71 citations · 98 across the 32 of their papers we have counts for
38 papers
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…
Learning to Steer: Input-dependent Steering for Multimodal LLMs
Jayneel Parekh, Pegah Khayatan, Mustafa Shukor +3
Steering has emerged as a practical approach to enable post-hoc guidance of LLMs towards enforcing a specific behavior. However, it remains largely underexplored for multimodal LLM…
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…
Network Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
The massive interest in deep neural networks (DNNs) for both computer vision and natural language processing has been sparked by the growth in computational power. However, this le…
MultIOD: Rehearsal-free Multihead Incremental Object Detector
Eden Belouadah, Arnaud Dapogny, Kevin Bailly
Class-Incremental learning (CIL) refers to the ability of artificial agents to integrate new classes as they appear in a stream. It is particularly interesting in evolving environm…
Gradient-Based Post-Training Quantization: Challenging the Status Quo
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Quantization has become a crucial step for the efficient deployment of deep neural networks, where floating point operations are converted to simpler fixed point operations. In its…
NUPES : Non-Uniform Post-Training Quantization via Power Exponent Search
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural network (DNN) deployment has been confined to larger hardware devices due to their expensive computational requirements. This challenge has recently reached another sca…
SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly +1
Deep neural networks (DNNs) demonstrate outstanding performance across most computer vision tasks. Some critical applications, such as autonomous driving or medical imaging, also r…
Designing strong baselines for ternary neural network quantization through support and mass equalization
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensiv…
Fighting over-fitting with quantization for learning deep neural networks on noisy labels
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon l…
Fighting noise and imbalance in Action Unit detection problems
Gauthier Tallec, Arnaud Dapogny, Kevin Bailly
Action Unit (AU) detection aims at automatically caracterizing facial expressions with the muscular activations they involve. Its main interest is to provide a low-level face repre…
PowerQuant: Automorphism Search for Non-Uniform Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Deep neural networks (DNNs) are nowadays ubiquitous in many domains such as computer vision. However, due to their high latency, the deployment of DNNs hinges on the development of…
Multi-Task Transformer with uncertainty modelling for Face Based Affective Computing
Gauthier Tallec, Jules Bonnard, Arnaud Dapogny +1
Face based affective computing consists in detecting emotions from face images. It is useful to unlock better automatic comprehension of human behaviours and could pave the way tow…
SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
The leap in performance in state-of-the-art computer vision methods is attributed to the development of deep neural networks. However it often comes at a computational price which…
To Fold or Not to Fold: a Necessary and Sufficient Condition on Batch-Normalization Layers Folding
Edouard Yvinec, Arnaud Dapogny, Kevin Bailly
Batch-Normalization (BN) layers have become fundamental components in the evermore complex deep neural network architectures. Such models require acceleration processes for deploym…
SPIQ: Data-Free Per-Channel Static Input Quantization
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community.…
Privileged Attribution Constrained Deep Networks for Facial Expression Recognition
Jules Bonnard, Arnaud Dapogny, Ferdinand Dhombres +1
Facial Expression Recognition (FER) is crucial in many research domains because it enables machines to better understand human behaviours. FER methods face the problems of relative…
Multi-label Transformer for Action Unit Detection
Gauthier Tallec, Edouard Yvinec, Arnaud Dapogny +1
Action Unit (AU) Detection is the branch of affective computing that aims at recognizing unitary facial muscular movements. It is key to unlock unbiased computational face represen…
Multi-Order Networks for Action Unit Detection
Gauthier Tallec, Arnaud Dapogny, Kevin Bailly
Action Units (AU) are muscular activations used to describe facial expressions. Therefore accurate AU recognition unlocks unbiaised face representation which can improve face-based…
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging
Edouard Yvinec, Arnaud Dapogny, Matthieu Cord +1
Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration. In this paper, we introduce a novel data-free pruning protocol RED+…