CVPR 2024 Papers

This paper presents a novel aerial-to-ground feature aggregation strategy tailored for the task of cross-view image-based geo-localization. Conventional vision-based methods heavily rely on matching ground-view image features with a pre-recorded image database often through establishing planar homography correspondences via a planar ground assumption. As such they tend to ignore features that are off-ground and not suited for handling visual occlusions leading to unreliable localization in challenging scenarios. We propose a Top-to-Ground Aggregation module that capitalizes aerial orthographic views to aggregate features down to the ground level leveraging reliable off-ground information to improve feature alignment. Furthermore we introduce a Cycle Domain Adaptation loss that ensures feature extraction robustness across domain changes. Additionally an Equidistant Re-projection loss is introduced to equalize the impact of all keypoints on orientation error leading to a more extended distribution of keypoints which benefits orientation estimation. On both KITTI and Ford Multi-AV datasets our method consistently achieves the lowest mean longitudinal and lateral translations across different settings and obtains the smallest orientation error when the initial pose is less accurate a more challenging setting. Further it can complete an entire route through continual vehicle pose estimation with initial vehicle pose given only at the starting point.

Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily rely on supervised learning using abundant 3D labels typically obtained through expensive and labor-intensive annotation on LiDAR point clouds. To tackle this problem we propose a novel weakly supervised 3D object detection framework named VSRD (Volumetric Silhouette Rendering for Detection) to train 3D object detectors without any 3D supervision but only weak 2D supervision. VSRD consists of multi-view 3D auto-labeling and subsequent training of monocular 3D object detectors using the pseudo labels generated in the auto-labeling stage. In the auto-labeling stage we represent the surface of each instance as a signed distance field (SDF) and render its silhouette as an instance mask through our proposed instance-aware volumetric silhouette rendering. To directly optimize the 3D bounding boxes through rendering we decompose the SDF of each instance into the SDF of a cuboid and the residual distance field (RDF) that represents the residual from the cuboid. This mechanism enables us to optimize the 3D bounding boxes in an end-to-end manner by comparing the rendered instance masks with the ground truth instance masks. The optimized 3D bounding boxes serve as effective training data for 3D object detection. We conduct extensive experiments on the KITTI-360 dataset demonstrating that our method outperforms the existing weakly supervised 3D object detection methods. The code is available at https://github.com/skmhrk1209/VSRD.

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.

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.

Depth completion is a vital task for autonomous driving as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation which are still insufficient to capture the fine-grained 3D geometry of the scene. To address this challenge we introduce Tri-Perspective View Decomposition (TPVD) a novel framework that can explicitly model 3D geometry. In particular (1) TPVD ingeniously decomposes the original point cloud into three 2D views one of which corresponds to the sparse depth input. (2) We design TPV Fusion to update the 2D TPV features through recurrent 2D-3D-2D aggregation where a Distance-Aware Spherical Convolution (DASC) is applied. (3) By adaptively choosing TPV affinitive neighbors the newly proposed Geometric Spatial Propagation Network (GSPN) further improves the geometric consistency. As a result our TPVD outperforms existing methods on KITTI NYUv2 and SUN RGBD. Furthermore we build a novel depth completion dataset named TOFDC which is acquired by the time-of-flight (TOF) sensor and the color camera on smartphones.

Although adversarial training (AT) has proven effective in enhancing the model's robustness the recently revealed issue of fairness in robustness has not been well addressed i.e. the robust accuracy varies significantly among different categories. In this paper instead of uniformly evaluating the model's average class performance we delve into the issue of robust fairness by considering the worst-case distribution across various classes. We propose a novel learning paradigm named Fairness-Aware Adversarial Learning (FAAL). As a generalization of conventional AT we re-define the problem of adversarial training as a min-max-max framework to ensure both robustness and fairness of the trained model. Specifically by taking advantage of distributional robust optimization our method aims to find the worst distribution among different categories and the solution is guaranteed to obtain the upper bound performance with high probability. In particular FAAL can fine-tune an unfair robust model to be fair within only two epochs without compromising the overall clean and robust accuracies. Extensive experiments on various image datasets validate the superior performance and efficiency of the proposed FAAL compared to other state-of-the-art methods.

