Existing person re-identification methods have achieved remarkable advances in appearance-based identity association across homogeneous cameras such as ground-ground matching. However as a more practical scenario aerial-ground person re-identification (AGPReID) among heterogeneous cameras has received minimal attention. To alleviate the disruption of discriminative identity representation by dramatic view discrepancy as the most significant challenge in AGPReID the view-decoupled transformer (VDT) is proposed as a simple yet effective framework. Two major components are designed in VDT to decouple view-related and view-unrelated features namely hierarchical subtractive separation and orthogonal loss where the former separates these two features inside the VDT and the latter constrains these two to be independent. In addition we contribute a large-scale AGPReID dataset called CARGO consisting of five/eight aerial/ground cameras 5000 identities and 108563 images. Experiments on two datasets show that VDT is a feasible and effective solution for AGPReID surpassing the previous method on mAP/Rank1 by up to 5.0%/2.7% on CARGO and 3.7%/5.2% on AG-ReID keeping the same magnitude of computational complexity. Our project is available at https://github.com/LinlyAC/VDT-AGPReID
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This paper targets high-fidelity and real-time view synthesis of dynamic 3D scenes at 4K resolution. Recent methods on dynamic view synthesis have shown impressive rendering quality. However their speed is still limited when rendering high-resolution images. To overcome this problem we propose 4K4D a 4D point cloud representation that supports hardware rasterization and network pre-computation to enable unprecedented rendering speed with a high rendering quality. Our representation is built on a 4D feature grid so that the points are naturally regularized and can be robustly optimized. In addition we design a novel hybrid appearance model that significantly boosts the rendering quality while preserving efficiency. Moreover we develop a differentiable depth peeling algorithm to effectively learn the proposed model from RGB videos. Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU which is 30x faster than previous methods and achieves the state-of-the-art rendering quality. Our project page is available at https://zju3dv.github.io/4k4d.
Referring expressions for visual objects often include descriptions of relative spatial arrangements to other objects -- e.g. "to the right of" -- that depend on the point of view of the speaker. In 2D referring expression tasks this viewpoint is captured unambiguously in the image. However grounding expressions with such spatial language in 3D without viewpoint annotations can be ambiguous. In this paper we investigate the significance of viewpoint information in 3D visual grounding -- introducing a model that explicitly predicts the speaker's viewpoint based on the referring expression and scene. We pretrain this model on a synthetically generated dataset that provides viewpoint annotations and then finetune on 3D referring expression datasets. Further we introduce an auxiliary uniform object representation loss to encourage viewpoint invariance in learned object representations. We find that our proposed ViewPoint Prediction Network (VPP-Net) achieves state-of-the-art performance on ScanRefer SR3D and NR3D -- improving Accuracy@0.25IoU by 1.06% 0.60% and 2.00% respectively compared to prior work.
Diffusion Models (DMs) have emerged as powerful generative models with unprecedented image generation capability. These models are widely used for data augmentation and creative applications. However DMs reflect the biases present in the training datasets. This is especially concerning in the context of faces where the DM prefers one demographic subgroup vs others (eg. female vs male). In this work we present a method for debiasing DMs without relying on additional reference data or model retraining. Specifically we propose Distribution Guidance which enforces the generated images to follow the prescribed attribute distribution. To realize this we build on the key insight that the latent features of denoising UNet hold rich demographic semantics and the same can be leveraged to guide debiased generation. We train Attribute Distribution Predictor (ADP) - a small mlp that maps the latent features to the distribution of attributes. ADP is trained with pseudo labels generated from existing attribute classifiers. The proposed Distribution Guidance with ADP enables us to do fair generation. Our method reduces bias across single/multiple attributes and outperforms the baseline by a significant margin for unconditional and text-conditional diffusion models. Further we present a downstream task of training a fair attribute classifier by augmenting the training set with our generated data.
