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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.