arXiv Computer Vision

CODE: Cross-Modal Calibration and Dynamic Suppression for Open World 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 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

arXiv AI

Aug 21

Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training

arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.

By Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao

llms computer-vision efficiency benchmarks safety

arXiv Machine Learning

Jul 1

T-QPM: Enabling Temporal Out-Of-Distribution Detection and Domain Generalization for Vision-Language Models in Open-World

arXiv:2603. 18481v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection remains a critical challenge in open-world learning, where models must adapt to evolving data distributions.

By Aditi Naiknaware, Salimeh Sekeh

llms multimodal benchmarks

arXiv AI

Jul 3

ProCal: Inference-Time Proposal Calibration for Open-Vocabulary Object Detection

arXiv:2607. 01759v1 Announce Type: cross Abstract: Open-vocabulary object detection aims to localize and classify objects beyond the fixed set of categories seen dur ing training.

By Jae-Ryung Hong, Ho-Joong Kim, Seong-Whan Lee

rag computer-vision multimodal

arXiv AI

Aug 11

Open-World Semantic Segmentation with Sensitivity Modeling

arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.

By Anastasios Romanos Varvarigos, Nikos Giakoumoglou, Tania Stathaki

rag computer-vision

arXiv Machine Learning

Jun 18

Semantic Robustness Certification for Vision-Language Models

arXiv:2606. 18839v1 Announce Type: new Abstract: Vision-language models (VLMs) are now widely used in downstream tasks.

By Peiyu Yang, Paul Montague, Feng Liu, Andrew C. Cullen, Amardeep Kaur, Christopher Leckie, Sarah M. Erfani

llms multimodal

arXiv AI

Aug 11

GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views

arXiv:2608. 09270v1 Announce Type: cross Abstract: Fine-grained cross-modal understanding in drone views is essential for aerial vision-language navigation.

By Jiahui Cui, Yan Zhao, Kan Wei, Enze Zhu, Peirong Zhang, Lei Wang, Yiru Wang

robotics multimodal benchmarks safety

Hugging Face Trending Papers

Aug 16

PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection

Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries. Recent vision-language detectors imp...

llms multimodal benchmarks

arXiv AI

Jun 24

MOCHA: Multi-modal Objects-aware Cross-arcHitecture Alignment

arXiv:2509. 14001v5 Announce Type: replace-cross Abstract: Personalized object detection aims to adapt a general-purpose detector to recognize user-specific instances from only a few examples.

By Elena Camuffo, Francesco Barbato, Mete Ozay, Simone Milani, Umberto Michieli

llms rag computer-vision efficiency multimodal benchmarks safety

arXiv Machine Learning

Aug 18

PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection

arXiv:2608. 15802v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection remains challenging for image classifiers, especially when near-OOD samples lie close to in-distribution (ID) class boundaries.

By Chengyao Jia, Ruixuan Wang

llms multimodal benchmarks

arXiv AI

Jun 26

Robust Onion: Peeling Open Vocab Object Detectors Under Noise

arXiv:2606. 26734v1 Announce Type: cross Abstract: The impact of real-world noise on Open Vocabulary Object Detectors (OV-ODs) remains poorly understood due to their architectural complexity.

By Priyank Pathak, Mukilan Karuppasamy, Aaditya Baranwal, Shruti Vyas, Yogesh S Rawat

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