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Found 10 papers, 2 papers with code
TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking
In this work we propose a novel, clip-based DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges.
Target-Free Text-guided Image Manipulation
We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training.
Paraphrasing Is All You Need for Novel Object Captioning
Since no ground truth captions are available for novel object images during training, our P2C leverages cross-modality (image-text) association modules to ensure the above caption characteristics can be properly preserved.
Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
Diffusion models (DMs) have shown great potential for high-quality image synthesis.
Scene Graph Expansion for Semantics-Guided Image Outpainting
In particular, we propose a novel network of Scene Graph Transformer (SGT), which is designed to take node and edge features as inputs for modeling the associated structural information.
Learning Visual-Linguistic Adequacy, Fidelity, and Fluency for Novel Object Captioning
Novel object captioning (NOC) learns image captioning models for describing objects or visual concepts which are unseen (i. e., novel) in the training captions.
LayoutTransformer: Scene Layout Generation With Conceptual and Spatial Diversity
To better exploit the text input, so that implicit objects or relationships can be properly inferred during layout generation, we propose a LayoutTransformer Network (LT-Net) in this paper.
LayoutTransformer: Relation-Aware Scene Layout Generation
In the areas of machine learning and computer vision, text-to-image synthesis aims at producing image outputs given the input text.
Natural World Distribution via Adaptive Confusion Energy Regularization
The Fine-Grained Visual Classification (FGVC) problem is notably characterized by two intriguing properties, significant inter-class similarity and intra-class variations, which cause learning an effective FGVC classifier a challenging task.
ACE: Adaptive Confusion Energy for Natural World Data Distribution
With the development of deep learning, standard classification problems have achieved good results.
Fine-Grained Image Classification
Fine-Grained Visual Recognition