3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times some work has emerged that aims to extend the functionality of NeRF beyond view synthesis for semantically aware tasks such as editing and segmentation using 3D feature field distillation from 2D foundation models. However these methods have two major limitations: (a) they are limited by the rendering speed of NeRF pipelines and (b) implicitly represented feature fields suffer from continuity artifacts reducing feature quality. Recently 3D Gaussian Splatting has shown state-of-the-art performance on real-time radiance field rendering. In this work we go one step further: in addition to radiance field rendering we enable 3D Gaussian splatting on arbitrary-dimension semantic features via 2D foundation model distillation. This translation is not straightforward: naively incorporating feature fields in the 3DGS framework encounters significant challenges notably the disparities in spatial resolution and channel consistency between RGB images and feature maps. We propose architectural and training changes to efficiently avert this problem. Our proposed method is general and our experiments showcase novel view semantic segmentation language-guided editing and segment anything through learning feature fields from state-of-the-art 2D foundation models such as SAM and CLIP-LSeg. Across experiments our distillation method is able to provide comparable or better results while being significantly faster to both train and render. Additionally to the best of our knowledge we are the first method to enable point and bounding-box prompting for radiance field manipulation by leveraging the SAM model. Project website at: https://feature-3dgs.github.io/
Decomposing an object's appearance into representations of its materials and the surrounding illumination is difficult even when the object's 3D shape is known beforehand. This problem is especially challenging for diffuse objects: it is ill-conditioned because diffuse materials severely blur incoming light and it is ill-posed because diffuse materials under high-frequency lighting can be indistinguishable from shiny materials under low-frequency lighting. We show that it is possible to recover precise materials and illumination---even from diffuse objects---by exploiting unintended shadows like the ones cast onto an object by the photographer who moves around it. These shadows are a nuisance in most previous inverse rendering pipelines but here we exploit them as signals that improve conditioning and help resolve material-lighting ambiguities. We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials the surrounding illumination environment and the shapes of the unseen light occluders who inadvertently cast shadows upon it.
Image stitching from different captures often results in non-rectangular boundaries which is often considered unappealing. To solve non-rectangular boundaries current solutions involve cropping which discards image content inpainting which can introduce unrelated content or warping which can distort non-linear features and introduce artifacts. To overcome these issues we introduce a novel diffusion-based learning framework RecDiffusion for image stitching rectangling. This framework combines Motion Diffusion Models (MDM) to generate motion fields effectively transitioning from the stitched image's irregular borders to a geometrically corrected intermediary. Followed by Content Diffusion Models (CDM) for image detail refinement. Notably our sampling process utilizes a weighted map to identify regions needing correction during each iteration of CDM. Our RecDiffusion ensures geometric accuracy and overall visual appeal surpassing all previous methods in both quantitative and qualitative measures when evaluated on public benchmarks. Code is released at https://github.com/lhaippp/RecDiffusion.
Despite exciting progress in automatic 3D reconstruction from images excessive and irregular triangular faces in the resulting meshes still constitute a significant challenge when it comes to adoption in practical artist workflows. Therefore we propose a method to extract regular quad-dominant meshes from posed images. More specifically we generate a high-quality 3D model through decomposition into an easily editable quad-dominant mesh with pixel-level details such as displacement materials and lighting. To enable end-to-end learning of shape and quad topology we QUADify a neural implicit representation using our novel differentiable re-meshing objective. Distinct from previous work our method exploits artifact-free Catmull-Clark subdivision combined with vertex displacement to extract pixel-level details linked to the base geometry. Finally we apply differentiable rendering techniques for material and lighting decomposition to optimize for image reconstruction. Our experiments show the benefits of end-to-end re-meshing and that our method yields state-of-the-art geometric accuracy while providing lightweight meshes with displacements and textures that are directly compatible with professional renderers and game engines.
