mtybilly/Instruct2DS huggingface dataset:
Adversarial Texture for Fooling Person Detectors in the Physical World
Authors: Siyuan Huang, Xiaopei Zhu, Fuchun Sun, Bo Zhang, Xiaolin Hu Zhanhao Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Nowadays, cameras equipped with AI systems can capture and analyze images to detect people automatically. However, the AI system can make mistakes when receiving deliberately designed patterns in the real world, i.e., physical adversarial examples. Prior works have shown that it is possible to print adversarial patches on clothes to evade DNN-based person detectors. However, these adversarial examples could have catastrophic drops in the attack success rate when the viewing angle (i.e., the camera's angle towards the object) changes. To perform a multi-angle attack, we propose Adversarial Texture (AdvTexture). AdvTexture can cover clothes with arbitrary shapes so that people wearing such clothes can hide from person detectors from different viewing angles. We propose a generative method, named Toroidal-Cropping-based Expandable Generative Attack (TC-EGA), to craft AdvTexture with repetitive structures. We printed several pieces of cloth with AdvTexure and then made T-shirts, skirts, and dresses in the physical world. Experiments showed that these clothes could fool person detectors in the physical world.
Focal Length and Object Pose Estimation via Render and Compare
Authors: Yann Labbé, Bryan Russell, Mathieu Aubry, Josef Sivic Georgy Ponimatkin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We introduce FocalPose, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are twofold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods.
TO-FLOW: Efficient Continuous Normalizing Flows With Temporal Optimization Adjoint With Moving Speed
Authors: Yihong Luo, Wei Chen, Jian Xu, Delu Zeng Shian Du
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Continuous normalizing flows (CNFs) construct invertible mappings between an arbitrary complex distribution and an isotropic Gaussian distribution using Neural Ordinary Differential Equations (neural ODEs). It has not been tractable on large datasets due to the incremental complexity of the neural ODE training. Optimal Transport theory has been applied to regularize the dynamics of the ODE to speed up training in recent works. In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training. In this appoach, we optimize the network weights of the CNF alternately with evolutionary time by coordinate descent. Further with temporal regularization, stability of the evolution is ensured. This approach can be used in conjunction with the original regularization approach. We have experimentally demonstrated that the proposed approach can significantly accelerate training without sacrifying performance over baseline models.
Arbitrary-Scale Image Synthesis
Authors: Mohamad Shahbazi, Iason Kastanis, Radu Timofte, Martin Danelljan, Luc Van Gool Evangelos Ntavelis
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator's layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis.
Cross-Modal Representation Learning for Zero-Shot Action Recognition
Authors: Kevin Lin, Lijuan Wang, Zicheng Liu, Linjie Li Chung-Ching Lin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present a cross-modal Transformer-based framework, which jointly encodes video data and text labels for zero-shot action recognition (ZSAR). Our model employs a conceptually new pipeline by which visual representations are learned in conjunction with visual-semantic associations in an end-to-end manner. The model design provides a natural mechanism for visual and semantic representations to be learned in a shared knowledge space, whereby it encourages the learned visual embedding to be discriminative and more semantically consistent. In zero-shot inference, we devise a simple semantic transfer scheme that embeds semantic relatedness information between seen and unseen classes to composite unseen visual prototypes. Accordingly, the discriminative features in the visual structure could be preserved and exploited to alleviate the typical zero-shot issues of information loss, semantic gap, and the hubness problem. Under a rigorous zero-shot setting of not pre-training on additional datasets, the experiment results show our model considerably improves upon the state of the arts in ZSAR, reaching encouraging top-1 accuracy on UCF101, HMDB51, and ActivityNet benchmark datasets. Code will be made available.
