4272 papers
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.
DriveMVS is a novel multi-view stereo framework for autonomous driving that leverages sparse LiDAR observations as geometric prompts and employs a spatio-temporal decoder to achieve state-of-the-art metric accuracy, temporal consistency, and cross-domain generalization.
This paper proposes a novel framework for small object detection in complex backgrounds that integrates Residual Haar Wavelet Downsampling, Global Relation Modeling, Cross-Scale Hybrid Attention, and a Center-Assisted Loss to preserve fine-grained details, suppress noise, and enhance localization accuracy, achieving state-of-the-art performance on the RGBT-Tiny benchmark.
This paper introduces TAP, a training-free framework that accelerates diffusion model inference by adaptively selecting the most accurate predictor for each token at every step based on a low-cost probe, achieving significant speedups with minimal quality loss.
To address model collapse and knowledge loss in long-term test-time adaptation, this paper proposes an Adaptive and Selective Reset (ASR) framework that dynamically determines optimal reset timing and scope while employing an importance-aware regularizer to recover essential knowledge and an on-the-fly adjustment scheme to enhance adaptability.