CVPR 2024 Papers

Given a set of images our goal is to map each image to a point in a feature space such that not only point proximity indicates visual similarity but where it is located directly encodes how prototypical the image is according to the dataset. Our key insight is to perform unsupervised feature learning in hyperbolic instead of Euclidean space where the distance between points still reflects image similarity yet we gain additional capacity for representing prototypicality with the location of the point: The closer it is to the origin the more prototypical it is. The latter property is simply emergent from optimizing the metric learning objective: The image similar to many training instances is best placed at the center of corresponding points in Euclidean space but closer to the origin in hyperbolic space. We propose an unsupervised feature learning algorithm in Hyperbolic space with sphere pACKing. HACK first generates uniformly packed particles in the Poincar'e ball of hyperbolic space and then assigns each image uniquely to a particle. With our feature mapper simply trained to spread out training instances in hyperbolic space we observe that images move closer to the origin with congealing - a warping process that aligns all the images and makes them appear more common and similar to each other validating our idea of unsupervised prototypicality discovery. We demonstrate that our data-driven prototypicality provides an easy and superior unsupervised instance selection to reduce sample complexity increase model generalization with atypical instances and robustness with typical ones.

Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy wrapped counterparts finding many applications in scientific imaging. Although supervised learning has shown promise in PU its utility is limited in ground-truth (GT) scarce scenarios. This paper presents an unsupervised learning approach that eliminates the need for GTs during end-to-end training. Our approach leverages the insight that both the gradients and wrapped gradients of wrapped phases serve as noisy labels for GT phase gradients along with sparse outliers induced by the wrapping operation. A recorruption-based self-reconstruction loss in the gradient domain is proposed to mitigate the adverse effects of label noise complemented with a self-distillation loss for improved generalization. Additionally by unfolding a variational model of PU that utilizes wrapped gradients of wrapped phases for its data-fitting term we develop a deep unrolling network that encodes physics of phase wrapping and incorporates special treatments on outliers. In the experiments on three types of phase data our approach outperforms existing GT-free methods and competes well against the supervised ones.

Significant progress in image deblurring has been achieved by deep learning methods especially the remarkable performance of supervised models on paired synthetic data. However real-world quality degradation is more complex than synthetic datasets and acquiring paired data in real-world scenarios poses significant challenges. To address these challenges we propose a novel unsupervised image deblurring framework based on self-enhancement. The framework progressively generates improved pseudo-sharp and blurry image pairs without the need for real paired datasets and the generated image pairs with higher qualities can be used to enhance the performance of the reconstructor. To ensure the generated blurry images are closer to the real blurry images we propose a novel re-degradation principal component consistency loss which enforces the principal components of the generated low-quality images to be similar to those of re-degraded images from the original sharp ones. Furthermore we introduce the self-enhancement strategy that significantly improves deblurring performance without increasing the computational complexity of network during inference. Through extensive experiments on multiple real-world blurry datasets we demonstrate the superiority of our approach over other state-of-the-art unsupervised methods.

Understanding 3D object structure from image collections of general object categories remains a long-standing challenge in computer vision. Due to the high relevance of image keypoints (e.g. for graph matching controlling generative models scene understanding etc.) in this work we specifically focus on inferring 3D structure in terms of sparse keypoints. Existing 3D keypoint inference approaches rely on strong priors such as spatio-temporal consistency multi-view images of the same object 3D shape priors (e.g. templates skeleton) or supervisory signals e.g. in the form of 2D keypoint annotations. In contrast we propose the first unsupervised 3D keypoint inference approach that can be trained for general object categories solely from an inhomogeneous image collection (containing different instances of objects from the same category). Our experiments show that our method not only improves upon unsupervised 2D keypoint inference but more importantly it also produces reasonable 3D structure for various object categories both qualitatively and quantitatively.

Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However common distance field based implicit representations specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes. In this paper we introduce a novel neural implicit representation based on unsigned orthogonal distance fields (UODFs). In UODFs the minimal unsigned distance from any spatial point to the shape surface is defined solely in one orthogonal direction contrasting with the multi-directional determination made by SDF and UDF. Consequently every point in the 3D UODFs can directly access its closest surface points along three orthogonal directions. This distinctive feature leverages the accurate reconstruction of surface points without interpolation errors. We verify the effectiveness of UODFs through a range of reconstruction examples extending from simple watertight or non-watertight shapes to complex shapes that include hollows internal or assembling structures.

Foundation segmentation models while powerful pose a significant risk: they enable users to effortlessly extract any objects from any digital content with a single click potentially leading to copyright infringement or malicious misuse. To mitigate this risk we introduce a new task "Anything Unsegmentable" to grant any image "the right to be unsegmented". The ambitious pursuit of the task is to achieve highly transferable adversarial attack against all prompt-based segmentation models regardless of model parameterizations and prompts. We highlight the non-transferable and heterogeneous nature of prompt-specific adversarial noises. Our approach focuses on disrupting image encoder features to achieve prompt-agnostic attacks. Intriguingly targeted feature attacks exhibit better transferability compared to untargeted ones suggesting the optimal update direction aligns with the image manifold. Based on the observations we design a novel attack named Unsegment Anything by Simulating Deformation (UAD). Our attack optimizes a differentiable deformation function to create a target deformed image which alters structural information while preserving achievable feature distance by adversarial example. Extensive experiments verify the effectiveness of our approach compromising a variety of promptable segmentation models with different architectures and prompt interfaces.

Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between sound and visual objects. Successful audio-visual learning requires two essential components: 1) a challenging dataset with high-quality pixel-level multi-class annotated images associated with audio files and 2) a model that can establish strong links between audio information and its corresponding visual object. However these requirements are only partially addressed by current methods with training sets containing biased audio-visual data and models that generalise poorly beyond this biased training set. In this work we propose a new cost-effective strategy to build challenging and relatively unbiased high-quality audio-visual segmentation benchmarks. We also propose a new informative sample mining method for audio-visual supervised contrastive learning to leverage discriminative contrastive samples to enforce cross-modal understanding. We show empirical results that demonstrate the effectiveness of our benchmark. Furthermore experiments conducted on existing AVS datasets and on our new benchmark show that our method achieves state-of-the-art (SOTA) segmentation accuracy.

Hyperspectral images (HSIs) have extensive applications in various fields such as medicine agriculture and industry. Nevertheless acquiring high signal-to-noise ratio HSI poses a challenge due to narrow-band spectral filtering. Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology. While most previous HSI denoising methods are supervised creating supervised training datasets for the diverse scenes hyperspectral cameras and scan parameters is impractical. In this work we present Diff-Unmix a self-supervised denoising method for HSI using diffusion denoising generative models. Specifically Diff-Unmix addresses the challenge of recovering noise-degraded HSI through a fusion of Spectral Unmixing and conditional abundance generation. Firstly it employs a learnable block-based spectral unmixing strategy complemented by a pure transformer-based backbone. Then we introduce a self-supervised generative diffusion network to enhance abundance maps from the spectral unmixing block. This network reconstructs noise-free Unmixing probability distributions effectively mitigating noise-induced degradations within these components. Finally the reconstructed HSI is reconstructed through unmixing reconstruction by blending the diffusion-adjusted abundance map with the spectral endmembers. Experimental results on both simulated and real-world noisy datasets show that Diff-Unmix achieves state-of-the-art performance.

In the realm of AI data serves as a pivotal resource. Real-world hyperspectral images (HSIs) bearing wide spectral characteristics are particularly valuable. However the acquisition of HSIs is always costly and time-intensive resulting in a severe data-thirsty issue in HSI research and applications. Current solutions have not been able to generate a sufficient volume of diverse and reliable synthetic HSIs. To this end our study formulates a novel generalized paradigm for HSI synthesis i.e. unmixing before fusion that initiates with unmixing across multi-source data and follows by fusion-based synthesis. By integrating unmixing this work maps unpaired HSI and RGB data to a low-dimensional abundance space greatly alleviating the difficulty of generating high-dimensional samples. Moreover incorporating abundances inferred from unpaired RGB images into generative models allows for cost-effective supplementation of various realistic spatial distributions in abundance synthesis. Our proposed paradigm can be instrumental with a series of deep generative models filling a significant gap in the field and enabling the generation of vast high-quality HSI samples for large-scale downstream tasks. Extension experiments on downstream tasks demonstrate the effectiveness of synthesized HSIs. The code is available at: HSI-Synthesis.github.io.

Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art performance with improved efficiency in various computer vision tasks. This suggests a promising paradigm shift of adapting pre-trained ViT models to Federated Learning (FL) settings. However the challenge of data heterogeneity among FL clients presents a significant hurdle in effectively deploying ViT models. Existing Generalized FL (GFL) and Personalized FL (PFL) methods have limitations in balancing performance across both global and local data distributions. In this paper we present a novel algorithm SGPT that integrates GFL and PFL approaches by employing a unique combination of both shared and group-specific prompts. This design enables SGPT to capture both common and group-specific features. A key feature of SGPT is its prompt selection module which facilitates the training of a single global model capable of automatically adapting to diverse local client data distributions without the need for local fine-tuning. To effectively train the prompts we utilize block coordinate descent (BCD) learning from common feature information (shared prompts) and then more specialized knowledge (group prompts) iteratively. Theoretically we justify that learning the proposed prompts can reduce the gap between global and local performance. Empirically we conduct experiments on both label and feature heterogeneity settings in comparison with state-of-the-art baselines along with extensive ablation studies to substantiate the superior performance of SGPT.

The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper we explore to harnesses this emergent feature to tackle few-shot semantic segmentation a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery we find that the concept of "relationship descriptors" initially conceived for enhancing the CLIP model for zero-shot semantic segmentation offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation extending its application across multiple layers to enhance performance. Building upon this adaptation we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets including PASCAL-5^ i and COCO-20^ i we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://github.com/ZiqinZhou66/FewSegwithRD.git.

Semantic image synthesis i.e. generating images from user-provided semantic label maps is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation but the image quality tends to suffer when modeling large and diverse datasets. In this work we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbones pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables leading to more diverse generated images. Our model which we dub DP-SIMS achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K COCO-Stuff and Cityscapes surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.

The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However its application in medical imaging presents challenges requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performance. This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure. In the initial stage H-SAM employs SAM's original decoder to generate a prior probabilistic mask guiding a more intricate decoding process in the second stage. Specifically we propose two key designs: 1) A class-balanced mask-guided self-attention mechanism addressing the unbalanced label distribution enhancing image embedding; 2) A learnable mask cross-attention mechanism spatially modulating the interplay among different image regions based on the prior mask. Moreover the inclusion of a hierarchical pixel decoder in H-SAM enhances its proficiency in capturing fine-grained and localized details. This approach enables SAM to effectively integrate learned medical priors facilitating enhanced adaptation for medical image segmentation with limited samples. Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants for multi-organ segmentation using only 10% of 2D slices. Notably without using any unlabeled data H-SAM even outperforms state-of-the-art semi-supervised models relying on extensive unlabeled training data across various medical datasets. Our code is available at https://github.com/Cccccczh404/H-SAM.

This paper investigates the effective utilization of unlabeled data for large-area cross-view geo-localization (CVGL) encompassing both unsupervised and semi-supervised settings. Common approaches to CVGL rely on ground-satellite image pairs and employ label-driven supervised training. However the cost of collecting precise cross-view image pairs hinders the deployment of CVGL in real-life scenarios. Without the pairs CVGL will be more challenging to handle the significant imaging and spatial gaps between ground and satellite images. To this end we propose an unsupervised framework including a cross-view projection to guide the model for retrieving initial pseudo-labels and a fast re-ranking mechanism to refine the pseudo-labels by leveraging the fact that "the perfectly paired ground-satellite image is located in a unique and identical scene". The framework exhibits competitive performance compared with supervised works on three open-source benchmarks. Our code and models will be released on https://github.com/liguopeng0923/UCVGL.

