40 citations · 77 across the 22 of their papers we have counts for
22 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
Voxtral Realtime
Mistral-AI, :, Alexander H. Liu +166
We introduce Voxtral Realtime, a natively streaming automatic speech recognition model that matches offline transcription quality at sub-second latency. Unlike approaches that adap…
Data Repetition Beats Data Scaling in Long-CoT Supervised Fine-Tuning
Dawid J. Kopiczko, Sagar Vaze, Tijmen Blankevoort +1
Supervised fine-tuning (SFT) on chain-of-thought data is an essential post-training step for reasoning language models. Standard machine learning intuition suggests that training w…
Ministral 3
Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116
We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…
SAM 3: Segment Anything with Concepts
Nicolas Carion, Laura Gustafson, Yuan-Ting Hu +35
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…
Devstral: Fine-tuning Language Models for Coding Agent Applications
Abhinav Rastogi, Adam Yang, Albert Q. Jiang +100
We introduce Devstral-Small, a lightweight open source model for code agents with the best performance among models below 100B size. In this technical report, we give an overview o…
Voxtral
Alexander H. Liu, Andy Ehrenberg, Andy Lo +103
We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art perfo…
Magistral
Mistral-AI, :, Abhinav Rastogi +98
We introduce Magistral, Mistral's first reasoning model and our own scalable reinforcement learning (RL) pipeline. Instead of relying on existing implementations and RL traces dist…
Pixtral 12B
Pravesh Agrawal, Szymon Antoniak, Emma Bou Hanna +39
We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance o…
Dissecting Out-of-Distribution Detection and Open-Set Recognition: A Critical Analysis of Methods and Benchmarks
Hongjun Wang, Sagar Vaze, Kai Han
Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent y…
HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts
Hongjun Wang, Sagar Vaze, Kai Han
Generalized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they…
SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning
Hongjun Wang, Sagar Vaze, Kai Han
Generalized Category Discovery (GCD) aims to classify unlabelled images from both `seen' and `unseen' classes by transferring knowledge from a set of labelled `seen' class images.…
No Representation Rules Them All in Category Discovery
Sagar Vaze, Andrea Vedaldi, Andrew Zisserman
In this paper we tackle the problem of Generalized Category Discovery (GCD). Specifically, given a dataset with labelled and unlabelled images, the task is to cluster all images in…
GeneCIS: A Benchmark for General Conditional Image Similarity
Sagar Vaze, Nicolas Carion, Ishan Misra
We argue that there are many notions of 'similarity' and that models, like humans, should be able to adapt to these dynamically. This contrasts with most representation learning me…
What's in a Name? Beyond Class Indices for Image Recognition
Kai Han, Xiaohu Huang, Yandong Li +3
Existing machine learning models demonstrate excellent performance in image object recognition after training on a large-scale dataset under full supervision. However, these models…
Zero-Shot Category-Level Object Pose Estimation
Walter Goodwin, Sagar Vaze, Ioannis Havoutis +1
Object pose estimation is an important component of most vision pipelines for embodied agents, as well as in 3D vision more generally. In this paper we tackle the problem of estima…
Generalized Category Discovery
Sagar Vaze, Kai Han, Andrea Vedaldi +1
In this paper, we consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled…
Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement
Walter Goodwin, Sagar Vaze, Ioannis Havoutis +1
Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-l…
Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
Sagar Vaze, Kai Han, Andrea Vedaldi +1
The ability to identify whether or not a test sample belongs to one of the semantic classes in a classifier's training set is critical to practical deployment of the model. This ta…
Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks
Sagar Vaze, James Foley, Mohamed Seddiq +2
The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural i…
SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework
Conrad James Foley, Sagar Vaze, Mohamed El Amine Seddiq +2
Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases…