Search Results for author:
Found 61 papers, 37 papers with code
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting
Finally, we propose to use a contrastive loss to mitigate this sensitivity and improve the robustness and generalization capabilities of LLMs.
Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching.
Temporal receptive field in dynamic graph learning: A comprehensive analysis
Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges.
ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation
Moreover, we extend the evaluation of GNN models for top-k recommendation tasks with an inductive user-centric protocol, providing a more accurate reflection of real-world applications.
Physics-Informed Model and Hybrid Planning for Efficient Dyna-Style Reinforcement Learning
Applying reinforcement learning (RL) to real-world applications requires addressing a trade-off between asymptotic performance, sample efficiency, and inference time.
GalLoP: Learning Global and Local Prompts for Vision-Language Models
Despite their success, most prompt learning methods trade-off between classification accuracy and robustness, e. g. in domain generalization or out-of-distribution (OOD) detection.
DiffCut: Catalyzing Zero-Shot Semantic Segmentation with Diffusion Features and Recursive Normalized Cut
Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks.
Energy Correction Model in the Feature Space for Out-of-Distribution Detection
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Deep learning enables automatic and robust extraction of cardiac function descriptors from echocardiographic sequences, such as ejection fraction or strain.
TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research
To validate the dataset's utility, 5 competitive Deep Learning models for automatic kidney segmentation were benchmarked, yielding average DICE scores from 83. 2% to 89. 1% for CT, and 61. 9% to 79. 4% for US images.
Optimization of Rank Losses for Image Retrieval
It provides an upperbound for rank losses and ensures robust training.
Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks
Vision-language foundation models such as CLIP have shown impressive zero-shot performance on many tasks and datasets, especially thanks to their free-text inputs.
VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing
In this work, we introduce VidEdit, a novel method for zero-shot text-based video editing that guarantees robust temporal and spatial consistency.
Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection
HEAT complements prior density estimators of the ID density, e. g. parametric models like the Gaussian Mixture Model (GMM), to provide an accurate yet robust density estimation.
Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
To perform future forecasting of pressure and velocity on the challenging EAGLE dataset, we introduce a new mesh transformer.
Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints
Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents.
Full Contextual Attention for Multi-resolution Transformers in Semantic Segmentation
In particular, vision transformers construct compressed global representations through self-attention and learnable class tokens.
Vision and Structured-Language Pretraining for Cross-Modal Food Retrieval
Finally, we validate the generalization of the approach to other tasks (i. e, Food Recognition) and domains with structured text such as the Medical domain on the ROCO dataset.
Ranked #1 on
Cross-Modal Retrieval
on Recipe1M+
Memory transformers for full context and high-resolution 3D Medical Segmentation
Transformer models achieve state-of-the-art results for image segmentation.
Take One Gram of Neural Features, Get Enhanced Group Robustness
Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts.
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction
Since BC is an approximate physical model violated in several situations, we propose to train a physically-constrained network complemented with a data-driven network.
Hierarchical Average Precision Training for Pertinent Image Retrieval
Image Retrieval is commonly evaluated with Average Precision (AP) or Recall@k. Yet, those metrics, are limited to binary labels and do not take into account errors' severity.
Ranked #1 on
Metric Learning
on DyML-Animal
Towards efficient feature sharing in MIMO architectures
To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features.
Swapping Semantic Contents for Mixing Images
Deep architecture have proven capable of solving many tasks provided a sufficient amount of labeled data.
Robust and Decomposable Average Precision for Image Retrieval
In image retrieval, standard evaluation metrics rely on score ranking, e. g. average precision (AP).
Ranked #2 on
Image Retrieval
on CUB-200-2011
Effective Uncertainty Estimation with Evidential Models for Open-World Recognition
A crucial property of KLoS is to be a class-wise divergence measure built from in-distribution samples and to not require OOD training data, in contrast to current second-order uncertainty measures.
Deep Time Series Forecasting with Shape and Temporal Criteria
This paper addresses the problem of multi-step time series forecasting for non-stationary signals that can present sudden changes.