Semantic scene completion (SSC) aims to predict complete 3D voxel occupancy and semantics from a single-view RGB-D image and recent SSC methods commonly adopt multi-modal inputs. However our investigation reveals two limitations: ineffective feature learning from single modalities and overfitting to limited datasets. To address these issues this paper proposes a novel SSC framework - Adversarial Modality Modulation Network (AMMNet) - with a fresh perspective of optimizing gradient updates. The proposed AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities and a customized adversarial training scheme leveraging dynamic gradient competition. Specifically the cross-modal modulation adaptively re-calibrates the features to better excite representation potentials from each single modality. The adversarial training employs a minimax game of evolving gradients with customized guidance to strengthen the generator's perception of visual fidelity from both geometric completeness and semantic correctness. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin providing a promising direction for improving the effectiveness and generalization of SSC methods.

Deep Convolutional Neural Networks (DCNNs) have achieved remarkable performance in synthetic aperture radar (SAR) object detection but this comes at the cost of tremendous computational resources partly due to extracting redundant features within a single convolutional layer. Recent works either delve into model compression methods or focus on the carefully-designed lightweight models both of which result in performance degradation. In this paper we propose an efficient convolution module for SAR object detection called SFS-Conv which increases feature diversity within each convolutional layer through a shunt-perceive-select strategy. Specifically we shunt input feature maps into space and frequency aspects. The former perceives the context of various objects by dynamically adjusting receptive field while the latter captures abundant frequency variations and textural features via fractional Gabor transformer. To adaptively fuse features from space and frequency aspects a parameter-free feature selection module is proposed to ensure that the most representative and distinctive information are preserved. With SFS-Conv we build a lightweight SAR object detection network called SFS-CNet. Experimental results show that SFS-CNet outperforms state-of-the-art (SoTA) models on a series of SAR object detection benchmarks while simultaneously reducing both the model size and computational cost.

We delve into Open Domain Generalization (ODG) marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing these pitfalls we introduce ODG-CLIP harnessing the semantic prowess of the vision-language model CLIP. Our framework brings forth three primary innovations: Firstly distinct from prevailing paradigms we conceptualize ODG as a multi-class classification challenge encompassing both known and novel categories. Central to our approach is modeling a unique prompt tailored for detecting unknown class samples and to train this we employ a readily accessible stable diffusion model elegantly generating proxy images for the open class. Secondly aiming for domain-tailored classification (prompt) weights while ensuring a balance of precision and simplicity we devise a novel visual style-centric prompt learning mechanism. Finally we infuse images with class-discriminative knowledge derived from the prompt space to augment the fidelity of CLIP's visual embeddings. We introduce a novel objective to safeguard the continuity of this infused semantic intel across domains especially for the shared classes. Through rigorous testing on diverse datasets covering closed and open-set DG contexts ODG-CLIP demonstrates clear supremacy consistently outpacing peers with performance boosts between 8%-16%. Code will be available at https://github.com/mainaksingha01/ODG-CLIP.

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.

This paper aims to achieve universal segmentation of arbitrary semantic level. Despite significant progress in recent years specialist segmentation approaches are limited to specific tasks and data distribution. Retraining a new model for adaptation to new scenarios or settings takes expensive computation and time cost which raises the demand for versatile and universal segmentation model that can cater to various granularity. Although some attempts have been made for unifying different segmentation tasks or generalization to various scenarios limitations in the definition of paradigms and input-output spaces make it difficult for them to achieve accurate understanding of content at arbitrary granularity. To this end we present UniLSeg a universal segmentation model that can perform segmentation at any semantic level with the guidance of language instructions. For training UniLSeg we reorganize a group of tasks from original diverse distributions into a unified data format where images with texts describing segmentation targets as input and corresponding masks are output. Combined with a automatic annotation engine for utilizing numerous unlabeled data UniLSeg achieves excellent performance on various tasks and settings surpassing both specialist and unified segmentation models.