CVPR 2024 Papers

Scene recovery the restoration of images degraded by adverse weather conditions presents significant challenges for existing methods. Physical models constrained by their inherent assumptions often fail when these assumptions are not met; Deep learning models are powerful they are limited by the diversity of their training datasets leading to poor generalization and high computational demands. To address these limitations we propose the Luminous Flux Prior (LFP) to recover degraded images under diverse adverse weather without learning. Luminous flux a physical measure that reflects image brightness has a rate of change that demonstrates a significant correlation with transmission. Consequently we leverage this rate of change in luminous flux as prior knowledge to estimate transmission which in turn assists in image recovery. This approach reduces dependency on physical parameters and enhances adaptability to various weather. Experimental validation under diverse conditions such as sandstorms underwater environments and haze attests to the robustness of LFP in restoring clear images. With a time complexity of \mathcal O (N\log N) LFP enables real-time recovery making it a suitable for devices with limited computational resources.

The recent success in revealing scene details from sparse 3D point clouds obtained via structure-from-motion has raised significant privacy concerns in visual localization. One prominent approach for mitigating this issue is to lift 3D points to 3D lines thereby reducing the effectiveness of the scene inversion attacks but this comes at the cost of increased algorithmic complexity for camera localization due to weaker geometric constraints induced by line clouds. To overcome this limitation we propose a new lifting approach called "ray cloud" whereby each lifted 3D line intersects at one of two predefined locations depicting omnidirectional rays from two cameras. This yields two benefits i) camera localization can now be cast as relative pose estimation between the query image and the calibrated rig of two perspective cameras which can be efficiently solved using a variant of the 5-point algorithm and ii) the ray cloud introduces erroneous estimations for the density-based inversion attack degrading the quality of scene recovery. Moreover we explore possible modifications of the inversion attack to better recover scenes from the ray clouds and propose a ray sampling technique to reduce the effectiveness of the modified attack. Experimental results on two public datasets show real-time localization speed as well as enhanced privacy-preserving capability over the state-of-the-art without overly sacrificing the localization accuracy.

Multi-task learning for dense prediction has emerged as a pivotal area in computer vision enabling simultaneous processing of diverse yet interrelated pixel-wise prediction tasks. However the substantial computational demands of state-of-the-art (SoTA) models often limit their widespread deployment. This paper addresses this challenge by introducing network binarization to compress resource-intensive multi-task dense predictors. Specifically our goal is to significantly accelerate multi-task dense prediction models via Binary Neural Networks (BNNs) while maintaining and even improving model performance at the same time. To reach this goal we propose a Binary Multi-task Dense Predictor Bi-MTDP and several variants of \bimtdp in which a multi-task dense predictor is constructed via specified binarized modules. Our systematical analysis of this predictor reveals that performance drop from binarization is primarily caused by severe information degradation. To address this issue we introduce a deep information bottleneck layer that enforces representations for downstream tasks satisfying Gaussian distribution in forward propagation. Moreover we introduce a knowledge distillation mechanism to correct the direction of information flow in backward propagation. Intriguingly one variant of Bi-MTDP outperforms full-precision (FP) multi-task dense prediction SoTAs ARTC (CNN-based) and InvPT (ViT-based). This result indicates that Bi-MTDP is not merely a naive trade-off between performance and efficiency but is rather a benefit of the redundant information flow thanks to the multi-task architecture.

Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however in the context of deep learning existing multi-scale algorithms not only require the use of complex modules for feature fusion of low-scale RGB images and deep semantics but also manually generate low-resolution pairs of images that do not have sufficient confidence. In this work we propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. This simplifies the complexity of algorithms based on a coarse-to-fine scheme. To alleviate restoration defects impacting detail information brought about by using a multi-scale architecture we combine the characteristics of real-world blurring trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images. In conclusion we propose a multi-scale network with a learnable discrete wavelet transform (MLWNet) which exhibits state-of-the-art performance on multiple real-world deblurred datasets in terms of both subjective and objective quality as well as computational efficiency.

