4157 papers
The paper proposes WCC-Net, a 3D wavelet-conditioned ControlNet framework that integrates explicit frequency-domain structural priors into a diffusion model to achieve superior anatomical consistency and denoising performance in whole-body low-dose PET imaging compared to existing CNN, GAN, and diffusion-based methods.
This paper presents an automated, multi-stage pipeline that identifies cervical spine fractures by fusing orthogonal 2D segmentations to estimate 3D volumes of interest, which are then analyzed using a 2.5D CNN-Transformer ensemble to achieve diagnostic performance comparable to expert radiologists while reducing computational dimensionality.
This paper proposes VRFT-Aug, a novel visual reinforcement fine-tuning framework that integrates perception and reasoning augmentation strategies to significantly outperform existing supervised and reinforcement learning baselines in high-stakes medical imaging tasks.
The First International StepUP Competition leveraged the newly released UNB StepUP-P150 dataset to advance biometric footstep recognition, culminating in a global contest where the top team achieved a 10.77% equal error rate while highlighting persistent challenges in generalizing to unfamiliar footwear.
VidEoMT is a simple, high-speed video segmentation model that eliminates complex tracking modules by leveraging a plain ViT encoder enhanced with a lightweight query propagation and fusion mechanism to achieve competitive accuracy at up to 160 FPS.
This paper proposes Conflict-aware Adaptive Safety Guidance (CASG), a training-free framework that dynamically identifies and applies category-specific safety directions to resolve harmful conflicts in text-to-image diffusion models, thereby significantly reducing overall harmful output rates compared to existing methods.
Skullptor bridges the gap between efficient single-image reconstruction and high-fidelity multi-view photogrammetry by combining a cross-view attention-based normal prediction model with inverse rendering optimization to achieve detailed 3D head reconstruction in seconds with reduced camera and computational requirements.
The paper proposes Momentum Memory Knowledge Distillation (MoMKD), a cross-modal framework that utilizes a momentum-updated memory to aggregate genomic and histopathology information across batches and decouples branch gradients, thereby overcoming the limitations of batch-local alignment and enabling robust, generalizable cancer diagnosis using histology-only inference.
This paper proposes a dynamic Visual Place Recognition mapping approach that automatically selects the optimal reference map density to guarantee that a user-specified local recall performance level is met across a defined proportion of the environment, thereby ensuring reliable long-term deployment without unnecessary over-densification.
This paper introduces Spatial Credit Redistribution (SCR), a training-free inference-time method that mitigates hallucinations in Vision-Language Models by redistributing suppressed visual attention from dominant patches to their spatial neighbors, thereby significantly reducing hallucination rates across multiple benchmarks while preserving generation quality and maintaining negligible latency.
This paper introduces EvalMVX, a comprehensive real-world dataset featuring 25 objects with aligned ground-truth meshes captured under diverse lighting and view conditions, to establish a unified benchmark for quantitatively evaluating and comparing neural multiview stereo, photometric stereo, and shape-from-polarization reconstruction methods.
This paper proposes an improved MambdaBDA framework that integrates Focal Loss, lightweight Attention Gates, and a compact Alignment Module to significantly enhance building damage assessment accuracy and generalization across diverse disaster domains by addressing class imbalance, background clutter, and domain shift.
This paper presents a unified study that systematically investigates the interactions between the temperature parameter and various training components in knowledge distillation, offering practical guidance for selecting optimal temperature values to improve student performance.
The paper proposes ITO, a framework that enhances image-text contrastive pretraining by synergizing multimodal multiple alignment with a lightweight, inference-free training-time fusion module to eliminate modality gaps and outperform existing baselines across various benchmarks.
This paper introduces Probe-Select, a plug-in module that predicts final image quality from early denoising activations to enable efficient early termination of unpromising seeds, thereby reducing sampling costs by over 60% while improving the quality of retained images in text-to-image generation.
This paper generalizes 3D Gaussian splatting to non-exponential radiative transfer regimes by introducing quadratic transmittance variants that achieve rendering quality comparable to the original method while significantly reducing overdraws and accelerating rendering speeds by up to 4x.
TRACE introduces a novel framework for universal multimodal retrieval that unifies generative reasoning with discriminative representation learning by generating and compressing Chain-of-Thought traces, thereby achieving state-of-the-art performance through autonomous task-adaptive reasoning and strong zero-shot transferability.
MoECLIP addresses the limitations of patch-agnostic designs in Zero-Shot Anomaly Detection by introducing a Mixture-of-Experts architecture that dynamically routes image patches to specialized LoRA experts, enhanced by Frozen Orthogonal Feature Separation and an ETF loss to ensure distinct and maximally equiangular representations, thereby achieving state-of-the-art performance across diverse industrial and medical benchmarks.
ProSMA-UNet introduces a novel decoder-conditioned sparse feature selection mechanism that employs multi-scale compatibility fields and an proximal operator to explicitly filter irrelevant skip-connection noise, thereby achieving state-of-the-art performance in challenging medical image segmentation tasks.
This paper proposes SpeciaRL, a specificity-aware reinforcement learning framework that fine-tunes reasoning Large Multimodal Models to achieve an optimal balance between correctness and specificity in open-world fine-grained image classification by employing a dynamic, verifier-based reward signal.