pwc-archive/papers-with-abstracts huggingface dataset:
Federated Domain Generalization With Generalization Adjustment
Authors: Ruipeng Zhang, Qinwei Xu, Jiangchao Yao, Ya zhang, Qi Tian, Yanfeng Wang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Domain Generalization, Fairness, Privacy Preserving

    Federated Domain Generalization (FedDG) attempts to learn a global model in a privacy-preserving manner that generalizes well to new clients possibly with domain shift. Recent exploration mainly focuses on designing an unbiased training strategy within each individual domain. However, without the support of multi-domain data jointly in the mini-batch training, almost all methods cannot guarantee the generalization under domain shift. To overcome this problem, we propose a novel global objective incorporating a new variance reduction regularizer to encourage fairness. A novel FL-friendly method named Generalization Adjustment (GA) is proposed to optimize the above objective by dynamically calibrating the aggregation weights. The theoretical analysis of GA demonstrates the possibility to achieve a tighter generalization bound with an explicit re-weighted aggregation, substituting the implicit multi-domain data sharing that is only applicable to the conventional DG settings. Besides, the proposed algorithm is generic and can be combined with any local client training-based methods. Extensive experiments on several benchmark datasets have shown the effectiveness of the proposed method, with consistent improvements over several FedDG algorithms when used in combination. The source code is released at https://github.com/MediaBrain-SJTU/FedDG-GA.
Neural Texture Synthesis With Guided Correspondence
Authors: Yang Zhou, Kaijian Chen, Rongjun Xiao, Hui Huang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Texture Synthesis

    Markov random fields (MRFs) are the cornerstone of classical approaches to example-based texture synthesis. Yet, it is not fully valued in the deep learning era. This paper aims to re-promote the combination of MRFs and neural networks, i.e., the CNNMRF model, for texture synthesis, with two key observations made. We first propose to compute the Guided Correspondence Distance in the nearest neighbor search, based on which a Guided Correspondence loss is defined to measure the similarity of the output texture to the example. Experiments show that our approach surpasses existing neural approaches in uncontrolled and controlled texture synthesis. More importantly, the Guided Correspondence loss can function as a general textural loss in, e.g., training generative networks for real-time controlled synthesis and inversion-based single-image editing. In contrast, existing textural losses, such as the Sliced Wasserstein loss, cannot work on these challenging tasks.
Ultrahigh Resolution Image/Video Matting With Spatio-Temporal Sparsity
Authors: Yanan sun, Chi-Keung Tang, Yu-Wing Tai
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Matting, Video Matting

    Commodity ultra-high definition (UHD) displays are becoming more affordable which demand imaging in ultra high resolution (UHR). This paper proposes SparseMat, a computationally efficient approach for UHR image/video matting. Note that it is infeasible to directly process UHR images at full resolution in one shot using existing matting algorithms without running out of memory on consumer-level computational platforms, e.g., Nvidia 1080Ti with 11G memory, while patch-based approaches can introduce unsightly artifacts due to patch partitioning. Instead, our method resorts to spatial and temporal sparsity for solving general UHR matting. During processing videos, huge computation redundancy can be reduced through the rational use of spatial and temporal sparsity. In this paper, we show how to effectively estimate spatio-temporal sparsity, which serves as a gate to activate input pixels for the matting model. Under the guidance of such sparsity, our method discards patch-based inference in lieu of memory-efficient and full-resolution matte refinement. Extensive experiments demonstrate that SparseMat can effectively and efficiently generate high-quality alpha matte for UHR images and videos in one shot.
PHA: Patch-Wise High-Frequency Augmentation for Transformer-Based Person Re-Identification
Authors: Guiwei Zhang, Yongfei Zhang, Tianyu Zhang, Bo Li, ShiLiang Pu
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Person Re-Identification

