← Search

Nicolas Carion

7 accepted papers

2026

SAM 3: Segment Anything with Concepts

ICLR 2026poster

We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., “yellow school bus”), image exemplars, or a combination of both. Promptable Concept Segmentation (P…

Cited by 687SourcecodeScholar
2025

SAM 2: Segment Anything in Images and Videos

ICLR 2025oral

We present Segment Anything Model 2 (SAM 2), a foundation model towards solving promptable visual segmentation in images and videos. We build a data engine, which improves model and data via user interaction, to collect the largest video segmentation dataset to date. Our model is a simple transforme…

2021

MDETR - Modulated Detection for End-to-End Multi-Modal Understanding

ICCV 2021poster

Multi-modal reasoning systems rely on a pre-trained object detector to extract regions of interest from the image. However, this crucial module is typically used as a black box, trained independently of the downstream task and on a fixed vocabulary of objects and attributes. This makes it challengin…

Cited by 980PDFcodeScholar
2020

End-to-End Object Detection with Transformers

ECCV 2020poster

We present a new method that views object detection as a direct set prediction. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge ab…

2019

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

NeurIPS 2019spotlight

Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training. In t…

2018

Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger

NeurIPS 2018poster

We formulate the problem of defogging as state estimation and future state prediction from previous, partial observations in the context of real-time strategy games. We propose to employ encoder-decoder neural networks for this task, and introduce proxy tasks and baselines for evaluation to assess t…