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From the 1 of 46 linked papers with an AI index.

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20242026
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cs.CV2026

ORCA: An Agentic Reasoning Framework for Hallucination and Adversarial Robustness in Vision-Language Models

Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian

Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and adversarial attacks from external expl…

cs.CV2026

Visual Reasoning Agent: Robust Vision Systems in Remote Sensing via Inference-Time Scaling

Chung-En Johnny Yu, Brian Jalaian, Nathaniel D. Bastian

Building robust vision systems for high-stakes domains such as remote sensing requires stronger visual reasoning than what single-pass inference typically provides; yet, retraining…

cs.CV2025

Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments

Yasir Ali Farrukh, Syed Wali, Irfan Khan +1

Unsupervised Domain Adaptation (UDA) is a critical challenge in real-world vision systems, especially in resource-constrained environments like drones, where memory and computation…

cs.CV2025

TOGA: Temporally Grounded Open-Ended Video QA with Weak Supervision

Ayush Gupta, Anirban Roy, Rama Chellappa +3

We address the problem of video question answering (video QA) with temporal grounding in a weakly supervised setup, without any temporal annotations. Given a video and a question,…

cs.CV2025

VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection

Aditya Taparia, Noel Ngu, Mario Leiva +6

Although fusing multiple sensor modalities can enhance object detection performance, existing fusion approaches often overlook subtle variations in environmental conditions and sen…

cs.CV2025

Hydra: An Agentic Reasoning Approach for Enhancing Adversarial Robustness and Mitigating Hallucinations in Vision-Language Models

Chung-En, Yu, Hsuan-Chih +3

To develop trustworthy Vision-Language Models (VLMs), it is essential to address adversarial robustness and hallucination mitigation, both of which impact factual accuracy in high-…