CVPR 2024 论文列表

The training of contemporary deep learning models heavily relies on publicly available data posing a risk of unauthorized access to online data and raising concerns about data privacy. Current approaches to creating unlearnable data involve incorporating small specially designed noises but these methods strictly limit data usability overlooking its potential usage in authorized scenarios. In this paper we extend the concept of unlearnable data to conditional data learnability and introduce UnGeneralizable Examples (UGEs). UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers. The protector defines the authorized network and optimizes UGEs to match the gradients of the original data and its ungeneralizable version ensuring learnability. To prevent unauthorized learning UGEs are trained by maximizing a designated distance loss in a common feature space. Additionally to further safeguard the authorized side from potential attacks we introduce additional undistillation optimization. Experimental results on multiple datasets and various networks demonstrate that the proposed UGEs framework preserves data usability while reducing training performance on hacker networks even under different types of attacks.

Computer vision applications predict on digital images acquired by a camera from physical scenes through light. However conventional robustness benchmarks rely on perturbations in digitized images diverging from distribution shifts occurring in the image acquisition process. To bridge this gap we introduce a new distribution shift dataset ImageNet-ES comprising variations in environmental and camera sensor factors by directly capturing 202k images with a real camera in a controllable testbed. With the new dataset we evaluate out-of-distribution (OOD) detection and model robustness. We find that existing OOD detection methods do not cope with the covariate shifts in ImageNet-ES implying that the definition and detection of OOD should be revisited to embrace real-world distribution shifts. We also observe that the model becomes more robust in both ImageNet-C and -ES by learning environment and sensor variations in addition to existing digital augmentations. Lastly our results suggest that effective shift mitigation via camera sensor control can significantly improve performance without increasing model size. With these findings our benchmark may aid future research on robustness OOD and camera sensor control for computer vision. Our code and dataset are available at https://github.com/Edw2n/ImageNet-ES.

Source-free Domain Adaptation (SFDA) is an emerging and challenging research area that addresses the problem of unsupervised domain adaptation (UDA) without source data. Though numerous successful methods have been proposed for SFDA a theoretical understanding of why these methods work well is still absent. In this paper we shed light on the theoretical perspective of existing SFDA methods. Specifically we find that SFDA loss functions comprising discriminability and diversity losses work in the same way as the training objective in the theory of self-training based on the expansion assumption which shows the existence of the target error bound. This finding brings two novel insights that enable us to build an improved SFDA method comprising 1) Model Training with Auto-Adjusting Diversity Constraint and 2) Augmentation Training with Teacher-Student Framework yielding a better recognition performance. Extensive experiments on three benchmark datasets demonstrate the validity of the theoretical analysis and our method.

This paper studies the problem of concept-based interpretability of transformer representations for videos. Concretely we seek to explain the decision-making process of video transformers based on high-level spatiotemporal concepts that are automatically discovered. Prior research on concept-based interpretability has concentrated solely on image-level tasks. Comparatively video models deal with the added temporal dimension increasing complexity and posing challenges in identifying dynamic concepts over time. In this work we systematically address these challenges by introducing the first Video Transformer Concept Discovery (VTCD) algorithm. To this end we propose an efficient approach for unsupervised identification of units of video transformer representations - concepts and ranking their importance to the output of a model. The resulting concepts are highly interpretable revealing spatio-temporal reasoning mechanisms and object-centric representations in unstructured video models. Performing this analysis jointly over a diverse set of supervised and self-supervised representations we discover that some of these mechanism are universal in video transformers. Finally we show that VTCD can be used for fine-grained action recognition and video object segmentation.

Human action anticipation aims at predicting what people will do in the future based on past observations. In this paper we introduce Uncertainty-aware Action Decoupling Transformer (UADT) for action anticipation. Unlike existing methods that directly predict action in a verb-noun pair format we decouple the action anticipation task into verb and noun anticipations separately. The objective is to make the two decoupled tasks assist each other and eventually improve the action anticipation task. Specifically we propose a two-stream Transformer-based architecture which is composed of a verb-to-noun model and a noun-to-verb model. The verb-to-noun model leverages the verb information to improve the noun prediction and the other way around. We extend the model in a probabilistic manner and quantify the predictive uncertainty of each decoupled task to select features. In this way the noun prediction leverages the most informative and redundancy-free verb features and verb prediction works similarly. Finally the two streams are combined dynamically based on their uncertainties to make the joint action anticipation. We demonstrate the efficacy of our method by achieving state-of-the-art performance on action anticipation benchmarks including EPIC-KITCHENS EGTEA Gaze+ and 50-Salads.

