mtybilly/Instruct2DS huggingface dataset:
Learning Modal-Invariant and Temporal-Memory for Video-Based Visible-Infrared Person Re-Identification
Authors: Jinxing Li, Zeyu Ma, Huafeng Li, Shuang Li, Kaixiong Xu, Guangming Lu, David Zhang Xinyu Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the "probe-to-gallery", almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal person Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code is released at: https://github.com/VCM-project233/MITML.
MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning
Authors: Ziming Hong, Guo-Sen Xie, Wenhan Yang, Qinmu Peng, Kai Wang, Jian Zhao, Xinge You Shiming Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The key challenge of zero-shot learning (ZSL) is how to infer the latent semantic knowledge between visual and attribute features on seen classes, and thus achieving a desirable knowledge transfer to unseen classes. Prior works either simply align the global features of an image with its associated class semantic vector or utilize unidirectional attention to learn the limited latent semantic representations, which could not effectively discover the intrinsic semantic knowledge (e.g., attribute semantics) between visual and attribute features. To solve the above dilemma, we propose a Mutually Semantic Distillation Network (MSDN), which progressively distills the intrinsic semantic representations between visual and attribute features for ZSL. MSDN incorporates an attribute->visual attention sub-net that learns attribute-based visual features, and a visual->attribute attention sub-net that learns visual-based attribute features. By further introducing a semantic distillation loss, the two mutual attention sub-nets are capable of learning collaboratively and teaching each other throughout the training process. The proposed MSDN yields significant improvements over the strong baselines, leading to new state-of-the-art performances on three popular challenging benchmarks. Our source codes, pre-trained models, and more results have been available at the anonymous project website: https://anonymous.4open.science/r/MSDN.
Oriented RepPoints for Aerial Object Detection
Authors: Yijie Chen, Kaixuan Hu, Jianke Zhu Wentong Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, this paper proposes an effective adaptive points learning approach to aerial object detection by taking advantage of the adaptive points representation, which is able to capture the geometric information of the arbitrary-oriented instances. To this end, three oriented conversion functions are presented to facilitate the classification and localization with accurate orientation. Moreover, we propose an effective quality assessment and sample assignment scheme for adaptive points learning toward choosing the representative oriented reppoints samples during training, which is able to capture the non-axis aligned features from adjacent objects or background noises. A spatial constraint is introduced to penalize the outlier points for roust adaptive learning. Experimental results on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD and DIOR-R, demonstrate the efficacy of our proposed approach. The source code is availabel at: https://github.com/LiWentomng/OrientedRepPoints.
OccAM's Laser: Occlusion-Based Attribution Maps for 3D Object Detectors on LiDAR Data
Authors: Georg Krispel, Horst Possegger, Peter M. Roth, Horst Bischof David Schinagl
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: While 3D object detection in LiDAR point clouds is well-established in academia and industry, the explainability of these models is a largely unexplored field. In this paper, we propose a method to generate attribution maps for the detected objects in order to better understand the behavior of such models. These maps indicate the importance of each 3D point in predicting the specific objects. Our method works with black-box models: We do not require any prior knowledge of the architecture nor access to the model's internals, like parameters, activations or gradients. Our efficient perturbation-based approach empirically estimates the importance of each point by testing the model with randomly generated subsets of the input point cloud. Our sub-sampling strategy takes into account the special characteristics of LiDAR data, such as the depth-dependent point density. We show a detailed evaluation of the attribution maps and demonstrate that they are interpretable and highly informative. Furthermore, we compare the attribution maps of recent 3D object detection architectures to provide insights into their decision-making processes.
BigDatasetGAN: Synthesizing ImageNet With Pixel-Wise Annotations
Authors: Huan Ling, Seung Wook Kim, Karsten Kreis, Sanja Fidler, Antonio Torralba Daiqing Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via a generative adversarial network (GAN) by exploiting a small set of manually labeled, GAN-generated images. Here, we scale DatasetGAN to ImageNet scale of class diversity. We take image samples from the class-conditional generative model BigGAN trained on ImageNet, and manually annotate only 5 images per class, for all 1k classes. By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN into a labeled dataset generator. We further show that VQGAN can similarly serve as a dataset generator, leveraging the already annotated data. We create a new ImageNet benchmark by labeling an additional set of real images and evaluate segmentation performance in a variety of settings. Through an extensive ablation study we show big gains in leveraging a large generated dataset to train different supervised and self-supervised backbone models on pixel-wise tasks. Furthermore, we demonstrate that using our synthesized datasets for pre-training leads to improvements over standard ImageNet pre-training on several downstream datasets, such as PASCAL-VOC, MS-COCO, Cityscapes and chest X-ray, as well as tasks (detection, segmentation). Our benchmark will be made public and maintain a leaderboard for this challenging task.
