1469 papers
This paper proposes a novel data augmentation paradigm that leverages Large Language Models to generate diverse, industry-resembling CAD programs by conditioning them on reference surfaces and modeling procedures, thereby addressing the scarcity of complex, spline-based geometric data in existing training sets.
PaQ-DETR is a unified object detection framework that addresses query utilization imbalance by dynamically generating image-specific queries from shared latent patterns and employing a quality-aware one-to-many assignment strategy, resulting in consistent mAP improvements across various DETR backbones.
The paper proposes DLRMamba, a novel framework for edge-based multispectral object detection that combines a Low-Rank SS2D module to reduce parameter redundancy with a Structure-Aware Distillation strategy to preserve feature fidelity, achieving superior efficiency and accuracy on resource-constrained hardware.
This paper introduces ESM-YOLO+, a lightweight visible-infrared fusion network that employs a Mask-Enhanced Attention Fusion module and training-time Structural Representation enhancement to achieve high-precision small-target detection in complex remote sensing scenes while significantly reducing model complexity compared to baselines.
This paper proposes HIERAMP, a method that leverages the coarse-to-fine generation capability of Vision Autoregressive (VAR) models to amplify hierarchical semantics through dynamic class token injection, thereby improving dataset distillation performance by better capturing object structures and details without explicitly optimizing global proximity.
This study presents a 3D histomorphometric analysis pipeline using nnU-Net segmentation on optically cleared prostatectomy specimens to extract features related to perineural and lymphovascular invasion, demonstrating that 3D perineural invasion features significantly outperform their 2D counterparts in predicting 5-year biochemical recurrence in prostate cancer.
This paper introduces Virtual Intraoperative CT (viCT), a method that sequentially updates preoperative CT scans during endoscopic sinus surgery by integrating monocular endoscopic video-derived 3D reconstructions to visualize evolving tissue resection boundaries with submillimeter accuracy, thereby addressing the limitations of static image guidance.
SurgCUT3R is a novel framework that addresses the challenges of data scarcity and pose drift in monocular endoscopic video reconstruction by leveraging a synthetic data generation pipeline, hybrid supervision, and a hierarchical inference strategy to achieve robust, accurate, and efficient 3D surgical scene understanding.
This paper introduces Conditional Unbalanced Optimal Transport Maps (CUOTM), a robust conditional generative framework that mitigates the outlier sensitivity of classical Conditional Optimal Transport by relaxing distribution-matching constraints via Csiszár divergence penalties while preserving conditioning marginals through a theoretically justified triangular -transform parameterization.
The paper proposes T2SGrid, a novel framework that reformulates video temporal grounding as a spatial understanding task by arranging video frames into composite grid images via overlapping sliding windows, thereby overcoming the limitations of existing temporal encoding methods and achieving superior performance on standard benchmarks.
This paper proposes a novel approach to image-based shape retrieval that leverages pre-aligned multi-modal encoders and a hard contrastive learning loss to achieve state-of-the-art performance in both zero-shot and supervised settings, eliminating the need for explicit view-based supervision or view synthesis.
This paper proposes a perception-aware multimodal reasoning framework that enhances Vision-Language Models' spatial understanding in monocular driving scenarios by representing objects with Visual Reference Tokens and utilizing a Multimodal Chain-of-Thought dataset, achieving significant performance gains on the SURDS benchmark through standard supervised fine-tuning.
This paper introduces ADAS-TO, the first large-scale naturalistic multimodal dataset of 15,659 ADAS-to-manual takeover events from 327 drivers, which combines kinematic and vision-language analysis to characterize safety-critical scenarios and demonstrate that actionable visual cues often precede takeovers by over three seconds.
MipSLAM is a novel 3D Gaussian Splatting SLAM framework that achieves high-fidelity anti-aliased rendering and robust pose estimation by integrating an Elliptical Adaptive Anti-aliasing algorithm, a Spectral-Aware Pose Graph Optimization module, and a local frequency-domain perceptual loss to overcome aliasing artifacts and trajectory drift.
AdaGen introduces a general, learnable framework that employs reinforcement learning with an adversarial reward to dynamically adapt step-specific parameters during iterative image synthesis, thereby overcoming the limitations of static, manually-designed schedules and achieving superior performance across diverse generative models with reduced inference costs.
TrajPred is a novel framework that enhances surgical instrument-tissue interaction recognition in vision-language models by encoding instrument trajectories to capture temporal motion cues and generating fine-grained visual semantic embeddings, thereby significantly improving performance and vision-text alignment on the CholecT50 benchmark.
This paper presents OV-DEIM, a real-time end-to-end DETR-style open-vocabulary object detector that combines the DEIMv2 framework with a query supplement strategy and a novel GridSynthetic data augmentation technique to achieve state-of-the-art performance and efficiency, particularly for rare categories.
This paper introduces TFM, a temporal attack framework that exploits the vulnerability of text-to-video models to generate harmful content by providing only sparse boundary conditions (start and end frames) and implicitly substituting sensitive cues, thereby bypassing existing safety filters and significantly increasing jailbreak success rates.
This paper proposes a novel fine-grained 3D facial reconstruction method for micro-expressions that integrates global dynamic features with locally-enriched cues from 2D motions, facial priors, and 3D geometry to overcome data scarcity and achieve superior geometric accuracy and perceptual detail compared to state-of-the-art approaches.
This paper proposes CAPL, a framework that mitigates multi-image hallucinations in large vision-language models by introducing a selectable image token interaction mechanism for fine-grained cross-image alignment and a preference learning strategy that trains the model to rely on genuine visual evidence rather than textual priors.