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Tree-like Decision Distillation

Tree-like Decision Distillation

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Song, Jie Zhang, Haofei Wang, Xinchao Xue, Mengqi Chen, Ying Sun, Li Tao, Dacheng Song, Mingli Zhejiang Univ Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore Univ Sydney Sydney NSW Australia Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model. Prior methods achieve this by making the student imitate shallow behaviors, such as soft targets, features, or attention, of the teacher. In this paper, we argue that what really matters for distillation is the intrinsic problem-solving process captured by the teacher. By dissecting the decision process in a layer-wise manner, we found that the decision-making procedure in the teacher model is conducted in a coarse-to-fine manner, where coarse-grained discrimination (e.g., animal vs vehicle) is attained in early layers, and fine-grained discrimination (e.g., dog vs cat, car vs truck) in latter layers. Motivated by this observation, we propose a new distillation method, dubbed as Tree-like Decision Distillation (TDD), to endow the student with the same problem-solving mechanism as that of the teacher. Extensive experiments demonstrated that TDD yields competitive performance compared to state of the arts. More importantly, it enjoys better interpretability due to its interpretable decision distillation instead of dark knowledge distillation.

关键词： Knowledge engineering computer vision Cats Decision making Dogs pattern recognition Problem-solving

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Exploring Heterogeneous Clues for Weakly-Supervised Audio-Visual Video Parsing

Exploring Heterogeneous Clues for Weakly-Supervised Audio-Vi...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wu, Yu Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

We investigate the weakly-supervised audio-visual video parsing task, which aims to parse a video into temporal event segments and predict the audible or visible event categories. The task is challenging since there only exist video-level event labels for training, without indicating the temporal boundaries and modalities. Previous works take the overall event labels to supervise both audio and visual model predictions. However, we argue that such overall labels harm the model training due to the audio-visual asynchrony. For example, commentators speak in a basketball video, but we cannot visually find the speakers. In this paper, we tackle this issue by leveraging the cross-modal correspondence of audio and visual signals. We generate reliable event labels individually for each modality by swapping audio and visual tracks with other unrelated videos. If the original visual/audio data contain event clues, the event prediction from the newly assembled data would still be highly confident. In this way, we could protect our models from being misled by ambiguous event labels. In addition, we propose the cross-modal audio-visual contrastive learning to induce temporal difference on attention models within videos, i.e., urging the model to pick the current temporal segment from all context candidates. Experiments show we outperform state-of-the-art methods by a large margin.

关键词： Training Visualization computer vision Target tracking Annotations Predictive models pattern recognition

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Asymmetric metric learning for knowledge transfer

Asymmetric metric learning for knowledge transfer

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Budnik, Mateusz Avrithis, Yannis Univ Rennes IRISA CNRS INRIA Rennes France

ISBN: (纸本)9781665445092

Knowledge transfer from large teacher models to smaller student models has recently been studied for metric learning, focusing on fine-grained classification. In this work, focusing on instance-level image retrieval, we study an asymmetric testing task, where the database is represented by the teacher and queries by the student. Inspired by this task, we introduce asymmetric metric learning, a novel paradigm of using asymmetric representations at training. This acts as a simple combination of knowledge transfer with the original metric learning task. We systematically evaluate different teacher and student models, metric learning and knowledge transfer loss functions on the new asymmetric testing as well as the standard symmetric testing task, where database and queries are represented by the same model. We find that plain regression is surprisingly effective compared to more complex knowledge transfer mechanisms, working best in asymmetric testing. Interestingly, our asymmetric metric learning approach works best in symmetric testing, allowing the student to even outperform the teacher. Our implementation is publicly available,(1) including trained student models for all loss functions and all pairs of teacher/student models.(2)

关键词： Measurement Training Databases Image retrieval Focusing pattern recognition Task analysis

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CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation

CoCosNet v2: Full-Resolution Correspondence Learning for Ima...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhou, Xingran Zhang, Bo Zhang, Ting Zhang, Pan Bao, Jianmin Chen, Dong Zhang, Zhongfei Wen, Fang Zhejiang Univ Hangzhou Zhejiang Peoples R China Microsoft Res Asia Beijing Peoples R China SUNY Binghamton Binghamton NY 13902 USA USTC Hefei Anhui Peoples R China

