DeepNLP/CVPR-2021-Accepted-Papers huggingface dataset:
Learning Compositional Representation for 4D Captures With Neural ODE
Authors: Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu

Abstract: Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively. Each component is represented by a latent code via a trained encoder. To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp. To this end, we propose an Identity Exchange Training (IET) strategy to encourage the network to learn effectively decoupling each component. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art deep learning based methods on 4D reconstruction, and significantly improves on various tasks, including motion transfer and completion.
Effective Snapshot Compressive-Spectral Imaging via Deep Denoising and Total Variation Priors
Authors: Haiquan Qiu, Yao Wang, Deyu Meng

Abstract: Snapshot compressive imaging (SCI) is a new type of compressive imaging system that compresses multiple frames of images into a single snapshot measurement, which enjoys low cost, low bandwidth, and high-speed sensing rate. By applying the existing SCI methods to deal with hyperspectral images, however, could not fully exploit the underlying structures, and thereby demonstrate unsatisfactory reconstruction performance. To remedy such issue, this paper aims to propose a new effective method by taking advantage of two intrinsic priors of the hyperspectral images, namely deep image denoising and total variation (TV) priors. Specifically, we propose an optimization objective to utilize these two priors. By solving this optimization objective, our method is equivalent to incorporate a weighted FFDNet and a 2DTV or 3DTV denoiser into the plug-and-play framework. Extensive numerical experiments demonstrate the outperformance of the proposed method over several state-of-the-art alternatives. Additionally, we provide a detailed convergence analysis of the resulting plug-and-play algorithm under relatively weak conditions such as without using diminishing step sizes. The code is available at https://github.com/ucker/SCI-TV-FFDNet.
LAFEAT: Piercing Through Adversarial Defenses With Latent Features
Authors: Yunrui Yu, Xitong Gao, Cheng-Zhong Xu

Abstract: Deep convolutional neural networks are susceptible to adversarial attacks. They can be easily deceived to give an incorrect output by adding a tiny perturbation to the input. This presents a great challenge in making CNNs robust against such attacks. An influx of new defense techniques have been proposed to this end. In this paper, we show that latent features in certain "robust" models are surprisingly susceptible to adversarial attacks. On top of this, we introduce a unified Linfinity white-box attack algorithm which harnesses latent features in its gradient descent steps, namely LAFEAT. We show that not only is it computationally much more efficient for successful attacks, but it is also a stronger adversary than the current state-of-the-art across a wide range of defense mechanisms. This suggests that model robustness could be contingent the effective use of the defender's hidden components, and it should no longer be viewed from a holistic perspective.
Exploiting Spatial Dimensions of Latent in GAN for Real-Time Image Editing
Authors: Hyunsu Kim, Yunjey Choi, Junho Kim, Sungjoo Yoo, Youngjung Uh

Abstract: Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN. Source code is available at https://github.com/naver-ai/StyleMapGAN.
Bidirectional Projection Network for Cross Dimension Scene Understanding
Authors: Wenbo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong

Abstract: 2D image representations are in regular grids and can be processed efficiently, whereas 3D point clouds are unordered and scattered in 3D space. The information inside these two visual domains is well complementary, e.g., 2D images have fine-grained texture while 3D point clouds contain plentiful geometry information. However, most current visual recognition systems process them individually. In this paper, we present a bidirectional projection network (BPNet) for joint 2D and 3D reasoning in an end-to-end manner. It contains 2D and 3D sub-networks with symmetric architectures, that are connected by our proposed bidirectional projection module (BPM). Via the BPM, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition. Extensive quantitative and qualitative experimental evaluations show that joint reasoning over 2D and 3D visual domains can benefit both 2D and 3D scene understanding simultaneously. Our BPNet achieves top performance on the ScanNetV2 benchmark for both 2D and 3D semantic segmentation. Code is available at https://github.com/wbhu/BPNet.
Event-Based Synthetic Aperture Imaging With a Hybrid Network
Authors: Xiang Zhang, Wei Liao, Lei Yu, Wen Yang, Gui-Song Xia

