CVPR 2024 Papers

Super-Resolution (SR) reconstructs high-resolution images from low-resolution ones. CNNs and window-attention methods are two major categories of canonical SR models. However these measures are rigid: in both operations each pixel gathers the same number of neighboring pixels hindering their effectiveness in SR tasks. Alternatively we leverage the flexibility of graphs and propose the Image Processing GNN (IPG) model to break the rigidity that dominates previous SR methods. Firstly SR is unbalanced in that most reconstruction efforts are concentrated to a small proportion of detail-rich image parts. Hence we leverage degree flexibility by assigning higher node degrees to detail-rich image nodes. Then in order to construct graphs for SR-effective aggregation we treat images as pixel node sets rather than patch nodes. Lastly we hold that both local and global information are crucial for SR performance. In the hope of gathering pixel information from both local and global scales efficiently via flexible graphs we search node connections within nearby regions to construct local graphs; and find connections within a strided sampling space of the whole image for global graphs. The flexibility of graphs boosts the SR performance of the IPG model. Experiment results on various datasets demonstrates that the proposed IPG outperforms State-of-the-Art baselines. Codes are available at https://github.com/huawei-noah/Efficient-Computing/tree/master/LowLevel/IPG.

To better understand the behavior of image classifiers it is useful to visualize the contribution of individual pixels to the model prediction. In this study we propose a method MoXI(Model eXplanation by Interactions) that efficiently and accurately identifies a group of pixels with high prediction confidence. The proposed method employs game-theoretic concepts Shapley values and interactions taking into account the effects of individual pixels and the cooperative influence of pixels on model confidence. Theoretical analysis and experiments demonstrate that our method better identifies the pixels that are highly contributing to the model outputs than widely-used by Grad-CAM Attention rollout and Shapley value. While prior studies have suffered from the exponential computational cost in the computation of Shapley value and interactions we show that this can be reduced to quadratic cost for our task. The code is available at https://github.com/KosukeSumiyasu/MoXI.

Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they're slow for interactive use lack precision at object boundaries and struggle to ensure multi-view consistency. We introduce IReNe to address these limitations enabling swift near real-time color editing in NeRF. Leveraging a pre-trained NeRF model and a single training image with user-applied color edits IReNe swiftly adjusts network parameters in seconds. This adjustment allows the model to generate new scene views accurately representing the color changes from the training image while also controlling object boundaries and view-specific effects. Object boundary control is achieved by integrating a trainable segmentation module into the model. The process gains efficiency by retraining only the weights of the last network layer. We observed that neurons in this layer can be classified into those responsible for view-dependent appearance and those contributing to diffuse appearance. We introduce an automated classification approach to identify these neuron types and exclusively fine-tune the weights of the diffuse neurons. This further accelerates training and ensures consistent color edits across different views. A thorough validation on a new dataset with edited object colors shows significant quantitative and qualitative advancements over competitors accelerating speeds by 5x and 500x.

Advanced video frame interpolation (VFI) algorithms approximate intermediate motions between two input frames to synthesize intermediate frame. However they struggle to handle complex scenarios with curvilinear motions since they overlook the latent acceleration information between the input frames. Moreover the supervision of predicted motions is tricky because ground-truth motions are not available. To this end we propose a novel framework for implicit quadratic video frame interpolation (IQ-VFI) which explores latent acceleration information and accurate intermediate motions via knowledge distillation. Specifically the proposed IQ-VFI consists of an implicit acceleration estimation network (IANet) and a VFI backbone the former fully leverages spatio-temporal information to explore latent acceleration priors between two input frames which is then used to progressively modulate linear motions from the latter into quadratic motions in coarse-to-fine manner. Furthermore to encourage both components to distill more acceleration and motion cues oriented towards VFI we propose a knowledge distillation strategy in which implicit acceleration distillation loss and implicit motion distillation loss are employed to adaptively guide latent acceleration priors and intermediate motions learning respectively. Extensive experiments show that our proposed IQ-VFI can achieve state-of-the-art performances on various benchmark datasets.

