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Baojin Huang

7 accepted papers

2026

Divide and Conquer: Reliable Multi-View Evidential Learning for Deepfake Detection

ICML 2026poster

With the evolution of generative models, deepfakes have achieved near-perfect semantic realism, leaving forensic traces only in subtle structural anomalies. However, existing single-view paradigms often fail to generalize, as dominant semantic features overwhelm subtle artifact cues within entangled…

Cited by 0SourceScholar
2026

Tutor-Student Reinforcement Learning: A Dynamic Curriculum for Robust Deepfake Detection

CVPR 2026

Standard supervised training for deepfake detection treats all samples with uniform importance, which can be suboptimal for learning robust and generalizable features. In this work, we propose a novel Tutor-Student Reinforcement Learning (TSRL) framework to dynamically optimize the training curricul

Cited by 0SourcecodeScholar
2025

ProJudge: A Multi-Modal Multi-Discipline Benchmark and Instruction-Tuning Dataset for MLLM-based Process Judges

ICCV 2025poster

As multi-modal large language models (MLLMs) frequently exhibit errors when solving scientific problems, evaluating the validity of their reasoning processes is critical for ensuring reliability and uncovering fine-grained model weaknesses. Since human evaluation is laborious and costly, prompting M…

2023

Continuous Learning for Blind Image Quality Assessment with Contrastive Transformer

ICASSP 2023accepted

Most existing blind image quality assessment (BIQA) models focus on improving performance on existing datasets and are weak in adapting to unknown distortion or degradation types. In this paper, we propose a Transformer-based BIQA contrastive continual learning approach to improve model transfer per…

Cited by 0SourceScholar
2023

Implicit Identity Driven Deepfake Face Swapping Detection

CVPR 2023poster

In this paper, we consider the face swapping detection from the perspective of face identity. Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake. We argue that the fake face contains the explicit ident…

Cited by 135SourcePDFScholar
2021

When Face Recognition Meets Occlusion: A New Benchmark

ICASSP 2021accepted

The existing face recognition datasets usually lack occlusion samples, which hinders the development of face recognition. Especially during the COVID-19 coronavirus epidemic, wearing a mask has become an effective means of preventing the virus spread. Traditional CNN-based face recognition models tr…

Cited by 0SourceScholar
2020

Multi-Scale Progressive Fusion Network for Single Image Deraining

CVPR 2020poster

Rain streaks in the air appear in various blurring degrees and resolutions due to different distances from their positions to the camera. Similar rain patterns are visible in a rain image as well as its multi-scale (or multi-resolution) versions, which makes it possible to exploit such complementary…

Cited by 844PDFcodeScholar