CVPR 2024 论文列表

In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.

Recent advances in neural network pruning have shown how it is possible to reduce the computational costs and memory demands of deep learning models before training. We focus on this framework and propose a new pruning at initialization algorithm that leverages the Neural Tangent Kernel (NTK) theory to align the training dynamics of the sparse network with that of the dense one. Specifically we show how the usually neglected data-dependent component in the NTK's spectrum can be taken into account by providing an analytical upper bound to the NTK's trace obtained by decomposing neural networks into individual paths. This leads to our Path eXclusion (PX) a foresight pruning method designed to preserve the parameters that mostly influence the NTK's trace. PX is able to find lottery tickets (i.e. good paths) even at high sparsity levels and largely reduces the need for additional training. When applied to pre-trained models it extracts subnetworks directly usable for several downstream tasks resulting in performance comparable to those of the dense counterpart but with substantial cost and computational savings.

Even when using large multi-modal foundation models few-shot learning is still challenging -- if there is no proper inductive bias it is nearly impossible to keep the nuanced class attributes while removing the visually prominent attributes that spuriously correlate with class labels. To this end we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes i.e. as the forward diffusion adds noise to an image at each time-step nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent. Building on this we propose Time-step Few-shot (TiF) learner. We train class-specific low-rank adapters for a text-conditioned DM to make up for the lost attributes such that images can be accurately reconstructed from their noisy ones given a prompt. Hence at a small time-step the adapter and prompt are essentially a parameterization of only the nuanced class attributes. For a test image we can use the parameterization to only extract the nuanced class attributes for classification. TiF learner significantly outperforms OpenCLIP and its adapters on a variety of fine-grained and customized few-shot learning tasks. Codes are in https://github.com/yue-zhongqi/tif.

Few-shot object detection (FSOD) aims to detect objects with only a few training examples. Visual feature extraction and query-support similarity learning are the two critical components. Existing works are usually developed based on ImageNet pre-trained vision backbones and design sophisticated metric-learning networks for few-shot learning but still have inferior accuracy. In this work we study few-shot object detection using modern foundation models. First vision-only contrastive pre-trained DINOv2 model is used for the vision backbone which shows strong transferable performance without tuning the parameters. Second Large Language Model (LLM) is employed for contextualized few-shot learning with the input of all classes and query image proposals. Language instructions are carefully designed to prompt the LLM to classify each proposal in context. The contextual information include proposal-proposal relations proposal-class relations and class-class relations which can largely promote few-shot learning. We comprehensively evaluate the proposed model (FM-FSOD) in multiple FSOD benchmarks achieving state-of-the-arts performance.

We introduce a novel approach for adapting deep stereo networks in a collaborative manner. By building over principles of federated learning we develop a distributed framework allowing for demanding the optimization process to a number of clients deployed in different environments. This makes it possible for a deep stereo network running on resourced-constrained devices to capitalize on the adaptation process carried out by other instances of the same architecture and thus improve its accuracy in challenging environments even when it cannot carry out adaptation on its own. Experimental results show how federated adaptation performs equivalently to on-device adaptation and even better when dealing with challenging environments.

Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are distributed in local clients and cannot be shared among clients. Fed-GCD aims to train a generic GCD model by client collaboration under the privacy-protected constraint. The Fed-GCD leads to two challenges: 1) representation degradation caused by training each client model with fewer data than centralized GCD learning and 2) highly heterogeneous label spaces across different clients. To this end we propose a novel Associated Gaussian Contrastive Learning (AGCL) framework based on learnable GMMs which consists of a Client Semantics Association (CSA) and a global-local GMM Contrastive Learning (GCL). On the server CSA aggregates the heterogeneous categories of local-client GMMs to generate a global GMM containing more comprehensive category knowledge. On each client GCL builds class-level contrastive learning with both local and global GMMs. The local GCL learns robust representation with limited local data. The global GCL encourages the model to produce more discriminative representation with the comprehensive category relationships that may not exist in local data. We build a benchmark based on six visual datasets to facilitate the study of Fed-GCD. Extensive experiments show that our AGCL outperforms multiple baselines on all datasets.

Federated learning is a promising framework to train neural networks with widely distributed data. However performance degrades heavily with heterogeneously distributed data. Recent work has shown this is due to the final layer of the network being most prone to local bias some finding success freezing the final layer as an orthogonal classifier. We investigate the training dynamics of the classifier by applying SVD to the weights motivated by the observation that freezing weights results in constant singular values. We find that there are differences when training in IID and non-IID settings. Based on this finding we introduce two regularization terms for local training to continuously emulate IID settings: (1) variance in the dimension-wise probability distribution of the classifier and (2) hyperspherical uniformity of representations of the encoder. These regularizations promote local models to act as if it were in an IID setting regardless of the local data distribution thus offsetting proneness to bias while being flexible to the data. On extensive experiments in both label-shift and feature-shift settings we verify that our method achieves highest performance by a large margin especially in highly non-IID cases in addition to being scalable to larger models and datasets.

Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods usually decouple global updates in deep neural networks by performing personalization on particular layers (i.e. classifier heads) and global aggregation for the rest of the network. However preselecting network layers for personalization may result in suboptimal storage of global knowledge. In this work we propose FedSelect a novel PFL algorithm inspired by the iterative subnetwork discovery procedure used for the Lottery Ticket Hypothesis. FedSelect incrementally expands subnetworks to personalize client parameters concurrently conducting global aggregations on the remaining parameters. This approach enables the personalization of both client parameters and subnetwork structure during the training process. Finally we show that FedSelect outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and demonstrates robustness to various real-world distributional shifts.

Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy it often suffers from significant performance degradation when clients have heterogeneous data distributions. This data heterogeneity causes the model to forget the global knowledge acquired from previously sampled clients after being trained on local datasets. Although the introduction of proximal objectives in local updates helps to preserve global knowledge it can also hinder local learning by interfering with local objectives. Inspired by Continual Learning (CL) we adopt an orthogonal learning strategy to balance these two conflicting objectives. However we observe that directly negating the proximal gradient in the local gradient significantly undermines local learning. To address the problem we propose a novel method Federated Stabilized Orthogonal Learning (FedSOL). FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective. Specifically FedSOL targets parameter regions where learning on the local objective is minimally influenced by proximal weight perturbations. Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios.

Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However its application on resource-constrained devices is challenging due to the high demand for computation and memory resources to train deep learning models. Neural network pruning techniques such as dynamic pruning could enhance model efficiency but directly adopting them in FL still poses substantial challenges including post-pruning performance degradation high activation memory usage etc. To address these challenges we propose FedMef a novel and memory-efficient federated dynamic pruning framework. FedMef comprises two key components. First we introduce the budget-aware extrusion that maintains pruning efficiency while preserving post-pruning performance by salvaging crucial information from parameters marked for pruning within a given budget. Second we propose scaled activation pruning to effectively reduce activation memory footprints which is particularly beneficial for deploying FL to memory-limited devices. Extensive experiments demonstrate the effectiveness of our proposed FedMef. In particular it achieves a significant reduction of 28.5% in memory footprint compared to state-of-the-art methods while obtaining superior accuracy.

Federated Learning (FL) enables joint training across distributed clients using their local data privately. Federated Multi-Task Learning (FMTL) builds on FL to handle multiple tasks assuming model congruity that identical model architecture is deployed in each client. To relax this assumption and thus extend real-world applicability we introduce a novel problem setting Hetero-Client Federated Multi-Task Learning (HC-FMTL) to accommodate diverse task setups. The main challenge of HC-FMTL is the model incongruity issue that invalidates conventional aggregation methods. It also escalates the difficulties in model aggregation to deal with data and task heterogeneity inherent in FMTL. To address these challenges we propose the FedHCA^2 framework which allows for federated training of personalized models by modeling relationships among heterogeneous clients. Drawing on our theoretical insights into the difference between multi-task and federated optimization we propose the Hyper Conflict-Averse Aggregation scheme to mitigate conflicts during encoder updates. Additionally inspired by task interaction in MTL the Hyper Cross Attention Aggregation scheme uses layer-wise cross attention to enhance decoder interactions while alleviating model incongruity. Moreover we employ learnable Hyper Aggregation Weights for each client to customize personalized parameter updates. Extensive experiments demonstrate the superior performance of FedHCA^2 in various HC-FMTL scenarios compared to representative methods. Code is available at https://github.com/innovator-zero/FedHCA2.

Personalized Federated Learning (PFL) is primarily designed to provide customized models for each client to better fit the non-iid distributed client data which is a inherent challenge in Federated Learning. However current PFL methods suffer from inconsistencies in both intra-client and inter-client levels: 1) The intra-client inconsistency stems from the asynchronous update strategy for personalized and shared parameters. In PFL clients update their shared parameters to communicate and learn from others while keeping personalized parts unchanged leading to poor coordination between these two components. 2) The Inter-client inconsistency arises from "stragglers" - inactive clients that communicate and train with the server less frequently. This results in their under-trained personalized models and impedes the collaborative training stage for other clients. In this paper we present a novel PFL framework named FedAS which uses Federated Parameter-Alignment and Client-Synchronization to overcome above challenges. Initially we enhance the localization of global parameters by infusing them with local insights. We make the shared parts learn from previous model thereby increasing their local relevance and reducing the impact of parameter inconsistency. Furthermore we design a robust aggregation method to mitigate the impact of stragglers by preventing the incorporation of their under-trained knowledge into aggregated model. Experimental results on Cifar10 and Cifar100 validate the effectiveness of our FedAS in achieving better performance and robustness against data heterogeneity.

