104 citations · 673 across the 47 of their papers we have counts for
17 papers · 1 filter
Velocity Scaling in Flow Matching
Youssef Saied, François Fleuret
Scaling a learned flow-matching velocity field by a gain was recently shown to greatly improve generation quality. Prior work argued that velocity fields trained with…
MODUS: Decoder-Only Any-to-Any Modeling of Diverse Modalities
Mingqiao Ye, Zhaochong An, Zhitong Gao +11
Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly…
PAUMER: Patch Pausing Transformer for Semantic Segmentation
Evann Courdier, Prabhu Teja Sivaprasad, François Fleuret
We study the problem of improving the efficiency of segmentation transformers by using disparate amounts of computation for different parts of the image. Our method, PAUMER, accomp…
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields
Mohammad Mahdi Johari, Camilla Carta, François Fleuret
We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a seque…
Borrowing from yourself: Faster future video segmentation with partial channel update
Evann Courdier, François Fleuret
Semantic segmentation is a well-addressed topic in the computer vision literature, but the design of fast and accurate video processing networks remains challenging. In addition, t…
GeoNeRF: Generalizing NeRF with Geometry Priors
Mohammad Mahdi Johari, Yann Lepoittevin, François Fleuret
We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a…
Real-Time Segmentation Networks should be Latency Aware
Evann Courdier, Francois Fleuret
As scene segmentation systems reach visually accurate results, many recent papers focus on making these network architectures faster, smaller and more efficient. In particular, stu…
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
Stepan Tulyakov, Anton Ivanov, Francois Fleuret
End-to-end deep-learning networks recently demonstrated extremely good perfor- mance for stereo matching. However, existing networks are difficult to use for practical applications…
The WILDTRACK Multi-Camera Person Dataset
Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6
People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…
Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection
Pierre Baqué, François Fleuret, Pascal Fua
People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking alg…
Semi-supervised learning of deep metrics for stereo reconstruction
Stepan Tulyakov, Anton Ivanov, Francois Fleuret
Deep-learning metrics have recently demonstrated extremely good performance to match image patches for stereo reconstruction. However, training such metrics requires large amount o…
Globally Consistent Multi-People Tracking using Motion Patterns
Andrii Maksai, Xinchao Wang, Francois Fleuret +1
Many state-of-the-art approaches to people tracking rely on detecting them in each frame independently, grouping detections into short but reliable trajectory segments, and then fu…
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
Timur Bagautdinov, Alexandre Alahi, François Fleuret +2
We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and est…
Predicting the dynamics of 2d objects with a deep residual network
François Fleuret
We investigate how a residual network can learn to predict the dynamics of interacting shapes purely as an image-to-image regression task. With a simple 2d physics simulator, we ge…
Multi-Modal Mean-Fields via Cardinality-Based Clamping
Pierre Baqué, François Fleuret, Pascal Fua
Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large…
Scalable Metric Learning via Weighted Approximate Rank Component Analysis
Cijo Jose, Francois Fleuret
We are interested in the large-scale learning of Mahalanobis distances, with a particular focus on person re-identification. We propose a metric learning formulation called Weighte…
Principled Parallel Mean-Field Inference for Discrete Random Fields
Pierre Baqué, Timur Bagautdinov, François Fleuret +1
Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and i…