6,813 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering
Earth vision research typically focuses on extracting geospatial object
locations and categories but neglects the exploration of relations between
objects and comprehensive reasoning. Based on city planning needs, we develop a
multi-modal multi-task VQA dataset (EarthVQA) to advance relational
reasoning-based judging, counting, and comprehensive analysis. The EarthVQA
dataset contains 6000 images, corresponding semantic masks, and 208,593 QA
pairs with urban and rural governance requirements embedded. As objects are the
basis for complex relational reasoning, we propose a Semantic OBject Awareness
framework (SOBA) to advance VQA in an object-centric way. To preserve refined
spatial locations and semantics, SOBA leverages a segmentation network for
object semantics generation. The object-guided attention aggregates object
interior features via pseudo masks, and bidirectional cross-attention further
models object external relations hierarchically. To optimize object counting,
we propose a numerical difference loss that dynamically adds difference
penalties, unifying the classification and regression tasks. Experimental
results show that SOBA outperforms both advanced general and remote sensing
methods. We believe this dataset and framework provide a strong benchmark for
Earth vision's complex analysis. The project page is at
https://Junjue-Wang.github.io/homepage/EarthVQA.Comment: Accepted By AAAI 202
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
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