CVPR 2024 论文列表

In the expansive domain of computer vision a myriad of pre-trained models are at our disposal. However most of these models are designed for natural RGB images and prove inadequate for spectral remote sensing (RS) images. Spectral RS images have two main traits: (1) multiple bands capturing diverse feature information (2) spatial alignment and consistent spectral sequencing within the spatial-spectral dimension. In this paper we introduce Spatial-SpectralMAE (S2MAE) a specialized pre-trained architecture for spectral RS imagery. S2MAE employs a 3D transformer for masked autoencoder modeling integrating learnable spectral-spatial embeddings with a 90% masking ratio. The model efficiently captures local spectral consistency and spatial invariance using compact cube tokens demonstrating versatility to diverse input characteristics. This adaptability facilitates progressive pretraining on extensive spectral datasets. The effectiveness of S2MAE is validated through continuous pretraining on two sizable datasets totaling over a million training images. The pre-trained model is subsequently applied to three distinct downstream tasks with in-depth ablation studies conducted to emphasize its efficacy.

Current 3D stylization methods often assume static scenes which violates the dynamic nature of our real world. To address this limitation we present S-DyRF a reference-based spatio-temporal stylization method for dynamic neural radiance fields. However stylizing dynamic 3D scenes is inherently challenging due to the limited availability of stylized reference images along the temporal axis. Our key insight lies in introducing additional temporal cues besides the provided reference. To this end we generate temporal pseudo-references from the given stylized reference. These pseudo-references facilitate the propagation of style information from the reference to the entire dynamic 3D scene. For coarse style transfer we enforce novel views and times to mimic the style details present in pseudo-references at the feature level. To preserve high-frequency details we create a collection of stylized temporal pseudo-rays from temporal pseudo-references. These pseudo-rays serve as detailed and explicit stylization guidance for achieving fine style transfer. Experiments on both synthetic and real-world datasets demonstrate that our method yields plausible stylized results of space-time view synthesis on dynamic 3D scenes.

This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly it introduces a fast patch selection method FPS for whole-slide image (WSI) analysis significantly reducing computational cost while maintaining accuracy. Secondly it presents PathDino a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks and only ? 9 million parameters markedly fewer than alternatives. Thirdly it introduces a rotation-agnostic representation learning paradigm using self-supervised learning effectively mitigating overfitting. We also show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets including both internal datasets spanning four sites (breast liver skin and colorectal) and seven public datasets (PANDA CAMELYON16 BRACS DigestPath Kather PanNuke and WSSS4LUAD). Notably even with a training dataset of ? 6 million histopathology patches from The Cancer Genome Atlas (TCGA) our approach demonstrates an average 8.5% improvement in patch-level majority vote performance. These contributions provide a robust framework for enhancing image analysis in digital pathology rigorously validated through extensive evaluation.

Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image Segmentation (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery leading to suboptimal segmentation results. To address these challenges we introduce the Rotated Multi-Scale Interaction Network (RMSIN) an innovative approach designed for the unique demands of RRSIS. RMSIN incorporates an Intra-scale Interaction Module (IIM) to effectively address the fine-grained detail required at multiple scales and a Cross-scale Interaction Module (CIM) for integrating these details coherently across the network. Furthermore RMSIN employs an Adaptive Rotated Convolution (ARC) to account for the diverse orientations of objects a novel contribution that significantly enhances segmentation accuracy. To assess the efficacy of RMSIN we have curated an expansive dataset comprising 17402 image-caption-mask triplets which is unparalleled in terms of scale and variety. This dataset not only presents the model with a wide range of spatial and rotational scenarios but also establishes a stringent benchmark for the RRSIS task ensuring a rigorous evaluation of performance. Experimental evaluations demonstrate the exceptional performance of RMSIN surpassing existing state-of-the-art models by a significant margin. Datasets and code are available at https://github.com/Lsan2401/RMSIN.

