mtybilly/Instruct2DS huggingface dataset:
Pooling Revisited: Your Receptive Field Is Suboptimal
Authors: Sanghyeok Chu, Joonhyuk Kim, Bohyung Han Dong-Hwan Jang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: The size and shape of the receptive field determine how the network aggregates local features, and affect the overall performance of a model considerably. Many components in a neural network, such as depth, kernel sizes, and strides for convolution and pooling, influence the receptive field. However, they still rely on hyperparameters, and the receptive fields of existing models result in suboptimal shapes and sizes. Hence, we propose a simple yet effective Dynamically Optimized Pooling operation, referred to as DynOPool, which learns the optimized scale factors of feature maps end-to-end. Moreover, DynOPool determines the proper resolution of a feature map by learning the desirable size and shape of its receptive field, which allows an operator in a deeper layer to observe an input image in the optimal scale. Any kind of resizing modules in a deep neural network can be replaced by DynOPool with minimal cost. Also, DynOPool controls the complexity of the model by introducing an additional loss term that constrains computational cost. Our experiments show that the models equipped with the proposed learnable resizing module outperform the baseline algorithms on multiple datasets in image classification and semantic segmentation.
Dual Task Learning by Leveraging Both Dense Correspondence and Mis-Correspondence for Robust Change Detection With Imperfect Matches
Authors: Ue-Hwan Kim, Seon-Hoon Lee, Jong-Hwan Kim Jin-Man Park
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Accurate change detection enables a wide range of tasks in visual surveillance, anomaly detection and mobile robotics. However, contemporary change detection approaches assume an ideal matching between the current and stored scenes, whereas only coarse matching is possible in real-world scenarios. Thus, contemporary approaches fail to show the reported performance in real-world settings. To overcome this limitation, we propose SimSaC. SimSaC concurrently conducts scene flow estimation and change detection and is able to detect changes with imperfect matches. To train SimSaC without additional manual labeling, we propose a training scheme with random geometric transformations and the cut-paste method. Moreover, we design an evaluation protocol which reflects performance in real-world settings. In designing the protocol, we collect a test benchmark dataset, which we claim as another contribution. Our comprehensive experiments verify that SimSaC displays robust performance even given imperfect matches and the performance margin compared to contemporary approaches is huge.
Show Me What and Tell Me How: Video Synthesis via Multimodal Conditioning
Authors: Jian Ren, Hsin-Ying Lee, Francesco Barbieri, Kyle Olszewski, Shervin Minaee, Dimitris Metaxas, Sergey Tulyakov Ligong Han
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Most methods for conditional video synthesis use a single modality as the condition. This comes with major limitations. For example, it is problematic for a model conditioned on an image to generate a specific motion trajectory desired by the user since there is no means to provide motion information. Conversely, language information can describe the desired motion, while not precisely defining the content of the video. This work presents a multimodal video generation framework that benefits from text and images provided jointly or separately. We leverage the recent progress in quantized representations for videos and apply a bidirectional transformer with multiple modalities as inputs to predict a discrete video representation. To improve video quality and consistency, we propose a new video token trained with self-learning and an improved mask-prediction algorithm for sampling video tokens. We introduce text augmentation to improve the robustness of the textual representation and diversity of generated videos. Our framework can incorporate various visual modalities, such as segmentation masks, drawings, and partially occluded images. It can generate much longer sequences than the one used for training. In addition, our model can extract visual information as suggested by the text prompt, e.g., "an object in image one is moving northeast", and generate corresponding videos. We run evaluations on three public datasets and a newly collected dataset labeled with facial attributes, achieving state-of-the-art generation results on all four. [Code](https://github.com/snap-research/MMVID) and [webpage](https://snap-research.github.io/MMVID/).
Patch Slimming for Efficient Vision Transformers
Authors: Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, Dacheng Tao Yehui Tang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: This paper studies the efficiency problem for visual transformers by excavating redundant calculation in given networks. The recent transformer architecture has demonstrated its effectiveness for achieving excellent performance on a series of computer vision tasks. However, similar to that of convolutional neural networks, the huge computational cost of vision transformers is still a severe issue. Considering that the attention mechanism aggregates different patches layer-by-layer, we present a novel patch slimming approach that discards useless patches in a top-down paradigm. We first identify the effective patches in the last layer and then use them to guide the patch selection process of previous layers. For each layer, the impact of a patch on the final output feature is approximated and patches with less impacts will be removed. Experimental results on benchmark datasets demonstrate that the proposed method can significantly reduce the computational costs of vision transformers without affecting their performances. For example, over 45% FLOPs of the ViT-Ti model can be reduced with only 0.2% top-1 accuracy drop on the ImageNet dataset.