We present TextureDreamer a novel image-guided texture synthesis method to transfer relightable textures from a small number of input images (3 to 5) to target 3D shapes across arbitrary categories. Texture creation is a pivotal challenge in vision and graphics. Industrial companies hire experienced artists to manually craft textures for 3D assets. Classical methods require densely sampled views and accurately aligned geometry while learning-based methods are confined to category-specific shapes within the dataset. In contrast TextureDreamer can transfer highly detailed intricate textures from real-world environments to arbitrary objects with only a few casually captured images potentially significantly democratizing texture creation. Our core idea personalized geometry-aware score distillation (PGSD) draws inspiration from recent advancements in diffuse models including personalized modeling for texture information extraction score distillation for detailed appearance synthesis and explicit geometry guidance with ControlNet. Our integration and several essential modifications substantially improve the texture quality. Experiments on real images spanning different categories show that TextureDreamer can successfully transfer highly realistic semantic meaningful texture to arbitrary objects surpassing the visual quality of previous state-of-the-art. Project page: https://texturedreamer.github.io

While pre-trained large-scale vision models have shown significant promise for semantic correspondence their features often struggle to grasp the geometry and orientation of instances. This paper identifies the importance of being geometry-aware for semantic correspondence and reveals a limitation of the features of current foundation models under simple post-processing. We show that incorporating this information can markedly enhance semantic correspondence performance with simple but effective solutions in both zero-shot and supervised settings. We also construct a new challenging benchmark for semantic correspondence built from an existing animal pose estimation dataset for both pre-training validating models. Our method achieves a PCK@0.10 score of 65.4 (zero-shot) and 85.6 (supervised) on the challenging SPair-71k dataset outperforming the state of the art by 5.5p and 11.0p absolute gains respectively. Our code and datasets are publicly available at: https://telling-left-from-right.github.io.

Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task mandating a dedicated decoder per task. In contrast traditional video codecs employ a flexible encoder controller enabling the adaptation of a single codec to different tasks through mechanisms like mode prediction. Drawing inspiration from this we introduce an innovative encoder controller for deep video compression for machines. This controller features a mode prediction and a Group of Pictures (GoP) selection module. Our approach centralizes control at the encoding stage allowing for adaptable encoder adjustments across different tasks such as detection and tracking while maintaining compatibility with a standard pre-trained DVC decoder. Empirical evidence demonstrates that our method is applicable across multiple tasks with various existing pre-trained DVCs. Moreover extensive experiments demonstrate that our method outperforms previous DVC by about 25% bitrate for different tasks with only one pre-trained decoder.

X-ray known for its ability to reveal internal structures of objects is expected to provide richer information for 3D reconstruction than visible light. Yet existing NeRF algorithms overlook this nature of X-ray leading to their limitations in capturing structural contents of imaged objects. In this paper we propose a framework Structure-Aware X-ray Neural Radiodensity Fields (SAX-NeRF) for sparse-view X-ray 3D reconstruction. Firstly we design a Line Segment-based Transformer (Lineformer) as the backbone of SAX-NeRF. Linefomer captures internal structures of objects in 3D space by modeling the dependencies within each line segment of an X-ray. Secondly we present a Masked Local-Global (MLG) ray sampling strategy to extract contextual and geometric information in 2D projection. Plus we collect a larger-scale dataset X3D covering wider X-ray applications. Experiments on X3D show that SAX-NeRF surpasses previous NeRF-based methods by 12.56 and 2.49 dB on novel view synthesis and CT reconstruction. https://github.com/caiyuanhao1998/SAX-NeRF

Regression-based keypoint localization shows advantages of high efficiency and better robustness to quantization errors than heatmap-based methods. However existing regression-based methods discard the spatial location prior in input image with a global pooling leading to inferior accuracy and are limited to single instance localization tasks. We study the regression-based keypoint localization from a new perspective by leveraging the spatial location prior. Instead of regressing on the pooled feature the proposed Spatial-Aware Regression (SAR) maintains the spatial location map and outputs spatial coordinates and confidence score for each grid which are optimized with a unified objective. Benefited by the location prior these spatial-aware outputs can be efficiently optimized resulting in better localization performance. Moreover incorporating spatial prior makes SAR more general and can be applied into various keypoint localization tasks. We test the proposed method in 4 keypoint localization tasks including single/multi-person 2D/3D pose estimation and the whole-body pose estimation. Extensive experiments demonstrate its promising performance e.g. consistently outperforming recent regressions-based methods.

Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here we present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to render 3D motions. Then the high-level motion semantics are incorporated into the motion retargeting process by feeding the vision-language model with the rendered images and aligning the extracted semantic embeddings. To ensure the preservation of fine-grained motion details and high-level semantics we adopt a two-stage pipeline consisting of skeleton-aware pre-training and fine-tuning with semantics and geometry constraints. Experimental results show the effectiveness of the proposed method in producing high-quality motion retargeting results while accurately preserving motion semantics. Project page can be found at https://sites.google.com/view/smtnet.

