CVPR 2024 Papers

We propose a method to control material attributes of objects like roughness metallic albedo and transparency in real images. Our method capitalizes on the generative prior of text-to-image models known for photorealism employing a scalar value and instructions to alter low-level material properties. Addressing the lack of datasets with controlled material attributes we generated an object-centric synthetic dataset with physically-based materials. Fine-tuning a modified pre-trained text-to-image model on this synthetic dataset enables us to edit material properties in real-world images while preserving all other attributes. We show the potential application of our model to material edited NeRFs.

Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However such purely geometric methods are understandably oblivious to plane semantics which are crucial to discerning distinct planes. To overcome this limitation we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.

Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body hand and expression estimation. Most existing methods have tackled this task in a two-stage manner first detecting the human body part with an off-the-shelf detection model and then inferring the different human body parts individually. Despite the impressive results achieved these methods suffer from 1) loss of valuable contextual information via cropping 2) introducing distractions and 3) lacking inter-association among different persons and body parts inevitably causing performance degradation especially for crowded scenes. To address these issues we introduce a novel all-in-one-stage framework AiOS for multiple expressive human pose and shape recovery without an additional human detection step. Specifically our method is built upon DETR which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection. We devise the decoder tokens and extend them to our task. Specifically we first employ a human token to probe a human location in the image and encode global features for each instance which provides a coarse location for the later transformer block. Then we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature which collaborates with the global feature to regress the whole-body mesh. This straightforward but effective model outperforms previous state-of-the-art methods by a 9 reduction in NMVE on AGORA a 30 reduction in PVE on EHF a 10 reduction in PVE on ARCTIC and a 3 reduction in PVE on EgoBody.

Convolutional neural networks benefit from translation equivariance achieving tremendous success. Equivariant networks further extend this property to other transformation groups. However most existing methods require discretization or sampling of groups leading to increased model sizes for larger groups such as the affine group. In this paper we build affine equivariant networks based on differential invariants from the viewpoint of symmetric PDEs without discretizing or sampling the group. To address the division-by-zero issue arising from fractional differential invariants of the affine group we construct a new kind of affine invariants by normalizing polynomial relative differential invariants to replace classical differential invariants. For further flexibility we design an equivariant layer which can be directly integrated into convolutional networks of various architectures. Moreover our framework for the affine group is also applicable to its continuous subgroups. We implement equivariant networks for the scale group the rotation-scale group and the affine group. Numerical experiments demonstrate the outstanding performance of our framework across classification tasks involving transformations of these groups. Remarkably under the out-of-distribution setting our model achieves a 3.37% improvement in accuracy over the main counterpart affConv on the affNIST dataset.

We present a neural radiance field method for urban-scale semantic and building-level instance segmentation from aerial images by lifting noisy 2D labels to 3D. This is a challenging problem due to two primary reasons. Firstly objects in urban aerial images exhibit substantial variations in size including buildings cars and roads which pose a significant challenge for accurate 2D segmentation. Secondly the 2D labels generated by existing segmentation methods suffer from the multi-view inconsistency problem especially in the case of aerial images where each image captures only a small portion of the entire scene. To overcome these limitations we first introduce a scale-adaptive semantic label fusion strategy that enhances the segmentation of objects of varying sizes by combining labels predicted from different altitudes harnessing the novel-view synthesis capabilities of NeRF. We then introduce a novel cross-view instance label grouping strategy based on the 3D scene representation to mitigate the multi-view inconsistency problem in the 2D instance labels. Furthermore we exploit multi-view reconstructed depth priors to improve the geometric quality of the reconstructed radiance field resulting in enhanced segmentation results. Experiments on multiple real-world urban-scale datasets demonstrate that our approach outperforms existing methods highlighting its effectiveness. The source code is available at https://github.com/zyqz97/Aerial_lifting.

