CVPR 2024 论文列表

The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places relying solely on visual cues. State-of-the-art pipelines focus on the aggregation of features extracted from a deep backbone in order to form a global descriptor for each image. In this context we introduce SALAD (Sinkhorn Algorithm for Locally Aggregated Descriptors) which reformulates NetVLAD's soft-assignment of local features to clusters as an optimal transport problem. In SALAD we consider both feature-to-cluster and cluster-to-feature relations and we also introduce a dustbin cluster designed to selectively discard features deemed non-informative enhancing the overall descriptor quality. Additionally we leverage and fine-tune DINOv2 as a backbone which provides enhanced description power for the local features and dramatically reduces the required training time. As a result our single-stage method not only surpasses single-stage baselines in public VPR datasets but also surpasses two-stage methods that add a re-ranking with significantly higher cost.

Estimating the pose of objects from images is a crucial task of 3D scene understanding and recent approaches have shown promising results on very large benchmarks. However these methods experience a significant performance drop when dealing with unseen objects. We believe that it results from the limited generalizability of image features. To address this problem we have an in-depth analysis on the features of diffusion models e.g. Stable Diffusion which hold substantial potential for modeling unseen objects. Based on this analysis we then innovatively introduce these diffusion features for object pose estimation. To achieve this we propose three distinct architectures that can effectively capture and aggregate diffusion features of different granularity greatly improving the generalizability of object pose estimation. Our approach outperforms the state-of-the-art methods by a considerable margin on three popular benchmark datasets LM O-LM and T-LESS. In particular our method achieves higher accuracy than the previous best arts on unseen objects: 98.2% vs. 93.5% on Unseen LM 85.9% vs. 76.3% on Unseen O-LM showing the strong generalizability of our method. Our code is released at https://github.com/Tianfu18/diff-feats-pose.

Generalizable NeRF can directly synthesize novel views across new scenes eliminating the need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these approaches is the extraction of a generalizable 3D representation by aggregating source-view features. In this paper we propose an Entangled View-Epipolar Information Aggregation method dubbed EVE-NeRF. Different from existing methods that consider cross-view and along-epipolar information independently EVE-NeRF conducts the view-epipolar feature aggregation in an entangled manner by injecting the scene-invariant appearance continuity and geometry consistency priors to the aggregation process. Our approach effectively mitigates the potential lack of inherent geometric and appearance constraint resulting from one-dimensional interactions thus further boosting the 3D representation generalizablity. EVE-NeRF attains state-of-the-art performance across various evaluation scenarios. Extensive experiments demonstate that compared to prevailing single-dimensional aggregation the entangled network excels in the accuracy of 3D scene geometry and appearance reconstruction. Our code is publicly available at https://github.com/tatakai1/EVENeRF.

Recently numerous approaches have achieved notable success in compressed video quality enhancement (VQE). However these methods usually ignore the utilization of valuable coding priors inherently embedded in compressed videos such as motion vectors and residual frames which carry abundant temporal and spatial information. To remedy this problem we propose the Coding Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial information from coding priors. The CPGA mainly consists of an inter-frame temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA) module. Specifically the ITA module aggregates temporal information from consecutive frames and coding priors while the MNA module globally captures spatial information guided by residual frames. In addition to facilitate research in VQE task we newly construct the Video Coding Priors (VCP) dataset comprising 300 videos with various coding priors extracted from corresponding bitstreams. It remedies the shortage of previous datasets on the lack of coding information. Experimental results demonstrate the superiority of our method compared to existing state-of-the-art methods. The code and dataset will be released at https://github.com/VQE-CPGA/CPGA.

The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework where clients update local models based on a global model aggregated by the server from the previous training round. This process can cause client drift especially with significant cross-client data heterogeneity impacting model performance and convergence of the FL algorithm. To address these challenges we introduce FedAF a novel aggregation-free FL algorithm. In this framework clients collaboratively learn condensed data by leveraging peer knowledge the server subsequently trains the global model using the condensed data and soft labels received from the clients. FedAF inherently avoids the issue of client drift enhances the quality of condensed data amid notable data heterogeneity and improves the global model performance. Extensive numerical studies on several popular benchmark datasets show FedAF surpasses various state-of-the-art FL algorithms in handling label-skew and feature-skew data heterogeneity leading to superior global model accuracy and faster convergence.