pwc-archive/papers-with-abstracts huggingface dataset:
Neumann Network With Recursive Kernels for Single Image Defocus Deblurring
Authors: Yuhui Quan, Zicong Wu, Hui Ji
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Deblurring, Image Defocus Deblurring

    Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the blob-type structure of defocus kernels, we propose a learnable recursive kernel representation (RKR) for defocus kernels that expresses a defocus kernel by a linear combination of recursive, separable and positive atom kernels, leading to a compact yet effective and physics-encoded parametrization of the spatially-varying defocus blurring process. Afterwards, a physics-driven and efficient deep model with a cross-scale fusion structure is presented for SIDD, with inspirations from the truncated Neumann series for approximating the matrix inversion of the RKR-based blurring operator. In addition, a reblurring loss is proposed to regularize the RKR learning. Extensive experiments show that, our proposed approach significantly outperforms existing ones, with a model size comparable to that of the top methods.
Angelic Patches for Improving Third-Party Object Detector Performance
Authors: Wenwen Si, Shuo Li, Sangdon Park, Insup Lee, Osbert Bastani
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Adversarial Attack, Object, object-detection, Object Detection

    Deep learning models have shown extreme vulnerability to simple perturbations and spatial transformations. In this work, we explore whether we can adopt the characteristics of adversarial attack methods to help improve perturbation robustness for object detection. We study a class of realistic object detection settings wherein the target objects have control over their appearance. To this end, we propose a reversed Fast Gradient Sign Method (FGSM) to obtain these angelic patches that significantly increase the detection probability, even without pre-knowledge of the perturbations. In detail, we apply the patch to each object instance simultaneously, strengthen not only classification but also bounding box accuracy. Experiments demonstrate the efficacy of the partial-covering patch in solving the complex bounding box problem. More importantly, the performance is also transferable to different detection models even under severe affine transformations and deformable shapes. To our knowledge, we are the first (object detection) patch that achieves both cross-model and multiple-patch efficacy. We observed average accuracy improvements of 30% in the real-world experiments, which brings large social value. Our code is available at: https://github.com/averysi224/angelic_patches.
Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection
Authors: Olga Veksler
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: object-detection, Object Detection, Salient Object Detection, Test-time Adaptation

    It is well known that CNNs tend to overfit to the training data. Test-time adaptation is an extreme approach to deal with overfitting: given a test image, the aim is to adapt the trained model to that image. Indeed nothing can be closer to the test data than the test image itself. The main difficulty of test-time adaptation is that the ground truth is not available. Thus test-time adaptation, while intriguing, applies to only a few scenarios where one can design an effective loss function that does not require ground truth. We propose the first approach for test-time Salient Object Detection (SOD) in the context of weak supervision. Our approach is based on a so called regularized loss function, which can be used for training CNN when pixel precise ground truth is unavailable. Regularized loss tends to have lower values for the more likely object segments, and thus it can be used to fine-tune an already trained CNN to a given test image, adapting to images unseen during training. We develop a regularized loss function particularly suitable for test-time adaptation and show that our approach significantly outperforms prior work for weakly supervised SOD.
EvShutter: Transforming Events for Unconstrained Rolling Shutter Correction
Authors: Julius Erbach, Stepan Tulyakov, Patricia Vitoria, Alfredo Bochicchio, Yuanyou Li
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Deblurring, distortion correction, Rolling Shutter Correction

    Widely used Rolling Shutter (RS) CMOS sensors capture high resolution images at the expense of introducing distortions and artifacts in the presence of motion. In such situations, RS distortion correction algorithms are critical. Recent methods rely on a constant velocity assumption and require multiple frames to predict the dense displacement field. In this work, we introduce a new method, called Eventful Shutter (EvShutter), that corrects RS artifacts using a single RGB image and event information with high temporal resolution. The method firstly removes blur using a novel flow-based deblurring module and then compensates RS using a double encoder hourglass network. In contrast to previous methods, it does not rely on a constant velocity assumption and uses a simple architecture thanks to an event transformation dedicated to RS, called Filter and Flip (FnF), that transforms input events to encode only the changes between GS and RS images. To evaluate the proposed method and facilitate future research, we collect the first dataset with real events and high-quality RS images with optional blur, called RS-ERGB. We generate the RS images from GS images using a newly proposed simulator based on adaptive interpolation. The simulator permits the use of inexpensive cameras with long exposure to capture high-quality GS images. We show that on this realistic dataset the proposed method outperforms the state-of-the-art image- and event-based methods by 9.16 dB and 0.75 dB respectively in terms of PSNR and an improvement of 23% and 21% in LPIPS.
AccelIR: Task-Aware Image Compression for Accelerating Neural Restoration
Authors: Juncheol Ye, Hyunho Yeo, Jinwoo Park, Dongsu Han
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Compression, Image Restoration, Super-Resolution

