4140 papers
The paper introduces Wallaroo, a simple autoregressive model that unifies multi-modal understanding, image generation, and editing through next-token prediction, featuring multi-resolution support and bilingual capabilities while achieving competitive performance via a decoupled visual encoding and four-stage training strategy.
This paper introduces TAPFormer, a transformer-based framework that achieves robust arbitrary point tracking by employing a Transient Asynchronous Fusion mechanism to effectively bridge the temporal gap between low-rate RGB frames and high-rate event streams, outperforming existing methods on both a newly constructed real-world dataset and standard benchmarks.
MultiGO++ is a novel framework for monocular 3D clothed human reconstruction that overcomes limitations in texture quality, geometric accuracy, and modality bias by integrating a multi-source texture synthesis strategy, a region-aware shape extraction module with Fourier encoding, and a dual reconstruction U-Net to achieve effective geometry-texture collaboration.
This paper introduces Lens2Zernike, a physics-consistent deep learning framework that simultaneously recovers optical parameters through Zernike coefficient regression, differentiable physics constraints, and spatial map predictions to enable stable, high-quality blind aberration recovery in mobile photography.
This paper presents a large-scale, multi-dimensional study evaluating the capabilities and limitations of generative image restoration models, revealing a paradigm shift from under-generation to over-generation challenges and proposing a new evaluation framework and IQA model to guide future advancements.
Tell2Adapt is a unified Source-Free Unsupervised Domain Adaptation framework that leverages Vision Foundation Models with Context-Aware Prompts Regularization and Visual Plausibility Refinement to achieve state-of-the-art performance across 10 domain shifts and 22 anatomical targets in medical image segmentation.
This paper introduces Semap, a diverse benchmark dataset, and a robust multiscale segmentation framework to enable generalizable semantic segmentation across heterogeneous historical map collections, thereby facilitating the integration of vast cartographic archives into historical geographic studies.
This paper proposes IRTTA, a test-time adaptation method that leverages informative intermediate reconstructions from iterative Optical Coherence Tomography algorithms to dynamically adjust a frozen segmentation network's normalization parameters, thereby improving performance and providing uncertainty estimates without altering the reconstruction or downstream models.
The paper proposes CoIn3D, a generalizable multi-camera 3D object detection framework that achieves strong cross-configuration transferability by explicitly addressing spatial prior discrepancies through spatial-aware feature modulation and training-free camera-aware data augmentation.
This paper proposes CLIP-PZSL, a novel framework that leverages CLIP and a progressive semantic mining strategy to address the challenge of ambiguous labels in zero-shot learning by dynamically refining candidate label weights to improve semantic alignment and recognition performance.
This paper presents a 360-degree multi-camera system for detecting blue emergency lights that utilizes a curated ABLDataset and an enhanced RT-DETR model with a color attention block to achieve high accuracy, azimuthal localization, and approach angle estimation for integration into Advanced Driver Assistance Systems.
The paper proposes MI-DETR, a bio-inspired dual-pathway detector that integrates a retina-inspired cellular automaton for explicit motion mapping with a parvocellular-magnocellular interconnection mechanism to achieve state-of-the-art infrared small target detection performance without requiring additional motion labels or alignment modules.
This paper introduces UniM, the first unified any-to-any interleaved multimodal benchmark containing 31K instances across seven modalities, along with a specialized evaluation suite and an agentic baseline model to assess and advance multimodal large language models in handling complex, intertwined understanding and generation tasks.
MoRe is a feed-forward 4D reconstruction transformer that efficiently recovers dynamic 3D scenes from monocular videos by employing an attention-forcing strategy to disentangle motion from static structure and utilizing grouped causal attention for temporally coherent geometry.
This paper proposes a novel framework called Orster that addresses the scarcity of 4D datasets by transferring disentangled spatial and temporal priors from 3D and video diffusion models into an STD-4D diffusion model enhanced with a spatial-temporal-aware HexPlane, thereby achieving superior high-quality 4D generation.
This paper proposes a novel Axiomatic On-Manifold Shapley framework that utilizes optimal generative flows and Wasserstein-2 geodesics to eliminate off-manifold artifacts, ensuring geometric efficiency, reparameterization invariance, and superior semantic alignment in model attribution.
GEM-TFL is a novel framework that bridges the performance gap between weakly and fully supervised temporal forgery localization by employing EM-guided label decomposition, graph-based proposal refinement, and training-free temporal consistency to overcome the limitations of binary video-level supervision.
Diff-ES is a novel framework that leverages evolutionary search to automatically optimize stage-wise sparsity schedules for diffusion models, enabling memory-efficient structural pruning that achieves significant speedups with minimal quality degradation.
This paper introduces BLINK, a trajectory-based recurrent state-space model that learns latent interaction dynamics from time-lapse microscopy to accurately predict and forecast NK cell cytotoxic outcomes while providing an interpretable representation of cellular behavioral modes.
UniPAR is a unified Transformer-based framework that overcomes the limitations of existing "one-model-per-dataset" approaches by integrating a phased fusion encoder and dynamic scheduling to simultaneously process heterogeneous modalities (RGB, video, and event streams) across diverse datasets, achieving state-of-the-art performance and enhanced cross-domain robustness.