1 citations · 1 across the 14 of their papers we have counts for
15 papers
SACE: Concept Erasure at the Semantic Singularity in Visual Autoregressive Models
Siya Yang, Nanxiang Jiang, Zhaoxin Fan +1
The rapid progress of visual autoregressive (VAR) models has unlocked a transformative frontier for high-fidelity text-to-image synthesis, while heightening concerns over the safet…
NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Suhang Yao +95
This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this c…
Mosaic: Multimodal Jailbreak against Closed-Source VLMs via Multi-View Ensemble Optimization
Yuqin Lan, Gen Li, Yuanze Hu +6
Vision-Language Models (VLMs) are powerful but remain vulnerable to multimodal jailbreak attacks. Existing attacks mainly rely on either explicit visual prompt attacks or gradient-…
HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
Boshui Chen, Zhaoxin Fan, Ke Wang +5
Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While recent hallucination detection…
Z-Erase: Enabling Concept Erasure in Single-Stream Diffusion Transformers
Nanxiang Jiang, Zhaoxin Fan, Baisen Wang +8
Concept erasure serves as a vital safety mechanism for removing unwanted concepts from text-to-image (T2I) models. While extensively studied in U-Net and dual-stream architectures…
EraseAnything++: Enabling Concept Erasure in Rectified Flow Transformers Leveraging Multi-Object Optimization
Zhaoxin Fan, Nanxiang Jiang, Daiheng Gao +2
Removing undesired concepts from large-scale text-to-image (T2I) and text-to-video (T2V) diffusion models while preserving overall generative quality remains a major challenge, par…
The Achilles' Heel of LLMs: How Altering a Handful of Neurons Can Cripple Language Abilities
Zixuan Qin, Qingchen Yu, Kunlin Lyu +2
Large Language Models (LLMs) have become foundational tools in natural language processing, powering a wide range of applications and research. Many studies have shown that LLMs sh…
Erased, But Not Forgotten: Erased Rectified Flow Transformers Still Remain Unsafe Under Concept Attack
Nanxiang Jiang, Zhaoxin Fan, Enhan Kang +6
Recent advances in text-to-image (T2I) diffusion models have enabled impressive generative capabilities, but they also raise significant safety concerns due to the potential to pro…
Can Structured Templates Facilitate LLMs in Tackling Harder Tasks? : An Exploration of Scaling Laws by Difficulty
Zhichao Yang, Zhaoxin Fan, Gen Li +6
Structured, procedural reasoning is essential for Large Language Models (LLMs), especially in mathematics. While post-training methods have improved LLM performance, they still fal…
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks
Yuanze Hu, Zhaoxin Fan, Xinyu Wang +8
Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. The prevailing approach to aligning vision and language models involves freezing…
RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks
Shiying Duan, Pei Ren, Nanxiang Jiang +5
Dual-arm robots play a crucial role in improving efficiency and flexibility in complex multitasking scenarios. While existing methods have achieved promising results in task planni…
AsynFusion: Towards Asynchronous Latent Consistency Models for Decoupled Whole-Body Audio-Driven Avatars
Tianbao Zhang, Jian Zhao, Yuer Li +5
Whole-body audio-driven avatar pose and expression generation is a critical task for creating lifelike digital humans and enhancing the capabilities of interactive virtual agents,…
LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation
Zhiying Li, Guanggang Geng, Yeying Jin +4
Expressive human pose and shape estimation (EHPS) plays a central role in digital human generation, particularly in live-streaming applications. However, most existing EHPS models…
Unveiling Hidden Vulnerabilities in Digital Human Generation via Adversarial Attacks
Zhiying Li, Yeying Jin, Fan Shen +9
Expressive human pose and shape estimation (EHPS) is crucial for digital human generation, especially in applications like live streaming. While existing research primarily focuses…
TinyLLaVA-Video: Towards Smaller LMMs for Video Understanding with Group Resampler
Xingjian Zhang, Xi Weng, Yihao Yue +3
Video behavior recognition and scene understanding are fundamental tasks in multimodal intelligence, serving as critical building blocks for numerous real-world applications. Throu…