2 citations · 3 across the 11 of their papers we have counts for
9 papers · 1 filter
Subtractive Modulative Network with Learnable Periodic Activations
Tiou Wang, Zhuoqian Yang, Markus Flierl +2
We propose the Subtractive Modulative Network (SMN), a novel, parameter-efficient Implicit Neural Representation (INR) architecture inspired by classical subtractive synthesis. The…
2-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisition
Liying Lu, Raphaël Achddou, Sabine Süsstrunk
Raw images taken in low-light conditions are very noisy due to low photon count and sensor noise. Learning-based denoisers have the potential to reconstruct high-quality images. Fo…
Enhancing Frequency Forgery Clues for Diffusion-Generated Image Detection
Daichi Zhang, Tong Zhang, Shiming Ge +1
Diffusion models have achieved remarkable success in image synthesis, but the generated high-quality images raise concerns about potential malicious use. Existing detectors often s…
Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection
Daichi Zhang, Tong Zhang, Jianmin Bao +2
With the rapid development of generative models, detecting generated fake images to prevent their malicious use has become a critical issue recently. Existing methods frame this ch…
Neural Cellular Automata: From Cells to Pixels
Ehsan Pajouheshgar, Yitao Xu, Ali Abbasi +3
Neural Cellular Automata (NCAs) are bio-inspired dynamical systems in which identical cells iteratively apply a learned local update rule to self-organize into complex patterns, ex…
VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models
Yufan Ren, Konstantinos Tertikas, Shalini Maiti +4
Large Vision-Language Models (LVLMs) struggle with puzzles, which require precise perception, rule comprehension, and logical reasoning. Assessing and enhancing their performance i…
FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing
Yufan Ren, Zicong Jiang, Tong Zhang +2
Text-guided image editing using Text-to-Image (T2I) models often fails to yield satisfactory results, frequently introducing unintended modifications, such as the loss of local det…
Unlocking Comics: The AI4VA Dataset for Visual Understanding
Peter Grönquist, Deblina Bhattacharjee, Bahar Aydemir +4
In the evolving landscape of deep learning, there is a pressing need for more comprehensive datasets capable of training models across multiple modalities. Concurrently, in digital…
Data Augmentation via Latent Diffusion for Saliency Prediction
Bahar Aydemir, Deblina Bhattacharjee, Tong Zhang +2
Saliency prediction models are constrained by the limited diversity and quantity of labeled data. Standard data augmentation techniques such as rotating and cropping alter scene co…