6357 papers
This paper introduces SCOTT, a sparse convolutional tokenizer combined with a MIM-JEPA training framework, which enables Vision Transformers to learn robust self-supervised representations from scratch on small-scale, fine-grained datasets, thereby challenging the necessity of big data and massive computational resources for effective vision representation learning.
This paper proposes CrackIVF, an adaptive, partition-based index for vector search in data lakes that progressively optimizes itself based on query workloads, enabling immediate query responses without upfront index construction while eventually matching the performance of conventional indexes.
This paper demonstrates that a simple iterative LQR algorithm using MuJoCo dynamics and finite-difference derivatives can achieve effective, real-time whole-body model-predictive control for quadruped and humanoid robots in the real world with minimal sim-to-real tuning, thereby lowering the barrier for future research.
The paper introduces NAMI, a Bridged Progressive Rectified Flow Transformer framework that significantly accelerates image generation and reduces inference time by 64% through a multi-resolution, spatially cascaded architecture with a BridgeFlow module, while maintaining state-of-the-art quality and introducing the NAMI-1K benchmark for evaluation.
ECLARE is an open-source, self-supervised super-resolution method that enhances anisotropic 2D MR volumes by estimating slice profiles and learning in-plane mappings without external data, thereby overcoming domain shift and outperforming existing techniques in both signal recovery and downstream tasks.
This paper introduces VISKY, a computationally efficient semi-active control strategy for magnetorheological suspensions that enhances ride comfort and stability by implementing a virtual inertia matrix through acceleration feedback, effectively outperforming conventional Skygroundhook methods across various road conditions.
The paper introduces EarthScape, a multimodal dataset and reproducible pipeline designed to automate surficial geologic mapping by integrating diverse geospatial data sources, demonstrating that terrain features provide the most robust predictive signal while highlighting the dataset's utility for benchmarking multimodal fusion and domain adaptation.
This paper introduces a new framework of psychophysical tests based on low-level vision principles—specifically contrast sensitivity, masking, and matching—to evaluate and reveal the perceptual strengths and weaknesses of 34 existing image and video quality metrics, demonstrating that standard evaluation protocols often fail to capture these fundamental human visual properties.
This paper presents a graph-based online Lidar odometry method that enhances trajectory estimation and map accuracy by registering scans against multiple overlapping submaps and performing retrospective refinement of their anchor points, achieving superior performance on automotive datasets while maintaining real-time operation.
This paper proposes GenCLIP, a novel framework for zero-shot anomaly detection that enhances generalization and stability through multi-layer prompting, a dual-branch inference strategy balancing category specificity with generalization, and an adaptive text prompt filtering mechanism.
FAST is an efficient scheduler designed to overcome the scalability and performance limitations of existing solutions for All-to-All(v) communication in dynamic Mixture-of-Experts workloads by addressing traffic skew and incast congestion while drastically reducing synthesis time on modern GPU clusters.
This paper introduces BusterX, an MLLM-powered framework for AI-generated video forgery detection that combines the comprehensive GenBuster-200K dataset and the multi-track GenBuster-Bench to shift from black-box classification to interpretable visual reasoning, achieving superior accuracy and explanation quality compared to leading models.
This paper introduces DVD-Quant, a novel data-free post-training quantization framework for Video Diffusion Transformers that utilizes Bounded-init Grid Refinement, Auto-scaling Rotated Quantization, and -Guided Bit Switching to achieve a 2 speedup and enable W4A4 quantization without compromising visual fidelity.
This paper introduces "Alchemist," a novel methodology that leverages pre-trained generative models to curate a compact, high-quality, general-purpose supervised fine-tuning dataset, which significantly enhances the aesthetic quality and alignment of public text-to-image models while preserving diversity.
This paper presents a systematic literature review of 80 studies on "systems of twinned systems," a paradigm that integrates the System of Systems and Digital Twin concepts to address modern engineering complexity, resulting in a new classification framework compatible with existing theories.
This paper introduces Instance Data Condensation (IDC), a novel framework utilizing Random Local Fourier Feature Extraction and Multi-level Feature Distribution Matching to synthesize a highly compact (10% volume) dataset for Image Super-Resolution that achieves performance comparable to the original full dataset while significantly reducing computational and storage requirements.
This paper introduces "Linear Layouts," a novel framework that models tensor layouts as linear algebra operations over to enable generic, efficient, and bug-free layout definitions and conversions for deep learning workloads, successfully integrating with the Triton compiler to overcome the limitations of existing case-by-case approaches.
This paper introduces a robustness-aware framework that jointly selects tools and plans contact-rich manipulation trajectories by leveraging an energy-based metric to optimize for disturbance resilience in robotic tool-use tasks.
This paper proposes a structured methodology that integrates the Robot Operating System (ROS) with Model-Based Systems Engineering (MBSE) through a specialized SysML metamodel called MeROS and an adapted V-model, aiming to enhance the semantic coherence, structural traceability, and reliable coordination of complex heterogeneous robotic systems.
VisualPrompter is a novel, training-free framework that enhances text-to-image synthesis by employing a self-reflection module to identify missing concepts and a fine-grained optimization mechanism to refine prompts, thereby achieving state-of-the-art semantic alignment between user descriptions and generated images.