9 citations · 32 across the 11 of their papers we have counts for
12 papers · 1 filter
Learning to Focus and Precise Cropping: A Reinforcement Learning Framework with Information Gaps and Grounding Loss for MLLMs
Xuanpu Zhao, Zhentao Tan, Dianmo Sheng +6
To enhance the perception and reasoning capabilities of multimodal large language models in complex visual scenes, recent research has introduced agent-based workflows. In these wo…
Training-Free In-Context Forensic Chain for Image Manipulation Detection and Localization
Rui Chen, Bin Liu, Changtao Miao +5
Advances in image tampering pose serious security threats, underscoring the need for effective image manipulation localization (IML). While supervised IML achieves strong performan…
LAKAN: Landmark-assisted Adaptive Kolmogorov-Arnold Network for Face Forgery Detection
Jiayao Jiang, Bin Liu, Qi Chu +1
The rapid development of deepfake generation techniques necessitates robust face forgery detection algorithms. While methods based on Convolutional Neural Networks (CNNs) and Trans…
Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal
Zhentao Tan, Yue Wu, Qiankun Liu +4
Image restoration under adverse weather conditions (e.g., rain, snow and haze) is a fundamental computer vision problem and has important indications for various downstream applica…
UIA-ViT: Unsupervised Inconsistency-Aware Method based on Vision Transformer for Face Forgery Detection
Wanyi Zhuang, Qi Chu, Zhentao Tan +5
Intra-frame inconsistency has been proved to be effective for the generalization of face forgery detection. However, learning to focus on these inconsistency requires extra pixel-l…
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
Qiankun Liu, Zhentao Tan, Dongdong Chen +6
Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer f…