Align Representations With Base: A New Approach to Self-Supervised Learning
Authors: Lyn Qiu, Feng Zhu, Junchi Yan, Hengrui Zhang, Rui Zhao, Hongyang Li, Xiaokang Yang Shaofeng Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Existing symmetric contrastive learning methods suffer from collapses (complete and dimensional) or quadratic complexity of objectives. Departure from these methods which maximize mutual information of two generated views, along either instance or feature dimension, the proposed paradigm introduces intermediate variables at the feature level, and maximizes the consistency between variables and representations of each view. Specifically, the proposed intermediate variables are the nearest group of base vectors to representations. Hence, we call the proposed method ARB (Align Representations with Base). Compared with other symmetric approaches, ARB 1) does not require negative pairs, which leads the complexity of the overall objective function is in linear order, 2) reduces feature redundancy, increasing the information density of training samples, 3) is more robust to output dimension size, which outperforms previous feature-wise arts over 28% Top-1 accuracy on ImageNet-100 under low-dimension settings.
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Authors: Tianyu Yang, Wei Ji, Jue Wang, Li Cheng Jingjing Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level labels. Most existing methods address this problem with a "localization-by-classification" pipeline that localizes action regions based on snippet-wise classification sequences. Snippet-wise classifications are unfortunately error prone due to the sparsity of video-level labels. Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting. This is enabled by three key designs: 1) an effective pseudo-label denoising module to alleviate the side effects caused by noisy contrastive features, 2) an efficient region-level feature contrast strategy with a region-level memory bank to capture "global" contrast across the entire dataset, and 3) a diverse contrastive learning strategy to enable action-background separation as well as intra-class compactness & inter-class separability. Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate the superior performance of our approach.
Pre-Train, Self-Train, Distill: A Simple Recipe for Supersizing 3D Reconstruction
Authors: Abhinav Gupta, Shubham Tulsiani Kalyan Vasudev Alwala
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Our work learns a unified model for single-view 3D reconstruction of objects from hundreds of semantic categories. As a scalable alternative to direct 3D supervision, our work relies on segmented image collections for learning 3D of generic categories. Unlike prior works that use similar supervision but learn independent category-specific models from scratch, our approach of learning a unified model simplifies the training process while also allowing the model to benefit from the common structure across categories. Using image collections from standard recognition datasets, we show that our approach allows learning 3D inference for over 150 object categories. We evaluate using two datasets and qualitatively and quantitatively show that our unified reconstruction approach improves over prior category-specific reconstruction baselines. Our final 3D reconstruction model is also capable of zero-shot inference on images from unseen object categories and we empirically show that increasing the number of training categories improves the reconstruction quality.
Meta Distribution Alignment for Generalizable Person Re-Identification
Authors: Jingkuan Song, Xiaopeng Luo, Feng Zheng, Wen Li, Heng Tao Shen Hao Ni
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Domain Generalizable (DG) person ReID is a challenging task which trains a model on source domains yet generalizes well on target domains. Existing methods use source domains to learn domain-invariant features, and assume those features are also irrelevant with target domains. However, they do not consider the target domain information which is unavailable in the training phrase of DG. To address this issue, we propose a novel Meta Distribution Alignment (MDA) method to enable them to share similar distribution in a test-time-training fashion. Specifically, since high-dimensional features are difficult to constrain with a known simple distribution, we first introduce an intermediate latent space constrained to a known prior distribution. The source domain data is mapped to this latent space and then reconstructed back. A meta-learning strategy is introduced to facilitate generalization and support fast adaption. To reduce their discrepancy, we further propose a test-time adaptive updating strategy based on the latent space which efficiently adapts model to unseen domains with a few samples. Extensive experimental results show that our model outperforms the state-of-the-art methods by up to 5.1% R-1 on average on the large-scale and 4.7% R-1 on the single-source domain generalization ReID benchmark.
