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Works35 from public data
- Spatially-Attentive Patch-Hierarchical Network for Adaptive Motion Deblurring336
This work proposes an efficient pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and proposes an effective content-aware global-local filtering module that significantly improves performance by considering not only global dependencies but also by dynamically exploiting neighboring pixel information.
- Spatially-Adaptive Image Restoration using Distortion-Guided Networks166
The key idea is to exploit the non-uniformity of heavy degradations in spatial-domain and suitably embed this knowledge within distortion-guided modules performing sparse normalization, feature extraction and attention.
- AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results110
The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.
- AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results101
The AIM 2020 challenge on efficient single image super-resolution was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images with focus on the proposed solutions and results.
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- AIM 2019 Challenge on Image Demoireing: Methods and Results57
This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019, and focuses on the proposed solutions and their results.
- AIM 2020 Challenge on Rendering Realistic Bokeh52
The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.
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- AIM 2020: Scene Relighting and Illumination Estimation Challenge50
The novel VIDIT dataset used in the AIM 2020 challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks are presented.
- An Efficient Framework for Dense Video Captioning50
This paper proposes a deep reinforcement-based approach which enables an agent to describe multiple events in a video by watching a portion of the frames, and reduces the computational cost by processing fewer frames while maintaining accuracy.
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- Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications17
VectorBrain running on local mobile devices, paired with a low-cost handheld imaging tool, is promising in transforming the mosquito vector surveillance programs by reducing the burden of expertise required and facilitating timely response based on accurate monitoring.
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- MultLFG: Training-free Multi-LoRA composition using Frequency-domain Guidance7
MultLFG is introduced, a novel framework for training-free multi-LoRA composition that utilizes frequency-domain guidance to achieve adaptive fusion of multiple LoRAs, and substantially enhances compositional fidelity and image quality across various styles and concept sets.
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- Unsupervised network for low-light enhancement1
An unsupervised low-light enhancement network using contextguided illumination-adaptive norm (CIN) and a region- Adaptive single input multiple output (SIMO) model that can generate multiple enhanced images from a single lowlight image.
- BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors–
This work builds a multiframe-aware diffusion model on top of a conventional BISR approach, which boosts image quality with minimal loss to fidelity, and introduces a novel degradation-aware conditioning mechanism and a robust sRGB-to-lRGB inverter.
- Zero-Shot Personalization of Objects via Textual Inversion–
This work proposes a novel framework that employs a learned network to predict object-specific textual inversion embeddings, which are subsequently integrated into the UNet timesteps of a diffusion model for text-conditional customization, marking the first attempt to achieve general-purpose, training-free personalization within diffusion models.
- Face2Scene: Using Facial Degradation as an Oracle for Diffusion-Based Scene Restoration–
This work proposes Face2Scene, a two-stage restoration framework that leverages the face as a perceptual oracle to estimate degradation and guide the restoration of the entire image.
- Spatially-Attentive Patch-Hierarchical Network with Adaptive Sampling for Motion Deblurring–
A pixel adaptive and feature attentive design for handling large blur variations across different spatial locations and process each test image adaptively and introduces a pixel-adaptive non-uniform sampling strategy that implicitly discovers the difficult-to-restore regions present in the image and performs fine-grained refinement in a progressive manner.
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- Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images–
The SemanticStyle Autoencoder (SSAE), a deep Generative Autoen coder model that leverages semantic mask-guided latent space manipulation for highly localized photorealistic style editing of real images is presented.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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