1,813 research outputs found
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
CHATEDIT: Towards Multi-turn Interactive Facial Image Editing via Dialogue
This paper explores interactive facial image editing via dialogue and
introduces the ChatEdit benchmark dataset for evaluating image editing and
conversation abilities in this context. ChatEdit is constructed from the
CelebA-HQ dataset, incorporating annotated multi-turn dialogues corresponding
to user edit requests on the images. The dataset is challenging, as it requires
the system to dynamically track user requests, edit images, and generate
appropriate responses. Accordingly, we propose three benchmark tasks: (i) user
edit request tracking, (ii) image editing, and (iii) response generation. We
present a novel baseline framework that integrates a dialogue module for both
tracking user requests and generating responses and an image editing module for
image editing. Unlike previous approaches, our framework directly tracks user
edit requests from the entire dialogue history up to the current turn and
modifies the original image rather than adjusting the previous turn's output,
thereby reducing error accumulation and preventing attribute forgetfulness.
Extensive experiments on the ChatEdit dataset underline our framework's
superior performance against prior models, while also highlighting potential
room for further research. We will release the code and data publicly to
facilitate advancements in complex interactive facial image editing.Comment: Accepted to EMNLP 2023 (Main Conference
Language-Based Image Editing with Recurrent Attentive Models
We investigate the problem of Language-Based Image Editing (LBIE). Given a
source image and a natural language description, we want to generate a target
image by editing the source image based on the description. We propose a
generic modeling framework for two sub-tasks of LBIE: language-based image
segmentation and image colorization. The framework uses recurrent attentive
models to fuse image and language features. Instead of using a fixed step size,
we introduce for each region of the image a termination gate to dynamically
determine after each inference step whether to continue extrapolating
additional information from the textual description. The effectiveness of the
framework is validated on three datasets. First, we introduce a synthetic
dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE
system. Second, we show that the framework leads to state-of-the-art
performance on image segmentation on the ReferIt dataset. Third, we present the
first language-based colorization result on the Oxford-102 Flowers dataset.Comment: Accepted to CVPR 2018 as a Spotligh
An HCI-Centric Survey and Taxonomy of Human-Generative-AI Interactions
Generative AI (GenAI) has shown remarkable capabilities in generating diverse
and realistic content across different formats like images, videos, and text.
In Generative AI, human involvement is essential, thus HCI literature has
investigated how to effectively create collaborations between humans and GenAI
systems. However, the current literature lacks a comprehensive framework to
better understand Human-GenAI Interactions, as the holistic aspects of
human-centered GenAI systems are rarely analyzed systematically. In this paper,
we present a survey of 291 papers, providing a novel taxonomy and analysis of
Human-GenAI Interactions from both human and Gen-AI perspectives. The
dimensions of design space include 1) Purposes of Using Generative AI, 2)
Feedback from Models to Users, 3) Control from Users to Models, 4) Levels of
Engagement, 5) Application Domains, and 6) Evaluation Strategies. Our work is
also timely at the current development stage of GenAI, where the Human-GenAI
interaction design is of paramount importance. We also highlight challenges and
opportunities to guide the design of Gen-AI systems and interactions towards
the future design of human-centered Generative AI applications
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