26 citations · 41 across the 20 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…
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…