CVPR 2024 论文列表

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.

As an important pillar of underwater intelligence Marine Animal Segmentation (MAS) involves segmenting animals within marine environments. Previous methods don't excel in extracting long-range contextual features and overlook the connectivity between discrete pixels. Recently Segment Anything Model (SAM) offers a universal framework for general segmentation tasks. Unfortunately trained with natural images SAM does not obtain the prior knowledge from marine images. In addition the single-position prompt of SAM is very insufficient for prior guidance. To address these issues we propose a novel feature learning framework named Dual-SAM for high-performance MAS. To this end we first introduce a dual structure with SAM's paradigm to enhance feature learning of marine images. Then we propose a Multi-level Coupled Prompt (MCP) strategy to instruct comprehensive underwater prior information and enhance the multi-level features of SAM's encoder with adapters. Subsequently we design a Dilated Fusion Attention Module (DFAM) to progressively integrate multi-level features from SAM's encoder. Finally instead of directly predicting the masks of marine animals we propose a Criss-Cross Connectivity Prediction (C3P) paradigm to capture the inter-connectivity between discrete pixels. With dual decoders it generates pseudo-labels and achieves mutual supervision for complementary feature representations resulting in considerable improvements over previous techniques. Extensive experiments verify that our proposed method achieves state-of-the-art performances on five widely-used MAS datasets. The code is available at https://github.com/Drchip61/Dual SAM.

Recent dataset deduplication techniques have demonstrated that content-aware dataset pruning can dramatically reduce the cost of training Vision-Language Pretrained (VLP) models without significant performance losses compared to training on the original dataset. These results have been based on pruning commonly used image-caption datasets collected from the web -- datasets that are known to harbor harmful social biases that may then be codified in trained models. In this work we evaluate how deduplication affects the prevalence of these biases in the resulting trained models and introduce an easy-to-implement modification to the recent SemDeDup algorithm that can reduce the negative effects that we observe. When examining CLIP-style models trained on deduplicated variants of LAION-400M we find our proposed FairDeDup algorithm consistently leads to improved fairness metrics over SemDeDup on the FairFace and FACET datasets while maintaining zero-shot performance on CLIP benchmarks.

Federated learning (FL) has emerged as a new paradigm for privacy-preserving collaborative training. Under domain skew the current FL approaches are biased and face two fairness problems. 1) Parameter Update Conflict: data disparity among clients leads to varying parameter importance and inconsistent update directions. These two disparities cause important parameters to potentially be overwhelmed by unimportant ones of dominant updates. It consequently results in significant performance decreases for lower-performing clients. 2) Model Aggregation Bias: existing FL approaches introduce unfair weight allocation and neglect domain diversity. It leads to biased model convergence objective and distinct performance among domains. We discover a pronounced directional update consistency in Federated Learning and propose a novel framework to tackle above issues. First leveraging the discovered characteristic we selectively discard unimportant parameter updates to prevent updates from clients with lower performance overwhelmed by unimportant parameters resulting in fairer generalization performance. Second we propose a fair aggregation objective to prevent global model bias towards some domains ensuring that the global model continuously aligns with an unbiased model. The proposed method is generic and can be combined with other existing FL methods to enhance fairness. Comprehensive experiments on Digits and Office-Caltech demonstrate the high fairness and performance of our method.

Face personalization aims to insert specific faces taken from images into pretrained text-to-image diffusion models. However it is still challenging for previous methods to preserve both the identity similarity and editability due to overfitting to training samples. In this paper we propose Face2Diffusion (F2D) for high-editability face personalization. The core idea behind F2D is that removing identity-irrelevant information from the training pipeline prevents the overfitting problem and improves editability of encoded faces. F2D consists of the following three novel components: 1) Multi-scale identity encoder provides well-disentangled identity features while keeping the benefits of multi-scale information which improves the diversity of camera poses. 2) Expression guidance disentangles face expressions from identities and improves the controllability of face expressions. 3) Class-guided denoising regularization encourages models to learn how faces should be denoised which boosts the text-alignment of backgrounds. Extensive experiments on the FaceForensics++ dataset and diverse prompts demonstrate our method greatly improves the trade-off between the identity- and text-fidelity compared to previous state-of-the-art methods. Code is available at https://github.com/mapooon/Face2Diffusion.

