most citedLearning to Discover Forgery Cues for Face Forgery Detection

37 citations · 40 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2024

FreeEdit: Mask-free Reference-based Image Editing with Multi-modal Instruction

Runze He, Kai Ma, Linjiang Huang +6

Introducing user-specified visual concepts in image editing is highly practical as these concepts convey the user's intent more precisely than text-based descriptions. We propose F…

cs.CV2024

Dynamic Prompting of Frozen Text-to-Image Diffusion Models for Panoptic Narrative Grounding

Hongyu Li, Tianrui Hui, Zihan Ding +5

Panoptic narrative grounding (PNG), whose core target is fine-grained image-text alignment, requires a panoptic segmentation of referred objects given a narrative caption. Previous…

cs.CR2024

AdaPPA: Adaptive Position Pre-Fill Jailbreak Attack Approach Targeting LLMs

Lijia Lv, Weigang Zhang, Xuehai Tang +4

Jailbreak vulnerabilities in Large Language Models (LLMs) refer to methods that extract malicious content from the model by carefully crafting prompts or suffixes, which has garner…

cs.CV202437 cited

Learning to Discover Forgery Cues for Face Forgery Detection

Jiahe Tian, Peng Chen, Cai Yu +4

Locating manipulation maps, i.e., pixel-level annotation of forgery cues, is crucial for providing interpretable detection results in face forgery detection. Related learning objec…

cs.CL20223 cited

InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings

Xing Wu, Chaochen Gao, Zijia Lin +3

Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constrai…

cs.CV2022

RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval

Xing Wu, Chaochen Gao, Zijia Lin +3

Video language pre-training methods have mainly adopted sparse sampling techniques to alleviate the temporal redundancy of videos. Though effective, sparse sampling still suffers i…