Conditional Prompt Learning for Vision-Language Models
Authors: Jingkang Yang, Chen Change Loy, Ziwei Liu Kaiyang Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: With the rise of powerful pre-trained vision-language models like CLIP, it becomes essential to investigate ways to adapt these models to downstream datasets. A recently proposed method named Context Optimization (CoOp) introduces the concept of prompt learning---a recent trend in NLP---to the vision domain for adapting pre-trained vision-language models. Specifically, CoOp turns context words in a prompt into a set of learnable vectors and, with only a few labeled images for learning, can achieve huge improvements over intensively-tuned manual prompts. In our study we identify a critical problem of CoOp: the learned context is not generalizable to wider unseen classes within the same dataset, suggesting that CoOp overfits base classes observed during training. To address the problem, we propose Conditional Context Optimization (CoCoOp), which extends CoOp by further learning a lightweight neural network to generate for each image an input-conditional token (vector). Compared to CoOp's static prompts, our dynamic prompts adapt to each instance and are thus less sensitive to class shift. Extensive experiments show that CoCoOp generalizes much better than CoOp to unseen classes, even showing promising transferability beyond a single dataset; and yields stronger domain generalization performance as well. Code is available at https://github.com/KaiyangZhou/CoOp.
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification
Authors: Ling Shao Shengcai Liao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Recent studies show that, both explicit deep feature matching as well as large-scale and diverse training data can significantly improve the generalization of person re-identification. However, the efficiency of learning deep matchers on large-scale data has not yet been adequately studied. Though learning with classification parameters or class memory is a popular way, it incurs large memory and computational costs. In contrast, pairwise deep metric learning within mini batches would be a better choice. However, the most popular random sampling method, the well-known PK sampler, is not informative and efficient for deep metric learning. Though online hard example mining has improved the learning efficiency to some extent, the mining in mini batches after random sampling is still limited. This inspires us to explore the use of hard example mining earlier, in the data sampling stage. To do so, in this paper, we propose an efficient mini-batch sampling method, called graph sampling (GS), for large-scale deep metric learning. The basic idea is to build a nearest neighbor relationship graph for all classes at the beginning of each epoch. Then, each mini batch is composed of a randomly selected class and its nearest neighboring classes so as to provide informative and challenging examples for learning. Together with an adapted competitive baseline, we improve the state of the art in generalizable person re-identification significantly, by 25.1% in Rank-1 on MSMT17 when trained on RandPerson. Besides, the proposed method also outperforms the competitive baseline, by 6.8% in Rank-1 on CUHK03-NP when trained on MSMT17. Meanwhile, the training time is significantly reduced, from 25.4 hours to 2 hours when trained on RandPerson with 8,000 identities. Code is available at https://github.com/ShengcaiLiao/QAConv.
Retrieval-Based Spatially Adaptive Normalization for Semantic Image Synthesis
Authors: Xiao Liu, Yuxiang Wei, Zhongqin Wu, Wangmeng Zuo Yupeng Shi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Semantic image synthesis is a challenging task with many practical applications. Albeit remarkable progress has been made in semantic image synthesis with spatially-adaptive normalization and existing methods normalize the feature activations under the coarse-level guidance (e.g., semantic class). However, different parts of a semantic object (e.g., wheel and window of car) are quite different in structures and textures, making blurry synthesis results usually inevitable due to the missing of fine-grained guidance. In this paper, we propose a novel normalization module, termed as REtrieval-based Spatially AdaptIve normaLization (RESAIL), for introducing pixel level fine-grained guidance to the normalization architecture. Specifically, we first present a retrieval paradigm by finding a content patch of the same semantic class from training set with the most similar shape to each test semantic mask. Then, RESAIL is presented to use the retrieved patch for guiding the feature normalization of corresponding region, and can provide pixel level fine-grained guidance, thereby greatly mitigating blurry synthesis results. Moreover, distorted ground-truth images are also utilized as alternatives of retrieval-based guidance for feature normalization, further benefiting model training and improving visual quality of generated images. Experiments on several challenging datasets show that our RESAIL performs favorably against state-of-the-arts in terms of quantitative metrics, visual quality, and subjective evaluation. The source code and pre-trained models will be publicly available.