    Recently, deep neural networks have been successfully applied for image restoration (IR) (e.g., super-resolution, de-noising, de-blurring). Despite their promising performance, running IR networks requires heavy computation. A large body of work has been devoted to addressing this issue by designing novel neural networks or pruning their parameters. However, the common limitation is that while images are saved in a compressed format before being enhanced by IR, prior work does not consider the impact of compression on the IR quality. In this paper, we present AccelIR, a framework that optimizes image compression considering the end-to-end pipeline of IR tasks. AccelIR encodes an image through IR-aware compression that optimizes compression levels across image blocks within an image according to the impact on the IR quality. Then, it runs a lightweight IR network on the compressed image, effectively reducing IR computation, while maintaining the same IR quality and image size. Our extensive evaluation using seven IR networks shows that AccelIR can reduce the computing overhead of super-resolution, de-nosing, and de-blurring by 49%, 29%, and 32% on average, respectively
ABCD: Arbitrary Bitwise Coefficient for De-Quantization
Authors: Woo Kyoung Han, Byeonghun Lee, Sang Hyun Park, Kyong Hwan Jin
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Quantization

    Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://github.com/WooKyoungHan/ABCD
Efficient Loss Function by Minimizing the Detrimental Effect of Floating-Point Errors on Gradient-Based Attacks
Authors: Yunrui Yu, Cheng-Zhong Xu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1

    Attackers can deceive neural networks by adding human imperceptive perturbations to their input data; this reveals the vulnerability and weak robustness of current deep-learning networks. Many attack techniques have been proposed to evaluate the model's robustness. Gradient-based attacks suffer from severely overestimating the robustness. This paper identifies that the relative error in calculated gradients caused by floating-point errors, including floating-point underflow and rounding errors, is a fundamental reason why gradient-based attacks fail to accurately assess the model's robustness. Although it is hard to eliminate the relative error in the gradients, we can control its effect on the gradient-based attacks. Correspondingly, we propose an efficient loss function by minimizing the detrimental impact of the floating-point errors on the attacks. Experimental results show that it is more efficient and reliable than other loss functions when examined across a wide range of defence mechanisms.
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation
Authors: Min Shi, Zihao Huang, Xianzheng Ma, Xiaowei Hu, Zhiguo Cao
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: 2D Pose Estimation, Category-Agnostic Pose Estimation, Decoder, Pose Estimation

    Category-agnostic pose estimation (CAPE) aims to predict keypoints for arbitrary categories given support images with keypoint annotations. Existing approaches match the keypoints across the image for localization. However, such a one-stage matching paradigm shows inferior accuracy: the prediction heavily relies on the matching results, which can be noisy due to the open set nature in CAPE. For example, two mirror-symmetric keypoints (e.g., left and right eyes) in the query image can both trigger high similarity on certain support keypoints (eyes), which leads to duplicated or opposite predictions. To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals. Then, the model learns to fetch relevant features to correct the initial proposals in the second stage. We instantiate the framework with a transformer model tailored for CAPE. The transformer encoder incorporates specific designs to improve the representation and similarity modeling in the first matching stage. In the second stage, similarity-aware proposals are packed as queries in the decoder for refinement via cross-attention. Our method surpasses the previous best approach by large margins on CAPE benchmark MP-100 on both accuracy and efficiency. Code available at https://github.com/flyinglynx/CapeFormer
Improving Robustness of Vision Transformers by Reducing Sensitivity To Patch Corruptions
Authors: Yong Guo, David Stutz, Bernt Schiele
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Sensitivity