Bijective Mapping Network for Shadow Removal
Authors: Jie Huang, Xueyang Fu, Feng Zhao, Qibin Sun, Zheng-Jun Zha Yurui Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature. Most existing deep learning-based methods individually remove the shadow by only considering the content of the matched paired images, barely taking into account the auxiliary supervision of shadow generation on shadow removal procedure. In this work, we argue that shadow removal and generation are interrelated and could provide useful informative supervision for each other. Specifically, we propose a new Bijective Mapping Network (BMNet), which couples the learning procedures of shadow removal and shadow generation in a unified parameter-shared framework. With consistent two-way constraints and synchronous optimization of the two procedures, BMNet could effectively recover the underlying background contents during the forward shadow removal procedure. In addition, through statistic analysis on real-world datasets, we observe and verify that shadow appearances under different color spectrums are inconsistent. This motivates us to design a Shadow-Invariant Color Guidance Module (SICGM), which can explicitly utilize the learned shadow-invariant color information to guide network color restoration, thereby further reducing color-bias effects. Experiments on the representative ISTD, ISTD+ and SRD benchmarks show that our proposed network outperforms the state-of-the-art method [??] in de-shadowing performance, while only using its 0.25% network parameters and 6.25% floating point operations (FLOPs).
End-to-End Semi-Supervised Learning for Video Action Detection
Authors: Yogesh Singh Rawat Akash Kumar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-temporal localization of actions. Therefore, we investigate two types of constraints, classification consistency, and spatio-temporal consistency. The presence of predominant background and static regions in a video makes it challenging to utilize spatio-temporal consistency for action detection. To address this, we propose two novel regularization constraints for spatio-temporal consistency; 1) temporal coherency, and 2) gradient smoothness. Both these aspects exploit the temporal continuity of action in videos and are found to be effective for utilizing unlabeled videos for action detection. We demonstrate the effectiveness of the proposed approach on two different action detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show the effectiveness of the proposed approach for video object segmentation on the Youtube-VOS which demonstrates its generalization capability The proposed approach achieves competitive performance by using merely 20% of annotations on UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively, compared to supervised approach.
Causal Transportability for Visual Recognition
Authors: Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick Chengzhi Mao
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poorly on out-of-distribution samples because spurious correlations between non-robust features and labels can be changed in a new environment. By analyzing procedures for out-of-distribution generalization with a causal graph, we show that standard classifiers fail because the association between images and labels is not transportable across settings. However, we then show that the causal effect, which severs all sources of confounding, remains invariant across domains. This motivates us to develop an algorithm to estimate the causal effect for image classification, which is transportable (i.e., invariant) across source and target environments. Without observing additional variables, we show that we can derive an estimand for the causal effect under empirical assumptions using representations in deep models as proxies. Theoretical analysis, empirical results, and visualizations show that our approach captures causal invariances and improves overall generalization.
Local Attention Pyramid for Scene Image Generation
Authors: Sangeek Hyun, DaeHyun Bae, Jae-Pil Heo Sang-Heon Shim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module.
Multi-Objective Diverse Human Motion Prediction With Knowledge Distillation
Authors: Jiachen Li, Ramtin Hosseini, Masayoshi Tomizuka, Chiho Choi Hengbo Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Obtaining accurate and diverse human motion prediction is essential to many industrial applications, especially robotics and autonomous driving. Recent research has explored several techniques to enhance diversity and maintain the accuracy of human motion prediction at the same time. However, most of them need to define a combined loss, such as the weighted sum of accuracy loss and diversity loss, and then decide their weights as hyperparameters before training. In this work, we aim to design a prediction framework that can balance the accuracy sampling and diversity sampling during the testing phase. In order to achieve this target, we propose a multi-objective conditional variational inference prediction model. We also propose a short-term oracle to encourage the prediction framework to explore more diverse future motions. We evaluate the performance of our proposed approach on two standard human motion datasets. The experiment results show that our approach is effective and on a par with state-of-the-art performance in terms of accuracy and diversity.
