4301 papers
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.
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.