With the escalating complexity and investment cost of training deep neural networks safeguarding them from unauthorized usage and intellectual property theft has become imperative. Especially the rampant misuse of prediction APIs to replicate models without access to the original data or architecture poses grave security threats. Diverse defense strategies have emerged to address these vulnerabilities yet these defenses either incur heavy inference overheads or assume idealized attack scenarios. To address these challenges we revisit the utilization of noise transition matrix as an efficient perturbation technique which injects noise into predicted posteriors in a linear manner and integrates seamlessly into existing systems with minimal overhead for model stealing defense. Provably with such perturbed posteriors the attacker's cloning process degrades into learning from noisy data. Toward optimizing the noise transition matrix we proposed a novel bi-level optimization training framework which performs fidelity on the victim model while the surrogate model adversarially. Comprehensive experimental results demonstrate that our method effectively thwarts model stealing attacks and achieves minimal utility trade-offs outperforming existing state-of-the-art defenses.

In this paper we explore the problem of event-based meshflow estimation a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start we generate a large-scale High-Resolution Event Meshflow (HREM) dataset which showcases its superiority by encompassing the merits of high resolution at 1280x720 handling dynamic objects and complex motion patterns and offering both optical flow and meshflow labels. These aspects have not been fully explored in previous works. Besides we propose Efficient Event-based MeshFlow (EEMFlow) network a lightweight model featuring a specially crafted encoder-decoder architecture to facilitate swift and accurate meshflow estimation. Furthermore we upgrade EEMFlow network to support dense event optical flow in which a Confidence-induced Detail Completion (CDC) module is proposed to preserve sharp motion boundaries. We conduct comprehensive experiments to show the exceptional performance and runtime efficiency (39x faster) of our EEMFlow model compared to recent state-of-the-art flow methods. Our code is available at https://github.com/boomluo02/EEMFlow.

Hyperparameter Optimization and Neural Architecture Search are powerful in attaining state-of-the-art machine learning models with Bayesian Optimization (BO) standing out as a mainstream method. Extending BO into the multi-fidelity setting has been an emerging research topic in this field but faces the challenge of determining an appropriate fidelity for each hyperparameter configuration to fit the surrogate model. To tackle the challenge we propose a multi-fidelity BO method named FastBO which excels in adaptively deciding the fidelity for each configuration and providing strong performance while ensuring efficient resource usage. These advantages are achieved through our proposed techniques based on the concepts of efficient point and saturation point for each configuration which can be obtained from the empirical learning curve of the configuration estimated from early observations. Extensive experiments demonstrate FastBO's superior anytime performance and efficiency in identifying high-quality configurations and architectures. We also show that our method provides a way to extend any single-fidelity method to the multi-fidelity setting highlighting the wide applicability of our approach.

Group synchronization plays a crucial role in global pipelines for Structure from Motion (SfM). Its formulation is nonconvex and it is faced with highly corrupted measurements. Cycle consistency has been effective in addressing these challenges. However computationally efficient solutions are needed for cycles longer than three especially in practical scenarios where 3-cycles are unavailable. To overcome this computational bottleneck we propose an algorithm for group synchronization that leverages information from cycles of lengths ranging from three to six with a complexity of O(n^3) (or O(n^ 2.373 ) when using a faster matrix multiplication algorithm). We establish non-trivial theory for this and related methods that achieves competitive sample complexity assuming the uniform corruption model. To advocate the practical need for our method we consider distributed group synchronization which requires at least 4-cycles and we illustrate state-of-the-art performance by our method in this context.

Dataset distillation reduces the storage and computational consumption of training a network by generating a small surrogate dataset that encapsulates rich information of the original large-scale one. However previous distillation methods heavily rely on the sample-wise iterative optimization scheme. As the images-per-class (IPC) setting or image resolution grows larger the necessary computation will demand overwhelming time and resources. In this work we intend to incorporate generative diffusion techniques for computing the surrogate dataset. Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models. We present a theoretical model of the process as hierarchical diffusion control demonstrating the flexibility of the diffusion process to target these criteria without jeopardizing the faithfulness of the sample to the desired distribution. The proposed method achieves state-of-the-art validation performance while demanding much less computational resources. Under the 100-IPC setting on ImageWoof our method requires less than one-twentieth the distillation time of previous methods yet yields even better performance. Source code and generated data are available in https://github.com/vimar-gu/MinimaxDiffusion.