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
Authors: Teppei Suzuki
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Optimization of image transformation functions for the purpose of data augmentation has been intensively studied. In particular, adversarial data augmentation strategies, which search augmentation maximizing task loss, show significant improvement in the model generalization for many tasks. However, the existing methods require careful parameter tuning to avoid excessively strong deformations that take away image features critical for acquiring generalization. In this paper, we propose a data augmentation optimization method based on the adversarial strategy called TeachAugment, which can produce informative transformed images to the model without requiring careful tuning by leveraging a teacher model. Specifically, the augmentation is searched so that augmented images are adversarial for the target model and recognizable for the teacher model. We also propose data augmentation using neural networks, which simplifies the search space design and allows for updating of the data augmentation using the gradient method. We show that TeachAugment outperforms existing methods in experiments of image classification, semantic segmentation, and unsupervised representation learning tasks.
SVIP: Sequence VerIfication for Procedures in Videos
Authors: Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, Shenghua Gao Yicheng Qian
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In this paper, we propose a novel sequence verification task that aims to distinguish positive video pairs performing the same action sequence from negative ones with step-level transformations but still conducting the same task. Such a challenging task resides in an open-set setting without prior action detection or segmentation that requires event-level or even frame-level annotations. To that end, we carefully reorganize two publicly available action-related datasets with step-procedure-task structure. To fully investigate the effectiveness of any method, we collect a scripted video dataset enumerating all kinds of step-level transformations in chemical experiments. Besides, a novel evaluation metric Weighted Distance Ratio is introduced to ensure equivalence for different step-level transformations during evaluation. In the end, a simple but effective baseline based on the transformer encoder with a novel sequence alignment loss is introduced to better characterize long-term dependency between steps, which outperforms other action recognition methods. Codes and data will be released.
Weakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning
Authors: Yanjie Huang, Qingchao Chen, Yuxin Peng, Yang Liu Minghang Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Temporal sentence grounding aims to detect the most salient moment corresponding to the natural language query from untrimmed videos. As labeling the temporal boundaries is labor-intensive and subjective, the weakly-supervised methods have recently received increasing attention. Most of the existing weakly-supervised methods generate the proposals by sliding windows, which are content-independent and of low quality. Moreover, they train their model to distinguish positive visual-language pairs from negative ones randomly collected from other videos, ignoring the highly confusing video segments within the same video. In this paper, we propose Contrastive Proposal Learning (CPL) to overcome the above limitations. Specifically, we use multiple learnable Gaussian functions to generate both positive and negative proposals within the same video that can characterize the multiple events in a long video. Then, we propose a controllable easy to hard negative proposal mining strategy to collect negative samples within the same video, which can ease the model optimization and enables CPL to distinguish highly confusing scenes. The experiments show that our method achieves state-of-the-art performance on Charades-STA and ActivityNet Captions datasets. The code and models are available at https://github.com/minghangz/cpl.
Low-Resource Adaptation for Personalized Co-Speech Gesture Generation
Authors: Dong Won Lee, Louis-Philippe Morency Chaitanya Ahuja
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Personalizing an avatar for co-speech gesture generation from spoken language requires learning the idiosyncrasies of a person's gesture style from a small amount of data. Previous methods in gesture generation require large amounts of data for each speaker, which is often infeasible. We propose an approach, named DiffGAN, that efficiently personalizes co-speech gesture generation models of a high-resource source speaker to target speaker with just 2 minutes of target training data. A unique characteristic of DiffGAN is its ability to account for the crossmodal grounding shift, while also addressing the distribution shift in the output domain. We substantiate the effectiveness of our approach a large scale publicly available dataset through quantitative, qualitative and user studies, which show that our proposed methodology significantly outperforms prior approaches for low-resource adaptation of gesture generation. Code and videos can be found at https://chahuja.com/diffgan
BoosterNet: Improving Domain Generalization of Deep Neural Nets Using Culpability-Ranked Features
Authors: Ghassan Hamarneh, Rafeef Garbi Nourhan Bayasi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Deep learning (DL) models trained to minimize empirical risk on a single domain often fail to generalize when applied to other domains. Model failures due to poor generalizability are quite common in practice and may prove quite perilous in mission-critical applications, e.g., diagnostic imaging where real-world data often exhibits pronounced variability. Such limitations have led to increased interest in domain generalization (DG) approaches that improve the ability of models learned from a single or multiple source domains to generalize to out-of-distribution (OOD) test domains. In this work, we propose BoosterNet, a lean add-on network that can be simply appended to any arbitrary core network to improve its generalization capability without requiring any changes in its architecture or training procedure. Specifically, using a novel measure of feature culpability, BoosterNet is trained episodically on the most and least culpable data features extracted from critical units in the core network based on their contribution towards class-specific prediction errors, which have shown to improve generalization. At inference time, corresponding test image features are extracted from the closest class-specific units, determined by smart gating via a Siamese network, and fed to BoosterNet for improved generalization. We evaluate the performance of BoosterNet within two very different classification problems, digits and skin lesions, and demonstrate a marked improvement in model generalization to OOD test domains compared to SOTA.
