Online Action Detection (OAD) in videos is proposed as a per-frame labeling task to address the real-time prediction tasks that can only obtain the previous and current video frames. This paper presents a novel learning-with-privileged based framework for online action detection where the future frames only observable at the training stages are considered as a form of privileged information. Knowledge distillation is employed to transfer the privileged information from the offline teacher to the online student. We note that this setting is different from conventional KD because the difference between the teacher and student models mostly lies in input data rather than the network architecture. We propose Privileged Knowledge Distillation (PKD) which (i) schedules a curriculum learning procedure and (ii) inserts auxiliary nodes to the student model, both for shrinking the information gap and improving learning performance. Compared to other OAD methods that explicitly predict future frames, our approach avoids learning unpredictable unnecessary yet inconsistent visual contents and achieves state-of-the-art accuracy on two popular OAD benchmarks, TVSeries and THUMOS14.
We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. In this work, we propose a method termed graph distillation that incorporates rich privileged inform…
OmniOPSD: Rationale-Privileged On-Policy Self-Distillation for Affective Computing
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；University of Science and Technology of China（中国科学技术大学）；Shenzhen Technology University（深圳技术大学）；Tongji University（同济大学）；Huawei（华为）
Beijing University of Posts and Telecommunications（北京邮电大学）；OPPO Research Institute（OPPO研究院）；Xmotors；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）
Performance of trimap-free image matting methods is limited when trying to decouple the deterministic and undetermined regions, especially in the scenes where foregrounds are semantically ambiguous, chromaless, or high transmittance. In this paper, we propose a novel framework named Privileged Prior Information Distillation for Image Matting (PPID-IM) that can effectively transfer privileged prior environment-aware information to improve the performance of students in solving hard foregrounds. The prior information of trimap regulates only the teacher model during the training stage, while not…
JDCNet: Confidence-Gated Privileged-Modality Distillation for Cost-Preserving X-ray Inference
Auckland University of Technology（奥克兰技术大学）；Guilin University of Electronic Technology（桂林电子科技大学）；Hikvision Technology Co., Ltd（海康威视技术有限公司）；Hebei University of Technology（河北工业大学）
Istituto Italiano di Tecnologia（意大利技术研究院）；University of Genova（热那亚大学）；University of Verona（维罗纳大学）
Heterogeneous data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while training data can be accurately collected to include a variety of sensory modalities, it is often the case that not all of them are available in real life (testing) scenarios, where a model has to be deployed. This raises the challenge of how to extract information from multimodal data in the training stage, in a form that can be exploited at test time, considering limitations such as noisy or missing modalities. This paper presents a …
Privileged Lesion-Context Relational Distillation for Mask-Free Skin Lesion Classification
Victoria University（维多利亚大学）；Bangaldesh University of Business and Technology（孟加拉国商业与技术大学）；Daffodil Institute of Information Technology（达芙妮信息技术学院）；American International University-Bangladesh (AIUB)（孟加拉国美国国际大学）；Multimedia University（多媒体大学）
Simon Fraser University（西蒙弗雷泽大学）；Borealis AI（Borealis人工智能研究院）
Knowledge distillation is a widely applicable technique for training a student neural network under the guidance of a trained teacher network. For example, in neural network compression, a high-capacity teacher is distilled to train a compact student; in privileged learning, a teacher trained with privileged data is distilled to train a student without access to that data. The distillation loss determines how a teacher's knowledge is captured and transferred to the student. In this paper, we propose a new form of knowledge distillation loss that is inspired by the observation that semantically…
Not Every Cue Is Evidence: Cue Supportability for Forensic Distillation in Face Forgery Detection
School of Software Technology, Zhejiang University（浙江大学软件学院）；Ant Group（蚂蚁集团）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science and Technology, Zhejiang University of Technology（浙江工业大学计算机科学与技术学院）
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology
School of Cancer Sciences, University of Glasgow（格拉斯哥大学癌症科学学院）；Cancer Research UK Scotland Institute（英国癌症研究苏格兰研究所）；School of Computing Science, University of Glasgow（格拉斯哥大学计算科学学院）；School of Mathematics and Statistics, University of Glasgow（格拉斯哥大学数学与统计学院）；Division of Biosciences, University College London（伦敦大学学院生物科学部）