GridShift: A Faster Mode-Seeking Algorithm for Image Segmentation and Object Tracking
Authors: Oladayo S. Ajani, Swagatam Das, Rammohan Mallipeddi Abhishek Kumar
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In machine learning, MeanShift is one of the popular clustering algorithms. It iteratively moves each data point to the weighted mean of its neighborhood data points. The computational cost required for finding neighborhood data points for each one is quadratic to the number of data points. Therefore, it is very slow for large-scale datasets. To address this issue, we propose a mode-seeking algorithm, GridShift, with faster computing and principally based on MeanShift that uses a grid-based approach. To speed up, GridShift employs a grid-based approach for neighbor search, which is linear to the number of data points. In addition, GridShift moves the active grid cells (grid cells associated with at least one data point) in place of data points towards the higher density, which provides more speed up. The runtime of GridShift is linear to the number of active grid cells and exponential to the number of features. Therefore, it is ideal for large-scale low-dimensional applications such as object tracking and image segmentation. Through extensive experiments, we showcase the superior performance of GridShift compared to other MeanShift-based algorithms and state-of-the-art algorithms in terms of accuracy and runtime on benchmark datasets, image segmentation. Finally, we provide a new object-tracking algorithm based on GridShift and show promising results for object tracking compared to camshift and MeanShift++.
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors
Authors: Wanli Jiang, Zhen Xu, Rundong Li, Junghyun Kwon, Siyi Li Yichun Shen
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: It's been a long history that most object detection methods obtain objects by using the non-maximum suppression (NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes. We challenge those NMS-based methods from three aspects: 1) The bounding box with highest confidence value may not be the true positive having the biggest overlap with the ground-truth box. 2) Not only suppression is required for redundant boxes, but also confidence enhancement is needed for those true positives. 3) Sorting candidate boxes by confidence values is not necessary so that full parallelism is achievable. In this paper, inspired by belief propagation (BP), we propose the Confidence Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully parallelizable as well as better in accuracy. In CP-Cluster, we borrow the message passing mechanism from BP to penalize redundant boxes and enhance true positives simultaneously in an iterative way until convergence. We verified the effectiveness of CP-Cluster by applying it to various mainstream detectors such as FasterRCNN, SSD, FCOS, YOLOv3, YOLOv5, Centernet etc. Experiments on MS COCO show that our plug and play method, without retraining detectors, is able to steadily improve average mAP of all those state-of-theart models with a clear margin from 0.3 to 1.9 respectively when compared with NMS-based methods.
Cluster-Guided Image Synthesis With Unconditional Models
Authors: James Oldfield, Grigorios G. Chrysos, Yannis Panagakis Markos Georgopoulos
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Generative Adversarial Networks (GANs) are the driving force behind the state-of-the-art in image generation. Despite their ability to synthesize high-resolution photo-realistic images, generating content with on-demand conditioning of different granularity remains a challenge. This challenge is usually tackled by annotating massive datasets with the attributes of interest, a laborious task that is not always a viable option. Therefore, it is vital to introduce control into the generation process of unsupervised generative models. In this work, we focus on controllable image generation by leveraging GANs that are well-trained in an unsupervised fashion. To this end, we discover that the representation space of intermediate layers of the generator forms a number of clusters that separate the data according to semantically meaningful attributes (e.g., hair color and pose). By conditioning on the cluster assignments, the proposed method is able to control the semantic class of the generated image. Our approach enables sampling from each cluster by Implicit Maximum Likelihood Estimation (IMLE). We showcase the efficacy of our approach on faces (CelebA-HQ and FFHQ), animals (Imagenet) and objects (LSUN) using different pre-trained generative models. The results highlight the ability of our approach to condition image generation on attributes like gender, pose and hair style on faces, as well as a variety of features on different object classes.
