2792 papers
This paper presents a semi-supervised deep generative anomaly detection framework, utilizing a residual autoencoder with a dense bottleneck, that achieves high-accuracy, real-time defect detection and localization on high-speed pharmaceutical Blow-Fill-Seal production lines while operating within strict 500 ms timing constraints.
This paper proposes 3DGS-HPC, a novel framework that improves 3D Gaussian Splatting in real-world environments by replacing fragile semantic cues with a robust patch-wise classification strategy and a hybrid metric to effectively identify and suppress transient distractors like moving objects and shadows.
This paper introduces StructAttack, a black-box jailbreak framework that exploits the semantic slot-filling vulnerability of Large Vision-Language Models by embedding benign-looking visual structures to covertly assemble and generate harmful content.
This paper proposes an efficient, end-to-end learned point cloud simplification method that combines feature embedding with attention-based sampling to achieve a superior balance between computational speed and accuracy for LiDAR-based object detection and classification compared to traditional sampling techniques.
EmbedTalk introduces a triplane-free talking head synthesis method that leverages learned embeddings to drive 3D Gaussian deformations, achieving superior rendering quality, lip synchronization, and motion consistency while enabling real-time performance (over 60 FPS) on mobile GPUs through significantly more compact models.
This paper proposes an unsupervised deep learning method using two neural-field networks with periodic activation functions to model water surface height and reconstruct undistorted underwater images from aerial views, outperforming existing approaches on both simulated and real data.
This paper proposes a novel visual representation framework that encodes signals as functions parametrized by low-rank adaptations on frozen diffusion models, enabling compact storage via single-vector hashing and bridging visual compression with generation through inference-time scaling and control.
This paper identifies "overthinking"—the propagation of incorrect intermediate hypotheses across decoder layers—as a primary cause of hallucinations in Vision Language Models and introduces the Overthinking Score, a layer-probing metric that significantly outperforms existing final-output-based detectors.
The paper proposes Duala, a dual-level alignment framework that enhances cross-subject fMRI decoding by ensuring semantic consistency at the stimulus level and capturing individual neural variations at the subject level, thereby achieving state-of-the-art performance in image-to-brain retrieval and reconstruction with minimal adaptation data.
This paper proposes Mobile GlottisNet, a lightweight and efficient deep learning framework utilizing spatial-decoupled feature learning and adaptive mechanisms to achieve real-time, high-speed glottis detection for nasotracheal intubation on resource-constrained edge devices.
This paper proposes a high-fidelity synthetic data generation pipeline using NVIDIA Omniverse to address data scarcity and privacy constraints in airport logistics, demonstrating that mixed training with synthetic data and only 40% of real annotations achieves performance comparable to full real-data baselines while reducing annotation effort by 25–35%.
The paper proposes AtomicVLA, a unified planning-and-execution framework that utilizes a Skill-Guided Mixture-of-Experts architecture to dynamically compose atomic skill abstractions, thereby significantly improving scalability and performance in long-horizon robotic manipulation and continual learning tasks compared to existing monolithic VLA models.
GLASS is a novel unsupervised framework that establishes dense 3D shape correspondence across challenging non-isometric and inter-class scenarios by integrating geometric spectral analysis with semantic priors from vision-language foundation models, achieving state-of-the-art performance through view-consistent feature extraction, language-injected vertex descriptors, and a graph-assisted contrastive loss.
This paper proposes a Self-Critical Inference (SCI) framework that enhances the robustness of Large Vision-Language Models against language bias and sensitivity through multi-round counterfactual reasoning with textual and visual perturbations, alongside a new Dynamic Robustness Benchmark (DRBench) for model-specific evaluation.
This paper introduces Holi-Spatial, the first fully automated, large-scale, spatially-aware multimodal dataset constructed from raw video streams without human intervention, which provides 4 million high-quality 3D semantic annotations and spatial QA pairs to significantly enhance the training and performance of Vision-Language Models on spatial reasoning tasks.
Ref-DGS is an efficient, rasterization-based framework that achieves state-of-the-art novel view synthesis on reflective scenes by decoupling surface geometry from specular reflections using a dual Gaussian representation and a lightweight adaptive mixing shader, thereby avoiding the high computational cost of explicit ray tracing.
This paper introduces FusionRegister, a general and efficient cross-modality registration framework guided by visual priors that directly corrects misalignment within fused infrared and visible images, thereby enhancing detail alignment and robustness without requiring extensive pre-registration.
UniUncer is a lightweight, unified framework for end-to-end autonomous driving that jointly estimates and leverages uncertainty for both static map elements and dynamic agents through probabilistic regression, uncertainty-aware query fusion, and adaptive gating, thereby significantly improving trajectory accuracy and planning robustness with minimal computational overhead.
FrameVGGT addresses the unbounded memory growth in streaming Visual Geometry Transformers by introducing a frame-driven rolling explicit-memory framework that aggregates frame-level evidence into compact prototypes, enabling stable long-sequence 3D perception under strict memory budgets.
This paper introduces RoboPCA, a pose-centered affordance learning framework that jointly predicts task-appropriate contact regions and poses from human demonstrations via the Human2Afford data curation pipeline, enabling robots to effectively manipulate objects with improved consistency and generalization across tasks and categories.