Publications related to Saliency Detection Using Regression Trees on Hierarchical Image Segments

Related publications (30)

SVGC-AVA: 360-Degree Video Saliency Prediction With Spherical Vector-Based Graph Convolution and Audio-Visual Attention

Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by joint ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Detecting Road Obstacles by Erasing Them

Pascal Fua, Mathieu Salzmann, Sina Honari, Krzysztof Maciej Lis

Vehicles can encounter a myriad of obstacles on the road, and it is impossible to record them all beforehand to train a detector. Instead, we select image patches and inpaint them with the surrounding road texture, which tends to remove obstacles from thos ...

2024

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Seyedbehzad Bozorgtabar, Devavrat Tomar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

IEEE2023

Convolutional neural network classifies visual stimuli from cortical response recorded with wide-field imaging in mice

Silvestro Micera

Objective. The optic nerve is a good location for a visual neuroprosthesis. It can be targeted when a subject cannot receive a retinal prosthesis and it is less invasive than a cortical implant. The effectiveness of an electrical neuroprosthesis depends on ...

IOP Publishing Ltd2023

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

Yufan Ren

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

Improving the Training of Compact Neural Networks for Visual Recognition

Shuxuan Guo

During the Artificial Intelligence (AI) revolution of the past decades, deep neural networks have been widely used and have achieved tremendous success in visual recognition. Unfortunately, deploying deep models is challenging because of their huge model s ...

EPFL2022

Visual Scene Understanding for Transportation: From Detecting Objects To Relationships

George Adaimi

Transportation, which deals with moving people and goods around, has a clear impact on the economic development of our society and our well-being. Traditionally, transportation was studied and analyzed using expensive sensors, such as induction loops, that ...

EPFL2022

Crowding and the importance of grouping and segmentation processes in human vision

Alban Bornet

Human vision has evolved to make sense of a world in which elements almost never appear in isolation. Surprisingly, the recognition of an element in a visual scene is strongly limited by the presence of other nearby elements, a phenomenon known as visual c ...

EPFL2021

Spatial and temporal integration of visual features

Oh-Hyeon Choung

Visual processing can be seen as the integration and segmentation of features. Objects are composed of contours, integrated into shapes and segmented from other contours. Information also needs to be integrated to solve the ill-posed problems of vision. Fo ...

EPFL2021

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

Frédéric Kaplan, Maud Ehrmann, Sofia Ares Oliveira, Raphaël Barman

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplyin ...

2021

The role of population receptive field size and recurrent processing in learning to “de-crowd”

Michael Herzog, Ayberk Ozkirli, Maya Anna Jastrzebowska

In crowding, the perception of a target is impeded by surrounding clutter. While traditional models are feedforward and local, there is increasing behavioral and neural evidence for a critical role of recurrent processing across the visual hierarchy in cro ...

SAGE Publishing2021

Unraveling brain interactions in vision: the example of crowding

Michael Herzog, Vitaly Chicherov, Maya Anna Jastrzebowska

Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a “bottleneck” at the earliest stages ...

2021

Human-Centered Scene Understanding via Crowd Counting

Weizhe Liu

Human-centered scene understanding is the process of perceiving and analysing a dynamic scene observed through a network of sensors with emphasis on human-related activities. It includes the visual perception of human-related activities from either single ...

EPFL2021

Capsule networks as recurrent models of grouping and segmentation

Michael Herzog, Adrien Christophe Doerig, Mauro Manassi

Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we pre ...

2020

Crowding and the Architecture of the Visual System

Adrien Christophe Doerig

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...

EPFL2020

Learning and leveraging shared domain semantics to counteract visual domain shifts

Róger Bermúdez Chacón

One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...

EPFL2020

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Mahsa Shoaran, Bingzhao Zhu

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Learning stereo reconstruction with deep neural networks

Stepan Tulyakov

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

EPFL2020

Painting by Numbers: Computational Methods and the History of Art

Leonardo Laurence Impett

This thesis presents a new methodology for computational art history, in the tradition of Distant Reading from literary criticism. This method is based on operationalisation, the transcription of a concept or theory from cultural history into an algorithm. ...

EPFL2020