3,648 research outputs found
An evolutionary algorithm with double-level archives for multiobjective optimization
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problemlevel and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed
GTAutoAct: An Automatic Datasets Generation Framework Based on Game Engine Redevelopment for Action Recognition
Current datasets for action recognition tasks face limitations stemming from
traditional collection and generation methods, including the constrained range
of action classes, absence of multi-viewpoint recordings, limited diversity,
poor video quality, and labor-intensive manually collection. To address these
challenges, we introduce GTAutoAct, a innovative dataset generation framework
leveraging game engine technology to facilitate advancements in action
recognition. GTAutoAct excels in automatically creating large-scale,
well-annotated datasets with extensive action classes and superior video
quality. Our framework's distinctive contributions encompass: (1) it
innovatively transforms readily available coordinate-based 3D human motion into
rotation-orientated representation with enhanced suitability in multiple
viewpoints; (2) it employs dynamic segmentation and interpolation of rotation
sequences to create smooth and realistic animations of action; (3) it offers
extensively customizable animation scenes; (4) it implements an autonomous
video capture and processing pipeline, featuring a randomly navigating camera,
with auto-trimming and labeling functionalities. Experimental results
underscore the framework's robustness and highlights its potential to
significantly improve action recognition model training
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection
The relation modeling between actors and scene context advances video action
detection where the correlation of multiple actors makes their action
recognition challenging. Existing studies model each actor and scene relation
to improve action recognition. However, the scene variations and background
interference limit the effectiveness of this relation modeling. In this paper,
we propose to select actor-related scene context, rather than directly leverage
raw video scenario, to improve relation modeling. We develop a Cycle
Actor-Context Relation network (CycleACR) where there is a symmetric graph that
models the actor and context relations in a bidirectional form. Our CycleACR
consists of the Actor-to-Context Reorganization (A2C-R) that collects actor
features for context feature reorganizations, and the Context-to-Actor
Enhancement (C2A-E) that dynamically utilizes reorganized context features for
actor feature enhancement. Compared to existing designs that focus on C2A-E,
our CycleACR introduces A2C-R for a more effective relation modeling. This
modeling advances our CycleACR to achieve state-of-the-art performance on two
popular action detection datasets (i.e., AVA and UCF101-24). We also provide
ablation studies and visualizations as well to show how our cycle actor-context
relation modeling improves video action detection. Code is available at
https://github.com/MCG-NJU/CycleACR.Comment: technical repor
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