in backgrounds of dynamic noise
We demonstrate that the search strategy corresponding to the Q-network is consistent with the IS search strategy.
FoveaTer: Foveated Transformer for Image Classification
We propose Foveated Transformer (FoveaTer) model, which uses pooling regions and eye movements to perform object classification tasks using a Vision Transformer architecture.
Comparing Visual Reasoning in Humans and AI
Results show that the machine/human agreement scene descriptions are much lower than human/human agreement for our complex scenes.
Gaze Perception in Humans and CNN-Based Model
Making accurate inferences about other individuals' locus of attention is essential for human social interactions and will be important for AI to effectively interact with humans.
M3L: Language-based Video Editing via Multi-Modal Multi-Level Transformers
LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video; 2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction.
Medical Image Quality Metrics for Foveated Model Observers
We show that the index of detectability across eccentricities weighted using the eye movement patterns of observers best predicted human performance in 2D vs. 3D search performance for a small microcalcification-like signal and a larger mass-like.
Foveated Model Observers for Visual Search in 3D Medical Images
Here, we compared standard linear model observers (ideal observers, non-pre-whitening matched filter with eye filter, and various versions of Channelized Hotelling models) to human performance searching in 3D 1/f$^{2. 8}$ filtered noise images and assessed its relationship to the more traditional location known exactly detection tasks and 2D search.
Assessment of Faster R-CNN in Man-Machine collaborative search
With the advent of modern expert systems driven by deep learning that supplement human experts (e. g. radiologists, dermatologists, surveillance scanners), we analyze how and when do such expert systems enhance human performance in a fine-grained small target visual search task.
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task.
Object Detection Through Exploration With A Foveated Visual Field
Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery.
