CVPR 2024 Papers

Novelty detection is a critical task for deploying machine learning models in the open world. A crucial property of novelty detection methods is universality which can be interpreted as generalization across various distributions of training or test data. More precisely for novelty detection distribution shifts may occur in the training set or the test set. Shifts in the training set refer to cases where we train a novelty detector on a new dataset and expect strong transferability. Conversely distribution shifts in the test set indicate the methods' performance when the trained model encounters a shifted test sample. We experimentally show that existing methods falter in maintaining universality which stems from their rigid inductive biases. Motivated by this we aim for more generalized techniques that have more adaptable inductive biases. In this context we leverage the fact that contrastive learning provides an efficient framework to easily switch and adapt to new inductive biases through the proper choice of augmentations in forming the negative pairs. We propose a novel probabilistic auto-negative pair generation method AutoAugOOD along with contrastive learning to yield a universal novelty detector method. Our experiments demonstrate the superiority of our method under different distribution shifts in various image benchmark datasets. Notably our method emerges universality in the lens of adaptability to different setups of novelty detection including one-class unlabeled multi-class and labeled multi-class settings.

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has achieved promising progress. However prior LSS methods are conventionally investigated and evaluated on datasets within the same domain in clear weather. The robustness of LSS models in unseen scenes and all weather conditions is crucial for ensuring safety and reliability in real applications. To this end we propose UniMix a universal method that enhances the adaptability and generalizability of LSS models. UniMix first leverages physically valid adverse weather simulation to construct a Bridge Domain which serves to bridge the domain gap between the clear weather scenes and the adverse weather scenes. Then a Universal Mixing operator is defined regarding spatial intensity and semantic distributions to create the intermediate domain with mixed samples from given domains. Integrating the proposed two techniques into a teacher-student framework UniMix efficiently mitigates the domain gap and enables LSS models to learn weather-robust and domain-invariant representations. We devote UniMix to two main setups: 1) unsupervised domain adaption adapting the model from the clear weather source domain to the adverse weather target domain; 2) domain generalization learning a model that generalizes well to unseen scenes in adverse weather. Extensive experiments validate the effectiveness of UniMix across different tasks and datasets all achieving superior performance over state-of-the-art methods. The code will be released.

Unsupervised Domain Adaptation (UDA) can effectively address domain gap issues in real-world image Super-Resolution (SR) by accessing both the source and target data. Considering privacy policies or transmission restrictions of source data in practical scenarios we propose a SOurce-free Domain Adaptation framework for image SR (SODA-SR) to address this issue i.e. adapt a source-trained model to a target domain with only unlabeled target data. SODA-SR leverages the source-trained model to generate refined pseudo-labels for teacher-student learning. To better utilize pseudo-labels we propose a novel wavelet-based augmentation method named Wavelet Augmentation Transformer (WAT) which can be flexibly incorporated with existing networks to implicitly produce useful augmented data. WAT learns low-frequency information of varying levels across diverse samples which is aggregated efficiently via deformable attention. Furthermore an uncertainty-aware self-training mechanism is proposed to improve the accuracy of pseudo-labels with inaccurate predictions being rectified by uncertainty estimation. To acquire better SR results and avoid overfitting pseudo-labels several regularization losses are proposed to constrain target LR and SR images in the frequency domain. Experiments show that without accessing source data SODA-SR outperforms state-of-the-art UDA methods in both synthetic->real and real->real adaptation settings and is not constrained by specific network architectures.

Single Image Super-Resolution is a classic computer vision problem that involves estimating high-resolution (HR) images from low-resolution (LR) ones. Although deep neural networks (DNNs) especially Transformers for super-resolution have seen significant advancements in recent years challenges still remain particularly in limited receptive field caused by window-based self-attention. To address these issues we introduce a group of auxiliary Adaptive Token Dictionary to SR Transformer and establish an ATD-SR method. The introduced token dictionary could learn prior information from training data and adapt the learned prior to specific testing image through an adaptive refinement step. The refinement strategy could not only provide global information to all input tokens but also group image tokens into categories. Based on category partitions we further propose a category-based self-attention mechanism designed to leverage distant but similar tokens for enhancing input features. The experimental results show that our method achieves the best performance on various single image super-resolution benchmarks.

