Visual perception and processing mechanisms
40 papers on this topic
Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?
We present the largest and most comprehensive empirical study of pre-trained visual representations (PVRs) or visual 'foundation models' for Embodied AI. First, we curate CortexBench, consisting...
What if Eye...? Computationally Recreating Vision Evolution
Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through countless...
A Robotics-Inspired Scanpath Model Reveals the Importance of Uncertainty and Semantic Object Cues for Gaze Guidance in Dynamic Scenes
The objects we perceive guide our eye movements when observing real-world dynamic scenes. Yet, gaze shifts and selective attention are critical for perceiving details and refining object...
Visualizing and Controlling Cortical Responses Using Voxel-Weighted Activation Maximization
Deep neural networks (DNNs) trained on visual tasks develop feature representations that resemble those in the human visual system. Although DNN-based encoding models can accurately predict brain...
The Information-Theoretic Imperative: Compression and the Epistemic Foundations of Intelligence
Why do brains and deep networks converge on similar representations? Task-optimized artificial neural networks quantitatively predict primate ventral stream responses despite radically different...
Natural Image Reconstruction from fMRI using Deep Learning: A Survey
With the advent of brain imaging techniques and machine learning tools, much effort has been devoted to building computational models to capture the encoding of visual information in the human...
Semantic Brain Decoding: from fMRI to conceptually similar image reconstruction of visual stimuli
Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs. Therefore, we propose a novel...
The Brain-Inspired Decoder for Natural Visual Image Reconstruction
Decoding images from brain activity has been a challenge. Owing to the development of deep learning, there are available tools to solve this problem. The decoded image, which aims to map neural...
Five Points to Check when Comparing Visual Perception in Humans and Machines
With the rise of machines to human-level performance in complex recognition tasks, a growing amount of work is directed towards comparing information processing in humans and machines. These...
Fixing the problems of deep neural networks will require better training data and learning algorithms
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We...
V1T: large-scale mouse V1 response prediction using a Vision Transformer
Accurate predictive models of the visual cortex neural response to natural visual stimuli remain a challenge in computational neuroscience. In this work, we introduce V1T, a novel Vision...
Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortex
Recently, there has been a surge in the popularity of pre trained large language models (LLMs) (such as GPT-4), sweeping across the entire Natural Language Processing (NLP) and Computer Vision...
Enhancing LGMD's Looming Selectivity for UAV with Spatial-temporal Distributed Presynaptic Connections
Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs). This is especially true for small or micro UAVs, due to their limited computational power. In nature,...
Modeling Human Eye Movements with Neural Networks in a Maze-Solving Task
From smoothly pursuing moving objects to rapidly shifting gazes during visual search, humans employ a wide variety of eye movement strategies in different contexts. While eye movements provide a...
Instantaneous Stereo Depth Estimation of Real-World Stimuli with a Neuromorphic Stereo-Vision Setup
The stereo-matching problem, i.e., matching corresponding features in two different views to reconstruct depth, is efficiently solved in biology. Yet, it remains the computational bottleneck for...
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs)....
Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review
Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging and so forth. A good number of motion detectors have been...
What can human minimal videos tell us about dynamic recognition models?
In human vision objects and their parts can be visually recognized from purely spatial or purely temporal information but the mechanisms integrating space and time are poorly understood. Here we...