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Found 12 papers, 5 papers with code
PixelMan: Consistent Object Editing with Diffusion Models via Pixel Manipulation and Generation
Recent research explores the potential of Diffusion Models (DMs) for consistent object editing, which aims to modify object position, size, and composition, etc., while preserving the consistency of objects and background without changing their texture and attributes.
Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric
To address this challenge, we conduct a pioneering study on human activity AGV quality assessment, focusing on visual quality evaluation and the identification of semantic distortions.
LMM-VQA: Advancing Video Quality Assessment with Large Multimodal Models
Then, we design a spatiotemporal vision encoder to extract spatial and temporal features to represent the quality characteristics of videos, which are subsequently mapped into the language space by the spatiotemporal projector for modality alignment.
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting
Through extensive evaluations, we show FRAP generates images with significantly higher prompt-image alignment to prompts from complex datasets, while having a lower average latency compared to recent latent code optimization methods, e. g., 4 seconds faster than D&B on the COCO-Subject dataset.
FunEditor: Achieving Complex Image Edits via Function Aggregation with Diffusion Models
This approach enables complex editing tasks, such as object movement, by aggregating multiple functions and applying them simultaneously to specific areas.
Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model
To bridge this gap, we propose the Unify Generated Video Quality assessment (UGVQ) model, designed to accurately evaluate the multi-dimensional quality of AIGC videos.
Building Optimal Neural Architectures using Interpretable Knowledge
Neural Architecture Search is a costly practice.
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration
Image restoration tasks traditionally rely on convolutional neural networks.
Ranked #1 on
Image Denoising
on SIDD
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models
Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks.
Exploring the Naturalness of AI-Generated Images
In this paper, we take the first step to benchmark and assess the visual naturalness of AI-generated images.
RepQ: Generalizing Quantization-Aware Training for Re-Parametrized Architectures
However, there are various methods to improve their efficiency.
Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping
We present a simple yet novel parameterized form of linear mapping to achieves remarkable network compression performance: a pseudo SVD called Ternary SVD (TSVD).
