105 research outputs found
Dynamical large deviations for a boundary driven stochastic lattice gas model with many conserved quantities
We prove the dynamical large deviations for a particle system in which
particles may have different velocities. We assume that we have two infinite
reservoirs of particles at the boundary: this is the so-called boundary driven
process. The dynamics we considered consists of a weakly asymmetric simple
exclusion process with collision among particles having different velocities
Evaluation of Explanation Methods of AI -- CNNs in Image Classification Tasks with Reference-based and No-reference Metrics
The most popular methods in AI-machine learning paradigm are mainly black
boxes. This is why explanation of AI decisions is of emergency. Although
dedicated explanation tools have been massively developed, the evaluation of
their quality remains an open research question. In this paper, we generalize
the methodologies of evaluation of post-hoc explainers of CNNs' decisions in
visual classification tasks with reference and no-reference based metrics. We
apply them on our previously developed explainers (FEM, MLFEM), and popular
Grad-CAM. The reference-based metrics are Pearson correlation coefficient and
Similarity computed between the explanation map and its ground truth
represented by a Gaze Fixation Density Map obtained with a psycho-visual
experiment. As a no-reference metric, we use stability metric, proposed by
Alvarez-Melis and Jaakkola. We study its behaviour, consensus with
reference-based metrics and show that in case of several kinds of degradation
on input images, this metric is in agreement with reference-based ones.
Therefore, it can be used for evaluation of the quality of explainers when the
ground truth is not available.Comment: Due to a bug found in the code, all tables and figures were redone.
The new results did not change the main conclusion, except for the best
explainer. FEM has performed better than MLFEM; 25 pages, 16 tables, 16
figures; Submitted to "Advances in Artificial Intelligence and Machine
Learning" (ISSN: 2582-9793
Real-Time Rough Extraction of Foreground Objects in MPEG1,2 Compressed Video
This paper describes a new approach to extract foreground objects in MPEG1,2 video streams, in the framework of “rough indexing paradigm”, that is starting from rough data obtained by only partially decoding the compressed stream. In this approach we use both P-frame motion information and I-frame colour information to identify and extract foreground objects. The particularity of our approach with regards to the state of the art methods consists in a robust estimation of camera motion and its use for localisation of real objects and filtering of parasite zones.
Secondly, a spatio-temporal filtering of roughly segmented objects at DC resolution is fulfilled using motion trajectory and gaussian-like shape characteristic function. This paradigm results in content description in real time, maintaining a good level of details
Three-stream 3D/1D CNN for fine-grained action classification and segmentation in table tennis
This paper proposes a fusion method of modalities extracted from videothrough a three-stream network with spatio-temporal and temporal convolutionsfor fine-grained action classification in sport. It is applied to TTStroke-21dataset which consists of untrimmed videos of table tennis games. The goal isto detect and classify table tennis strokes in the videos, the first step of abigger scheme aiming at giving feedback to the players for improving theirperformance. The three modalities are raw RGB data, the computed optical flowand the estimated pose of the player. The network consists of three brancheswith attention blocks. Features are fused at the latest stage of the networkusing bilinear layers. Compared to previous approaches, the use of threemodalities allows faster convergence and better performances on both tasks:classification of strokes with known temporal boundaries and joint segmentationand classification. The pose is also further investigated in order to offerricher feedback to the athletes.<br
Sports video: Fine-grained action detection and classification of table tennis strokes from videos for MediaEval 2021
This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CNN architecture and the training process of the model are tailored according to the addressed subtask. The method has the purpose of helping the participants to solve the task and is not meant to reach stateof-the-art performance. Still, for the detection task, the baseline is performing better than the other participants, which stresses the difficulty of such a task
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
3D convolutional networks is a good means to perform tasks such as video
segmentation into coherent spatio-temporal chunks and classification of them
with regard to a target taxonomy. In the chapter we are interested in the
classification of continuous video takes with repeatable actions, such as
strokes of table tennis. Filmed in a free marker less ecological environment,
these videos represent a challenge from both segmentation and classification
point of view. The 3D convnets are an efficient tool for solving these problems
with window-based approaches.Comment: Multi-faceted Deep Learning, 202
Clustering of scene repeats for essential rushes preview
This paper focuses on a specific type of unedited video content, called rushes, which are used for movie editing and usually present a high-level of redundancy. Our goal is to automatically extract a summarized preview, where redundant material is diminished without discarding any important event. To achieve this, rushes content has been first analysed and modeled. Then different clustering techniques on shot key-frames are presented and compared in order to choose the best representative segments to enter the preview. Experiments performed on TRECVID data are evaluated by computing the mutual information between the obtained results and a manually annotated ground-truth
Mumford dendrograms and discrete p-adic symmetries
In this article, we present an effective encoding of dendrograms by embedding
them into the Bruhat-Tits trees associated to -adic number fields. As an
application, we show how strings over a finite alphabet can be encoded in
cyclotomic extensions of and discuss -adic DNA encoding. The
application leads to fast -adic agglomerative hierarchic algorithms similar
to the ones recently used e.g. by A. Khrennikov and others. From the viewpoint
of -adic geometry, to encode a dendrogram in a -adic field means
to fix a set of -rational punctures on the -adic projective line
. To is associated in a natural way a
subtree inside the Bruhat-Tits tree which recovers , a method first used by
F. Kato in 1999 in the classification of discrete subgroups of
.
Next, we show how the -adic moduli space of
with punctures can be applied to the study of time series of
dendrograms and those symmetries arising from hyperbolic actions on
. In this way, we can associate to certain classes of dynamical
systems a Mumford curve, i.e. a -adic algebraic curve with totally
degenerate reduction modulo .
Finally, we indicate some of our results in the study of general discrete
actions on , and their relation to -adic Hurwitz spaces.Comment: 14 pages, 6 figure
A -adic RanSaC algorithm for stereo vision using Hensel lifting
A -adic variation of the Ran(dom) Sa(mple) C(onsensus) method for solving
the relative pose problem in stereo vision is developped. From two 2-adically
encoded images a random sample of five pairs of corresponding points is taken,
and the equations for the essential matrix are solved by lifting solutions
modulo 2 to the 2-adic integers. A recently devised -adic hierarchical
classification algorithm imitating the known LBG quantisation method classifies
the solutions for all the samples after having determined the number of
clusters using the known intra-inter validity of clusterings. In the successful
case, a cluster ranking will determine the cluster containing a 2-adic
approximation to the "true" solution of the problem.Comment: 15 pages; typos removed, abstract changed, computation error remove
Scaling limits of a tagged particle in the exclusion process with variable diffusion coefficient
We prove a law of large numbers and a central limit theorem for a tagged
particle in a symmetric simple exclusion process in the one-dimensional lattice
with variable diffusion coefficient. The scaling limits are obtained from a
similar result for the current through -1/2 for a zero-range process with bond
disorder. For the CLT, we prove convergence to a fractional Brownian motion of
Hurst exponent 1/4.Comment: 9 page
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