All papers

The paper uses attention maps from vision-language models to directly guide pixel decoders for referring image segmentation.

This paper introduces a dataset and deep learning model to detect concealed handguns using WiFi channel state information.

The paper proposes a context-aware Bayesian late fusion method to dynamically adjust object detector outputs for autonomous driving in adverse conditions.

This paper presents a hybrid layer-set diffusion framework for generating editable and visually rich educational infographics.

This paper proposes a multi-context spatial transformer architecture to improve gene expression prediction from standard histology tissue images.

The paper presents the design and dataset for an international challenge benchmarking federated learning methods for tumor segmentation and classification.

This paper introduces an attention distillation method that transfers geometric correspondences from a frozen model to improve consistency in video generation.

The paper analyzes agentic remote sensing benchmarks and argues that autonomous decision-making should require demonstrated reliability and calibrated uncertainty.

This paper presents an anatomical representation approach driving iterative solvers for longitudinal whole-body PET and CT image registration.

This paper proposes an image editing framework that coordinates prompt understanding and image generation within a single unified model.

This paper presents a benchmark evaluating action-conditioned world models against paired real-world executions and physical task outcomes.

This paper introduces a benchmark to analyze whether visual reasoning skills evaluated in toy games transfer to real-world tasks.

This paper introduces a benchmark evaluating high-level reasoning and multi-step planning in vision-language models for embodied tasks.

This paper develops an online entropic optimal transport approximation to scale minibatch coupling for stable flow matching.

This paper introduces a benchmark to diagnose the ability of video language models to count repeated action cycles.

This paper proposes a generative framework to reconstruct complete, large-scale 3D scene meshes from unposed multi-view RGB images.

This paper applies mixture-of-experts architectures to scale capacity in pixel-space diffusion models without needing learned tokenizers.

The paper discusses visual budgeting strategies to manage token redundancy for scalable multi-image reasoning in multimodal language models.

This paper proposes a progressive layout generation framework that dynamically adapts graphic design compositions to the rendered canvas.

This paper proposes a dimensionless prior to complete dense mesh motion from sparse observations across different geometry families.

This paper presents a framework to pretrain and fine-tune text-to-image diffusion transformers using four consumer GPUs.

This paper introduces a 100-hour multimodal dataset capturing egocentric video, body motion, and touch for human loco-manipulation tasks.

This paper introduces a closed-loop world-action model that improves generalist robot policies using deployment rollouts of mixed quality.

This paper evaluates whether existing deepfake detection models can identify physical humanoid face robots performing speech-synchronized lip movements.

The paper surveys security vulnerabilities and defenses in edge-deployed audio language model agents across the sensing-to-action pipeline.

The paper presents a framework using random noise and Sigmoid function analysis to detect directional adversarial attacks on medical imaging models.

The paper evaluates the adversarial robustness of five camera-radar fusion algorithms against sensor perturbations in autonomous driving settings.

The paper evaluates the effectiveness of randomized interactive gesture challenges in detecting real-time face-swap attacks during remote identity verification.

The paper proposes design requirements and evaluates defenses against model-side protection removal in open-weight image diffusion models.

This paper proposes using state space models to guide black-box image perturbations that disrupt facial recognition systems.

The paper presents OpenMobile-2, combining open mobile environments, trajectory datasets, and hybrid GUI-tool actions for mobile agents.

This paper introduces a benchmark to measure vision-language model hallucinations caused by deceptive textual premises paired with visually confusable images.

This paper presents a multimodal fusion network for depression detection combining gait, audio-visual data, and textual personality descriptions.

The paper introduces a Persian vision-language-action dataset and benchmark to evaluate robotic instruction grounding under deliberate ambiguity and infeasibility.

This paper introduces an instruction-preserving decoding strategy to prevent visual contrastive decoding from discarding valid abstention responses.

The paper evaluates whether providing page images to vision-language models improves the detection of errors in Japanese-English manga translation.

This paper introduces a benchmark to evaluate multimodal models on joint audio-visual temporal grounding, spatial grounding, and question answering.

This paper frames Earth observation model development as an automated loop where coding agents build and iteratively refine models using verifiable harnesses.

This paper presents a robotic manipulation framework combining high-level language planning with vision-language verification to enforce physical and kinematic constraints.

This paper evaluates whether combining radiomics with deep image representations improves probability calibration and discrimination in renal cancer classification models.

This paper analyzes decision logits in radiomics-image fusion models to distinguish gate weight values from actual feature contributions during cancer classification.

The paper develops and evaluates a multimodal foundation model for interpreting oncologic chest, abdomen, and pelvis CT scans.

The paper presents a latent world action model that predicts action-induced state transitions rather than full-screen pixel reconstructions for graphical agents.

The paper introduces a sparse interaction diffusion method that coordinates multi-robot trajectories using a frozen single-robot prior and modular corrections.

The authors introduce a framework that uses cross-view depth consistency as a single reward to improve 4D video generation geometry.

The paper proposes a training-free method leveraging pullback directions to increase sample diversity in conditional diffusion generation.

The paper introduces a receding-horizon optimal control algorithm for constrained sampling in flow matching generative models.

The authors present a feed-forward network that aggregates local wall predictions to reconstruct floor plans from sparse unposed panoramas.

The paper presents an elastic vision architecture that dynamically reconfigures width and supports partitionable inference across heterogeneous edge devices.

The paper introduces a post-training residual correction method to predict and correct endpoint errors during low-step generative model sampling.

The authors present a foundation controller that converts motion drafts into contact-consistent joint commands for dexterous robotic hands.

The paper proposes an uncurated multi-stream latent memory framework that augments vision-language-action robotic policies while maintaining low inference latency.

The paper introduces a benchmark and framework to evaluate how multimodal models track entities and reason about future scene evolution in videos.

The paper presents a feature-proxy method to selectively erase combined object-style concepts from diffusion models without degrading general generative performance.

The paper specifies a verifier-governed protocol for panvascular research and benchmarks a baseline model on intervention imaging tasks.

The paper presents a neural-symbolic method for multi-robot systems to evaluate robot and object hypothesis trust purely via inter-robot observation consistency.

The paper proposes a structured chain-of-thought grounding method to track visual evidence across video frames for goal-oriented embodied navigation.

The paper releases a multimodal dataset and dual-encoder model to align natural language descriptions with global Earth system meteorological data.

The paper introduces a multi-head prediction method that injects future long-range supervision to improve autoregressive video generation consistency.

The paper introduces a hybrid kinematic graph neural network and elastic transformer framework for efficient Indian sign language recognition from video.