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Found 9 papers, 1 papers with code
EGSRAL: An Enhanced 3D Gaussian Splatting based Renderer with Automated Labeling for Large-Scale Driving Scene
To address these issues, we propose EGSRAL, a 3D GS-based method that relies solely on training images without extra annotations.
FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing
To address these issues, we propose a fine-grained token-wise pruning approach for the LLMs, which presents a learnable router to adaptively identify the less important tokens and skip them across model blocks to reduce computational cost during inference.
Fast Occupancy Network
To address this problem, we make an analysis of the bottleneck of Occupancy Network inference cost, and present a simple and fast Occupancy Network model, which adopts a deformable 2D convolutional layer to lift BEV feature to 3D voxel feature and presents an efficient voxel feature pyramid network (FPN) module to improve performance with few computational cost.
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization
Finally, our method can identify an optimal SubNet through few-step gradient optimization and a simple post-processing procedure.
Amphista: Bi-directional Multi-head Decoding for Accelerating LLM Inference
On MT-Bench, Amphista delivers up to 2. 75$\times$ speedup over vanilla autoregressive decoding and 1. 40$\times$ over Medusa on Vicuna 33B in wall-clock time.
Sparse Laneformer
We analyze that dense anchors are not necessary for lane detection, and propose a transformer-based lane detection framework based on a sparse anchor mechanism.
UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer
Our method also achieves state-of-the-art pruning performance on the vision transformer model.
Separated RoadTopoFormer
Previous works such as map learning and BEV lane detection neglect the connection relationship between lane instances, and traffic elements detection tasks usually neglect the relationship with lane lines.
Ranked #3 on
3D Lane Detection
on OpenLane-V2 val
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving
Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.
