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Long Tang

5 accepted papers

2026

Life-IQA: Boosting Blind Image Quality Assessment through GCN-enhanced Layer Interaction and MoE-based Feature Decoupling

CVPR 2026

Blind image quality assessment (BIQA) plays a crucial role in evaluating and optimizing visual experience. Most existing BIQA approaches fuse shallow and deep features extracted from backbone networks, while overlooking the unequal contributions to quality prediction. Moreover, while various vision

Cited by 0SourceScholar
2026

When Generalized Zero-Shot Learning Meets PU Learning: A Plug-and-Play Framework for Seen-Class Bias Mitigation

ICML 2026poster

Generalized Zero-Shot Learning (GZSL) suffers from severe seen-class bias, a challenge stemming from the label incompleteness inherent in mixed test distributions. To address this, we propose PUFE, a unified plug-and-play framework that recasts GZSL inference as a Positive-Unlabeled (PU) learning ta…

Cited by 0SourceScholar
2025

Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection

AAAI 2025technical

Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Although past methods have achieved good performance on detecting pure machine-generated text, those detectors have poor performance on distinguishing machine-revised t…

2024

Once and for All: Universal Transferable Adversarial Perturbation against Deep Hashing-Based Facial Image Retrieval

AAAI 2024technical

Deep Hashing (DH)-based image retrieval has been widely applied to face-matching systems due to its accuracy and efficiency. However, this convenience comes with an increased risk of privacy leakage. DH models inherit the vulnerability to adversarial attacks, which can be used to prevent the retriev…

Cited by 9SourcePDFScholar
2023

Voice Guard: Protecting Voice Privacy with Strong and Imperceptible Adversarial Perturbation in the Time Domain

IJCAI 2023poster

Adversarial example is a rising tool for voice privacy protection. By adding imperceptible noise to public audio, it prevents tampers from using zero-shot Voice Conversion (VC) to synthesize high quality speech with target speaker identity. However, many existing studies ignore the human perception…

Cited by 7SourcePDFScholar