7529 papers
This paper addresses the limitations of existing formula-driven synthetic data by proposing a Physics-inspired Spatially-Decoupled Synthesis framework that resolves the texture-shape conflict through a gradient-shielded buffer zone and spectral texture injection, thereby significantly enhancing the performance of 3D medical Vision Transformers on BTCV and MSD datasets without relying on real patient data.
This paper presents a comprehensive technical survey on foundation models in remote sensing, exploring their evolution from unimodal to multimodal approaches while providing a tutorial-like guide to help researchers understand, train, and apply these models to real-world tasks.
MLRecon is a robust, low-cost framework for markerless freehand 3D ultrasound reconstruction that utilizes a commodity RGB-D camera and a vision foundation model-based pipeline with a dual-stage refinement network to achieve drift-resilient, sub-millimeter accurate probe pose tracking and high-quality volumetric imaging.
The paper proposes GeodesicNVS, a novel view synthesis framework that combines deterministic Data-to-Data Flow Matching with Probability Density Geodesic constraints derived from pretrained diffusion models to achieve superior view consistency and geometric coherence compared to traditional diffusion-based approaches.
This paper discusses the implementation of car license plate recognition systems, highlighting their growing necessity in Malaysia due to increasing vehicle numbers and their diverse applications in traffic management, law enforcement, and other specialized fields.
This paper proposes RaUF, a spatial uncertainty field learning framework that addresses the low fidelity and ambiguity of millimeter-wave radar by modeling anisotropic probabilistic uncertainty and employing a bidirectional domain attention mechanism to suppress spurious returns, thereby delivering highly reliable spatial detections with well-calibrated uncertainty for downstream perception tasks.
This paper proposes Content-Aware Frequency Encoding (CAFE) and its enhanced variant CAFE+, which utilize learnable parallel linear layers and Chebyshev features to overcome the spectral bias of Implicit Neural Representations by explicitly synthesizing and selecting task-relevant frequency bases for superior signal reconstruction.
The paper proposes VL-Anomaly, a novel framework that leverages vision-language model priors and a multi-source inference strategy to significantly improve road anomaly segmentation by reducing false positives on normal backgrounds and enhancing the detection of out-of-distribution obstacles.
To address the limitations of traditional visual methods in robot automated assembly, this paper proposes SMR-Net, a self-attention-based multi-scale detection algorithm paired with a dedicated sensor, which significantly improves snap localization precision and robustness in complex scenarios by integrating attention-enhanced feature extraction, parallel multi-scale processing, and adaptive reweighting.
The paper proposes TAR-FAS, a tool-augmented reasoning framework that enhances generalizable Face Anti-Spoofing by enabling MLLMs to combine intuitive observations with adaptive, fine-grained visual tool investigations through a specialized dataset and training pipeline.
The paper introduces MM-DeepResearch, a multimodal deep research agent that overcomes data scarcity, trajectory generation, and training cost challenges through Hyper-Search for data synthesis, DR-TTS for specialized tool optimization and trajectory planning, and an offline search engine for cost-effective reinforcement learning.
This paper proposes ELF-VLA, a framework that enhances Vision-Language-Action models for autonomous driving by replacing vague scalar rewards with explicit, diagnostic failure feedback to guide targeted policy refinement, thereby overcoming exploration limitations and achieving state-of-the-art performance on the NAVSIM benchmark.
LLaDA-o is a state-of-the-art, length-adaptive omni diffusion model that leverages a Mixture of Diffusion framework with a shared attention backbone to effectively unify discrete text understanding and continuous visual generation, achieving top-tier performance on multimodal benchmarks.
This paper presents SHIELD8-UAV, a low-energy, sequential 8-bit hardware accelerator for UAV acoustic detection that achieves real-time, precision-aware inference on resource-constrained edge devices through a shared multi-precision datapath, layer-sensitivity quantization, and structured channel pruning.
The paper proposes A3Point, an adaptive framework that enhances LiDAR semantic segmentation robustness under adverse weather by utilizing a semantic confusion prior and shift region localization to effectively leverage diverse augmentations while mitigating semantic shifts.
This paper introduces MCMR, a large-scale benchmark designed to evaluate fine-grained, multi-condition multimodal retrieval across five product domains, revealing that while visual cues drive early precision, MLLM-based rerankers significantly enhance compositional matching by verifying complex query-candidate consistency.
This paper introduces AesEval-Bench, a comprehensive benchmark and training dataset designed to systematically evaluate and enhance Vision Language Models' ability to assess graphic design aesthetics across multiple dimensions, tasks, and indicators.
This paper introduces V-SONAR, a unified vision-language embedding space aligned with the multilingual SONAR text space, and leverages it to develop V-LCM, a model that achieves state-of-the-art performance in video captioning and significantly outperforms existing vision-language models across 61 diverse languages through concept space alignment and latent diffusion training.
This paper introduces DP-RGMI, a framework that analyzes differential privacy in medical imaging by decomposing utility loss into representation geometry and task-head utilization, revealing that privacy mechanisms induce non-uniform anisotropic reshaping of features and create a utilization gap even when linear separability is preserved.
This paper proposes StrokeDiff, a data-efficient diffusion-based framework with Smooth Regularization that generates diverse, controllable, and human-like oil painting brushstrokes from a small dataset, enabling structured and expressive multimedia content creation.