Search Results for author:
Found 14 papers, 4 papers with code
OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation
Text-to-image (T2I) models have garnered significant attention for generating high-quality images aligned with text prompts.
Step1X-Edit: A Practical Framework for General Image Editing
In recent years, image editing models have witnessed remarkable and rapid development.
Ranked #3 on
Image Editing
on ImgEdit-Data
Dynamic Pyramid Network for Efficient Multimodal Large Language Model
To maximize the benefit of DPN, we further propose an innovative Dynamic Pooling Experts (DPE) that can dynamically choose the optimal visual compression rate according to input features.
3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised Anomaly
The key features of 3CAD are that it covers anomalous regions of different sizes, multiple anomaly types, and the possibility of multiple anomalous regions and multiple anomaly types per anomaly image.
InstantIR: Blind Image Restoration with Instant Generative Reference
In this paper, we introduce Instant-reference Image Restoration (InstantIR), a novel diffusion-based BIR method which dynamically adjusts generation condition during inference.
CSGO: Content-Style Composition in Text-to-Image Generation
Based on this pipeline, we construct a dataset IMAGStyle, the first large-scale style transfer dataset containing 210k image triplets, available for the community to explore and research.
EXCGEC: A Benchmark of Edit-wise Explainable Chinese Grammatical Error Correction
Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited scenario, where they ignore the interaction between corrections and explanations.
InstantStyle-Plus: Style Transfer with Content-Preserving in Text-to-Image Generation
Style transfer is an inventive process designed to create an image that maintains the essence of the original while embracing the visual style of another.
A Recover-then-Discriminate Framework for Robust Anomaly Detection
Specifically, by Case-1, we found that the main reasons detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has-not/has been recovered into its original state.
Inv-Adapter: ID Customization Generation via Image Inversion and Lightweight Adapter
The remarkable advancement in text-to-image generation models significantly boosts the research in ID customization generation.
Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework
Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences.
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection
However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations, and 2) the features of the teacher network serve solely as a ``reference standard" and are not fully leveraged.
Visual Anomaly Detection Via Partition Memory Bank Module and Error Estimation
Reconstruction method based on the memory module for visual anomaly detection attempts to narrow the reconstruction error for normal samples while enlarging it for anomalous samples.
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection
In this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is proposed by jointly exploring effective generation method of forged anomalous samples and the normal sample features as the guidance information of segmentation for anomaly detection.
