cs.CV papers | Gist.Science

EventVGGT: Exploring Cross-Modal Distillation for Consistent Event-based Depth Estimation

EventVGGT is a novel framework that addresses the scarcity of depth annotations and temporal inconsistency in event-based monocular depth estimation by treating event streams as coherent video sequences and distilling spatio-temporal and multi-view geometric priors from the Visual Geometry Grounded Transformer (VGGT) through a tri-level distillation strategy, achieving state-of-the-art performance and robust zero-shot generalization.

Yinrui Ren, Jinjing Zhu, Kanghao Chen, Zhuoxiao Li, Jing Ou, Zidong Cao, Tongyan Hua, Peilun Shi, Yingchun Fu, Wufan Zhao, Hui Xiong2026-03-11💻 cs

Training-Free Coverless Multi-Image Steganography with Access Control

The paper proposes MIDAS, a training-free diffusion-based framework that enables coverless multi-image steganography with user-specific access control through latent-level fusion, demonstrating superior performance in image quality, robustness, and security compared to existing methods.

Minyeol Bae, Si-Hyeon Lee2026-03-11💻 cs

ICDAR 2025 Competition on End-to-End Document Image Machine Translation Towards Complex Layouts

This paper presents the ICDAR 2025 competition on end-to-end document image machine translation, detailing its dual-track structure for small and large models, participation statistics, and findings that highlight large-model approaches as a promising paradigm for handling complex document layouts.

Yaping Zhang, Yupu Liang, Zhiyang Zhang, Zhiyuan Chen, Lu Xiang, Yang Zhao, Yu Zhou, Chengqing Zong2026-03-11🤖 cs.AI

YOLO-NAS-Bench: A Surrogate Benchmark with Self-Evolving Predictors for YOLO Architecture Search

This paper introduces YOLO-NAS-Bench, the first surrogate benchmark for YOLO-style object detectors, which employs a self-evolving mechanism to iteratively refine a LightGBM predictor, enabling efficient and accurate discovery of high-performing architectures that surpass official YOLO baselines.

Zhe Li, Xiaoyu Ding, Jiaxin Zheng, Yongtao Wang2026-03-11💻 cs

Reviving ConvNeXt for Efficient Convolutional Diffusion Models

This paper introduces the Fully Convolutional Diffusion Model (FCDM), a ConvNeXt-based architecture that achieves competitive generative performance with significantly fewer computational resources and training steps than Transformer-based counterparts, demonstrating that modern convolutional designs remain a highly efficient alternative for scaling diffusion models.

Taesung Kwon, Lorenzo Bianchi, Lennart Wittke, Felix Watine, Fabio Carrara, Jong Chul Ye, Romann Weber, Vinicius Azevedo2026-03-11🤖 cs.AI

RiO-DETR: DETR for Real-time Oriented Object Detection

RiO-DETR is the first real-time oriented object detection transformer that addresses challenges in angle estimation, periodicity, and convergence through novel designs like Content-Driven Angle Estimation and Decoupled Periodic Refinement, achieving a new speed-accuracy trade-off on benchmark datasets.

Zhangchi Hu, Yifan Zhao, Yansong Peng, Wenzhang Sun, Xiangchen Yin, Jie Chen, Peixi Wu, Hebei Li, Xinghao Wang, Dongsheng Jiang, Xiaoyan Sun2026-03-11💻 cs

PromptDLA: A Domain-aware Prompt Document Layout Analysis Framework with Descriptive Knowledge as a Cue

This paper introduces PromptDLA, a domain-aware framework that leverages descriptive knowledge as cues to customize prompts for integrating domain priors, thereby overcoming the limitations of directly merging diverse datasets and achieving state-of-the-art performance in Document Layout Analysis across multiple benchmarks.

Zirui Zhang, Yaping Zhang, Lu Xiang, Yang Zhao, Feifei Zhai, Yu Zhou, Chengqing Zong2026-03-11🤖 cs.AI

CIGPose: Causal Intervention Graph Neural Network for Whole-Body Pose Estimation

CIGPose introduces a Causal Intervention Graph Neural Network framework that enhances whole-body pose estimation robustness by using a Structural Causal Model to identify and replace context-confounded keypoint representations with invariant embeddings, thereby achieving state-of-the-art performance on COCO-WholeBody without relying on extra training data.

Bohao Li, Zhicheng Cao, Huixian Li, Yangming Guo2026-03-11💻 cs

MetaDAT: Generalizable Trajectory Prediction via Meta Pre-training and Data-Adaptive Test-Time Updating

The paper proposes MetaDAT, a trajectory prediction framework that combines meta-learning pre-training with a data-adaptive test-time updating mechanism to achieve robust, fast, and accurate online adaptation under distribution shifts by dynamically adjusting learning rates and focusing on informative hard samples.

Yuning Wang, Pu Zhang, Yuan He, Ke Wang, Jianru Xue2026-03-11💻 cs

Open-World Motion Forecasting

This paper introduces "Open-World Motion Forecasting," an end-to-end class-incremental framework that predicts future trajectories directly from camera images while mitigating catastrophic forgetting through pseudo-labeling with vision-language models and a novel query feature variance-based replay strategy, enabling continual adaptation to evolving object taxonomies in real-world autonomous driving.