Single-point annotation in visual tasks with the goal of minimizing labeling costs is becoming increasingly prominent in research. Recently visual foundation models such as Segment Anything (SAM) have gained widespread usage due to their robust zero-shot capabilities and exceptional annotation performance. However SAM's class-agnostic output and high confidence in local segmentation introduce semantic ambiguity posing a challenge for precise category-specific segmentation. In this paper we introduce a cost-effective category-specific segmenter using SAM. To tackle this challenge we have devised a Semantic-Aware Instance Segmentation Network (SAPNet) that integrates Multiple Instance Learning (MIL) with matching capability and SAM with point prompts. SAPNet strategically selects the most representative mask proposals generated by SAM to supervise segmentation with a specific focus on object category information. Moreover we introduce the Point Distance Guidance and Box Mining Strategy to mitigate inherent challenges: group and local issues in weakly supervised segmentation. These strategies serve to further enhance the overall segmentation performance. The experimental results on Pascal VOC and COCO demonstrate the promising performance of our proposed SAPNet emphasizing its semantic matching capabilities and its potential to advance point-prompted instance segmentation. The code is available at https://github.com/zhaoyangwei123/SAPNet.

Despite its importance generating attacks for multi label learning (MLL) models has received much less attention compared to multi-class recognition. Attacking an MLL model by optimizing a loss on the target set of labels has often the undesired consequence of changing the predictions for other labels. On the other hand adding a loss on the remaining labels to keep them fixed leads to highly negatively correlated gradient directions reducing the attack effectiveness. In this paper we develop a framework for crafting effective and semantic aware adversarial attacks for MLL. First to obtain an attack that leads to semantically consistent predictions across all labels we find a minimal superset of the target labels referred to as consistent target set. To do so we develop an efficient search algorithm over a knowledge graph which encodes label dependencies. Next we propose an optimization that searches for an attack that modifies the predictions of labels in the consistent target set while ensuring other labels will not get affected. This leads to an efficient algorithm that projects the gradient of the consistent target set loss onto the orthogonal direction of the gradient of the loss on other labels. Our framework can generate attacks on different target set sizes and for MLL with thousands of labels (as in OpenImages). Finally by extensive experiments on three datasets and several MLL models we show that our method generates both successful and semantically consistent attacks.

Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However as a consequence of the heavy quality degradation of input low-resolution (LR) images the destruction of local structures can lead to ambiguous image semantics. As a result the content of reproduced high-resolution image may have semantic errors deteriorating the super-resolution performance. To address this issue we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First we train a degradation-aware prompt extractor which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags aiming to enhance the local perception ability of the T2I model while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts encourage the T2I model to generate detailed and semantically accurate results. Furthermore during the inference process we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics. The source code of our method can be found at https://github.com/cswry/SeeSR

Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner. However the distribution learning is overly coarse-grained which is susceptible to virtual textures and causes counter-intuitive generation results. To mitigate this we propose the simple and effective Semantic-aware Discriminator (denoted as SeD) which encourages the SR network to learn the fine-grained distributions by introducing the semantics of images as a condition. Concretely we aim to excavate the semantics of images from a well-trained semantic extractor. Under different semantics the discriminator is able to distinguish the real-fake images individually and adaptively which guides the SR network to learn the more fine-grained semantic-aware textures. To obtain accurate and abundant semantics we take full advantage of recently popular pretrained vision models (PVMs) with extensive datasets and then incorporate its semantic features into the discriminator through a well-designed spatial cross-attention module. In this way our proposed semantic-aware discriminator empowered the SR network to produce more photo-realistic and pleasing images. Extensive experiments on two typical tasks i.e. SR and Real SR have demonstrated the effectiveness of our proposed methods. The code will be available at https://github.com/lbc12345/SeD.

We present SPAD a novel approach for creating consistent multi-view images from text prompts or single images. To enable multi-view generation we repurpose a pretrained 2D diffusion model by extending its self-attention layers with cross-view interactions and fine-tune it on a high quality subset of Objaverse. We find that a naive extension of the self-attention proposed in prior work (e.g. MVDream) leads to content copying between views. Therefore we explicitly constrain the cross-view attention based on epipolar geometry. To further enhance 3D consistency we utilize Pl ?ucker coordinates derived from camera rays and inject them as positional encoding. This enables SPAD to reason over spatial proximity in 3D well. Compared to concurrent works that can only generate views at fixed azimuth and elevation (e.g. MVDream SyncDreamer) SPAD offers full camera control and achieves state-of-the-art results in novel view synthesis on unseen objects from the Objaverse and Google Scanned Objects datasets. Finally we demonstrate that text-to-3D generation using SPAD prevents the multi-face Janus issue.