All papers

Proposes explicit learning of physical state transitions between current and future scenes to train robust vision-language-action robotic policies.

Reduces the inference computational cost of vision graph neural networks by applying graph coarsening via MinCUT pooling.

Introduces a framework that enables vision-language models to iteratively adapt frozen visual encoder computation based on the input question.

Presents a GPU nearest-neighbor search method that uses half-precision certified exclusion to deliver exact visual retrieval efficiently.

Proposes invertible bi-Lipschitz adapters to align pretrained latent spaces with semantic representations for flow matching without retraining encoders.

Presents an auditable framework that uses risk budgeting to selectively deploy generative enhancers over base restoration models in low-light images.

Introduces a label-efficient self-supervised learning framework that splits volumetric CT scans into local slice and global context representations.

Proposes a script-agnostic one-shot font generation framework separating style and content representations across multiple spatial scales.

Presents a backpropagation-free test-time adaptation method that purges unreliable 3D point cloud tokens without requiring candidate size search.

Introduces an attribution metric to diagnose whether perceptual limits or reasoning gaps constrain reinforcement learning gains in vision-language models.

Presents a keypoint descriptor method that leverages global line correspondences to improve feature matching in repetitive or textureless scenes.

Proposes a framework that increases image diffusion throughput by fusing and processing multiple generation samples simultaneously in manifold space.

Introduces a benchmark and dataset of over two million images to evaluate deepfake detection models on partial-face manipulations.

Presents a multi-modal registration framework for longitudinal whole-body PET/CT that preserves metabolic tumor quantification metrics.

Proposes a dual-loop harness augmenting frozen robot policies with persistent user preferences and proactive safety assurance.

Introduces cross-prompt inference to recover rare-class recall drops caused by class-imbalanced finetuning in vision-language models.

Presents an agentic framework that iteratively refines 3D scene geometry from multi-view video captures against standing visual observations.

Combines lightweight learned guidance with classical filter banks for efficient, low-latency video denoising in communication pipelines.

Identifies early-stage issues with classifier-free guidance in flow matching models and proposes a correction for controllable image editing.

Proposes a decoupled inference-time guidance algorithm to filter unsafe outputs in text-to-image diffusion models without altering weights.

Introduces a benchmark evaluating decision tree reasoning in vision-language models, revealing accuracy drops compared to direct zero-shot prompting.

Releases a spatial audio-visual benchmark and model architecture designed to identify, localize, and reason about sound sources in scenes.

Introduces an evaluation protocol for medical image segmentation uncertainty metrics based on clinical deferral policies and fixed review budgets.

Proposes a training-free closed-form method to refine target object retrieval in 3D Gaussian language maps while preserving open-vocabulary queries.

Presents a feed-forward 3D Gaussian splatting method that uses entropy-guided 3D anchors instead of pixel-aligned predictions for efficient reconstruction.

Proposes a method to dynamically adjust stochastic spread during inference to improve the reliability of weather ensemble forecasts.

Presents a training-free token merging approach combining spatial consistency and feature similarity to speed up vision transformers in segmentation.

Releases an extended 50-year multispectral satellite benchmark for typhoon intensity regression and analyzes systematic model biases.

Develops a robustness auditing framework that evaluates multimodal models against pixel perturbations and competing rendered-text visual overlays.

Introduces a text-to-4D generation method that separates static backgrounds and dynamic characters to allow modular editing and camera control.

Presents a clinical workflow using intraoperative fluoroscopy to provide subsurface ground truth for validating markerless spinal tracking systems.

Proposes an adaptive multi-sequence feature difference module to synthesize contrast-enhanced MRI scans from non-contrast imaging sequences.

Validates an unsupervised domain adaptation model to classify normal and abnormal retinal development across diverse optical coherence tomography systems.

Introduces a dormant spiking neuron mechanism that skips subthreshold computations to reduce computational overhead in spiking neural networks.

Proposes an AI-generated video quality assessment method using visually grounded captions instead of relying solely on source text prompts.

Evaluates whether multimodal language models can detect AI-generated synthetic facial images using visual reasoning or digital artifacts.

Introduces a parameter-efficient multimodal fusion framework inside frozen vision backbones that handles missing modalities during semantic segmentation.

Trains vision-language models via reinforcement learning to actively navigate and inspect 3D environments using single views before answering questions.

Introduces an open-source Python toolbox combining computer vision and speech models to analyze multimodal human nonverbal communication signals.

Presents an agentic multimodal framework using a plan-act-observe loop to generate structured, protocol-compliant chest X-ray radiology reports.

Proposes a single-stage joint diffusion method to forecast solar irradiance directly from sky images without requiring a separate regression model.

Refines DINO foundation features using self-supervised low-rank adaptation to improve partial 3D shape matching.

The paper benchmarks the performance degradation of medical foundation models when transferring from adult chest X-rays to pediatric patients.

The paper applies implicit neural representations with temporal consistency constraints to register periodic medical image sequences across time.

The paper presents a dual-frame coordinate transformer to generate 3D assets with separable, editable component parts.

The paper reconstructs relightable 3D garment meshes from 2D sketches annotated with sparse lighting symbols.

The paper introduces an event monitoring system for surgical video streams that decouples visual perception from procedure-agnostic logic reasoning.

The paper benchmarks how language priors override visual evidence in multimodal models and proposes an adaptation technique to mitigate them.

The paper introduces an image-conditioned evidence refinement method to improve fine-grained compositional matching in vision-language models.

The paper introduces a graph-structured relational reward shaping framework for diffusion-based trajectory planning in autonomous driving.

The paper presents a multimodal vision transformer framework combining thermal guidance and RGB features for image segmentation in low-light environments.

The paper introduces a sparse convolutional masked autoencoder framework to improve data-efficient pretraining on radar satellite imagery.

The paper introduces a benchmark to measure how often vision-language models accept incorrect Qur'anic citations across various visual presentations.

The paper evaluates vision-language models on Arabic calligraphy recognition and identifies frequent default-class prediction failures.

The paper trains discrete image tokenizers by reconstructing pretrained self-supervised feature embeddings rather than raw pixel values.

The paper reduces vision-language model parameters by pruning layers and applying depth recurrence over remaining language backbone blocks.

The paper tests whether pathology foundation models can retrieve relevant historical cases for rare salivary gland tumors without task-specific fine-tuning.

The paper introduces a multiple instance learning method to recover coexisting secondary lesions from whole-slide pathology images labeled only by dominant severity.

The paper presents a contrastive learning framework that handles false-negative pairs during zero-shot chest X-ray vision-language pre-training.

The paper develops an FPGA-based vision system that enforces explicit latency bounds for autonomous vehicle 3D occupancy prediction.