7824 papers
TextPecker addresses the critical bottleneck of structural anomaly perception in Visual Text Rendering by introducing a plug-and-play RL strategy supported by a specialized recognition dataset and stroke-editing synthesis engine, which significantly enhances the structural fidelity and semantic alignment of text-to-image models.
NORD is a data-efficient Vision-Language-Action model for autonomous driving that achieves competitive performance on Waymo and NAVSIM benchmarks using less than 60% of the training data and no reasoning annotations by addressing the difficulty bias in standard Group Relative Policy Optimization through the Dr. GRPO algorithm.
The paper proposes Durian, a difficulty-aware group normalization method that re-groups multimodal samples by perceptual complexity and reasoning uncertainty to stabilize reward normalization and enhance reasoning performance in multimodal large language models.
The paper proposes EndoDDC, a novel diffusion-based framework that integrates image features with sparse depth and gradient information to achieve robust and accurate dense depth reconstruction for endoscopic robotic navigation, effectively overcoming challenges like weak textures and light reflections.
CoLoGen is a unified diffusion framework that resolves the representational conflict between conceptual understanding and spatial localization in conditional image generation by employing a progressive learning curriculum and a novel Progressive Representation Weaving module to dynamically integrate specialized expert features.
The paper introduces Solaris, a multiplayer video world model for Minecraft that leverages a novel automated data collection system and a staged training pipeline to overcome the limitations of single-agent models by simulating consistent multi-view observations and complex multi-agent interactions.
This paper proposes an adaptive prefiltering framework for high-dimensional similarity search that dynamically allocates computational budgets based on query frequency patterns and cluster coherence, achieving equivalent recall with 20.4% fewer distance computations than static methods while maintaining sub-millisecond latency.
CrossLLM-Mamba is a novel, scalable framework that leverages bidirectional Mamba encoders to model RNA interaction prediction as a dynamic state-space alignment problem, achieving state-of-the-art performance across RNA-protein, RNA-small molecule, and RNA-RNA tasks by capturing context-dependent molecular binding more effectively than static fusion methods.
This paper demonstrates that introducing novel robustness losses during the training of downstream task-specific models effectively mitigates technical biases in histopathology foundation models, thereby enhancing both prediction accuracy and clinical applicability without requiring the retraining of the foundation models themselves.
This paper introduces MNAS-Unet, a novel medical image segmentation framework that leverages Monte Carlo Tree Search to dynamically optimize network architecture, achieving superior accuracy on multiple datasets while significantly reducing search costs and model size compared to state-of-the-art methods.
This paper presents AeroDGS, a physics-guided 4D Gaussian splatting framework that leverages a monocular geometry lifting module and physics-based optimization priors to achieve robust, high-fidelity dynamic reconstruction from single-view aerial UAV videos, addressing the inherent depth ambiguity and motion instability of such scenarios.
This study introduces a deep learning framework utilizing an Organ Focused Attention (OFA) loss function to accurately predict renal tumor malignancy from 3D CT images without requiring labor-intensive manual segmentation, achieving performance that surpasses conventional segmentation-based models on both private and public datasets.
This paper identifies that artifacts in Vision Transformers stem from a "lazy aggregation" behavior where the model relies on irrelevant background patches as shortcuts for global semantics, and proposes a solution that selectively integrates patch features into the CLS token to mitigate this issue and improve performance across diverse supervision paradigms.
MolFM-Lite is a multi-modal machine learning model that improves molecular property prediction by jointly encoding 1D sequences, 2D graphs, and 3D conformer ensembles through cross-attention fusion and FiLM conditioning, achieving significant performance gains over single-modality baselines on MoleculeNet benchmarks.
This paper introduces SimpleOCR, a plug-and-play training strategy that renders text queries directly onto images to force Multimodal Large Language Models to overcome "modality laziness" and genuinely read visual text, achieving significant performance gains on out-of-distribution benchmarks with extreme data efficiency.
This paper demonstrates the feasibility of deploying privacy-preserving, real-time episodic memory question answering on edge devices by utilizing a two-threaded pipeline with Multimodal Large Language Models, achieving competitive accuracy and low latency compared to cloud-based solutions.
MammoWise is a practical, local, multi-model pipeline that leverages open-source Vision Language Models enhanced with few-shot prompting and Retrieval Augmented Generation to generate high-quality mammography reports and perform accurate clinical classifications while ensuring privacy and reproducibility.
This paper proposes Space Syntax-guided Post-training (SSPT), a framework that integrates architectural theory into residential floor plan generation via a non-differentiable oracle and reinforcement learning to enhance public space dominance and functional hierarchy while outperforming distribution-fitted baselines in efficiency and stability.
Pix2Key is a novel composed image retrieval framework that utilizes semantic decomposition and self-supervised visual dictionary learning to represent queries and candidates as open-vocabulary dictionaries, thereby achieving superior intent-aware matching and diversity-aware reranking without relying on supervised triplets.
This paper introduces HARU-Net, a novel Hybrid Attention Residual U-Net architecture that integrates hybrid attention transformers and residual learning to effectively denoise low-dose Cone-Beam Computed Tomography (CBCT) images while preserving critical anatomical edges, outperforming state-of-the-art methods in both image quality metrics and computational efficiency.