The image is iteratively enhanced using the enhancement curve and the enhancement parameters. Second, the unsupervised algorithm needs to design an image non-reference loss function in training. Four non-reference loss functions are introduced to train the parameter estimation network. Experiments on ...
Fig. 5: Zero-shot denoising and resolution enhancement in multimodal SIM data. a Schematic of the training procedure of ZS-DeconvNet for SIM. b Progression of SNR and resolution improvement across the CCPs in a SUM-159 cell, from raw SIM images (left), conventional SIM image (right), and...
Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods lack effective bridging capabilities for unknown degradation. To address these limitations, we propose a novel zero-reference light...
Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning...
Publication|Publication Understanding illumination and reducing the need for supervision pose a significant challenge in low-light enhancement. Current approaches are highly sensitive to data usage during training and illumination-specific hyper-parameters, limiting their ability to handle...
thank your contribution, I also encountered some problems when using this project, i need some suggestion, I use yolox-tiny to train my own VOC data, batch_size: 32, gpu_num:2, img_size:[224x224], an error occurs when the training reaches the 30th-40th epoch, the error message is ...
patch matching and frame blending. Our framework achieves global style and local texture temporal consistency at a low cost (without re-training or optimization). The adaptation is compatible with existing image diffusion techniques, allowing our framework to take advantage of them, such as ...
This integrated neglect will make the model lose its grasp of the main components of the image. Relying only on the local existence of seen classes during the inference stage introduces unavoidable bias. In this paper, we propose a novel and effective group bi-enhancement framework for MLZSL, ...
Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However, the soft constraint imposed on determining where to attend to valid features can sometimes be ...
Caption enhancement algorithm described in the paper "TROPE: TRaining-Free Object-Part Enhancement for Seamlessly Improving Fine-Grained Zero-Shot Image Captioning" (EMNLP 2024 Findings). arXiv: arriving October 1st ACL Anthology: arriving soon Overview TROPE is an automatic caption evaluation metric th...