    Although recent studies empirically show that injecting Convolutional Neural Networks (CNNs) into Vision Transformers (ViTs) can improve the performance of person re-identification, the rationale behind it remains elusive. From a frequency perspective, we reveal that ViTs perform worse than CNNs in preserving key high-frequency components (e.g, clothes texture details) since high-frequency components are inevitably diluted by low-frequency ones due to the intrinsic Self-Attention within ViTs. To remedy such inadequacy of the ViT, we propose a Patch-wise High-frequency Augmentation (PHA) method with two core designs. First, to enhance the feature representation ability of high-frequency components, we split patches with high-frequency components by the Discrete Haar Wavelet Transform, then empower the ViT to take the split patches as auxiliary input. Second, to prevent high-frequency components from being diluted by low-frequency ones when taking the entire sequence as input during network optimization, we propose a novel patch-wise contrastive loss. From the view of gradient optimization, it acts as an implicit augmentation to improve the representation ability of key high-frequency components. This benefits the ViT to capture key high-frequency components to extract discriminative person representations. PHA is necessary during training and can be removed during inference, without bringing extra complexity. Extensive experiments on widely-used ReID datasets validate the effectiveness of our method.
Toward Stable, Interpretable, and Lightweight Hyperspectral Super-Resolution
Authors: Weiying Xie, Kai Jiang, Yunsong Li, Jie Lei, Leyuan Fang, Wen-jin Guo
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Super-Resolution

    For real applications, existing HSI-SR methods are mostly not only limited to unstable performance under unknown scenarios but also suffer from high computation consumption. In this paper, we develop a new coordination optimization framework for stable, interpretable, and lightweight HSI-SR. Specifically, we create a positive cycle between fusion and degradation estimation under a new probabilistic framework. The estimated degradation is applied to fusion as guidance for a degradation-aware HSI-SR. Under the framework, we establish an explicit degradation estimation method to tackle the indeterminacy and unstable performance driven by black-box simulation in previous methods. Considering the interpretability in fusion, we integrate spectral mixing prior to the fusion process, which can be easily realized by a tiny autoencoder, leading to a dramatic release of the computation burden. We then develop a partial fine-tune strategy in inference to reduce the computation cost further. Comprehensive experiments demonstrate the superiority of our method against state-of-the-art under synthetic and real datasets. For instance, we achieve a 2.3 dB promotion on PSNR with 120x model size reduction and 4300x FLOPs reduction under the CAVE dataset. Code is available in https://github.com/WenjinGuo/DAEM.
RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer
Authors: Jiahao Wang, Songyang Zhang, Yong liu, Taiqiang Wu, Yujiu Yang, Xihui Liu, Kai Chen, Ping Luo, Dahua Lin
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Inductive Bias

    This paper studies how to keep a vision backbone effective while removing token mixers in its basic building blocks. Token mixers, as self-attention for vision transformers (ViTs), are intended to perform information communication between different spatial tokens but suffer from considerable computational cost and latency. However, directly removing them will lead to an incomplete model structure prior, and thus brings a significant accuracy drop. To this end, we first develop an RepIdentityFormer base on the re-parameterizing idea, to study the token mixer free model architecture. And we then explore the improved learning paradigm to break the limitation of simple token mixer free backbone, and summarize the empirical practice into 5 guidelines. Equipped with the proposed optimization strategy, we are able to build an extremely simple vision backbone with encouraging performance, while enjoying the high efficiency during inference. Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy. We hope this work can serve as a starting point for the exploration of optimization-driven efficient network design.
Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning
Authors: Zesen Wu, Mang Ye
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Contrastive Learning, Graph Matching, Person Re-Identification

    Unsupervised visible-infrared person re-identification is a challenging task due to the large modality gap and the unavailability of cross-modality correspondences. Cross-modality correspondences are very crucial to bridge the modality gap. Some existing works try to mine cross-modality correspondences, but they focus only on local information. They do not fully exploit the global relationship across identities, thus limiting the quality of the mined correspondences. Worse still, the number of clusters of the two modalities is often inconsistent, exacerbating the unreliability of the generated correspondences. In response, we devise a Progressive Graph Matching method to globally mine cross-modality correspondences under cluster imbalance scenarios. PGM formulates correspondences mining as a graph matching process and considers the global information by minimizing the global matching cost, where the matching cost measures the dissimilarity of clusters. Besides, PGM adopts a progressive strategy to address the imbalance issue with multiple dynamic matching processes. Based on PGM, we design an Alternate Cross Contrastive Learning (ACCL) module to reduce the modality gap with the mined cross-modality correspondences, while mitigating the effect of noise in correspondences through an alternate scheme. Extensive experiments demonstrate the reliability of the generated correspondences and the effectiveness of our method.
A Soma Segmentation Benchmark in Full Adult Fly Brain
Authors: Xiaoyu Liu, Bo Hu, Mingxing Li, Wei Huang, Yueyi Zhang, Zhiwei Xiong
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1

