arXiv subjects

Jiaying Zhu

Publications and source records attributed to Jiaying Zhu.

26 records · Page 2Linked to original sources

arXiv2025

ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA

We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RTL) code, Post-mapping (PM) netlists, And-Inverter Graphs (AIGs), and placed netlists, enabling comprehensive analysis and development. We demonstrate ForgeEDA's utility by benchmarking state-of-the-art EDA algorithms on critical tasks such as Power, Performance, and Area (PPA) optimization, highlighting its ability to expose performance gaps and drive advancements. Additionally, ForgeEDA's scale and diversity facilitate the training of AI models for EDA tasks, demonstrating its potential to improve model performance and generalization. By addressing limitations in existing datasets, ForgeEDA aims to catalyze breakthroughs in modern IC design and support the next generation of innovations in EDA.

cs.AR↗

arXiv2025

NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results

This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for single-image deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of event-based image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.

cs.CV↗

arXiv2024

Quantifying Semantic Emergence in Language Models

Large language models (LLMs) are widely recognized for their exceptional capacity to capture semantics meaning. Yet, there remains no established metric to quantify this capability. In this work, we introduce a quantitative metric, Information Emergence (IE), designed to measure LLMs' ability to extract semantics from input tokens. We formalize ``semantics'' as the meaningful information abstracted from a sequence of tokens and quantify this by comparing the entropy reduction observed for a sequence of tokens (macro-level) and individual tokens (micro-level). To achieve this, we design a lightweight estimator to compute the mutual information at each transformer layer, which is agnostic to different tasks and language model architectures. We apply IE in both synthetic in-context learning (ICL) scenarios and natural sentence contexts. Experiments demonstrate informativeness and patterns about semantics. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights.

cs.CL↗

arXiv2024

DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera

Hybrid Event-Based Vision Sensor (HybridEVS) is a novel sensor integrating traditional frame-based and event-based sensors, offering substantial benefits for applications requiring low-light, high dynamic range, and low-latency environments, such as smartphones and wearable devices. Despite its potential, the lack of Image signal processing (ISP) pipeline specifically designed for HybridEVS poses a significant challenge. To address this challenge, in this study, we propose a coarse-to-fine framework named DemosaicFormer which comprises coarse demosaicing and pixel correction. Coarse demosaicing network is designed to produce a preliminary high-quality estimate of the RGB image from the HybridEVS raw data while the pixel correction network enhances the performance of image restoration and mitigates the impact of defective pixels. Our key innovation is the design of a Multi-Scale Gating Module (MSGM) applying the integration of cross-scale features, which allows feature information to flow between different scales. Additionally, the adoption of progressive training and data augmentation strategies further improves model's robustness and effectiveness. Experimental results show superior performance against the existing methods both qualitatively and visually, and our DemosaicFormer achieves the best performance in terms of all the evaluation metrics in the MIPI 2024 challenge on Demosaic for Hybridevs Camera. The code is available at https://github.com/QUEAHREN/DemosaicFormer.

cs.CV↗

arXiv2024

MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2024/.

eess.IV↗

arXiv2024

Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey

This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. Th goal of this challenge is to upscale RAW Bayer images by 2x, considering unknown degradations such as noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. The performance of the top-5 submissions is reviewed and provided here as a gauge for the current state-of-the-art in RAW Image Super-Resolution.

cs.CV↗

arXiv2023

A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale

Background: Depression is a common mental disorder with societal and economic burden. Current diagnosis relies on self-reports and assessment scales, which have reliability issues. Objective approaches are needed for diagnosing depression. Objective: Evaluate the potential of GPT technology in diagnosing depression. Assess its ability to simulate individuals with depression and investigate the influence of depression scales. Methods: Three depression-related assessment tools (HAMD-17, SDS, GDS-15) were used. Two experiments simulated GPT responses to normal individuals and individuals with depression. Compare GPT's responses with expected results, assess its understanding of depressive symptoms, and performance differences under different conditions. Results: GPT's performance in depression assessment was evaluated. It aligned with scoring criteria for both individuals with depression and normal individuals. Some performance differences were observed based on depression severity. GPT performed better on scales with higher sensitivity. Conclusion: GPT accurately simulates individuals with depression and normal individuals during depression-related assessments. Deviations occur when simulating different degrees of depression, limiting understanding of mild and moderate cases. GPT performs better on scales with higher sensitivity, indicating potential for developing more effective depression scales. GPT has important potential in depression assessment, supporting clinicians and patients.

q-bio.NC↗

arXiv2021

LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering

Most existing Visual Question Answering (VQA) systems tend to overly rely on language bias and hence fail to reason from the visual clue. To address this issue, we propose a novel Language-Prior Feedback (LPF) objective function, to re-balance the proportion of each answer's loss value in the total VQA loss. The LPF firstly calculates a modulating factor to determine the language bias using a question-only branch. Then, the LPF assigns a self-adaptive weight to each training sample in the training process. With this reweighting mechanism, the LPF ensures that the total VQA loss can be reshaped to a more balanced form. By this means, the samples that require certain visual information to predict will be efficiently used during training. Our method is simple to implement, model-agnostic, and end-to-end trainable. We conduct extensive experiments and the results show that the LPF (1) brings a significant improvement over various VQA models, (2) achieves competitive performance on the bias-sensitive VQA-CP v2 benchmark.

cs.CV↗