265 research outputs found
Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution
Color-guided depth super-resolution (DSR) is an encouraging paradigm that
enhances a low-resolution (LR) depth map guided by an extra high-resolution
(HR) RGB image from the same scene. Existing methods usually use interpolation
to upscale the depth maps before feeding them into the network and transfer the
high-frequency information extracted from HR RGB images to guide the
reconstruction of depth maps. However, the extracted high-frequency information
usually contains textures that are not present in depth maps in the existence
of the cross-modality gap, and the noises would be further aggravated by
interpolation due to the resolution gap between the RGB and depth images. To
tackle these challenges, we propose a novel Symmetric Uncertainty-aware Feature
Transmission (SUFT) for color-guided DSR. (1) For the resolution gap, SUFT
builds an iterative up-and-down sampling pipeline, which makes depth features
and RGB features spatially consistent while suppressing noise amplification and
blurring by replacing common interpolated pre-upsampling. (2) For the
cross-modality gap, we propose a novel Symmetric Uncertainty scheme to remove
parts of RGB information harmful to the recovery of HR depth maps. Extensive
experiments on benchmark datasets and challenging real-world settings suggest
that our method achieves superior performance compared to state-of-the-art
methods. Our code and models are available at
https://github.com/ShiWuxuan/SUFT.Comment: 10 pages, 9 figures, accepted by the 30th ACM International
Conference on Multimedia (ACM MM 22
Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning
Federated learning is an important privacy-preserving multi-party learning
paradigm, involving collaborative learning with others and local updating on
private data. Model heterogeneity and catastrophic forgetting are two crucial
challenges, which greatly limit the applicability and generalizability. This
paper presents a novel FCCL+, federated correlation and similarity learning
with non-target distillation, facilitating the both intra-domain
discriminability and inter-domain generalization. For heterogeneity issue, we
leverage irrelevant unlabeled public data for communication between the
heterogeneous participants. We construct cross-correlation matrix and align
instance similarity distribution on both logits and feature levels, which
effectively overcomes the communication barrier and improves the generalizable
ability. For catastrophic forgetting in local updating stage, FCCL+ introduces
Federated Non Target Distillation, which retains inter-domain knowledge while
avoiding the optimization conflict issue, fulling distilling privileged
inter-domain information through depicting posterior classes relation.
Considering that there is no standard benchmark for evaluating existing
heterogeneous federated learning under the same setting, we present a
comprehensive benchmark with extensive representative methods under four domain
shift scenarios, supporting both heterogeneous and homogeneous federated
settings. Empirical results demonstrate the superiority of our method and the
efficiency of modules on various scenarios
Transformer for Object Re-Identification: A Survey
Object Re-Identification (Re-ID) aims to identify and retrieve specific
objects from varying viewpoints. For a prolonged period, this field has been
predominantly driven by deep convolutional neural networks. In recent years,
the Transformer has witnessed remarkable advancements in computer vision,
prompting an increasing body of research to delve into the application of
Transformer in Re-ID. This paper provides a comprehensive review and in-depth
analysis of the Transformer-based Re-ID. In categorizing existing works into
Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal
Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages
demonstrated by the Transformer in addressing a multitude of challenges across
these domains. Considering the trending unsupervised Re-ID, we propose a new
Transformer baseline, UntransReID, achieving state-of-the-art performance on
both single-/cross modal tasks. Besides, this survey also covers a wide range
of Re-ID research objects, including progress in animal Re-ID. Given the
diversity of species in animal Re-ID, we devise a standardized experimental
benchmark and conduct extensive experiments to explore the applicability of
Transformer for this task to facilitate future research. Finally, we discuss
some important yet under-investigated open issues in the big foundation model
era, we believe it will serve as a new handbook for researchers in this field
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of
federated learning, an influx of approaches have delivered towards different
realistic challenges. In this survey, we provide a systematic overview of the
important and recent developments of research on federated learning. Firstly,
we introduce the study history and terminology definition of this area. Then,
we comprehensively review three basic lines of research: generalization,
robustness, and fairness, by introducing their respective background concepts,
task settings, and main challenges. We also offer a detailed overview of
representative literature on both methods and datasets. We further benchmark
the reviewed methods on several well-known datasets. Finally, we point out
several open issues in this field and suggest opportunities for further
research. We also provide a public website to continuously track developments
in this fast advancing field: https://github.com/WenkeHuang/MarsFL.Comment: 22 pages, 4 figure
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