Exemplar-free Class Incremental Learning (EFCIL) aims to sequentially learn tasks with access only to data from the current one. EFCIL is of interest because it mitigates concerns about privacy and long-term storage of data while at the same time alleviating the problem of catastrophic forgetting in incremental learning. In this work we introduce task-adaptive saliency for EFCIL and propose a new framework which we call Task-Adaptive Saliency Supervision (TASS) for mitigating the negative effects of saliency drift between different tasks. We first apply boundary-guided saliency to maintain task adaptivity and plasticity on model attention. Besides we introduce task-agnostic low-level signals as auxiliary supervision to increase the stability of model attention. Finally we introduce a module for injecting and recovering saliency noise to increase the robustness of saliency preservation. Our experiments demonstrate that our method can better preserve saliency maps across tasks and achieve state-of-the-art results on the CIFAR-100 Tiny-ImageNet and ImageNet-Subset EFCIL benchmarks. Code is available at https://github.com/scok30/tass.

We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation which aims to adapt a source-trained model to the target domain without accessing the source data. Many works have been proposed to address this challenging problem among which uncertainty based self-training is a predominant approach. However without comprehensive denoising mechanisms they still largely fall into biased estimates when dealing with different domains and confirmation bias. In this paper we observe that pseudo-label noise is mainly contained in unstable samples in which the predictions of most pixels undergo significant variations during self-training. Inspired by this we propose a novel mechanism to denoise unstable samples with stable ones. Specifically we introduce the Stable Neighbor Denoising (SND) approach which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning. Moreover we compensate for the stable set by object-level object paste which can further eliminate the bias caused by less learned classes. Our SND enjoys two advantages. First SND does not require a specific segmentor structure endowing its universality. Second SND simultaneously addresses the issues of class domain and confirmation biases during adaptation ensuring its effectiveness. Extensive experiments show that SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings. In addition SND can be easily integrated with other approaches obtaining further improvements. The source code will be publicly available.

Predicting the future motion of surrounding agents is essential for autonomous vehicles (AVs) to operate safely in dynamic human-robot-mixed environments. Context information such as road maps and surrounding agents' states provides crucial geometric and semantic information for motion behavior prediction. To this end recent works explore two-stage prediction frameworks where coarse trajectories are first proposed and then used to select critical context information for trajectory refinement. However they either incur a large amount of computation or bring limited improvement if not both. In this paper we introduce a novel scenario-adaptive refinement strategy named SmartRefine to refine prediction with minimal additional computation. Specifically SmartRefine can comprehensively adapt refinement configurations based on each scenario's properties and smartly chooses the number of refinement iterations by introducing a quality score to measure the prediction quality and remaining refinement potential of each scenario. SmartRefine is designed as a generic and flexible approach that can be seamlessly integrated into most state-of-the-art motion prediction models. Experiments on Argoverse (1 & 2) show that our method consistently improves the prediction accuracy of multiple state-of-the-art prediction models. Specifically by adding SmartRefine to QCNet we outperform all published ensemble-free works on the Argoverse 2 leaderboard (single agent track) at submission. Comprehensive studies are also conducted to ablate design choices and explore the mechanism behind multi-iteration refinement. Codes are available at https://github.com/opendilab/SmartRefine/.

Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content eliminating any distortions or synthetic details. While traditional diffusion-based SR techniques have demonstrated remarkable abilities to enhance image detail they are prone to artifact introduction during iterative procedures. Such artifacts ranging from trivial noise to unauthentic textures deviate from the true structure of the source image thus challenging the integrity of the super-resolution process. In this work we propose Self-Adaptive Reality-Guided Diffusion (SARGD) a training-free method that delves into the latent space to effectively identify and mitigate the propagation of artifacts. Our SARGD begins by using an artifact detector to identify implausible pixels creating a binary mask that highlights artifacts. Following this the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations improving alignment with the original image. Nonetheless initial realistic-latent representations from lower-quality images result in over-smoothing in the final output. To address this we introduce a Self-Adaptive Guidance (SAG) mechanism. It dynamically computes a reality score enhancing the sharpness of the realistic latent. These alternating mechanisms collectively achieve artifact-free super-resolution. Extensive experiments demonstrate the superiority of our method delivering detailed artifact-free high-resolution images while reducing sampling steps by 2X. We release our code at https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git.