    Despite their success, vision transformers still remain vulnerable to image corruptions, such as noise or blur. Indeed, we find that the vulnerability mainly stems from the unstable self-attention mechanism, which is inherently built upon patch-based inputs and often becomes overly sensitive to the corruptions across patches. For example, when we only occlude a small number of patches with random noise (e.g., 10%), these patch corruptions would lead to severe accuracy drops and greatly distract intermediate attention layers. To address this, we propose a new training method that improves the robustness of transformers from a new perspective -- reducing sensitivity to patch corruptions (RSPC). Specifically, we first identify and occlude/corrupt the most vulnerable patches and then explicitly reduce sensitivity to them by aligning the intermediate features between clean and corrupted examples. We highlight that the construction of patch corruptions is learned adversarially to the following feature alignment process, which is particularly effective and essentially different from existing methods. In experiments, our RSPC greatly improves the stability of attention layers and consistently yields better robustness on various benchmarks, including CIFAR-10/100-C, ImageNet-A, ImageNet-C, and ImageNet-P.
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning
Authors: Chengzhi Cao, Xueyang Fu, Hongjian Liu, Yukun Huang, Kunyu Wang, Jiebo Luo, Zheng-Jun Zha
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Person Re-Identification, Representation Learning, Video-Based Person Re-Identification

    Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications. Most existing methods utilize spatial and temporal correlations in frame sequences to obtain discriminative person features. However, inevitable degradations, e.g., motion blur contained in frames often cause ambiguity texture noise and temporal disturbance, leading to the loss of identity-discriminating cues. Recently, a new bio-inspired sensor called event camera, which can asynchronously record intensity changes, brings new vitality to the Re-ID task. With the microsecond resolution and low latency, event cameras can accurately capture the movements of pedestrians even in the aforementioned degraded environments. Inspired by the properties of event cameras, in this work, we propose a Sparse-Dense Complementary Learning Framework, which effectively extracts identity features by fully exploiting the complementary information of dense frames and sparse events. Specifically, for frames, we build a CNN-based module to aggregate the dense features of pedestrian appearance step-by-step, while for event streams, we design a bio-inspired spiking neural backbone, which encodes event signals into sparse feature maps in a spiking form, to present the dynamic motion cues of pedestrians. Finally, a cross feature alignment module is constructed to complementarily fuse motion information from events and appearance cues from frames to enhance identity representation learning. Experiments on several benchmarks show that by employing events and SNN into Re-ID, our method significantly outperforms competitive methods.
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning
Authors: Kai Li, Deep Patel, Erik Kruus, Martin Renqiang Min
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Domain Adaptation, Source-Free Domain Adaptation, Unsupervised Domain Adaptation

    Source-free domain adaptation (SFDA) is an emerging research topic that studies how to adapt a pretrained source model using unlabeled target data. It is derived from unsupervised domain adaptation but has the advantage of not requiring labeled source data to learn adaptive models. This makes it particularly useful in real-world applications where access to source data is restricted. While there has been some SFDA work for images, little attention has been paid to videos. Naively extending image-based methods to videos without considering the unique properties of videos often leads to unsatisfactory results. In this paper, we propose a simple and highly flexible method for Source-Free Video Domain Adaptation (SFVDA), which extensively exploits consistency learning for videos from spatial, temporal, and historical perspectives. Our method is based on the assumption that videos of the same action category are drawn from the same low-dimensional space, regardless of the spatio-temporal variations in the high-dimensional space that cause domain shifts. To overcome domain shifts, we simulate spatio-temporal variations by applying spatial and temporal augmentations on target videos, and encourage the model to make consistent predictions from a video and its augmented versions. Due to the simple design, our method can be applied to various SFVDA settings, and experiments show that our method achieves state-of-the-art performance for all the settings.
Unsupervised 3D Point Cloud Representation Learning by Triangle Constrained Contrast for Autonomous Driving
Authors: Bo Pang, Hongchi Xia, Cewu Lu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Autonomous Driving, Representation Learning, Semantic Segmentation, Temporal Sequences, Triplet

    Due to the difficulty of annotating the 3D LiDAR data of autonomous driving, an efficient unsupervised 3D representation learning method is important. In this paper, we design the Triangle Constrained Contrast (TriCC) framework tailored for autonomous driving scenes which learns 3D unsupervised representations through both the multimodal information and dynamic of temporal sequences. We treat one camera image and two LiDAR point clouds with different timestamps as a triplet. And our key design is the consistent constraint that automatically finds matching relationships among the triplet through "self-cycle" and learns representations from it. With the matching relations across the temporal dimension and modalities, we can further conduct a triplet contrast to improve learning efficiency. To the best of our knowledge, TriCC is the first framework that unifies both the temporal and multimodal semantics, which means it utilizes almost all the information in autonomous driving scenes. And compared with previous contrastive methods, it can automatically dig out contrasting pairs with higher difficulty, instead of relying on handcrafted ones. Extensive experiments are conducted with Minkowski-UNet and VoxelNet on several semantic segmentation and 3D detection datasets. Results show that TriCC learns effective representations with much fewer training iterations and improves the SOTA results greatly on all the downstream tasks. Code and models can be found at https://bopang1996.github.io/.
Autoregressive Visual Tracking
Authors: Xing Wei, Yifan Bai, Yongchao Zheng, Dahu Shi, Yihong Gong
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 2023 2
Tasks: Object, Object Tracking, Template Matching, Video Object Tracking, Visual Object Tracking, Visual Tracking

