1877 papers
ImageEdit-R1 is a novel multi-agent framework that employs reinforcement learning to coordinate specialized vision-language and generative agents, enabling dynamic, context-aware image editing that outperforms existing monolithic models and baselines in handling complex, multi-step user instructions.
This paper proposes a novel plug-and-play ranking architecture that leverages Large Vision-Language Models (LVLMs) and a relational-aware loss function to explicitly model cross-view interactions, thereby significantly enhancing the accuracy and stability of UAV-to-satellite image geolocalization.
This paper proposes a novel evaluation framework for generative models that replaces traditional continuous feature-based metrics with training-free and no-reference metrics operating in discrete visual token space, demonstrating superior correlation with human judgments across a new large-scale benchmark called VisForm.
This paper proposes a training-free pipeline using multimodal large language models to generate diverse, high-fidelity synthetic defect images for power line insulators, which significantly improves classification performance and data efficiency in low-data regimes by augmenting limited real-world datasets.
The paper proposes TALON, a test-time adaptive learning framework for on-the-fly category discovery that overcomes the limitations of static hash-based methods by dynamically updating semantic prototypes and the feature encoder to continuously integrate new knowledge, while employing margin-aware logit calibration to prevent category explosion and significantly improve novel-class accuracy.
This paper proposes a "Map-Based AI" framework that integrates a LoRA-fine-tuned Llama-2 model for semantic zone inference with a hybrid topological-grid mapping system to enable systematic, TSP-optimized exploration, significantly outperforming traditional reactive baselines in Object-Goal Navigation tasks within the AI2-THOR simulator.
This paper introduces DSH-Bench, a comprehensive benchmark featuring a hierarchical subject taxonomy, granular difficulty and scenario classification, and a novel Subject Identity Consistency Score (SICS) metric to systematically evaluate and diagnose subject-driven text-to-image generation models.
TrianguLang is a feed-forward, pose-free framework for 3D object localization that leverages Geometry-Aware Semantic Attention to achieve state-of-the-art accuracy and geometric consistency across multiple views without requiring camera calibration or per-scene optimization.
This paper proposes Adaptive MLP Pruning (AMP), a method that utilizes a label-free information entropy criterion for accurate neuron importance evaluation and a binary search algorithm for adaptive pruning, achieving roughly 40% parameter and FLOPs reduction in large vision transformers like CLIP and DINOv2 with near-lossless performance.
The paper proposes SAMoE-VLA, a novel Vision-Language-Action framework for autonomous driving that replaces unstable token-level Mixture-of-Experts with a scene-adaptive mechanism driven by bird's-eye-view features and a conditional cross-modal causal attention module, achieving state-of-the-art performance with fewer parameters on both open-loop and closed-loop benchmarks.
FoleyFlow introduces a novel video-to-audio generation framework that achieves superior semantic and rhythmic synchronization by aligning unimodal encoders through masked audio-visual modeling and employing a dynamic conditional flow that utilizes temporally varying video features to guide audio synthesis.
UniGround introduces a novel, training-free framework for universal 3D visual grounding that leverages global candidate filtering and local precision reasoning to achieve state-of-the-art zero-shot performance in localizing arbitrary objects within complex 3D environments without relying on pre-trained models or 3D supervision.
FLED-GS is a fast framework for novel view synthesis in 3D dark scenes that addresses compound low-light, noise, and motion blur degradations by reformulating restoration as an alternating cycle of 2D deblurring and noise-aware 3D Gaussian Splatting reconstruction, achieving superior performance with significantly faster training and rendering speeds compared to state-of-the-art methods.
This paper introduces VesselFusion, a diffusion model-based approach that utilizes coarse-to-fine representation and voting-based aggregation to achieve more accurate and natural vessel centerline extraction from 3D CT images compared to conventional deterministic methods.
This paper introduces MV-Fashion, a large-scale multi-view video dataset featuring 3,273 sequences with pixel-level annotations, ground-truth material properties, and paired flat/worn garment images, designed to bridge the realism and annotation gaps in existing datasets for virtual try-on and size estimation tasks.
Edged USLAM is a hybrid visual-inertial SLAM system that integrates an edge-aware front-end and a lightweight depth module to achieve robust, drift-minimized localization on UAVs, particularly excelling in slow or structured trajectories under challenging illumination where purely event-based or learning-based methods may struggle.
The paper introduces MERLIN, a novel training framework accompanied by the EM-100k dataset and EM-Bench benchmark, to overcome data scarcity, evaluation gaps, and low-SNR fragility in building robust Multimodal Large Language Models for electromagnetic signals.
The paper proposes ALOOD, a novel approach that leverages vision-language model representations to align LiDAR object features with language embeddings, thereby enabling zero-shot out-of-distribution object detection for safer autonomous driving.
Fusion-Poly is a novel spatial-temporal fusion framework for 3D multi-object tracking that effectively leverages asynchronous LiDAR and camera observations to enable higher-frequency state updates and achieve state-of-the-art performance on the nuScenes benchmark.
This paper proposes MM-TS, a novel framework for multi-modal contrastive learning that dynamically adjusts temperature and margin schedules based on local data distribution to address long-tail imbalances, unifying InfoNCE and max-margin objectives to achieve state-of-the-art performance across multiple image- and video-language datasets.