arXiv:2610.35450v1 Announce Type: new Abstract: Referring expression grounding in overhead imagery requires oriented bounding boxes rather than horizontal ones, since aerial objects appear at arbitra...
Groundbench is a new benchmark that evaluates vision‑language models on multi‑resolution polygon grounding, using the same 1,500 image‑expression‑referent triples but targeting exact‑N polygons with five different vertex budgets. It audits both filled‑region intersection‑over‑union (IoU) and legal‑polygon completion, revealing that the best models achieve 88.2 box IoU and 97.1 accuracy at IoU ≥ 0.5, while direct polygon predictions lag at 57.7 and 69.2. The study shows performance is non‑monotonic across budgets, collapses at the densest budget due to legality failures, and highlights that false spatial cues hurt more than false colour cues, underscoring an operational geometry gap beyond latent boundary perception.
The paper introduces O$^2$-VG, a unified framework for oriented object visual grounding in remote sensing images, comprising three complementary models: O$^2$-VG-Trans, a cross‑modality transformer; O$^2$-VG-Uni, which predicts universal oriented proposals; and O$^2$-VG-VLM, an autoregressive vision‑language model that generates oriented bounding boxes. It also presents DIOR‑R‑SVG, a new dataset containing image, expression, and oriented box triplets for training and evaluation. The framework demonstrates superior performance across multiple benchmarks and is supported by publicly available code.
Jag is a method that transforms a compact pretrained multimodal model into a direct box predictor for visual grounding. It adds a lightweight regression head that, conditioned on the image and referring expression, outputs all four box coordinates in a single forward pass without sequential decoding. Geometric supervision jointly fine‑tunes the language backbone, visual merger, and regression head, leading to improved accuracy and lower latency compared to existing specialized grounding models.
arXiv:2605. 20306v2 Announce Type: replace-cross Abstract: We introduce WildRoadBench, a wild aerial road-damage grounding benchmark that couples direct visual grounding by vision-language models with autonomous research-and-engineering by LLM-driven agents on a single professionally annotated UAV corpus.
Remote-sensing and UAV applications need models that generalize across platforms and viewpoints without task-specific training. Yet training-free pipelines often falter on oriented geometry, scale/rotation variation, and crowded ports or airfields, and rarely unify detection and segmentation.
arXiv:2603. 14579v3 Announce Type: replace-cross Abstract: Vision language models (VLMs) have shown significant promise in visual grounding for images as well as videos.
The paper introduces MV-PVMapper, a training‑free framework that combines vision foundation models (SAM 3) with UAV photogrammetry to produce high‑fidelity maps of photovoltaic modules. By generating candidate contours, correcting for surface slope, and iteratively refining segmentation across multiple views, the method recovers 7,710 of 7,711 modules with no false positives and achieves macro‑averaged precision and recall above 98 %. The approach outperforms orthomosaic segmentation and multi‑view pixel‑voting baselines, reaching a Jaccard index of 97.55 % without any task‑specific training data.
The paper addresses lifelong aerial autonomy by treating visual place recognition (VPR) as a mission‑based domain‑incremental learning problem. It introduces a heterogeneous memory framework that first trains on a static satellite exemplar memory and then uses a bounded replay buffer to retain selected airborne observations across missions. The proposed DBS‑Hybrid replay strategy, which blends prototype‑based diversity trimming with representative‑first feature‑space coverage, outperforms baseline methods in accuracy, generalization, and knowledge retention across multiple UAV missions.
In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation. Naively applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial memory.
AMIGO (Agentic Multi-Image Grounding Oracle Benchmark) is a long-horizon evaluation framework for vision‑language models that tests hidden‑target identification across galleries of visually similar images. The benchmark requires a model to ask a sequence of attribute‑focused Yes/No questions, receiving Yes/No/Unsure feedback and penalizing invalid actions with Skip, thereby stressing question selection under uncertainty, constraint tracking, and fine‑grained discrimination. Using the Guess My Preferred Dress task, the study shows that final‑answer accuracy alone overstates performance, as models may guess correctly without verified evidence, waste turns, or violate the protocol, highlighting the need for combined visual discrimination, informative questioning, and robust protocol adherence.
CoEvolve is a construct-to-edit framework for visual grounding that separates the task into explicit state construction and state editing. It uses Region‑Evolution Reinforcement to progressively refine candidate regions and Bidirectional Denoising Refiner to adjust coordinate fields based on fixed semantic context. The approach achieves high grounding accuracy with a 9B backbone, rivaling much larger models, and can recover over 27 percentage points in mean box overlap after a single refinement pass.