Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Shizhong Han, Yan Tong
A novel Identity-Free conditional Generative Adversarial Network (IF-GAN) was proposed for Facial Expression Recognition (FER) to explicitly reduce high inter-subject variations caused by identity-related facial attributes, e.g., age, race, and gender. As part of an end-to-end system, a cGAN was designed to transform a given input facial expression image to an "average" identity face with the same expression as the input. Then, identity-free FER is possible since the generated images have the same synthetic "average" identity and differ only in their displayed expressions. Experiments on four facial expression datasets, one with spontaneous expressions, show that IF-GAN outperforms the baseline CNN and achieves state-of-the-art performance for FER.
Jie Cai, Sarah J Ryu, Hyejin Hannah Kum-Biocca, Donghee Yvette Wohn
While much work focuses on the impacts of the pandemic on people's psychological and physical health, it is still unclear about the practical changes and adaptations. In this work, we interviewed 46 participants who were forced to work from home. Results show that there is an increased reliance on asynchronous communication, which slowed communication efficiency and decreased initiative to communicate. The home environment causes distraction from households and lacked facilities but is embraced by a group of people. Many people had to passively adapt to the communication and environmental changes and accept the limitations of technology, a situation that is not sustainable in the long run. We pointed out how technology can potentially play a larger role in supporting communication and coping with environmental changes in the future.
Jie Cai, Zibo Meng, Jiaming Ding, Chiu Man Ho
Image Super-Resolution (ISR), which aims at recovering High-Resolution (HR) images from the corresponding Low-Resolution (LR) counterparts. Although recent progress in ISR has been remarkable. However, they are way too computationally intensive to be deployed on edge devices, since most of the recent approaches are deep learning-based. Besides, these methods always fail in real-world scenes, since most of them adopt a simple fixed "ideal" bicubic downsampling kernel from high-quality images to construct LR/HR training pairs which may lose track of frequency-related details. In this work, an approach for real-time ISR on mobile devices is presented, which is able to deal with a wide range of degradations in real-world scenarios. Extensive experiments on traditional super-resolution datasets (Set5, Set14, BSD100, Urban100, Manga109, DIV2K) and real-world images with a variety of degradations demonstrate that our method outperforms the state-of-art methods, resulting in higher PSNR and SSIM, lower noise and better visual quality. Most importantly, our method achieves real-time performance on mobile or edge devices.
Yao Lyu, He Zhang, Shuo Niu, Jie Cai
Content creators increasingly utilize generative artificial intelligence (Gen-AI) on platforms such as YouTube, TikTok, Instagram, and various blogging sites to produce imaginative images, AI-generated videos, and articles using Large Language Models (LLMs). Despite its growing popularity, there remains an underexplored area concerning the specific domains where AI-generated content is being applied, and the methodologies content creators employ with Gen-AI tools during the creation process. This study initially explores this emerging area through a qualitative analysis of 68 YouTube videos demonstrating Gen-AI usage. Our research focuses on identifying the content domains, the variety of tools used, the activities performed, and the nature of the final products generated by Gen-AI in the context of user-generated content.
Jie Cai, Kangning Yang, Jiaming Ding, Lan Fu, Ling Ouyang, Jiang Li, Jinglin Shen, Zibo Meng
Image degradation is a prevalent issue in various real-world applications, affecting visual quality and downstream processing tasks. In this study, we propose a novel framework that employs a Vision-Language Model (VLM) to automatically classify degraded images into predefined categories. The VLM categorizes an input image into one of four degradation types: (A) super-resolution degradation (including noise, blur, and JPEG compression), (B) reflection artifacts, (C) motion blur, or (D) no visible degradation (high-quality image). Once classified, images assigned to categories A, B, or C undergo targeted restoration using dedicated models tailored for each specific degradation type. The final output is a restored image with improved visual quality. Experimental results demonstrate the effectiveness of our approach in accurately classifying image degradations and enhancing image quality through specialized restoration models. Our method presents a scalable and automated solution for real-world image enhancement tasks, leveraging the capabilities of VLMs in conjunction with state-of-the-art restoration techniques.
