arXiv Computer Vision
EmbPASS: Towards Cross-Embodiment Open Panoramic Segmentation
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arXiv AI
Aug 19
Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing
The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.
arXiv Computer Vision
5d ago
XEmbodied: A Foundation Model with Enhanced Geometric and Physical Cues for Large-Scale Embodied Environments
arXiv:2604.18484v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models drive next-generation autonomous systems, but training them requires scalable, high-quality annotations from co...
arXiv AI
Sep 10
GoDeep: Annotation-Free Open-Vocabulary 3D Scene Understanding via Language-Space Lifting
GoDeep is an annotation‑free method for open‑vocabulary 3D scene understanding that uses a vision‑language model solely as a translator to generate structured, entity‑level descriptions of each image. These descriptions are projected and aggregated in a language‑only embedding space, eliminating the need for a 3D training corpus or domain‑specific encoder. The approach achieves competitive performance on ScanNet++ and a cultural heritage benchmark, accurately localizes out‑of‑vocabulary objects, and offers explainable, point‑level predictions.
arXiv AI
6d ago
UniAfford: Token-Routed Multitask Learning for Generalizable 2D-3D Affordance Perception
arXiv:2609.37264v1 Announce Type: cross Abstract: Affordance perception aims to localize actionable regions supporting embodied interaction, yet 2D and 3D affordance grounding have evolved as separat...
arXiv Computer Vision
Sep 16
NeuroSymbEAD: A Large Scale Neuro-Symbolic Caption Dataset for Omni-Directional Embodied Autonomous Driving
NeuroSymbEAD is a large‑scale neuro‑symbolic caption dataset that builds an ego‑centric knowledge graph of static and dynamic objects on the KITTI‑360 dataset, annotating classes, categories, heading directions, orientations, and distances from the ego‑vehicle. The dataset generates multilevel textual captions that serve as a lightweight representation of an ego‑centric scene map, enabling outdoor scene‑map reconstruction, visual recognition, and object grounding. Baselines for driving common sense and traffic/scene understanding are established, and the dataset is benchmarked using pre‑trained grounding and learned auto‑regressive captioning networks to support vision‑language and foundation models for traffic‑scene explanation, 3D reasoning, and interpretable autonomous‑driving perception.
arXiv AI
Aug 13
CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D
arXiv:2509. 24528v4 Announce Type: replace-cross Abstract: Object retrieval from a scene has become a new trend of research due to its numerous applications.