ISNet: Shape Matters for Infrared Small Target Detection
Authors: Rui Zhang, Yuxiang Yang, Haichen Bai, Jing Zhang, Jie Guo Mingjin Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Infrared small target detection (IRSTD) refers to extracting small and dim targets from blurred backgrounds, which has a wide range of applications such as traffic management and marine rescue. Due to the low signal-to-noise ratio and low contrast, infrared targets are easily submerged in the background of heavy noise and clutter. How to detect the precise shape information of infrared targets remains challenging. In this paper, we propose a novel infrared shape network (ISNet), where Taylor finite difference (TFD)-inspired edge block and two-orientation attention aggregation (TOAA) block are devised to address this problem. Specifically, TFD-inspired edge block aggregates and enhances the comprehensive edge information from different levels, in order to improve the contrast between target and background and also lay a foundation for extracting shape information with mathematical interpretation. TOAA block calculates the low-level information with attention mechanism in both row and column directions and fuses it with the high-level information to capture the shape characteristic of targets and suppress noises. In addition, we construct a new benchmark consisting of 1,000 realistic images in various target shapes, different target sizes, and rich clutter backgrounds with accurate pixel-level annotations, called IRSTD-1k. Experiments on public datasets and IRSTD-1k demonstrate the superiority of our approach over representative state-of-the-art IRSTD methods. The dataset and code are available at github.com/RuiZhang97/ISNet.
Robust Region Feature Synthesizer for Zero-Shot Object Detection
Authors: Junwei Han, De Cheng, Dingwen Zhang Peiliang Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Zero-shot object detection aims at incorporating class semantic vectors to realize the detection of (both seen and) unseen classes given an unconstrained test image. In this study, we reveal the core challenges in this research area: how to synthesize robust region features (for unseen objects) that are as intra-class diverse and inter-class separable as the real samples, so that strong unseen object detectors can be trained upon them. To address these challenges, we build a novel zero-shot object detection framework that contains an Intra-class Semantic Diverging component and an Inter-class Structure Preserving component. The former is used to realize the one-to-more mapping to obtain diverse visual features from each class semantic vector, preventing miss-classifying the real unseen objects as image backgrounds. While the latter is used to avoid the synthesized features too scattered to mix up the inter-class and foreground-background relationship. To demonstrate the effectiveness of the proposed approach, comprehensive experiments on PASCAL VOC, COCO, and DIOR datasets are conducted. Notably, our approach achieves the new state-of-the-art performance on PASCAL VOC and COCO and it is the first study to carry out zero-shot object detection in remote sensing imagery.
Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
Authors: Anqi Joyce Yang, Shenlong Wang, Raquel Urtasun, Antonio Torralba Wei-Chiu Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Recovering the spatial layout of the cameras and the geometry of the scene from extreme-view images is a longstanding challenge in computer vision. Prevailing 3D reconstruction algorithms often adopt the image matching paradigm and presume that a portion of the scene is co-visible across images, yielding poor performance when there is little overlap among inputs. In contrast, humans can associate visible parts in one image to the corresponding invisible components in another image via prior knowledge of the shapes. Inspired by this fact, we present a novel concept called virtual correspondences (VCs). VCs are a pair of pixels from two images whose camera rays intersect in 3D. Similar to classic correspondences, VCs conform with epipolar geometry; unlike classic correspondences, VCs do not need to be co-visible across views. Therefore VCs can be established and exploited even if images do not overlap. We introduce a method to find virtual correspondences based on humans in the scene. We showcase how VCs can be seamlessly integrated with classic bundle adjustment to recover camera poses across extreme views. Experiments show that our method significantly outperforms state-of-the-art camera pose estimation methods in challenging scenarios and is comparable in the traditional densely captured setup. Our approach also unleashes the potential of multiple downstream tasks such as scene reconstruction from multi-view stereo and novel view synthesis in extreme-view scenarios.
Segment, Magnify and Reiterate: Detecting Camouflaged Objects the Hard Way
Authors: Shuilian Yao, Yu Liu, Xin Fan, Risheng Liu, Zhongxuan Luo Qi Jia
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: It is challenging to accurately detect camouflaged objects from their highly similar surroundings. Existing methods mainly leverage a single-stage detection fashion, while neglecting small objects with low-resolution fine edges requires more operations than the larger ones. To tackle camouflaged object detection (COD), we are inspired by humans attention coupled with the coarse-to-fine detection strategy, and thereby propose an iterative refinement framework, coined SegMaR, which integrates Segment, Magnify and Reiterate in a multi-stage detection fashion. Specifically, we design a new discriminative mask which makes the model attend on the fixation and edge regions. In addition, we leverage an attention-based sampler to magnify the object region progressively with no need of enlarging the image size. Extensive experiments show our SegMaR achieves remarkable and consistent improvements over other state-of-the-art methods. Especially, we surpass two competitive methods 7.4% and 20.0% respectively in average over standard evaluation metrics on small camouflaged objects. Additional studies provide more promising insights into SegMaR, including its effectiveness on the discriminative mask and its generalization to other network architectures.