3D head avatars built with neural implicit volumetric representations have achieved unprecedented levels of photorealism. However the computational cost of these methods remains a significant barrier to their widespread adoption particularly in real-time applications such as virtual reality and teleconferencing. While attempts have been made to develop fast neural rendering approaches for static scenes these methods cannot be simply employed to support realistic facial expressions such as in the case of a dynamic facial performance. To address these challenges we propose a novel fast 3D neural implicit head avatar model that achieves real-time rendering while maintaining fine-grained controllability and high rendering quality. Our key idea lies in the introduction of local hash table blendshapes which are learned and attached to the vertices of an underlying face parametric model. These per-vertex hash-tables are linearly merged with weights predicted via a CNN resulting in expression dependent embeddings. Our novel representation enables efficient density and color predictions using a lightweight MLP which is further accelerated by a hierarchical nearest neighbor search method. Extensive experiments show that our approach runs in real-time while achieving comparable rendering quality to state-of-the-arts and decent results on challenging expressions.

Image-based mirror detection has recently undergone rapid research due to its significance in applications such as robotic navigation semantic segmentation and scene reconstruction. Recently VMD-Net was proposed as the first video mirror detection technique by modeling dual correspondences between the inside and outside of the mirror both spatially and temporally. However this approach is not reliable as correspondences can occur completely inside or outside of the mirrors. In addition the proposed dataset VMD-D contains many small mirrors limiting its applicability to real-world scenarios. To address these problems we developed a more challenging dataset that includes mirrors of various shapes and sizes at different locations of the frames providing a better reflection of real-world scenarios. Next we observed that the motions between the inside and outside of the mirror are often inconsistent. For instance when moving in front of a mirror the motion inside the mirror is often much smaller than the motion outside due to increased depth perception. With these observations we propose modeling inconsistent motion cues to detect mirrors and a new network with two novel modules. The Motion Attention Module (MAM) explicitly models inconsistent motions around mirrors via optical flow and the Motion-Guided Edge Detection Module (MEDM) uses motions to guide mirror edge feature learning. Experimental results on our proposed dataset show that our method outperforms state-of-the-arts. The code and dataset are available at https://github.com/AlexAnthonyWarren/MG-VMD.

In the era of AI-generated content (AIGC) malicious tampering poses imminent threats to copyright integrity and information security. Current deep image watermarking while widely accepted for safeguarding visual content can only protect copyright and ensure traceability. They fall short in localizing increasingly realistic image tampering potentially leading to trust crises privacy violations and legal disputes. To solve this challenge we propose an innovative proactive forensics framework EditGuard to unify copyright protection and tamper-agnostic localization especially for AIGC-based editing methods. It can offer a meticulous embedding of imperceptible watermarks and precise decoding of tampered areas and copyright information. Leveraging our observed fragility and locality of image-into-image steganography the realization of EditGuard can be converted into a united image-bit steganography issue thus completely decoupling the training process from the tampering types. Extensive experiments verify that our EditGuard balances the tamper localization accuracy copyright recovery precision and generalizability to various AIGC-based tampering methods especially for image forgery that is difficult for the naked eye to detect.

In recent years image editing has advanced remarkably. With increased human control it is now possible to edit an image in a plethora of ways; from specifying in text what we want to change to straight up dragging the contents of the image in an interactive point-based manner. However most of the focus has remained on editing single images at a time. Whether and how we can simultaneously edit large batches of images has remained understudied. With the goal of minimizing human supervision in the editing process this paper presents a novel method for interactive batch image editing using StyleGAN as the medium. Given an edit specified by users in an example image (e.g. make the face frontal) our method can automatically transfer that edit to other test images so that regardless of their initial state (pose) they all arrive at the same final state (e.g. all facing front). Extensive experiments demonstrate that edits performed using our method have similar visual quality to existing single-image-editing methods while having more visual consistency and saving significant time and human effort.