We present the content deformation field (CoDeF) as a new type of video representation which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from the canonical image (i.e. rendered from the canonical content field) to each individual frame along the time axis. Given a target video these two fields are jointly optimized to reconstruct it through a carefully tailored rendering pipeline. We advisedly introduce some regularizations into the optimization process urging the canonical content field to inherit semantics (e.g. the object shape) from the video. With such a design CoDeF naturally supports lifting image algorithms for video processing in the sense that one can apply an image algorithm to the canonical image and effortlessly propagate the outcomes to the entire video with the aid of the temporal deformation field. We experimentally show that CoDeF is able to lift image-to-image translation to video-to-video translation and lift keypoint detection to keypoint tracking without any training. More importantly thanks to our lifting strategy that deploys the algorithms on only one image we achieve superior cross-frame consistency in processed videos compared to existing video-to-video translation approaches and even manage to track non-rigid objects like water and smog. Code will be made publicly available.
Few-shot image generation as an important yet challenging visual task still suffers from the trade-off between generation quality and diversity. According to the principle of feature-matching learning existing fusion-based methods usually fuse different features by using similarity measurements or attention mechanisms which may match features inaccurately and lead to artifacts in the texture and structure of generated images. In this paper we propose an exact Fusion via Feature Distribution matching Generative Adversarial Network (F2DGAN) for few-shot image generation. The rationale behind this is that feature distribution matching is much more reliable than feature matching to explore the statistical characters in image feature space for limited real-world data. To model feature distributions from only a few examples for feature fusion we design a novel variational feature distribution matching fusion module to perform exact fusion by empirical cumulative distribution functions. Specifically we employ a variational autoencoder to transform deep image features into distributions and fuse different features exactly by applying histogram matching. Additionally we formulate two effective losses to guide the matching process for better fitting our fusion strategy. Extensive experiments compared with state-of-the-art methods on three public datasets demonstrate the superiority of F2DGAN for few-shot image generation in terms of generation quality and diversity and the effectiveness of data augmentation in downstream classification tasks.
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this work we introduce Universal Semi-Supervised Domain Adaptation (UniSSDA) a practical yet challenging setting where the target domain is partially labeled and the source and target label space may not strictly match. UniSSDA is at the intersection of Universal Domain Adaptation (UniDA) and Semi-Supervised Domain Adaptation (SSDA): the UniDA setting does not allow for fine-grained categorization of target private classes not represented in the source domain while SSDA focuses on the restricted closed-set setting where source and target label spaces match exactly. Existing UniDA and SSDA methods are susceptible to common-class bias in UniSSDA settings where models overfit to data distributions of classes common to both domains at the expense of private classes. We propose a new prior-guided pseudo-label refinement strategy to reduce the reinforcement of common-class bias due to pseudo-labeling a common label propagation strategy in domain adaptation. We demonstrate the effectiveness of the proposed strategy on benchmark datasets Office-Home DomainNet and VisDA. The proposed strategy attains the best performance across UniSSDA adaptation settings and establishes a new baseline for UniSSDA.
Instance-Adaptive and Geometric-Aware Keypoint Learning for Category-Level 6D Object Pose Estimation
PDF ↗Category-level 6D object pose estimation aims to estimate the rotation translation and size of unseen instances within specific categories. In this area dense correspondence-based methods have achieved leading performance. However they do not explicitly consider the local and global geometric information of different instances resulting in poor generalization ability to unseen instances with significant shape variations. To deal with this problem we propose a novel Instance-adaptive and To deal with this problem we propose a novel Instance-Adaptive and Geometric-Aware Keypoint Learning method for category-level 6D object pose estimation (AG-Pose) which includes two key designs: (1) The first design is an Instance-Adaptive Keypoint Detection module which can adaptively detect a set of sparse keypoints for various instances to represent their geometric structures. (2) The second design is a Geometric-Aware Feature Aggregation module which can efficiently integrate the local and global geometric information into keypoint features. These two modules can work together to establish robust keypoint-level correspondences for unseen instances thus enhancing the generalization ability of the model.Experimental results on CAMERA25 and REAL275 datasets show that the proposed AG-Pose outperforms state-of-the-art methods by a large margin without category-specific shape priors.
