Multilingual pre-trained models have been widely applied in natural language processing (NLP) tasks, including text classification. However, due to the varying amounts of language resources, these models exhibit performance imbalance across different languages, a phenomenon known as language imbalance. Existing research on mitigating language imbalance primarily harnesses text and image data, neglecting the auditory aspects of languages. This neglect results in an incomplete solution to language imbalance, as it fails to exploit the rich linguistic nuances conveyed through speech. To address these issues, this paper introduces a novel framework called MultiLingual Contrastive Learning (MLCL) to reduce language imbalance. By incorporating concepts from comparative linguistics into neural networks, we utilize the phonetic similarities among languages within the Sino-Tibetan family to tackle the problem of language imbalance in multilingual pre-trained models. To evaluate our method's effectiveness, we conducted tests using two synthetic datasets derived from the Flores200 and mms datasets across various models. The experimental results show that, in terms of language imbalance metrics, our model surpasses all baseline models.
We present a new convergence analysis for the over-relaxed alternating direction method of multipliers (ADMM) when the subproblem cannot be exactly solved, i.e., inexact over-relaxed ADMM. Our method builds on (Hu and Lessard, 2017) that relates the convergence analysis of optimization algorithms to the stability of a discrete-time linear dynamic system. By expressing the inexact over-relaxed ADMM as a discrete-time linear dynamic system, we show that both the linear and sublinear convergence of inexact over-relaxed ADMM can be obtained by solving or verifying the feasibility of a small semidefinite program (SDP). More importantly, we prove that the associated SDP has an analytical solution for various parameters. We demonstrate the theoretical result by applying the inexact over-relaxed ADMM to solve a distributed l(1)-norm regularized logistic regression problem.
With the widespread use of virtual reality applications, 3D scene generation has become a challenging new research frontier. 3D scenes have highly complex structures, so it is crucial to ensure that the output is dense, coherent, and includes all necessary structures. Many current 3D scene generation methods rely on pre-trained text-to-image diffusion models and monocular depth estimators, but they often lack rich geometric constraint information within the scene, leading to geometric distortion in the generated results. Therefore, we propose a two-stage geometry-aware progressive scene generation framework, Scene-Weaver, which creates diverse, high-quality 3D scenes from text or image inputs. In the first stage, we introduce a multi-level depth refinement mechanism combined with image inpainting and point cloud updating strategies to construct a high-quality initial point cloud. In the second stage, 3D Gaussians are initialized based on the point cloud and continuously optimized. To address the challenge of insufficient geometric constraints in the Gaussian Splatting optimization process, we utilize the rich appearance and geometry information within the scene to perform a geometry-aware optimization, resulting in high-quality scene generation results. Comprehensive experiments across multiple scenes demonstrate the significant potential and advantages of our framework compared with several baselines.
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients. Images uploaded by each client typically come from a small subset of classes. From the cloud-client discrepancy on the range of image classes, the recognition model is desired to have strong adaptiveness, intuitively by focusing on each client's local dynamic class subset, while incurring negligible overhead. In this work, we propose to plug a new intra-client and inter-image attention (ICIIA) module into existing backbone recognition models, requiring only one-time cloud-based training to be client-adaptive. In particular, given an image to be recognized from a certain client, ICIIA introduces multi-head self-attention to retrieve relevant images from the client's local images, thereby calibrating the focus and the recognition result. We further identify the bottleneck of ICIIA's overhead being in linear projection, propose to group and shuffle the features before the projections, and allow increasing the number of feature groups to dramatically improve efficiency without scarifying much accuracy. We extensively evaluate ICIIA and compare its performance against several baselines, demonstrating effectiveness and efficiency. Specifically, for a partitioned version of ImageNet-1K with the backbone models of MobileNetV3-L and SwinB, ICIIA improves the classification accuracy to 83.37% (+8.11%) and 88.86% (+5.28%), while adding only 1.62% and 0.02% of FLOPs, respectively. Source code is available in the supplementary materials.
Graph Neural Networks (GNNs) are known to suffer from degraded performance with more layers. Most prior works explained it from graph propagation, arguing that it inevitably leads to indistinguishable node features under more depth, known as oversmoothing. However, we notice that these analyses largely ignore the role of GNN weights either directly or by unrealistically strong assumptions. In this paper, we rediscover the role of GNN weights on oversmoothing with a systematic study. Notably, contrary to previous findings, we show that when learned freely, there always exist ideal weights such that vanilla GNNs completely avoid oversmoothing, even after infinite propagation steps. It indicates that oversmoothing is a problem of learning disabilities instead of the doom of GNNs themselves. To facilitate the learning of proper weights, we propose Weight Reparameterization (WeightRep) as a way to adaptively maintain the ideal weights in vanilla GNNs along the learning process. We theoretically show that for linear GNNs, WeightRep can always mitigate oversmoothing (full collapse) as well as dimensional collapse. Extensive experiments on nine benchmark datasets demonstrate its effectiveness and efficiency in practice.
