Search Results for author:
Found 15 papers, 12 papers with code
Translating Images to Road Network: A Sequence-to-Sequence Perspective
Instead, our work establishes a unified representation of both types of data domain by projecting both Euclidean and non-Euclidean data into an integer series called RoadNet Sequence.
S-Agents: Self-organizing Agents in Open-ended Environments
Leveraging large language models (LLMs), autonomous agents have significantly improved, gaining the ability to handle a variety of tasks.
S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation
Autonomous driving simulation system plays a crucial role in enhancing self-driving data and simulating complex and rare traffic scenarios, ensuring navigation safety.
LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement
Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG.
WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation
Generating multi-camera street-view videos is critical for augmenting autonomous driving datasets, addressing the urgent demand for extensive and varied data.
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
In this framework, a significant challenge arises: To compute gradients for individual image pixels, it is necessary to backpropagate gradients from the designated latent space through the frozen components of the image model, such as the VAE encoder used within LDM.
SUIT: Learning Significance-guided Information for 3D Temporal Detection
To this end, we propose to learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames.
Generative Semantic Segmentation
To that end, the segmentation mask is expressed with a special type of image (dubbed as maskige).
SeaFormer++: Squeeze-enhanced Axial Transformer for Mobile Visual Recognition
Since the introduction of Vision Transformers, the landscape of many computer vision tasks (e. g., semantic segmentation), which has been overwhelmingly dominated by CNNs, recently has significantly revolutionized.
Translating Images to Road Network: A Non-Autoregressive Sequence-to-Sequence Approach
The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections.
Vision Transformers: From Semantic Segmentation to Dense Prediction
Extensive experiments show that our methods achieve appealing performance on a variety of dense prediction tasks (e. g., object detection and instance segmentation and semantic segmentation) as well as image classification.
Softmax-free Linear Transformers
With linear complexity, much longer token sequences are permitted by SOFT, resulting in superior trade-off between accuracy and complexity.
Learning Ego 3D Representation as Ray Tracing
A self-driving perception model aims to extract 3D semantic representations from multiple cameras collectively into the bird's-eye-view (BEV) coordinate frame of the ego car in order to ground downstream planner.
SOFT: Softmax-free Transformer with Linear Complexity
Crucially, with a linear complexity, much longer token sequences are permitted in SOFT, resulting in superior trade-off between accuracy and complexity.
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task.
Ranked #2 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
