3,564 research outputs found
Learning to Detect and Segment for Open Vocabulary Object Detection
Open vocabulary object detection has been greatly advanced by the recent
development of vision-language pretrained model, which helps recognize novel
objects with only semantic categories. The prior works mainly focus on
knowledge transferring to the object proposal classification and employ
class-agnostic box and mask prediction. In this work, we propose CondHead, a
principled dynamic network design to better generalize the box regression and
mask segmentation for open vocabulary setting. The core idea is to
conditionally parameterize the network heads on semantic embedding and thus the
model is guided with class-specific knowledge to better detect novel
categories. Specifically, CondHead is composed of two streams of network heads,
the dynamically aggregated head and the dynamically generated head. The former
is instantiated with a set of static heads that are conditionally aggregated,
these heads are optimized as experts and are expected to learn sophisticated
prediction. The latter is instantiated with dynamically generated parameters
and encodes general class-specific information. With such a conditional design,
the detection model is bridged by the semantic embedding to offer strongly
generalizable class-wise box and mask prediction. Our method brings significant
improvement to the state-of-the-art open vocabulary object detection methods
with very minor overhead, e.g., it surpasses a RegionClip model by 3.0
detection AP on novel categories, with only 1.1% more computation.Comment: Accepted to CVPR2023, code will be available late
CONA: A novel CONtext-Aware instruction paradigm for communication using large language model
We introduce CONA, a novel context-aware instruction paradigm for effective
knowledge dissemination using generative pre-trained transformer (GPT) models.
CONA is a flexible framework designed to leverage the capabilities of Large
Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge,
Wisdom) hierarchy to automatically instruct and optimise presentation content,
anticipate potential audience inquiries, and provide context-aware answers that
adaptive to the knowledge level of the audience group. The unique aspect of the
CONA paradigm lies in its combination of an independent advisory mechanism and
a recursive feedback loop rooted on the DIKW hierarchy. This synergy
significantly enhances context-aware contents, ensuring they are accessible and
easily comprehended by the audience. This paradigm is an early pioneer to
explore new methods for knowledge dissemination and communication in the LLM
era, offering effective support for everyday knowledge sharing scenarios. We
conduct experiments on a range of audience roles, along with materials from
various disciplines using GPT4. Both quantitative and qualitative results
demonstrated that the proposed CONA paradigm achieved remarkable performance
compared to the outputs guided by conventional prompt engineering
Passive Eavesdropping Can Significantly Slow Down RIS-Assisted Secret Key Generation
Reconfigurable Intelligent Surface (RIS) assisted physical layer key
generation has shown great potential to secure wireless communications by
smartly controlling signals such as phase and amplitude. However, previous
studies mainly focus on RIS adjustment under ideal conditions, while the
correlation between the eavesdropping channel and the legitimate channel, a
more practical setting in the real world, is still largely under-explored for
the key generation. To fill this gap, this paper aims to maximize the
RIS-assisted physical-layer secret key generation by optimizing the RIS units
switching under the eavesdropping channel. Firstly, we theoretically show that
passive eavesdropping significantly reduces RIS-assisted secret key generation.
Keeping this in mind, we then introduce a mathematical formulation to maximize
the key generation rate and provide a step-by-step analysis. Extensive
experiments show the effectiveness of our method in benefiting the secret key
capacity under the eavesdropping channel. We also observe that the key
randomness, and unmatched key rate, two metrics that measure the secret key
quality, are also significantly improved, potentially paving the way to
RIS-assisted key generation in real-world scenarios.Comment: Accepted by Globecom 202
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