RefAerial is a new benchmark dataset for text-based object detection in aerial imagery, accompanied by an SCS model that handles scale diversity better than prior ground-image methods.
Deep residual learning for image recognition
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
ReWeaver reconstructs topology-accurate 3D garments and sewing patterns from sparse multi-view images by predicting seams and panels in 2D UV and 3D space using a new 100k-sample synthetic dataset.
UST-Hand is a self-supervised 3D hand pose estimation method using conditional normalizing flows for uncertainty-aware hypothesis sampling and probabilistic point cloud interactions to achieve up to 37.8% better MPVPE than prior self-supervised approaches on three datasets.
ComMark embeds covert watermarks in models using frequency-domain compressed samples and simulated attacks, claiming state-of-the-art covertness and robustness across image, speech, text, and video tasks.
SemLT3D introduces semantic-guided expert distillation with a language MoE module and CLIP projection to enrich features for long-tailed classes in camera-only 3D detection.
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.
citing papers explorer
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RefAerial: A Benchmark and Approach for Referring Detection in Aerial Images
RefAerial is a new benchmark dataset for text-based object detection in aerial imagery, accompanied by an SCS model that handles scale diversity better than prior ground-image methods.
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ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction
ReWeaver reconstructs topology-accurate 3D garments and sewing patterns from sparse multi-view images by predicting seams and panels in 2D UV and 3D space using a new 100k-sample synthetic dataset.
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UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation
UST-Hand is a self-supervised 3D hand pose estimation method using conditional normalizing flows for uncertainty-aware hypothesis sampling and probabilistic point cloud interactions to achieve up to 37.8% better MPVPE than prior self-supervised approaches on three datasets.
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ComMark: Covert and Robust Black-Box Model Watermarking with Compressed Samples
ComMark embeds covert watermarks in models using frequency-domain compressed samples and simulated attacks, claiming state-of-the-art covertness and robustness across image, speech, text, and video tasks.
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SemLT3D: Semantic-Guided Expert Distillation for Camera-only Long-Tailed 3D Object Detection
SemLT3D introduces semantic-guided expert distillation with a language MoE module and CLIP projection to enrich features for long-tailed classes in camera-only 3D detection.
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Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.