4335 papers
This paper presents a user-friendly framework that enables domain scientists to generate 3D animations of petascale, time-varying climate data on commodity hardware using an LLM-assisted conversational interface, thereby eliminating the need for specialized visualization expertise and high-performance computing resources.
This paper proposes a bi-directional digital twin prototype anchoring framework enhanced with multi-periodicity learning to achieve robust few-shot fault diagnosis by leveraging meta-training in a virtual simulation space and test-time adaptation in the physical domain, thereby overcoming the limitations of traditional methods that require abundant labeled or unlabeled target data.
SODA introduces a sensitivity-oriented dynamic acceleration framework for Diffusion Transformers that adaptively optimizes caching and pruning strategies through fine-grained sensitivity modeling and dynamic programming, achieving state-of-the-art generation fidelity under controllable acceleration ratios.
This paper introduces GuideTWSI, a diverse dataset combining synthetic and real-world images to address the scarcity of Tactile Walking Surface Indicator (TWSI) data, specifically bridging the gap between East Asian directional bars and North American/European truncated domes to improve navigation safety for blind and low-vision individuals.
This paper introduces Marauder, a declarative framework that unifies diverse hand-crafted mutation representations through a common intermediate form and a mutation algebra to enable efficient, expressive, and lossless management of mutants for evaluating testing tools.
MedSteer is a training-free activation-steering framework that generates structurally preserved counterfactual endoscopic images by manipulating cross-attention activations in diffusion transformers, outperforming existing methods in concept editing and downstream medical detection tasks.
This paper explores the geometry of popup structures by combining origami and kirigami principles to establish a design pipeline that uses discrete surface curvature definitions to generate cut-fold patterns capable of creating complex, deployable shapes with applications in drag reduction, packaging, and architecture.
This paper proposes a morphology-independent facial expression imitation method that decouples expression semantics from facial morphology using self-supervised learning and an error-perceiving transfer module, validated on a custom-designed human-face robot named Pengrui to achieve more natural and accurate human-robot interaction.
This paper proposes and validates a novel approach using GPT-4-powered dialogue systems to facilitate user review creation through interview-based information gathering, demonstrating that the resulting system-generated reviews require less editing and are perceived as more helpful by readers than human-written ones, despite some fluency challenges.
This paper introduces VirtueBench, a new benchmark designed to evaluate the trustworthiness of Vision-Language Models in long video understanding by distinguishing between answerable and unanswerable cases to prevent misleading accuracy scores caused by guessing under uncertainty.
This paper introduces PhyVLM-CR, a novel unified framework that integrates Vision-Language Model semantic priors with physical scattering parameters to seamlessly remove both thin and thick clouds from optical remote sensing imagery without explicit cloud-type segmentation, thereby achieving high-fidelity, hallucination-free surface reconstruction.
This paper proposes PSG-UIENet, a novel underwater image enhancement network that integrates Retinex-based illumination correction with CLIP-derived textual semantics to overcome the limitations of existing methods, supported by the introduction of a new large-scale image-text dataset (LUIQD-TD) and a specialized semantic similarity loss function.
This paper introduces a novel framework for EEG-based visual decoding that aligns brain signals with intermediate visual layers via a proposed "Neural Visibility" concept and a Hierarchically Complementary Fusion mechanism, achieving state-of-the-art performance by significantly reducing cross-modal information mismatch.
The paper proposes Multi-TAP, a multi-criteria target-adaptive persona framework that addresses data sparsity and intra-domain heterogeneity in cross-domain recommendation by explicitly modeling semantic personas and selectively transferring relevant source-domain signals, thereby outperforming state-of-the-art methods on real-world datasets.
The paper introduces mAVE, a novel watermarking framework that cryptographically binds audio and video latents in joint generation models to eliminate the "Binding Vulnerability" of existing methods and robustly defend against adversarial Swap Attacks without requiring model fine-tuning.
This study benchmarks ten small language models on architectural decision record generation to establish a multidimensional evaluation framework, revealing that models exceeding 3 billion parameters excel in zero-shot reasoning while sub-2 billion models benefit most from fine-tuning, and that few-shot prompting effectively calibrates mid-sized models despite high semantic diversity often correlating with hallucinations.
This paper proposes a facial expression generation method for natural dyadic interaction that leverages human feedback within a vision-language-action framework and reinforcement learning strategy to produce contextually appropriate, identity-independent expressions aligned with human preferences.
This paper investigates concurrent graph games with distributed randomization where team players lack a shared random source, establishing that memoryless strategies suffice for the threshold problem (placing it in and proving NP-hardness) and that almost-sure reachability is NP-complete, while introducing the IRATL logic and a corresponding solver.
This paper proposes ACLM, an ADMM-based distributed model predictive control framework that enables scalable, real-time collaborative loco-manipulation of heavy payloads by decomposing the global optimization problem into parallel robot-level subproblems while preserving dynamic coupling through consensus constraints.
This paper proposes a scalable, structured multitask variational Gaussian Process framework for full-body human motion prediction that achieves competitive accuracy with significantly fewer parameters and provides well-calibrated, interpretable uncertainty estimates essential for safe real-time human-robot collaboration.