Nicolas Schischka, Nikhil Gosala, B Ravi Kiran, Senthil Yogamani, Abhinav Valada2026-03-11🤖 cs.AI

GIIM: Graph-based Learning of Inter- and Intra-view Dependencies for Multi-view Medical Image Diagnosis

The paper proposes GIIM, a novel graph-based framework that enhances multi-view medical image diagnosis by simultaneously modeling intra-view relationships and inter-view dynamics while effectively handling missing data to improve predictive accuracy and robustness.

Tran Bao Sam, Hung Vu, Dao Trung Kien, Tran Dat Dang, Van Ha Tang, Steven Truong2026-03-11💻 cs

A Guideline-Aware AI Agent for Zero-Shot Target Volume Auto-Delineation

This paper introduces OncoAgent, a novel guideline-aware AI agent that achieves zero-shot, training-free auto-delineation of clinical target volumes by converting textual clinical guidelines into 3D contours, demonstrating superior adaptability and physician preference over traditional supervised deep learning models.

Yoon Jo Kim, Wonyoung Cho, Jongmin Lee, Han Joo Chae, Hyunki Park, Sang Hoon Seo, Noh Jae Myung, Kyungmi Yang, Dongryul Oh, Jin Sung Kim2026-03-11🤖 cs.AI

EvoDriveVLA: Evolving Autonomous Driving Vision-Language-Action Model via Collaborative Perception-Planning Distillation

EvoDriveVLA is a novel Vision-Language-Action model for autonomous driving that overcomes perception degradation and planning instability through a collaborative distillation framework combining self-anchored visual constraints and oracle-guided trajectory optimization to achieve state-of-the-art performance.

Jiajun Cao, Xiaoan Zhang, Xiaobao Wei, Liyuqiu Huang, Wang Zijian, Hanzhen Zhang, Zhengyu Jia, Wei Mao, Hao Wang, Xianming Liu, Shuchang Zhou Liu, Yang Wang, Shanghang Zhang2026-03-11🤖 cs.AI

TopoOR: A Unified Topological Scene Representation for the Operating Room

TopoOR introduces a novel topological scene representation for surgical operating rooms that leverages higher-order structures and attention mechanisms to preserve complex multimodal relationships and manifold geometry, thereby outperforming traditional graph and LLM-based methods in safety-critical tasks like sterility breach detection and robot phase prediction.

Tony Danjun Wang, Ka Young Kim, Tolga Birdal, Nassir Navab, Lennart Bastian2026-03-11💻 cs

The Patrologia Graeca Corpus: OCR, Annotation, and Open Release of Noisy Nineteenth-Century Polytonic Greek Editions

This paper introduces the Patrologia Graeca Corpus, a large-scale open resource featuring OCR-processed, lemmatized, and part-of-speech tagged text from degraded nineteenth-century bilingual Greek-Latin editions, which achieves state-of-the-art recognition accuracy and establishes a new benchmark for noisy polytonic Greek processing.

Chahan Vidal-Gorène (CJM, LIPN), Bastien Kindt2026-03-11💻 cs

OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks

This paper introduces OmniEarth, a comprehensive benchmark comprising 9,275 images and 44,210 verified instructions that evaluates Vision-Language Models across 28 geospatial tasks with a focus on perception, reasoning, and robustness, revealing significant performance gaps in current models for remote sensing applications.

Ronghao Fu, Haoran Liu, Weijie Zhang, Zhiwen Lin, Xiao Yang, Peng Zhang, Bo Yang2026-03-11💻 cs

Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity

PruneSID is a training-free, synergistic importance-diversity framework that significantly enhances Vision-Language Model efficiency by employing Principal Semantic Components Analysis and Intra-group Non-Maximum Suppression to achieve state-of-the-art accuracy with extreme token compression and faster prefilling speeds.

Zhengyao Fang, Pengyuan Lyu, Chengquan Zhang, Guangming Lu, Jun Yu, Wenjie Pei2026-03-11💻 cs

Component-Aware Sketch-to-Image Generation Using Self-Attention Encoding and Coordinate-Preserving Fusion

This paper proposes a novel component-aware, self-refining framework that combines a Self-Attention-based Autoencoder, a Coordinate-Preserving Gated Fusion module, and a Spatially Adaptive Refinement Revisor to generate high-fidelity, semantically accurate photorealistic images from freehand sketches, significantly outperforming existing GAN and diffusion models across diverse facial and non-facial datasets.

Ali Zia, Muhammad Umer Ramzan, Usman Ali, Muhammad Faheem, Abdelwahed Khamis, Shahnawaz Qureshi2026-03-11💻 cs

Streaming Autoregressive Video Generation via Diagonal Distillation

This paper introduces Diagonal Distillation, an asymmetric autoregressive framework that leverages temporal context and implicit optical flow to enable high-fidelity, real-time streaming video generation with a 277.3x speedup while mitigating error accumulation and motion incoherence.

Jinxiu Liu, Xuanming Liu, Kangfu Mei, Yandong Wen, Ming-HsuanYang, Weiyang Liu2026-03-11💻 cs

Evolving Prompt Adaptation for Vision-Language Models

The paper proposes EvoPrompt, a novel framework that achieves stable, knowledge-preserving adaptation of vision-language models in few-shot scenarios by employing a modality-shared projector and an evolutionary training strategy that decouples prompt updates into directional and magnitude components to prevent catastrophic forgetting.

Enming Zhang, Jiayang Li, Yanru Wu, Zhenyu Liu, Yang Li2026-03-11🤖 cs.AI

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