All papers

Uses contrastive uncertainty learning during training to improve anatomical boundary segmentation in pelvic magnetic resonance imaging.

Introduces a geometry-grounded scene representation with continuous levels of detail to compress large view synthesis models.

Investigates the interaction between learnable image resizing and spectral-domain convolutional filter parameterization in neural networks.

Presents an adversarial evasion attack that fools face recognition systems while preserving embedded watermarks.

Compares event and conventional frame cameras via simulations for synthetic-aperture imaging through occluding scenes like foliage.

Decouples geometry and appearance learning in Gaussian reconstruction to improve three-dimensional scene rendering across diverse views.

This paper introduces ReferFlow-Bench and an accompanying method to evaluate and perform multi-step trajectory-level referring expression comprehension.

This paper proposes PreActIR, an agentic image restoration framework that forecasts tool consequences using a world model before taking action.

This paper introduces STP-GUI, a spatially aware token-pruning framework that preserves local interface structures for efficient web-GUI vision-language agents.

This paper proposes a reusable, label-aware front end to allocate fixed budgets of 3D Gaussians for cinematic CT visualization without per-case optimization.

This paper presents a hybrid instance segmentation pipeline that trains ancillary models to suppress label noise when combining bounding boxes with pixel masks.

This paper assesses ten fingerprint quality assessment methods on longitudinal infant data and proposes parameter tuning routines to improve recognition.

This paper introduces OLiVES, a benchmark dataset of dynamic outdoor low-light videos designed for joint video enhancement and object segmentation.

This paper develops a self-supervised representation learning approach based on pointwise mutual information factorisation for scalable generative latent tokenization.

This paper introduces RM-Net, a reliability-aware network designed to maintain multimodal sentiment prediction accuracy when spans of modalities are missing.

This paper proposes Speculative Sampling to combine large bidirectional sampling windows with low-latency interaction in interactive video generation.

This paper presents A3PreCore, a six-degree-of-freedom rigid-registration method for robust T1-to-FLAIR brain-mask transfer alongside quantitative quality control metrics.

This paper introduces Any Time, Any Space, a world-state memory framework enabling consistent multi-shot long-form agentic video generation.

This paper proposes a mixture-of-experts transformer architecture using discrete Hodge-Laplacian operators to solve steady partial differential equations on unstructured meshes.

This paper presents a feed-forward framework that encodes multi-view image features into a compact bitstream for 3D Gaussian Splatting scene reconstruction.

This paper proposes GeoThinker-F, an efficient architecture that predicts spatial representations directly from multimodal hidden states without dedicated 3D encoders.

This paper presents DynThinker, a framework using parameter averaging and spatial-dynamic fusion to balance static 3D and dynamic 4D reasoning.

This paper introduces CommerceGen-Studio, an end-to-end framework using specialized agents and diffusion models to generate e-commerce product images.

This paper presents VITAL, an action tokenizer coupling visual motion scaffolding with residual error encoding for autonomous driving decision-making.

This paper proposes representation conditioning to feed vision encoder features into later layers of diffusion transformers for improved generation semantics.

This paper introduces Ask4NV, combining multimodal 3D language understanding with camera geometry to guide novel-view image generation.

This paper investigates 36 handcrafted physiological markers from speech production to detect deepfake speech using standard classifiers.

The authors present IDGuardian, an adversarial perturbation framework designed to defend facial images against unauthorized personalized diffusion models.

This paper introduces DarKnight, a framework that uses locally hosted vision-language models to solve dark web CAPTCHAs during threat intelligence gathering.

The paper presents a vision-language policy combining pre-aligned representations and multi-stage learning to improve robotic person tracking among distractors.

The paper proposes an agentic framework enabling multimodal models to identify information gaps and retrieve in-context demonstrations iteratively.

This paper introduces AdaRSI, an on-demand recursive self-improvement framework for embodied agents that isolates and fine-tunes only persistent sub-step failures.

Integrates clinical text and deformation history into medical image registration models for adaptive radiotherapy planning.

Introduces a generalist policy that tracks diverse dexterous hand-object interactions using value-guided reset allocation during training.

Presents a large graphical user interface grounding dataset and a combined distillation and reinforcement learning training method for agents.

Surveys dataset design, construction methods, and evaluation protocols for improving spatial reasoning in multimodal language models.

The paper demonstrates onboard thermal segmentation training and closed-loop attitude control on a satellite in low Earth orbit.

The paper introduces a framework that incrementally updates robot spatial judgments after environmental changes without full scene recomputation.

The paper provides an analytical framework for how multimodal agents form, represent, and adapt memory over long-horizon tasks.

The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence.

The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning.

The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models.

The paper introduces a multi-task benchmark and a masked autoencoder to evaluate transfer learning for wrist-worn inertial measurement units.

The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion.

The paper proposes an in-context reinforcement learning method enabling vision-language-action policies to adapt during robot deployment.

The paper introduces a spatial reasoning benchmark to evaluate how models handle varying reference frames and altered visual evidence.

The paper separates parameter specification, instruction generation, and code synthesis for language-conditioned robotic arm control using reinforcement learning.

The paper combines a vision-language model with a Transformer-VAE to generate expressive, human-like gaze dynamics for humanoid robots.

The paper proposes a 3D-anchored latent space representation for simultaneous scene reconstruction and view generation.

The paper introduces an uncertainty-driven self-play method where exploring agents target policy weaknesses to learn robust autonomous driving policies.

This paper introduces a task and benchmark for tracking candidate sets of referent objects across ambiguous interactive dialogue turns.

This paper presents a training-free contrastive decoding framework using attention-guided perturbations to reduce hallucinations in video language models.

This paper analyzes three distinct failure modes where reinforcement learning agents misgeneralize goals despite competent behavior.

This paper presents a dual-branch latent diffusion method to generate LiDAR point clouds from multi-view camera images.

This paper pairs language models with video diffusion to maintain persistent abstract states across dynamic video world interactions.

This paper proposes an agentic video framework combining change-aware memory and targeted replay for streaming video understanding.

The paper describes a vision-language model using task-specific low-rank adapters for multimodal medical image parsing.

The paper proposes a modular manipulation framework that sequences verified robot capability units for long-horizon tasks.

The paper proposes a contextual parameterization framework that dynamically adjusts robot policy parameters as interaction contexts change.

The paper introduces an agentic framework that removes peripheral visual clues to protect geolocation privacy in images without adding visible artifacts.