7 research outputs found
Learning Discriminative Features with Multiple Granularities for Person Re-Identification
The combination of global and partial features has been an essential solution
to improve discriminative performances in person re-identification (Re-ID)
tasks. Previous part-based methods mainly focus on locating regions with
specific pre-defined semantics to learn local representations, which increases
learning difficulty but not efficient or robust to scenarios with large
variances. In this paper, we propose an end-to-end feature learning strategy
integrating discriminative information with various granularities. We carefully
design the Multiple Granularity Network (MGN), a multi-branch deep network
architecture consisting of one branch for global feature representations and
two branches for local feature representations. Instead of learning on semantic
regions, we uniformly partition the images into several stripes, and vary the
number of parts in different local branches to obtain local feature
representations with multiple granularities. Comprehensive experiments
implemented on the mainstream evaluation datasets including Market-1501,
DukeMTMC-reid and CUHK03 indicate that our method has robustly achieved
state-of-the-art performances and outperformed any existing approaches by a
large margin. For example, on Market-1501 dataset in single query mode, we
achieve a state-of-the-art result of Rank-1/mAP=96.6%/94.2% after re-ranking.Comment: 9 pages, 5 figures. To appear in ACM Multimedia 201
Accurate Temporal Action Proposal Generation with Relation-Aware Pyramid Network
Accurate temporal action proposals play an important role in detecting
actions from untrimmed videos. The existing approaches have difficulties in
capturing global contextual information and simultaneously localizing actions
with different durations. To this end, we propose a Relation-aware pyramid
Network (RapNet) to generate highly accurate temporal action proposals. In
RapNet, a novel relation-aware module is introduced to exploit bi-directional
long-range relations between local features for context distilling. This
embedded module enhances the RapNet in terms of its multi-granularity temporal
proposal generation ability, given predefined anchor boxes. We further
introduce a two-stage adjustment scheme to refine the proposal boundaries and
measure their confidence in containing an action with snippet-level actionness.
Extensive experiments on the challenging ActivityNet and THUMOS14 benchmarks
demonstrate our RapNet generates superior accurate proposals over the existing
state-of-the-art methods.Comment: accepted by AAAI-2
SoccerNet 2023 Challenges Results
peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding
challenges organized by the SoccerNet team. For this third edition, the
challenges were composed of seven vision-based tasks split into three main
themes. The first theme, broadcast video understanding, is composed of three
high-level tasks related to describing events occurring in the video
broadcasts: (1) action spotting, focusing on retrieving all timestamps related
to global actions in soccer, (2) ball action spotting, focusing on retrieving
all timestamps related to the soccer ball change of state, and (3) dense video
captioning, focusing on describing the broadcast with natural language and
anchored timestamps. The second theme, field understanding, relates to the
single task of (4) camera calibration, focusing on retrieving the intrinsic and
extrinsic camera parameters from images. The third and last theme, player
understanding, is composed of three low-level tasks related to extracting
information about the players: (5) re-identification, focusing on retrieving
the same players across multiple views, (6) multiple object tracking, focusing
on tracking players and the ball through unedited video streams, and (7) jersey
number recognition, focusing on recognizing the jersey number of players from
tracklets. Compared to the previous editions of the SoccerNet challenges, tasks
(2-3-7) are novel, including new annotations and data, task (4) was enhanced
with more data and annotations, and task (6) now focuses on end-to-end
approaches. More information on the tasks, challenges, and leaderboards are
available on https://www.soccer-net.org. Baselines and development kits can be
found on https://github.com/SoccerNet
SoccerNet 2022 Challenges Results
peer reviewedThe SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet .Applications et Recherche pour une Intelligence Artificielle de Confiance (ARIAC
SoccerNet 2022 Challenges Results
peer reviewedThe SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks: (1) action spotting, focusing on retrieving action timestamps in long untrimmed videos, (2) replay grounding, focusing on retrieving the live moment of an action shown in a replay, (3) pitch localization, focusing on detecting line and goal part elements, (4) camera calibration, dedicated to retrieving the intrinsic and extrinsic camera parameters, (5) player re-identification, focusing on retrieving the same players across multiple views, and (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams. Compared to last year's challenges, tasks (1-2) had their evaluation metrics redefined to consider tighter temporal accuracies, and tasks (3-6) were novel, including their underlying data and annotations. More information on the tasks, challenges and leaderboards are available on https://www.soccer-net.org. Baselines and development kits are available on https://github.com/SoccerNet .Applications et Recherche pour une Intelligence Artificielle de Confiance (ARIAC
SoccerNet 2023 challenges results
SoccerNet 2023 Challenges ResultsThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet