5280 papers
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.
This paper proposes DM-CFO, a diffusion model-based framework that integrates text and graph constraints for layout generation with collision-free optimization via 3D Gaussian updates and distance regularization to produce realistic, intersection-free compositional 3D tooth designs.
This paper proposes a framework that enhances penguin detection and identification in animal facilities by integrating motion cues into a modified YOLO11 detector for improved temporal consistency and employing tracklet-based contrastive learning to generate coherent feature embeddings for individual recognition.
This paper proposes a robust method for tracking feral horses in aerial video by employing oriented bounding boxes and a novel head-orientation estimation technique using multi-detector voting to resolve 180° flipping ambiguities, thereby achieving 99.3% accuracy in distinguishing head from tail for continuous trajectory analysis.
The paper proposes ParaHydra, a novel distributed multi-view image compression framework featuring an OmniParallax Attention Mechanism and a Parallax Multi Information Fusion Module that adaptively aligns and integrates inter-view correlations, enabling it to significantly outperform state-of-the-art multi-view codecs in both bitrate efficiency and computational speed.
This paper introduces LeafInst, a novel instance segmentation network designed for fine-grained forestry leaf analysis in open-field UAV imagery, and validates its superior performance on the newly constructed Poplar-leaf benchmark and the public PhenoBench dataset.