3575 papers
FedEU is a novel federated learning framework that enhances remote sensing image segmentation by integrating evidential uncertainty quantification and client-specific feature embeddings to guide adaptive global aggregation, thereby improving model robustness and reliability across heterogeneous distributed datasets.
This paper introduces Early Vision-Language Fusion (EVLF), a plug-and-play method that aligns textual and visual embeddings early in the diffusion process to overcome the visual dominance issues of late-stage guidance, thereby generating semantically faithful and visually coherent synthetic datasets that improve downstream classification accuracy.
This paper proposes a Multi-Modal Decouple and Recouple Network that enhances robust 3D object detection under data corruption by explicitly separating BEV features into invariant and specific components to enable cross-modal compensation, followed by an adaptive fusion of three specialized experts tailored to different corruption scenarios.
This paper introduces RobustSCI, a pioneering framework that shifts snapshot compressive imaging from simple reconstruction to robust restoration by proposing a novel network architecture and a large-scale benchmark to effectively recover pristine scenes from real-world degraded measurements caused by motion blur and low light.
The paper proposes RayD3D, a novel cross-modal distillation framework that transfers depth knowledge specifically along the camera-to-object ray to filter out irrelevant LiDAR information, thereby significantly enhancing the robustness of multi-view 3D object detection models against real-world data corruptions without increasing inference costs.
DocCogito is a unified framework for document understanding that aligns global layout perception with structured, region-grounded reasoning through a lightweight layout tower and a deterministic Visual-Semantic Chain, achieving state-of-the-art performance on multiple benchmarks by enforcing systematic coupling between layout priors and evidence-based reasoning.
This paper proposes AMR-CCR, an anchored modular retrieval framework with script-conditioned injection and multi-prototype dictionaries to address the challenges of continual, class-incremental ancient Chinese character recognition, accompanied by the new EvoCON benchmark for systematic evaluation.
This paper proposes a skeletal latent diffusion framework that leverages a differentiable skeletonization module and a large-scale MedSDF dataset to achieve high-fidelity, computationally efficient medical shape generation while effectively addressing challenges posed by anatomical geometric complexity and data scarcity.
This paper establishes a unified theoretical framework demonstrating that drifting models, which optimize kernel-based mean-shift discrepancies, are mathematically equivalent to score-matching objectives on kernel-smoothed distributions, thereby precisely connecting them to diffusion models and clarifying their relationship with Distribution Matching Distillation.
The paper proposes EvolveReason, a self-evolving reasoning paradigm that combines a human-like chain-of-thought framework, a forgery latent-space distribution capture module, and a reinforcement learning-based self-evolution strategy to enhance the accuracy, detail, and reliability of explainable deepfake facial image identification.
This paper introduces SketchGraphNet, a memory-efficient hybrid graph transformer that models free-hand sketches as structured graphs to achieve state-of-the-art recognition accuracy on the newly constructed 3.44-million-sample SketchGraph benchmark while significantly reducing computational resource requirements.
The paper proposes ACCURATE, a robust 3D reconstruction framework that combines image segmentation with geometry-constrained topology traversal and dynamic programming to achieve high-accuracy reconstruction of arbitrary-shaped, deformable continuum bodies like guidewires and catheters under biplanar X-ray imaging.
This paper proposes a semantic geometric framework that leverages small vehicles as metric anchors within a decoupled stereoscopic projection model to recover absolute scale from monocular UAV images, thereby enabling scale-adaptive satellite image cropping and significantly improving cross-view geo-localization robustness under real-world scale ambiguity.
This paper introduces UniLongGen, a training-free inference strategy that improves long-horizon interleaved image generation by dynamically curating context to discard accumulated visual noise, thereby overcoming the reliability collapse caused by dense visual token interference in unified multimodal models.
The paper introduces CONSTANT, a novel one-shot handwriting generation framework that leverages Style-Aware Quantization and a latent patch-based contrastive objective within a diffusion model to overcome existing limitations in capturing diverse writer styles and generating high-quality, realistic handwritten images across multiple languages.
DreamSAC is a framework that enhances extrapolative generalization in physics simulations by combining an unsupervised symmetry exploration strategy, which actively probes conservation laws via a Hamiltonian-based curiosity bonus, with a Hamiltonian-based world model that learns invariant physical states from raw observations through a novel contrastive objective.
ReconDrive is a fast, feed-forward framework that adapts the VGGT foundation model with hybrid prediction heads and static-dynamic composition to achieve high-fidelity, scalable 4D Gaussian Splatting for autonomous driving scenes, outperforming existing feed-forward methods while matching the quality of slower optimization-based approaches.
This paper proposes an active inference-based framework for micro-gesture recognition that utilizes Expected Free Energy-guided temporal sampling and uncertainty-aware adaptive learning to overcome challenges like low amplitude, noise, and inter-subject variability, demonstrating significant performance improvements on the SMG dataset.
PureCC is a novel concept customization framework that employs a decoupled learning objective and a dual-branch training pipeline to achieve high-fidelity text-to-image personalization while effectively preserving the original model's behavior and capabilities.
Brain-WM is a pioneering brain glioblastoma world model that utilizes a novel Y-shaped Mixture-of-Transformers architecture to unify next-step treatment prediction and future MRI generation, effectively capturing the co-evolutionary dynamics between tumor progression and treatment response to optimize clinical outcomes.