Hugging Face Trending Papers August 12, 2026

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation

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This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations. Existing methods often suffer from noisy pseudo-masks, limited visual-textual grounding, and difficulty handling synonyms or out-of-vocabulary (OOV) words.

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arXiv AI

Aug 13

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation

arXiv:2608. 11681v1 Announce Type: cross Abstract: This work addresses the challenge of open-vocabulary instance segmentation (OVIS) and open-set panoptic segmentation (OSPS), which aim to recognize both predefined and unseen object categories without exhaustive human annotations.

By Duy Tran Thanh, Yeejin Lee, Byeongkeun Kang

llms computer-vision multimodal benchmarks safety

Hugging Face Trending Papers

Sep 16

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image ca...

llms computer-vision multimodal benchmarks

arXiv Computation and Language

Sep 17

PANORAMA: Panoptic Grounded Captioning via Mask Proposal Selection

PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.

By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid

llms computer-vision multimodal benchmarks

arXiv AI

Aug 11

Vision-Language Grounding as Bidirectional Concept Correspondence

arXiv:2608. 07886v1 Announce Type: cross Abstract: Vision-language grounding connects language to visual content, yet most existing formulations reduce grounding to a unidirectional localization problem: given a prespecified text phrase or category name, identify the corresponding image region.

By Jieyu Zhang, Ziqi Gao, Luke Zettlemoyer, Ranjay Krishna

llms computer-vision multimodal safety

arXiv AI

Jul 14

SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception

arXiv:2607. 11008v1 Announce Type: cross Abstract: Open-vocabulary dense perception (OVDP) aims to localize objects unseen during training by leveraging textual knowledge.

By Mingjie Xie, Guangjun He, Dongli Xu, Youtian Lin, Hongjue Li, Pengming Feng, Jian Guan, Yue Deng

benchmarks safety

arXiv AI

Sep 1

Cost-efficient Active Learning for Referring Image Segmentation and Grounding

arXiv:2608.30621v1 Announce Type: cross Abstract: Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (V...

By Junbeom Hong, Seonghoon Yu, Hyung Rok Jung, Sundong Kim, Jeany Son

computer-vision benchmarks

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