3193 papers
OVerSeeC is a zero-shot modular framework that leverages large language models and open-vocabulary segmentation to generate executable global costmaps from satellite imagery and natural language instructions, enabling autonomous navigation to adapt to novel entities and dynamic mission constraints without requiring fixed ontologies.
This paper introduces Open-Vocabulary Domain Generalization in Semantic Segmentation (OVDG-SS), a new setting and benchmark for autonomous driving that addresses both unseen domains and categories, and proposes S2-Corr, a state-space-driven mechanism to refine text-image correlations in Vision-Language Models to achieve robust performance across diverse urban environments.
UniMatch is a novel coarse-to-fine framework that establishes dense semantic correspondences between strongly non-isometric, cross-category 3D shapes by leveraging class-agnostic segmentation, multimodal language models for part identification, and a rank-based contrastive learning scheme to overcome the limitations of prior isometry-dependent methods.
InfScene-VF proposes a diffusion-based framework for arbitrary-size image super-resolution that eliminates boundary artifacts and enables memory-efficient, parallelized inference on gigapixel imagery by introducing Variance-Corrected Fusion and Spatially-Decoupled Variance Correction to achieve spatially continuous joint-denoising.
This paper proposes an online data manipulation scheme that decomposes training images into independent object, scene, and camera components and recomposes them with perturbed poses to generate diverse training data, thereby improving the data efficiency and performance of monocular 3D object detection models across both fully and sparsely supervised settings.
This paper presents a cycle-consistent tuning framework that leverages lightweight LoRA adaptation of pretrained diffusion models to achieve robust, high-fidelity layered image decomposition, specifically for challenging logo-object separation, by enforcing bidirectional reconstruction consistency and iteratively refining performance through a progressive self-improving process.
This paper proposes "See It, Say It, Sorted," a lightweight, training-free, and plug-and-play framework that mitigates visual hallucination in large vision-language models by iteratively supervising each reasoning step with dynamically extracted visual evidence, thereby significantly improving reasoning accuracy without requiring additional model training.
This paper introduces a unified language modeling approach for semantic and panoptic segmentation in images and videos that discretizes masks into run-length encoded tokens, employing novel compression strategies to enable autoregressive generation despite computational constraints.
WISER is a training-free framework for Zero-Shot Composed Image Retrieval that unifies Text-to-Image and Image-to-Image paradigms through a "retrieve-verify-refine" pipeline, leveraging wider search, adaptive fusion, and self-reflection to significantly outperform existing methods across diverse benchmarks.
The paper introduces PackUV, a novel 4D Gaussian representation and fitting method that maps volumetric video attributes into structured UV atlases for efficient, codec-compatible storage and streaming, while demonstrating superior temporal consistency and rendering fidelity on the newly proposed large-scale PackUV-2B dataset.
This paper proposes HART, an annotation-free framework that leverages a novel Advantage Preference Group Relative Policy Optimization (AP-GRPO) algorithm to enable Large Multimodal Models to autonomously identify and verify key high-resolution image regions, thereby improving reasoning performance without requiring costly human grounding labels.
This paper introduces Infinite Self-Attention (InfSA) and its linear-time variant, Linear-InfSA, a spectral reformulation of self-attention as a diffusion process on token graphs that achieves state-of-the-art ImageNet accuracy and enables efficient, memory-free inference at ultra-high resolutions (up to 9216×9216) by replacing the quadratic softmax cost with a Neumann series approximation.
This paper introduces WildActor, a framework for unconstrained identity-preserving human video generation that leverages the large-scale Actor-18M dataset and novel attention mechanisms to overcome existing limitations in maintaining consistent full-body identities across dynamic shots, viewpoints, and motions.
This position paper argues that the evaluation of modern visual processing systems must shift from a reliance on single-metric benchmarks toward a human-centered, context-aware paradigm to better align with human perception and foster genuine innovation.
The paper proposes DeAR, a fine-grained adaptation framework for Vision-Language Models that decomposes attention heads into functional roles (Attribute, Generalization, and Mixed) using a Concept Entropy metric to selectively isolate task-specific learning from generalization capabilities, thereby achieving superior performance across diverse tasks while preserving zero-shot robustness.
The paper proposes the MSP framework, which mitigates hairstyle distraction and preserves structural information through Hairstyle-Oriented Augmentation, Cloth-Preserved Random Erasing, and Region-based Parsing Attention to achieve state-of-the-art performance in cloth-changing person re-identification.
This paper presents a computationally efficient, detection-gated deep learning pipeline that achieves state-of-the-art robustness and cross-dataset generalization in glottal segmentation from high-speed videoendoscopy, enabling reliable extraction of clinical biomarkers for distinguishing healthy from pathological vocal function.
This paper proposes a robust framework combining the hybrid CoAtNet architecture with model soups ensembling to effectively classify Intangible Cultural Heritage images from the Mekong Delta, achieving state-of-the-art performance on the ICH-17 dataset by reducing variance and enhancing generalization in data-scarce, high-similarity settings.
This paper evaluates DINOv3 as a frozen backbone for blueberry robotic harvesting tasks, finding that while it excels in segmentation through stable patch-level representations, its detection performance is limited by scale variation and spatial aggregation challenges, suggesting it functions best as a semantic backbone requiring downstream spatial modeling tailored to fruit structures.
This paper introduces GramCol and a motion-feature selection algorithm to generate Interpretable Motion-Attentive Maps (IMAPs) that effectively localize both motion and non-motion concepts in Video Diffusion Transformers without requiring gradient calculations or parameter updates.