Generative object compositing emerges as a promising new avenue for compositional image editing. However the requirement of object identity preservation poses a significant challenge limiting practical usage of most existing methods. In response this paper introduces IMPRINT a novel diffusion-based generative model trained with a two-stage learning framework that decouples learning of identity preservation from that of compositing. The first stage is targeted for context-agnostic identity-preserving pretraining of the object encoder enabling the encoder to learn an embedding that is both view-invariant and conducive to enhanced detail preservation. The subsequent stage leverages this representation to learn seamless harmonization of the object composited to the background. In addition IMPRINT incorporates a shape-guidance mechanism offering user-directed control over the compositing process. Extensive experiments demonstrate that IMPRINT significantly outperforms existing methods and various baselines on identity preservation and composition quality.

Deep learning-based image registration (DLIR) methods have achieved remarkable success in deformable image registration. We observe that iterative inference can exploit the well-trained registration network to the fullest extent. In this work we propose a novel Iterative Inference Residual Pyramid Network (IIRP-Net) to enhance registration performance without any additional training costs. In IIRP-Net we construct a streamlined pyramid registration network consisting of a feature extractor and residual flow estimators (RP-Net) to achieve generalized capabilities in feature extraction and registration. Then in the inference phase IIRP-Net employs an iterative inference strategy to enhance RP-Net by iteratively reutilizing residual flow estimators from coarse to fine. The number of iterations is adaptively determined by the proposed IterStop mechanism. We conduct extensive experiments on the FLARE and Mindboggle datasets and the results verify the effectiveness of the proposed method outperforming state-of-the-art deformable image registration methods. Our code is available at https://github.com/Torbjorn1997/IIRP-Net.

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.

Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However they may still face limitations in effectively distinguishing between the most challenging OOD samples that are much like in-distribution (ID) data i.e. ID-like samples. To this end we propose a novel OOD detection framework that discovers ID-like outliers using CLIP from the vicinity space of the ID samples thus helping to identify these most challenging OOD samples. Then a prompt learning framework is proposed that utilizes the identified ID-like outliers to further leverage the capabilities of CLIP for OOD detection. Benefiting from the powerful CLIP we only need a small number of ID samples to learn the prompts of the model without exposing other auxiliary outlier datasets. By focusing on the most challenging ID-like OOD samples and elegantly exploiting the capabilities of CLIP our method achieves superior few-shot learning performance on various real-world image datasets (e.g. in 4-shot OOD detection on the ImageNet-1k dataset our method reduces the average FPR95 by 12.16% and improves the average AUROC by 2.76% compared to state-of-the-art methods).

Image deblurring aims to remove undesired blurs from an image captured in a dynamic scene. Much research has been dedicated to improving deblurring performance through model architectural designs. However there is little work on data augmentation for image deblurring. Since continuous motion causes blurred artifacts during image exposure we aspire to develop a groundbreaking blur augmentation method to generate diverse blurred images by simulating motion trajectories in a continuous space. This paper proposes Implicit Diffusion-based reBLurring AUgmentation (ID-Blau) utilizing a sharp image paired with a controllable blur condition map to produce a corresponding blurred image. We parameterize the blur patterns of a blurred image with their orientations and magnitudes as a pixel-wise blur condition map to simulate motion trajectories and implicitly represent them in a continuous space. By sampling diverse blur conditions ID-Blau can generate various blurred images unseen in the training set. Experimental results demonstrate that ID-Blau can produce realistic blurred images for training and thus significantly improve performance for state-of-the-art deblurring models. The source code is available at https://github.com/plusgood-steven/ID-Blau.

Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. Prevalent methods consider scene flow as point-wise unconstrained flow vectors that can be learned by either large-scale training beforehand or time-consuming optimization at inference. However these methods do not take into account that objects in autonomous driving often move rigidly. We incorporate this rigid-motion assumption into our design where the goal is to associate objects over scans and then estimate the locally rigid transformations. We propose ICP-Flow a learning-free flow estimator. The core of our design is the conventional Iterative Closest Point (ICP) algorithm which aligns the objects over time and outputs the corresponding rigid transformations. Crucially to aid ICP we propose a histogram-based initialization that discovers the most likely translation thus providing a good starting point for ICP. The complete scene flow is then recovered from the rigid transformations. We outperform state-of-the-art baselines including supervised models on the Waymo dataset and perform competitively on Argoverse-v2 and nuScenes. Further we train a feedforward neural network supervised by the pseudo labels from our model and achieve top performance among all models capable of real-time inference. We validate the advantage of our model on scene flow estimation with longer temporal gaps up to 0.4 seconds where other models fail to deliver meaningful results.

Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However NeRF training requires accurate camera pose for each input view typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON) an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further ICON introduces "confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON without prior pose initialization achieves superior performance in both CO3D and HO3D versus methods which use SfM pose.

In this paper we address the challenging problem of visual SLAM with neural scene representations. Recently neural scene representations have shown promise for SLAM to produce dense 3D scene reconstruction with high quality. However existing methods require scene-specific optimization leading to time-consuming mapping processes for each individual scene. To overcome this limitation we propose IBD-SLAM an Image-Based Depth fusion framework for generalizable SLAM. In particular we adopt a Neural Radiance Field (NeRF) for scene representation. Inspired by multi-view image-based rendering instead of learning a fixed-grid scene representation we propose to learn an image-based depth fusion model that fuses depth maps of multiple reference views into a xyz-map representation. Once trained this model can be applied to new uncalibrated monocular RGBD videos of unseen scenes without the need for retraining and reconstructs full 3D scenes efficiently with a light-weight pose optimization procedure. We thoroughly evaluate IBD-SLAM on public visual SLAM benchmarks outperforming the previous state-of-the-art while being 10x faster in the mapping stage. Project page: https://visual-ai.github.io/ibd-slam.

We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper we present I'm-HOI a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter we tailor a category-aware motion diffusion model which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body hand and object motions. Moreover we contribute a large dataset with ground truth human and object motions dense RGB inputs and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community.

Aiming to enhance the utilization of metric space by the parametric softmax classifier recent studies suggest replacing it with a non-parametric alternative. Although a non-parametric classifier may provide better metric space utilization it introduces the challenge of capturing inter-class relationships. A shared characteristic among prior non-parametric classifiers is the static assignment of labels to prototypes during the training i.e. each prototype consistently represents a class throughout the training course. Orthogonal to previous works we present a simple yet effective method to optimize the category assigned to each prototype (label-to-prototype assignment) during the training. To this aim we formalize the problem as a two-step optimization objective over network parameters and label-to-prototype assignment mapping. We solve this optimization using a sequential combination of gradient descent and Bipartide matching. We demonstrate the benefits of the proposed approach by conducting experiments on balanced and long-tail classification problems using different backbone network architectures. In particular our method outperforms its competitors by 1.22% accuracy on CIFAR-100 and 2.15% on ImageNet-200 using a metric space dimension half of the size of its competitors. \href https://github.com/msed-Ebrahimi/DL2PA_CVPR24 Code

Open-world detection poses significant challenges as it requires the detection of any object using either object class labels or free-form texts. Existing related works often use large-scale manual annotated caption datasets for training which are extremely expensive to collect. Instead we propose to transfer knowledge from vision-language models (VLMs) to enrich the open-vocabulary descriptions automatically. Specifically we bootstrap dense synthetic captions using pre-trained VLMs to provide rich descriptions on different regions in images and incorporate these captions to train a novel detector that generalizes to novel concepts. To mitigate the noise caused by hallucination in synthetic captions we also propose a novel hyperbolic vision-language learning approach to impose a hierarchy between visual and caption embeddings. We call our detector "HyperLearner". We conduct extensive experiments on a wide variety of open-world detection benchmarks (COCO LVIS Object Detection in the Wild RefCOCO) and our results show that our model consistently outperforms existing state-of-the-art methods such as GLIP GLIPv2 and Grounding DINO when using the same backbone.

