74 citations · 315 across the 165 of their papers we have counts for
12 papers · 2 filters
DIME: Scaling Facial Representation Learning via Differential Masked Autoencoding
Hao Yu, Haoyu Chen, Hui Wei +3
Scaling up data and model capacity has driven substantial progress in visual representation learning. However, conventional pretext tasks exhibit diminishing returns on fine-graine…
Localize Any Object in X-Ray Security Scans without Human Annotation
Yaqi Cai, Mingxuan Liu, Lorenzo Vaquero +5
Universal object localization in X-ray security inspection is critical for automated threat detection in safety-critical venues. However, unlike everyday RGB images that dominate w…
Hallucination Early Detection in Diffusion Models
Federico Betti, Lorenzo Baraldi, Rita Cucchiara +1
Text-to-Image generation has seen significant advancements in output realism with the advent of diffusion models. However, diffusion models encounter difficulties when tasked with…
The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Xingyu Qiu, Yuqian Fu, Jiawei Geng +70
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across…
Finetune Like You Pretrain: Boosting Zero-shot Adversarial Robustness in Vision-language Models
Songlong Xing, Weijie Wang, Zhengyu Zhao +3
Despite their impressive zero-shot abilities, vision-language models such as CLIP have been shown to be susceptible to adversarial attacks. To enhance its adversarial robustness, r…
Open-Vocabulary Domain Generalization in Urban-Scene Segmentation
Dong Zhao, Qi Zang, Nan Pu +3
Domain Generalization in Semantic Segmentation (DG-SS) aims to enable segmentation models to perform robustly in unseen environments. However, conventional DG-SS methods are restri…
Generalizable Knowledge Distillation from Vision Foundation Models for Semantic Segmentation
Chonghua Lv, Dong Zhao, Shuang Wang +4
Knowledge distillation (KD) has been widely applied in semantic segmentation to compress large models, but conventional approaches primarily preserve in-domain accuracy while negle…
Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving
Jiangxin Sun, Feng Xue, Teng Long +4
With advances in imitation learning (IL) and large-scale driving datasets, end-to-end autonomous driving (E2E-AD) has made great progress recently. Currently, IL-based methods have…
Curriculum-DPO++: Direct Preference Optimization via Data and Model Curricula for Text-to-Image Generation
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu +2
Direct Preference Optimization (DPO) has been proposed as an effective and efficient alternative to reinforcement learning from human feedback (RLHF). However, neither RLHF nor DPO…
A Unified Masked Jigsaw Puzzle Framework for Vision and Language Models
Weixin Ye, Wei Wang, Yahui Liu +5
In federated learning, Transformer, as a popular architecture, faces critical challenges in defending against gradient attacks and improving model performance in both Computer Visi…
In defense of the two-stage framework for open-set domain adaptive semantic segmentation
Wenqi Ren, Weijie Wang, Meng Zheng +4
Open-Set Domain Adaptation for Semantic Segmentation (OSDA-SS) presents a significant challenge, as it requires both domain adaptation for known classes and the distinction of unkn…
POCI-Diff: 3D-Layout Guided Diffusion for Controllable Synthetic Surveillance Data Generation
Andrea Rigo, Luca Stornaiuolo, Weijie Wang +3
Training robust visual surveillance models requires large-scale datasets with precise spatial annotations, yet collecting real surveillance data is costly, privacy-sensitive, and o…