All papers

Presents a quadratic selection pruning algorithm for 3D Gaussian Splatting that coordinates regional budgets and accounts for joint deletion effects.

Develops a robotic action-conditioned world model trained on egocentric human interactions and failure dynamics to improve execution foresight.

Presents a teacher-student framework to evaluate whether remote sensing visual queries are genuinely supported before predicting bounding boxes.

Introduces an error-driven, confidence-aware densification method to place primitives more effectively in 3D Gaussian Splatting under fixed budgets.

Proposes an object-centric slot adaptation framework with prototype memory for remote sensing image super-resolution using diffusion Transformers.

Audits vision-language model benchmarks by analyzing saved evaluation artifacts and removing image inputs to detect ungrounded questions.

Introduces ELMA, a benchmark dataset for generating long-term human motions from multi-segment instructions with anaphoric references.

Applies multi-directional spatial context modeling and high-frequency guidance for cross-view geographic localization.

Uses fMRI encoding representations to guide targeted emotional edits on images while preserving visual content.

Combines deep segmentation networks with conformal prediction to estimate pixel-level uncertainty on thyroid ultrasound images.

Develops a class-conditioned boundary evidence method for open-set recognition of visually similar insect species.

Introduces an evaluation protocol and dataset to measure consistency between textual answers and visual grounding in remote sensing VQA.

Benchmarks multimodal decision models across multiple prompts for classifying breast ultrasound imaging datasets.

Reconstructs physical 3D objects with controllable material properties and interaction dynamics via video diffusion models.

Proposes a memory-efficient cross-attention method for distilling large paired audio-visual datasets into compact synthetic sets.

Uses generative models and vision-language labeling to detect and synthesize missing semantic domains in test datasets.

Designs a global-local memory interface with multi-precision spectral encoding for consistent video world models.

Introduces a state-isolation routing mechanism for Vision Mamba models to enhance spatially heterogeneous underwater images.

Presents perturbation techniques to protect personal video data from unauthorized identity replication by generative models.

Improves 2D Gaussian Splatting rendering of near-field reflections using observed image environment data.

Reconstructs images and videos from fMRI signals using cortex-inspired serial decoding and multimodal pre-training.

Evaluates shortcut learning and out-of-distribution failure in geospatial foundation models on cross-disaster damage assessment tasks.

Provides an attribution method for selective state-space video models by tracing accumulated recurrence in a single forward pass.

Benchmarks heuristic, machine learning, and deep learning methods for identifying rare dead cells in fluorescence microscopy.

Presents a unified plug-and-play posterior sampling framework combining diffusion and flow models via exact Gaussian conditioning.

Pre-trains vision transformers on multi-frame event camera data using masked auto-encoding for action recognition.

Reviews satellite-based building damage assessment across data, evaluation protocols, and method formulations.

Introduces a differentiable token merging operator running in linear time with an exact sequence output budget.

Animates flowing water in static 3D Gaussian Splatting scenes using 2D optical flow lifted into a 3D Eulerian motion field.

Replaces random crop sampling in 3D medical self-supervised learning with a guided probe bank based on anatomical priors.

Introduces a generate-verify-optimize pipeline to enable non-verbal visual reasoning in machine learning systems.

Improves multimodal diffusion transformer generation by suppressing image-to-text feedback loops during iterative denoising.

Introduces a shared latent interface enabling handoff and translation of denoising trajectories across heterogeneous image diffusion models.

Presents a multi-agent generation pipeline and benchmark for creating physically grounded, articulated 3D digital assets.

The paper proposes embedding multi-scale wavelet-domain watermarks into initial diffusion noise to enable robust provenance tracking under image removal attacks.

The paper evaluates label-only backdoor defenses and demonstrates that inspecting representations in unpoisoned feature spaces improves poison detection accuracy.

The paper presents an adversarial perturbation defense that disrupts the value vector diversity in image-to-video diffusion models to prevent unauthorized generation.

The paper proposes a deep convolutional, quantization-free watermarking framework to embed metadata into electrocardiogram signals without hurting diagnostic details.

Develops a framework for egocentric multimodal assistants to decide both when to speak and what guidance to provide.

Introduces a benchmark evaluating how well vision-language models predict unseen physical outcomes following scene interventions.

Proposes a framework that encodes visual trajectories into reusable skill cards to reinforce vision-language agent planning and rewards.

Presents a benchmark and dataset using generative video rollouts to train and evaluate visual transition reasoning in multimodal models.

Introduces a benchmark evaluating streaming vision-language models on fast-moving, high-dynamic events in real-world video streams.

Introduces a benchmark evaluating vision-language models on graph-theoretic reasoning where evidence is split across multiple visual graph views.

Tests whether self-supervised vision transformers represent semantic meanings of rendered words without paired text data.

Analyzes the emergence and behavior of attention sinks in text-to-image diffusion and autoregressive Transformers.

The paper identifies a linear spatial workspace subspace in multimodal language models that encodes continuous 3D coordinate representations.

The paper extends attention residuals across model depth by introducing multi-stream depth attention with causal convolutions.

The paper presents a benchmark evaluating how effectively vision-language models recover fully occluded objects using remaining visual cues.

Introduces a Minecraft diagnostic benchmark testing whether multiplayer world models maintain consistent and persistent representations across independently controlled views.

Proposes an external memory framework allowing frozen language model agents to learn and iteratively refine natural-language world models compiled into executable code.

Evaluates eight lightweight open-source vision-language models on optical character recognition and schema-compliant JSON extraction across heritage document collections.

Introduces a training-free inference method using Gaussian optimal transport to reduce object hallucination in vision-language models without suppressing valid representations.

Proposes an adaptive multi-source steering decoding method to mitigate hallucinations in vision-language models by balancing visual and textual context contributions.

Introduces a benchmark evaluating multimodal language models on extracting data, tracking narrative flows, and answering questions from animated data videos.

Proposes a rolling-denoising framework for vision-language-action models to improve robot control reactivity without sacrificing long-horizon execution consistency.

Introduces a world action model for humanoid robots that jointly models robot internal states and scene evolution to close the execution gap.

Introduces an immutable, provenance-aware state representation framework with deterministic replay and audit logging for robotic systems.

Transfers editing capabilities from image pairs to video diffusion models using in-context visual demonstrations across decomposed sub-tasks.

Synthesizes novel views by reusing nearby rendered RGB-D images and applying pose-guided warping with residual refinement.