This paper proposes to correct the rolling shutter (RS) distorted images by estimating the distortion flow from the global shutter (GS) to RS directly. Existing methods usually perform correction using the undistortion flow from the RS to GS. They initially predict the flow from consecutive RS frames subsequently rescaling it as the displacement fields from the RS frame to the underlying GS image using time-dependent scaling factors. Following this RS-aware forward warping is employed to convert the RS image into its GS counterpart. Nevertheless this strategy is prone to two shortcomings. First the undistortion flow estimation is rendered inaccurate by merely linear scaling the flow due to the complex non-linear motion nature. Second RS-aware forward warping often results in unavoidable artifacts. To address these limitations we introduce a new framework that directly estimates the distortion flow and rectifies the RS image with the backward warping operation. More specifically we first propose a global correlation-based flow attention mechanism to estimate the initial distortion flow and GS feature jointly which are then refined by the following coarse-to-fine decoder layers. Additionally a multi-distortion flow prediction strategy is integrated to mitigate the issue of inaccurate flow estimation further. Experimental results validate the effectiveness of the proposed method which outperforms state-of-the-art approaches on various benchmarks while maintaining high efficiency. The project is available at https://github.com/ljzycmd/DFRSC.

With advancements in domain generalized stereo matching networks models pre-trained on synthetic data demonstrate strong robustness to unseen domains. However few studies have investigated the robustness after fine-tuning them in real-world scenarios during which the domain generalization ability can be seriously degraded. In this paper we explore fine-tuning stereo matching networks without compromising their robustness to unseen domains. Our motivation stems from comparing Ground Truth (GT) versus Pseudo Label (PL) for fine-tuning: GT degrades but PL preserves the domain generalization ability. Empirically we find the difference between GT and PL implies valuable information that can regularize networks during fine-tuning. We also propose a framework to utilize this difference for fine-tuning consisting of a frozen Teacher an exponential moving average (EMA) Teacher and a Student network. The core idea is to utilize the EMA Teacher to measure what the Student has learned and dynamically improve GT and PL for fine-tuning. We integrate our framework with state-of-the-art networks and evaluate its effectiveness on several real-world datasets. Extensive experiments show that our method effectively preserves the domain generalization ability during fine-tuning.

The problem of self-calibration of two cameras from a given fundamental matrix is one of the basic problems in geometric computer vision. Under the assumption of known principal points and square pixels the Bougnoux formula offers a means to compute the two unknown focal lengths. However in many practical situations the formula yields inaccurate results due to commonly occurring singularities. Moreover the estimates are sensitive to noise in the computed fundamental matrix and to the assumed positions of the principal points. In this paper we therefore propose an efficient and robust iterative method to estimate the focal lengths along with the principal points of the cameras given a fundamental matrix and priors for the estimated camera intrinsics. In addition we study a computationally efficient check of models generated within RANSAC that improves the accuracy of the estimated models while reducing the total computational time. Extensive experiments on real and synthetic data show that our iterative method brings significant improvements in terms of the accuracy of the estimated focal lengths over the Bougnoux formula and other state-of-the-art methods even when relying on inaccurate priors. The code for the methods and experiments is available at https://github.com/kocurvik/robust_self_calibration

Numerous studies have demonstrated the susceptibility of deep neural networks (DNNs) to subtle adversarial perturbations prompting the development of many advanced adversarial defense methods aimed at mitigating adversarial attacks. Current defense strategies usually train DNNs for a specific adversarial attack method and can achieve good robustness in defense against this type of adversarial attack. Nevertheless when subjected to evaluations involving unfamiliar attack modalities empirical evidence reveals a pronounced deterioration in the robustness of DNNs. Meanwhile there is a trade-off between the classification accuracy of clean examples and adversarial examples. Most defense methods often sacrifice the accuracy of clean examples in order to improve the adversarial robustness of DNNs. To alleviate these problems and enhance the overall robust generalization of DNNs we propose the Test-Time Pixel-Level Adversarial Purification (TPAP) method. This approach is based on the robust overfitting characteristic of DNNs to the fast gradient sign method (FGSM) on training and test datasets. It utilizes FGSM for adversarial purification to process images for purifying unknown adversarial perturbations from pixels at testing time in a "counter changes with changelessness" manner thereby enhancing the defense capability of DNNs against various unknown adversarial attacks. Extensive experimental results show that our method can effectively improve both overall robust generalization of DNNs notably over previous methods. Code is available https://github.com/tly18/TPAP.

