5,212 research outputs found
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
A robust motion estimation and segmentation approach to represent moving images with layers
The paper provides a robust representation of moving images based on layers. To that goal, we have designed efficient motion estimation and segmentation techniques by affine model fitting suitable for the construction of layers. Layered representations, originally introduced by Wang and Adelson (see IEEE Transactions on Image Processing, vol.3, no.5, p.625-38, 1994) are important in several applications. In particular they are very appropriate for object tracking, object manipulation and content-based scalability which are among the main functionalities of the future MPEG-4 standard. In addition a variety of examples are provided that give a deep insight into the performance bounds of the representation of moving images using layers.Peer ReviewedPostprint (published version
Image-based rendering and synthesis
Multiview imaging (MVI) is currently the focus of some research as it has a wide range of applications and opens up research in other topics and applications, including virtual view synthesis for three-dimensional (3D) television (3DTV) and entertainment. However, a large amount of storage is needed by multiview systems and are difficult to construct. The concept behind allowing 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction is image-based rendering (IBR). Using images as the primary substrate, IBR has many potential applications including for video games, virtual travel and others. The technique creates new views of scenes which are reconstructed from a collection of densely sampled images or videos. The IBR concept has different classification such as knowing 3D models and the lighting conditions and be rendered using conventional graphic techniques. Another is lightfield or lumigraph rendering which depends on dense sampling with no or very little geometry for rendering without recovering the exact 3D-models.published_or_final_versio
3D coding tools final report
Livrable D4.3 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.3 du projet. Son titre : 3D coding tools final repor
Visual pathways from the perspective of cost functions and multi-task deep neural networks
Vision research has been shaped by the seminal insight that we can understand
the higher-tier visual cortex from the perspective of multiple functional
pathways with different goals. In this paper, we try to give a computational
account of the functional organization of this system by reasoning from the
perspective of multi-task deep neural networks. Machine learning has shown that
tasks become easier to solve when they are decomposed into subtasks with their
own cost function. We hypothesize that the visual system optimizes multiple
cost functions of unrelated tasks and this causes the emergence of a ventral
pathway dedicated to vision for perception, and a dorsal pathway dedicated to
vision for action. To evaluate the functional organization in multi-task deep
neural networks, we propose a method that measures the contribution of a unit
towards each task, applying it to two networks that have been trained on either
two related or two unrelated tasks, using an identical stimulus set. Results
show that the network trained on the unrelated tasks shows a decreasing degree
of feature representation sharing towards higher-tier layers while the network
trained on related tasks uniformly shows high degree of sharing. We conjecture
that the method we propose can be used to analyze the anatomical and functional
organization of the visual system and beyond. We predict that the degree to
which tasks are related is a good descriptor of the degree to which they share
downstream cortical-units.Comment: 16 pages, 5 figure
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