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Found 8 papers, 1 papers with code
Exploring Limits of Diffusion-Synthetic Training with Weakly Supervised Semantic Segmentation
The advance of generative models for images has inspired various training techniques for image recognition utilizing synthetic images.
Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning
Siamese-network-based self-supervised learning (SSL) suffers from slow convergence and instability in training.
Context-Free TextSpotter for Real-Time and Mobile End-to-End Text Detection and Recognition
In the deployment of scene-text spotting systems on mobile platforms, lightweight models with low computation are preferable.
Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach
Detecting tiny objects in a high-resolution video is challenging because the visual information is little and unreliable.
Hybrid Loss for Learning Single-Image-based HDR Reconstruction
It makes the reconstruction faithful to the input.
Classification-Reconstruction Learning for Open-Set Recognition
Existing open-set classifiers rely on deep networks trained in a supervised manner on known classes in the training set; this causes specialization of learned representations to known classes and makes it hard to distinguish unknowns from knowns.
Cross-connected Networks for Multi-task Learning of Detection and Segmentation
We evaluated our proposed architecture on a combination of detection and segmentation using two datasets.
Differentiating Objects by Motion: Joint Detection and Tracking of Small Flying Objects
While generic object detection has achieved large improvements with rich feature hierarchies from deep nets, detecting small objects with poor visual cues remains challenging.
