3931 papers
The paper introduces CHMv2, a new global meter-resolution canopy height map that significantly improves accuracy and structural detail over existing products by leveraging a DINOv3-based depth estimation model trained on expanded, diverse airborne laser scanning data and validated against millions of satellite observations.
This paper introduces a prompt group-aware training framework that enhances the robustness and generalization of text-guided nuclei segmentation by enforcing consistency among semantically related prompts through quality-guided regularization and logit-level constraints, achieving significant performance gains without altering model architecture or inference.
REACT++ is a new state-of-the-art model for real-time Scene Graph Generation that leverages efficient feature extraction and subject-to-object cross-attention to simultaneously achieve the highest inference speed, improved relation prediction accuracy, and maintained object detection performance, outperforming its predecessor by being 20% faster with a 10% accuracy gain.
This paper proposes a human-in-the-loop framework that combines an automatic relaxation-labeling solver with interactive guidance strategies to effectively and efficiently reassemble large-scale, fragmented cultural heritage artifacts in real-world conditions where traditional methods fail.
The paper introduces DiffInf, a self-influence-guided diffusion framework that identifies and generatively corrects annotation-inconsistent facial images to align visual content with labels, thereby improving classification performance without discarding data or sacrificing distributional coverage.
This paper identifies that the Vision Transformer BEiT resolves the perceptual ambiguity between concave and convex figure-ground completions in its later layers, specifically through a weak convexity bias introduced by attention head L0H9, which can be modulated to allow concave evidence to guide the outcome.
This paper introduces Physical Simulator In-the-loop Video Generation (PSIVG), a framework that integrates a physical simulator and a test-time texture optimization technique into the video diffusion process to generate visually realistic videos that strictly adhere to real-world physical laws like gravity and collision.
This paper proposes a non-invasive, refraction-aware stereo vision system that utilizes a YOLOv11-Pose network and specialized epipolar constraints to accurately estimate the length of small freshwater fish in home aquariums, validated on a new dataset of endangered Sulawesi ricefish.
The paper proposes CLoPA, a continual low-parameter adaptation strategy that efficiently tunes a small fraction of the nnInteractive model on incoming annotation data, rapidly achieving expert-level performance across diverse medical imaging tasks without requiring new parameters or altering the inference pipeline.
This paper introduces WanderDream, the first large-scale dataset comprising panoramic videos and question-answer pairs that enables agents to perform situated reasoning through emulative mental simulation of future trajectories, thereby overcoming the physical and safety constraints of active real-world exploration.
The paper introduces CaTok, a novel 1D causal image tokenizer equipped with a MeanFlow decoder and REPA-A regularization that overcomes existing limitations in visual tokenization to achieve state-of-the-art ImageNet reconstruction and competitive autoregressive generation performance.
Pinterest introduces Canvas, a large-scale image generation system that employs a foundational diffusion model rapidly fine-tuned into specialized variants for specific editing tasks, achieving significant engagement lifts and outperforming third-party models in production A/B tests.
This paper systematically investigates how loss weighting and output parameterization choices in flow matching models interact with data dimensionality, architecture, and dataset size to provide practical design insights for optimizing both denoising accuracy and generative quality.
This paper demonstrates that frozen vision-language model features contain rich, continuous geometric information that outperforms text-based outputs by 3.3x, revealing that the accuracy bottleneck stems from training objectives and autoregressive generation rather than representational limitations, as evidenced by high-precision linear probes and consistent performance across diverse encoder architectures.
GreenRFM introduces a resource-efficient pre-training framework utilizing principled "MUST" supervision to achieve state-of-the-art radiology foundation model performance with significantly reduced computational requirements, challenging the prevailing "scale is all you need" paradigm.
Match4Annotate is a lightweight framework that enables efficient, high-quality propagation of sparse point and mask annotations across and within video sequences by fitting test-time implicit neural representations to DINOv3 features, offering a scalable solution for annotation bottlenecks in specialized domains like medical imaging.
This paper introduces Self-Flow, a self-supervised flow matching paradigm that utilizes a Dual-Timestep Scheduling mechanism to integrate representation learning directly into the generative framework, thereby eliminating the need for external models and achieving superior, scalable multi-modal synthesis across image, video, and audio.
This paper introduces SG-DOR, a relational framework that utilizes a direction-aware graph neural network to infer scene graphs encoding physical attachments and direction-conditioned occlusion for robotic harvesting of pepper plants in dense canopies.
This paper presents an artificial intelligence system trained on over 45,000 ultrasound images that achieves diagnostic accuracy comparable to senior radiologists for fetal orofacial clefts, significantly enhances junior radiologists' performance when used as a copilot, and accelerates clinical expertise development for rare conditions.
This paper introduces SCAN, a universal framework that leverages an AutoEncoder-based approach guided by the Information Bottleneck principle to generate high-resolution, faithful self-confidence maps, effectively overcoming the trade-off between fidelity and applicability in visual explanations for both CNN and Transformer architectures.