CVPR 2024 Papers

This paper studies amodal image segmentation: predicting entire object segmentation masks including both visible and invisible (occluded) parts. In previous work the amodal segmentation ground truth on real images is usually predicted by manual annotaton and thus is subjective. In contrast we use 3D data to establish an automatic pipeline to determine authentic ground truth amodal masks for partially occluded objects in real images. This pipeline is used to construct an amodal completion evaluation benchmark MP3D-Amodal consisting of a variety of object categories and labels. To better handle the amodal completion task in the wild we explore two architecture variants: a two-stage model that first infers the occluder followed by amodal mask completion; and a one-stage model that exploits the representation power of Stable Diffusion for amodal segmentation across many categories. Without bells and whistles our method achieves a new state-of-the-art performance on Amodal segmentation datasets that cover a large variety of objects including COCOA and our new MP3D-Amodal dataset. The dataset model and code are available at https://www. robots.ox.ac.uk/ vgg/research/amodal/.

Data privacy is of great concern in cloud machine-learning service platforms when sensitive data are exposed to service providers. While private computing environments (e.g. secure enclaves) and cryptographic approaches (e.g. homomorphic encryption) provide strong privacy protection their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance we propose Delta a new private training and inference framework with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10 CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34 showing that Delta achieves strong privacy protection fast training and inference without significantly compromising the model utility.

Despite the recent progress made in Video Question-Answering (VideoQA) these methods typically function as black-boxes making it difficult to understand their reasoning processes and perform consistent compositional reasoning. To address these challenges we propose a model-agnostic Video Alignment and Answer Aggregation (VA3) framework which is capable of enhancing both compositional consistency and accuracy of existing VidQA methods by integrating video aligner and answer aggregator modules. The video aligner hierarchically selects the relevant video clips based on the question while the answer aggregator deduces the answer to the question based on its sub-questions with compositional consistency ensured by the information flow along the question decompose graph and the contrastive learning strategy. We evaluate our framework on three settings of the AGQA-Decomp dataset with three baseline methods and propose new metrics to measure the compositional consistency of VidQA methods more comprehensively. Moreover we propose a large language model (LLM) based automatic question decompose pipeline to apply our framework on any VidQA data. We extend MSVD and NExT-QA datasets with it to evaluate such scheme and our VA3 framework on broader scenarios. Extensive experiments show that our framework improves both compositional consistency and accuracy of existing methods leading to more interpretable models in real-world applications.

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.

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.

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.

In this work we study a novel problem which focuses on person identification while performing daily activities. Learning biometric features from RGB videos is challenging due to spatio-temporal complexity and presence of appearance biases such as clothing color and background. We propose ABNet a novel framework which leverages disentanglement of biometric and non-biometric features to perform effective person identification from daily activities. ABNet relies on a bias-less teacher to learn biometric features from RGB videos and explicitly disentangle non-biometric features with the help of biometric distortion. In addition ABNet also exploits activity prior for biometrics which is enabled by joint biometric and activity learning. We perform comprehensive evaluation of the proposed approach across five different datasets which are derived from existing activity recognition benchmarks. Furthermore we extensively compare ABNet with existing works in person identification and demonstrate its effectiveness for activity-based biometrics across all five datasets. The code and dataset can be accessed at: https://github.com/sacrcv/Activity-Biometrics/

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case or one scaled and oriented correspondence given a known vertical direction. Nowadays assuming a known direction is not particularly restrictive as modern consumer devices such as smartphones or drones are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences our solvers need a smaller minimal sample reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://github.com/danini/absolute-pose-from-oriented-and-scaled-features .

Facial action unit (AU) intensity plays a pivotal role in quantifying fine-grained expression behaviors which is an effective condition for facial expression manipulation. However publicly available datasets containing intensity annotations for multiple AUs remain severely limited often featuring a restricted number of subjects. This limitation places challenges to the AU intensity manipulation in images due to disentanglement issues leading researchers to resort to other large datasets with pretrained AU intensity estimators for pseudo labels. In addressing this constraint and fully leveraging manual annotations of AU intensities for precise manipulation we introduce AUEditNet. Our proposed model achieves impressive intensity manipulation across 12 AUs trained effectively with only 18 subjects. Utilizing a dual-branch architecture our approach achieves comprehensive disentanglement of facial attributes and identity without necessitating additional loss functions or implementing with large batch sizes. This approach offers a potential solution to achieve desired facial attribute editing despite the dataset's limited subject count. Our experiments demonstrate AUEditNet's superior accuracy in editing AU intensities affirming its capability in disentangling facial attributes and identity within a limited subject pool. AUEditNet allows conditioning by either intensity values or target images eliminating the need for constructing AU combinations for specific facial expression synthesis. Moreover AU intensity estimation as a downstream task validates the consistency between real and edited images confirming the effectiveness of our proposed AU intensity manipulation method.