Multiple instance learning (MIL) is the most widely used framework in computational pathology encompassing sub-typing diagnosis prognosis and more. However the existing MIL paradigm typically requires an offline instance feature extractor such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning within the specific downstream tasks limiting its adaptability and performance. To address this issue we propose a Re-embedded Regional Transformer (RRT) for re-embedding the instance features online which captures fine-grained local features and establishes connections across different regions. Unlike existing works that focus on pre-training powerful feature extractor or designing sophisticated instance aggregator RRT is tailored to re-embed instance features online. It serves as a portable module that can seamlessly integrate into mainstream MIL models. Extensive experimental results on common computational pathology tasks validate that: 1) feature re-embedding improves the performance of MIL models based on ResNet-50 features to the level of foundation model features and further enhances the performance of foundation model features; 2) the RRT can introduce more significant performance improvements to various MIL models; 3) RRT-MIL as an RRT-enhanced AB-MIL outperforms other latest methods by a large margin. The code is available at: https://github.com/DearCaat/RRT-MIL.

3D correspondence i.e. a pair of 3D points is a fundamental concept in computer vision. A set of 3D correspondences when equipped with compatibility edges forms a correspondence graph. This graph is a critical component in several state-of-the-art 3D point cloud registration approaches e.g. the one based on maximal cliques (MAC). However its properties have not been well understood. So we present the first study that introduces graph signal processing into the domain of correspondence graph. We exploit the generalized degree signal on correspondence graph and pursue sampling strategies that preserve high-frequency components of this signal. To address time-consuming singular value decomposition in deterministic sampling we resort to a stochastic approximate sampling strategy. As such the core of our method is the stochastic spectral sampling of correspondence graph. As an application we build a complete 3D registration algorithm termed as FastMAC that reaches real-time speed while leading to little to none performance drop. Through extensive experiments we validate that FastMAC works for both indoor and outdoor benchmarks. For example FastMAC can accelerate MAC by 80 times while maintaining high registration success rate on KITTI. Codes are publicly available at https://github.com/Forrest-110/FastMAC.

Domain shift is a challenge for supervised human pose estimation where the source data and target data come from different distributions. This is why pose estimation methods generally perform worse on the test set than on the training set. Recently test-time adaptation has proven to be an effective way to deal with domain shift in human pose estimation. Although the performance on the target domain has been improved existing methods require a large number of weight updates for convergence which is time-consuming and brings catastrophic forgetting. To solve these issues we propose a meta-auxiliary learning method to achieve fast adaptation for domain shift during inference. Specifically we take human pose estimation as the supervised primary task and propose body-specific image inpainting as a self-supervised auxiliary task. First we jointly train the primary and auxiliary tasks to get a pre-trained model on the source domain. Then meta-training correlates the performance of the two tasks to learn a good weight initialization. Finally meta-testing adapts the meta-learned model to the target data through self-supervised learning. Benefiting from the meta-learning paradigm the proposed method enables fast adaptation to the target domain while preserving the source domain knowledge. The carefully designed auxiliary task better pays attention to human-related semantics in a single image. Extensive experiments demonstrate the effectiveness of our test-time fast adaptation.

Due to the high potential for abuse of GenAI systems the task of detecting synthetic images has recently become of great interest to the research community. Unfortunately existing image space detectors quickly become obsolete as new high-fidelity text-to-image models are developed at blinding speed. In this work we propose a new synthetic image detector that uses features obtained by inverting an open-source pre-trained Stable Diffusion model. We show that these inversion features enable our detector to generalize well to unseen generators of high visual fidelity (e.g. DALL*E 3) even when the detector is trained only on lower fidelity fake images generated via Stable Diffusion. This detector achieves new state-of-the-art across multiple training and evaluation setups. Moreover we introduce a new challenging evaluation protocol that uses reverse image search to mitigate stylistic and thematic biases in the detector evaluation. We show that the resulting evaluation scores align well with detectors' in-the-wild performance and release these datasets as public benchmarks for future research.

In this paper we introduce Fairy a minimalist yet robust adaptation of image-editing diffusion models enhancing them for video editing applications. Our approach centers on the concept of anchor-based cross-frame attention a mechanism that implicitly propagates diffusion features across frames ensuring superior temporal coherence and high-fidelity synthesis. Fairy not only addresses limitations of previous models including memory and processing speed. It also improves temporal consistency through a unique data augmentation strategy. This strategy renders the model equivariant to affine transformations in both source and target images. Remarkably efficient Fairy generates 120-frame 512x384 videos (4-second duration at 30 FPS) in just 14 seconds outpacing prior works by at least 44x. A comprehensive user study involving 1000 generated samples confirms that our approach delivers superior quality decisively outperforming established methods.

Existing text-to-image generative models reflect or even amplify societal biases ingrained in their training data. This is especially concerning for human image generation where models are biased against certain demographic groups. Existing attempts to rectify this issue are hindered by the inherent limitations of the pre-trained models and fail to substantially improve demographic diversity. In this work we introduce Fair Retrieval Augmented Generation (FairRAG) a novel framework that conditions pre-trained generative models on reference images retrieved from an external image database to improve fairness in human generation. FairRAG enables conditioning through a lightweight linear module that projects reference images into the textual space. To enhance fairness FairRAG applies simple-yet-effective debiasing strategies providing images from diverse demographic groups during the generative process. Extensive experiments demonstrate that FairRAG outperforms existing methods in terms of demographic diversity image-text alignment and image fidelity while incurring minimal computational overhead during inference.