    Neuron reconstruction in a full adult fly brain from high-resolution electron microscopy (EM) data is regarded as a cornerstone for neuroscientists to explore how neurons inspire intelligence. As the central part of neurons, somas in the full brain indicate the origin of neurogenesis and neural functions. However, due to the absence of EM datasets specifically annotated for somas, existing deep learning-based neuron reconstruction methods cannot directly provide accurate soma distribution and morphology. Moreover, full brain neuron reconstruction remains extremely time-consuming due to the unprecedentedly large size of EM data. In this paper, we develop an efficient soma reconstruction method for obtaining accurate soma distribution and morphology information in a full adult fly brain. To this end, we first make a high-resolution EM dataset with fine-grained 3D manual annotations on somas. Relying on this dataset, we propose an efficient, two-stage deep learning algorithm for predicting accurate locations and boundaries of 3D soma instances. Further, we deploy a parallelized, high-throughput data processing pipeline for executing the above algorithm on the full brain. Finally, we provide quantitative and qualitative benchmark comparisons on the testset to validate the superiority of the proposed method, as well as preliminary statistics of the reconstructed somas in the full adult fly brain from the biological perspective. We release our code and dataset at https://github.com/liuxy1103/EMADS.
Exploring and Utilizing Pattern Imbalance
Authors: Shibin Mei, Chenglong Zhao, Shengchao Yuan, Bingbing Ni
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Domain Generalization

    In this paper, we identify pattern imbalance from several aspects, and further develop a new training scheme to avert pattern preference as well as spurious correlation. In contrast to prior methods which are mostly concerned with category or domain granularity, ignoring the potential finer structure that existed in datasets, we give a new definition of seed category as an appropriate optimization unit to distinguish different patterns in the same category or domain. Extensive experiments on domain generalization datasets of diverse scales demonstrate the effectiveness of the proposed method.
Light Source Separation and Intrinsic Image Decomposition Under AC Illumination
Authors: Yusaku Yoshida, Ryo Kawahara, Takahiro Okabe
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Intrinsic Image Decomposition

    Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, we show that the flickers due to AC illumination is useful for intrinsic image decomposition (IID). Our proposed method conducts the light source separation (LSS) followed by the IID under AC illumination. In particular, we reveal the ambiguity in the blind LSS via matrix factorization and the ambiguity in the IID assuming the Lambert model, and then show why and how those ambiguities can be resolved. We experimentally confirmed that our method can recover the colors of the light sources, the diffuse reflectance values, and the diffuse and specular intensities (shadings) under each of the light sources, and that the IID under AC illumination is effective for application to auto white balancing.
Multi-Modal Gait Recognition via Effective Spatial-Temporal Feature Fusion
Authors: Yufeng Cui, Yimei Kang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Gait Recognition

    Gait recognition is a biometric technology that identifies people by their walking patterns. The silhouettes-based method and the skeletons-based method are the two most popular approaches. However, the silhouette data are easily affected by clothing occlusion, and the skeleton data lack body shape information. To obtain a more robust and comprehensive gait representation for recognition, we propose a transformer-based gait recognition framework called MMGaitFormer, which effectively fuses and aggregates the spatial-temporal information from the skeletons and silhouettes. Specifically, a Spatial Fusion Module (SFM) and a Temporal Fusion Module (TFM) are proposed for effective spatial-level and temporal-level feature fusion, respectively. The SFM performs fine-grained body parts spatial fusion and guides the alignment of each part of the silhouette and each joint of the skeleton through the attention mechanism. The TFM performs temporal modeling through Cycle Position Embedding (CPE) and fuses temporal information of two modalities. Experiments demonstrate that our MMGaitFormer achieves state-of-the-art performance on popular gait datasets. For the most challenging "CL" (i.e., walking in different clothes) condition in CASIA-B, our method achieves a rank-1 accuracy of 94.8%, which outperforms the state-of-the-art single-modal methods by a large margin.
Dual-Bridging With Adversarial Noise Generation for Domain Adaptive rPPG Estimation
Authors: Jingda Du, Si-Qi Liu, Bochao Zhang, Pong C. Yuen
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Video Compression