While real-world anime super-resolution (SR) has gained increasing attention in the SR community existing methods still adopt techniques from the photorealistic domain. In this paper we analyze the anime production workflow and rethink how to use characteristics of it for the sake of the real-world anime SR. First we argue that video networks and datasets are not necessary for anime SR due to the repetition use of hand-drawing frames. Instead we propose an anime image collection pipeline by choosing the least compressed and the most informative frames from the video sources. Based on this pipeline we introduce the Anime Production-oriented Image (API) dataset. In addition we identify two anime-specific challenges of distorted and faint hand-drawn lines and unwanted color artifacts. We address the first issue by introducing a prediction-oriented compression module in the image degradation model and a pseudo-ground truth preparation with enhanced hand-drawn lines. In addition we introduce the balanced twin perceptual loss combining both anime and photorealistic high-level features to mitigate unwanted color artifacts and increase visual clarity. We evaluate our method through extensive experiments on the public benchmark showing our method outperforms state-of-the-art anime dataset-trained approaches.

Binary neural networks utilize 1-bit quantized weights and activations to reduce both the model's storage demands and computational burden. However advanced binary architectures still incorporate millions of inefficient and nonhardware-friendly full-precision multiplication operations. A&B BNN is proposed to directly remove part of the multiplication operations in a traditional BNN and replace the rest with an equal number of bit operations introducing the mask layer and the quantized RPReLU structure based on the normalizer-free network architecture. The mask layer can be removed during inference by leveraging the intrinsic characteristics of BNN with straightforward mathematical transformations to avoid the associated multiplication operations. The quantized RPReLU structure enables more efficient bit operations by constraining its slope to be integer powers of 2. Experimental results achieved 92.30% 69.35% and 66.89% on the CIFAR-10 CIFAR-100 and ImageNet datasets respectively which are competitive with the state-of-the-art. Ablation studies have verified the efficacy of the quantized RPReLU structure leading to a 1.14% enhancement on the ImageNet compared to using a fixed slope RLeakyReLU. The proposed add&bit-operation-only BNN offers an innovative approach for hardware-friendly network architecture.

Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To address this problem a unified framework including the multi-pyramid transformer (MPT) and extended frequency contrastive regularization (EFCR) is proposed to tackle two outstanding challenges in microscopy deblur: longer attention span and data deficiency. The MPT employs an explicit pyramid structure at each network stage that integrates the cross-scale window attention (CSWA) the intra-scale channel attention (ISCA) and the feature-enhancing feed-forward network (FEFN) to capture long-range cross-scale spatial interaction and global channel context. The EFCR addresses the data deficiency problem by exploring latent deblur signals from different frequency bands. It also enables deblur knowledge transfer to learn cross-domain information from extra data improving deblur performance for labeled and unlabeled data. Extensive experiments and downstream task validation show the framework achieves state-of-the-art performance across multiple datasets. Project page: https://github.com/PieceZhang/MPT-CataBlur.

Event cameras exhibit remarkable attributes such as high dynamic range asynchronicity and low latency making them highly suitable for vision tasks that involve high-speed motion in challenging lighting conditions. These cameras implicitly capture movement and depth information in events making them appealing sensors for Camera Pose Relocalization (CPR) tasks. Nevertheless existing CPR networks based on events neglect the pivotal fine-grained temporal information in events resulting in unsatisfactory performance. Moreover the energy-efficient features are further compromised by the use of excessively complex models hindering efficient deployment on edge devices. In this paper we introduce PEPNet a simple and effective point-based network designed to regress six degrees of freedom (6-DOFs) event camera poses. We rethink the relationship between the event camera and CPR tasks leveraging the raw Point Cloud directly as network input to harness the high-temporal resolution and inherent sparsity of events. PEPNet is adept at abstracting the spatial and implicit temporal features through hierarchical structure and explicit temporal features by Attentive Bi-directional Long Short-Term Memory (A-Bi-LSTM). By employing a carefully crafted lightweight design PEPNet delivers state-of-the-art (SOTA) performance on both indoor and outdoor datasets with meager computational resources. Specifically PEPNet attains a significant 38% and 33% performance improvement on the random split IJRR and M3ED datasets respectively. Moreover the lightweight design version PEPNet_ tiny accomplishes results comparable to the SOTA while employing a mere 0.5% of the parameters.