    We present ARTrack, an autoregressive framework for visual object tracking. ARTrack tackles tracking as a coordinate sequence interpretation task that estimates object trajectories progressively, where the current estimate is induced by previous states and in turn affects subsequences. This time-autoregressive approach models the sequential evolution of trajectories to keep tracing the object across frames, making it superior to existing template matching based trackers that only consider the per-frame localization accuracy. ARTrack is simple and direct, eliminating customized localization heads and post-processings. Despite its simplicity, ARTrack achieves state-of-the-art performance on prevailing benchmark datasets.
Learning To Detect Mirrors From Videos via Dual Correspondences
Authors: Jiaying Lin, Xin Tan, Rynson W.H. Lau
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Mirror Detection

    Detecting mirrors from static images has received significant research interest recently. However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to address the VMD problem from a deep-learning-based perspective. Our observation is that there are often correspondences between the contents inside (reflected) and outside (real) of a mirror, but such correspondences may not always appear in every frame, e.g., due to the change of camera pose. This inspires us to propose a video mirror detection method, named VMD-Net, that can tolerate spatially missing correspondences by considering the mirror correspondences at both the intra-frame level as well as inter-frame level via a dual correspondence module that looks over multiple frames spatially and temporally for correlating correspondences. We further propose a first large-scale dataset for VMD (named VMD-D), which contains 14,987 image frames from 269 videos with corresponding manually annotated masks. Experimental results show that the proposed method outperforms SOTA methods from relevant fields. To enable real-time VMD, our method efficiently utilizes the backbone features by removing the redundant multi-level module design and gets rid of post-processing of the output maps commonly used in existing methods, making it very efficient and practical for real-time video-based applications. Code, dataset, and models are available at https://jiaying.link/cvpr2023-vmd/
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection
Authors: Xiaolin Song, Binghui Chen, Pengyu Li, Jun-Yan He, Biao Wang, Yifeng Geng, Xuansong Xie, Honggang Zhang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Pedestrian Detection

    End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial applications. In this paper, we intend to bridge this gap and propose an Optimal Proposal Learning (OPL) framework for deployable end-to-end pedestrian detection. Specifically, we achieve this goal by using CNN-based light detector and introducing two novel modules, including a Coarse-to-Fine (C2F) learning strategy for proposing precise positive proposals for the Ground-Truth (GT) instances by reducing the ambiguity of sample assignment/output in training/testing respectively, and a Completed Proposal Network (CPN) for producing extra information compensation to further recall the hard pedestrian samples. Extensive experiments are conducted on CrowdHuman, TJU-Ped and Caltech, and the results show that our proposed OPL method significantly outperforms the competing methods.
Human Body Shape Completion With Implicit Shape and Flow Learning
Authors: Boyao Zhou, Di Meng, Jean-Sébastien Franco, Edmond Boyer
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1

    In this paper, we investigate how to complete human body shape models by combining shape and flow estimation given two consecutive depth images. Shape completion is a challenging task in computer vision that is highly under-constrained when considering partial depth observations. Besides model based strategies that exploit strong priors, and consequently struggle to preserve fine geometric details, learning based approaches build on weaker assumptions and can benefit from efficient implicit representations. We adopt such a representation and explore how the motion flow between two consecutive frames can contribute to the shape completion task. In order to effectively exploit the flow information, our architecture combines both estimations and implements two features for robustness: First, an all-to-all attention module that encodes the correlation between points in the same frame and between corresponding points in different frames; Second, a coarse-dense to fine-sparse strategy that balances the representation ability and the computational cost. Our experiments demonstrate that the flow actually benefits human body model completion. They also show that our method outperforms the state-of-the-art approaches for shape completion on 2 benchmarks, considering different human shapes, poses, and clothing.