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks. Previous empirical analysis has illustrated an orthogonal performance gain between MSR and conventional offline Knowledge Distillation (KD). To be more specific, student networks can be enhanced with the involvement of MSR in the training stage of sequential distillation. Yet, the interplay between MSR and online knowledge distillation, where an ensemble of peer students learn mutually from each other, remains unexplored. To bridge the gap, we make t…
Online knowledge distillation (KD) has received increasing attention in recent years. However, while most existing online KD methods focus on developing complicated model structures and training strategies to improve the distillation of high-level knowledge like probability distribution, the effects of the multi-level knowledge in the online KD are greatly overlooked, especially the low-level knowledge. Thus, to provide a novel viewpoint to online KD, we propose MetaMixer, a regularization strategy that can strengthen the distillation by combining the low-level knowledge that impacts the local…
University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）
In this paper, we present and discuss a deep mixture model with online knowledge distillation (MOD) for large-scale video temporal concept localization, which is ranked 3rd in the 3rd YouTube-8M Video Understanding Challenge. Specifically, we find that by enabling knowledge sharing with online distillation, fintuning a mixture model on a smaller dataset can achieve better evaluation performance. Based on this observation, in our final solution, we trained and fintuned 12 NeXtVLAD models in parallel with a 2-layer online distillation structure. The experimental results show that the proposed di…
University of Maryland, College Park（马里兰大学帕克分校）；Georgia Institute of Technology（佐治亚理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of Oxford（牛津大学）；UC San Diego（加利福尼亚大学圣迭戈分校）
Recently, researchers have shown an increased interest in the online knowledge distillation. Adopting an one-stage and end-to-end training fashion, online knowledge distillation uses aggregated intermediated predictions of multiple peer models for training. However, the absence of a powerful teacher model may result in the homogeneity problem between group peers, affecting the effectiveness of group distillation adversely. In this paper, we propose a novel online knowledge distillation method, \textbf{C}hannel \textbf{S}elf-\textbf{S}upervision for Online Knowledge Distillation (CSS), which st…
Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Key Laboratory of Space-ground Interconnection and Convergence（北京天地互联与融合重点实验室）
Online knowledge distillation conducts knowledge transfer among all student models to alleviate the reliance on pre-trained models. However, existing online methods rely heavily on the prediction distributions and neglect the further exploration of the representational knowledge. In this paper, we propose a novel Multi-scale Feature Extraction and Fusion method (MFEF) for online knowledge distillation, which comprises three key components: Multi-scale Feature Extraction, Dual-attention and Feature Fusion, towards generating more informative feature maps for distillation. The multiscale feature…
In this paper, we present offline-to-online knowledge distillation (OOKD) for video instance segmentation (VIS), which transfers a wealth of video knowledge from an offline model to an online model for consistent prediction. Unlike previous methods that having adopting either an online or offline model, our single online model takes advantage of both models by distilling offline knowledge. To transfer knowledge correctly, we propose query filtering and association (QFA), which filters irrelevant queries to exact instances. Our KD with QFA increases the robustness of feature matching by encodin…
Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
Knowledge Distillation has been established as a highly promising approach for training compact and faster models by transferring knowledge from heavyweight and powerful models. However, KD in its conventional version constitutes an enduring, computationally and memory demanding process. In this paper, Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feat…
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning
Xi’an Jiaotong University（西安交通大学）；MOE KLINNS Lab（MOE KLINNS实验室）；Shaanxi Province Key Laboratory of Big Data Knowledge Engineering（陕西省大数据知识工程重点实验室）；Sun Yat-sen University（中山大学）
VAD: Attributing Visual Evidence for Target Reconstruction in Multimodal On-Policy Distillation
Shanghai Jiao Tong University（上海交通大学）；Xiaohongshu Inc.（小红书公司）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）；Southeast University（东南大学）
Traditional knowledge distillation uses a two-stage training strategy to transfer knowledge from a high-capacity teacher model to a compact student model, which relies heavily on the pre-trained teacher. Recent online knowledge distillation alleviates this limitation by collaborative learning, mutual learning and online ensembling, following a one-stage end-to-end training fashion. However, collaborative learning and mutual learning fail to construct an online high-capacity teacher, whilst online ensembling ignores the collaboration among branches and its logit summation impedes the further op…