Anomaly detection is a challenging computer vision task in industrial scenario. Advancements in deep learning constantly revolutionize vision-based anomaly detection methods and considerable progress has been made in both supervised and self-supervised anomaly detection. The commonly-used pipeline is to optimize the model by constraining the feature embeddings using a distance-based loss function. However these methods work in Euclidean space and they cannot well exploit the data lied in non-Euclidean space. In this paper we are the first to explore anomaly detection task in hyperbolic space that is a representative of non-Euclidean space and propose a hyperbolic anomaly detection (HypAD) method. Specifically we first extract image features and then map them from Euclidean space to hyperbolic space where the hyperbolic distance metric is employed to optimize the proposed HypAD. Extensive experiments on the benchmarking datasets including MVTec AD and VisA show that our HypAD approach obtains the state-of-the-art performance demonstrating the effectiveness of our HypAD and the promise of investigating anomaly detection in hyperbolic space.

3D shape generation from text is a fundamental task in 3D representation learning. The text-shape pairs exhibit a hierarchical structure where a general text like "chair" covers all 3D shapes of the chair while more detailed prompts refer to more specific shapes. Furthermore both text and 3D shapes are inherently hierarchical structures. However existing Text2Shape methods such as SDFusion do not exploit that. In this work we propose HyperSDFusion a dual-branch diffusion model that generates 3D shapes from a given text. Since hyperbolic space is suitable for handling hierarchical data we propose to learn the hierarchical representations of text and 3D shapes in hyperbolic space. First we introduce a hyperbolic text-image encoder to learn the sequential and multi-modal hierarchical features of text in hyperbolic space. In addition we design a hyperbolic text-graph convolution module to learn the hierarchical features of text in hyperbolic space. In order to fully utilize these text features we introduce a dual-branch structure to embed text features in 3D feature space. At last to endow the generated 3D shapes with a hierarchical structure we devise a hyperbolic hierarchical loss. Our method is the first to explore the hyperbolic hierarchical representation for text-to-shape generation. Experimental results on the existing text-to-shape paired dataset Text2Shape achieved state-of-the-art results. We release our implementation under HyperSDFusion.github.io.

Personalization has emerged as a prominent aspect within the field of generative AI enabling the synthesis of individuals in diverse contexts and styles while retaining high-fidelity to their identities. However the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges we propose HyperDreamBooth - a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model coupled with fast finetuning HyperDreamBooth can generate a person's face in various contexts and styles with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds 25x faster than DreamBooth and 125x faster than Textual Inversion using as few as one reference image with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10000x smaller than a normal DreamBooth model.

Mesh denoising (MD) is a critical task in geometry processing as meshes from scanning or AIGC techniques are susceptible to noise contamination. The challenge of MD lies in the diverse nature of mesh facets in terms of geometric characteristics and noise distributions. Despite recent advancements in deep learning-based MD methods existing MD networks typically neglect the consideration of geometric characteristics and noise distributions. In this paper we propose Hyper-MD a hyper-network-based approach that addresses this limitation by dynamically customizing denoising parameters for each facet based on its noise intensity and geometric characteristics. Specifically Hyper-MD is composed of a hyper-network and an MD network. For each noisy facet the hyper-network takes two angles as input to customize parameters for the MD network. These two angles are specially defined to reveal the noise intensity and geometric characteristics of the current facet respectively. The MD network receives a facet patch as input and outputs the denoised normal using the customized parameters. Experimental results on synthetic and real-scanned meshes demonstrate that Hyper-MD outperforms state-of-the-art mesh denoising methods.