11 citations · 15 across the 14 of their papers we have counts for
22 papers · 1 filter
JoLT: Joint Latent Trajectories for Context-Guided High-Resolution Tiled Generation
Mathis Koroglu, Guillaume Jeanneret, Hugo Caselles-Dupré +2
Although text-to-image generative models produce impressive results, they struggle to generate densely detailed, high-resolution (HR) images. Current literature addresses this issu…
Spatially-Grounded Text-to-Video Generation via Inference-Time Gradient-Free Optimization
Guillaume Jeanneret, Mathis Koroglu, Hugo Caselles-Dupré +2
Diffusion Transformer Text-to-Video models have achieved remarkable synthesis quality, yet fine-grained spatial controllability remains a significant challenge. While existing trai…
When Prompts Override Vision: Prompt-Induced Hallucinations in LVLMs
Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny +3
Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs that are not grounded in the v…
FrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion
Hugo Caselles-Dupré, Mathis Koroglu, Guillaume Jeanneret +2
Diffusion-based image-to-video (I2V) models are increasingly effective, yet they struggle to scale to ultra-high-resolution inputs (e.g., 4K). Generating videos at the model's nati…
PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, w…
Archtree: on-the-fly tree-structured exploration for latency-aware pruning of deep neural networks
Rémi Ouazan Reboul, Edouard Yvinec, Arnaud Dapogny +1
Deep neural networks (DNNs) have become ubiquitous in addressing a number of problems, particularly in computer vision. However, DNN inference is computationally intensive, which c…