Task-Specific Inconsistency Alignment for Domain Adaptive Object Detection
Authors: Limin Wang Liang Zhao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Detectors trained with massive labeled data often exhibit dramatic performance degradation in some particular scenarios with data distribution gap. To alleviate this problem of domain shift, conventional wisdom typically concentrates solely on reducing the discrepancy between the source and target domains via attached domain classifiers, yet ignoring the difficulty of such transferable features in coping with both classification and localization subtasks in object detection. To address this issue, in this paper, we propose Task-specific Inconsistency Alignment (TIA), by developing a new alignment mechanism in separate task spaces, improving the performance of the detector on both subtasks. Specifically, we add a set of auxiliary predictors for both classification and localization branches, and exploit their behavioral inconsistencies as finer-grained domain-specific measures. Then, we devise task-specific losses to align such cross-domain disagreement of both subtasks. By optimizing them individually, we are able to well approximate the category- and boundary-wise discrepancies in each task space, and therefore narrow them in a decoupled manner. TIA demonstrates superior results on various scenarios to the previous state-of-the-art methods. It is also observed that both the classification and localization capabilities of the detector are sufficiently strengthened, further demonstrating the effectiveness of our TIA method. Code and trained models are publicly available at https://github.com/MCG-NJU/TIA.
HDR-NeRF: High Dynamic Range Neural Radiance Fields
Authors: Qi Zhang, Ying Feng, Hongdong Li, Xuan Wang, Qing Wang Xin Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to +infty), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors, and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range.
MS2DG-Net: Progressive Correspondence Learning via Multiple Sparse Semantics Dynamic Graph
Authors: Yizhang Liu, Jiayi Ma, Lifang Wei, Taotao Lai, Changcai Yang, Riqing Chen Luanyuan Dai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Establishing superior-quality correspondences in an image pair is pivotal to many subsequent computer vision tasks. Using Euclidean distance between correspondences to find neighbors and extract local information is a common strategy in previous works. However, most such works ignore similar sparse semantics information between two given images and cannot capture local topology among correspondences well. Therefore, to deal with the above problems, Multiple Sparse Semantics Dynamic Graph Network (MS^ 2 DG-Net) is proposed, in this paper, to predict probabilities of correspondences as inliers and recover camera poses. MS^ 2 DG-Net dynamically builds sparse semantics graphs based on sparse semantics similarity between two given images, to capture local topology among correspondences, while maintaining permutation-equivariant. Extensive experiments prove that MS^ 2 DG-Net outperforms state-of-the-art methods in outlier removal and camera pose estimation tasks on the public datasets with heavy outliers. Source code:https://github.com/changcaiyang/MS2DG-Net
Neural Emotion Director: Speech-Preserving Semantic Control of Facial Expressions in "In-the-Wild" Videos
Authors: Panagiotis P. Filntisis, Petros Maragos, Anastasios Roussos Foivos Paraperas Papantoniou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In this paper, we introduce a novel deep learning method for photo-realistic manipulation of the emotional state of actors in "in-the-wild" videos. The proposed method is based on a parametric 3D face representation of the actor in the input scene that offers a reliable disentanglement of the facial identity from the head pose and facial expressions. It then uses a novel deep domain translation framework that alters the facial expressions in a consistent and plausible manner, taking into account their dynamics. Finally, the altered facial expressions are used to photo-realistically manipulate the facial region in the input scene based on an especially-designed neural face renderer. To the best of our knowledge, our method is the first to be capable of controlling the actor's facial expressions by even using as a sole input the semantic labels of the manipulated emotions, while at the same time preserving the speech-related lip movements. We conduct extensive qualitative and quantitative evaluations and comparisons, which demonstrate the effectiveness of our approach and the especially promising results that we obtain. Our method opens a plethora of new possibilities for useful applications of neural rendering technologies, ranging from movie post-production and video games to photo-realistic affective avatars.
