Work Force

D2AF: A Dual-Driven Annotation and Filtering Framework for Visual Grounding

Visual Grounding is a task that aims to localize a target region in an image based on a free-form natural language description. With the rise of Transformer architectures, there is an increasing need for larger datasets to boost performance. However, the high cost of manual annotation poses a challenge, hindering the scale of data and the ability of large models to enhance their effectiveness. P...

Methylomes Reveal Recent Evolutionary Changes in Populations of Two Plant Species.

Plant DNA methylation changes occur hundreds to thousands of times faster than DNA mutations and can be transmitted transgenerationally, making them useful for studying population-scale patterns in clonal or selfing species. However, a state-of-the-art approach to use them for inferring population genetic processes and demographic histories is lacking. To address this, we compare evolutionary sign...

Bridging the Gap: Enhancing Digital Accessibility for Medicaid Populations in Telehealth Adoption

The swift evolution of telehealth has revolutionized how medical professionals deliver healthcare services and boost convenience and accessibility. ...

Can LLMs Deceive CLIP? Benchmarking Adversarial Compositionality of Pre-trained Multimodal Representation via Text Updates

While pre-trained multimodal representations (e.g., CLIP) have shown impressive capabilities, they exhibit significant compositional vulnerabilities...

ImageReFL: Balancing Quality and Diversity in Human-Aligned Diffusion Models

Recent advances in diffusion models have led to impressive image generation capabilities, but aligning these models with human preferences remains c...

Understanding Adversarial Training with Energy-based Models

We aim at using Energy-based Model (EBM) framework to better understand adversarial training (AT) in classifiers, and additionally to analyze the in...

One-Way Ticket:Time-Independent Unified Encoder for Distilling Text-to-Image Diffusion Models

Text-to-Image (T2I) diffusion models have made remarkable advancements in generative modeling; however, they face a trade-off between inference spee...

Towards Robust Assessment of Pathological Voices via Combined Low-Level Descriptors and Foundation Model Representations

Perceptual voice quality assessment is essential for diagnosing and monitoring voice disorders by providing standardized evaluations of vocal functi...

Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance

Classifier-Free Guidance (CFG) is a widely used technique for improving conditional diffusion models by linearly combining the outputs of conditiona...

Enhancing Transformation from Natural Language to Signal Temporal Logic Using LLMs with Diverse External Knowledge

Temporal Logic (TL), especially Signal Temporal Logic (STL), enables precise formal specification, making it widely used in cyber-physical systems s...

It's Not Just Labeling -- A Research on LLM Generated Feedback Interpretability and Image Labeling Sketch Features

The quality of training data is critical to the performance of machine learning applications in domains like transportation, healthcare, and robotic...

Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals

Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has ...

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images

Natural images exhibit label diversity (clean vs. noisy) in noisy-labeled image classification and prevalence diversity (abundant vs. sparse) in lon...

Improved Immiscible Diffusion: Accelerate Diffusion Training by Reducing Its Miscibility

The substantial training cost of diffusion models hinders their deployment. Immiscible Diffusion recently showed that reducing diffusion trajectory ...

Accelerating Learned Image Compression Through Modeling Neural Training Dynamics

As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper...

F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of Nanoparticles

Nanomaterial research is becoming a vital area for energy, medicine, and materials science, and accurate analysis of the nanoparticle topology is es...

Scaling Image and Video Generation via Test-Time Evolutionary Search

As the marginal cost of scaling computation (data and parameters) during model pre-training continues to increase substantially, test-time scaling (...

Diagnosing Vision Language Models' Perception by Leveraging Human Methods for Color Vision Deficiencies

Large-scale Vision Language Models (LVLMs) are increasingly being applied to a wide range of real-world multimodal applications, involving complex v...

Increasing the ethnic diversity of senior leadership within the English National Health Service: using an artificial intelligence approach to evaluate inclusive recruitment strategies in hospital settings.

BACKGROUND: The English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) repres...

Large-Scale Non-Adiabatic Dynamics Simulation Based on Machine Learning Hamiltonian and Force Field: The Case of Charge Transport in Monolayer MoS.

We present an efficient and reliable large-scale non-adiabatic dynamics simulation method based on machine learning Hamiltonian and force field. The q...