3927 papers
This paper proposes ADiVA, a generative zero-shot learning framework that addresses the class-instance and semantic-visual domain gaps by jointly modeling attribute distributions to capture instance-specific variability and employing visual-guided alignment to refine semantic representations, thereby significantly outperforming state-of-the-art methods on benchmark datasets.
FlowMotion is a novel training-free framework that achieves efficient and flexible video motion transfer by directly leveraging early latent predictions from flow-based text-to-video models to extract motion representations and align patterns, while employing velocity regularization to ensure smooth motion evolution.
This paper proposes a novel 3D dental implant inpainting method using perpendicular score-based diffusion models that operate in the projection domain to capture inter-projection correlations, thereby generating high-quality, artifact-reduced CBCT images with improved consistency compared to existing 2D-based approaches.
The paper introduces DEX-AR, a novel dynamic explainability method that generates per-token and sequence-level heatmaps for autoregressive Vision-Language Models by computing layer-wise gradients and employing dynamic filtering to distinguish visually-grounded from linguistic tokens, thereby improving interpretability and performance across multiple benchmarks.
The paper proposes LTA, a transfer-based adversarial attack that optimizes perturbations within the latent space of a pretrained Stable Diffusion VAE to generate spatially coherent, low-frequency examples that achieve superior transferability and robustness against common preprocessing compared to traditional pixel-space methods.
This paper proposes WMoE-CLIP, a zero-shot anomaly detection method that overcomes the limitations of fixed prompts and spatial-only features by integrating variational autoencoder-based semantic modeling, wavelet decomposition for multi-frequency feature refinement, and a semantic-aware mixture-of-experts module, achieving state-of-the-art performance across 14 industrial and medical datasets.
This paper introduces P-SLCR, a novel unsupervised point cloud semantic segmentation framework that leverages Consistent Structure Learning and Semantic Relation Consistent Reasoning to achieve state-of-the-art performance on S3DIS, SemanticKITTI, and Scannet datasets, even surpassing the fully supervised PointNet on the S3DIS Area-5 benchmark.
This study evaluates ChatGPT's ability to generate AI pastiches of contemporary artworks by combining human feedback from twelve international artists with computational analysis, revealing a significant gap between superficial visual similarities and the lack of conceptual depth, dimensionality, and emotional resonance in the AI-generated results, thereby advocating for a multi-metric "style transfer dashboard" for more comprehensive evaluation.
WorldCache is a novel caching framework that accelerates diffusion-based world models by up to 3.7 without retraining, overcoming challenges of token heterogeneity and non-uniform temporal dynamics through curvature-guided prediction and chaotic-prioritized adaptive skipping to maintain high rollout quality.
K-MaT is a novel prompt-learning framework that enables the zero-shot transfer of large-scale biomedical vision-language models from high-end to low-end imaging modalities by anchoring prompts to clinical text and aligning their decision manifolds via Fused Gromov-Wasserstein optimal transport, thereby achieving state-of-the-art performance while mitigating catastrophic forgetting.
The paper introduces the Dynamic Chunking Diffusion Transformer (DC-DiT), a model that adaptively compresses image tokens based on spatial detail and diffusion timesteps to achieve superior generation quality and efficiency over standard DiTs while requiring minimal additional training.
This paper introduces LATO, a novel framework that enables scalable, flow matching-based synthesis of explicit 3D meshes with complex geometry and well-formed topology by representing them as vertex displacement fields within a structured, topology-preserving sparse voxel latent space, thereby eliminating the need for isosurface extraction or heuristic meshing.
This paper presents computer vision-based methods, utilizing a dual-camera system (BIODISCOVER) to capture sinking speed and area, that accurately estimate invertebrate dry mass with 10–20% median error, offering a scalable, non-destructive alternative to manual weighing for biodiversity monitoring.
This paper introduces OralGPT-Plus, an agentic vision-language model that leverages a new dataset (DentalProbe), a reinspection-driven reinforcement learning framework, and a holistic benchmark (MMOral-X) to enable iterative, symmetry-aware, and clinically reliable diagnostic reasoning for panoramic dental radiographs.
Rewis3d is a novel framework that significantly improves weakly-supervised semantic segmentation on 2D images by leveraging feed-forward 3D reconstruction as an auxiliary supervisory signal to propagate sparse annotations across scenes via a dual student-teacher architecture, achieving state-of-the-art performance without additional labels or inference overhead.
The paper proposes MoEMambaMIL, a structure-aware selective state space model that integrates region-nested selective scanning with mixture-of-experts mechanisms to effectively capture hierarchical spatial dependencies in whole-slide images, achieving state-of-the-art performance across nine downstream tasks.
The paper introduces CHMv2, a new global meter-resolution canopy height map that significantly improves accuracy and structural detail over existing products by leveraging a DINOv3-based depth estimation model trained on expanded, diverse airborne laser scanning data and validated against millions of satellite observations.
This paper introduces a prompt group-aware training framework that enhances the robustness and generalization of text-guided nuclei segmentation by enforcing consistency among semantically related prompts through quality-guided regularization and logit-level constraints, achieving significant performance gains without altering model architecture or inference.
REACT++ is a new state-of-the-art model for real-time Scene Graph Generation that leverages efficient feature extraction and subject-to-object cross-attention to simultaneously achieve the highest inference speed, improved relation prediction accuracy, and maintained object detection performance, outperforming its predecessor by being 20% faster with a 10% accuracy gain.
This paper proposes a human-in-the-loop framework that combines an automatic relaxation-labeling solver with interactive guidance strategies to effectively and efficiently reassemble large-scale, fragmented cultural heritage artifacts in real-world conditions where traditional methods fail.