4,659 research outputs found
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
Going Deeper into Action Recognition: A Survey
Understanding human actions in visual data is tied to advances in
complementary research areas including object recognition, human dynamics,
domain adaptation and semantic segmentation. Over the last decade, human action
analysis evolved from earlier schemes that are often limited to controlled
environments to nowadays advanced solutions that can learn from millions of
videos and apply to almost all daily activities. Given the broad range of
applications from video surveillance to human-computer interaction, scientific
milestones in action recognition are achieved more rapidly, eventually leading
to the demise of what used to be good in a short time. This motivated us to
provide a comprehensive review of the notable steps taken towards recognizing
human actions. To this end, we start our discussion with the pioneering methods
that use handcrafted representations, and then, navigate into the realm of deep
learning based approaches. We aim to remain objective throughout this survey,
touching upon encouraging improvements as well as inevitable fallbacks, in the
hope of raising fresh questions and motivating new research directions for the
reader
RADAR: Robust AI-Text Detection via Adversarial Learning
Recent advances in large language models (LLMs) and the intensifying
popularity of ChatGPT-like applications have blurred the boundary of
high-quality text generation between humans and machines. However, in addition
to the anticipated revolutionary changes to our technology and society, the
difficulty of distinguishing LLM-generated texts (AI-text) from human-generated
texts poses new challenges of misuse and fairness, such as fake content
generation, plagiarism, and false accusation of innocent writers. While
existing works show that current AI-text detectors are not robust to LLM-based
paraphrasing, this paper aims to bridge this gap by proposing a new framework
called RADAR, which jointly trains a Robust AI-text Detector via Adversarial
leaRning. RADAR is based on adversarial training of a paraphraser and a
detector. The paraphraser's goal is to generate realistic contents to evade
AI-text detection. RADAR uses the feedback from the detector to update the
paraphraser, and vice versa. Evaluated with 8 different LLMs (Pythia, Dolly
2.0, Palmyra, Camel, GPT-J, Dolly 1.0, LLaMA, and Vicuna) across 4 datasets,
experimental results show that RADAR significantly outperforms existing AI-text
detection methods, especially when paraphrasing is in place. We also identify
the strong transferability of RADAR from instruction-tuned LLMs to other LLMs,
and evaluate the improved capability of RADAR via GPT-3.5.Comment: Preprint. Project page and demos: https://radar.vizhub.a
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