arXiv Computer Vision

InterHier: Learning Interconnected Hierarchical Semantics for Open-Vocabulary Object Detection

Hugging Face Trending Papers

Jul 27

Multimodal Semantic-Probabilistic Objectness for Open World Object Detection

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.

computer-vision multimodal

arXiv Computer Vision

Aug 31

Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection

arXiv:2509.24192v2 Announce Type: replace Abstract: Vision-language models (VLMs) have advanced multimodal perception, demonstrated by open-vocabulary object detection with simple language queries. S...

By Sojung An, Kwanyong Park, Yong Jae Lee, Donghyun Kim

llms rag computer-vision multimodal benchmarks

arXiv AI

Jul 28

Multimodal Semantic-Probabilistic Objectness for Open World Object Detection

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.

By Weijun Tian, Rui Liu

computer-vision multimodal

Hugging Face Trending Papers

Sep 9

Vague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection

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...

llms rag computer-vision fine-tuning benchmarks

arXiv Computation and Language

Sep 10

Vague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection

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.

By Ibrohimjon Muminov (Dongguk University, Seoul, South Korea), Jihie Kim (Dongguk University, Seoul, South Korea)

llms rag computer-vision fine-tuning benchmarks

arXiv AI

Jul 13

H3Former: Hypergraph-based Semantic-Aware Aggregation via Hyperbolic Hierarchical Contrastive Loss for Fine-Grained Visual Classification

arXiv:2511. 10260v2 Announce Type: replace-cross Abstract: Fine-Grained Visual Classification (FGVC) remains a challenging task due to subtle inter-class differences and large intra-class variations.

By Yongji Zhang, Siqi Li, Kuiyang Huang, Yue Gao, Yu Jiang

rag benchmarks

arXiv AI

Aug 11

Explaining, Verifying, and Aligning Semantic Hierarchies in Vision-Language Model Embeddings

arXiv:2603. 26798v2 Announce Type: replace-cross Abstract: Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected.

By Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert, Annika M\"utze, Marvin Keller, Sparsh Tiwari, Georgii Mikriukov, Diedrich Wolter, Jae Hee Lee, Matthias Rottmann

llms rag multimodal safety

arXiv AI

Jul 7

Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models

arXiv:2607. 02909v1 Announce Type: cross Abstract: Taxonomies provide key information about the semantic relationships between concepts and the inherent organization of vision and language.

By Hulingxiao He, Zhi Tan, Yuxin Peng

llms rag fine-tuning multimodal

arXiv AI

Jul 10

VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval

arXiv:2607. 08541v1 Announce Type: cross Abstract: Open-vocabulary object detection and segmentation aim to recognize arbitrary objects beyond predefined categories.

By ZhiXin Sun

rag computer-vision nlp multimodal

arXiv Computer Vision

Aug 25

C$^2$Path: Class-Conditional Pathway Decoupling for Vision-Language Incremental Object Detection

arXiv:2608.21937v1 Announce Type: new Abstract: Incremental Object Detection (IOD) aims to enable detectors to continuously learn novel categories while preserving previously acquired knowledge. Howe...

By Lecheng Xu, Feifei Shao, Ouyangzi Ye, Zhen Wang, Lin Li, Kexin Li, Zhao Wang, Changqin Huang

computer-vision multimodal benchmarks

arXiv Computer Vision

Sep 1

OPUS: A Simple yet Effective Unified Framework for Open-Vocabulary Detection

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...

By Xiaoyan Wei, Zhimin Yao, Ruilin Yang, Wei Zhang, Yong Dai, Yi Zhang, Wei Ge

llms multimodal benchmarks safety

arXiv AI

Sep 1

Background-Free Objectness Learning for Class-Agnostic Detection

Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.

By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella

benchmarks safety

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