Na Li, Chuhao Wu, Hongyang Zhou, Huiran Yi, Xuefei Wang, Jie Cai, Xinyi Fu, John Carroll
With growing awareness of long-term health and wellness, everyday body management has become a widespread practice. Social media platforms and health-related applications offer abundant information for those pursuing healthier lifestyles and more positive body images. While prior Human-Computer Interaction research has focused extensively on technology-mediated health interventions, the user-initiated practices of browsing and evaluating body management information remain underexplored. In this paper, we study a female-dominant social media platform in China to examine how users seek such information and how it shapes their lifestyle choices. Through semi-structured interviews with 18 users, we identify factors including consumerism, poster popularity, and perceived authenticity that influence decision-making, alongside challenges such as discerning reliable methods and managing body anxiety triggered by social media. We contribute insights into how content and media formats interact to shape users' information evaluation, and we outline design implications for supporting more reliable and healthy engagements with body management information.
Jie Cai, Zhengzhou Zhu, Ping Nie, Qian Liu
Pre-trained models have brought significant improvements to many NLP tasks and have been extensively analyzed. But little is known about the effect of fine-tuning on specific tasks. Intuitively, people may agree that a pre-trained model already learns semantic representations of words (e.g. synonyms are closer to each other) and fine-tuning further improves its capabilities which require more complicated reasoning (e.g. coreference resolution, entity boundary detection, etc). However, how to verify these arguments analytically and quantitatively is a challenging task and there are few works focus on this topic. In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e.g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task. Specifically, we identify five phenomena in MRC. According to pairwise probing tasks, we compare the performance of each layer's hidden representation of pre-trained and fine-tuned BERT. The proposed pairwise probe alleviates the problem of distraction from inaccurate model training and makes a robust and quantitative comparison. Our experimental analysis leads to highly confident conclusions: (1) Fine-tuning has little effect on the fundamental and low-level information and general semantic tasks. (2) For specific abilities required for downstream tasks, fine-tuned BERT is better than pre-trained BERT and such gaps are obvious after the fifth layer.
Jie Cai, Aashka Patel, Azadeh Naderi, Donghee Yvette Wohn
Social media users may perceive moderation decisions by the platform differently, which can lead to frustration and dropout. This study investigates users' perceived justice and fairness of online moderation decisions when they are exposed to various illegal versus legal scenarios, retributive versus restorative moderation strategies, and user-moderated versus commercially moderated platforms. We conduct an online experiment on 200 American social media users of Reddit and Twitter. Results show that retributive moderation delivers higher justice and fairness for commercially moderated than for user-moderated platforms in illegal violations; restorative moderation delivers higher fairness for legal violations than illegal ones. We discuss the opportunities for platform policymaking to improve moderation system design.
Jie Cai, Kangning Yang, Ling Ouyang, Lan Fu, Jiaming Ding, Huiming Sun, Chiu Man Ho, Zibo Meng
Single Image Reflection Removal (SIRR) technique plays a crucial role in image processing by eliminating unwanted reflections from the background. These reflections, often caused by photographs taken through glass surfaces, can significantly degrade image quality. SIRR remains a challenging problem due to the complex and varied reflections encountered in real-world scenarios. These reflections vary significantly in intensity, shapes, light sources, sizes, and coverage areas across the image, posing challenges for most existing methods to effectively handle all cases. To address these challenges, this paper introduces a U-shaped Fast Fourier Transform Transformer and Hierarchical Transformer (F2T2-HiT) architecture, an innovative Transformer-based design for SIRR. Our approach uniquely combines Fast Fourier Transform (FFT) Transformer blocks and Hierarchical Transformer blocks within a UNet framework. The FFT Transformer blocks leverage the global frequency domain information to effectively capture and separate reflection patterns, while the Hierarchical Transformer blocks utilize multi-scale feature extraction to handle reflections of varying sizes and complexities. Extensive experiments conducted on three publicly available testing datasets demonstrate state-of-the-art performance, validating the effectiveness of our approach.
Ya-Fang Lin, Xiaotian Li, Wan-Hsuan Huang, Charan Pushpanathan Prabavathi, Jie Cai, John M. Carroll
Couples often experience a decrease in closeness as they cope with the demands of parenthood. Existing technologies have supported parenting and parental collaboration. However, these technologies do not adequately support closeness in co-parenting. We use scenarios and design probes to brainstorm with 10 new parent couples to explore and envision possibilities for technologies to support closeness. We reported parents' current technology use for co-parenting and how participants considered and envisioned co-parenting technology for closeness, including information and task sharing, emotion awareness and disclosure, and fostering fun interaction. We discuss the potential technology has for fostering closeness in co-parenting by (1) fostering interdependence by supporting parental competence and (2) integrating positive emotions and experiences, such as validation and fun, in parenting. Based on our findings, we expand the design space of technology for closeness to include interdependence. We also expand the design space for co-parenting technology by integrating more positive emotions.
