💻 Category

cs

3827 papers

VB: Visibility Benchmark for Visibility and Perspective Reasoning in Images

This paper introduces VB, a novel benchmark designed to evaluate vision-language models' ability to determine image visibility and appropriately abstain from answering when evidence is insufficient, utilizing controlled minimal edits and specialized metrics to reveal that top-tier models like GPT-4o and Gemini 3.1 Pro significantly outperform open-source alternatives in confidence-aware accuracy and perspective reasoning.

Neil Tripathi2026-03-10💻 cs

RADAR: A Multimodal Benchmark for 3D Image-Based Radiology Report Review

The paper introduces RADAR, a multimodal benchmark comprising expert-annotated 3D abdominal CT scans and radiology report edits that enables the systematic evaluation of AI models on fine-grained clinical reasoning tasks, specifically image-text alignment and discrepancy assessment during the radiology report review process.

Zhaoyi Sun, Minal Jagtiani, Wen-wai Yim, Fei Xia, Martin Gunn, Meliha Yetisgen, Asma Ben Abacha2026-03-10💻 cs

ECHO: Event-Centric Hypergraph Operations via Multi-Agent Collaboration for Multimedia Event Extraction

The paper proposes ECHO, a multi-agent framework that utilizes iterative hypergraph operations and a "Link-then-Bind" strategy to mitigate cascading errors in Multimedia Event Extraction, achieving significant performance improvements over state-of-the-art methods on the M2E2 benchmark.

Hailong Chu, Shuo Zhang, Yunlong Chu, Shutai Huang, Xingyue Zhang, Tinghe Yan, Jinsong Zhang, Lei Li2026-03-10💻 cs

Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMind

This study evaluates the adaptability of the TerraMind geospatial foundation model to hyperspectral imaging tasks without native pretraining, finding that while band selection strategies allow for moderate performance, deep learning models with native spectral support remain superior, thereby highlighting the critical need for future architectures to incorporate native spectral tokenization.

Julia Anna Leonardi, Johannes Jakubik, Paolo Fraccaro, Maria Antonia Brovelli2026-03-10💻 cs

HARP: HARmonizing in-vivo diffusion MRI using Phantom-only training

This paper introduces HARP, a deep learning framework that harmonizes multi-site in-vivo diffusion MRI data by training exclusively on easily transportable phantom scans, thereby eliminating the need for impractical multi-site human cohorts while significantly reducing inter-scanner variability.

Hwihun Jeong, Qiang Liu, Kathryn E. Keenan, Elisabeth A. Wilde, Walter Schneider, Sudhir Pathak, Anthony Zuccolotto, Lauren J. O'Donnell, Lipeng Ning, Yogesh Rathi2026-03-10💻 cs

Thinking with Gaze: Sequential Eye-Tracking as Visual Reasoning Supervision for Medical VLMs

This paper introduces a method that enhances medical Vision-Language Models by using sequential eye-tracking data as supervision to train dedicated gaze tokens, enabling the models to mimic radiologists' visual search patterns and achieve state-of-the-art performance in both in-domain and out-of-domain medical reasoning tasks.

Yiwei Li, Zihao Wu, Yanjun Lv, Hanqi Jiang, Weihang You, Zhengliang Liu, Dajiang Zhu, Xiang Li, Quanzheng Li, Tianming Liu, Lin Zhao2026-03-10💻 cs

Asymmetric Distillation and Information Retention in Capacity-Constrained Cross-Modal Transfer

This paper investigates the severe dimensional collapse and resulting robustness fragility that occur when distilling a large Vision Transformer into capacity-constrained CNNs, revealing that while larger student models pack information densely but lose noise immunity, extremely small models act as robust low-pass filters due to fundamental geometric limitations in asymmetric cross-modal transfer.

Kabir Thayani2026-03-10💻 cs

SIQA: Toward Reliable Scientific Image Quality Assessment

This paper introduces the SIQA framework, which redefines scientific image quality assessment by distinguishing between perceptual alignment and scientific correctness, and demonstrates through a new benchmark that current multimodal models often achieve high scoring consistency with experts while lacking genuine scientific understanding.

Wenzhe Li, Liang Chen, Junying Wang, Yijing Guo, Ye Shen, Farong Wen, Chunyi Li, Zicheng Zhang, Guangtao Zhai2026-03-10💻 cs

Mining Beyond the Bools: Learning Data Transformations and Temporal Specifications

This paper proposes a novel approach to mining data-aware temporal specifications from execution traces by combining Syntax Guided Synthesis with a finite-prefix interpretation of Temporal Stream Logic (TSLf_f), enabling the robust and sample-efficient synthesis of reactive programs that capture both data transformations and temporal behaviors.

Sam Nicholas Kouteili, William Fishell, Christian Scaff, Mark Santolucito, Ruzica Piskac2026-03-10💻 cs

Dynamic Targeting of Satellite Observations Using Supplemental Geostationary Satellite Data and Hierarchical Planning

This paper proposes a hierarchical planning approach that integrates supplemental geostationary satellite data to extend lookahead horizons for Dynamic Targeting missions, demonstrating up to a 41% performance improvement over traditional onboard-only planners, particularly in scenarios with sparsely distributed targets.

Akseli Kangaslahti, Itai Zilberstein, Alberto Candela, Steve Chien2026-03-10💻 cs

UWPD: A General Paradigm for Invisible Watermark Detection Agnostic to Embedding Algorithms

This paper introduces Universal Watermark Presence Detection (UWPD), a novel task for identifying invisible watermarks without prior algorithm knowledge, supported by the UniFreq-100K dataset and the Frequency Shield Network (FSNet) model that achieves superior zero-shot detection by dynamically amplifying high-frequency watermark signals while suppressing semantic content.

Xiang Ao, Yiling Du, Zidan Wang, Mengru Chen2026-03-10💻 cs

HERO: Hierarchical Embedding-Refinement for Open-Vocabulary Temporal Sentence Grounding in Videos

This paper introduces the Open-Vocabulary Temporal Sentence Grounding (OV-TSGV) task with new benchmarks (Charades-OV and ActivityNet-OV) and proposes HERO, a hierarchical embedding-refinement framework that achieves state-of-the-art performance by effectively generalizing to novel linguistic expressions through multi-level semantic modeling and cross-modal refinement.

Tingting Han, Xinsong Tao, Yufei Yin, Min Tan, Sicheng Zhao, Zhou Yu2026-03-10💻 cs

Vessel-Aware Deep Learning for OCTA-Based Detection of AMD

This paper proposes a vessel-aware deep learning framework for detecting age-related macular degeneration (AMD) in OCTA images by integrating external multiplicative attention with clinically meaningful vascular biomarkers, specifically tortuosity and dropout maps, to guide the model toward physiologically relevant regions and improve interpretability.

Margalit G. Mitzner, Moinak Bhattacharya, Zhilin Zou, Chao Chen, Prateek Prasanna2026-03-10💻 cs