3 citations · 3 across the 4 of their papers we have counts for
5 papers
ForensicZoom: Adaptive Visual Inspection with Multimodal LLMs for Industrial-Grade Face Forgery Detection
Hang Zhou, Yiming Tang, Kun Yu +3
Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excessive false positives disrupt…
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
Zeyang Zhang, Xin Wang, Ziwei Zhang +5
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution se…
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
Kun He, Xin Liu, Yichen Yang +4
Adversarial training has been demonstrated to be the most effective approach to defend against adversarial attacks. However, existing adversarial training methods show apparent osc…
Compare learning: bi-attention network for few-shot learning
Li Ke, Meng Pan, Weigao Wen +1
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only. One of the Few-shot learning methods cal…
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Runshi Liu, Pengda Qin, Yuhong Li +4
E-commerce companies have to face abnormal sellers who sell potentially-risky products. Typically, the risk can be identified by jointly considering product content (e.g., title an…