1 citations · 1 across the 12 of their papers we have counts for
16 papers
Scaling Latent Motor Adaptation to Coordinated Bimanual Manipulation
Seung-Bin Joo, Alessandro Albini, Luca Wiehe +3
Robust manipulation under uncertain object dynamics and geometry remains a core challenge in real-world robotics. Policies trained under fixed assumptions often degrade when object…
S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation
Jeffrey Hu, Daniel Olmeda Reino, Ayush Tewari
Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural generators, continue to viola…
Ego: Embedding-Guided Personalization of Vision-Language Models
Soroush Seifi, Simon Gardier, Vaggelis Dorovatas +2
AI assistants that support humans in daily life are becoming increasingly feasible, driven by the rapid advancements in multimodal language models. A key challenge lies in overcomi…
Efficient Few-Shot Continual Learning in Vision-Language Models
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino +1
Vision-language models (VLMs) excel in tasks such as visual question answering and image captioning. However, VLMs are often limited by their use of pretrained image encoders, like…
Personalization Toolkit: Training Free Personalization of Large Vision Language Models
Soroush Seifi, Vaggelis Dorovatas, Matteo Cassinelli +3
Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate contextually tailored responses.…
Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino +1
Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. Ho…
VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field
Fei Xue, Ignas Budvytis, Daniel Olmeda Reino +1
Visual relocalization is a key technique to autonomous driving, robotics, and virtual/augmented reality. After decades of explorations, absolute pose regression (APR), scene coordi…
Annotation Free Semantic Segmentation with Vision Foundation Models
Soroush Seifi, Daniel Olmeda Reino, Fabien Despinoy +1
Semantic Segmentation is one of the most challenging vision tasks, usually requiring large amounts of training data with expensive pixel level annotations. With the success of foun…
OOD Aware Supervised Contrastive Learning
Soroush Seifi, Daniel Olmeda Reino, Nikolay Chumerin +1
Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e.…
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino +2
In Class-Incremental Learning (CIL) an image classification system is exposed to new classes in each learning session and must be updated incrementally. Methods approaching this pr…
Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
Farzaneh Rezaeianaran, Rakshith Shetty, Rahaf Aljundi +3
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate…
MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation
Jonas Heylen, Mark De Wolf, Bruno Dawagne +5
Monocular 3D object detection has recently shown promising results, however there remain challenging problems. One of those is the lack of invariance to different camera intrinsic…
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz +1
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle a…
Continual Novelty Detection
Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin +1
Novelty Detection methods identify samples that are not representative of a model's training set thereby flagging misleading predictions and bringing a greater flexibility and tran…
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception
Christos Sakaridis, Haoran Wang, Ke Li +6
Level-5 driving automation requires a robust visual perception system that can parse input images under any condition. However, existing driving datasets for dense semantic percept…
Identifying Wrongly Predicted Samples: A Method for Active Learning
Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino
State-of-the-art machine learning models require access to significant amount of annotated data in order to achieve the desired level of performance. While unlabelled data can be l…