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Found 12 papers, 8 papers with code
CoSimGNN: Towards Large-scale Graph Similarity Computation
In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching" framework CoSimGNN, which first embeds and coarsens large graphs with adaptive pooling operation and then deploys fine-grained interactions on the coarsened graphs for final similarity scores.
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation
Next, a novel graph neural network with an attention mechanism is designed to map each subgraph into an embedding vector.
RaLL: End-to-end Radar Localization on Lidar Map Using Differentiable Measurement Model
In this paper, we propose an end-to-end deep learning framework for Radar Localization on Lidar Map (RaLL) to bridge the gap, which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping.
CycleMLP: A MLP-like Architecture for Dense Prediction
We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e. g., Swin Transformer, while using fewer parameters and FLOPs.
Ranked #15 on
Semantic Segmentation
on DensePASS
RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
Blind face restoration is to recover a high-quality face image from unknown degradations.
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
In this paper, we propose CO^3, namely Cooperative Contrastive Learning and Contextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner.
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
In visual control, learning transferable state representation that can transfer between different control tasks is important to reduce the training sample size.
MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training
This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset.
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Autonomous Driving
Our contributions are threefold: (1) Occupancy prediction is shown to be promising for learning general representations, which is demonstrated by extensive experiments on plenty of datasets and tasks.
CurriculumLoc: Enhancing Cross-Domain Geolocalization through Multi-Stage Refinement
Visual geolocalization is a cost-effective and scalable task that involves matching one or more query images, taken at some unknown location, to a set of geo-tagged reference images.
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI.
Ranked #72 on
Visual Question Answering
on MM-Vet
Towards Implicit Prompt For Text-To-Image Models
We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.