SIMBAR: Single Image-Based Scene Relighting for Effective Data Augmentation for Automated Driving Vision Tasks
Authors: Nathan Tseng, Ameerah Syed, Rohan Bhasin, Nikita Jaipuria Xianling Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Real-world autonomous driving datasets comprise of images aggregated from different drives on the road. The ability to relight captured scenes to unseen lighting conditions, in a controllable manner, presents an opportunity to augment datasets with a richer variety of lighting conditions, similar to what would be encountered in the real-world. This paper presents a novel image-based relighting pipeline, SIMBAR, that can work with a single image as input. To the best of our knowledge, there is no prior work on scene relighting leveraging explicit geometric representations from a single image. We present qualitative comparisons with prior multi-view scene relighting baselines. To further validate and effectively quantify the benefit of leveraging SIMBAR for data augmentation for automated driving vision tasks, object detection and tracking experiments are conducted with a state-of-the-art method, a Multiple Object Tracking Accuracy (MOTA) of 93.3% is achieved with CenterTrack on SIMBAR-augmented KITTI - an impressive 9.0% relative improvement over the baseline MOTA of 85.6% with CenterTrack on original KITTI, both models trained from scratch and tested on Virtual KITTI. For more details and SIMBAR relit datasets, please visit our project website (https://simbarv1.github.io/).
Shape From Thermal Radiation: Passive Ranging Using Multi-Spectral LWIR Measurements
Authors: Takahiro Kushida, Kenichiro Tanaka, Takuya Funatomi, Yasuhiro Mukaigawa Yasuto Nagase
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: In this paper, we propose a new cue of depth sensing using thermal radiation. Our method realizes passive, texture independent, far range, and dark scene applicability, which can broaden the depth sensing subjects. A key observation is that thermal radiation is attenuated by the air and is wavelength dependent. By modeling the wavelength-dependent attenuation by the air and building a multi-spectral LWIR measurement system, we can jointly estimate the depth, temperature, and emissivity of the target. We analytically show the capability of the thermal radiation cue and show the effectiveness of the method in real-world scenes using an imaging system with a few bandpass filters.
Multi-Label Classification With Partial Annotations Using Class-Aware Selective Loss
Authors: Tal Ridnik, Itamar Friedman, Avi Ben-Cohen, Nadav Zamir, Asaf Noy, Lihi Zelnik-Manor Emanuel Ben-Baruch
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Different methods for handling the missing labels induce different properties on the model and impact its accuracy. In this work, we analyze the partial labeling problem, then propose a solution based on two key ideas. First, un-annotated labels should be treated selectively according to two probability quantities: the class distribution in the overall dataset and the specific label likelihood for a given data sample. We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations. Second, during the training of the target model, we emphasize the contribution of annotated labels over originally un-annotated labels by using a dedicated asymmetric loss. With our novel approach, we achieve state-of-the-art results on OpenImages dataset (e.g. reaching 87.3 mAP on V6). In addition, experiments conducted on LVIS and simulated-COCO demonstrate the effectiveness of our approach. Code is available at https://github.com/Alibaba-MIIL/PartialLabelingCSL.
HSC4D: Human-Centered 4D Scene Capture in Large-Scale Indoor-Outdoor Space Using Wearable IMUs and LiDAR
Authors: Yitai Lin, Chenglu Wen, Siqi Shen, Lan Xu, Jingyi Yu, Yuexin Ma, Cheng Wang Yudi Dai
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: We propose Human-centered 4D Scene Capture (HSC4D) to accurately and efficiently create a dynamic digital world, containing large-scale indoor-outdoor scenes, diverse human motions, and rich interactions between humans and environments. Using only body-mounted IMUs and LiDAR, HSC4D is space-free without any external devices' constraints and map-free without pre-built maps. Considering that IMUs can capture human poses but always drift for long-period use, while LiDAR is stable for global localization but rough for local positions and orientations, HSC4D makes both sensors complement each other by a joint optimization and achieves promising results for long-term capture. Relationships between humans and environments are also explored to make their interaction more realistic. To facilitate many down-stream tasks, like AR, VR, robots, autonomous driving, etc., we propose a dataset containing three large scenes (1k-5k m^2 ) with accurate dynamic human motions and locations. Diverse scenarios (climbing gym, multi-story building, slope, etc.) and challenging human activities (exercising, walking up/down stairs, climbing, etc.) demonstrate the effectiveness and the generalization ability of HSC4D. The dataset and code is available at lidarhumanmotion.net/hsc4d.
