arXiv AI

Low-cost concept-based localized explanations: How far can we get with training-free approaches?

arXiv:2606. 29069v1 Announce Type: new Abstract: Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations.

arXiv AI

Jul 7

Explainable Novel Category Discovery in Semantic Concept Space

arXiv:2607. 04548v1 Announce Type: cross Abstract: Novel category discovery aims to identify unseen classes from unlabeled data by transferring knowledge from labeled categories, but most existing methods perform discovery in opaque latent feature spaces.

By Ifrat Ikhtear Uddin, Yang Zhou, KC Santosh, Longwei Wang

multimodal

arXiv AI

Jun 9

From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs

arXiv:2606. 09134v1 Announce Type: cross Abstract: Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to formal ontology classes, still relies on manually curated dictionaries that are brittle and do not generalize across assets.

By Jiangtao Shuai, Zongxiong Chen, Manfred Hauswirth, Sonja Schimmler

llms rag robotics benchmarks

arXiv AI

Jul 13

Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers

arXiv:2605. 28215v2 Announce Type: replace Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples.

By Carmen Quiles-Ram\'irez, Leticia L. Rodr\'iguez, Nicol\'as Martorell, Natalia D\'iaz-Rodr\'iguez

llms multimodal benchmarks

arXiv AI

1d ago

Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification

arXiv:2608. 15731v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust.

By Haadia Amjad, Ronald Tetzlaff

agents computer-vision robotics safety

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 AI

Jun 16

XAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Models

arXiv:2606. 16137v1 Announce Type: cross Abstract: Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-making.

By Yupei Li, Qiyang Sun, Xiaoliang Wu, Chenxi Wang, Berrak Sisman, Bj\"orn W. Schuller

llms multimodal

arXiv Machine Learning

Jul 2

AdaBoosting Text Prompts for Vision-Language Models

arXiv:2607. 00684v1 Announce Type: new Abstract: The classification accuracy of pretrained Vision-Language Models (VLMs) relies on the quality of the text prompts.

By Seokhee Jin, Changhwan Sung, Sunung Mun, Hoyoung Kim, Jungseul Ok

llms multimodal benchmarks

arXiv Machine Learning

Jun 26

LLMTabBench: Evaluating LLMs on Binary Tabular Classification From Zero to Few Shots

arXiv:2605. 24417v2 Announce Type: replace Abstract: Supervised classification on tabular data remains a central machine learning task, but its dependence on large labeled datasets limits its applicability in data-scarce settings.

By Daria Grushina, Kseniia Kuvshinova, Alina Kostromina, Aziz Temirkhanov, Mile Mitrovic, Dmitry Simakov

llms benchmarks

arXiv AI

Jul 7

Token-Based Affordance Grounding with Large Vision-Language Models

arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.

By Seung Il Lee, Qinqian Lei, Daguang Xu, Dong Yang, Robby T. Tan, Yixin Chen, Bo Wang

llms robotics 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

Aug 5

From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model

arXiv:2508. 00955v3 Announce Type: replace-cross Abstract: Adapting generative Multimodal Large Language Models (MLLMs) into universal embedding models typically demands resource-intensive contrastive pre-training, while traditional hard negative mining methods suffer from severe false negative contamination.

By Yeong-Joon Ju, Seong-Whan Lee

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 16

Beyond Scalar Distances: Semantic Attribute Gradients from Frozen MLLMs for Visual Embeddings

arXiv:2606. 15134v1 Announce Type: cross Abstract: Vision encoders for retrieval are typically trained with class-label supervision: each training pair reduces to a scalar that uniformly pushes the embedding apart or pulls it together, as if every visual attribute either differed or matched.

By Shubhang Bhatnagar, Dheeraj Baiju, Narendra Ahuja

llms rag efficiency multimodal benchmarks

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