CVPR 2024 论文列表

Recent developments in face restoration have achieved remarkable results in producing high-quality and lifelike outputs. The stunning results however often fail to be faithful with respect to the identity of the person as the models lack necessary context. In this paper we explore the potential of personalized face restoration with diffusion models. In our approach a restoration model is personalized using a few images of the identity leading to tailored restoration with respect to the identity while retaining fine-grained details. By using independent trainable blocks for personalization the rich prior of a base restoration model can be exploited to its fullest. To avoid the model relying on parts of identity left in the conditioning low-quality images a generative regularizer is employed. With a learnable parameter the model learns to balance between the details generated based on the input image and the degree of personalization. Moreover we improve the training pipeline of face restoration models to enable an alignment-free approach. We showcase the robust capabilities of our approach in several real-world scenarios with multiple identities demonstrating our method's ability to generate fine-grained details with faithful restoration. In the user study we evaluate the perceptual quality and faithfulness of the generated details with our method being voted best 61% of the time compared to the second best with 25% of the votes.

Recent transformer-based architectures have shown impressive results in the field of image segmentation. Thanks to their flexibility they obtain outstanding performance in multiple segmentation tasks such as semantic and panoptic under a single unified framework. To achieve such impressive performance these architectures employ intensive operations and require substantial computational resources which are often not available especially on edge devices. To fill this gap we propose Prototype-based Efficient MaskFormer (PEM) an efficient transformer-based architecture that can operate in multiple segmentation tasks. PEM proposes a novel prototype-based cross-attention which leverages the redundancy of visual features to restrict the computation and improve the efficiency without harming the performance. In addition PEM introduces an efficient multi-scale feature pyramid network capable of extracting features that have high semantic content in an efficient way thanks to the combination of deformable convolutions and context-based self-modulation. We benchmark the proposed PEM architecture on two tasks semantic and panoptic segmentation evaluated on two different datasets Cityscapes and ADE20K. PEM demonstrates outstanding performance on every task and dataset outperforming task-specific architectures while being comparable and even better than computationally expensive baselines. Code is available at https://github.com/NiccoloCavagnero/PEM.

Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Recent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy however leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically when applied to three vision-only and one vision-language Transformer models our approach often demonstrates a merely 0.6-point decrease in performance while reducing the original parameter size by 1/3 to 2/3.

We present PEGASUS a method for constructing a personalized generative 3D face avatar from monocular video sources. Our generative 3D avatar enables disentangled controls to selectively alter the facial attributes (e.g. hair or nose) while preserving the identity. Our approach consists of two stages: synthetic database generation and constructing a personalized generative avatar. We generate a synthetic video collection of the target identity with varying facial attributes where the videos are synthesized by borrowing the attributes from monocular videos of diverse identities. Then we build a person-specific generative 3D avatar that can modify its attributes continuously while preserving its identity. Through extensive experiments we demonstrate that our method of generating a synthetic database and creating a 3D generative avatar is the most effective in preserving identity while achieving high realism. Subsequently we introduce a zero-shot approach to achieve the same goal of generative modeling more efficiently by leveraging a previously constructed personalized generative model.

Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage.

Existing point cloud semantic segmentation networks cannot identify unknown classes and update their knowledge due to a closed-set and static perspective of the real world which would induce the intelligent agent to make bad decisions. To address this problem we propose a Probability-Driven Framework (PDF) for open world semantic segmentation that includes (i) a lightweight U-decoder branch to identify unknown classes by estimating the uncertainties (ii) a flexible pseudo-labeling scheme to supply geometry features along with probability distribution features of unknown classes by generating pseudo labels and (iii) an incremental knowledge distillation strategy to incorporate novel classes into the existing knowledge base gradually. Our framework enables the model to behave like human beings which could recognize unknown objects and incrementally learn them with the corresponding knowledge. Experimental results on the S3DIS and ScanNetv2 datasets demonstrate that the proposed PDF outperforms other methods by a large margin in both important tasks of open world semantic segmentation.