Few-shot learning (FSL) facilitates a variety of computer vision tasks yet remains vulnerable to adversarial attacks. Existing adversarially robust FSL methods rely on either visual similarity learning or class concept learning. Our analysis reveals that these two learning paradigms are complementary exhibiting distinct robustness due to their unique decision boundary types (concepts clustering by the visual similarity label vs. classification by the class labels). To bridge this gap we propose a novel framework unifying adversarially robust similarity learning and class concept learning. Specifically we distill parameters from both network branches into a "unified embedding model" during robust optimization and redistribute them to individual network branches periodically. To capture generalizable robustness across diverse branches we initialize adversaries in each episode with cross-branch class-wise "global adversarial perturbations" instead of less informative random initialization. We also propose a branch robustness harmonization to modulate the optimization of similarity and class concept learners via their relative adversarial robustness. Extensive experiments demonstrate the state-of-the-art performance of our method in diverse few-shot scenarios.

Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator that controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retains the properties of continuous generative models.

Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale manifested as over-smoothness or instability at small CFG scales while over-saturation at large ones. To explain and analyze these issues we revisit the derivation of Score Distillation Sampling (SDS) and decipher existing score distillation with the Wasserstein Generative Adversarial Network (WGAN) paradigm. With the WGAN paradigm we find that existing score distillation either employs a fixed sub-optimal discriminator or conducts incomplete discriminator optimization resulting in the scale-sensitive issue. We propose the Adversarial Score Distillation (ASD) which maintains an optimizable discriminator and updates it using the complete optimization objective. Experiments show that the proposed ASD performs favorably in 2D distillation and text-to-3D tasks against existing methods. Furthermore to explore the generalization ability of our paradigm we extend ASD to the image editing task which achieves competitive results. The project page and code are at https://github.com/2y7c3/ASD

Adversarial distillation (AD) is a highly effective method for enhancing the robustness of small models. Contrary to expectations a high-performing teacher model does not always result in a more robust student model. This is due to two main reasons. First when there are significant differences in predictions between the teacher model and the student model exact matching of predicted values using KL divergence interferes with training leading to poor performance of existing methods. Second matching solely based on the output prevents the student model from fully understanding the behavior of the teacher model. To address these challenges this paper proposes a novel AD method named SmaraAD. During the training process we facilitate the student model in better understanding the teacher model's behavior by aligning the attribution region that the student model focuses on with that of the teacher model. Concurrently we relax the condition of exact matching in KL divergence and replace it with a more flexible matching criterion thereby enhancing the model's robustness. Extensive experiments substantiate the effectiveness of our method in improving the robustness of small models outperforming previous SOTA methods.

In autonomous driving behavior prediction is fundamental for safe motion planning hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor attack against trajectory prediction models as a means of studying their potential vulnerabilities. Our attack affects the victim at training time via naturalistic hence stealthy poisoned samples crafted using a novel two-step approach. First the triggers are crafted by perturbing the trajectory of attacking vehicle and then disguised by transforming the scene using a bi-level optimization technique. The proposed attack does not depend on a particular model architecture and operates in a black-box manner thus can be effective without any knowledge of the victim model. We conduct extensive empirical studies using state-of-the-art prediction models on two benchmark datasets using metrics customized for trajectory prediction. We show that the proposed attack is highly effective as it can significantly hinder the performance of prediction models unnoticeable by the victims and efficient as it forces the victim to generate malicious behavior even under constrained conditions. Via ablative studies we analyze the impact of different attack design choices followed by an evaluation of existing defence mechanisms against the proposed attack.

Saliency ranking detection (SRD) has emerged as a challenging task in computer vision aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primarily using mouse-trajectory data which inadequately captures the intricacies of human visual perception. Addressing this gap this paper introduces the first large-scale SRD dataset SIFR constructed using genuine human fixation data thereby aligning more closely with real visual perceptual processes. To establish a baseline for this dataset we propose QAGNet a novel model that leverages salient instance query features from a transformer detector within a tri-tiered nested graph. Through extensive experiments we demonstrate that our approach outperforms existing state-of-the-art methods across two widely used SRD datasets and our newly proposed dataset. Code and dataset are available at https://github.com/EricDengbowen/QAGNet.

