Alexandre Massoud Alahi

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Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, traffic operations, deployment of social robots in crowded environments, to name a fe ...

A large number of vision applications rely on matching keypoints across images. The last decade featured an arms-race towards faster and more robust keypoints and association algorithms: Scale Invariant Feature Transform (SIFT), Speed-up Robust Feature (SU ...

Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data streams through on-device active learning allows us to directly sample from the ...

Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, ...

A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algorithms t ...

We present OSKAR, the first multimodal foundation model based on bootstrapped latent feature prediction. Unlike generative or contrastive methods, it avoids memorizing unnecessary details (e.g., pixels), and does not require negative pairs, large memory banks, or hand-crafted augmentations. We propose a novel pretraining strategy: given masked tokens from multiple modalities, predict a subset of missing tokens per modality, supervised by momentum-updated uni-modal target encoders. This design efficiently ut

Automated severity rating of rapid eye movement (REM) sleep behavior disorder (RBD) movements would enable multi-night monitoring to detect potentially injurious behaviors and provide objective endpoints for clinical trials. We compared a heuristic classifier using optical flow-derived features against V-JEPA2, a self-supervised video foundation model, for clip-level severity classification (3329 mild versus 284 moderate-to-severe) of in-laboratory video-polysomnography infrared recordings in 86 isolated RB

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are ...

Perceiving humans in the context of Intelligent Transportation Systems (ITS) often relies on multiple cameras or expensive LiDAR sensors. In this work, we present a new cost-effective vision-based method that perceives humans' locations in 3D and their bod ...

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. Existing algorithms treat each image in isolation, but ...

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

Cross-view localization, the task of estimating a camera's 3-degrees-of-freedom (3-DoF) pose by aligning ground-level images with satellite images, is crucial for large-scale outdoor applications like autonomous navigation and augmented reality. Existing m ...

Occluded Person Re-Identification (ReID) is a metric learning task that involves matching occluded individuals based on their appearance. While many studies have tackled occlusions caused by objects, multi-person occlusions remain less explored. In this wo ...

Intelligent devices for supporting persons with vision impairment are becoming more widespread, but they are lacking behind the advancements in intelligent driver assistant system. To make a first step forward, this work discusses the integration of the risk model technology, previously used in autonomous driving and advanced driver assistance systems, into an assistance device for persons with vision impairment. The risk model computes a probabilistic collision risk given object trajectories which has prev

Conventional human trajectory prediction models rely on clean curated data, requiring specialized equipment or manual labeling, which is often impractical for robotic applications. The existing predictors tend to overfit to clean observation affecting thei ...

As Large Language Models (LLMs) become increasingly widespread and integrated into everyday assistant applications, achieving a reliable behavior is critical for effective human–AI interaction. We argue that trustworthy AI assistants rely on two key dimensions: (i) persona consistency, i.e., maintaining a stable personality profile grounded in well-established trait descriptions, and (ii) emotion understanding, i.e., the ability to appropriately infer affective signals. In this work, we evaluate a set of Sm

Efficiently leveraging of the capabilities of contemporary large language models (LLMs) is increasingly challenging, particularly when direct finetuning is expensive and often impractical. Existing training-free methods, including manually or automated designed workflows, typically demand substantial human effort or yield suboptimal results. This paper proposes Weak-for-Strong Harnessing (W4S), a novel framework that customizes smaller, cost-efficient language models to design and optimize workflows for har

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

Predicting 3D human poses in real-world scenarios, also known as human pose forecasting, is inevitably subject to noisy inputs arising from inaccurate 3D pose estimations and occlusions. To address these challenges, we propose a diffusion-based approach th ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

Alexandre Massoud Alahi

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Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

FREAK: Fast Retina Keypoint

VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation

A Master-Slave Approach for Object Detection and Matching with Fixed and Mobile Cameras

Principles and Guidelines for Evaluating Social RobotNavigation Algorithms

OSKAR: Omnimodal Self-supervised Knowledge Abstraction and Representation

Vision-Based Automated Severity Rating of REM Sleep Behavior Disorder: From Heuristic Features to Foundation Models

Body Part-Based Representation Learning for Occluded Person Re-Identification

Perceiving Humans: from Monocular 3D Localization to Social Distancing

Using A Priori Knowledge to Improve Scene Understanding

Predicting the long-term collective behaviour of fish pairs with deep learning

GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization

Keypoint Promptable Re-Identification

Probabilistic Collision Risk Estimation for Pedestrian Navigation

Unified Human Localization and Trajectory Prediction with Monocular Vision

Evaluating Persona Consistency and Emotion Understanding in Small Language Models

Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

A generic diffusion-based approach for 3D human pose prediction in the wild

Social NCE: Contrastive Learning of Socially-aware Motion Representations