Jie Cai, Ya-Fang Lin, He Zhang, John M. Carroll
Community management is critical for stakeholders to collaboratively build and sustain communities with socio-technical support. However, most of the existing research has mainly focused on the community members and the platform, with little attention given to the developers who act as intermediaries between the platform and community members and develop tools to support community management. This study focuses on third-party developers (TPDs) for the live streaming platform Twitch and explores their tool development practices. Using a mixed method with in-depth qualitative analysis, we found that TPDs maintain complex relationships with different stakeholders (streamers, viewers, platform, professional developers), and the multi-layered policy restricts their agency regarding idea innovation and tool development. We argue that HCI research should shift its focus from tool users to tool developers with regard to community management. We propose designs to support closer collaboration between TPDS and the platform and professional developers and streamline TPDs' development process with unified toolkits and policy documentation.
Jie Cai, Yinren Shou, Yixing Geng, Liqi Han, Xinlu Xu, Shuangchung Wen, Baifei Shen, Jinqing Yu, Xueqing Yan
The production of broadband, terawatt terahertz (THz) pulses has been demonstrated by irradiating relativistic lasers on solid targets. However, the generation of extremely powerful, narrow-band, and frequency-tunable THz pulses remains a challenge. Here, we present a novel approach for such THz pulses, in which a plasma wiggler is elaborated by a table-top laser and a near-critical density plasma. In such a wiggler, the laser-accelerated electrons emit THz radiations with a period closely related to the plasma thickness. Theoretical model and numerical simulations predict a THz pulse with a laser-THz energy conversion over 2.0$\%$, an ultra-strong field exceeding 80 GV/m, a divergence angle approximately 20$^\circ$, and a center-frequency tunable from 4.4 to 1.5 THz, can be generated from a laser of 430 mJ. Furthermore, we demonstrate that this method can work across a wide range of laser and plasma parameters, offering potential for future applications with extremely powerful THz pulse.
Jie Cai, Sagnik Chowdhury, Hongyang Zhou, Donghee Yvette Wohn
Online harassment and content moderation have been well-documented in online communities. However, new contexts and systems always bring new ways of harassment and need new moderation mechanisms. This study focuses on hate raids, a form of group attack in real-time in live streaming communities. Through a qualitative analysis of hate raids discussion in the Twitch subreddit (r/Twitch), we found that (1) hate raids as a human-bot coordinated group attack leverages the live stream system to attack marginalized streamers and other potential groups with(out) breaking the rules, (2) marginalized streamers suffer compound harms with insufficient support from the platform, (3) moderation strategies are overwhelmingly technical, but streamers still struggle to balance moderation and participation considering their marginalization status and needs. We use affordances as a lens to explain how hate raids happens in live streaming systems and propose moderation-by-design as a lens when developing new features or systems to mitigate the potential abuse of such designs.
Jie Cai, Donghee Yvette Wohn
Volunteer moderators (mods) play significant roles in developing moderation standards and dealing with harmful content in their micro-communities. However, little work explores how volunteer mods work as a team. In line with prior work about understanding volunteer moderation, we interview 40 volunteer mods on Twitch - a leading live streaming platform. We identify how mods collaborate on tasks (off-streaming coordination and preparation, in-stream real-time collaboration, and relationship building both off-stream and in-stream to reinforce collaboration) and how mods contribute to moderation standards (collaboratively working on the community rulebook and individually shaping community norms). We uncover how volunteer mods work as an effective team. We also discuss how the affordances of multi-modal communication and informality of volunteer moderation contribute to task collaboration, standards development, and mod's roles and responsibilities.
Christine L. Cook, Jie Cai, Donghee Yvette Wohn
When people have the freedom to create and post content on the internet, particularly anonymously, they do not always respect the rules and regulations of the websites on which they post, leaving other unsuspecting users vulnerable to sexism, racism, threats, and other unacceptable content in their daily cyberspace diet. However, content moderators witness the worst of humanity on a daily basis in place of the average netizen. This takes its toll on moderators, causing stress, fatigue, and emotional distress akin to the symptomology of post-traumatic stress disorder (PTSD). The goal of the present study was to explore whether adding positive stimuli to breaktimes-images of baby animals or beautiful, aweinspiring landscapes-could help reduce the negative side-effects of being a content moderator. To test this, we had over 300 experienced content moderators read and decide whether 200 fake text-based social media posts were acceptable or not for public consumption. Although we set out to test positive emotional stimulation, however, we actually found that it is the cumulative nature of the negative emotions that likely negates most of the effects of the intervention: the longer the person had practiced content moderation, the stronger their negative experience. Connections to compassion fatigue and how best to spend work breaks as a content moderator are discussed.
Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, Shizhong Han, Ping Liu, Min Chen, Yan Tong
Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in "close-to-real-world" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the audio channel. Two kinds of fusion methods, i,e., feature-level fusion and model-level fusion, were developed to utilize the information extracted from the two channels. Experimental results on the EmotiW2018 AFEW dataset have shown that the proposed fusion methods outperform the baseline methods significantly and achieve better or at least comparable performance compared with the state-of-the-art methods, where the model-level fusion performs better when one of the channels totally fails.
Jie Cai, Kangning Yang, Ling Ouyang, Lan Fu, Jiaming Ding, Jinglin Shen, Zibo Meng
Removing reflections is a crucial task in computer vision, with significant applications in photography and image enhancement. Nevertheless, existing methods are constrained by the absence of large-scale, high-quality, and diverse datasets. In this paper, we present a novel benchmark for Single Image Reflection Removal (SIRR). We have developed a large-scale dataset containing 5,300 high-quality, pixel-aligned image pairs, each consisting of a reflection image and its corresponding clean version. Specifically, the dataset is divided into two parts: 5,000 images are used for training, and 300 images are used for validation. Additionally, we have included 100 real-world testing images without ground truth (GT) to further evaluate the practical performance of reflection removal methods. All image pairs are precisely aligned at the pixel level to guarantee accurate supervision. The dataset encompasses a broad spectrum of real-world scenarios, featuring various lighting conditions, object types, and reflection patterns, and is segmented into training, validation, and test sets to facilitate thorough evaluation. To validate the usefulness of our dataset, we train a U-Net-based model and evaluate it using five widely-used metrics, including PSNR, SSIM, LPIPS, DISTS, and NIQE. We will release both the dataset and the code on https://github.com/caijie0620/OpenRR-5k to facilitate future research in this field.
Kai Qin, Kexin Du, Yimeng Chen, Yueyan Liu, Jie Cai, Zhiqiang Nie, Nan Gao, Guohui Wei, Shengzhu Wang, Chun Yu
The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memorizing) but also introduces new burdens (e.g., earning, compliance, psychological burdens). This research contributes to a more nuanced understanding of AI integration in organizational settings and highlights the efforts and burdens undertaken by CSRs to adapt to the updated system.
Jie Cai, Kangning Yang, Lan Fu, Jiaming Ding, Jinlong Li, Huiming Sun, Daitao Xing, Jinglin Shen, Zibo Meng
We introduce CompareBench, a benchmark for evaluating visual comparison reasoning in vision-language models (VLMs), a fundamental yet understudied skill. CompareBench consists of 1000 QA pairs across four tasks: quantity (600), temporal (100), geometric (200), and spatial (100). It is derived from two auxiliary datasets that we constructed: TallyBench (2000 counting images with QA) and HistCaps (515 historical images with bilingual captions). We evaluate both closed-source APIs (OpenAI, Gemini, Claude) and open-source models (Qwen2.5-VL and Qwen3-VL series). Results show clear scaling trends but also reveal critical limitations: even the strongest models consistently fail at temporal ordering and spatial relations, and they often make mistakes in basic counting and geometric comparisons that are trivial for humans. These findings demonstrate that visual comparison remains a systematic blind spot for current VLMs. By providing controlled, diverse, and diagnostic evaluation, CompareBench establishes a foundation for advancing more reliable multimodal reasoning.
Jie Cai, He Zhang, Yueyan Liu, John M. Carroll, Chun Yu
Third-party developers (TPDs) often turn to online communities for support when they can't get immediate responses from the platform. Twitch, as a leading live streaming platform, attracted many TPDs and formed an online support community on Discord. This study explores TPDs' support practices via mixed method (a topic modeling to identify topics related to support seeking and provision first and a follow-up in-depth qualitative analysis with these topics) and found that: (1) TPDs' support-seeking practices around social, technical, and policy matters are highly dependent on Twitch, and this dependence acts as a form of platform labor; (2) TPDs need to switch between Discord and Twitch regarding seeking and provision, exacerbating TPDs' platform labor; (3) TPDs' flexible role practices reflect the community's flourishing on Discord but require roles to bridge the two platforms and transfer informal support seeking to possible formal support from Twitch. We propose implications for effectively managing support seeking and provision between formal and informal spaces to improve the development of TPDs. We also contribute to community support practice and to platform ecology work in CSCW.