6740 papers
This study reveals that medical image segmentation follows a geometry-limited power-law scaling behavior characterized by early performance saturation, which can be improved through topology-aware augmentation that enhances sample efficiency by expanding effective topological coverage without altering the fundamental scaling law.
This paper proposes VeCoR, a velocity contrastive regularization method that enhances Flow Matching models by introducing a two-sided attract-repel training scheme to prevent off-manifold errors and significantly improve image quality and stability, particularly in low-step and lightweight configurations.
This paper introduces UltraViCo, a training-free method that overcomes video length extrapolation limits in Diffusion Transformers by identifying and suppressing attention dispersion, thereby eliminating both periodic repetition and quality degradation to achieve up to 4x extrapolation with significant performance gains.
The paper proposes ReSAM, a point-supervised self-prompting framework that adapts the Segment Anything Model to remote sensing images through a Refine-Requery-Reinforce loop, achieving superior segmentation performance without requiring dense mask annotations.
This paper introduces InnoGym, the first benchmark and framework designed to evaluate the innovation potential of AI agents by measuring both the performance gains over existing solutions and the novelty of their methodologies across 18 real-world engineering and scientific tasks.
AdaptVision is an efficient Vision-Language Model paradigm that mimics human active vision by using a reinforcement learning framework with Decoupled Turn Policy Optimization to autonomously determine and acquire the minimum necessary visual tokens via a coarse-to-fine process, thereby achieving superior performance with significantly reduced computational overhead compared to existing methods.
This paper proposes Fourier-Attentive Representation Learning (FARL), a novel framework that enhances few-shot generalization in Vision-Language Models by explicitly disentangling image structure and style via Fourier analysis and a dual cross-attention mechanism to guide robust vision-language alignment.
This study introduces a lightweight, unsupervised Variational Auto-Encoder model utilizing 3-meter 4-band Planet Labs imagery to detect conflict-related fires in Sudan within 24 to 30 hours, demonstrating superior performance in recall and F1-scores compared to traditional change detection methods for near-real-time war zone monitoring.
This paper presents a systematic study isolating the impact of masking families in continual test-time adaptation, revealing that spatial masking generally outperforms frequency masking on patch-tokenized architectures by preserving structural coherence, while the optimal choice ultimately depends on the alignment between the specific architecture and task.
Brain-Semantoks is a self-supervised foundation model for fMRI time series that leverages a semantic tokenizer and a self-distillation objective to learn robust, abstract representations of brain dynamics, enabling strong downstream performance and scalable out-of-distribution generalization without extensive fine-tuning.
This paper introduces -CLIP, a multi-granular text-conditioned contrastive learning framework that employs cross-attention and a novel -Contextualized Contrastive Alignment Loss to achieve state-of-the-art dense vision-language alignment by hierarchically matching textual descriptions of varying lengths to corresponding visual regions.
CRISP is a novel method that recovers physically plausible, simulation-ready human motion and scene geometry from monocular video by fitting planar primitives to point clouds, leveraging contact modeling for occluded regions, and validating interactions through reinforcement learning, thereby significantly reducing motion tracking failures and accelerating real-to-sim applications.
SoFlow introduces a one-step generative modeling framework that leverages a novel Flow Matching loss and a Jacobian-free solution consistency loss to achieve superior ImageNet 256x256 generation performance compared to MeanFlow models while avoiding computationally expensive operations.
This research proposes a comprehensive, interpretable multi-modal AI framework that integrates deep learning image analysis with family history data to enhance the accuracy and personalization of dermatological diagnosis, with plans for prospective clinical trials to validate its real-world implementation.
The paper proposes GeoTeacher, a semi-supervised 3D object detection framework that enhances student model performance on limited labeled data by employing a keypoint-based geometric relation supervision module and a distance-decay voxel-wise data augmentation strategy to better capture and understand object geometries, achieving state-of-the-art results on the ONCE and Waymo datasets.
This research proposes "ForCM," a novel forest cover mapping approach that integrates deep learning models with Object-Based Image Analysis (OBIA) on Sentinel-2 imagery, achieving higher accuracy (up to 95.64%) than traditional OBIA methods to support global environmental monitoring.
This paper introduces CEM, a model-agnostic, plug-and-play plugin that utilizes a dynamic programming algorithm guided by cumulative error minimization to dynamically optimize caching strategies, thereby significantly enhancing the generation fidelity of accelerated Diffusion Transformer models without incurring additional computational overhead.
This paper introduces Pointwise-interpretable Networks (PiNets), a modeling framework that ensures "explanatory alignment" by combining statistical intelligence with a pseudo-linear structure to produce neural network explanations that directly underlie predictions rather than merely rationalizing them.
TP-Blend is a lightweight, training-free framework that achieves precise object-style blending in diffusion models by combining Cross-Attention Object Fusion for spatially aware feature reassignment and Self-Attention Style Fusion for detail-sensitive texture modulation, enabling simultaneous high-fidelity object replacement and style transfer.
This paper presents a zero-shot 3D object alignment framework that optimizes relative pose using CLIP-driven gradients and geometry-aware constraints via a differentiable renderer, achieving semantically faithful and physically plausible results without requiring new model training.