Abstract: Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on conventional frame-based cameras, leading to performance degeneration. To address these problems, we propose a novel SAI system based on the event camera which can produce asynchronous events with extremely low latency and high dynamic range. Thus, it can eliminate the interference of dense occlusions by measuring with almost continuous views, and simultaneously tackle the over/under exposure problems. To reconstruct the occluded targets, we propose a hybrid encoder-decoder network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs). In the hybrid network, the spatio-temporal information of the collected events is first encoded by SNN layers, and then transformed to the visual image of the occluded targets by a style-transfer CNN decoder. Through experiments, the proposed method shows remarkable performance in dealing with very dense occlusions and extreme lighting conditions, and high quality visual images can be reconstructed using pure event data.
RSG: A Simple but Effective Module for Learning Imbalanced Datasets
Authors: Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Jianfei Cai, Zhenghua Xu

Abstract: Imbalanced datasets widely exist in practice and are a great challenge for training deep neural models with a good generalization on infrequent classes. In this work, we propose a new rare-class sample generator (RSG) to solve this problem. RSG aims to generate some new samples for rare classes during training, and it has in particular the following advantages: (1) it is convenient to use and highly versatile, because it can be easily integrated into any kind of convolutional neural network, and it works well when combined with different loss functions, and (2) it is only used during the training phase, and therefore, no additional burden is imposed on deep neural networks during the testing phase. In extensive experimental evaluations, we verify the effectiveness of RSG. Furthermore, by leveraging RSG, we obtain competitive results on Imbalanced CIFAR and new state-of-the-art results on Places-LT, ImageNet-LT, and iNaturalist 2018. The source code is available at https://github.com/Jianf-Wang/RSG.
Learning Statistical Texture for Semantic Segmentation
Authors: Lanyun Zhu, Deyi Ji, Shiping Zhu, Weihao Gan, Wei Wu, Junjie Yan

Abstract: Existing semantic segmentation works mainly focus on learning the contextual information in high-level semantic features with CNNs. In order to maintain a precise boundary, low-level texture features are directly skip-connected into the deeper layers. Nevertheless, texture features are not only about local structure, but also include global statistical knowledge of the input image. In this paper, we fully take advantages of the low-level texture features and propose a novel Statistical Texture Learning Network (STLNet) for semantic segmentation. For the first time, STLNet analyzes the distribution of low level information and efficiently utilizes them for the task. Specifically, a novel Quantization and Counting Operator (QCO) is designed to describe the texture information in a statistical manner. Based on QCO, two modules are introduced: (1) Texture Enhance Module (TEM), to capture texture-related information and enhance the texture details; (2) Pyramid Texture Feature Extraction Module (PTFEM), to effectively extract the statistical texture features from multiple scales. Through extensive experiments, we show that the proposed STLNet achieves state-of-the-art performance on three semantic segmentation benchmarks: Cityscapes, PASCAL Context and ADE20K.
Neural Feature Search for RGB-Infrared Person Re-Identification
Authors: Yehansen Chen, Lin Wan, Zhihang Li, Qianyan Jing, Zongyuan Sun

Abstract: RGB-Infrared person re-identification (RGB-IR ReID) is a challenging cross-modality retrieval problem, which aims at matching the person-of-interest over visible and infrared camera views. Most existing works achieve performance gains through manually-designed feature selection modules, which often require significant domain knowledge and rich experience. In this paper, we study a general paradigm, termed Neural Feature Search (NFS), to automate the process of feature selection. Specifically, NFS combines a dual-level feature search space and a differentiable search strategy to jointly select identity-related cues in coarse-grained channels and fine-grained spatial pixels. This combination allows NFS to adaptively filter background noises and concentrate on informative parts of human bodies in a data-driven manner. Moreover, a cross-modality contrastive optimization scheme further guides NFS to search features that can minimize modality discrepancy whilst maximizing inter-class distance. Extensive experiments on mainstream benchmarks demonstrate that our method outperforms state-of-the-arts, especially achieving better performance on the RegDB dataset with significant improvement of 11.20% and 8.64% in Rank-1 and mAP, respectively.
FP-NAS: Fast Probabilistic Neural Architecture Search
Authors: Zhicheng Yan, Xiaoliang Dai, Peizhao Zhang, Yuandong Tian, Bichen Wu, Matt Feiszli