While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance.

Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination---even from diffuse objects---by exploiting unintended shadows like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials the surrounding illumination environment and the shapes of the unseen light occluders who inadvertently cast shadows upon it.

Generative models are gaining increasing popularity and the demand for precisely generating images is on the rise. However generating an image that perfectly aligns with users' expectations is extremely challenging. The shapes of objects the poses of animals the structures of landscapes and more may not match the user's desires and this applies to real images as well. This is where point-based image editing becomes essential. An excellent image editing method needs to meet the following criteria: user-friendly interaction high performance and good generalization capability. Due to the limitations of StyleGAN DragGAN exhibits limited robustness across diverse scenarios while DragDiffusion lacks user-friendliness due to the necessity of LoRA fine-tuning and masks. In this paper we introduce a novel interactive point-based image editing framework called EasyDrag that leverages pretrained diffusion models to achieve high-quality editing outcomes and user-friendship. Extensive experimentation demonstrates that our approach surpasses DragDiffusion in terms of both image quality and editing precision for point-based image manipulation tasks.

Astronaut photography spanning six decades of human spaceflight presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite its significance accurately localizing the geographical extent of these images crucial for effective utilization poses substantial challenges. Current manual localization efforts are time-consuming motivating the need for automated solutions. We propose a novel approach - leveraging image retrieval - to address this challenge efficiently. We introduce innovative training techniques including Year-Wise Data Augmentation and a Neutral-Aware Multi-Similarity Loss which contribute to the development of a high-performance model EarthLoc. We develop six evaluation datasets and perform a comprehensive benchmark comparing EarthLoc to existing methods showcasing its superior efficiency and accuracy. Our approach marks a significant advancement in automating the localization of astronaut photography which will help bridge a critical gap in Earth observations data. Code and datasets are available at this https://github.com/gmberton/EarthLoc

Distribution shift widely exists in medical images acquired from different medical centres and poses a significant obstacle to deploying the pre-trained semantic segmentation model in real-world applications. Test-time adaptation has proven its effectiveness in tackling the cross-domain distribution shift during inference. However most existing methods achieve adaptation by updating the pre-trained models rendering them susceptible to error accumulation and catastrophic forgetting when encountering a series of distribution shifts (i.e. under the continual test-time adaptation setup). To overcome these challenges caused by updating the models in this paper we freeze the pre-trained model and propose the Visual Prompt-based Test-Time Adaptation (VPTTA) method to train a specific prompt for each test image to align the statistics in the batch normalization layers. Specifically we present the low-frequency prompt which is lightweight with only a few parameters and can be effectively trained in a single iteration. To enhance prompt initialization we equip VPTTA with a memory bank to benefit the current prompt from previous ones. Additionally we design a warm-up mechanism which mixes source and target statistics to construct warm-up statistics thereby facilitating the training process. Extensive experiments demonstrate the superiority of our VPTTA over other state-of-the-art methods on two medical image segmentation benchmark tasks. The code and weights of pre-trained source models are available at https://github.com/Chen-Ziyang/VPTTA.

Event-based Vision Sensors (EVS) gain popularity in enhancing CMOS Image Sensor (CIS) video capture. Nonidealities of EVS such as pixel or readout latency can significantly influence the quality of the enhanced images and warrant dedicated consideration in the design of fusion algorithms. A novel approach for jointly computing deblurred rolling-shutter artifact corrected high-speed videos with frame rates up to 10000 FPS using inherently blurry rolling shutter CIS frames of 120 FPS to 150 FPS in conjunction with EVS data from a hybrid CIS-EVS sensor is presented. EVS pixel latency readout latency and the sensor's refractory period are explicitly incorporated into the measurement model. This inverse function problem is solved on a per-pixel manner using an optimization-based framework. The interpolated images are subsequently processed by a novel refinement network. The proposed method is evaluated using simulated and measured datasets under natural and controlled environments. Extensive experiments show reduced shadowing effect a 4 dB increment in PSNR and a 12% improvement in LPIPS score compared to state-of-the-art methods.