5561 papers
To address the limitations of traditional visual methods in robot automated assembly, this paper proposes SMR-Net, a self-attention-based multi-scale detection algorithm paired with a dedicated sensor, which significantly improves snap localization precision and robustness in complex scenarios by integrating attention-enhanced feature extraction, parallel multi-scale processing, and adaptive reweighting.
The paper proposes TAR-FAS, a tool-augmented reasoning framework that enhances generalizable Face Anti-Spoofing by enabling MLLMs to combine intuitive observations with adaptive, fine-grained visual tool investigations through a specialized dataset and training pipeline.
The paper introduces MM-DeepResearch, a multimodal deep research agent that overcomes data scarcity, trajectory generation, and training cost challenges through Hyper-Search for data synthesis, DR-TTS for specialized tool optimization and trajectory planning, and an offline search engine for cost-effective reinforcement learning.
This paper proposes ELF-VLA, a framework that enhances Vision-Language-Action models for autonomous driving by replacing vague scalar rewards with explicit, diagnostic failure feedback to guide targeted policy refinement, thereby overcoming exploration limitations and achieving state-of-the-art performance on the NAVSIM benchmark.
LLaDA-o is a state-of-the-art, length-adaptive omni diffusion model that leverages a Mixture of Diffusion framework with a shared attention backbone to effectively unify discrete text understanding and continuous visual generation, achieving top-tier performance on multimodal benchmarks.
This paper presents SHIELD8-UAV, a low-energy, sequential 8-bit hardware accelerator for UAV acoustic detection that achieves real-time, precision-aware inference on resource-constrained edge devices through a shared multi-precision datapath, layer-sensitivity quantization, and structured channel pruning.
The paper proposes A3Point, an adaptive framework that enhances LiDAR semantic segmentation robustness under adverse weather by utilizing a semantic confusion prior and shift region localization to effectively leverage diverse augmentations while mitigating semantic shifts.
This paper introduces MCMR, a large-scale benchmark designed to evaluate fine-grained, multi-condition multimodal retrieval across five product domains, revealing that while visual cues drive early precision, MLLM-based rerankers significantly enhance compositional matching by verifying complex query-candidate consistency.
This paper introduces AesEval-Bench, a comprehensive benchmark and training dataset designed to systematically evaluate and enhance Vision Language Models' ability to assess graphic design aesthetics across multiple dimensions, tasks, and indicators.
This paper introduces V-SONAR, a unified vision-language embedding space aligned with the multilingual SONAR text space, and leverages it to develop V-LCM, a model that achieves state-of-the-art performance in video captioning and significantly outperforms existing vision-language models across 61 diverse languages through concept space alignment and latent diffusion training.
This paper introduces DP-RGMI, a framework that analyzes differential privacy in medical imaging by decomposing utility loss into representation geometry and task-head utilization, revealing that privacy mechanisms induce non-uniform anisotropic reshaping of features and create a utilization gap even when linear separability is preserved.
This paper proposes StrokeDiff, a data-efficient diffusion-based framework with Smooth Regularization that generates diverse, controllable, and human-like oil painting brushstrokes from a small dataset, enabling structured and expressive multimedia content creation.
This paper introduces Egocentric Co-Pilot, a web-native neuro-symbolic framework for smart glasses that combines an LLM-orchestrated toolset with advanced temporal reasoning and multimodal intent mapping to deliver state-of-the-art, always-on assistive AI for navigation and daily tasks, demonstrating superior performance and user satisfaction over commercial baselines through both cloud and local deployment evaluations.
This paper introduces GroundedSurg, the first multi-procedure benchmark designed to evaluate language-conditioned, instance-level surgical tool segmentation by pairing surgical images with natural language descriptions and precise spatial annotations to address the limitations of existing category-level evaluation paradigms in clinical AI.
GuiDINO introduces a framework that leverages DINOv3 as a visual guidance generator to produce spatial guide masks via a lightweight TokenBook mechanism, effectively enhancing medical image segmentation across diverse datasets and backbones without requiring full fine-tuning of the foundation model.
ClinCoT is a novel framework that enhances medical vision-language models by introducing a clinical-aware visual chain-of-thought mechanism and an iterative, scoring-based preference optimization strategy to improve factual grounding and reduce hallucinations through region-level reasoning.
This paper proposes PR-ACL, a novel framework for compositional visual relations that combines augmented anomaly contrastive learning with a predictive-and-verify paradigm to effectively identify outlier images by iteratively predicting and verifying compositional rules.
The paper proposes TCD-Net, a Vision Transformer-based image denoising framework that utilizes teacher-guided causal interventions, including environmental bias adjustment and orthogonal content-noise disentanglement, to eliminate spurious correlations and achieve state-of-the-art fidelity and real-time performance.
ArtLLM is a novel framework that leverages a 3D multimodal large language model to autoregressively predict kinematic structures and generate high-fidelity articulated 3D assets directly from complete meshes, significantly outperforming existing methods in accuracy and generalization for applications like robotics and simulation.
This paper proposes TC-SSA, a learnable token compression framework that utilizes gated semantic slot aggregation to efficiently process gigapixel whole slide images by reducing visual tokens to 1.7% of the original sequence while preserving diagnostically critical information and outperforming existing sampling-based methods in both reasoning and classification tasks.