Search Results for author:
Found 23 papers, 16 papers with code
Computer Vision for Primate Behavior Analysis in the Wild
With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior.
Most discriminative stimuli for functional cell type clustering
Thus, for unbiased identification of the functional cell types in retina and visual cortex, new approaches are needed.
The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
We hope this competition will continue to strengthen the accompanying Sensorium benchmarks collection as a standard tool to measure progress in large-scale neural system identification models of the entire mouse visual hierarchy and beyond.
The Sensorium competition on predicting large-scale mouse primary visual cortex activity
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input.
Self-Supervised Graph Representation Learning for Neuronal Morphologies
GraphDINO is a novel transformer-based representation learning method for spatially-embedded graphs.
Image Segmentation Using Text and Image Prompts
After training on an extended version of the PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on an additional image expressing the query.
Ranked #3 on
Referring Image Matting (Keyword-based)
on RefMatte
A Broad Dataset is All You Need for One-Shot Object Detection
We here show that this generalization gap can be nearly closed by increasing the number of object categories used during training.
Benchmarking Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding.
Rotation-invariant clustering of neuronal responses in primary visual cortex
Similar to a convolutional neural network (CNN), the mammalian retina encodes visual information into several dozen nonlinear feature maps, each formed by one ganglion cell type that tiles the visual space in an approximately shift-equivariant manner.
How well do deep neural networks trained on object recognition characterize the mouse visual system?
Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Ranked #1 on
Robust Object Detection
on MS COCO
Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
However, in many cases this approach requires that the model is able to generalize to stimulus statistics that it was not trained on, such as band-limited noise and other parameterized stimuli.
One-Shot Instance Segmentation
We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.
Ranked #1 on
One-Shot Instance Segmentation
on MS COCO
A rotation-equivariant convolutional neural network model of primary visual cortex
We present a framework to identify common features independent of individual neurons' orientation selectivity by using a rotation-equivariant convolutional neural network, which automatically extracts every feature at multiple different orientations.
Diverse feature visualizations reveal invariances in early layers of deep neural networks
Here we propose a method to discover invariances in the responses of hidden layer units of deep neural networks.
One-Shot Segmentation in Clutter
We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example.
Ranked #1 on
One-Shot Segmentation
on Cluttered Omniglot
Neural system identification for large populations separating “what” and “where”
Traditional methods for neural system identification do not capitalize on this separation of “what” and “where”.
Neural system identification for large populations separating "what" and "where"
Our network scales well to thousands of neurons and short recordings and can be trained end-to-end.
Synthesising Dynamic Textures using Convolutional Neural Networks
Here we present a parametric model for dynamic textures.
Controlling Perceptual Factors in Neural Style Transfer
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
Image Style Transfer Using Convolutional Neural Networks
Rendering the semantic content of an image in different styles is a difficult image processing task.
A Neural Algorithm of Artistic Style
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image.
Texture Synthesis Using Convolutional Neural Networks
Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition.