Existing few-shot segmentation methods usually extract foreground prototypes from support images to guide query image segmentation. However different background contexts of support and query images can cause their foreground features to be misaligned. This phenomenon known as background context bias can hinder the effectiveness of support prototypes in guiding query image segmentation. In this work we propose a novel framework with an iterative structure to address this problem. In each iteration of the framework we first generate a query prediction based on a support foreground feature. Next we extract background context from the query image to modulate the support foreground feature thus eliminating the foreground feature misalignment caused by the different backgrounds. After that we design a confidence-biased attention to eliminate noise and cleanse information. By integrating these components through an iterative structure we create a novel network that can leverage the synergies between different modules to improve their performance in a mutually reinforcing manner. Through these carefully designed components and structures our network can effectively eliminate background context bias in few-shot segmentation thus achieving outstanding performance. We conduct extensive experiments on the PASCAL-5^ i and COCO-20^ i datasets and achieve state-of-the-art (SOTA) results which demonstrate the effectiveness of our approach.

Visual-inertial odometry (VIO) has demonstrated remarkable success due to its low-cost and complementary sensors. However existing VIO methods lack the generalization ability to adjust to different environments and sensor attributes. In this paper we propose Adaptive VIO a new monocular visual-inertial odometry that combines online continual learning with traditional nonlinear optimization. Adaptive VIO comprises two networks to predict visual correspondence and IMU bias. Unlike end-to-end approaches that use networks to fuse the features from two modalities (camera and IMU) and predict poses directly we combine neural networks with visual-inertial bundle adjustment in our VIO system. The optimized estimates will be fed back to the visual and IMU bias networks refining the networks in a self-supervised manner. Such a learning-optimization-combined framework and feedback mechanism enable the system to perform online continual learning. Experiments demonstrate that our Adaptive VIO manifests adaptive capability on EuRoC and TUM-VI datasets. The overall performance exceeds the currently known learning-based VIO methods and is comparable to the state-of-the-art optimization-based methods.

Softassign is a pivotal method in graph matching and other learning tasks. Many softassign-based algorithms exhibit performance sensitivity to a parameter in the softassign. However tuning the parameter is challenging and almost done empirically. This paper proposes an adaptive softassign method for graph matching by analyzing the relationship between the objective score and the parameter. This method can automatically tune the parameter based on a given error bound to guarantee accuracy. The Hadamard-Equipped Sinkhorn formulas introduced in this study significantly enhance the efficiency and stability of the adaptive softassign. Moreover these formulas can also be used in optimal transport problems. The resulting adaptive softassign graph matching algorithm enjoys significantly higher accuracy than previous state-of-the-art large graph matching algorithms while maintaining comparable efficiency.

Object-centric learning (OCL) extracts the representation of objects with slots offering an exceptional blend of flexibility and interpretability for abstracting low-level perceptual features. A widely adopted method within OCL is slot attention which utilizes attention mechanisms to iteratively refine slot representations. However a major drawback of most object-centric models including slot attention is their reliance on predefining the number of slots. This not only necessitates prior knowledge of the dataset but also overlooks the inherent variability in the number of objects present in each instance. To overcome this fundamental limitation we present a novel complexity-aware object auto-encoder framework. Within this framework we introduce an adaptive slot attention (AdaSlot) mechanism that dynamically determines the optimal number of slots based on the content of the data. This is achieved by proposing a discrete slot sampling module that is responsible for selecting an appropriate number of slots from a candidate list. Furthermore we introduce a masked slot decoder that suppresses unselected slots during the decoding process. Our framework tested extensively on object discovery tasks with various datasets shows performance matching or exceeding top fixed-slot models. Moreover our analysis substantiates that our method exhibits the capability to dynamically adapt the slot number according to each instance's complexity offering the potential for further exploration in slot attention research. Project will be available at https://kfan21.github.io/AdaSlot/

