Decentralized Federated Learning (DFL) has become popular due to its
robustness and avoidance of centralized coordination. In this paradigm, clients
actively engage in training by exchanging models with their networked
neighbors. However, DFL introduces increased costs in terms of training and
communication. Existing methods focus on minimizing communication often
overlooking training efficiency and data heterogeneity. To address this gap, we
propose a novel \textit{sparse-to-sparser} training scheme: DA-DPFL. DA-DPFL
initializes with a subset of model parameters, which progressively reduces
during training via \textit{dynamic aggregation} and leads to substantial
energy savings while retaining adequate information during critical learning
periods.
Our experiments showcase that DA-DPFL substantially outperforms DFL baselines
in test accuracy, while achieving up to $5$ times reduction in energy costs. We
provide a theoretical analysis of DA-DPFL's convergence by solidifying its
applicability in decentralized and personalized learning. The code is available
at:this https URL

提出了一种新颖的 DA-DPFL 稀疏到更稀疏的训练方案，通过动态聚合逐渐减少模型参数，从而在保留关键学习期间的足够信息的同时实现了能耗大幅降低，并在测试准确性上明显优于 DFL 基准。

基于条件稀疏到更稀疏方案的去中心化个性化联邦学习

Decentralized Personalized Federated Learning based on a Conditional  Sparse-to-Sparser Scheme

Few-shot object detection, expecting detectors to detect novel classes with a
few instances, has made conspicuous progress. However, the prototypes extracted
by existing meta-learning based methods still suffer from insufficient
representative information and lack awareness of query images, which cannot be
adaptively tailored to different query images. Firstly, only the support images
are involved for extracting prototypes, resulting in scarce perceptual
information of query images. Secondly, all pixels of all support images are
treated equally when aggregating features into prototype vectors, thus the
salient objects are overwhelmed by the cluttered background. In this paper, we
propose an Information-Coupled Prototype Elaboration (ICPE) method to generate
specific and representative prototypes for each query image. Concretely, a
conditional information coupling module is introduced to couple information
from the query branch to the support branch, strengthening the query-perceptual
information in support features. Besides, we design a prototype dynamic
aggregation module that dynamically adjusts intra-image and inter-image
aggregation weights to highlight the salient information useful for detecting
query images. Experimental results on both Pascal VOC and MS COCO demonstrate
that our method achieves state-of-the-art performance in almost all settings.

本文提出了一种信息耦合的样本原型扩展 (ICPE) 方法，该方法可以灵活地为每个查询图像生成特定和刻画性的原型。ICPE 包括一个条件信息耦合模块和一个原型动态聚合模块，前者可以增强检测器中支持模块中的知觉信息，后者可以动态调整原型向量的聚合权重，以突出与检测查询图像有关的显著信息。实验证明该方法在 Pascal VOC 和 MS COCO 数据集上都取得了最新的实验效果。

Breaking Immutable: 紧密耦合信息的原型展开用于少样本目标检测

Breaking Immutable: Information-Coupled Prototype Elaboration for  Few-Shot Object Detection

We propose a novel Siamese Natural Language Tracker (SNLT), which brings the
advancements in visual tracking to the tracking by natural language (NL)
descriptions task. The proposed SNLT is applicable to a wide range of Siamese
trackers, providing a new class of baselines for the tracking by NL task and
promising future improvements from the advancements of Siamese trackers. The
carefully designed architecture of the Siamese Natural Language Region Proposal
Network (SNL-RPN), together with the Dynamic Aggregation of vision and language
modalities, is introduced to perform the tracking by NL task. Empirical results
over tracking benchmarks with NL annotations show that the proposed SNLT
improves Siamese trackers by 3 to 7 percentage points with a slight tradeoff of
speed. The proposed SNLT outperforms all NL trackers to-date and is competitive
among state-of-the-art real-time trackers on LaSOT benchmarks while running at
50 frames per second on a single GPU.

提出 “Siamese Natural Language Tracker” 的新方法，应用于自然语言描述任务的跟踪。相关实验证明，该算法比 “Siamese trackers” 有显著提升，是目前最好的实时跟踪器之一。