Learning To Listen: Modeling Non-Deterministic Dyadic Facial Motion
Authors: Hanbyul Joo, Liwen Hu, Hao Li, Trevor Darrell, Angjoo Kanazawa, Shiry Ginosar Evonne Ng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present a framework for modeling interactional communication in dyadic conversations: given multimodal inputs of a speaker, we autoregressively output multiple possibilities of corresponding listener motion. We combine the motion and speech audio of the speaker using a motion-audio cross attention transformer. Furthermore, we enable non-deterministic prediction by learning a discrete latent representation of realistic listener motion with a novel motion-encoding VQ-VAE. Our method organically captures the multimodal and non-deterministic nature of nonverbal dyadic interactions. Moreover, it produces realistic 3D listener facial motion synchronous with the speaker (see video). We demonstrate that our method outperforms baselines qualitatively and quantitatively via a rich suite of experiments. To facilitate this line of research, we introduce a novel and large in-the-wild dataset of dyadic conversations. Code, data, and videos available at http://people.eecs.berkeley.edu/ evonne_ng/projects/learning2listen/.
3PSDF: Three-Pole Signed Distance Function for Learning Surfaces With Arbitrary Topologies
Authors: Cheng Lin, Weiyang Li, Bo Yang Weikai Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Recent advances in learning 3D shapes using neural implicit functions have achieved impressive results by breaking the previous barrier of resolution and diversity for varying topologies. However, most of such approaches are limited to closed surfaces as they require the space to be divided into inside and outside. More recent works based on unsigned distance function have been proposed to handle complex geometry containing both the open and closed surfaces. Nonetheless, as their direct outputs are point clouds, robustly obtaining high-quality meshing results from discrete points remains an open question. We present a novel learnable implicit representation, called the three-pole signed distance function (3PSDF), that can represent non-watertight 3D shapes with arbitrary topologies while supporting easy field-to-mesh conversion using the classic Marching Cubes algorithm. The key to our method is the introduction of a new sign, the NULL sign, in addition to the conventional in and out labels. The existence of the null sign could stop the formation of a closed isosurface derived from the bisector of the in/out regions. Further, we propose a dedicated learning framework to effectively learn 3PSDF without worrying about the vanishing gradient due to the null labels. Experimental results show that our approach outperforms the previous state-of-the-art methods in a wide range of benchmarks both quantitatively and qualitatively.
Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation From Monocular Video
Authors: Jen-Chun Lin, Tyng-Luh Liu, Hong-Yuan Mark Liao Wen-Li Wei
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human motion. To address this problem, we propose a motion pose and shape network (MPS-Net) to effectively capture humans in motion to estimate accurate and temporally coherent 3D human pose and shape from a video. Specifically, we first propose a motion continuity attention (MoCA) module that leverages visual cues observed from human motion to adaptively recalibrate the range that needs attention in the sequence to better capture the motion continuity dependencies. Then, we develop a hierarchical attentive feature integration (HAFI) module to effectively combine adjacent past and future feature representations to strengthen temporal correlation and refine the feature representation of the current frame. By coupling the MoCA and HAFI modules, the proposed MPS-Net excels in estimating 3D human pose and shape in the video. Though conceptually simple, our MPS-Net not only outperforms the state-of-the-art methods on the 3DPW, MPI-INF-3DHP, and Human3.6M benchmark datasets, but also uses fewer network parameters. The video demos can be found at https://mps-net.github.io/MPS-Net/.
MixFormer: End-to-End Tracking With Iterative Mixed Attention
Authors: Cheng Jiang, Limin Wang, Gangshan Wu Yutao Cui
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Tracking often uses a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer tracking framework simply by stacking multiple MAMs with progressive patch embedding and placing a localization head on top. In addition, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer sets a new state-of-the-art performance on five tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on VOT2020. We also perform in-depth ablation studies to demonstrate the effectiveness of simultaneous feature extraction and information integration. Code and trained models are publicly available at https://github.com/MCG-NJU/MixFormer.