CADTransformer: Panoptic Symbol Spotting Transformer for CAD Drawings
Authors: Tianlong Chen, Peihao Wang, Zhangyang Wang Zhiwen Fan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Understanding 2D computer-aided design (CAD) drawings plays a crucial role for creating 3D prototypes in architecture, engineering and construction (AEC) industries. The task of automated panoptic symbol spotting, i.e., to spot and parse both countable object instances (windows, doors, tables, etc.) and uncountable stuff (wall, railing, etc.) from CAD drawings, has recently drawn interests from the computer vision community. Unfortunately, the highly irregular ordering and orientations set major roadblocks for this task. Existing methods, based on convolutional neural networks (CNNs) and/or graph neural networks (GNNs), regress instance bounding boxes in the pixel domain and then convert the predictions into symbols. In this paper, we present a novel framework named CADTransformer, that can painlessly modify existing vision transformer (ViT) backbones to tackle the above limitations for the panoptic symbol spotting task. CADTransformer tokenizes directly from the set of graphical primitives in CAD drawings, and correspondingly optimizes line-grained semantic and instance symbol spotting altogether by a pair of prediction heads. The backbone is further enhanced with a few plug-and-play modifications, including a neighborhood aware self-attention, hierarchical feature aggregation, and graphic entity position encoding, to bake in the structure prior while optimizing the efficiency. Besides, a new data augmentation method, termed Random Layer, is proposed by the layer-wise separation and recombination of a CAD drawing. Overall, CADTransformer significantly boosts the previous state-of-the-art from 0.595 to 0.685 in the panoptic quality (PQ) metric, on the recently released FloorPlanCAD dataset. We further demonstrate that our model can spot symbols with irregular shapes and arbitrary orientations. Our codes are available in https://github.com/VITA-Group/CADTransformer.
IntraQ: Learning Synthetic Images With Intra-Class Heterogeneity for Zero-Shot Network Quantization
Authors: Mingbao Lin, Gongrui Nan, Jianzhuang Liu, Baochang Zhang, Yonghong Tian, Rongrong Ji Yunshan Zhong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of the real data. In this paper, we observe an interesting phenomenon of intra-class heterogeneity in real data and show that existing methods fail to retain this property in their synthetic images, which causes a limited performance increase. To address this issue, we propose a novel zero-shot quantization method referred to as IntraQ. First, we propose a local object reinforcement that locates the target objects at different scales and positions of the synthetic images. Second, we introduce a marginal distance constraint to form class-related features distributed in a coarse area. Lastly, we devise a soft inception loss which injects a soft prior label to prevent the synthetic images from being overfitting to a fixed object. Our IntraQ is demonstrated to well retain the intra-class heterogeneity in the synthetic images and also observed to perform state-of-the-art. For example, compared to the advanced ZSQ, our IntraQ obtains 9.17% increase of the top-1 accuracy on ImageNet when all layers of MobileNetV1 are quantized to 4-bit. Code is at https://github.com/zysxmu/IntraQ.
M3L: Language-Based Video Editing via Multi-Modal Multi-Level Transformers
Authors: Xin Eric Wang, Scott T. Grafton, Miguel P. Eckstein, William Yang Wang Tsu-Jui Fu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Video editing tools are widely used nowadays for digital design. Although the demand for these tools is high, the prior knowledge required makes it difficult for novices to get started. Systems that could follow natural language instructions to perform automatic editing would significantly improve accessibility. This paper introduces the language-based video editing (LBVE) task, which allows the model to edit, guided by text instruction, a source video into a target video. LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video; 2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction. We propose a Multi-Modal Multi-Level Transformer (M3L-Transformer) to carry out LBVE. The M3L-Transformer dynamically learns the correspondence between video perception and language semantic at different levels, which benefits both the video understanding and video frame synthesis. We build three new datasets for evaluation, including two diagnostic and one from natural videos with human-labeled text. Extensive experimental results show that M3L-Transformer is effective for video editing and that LBVE can lead to a new field toward vision-and-language research.
