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Found 9 papers, 7 papers with code
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs.
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
Masked Pre-trained Model Enables Universal Zero-shot Denoiser
In this work, we observe that the model, which is trained on vast general images using masking strategy, has been naturally embedded with the distribution knowledge regarding natural images, and thus spontaneously attains the underlying potential for strong image denoising.
Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation
Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability, we introduce a robust fine-tuning approach, namely Rein, to parameter-efficiently harness VFMs for DGSS.
Disentangle then Parse:Night-time Semantic Segmentation with Illumination Disentanglement
2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions.
FreeDrag: Feature Dragging for Reliable Point-based Image Editing
To serve the intricate and varied demands of image editing, precise and flexible manipulation in image content is indispensable.
Disentangle then Parse: Night-time Semantic Segmentation with Illumination Disentanglement
2) Based on the observation that the illumination component can serve as a cue for some semantically confused regions, we further introduce an Illumination-Aware Parser (IAParser) to explicitly learn the correlation between semantics and lighting, and aggregate the illumination features to yield more precise predictions.
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
In this work, we resort to data mixing to establish a deliberated domain bridging (DDB) for DASS, through which the joint distributions of source and target domains are aligned and interacted with each in the intermediate space.
Ranked #1 on
Domain Adaptation
on GTAV+Synscapes to Cityscapes
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
Such NWD can be coupled with the classifier to serve as a discriminator satisfying the K-Lipschitz constraint without the requirements of additional weight clipping or gradient penalty strategy.
Ranked #2 on
Domain Adaptation
on ImageCLEF-DA
