4147 papers
This paper introduces Persistent Homology Convolutions, a novel method that captures local topological features in histopathology slides, demonstrating that this approach outperforms standard CNNs in classification accuracy and hyperparameter robustness by effectively integrating geometric information into deep learning models.
This paper proposes Image-Adaptive Prompt Learning (IAPL), a novel paradigm that dynamically adjusts encoder prompts for each test image using conditional information and test-time adaptive tokens to significantly improve the generalization and robustness of AI-generated image detection against unseen forgery generators.
The paper proposes "GaitSnippet," a novel gait recognition framework that overcomes the limitations of existing set-based and sequence-based methods by modeling gait as a composition of randomly sampled, multi-scale temporal snippets to capture both short-range and long-range dependencies, achieving state-of-the-art performance on major datasets.
The paper introduces Veason-R1, a specialized Large Vision-Language Model for Video Reasoning Segmentation that leverages Chain-of-Thought initialization and Group Relative Policy Optimization with a holistic reward mechanism to enhance spatiotemporal reasoning, achieving state-of-the-art performance and improved robustness against hallucinations.
This paper introduces S-Guidance, a training-free method that leverages stochastic block-dropping to construct sub-networks for refining suboptimal predictions in diffusion models, thereby overcoming the semantic incoherence and low-quality outputs associated with traditional Classifier-free Guidance.
This foundational concept paper proposes the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys), an architecture integrating quantized neural networks and adaptive multi-sensor fusion to enable real-time, high-fidelity crater detection on resource-constrained, radiation-hardened space exploration hardware.
The paper introduces ROBUST-MIPS, a novel dataset combining skeletal pose and instance segmentation annotations for laparoscopic surgical instruments to address data scarcity, demonstrate the efficiency of pose-based annotation, and facilitate benchmarking for computer-assisted surgical technologies.
This paper proposes a lightweight token pruning framework that filters non-informative background regions and refines fragmented text areas in document images to significantly reduce computational costs in vision-language models while maintaining comparable accuracy.
QDFlow is an open-source Python package that simulates realistic quantum dot device data with ground-truth labels by combining a self-consistent Thomas-Fermi solver, dynamic capacitance modeling, and customizable noise modules to address the scarcity of experimental datasets needed for machine learning applications.
This paper proposes a novel, architecture-agnostic Dynamic Skip Connection (DSC) block that integrates Test-Time Training and Dynamic Multi-Scale Kernel modules to overcome the static and multi-scale limitations of conventional skip connections, thereby significantly enhancing feature fusion and segmentation performance across diverse U-like network architectures.
This paper addresses the unexplored challenge of label noise in action-based video object segmentation by introducing the ActiSeg-NL benchmark, analyzing the impact of textual and mask annotation noise, and proposing a Parallel Mask Head Mechanism to enhance robustness for embodied intelligence applications.
This paper presents a fast local solver for category-level object shape and pose estimation that leverages learned semantic keypoints and active shape models to achieve global optimality certification in under a millisecond via an efficient self-consistent field iteration.
This paper introduces RawJPEG Adapter, a lightweight and invertible preprocessing pipeline that transforms raw images for efficient standard JPEG compression while enabling accurate reconstruction by storing compact transform parameters in the JPEG comment field.
Vision-Zero is a scalable, label-free framework that enables vision-language models to self-improve through strategic multi-agent self-play games generated from arbitrary images, utilizing an Iterative Self-Play Policy Optimization algorithm to achieve state-of-the-art performance without human annotation.
This paper introduces a training-free framework for reward-guided image editing that formulates the process as a trajectory optimal control problem, enabling the model to enhance specific objectives while preserving source image fidelity without requiring additional training or suffering from reward hacking.
This paper addresses the limitations of current image generation models in handling structured visuals by introducing a comprehensive framework that includes a 1.3-million-pair dataset, a unified VLM-FLUX.1 model trained with a three-stage curriculum and external reasoning, and the StructBench benchmark with StructScore metric to evaluate and improve factual fidelity in chart and diagram generation and editing.
TIGeR is a novel framework that enhances Vision-Language Models for robotics by integrating external computational tools to perform precise geometric calculations, supported by a new dataset and a two-stage training pipeline, thereby achieving centimeter-level accuracy in real-world manipulation tasks.
This paper introduces ToMCLIP, a topology-aware framework that enhances multilingual vision-language alignment by applying persistent homology to preserve the global geometric structure of shared embedding spaces, thereby improving zero-shot accuracy and retrieval performance compared to existing instance-level methods.
This paper introduces COGS, a data-efficient framework that synthesizes large-scale reasoning datasets by decomposing seed questions into primitive factors and recomposing them with new images, thereby significantly enhancing the visual reasoning capabilities of multi-modal large language models in annotation-scarce domains like charts and webpages.
This paper reveals that the reliability of Mahalanobis-based out-of-distribution detection is highly dependent on the geometric properties of the feature space, specifically within-class spectral structure and local intrinsic dimensionality, and proposes a radially scaled normalization method that dynamically adjusts feature radii to optimize detection performance based on these geometric signals.