Using higher-resolution feature maps in the network is an effective approach for detecting small objects. However, high-resolution feature maps face the challenge of lacking semantic information. This has led previous methods to rely on downsampling feature maps, applying large-kernel convolution layers, and then upsampling the feature maps to obtain semantic information. However, these methods have certain limitations: first, large kernel convolutions in deeper layers typically provide significant global semantic information, but our experiments reveal that such prominent semantic information introduces background smear, which in turn leads to overfitting. Second, deep features often contain substantial redundant information, and the features of small objects are either minimal or have disappeared, which causes a degradation in detection performance when directly relying on deep features. To address these issues, we propose a high-resolution network based on local contextual semantics (HR-SemNet). The network is built on the proposed high-resolution backbone (HRB), which replaces the traditional backbone-FPN architecture by focusing all computational resources of large kernel convolutions on high-resolution feature layers to capture clearer features of small objects. Additionally, a local context semantic module (LCSM) is employed to extract semantic information from the background, confining the semantic extraction to a local window to avoid interference from large-scale backgrounds and objects. HR-SemNet decouples small object semantics from contextual semantics, with HRB and LCSM independently extracting these features. Extensive experiments and comprehensive evaluations on the VisDrone, AI-TOD, and TinyPerson datasets validate the effectiveness of the method. On the VisDrone dataset, which contains a large number of small objects, HR-SemNet improves the mean average precision (mAP) by 4.6%, reduces the computational cost (GFLOPs) by 49.9%, and decreases the parameter count by 94.9%.

Effective training of large Vision-Language Models (VLMs) on resource-constrained client devices in Federated Learning (FL) requires the usage of parameter-efficient finetuning (PEFT) strategies. To this end, we demonstrate the impact of two factors viz., client-specific layer importance score that selects the most important VLM layers for finetuning and inter-client layer diversity score that encourages diverse layer selection across clients for optimal VLM layer selection. We first theoretically motivate and leverage the principal eigenvalue magnitude of layerwise Neural Tangent Kernels and show its effectiveness as client-specific layer importance score. Next, we propose a novel layer updating strategy dubbed F3OCUS that jointly optimizes the layer importance and diversity factors by employing a data-free, multi-objective, meta-heuristic optimization on the server. We explore 5 different meta-heuristic algorithms and compare their effectiveness for selecting model layers and adapter layers towards PEFT-FL. Furthermore, we release a new MedVQA-FL dataset involving overall 707,962 VQA triplets and 9 modality-specific clients and utilize it to train and evaluate our method. Overall, we conduct more than 10,000 client-level experiments on 6 Vision-Language FL task settings involving 58 medical image datasets and 4 different VLM architectures of varying sizes to demonstrate the effectiveness of the proposed method. Project Page: https://pramitsaha.github.io/FOCUS/

Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well studied, while Graph Few-Shot Class Incremental Learning (GFSCIL), which involves learning a GNN classifier as graph nodes and classes growing over time sporadically, has received much less attention despite its importance. We introduce inductive GFSCIL that continually learns novel classes with newly emerging nodes while maintaining performance on old classes without accessing previous data. This addresses the practical concern of transductive GFSCIL, which requires storing the entire graph with historical data. Compared to the transductive GFSCIL, the inductive setting exacerbates catastrophic forgetting due to inaccessible previous data during incremental training, in addition to the overfitting issue caused by label sparsity. Thus, we propose a novel method, called Topology-based class Augmentation and Prototype calibration (TAP). To be specific, it first performs a topology-based class augmentation method, helping replicate the setting of disjoint subgraphs with nodes of novel classes received in incremental sessions, to enhance backbone versatility. In incremental learning, given the limited number of novel class samples, we propose an iterative prototype calibration to improve the separation of class prototypes. Furthermore, as backbone fine-tuning poses the feature distribution drift, prototypes of old classes start failing over time, we propose the prototype shift method for old classes to compensate for the drift. We showcase the proposed method on four datasets.

Deep learning models often suffer from catastrophic forgetting when fine-tuned with samples of new classes. This issue becomes even more challenging when there is a domain shift between training and testing data. In this paper, we address the critical yet less explored Domain-Generalized Class-Incremental Learning (DGCIL) task. We propose a DGCIL approach designed to memorize old classes, adapt to new classes, and reliably classify objects from unseen domains. Specifically, our loss formulation maintains classification boundaries while suppressing domain-specific information for each class. Without storing old exemplars, we employ knowledge distillation and estimate the drift of old class prototypes as incremental training progresses. Our prototype representations are based on multivariate Normal distributions , with means and covariances continually adapted to reflect evolving model features, providing effective representations for old classes. We then sample pseudo-features for these old classes from the adapted Normal distributions using Cholesky decomposition . Unlike previous pseudo-feature sampling strategies that rely solely on average mean prototypes, our method captures richer semantic variations. Experiments on several benchmarks demonstrate the superior performance of our method compared to the state of the art.

Current medical image synthetic augmentation techniques rely on the intensive use of generative adversarial networks (GANs). However, the nature of GAN architecture leads to heavy computational resources to produce synthetic images and the augmentation process requires multiple stages to complete. To address these challenges, we introduce a novel generative meta curriculum learning method that trains the task-specific model (student) end-to-end with only one additional teacher model. The teacher learns to generate curriculum to feed into the student model for data augmentation and guides the student to improve performance in a meta-learning style. In contrast to the generator and discriminator in GAN, which compete with each other, the teacher and student collaborate to improve the student's performance on the target tasks. Extensive experiments on the histopathology datasets show that leveraging our framework results in significant and consistent improvements in classification performance.

The inference of the network traffic matrix from partial measurement data becomes increasingly critical for various network engineering tasks, such as capacity planning, load balancing, path setup, network provisioning, anomaly detection, and failure recovery. The recent study shows it is promising to more accurately interpolate the missing data with a three-dimensional tensor as compared to interpolation methods based on two-dimensional matrix. Despite the potential, it is difficult to form a tensor with measurements taken at varying rate in a practical network. To address the issues, we propose Reshape-Align scheme to form the regular tensor with data from dynamic measurements, and introduce user-domain and temporal-domain factor matrices which takes full advantage of features from both domains to translate the matrix completion problem to the tensor completion problem based on CP decomposition for more accurate missing data recovery. Our performance results demonstrate that our Reshape-Align scheme can achieve significantly better performance in terms of two metrics: error ratio and mean absolute error (MAE).