MLRS

More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe

Accepted to ACCV 2026. 📄 Paper: https://arxiv.org/abs/2607.15942 🤗 Weights: https://huggingface.co/INSAIT-Institute/MLRS

MLRS is a remote-sensing vision-language model that answers questions and performs reasoning segmentation. MLRS supports wide range of inputs: ultra-high-res, SAR, thermal, false-color, multi-temporal, multi-view, visual prompting, multi-image. A LoRA-finetuned InternVL3.5-8B reasons about the image (or multiple images in rgb format) and either answers in text or calls a segmentation tool:

<think>...</think><segmentation>{"noun phrase": "...", "objects": [{"bbox": [x1, y1, x2, y2]}]}</segmentation>

Each noun phrase, its boxes (on a 0–999 grid), and the last rgb image are passed to SAM3, whose mask decoder was finetuned jointly via GRTO. SAM3 returns one mask per phrase, clipped to the predicted boxes. Text-only tasks answer with <answer>...</answer> and return no masks.

This repository only holds the finetuned deltas (a LoRA adapter and SAM3 mask-decoder weights). The base models are downloaded from the Hub at the revisions pinned in config.json.

facebook/sam3 is gated. Accept its license on the Hub and log in (hf auth login or set HF_TOKEN env.variable) before loading.

python usage_example.py --model INSAIT-Institute/MLRS \
        --image scene.png --prompt "Segment the buildings damaged by the flood."

or

import torch
from PIL import Image
from transformers import AutoModel, AutoProcessor

repo = "INSAIT-Institute/MLRS"
model = AutoModel.from_pretrained(repo, trust_remote_code=True, dtype=torch.bfloat16, device_map="cuda")
processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True)

image = Image.open("scene.png")
inputs = processor(images=image, text="Segment the buildings damaged by the flood.")
out = model.generate(**inputs, max_new_tokens=1024)

out.text[0]      # full generation, including <think> and the tool call / answer
out.phrases[0]   # noun phrases sent to SAM3
out.boxes[0]     # per phrase: pixel boxes [x1, y1, x2, y2]
masks = processor.post_process_masks(out, inputs)[0]  # bool [K, H, W], one mask per phrase

Multiple images (for example, pre/post-event pairs): processor(images=[pre, post], text=...). The segmentation tool runs on the last image; use primary_image_index= to pick a different one.

Batching: processor(images=[[img_a], [img_b1, img_b2]], text=[q_a, q_b]).

model.generate returns MLRSOutput(text, masks, phrases, boxes). masks[i] is a bool tensor of shape [K, H, W] at sam_original_sizes resolution. processor.post_process_masks resizes the masks back to the size of the input image.

• The defaults reproduce the evaluation setup: greedy decoding, max_new_tokens=1024, and the LoRA adapter kept unmerged. AutoModel.from_pretrained(..., merge_lora=True) generates faster, with small numerical differences.
• Tested with transformers==5.18.0, peft==0.18.0, torch==2.9.0.

@misc{ailuro2026mlrs,
      title={{More with Less}: a Large Scale Remote Sensing {VLM} with a Simple Recipe}, 
      author={Stefan Maria Ailuro and Mario Markov and Mohammad Mahdi and Luc Van Gool and Danda Pani Paudel},
      year={2026},
      eprint={2607.15942},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.15942}, 
}