Search Results for author:
Found 17 papers, 2 papers with code
Boosting Box-supervised Instance Segmentation with Pseudo Depth
This innovative approach empowers the network to simultaneously predict masks and depth, enhancing its ability to capture nuanced depth-related information during the instance segmentation process.
Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive Consistency Constraints
3D scene reconstruction from 2D images has been a long-standing task.
ShiftNAS: Improving One-shot NAS via Probability Shift
To address the problem of uniform sampling, we propose ShiftNAS, a method that can adjust the sampling probability based on the complexity of subnets.
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks.
LoRAPrune: Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
This is due to their utilization of unstructured pruning on LPMs, impeding the merging of LoRA weights, or their dependence on the gradients of pre-trained weights to guide pruning, which can impose significant memory overhead.
CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection
The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies.
MKIoU Loss: Towards Accurate Oriented Object Detection in Aerial Images
Thus, the Gaussian Angle Loss (GA Loss) is presented to solve this problem by adding a corrected loss for square targets.
Multi-subgoal Robot Navigation in Crowds with History Information and Interactions
Firstly, based on subgraph network, the history information of all agents is aggregated before encoding interactions through a graph neural network, so as to improve the ability of the robot to anticipate the future scenarios implicitly.
Efficient Re-parameterization Operations Search for Easy-to-Deploy Network Based on Directional Evolutionary Strategy
However, the performance of architecture is limited by the type of operations and prior knowledge.
Real-time Rail Recognition Based on 3D Point Clouds
Accurate rail location is a crucial part in the railway support driving system for safety monitoring.
Conditional Generative Data-free Knowledge Distillation
In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data.
Conditional Image Generation
Data-free Knowledge Distillation
+1
Across-Task Neural Architecture Search via Meta Learning
In this paper, an across-task neural architecture search (AT-NAS) is proposed to address the problem through combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks.
Oriented Object Detection in Aerial Images Based on Area Ratio of Parallelogram
Oriented object detection is a challenging task in aerial images since the objects in aerial images are displayed in arbitrary directions and are frequently densely packed.
RepNAS: Searching for Efficient Re-parameterizing Blocks
However, it is still challenging to search for efficient networks due to the gap between the searched constraint and real inference time exists.
Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization
A specific view-attribute is composed by the extracted attribute feature and four view scores which are predicted by view predictor as the confidences for attribute from different views.
Effective Model Compression via Stage-wise Pruning
To overcome the unfull training, a stage-wise pruning(SWP) method is proposed, which splits a deep supernet into several stage-wise supernets to reduce the candidate number and utilize inplace distillation to supervise the stage training.
Graph Pruning for Model Compression
Different from previous work, we take the node features from a well-trained graph aggregator instead of the hand-craft features, as the states in reinforcement learning.
