Floating waste in inland waterways threatens aquatic ecosystems and requires timely monitoring under cluttered, multi-object conditions. Existing aquatic-waste datasets provide limited geographic cove...
arXiv:2610.04425v2 Announce Type: replace-cross Abstract: Agricultural visual models are typically evaluated for either recognition or localization, but reliable diagnosis requires identifying what i...
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.
SDGBiasBench is a large-scale benchmark suite designed to evaluate and mitigate biases in vision–language models (VLMs) when reasoning about Sustainable Development Goals (SDGs). It contains 500k expert‑involved multiple‑choice questions and 50k regression tasks, allowing assessment of both decision‑level and estimation‑level bias. Experiments show that current VLMs exhibit intrinsic SDG bias, often relying on priors rather than multimodal evidence, and the proposed CADE method significantly reduces this bias, improving accuracy and reducing mean absolute error.
arXiv:2607. 24064v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) have achieved remarkable success in visual understanding, driven by the growing availability of high-quality image-text pairs.
arXiv:2608. 12220v1 Announce Type: cross Abstract: Existing Vision-Language Models (VLMs) exhibits a critical bottleneck in robust spatial reasoning.
arXiv:2601. 19099v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) achieve strong performance on many multimodal benchmarks but remain brittle on spatial reasoning tasks that require aligning abstract overhead representations with egocentric views.
Det‑LIME is a detector‑aware, multi‑instance adaptation of LIME designed to explain black‑box object detectors used in marine mammal research. It generates instance‑specific, box‑aligned explanations by weighting detections, applying a proximity kernel, and using IoU‑based matching to track instances across perturbations. Evaluated on aerial drone imagery of harbor seals and a seabird case study, Det‑LIME outperformed vanilla LIME, Stabilized LIME, Deterministic LIME, and gradient‑based methods in Attribution Ratio and Max Saliency Hit Rate, offering higher‑resolution, instance‑aware explanations that aid debugging, data augmentation, and modeling improvements.
arXiv:2606. 27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems.
arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.
GrabVG is a visual grounding framework for UAV imagery that tackles the challenges of small, densely packed, and visually similar objects by separating the task into preattentive hypothesis search and graph-attentive feature binding. It first generates a compact set of reliable object hypotheses using distillation-guided proposal induction and text-aware filtering, then constructs a sparse graph where language-guided visual cues and inter-instance topological relationships are jointly bound and propagated via graph attention. Experiments on AerialVG and AerialSense demonstrate that GrabVG achieves a strong accuracy–speed trade‑off, reaching 67.31% and 80.34% Acc@0.5 and outperforming baselines by 10.55 and 8.76 percentage points.
GrabVG is a visual grounding framework for UAV imagery that tackles the challenges of small, densely packed, and visually similar objects. It splits the task into preattentive hypothesis search and graph‑attentive feature binding, using distillation‑guided proposals and a sparse graph to capture intra‑ and inter‑instance relationships. Experiments on AerialVG and AerialSense show that GrabVG achieves higher accuracy and speed, outperforming baselines by significant margins.