ISBN: (纸本)9781665445092

We present the full-resolution correspondence learning for cross-domain images, which aids image translation. We adopt a hierarchical strategy that uses the correspondence from coarse level to guide the fine levels. At each hierarchy, the correspondence can be efficiently computed via PatchMatch that iteratively leverages the matchings from the neighborhood. Within each PatchMatch iteration, the ConvGRU module is employed to refine the current correspondence considering not only the matchings of larger context but also the historic estimates. The proposed CoCosNet v2, a GRU-assisted PatchMatch approach, is fully differentiable and highly efficient. When jointly trained with image translation, full-resolution semantic correspondence can be established in an unsupervised manner, which in turn facilitates the exemplar-based image translation. Experiments on diverse translation tasks show that CoCosNet v2 performs considerably better than state-of-the-art literature on producing high-resolution images.

关键词： computer vision Image resolution Semantics Iterative algorithms pattern recognition Task analysis

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Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation

Instance Level Affinity-Based Transfer for Unsupervised Doma...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Sharma, Astuti Kalluri, Tarun Chandraker, Manmohan Univ Calif San Diego La Jolla CA 92093 USA

ISBN: (纸本)9781665445092

Domain adaptation deals with training models using large scale labeled data from a specific source domain and then adapting the knowledge to certain target domains that have few or no labels. Many prior works learn domain agnostic feature representations for this purpose using a global distribution alignment objective which does not take into account the finer class specific structure in the source and target domains. We address this issue in our work and propose an instance affinity based criterion for source to target transfer during adaptation, called ILA-DA. We first propose a reliable and efficient method to extract similar and dissimilar samples across source and target, and utilize a multi-sample contrastive loss to drive the domain alignment process. ILA-DA simultaneously accounts for intra-class clustering as well as inter-class separation among the categories, resulting in less noisy classifier boundaries, improved transferability and increased accuracy. We verify the effectiveness of ILA-DA by observing consistent improvements in accuracy over popular domain adaptation approaches on a variety of benchmark datasets and provide insights into the proposed alignment approach. Code will be made publicly available at https://***/astuti/ILA-DA.

关键词： Training Adaptation models computer vision Benchmark testing Drives Data models Numerical models

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CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models

CausalVAE: Disentangled Representation Learning via Neural S...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Mengyue Liu, Furui Chen, Zhitang Shen, Xinwei Hao, Jianye Wang, Jun Huawei Noahs Ark Lab Shenzhen Peoples R China UCL London England Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of variational autoencoder (VAE) is commonly used to disentangle independent factors from observations. However, in real scenarios, factors with semantics are not necessarily independent. Instead, there might be an underlying causal structure which renders these factors dependent. We thus propose a new VAE based framework named CausalVAE, which includes a Causal Layer to transform independent exogenous factors into causal endogenous ones that correspond to causally related concepts in data. We further analyze the model identifiabitily, showing that the proposed model learned from observations recovers the true one up to a certain degree. Experiments are conducted on various datasets, including synthetic and real word benchmark CelebA. Results show that the causal representations learned by CausalVAE are semantically interpretable, and their causal relationship as a Directed Acyclic Graph (DAG) is identified with good accuracy. Furthermore, we demonstrate that the proposed CausalVAE model is able to generate counterfactual data through "do-operation" to the causal factors.

关键词： Analytical models Directed acyclic graph computer vision Semantics Transforms Benchmark testing Data models

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Pixel-aligned Volumetric Avatars

Pixel-aligned Volumetric Avatars

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Raj, Amit Zollhofer, Michael Simon, Tomas Saragih, Jason Saito, Shunsuke Hays, James Lombardi, Stephen Georgia Inst Technol Atlanta GA 30332 USA Facebook Real Labs Res Menlo Pk CA USA

ISBN: (纸本)9781665445092

Acquisition and rendering of photo-realistic human heads is a highly challenging research problem of particular importance for virtual telepresence. Currently, the highest quality is achieved by volumetric approaches trained in a person-specific manner on multi-view data. These models better represent fine structure, such as hair, compared to simpler mesh-based models. Volumetric models typically employ a global code to represent facial expressions, such that they can be driven by a small set of animation parameters. While such architectures achieve impressive rendering quality, they can not easily be extended to the multi-identity setting. In this paper, we devise a novel approach for predicting volumetric avatars of the human head given just a small number of inputs. We enable generalization across identities by a novel parameterization that combines neural radiance fields with local, pixel-aligned features extracted directly from the inputs, thus side-stepping the need for very deep or complex networks. Our approach is trained in an end-to-end manner solely based on a photometric re-rendering loss without requiring explicit 3D supervision. We demonstrate that our approach outperforms the existing state of the art in terms of quality and is able to generate faithful facial expressions in a multi-identity setting.

