Search Results for author:
Found 8 papers, 3 papers with code
Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty
In this paper, we aim to tackle this problem with a unified framework consisting of an end-to-end trainable method and a planning algorithm.
LACMA: Language-Aligning Contrastive Learning with Meta-Actions for Embodied Instruction Following
Additional analysis shows that the contrastive objective and meta-actions are complementary in achieving the best results, and the resulting agent better aligns its states with corresponding instructions, making it more suitable for real-world embodied agents.
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.
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.
