4196 papers
This paper introduces RAGTrack, a novel Retrieval-Augmented Generation framework that enhances RGB-Thermal tracking by integrating textual descriptions via Multi-modal Large Language Models and employing adaptive token fusion with context-aware reasoning to overcome appearance variations and modality gaps.
The paper introduces CoRe-BT, a multimodal benchmark comprising 310 patients with MRI, histopathology, and pathology reports, designed to evaluate robust brain tumor typing under realistic conditions of missing clinical data.
This paper presents a rigorous framework that extends neural operators to robustly handle out-of-distribution input functions by leveraging kernel approximations and Reproducing Kernel Hilbert Space theory to ensure accurate prediction of both function values and derivatives, validated through solutions of elliptic partial differential equations on manifolds.
This paper introduces Image-based Prompt Injection (IPI), a black-box attack that embeds adversarial instructions into natural images to hijack Multimodal Large Language Models, demonstrating a 64% success rate in manipulating model outputs while remaining visually imperceptible to humans.
The paper presents InfinityStory, a novel framework, dataset, and model that overcome key limitations in long-form video generation by ensuring background and character consistency across shots while enabling seamless multi-subject transitions for hour-long narratives.
The paper proposes SCFlowFR, a one-step face restoration method that leverages data-dependent coupling, conditional mean estimation, and a shortcut constraint to model low-to-high quality dependencies, thereby eliminating path crossovers and enabling high-quality, single-step inference.
This dissertation presents a comprehensive field imaging framework that leverages advanced computer vision algorithms, including 2D instance segmentation and an integrated 3D reconstruction-segmentation-completion approach, to overcome the limitations of traditional methods and enable accurate morphological characterization of construction aggregates across diverse field scenarios.
This paper introduces InEdit-Bench, the first benchmark designed to evaluate the ability of multimodal generative models to reason over intermediate logical pathways in complex image editing tasks, revealing significant shortcomings in current models' capacity for dynamic reasoning and causal understanding.
This paper introduces MAP, a novel facial privacy protection method that employs human emotion editing to disguise original identities as target identities, effectively overcoming the limitations of traditional countermeasures in black-box settings and across diverse demographics while maintaining high perceptual quality.
EvoPrune is an early-stage visual token pruning method that performs layer-wise pruning guided by token similarity, diversity, and attention importance during visual encoding, achieving a 2 inference speedup with minimal performance degradation on high-resolution images and videos.
This paper proposes a wavelet-based polyp segmentation model that integrates grayscale and RGB representations through complementary frequency-consistent interaction to overcome low-contrast challenges and achieve superior boundary precision, as validated by extensive experiments on four benchmark datasets.
This paper proposes Embedded Runge-Kutta Guidance (ERK-Guid), a novel sampling method that leverages solver-induced local truncation errors as a guidance signal to detect stiffness and stabilize diffusion model generation, thereby outperforming state-of-the-art methods on benchmarks like ImageNet.
MPFlow is a zero-shot multi-modal MRI reconstruction framework that leverages a self-supervised pretraining strategy (PAMRI) to guide rectified flow sampling with auxiliary structural scans, thereby significantly reducing hallucinations and improving anatomical fidelity compared to single-modality baselines while requiring fewer sampling steps.
This paper identifies and quantifies "Order-to-Space Bias" (OTS), a systematic flaw in modern image generation models where the textual order of entities incorrectly dictates their spatial layout, and demonstrates that this data-driven issue can be effectively mitigated through targeted fine-tuning and early-stage interventions without compromising generation quality.
This paper introduces a novel dual-backbone architecture that fuses general visual features from a frozen DINOv3 model with task-specific features from a supervised Swin model to achieve state-of-the-art glass segmentation performance across multiple datasets while maintaining competitive inference speed.
To address the generalization challenges in No-Reference Point Cloud Quality Assessment caused by data scarcity, this paper proposes QD-PCQA, a novel unsupervised domain adaptation framework that transfers quality priors from images to point clouds through a Rank-weighted Conditional Alignment strategy and a Quality-guided Feature Augmentation module to enhance perceptual quality ranking and feature alignment.
The paper proposes PROSPECT, a unified streaming vision-language navigation agent that integrates CUT3R-based spatial encoding with SigLIP semantic features and employs latent predictive representation learning to achieve state-of-the-art performance and robustness in long-horizon navigation tasks.
DAGE introduces a dual-stream transformer architecture that efficiently estimates accurate, view-consistent geometry and camera poses from uncalibrated multi-view inputs by disentangling global coherence in a low-resolution stream from fine details in a high-resolution stream, achieving state-of-the-art performance while supporting high resolutions and long sequences.
This paper proposes WSI-INR, a novel patch-free framework utilizing Implicit Neural Representations and multi-resolution hash grid encoding to model whole-slide images as continuous functions, thereby overcoming the spatial fragmentation and resolution sensitivity of existing methods to achieve robust and accurate lesion segmentation across varying scales.
The paper introduces KFRA, a knowledge-augmented agent that emulates expert analysis through a three-stage closed reasoning loop to achieve superior open-set fine-grained visual understanding and interpretable, evidence-driven reasoning, validated by the newly constructed FGExpertBench.