Undoing the Damage of Label Shift for Cross-Domain Semantic Segmentation
Authors: Jinhong Deng, Jiale Tao, Tong Chu, Lixin Duan, Wen Li Yahao Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Existing works typically treat cross-domain semantic segmentation(CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional distribution. However, the label shift issue is unfortunately overlooked, which actually commonly exists in the CDSS task, and often causes a classifier bias in the learnt model. In this paper, we give an in-depth analysis and show that the damage of label shift can be overcome by aligning the data conditional distribution and correcting the posterior probability. To this end, we propose a novel approach to undo the damage of the label shift problem in CDSS. In implementation, we adopt class-level feature alignment for conditional distribution alignment, as well as two simple yet effective methods to rectify the classifier bias from source to target by remolding the classifier predictions. We conduct extensive experiments on the benchmark datasets of urban scenes, including GTA5 to Cityscapes and SYNTHIA to Cityscapes, where our proposed approach outperforms previous methods by a large margin. For instance, our model equipped with a self-training strategy reaches 59.3% mIoU on GTA5 to Cityscapes, pushing to a new state-of-the-art. The code will be available at https://github.com/manmanjun/Undoing_UDA.
GPV-Pose: Category-Level Object Pose Estimation via Geometry-Guided Point-Wise Voting
Authors: Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xiangyang Ji, Nassir Navab, Federico Tombari Yan Di
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: While 6D object pose estimation has recently made a huge leap forward, most methods can still only handle a single or a handful of different objects, which limits their applications. To circumvent this problem, category-level object pose estimation has recently been revamped, which aims at predicting the 6D pose as well as the 3D metric size for previously unseen instances from a given set of object classes. This is, however, a much more challenging task due to severe intra-class shape variations. To address this issue, we propose GPV-Pose, a novel framework for robust category-level pose estimation, harnessing geometric insights to enhance the learning of category-level pose-sensitive features. First, we introduce a decoupled confidence-driven rotation representation, which allows geometry-aware recovery of the associated rotation matrix. Second, we propose a novel geometry-guided point-wise voting paradigm for robust retrieval of the 3D object bounding box. Finally, leveraging these different output streams, we can enforce several geometric consistency terms, further increasing performance, especially for non-symmetric categories. GPV-Pose produces superior results to state-of-the-art competitors on common public benchmarks, whilst almost achieving real-time inference speed at 20 FPS. Code and trained models will be made publicly available.
Dynamic 3D Gaze From Afar: Deep Gaze Estimation From Temporal Eye-Head-Body Coordination
Authors: Shohei Nobuhara, Ko Nishino Soma Nonaka
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We introduce a novel method and dataset for 3D gaze estimation of a freely moving person from a distance, typically in surveillance views. Eyes cannot be clearly seen in such cases due to occlusion and lacking resolution. Existing gaze estimation methods suffer or fall back to approximating gaze with head pose as they primarily rely on clear, close-up views of the eyes. Our key idea is to instead leverage the intrinsic gaze, head, and body coordination of people. Our method formulates gaze estimation as Bayesian prediction given temporal estimates of head and body orientations which can be reliably estimated from a far. We model the head and body orientation likelihoods and the conditional prior of gaze direction on those with separate neural networks which are then cascaded to output the 3D gaze direction. We introduce an extensive new dataset that consists of surveillance videos annotated with 3D gaze directions captured in 5 indoor and outdoor scenes. Experimental results on this and other datasets validate the accuracy of our method and demonstrate that gaze can be accurately estimated from a typical surveillance distance even when the person's face is not visible to the camera.
Expressive Talking Head Generation With Granular Audio-Visual Control
Authors: Yan Pan, Zhizhi Guo, Hang Zhou, Zhibin Hong, Xiaoguang Han, Junyu Han, Jingtuo Liu, Errui Ding, Jingdong Wang Borong Liang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces realistic. In this paper, we propose the Granularly Controlled Audio-Visual Talking Heads (GC-AVT), which controls lip movements, head poses, and facial expressions of a talking head in a granular manner. Our insight is to decouple the audio-visual driving sources through prior-based pre-processing designs. Detailedly, we disassemble the driving image into three complementary parts including: 1) a cropped mouth that facilitates lip-sync; 2) a masked head that implicitly learns pose; and 3) the upper face which works corporately and complementarily with a time-shifted mouth to contribute the expression. Interestingly, the encoded features from the three sources are integrally balanced through reconstruction training. Extensive experiments show that our method generates expressive faces with not only synced mouth shapes, controllable poses, but precisely animated emotional expressions as well.
