5306 papers
The paper introduces DISC, a fully GPU-accelerated framework that utilizes a novel single-pass, distance-weighted mechanism to extract dense semantic context from CLIP embeddings, enabling efficient, real-time open-set semantic mapping that significantly outperforms existing state-of-the-art methods in accuracy and scalability.
This paper presents CMDR-IAD, a lightweight unsupervised framework that achieves state-of-the-art industrial anomaly detection by combining bidirectional 2D-3D cross-modal mapping with dual-branch reconstruction to robustly handle noisy, weak-texture, or missing modalities without relying on memory banks.
This study demonstrates that a DenseNet121-based deep learning model effectively detects hyper- and hypointense intensity artifacts on high b-value (1500 s/mm²) breast diffusion-weighted MRI slices, achieving high AUROC scores and providing promising results for automated quality assessment.
This paper introduces Spatial Causal Prediction (SCP), a new task paradigm and benchmark (SCP-Bench) designed to evaluate and improve video models' ability to infer unseen spatial states and causal outcomes beyond visible observations, revealing significant gaps between current AI and human intelligence in this domain.
The paper introduces RVN-Bench, a new collision-aware benchmark built on Habitat 2.0 and HM3D scenes that enables the training and evaluation of safe, robust indoor visual navigation policies for mobile robots in unseen, cluttered environments.
This paper proposes a training data synthesis method that generates diverse, unaligned image pairs from single inputs alongside a novel network architecture to enhance the robustness and generalization of multimodal homography estimation across unseen domains.
This paper proposes the Structural Action Transformer (SAT), a novel 3D dexterous manipulation policy that reframes actions as variable-length, unordered joint trajectories and utilizes an Embodied Joint Codebook to achieve superior sample efficiency and cross-embodiment skill transfer from heterogeneous datasets.
The paper introduces ProFound, a domain-specialized vision foundation model pre-trained on over 22,000 prostate MRI volumes via self-supervised learning, which demonstrates superior or competitive performance across 11 diverse clinical tasks compared to state-of-the-art specialized and foundation models.
This paper introduces BLOCK, an open-source bi-stage pipeline that leverages a large multimodal model to generate consistent 3D character previews and a fine-tuned FLUX.2 model with a novel EvolveLoRA curriculum to decode these previews into pixel-perfect Minecraft skins.
This paper proposes UniRain, a unified image deraining framework that combines a RAG-based dataset distillation pipeline for selecting high-quality training samples and a multi-objective reweighted optimization strategy within an asymmetric MoE architecture to effectively restore images degraded by diverse rain streaks and raindrops across both daytime and nighttime conditions.
This paper proposes a novel self-supervised pretraining method that leverages structure-aware distillation from visual foundation models to overcome annotation bottlenecks and semantic collapse, enabling scalable learning of versatile, fine-grained representations from dense event streams.
Dual-Solver is a generalized ODE solver for diffusion models that employs learnable parameters to dynamically interpolate prediction types, select integration domains, and adjust residuals, thereby significantly improving image quality and CLIP scores in low-function-evaluation regimes across various backbones.
This technical report introduces Phi-4-reasoning-vision-15B, a compact open-weight multimodal model that achieves competitive performance in scientific, mathematical, and UI reasoning through strategic architecture choices, rigorous data curation, and a hybrid training approach, demonstrating that smaller models can excel with significantly less compute.
GeoSeg is a training-free, zero-shot framework that leverages MLLM reasoning combined with bias-aware coordinate refinement and dual-route prompting to achieve instruction-grounded segmentation in remote sensing imagery, addressing the lack of generalizable solutions and data scarcity in the domain.
This paper introduces RIVER, a novel benchmark and framework designed to evaluate and improve the real-time interactive capabilities of video large language models by addressing their current limitations in online processing, long-term memory, and proactive anticipation through a three-task system of Retrospective Memory, Live-Perception, and Proactive Anticipation.
This paper introduces a diagnostic framework using face pareidolia to reveal that vision models' behavior under visual ambiguity is primarily governed by their representational architecture, with vision-language models exhibiting semantic overactivation, pure vision models adopting uncertainty-based abstention, and detection models relying on conservative priors to suppress false positives.
This paper introduces SAFE, a two-stage framework that leverages weak supervision, contrastive learning, and feature-space ensemble methods to systematically expand sparse expert annotations of diabetic retinopathy lesions, thereby significantly improving both patch-level detection accuracy and downstream disease classification performance.
The paper proposes DPAD, a method that enhances reasoning segmentation by introducing a discriminative perception mechanism through anchored object descriptions, which effectively guides Multimodal Large Language Models to generate more focused and efficient reasoning chains while significantly improving localization accuracy and reducing verbosity.
This paper proposes a novel framework for radiology report generation that enhances reinforcement learning efficiency through a diagnostic diversity-based data sampling strategy and a Diagnostic Token-weighted Policy Optimization (DiTPO) method, achieving state-of-the-art clinical accuracy with significantly fewer training samples by prioritizing diagnostically critical content.
The paper proposes Volumetric Directional Diffusion (VDD), a novel framework that anchors generative trajectories to a deterministic consensus prior to predict 3D boundary residuals, thereby achieving state-of-the-art anatomically coherent uncertainty quantification for ambiguous medical image segmentation while avoiding the topological fractures common in standard diffusion models.