Hefei University of Technology（合肥工业大学）；The University of Queensland（昆士兰大学）
Online Knowledge Distillation (OKD) improves the involved models by reciprocally exploiting the difference between teacher and student. Several crucial bottlenecks over the gap between them -- e.g., Why and when does a large gap harm the performance, especially for student? How to quantify the gap between teacher and student? -- have received limited formal study. In this paper, we propose Switchable Online Knowledge Distillation (SwitOKD), to answer these questions. Instead of focusing on the accuracy gap at test phase by the existing arts, the core idea of SwitOKD is to adaptively calibrate …
Seeing Before Reasoning: Decoupling Perception and Reasoning for Shortcut-Resilient Multimodal On-Policy Self-Distillation
State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences（机器人与智能系统国家重点实验室，沈阳自动化研究所，中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）
IoU-PD: IoU-Aware Privileged Distillation for Visual Grounding with Multimodal Large Language Models
University of Chinese Academy of Sciences（中国科学院大学）；State Key Laboratory of Communication Content Cognition（通信内容认知技术国家重点实验室）；Peng Cheng Laboratory（鹏城实验室）
We design a real-time portrait matting pipeline for everyday use, particularly for "virtual backgrounds" in video conferences. Existing segmentation and matting methods prioritize accuracy and quality over throughput and efficiency, and our pipeline enables trading off a controllable amount of accuracy for better throughput by leveraging online distillation on the input video stream. We construct our own dataset of simulated video calls in various scenarios, and show that our approach delivers a 5x speedup over a saliency detection based pipeline in a non-GPU accelerated setting while deliveri…
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowledge in the teacher network and often lacks the ability for feature generalization, particularly in situations when teacher and student are built to address different classification tasks. We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both…
Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS)（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xiamen Data Intelligence Academy of ICT, CAS（中国科学院计算技术研究所厦门数据智能研究院）
Previous Online Knowledge Distillation (OKD) often carries out mutually exchanging probability distributions, but neglects the useful representational knowledge. We therefore propose Multi-view Contrastive Learning (MCL) for OKD to implicitly capture correlations of feature embeddings encoded by multiple peer networks, which provide various views for understanding the input data instances. Benefiting from MCL, we can learn a more discriminative representation space for classification than previous OKD methods. Experimental results on image classification demonstrate that our MCL-OKD outperform…
Feature maps contain rich information about image intensity and spatial correlation. However, previous online knowledge distillation methods only utilize the class probabilities. Thus in this paper, we propose an online knowledge distillation method that transfers not only the knowledge of the class probabilities but also that of the feature map using the adversarial training framework. We train multiple networks simultaneously by employing discriminators to distinguish the feature map distributions of different networks. Each network has its corresponding discriminator which discriminates the…
RS-OPSD: Reliable Privileged On-Policy-Self-Distillation for Ultra-High-Resolution Remote Sensing VQA
Tsinghua University（清华大学）；Zhejiang University（浙江大学）；Central University of Finance and Economics（中央财经大学）；East China Normal University（华东师范大学）；Key Laboratory of Geographic Information Science（地理信息科学重点实验室）
From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting
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）；SGIT AI Lab, State Grid Corporation of China（国家电网公司SGIT人工智能实验室）；Zhejiang University of Technology（浙江工业大学）；Baidu（百度）
Convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Most SR methods based on CNNs have focused on achieving performance gains in terms of quality metrics, such as PSNR and SSIM, over classical approaches. They typically require a large amount of memory and computational units. FSRCNN, consisting of few numbers of convolutional layers, has shown promising results, while using an extremely small number of network parameters. We introduce in this paper a novel distillation framework, consisting of teacher and student …
OPD-V: Visual On-Policy Self-Distillation with Modality Balance
National University of Singapore（新加坡国立大学）；Ludwig Maximilian University of Munich（慕尼黑大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Sun Yat-sen University（中山大学）
