RSBuilding: Towards General Remote Sensing Image Building Extraction and Change Detection with Foundation Model

The intelligent interpretation of buildings plays a significant role in urban planning and management, macroeconomic analysis, population dynamics, etc. Remote sensing image building interpretation primarily encompasses building extraction and change detection. However, current methodologies often treat these two tasks as separate entities, thereby failing to leverage shared knowledge. Moreover, the complexity and diversity of remote sensing image scenes pose additional challenges, as most algorithms are designed to model individual small datasets, thus lacking cross-scene generalization. In this paper, we propose a comprehensive remote sensing image building understanding model, termed RSBuilding, developed from the perspective of the foundation model. RSBuilding is designed to enhance cross-scene generalization and task universality. Specifically, we extract image features based on the prior knowledge of the foundation model and devise a multi-level feature sampler to augment scale information. To unify task representation and integrate image spatiotemporal clues, we introduce a cross-attention decoder with task prompts. Addressing the current shortage of datasets that incorporate annotations for both tasks, we have developed a federated training strategy to facilitate smooth model convergence even when supervision for some tasks is missing, thereby bolstering the complementarity of different tasks. Our model was trained on a dataset comprising up to 245,000 images and validated on multiple building extraction and change detection datasets. The experimental results substantiate that RSBuilding can concurrently handle two structurally distinct tasks and exhibits robust zero-shot generalization capabilities.

1st Place Solution for the 5th LSVOS Challenge: Video Instance Segmentation

Video instance segmentation is a challenging task that serves as the cornerstone of numerous downstream applications, including video editing and autonomous driving. In this report, we present further improvements to the SOTA VIS method, DVIS. First, we introduce a denoising training strategy for the trainable tracker, allowing it to achieve more stable and accurate object tracking in complex and long videos. Additionally, we explore the role of visual foundation models in video instance segmentation. By utilizing a frozen VIT-L model pre-trained by DINO v2, DVIS demonstrates remarkable performance improvements. With these enhancements, our method achieves 57.9 AP and 56.0 AP in the development and test phases, respectively, and ultimately ranked 1st in the VIS track of the 5th LSVOS Challenge. The code will be available at https://github.com/zhang-tao-whu/DVIS.

PiClick: Picking the desired mask in click-based interactive segmentation

Click-based interactive segmentation enables productive pixel-level annotation and image editing with simple user clicks, whereas target ambiguity remains a problem hindering precise segmentation. That is, in scenes with rich context, one click may refer to multiple potential targets residing in corresponding masks, while most interactive segmentors can only generate one single mask and fail to capture the rich context. To resolve target ambiguity, we here propose PiClick to produce semantically diversified masks. PiClick leverages a transformer network design wherein mutually interactive mask queries are integrated to infuse target priors. Moreover, a Target Reasoning Module is designed in PiClick to automatically imply the best-matched mask from all proposals, significantly relieving target ambiguity as well as extra human intervention. Extensive experiments conducted on all 9 interactive segmentation datasets not only demonstrate the state-of-the-art segmentation performance of PiClick, but also reduces human interventions with multiple proposal generation and target reasoning. To promote direct usage and future endeavors, we release the source code of PiClick together with a plug-and-play annotation tool at https://github.com/cilinyan/PiClick.

Towards Open-Vocabulary Video Instance Segmentation

Video Instance Segmentation(VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel categories in real-world videos. To address this limitation, we make the following three contributions. First, we introduce the novel task of Open-Vocabulary Video Instance Segmentation, which aims to simultaneously segment, track, and classify objects in videos from open-set categories, including novel categories unseen during training. Second, to benchmark Open-Vocabulary VIS, we collect a Large-Vocabulary Video Instance Segmentation dataset(LV-VIS), that contains well-annotated objects from 1,212 diverse categories, significantly surpassing the category size of existing datasets by more than one order of magnitude. Third, we propose an efficient Memory-Induced Vision-Language Transformer, MindVLT, to first achieve Open-Vocabulary VIS in an end-to-end manner with near real-time inference speed. Extensive experiments on LV-VIS and four existing VIS datasets demonstrate the strong zero-shot generalization ability of MindVLT on novel categories. We will release the dataset and code to facilitate future endeavors.