Sparse Fuse Dense: Towards High Quality 3D Detection With Depth Completion
Authors: Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, Deng Cai Xiaopei Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different representations of images and point clouds make it difficult to fuse them, resulting in suboptimal performance. In this paper, we present a novel multi-modal framework SFD (Sparse Fuse Dense), which utilizes pseudo point clouds generated from depth completion to tackle the issues mentioned above. Different from prior works, we propose a new RoI fusion strategy 3D-GAF (3D Grid-wise Attentive Fusion) to make fuller use of information from different types of point clouds. Specifically, 3D-GAF fuses 3D RoI features from the pair of point clouds in a grid-wise attentive way, which is more fine-grained and more precise. In addition, we propose a SynAugment (Synchronized Augmentation) to enable our multi-modal framework to utilize all data augmentation approaches tailored to LiDAR-only methods. Lastly, we customize an effective and efficient feature extractor CPConv (Color Point Convolution) for pseudo point clouds. It can explore 2D image features and 3D geometric features of pseudo point clouds simultaneously. Our method holds the highest entry on the KITTI car 3D object detection leaderboard, demonstrating the effectiveness of our SFD. Code will be made publicly available.
GIRAFFE HD: A High-Resolution 3D-Aware Generative Model
Authors: Yuheng Li, Krishna Kumar Singh, Yong Jae Lee Yang Xue
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: 3D-aware generative models have shown that the introduction of 3D information can lead to more controllable image generation. In particular, the current state-of-the-art model GIRAFFE can control each object's rotation, translation, scale, and scene camera pose without corresponding supervision. However, GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's controllable features while generating high-quality, high-resolution images (512^2 resolution and above). The key idea is to leverage a style-based neural renderer, and to independently generate the foreground and background to force their disentanglement while imposing consistency constraints to stitch them together to composite a coherent final image. We demonstrate state-of-the-art 3D controllable high-resolution image generation on multiple natural image datasets.
InOut: Diverse Image Outpainting via GAN Inversion
Authors: Chieh Hubert Lin, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Ming-Hsuan Yang Yen-Chi Cheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. Compared to inpainting --- filling in missing pixels in a way coherent with the neighboring pixels --- outpainting can be achieved in more diverse ways since the problem is less constrained by the surrounding pixels. Existing image outpainting methods pose the problem as a conditional image-to-image translation task, often generating repetitive structures and textures by replicating the content available in the input image. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image. To outpaint an image, we seek for multiple latent codes not only recovering available patches but also synthesizing diverse outpainting by patch-based generation. This leads to richer structure and content in the outpainted regions. Furthermore, our formulation allows for outpainting conditioned on the categorical input, thereby enabling flexible user controls. Extensive experimental results demonstrate the proposed method performs favorably against existing in- and outpainting methods, featuring higher visual quality and diversity.
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Authors: Fumin Shen, Dan Huang, Qiong Wang, Xiangbo Shu, Yazhou Yao, Jinhui Tang Zeren Sun
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data. Prior literature primarily resorts to sample selection methods for combating noisy labels. However, these approaches focus on dividing samples by order sorting or threshold selection, inevitably introducing hyper-parameters (e.g., selection ratio / threshold) that are hard-to-tune and dataset-dependent. To this end, we propose a simple yet effective approach named PNP (Probabilistic Noise Prediction) to explicitly model label noise. Specifically, we simultaneously train two networks, in which one predicts the category label and the other predicts the noise type. By predicting label noise probabilistically, we identify noisy samples and adopt dedicated optimization objectives accordingly. Finally, we establish a joint loss for network update by unifying the classification loss, the auxiliary constraint loss, and the in-distribution consistency loss. Comprehensive experimental results on synthetic and real-world datasets demonstrate the superiority of our proposed method.
Estimating Structural Disparities for Face Models
Authors: Cristina Segalin, Nathan Kallus Shervin Ardeshir
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In machine learning, disparity metrics are often defined by measuring the difference in the performance or outcome of a model, across different sub-populations (groups) of datapoints. Thus, the inputs to disparity quantification consist of a model's predictions y', the ground-truth labels for the predictions y, and group labels g for the data points. Performance of the model for each group is calculated by comparing y' and y for the datapoints within a specific group, and as a result, disparity of performance across the different groups can be calculated. In many real world scenarios however, group labels (g) may not be available at scale during training and validation time, or collecting them might not be feasible or desirable as they could often be sensitive information. As a result, evaluating disparity metrics across categorical groups would not be feasible. On the other hand, in many scenarios noisy groupings may be obtainable using some form of a proxy, which would allow measuring disparity metrics across sub-populations. Here we explore performing such analysis on computer vision models trained on human faces, and on tasks such as face attribute prediction and affect estimation. Our experiments indicate that embeddings resulting from an off-the-shelf face recognition model, could meaningfully serve as a proxy for such estimation.
