6576 papers
This paper proposes a framework that bridges Multi-modal Large Language Models and physical robot control by introducing "analytic concepts"—procedurally defined mathematical representations—to ground commonsense knowledge for generalized articulated object manipulation.
The paper proposes OSDM-MReg, a novel multimodal image registration framework that utilizes a one-step unaligned target-guided conditional diffusion model to translate images into a unified domain and a multimodal multiscale registration network to achieve superior alignment accuracy between SAR and optical images.
This paper introduces VR-FuseNet, a hybrid deep learning model that fuses VGG19 and ResNet50V2 architectures with a balanced, preprocessed multi-source dataset and explainable AI techniques to achieve 91.824% accuracy and clinically interpretable detection of diabetic retinopathy.
This paper argues that computer vision models for ecology and biology should be evaluated using application-specific metrics rather than standard machine learning benchmarks, demonstrating through case studies on chimpanzee abundance and pigeon gaze estimation that high technical performance does not guarantee accuracy in downstream scientific analyses.
This paper introduces an Explainable AI-based method to attribute Wasserstein distances to specific data components, enabling a deeper understanding of dataset shifts and transport phenomena across diverse applications.
To address the performance degradation caused by noisy correspondence in large-scale text-image datasets, this paper proposes the Dynamic Uncertainty and Relational Alignment (DURA) framework, which utilizes a Key Feature Selector and a Dynamic Softmax Hinge Loss to model noise uncertainty and adaptively handle negative samples, thereby significantly improving retrieval robustness across varying noise levels.
This paper presents the first theoretical analysis demonstrating that adversarially pretrained single-layer linear transformers can function as universally robust foundation models, enabling them to adapt to diverse unseen downstream tasks with robustness for free through in-context learning from clean demonstrations.
This paper introduces the Flexible-weighted Chamfer Distance (FCD), a plug-and-play objective function that decouples local precision and global completeness with an asymmetric weighting strategy to mitigate point aggregation and structural defects, thereby significantly enhancing the quality and uniformity of generated point clouds across various completion and upsampling tasks.
DeepEyes is a Large Vision-Language Model that leverages reinforcement learning to natively learn "thinking with images" through active perception, enabling it to strategically ground reasoning in visual information without requiring pre-collected reasoning data and achieving significant improvements in perception, reasoning, and grounding tasks.
This paper introduces GradPCA, a reliable Out-of-Distribution detection method that leverages the low-rank structure of neural network gradients induced by Neural Tangit Kernel alignment through Principal Component Analysis, supported by both theoretical insights and extensive experimental validation.
This paper introduces DTR, a novel inference-time defense that dynamically reweights visual tokens by optimizing key-value caches to effectively mitigate multimodal jailbreak attacks in vision-language models while preserving general capabilities and efficiency.
Seek-CAD is a pioneering training-free framework that leverages the locally deployed open-source DeepSeek-R1 model combined with visual and Chain-of-Thought feedback from a Vision Language Model to iteratively self-refine 3D parametric CAD models, supported by a novel SSR-structured dataset for industrial applications.
This paper introduces deterministic, projection-based probabilistic kernel functions for fast angle testing in high-dimensional spaces that eliminate asymptotic assumptions and achieve 2.5x–3x higher query throughput than HNSW in approximate nearest neighbor search.
The paper introduces Point-MoE, a Mixture-of-Experts framework that enables effective large-scale joint training of 3D semantic segmentation models across diverse, unlabeled datasets by using a lightweight router to dynamically assign tokens to specialized experts, thereby overcoming the performance degradation caused by naive data mixing and achieving state-of-the-art results without requiring dataset-specific labels.
This paper introduces SenseFlow, a novel distillation framework that overcomes the convergence challenges of vanilla Distribution Matching Distillation on large-scale flow-based text-to-image models like SD 3.5 and FLUX.1 by employing implicit distribution alignment and intra-segment guidance to achieve superior performance across both diffusion and flow-matching architectures.
This paper addresses the generalization limitations of traditional Face Anti-Spoofing by introducing FaceCoT, the first large-scale Visual Question Answering dataset enriched with Chain-of-Thought reasoning and generated via reinforcement learning, alongside a CEPL training strategy that collectively enable Multimodal Large Language Models to achieve superior robustness and interpretability across diverse spoofing attacks.
This paper introduces OmniSpatial, a comprehensive benchmark grounded in cognitive psychology with over 8.4K annotated samples across four major categories, which reveals significant limitations in current vision-language models' spatial reasoning capabilities and explores strategies like PointGraph and SpatialCoT to address them.
The paper proposes UniCUE, a unified framework that directly generates speech from Chinese Cued Speech videos by integrating recognition and generation tasks to overcome the limitations of text-intermediate pipelines, supported by the newly constructed large-scale UniCUE-HI dataset.
This study addresses the challenge of overconfident predictions in closed-world animal classification by demonstrating that feature-based out-of-distribution detection methods, particularly Nearest Class Mean with ImageNet pre-trained features, significantly outperform existing techniques in identifying unknown wildlife species within the context of Africa's Big Five.
The paper proposes Meta-Adaptive Prompt Distillation, a meta-learning framework that distills task-relevant visual features into adaptable soft prompts via an attention-mapper module, significantly outperforming standard in-context learning and parameter-efficient fine-tuning for few-shot Visual Question Answering in Large Multimodal Models.