Trustworthy Long-Tailed Classification
Authors: Zongbo Han, Haining Li, Huazhu Fu, Changqing Zhang Bolian Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Classification on long-tailed distributed data is a challenging problem, which suffers from serious class-imbalance and accordingly unpromising performance especially on tail classes. Recently, the ensembling based methods achieve the state-of-the-art performance and show great potential. However, there are two limitations for current methods. First, their predictions are not trustworthy for failure-sensitive applications. This is especially harmful for the tail classes where the wrong predictions is basically frequent. Second, they assign unified numbers of experts to all samples, which is redundant for easy samples with excessive computational cost. To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework. Our TLC obtains the evidence-based uncertainty (EvU) and evidence for each expert, and then combines these uncertainties and evidences under the Dempster-Shafer Evidence Theory (DST). Moreover, we propose a dynamic expert engagement to reduce the number of engaged experts for easy samples and achieve efficiency while maintaining promising performances. Finally, we conduct comprehensive experiments on the tasks of classification, tail detection, OOD detection and failure prediction. The experimental results show that the proposed TLC outperforms existing methods and is trustworthy with reliable uncertainty.
Primitive3D: 3D Object Dataset Synthesis From Randomly Assembled Primitives
Authors: Henghui Ding, Zekun Tong, Yuwei Wu, Yeow Meng Chee Xinke Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Numerous advancements of deep learning can be attributed to access to large-scale and well-annotated datasets. However, such a dataset is prohibitively expensive in 3D computer vision due to the substantial collection cost. To alleviate this issue, we propose a cost-effective method for automatically generating a large amount of 3D objects with annotations. In particular, we synthesize objects simply by assembling multiple random primitives. These objects are thus auto-annotated with part-based labels originating from primitives. This allows us to perform multi-task learning by combining the supervised segmentation with unsupervised reconstruction. Considering the large overhead of learning on the generated dataset, we further propose a dataset distillation strategy to remove redundant samples regarding a target dataset. We conduct extensive experiments for the downstream tasks of 3D object classification. The results indicate that our dataset, together with multi-task pretraining on its annotations, achieves the best performance compared to other commonly used datasets. Further study suggests that our strategy can improve the model performance by pretraining and fine-tuning scheme, especially for a dataset with a small scale. In addition, pretraining with the proposed dataset distillation method can save 86% of the pretraining time with negligible performance degradation. We expect that our attempt provides a new data-centric perspective for training 3D deep models.
Mix and Localize: Localizing Sound Sources in Mixtures
Authors: Ziyang Chen, Andrew Owens Xixi Hu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present a method for simultaneously localizing multiple sound sources within a visual scene. This task requires a model to both group a sound mixture into individual sources, and to associate them with a visual signal. Our method jointly solves both tasks at once, using a formulation inspired by the contrastive random walk of Jabri et al. We create a graph in which images and separated sounds each correspond to nodes, and train a random walker to transition between nodes from different modalities with high return probability. The transition probabilities for this walk are determined by an audio-visual similarity metric that is learned by our model. We show through experiments with musical instruments and human speech that our model can successfully localize multiple sounds, outperforming other self-supervised methods.
