4361 papers
MoECLIP addresses the limitations of patch-agnostic designs in Zero-Shot Anomaly Detection by introducing a Mixture-of-Experts architecture that dynamically routes image patches to specialized LoRA experts, enhanced by Frozen Orthogonal Feature Separation and an ETF loss to ensure distinct and maximally equiangular representations, thereby achieving state-of-the-art performance across diverse industrial and medical benchmarks.
ProSMA-UNet introduces a novel decoder-conditioned sparse feature selection mechanism that employs multi-scale compatibility fields and an proximal operator to explicitly filter irrelevant skip-connection noise, thereby achieving state-of-the-art performance in challenging medical image segmentation tasks.
This paper proposes SpeciaRL, a specificity-aware reinforcement learning framework that fine-tunes reasoning Large Multimodal Models to achieve an optimal balance between correctness and specificity in open-world fine-grained image classification by employing a dynamic, verifier-based reward signal.
This paper presents a comprehensive survey of Deep Sketch-Based 3D Modeling (DS-3DM) by introducing the novel MORPHEUS design space, which categorizes recent advancements within an Input-Model-Output framework to highlight current limitations and identify future interdisciplinary opportunities for enhancing user-centered, controllable, and information-rich 3D creation.
This study demonstrates that leveraging the iconicity of signs in transfer learning from Chinese to Arabic and Greek to Flemish significantly improves sign language recognition performance, particularly yielding a 7.02% gain for Arabic, by utilizing MediaPipe-extracted spatial and temporal features processed through MLP and GRU architectures.
This paper introduces mHC-HSI, a clustering-guided Hyper-Connection Mamba model that enhances hyperspectral image classification accuracy and interpretability by integrating spatial-spectral feature learning, soft cluster-based residual matrices, and physically-meaningful spectral band grouping.
This paper introduces a counterfactual evaluation framework revealing that while reinforcement learning with verifiable rewards improves accuracy on medical VQA benchmarks, it often degrades genuine visual grounding by enabling models to rely on text shortcuts and hallucinate visual reasoning, necessitating new evaluation metrics and training objectives that explicitly enforce visual dependence.
This paper introduces Proact-VL, a general framework designed to transform multimodal language models into proactive, real-time AI companions that overcome latency and decision-making challenges, validated through the new Live Gaming Benchmark across commentary and guidance scenarios.
This paper investigates how various localization errors degrade label quality in online HD map construction, revealing that heading angle errors have a more significant impact than position errors and that model performance decreases non-linearly with increasing noise, while also proposing a distance-based metric to better evaluate these effects.
The paper introduces PERSIST, a novel world model paradigm that simulates the evolution of a latent 3D scene to overcome the spatial memory and consistency limitations of existing video generation methods, thereby enabling coherent, long-horizon interactive experiences with persistent 3D state and geometry-aware control.
This paper introduces Phys4D, a three-stage training pipeline that transforms appearance-driven video diffusion models into physics-consistent 4D world representations by combining pseudo-supervised pretraining, simulation-grounded fine-tuning, and reinforcement learning to achieve fine-grained spatiotemporal and physical consistency.
This study proposes a novel Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer that fuses Sentinel-1, RCM, and AMSR2 data to generate 200m resolution pan-Arctic sea ice concentration maps with reliable uncertainty estimates, effectively overcoming challenges related to subtle feature extraction, inexact labels, and data heterogeneity.
PhyPrompt introduces a two-stage reinforcement learning framework that automatically refines text-to-video prompts through physics-focused fine-tuning and a dynamic reward curriculum, significantly enhancing physical plausibility and semantic adherence across diverse models while outperforming much larger general-purpose LLMs.
This paper introduces PinCLIP, a large-scale foundational multimodal representation model for Pinterest that employs a novel hybrid Vision Transformer architecture and neighbor alignment objectives to overcome VLM integration challenges, resulting in significant improvements in multi-modal retrieval accuracy, cold-start content distribution, and overall user engagement.
This paper introduces PolyV, a unified large vision model that achieves cross-vision synergy across images, videos, and 3D data through a sparse Mixture-of-Experts architecture with dynamic routing and a synergy-aware training paradigm, resulting in significant performance improvements over existing models.
This paper proposes a novel confidence-aware monocular depth estimation framework for minimally invasive surgery that leverages calibrated confidence targets and a specialized loss function to improve depth accuracy and provide reliable per-pixel confidence maps, thereby addressing challenges posed by endoscopic image artifacts like smoke and blur.
This paper introduces L2G-Det, a novel framework that detects and segments specific object instances in open-world scenes by leveraging dense local patch matching to generate candidate points, which are then refined and used to prompt an augmented Segment Anything Model for robust mask reconstruction without relying on traditional object proposals.
This paper introduces Ambient Radio Sensing (ARS), a novel ISAC approach that repurposes ambient 5G signals for human activity detection via a passive self-mixing hardware architecture and a cross-modal learning framework, effectively overcoming spectrum scarcity while preserving privacy.
This paper proposes a VQA-inspired data augmentation framework that generates natural-language questions about character-level attributes to enhance scene and handwritten text recognition models, resulting in significant improvements in transcription accuracy on benchmark datasets.
This paper introduces a novel Traffic Scene Graph Generation framework that leverages accident data and depth cues to model safety-relevant relations between hazards and the ego vehicle, thereby enhancing situational awareness in complex driving scenarios.