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Found 10 papers, 6 papers with code
Diffusion-based Blind Text Image Super-Resolution
Since text prior is important to guarantee the correctness of the restored text structure according to existing arts, we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures.
MotionCtrl: A Unified and Flexible Motion Controller for Video Generation
Therefore, this paper presents MotionCtrl, a unified and flexible motion controller for video generation designed to effectively and independently control camera and object motion.
StyleAdapter: A Single-Pass LoRA-Free Model for Stylized Image Generation
StyleAdapter can generate high-quality images that match the content of the prompts and adopt the style of the references (even for unseen styles) in a single pass, which is more flexible and efficient than previous methods.
RestoreFormer++: Towards Real-World Blind Face Restoration from Undegraded Key-Value Pairs
In this work, we propose RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand, explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap.
RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
Blind face restoration is to recover a high-quality face image from unknown degradations.
Learning a Reinforced Agent for Flexible Exposure Bracketing Selection
Automatically selecting exposure bracketing (images exposed differently) is important to obtain a high dynamic range image by using multi-exposure fusion.
Deep Reasoning with Knowledge Graph for Social Relationship Understanding
And this structured knowledge can be efficiently integrated into the deep neural network architecture to promote social relationship understanding by an end-to-end trainable Graph Reasoning Model (GRM), in which a propagation mechanism is learned to propagate node message through the graph to explore the interaction between persons of interest and the contextual objects.
Ranked #2 on
Visual Social Relationship Recognition
on PIPA
Recurrent Attentional Reinforcement Learning for Multi-label Image Recognition
Recognizing multiple labels of images is a fundamental but challenging task in computer vision, and remarkable progress has been attained by localizing semantic-aware image regions and predicting their labels with deep convolutional neural networks.
LSTM Pose Machines
Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e. g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames.
Ranked #3 on
Pose Estimation
on J-HMDB
Multi-label Image Recognition by Recurrently Discovering Attentional Regions
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding.
