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Found 18 papers, 15 papers with code
Structural Alignment Improves Graph Test-Time Adaptation
Graph-based learning has achieved remarkable success in domains ranging from recommendation to fraud detection and particle physics by effectively capturing underlying interaction patterns.
Model Generalization on Text Attribute Graphs: Principles with Large Language Models
Large language models (LLMs) have recently been introduced to graph learning, aiming to extend their zero-shot generalization success to tasks where labeled graph data is scarce.
Learning Flow Fields in Attention for Controllable Person Image Generation
Additionally, we show that our loss is model-agnostic and can be used to improve the performance of other diffusion models.
Ranked #1 on
Pose Transfer
on Deep-Fashion
(FID metric)
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale
We introduce MarDini, a new family of video diffusion models that integrate the advantages of masked auto-regression (MAR) into a unified diffusion model (DM) framework.
Pairwise Alignment Improves Graph Domain Adaptation
Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from the graph used for testing.
EscherNet: A Generative Model for Scalable View Synthesis
We introduce EscherNet, a multi-view conditioned diffusion model for view synthesis.
GeSS: Benchmarking Geometric Deep Learning under Scientific Applications with Distribution Shifts
Geometric deep learning (GDL) has gained significant attention in scientific fields, for its proficiency in modeling data with intricate geometric structures.
Structural Re-weighting Improves Graph Domain Adaptation
This work examines different impacts of distribution shifts caused by either graph structure or node attributes and identifies a new type of shift, named conditional structure shift (CSS), which current GDA approaches are provably sub-optimal to deal with.
Prismer: A Vision-Language Model with Multi-Task Experts
Recent vision-language models have shown impressive multi-modal generation capabilities.
Ranked #1 on
Image Captioning
on nocaps val
vMAP: Vectorised Object Mapping for Neural Field SLAM
We present vMAP, an object-level dense SLAM system using neural field representations.
Auto-Lambda: Disentangling Dynamic Task Relationships
Unlike previous methods where task relationships are assumed to be fixed, Auto-Lambda is a gradient-based meta learning framework which explores continuous, dynamic task relationships via task-specific weightings, and can optimise any choice of combination of tasks through the formulation of a meta-loss; where the validation loss automatically influences task weightings throughout training.
Ranked #3 on
Robot Manipulation
on RLBench
(Succ. Rate (10 tasks, 100 demos/task) metric)
Semi-supervised Graph Neural Network for Particle-level Noise Removal
The graph neural network is trained on charged particles with well-known labels, which can be obtained from simulation truth information or measurements from data, and inferred on neutral particles of which such labeling is missing.
Bootstrapping Semantic Segmentation with Regional Contrast
We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation.
iMAP: Implicit Mapping and Positioning in Real-Time
We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera.
Shape Adaptor: A Learnable Resizing Module
We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution.
Self-Supervised Generalisation with Meta Auxiliary Learning
The loss for the label-generation network incorporates the loss of the multi-task network, and so this interaction between the two networks can be seen as a form of meta learning with a double gradient.
End-to-End Multi-Task Learning with Attention
Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #6 on
3D Object Recognition
on ModelNet40
