4758 papers
This paper proposes a novel framework for radiology report generation that enhances reinforcement learning efficiency through a diagnostic diversity-based data sampling strategy and a Diagnostic Token-weighted Policy Optimization (DiTPO) method, achieving state-of-the-art clinical accuracy with significantly fewer training samples by prioritizing diagnostically critical content.
The paper proposes Volumetric Directional Diffusion (VDD), a novel framework that anchors generative trajectories to a deterministic consensus prior to predict 3D boundary residuals, thereby achieving state-of-the-art anatomically coherent uncertainty quantification for ambiguous medical image segmentation while avoiding the topological fractures common in standard diffusion models.
The paper proposes DQE-CIR, a novel composed image retrieval method that enhances query discriminativeness and fine-grained retrieval accuracy by integrating learnable attribute weights for precise vision-language alignment and a target relative negative sampling strategy to mitigate relevance suppression and semantic confusion.
This paper addresses the lack of benchmarks for long-term visual localization in dynamic benthic environments by introducing a curated multi-year underwater dataset, a novel footprint-based ground-truthing method that outperforms traditional distance-threshold approaches, and a benchmark evaluation demonstrating that state-of-the-art visual place recognition methods struggle significantly in these challenging underwater settings.
This paper introduces MELT, a lightweight backdoor attack framework for multi-encoder diffusion models like Stable Diffusion 3, demonstrating that tuning fewer than 0.2% of parameters via low-rank adapters is sufficient to achieve effective attacks while identifying the minimal encoder subsets required for different objectives.
This study demonstrates that for cell-level histopathological image analysis under extreme spatial constraints, task-specific architectures trained on sufficient data outperform foundation models in both accuracy and efficiency, while offering comparable robustness to blur perturbations.
EgoPoseFormer v2 is a transformer-based framework that significantly advances egocentric human motion estimation for AR/VR by combining a novel architecture with an uncertainty-aware auto-labeling system to achieve state-of-the-art accuracy and temporal consistency on large-scale unlabeled datasets.
This paper proposes a CLIP-guided multi-task regression framework that leverages level-aware vision-language embeddings to robustly predict plant age and leaf count from multi-view imagery, achieving significant accuracy improvements on the GroMo25 benchmark while simplifying the pipeline and handling incomplete inputs.
This paper introduces a training-free, capture-time frame curation method for always-on egocentric cameras that leverages gaze stability and pupil-derived novelty as complementary criteria to efficiently select high-quality, informative frames, achieving full-stream classification performance with only 10% of the data while respecting wearable device constraints.
This paper introduces HPENets, an efficient suite of MLP-based point cloud networks that leverage a two-stage abstraction-refinement paradigm, high-dimensional positional encoding, and non-local MLPs to achieve superior performance with significantly reduced computational costs compared to state-of-the-art models.
This paper proposes a disentangled representation learning framework for brain MRI to demonstrate that demographic predictability primarily stems from anatomical variation rather than acquisition-dependent contrast, highlighting the need for targeted mitigation strategies that address these distinct sources to ensure robust bias reduction.
This paper introduces Any2Any, a unified latent diffusion framework that enables efficient and generalizable arbitrary modality translation in remote sensing by projecting heterogeneous inputs into a shared geometrically aligned latent space, supported by the newly proposed million-scale RST-1M dataset.
TextBoost addresses the challenge of preserving small-font scene text in ultra-low bitrate image compression by transmitting negligible OCR-derived semantic guidance to the decoder, where it is fused with image features and enforced via a regularizing loss to significantly improve text recognition fidelity without compromising global image quality.
This paper addresses the underexplored challenge of Few-Shot Open-Set Action Recognition in video data by proposing a Feature-Residual Discriminator (FR-Disc) that significantly improves unknown action rejection without sacrificing closed-set accuracy, establishing a new state-of-the-art benchmark across five datasets.
Crab is a scalable and unified audio-visual scene understanding model that overcomes the negative transfer issues of conventional multi-task methods by introducing the AV-UIE v2 dataset with explicit reasoning and an Interaction-aware LoRA mechanism to enable effective explicit cooperation across heterogeneous tasks.
This paper proposes an inference-time attention regulation framework that utilizes anatomy-aware gating and pathology-guided latent corrections to achieve anatomically consistent and precisely localized counterfactual chest X-ray synthesis, effectively overcoming the structural drift and unstable pathology expression issues common in standard diffusion-based editing methods.
This paper proposes HBRB-BoW, a refined hierarchical training algorithm that integrates global real-valued flows to preserve high-fidelity descriptor information before final binarization, thereby overcoming the precision loss of traditional binary clustering and significantly enhancing the discriminative power and performance of ORB-SLAM in loop closing and relocalization tasks.
This paper proposes a LISTA-Transformer model that integrates Learnable Iterative Shrinkage Threshold Algorithm-based sparse coding with the Transformer architecture to overcome the limitations of CNNs and standard Transformers in local and global feature modeling, achieving a 98.5% fault recognition rate on the CWRU dataset through time-frequency signal analysis.
This paper introduces a degradation-based augmented training framework that artificially diversifies image degradations during training to significantly improve the robustness and accuracy of deep learning models for individual animal re-identification across various species and real-world conditions.
The paper introduces PlaneCycle, a training-free and adapter-free method that lifts pretrained 2D foundation models to 3D by cyclically distributing spatial aggregation across orthogonal planes, enabling strong 3D performance without architectural modifications or additional parameters.