activity
20212026
most citedESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding

69 citations · 127 across the 30 of their papers we have counts for

collaborators
Showing 2024Show all

13 papers · 1 filter

cs.CV2024★ 8 cited

Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression

Shaofei Huang, Zhenwei Shen, Zehao Huang +4

In this paper, we focus on the challenging task of monocular 3D lane detection. Previous methods typically adopt inverse perspective mapping (IPM) to transform the Front-Viewed (FV…

cs.CV2024

Multimodal Music Generation with Explicit Bridges and Retrieval Augmentation

Baisen Wang, Le Zhuo, Zhaokai Wang +7

Multimodal music generation aims to produce music from diverse input modalities, including text, videos, and images. Existing methods use a common embedding space for multimodal fu…

cs.LG2024

The Dark Side of Trust: Authority Citation-Driven Jailbreak Attacks on Large Language Models

Xikang Yang, Xuehai Tang, Jizhong Han +1

The widespread deployment of large language models (LLMs) across various domains has showcased their immense potential while exposing significant safety vulnerabilities. A major co…

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.CV2024★ 37 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.CV2024

Unleashing the Temporal-Spatial Reasoning Capacity of GPT for Training-Free Audio and Language Referenced Video Object Segmentation

Shaofei Huang, Rui Ling, Hongyu Li +5

In this paper, we propose an Audio-Language-Referenced SAM 2 (AL-Ref-SAM 2) pipeline to explore the training-free paradigm for audio and language-referenced video object segmentati…

cs.CV2024

Unveiling Structural Memorization: Structural Membership Inference Attack for Text-to-Image Diffusion Models

Qiao Li, Xiaomeng Fu, Xi Wang +4

With the rapid advancements of large-scale text-to-image diffusion models, various practical applications have emerged, bringing significant convenience to society. However, model…

cs.MM2024

Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens

Xikang Yang, Xuehai Tang, Fuqing Zhu +2

Vision-language models (VLMs) seamlessly integrate visual and textual data to perform tasks such as image classification, caption generation, and visual question answering. However…

cs.CL2024★ 1 cited

Chain of Attack: a Semantic-Driven Contextual Multi-Turn attacker for LLM

Xikang Yang, Xuehai Tang, Songlin Hu +1

Large language models (LLMs) have achieved remarkable performance in various natural language processing tasks, especially in dialogue systems. However, LLM may also pose security…

cs.CV2024

Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection

Cai Yu, Shan Jia, Xiaomeng Fu +6

With the rising prevalence of deepfakes, there is a growing interest in developing generalizable detection methods for various types of deepfakes. While effective in their specific…

cs.CV2024

Model Will Tell: Training Membership Inference for Diffusion Models

Xiaomeng Fu, Xi Wang, Qiao Li +3

Diffusion models pose risks of privacy breaches and copyright disputes, primarily stemming from the potential utilization of unauthorized data during the training phase. The Traini…