I M Avatar: Implicit Morphable Head Avatars From Videos
Authors: Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, Otmar Hilliges Yufeng Zheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Traditional 3D morphable face models (3DMMs) provide fine-grained control over expression but cannot easily capture geometric and appearance details. Neural volumetric representations approach photorealism but are hard to animate and do not generalize well to unseen expressions. To tackle this problem, we propose IMavatar (Implicit Morphable avatar), a novel method for learning implicit head avatars from monocular videos. Inspired by the fine-grained control mechanisms afforded by conventional 3DMMs, we represent the expression- and pose- related deformations via learned blendshapes and skinning fields. These attributes are pose-independent and can be used to morph the canonical geometry and texture fields given novel expression and pose parameters. We employ ray marching and iterative root-finding to locate the canonical surface intersection for each pixel. A key contribution is our novel analytical gradient formulation that enables end- to-end training of IMavatars from videos. We show quantitatively and qualitatively that our method improves geometry and covers a more complete expression space compared to state-of-the-art methods. Code and data can be found at https://ait.ethz.ch/projects/2022/IMavatar/.
BodyMap: Learning Full-Body Dense Correspondence Map
Authors: Nikolaos Sarafianos, Yuanlu Xu, Ignacio Rocco, Tony Tung Anastasia Ianina
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Dense correspondence between humans carries powerful semantic information that can be utilized to solve fundamental problems for full-body understanding such as in-the-wild surface matching, tracking and reconstruction. In this paper we present BodyMap, a new framework for obtaining high-definition full-body and continuous dense correspondence between in-the-wild images of clothed humans and the surface of a 3D template model. The correspondences cover fine details such as hands and hair, while capturing regions far from the body surface, such as loose clothing. Prior methods for estimating such dense surface correspondence i) cut a 3D body into parts which are unwrapped to a 2D UV space, producing discontinuities along part seams, or ii) use a single surface for representing the whole body, but none handled body details. Here, we introduce a novel network architecture with Vision Transformers that learn fine-level features on a continuous body surface. BodyMap outperforms prior work on various metrics and datasets, including DensePose-COCO by a large margin. Furthermore, we show various applications ranging from multi-layer dense cloth correspondence, neural rendering with novel-view synthesis and appearance swapping.
Weakly-Supervised Metric Learning With Cross-Module Communications for the Classification of Anterior Chamber Angle Images
Authors: Yue Ning, Dong Nie, Linan Guan, Xiping Jia Jingqi Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: As the basis for developing glaucoma treatment strategies, Anterior Chamber Angle (ACA) evaluation is usually dependent on experts' judgements. However, experienced ophthalmologists needed for these judgements are not widely available. Thus, computer-aided ACA evaluations become a pressing and efficient solution for this issue. In this paper, we propose a novel end-to-end framework GCNet for automated Glaucoma Classification based on ACA images or other Glaucoma-related medical images. We first collect and label an ACA image dataset with some pixel-level annotations. Next, we introduce a segmentation module and an embedding module to enhance the performance of classifying ACA images. Within GCNet, we design a Cross-Module Aggregation Net (CMANet) which is a weakly-supervised metric learning network to capture contextual information exchanging across these modules. We conduct experiments on the ACA dataset and two public datasets REFUGE and SIGF. Our experimental results demonstrate that GCNet outperforms several state-of-the-art deep models in the tasks of glaucoma medical image classifications. The source code of GCNet can be found at https://github.com/Jingqi-H/GCNet.
A Hybrid Egocentric Activity Anticipation Framework via Memory-Augmented Recurrent and One-Shot Representation Forecasting
Authors: Kin-Man Lam Tianshan Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Egocentric activity anticipation involves identifying the interacted objects and target action patterns in the near future. A standard activity anticipation paradigm is recurrently forecasting future representations to compensate the missing activity semantics of the unobserved sequence. However, the limitations of current recursive prediction models arise from two aspects: (i) The vanilla recurrent units are prone to accumulated errors in relatively long periods of anticipation. (ii) The anticipated representations may be insufficient to reflect the desired semantics of the target activity, due to lack of contextual clues. To address these issues, we propose "HRO", a hybrid framework that integrates both the memory-augmented recurrent and one-shot representation forecasting strategies. Specifically, to solve the limitation (i), we introduce a memory-augmented contrastive learning paradigm to regulate the process of the recurrent representation forecasting. Since the external memory bank maintains long-term prototypical activity semantics, it can guarantee that the anticipated representations are reconstructed from the discriminative activity prototypes. To further guide the learning of the memory bank, two auxiliary loss functions are designed, based on the diversity and sparsity mechanisms, respectively. Furthermore, to resolve the limitation (ii), a one-shot transferring paradigm is proposed to enrich the forecasted representations, by distilling the holistic activity semantics after the target anticipation moment, in the offline training. Extensive experimental results on two large-scale data sets validate the effectiveness of our proposed HRO method.