Abstract: Differential Neural Architecture Search (NAS) requires all layer choices to be held in memory simultaneously; this limits the size of both search space and final architecture. In contrast, Probabilistic NAS, such as PARSEC, learns a distribution over high-performing architectures, and uses only as much memory as needed to train a single model. Nevertheless, it needs to sample many architectures, making it computationally expensive for searching in an extensive space. To solve these problems, we propose a sampling method adaptive to the distribution entropy, drawing more samples to encourage explorations at the beginning, and reducing samples as learning proceeds. Furthermore, to search fast in the multi-variate space, we propose a coarse-to-fine strategy by using a factorized distribution at the beginning which can reduce the number of architecture parameters by over an order of magnitude. We call this method Fast Probabilistic NAS (FP-NAS). Compared with PARSEC, it can sample 64% fewer architectures and search 2.1x faster. Compared with FBNetV2, FP-NAS is 1.9x - 3.5x faster, and the searched models outperform FBNetV2 models on ImageNet. FP-NAS allows us to expand the giant FBNetV2 space to be wider (i.e. larger channel choices) and deeper (i.e. more blocks), while adding Split-Attention block and enabling the search over the number of splits. When searching a model of size 0.4G FLOPS, FP-NAS is 132x faster than EfficientNet, and the searched FP-NAS-L0 model outperforms EfficientNet-B0 by 0.7% accuracy. Without using any architecture surrogate or scaling tricks, we directly search large models up to 1.0G FLOPS. Our FP-NAS-L2 model with simple distillation outperforms BigNAS-XL with advanced in-place distillation by 0.7% accuracy using similar FLOPS.
Fast Sinkhorn Filters: Using Matrix Scaling for Non-Rigid Shape Correspondence With Functional Maps
Authors: Gautam Pai, Jing Ren, Simone Melzi, Peter Wonka, Maks Ovsjanikov

Abstract: In this paper, we provide a theoretical foundation for pointwise map recovery from functional maps and highlight its relation to a range of shape correspondence methods based on spectral alignment. With this analysis in hand, we develop a novel spectral registration technique: Fast Sinkhorn Filters, which allows for the recovery of accurate and bijective pointwise correspondences with a superior time and memory complexity in comparison to existing approaches. Our method combines the simple and concise representation of correspondence using functional maps with the matrix scaling schemes from computational optimal transport. By exploiting the sparse structure of the kernel matrices involved in the transport map computation, we provide an efficient trade-off between acceptable accuracy and complexity for the problem of dense shape correspondence.
Bilevel Online Adaptation for Out-of-Domain Human Mesh Reconstruction
Authors: Shanyan Guan, Jingwei Xu, Yunbo Wang, Bingbing Ni, Xiaokang Yang

Abstract: This paper considers a new problem of adapting a pre-trained model of human mesh reconstruction to out-of-domain streaming videos. However, most previous methods based on the parametric SMPL model underperform in new domains with unexpected, domain-specific attributes, such as camera parameters, lengths of bones, backgrounds, and occlusions. Our general idea is to dynamically fine-tune the source model on test video streams with additional temporal constraints, such that it can mitigate the domain gaps without over-fitting the 2D information of individual test frames. A subsequent challenge is how to avoid conflicts between the 2D and temporal constraints. We propose to tackle this problem using a new training algorithm named Bilevel Online Adaptation (BOA), which divides the optimization process of overall multi-objective into two steps of weight probe and weight update in a training iteration. We demonstrate that BOA leads to state-of-the-art results on two human mesh reconstruction benchmarks.
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
Authors: Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, Michael Firman

Abstract: Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the-shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available. We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time.
Distribution-Aware Adaptive Multi-Bit Quantization
Authors: Sijie Zhao, Tao Yue, Xuemei Hu

