Vague2Detect is a hybrid pipeline that improves object detection for ambiguous prompts by combining a fine‑tuned Sentence‑BERT to retrieve candidates from a structured household knowledge base, YOLO‑World to verify their presence in images, and a GPT‑3.5‑turbo fallback to generate new candidate descriptions when prompts fall outside the knowledge base. On a benchmark of household scenes, Vague2Detect raises the vague prompt success rate from 32% (YOLO‑World alone) to 61% with high precision, and up to 85% when the GPT fallback is used.
Real-world detectors must often interpret functional or ambiguous prompts, yet conventional models such as YOLO remain restricted to fixed class lists. Even open-vocabulary models like YOLO-World freq...
Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes. PROB improves unknown discovery by modeling class-agnostic probabilistic objectness in the decoder-query space.
arXiv:2607. 23981v1 Announce Type: cross Abstract: Open-world object detection (OWOD) requires a detector to recognize known categories, discover unnamed objects from unseen categories, and incrementally learn newly annotated classes.
The paper introduces OpenRef, a benchmark for Referring Expression Comprehension (REC) designed for open‑world scenarios. OpenRef expands beyond simple settings by including diverse visual domains, variable target counts (multi‑target and none‑target), and a rich vocabulary with proper nouns, polysemous words, and ordinal terms. It also proposes new evaluation metrics—F1 for grounding accuracy and N3R for negative expression rejection—and presents a training‑free Multi‑task Consistency Checker (MCC) that improves model performance with a single click.
arXiv:2602. 00593v4 Announce Type: replace-cross Abstract: Despite progress on general tasks, vision-language models (VLMs) still struggle with challenges that demand both fine-grained visual grounding and external knowledge, a synergy overlooked by existing benchmarks that evaluate these abilities in isolation.
arXiv:2608.30247v1 Announce Type: new Abstract: Recent unified open-vocabulary detection (OVD) supports heterogeneous prompts, including text queries, visual exemplars, and their combinations, but of...
arXiv:2609.27076v1 Announce Type: new Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perce...
arXiv:2609.24026v1 Announce Type: new Abstract: In this paper, we investigate the limitations of fixed, hand-crafted connectors in hierarchical semantic representations for open-vocabulary object det...
CODE: Cross-Modal Calibration and Dynamic Suppression for Open World Object Detection introduces a unified inference-time framework that addresses semantic ambiguity and over-suppression in multimodal OWOD systems. It comprises Cross-Modal Joint Confidence Calibration, Uncertainty-Guided Universal Objectness Enhancement, and Dynamic Outlier Suppression via Confidence Margin. Experiments on the Real-World Detection benchmark with the OWL‑ViT L/14 backbone show CODE achieving 21.7 U‑mAP and 40.8 K‑mAP, surpassing prior state‑of‑the‑art results by 2.6 and 2.3 points respectively.
arXiv:2608.28707v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable progress in Visual Question Answering (VQA), yet they continue to struggle with quest...
arXiv:2609.23431v1 Announce Type: new Abstract: Human-object interaction (HOI) detection requires grounding an interacting human-object pair and recognizing the verb that links them, often under seve...
Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants by requiring external information beyond a provided image to answer questions. KI-VQA involves multiple sub-problems -referring expression understanding, visual grounding, object recognition, knowledge retrieval, and reasoning-yet existing benchmarks typically report only end-task accuracy, obscuring where failures arise.