collaborators

10 papers

cs.LG2026

The Illusion of Forgetting: Attack Unlearned Diffusion via Initial Latent Variable Optimization

Manyi Li, Yufan Liu, Lai Jiang +3

Text-to-image diffusion models (DMs) are frequently abused to produce harmful or copyrighted content, violating public interests. Concept erasure (unlearning) is a promising paradi…

cs.CV2026

OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models

Yiwei Zhang, Xuesong Chen, Jin Gao +5

Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured outputs and heterogeneous decod…

cs.CV2026

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)

Guanyi Qin, Jie Liang, Bingbing Zhang +50

In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Asses…

cs.CV2026

SEATrack: Simple, Efficient, and Adaptive Multimodal Tracker

Junbin Su, Ziteng Xue, Shihui Zhang +3

Parameter-efficient fine-tuning (PEFT) in multimodal tracking reveals a concerning trend where recent performance gains are often achieved at the cost of inflated parameter budgets…

cs.CV2026

Low-Data Supervised Adaptation Outperforms Prompting for Cloud Segmentation Under Domain Shift

Harshith Kethavath, Weiming Hu

Adapting vision-language models to remote sensing imagery presents a fundamental challenge: both the visual and linguistic distributions of satellite data lie far outside natural i…

cs.CV2026

Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation

Zhiheng Li, Zongyang Ma, Yuntong Pan +8

Multimodal Large Language Models (MLLMs) are increasingly being deployed as automated content moderators. Within this landscape, we uncover a critical threat: Adversarial Smuggling…