Modern object detection methods can be divided into one-stage approaches and two-stage ones. One-stage detectors are more efficient owing to straightforward architectures, but the two-stage detectors still take the lead in accuracy. Although recent work try to improve the one-stage detectors by imitating the structural design of the two-stage ones, the accuracy gap is still significant. In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors. Unlike conventional mimic methods, MimicDet has a shared backbone for one-stage and two-stage detectors, then it branches into two heads which are well designed to have compatible features for mimicking. Thus MimicDet can be end-to-end trained without the pre-train of the teacher network. And the cost does not increase much, which makes it practical to adopt large networks as backbones. We also make several specialized designs such as dual-path mimicking and staggered feature pyramid to facilitate the mimicking process. Experiments on the challenging COCO detection benchmark demonstrate the effectiveness of MimicDet. It achieves 46.1 mAP with ResNeXt-101 backbone on the COCO test-dev set, which significantly surpasses current state-of-the-art methods.
Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets
National University of Singapore I2R, A*STAR（新加坡国立大学I2R，A*STAR）；University of Cambridge（剑桥大学）；National University of Singapore ASUS Intelligent Cloud Services（新加坡国立大学ASUS智能云服务）
InverseDraping: Recovering Sewing Patterns from 3D Garment Surfaces via BoxMesh Bridging
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；FNii-Shenzhen（深圳市未来网络智能研究院）；Guangdong Provincial Key Laboratory of Future Networks of Intelligence（广东省未来网络智能重点实验室）；Pinscreen
Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications
School of Computer Engineering, Iran University of Science and Technology（伊朗科学技术大学计算机工程学院）；George Mason University Department of Computer Science（乔治·梅奥大学计算机科学系）
Bridging Diffusion Pruning and Step Distillation with Teacher-Aligned Repair
School of Big Data and Artificial Intelligence（大数据与人工智能学院）；Guangdong University of Finance and Economics（广东财经大学）；School of Computer Science（计算机科学学院）；Shandong University（山东大学）；GDUFE-UCM Joint Institute（广东财经大学-UCM联合学院）
ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Bytedance Inc.（字节跳动公司）
PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models
Beijing University of Posts and Telecommunications, China（北京邮电大学）；National Institute of Informatics, Japan（日本国立信息机构）；Peking University, China（北京大学）；The University of Tokyo, Japan（东京大学）
Cycle Training with Semi-Supervised Domain Adaptation: Bridging Accuracy and Efficiency for Real-Time Mobile Scene Detection
Ho Chi Minh University of Technology, VNU-HCM（胡志明市理工大学（胡志明市国家大学））；Michigan State University（密歇根州立大学）；LPL Financial（LPL金融公司）；Georgia State University（佐治亚州立大学）；Posts and Telecommunications Institute of Technology, Hanoi, Vietnam（河内邮电技术学院）；York University（约克大学）；AI VIETNAM Lab（越南人工智能实验室）
Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference
School of AI, Shanghai Jiao Tong University（上海交通大学人工智能学院）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；BAAI（北京人工智能研究院）
Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（中国科学院自动化研究所多模态人工智能系统全国重点实验室）；School of Artificial Intelligence, UCAS（中国科学院大学人工智能学院）
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
Om AI Research（Om AI研究机构）；Binjiang Institute of Zhejiang University（浙江大学滨江研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；DAMO Academy, Alibaba Group（阿里巴巴集团 DAMO 院）；Hupan Lab, Zhejiang Province（浙江省 Hupan 实验室）
