The paper investigates using Vision Language Models (VLMs) to accelerate verification and validation (V&V) of classification models by automatically detecting systematic errors. It introduces a VLM-based error slice detection (ESD) method that groups and labels errors, demonstrating its ability to identify perturbations in a non-military dataset and to cluster images by surroundings in a military context. The study highlights challenges such as underrepresentation of defence data in VLM training and limited contextual diversity, and suggests that while fully automated V&V is not yet feasible, VLMs could speed up the process in the future.
arXiv:2609.38603v1 Announce Type: new Abstract: While earth observation models have advanced substantially, they still lack interpretability. While concept-bottleneck models provide interpretability...
arXiv:2609.36648v1 Announce Type: new Abstract: Vision-language pre-training has reshaped image clustering, giving rise to language-assisted image clustering (LaIC), which leverages textual semantics...
The paper reviews domain generalization (DG) for object detection, highlighting its scarcity and unique challenges such as localization and multi-scale representation. It examines synthetic data from three angles: as an enabler that diversifies and aligns training data, as a probe that allows controlled experiments to uncover failure modes, and as a source of a synthetic‑to‑real gap that hampers deployment on real imagery. The authors conclude that future DG research must develop representation‑aware methods that explicitly address both localization and classification under domain shift.
The paper evaluates out‑of‑the‑box object detection models for automatic target detection and recognition (ATD/R) in military settings. Six YOLO variants and two DETR variants were benchmarked on a new military dataset featuring vehicles, occlusions, and small targets, with performance measured in mAP@0.5 and mAP@0.5:0.95 across air‑to‑ground and ground‑to‑ground perspectives. Findings show larger models and DETR-based approaches perform best, fine‑tuning on the VisDrone dataset improves air‑to‑ground and small‑object performance, yet all models still struggle with small targets in air‑to‑ground scenarios.
DiaVLo is a diagnostic framework for vision‑language models (VLMs) that uses human curation and VLM generation to create specifications of desired and observed behaviours, revealing potential misalignments. It also offers causal estimates to pinpoint the most influential concepts driving VLM behaviour. Experiments on several open‑source VLMs under classification and generation tasks show that DiaVLo’s behaviour labels correlate with model performance and illuminate how VLMs perceive, organise, and prioritise concepts.
arXiv:2610.29241v1 Announce Type: new Abstract: In this paper, we investigate why vision-language models (VLMs) produce errors by decomposing them into two sources: visual-representation failures, in...
The paper introduces a unified evaluation framework for assessing the physical adversarial robustness of Vision‑Language Models (VLMs) deployed at the edge. It defines a conditional attack success rate (cASR) metric that counts only images where the model still localizes objects under a gray occluder, and categorizes failures into omission, substitution, and displacement. Benchmarking shows that compact grounding VLMs can be as vulnerable as traditional object detectors, with omission being the only failure visible to downstream systems, while displacement and substitution pose potentially more dangerous risks, especially under distribution shift.
arXiv:2608.29783v1 Announce Type: new Abstract: Industrial anomaly detection is a critical component of modern manufacturing. Most traditional unsupervised methods rely on modelling normal feature di...
arXiv:2605. 09697v3 Announce Type: replace-cross Abstract: In many real-world computer vision applications, including medical imaging and industrial inspection, binary classification tasks are characterized by a severe scarcity of positive samples.
The paper investigates how evaluation protocols and ground‑truth quality affect the classification performance of Multimodal Large Language Models (MLLMs). It identifies and corrects key issues such as discarded out‑of‑list outputs, inflated distractor choices, and poor open‑world mapping, and shows that design choices like batch size, image ordering, and text encoder selection significantly influence accuracy. Using a multilabel reannotation of 625 ImageNet‑1k classes (ReGT), the study finds that corrected labels can boost MLLM performance by up to 10.8%, narrowing the gap with supervised models, and demonstrates that MLLMs can assist human annotators in about half of difficult cases.
The paper proposes a method called Restrict, Don't Retrain that enhances zero-shot aerial segmentation by using inference-time guidance from a vision‑language model (VLM). It combines a frozen foundation model that labels every pixel with two VLM queries: one to select relevant classes and another to locate small objects missed by the base model. Experiments on four aerial datasets show consistent performance gains at each stage where the base model is competent.
arXiv:2610.10436v1 Announce Type: new Abstract: Adversarial perturbations can alter the predictions of frozen vision-language models (VLMs) while leaving their confidence and image--text similarity p...