Search Results for author:
Found 10 papers, 7 papers with code
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation.
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving.
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
MixSKD mutually distills feature maps and probability distributions between the random pair of original images and their mixup images in a meaningful way.
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks.
HOPE: Hierarchical Spatial-temporal Network for Occupancy Flow Prediction
In this report, we introduce our solution to the Occupancy and Flow Prediction challenge in the Waymo Open Dataset Challenges at CVPR 2022, which ranks 1st on the leaderboard.
Cross-Image Relational Knowledge Distillation for Semantic Segmentation
Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher's structured information generated from individual data samples.
Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition
In particular, audio and visual front-ends are trained on large-scale unimodal datasets, then we integrate components of both front-ends into a larger multimodal framework which learns to recognize parallel audio-visual data into characters through a combination of CTC and seq2seq decoding.
Ranked #2 on
Automatic Speech Recognition (ASR)
on LRS2
Audio-Visual Speech Recognition
Automatic Speech Recognition (ASR)
+7
Rethinking Soft Labels for Knowledge Distillation: A Bias-Variance Tradeoff Perspective
The outputs from the teacher network are used as soft labels for supervising the training of a new network.
Ranked #25 on
Knowledge Distillation
on ImageNet
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective
In this paper, we investigate the bias-variance tradeoff brought by distillation with soft labels.
VarGNet: Variable Group Convolutional Neural Network for Efficient Embedded Computing
In this paper, we propose a novel network design mechanism for efficient embedded computing.
Ranked #5 on
Face Verification
on CFP-FP
