5549 papers
The paper introduces eMIGM, a unified masked image generation model that effectively combines masked image and diffusion paradigms to achieve state-of-the-art performance on ImageNet generation with significantly fewer function evaluations and parameters compared to existing methods.
The paper introduces SPEED, an efficient concept erasure framework for diffusion models that directly edits parameters within a null space—enhanced by influence-based filtering, directed prior augmentation, and invariant equality constraints—to achieve scalable, precise removal of multiple concepts in seconds while preserving the quality of non-target generations.
This paper introduces a model-agnostic, multi-objective evaluation framework that utilizes radar charts and comprehensive metrics to systematically analyze and visualize utility-fairness trade-offs in machine learning systems, with a specific focus on mitigating disparities in medical imaging applications.
This paper presents a target-aware video diffusion model that generates videos of actors interacting with specific objects defined by segmentation masks and text prompts, utilizing a specialized token and fine-tuned cross-attention mechanism to enable precise human-object interaction planning for applications like 3D motion synthesis and long-term video creation.
AdaRank is a novel model merging framework that adaptively prunes detrimental singular components of task vectors during test-time via entropy minimization to mitigate cross-task interference and achieve state-of-the-art performance across various backbones and task configurations.
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.