The diffusion-based text-to-image model harbors immense potential in transferring reference style. However current encoder-based approaches significantly impair the text controllability of text-to-image models while transferring styles. In this paper we introduce DEADiff to address this issue using the following two strategies: 1) a mechanism to decouple the style and semantics of reference images. The decoupled feature representations are first extracted by Q-Formers which are instructed by different text descriptions. Then they are injected into mutually exclusive subsets of cross-attention layers for better disentanglement. 2) A non-reconstructive learning method. The Q-Formers are trained using paired images rather than the identical target in which the reference image and the ground-truth image are with the same style or semantics. We show that DEADiff attains the best visual stylization results and optimal balance between the text controllability inherent in the text-to-image model and style similarity to the reference image as demonstrated both quantitatively and qualitatively. Our project page is https://tianhao-qi.github.io/DEADiff/.
Despite their ability to generate high-resolution and diverse images from text prompts text-to-image diffusion models often suffer from slow iterative sampling processes. Model distillation is one of the most effective directions to accelerate these models. However previous distillation methods fail to retain the generation quality while requiring a significant amount of images for training either from real data or synthetically generated by the teacher model. In response to this limitation we present a novel image-free distillation scheme named SwiftBrush. Drawing inspiration from text-to-3D synthesis in which a 3D neural radiance field that aligns with the input prompt can be obtained from a 2D text-to-image diffusion prior via a specialized loss without the use of any 3D data ground-truth our approach re-purposes that same loss for distilling a pretrained multi-step text-to-image model to a student network that can generate high-fidelity images with just a single inference step. In spite of its simplicity our model stands as one of the first one-step text-to-image generators that can produce images of comparable quality to Stable Diffusion without reliance on any training image data. Remarkably SwiftBrush achieves an FID score of 16.67 and a CLIP score of 0.29 on the COCO-30K benchmark achieving competitive results or even substantially surpassing existing state-of-the-art distillation techniques.
In this work we propose IDGuard a novel proactive defense method from the perspective of developers to protect Persons-of-Interest (POI) such as national leaders from face editing abuse. We build a bridge between identities and model behavior safeguarding POI identities rather than merely certain face images. Given a face editing model IDGuard enables it to reject editing any image containing POI identities while retaining its editing functionality for regular use. Specifically we insert an ID Normalization Layer into the original face editing model and introduce an ID Extractor to extract the identities of input images. To differentiate the editing behavior between POI and nonPOI we use a transformer-based ID Encoder to encode extracted POI identities as parameters of the ID Normalization Layer. Our method supports the simultaneous protection of multiple POI and allows for the addition of new POI in the inference stage without the need for retraining. Extensive experiments show that our method achieves 100% protection accuracy on POI images even if they are neither included in the training set nor subject to any preprocessing. Notably our method exhibits excellent robustness against image and model attacks and maintains 100% protection performance when generalized to various face editing models further demonstrating its practicality.
Most existing dynamic or runtime channel pruning methods have to store all weights to achieve efficient inference which brings extra storage costs. Static pruning methods can reduce storage costs directly but their performance is limited by using a fixed sub-network to approximate the original model. Most existing pruning works suffer from these drawbacks because they were designed to only conduct either static or dynamic pruning. In this paper we propose a novel method to solve both efficiency and storage challenges via simultaneously conducting dynamic and static channel pruning for convolutional neural networks. We propose a new bi-level optimization based model to naturally integrate the static and dynamic channel pruning. By doing so our method enjoys benefits from both sides and the disadvantages of dynamic and static pruning are reduced. After pruning we permanently remove redundant parameters and then finetune the model with dynamic flexibility. Experimental results on CIFAR-10 and ImageNet datasets suggest that our method can achieve state-of-the-art performance compared to existing dynamic and static channel pruning methods.
Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain Adaptation
PDF ↗Open-Set Source-Free Domain Adaptation aims to transfer knowledge in realistic scenarios where the target domain has additional unknown classes compared to the limited-access source domain. Due to the absence of information on unknown classes existing methods mainly transfer knowledge of known classes while roughly grouping unknown classes as one attenuating the knowledge transfer and generalization. In contrast this paper advocates that exploring unknown classes can better identify known ones and proposes a domain adaptation model to transfer knowledge on known and unknown classes jointly. Specifically given a source pre-trained model we first introduce an unknown diffuser that can determine whether classes in space need to be split and merged through similarity measures to estimate and generate a wider class space distribution including known and unknown classes. Based on such a wider space distribution we enhance the reliability of known class knowledge in the source pre-trained model through contrastive constraint. Finally various supervision information including reliable known class knowledge and clustered pseudo-labels optimize the model for impressive knowledge transfer and generalization. Extensive experiments show that our network can achieve superior exploration and knowledge generalization on unknown classes while with excellent known class transfer. The code is available at https://github.com/xdwfl/UPUK.
UniRepLKNet: A Universal Perception Large-Kernel ConvNet for Audio Video Point Cloud Time-Series and Image Recognition
PDF ↗Large-kernel convolutional neural networks (ConvNets) have recently received extensive research attention but two unresolved and critical issues demand further investigation. 1) The architectures of existing large-kernel ConvNets largely follow the design principles of conventional ConvNets or transformers while the architectural design for large-kernel ConvNets remains under-addressed. 2) As transformers have dominated multiple modalities it remains to be investigated whether ConvNets also have a strong universal perception ability in domains beyond vision. In this paper we contribute from two aspects. 1) We propose four architectural guidelines for designing large-kernel ConvNets the core of which is to exploit the essential characteristics of large kernels that distinguish them from small kernels - they can see wide without going deep. Following such guidelines our proposed large-kernel ConvNet shows leading performance in image recognition (ImageNet accuracy of 88.0% ADE20K mIoU of 55.6% and COCO box AP of 56.4%) demonstrating better performance and higher speed than the recent powerful competitors. 2) We discover large kernels are the key to unlocking the exceptional performance of ConvNets in domains where they were originally not proficient. With certain modality-related preprocessing approaches the proposed model achieves state-of-the-art performance on time-series forecasting and audio recognition tasks even without modality-specific customization to the architecture. All the code and models are publicly available on GitHub and Huggingface.
Before developing a Document Layout Analysis (DLA) model in real-world applications conducting comprehensive robustness testing is essential. However the robustness of DLA models remains underexplored in the literature. To address this we are the first to introduce a robustness benchmark for DLA models which includes 450K document images of three datasets. To cover realistic corruptions we propose a perturbation taxonomy with 12 common document perturbations with 3 severity levels inspired by real-world document processing. Additionally to better understand document perturbation impacts we propose two metrics Mean Perturbation Effect (mPE) for perturbation assessment and Mean Robustness Degradation (mRD) for robustness evaluation. Furthermore we introduce a self-titled model i.e. Robust Document Layout Analyzer (RoDLA) which improves attention mechanisms to boost extraction of robust features. Experiments on the proposed benchmarks (PubLayNet-P DocLayNet-P and M6Doc-P) demonstrate that RoDLA obtains state-of-the-art mRD scores of 115.7 135.4 and 150.4 respectively. Compared to previous methods RoDLA achieves notable improvements in mAP of +3.8% +7.1% and +12.1% respectively.
Visual odometry estimates the motion of a moving camera based on visual input. Existing methods mostly focusing on two-view point tracking often ignore the rich temporal context in the image sequence thereby overlooking the global motion patterns and providing no assessment of the full trajectory reliability. These shortcomings hinder performance in scenarios with occlusion dynamic objects and low-texture areas. To address these challenges we present the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively combines visual inter-track and temporal cues with mindfully selected anchors for dynamic track estimation. Moreover LEAP's temporal probabilistic formulation integrates distribution updates into a learnable iterative refinement module to reason about point-wise uncertainty. Based on these traits we develop LEAP-VO a robust visual odometry system adept at handling occlusions and dynamic scenes. Our mindful integration showcases a novel practice by employing long-term point tracking as the front-end. Extensive experiments demonstrate that the proposed pipeline significantly outperforms existing baselines across various visual odometry benchmarks.