Contemporary segmentation methods are usually based on deep fully convolutional networks (FCNs). However, the layer-by-layer convolutions with a growing receptive field is not good at capturing long-range contexts such as lane markers in the scene. In this paper, we address this issue by designing a distillation method that exploits label structure when training segmentation network. The intuition is that the ground-truth lane annotations themselves exhibit internal structure. We broadcast the structure hints throughout a teacher network, i.e., we train a teacher network that consumes a lane label map as input and attempts to replicate it as output. Then, the attention maps of the teacher network are adopted as supervisors of the student segmentation network. The teacher network, with label structure information embedded, knows distinctly where the convolution layers should pay visual attention into. The proposed method is named as Label-guided Attention Distillation (LGAD). It turns out that the student network learns significantly better with LGAD than when learning alone. As the teacher network is deprecated after training, our method do not increase the inference time. Note that LGAD can be easily incorporated in any lane segmentation network.
The Chinese University of Hong Kong（香港中文大学）；SenseTime Group Limited（商汤集团有限公司）；Nanyang Technological University（南洋理工大学）
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations. Without learning from much richer context, these models often fail in challenging scenarios, e.g., severe occlusion, ambiguous lanes, and poor lighting conditions. In this paper, we present a novel knowledge distillation approach, i.e., Self Attention Distillation (SAD), which allows a model to learn from itself and gains substantial improvement without any additional supervision or labels. Specifically, we observe that attention maps extracted from a model…
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark data…
Lane Departure Accident Prevention in Foggy Conditions: A Prior-Guided Dynamic Feature Fusion Transformer Framework for Real-Time Lane Detection
Guangdong Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University（广东省智能交通系统重点实验室，智能系统工程学院，中山大学）；School of Civil Engineering and Transportation, South China University of Technology（土木工程与交通学院，华南理工大学）；College of Automotive Studies, Tongji University（汽车研究学院，同济大学）；College of Transportation Engineering, Tongji University（交通运输工程学院，同济大学）；Department of Civil and Environmental Engineering, University of Alberta（土木与环境工程系，阿尔伯塔大学）；State Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University（汽车底盘集成与生物力学国家重点实验室，吉林大学）
Deep learning models have demonstrated remarkable success in object detection, yet their complexity and computational intensity pose a barrier to deploying them in real-world applications (e.g., self-driving perception). Knowledge Distillation (KD) is an effective way to derive efficient models. However, only a small number of KD methods tackle object detection. Also, most of them focus on mimicking the plain features of the teacher model but rarely consider how the features contribute to the final detection. In this paper, we propose a novel approach for knowledge distillation in object detec…
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）
Current lane detection methods are struggling with the invisibility lane issue caused by heavy shadows, severe road mark degradation, and serious vehicle occlusion. As a result, discriminative lane features can be barely learned by the network despite elaborate designs due to the inherent invisibility of lanes in the wild. In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild. To achieve this, we propose a novel Repainting and Imitating Learning (RIL) framework containing a pair of tea…
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 facil…
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）
Knowledge distillation, aimed at transferring the knowledge from a heavy teacher network to a lightweight student network, has emerged as a promising technique for compressing neural networks. However, due to the capacity gap between the heavy teacher and the lightweight student, there still exists a significant performance gap between them. In this paper, we see knowledge distillation in a fresh light, using the knowledge gap, or the residual, between a teacher and a student as guidance to train a much more lightweight student, called a res-student. We combine the student and the res-student …
Recently, large-scale pre-trained models have shown their advantages in many tasks. However, due to the huge computational complexity and storage requirements, it is challenging to apply the large-scale model to real scenes. A common solution is knowledge distillation which regards the large-scale model as a teacher model and helps to train a small student model to obtain a competitive performance. Cross-task Knowledge distillation expands the application scenarios of the large-scale pre-trained model. Existing knowledge distillation works focus on directly mimicking the final prediction or th…
CEA, LIST（法国原子能与替代能源委员会 信息与技术系统实验室）；CERMICS, Ecole des Ponts（巴黎路桥学院 数学与科学计算中心）
A thorough comprehension of image content demands a complex grasp of the interactions that may occur in the natural world. One of the key issues is to describe the visual relationships between objects. When dealing with real world data, capturing these very diverse interactions is a difficult problem. It can be alleviated by incorporating common sense in a network. For this, we propose a framework that makes use of semantic knowledge and estimates the relevance of object pairs during both training and test phases. Extracted from precomputed models and training annotations, this information is …
With the success of deep neural networks, knowledge distillation which guides the learning of a small student network from a large teacher network is being actively studied for model compression and transfer learning. However, few studies have been performed to resolve the poor learning issue of the student network when the student and teacher model sizes significantly differ. In this paper, we propose a densely guided knowledge distillation using multiple teacher assistants that gradually decreases the model size to efficiently bridge the large gap between the teacher and student networks. To…
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）
Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；SenseTime Research（商汤科技研究院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；State Key Laboratory of Complex & Critical Software Environment, Beihang University（北京航空航天大学复杂与关键软件环境国家重点实验室）
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）
Nanjing University of Science and Technology（南京理工大学）；Sensetime Research（商汤科技研究院）
Knowledge Distillation (KD) is a widely-used technology to inherit information from cumbersome teacher models to compact student models, consequently realizing model compression and acceleration. Compared with image classification, object detection is a more complex task, and designing specific KD methods for object detection is non-trivial. In this work, we elaborately study the behaviour difference between the teacher and student detection models, and obtain two intriguing observations: First, the teacher and student rank their detected candidate boxes quite differently, which results in the…
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）
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）
Stony Brook University（石溪大学）；Brookhaven National Laboratory（布鲁克海文国家实验室）
While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully designed adversarial attacks. We propose a novel adversarial training-based model by Attention Guided Knowledge Distillation and Bi-directional Metric Learning (AGKD-BML). The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model. In this way, the student model is able to focus on the correct region, as well as corre…
Feature distillation is an effective way to improve the performance for a smaller student model, which has fewer parameters and lower computation cost compared to the larger teacher model. Unfortunately, there is a common obstacle - the gap in semantic feature structure between the intermediate features of teacher and student. The classic scheme prefers to transform intermediate features by adding the adaptation module, such as naive convolutional, attention-based or more complicated one. However, this introduces two problems: a) The adaptation module brings more parameters into training. b) T…
Shanghai Jiao Tong University（上海交通大学）；UC Santa Cruz（加州大学圣克鲁兹分校）；The University of Hong Kong（香港大学）
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains challenging to deal with heterogeneous data clients, as the discrepant data distributions usually prevent the global model from delivering good generalization ability on each participating client. In this paper, we propose CD^2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. Different from previous works which est…
Illinois Institute of Technology（伊利诺伊理工学院）；Texas State University（德克萨斯州立大学）；Shandong University（山东大学）
Knowledge distillation has become one of the most important model compression techniques by distilling knowledge from larger teacher networks to smaller student ones. Although great success has been achieved by prior distillation methods via delicately designing various types of knowledge, they overlook the functional properties of neural networks, which makes the process of applying those techniques to new tasks unreliable and non-trivial. To alleviate such problem, in this paper, we initially leverage Lipschitz continuity to better represent the functional characteristic of neural networks a…
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD is affected by the large capacity gap between the teacher and student networks. Recent methods have resorted to a multiple teacher assistant (TA) setting for KD, which sequentially decreases the size of the teacher model to relatively bridge the size gap between these models. This paper proposes a new technique called Curriculum Expert Selection for Knowledge Distillation (CES-KD) to efficiently enhance the learning of a compact student under the capacity…
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）