The surge in multi-modal data has propelled cross-modal matching to the forefront of research interest. However the challenge lies in the laborious and expensive process of curating a large and accurately matched multimodal dataset. Commonly sourced from the Internet these datasets often suffer from a significant presence of mismatched data impairing the performance of matching models. To address this problem we introduce a novel regularization approach named Equivariant Similarity Consistency (ESC) which can facilitate robust clean and noisy data separation and improve the training for cross-modal matching. Intuitively our method posits that the semantic variations caused by image changes should be proportional to those caused by text changes for any two matched samples. Accordingly we first calculate the ESC by comparing image and text semantic variations between a set of elaborated anchor points and other undivided training data. Then pairs with high ESC are filtered out as noisy correspondence pairs. We implement our method by combining the ESC with a traditional hinge-based triplet loss. Extensive experiments on three widely used datasets including Flickr30K MS-COCO and Conceptual Captions verify the effectiveness of our method.

Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks which are trained using a limited spectrum of real noise distributions and thus show poor robustness to out-of-distribution real noise types. To alleviate this issue we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics all the while preserving the properties of authentic real-world noise. Subsequently incorporating these noisy images into the training pipeline enhances the denoising network's robustness to variations in noise distributions. Extensive experiments and analyses conducted on a wide range of real noise benchmarks demonstrate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions. The code is available at https://github.com/dhryougit/AFM.

Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation causing severe performance bottlenecks and confounding valuable context priors. In this paper we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes resulting in bias mitigation and robust prediction. As a model-agnostic framework CLEF can be readily integrated into existing methods bringing consistent performance gains.

Adversarially robust knowledge distillation aims to compress large-scale models into lightweight models while preserving adversarial robustness and natural performance on a given dataset. Existing methods typically align probability distributions of natural and adversarial samples between teacher and student models but they overlook intermediate adversarial samples along the "adversarial path" formed by the multi-step gradient ascent of a sample towards the decision boundary. Such paths capture rich information about the decision boundary. In this paper we propose a novel adversarially robust knowledge distillation approach by incorporating such adversarial paths into the alignment process. Recognizing the diverse impacts of intermediate adversarial samples (ranging from benign to noisy) we propose an adaptive weighting strategy to selectively emphasize informative adversarial samples thus ensuring efficient utilization of lightweight model capacity. Moreover we propose a dual-branch mechanism exploiting two following insights: (i) complementary dynamics of adversarial paths obtained by targeted and untargeted adversarial learning and (ii) inherent differences between the gradient ascent path from class c_i towards the nearest class boundary and the gradient descent path from a specific class c_j towards the decision region of c_i (i \neq j). Comprehensive experiments demonstrate the effectiveness of our method on lightweight models under various settings.

Existing depth sensors are imperfect and may provide inaccurate depth values in challenging scenarios such as in the presence of transparent or reflective objects. In this work we present a general framework that leverages polarization imaging to improve inaccurate depth measurements from various depth sensors. Previous polarization-based depth enhancement methods focus on utilizing pure physics-based formulas for a single sensor. In contrast our method first adopts a learning-based strategy where a neural network is trained to estimate a dense and complete depth map from polarization data and a sensor depth map from different sensors. To further improve the performance we propose a Polarization Prompt Fusion Tuning (PPFT) strategy to effectively utilize RGB-based models pre-trained on large-scale datasets as the size of the polarization dataset is limited to train a strong model from scratch. We conducted extensive experiments on a public dataset and the results demonstrate that the proposed method performs favorably compared to existing depth enhancement baselines. Code and demos are available at https://lastbasket.github.io/PPFT/.

Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene and dense methods estimate all such correspondences. The aim is to learn a robust model i.e. a model able to match under challenging real-world changes. In this work we propose such a model leveraging frozen pretrained features from the foundation model DINOv2. Although these features are significantly more robust than local features trained from scratch they are inherently coarse. We therefore combine them with specialized ConvNet fine features creating a precisely localizable feature pyramid. To further improve robustness we propose a tailored transformer match decoder that predicts anchor probabilities which enables it to express multimodality. Finally we propose an improved loss formulation through regression-by-classification with subsequent robust regression. We conduct a comprehensive set of experiments that show that our method RoMa achieves significant gains setting a new state-of-the-art. In particular we achieve a 36% improvement on the extremely challenging WxBS benchmark. Code is provided at github.com/Parskatt/RoMa.

