6554 papers
MVD-HuGaS is a novel framework that achieves state-of-the-art free-view 3D human rendering from a single image by leveraging a fine-tuned multi-view diffusion model to generate consistent multi-view images, an alignment module for joint Gaussian and pose optimization, and a depth-based facial distortion mitigation module to ensure high-fidelity reconstruction.
This paper introduces 3D-DRES, a new task for fine-grained phrase-to-3D-instance mapping, supported by the DetailRefer dataset with pioneering phrase-instance annotations and the DetailBase baseline, which together enhance 3D vision-language understanding and improve performance on traditional 3D-RES benchmarks.
The paper proposes ProGIC, a progressive and lightweight generative image compression codec based on residual vector quantization and a compact backbone, which achieves significant bitrate savings, faster encoding/decoding speeds, and flexible progressive transmission compared to existing methods.
This paper introduces the Harmonic Beltrami Signature Network (HBSN), a novel deep learning architecture that efficiently computes rotation-, scale-, and translation-invariant shape representations to serve as a versatile shape prior module, thereby enhancing the performance of existing computer vision segmentation models.
This paper presents Articulation in Motion (AiM), a prior-free framework that leverages a dual-Gaussian scene representation and sequential RANSAC to automatically segment articulated objects into rigid parts, estimate their kinematics, and reconstruct interactive 3D replicas from a single static scan and an interaction video without requiring prior knowledge of the number of parts.
HDINO is a concise and efficient open-vocabulary detector that eliminates reliance on manually curated datasets and resource-intensive feature extraction by employing a two-stage training strategy with a One-to-Many Semantic Alignment Mechanism and Difficulty Weighted Classification Loss to achieve state-of-the-art performance on COCO with significantly fewer training images than existing methods.
GloPath is a scalable, entity-centric foundation model trained on over one million glomeruli that outperforms state-of-the-art methods in diverse lesion assessment tasks and uncovers significant clinicopathological associations, advancing the clinical translation of AI in renal pathology.
TC-Padé is a novel feature prediction framework that leverages Trajectory-Consistent Padé approximation with adaptive coefficient modulation and step-aware strategies to significantly accelerate diffusion models in low-step regimes while maintaining high generation quality and overcoming the trajectory drift limitations of existing polynomial-based methods.
This paper proposes a lightweight semi-supervised learning framework that introduces a stochastic Proportion Loss, adapted from learning from label proportions, to align model predictions with global class distributions and effectively mitigate class imbalance issues in SSL.
This paper proposes an efficient semi-supervised method for few-shot adaptation of vision-language models in medical imaging that leverages unlabeled data to propagate text-informed pseudo-labels, thereby reducing annotation requirements by over 50% while addressing class imbalance challenges.
This paper proposes a framework combining a foundation-model-based anomaly synthesis pipeline (FMAS) and a Wavelet Domain Attention Module (WDAM) to generate realistic synthetic anomalies and enhance feature extraction, significantly improving industrial anomaly detection performance on benchmark datasets without requiring fine-tuning.
TagaVLM is an end-to-end framework that enhances Vision-Language Navigation by explicitly injecting topological structures into the VLM backbone via Spatial Topology Aware Residual Attention and Interleaved Navigation Prompts, achieving state-of-the-art performance on the R2R benchmark and demonstrating that targeted architectural improvements on smaller models can outperform brute-force scaling for embodied spatial reasoning.
This paper proposes a memory-efficient unsupervised anomaly detection framework that leverages a 2D autoregressive CNN to explicitly model spatial dependencies in DINOv3 patch embeddings, achieving competitive performance on medical imaging benchmarks while significantly reducing inference time and memory overhead compared to existing prototype-based methods.
The Dresden Dataset (D4D) is a comprehensive benchmark comprising over 300,000 frames and 369 point clouds from porcine cadaver surgeries, providing paired endoscopic video and high-quality structured-light geometry to enable quantitative evaluation of non-rigid 4D reconstruction, SLAM, and depth estimation methods in realistic abdominal surgical scenes.
VIRGi is a novel method for rapidly and photorealistically recoloring 3D Gaussian Splatting scenes by decomposing color into diffuse and view-dependent components, enabling real-time, single-image editing that preserves specular highlights and outperforms NeRF-based approaches.
The paper proposes the Ultra Resolution Geometry Transformer (URGT), a unified multi-patch framework that leverages cross-patch attention and a GridMix sampling strategy to achieve state-of-the-art, high-resolution monocular depth and normal estimation with enhanced global consistency and generalization.
The paper introduces BRIGHT, a collaborative generalist-specialist foundation model trained on a massive breast-specific dataset that outperforms existing generalist models across 24 diverse clinical tasks, establishing a new state-of-the-art for breast pathology and validating a scalable paradigm for organ-specific AI development.
This paper introduces EduAIGV-1k, the first benchmark dataset for evaluating AI-generated educational videos on math concepts, and proposes EduVQA, a novel assessment framework featuring a Structured 2D Mixture-of-Experts module that leverages fine-grained perceptual and prompt-alignment annotations to outperform existing video quality baselines.
The paper introduces TikZilla, a family of small open-source models trained on the high-quality DaTikZ-V4 dataset using a two-stage pipeline of supervised fine-tuning and reinforcement learning with image-based rewards, which significantly outperforms prior approaches and matches GPT-5 in generating accurate TikZ figures from text descriptions.
This paper introduces TinyIceNet, a compact, low-power semantic segmentation network co-designed for FPGA deployment that enables near-real-time, energy-efficient sea ice mapping directly on-board satellites using Sentinel-1 SAR imagery.