In this paper we present an end-to-end 3D-building-wireframe reconstruction method to regress edges directly from aerial light-detection-and-ranging (LiDAR) point clouds. Our method named parametric-building-wireframe reconstruction (PBWR) takes aerial LiDAR point clouds and initial edge entities as input and fully uses the self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarity to remove redundant edges. Additionally a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters when the simple use of the edge distance loss is not suitable. In our experiments our proposed method demonstrated state-of-the-art results on the Building3D dataset achieving an improvement of approximately 36% in Entry-level dataset edge accuracy and around a 42% improvement in the Tallinn dataset.

Recent works have proposed end-to-end autonomous vehicle (AV) architectures comprised of differentiable modules achieving state-of-the-art driving performance. While they provide advantages over the traditional perception-prediction-planning pipeline (e.g. removing information bottlenecks between components and alleviating integration challenges) they do so using a diverse combination of tasks modules and their interconnectivity. As of yet however there has been no systematic analysis of the necessity of these modules or the impact of their connectivity placement and internal representations on overall driving performance. Addressing this gap our work conducts a comprehensive exploration of the design space of end-to-end modular AV stacks. Our findings culminate in the development of PARA-Drive: a fully parallel end-to-end AV architecture. PARA-Drive not only achieves state-of-the-art performance in perception prediction and planning but also significantly enhances runtime speed by nearly 3x without compromising on interpretability or safety.

We propose the problem of point-level 3D scene interpolation which aims to simultaneously reconstruct a 3D scene in two states from multiple views synthesize smooth point-level interpolations between them and render the scene from novel viewpoints all without any supervision between the states. The primary challenge is on achieving a smooth transition between states that may involve significant and non-rigid changes. To address these challenges we introduce "PAPR in Motion" a novel approach that builds upon the recent Proximity Attention Point Rendering (PAPR) technique which can deform a point cloud to match a significantly different shape and render a visually coherent scene even after non-rigid deformations. Our approach is specifically designed to maintain the temporal consistency of the geometric structure by introducing various regularization techniques for PAPR. The result is a method that can effectively bridge large scene changes and produce visually coherent and temporally smooth interpolations in both geometry and appearance. Evaluation across diverse motion types demonstrates that "PAPR in Motion" outperforms the leading neural renderer for dynamic scenes. For more results and code please visit our project website at https://niopeng.github.io/PAPR-in-Motion/.

Generative image editing has recently witnessed extremely fast-paced growth. Some works use high-level conditioning such as text while others use low-level conditioning. Nevertheless most of them lack fine-grained control over the properties of the different objects present in the image i.e. object-level image editing. In this work we tackle the task by perceiving the images as an amalgamation of various objects and aim to control the properties of each object in a fine-grained manner. Out of these properties we identify structure and appearance as the most intuitive to understand and useful for editing purposes. We propose PAIR Diffusion a generic framework that enables a diffusion model to control the structure and appearance properties of each object in the image. We show that having control over the properties of each object in an image leads to comprehensive editing capabilities. Our framework allows for various object-level editing operations on real images such as reference image-based appearance editing free-form shape editing adding objects and variations. Thanks to our design we do not require any inversion step. Additionally we propose multimodal classifier-free guidance which enables editing images using both reference images and text when using our approach with foundational diffusion models. We validate the above claims by extensively evaluating our framework on both unconditional and foundational diffusion models.

Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods which rely on attack data or prior knowledge struggle to effectively address a wide range of adversarial patches. In this paper we show two inherent characteristics of adversarial patches semantic independence and spatial heterogeneity independent of their appearance shape size quantity and location. Semantic independence indicates that adversarial patches operate autonomously within their semantic context while spatial heterogeneity manifests as distinct image quality of the patch area that differs from original clean image due to the independent generation process. Based on these observations we propose PAD a novel adversarial patch localization and removal method that does not require prior knowledge or additional training. PAD offers patch-agnostic defense against various adversarial patches compatible with any pre-trained object detectors. Our comprehensive digital and physical experiments involving diverse patch types such as localized noise printable and naturalistic patches exhibit notable improvements over state-of-the-art works. Our code is available at https://github.com/Lihua-Jing/PAD.