While fine-tuning is a de facto standard method for training deep neural networks it still suffers from overfitting when using small target datasets. Previous methods improve fine-tuning performance by maintaining knowledge of the source datasets or introducing regularization terms such as contrastive loss. However these methods require auxiliary source information (e.g. source labels or datasets) or heavy additional computations. In this paper we propose a simple method called adaptive random feature regularization (AdaRand). AdaRand helps the feature extractors of training models to adaptively change the distribution of feature vectors for downstream classification tasks without auxiliary source information and with reasonable computation costs. To this end AdaRand minimizes the gap between feature vectors and random reference vectors that are sampled from class conditional Gaussian distributions. Furthermore AdaRand dynamically updates the conditional distribution to follow the currently updated feature extractors and balance the distance between classes in feature spaces. Our experiments show that AdaRand outperforms the other fine-tuning regularization requiring auxiliary source information and heavy computation costs.

Despite the great success of deep learning in stereo matching recovering accurate disparity maps is still challenging. Currently L1 and cross-entropy are the two most widely used losses for stereo network training. Compared with the former the latter usually performs better thanks to its probability modeling and direct supervision to the cost volume. However how to accurately model the stereo ground-truth for cross-entropy loss remains largely under-explored. Existing works simply assume that the ground-truth distributions are uni-modal which ignores the fact that most of the edge pixels can be multi-modal. In this paper a novel adaptive multi-modal cross-entropy loss (ADL) is proposed to guide the networks to learn different distribution patterns for each pixel. Moreover we optimize the disparity estimator to further alleviate the bleeding or misalignment artifacts in inference. Extensive experimental results show that our method is generic and can help classic stereo networks regain state-of-the-art performance. In particular GANet with our method ranks 1st on both the KITTI 2015 and 2012 benchmarks among the published methods. Meanwhile excellent synthetic-to-realistic generalization performance can be achieved by simply replacing the traditional loss with ours. Code is available at https://github.com/xxxupeng/ADL.

In Federated Learning (FL) the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' data vary in modalities. In response to these issues of statistical heterogeneity and modality incompatibility we propose the Adaptive Hyper-graph Aggregation framework a novel solution for Modality-Agnostic Federated Learning. We design a Modular Architecture for Local Model with single modality setting the stage for efficient intra-modality sharing and inter-modality complementarity. An innovative Global Consensus Prototype Enhancer is crafted to assimilate and broadcast global consensus knowledge within the network. At the core of our approach lies the Adaptive Hyper-graph Learning Strategy which effectively tackles the inherent challenges of modality incompatibility and statistical heterogeneity within federated learning environments accomplishing this adaptively even without the server being aware of the clients' modalities. Our approach tested on three multimodal benchmark datasets demonstrated strong performance across diverse data distributions affirming its effectiveness in multimodal federated learning.

Multi-view depth estimation has achieved impressive performance over various benchmarks. However almost all current multi-view systems rely on given ideal camera poses which are unavailable in many real-world scenarios such as autonomous driving. In this work we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings. Surprisingly we find current multi-view depth estimation methods or single-view and multi-view fusion methods will fail when given noisy pose settings. To address this challenge we propose a single-view and multi-view fused depth estimation system which adaptively integrates high-confident multi-view and single-view results for both robust and accurate depth estimations. The adaptive fusion module performs fusion by dynamically selecting high-confidence regions between two branches based on a wrapping confidence map. Thus the system tends to choose the more reliable branch when facing textureless scenes inaccurate calibration dynamic objects and other degradation or challenging conditions. Our method outperforms state-of-the-art multi-view and fusion methods under robustness testing. Furthermore we achieve state-of-the-art performance on challenging benchmarks (KITTI and DDAD) when given accurate pose estimations. Project website: https://github.com/Junda24/AFNet/