7811 papers
This paper proposes "Sparse Imagination," a transformer-based visual world model planning method that utilizes a randomized grouped attention strategy to dynamically reduce token processing during latent rollout, thereby significantly accelerating inference efficiency while maintaining high control fidelity for real-time robotic applications.
LinGuinE is a novel, training-free PyTorch framework that achieves state-of-the-art longitudinal volumetric tumour segmentation and lesion tracking across multiple datasets by combining image registration with guided segmentation from a single radiologist prompt, enabling flexible, direction-agnostic analysis without requiring longitudinal data training.
This paper proposes a novel human-guided framework for CBCT-to-MDCT translation that leverages a Schrödinger Bridge formulation with conditional diffusion and classifier-free guidance to effectively suppress shade artifacts while preserving anatomical fidelity and aligning with clinical preferences through iterative human feedback.
This paper addresses the "Data Addition Dilemma" in medical image segmentation by proposing an exchangeability-based framework that controls foreground-background feature discrepancies across deep network layers, achieving state-of-the-art performance on five datasets including a novel curated ultrasound collection.
LayerT2V is a unified framework that generates semantically consistent, editable multi-layer videos (including background, foregrounds, and alpha mattes) in a single inference pass by leveraging temporal serialization within a shared DiT backbone, supported by the new large-scale VidLayer dataset.
RAP is a unified framework that enables real-time, high-quality audio-driven portrait animation by introducing a hybrid attention mechanism for fine-grained audio control and a static-dynamic training-inference paradigm to overcome the limitations of compressed latent representations.
This paper proposes MixCache, a training-free framework that accelerates video DiT inference by employing a context-aware triggering mechanism and an adaptive hybrid strategy to dynamically select optimal caching granularities, thereby significantly improving both generation speed and quality.
This paper introduces Dyslexify, a training-free defense mechanism that selectively ablates specific attention heads in CLIP vision encoders to neutralize typographic attacks, significantly improving robustness against text-based manipulations while preserving standard recognition accuracy.
This paper addresses the limitations of current risk-oriented methods in constructing multimodal safety datasets by proposing a novel image-oriented self-adaptive pipeline that automatically generates a 35k real-world safety dataset and introduces a standardized evaluation metric to validate its effectiveness across various tasks.
This paper proposes Loc, an interpretable and lightweight cross-view localization method that estimates ground-level camera pose by learning direct ground-aerial feature correspondences, lifting them to bird's-eye-view space via monocular depth, and applying scale-aware Procrustes alignment without requiring pixel-level annotations.
This paper proposes ST-GS, a novel framework that enhances vision-based 3D semantic occupancy prediction for autonomous driving by introducing a guidance-informed spatial aggregation strategy and a geometry-aware temporal fusion scheme to achieve state-of-the-art performance and superior temporal consistency on the nuScenes benchmark.
This paper introduces Visual Instruction Pretraining (ViTP), a novel paradigm that leverages high-level reasoning to enhance low-level perceptual features through end-to-end pretraining of a Vision Transformer within a Vision-Language Model, achieving state-of-the-art performance across diverse remote sensing and medical imaging benchmarks.
PartSAM is the first promptable 3D part segmentation model trained natively on a large-scale dataset of over five million shape-part pairs, utilizing a triplane-based encoder-decoder architecture to achieve superior open-world generalization and accurate decomposition of both surface and internal structures compared to existing 2D-transfer methods.
This paper introduces the first diffusion-based framework for secure and reversible face anonymization that utilizes secret-key conditioning to enable high-quality identity protection and authorized reconstruction while preventing unauthorized de-anonymization.
This paper proposes Asynchronous Denoising Diffusion Models, a novel framework that assigns distinct timesteps to individual pixels to enable prompt-related regions to leverage clearer contextual information from unrelated areas, thereby significantly improving text-to-image alignment.
This study demonstrates that pre-trained multimodal large language models, particularly when enhanced with two-stage prompting strategies that leverage reference objects, can effectively detect and measure hailstone diameters from crowdsourced social media images with an average error of 1.12cm, offering a promising complement to traditional hail sensors for rapid severe weather assessment.
The paper proposes FlowRVS, a novel one-stage generative framework that reformulates Referring Video Object Segmentation as a language-guided continuous flow deformation problem, leveraging pretrained text-to-video models to achieve state-of-the-art performance by directly mapping video representations to target masks while overcoming the limitations of traditional cascaded approaches.
G4Splat is a novel 3D reconstruction method that leverages accurate metric-scale geometry derived from planar structures to guide a generative prior, effectively resolving multi-view inconsistencies and enabling high-quality scene completion in both observed and unobserved regions.
This paper introduces PoSh, a scene graph-guided LLM-as-a-Judge metric for evaluating detailed image descriptions, and validates it through the new DOCENT benchmark, demonstrating superior correlation with human judgments and robustness across diverse image types compared to existing metrics.
This study demonstrates that self-supervised deep learning, specifically the "Bootstrap Your Own Latent" strategy, enables highly accurate statewide 1-meter land cover classification using only 1,000 annotated patches, effectively overcoming the data scarcity barrier for large-scale, high-resolution mapping.