Stereo matching methods based on iterative optimization like RAFT-Stereo and IGEV-Stereo have evolved into a cornerstone in the field of stereo matching. However these methods struggle to simultaneously capture high-frequency information in edges and low-frequency information in smooth regions due to the fixed receptive field. As a result they tend to lose details blur edges and produce false matches in textureless areas. In this paper we propose Selective Recurrent Unit (SRU) a novel iterative update operator for stereo matching. The SRU module can adaptively fuse hidden disparity information at multiple frequencies for edge and smooth regions. To perform adaptive fusion we introduce a new Contextual Spatial Attention (CSA) module to generate attention maps as fusion weights. The SRU empowers the network to aggregate hidden disparity information across multiple frequencies mitigating the risk of vital hidden disparity information loss during iterative processes. To verify SRU's universality we apply it to representative iterative stereo matching methods collectively referred to as Selective-Stereo. Our Selective-Stereo ranks first on KITTI 2012 KITTI 2015 ETH3D and Middlebury leaderboards among all published methods. Code is available at https://github.com/Windsrain/Selective-Stereo.

While recent Transformer-based approaches have shown impressive performances on event-based object detection tasks their high computational costs still diminish the low power consumption advantage of event cameras. Image-based works attempt to reduce these costs by introducing sparse Transformers. However they display inadequate sparsity and adaptability when applied to event-based object detection since these approaches cannot balance the fine granularity of token-level sparsification and the efficiency of window-based Transformers leading to reduced performance and efficiency. Furthermore they lack scene-specific sparsity optimization resulting in information loss and a lower recall rate. To overcome these limitations we propose the Scene Adaptive Sparse Transformer (SAST). SAST enables window-token co-sparsification significantly enhancing fault tolerance and reducing computational overhead. Leveraging the innovative scoring and selection modules along with the Masked Sparse Window Self-Attention SAST showcases remarkable scene-aware adaptability: It focuses only on important objects and dynamically optimizes sparsity level according to scene complexity maintaining a remarkable balance between performance and computational cost. The evaluation results show that SAST outperforms all other dense and sparse networks in both performance and efficiency on two large-scale event-based object detection datasets (1Mpx and Gen1). Code: https://github.com/Peterande/SAST

Scene-aware Adaptive Compressive Sensing (ACS) has constituted a persistent pursuit holding substantial promise for the enhancement of Compressive Sensing (CS) performance. Cascaded ACS furnishes a proficient multi-stage framework for adaptively allocating the CS sampling based on previous CS measurements. However reconstruction is commonly required for analyzing and steering the successive CS sampling which bottlenecks the ACS speed and impedes the practical application in time-sensitive scenarios. Addressing this challenge we propose a reconstruction-free cascaded ACS method which requires NO reconstruction during the adaptive sampling process. A lightweight Score Network (ScoreNet) is proposed to directly determine the ACS allocation with previous CS measurements and a differentiable adaptive sampling module is proposed for end-to-end training. For image reconstruction we propose a Multi-Grid Spatial-Attention Network (MGSANet) that could facilitate efficient multi-stage training and inferencing. By introducing the reconstruction-fidelity supervision outside the loop of the multi-stage sampling process ACS can be efficiently optimized and achieve high imaging fidelity. The effectiveness of the proposed method is demonstrated with extensive quantitative and qualitative experiments compared with the state-of-the-art CS algorithms.

Most of the previous exposure correction methods learn dense pixel-wise transformations to achieve promising results but consume huge computational resources. Recently Learnable 3D lookup tables (3D LUTs) have demonstrated impressive performance and efficiency for image enhancement. However these methods can only perform global transformations and fail to finely manipulate local regions. Moreover they uniformly downsample the input image which loses the rich color information and limits the learning of color transformation capabilities. In this paper we present a collaborative transformation framework (CoTF) for real-time exposure correction which integrates global transformation with pixel-wise transformations in an efficient manner. Specifically the global transformation adjusts the overall appearance using image-adaptive 3D LUTs to provide decent global contrast and sharp details while the pixel transformation compensates for local context. Then a relation-aware modulation module is designed to combine these two components effectively. In addition we propose an adaptive sampling strategy to preserve more color information by predicting the sampling intervals thus providing higher quality input data for the learning of 3D LUTs. Extensive experiments demonstrate that our method can process high-resolution images in real-time on GPUs while achieving comparable performance against current state-of-the-art methods. The code is available at https://github.com/HUST-IAL/CoTF.