FisherMatch: Semi-Supervised Rotation Regression via Entropy-Based Filtering
Authors: Yingcheng Cai, He Wang, Baoquan Chen Yingda Yin
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. Recent works achieve good performance relying on a large amount of expensive-to-obtain labeled data. To reduce the amount of supervision, we for the first time propose a general framework, FisherMatch, for semi-supervised rotation regression, without assuming any domain-specific knowledge or paired data. Inspired by the popular semi-supervised approach, FixMatch, we propose to leverage pseudo label filtering to facilitate the information flow from labeled data to unlabeled data in a teacher-student mutual learning framework. However, incorporating the pseudo label filtering mechanism into semi-supervised rotation regression is highly non-trivial, mainly due to the lack of a reliable confidence measure for rotation prediction. In this work, we propose to leverage matrix Fisher distribution to build a probabilistic model of rotation and devise a matrix Fisher-based regressor for jointly predicting rotation along with its prediction uncertainty. We then propose to use the entropy of the predicted distribution as a confidence measure, which enables us to perform pseudo label filtering for rotation regression. For supervising such distribution-like pseudo labels, we further investigate the problem of how to enforce loss between two matrix Fisher distributions. Our extensive experiments show that our method can work well even under very low labeled data ratios on different benchmarks, achieving significant and consistent performance improvement over supervised learning and other semi-supervised learning baselines. Our project page is at https://yd-yin.github.io/FisherMatch.
NPBG++: Accelerating Neural Point-Based Graphics
Authors: Andrei-Timotei Ardelean, Victor Lempitsky, Evgeny Burnaev Ruslan Rakhimov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present a new system (NPBG++) for the novel view synthesis (NVS) task that achieves high rendering realism with low scene fitting time. Our method efficiently leverages the multiview observations and the point cloud of a static scene to predict a neural descriptor for each point, improving upon the pipeline of Neural Point-Based Graphics in several important ways. By predicting the descriptors with a single pass through the source images, we lift the requirement of per-scene optimization while also making the neural descriptors view-dependent and more suitable for scenes with strong non-Lambertian effects. In our comparisons, the proposed system outperforms previous NVS approaches in terms of fitting and rendering runtimes while producing images of similar quality.
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis
Authors: Yunfei Zhang, Xiaoyang Huo, Si Wu, Yong Xu, Hau San Wong Tianyi Chen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Generative Adversarial Network (GAN)-based models have greatly facilitated image synthesis. However, the model performance may be degraded when applied to fine-grained data, due to limited training samples and subtle distinction among categories. Different from generic GANs, we address the issue from a new perspective of discovering and utilizing the underlying structure of real data to explicitly regularize the spatial organization of latent space. To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN. By projecting random vectors drawn from a prior distribution onto a hyper-sphere, we can model more complex distributions, while at the same time the similarity between the resulting latent vectors depends only on the angle, but not on their magnitudes. On the other hand, we also incorporate a mapping network to map real images onto the hyper-sphere, and match latent vectors with the underlying structure of real data via real-fake cluster alignment. As a result, we obtain a spatially organized latent space, which is useful for capturing class-independent variation factors. The experimental results suggest that our SphericGAN achieves state-of-the-art performance in synthesizing high-fidelity images with precise class semantics.
HairMapper: Removing Hair From Portraits Using GANs
Authors: Yong-Liang Yang, Xiaogang Jin Yiqian Wu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Removing hair from portrait images is challenging due to the complex occlusions between hair and face, as well as the lack of paired portrait data with/without hair. To this end, we present a dataset and a baseline method for removing hair from portrait images using generative adversarial networks (GANs). Our core idea is to train a fully connected network HairMapper to find the direction of hair removal in the latent space of StyleGAN for the training stage. We develop a new separation boundary and diffuse method to generate paired training data for males, and a novel "female-male-bald" pipeline for paired data of females. Experiments show that our method can naturally deal with portrait images with variations on gender, age, etc. We validate the superior performance of our method by comparing it to state-of-the-art methods through extensive experiments and user studies. We also demonstrate its applications in hair design and 3D face reconstruction.
Affine Medical Image Registration With Coarse-To-Fine Vision Transformer
Authors: Albert C. S. Chung Tony C. W. Mok
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Affine registration is indispensable in a comprehensive medical image registration pipeline. However, only a few studies focus on fast and robust affine registration algorithms. Most of these studies utilize convolutional neural networks (CNNs) to learn joint affine and non-parametric registration, while the standalone performance of the affine subnetwork is less explored. Moreover, existing CNN-based affine registration approaches focus either on the local misalignment or the global orientation and position of the input to predict the affine transformation matrix, which are sensitive to spatial initialization and exhibit limited generalizability apart from the training dataset. In this paper, we present a fast and robust learning-based algorithm, Coarse-to-Fine Vision Transformer (C2FViT), for 3D affine medical image registration. Our method naturally leverages the global connectivity and locality of the convolutional vision transformer and the multi-resolution strategy to learn the global affine registration. We evaluate our method on 3D brain atlas registration and template-matching normalization. Comprehensive results demonstrate that our method is superior to the existing CNNs-based affine registration methods in terms of registration accuracy, robustness and generalizability while preserving the runtime advantage of the learning-based methods. The source code is available at https://github.com/cwmok/C2FViT.