We present a highly scalable self-training framework for incrementally adapting vision-based end-to-end autonomous driving policies in a semi-supervised manner i.e. over a continual stream of incoming video data. To facilitate large-scale model training (e.g. open web or unlabeled data) we do not assume access to ground-truth labels and instead estimate pseudo-label policy targets for each video. Our framework comprises three key components: knowledge distillation a sample purification module and an exploration and knowledge retention mechanism. First given sequential image frames we pseudo-label the data and estimate uncertainty using an ensemble of inverse dynamics models. The uncertainty is used to select the most informative samples to add to an experience replay buffer. We specifically select high-uncertainty pseudo-labels to facilitate the exploration and learning of new and diverse driving skills. However in contrast to prior work in continual learning that assumes ground-truth labeled samples the uncertain pseudo-labels can introduce significant noise. Thus we also pair the exploration with a label refinement module which makes use of consistency constraints to re-label the noisy exploratory samples and effectively learn from diverse data. Trained as a complete never-ending learning system we demonstrate state-of-the-art performance on training from domain-changing data as well as millions of images from the open web.

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.

In ill-posed inverse problems it is commonly desirable to obtain insight into the full spectrum of plausible solutions rather than extracting only a single reconstruction. Information about the plausible solutions and their likelihoods is encoded in the posterior distribution. However for high-dimensional data this distribution is challenging to visualize. In this work we introduce a new approach for estimating and visualizing posteriors by employing energy-based models (EBMs) over low-dimensional subspaces. Specifically we train a conditional EBM that receives an input measurement and a set of directions that span some low-dimensional subspace of solutions and outputs the probability density function of the posterior within that space. We demonstrate the effectiveness of our method across a diverse range of datasets and image restoration problems showcasing its strength in uncertainty quantification and visualization. As we show our method outperforms a baseline that projects samples from a diffusion-based posterior sampler while being orders of magnitude faster. Furthermore it is more accurate than a baseline that assumes a Gaussian posterior.

Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However existing methods attempt to extract domain-invariant features neglecting that the biased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end we propose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically we formulate SDG in object detection from a causal perspective and construct a Structural Causal Model (SCM) to analyze the data bias and feature bias in the task which are caused by scene confounders and object attribute confounders. Based on the SCM we design a Global-Local Transformation module for data augmentation which effectively simulates domain diversity and mitigates the data bias. Additionally we introduce a Causal Attention Learning module that incorporates a designed attention invariance loss to learn image-level features that are robust to scene confounders. Moreover we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint which further alleviates the negative impact of object attribute confounders. Experimental results on five scenes demonstrate the prominent generalization ability of our method with an improvement of 3.9% mAP on the Night-Clear scene.

In the literature points and conics have been major features for camera geometric calibration. Although conics are more informative features than points the loss of the conic property under distortion has critically limited the utility of conic features in camera calibration. Many existing approaches addressed conic-based calibration by ignoring distortion or introducing 3D spherical targets to circumvent this limitation. In this paper we present a novel formulation for conic-based calibration using moments. Our derivation is based on the mathematical finding that the first moment can be estimated without bias even under distortion. This allows us to track moment changes during projection and distortion ensuring the preservation of the first moment of the distorted conic. With an unbiased estimator the circular patterns can be accurately detected at the sub-pixel level and can now be fully exploited for an entire calibration pipeline resulting in significantly improved calibration. The entire code is readily available from https://github.com/ChaehyeonSong/discocal.

3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive manual 3D annotations. We propose UnScene3D the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentation of indoor scans. UnScene3D first generates pseudo masks by leveraging self-supervised color and geometry features to find potential object regions. We operate on a basis of 3D segment primitives enabling efficient representation and learning on high-resolution 3D data. The coarse proposals are then refined through self-training our model on its predictions. Our approach improves over state-of-the-art unsupervised 3D instance segmentation methods by more than 300% Average Precision score demonstrating effective instance segmentation even in challenging cluttered 3D scenes.

Traditional unsupervised optical flow methods are vulnerable to occlusions and motion boundaries due to lack of object-level information. Therefore we propose UnSAMFlow an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further aggregate features on the object level. With all these adaptations our method produces clear optical flow estimation with sharp boundaries around objects which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.

Perceiving the world and forecasting its future state is a critical task for self-driving. Supervised approaches leverage annotated object labels to learn a model of the world --- traditionally with object detections and trajectory predictions or temporal bird's-eye-view (BEV) occupancy fields. However these annotations are expensive and typically limited to a set of predefined categories that do not cover everything we might encounter on the road. Instead we learn to perceive and forecast a continuous 4D (spatio-temporal) occupancy field with self-supervision from LiDAR data. This unsupervised world model can be easily and effectively transferred to downstream tasks. We tackle point cloud forecasting by adding a lightweight learned renderer and achieve state-of-the-art performance in Argoverse 2 nuScenes and KITTI. To further showcase its transferability we fine-tune our model for BEV semantic occupancy forecasting and show that it outperforms the fully supervised state-of-the-art especially when labeled data is scarce. Finally when compared to prior state-of-the-art on spatio-temporal geometric occupancy prediction our 4D world model achieves a much higher recall of objects from classes relevant to self-driving.