关键词： Hair computer vision Head Three-dimensional displays Telepresence Avatars Neural networks

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Person Re-identification using Heterogeneous Local Graph Attention Networks

Person Re-identification using Heterogeneous Local Graph Att...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Zhong Zhang, Haijia Liu, Shuang Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin Peoples R China

ISBN: (纸本)9781665445092

Recently, some methods have focused on learning local relation among parts of pedestrian images for person reidentification (Re-ID), as it offers powerful representation capabilities. However, they only provide the intra-local relation among parts within single pedestrian image and ignore the inter-local relation among parts from different images, which results in incomplete local relation information. In this paper, we propose a novel deep graph model named Heterogeneous Local Graph Attention Networks (HLGAT) to model the inter-local relation and the intra-local relation in the completed local graph, simultaneously. Specifically, we first construct the completed local graph using local features, and we resort to the attention mechanism to aggregate the local features in the learning process of inter-local relation and intra-local relation so as to emphasize the importance of different local features. As for the inter-local relation, we propose the attention regularization loss to constrain the attention weights based on the identities of local features in order to describe the inter-local relation accurately. As for the intra-local relation, we propose to inject the contextual information into the attention weights to consider structure information. Extensive experiments on Market-1501, CUHK03, DukeMTMC-reID and MSMT17 demonstrate that the proposed HLGAT outperforms the state-of-the-art methods.

关键词： computer vision Aggregates pattern recognition Context modeling

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Hierarchical Lovasz Embeddings for Proposal-free Panoptic Segmentation

Hierarchical Lovasz Embeddings for Proposal-free Panoptic Se...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Kerola, Tommi Li, Jie Kanehira, Atsushi Kudo, Yasunori Vallet, Alexis Gaidon, Adrien Preferred Networks Inc Tokyo Japan Toyota Res Inst TRI Los Altos CA USA

ISBN: (纸本)9781665445092

Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simultaneously instance-specific and category-specific (i.e. instance-agnostic) representations jointly. Hence, state-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. In contrast, we propose Hierarchical Lovasz Embeddings, per pixel feature vectors that simultaneously encode instance- and category-level discriminative information. We use a hierarchical Lovasz hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals. Besides modeling instances precisely in a proposal-free manner, our Hierarchical Lovasz Embeddings generalize to categories by using a simple Nearest-Class-Mean classifier, including for non-instance "stuff" classes where instance segmentation methods are not applicable. Our simple model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas. Furthermore, our model demonstrates temporal stability between video frames.

关键词： Semantics Fasteners Predictive models Ontologies Stability analysis pattern recognition Proposals

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Generic Perceptual Loss for Modeling Structured Output Dependencies

Generic Perceptual Loss for Modeling Structured Output Depen...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yifan Chen, Hao Chen, Yu Yin, Wei Shen, Chunhua Univ Adelaide Adelaide SA Australia Automind Vancouver BC Canada Monash Univ Clayton Vic Australia

ISBN: (纸本)9781665445092

The perceptual loss has been widely used as an effective loss term in image synthesis tasks including image super-resolution [16], and style transfer [14]. It was believed that the success lies in the high-level perceptual feature representations extracted from CNNs pretrained with a large set of images. Here we reveal that, what matters is the network structure instead of the trained weights. Without any learning, the structure of a deep network is sufficient to capture the dependencies between multiple levels of variable statistics using multiple layers of CNNs. This insight removes the requirements of pre-training and a particular network structure (commonly, VGG) that are previously assumed for the perceptual loss, thus enabling a significantly wider range of applications. To this end, we demonstrate that a randomly-weighted deep CNN can be used to model the structured dependencies of outputs. On a few dense perpixel prediction tasks such as semantic segmentation, depth estimation and instance segmentation, we show improved results of using the extended randomized perceptual loss, compared to the baselines using pixel-wise loss alone. We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.

关键词： Training computer vision Image segmentation Image synthesis Semantics Superresolution Estimation

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