Federated learning (FL) is a powerful technology that enables collaborative training of machine learning models without sharing private data among clients. The fundamental challenge in FL lies in learning over extremely heterogeneous data distributions device capacities and device state availabilities all of which adversely impact performance and communication efficiency. While data heterogeneity has been well-studied in the literature this paper introduces FLHetBench the first FL benchmark targeted toward understanding device and state heterogeneity. FLHetBench comprises two new sampling methods to generate real-world device and state databases with varying heterogeneity and new metrics for quantifying the success of FL methods under these real-world constraints. Using FLHetBench we conduct a comprehensive evaluation of existing methods and find that they struggle under these settings which inspires us to propose BiasPrompt+ a new method employing staleness-aware aggregation and fast weights to tackle these new heterogeneity challenges. Experiments on various FL tasks and datasets validate the effectiveness of our BiasPrompt+ method and highlight the value of FLHetBench in fostering the development of more efficient and robust FL solutions under real-world device and state constraints.

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging thin filamentous and widely branching morphologies multiple neurons are tightly inter-weaved and partial volume effects uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing to date methods are typically benchmarked on synthetic datasets. To address this gap we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies and facilitate scientific discovery in basic neuroscience.

Non-Exemplar Class Incremental Learning (NECIL) involves learning a classification model on a sequence of data without access to exemplars from previously encountered old classes. Such a stringent constraint always leads to catastrophic forgetting of the learned knowledge. Currently existing methods either employ knowledge distillation techniques or preserved class prototypes to sustain prior knowledge. However two critical issues still persist. On the one hand as the model is continually updated the preserved prototypes of old classes will inevitably derive from the suitable location in the feature space of the new model. On the other hand due to the lack of exemplars the features of new classes will take the place of similar old classes which breaks the classification boundary. To address these challenges we propose a Feature Calibration and Separation (FCS) method for NECIL. Our approach comprises a Feature Calibration Network (FCN) that adapts prototypes of old classes to the new model via optimal transport learning approximating the drift of prototypes caused by model evolution. Additionally we also propose a Prototype-Involved Contrastive Loss (PIC) that enhances feature separation among different classes. Specifically to mitigate the boundary distortion arising from the interplay of classes from different learning stages prototypes are involved in pushing the feature of new classes away from the old classes. Extensive experiments on three datasets with different settings have demonstrated the superiority of our FCS method against the state-of-the-art class incremental learning approaches. Code is available at https://github.com/zhoujiahuan1991/CVPR2024-FCS.

Learning fair representation in deep learning is essential to mitigate discriminatory outcomes and enhance trustworthiness. However previous research has been commonly established on inappropriate assumptions prone to unrealistic counterfactuals and performance degradation. Although some proposed alternative approaches such as employing correlation-aware causal graphs or proxies for mutual information these methods are less practical and not applicable in general. In this work we propose FAir DisEntanglement with Sensitive relevance (FADES) a novel approach that leverages conditional mutual information from the information theory perspective to address these challenges. We employ sensitive relevant code to direct correlated information between target labels and sensitive attributes by imposing conditional independence allowing better separation of the features of interest in the latent space. Utilizing an intuitive disentangling approach FADES consistently achieves superior performance and fairness both quantitatively and qualitatively with its straightforward structure. Specifically the proposed method outperforms existing works in downstream classification and counterfactual generations on various benchmarks.

Dataset distillation has emerged as a promising approach in deep learning enabling efficient training with small synthetic datasets derived from larger real ones. Particularly distribution matching-based distillation methods attract attention thanks to its effectiveness and low computational cost. However these methods face two primary limitations: the dispersed feature distribution within the same class in synthetic datasets reducing class discrimination and an exclusive focus on mean feature consistency lacking precision and comprehensiveness. To address these challenges we introduce two novel constraints: a class centralization constraint and a covariance matching constraint. The class centralization constraint aims to enhance class discrimination by more closely clustering samples within classes. The covariance matching constraint seeks to achieve more accurate feature distribution matching between real and synthetic datasets through local feature covariance matrices particularly beneficial when sample sizes are much smaller than the number of features. Experiments demonstrate notable improvements with these constraints yielding performance boosts of up to 6.6% on CIFAR10 2.9% on SVHN 2.5% on CIFAR100 and 2.5% on TinyImageNet compared to the state-of-the-art relevant methods. In addition our method maintains robust performance in cross-architecture settings with a maximum performance drop of 1.7% on four architectures. Code is available at https://github.com/VincenDen/IID.

