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Found 12 papers, 4 papers with code
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
The goal of image cropping is to identify visually appealing crops within an image.
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling
Recognizing and disentangling visual attributes from objects is a foundation to many computer vision applications.
Optical Diffusion Models for Image Generation
Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution.
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
We use the novel multi-reward optimization algorithm to jointly optimize the T2I model and a prompt expansion network, resulting in significant improvement of image quality and also allow to control the trade-off of different rewards using a reward related prompt during inference.
Rich Human Feedback for Text-to-Image Generation
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
Forward-Forward Training of an Optical Neural Network
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations.
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines.
Ranked #46 on
Video Quality Assessment
on MSU SR-QA Dataset
MRET: Multi-resolution Transformer for Video Quality Assessment
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience.
MUSIQ: Multi-scale Image Quality Transformer
To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation.
Ranked #3 on
Image Quality Assessment
on MSU NR VQA Database
Rich Features for Perceptual Quality Assessment of UGC Videos
Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality.
Adversarially Adaptive Normalization for Single Domain Generalization
As a generic tool, the improvement introduced by ASR-Norm is agnostic to the choice of ADA methods.
Multi-path Neural Networks for On-device Multi-domain Visual Classification
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