    The remote photoplethysmography (rPPG) technique can estimate pulse-related metrics (e.g. heart rate and respiratory rate) from facial videos and has a high potential for health monitoring. The latest deep rPPG methods can model in-distribution noise due to head motion, video compression, etc., and estimate high-quality rPPG signals under similar scenarios. However, deep rPPG models may not generalize well to the target test domain with unseen noise and distortions. In this paper, to improve the generalization ability of rPPG models, we propose a dual-bridging network to reduce the domain discrepancy by aligning intermediate domains and synthesizing the target noise in the source domain for better noise reduction. To comprehensively explore the target domain noise, we propose a novel adversarial noise generation in which the noise generator indirectly competes with the noise reducer. To further improve the robustness of the noise reducer, we propose hard noise pattern mining to encourage the generator to learn hard noise patterns contained in the target domain features. We evaluated the proposed method on three public datasets with different types of interferences. Under different cross-domain scenarios, the comprehensive results show the effectiveness of our method.
Cross-Guided Optimization of Radiance Fields With Multi-View Image Super-Resolution for High-Resolution Novel View Synthesis
Authors: Youngho Yoon, Kuk-Jin Yoon
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Image Super-Resolution, NeRF, Novel View Synthesis, Super-Resolution

    Novel View Synthesis (NVS) aims at synthesizing an image from an arbitrary viewpoint using multi-view images and camera poses. Among the methods for NVS, Neural Radiance Fields (NeRF) is capable of NVS for an arbitrary resolution as it learns a continuous volumetric representation. However, radiance fields rely heavily on the spectral characteristics of coordinate-based networks. Thus, there is a limit to improving the performance of high-resolution novel view synthesis (HRNVS). To solve this problem, we propose a novel framework using cross-guided optimization of the single-image super-resolution (SISR) and radiance fields. We perform multi-view image super-resolution (MVSR) on train-view images during the radiance fields optimization process. It derives the updated SR result by fusing the feature map obtained from SISR and voxel-based uncertainty fields generated by integrated errors of train-view images. By repeating the updates during radiance fields optimization, train-view images for radiance fields optimization have multi-view consistency and high-frequency details simultaneously, ultimately improving the performance of HRNVS. Experiments of HRNVS and MVSR on various benchmark datasets show that the proposed method significantly surpasses existing methods.
Incrementer: Transformer for Class-Incremental Semantic Segmentation With Knowledge Distillation Focusing on Old Class
Authors: Chao Shang, Hongliang Li, Fanman Meng, Qingbo Wu, Heqian Qiu, Lanxiao Wang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1
Tasks: Class-Incremental Semantic Segmentation, Decoder, Knowledge Distillation, Segmentation, Semantic Segmentation

    Class-incremental semantic segmentation aims to incrementally learn new classes while maintaining the capability to segment old ones, and suffers catastrophic forgetting since the old-class labels are unavailable. Most existing methods are based on convolutional networks and prevent forgetting through knowledge distillation, which (1) need to add additional convolutional layers to predict new classes, and (2) ignore to distinguish different regions corresponding to old and new classes during knowledge distillation and roughly distill all the features, thus limiting the learning of new classes. Based on the above observations, we propose a new transformer framework for class-incremental semantic segmentation, dubbed Incrementer, which only needs to add new class tokens to the transformer decoder for new-class learning. Based on the Incrementer, we propose a new knowledge distillation scheme that focuses on the distillation in the old-class regions, which reduces the constraints of the old model on the new-class learning, thus improving the plasticity. Moreover, we propose a class deconfusion strategy to alleviate the overfitting to new classes and the confusion of similar classes. Our method is simple and effective, and extensive experiments show that our method outperforms the SOTAs by a large margin (5 15 absolute points boosts on both Pascal VOC and ADE20k). We hope that our Incrementer can serve as a new strong pipeline for class-incremental semantic segmentation.
Progressive Neighbor Consistency Mining for Correspondence Pruning
Authors: Xin Liu, Jufeng Yang
Date: 2023-01-01 00:00:00
Proceeding: CVPR 2023 1

    The goal of correspondence pruning is to recognize correct correspondences (inliers) from initial ones, with applications to various feature matching based tasks. Seeking neighbors in the coordinate and feature spaces is a common strategy in many previous methods. However, it is difficult to ensure that these neighbors are always consistent, since the distribution of false correspondences is extremely irregular. For addressing this problem, we propose a novel global-graph space to search for consistent neighbors based on a weighted global graph that can explicitly explore long-range dependencies among correspondences. On top of that, we progressively construct three neighbor embeddings according to different neighbor search spaces, and design a Neighbor Consistency block to extract neighbor context and explore their interactions sequentially. In the end, we develop a Neighbor Consistency Mining Network (NCMNet) for accurately recovering camera poses and identifying inliers. Experimental results indicate that our NCMNet achieves a significant performance advantage over state-of-the-art competitors on challenging outdoor and indoor matching scenes. The source code can be found at https://github.com/xinliu29/NCMNet.