We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory or the content of the reference video consequently overlooking the potential diversity of human motion within such scenarios. This limitation restricts the ability to generate pedestrian behaviors that exhibit a wider range of variations and realistic motions and therefore restricts its usage to provide rich motion content for other components in the driving simulation system e.g. suddenly changed motion to which the autonomous vehicle should respond. In our approach we strive to surpass the limitation by showcasing diverse human motions obtained from various sources such as generated human motions in addition to following the given trajectory. The fundamental contribution of our framework lies in combining the motion tracking task with trajectory following which enables the tracking of specific motion parts (e.g. upper body) while simultaneously following the given trajectory by a single policy. This way we significantly enhance both the diversity of simulated human motion within the given scenario and the controllability of the content including language-based control. Our framework facilitates the generation of a wide range of human motions contributing to greater realism and adaptability in pedestrian simulations for driving scenarios.

Nowadays the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services. In this paper we investigate latency attacks on deep learning applications. Unlike common adversarial attacks for misclassification the goal of latency attacks is to increase the inference time which may stop applications from responding to the requests within a reasonable time. This kind of attack is ubiquitous for various applications and we use object detection to demonstrate how such kind of attacks work. We also design a framework named Overload to generate latency attacks at scale. Our method is based on a newly formulated optimization problem and a novel technique called spatial attention. This attack serves to escalate the required computing costs during the inference time consequently leading to an extended inference time for object detection. It presents a significant threat especially to systems with limited computing resources. We conducted experiments using YOLOv5 models on Nvidia NX. Compared to existing methods our method is simpler and more effective. The experimental results show that with latency attacks the inference time of a single image can be increased ten times longer in reference to the normal setting. Moreover our findings pose a potential new threat to all object detection tasks requiring non-maximum suppression (NMS) as our attack is NMS-agnostic.

Foundation models encompass an extensive knowledge base and offer remarkable transferability. However this knowledge becomes outdated or insufficient over time. The challenge lies in continuously updating foundation models to accommodate novel information while retaining their original capabilities. Leveraging the fact that foundation models have initial knowledge on various tasks and domains we propose a novel approach that instead of updating all parameters equally localizes the updates to a sparse set of parameters relevant to the task being learned. We strike a balance between efficiency and new task performance while maintaining the transferability and generalizability of foundation models. We extensively evaluate our method on foundational vision-language models with a diverse spectrum of continual learning tasks. Our method achieves improvements on the accuracy of the newly learned tasks up to 7% while preserving the pretraining knowledge with a negligible decrease of 0.9% on a representative control set accuracy.

We aim to generate fine-grained 3D geometry from large-scale sparse LiDAR scans abundantly captured by autonomous vehicles (AV). Contrary to prior work on AV scene completion we aim to extrapolate fine geometry from unlabeled and beyond spatial limits of LiDAR scans taking a step towards generating realistic high-resolution simulation-ready 3D street environments. We propose hierarchical Generative Cellular Automata (hGCA) a spatially scalable conditional 3D generative model which grows geometry recursively with local kernels following GCAs in a coarse-to-fine manner equipped with a light-weight planner to induce global consistency. Experiments on synthetic scenes show that hGCA generates plausible scene geometry with higher fidelity and completeness compared to state-of-the-art baselines. Our model generalizes strongly from sim-to-real qualitatively outperforming baselines on the Waymo-open dataset. We also show anecdotal evidence of the ability to create novel objects from real-world geometric cues even when trained on limited synthetic content.

Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However current MLLMs primarily focus on image-level or box-level understanding falling short in achieving fine-grained vision-language alignment at pixel level. Besides the lack of mask-based instruction data limits their advancements. In this paper we propose Osprey a mask-text instruction tuning approach to extend MLLMs by incorporating fine-grained mask regions into language instruction aiming at achieving pixel-wise visual understanding. To achieve this goal we first meticulously curate a mask-based region-text dataset with 724K samples and then design a vision-language model by injecting pixel-level representation into LLM. Specifically Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimental results demonstrate Osprey's superiority in various region understanding tasks showcasing its new capability for pixel-level instruction tuning. In particular Osprey can be integrated with Segment Anything Model (SAM) seamlessly to obtain multi-granularity semantics. The source code dataset and demo can be found at https://github.com/CircleRadon/Osprey.

Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited pre-defined set of them they fall short of achieving scalability where a single model can seamlessly render countless concepts. In this paper we address a new problem called Modular Customization with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts. This allows the merged model to jointly synthesize concepts in one image without compromising fidelity or incurring any additional computational costs. To address this problem we introduce Orthogonal Adaptation a method designed to encourage the customized models which do not have access to each other during fine-tuning to have orthogonal residual weights. This ensures that during inference time the customized models can be summed with minimal interference. Our proposed method is both simple and versatile applicable to nearly all optimizable weights in the model architecture. Through an extensive set of quantitative and qualitative evaluations our method consistently outperforms relevant baselines in terms of efficiency and identity preservation demonstrating a significant leap toward scalable customization of diffusion models.

Redundancy is a persistent challenge in Capsule Networks (CapsNet) leading to high computational costs and parameter counts. Although previous studies have introduced pruning after the initial capsule layer dynamic routing's fully connected nature and non-orthogonal weight matrices reintroduce redundancy in deeper layers. Besides dynamic routing requires iterating to converge further increasing computational demands. In this paper we propose an Orthogonal Capsule Network (OrthCaps) to reduce redundancy improve routing performance and decrease parameter counts. Firstly an efficient pruned capsule layer is introduced to discard redundant capsules. Secondly dynamic routing is replaced with orthogonal sparse attention routing eliminating the need for iterations and fully connected structures. Lastly weight matrices during routing are orthogonalized to sustain low capsule similarity which is the first approach to use Householder orthogonal decomposition to enforce orthogonality in CapsNet. Our experiments on baseline datasets affirm the efficiency and robustness of OrthCaps in classification tasks in which ablation studies validate the criticality of each component. OrthCaps-Shallow outperforms other Capsule Network benchmarks on four datasets utilizing only 110k parameters - a mere 1.25% of a standard Capsule Network's total. To the best of our knowledge it achieves the smallest parameter count among existing Capsule Networks. Similarly OrthCaps-Deep demonstrates competitive performance across four datasets utilizing only 1.2% of the parameters required by its counterparts.

To accommodate real-world dynamics artificial intelligence systems need to cope with sequentially arriving content in an online manner. Beyond regular Continual Learning (CL) attempting to address catastrophic forgetting with offline training of each task Online Continual Learning (OCL) is a more challenging yet realistic setting that performs CL in a one-pass data stream. Current OCL methods primarily rely on memory replay of old training samples. However a notable gap from CL to OCL stems from the additional overfitting-underfitting dilemma associated with the use of rehearsal buffers: the inadequate learning of new training samples (underfitting) and the repeated learning of a few old training samples (overfitting). To this end we introduce a novel approach Multi-level Online Sequential Experts (MOSE) which cultivates the model as stacked sub-experts integrating multi-level supervision and reverse self-distillation. Supervision signals across multiple stages facilitate appropriate convergence of the new task while gathering various strengths from experts by knowledge distillation mitigates the performance decline of old tasks. MOSE demonstrates remarkable efficacy in learning new samples and preserving past knowledge through multi-level experts thereby significantly advancing OCL performance over state-of-the-art baselines (e.g. up to 7.3% on Split CIFAR-100 and 6.1% on Split Tiny-ImageNet).