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Found 9 papers, 1 papers with code
Enhancing Prompt Following with Visual Control Through Training-Free Mask-Guided Diffusion
Meanwhile, a network, dubbed as Masked ControlNet, is designed to utilize these object masks for object generation in the misaligned visual control region.
Accelerating Image Generation with Sub-path Linear Approximation Model
Diffusion models have significantly advanced the state of the art in image, audio, and video generation tasks.
RHanDS: Refining Malformed Hands for Generated Images with Decoupled Structure and Style Guidance
The style guidance is a hand image, e. g., the malformed hand itself, and is employed to furnish the style reference for hand refining.
Long Short-Term Planning for Conversational Recommendation Systems
Specifically, the recommendation predicts the long-term recommendation target based on the conversational context and the user history.
Multi-Scenario Ranking with Adaptive Feature Learning
Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.
Beyond Bounding Box: Multimodal Knowledge Learning for Object Detection
In this paper, we take advantage of language prompt to introduce effective and unbiased linguistic supervision into object detection, and propose a new mechanism called multimodal knowledge learning (\textbf{MKL}), which is required to learn knowledge from language supervision.
Visual Encoding and Debiasing for CTR Prediction
In this paper, we present a visual encoding framework for CTR prediction to overcome these problems.
Training Generative Adversarial Networks in One Stage
Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort.
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
This report demonstrates our solution for the Open Images 2018 Challenge.
