685 research outputs found
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers
Online Multi-Object Tracking (MOT) from videos is a challenging computer
vision task which has been extensively studied for decades. Most of the
existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm
combined with popular machine learning approaches which largely reduce the
human effort to tune algorithm parameters. However, the commonly used
supervised learning approaches require the labeled data (e.g., bounding boxes),
which is expensive for videos. Also, the TBD framework is usually suboptimal
since it is not end-to-end, i.e., it considers the task as detection and
tracking, but not jointly. To achieve both label-free and end-to-end learning
of MOT, we propose a Tracking-by-Animation framework, where a differentiable
neural model first tracks objects from input frames and then animates these
objects into reconstructed frames. Learning is then driven by the
reconstruction error through backpropagation. We further propose a
Reprioritized Attentive Tracking to improve the robustness of data association.
Experiments conducted on both synthetic and real video datasets show the
potential of the proposed model. Our project page is publicly available at:
https://github.com/zhen-he/tracking-by-animationComment: CVPR 201
Using an interactive whiteboard and a computer-programming tool to support the development of the key competencies in the New Zealand curriculum
Does childrenâs use of the software Scratch provide potential for the enhancement of key competencies as they work in pairs at the interactive whiteboard (IWB)? This article looks at how children using Scratch collaborated and managed their projects as they set about designing, constructing, testing and evaluating a game for others to play, a task that provided a sustained challenge over six weeks and beyond.
The findings showed that the key competencies of participating, contributing, and relating to others were enhanced by the collaborative use of Scratch at the IWB, and that creative and conceptual thinking processes were sustained. Children became increasingly adept at using Scratch, and some children, previously thought to have poor social skills, began to articulate their understandings to others. While a guiding and scaffolding role was evident in teachersâ actions, close monitoring of group progress and direct input from teachers is required to keep the challenge high but achievable, and to extend childrenâs knowledge and thinking as they use Scratch at the IWB
Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time
We present a video decomposition method that facilitates layer-based editing
of videos with spatiotemporally varying lighting and motion effects. Our neural
model decomposes an input video into multiple layered representations, each
comprising a 2D texture map, a mask for the original video, and a
multiplicative residual characterizing the spatiotemporal variations in
lighting conditions. A single edit on the texture maps can be propagated to the
corresponding locations in the entire video frames while preserving other
contents' consistencies. Our method efficiently learns the layer-based neural
representations of a 1080p video in 25s per frame via coordinate hashing and
allows real-time rendering of the edited result at 71 fps on a single GPU.
Qualitatively, we run our method on various videos to show its effectiveness in
generating high-quality editing effects. Quantitatively, we propose to adopt
feature-tracking evaluation metrics for objectively assessing the consistency
of video editing. Project page: https://lightbulb12294.github.io/hashing-nvd
Non-Parametric Probabilistic Image Segmentation
We propose a simple probabilistic generative model for
image segmentation. Like other probabilistic algorithms
(such as EM on a Mixture of Gaussians) the proposed model
is principled, provides both hard and probabilistic cluster
assignments, as well as the ability to naturally incorporate
prior knowledge. While previous probabilistic approaches
are restricted to parametric models of clusters (e.g., Gaussians)
we eliminate this limitation. The suggested approach
does not make heavy assumptions on the shape of the clusters
and can thus handle complex structures. Our experiments
show that the suggested approach outperforms previous
work on a variety of image segmentation tasks
Automated Video Game Testing Using Synthetic and Human-Like Agents
In this paper, we present a new methodology that employs tester agents to
automate video game testing. We introduce two types of agents -synthetic and
human-like- and two distinct approaches to create them. Our agents are derived
from Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) agents, but
focus on finding defects. The synthetic agent uses test goals generated from
game scenarios, and these goals are further modified to examine the effects of
unintended game transitions. The human-like agent uses test goals extracted by
our proposed multiple greedy-policy inverse reinforcement learning (MGP-IRL)
algorithm from tester trajectories. MGPIRL captures multiple policies executed
by human testers. These testers' aims are finding defects while interacting
with the game to break it, which is considerably different from game playing.
We present interaction states to model such interactions. We use our agents to
produce test sequences, run the game with these sequences, and check the game
for each run with an automated test oracle. We analyze the proposed method in
two parts: we compare the success of human-like and synthetic agents in bug
finding, and we evaluate the similarity between humanlike agents and human
testers. We collected 427 trajectories from human testers using the General
Video Game Artificial Intelligence (GVG-AI) framework and created three games
with 12 levels that contain 45 bugs. Our experiments reveal that human-like and
synthetic agents compete with human testers' bug finding performances.
Moreover, we show that MGP-IRL increases the human-likeness of agents while
improving the bug finding performance
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