Abstract: In this paper, we explore the compression of deep neural networks by quantizing the weights and activations into multi-bit binary networks (MBNs). A distribution-aware multi-bit quantization (DMBQ) method that incorporates the distribution prior into the optimization of quantization is proposed. Instead of solving the optimization in each iteration, DMBQ search the optimal quantization scheme over the distribution space beforehand, and select the quantization scheme during training using a fast lookup table based strategy. Based upon DMBQ, we further propose loss-guided bit-width allocation (LBA) to adaptively quantize and even prune the neural network. The first-order Taylor expansion is applied to build a metric for evaluating the loss sensitivity of the quantization of each channel, and automatically adjust the bit-width of weights and activations channel-wisely. We extend our method to image classification tasks and experimental results show that our method not only outperforms state-of-the-art quantized networks in terms of accuracy but also is more efficient in terms of training time compared with state-of-the-art MBNs, even for the extremely low bit width (below 1-bit) quantization cases.
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
Authors: Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, Marcus Rohrbach

Abstract: One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, the setting when the knowledge required to answer a question is not given/annotated, neither at training nor test time. We tap into two types of knowledge representations and reasoning. First, implicit knowledge which can be learned effectively from unsupervised language pretraining and supervised training data with transformer-based models. Second, explicit, symbolic knowledge encoded in knowledge bases. Our approach combines both---exploiting the powerful implicit reasoning of transformer models for answer prediction, and integrating symbolic representations from a knowledge graph, while never losing their explicit semantics to an implicit embedding. We combine diverse sources of knowledge to cover the wide variety of knowledge needed to solve knowledge-based questions. We show our approach, KRISP, significantly outperforms state-of-the-art on OK-VQA, the largest available dataset for open-domain knowledge-based VQA. We show with extensive ablations that while our model successfully exploits implicit knowledge reasoning, the symbolic answer module which explicitly connects the knowledge graph to the answer vocabulary is critical to the performance of our method and generalizes to rare answers.
Amalgamating Knowledge From Heterogeneous Graph Neural Networks
Authors: Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, Dacheng Tao

Abstract: In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs). We strive to train a multi-talented student GNN, without accessing human annotations, that "amalgamates" knowledge from a couple of teacher GNNs with heterogeneous architectures and handling distinct tasks. The student derived in this way is expected to integrate the expertise from both teachers while maintaining a compact architecture. To this end, we propose an innovative approach to train a slimmable GNN that enables learning from teachers with varying feature dimensions. Meanwhile, to explicitly align topological semantics between the student and teachers, we introduce a topological attribution map (TAM) to highlight the structural saliency in a graph, based on which the student imitates the teachers' ways of aggregating information from neighbors. Experiments on seven datasets across various tasks, including multi-label classification and joint segmentation-classification, demonstrate that the learned student, with a lightweight architecture, achieves gratifying results on par with and sometimes even superior to those of the teachers in their specializations. Our code is publicly available at https://github.com/ycjing/AmalgamateGNN.PyTorch.
MetaSets: Meta-Learning on Point Sets for Generalizable Representations
Authors: Chao Huang, Zhangjie Cao, Yunbo Wang, Jianmin Wang, Mingsheng Long

Abstract: Deep learning techniques for point clouds have achieved strong performance on a range of 3D vision tasks. However, it is costly to annotate large-scale point sets, making it critical to learn generalizable representations that can transfer well across different point sets. In this paper, we study a new problem of 3D Domain Generalization (3DDG) with the goal to generalize the model to other unseen domains of point clouds without any access to them in the training process. It is a challenging problem due to the substantial geometry shift from simulated to real data, such that most existing 3D models underperform due to overfitting the complete geometries in the source domain. We propose to tackle this problem with MetaSets, which meta-learns point cloud representations from a set of classification tasks on carefully-designed transformed point sets containing specific geometry priors. The learned representations are more generalizable to various unseen domains of different geometries. We design two benchmarks for Sim-to-Real transfer of 3D point clouds. Experimental results show that MetaSets outperforms existing 3D deep learning methods by large margins.