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Found 11 papers, 7 papers with code
Learning and Simulation in Generative Structured World Models
The G-SWM not only unifies the key properties of previous models in a principled framework but also achieves two crucial new abilities, multi-modal uncertainty and situated behavior.
Slot State Space Models
Unlike conventional SSMs that maintain a monolithic state vector, SlotSSMs maintains the state as a collection of multiple vectors called slots.
Layout Agnostic Scene Text Image Synthesis with Diffusion Models
While diffusion models have significantly advanced the quality of image generation their capability to accurately and coherently render text within these images remains a substantial challenge.
Improving Tuning-Free Real Image Editing with Proximal Guidance
Null-text inversion (NTI) optimizes null embeddings to align the reconstruction and inversion trajectories with larger CFG scales, enabling real image editing with cross-attention control.
Object-Centric Slot Diffusion
The recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes.
Generative Neurosymbolic Machines
In this paper, we propose Generative Neurosymbolic Machines, a generative model that combines the benefits of distributed and symbolic representations to support both structured representations of symbolic components and density-based generation.
Improving Generative Imagination in Object-Centric World Models
Third, a few key abilities for more faithful temporal imagination such as multimodal uncertainty and situation-awareness are missing.
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes.
SCALOR: Generative World Models with Scalable Object Representations
Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning.
RedNet: Residual Encoder-Decoder Network for indoor RGB-D Semantic Segmentation
Indoor semantic segmentation has always been a difficult task in computer vision.
Ranked #3 on
Semantic Segmentation
on THUD Robotic Dataset
Incorporating Depth into both CNN and CRF for Indoor Semantic Segmentation
To improve segmentation performance, a novel neural network architecture (termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional neural network (DFCN) with a depth-sensitive fully-connected conditional random field (DCRF).