Active recognition enables robots to intelligently explore novel observations thereby acquiring more information while circumventing undesired viewing conditions. Recent approaches favor learning policies from simulated or collected data wherein appropriate actions are more frequently selected when the recognition is accurate. However most recognition modules are developed under the closed-world assumption which makes them ill-equipped to handle unexpected inputs such as the absence of the target object in the current observation. To address this issue we propose treating active recognition as a sequential evidence-gathering process providing by-step uncertainty quantification and reliable prediction under the evidence combination theory. Additionally the reward function developed in this paper effectively characterizes the merit of actions when operating in open-world environments. To evaluate the performance we collect a dataset from an indoor simulator encompassing various recognition challenges such as distance occlusion levels and visibility. Through a series of experiments on recognition and robustness analysis we demonstrate the necessity of introducing uncertainties to active recognition and the superior performance of the proposed method.

Event sensors offer high temporal resolution visual sensing which makes them ideal for perceiving fast visual phenomena without suffering from motion blur. Certain applications in robotics and vision-based navigation require 3D perception of an object undergoing circular or spinning motion in front of a static camera such as recovering the angular velocity and shape of the object. The setting is equivalent to observing a static object with an orbiting camera. In this paper we propose event-based structure-from-orbit (eSfO) where the aim is to simultaneously reconstruct the 3D structure of a fast spinning object observed from a static event camera and recover the equivalent orbital motion of the camera. Our contributions are threefold: since state-of-the-art event feature trackers cannot handle periodic self-occlusion due to the spinning motion we develop a novel event feature tracker based on spatio-temporal clustering and data association that can better track the helical trajectories of valid features in the event data. The feature tracks are then fed to our novel factor graph-based structure-from-orbit back-end that calculates the orbital motion parameters (e.g. spin rate relative rotational axis) that minimize the reprojection error. For evaluation we produce a new event dataset of objects under spinning motion. Comparisons against ground truth indicate the efficacy of eSfO.

Tracking with bio-inspired event cameras has garnered increasing interest in recent years. Existing works either utilize aligned RGB and event data for accurate tracking or directly learn an event-based tracker. The former incurs higher inference costs while the latter may be susceptible to the impact of noisy events or sparse spatial resolution. In this paper we propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals. Specifically a teacher Transformer-based multi-modal tracking framework is first trained by feeding the RGB frame and event stream simultaneously. Then we design a new hierarchical knowledge distillation strategy which includes pairwise similarity feature representation and response maps-based knowledge distillation to guide the learning of the student Transformer network. In particular since existing event-based tracking datasets are all low-resolution (346 * 260) we propose the first large-scale high-resolution (1280 * 720) dataset named EventVOT. It contains 1141 videos and covers a wide range of categories such as pedestrians vehicles UAVs ping pong etc. Extensive experiments on both low-resolution (FE240hz VisEvent COESOT) and our newly proposed high-resolution EventVOT dataset fully validated the effectiveness of our proposed method. The dataset evaluation toolkit and source code will be released.

Retrieval tasks play central roles in real-world machine learning systems such as search engines recommender systems and retrieval-augmented generation (RAG). Achieving decent performance in these tasks often requires fine-tuning various pre-trained models on specific datasets and selecting the best candidate a process that can be both time and resource-consuming. To tackle the problem we introduce a novel and efficient method called RetMMD that leverages Maximum Mean Discrepancy (MMD) and kernel methods to assess the transferability of pretrained models in retrieval tasks. RetMMD is calculated on pretrained model and target dataset without any fine-tuning involved. Specifically given some query we quantify the distribution discrepancy between relevant and irrelevant document embeddings by estimating the similarities within their mappings in the fine-tuned embedding space through kernel method. This discrepancy is averaged over multiple queries taking into account the distribution characteristics of the target dataset. Experiments suggest that the proposed metric calculated on pre-trained models closely aligns with retrieval performance post-fine-tuning. The observation holds across a variety of datasets including image text and multi-modal domains indicating the potential of using MMD and kernel methods for transfer learning evaluation in retrieval scenarios. In addition we also design a way of evaluating dataset transferability for retrieval tasks with experimental results demonstrating the effectiveness of the proposed approach.

