It is challenging to bridge the performance gap between Binary CNN (BCNN) and Floating point CNN (FCNN). We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN. To minimize the performance gap, we enforce BCNN to produce similar intermediate feature maps with the ones of FCNN. This training strategy, i.e., optimizing each binary convolutional block with block-wise distillation loss derived from FCNN, leads to a more effective optimization to BCNN. It also motivates us to update the binary convolutional block architecture to facilitate the optimization of block-wise distillation loss. Specifically, a lightweight shortcut branch is inserted into each binary convolutional block to complement residuals at each block. Benefited from its Squeeze-and-Interaction (SI) structure, this shortcut branch introduces a fraction of parameters, e.g., 10\% overheads, but effectively complements the residuals. Extensive experiments on ImageNet demonstrate the superior performance of our method in both classification efficiency and accuracy, e.g., BCNN trained with our methods achieves the accuracy of 60.45\% on ImageNet.
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation
The Hong Kong Polytechnic University（香港理工大学）；Centre for Artificial Intelligence and Robotics, HKISI, CAS（中国科学院香港智能研究院人工智能与机器人中心）；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国家重点实验室）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）
Teacher-Guided Student Self-Knowledge Distillation Using Diffusion Model
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
OsteoFlow: Lyapunov-Guided Flow Distillation for Predicting Bone Remodeling after Mandibular Reconstruction
Department of Electrical and Computer Engineering, University of British Columbia, Canada（电气与计算机工程系，不列颠哥伦比亚大学，加拿大）；Department of Mechanical Engineering, University of British Columbia, Canada（机械工程系，不列颠哥伦比亚大学，加拿大）；Department of Surgery, University of British Columbia, Canada（外科系，不列颠哥伦比亚大学，加拿大）；Sunnybrook Research Institute, University of Toronto, Canada（阳光医院研究学院，多伦多大学，加拿大）
Sparse Depth-Guided Attention for Accurate Depth Completion: A Stereo-Assisted Monitored Distillation Approach
Huawei Cloud Computing Technology Co., Ltd.（华为云计算技术有限公司）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人学会）；Institute of Robotics and Intelligent Manufacturing, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）机器人与智能制造研究院）
Expandable Residual Approximation for Knowledge Distillation
School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences（电子、电气与通信工程学院，中国科学院大学）
本文提出可扩展残差逼近（ERA）方法，通过多分支残差网络分步分解残差知识并整合教师权重，有效缓解师生容量差距，在 ImageNet 和 MS COCO 等视觉任务上取得显著提升。
Lightweight Model Pre-training via Language Guided Knowledge Distillation
School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；Tencent GY-Laboratory（腾讯GY实验室）；Academy for Engineering and Technology, Fudan University（复旦大学工程技术研究院）
Single-Teacher View Augmentation: Enhancing Knowledge Distillation with Student-Guided Perturbations
State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University（西安交通大学人工智能与机器人研究所人机混合增强智能技术国家重点实验室）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）；Institute of Collaborative Innovation, University of Macau（澳门大学协同创新研究院）；Beijing Huahang Institute of Radio Measurement（北京华航无线电测量研究所）
ATMS-KD: Adaptive Temperature and Mixed Sample Knowledge Distillation for a Lightweight Residual CNN in Agricultural Embedded Systems
TEDAEEP Research Group, FPL, Abdelmalek saadi University（TEDAEEP研究组，FPL，阿德莱尔马萨迪大学）；Smart System Laboratory (SSL), ENSIAS, Mohammed V University（智能系统实验室（SSL），ENSIAS，穆罕默德五世大学）
Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
Computer Vision and Learning Systems, Department of Electrical Engineering, Linköping University（计算机视觉与学习系统，电气工程系，利厄普大学）；Helmholtz-Zentrum Dresden-Rossendorf, Responsible AI Group（海姆霍尔茨·德累斯顿-罗斯托克研究中心，负责任人工智能小组）；University of Iceland, Faculty of Electrical and Computer Engineering（冰岛大学，电气与计算机工程学院）
RPM-Distill: Physiology-guided Adaptive Cross-modal Distillation for Robust Remote Physiological Measurement
McGill University（麦吉尔大学）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Tsinghua University（清华大学）
Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Shanghai Jiao Tong University（上海交通大学）
MuDD: A Multimodal Deception Detection Dataset and GSR-Guided Progressive Distillation for Non-Contact Deception Detection
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models
School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）；University of Technology Sydney（悉尼科技大学）；Advanced Institute of Big Data（大数据先进研究院）