Recent advancements in large-scale pre-trained text-to-image models have led to remarkable progress in semantic image synthesis. Nevertheless synthesizing high-quality images with consistent semantics and layout remains a challenge. In this paper we propose the adaPtive LAyout-semantiC fusion modulE (PLACE) that harnesses pre-trained models to alleviate the aforementioned issues. Specifically we first employ the layout control map to faithfully represent layouts in the feature space. Subsequently we combine the layout and semantic features in a timestep-adaptive manner to synthesize images with realistic details. During fine-tuning we propose the Semantic Alignment (SA) loss to further enhance layout alignment. Additionally we introduce the Layout-Free Prior Preservation (LFP) loss which leverages unlabeled data to maintain the priors of pre-trained models thereby improving the visual quality and semantic consistency of synthesized images. Extensive experiments demonstrate that our approach performs favorably in terms of visual quality semantic consistency and layout alignment. The source code and model are available at \href https://github.com/cszy98/PLACE/tree/main PLACE .

We present a method to reconstruct indoor and outdoor static scene geometry and appearance from an omnidirectional video moving in a small circular sweep. This setting is challenging because of the small baseline and large depth ranges making it difficult to find ray crossings. To better constrain the optimization we estimate geometry as a signed distance field within a spherical binoctree data structure and use a complementary efficient tree traversal strategy based on a breadth-first search for sampling. Unlike regular grids or trees the shape of this structure well-matches the camera setting creating a better memory-quality trade-off. From an initial depth estimate the binoctree is adaptively subdivided throughout the optimization; previous methods use a fixed depth that leaves the scene undersampled. In comparison with three neural optimization methods and two non-neural methods ours shows decreased geometry error on average especially in a detailed scene while significantly reducing the required number of voxels to represent such details.

The booming of 3D recognition in the 2020s began with the introduction of point cloud transformers. They quickly overwhelmed sparse CNNs and became state-of-the-art models especially in 3D semantic segmentation. However sparse CNNs are still valuable networks due to their efficiency treasure and ease of application. In this work we reexamine the design distinctions and test the limits of what a sparse CNN can achieve. We discover that the key credit to the performance difference is adaptivity. Specifically we propose two key components i.e. adaptive receptive fields (spatially) and adaptive relation to bridge the gap. This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs) a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost. Without any self-attention modules OA-CNNs favorably surpass point transformers in terms of accuracy in both indoor and outdoor scenes with much less latency and memory cost. Notably it achieves 76.1% 78.9% and 70.6% mIoU on ScanNet v2 nuScenes and SemanticKITTI validation benchmarks respectively while maintaining at most 5x better speed than transformer counterparts. This revelation highlights the potential of pure sparse CNNs to outperform transformer-related networks. Our code is built upon Pointcept which is available at https://github.com/Pointcept/Pointcept.

Nighttime conditions pose a significant challenge to color constancy due to the diversity of lighting conditions and the presence of substantial low-light noise. Existing color constancy methods struggle with nighttime scenes frequently leading to imprecise light color estimations. To tackle nighttime color constancy we propose a novel unsupervised domain adaptation approach that utilizes labeled daytime data to facilitate learning on unlabeled nighttime images. To specifically address the unique lighting conditions of nighttime and ensure the robustness of pseudo labels we propose adaptive channel masking and light uncertainty. By selectively masking channels that are less sensitive to lighting conditions adaptive channel masking directs the model to progressively focus on features less affected by variations in light colors and noise. Additionally our model leverages light uncertainty to provide a pixel-wise uncertainty estimation regarding light color prediction which helps avoid learning from incorrect labels. Our model demonstrates a significant improvement in accuracy achieving 21.5% lower Mean Angular Error (MAE) compared to the state-of-the-art method on our nighttime dataset.

Eliminating image blur produced by various kinds of motion has been a challenging problem. Dominant approaches rely heavily on model capacity to remove blurring by reconstructing residual from blurry observation in feature space. These practices not only prevent the capture of spatially variable motion in the real world but also ignore the tailored handling of various motions in image space. In this paper we propose a novel real-world deblurring filtering model called the Motion-adaptive Separable Collaborative (MISC) Filter. In particular we use a motion estimation network to capture motion information from neighborhoods thereby adaptively estimating spatially-variant motion flow mask kernels weights and offsets to obtain the MISC Filter. The MISC Filter first aligns the motion-induced blurring patterns to the motion middle along the predicted flow direction and then collaboratively filters the aligned image through the predicted kernels weights and offsets to generate the output. This design can handle more generalized and complex motion in a spatially differentiated manner. Furthermore we analyze the relationships between the motion estimation network and the residual reconstruction network. Extensive experiments on four widely used benchmarks demonstrate that our method provides an effective solution for real-world motion blur removal and achieves state-of-the-art performance. Code is available at https://github.com/ChengxuLiu/MISCFilter.