26 citations · 41 across the 20 of their papers we have counts for
22 papers
Overloading Large Vision-Language Models for Jailbreaking
Haoyu Zhang, Yangyang Guo, Mohan Kankanhalli
Large Vision-Language Models (LVLMs) exhibit remarkable vision-language capabilities and are increasingly deployed in real-world applications such as personal assistants, document…
Fair Deepfake Detectors Can Generalize
Harry Cheng, Ming-Hui Liu, Yangyang Guo +3
Deepfake detection models face two critical challenges: generalization to unseen manipulations and demographic fairness among population groups. However, existing approaches often…
The VLLM Safety Paradox: Dual Ease in Jailbreak Attack and Defense
Yangyang Guo, Fangkai Jiao, Liqiang Nie +1
The vulnerability of Vision Large Language Models (VLLMs) to jailbreak attacks appears as no surprise. However, recent defense mechanisms against these attacks have reached near-sa…
Social Debiasing for Fair Multi-modal LLMs
Harry Cheng, Yangyang Guo, Qingpei Guo +4
Multi-modal Large Language Models (MLLMs) have dramatically advanced the research field and delivered powerful vision-language understanding capabilities. However, these models oft…
Do Vision-Language Transformers Exhibit Visual Commonsense? An Empirical Study of VCR
Zhenyang Li, Yangyang Guo, Kejie Wang +3
Visual Commonsense Reasoning (VCR) calls for explanatory reasoning behind question answering over visual scenes. To achieve this goal, a model is required to provide an acceptable…
Diffusion Facial Forgery Detection
Harry Cheng, Yangyang Guo, Tianyi Wang +2
Detecting diffusion-generated images has recently grown into an emerging research area. Existing diffusion-based datasets predominantly focus on general image generation. However,…
Towards Generalizable Deepfake Detection by Primary Region Regularization
Harry Cheng, Yangyang Guo, Tianyi Wang +2
The existing deepfake detection methods have reached a bottleneck in generalizing to unseen forgeries and manipulation approaches. Based on the observation that the deepfake detect…
Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
Harry Cheng, Yangyang Guo, Liqiang Nie +2
Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets. Current methods primarily aim to eithe…
Learning to Agree on Vision Attention for Visual Commonsense Reasoning
Zhenyang Li, Yangyang Guo, Kejie Wang +3
Visual Commonsense Reasoning (VCR) remains a significant yet challenging research problem in the realm of visual reasoning. A VCR model generally aims at answering a textual questi…
Chairs Can be Stood on: Overcoming Object Bias in Human-Object Interaction Detection
Guangzhi Wang, Yangyang Guo, Yongkang Wong +1
Detecting Human-Object Interaction (HOI) in images is an important step towards high-level visual comprehension. Existing work often shed light on improving either human and object…
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
Yangyang Guo, Liqiang Nie, Yongkang Wong +3
Knowledge-based Visual Question Answering (VQA) expects models to rely on external knowledge for robust answer prediction. Though significant it is, this paper discovers several le…
Voice-Face Homogeneity Tells Deepfake
Harry Cheng, Yangyang Guo, Tianyi Wang +3
Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit w…
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning
Fangkai Jiao, Yangyang Guo, Xuemeng Song +1
Logical reasoning is of vital importance to natural language understanding. Previous studies either employ graph-based models to incorporate prior knowledge about logical relations…
On Modality Bias Recognition and Reduction
Yangyang Guo, Liqiang Nie, Harry Cheng +3
Making each modality in multi-modal data contribute is of vital importance to learning a versatile multi-modal model. Existing methods, however, are often dominated by one or few o…
Joint Answering and Explanation for Visual Commonsense Reasoning
Zhenyang Li, Yangyang Guo, Kejie Wang +3
Visual Commonsense Reasoning (VCR), deemed as one challenging extension of the Visual Question Answering (VQA), endeavors to pursue a more high-level visual comprehension. It is co…
When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
Xiangkun Yin, Yangyang Guo, Liqiang Nie +1
Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product…
Review Polarity-wise Recommender
Han Liu, Yangyang Guo, Jianhua Yin +2
Utilizing review information to enhance recommendation, the de facto review-involved recommender systems, have received increasing interests over the past few years. Thereinto, one…
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Fangkai Jiao, Yangyang Guo, Yilin Niu +3
Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years. Although the general language representation learned…
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Yangyang Guo, Liqiang Nie, Zhiyong Cheng +3
A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions ba…
Feature-level Attentive ICF for Recommendation
Zhiyong Cheng, Fan Liu, Shenghan Mei +3
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender sys…
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
Yangyang Guo, Liqiang Nie, Zhiyong Cheng +2
Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem, which refers to making predictio…
Enhancing Factorization Machines with Generalized Metric Learning
Yangyang Guo, Zhiyong Cheng, Jiazheng Jing +3
Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the…