Recent advances in 3D avatar generation have gained significant attention. These breakthroughs aim to produce more realistic animatable avatars narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss combined with a differentiable renderer and text condition to guide a diffusion model in generating 3D avatars. However SDS often generates over-smoothed results with few facial details thereby lacking the diversity compared with ancestral sampling. On the other hand other works generate 3D avatar from a single image where the challenges of unwanted lighting effects perspective views and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method outperforming the state-of-the-art methods by a large margin in the experiments.

Learning-based underwater image enhancement (UIE) methods have made great progress. However the lack of large-scale and high-quality paired training samples has become the main bottleneck hindering the development of UIE. The inter-frame information in underwater videos can accelerate or optimize the UIE process. Thus we constructed the first large-scale high-resolution underwater video enhancement benchmark (UVEB) to promote the development of underwater vision.It contains 1308 pairs of video sequences and more than 453000 high-resolution with 38% Ultra-High-Definition (UHD) 4K frame pairs. UVEB comes from multiple countries containing various scenes and video degradation types to adapt to diverse and complex underwater environments. We also propose the first supervised underwater video enhancement method UVE-Net. UVE-Net converts the current frame information into convolutional kernels and passes them to adjacent frames for efficient inter-frame information exchange. By fully utilizing the redundant degraded information of underwater videos UVE-Net completes video enhancement better. Experiments show the effective network design and good performance of UVE-Net.

3D face reconstruction aims at generating high-fidelity 3D face shapes and textures from single-view or multi-view images. However current prevailing facial texture generation methods generally suffer from low-quality texture identity information loss and inadequate handling of occlusions. To solve these problems we introduce an Identity-Conditioned Latent Diffusion Model for face UV-texture generation (UV-IDM) to generate photo-realistic textures based on the Basel Face Model (BFM). UV-IDM leverages the powerful texture generation capacity of a latent diffusion model (LDM) to obtain detailed facial textures. To preserve the identity during the reconstruction procedure we design an identity-conditioned module that can utilize any in-the-wild image as a robust condition for the LDM to guide texture generation. UV-IDM can be easily adapted to different BFM-based methods as a high-fidelity texture generator. Furthermore in light of the limited accessibility of most existing UV-texture datasets we build a large-scale and publicly available UV-texture dataset based on BFM termed BFM-UV. Extensive experiments show that our UV-IDM can generate high-fidelity textures in 3D face reconstruction within seconds while maintaining image consistency bringing new state-of-the-art performance in facial texture generation.

The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (SAM) have shown superior performance in generating class-agnostic image segments. The main challenge in open-vocabulary image segmentation now lies in accurately classifying these segments into text-defined categories. In this paper we introduce the Universal Segment Embedding (USE) framework to address this challenge. This framework is comprised of two key components: 1) a data pipeline designed to efficiently curate a large amount of segment-text pairs at various granularities and 2) a universal segment embedding model that enables precise segment classification into a vast range of text-defined categories. The USE model can not only help open-vocabulary image segmentation but also facilitate other downstream tasks (e.g. querying and ranking). Through comprehensive experimental studies on semantic segmentation and part segmentation benchmarks we demonstrate that the USE framework outperforms state-of-the-art open-vocabulary segmentation methods.

Existing photorealistic relightable hand models require extensive identity-specific observations in different views poses and illuminations and face challenges in generalizing to natural illuminations and novel identities. To bridge this gap we present URHand the first universal relightable hand model that generalizes across viewpoints poses illuminations and identities. Our model allows few-shot personalization using images captured with a mobile phone and is ready to be photorealistically rendered under novel illuminations. To simplify the personalization process while retaining photorealism we build a powerful universal relightable prior based on neural relighting from multi-view images of hands captured in a light stage with hundreds of identities. The key challenge is scaling the cross-identity training while maintaining personalized fidelity and sharp details without compromising generalization under natural illuminations. To this end we propose a spatially varying linear lighting model as the neural renderer that takes physics-inspired shading as input feature. By removing non-linear activations and bias our specifically designed lighting model explicitly keeps the linearity of light transport. This enables single-stage training from light-stage data while generalizing to real-time rendering under arbitrary continuous illuminations across diverse identities. In addition we introduce the joint learning of a physically based model and our neural relighting model which further improves fidelity and generalization. Extensive experiments show that our approach achieves superior performance over existing methods in terms of both quality and generalizability. We also demonstrate quick personalization of URHand from a short phone scan of an unseen identity.