8028 papers
This paper proposes the Deferred Visual Ingestion (DVI) framework, which replaces the lossy pre-embedding of visual content with a structure-based hierarchical indexing and deferred VLM analysis strategy, achieving significantly higher accuracy on visual-dense engineering document QA by overcoming the retrieval and detail-loss limitations of existing Pre-Ingestion methods.
This paper presents a feasible global optimization approach for depth from defocus that utilizes alternating minimization between convex optimization and parallel grid search to achieve high-resolution depth recovery on both synthetic and real datasets, outperforming current deep learning methods.
This paper introduces Hadamard spectral codes, a compact latent representation that enables efficient spectral rendering by approximating complex wavelength-dependent interactions through a small number of standard RGB rendering passes while preserving linear operations and approximating multiplicative spectral relationships.
This paper proposes a novel skin-colour disentangling framework that utilizes randomized decolourization and geometry-aligned post-processing to learn a structured latent space for generating diverse, equitable skin lesion datasets, thereby improving machine learning model performance across varying skin tones and imaging conditions.
The paper introduces FUSAR-GPT, a specialized Visual Language Model for SAR imagery that overcomes existing limitations by leveraging an inaugural SAR Image-Text-AlphaEarth dataset, embedding multi-source spatiotemporal features via "spatiotemporal anchors," and employing a two-stage decoupled training strategy to achieve state-of-the-art performance in remote sensing interpretation.
This paper introduces DICArt, a novel framework that advances category-level articulated object pose estimation by formulating the task as a conditional discrete diffusion process enhanced with a flexible flow decider and hierarchical kinematic coupling to overcome the limitations of existing continuous-space methods.
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.