6816 papers
To overcome the challenges of individual variability in echocardiography probe guidance, the authors propose VA-Adapter, a lightweight module that integrates vision-action sequences into an ultrasound foundation model to enable online inference of individual 3D cardiac structures, achieving superior performance with significantly fewer parameters than existing methods.
TTOM is a training-free framework that enhances compositional video generation by optimizing new parameters guided by layout-attention objectives and employing a parametric memory mechanism for streaming inference, thereby achieving superior text-image alignment and generalization without direct latent intervention.
The paper introduces "Splat the Net," a novel volumetric representation that combines the high expressivity of neural radiance fields with the real-time efficiency of 3D Gaussian Splatting by using neural primitives with exact analytical line integrals, achieving comparable quality and speed with significantly fewer primitives and parameters.
This paper introduces LinearSR, a holistic framework that enables stable and efficient photorealistic image super-resolution by overcoming linear attention's historical training instability and perception-distortion trade-off through novel strategies like ESGF, SNR-based MoE, and TAG, achieving state-of-the-art quality with exceptional computational efficiency.
The paper proposes PHyCLIP, a vision-language model that unifies hierarchical and compositional semantic structures by employing an -product metric on a Cartesian product of hyperbolic factors, thereby outperforming existing single-space approaches in various representation learning tasks.
This paper proposes a Co-learning framework (CSL) for incomplete multi-label image recognition that unifies semantic-aware feature learning and label recovery through a collaborative mechanism to simultaneously enhance feature discriminability and infer missing labels, achieving state-of-the-art performance on benchmark datasets.
UniFlow introduces a unified pixel flow tokenizer that resolves the inherent trade-off between visual understanding and generation by leveraging layer-wise adaptive self-distillation on pretrained encoders and a lightweight patch-wise pixel flow decoder, achieving superior performance across diverse benchmarks without sacrificing fidelity.
This paper introduces a novel two-stage self-supervised pre-training framework that enables end-to-end pixel-space generative modeling, achieving state-of-the-art performance on ImageNet with significantly improved efficiency and quality compared to both prior pixel-space methods and latent-space VAE-based counterparts.
Inspired by the fly olfactory circuit, Fly-CL is a bio-inspired framework that enhances pre-trained model-based continual representation learning by efficiently resolving multicollinearity to reduce training time while maintaining or exceeding state-of-the-art performance.
The paper introduces Mono4DGS-HDR, a novel two-stage Gaussian Splatting framework that reconstructs renderable 4D high dynamic range scenes from unposed monocular videos with alternating exposures, achieving superior quality and speed through pose-free initial learning, joint refinement, and temporal luminance regularization.
LightMem is a lightweight, efficient memory-augmented generation system inspired by the Atkinson-Shiffrin human memory model that utilizes a three-stage process of sensory filtering, topic-aware short-term consolidation, and offline long-term updates to significantly improve QA accuracy while drastically reducing token usage and API calls compared to existing baselines.
This paper introduces BioCAP, a biological foundation model that leverages synthetic, instance-specific captions generated by multimodal large language models to enhance species classification and text-image retrieval by capturing rich semantic traits beyond simple labels.
VoMP is a fast, feed-forward deep learning method that predicts spatially-varying volumetric mechanical properties (Young's modulus, Poisson's ratio, and density) for 3D objects by aggregating multi-view features through a Geometry Transformer and decoding them via a physically plausible material manifold learned from a novel, multi-source annotated dataset.
Concerto is a minimalist self-supervised learning framework that synergistically combines 2D-3D joint embedding and 3D intra-modal self-distillation to emerge superior spatial representations, achieving state-of-the-art performance in 3D scene perception and enabling open-world perception through language alignment.
This paper introduces ProMoE, a Mixture-of-Experts framework for Diffusion Transformers that overcomes the limitations of existing vision MoE approaches by employing a two-step router with explicit guidance to partition tokens by function and refine assignments via prototypes, thereby achieving state-of-the-art performance on ImageNet.
The paper introduces "Brain-IT," a novel framework utilizing a Brain Interaction Transformer to model functional brain-voxel clusters for predicting complementary semantic and structural image features, thereby achieving highly faithful fMRI-to-image reconstructions that surpass state-of-the-art methods while requiring significantly less training data.
This paper presents a novel method for generating high-resolution, high-quality talking face videos exclusively from a single speech input by utilizing a speech-conditioned diffusion model with statistical facial priors, region-enhanced lip synchronization, and a Transformer-based discrete codebook for end-to-end detail refinement.
The paper introduces ThinkMorph, a unified model fine-tuned on high-quality interleaved reasoning traces that treats text and image thoughts as complementary modalities, achieving significant performance gains on vision-centric benchmarks and demonstrating emergent multimodal intelligence such as adaptive reasoning and unseen visual manipulation skills.
This study reveals that medical image segmentation follows a geometry-limited power-law scaling behavior characterized by early performance saturation, which can be improved through topology-aware augmentation that enhances sample efficiency by expanding effective topological coverage without altering the fundamental scaling law.
This paper proposes VeCoR, a velocity contrastive regularization method that enhances Flow Matching models by introducing a two-sided attract-repel training scheme to prevent off-manifold errors and significantly improve image quality and stability, particularly in low-step and lightweight configurations.