U-Net Transformer: Self and Cross Attention for Medical Image Segmentation
Medical image segmentation remains particularly challenging for complex and low-contrast anatomical structures.
Confidence Estimation via Auxiliary Models
In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).
Probabilistic Time Series Forecasting with Shape and Temporal Diversity
We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate.
Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
In this work, we introduce the APHYNITY framework, a principled approach for augmenting incomplete physical dynamics described by differential equations with deep data-driven models.
Disentangling Physical Dynamics from Unknown Factors for Unsupervised Video Prediction
Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods.
Ranked #2 on
Video Prediction
on SynpickVP
Addressing Failure Detection by Learning Model Confidence
In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP).
Addressing Failure Prediction by Learning Model Confidence
In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP).
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
DualDis: Dual-Branch Disentangling with Adversarial Learning
To effectively separate the information, we propose to use a combination of regular and adversarial classifiers to guide the two branches in specializing for class and attribute information respectively.
MUREL: Multimodal Relational Reasoning for Visual Question Answering
In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images.
Ranked #1 on
Visual Question Answering (VQA)
on TDIUC
BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities.
Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection
Multi-Task Learning (MTL) is appealing for deep learning regularization.
HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet.
Ranked #52 on
Image Classification
on STL-10
Manifold Learning in Quotient Spaces
Typically, we propose to quotient the space of 3D models by the action of rotations.
SHADE: Information-Based Regularization for Deep Learning
Regularization is a big issue for training deep neural networks.
Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings
Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them.
Ranked #9 on
Cross-Modal Retrieval
on Recipe1M
SHADE: Information Based Regularization for Deep Learning
Regularization is a big issue for training deep neural networks.
SHADE: SHAnnon DEcay Information-Based Regularization for Deep Learning
Regularization is a big issue for training deep neural networks.
Deformable Part-based Fully Convolutional Network for Object Detection
Existing region-based object detectors are limited to regions with fixed box geometry to represent objects, even if those are highly non-rectangular.
WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation
This paper introduces WILDCAT, a deep learning method which jointly aims at aligning image regions for gaining spatial invariance and learning strongly localized features.
Ranked #3 on
Weakly Supervised Object Detection
on MS COCO
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks.
Ranked #35 on
Visual Question Answering (VQA)
on VQA v2 test-std
Gossip training for deep learning
We address the issue of speeding up the training of convolutional networks.
Maxmin convolutional neural networks for image classification
Convolutional neural networks (CNN) are widely used in computer vision, especially in image classification.
Master's Thesis : Deep Learning for Visual Recognition
Our last contribution is a framework, build on top of Torch7, for training and testing deep models on any visual recognition tasks and on datasets of any scale.
M2CAI Workflow Challenge: Convolutional Neural Networks with Time Smoothing and Hidden Markov Model for Video Frames Classification
Our approach is among the three best to tackle the M2CAI Workflow challenge.
WELDON: Weakly Supervised Learning of Deep Convolutional Neural Networks
In this paper, we introduce a novel framework for WEakly supervised Learning of Deep cOnvolutional neural Networks (WELDON).
Deep Neural Networks Under Stress
In recent years, deep architectures have been used for transfer learning with state-of-the-art performance in many datasets.
MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking
For ranking, we propose efficient solutions to exactly solve the inference and the loss-augmented problems.
Recipe recognition with large multimodal food dataset
We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion.
Fantope Regularization in Metric Learning
This paper introduces a regularization method to explicitly control the rank of a learned symmetric positive semidefinite distance matrix in distance metric learning.
Sequentially Generated Instance-Dependent Image Representations for Classification
In this paper, we investigate a new framework for image classification that adaptively generates spatial representations.
Top-Down Regularization of Deep Belief Networks
We suggest a deep learning strategy that bridges the gap between the two phases, resulting in a three-phase learning procedure.
Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis
In this paper, we address the challenging problem of categorizing video sequences composed of dynamic natural scenes.
