arXiv AI

Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis

arXiv:2605. 07821v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models.

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

Aug 12

Token-Based Detection of Spurious Correlations in Vision Transformers

arXiv:2509. 04009v2 Announce Type: replace-cross Abstract: Due to their powerful feature association capabilities, neural network-based computer vision models have the ability to detect and exploit unintended patterns within the data, potentially leading to correct predictions based on incorrect or unintended but statistically relevant signals.

By Solha Kang, Esla Timothy Anzaku, Wesley De Neve, Arnout Van Messem, Joris Vankerschaver, Francois Rameau, Utku Ozbulak

llms computer-vision

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 Machine Learning

Aug 4

Generated Images Are Easier to Forget: A Machine Unlearning Perspective for Synthetic Image Detection

arXiv:2608. 00716v1 Announce Type: cross Abstract: Robust detection of generated images is critical to counter the misuse of generative models.

By Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian

benchmarks

arXiv AI

Jun 12

DIMOS: Disentangling Instance-level Moving Object Segmentation

arXiv:2606. 12826v1 Announce Type: cross Abstract: Moving instance segmentation (MIS) attracts increasing attention due to its broad applications in traffic surveillance, autonomous driving, and animal tracking.

By Hongxiang Huang, Hongwei Ren, Xiaopeng Lin, Yulong Huang, Zeke Xie, Bojun Cheng

agents computer-vision robotics multimodal benchmarks safety

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

arXiv AI

Jul 16

Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild

arXiv:2607. 13881v1 Announce Type: cross Abstract: Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories.

By Ting Lei, Jialin Liu, Zhu Xu, Yuxin Peng, Yang Liu

llms agents multimodal benchmarks

arXiv Machine Learning

Jun 9

phepy: Visual benchmarks and improvements for out-of-distribution detectors

arXiv:2503. 05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on inputs outside its training domain.

By Felix Krumbiegel, Juniper Tyree, Michael Boy, Petri Clusius, Andreas Rupp

benchmarks

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 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

Jul 15

Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Model

arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.

By Kun Fang, Zuopeng Yang, Haibo Hu, Xiaolin Huang, Jie Yang, Qinghua Tao

diffusion

Hugging Face Trending Papers

Jul 9

Do Egocentric Video-Language Models Capture Both Hand- and Object-Centric Cues?

Hand-object interaction (HOI) recognition requires capturing both hand manipulations and object transformations. However, existing video-language models often fall into shortcuts by relying on spurious correlations among hands, objects, or environmental context, rather than reasoning from the appearance and dynamics of hands and objects themselves.

llms rag robotics

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