1352 papers
The paper proposes TriFusion-SR, a wavelet-guided conditional diffusion framework that jointly performs tri-modal medical image fusion and super-resolution by decomposing features into frequency bands and employing rectified wavelet features with adaptive spatial-frequency fusion to achieve state-of-the-art performance in resolution and perceptual quality.
The paper introduces TemporalDoRA, a parameter-efficient fine-tuning method that integrates lightweight temporal attention into the low-rank adaptation branch of vision encoders to enhance robustness against linguistic variations in surgical video question answering, validated by a new colonoscopy dataset and improved Out-of-Template performance.
This paper introduces DRIFT, a dual-path Transformer model that effectively fuses fine-grained local and coarse-grained global features from sparse 4D radar point clouds to achieve state-of-the-art performance in automated driving perception tasks like object detection and free road estimation.
This paper introduces FootMR, a method that refines 3D foot motion in markerless monocular human motion capture by lifting 2D keypoints to 3D using large-scale motion capture data and context-aware residual prediction, alongside the new MOOF dataset, to significantly outperform existing state-of-the-art approaches in accuracy and generalization.
VarSplat is an uncertainty-aware 3D Gaussian Splatting SLAM system that explicitly learns per-splat appearance variance to render differentiable uncertainty maps, thereby guiding tracking and optimization toward reliable regions to achieve robust pose estimation and high-fidelity reconstruction in challenging real-world environments.
The paper presents DiffWind, a physics-informed differentiable framework that unifies wind-object interaction modeling, video-based reconstruction, and forward simulation by combining 3D Gaussian Splatting, the Material Point Method, and Lattice Boltzmann constraints to accurately recover and simulate wind-driven object dynamics from video observations.
This paper presents OTPL-VIO, a robust stereo visual-inertial odometry system that enhances performance in low-texture and illumination-challenging environments by employing a training-free deep descriptor with entropy-regularized optimal transport for line association and introducing adaptive uncertainty weighting to stabilize estimation.
This paper introduces Quantization-aware Distilled Restoration (QDR), a novel framework that employs Decoder-Free Distillation and Learnable Magnitude Reweighting to overcome bottlenecks in quantization-aware training, enabling high-performance, edge-deployed image restoration models that achieve near-FP32 accuracy and significant speedups.
This paper proposes a zero-shot MRI super-resolution framework that leverages explicit, physics-driven 3D Gaussian representations and parallel brick-based rasterization to achieve high-quality, efficient reconstruction without relying on costly paired training data.
This paper proposes a saccade-inspired image classification method that leverages DINO's Vision Transformer attention maps to selectively focus processing on task-relevant regions, achieving performance comparable to or better than full-image analysis while offering a biologically plausible approach to efficient visual processing.
The paper proposes ParTY, a novel framework that improves text-to-motion synthesis by introducing part-guided networks, part-aware text grounding, and holistic-part fusion to overcome the limitations of existing methods in aligning specific body part actions with text while maintaining full-body motion coherence.
This paper introduces BinaryAttention, a theoretically grounded method that replaces floating-point dot products with 1-bit sign-based operations and learnable biases to achieve over 2x speedup over FlashAttention2 while matching or exceeding full-precision accuracy in vision and diffusion transformers.
This paper introduces the Panorama-Language Modeling (PLM) paradigm and the PanoVQA dataset to enable holistic $360^\circ$ vision-language reasoning in adverse omni-scenes, demonstrating that a unified panoramic approach yields superior understanding compared to stitching multiple narrow-field-of-view inputs.
The paper introduces GeoAlignCLIP, a unified framework that enhances fine-grained vision-language alignment in remote sensing by leveraging multi-granular semantic learning and intra-modal consistency, supported by a newly constructed hierarchical dataset (RSFG-100k) to outperform existing methods on diverse benchmarks.
The paper introduces GeoSolver, a framework that enhances remote sensing reasoning by leveraging a large-scale process supervision dataset (Geo-PRM-2M) and a novel Process-Aware Tree-GRPO algorithm to train a token-level reward model (GeoPRM), thereby enabling verifiable, step-by-step reasoning and robust test-time scaling for both specialized and general-purpose Vision-Language Models.
This paper presents a comprehensive study of Time-of-Flight non-line-of-sight imaging methods by unifying their theoretical formulations and hardware implementations to establish a common framework for analysis and demonstrate that, under equal constraints, existing techniques share similar performance limitations despite method-specific differences.
This paper introduces DynHiL-EQA, a new dataset for dynamic human-in-the-loop Embodied Question Answering, and proposes DIVRR, a training-free framework that enhances robustness and inference efficiency by refining ambiguous views and selectively managing memory in dynamic environments.
This paper proposes a reinforcement learning-based post-training strategy that extends Group Relative Policy Optimization (GRPO) with hybrid and process-level rewards to enable existing unified vision-language models to generate high-quality multimodal interleaved outputs without relying on large-scale interleaved datasets.
This paper proposes DCAU-Net, a novel medical image segmentation framework that combines Differential Cross Attention to efficiently model long-range dependencies while reducing computational complexity, and a Channel-Spatial Feature Fusion strategy to adaptively integrate semantic and spatial details, thereby achieving enhanced segmentation accuracy and robustness.
The paper introduces RESBev, a plug-and-play framework that enhances the robustness of Bird's-eye-view (BEV) perception systems against sensor degradation and adversarial attacks by reframing the problem as latent semantic prediction to reconstruct clean features from corrupted observations.