The increasing frequency and intensity of natural disasters demand more
sophisticated approaches for rapid and precise damage assessment. To tackle
this issue, researchers have developed various methods on disaster benchmark
datasets from satellite imagery to aid in detecting disaster damage. However,
the diverse nature of geographical landscapes and disasters makes it
challenging to apply existing methods to regions unseen during training. We
present DAVI (Disaster Assessment with VIsion foundation model), which
overcomes domain disparities and detects structural damage (e.g., building)
without requiring ground-truth labels of the target region. DAVI integrates
task-specific knowledge from a model trained on source regions with an image
segmentation foundation model to generate pseudo labels of possible damage in
the target region. It then employs a two-stage refinement process, targeting
both the pixel and overall image, to more accurately pinpoint changes in
disaster-struck areas based on before-and-after images. Comprehensive
evaluations demonstrate that DAVI achieves exceptional performance across
diverse terrains (e.g., USA and Mexico) and disaster types (e.g., wildfires,
hurricanes, and earthquakes). This confirms its robustness in assessing
disaster impact without dependence on ground-truth labels.

DAVI 是一种基于图像分割和任务特定知识的灾害评估方法，能够在无需目标地区地面真值标签的情况下，准确检测结构性损害，并在不同地理景观和灾害类型的情况下展现出优异性能。

基于视觉基础模型的通用灾害损害评估通过变化检测

Generalizable Disaster Damage Assessment via Change Detection with  Vision Foundation Model

Answer grounding aims to reveal the visual evidence for visual question
answering (VQA), which entails highlighting relevant positions in the image
when answering questions about images. Previous attempts typically tackle this
problem using pretrained object detectors, but without the flexibility for
objects not in the predefined vocabulary. However, these black-box methods
solely concentrate on the linguistic generation, ignoring the visual
interpretability. In this paper, we propose Dual Visual-Linguistic Interaction
(DaVI), a novel unified end-to-end framework with the capability for both
linguistic answering and visual grounding. DaVI innovatively introduces two
visual-linguistic interaction mechanisms: 1) visual-based linguistic encoder
that understands questions incorporated with visual features and produces
linguistic-oriented evidence for further answer decoding, and 2)
linguistic-based visual decoder that focuses visual features on the
evidence-related regions for answer grounding. This way, our approach ranked
the 1st place in the answer grounding track of 2022 VizWiz Grand Challenge.

本文提出了一种名为 DaVI 的新型统一端到端框架，具有语言回答和视觉定位的能力，引入了两种视觉 - 语言交互机制来解决答案定位问题，并在 2022 年 VizWiz 大挑战中获得了第一名。