Revisiting the Transferability of Supervised Pretraining: An MLP Perspective
Authors: Shixiang Tang, Feng Zhu, Lei Bai, Rui Zhao, Donglian Qi, Wanli Ouyang Yizhou Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervised counterparts. This paper revisits this phenomenon and sheds new light on understanding the transferability gap between unsupervised and supervised pretraining from a multilayer perceptron (MLP) perspective. While previous works focus on the effectiveness of MLP on unsupervised image classification where pretraining and evaluation are conducted on the same dataset, we reveal that the MLP projector is also the key factor to better transferability of unsupervised pretraining methods than supervised pretraining methods. Based on this observation, we attempt to close the transferability gap between supervised and unsupervised pretraining by adding an MLP projector before the classifier in supervised pretraining. Our analysis indicates that the MLP projector can help retain intra-class variation of visual features, decrease the feature distribution distance between pretraining and evaluation datasets, and reduce feature redundancy. Extensive experiments on public benchmarks demonstrate that the added MLP projector significantly boosts the transferability of supervised pretraining, e.g. +7.2% top-1 accuracy on the concept generalization task, +5.8% top-1 accuracy for linear evaluation on 12-domain classification tasks, and +0.8% AP on COCO object detection task, making supervised pretraining comparable or even better than unsupervised pretraining.
Plenoxels: Radiance Fields Without Neural Networks
Authors: Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa Sara Fridovich-Keil
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We introduce Plenoxels (plenoptic voxels), a system for photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regularization without any neural components. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality. For video and code, please see https://alexyu.net/plenoxels.
What Matters for Meta-Learning Vision Regression Tasks?
Authors: Hanna Ziesche, Ngo Anh Vien, Michael Volpp, Gerhard Neumann Ning Gao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Meta-learning is widely used in few-shot classification and function regression due to its ability to quickly adapt to unseen tasks. However, it has not yet been well explored on regression tasks with high dimensional inputs such as images. This paper makes two main contributions that help understand this barely explored area. First, we design two new types of cross-category level vision regression tasks, namely object discovery and pose estimation of unprecedented complexity in the meta-learning domain for computer vision. To this end, we (i) exhaustively evaluate common meta-learning techniques on these tasks, and (ii) quantitatively analyze the effect of various deep learning techniques commonly used in recent meta-learning algorithms in order to strengthen the generalization capability: data augmentation, domain randomization, task augmentation and meta-regularization. Finally, we (iii) provide some insights and practical recommendations for training meta-learning algorithms on vision regression tasks. Second, we propose the addition of functional contrastive learning (FCL) over the task representations in Conditional Neural Processes (CNPs) and train in an end-to-end fashion. The experimental results show that the results of prior work are misleading as a consequence of a poor choice of the loss function as well as too small meta-training sets. Specifically, we find that CNPs outperform MAML on most tasks without fine-tuning. Furthermore, we observe that naive task augmentation without a tailored design results in underfitting.
Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition
Authors: Shangfei Wang Yanan Chang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Facial action unit (AU) recognition is formulated as a supervised learning problem by recent works. However, the complex labeling process makes it challenging to provide AU annotations for large amounts of facial images. To remedy this, we utilize AU labeling rules defined by the Facial Action Coding System (FACS) to design a novel knowledge-driven self-supervised representation learning framework for AU recognition. The representation encoder is trained using large amounts of facial images without AU annotations. AU labeling rules are summarized from FACS to design facial partition manners and determine correlations between facial regions. The method utilizes a backbone network to extract local facial area representations and a project head to map the representations into a low-dimensional latent space. In the latent space, a contrastive learning component leverages the inter-area difference to learn AU-related local representations while maintaining intra-area instance discrimination. Correlations between facial regions summarized from AU labeling rules are also explored to further learn representations using a predicting learning component. Evaluation on two benchmark databases demonstrates that the learned representation is powerful and data-efficient for AU recognition.