550 citations · 824 across the 21 of their papers we have counts for
8 papers · 1 filter
MalRAG: A Retrieval-Augmented LLM Framework for Open-set Malicious Traffic Identification
Xiang Luo, Chang Liu, Gang Xiong +4
Fine-grained identification of IDS-flagged suspicious traffic is crucial in cybersecurity. In practice, cyber threats evolve continuously, making the discovery of novel malicious t…
MMSD3.0: A Multi-Image Benchmark for Real-World Multimodal Sarcasm Detection
Haochen Zhao, Yuyao Kong, Yongxiu Xu +4
Despite progress in multimodal sarcasm detection, existing datasets and methods predominantly focus on single-image scenarios, overlooking potential semantic and affective relation…
T2VParser: Adaptive Decomposition Tokens for Partial Alignment in Text to Video Retrieval
Yili Li, Gang Xiong, Gaopeng Gou +4
Text-to-video retrieval essentially aims to train models to align visual content with textual descriptions accurately. Due to the impressive general multimodal knowledge demonstrat…
Respond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification
Xinjie Lin, Gang Xiong, Gaopeng Gou +4
Encrypted traffic classification is highly challenging in network security due to the need for extracting robust features from content-agnostic traffic data. Existing approaches fa…
DecETT: Accurate App Fingerprinting Under Encrypted Tunnels via Dual Decouple-based Semantic Enhancement
Zheyuan Gu, Chang Liu, Xiyuan Zhang +5
Due to the growing demand for privacy protection, encrypted tunnels have become increasingly popular among mobile app users, which brings new challenges to app fingerprinting (AF)-…
Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval
Yuanmin Tang, Jing Yu, Keke Gai +4
Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key cha…
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification
Xiangyan Qu, Jing Yu, Jiamin Zhuang +3
Zero-shot learning (ZSL) aims to train a model on seen classes and recognize unseen classes by knowledge transfer through shared auxiliary information. Recent studies reveal that d…
ProAPO: Progressively Automatic Prompt Optimization for Visual Classification
Xiangyan Qu, Gaopeng Gou, Jiamin Zhuang +5
Vision-language models (VLMs) have made significant progress in image classification by training with large-scale paired image-text data. Their performances largely depend on the p…