Separating the direct and global components of a scene aids in shape recovery and basic material understanding. Conventional methods capture multiple frames under high frequency illumination patterns or shadows requiring the scene to keep stationary during the image acquisition process. Single-frame methods simplify the capture procedure but yield lower-quality separation results. In this paper we leverage the event camera to facilitate the separation of direct and global components enabling video-rate separation of high quality. In detail we adopt an event camera to record rapid illumination changes caused by the shadow of a line occluder sweeping over the scene and reconstruct the coarse separation results through event accumulation. We then design a network to resolve the noise in the coarse separation results and restore color information. A real-world dataset is collected using a hybrid camera system for network training and evaluation. Experimental results show superior performance over state-of-the-art methods.

Recent advancements in the study of Neural Radiance Fields (NeRF) for dynamic scenes often involve explicit modeling of scene dynamics. However this approach faces challenges in modeling scene dynamics in urban environments where moving objects of various categories and scales are present. In such settings it becomes crucial to effectively eliminate moving objects to accurately reconstruct static backgrounds. Our research introduces an innovative method termed here as Entity-NeRF which combines the strengths of knowledge-based and statistical strategies. This approach utilizes entity-wise statistics leveraging entity segmentation and stationary entity classification through thing/stuff segmentation. To assess our methodology we created an urban scene dataset masked with moving objects. Our comprehensive experiments demonstrate that Entity-NeRF notably outperforms existing techniques in removing moving objects and reconstructing static urban backgrounds both quantitatively and qualitatively.

One primary topic of multimodal learning is to jointly incorporate heterogeneous information from different modalities. However most models often suffer from unsatisfactory multimodal cooperation which cannot jointly utilize all modalities well. Some methods are proposed to identify and enhance the worse learnt modality but they are often hard to provide the fine-grained observation of multimodal cooperation at sample-level with theoretical support. Hence it is essential to reasonably observe and improve the fine-grained cooperation between modalities especially when facing realistic scenarios where the modality discrepancy could vary across different samples. To this end we introduce a sample-level modality valuation metric to evaluate the contribution of each modality for each sample. Via modality valuation we observe that modality discrepancy indeed could be different at sample-level beyond the global contribution discrepancy at dataset-level. We further analyze this issue and improve cooperation between modalities at sample-level by enhancing the discriminative ability of low-contributing modalities in a targeted manner. Overall our methods reasonably observe the fine-grained uni-modal contribution and achieve considerable improvement. The source code and dataset are available at https://github.com/GeWu-Lab/Valuate-and-Enhance-Multimodal-Cooperation.

360 depth estimation has recently received great attention for 3D reconstruction owing to its omnidirectional field of view (FoV). Recent approaches are predominantly focused on cross-projection fusion with geometry-based re-projection: they fuse 360 images with equirectangular projection (ERP) and another projection type e.g. cubemap projection to estimate depth with the ERP format. However these methods suffer from 1) limited local receptive fields making it hardly possible to capture large FoV scenes and 2) prohibitive computational cost caused by the complex cross-projection fusion module design. In this paper we propose Elite360D a novel framework that inputs the ERP image and icosahedron projection (ICOSAP) point set which is undistorted and spatially continuous. Elite360D is superior in its capacity in learning a representation from a local-with-global perspective. With a flexible ERP image encoder it includes an ICOSAP point encoder and a Bi-projection Bi-attention Fusion (B2F) module (totally 1M parameters). Specifically the ERP image encoder can take various perspective image-trained backbones (e.g. ResNet Transformer) to extract local features. The point encoder extracts the global features from the ICOSAP. Then the B2F module captures the semantic- and distance-aware dependencies between each pixel of the ERP feature and the entire ICOSAP feature set. Without specific backbone design and obvious computational cost increase Elite360D outperforms the prior arts on several benchmark datasets.

In this work we explore egocentric whole-body motion capture using a single fisheye camera which simultaneously estimates human body and hand motion. This task presents significant challenges due to three factors: the lack of high-quality datasets fisheye camera distortion and human body self-occlusion. To address these challenges we propose a novel approach that leverages FisheyeViT to extract fisheye image features which are subsequently converted into pixel-aligned 3D heatmap representations for 3D human body pose prediction. For hand tracking we incorporate dedicated hand detection and hand pose estimation networks for regressing 3D hand poses. Finally we develop a diffusion-based whole-body motion prior model to refine the estimated whole-body motion while accounting for joint uncertainties. To train these networks we collect a large synthetic dataset EgoWholeBody comprising 840000 high-quality egocentric images captured across a diverse range of whole-body motion sequences. Quantitative and qualitative evaluations demonstrate the effectiveness of our method in producing high-quality whole-body motion estimates from a single egocentric camera.