Search Results for author:
Found 16 papers, 11 papers with code
CAT: Enhancing Multimodal Large Language Model to Answer Questions in Dynamic Audio-Visual Scenarios
This paper focuses on the challenge of answering questions in scenarios that are composed of rich and complex dynamic audio-visual components.
Audio-visual Question Answering
Audio-Visual Question Answering (AVQA)
+5
Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought
In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of LLMs on various emotional generation tasks by aligning with human emotional intelligence guidelines.
LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge
1) Progressive incorporation of fine-grained spatial-aware visual knowledge.
Robust Sequential DeepFake Detection
However, existing methods only focus on detecting one-step facial manipulation.
Detecting and Grounding Multi-Modal Media Manipulation and Beyond
HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning.
DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection
Unlike existing deepfake detection methods merely focusing on low-level forgery patterns, the forgery detection process of our model can be regularized by generalizable high-level semantics from a pre-trained ViT and adapted by global and local low-level forgeries of deepfake data.
Detecting and Grounding Multi-Modal Media Manipulation
In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM^4).
Mixup for Test-Time Training
Firstly, it will lead to overfitting to the test-time procedure thus hurt the performance on the main task.
Detecting and Recovering Sequential DeepFake Manipulation
Moreover, we build a comprehensive benchmark and set up rigorous evaluation protocols and metrics for this new research problem.
Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem.
Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation
The generalization ability of face presentation attack detection models to unseen attacks has become a key issue for real-world deployment, which can be improved when models are trained with face images from different input distributions and different types of spoof attacks.
Federated Generalized Face Presentation Attack Detection
A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.
Open-set Adversarial Defense
In this paper, we show that open-set recognition systems are vulnerable to adversarial attacks.
Federated Face Presentation Attack Detection
A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.
Regularized Fine-grained Meta Face Anti-spoofing
Besides, to further enhance the generalization ability of our model, the proposed framework adopts a fine-grained learning strategy that simultaneously conducts meta-learning in a variety of domain shift scenarios in each iteration.
Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection
This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of the domain generalization.