We propose RoHM an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train neural networks to directly regress motion in 3D or learn data-driven motion priors and combine them with optimization at test time. RoHM is a novel diffusion-based motion model that conditioned on noisy and occluded input data reconstructs complete plausible motions in consistent global coordinates. Given the complexity of the problem -- requiring one to address different tasks (denoising and infilling) in different solution spaces (local and global motion) -- we decompose it into two sub-tasks and learn two models one for global trajectory and one for local motion. To capture the correlations between the two we then introduce a novel conditioning module combining it with an iterative inference scheme. We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling. Extensive experiments on three popular datasets show that our method outperforms state-of-the-art approaches qualitatively and quantitatively while being faster at test time. The code is available at https://sanweiliti.github.io/ROHM/ROHM.html.

Before developing a Document Layout Analysis (DLA) model in real-world applications conducting comprehensive robustness testing is essential. However the robustness of DLA models remains underexplored in the literature. To address this we are the first to introduce a robustness benchmark for DLA models which includes 450K document images of three datasets. To cover realistic corruptions we propose a perturbation taxonomy with 12 common document perturbations with 3 severity levels inspired by real-world document processing. Additionally to better understand document perturbation impacts we propose two metrics Mean Perturbation Effect (mPE) for perturbation assessment and Mean Robustness Degradation (mRD) for robustness evaluation. Furthermore we introduce a self-titled model i.e. Robust Document Layout Analyzer (RoDLA) which improves attention mechanisms to boost extraction of robust features. Experiments on the proposed benchmarks (PubLayNet-P DocLayNet-P and M6Doc-P) demonstrate that RoDLA obtains state-of-the-art mRD scores of 115.7 135.4 and 150.4 respectively. Compared to previous methods RoDLA achieves notable improvements in mAP of +3.8% +7.1% and +12.1% respectively.

Deep neural networks for learning Symmetric Positive Definite (SPD) matrices are gaining increasing attention in machine learning. Despite the significant progress most existing SPD networks use traditional Euclidean classifiers on an approximated space rather than intrinsic classifiers that accurately capture the geometry of SPD manifolds. Inspired by Hyperbolic Neural Networks (HNNs) we propose Riemannian Multinomial Logistics Regression (RMLR) for the classification layers in SPD networks. We introduce a unified framework for building Riemannian classifiers under the metrics pulled back from the Euclidean space and showcase our framework under the parameterized Log-Euclidean Metric (LEM) and Log-Cholesky Metric (LCM). Besides our framework offers a novel intrinsic explanation for the most popular LogEig classifier in existing SPD networks. The effectiveness of our method is demonstrated in three applications: radar recognition human action recognition and electroencephalography (EEG) classification. The code is available at https://github.com/GitZH-Chen/SPDMLR.git.

Recent Text-to-Image (T2I) generation models such as Stable Diffusion and Imagen have made significant progress in generating high-resolution images based on text descriptions. However many generated images still suffer from issues such as artifacts/implausibility misalignment with text descriptions and low aesthetic quality. Inspired by the success of Reinforcement Learning with Human Feedback (RLHF) for large language models prior works collected human-provided scores as feedback on generated images and trained a reward model to improve the T2I generation. In this paper we enrich the feedback signal by (i) marking image regions that are implausible or misaligned with the text and (ii) annotating which words in the text prompt are misrepresented or missing on the image. We collect such rich human feedback on 18K generated images (RichHF-18K) and train a multimodal transformer to predict the rich feedback automatically. We show that the predicted rich human feedback can be leveraged to improve image generation for example by selecting high-quality training data to finetune and improve the generative models or by creating masks with predicted heatmaps to inpaint the problematic regions. Notably the improvements generalize to models (Muse) beyond those used to generate the images on which human feedback data were collected (Stable Diffusion variants). The RichHF-18K data set will be released in our GitHub repository: https://github.com/google-research/google-research/tree/master/richhf_18k.

Recent studies have drawn attention to the untapped potential of the "star operation" (element-wise multiplication) in network design. While intuitive explanations abound the foundational rationale behind its application remains largely unexplored. Our study attempts to reveal the star operation's ability of mapping inputs into high-dimensional non-linear feature spaces--akin to kernel tricks--without widening the network. We further introduce StarNet a simple yet powerful prototype demonstrating impressive performance and low latency under compact network structure and efficient budget. Like stars in the sky the star operation appears unremarkable but holds a vast universe of potential. Our work encourages further exploration across tasks with codes available at https://github.com/ma-xu/Rewrite-the-Stars.