Hand mesh reconstruction has attracted considerable attention in recent years with various approaches and techniques being proposed. Some of these methods incorporate complex components and designs which while effective may complicate the model and hinder efficiency. In this paper we decompose the mesh decoder into token generator and mesh regressor. Through extensive ablation experiments we found that the token generator should select discriminating and representative points while the mesh regressor needs to upsample sparse keypoints into dense meshes in multiple stages. Given these functionalities we can achieve high performance with minimal computational resources. Based on this observation we propose a simple yet effective baseline that outperforms state-of-the-art methods by a large margin while maintaining real-time efficiency. Our method outperforms existing solutions achieving state-of-the-art (SOTA) results across multiple datasets. On the FreiHAND dataset our approach produced a PA-MPJPE of 5.8mm and a PA-MPVPE of 6.1mm. Similarly on the DexYCB dataset we observed a PA-MPJPE of 5.5mm and a PA-MPVPE of 5.5mm. As for performance speed our method reached up to 33 frames per second (fps) when using HRNet and up to 70 fps when employing FastViT-MA36. Code will be made available.

The ability to detect unfamiliar or unexpected images is essential for safe deployment of computer vision systems. In the context of classification the task of detecting images outside of a model's training domain is known as out-of-distribution (OOD) detection. While there has been a growing research interest in developing post-hoc OOD detection methods there has been comparably little discussion around how these methods perform when the underlying classifier is not trained on a clean carefully curated dataset. In this work we take a closer look at 20 state-of-the-art OOD detection methods in the (more realistic) scenario where the labels used to train the underlying classifier are unreliable (e.g. crowd-sourced or web-scraped labels). Extensive experiments across different datasets noise types & levels architectures and checkpointing strategies provide insights into the effect of class label noise on OOD detection and show that poor separation between incorrectly classified ID samples vs. OOD samples is an overlooked yet important limitation of existing methods. Code: https://github.com/glhr/ood-labelnoise

Comparing different age estimation methods poses a challenge due to the unreliability of published results stemming from inconsistencies in the benchmarking process. Previous studies have reported continuous performance improvements over the past decade using specialized methods; however our findings challenge these claims. This paper identifies two trivial yet persistent issues with the currently used evaluation protocol and describes how to resolve them. We offer an extensive comparative analysis for state-of-the-art facial age estimation methods. Surprisingly we find that the performance differences between the methods are negligible compared to the effect of other factors such as facial alignment facial coverage image resolution model architecture or the amount of data used for pretraining. We use the gained insights to propose using FaRL as the backbone model and demonstrate its effectiveness on all public datasets. We make the source code and exact data splits public on GitHub and in the supplementary material.

The studies of human clothing for digital avatars have predominantly relied on synthetic datasets. While easy to collect synthetic data often fall short in realism and fail to capture authentic clothing dynamics. Addressing this gap we introduce 4D-DRESS the first real-world 4D dataset advancing human clothing research with its high-quality 4D textured scans and garment meshes. 4D-DRESS captures 64 outfits in 520 human motion sequences amounting to 78k textured scans. Creating a real-world clothing dataset is challenging particularly in annotating and segmenting the extensive and complex 4D human scans. To address this we develop a semi-automatic 4D human parsing pipeline. We efficiently combine a human-in-the-loop process with automation to accurately label 4D scans in diverse garments and body movements. Leveraging precise annotations and high-quality garment meshes we establish several benchmarks for clothing simulation and reconstruction. 4D-DRESS offers realistic and challenging data that complements synthetic sources paving the way for advancements in research of lifelike human clothing. Website: https://ait.ethz.ch/4d-dress