It's All in the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
Authors: Hye Yoon Lee, Deokki Hong, Joonsang Yu, Noseong Park, Youngsok Kim, Jinho Lee Kanghyun Choi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks. In real-world environments, however, such a method is frequently infeasible because training data is unavailable due to security, privacy, or confidentiality concerns. Zero-shot quantization addresses such problems, usually by taking information from the weights of a full-precision teacher network to compensate the performance drop of the quantized networks. In this paper, we first analyze the loss surface of state-of-the-art zero-shot quantization techniques and provide several findings. In contrast to usual knowledge distillation problems, zero-shot quantization often suffers from 1) the difficulty of optimizing multiple loss terms together, and 2) the poor generalization capability due to the use of synthetic samples. Furthermore, we observe that many weights fail to cross the rounding threshold during training the quantized networks even when it is necessary to do so for better performance. Based on the observations, we propose AIT, a simple yet powerful technique for zero-shot quantization, which addresses the aforementioned two problems in the following way: AIT i) uses a KL distance loss only without a cross-entropy loss, and ii) manipulates gradients to guarantee that a certain portion of weights are properly updated after crossing the rounding thresholds. Experiments show that AIT outperforms the performance of many existing methods by a great margin, taking over the overall state-of-the-art position in the field.
Improving Segmentation of the Inferior Alveolar Nerve Through Deep Label Propagation
Authors: Stefano Allegretti, Federico Bolelli, Federico Pollastri, Costantino Grana Marco Cipriano
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Many recent works in dentistry and maxillofacial imagery focused on the Inferior Alveolar Nerve (IAN) canal detection. Unfortunately, the small extent of available 3D maxillofacial datasets has strongly limited the performance of deep learning-based techniques. On the other hand, a huge amount of sparsely annotated data is produced every day from the regular procedures in the maxillofacial practice. Despite the amount of sparsely labeled images being significant, the adoption of those data still raises an open problem. Indeed, the deep learning approach frames the presence of dense annotations as a crucial factor. Recent efforts in literature have hence focused on developing label propagation techniques to expand sparse annotations into dense labels.However, the proposed methods proved only marginally effective for the purpose of segmenting the alveolar nerve in CBCT scans.This paper exploits and publicly releases a new 3D densely annotated dataset, through which we are able to train a deep label propagation model which obtains better results than those available in literature. By combining a segmentation model trained on the 3D annotated data and label propagation, we significantly improve the state of the art in the Inferior Alveolar Nerve segmentation.
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-Resolution
Authors: Zhetong Liang, Lei Zhang Jianqi Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Main Conference Track), 2022

Abstract: Scene text image super-resolution aims to increase the resolution and readability of the text in low-resolution images. Though significant improvement has been achieved by deep convolutional neural networks (CNNs), it remains difficult to reconstruct high-resolution images for spatially deformed texts, especially rotated and curve-shaped ones. This is because the current CNN-based methods adopt locality-based operations, which are not effective to deal with the variation caused by deformations. In this paper, we propose a CNN based Text ATTention network (TATT) to address this problem. The semantics of the text are firstly extracted by a text recognition module as text prior information. Then we design a novel transformer-based module, which leverages global attention mechanism, to exert the semantic guidance of text prior to the text reconstruction process. In addition, we propose a text structure consistency loss to refine the visual appearance by imposing structural consistency on the reconstructions of regular and deformed texts. Experiments on the benchmark TextZoom dataset show that the proposed TATT not only achieves state-of-the-art performance in terms of PSNR/SSIM metrics, but also significantly improves the recognition accuracy in the downstream text recognition task, particularly for text instances with multi-orientation and curved shapes. Code is available at https://github.com/mjq11302010044/TATT.