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Found 10 papers, 3 papers with code
Social Debiasing for Fair Multi-modal LLMs
Multi-modal Large Language Models (MLLMs) have advanced significantly, offering powerful vision-language understanding capabilities.
Bridging the Intent Gap: Knowledge-Enhanced Visual Generation
To address these challenges, we propose a knowledge-enhanced iterative refinement framework for visual content generation.
LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation
In this paper, we present LOC-ZSON, a novel Language-driven Object-Centric image representation for object navigation task within complex scenes.
Diffusion Facial Forgery Detection
In particular, this dataset leverages 30, 000 carefully collected textual and visual prompts, ensuring the synthesis of images with both high fidelity and semantic consistency.
Robust Identity Perceptual Watermark Against Deepfake Face Swapping
Falsification and source tracing are accomplished by justifying the consistency between the content-matched identity perceptual watermark and the recovered robust watermark from the image.
Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets.
Towards Generalizable Deepfake Detection by Primary Region Regularization
The existing deepfake detection methods have reached a bottleneck in generalizing to unseen forgeries and manipulation approaches.
Deep Convolutional Pooling Transformer for Deepfake Detection
Most existing deep learning methods mainly focus on local features and relations within the face image using convolutional neural networks as a backbone.
Voice-Face Homogeneity Tells Deepfake
To this end, a voice-face matching method is devised to measure the matching degree of these two.
On Modality Bias Recognition and Reduction
From the results on four datasets regarding the above three tasks, our method yields remarkable performance improvements compared with the baselines, demonstrating its superiority on reducing the modality bias problem.
