Staffing & Scheduling

Balancing Stability and Plasticity in Pretrained Detector: A Dual-Path Framework for Incremental Object Detection

The balance between stability and plasticity remains a fundamental challenge in pretrained model-based incremental object detection (PTMIOD). While existing PTMIOD methods demonstrate strong performance on in-domain tasks aligned with pretraining data, their plasticity to cross-domain scenarios remains underexplored. Through systematic component-wise analysis of pretrained detectors, we reveal a...

Mavors: Multi-granularity Video Representation for Multimodal Large Language Model

Long-context video understanding in multimodal large language models (MLLMs) faces a critical challenge: balancing computational efficiency with the retention of fine-grained spatio-temporal patterns. Existing approaches (e.g., sparse sampling, dense sampling with low resolution, and token compression) suffer from significant information loss in temporal dynamics, spatial details, or subtle inte...

AimTS: Augmented Series and Image Contrastive Learning for Time Series Classification

Time series classification (TSC) is an important task in time series analysis. Existing TSC methods mainly train on each single domain separately, s...

Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training

Training diffusion models (DMs) requires substantial computational resources due to multiple forward and backward passes across numerous timesteps, ...

Reduction of Supervision for Biomedical Knowledge Discovery

Knowledge discovery is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of in...

Bayesian Cross-Modal Alignment Learning for Few-Shot Out-of-Distribution Generalization

Recent advances in large pre-trained models showed promising results in few-shot learning. However, their generalization ability on two-dimensional ...

SpecEE: Accelerating Large Language Model Inference with Speculative Early Exiting

Early exiting has recently emerged as a promising technique for accelerating large language models (LLMs) by effectively reducing the hardware compu...

Enhancing knowledge retention for continual learning with domain-specific adapters and features gating

Continual learning empowers models to learn from a continuous stream of data while preserving previously acquired knowledge, effectively addressing ...

Leveraging Synthetic Adult Datasets for Unsupervised Infant Pose Estimation

Human pose estimation is a critical tool across a variety of healthcare applications. Despite significant progress in pose estimation algorithms tar...

Tuning-Free Image Editing with Fidelity and Editability via Unified Latent Diffusion Model

Balancing fidelity and editability is essential in text-based image editing (TIE), where failures commonly lead to over- or under-editing issues. Ex...

Using C4.5 Algorithm to Gain Insights on Stakeholder Engagement and Use of Artificial Intelligence on Social Media in Dementia Caregiving Disparity Research.

We applied machine learning techniques to build models that predict perceived risks and benefits of using artificial intelligence (AI) algorithms to r...

Correcting Class Imbalances with Self-Training for Improved Universal Lesion Detection and Tagging

Universal lesion detection and tagging (ULDT) in CT studies is critical for tumor burden assessment and tracking the progression of lesion status (g...

Exploring Kernel Transformations for Implicit Neural Representations

Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes...

CATS: Mitigating Correlation Shift for Multivariate Time Series Classification

Unsupervised Domain Adaptation (UDA) leverages labeled source data to train models for unlabeled target data. Given the prevalence of multivariate t...

Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition

Personalized facial expression recognition (FER) involves adapting a machine learning model using samples from labeled sources and unlabeled target ...

FaR: Enhancing Multi-Concept Text-to-Image Diffusion via Concept Fusion and Localized Refinement

Generating multiple new concepts remains a challenging problem in the text-to-image task. Current methods often overfit when trained on a small numb...

Symbiotic AI: Augmenting Human Cognition from PCs to Cars

As AI takes on increasingly complex roles in human-computer interaction, fundamental questions arise: how can HCI help maintain the user as the prim...

ESC: Erasing Space Concept for Knowledge Deletion

As concerns regarding privacy in deep learning continue to grow, individuals are increasingly apprehensive about the potential exploitation of their...

AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening

Resume screening is a critical yet time-intensive process in talent acquisition, requiring recruiters to analyze vast volume of job applications whi...

Timely Trajectory Reconstruction in Finite Buffer Remote Tracking Systems

Remote tracking systems play a critical role in applications such as IoT, monitoring, surveillance and healthcare. In such systems, maintaining both...