SMPL-A: Modeling Person-Specific Deformable Anatomy
Authors: Benjamin Planche, Meng Zheng, Srikrishna Karanam, Terrence Chen, Ziyan Wu Hengtao Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: A variety of diagnostic and therapeutic protocols rely on locating in vivo target anatomical structures, which can be obtained from medical scans. However, organs move and deform as the patient changes his/her pose. In order to obtain accurate target location information, clinicians have to either conduct frequent intraoperative scans, resulting in higher exposition of patients to radiations, or adopt proxy procedures (e.g., creating and using custom molds to keep patients in the exact same pose during both preoperative organ scanning and subsequent treatment. Such custom proxy methods are typically sub-optimal, constraining the clinicians and costing precious time and money to the patients. To the best of our knowledge, this work is the first to present a learning-based approach to estimate the patient's internal organ deformation for arbitrary human poses in order to assist with radiotherapy and similar medical protocols. The underlying method first leverages medical scans to learn a patient-specific representation that potentially encodes the organ's shape and elastic properties. During inference, given the patient's current body pose information and the organ's representation extracted from previous medical scans, our method can estimate their current organ deformation to offer guidance to clinicians. We conduct experiments on a well-sized dataset which is augmented through real clinical data using finite element modeling. Our results suggest that pose-dependent organ deformation can be learned through a point cloud autoencoder conditioned on the parametric pose input. We hope that this work can be a starting point for future research towards closing the loop between human mesh recovery and anatomical reconstruction, with applications beyond the medical domain.
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
Authors: Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, Chongyi Li Chun-Le Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Despite single image dehazing has been made promising progress with Convolutional Neural Networks (CNNs), the inherent equivariance and locality of convolution still bottleneck dehazing performance. Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance. The key insight of this study is to investigate how to combine CNN and Transformer for image dehazing. To solve the feature inconsistency issue between Transformer and CNN, we propose to modulate CNN features via learning modulation matrices (i.e., coefficient matrix and bias matrix) conditioned on Transformer features instead of simple feature addition or concatenation. The feature modulation naturally inherits the global context modeling capability of Transformer and the local representation capability of CNN. We bring a haze density-related prior into Transformer via a novel transmission-aware 3D position embedding module, which not only provides the relative position but also suggests the haze density of different spatial regions. Extensive experiments demonstrate that our method, DeHamer, attains state-of-the-art performance on several image dehazing benchmarks.
Out-of-Distribution Generalization With Causal Invariant Transformations
Authors: Mingyang Yi, Zhitang Chen, Shengyu Zhu Ruoyu Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In real-world applications, it is important and desirable to learn a model that performs well on out-of-distribution (OOD) data. Recently, causality has become a powerful tool to tackle the OOD generalization problem, with the idea resting on the causal mechanism that is invariant across domains of interest. To leverage the generally unknown causal mechanism, existing works assume a linear form of causal feature or require sufficiently many and diverse training domains, which are usually restrictive in practice. In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature. Our approach is based on transformations that modify the non-causal feature but leave the causal part unchanged, which can be either obtained from prior knowledge or learned from the training data in the multi-domain scenario. Under the setting of invariant causal mechanism, we theoretically show that if all such transformations are available, then we can learn a minimax optimal model across the domains using only single domain data. Noticing that knowing a complete set of these causal invariant transformations may be impractical, we further show that it suffices to know only a subset of these transformations. Based on the theoretical findings, a regularized training procedure is proposed to improve the OOD generalization capability. Extensive experimental results on both synthetic and real datasets verify the effectiveness of the proposed algorithm, even with only a few causal invariant transformations.
Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmap
Authors: Xiao He, Yang Wen, Yuan Gao, Xiaoqiang Cheng, Dan Zhang Jinke Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation. However, in terms of speed and accuracy, existing LiDAR methods in the field are still limited. In this paper, we propose a fast and high-performance LiDAR-based framework, referred to as Panoptic-PHNet, with three attractive aspects: 1) We introduce a clustering pseudo heatmap as a new paradigm, which, followed by a center grouping module, yields instance centers for efficient clustering without object-level learning tasks. 2) A knn-transformer module is proposed to model the interaction among foreground points for accurate offset regression. 3) For backbone design, we fuse the fine-grained voxel features and the 2D Bird's Eye View (BEV) features with different receptive fields to utilize both detailed and global information. Extensive experiments on both SemanticKITTI dataset and nuScenes dataset show that our Panoptic-PHNet surpasses state-of-the-art methods by remarkable margins with a real-time speed. We achieve the 1st place on the public leaderboard of SemanticKITTI and leading performance on the recently released leaderboard of nuScenes.
Dual-Key Multimodal Backdoors for Visual Question Answering
Authors: Karan Sikka, Indranil Sur, Abhinav Shrivastava, Susmit Jha Matthew Walmer
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The success of deep learning has enabled advances in multimodal tasks that require non-trivial fusion of multiple input domains. Although multimodal models have shown potential in many problems, their increased complexity makes them more vulnerable to attacks. A Backdoor (or Trojan) attack is a class of security vulnerability wherein an attacker embeds a malicious secret behavior into a network (e.g. targeted misclassification) that is activated when an attacker-specified trigger is added to an input. In this work, we show that multimodal networks are vulnerable to a novel type of attack that we refer to as Dual-Key Multimodal Backdoors. This attack exploits the complex fusion mechanisms used by state-of-the-art networks to embed backdoors that are both effective and stealthy. Instead of using a single trigger, the proposed attack embeds a trigger in each of the input modalities and activates the malicious behavior only when both the triggers are present. We present an extensive study of multimodal backdoors on the Visual Question Answering (VQA) task with multiple architectures and visual feature backbones. A major challenge in embedding backdoors in VQA models is that most models use visual features extracted from a fixed pretrained object detector. This is challenging for the attacker as the detector can distort or ignore the visual trigger entirely, which leads to models where backdoors are over-reliant on the language trigger. We tackle this problem by proposing a visual trigger optimization strategy designed for pretrained object detectors. Through this method, we create Dual-Key Backdoors with over a 98% attack success rate while only poisoning 1% of the training data. Finally, we release TrojVQA, a large collection of clean and trojan VQA models to enable research in defending against multimodal backdoors.
A Differentiable Two-Stage Alignment Scheme for Burst Image Reconstruction With Large Shift
Authors: Xi Yang, Jianqi Ma, Gaofeng Ren, Lei Zhang Shi Guo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. State-of-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-to-end manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available at https://github.com/GuoShi28/2StageAlign.
Unifying Panoptic Segmentation for Autonomous Driving
Authors: Matthias Schörghuber, Bernhard Rainer, Markus Murschitz, Csaba Beleznai Oliver Zendel
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: This paper aims to improve panoptic segmentation for real-world applications in three ways. First, we present a label policy that unifies four of the most popular panoptic segmentation datasets for autonomous driving. We also clean up label confusion by adding the new vehicle labels pickup and van. Full relabeling information for the popular Mapillary Vistas, IDD, and Cityscapes dataset are provided to add these new labels to existing setups. Second, we introduce Wilddash2 (WD2), a new dataset and public benchmark service for panoptic segmentation. The dataset consists of more than 5000 unique driving scenes from all over the world with a focus on visually challenging scenes, such as diverse weather conditions, lighting situations, and camera characteristics. We showcase experimental visual hazard classifiers which help to pre-filter challenging frames during dataset creation. Finally, to characterize the robustness of algorithms in out-of-distribution situations, we introduce hazard-aware and negative testing for panoptic segmentation as well as statistical significance calculations that increase confidence for both concepts. Additionally, we present a novel technique for visualizing panoptic segmentation errors. Our experiments show the negative impact of visual hazards on panoptic segmentation quality. Additional data from the WD2 dataset improves performance for visually challenging scenes and thus robustness in real-world scenarios.
Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans From a Single Camera
Authors: Duygu Ceylan, Tuanfeng Y. Wang, Jingwan Lu, Jimei Yang, Zhixin Shu, Hyun Soo Park Jae Shin Yoon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Appearance of dressed humans undergoes a complex geometric transformation induced not only by the static pose but also by its dynamics, i.e., there exists a number of cloth geometric configurations given a pose depending on the way it has moved. Such appearance modeling conditioned on motion has been largely neglected in existing human rendering methods, resulting in rendering of physically implausible motion. A key challenge of learning the dynamics of the appearance lies in the requirement of a prohibitively large amount of observations. In this paper, we present a compact motion representation by enforcing equivariance---a representation is expected to be transformed in the way that the pose is transformed. We model an equivariant encoder that can generate the generalizable representation from the spatial and temporal derivatives of the 3D body surface. This learned representation is decoded by a compositional multi-task decoder that renders high fidelity time-varying appearance. Our experiments show that our method can generate a temporally coherent video of dynamic humans for unseen body poses and novel views given a single view video.
On the Road to Online Adaptation for Semantic Image Segmentation
Authors: Pau De Jorge, Diane Larlus, Gabriela Csurka Riccardo Volpi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goal is fostering the development of adaptive learning systems that will continuously learn, without supervision, in ever-changing environments. Typical protocols that study adaptation algorithms for segmentation models are limited to few domains, adaptation happens offline, and human intervention is generally required, at least to annotate data for hyper-parameter tuning. We argue that such constraints are incompatible with algorithms that can continuously adapt to different real-world situations. To address this, we propose a protocol where models need to learn online, from sequences of temporally correlated images, requiring continuous, frame-by-frame adaptation. We accompany this new protocol with a variety of baselines to tackle the proposed formulation, as well as an extensive analysis of their behaviors, which can serve as a starting point for future research.
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
Authors: Alina Jade Barnett, Chaofan Chen Jon Donnelly
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yielding explanations in the form of "this looks like that." However, while previous methods use spatially rigid prototypes, we address this shortcoming by proposing spatially flexible prototypes. Each prototype is made up of several prototypical parts that adaptively change their relative spatial positions depending on the input image. Consequently, a Deformable ProtoPNet can explicitly capture pose variations and context, improving both model accuracy and the richness of explanations provided. Compared to other case-based interpretable models using prototypes, our approach achieves state-of-the-art accuracy and gives an explanation with greater context. The code is available at https://github.com/jdonnelly36/Deformable-ProtoPNet.
Context-Aware Sequence Alignment Using 4D Skeletal Augmentation
Authors: Bugra Tekin, Siyu Tang, Marc Pollefeys Taein Kwon
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Temporal alignment of fine-grained human actions in videos is important for numerous applications in computer vision, robotics, and mixed reality. State-of-the-art methods directly learn image-based embedding space by leveraging powerful deep convolutional neural networks. While being straightforward, their results are far from satisfactory, the aligned videos exhibit severe temporal discontinuity without additional post-processing steps. The recent advancements in human body and hand pose estimation in the wild promise new ways of addressing the task of human action alignment in videos. In this work, based on off-the-shelf human pose estimators, we propose a novel context-aware self-supervised learning architecture to align sequences of actions. We name it CASA. Specifically, CASA employs self-attention and cross-attention mechanisms to incorporate the spatial and temporal context of human actions, which can solve the temporal discontinuity problem. Moreover, we introduce a self-supervised learning scheme that is empowered by novel 4D augmentation techniques for 3D skeleton representations. We systematically evaluate the key components of our method. Our experiments on three public datasets demonstrate CASA significantly improves phase progress and Kendall's Tau scores over the previous state-of-the-art methods.