16 citations · 41 across the 9 of their papers we have counts for
10 papers
FocSAM: Delving Deeply into Focused Objects in Segmenting Anything
You Huang, Zongyu Lan, Liujuan Cao +4
The Segment Anything Model (SAM) marks a notable milestone in segmentation models, highlighted by its robust zero-shot capabilities and ability to handle diverse prompts. SAM follo…
DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis
Zhongxi Chen, Ke Sun, Ziyin Zhou +4
The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. Existing face forger…
Improving Human-Object Interaction Detection via Virtual Image Learning
Shuman Fang, Shuai Liu, Jie Li +3
Human-Object Interaction (HOI) detection aims to understand the interactions between humans and objects, which plays a curtail role in high-level semantic understanding tasks. Howe…
HODN: Disentangling Human-Object Feature for HOI Detection
Shuman Fang, Zhiwen Lin, Ke Yan +3
The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances cur…
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models
Zhongxi Chen, Ke Sun, Xianming Lin +1
Camouflaged Object Detection (COD) is a challenging task in computer vision due to the high similarity between camouflaged objects and their surroundings. Existing COD methods prim…
Latent Feature Relation Consistency for Adversarial Robustness
Xingbin Liu, Huafeng Kuang, Hong Liu +3
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial…
CAT:Collaborative Adversarial Training
Xingbin Liu, Huafeng Kuang, Xianming Lin +2
Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained…
Exploring Invariant Representation for Visible-Infrared Person Re-Identification
Lei Tan, Yukang Zhang, Shengmei Shen +5
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy. In this pap…
Exploring Target Representations for Masked Autoencoders
Xingbin Liu, Jinghao Zhou, Tao Kong +2
Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the…
Learning to Learn Transferable Attack
Shuman Fang, Jie Li, Xianming Lin +1
Transfer adversarial attack is a non-trivial black-box adversarial attack that aims to craft adversarial perturbations on the surrogate model and then apply such perturbations to t…