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05/02/2023
FlowMap: Path Generation for Automated Vehicles in Open Space Using Traffic Flow
There is extensive literature on perceiving road structures by fusing va...
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03/03/2023
Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving
Multi-task learning has emerged as a powerful paradigm to solve a range ...
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12/14/2022
NLIP: Noise-robust Language-Image Pre-training
Large-scale cross-modal pre-training paradigms have recently shown ubiqu...
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11/02/2022
P^3OVD: Fine-grained Visual-Text Prompt-Driven Self-Training for Open-Vocabulary Object Detection
Inspired by the success of visual-language methods (VLMs) in zero-shot c...
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09/20/2022
DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection
Open-world object detection, as a more general and challenging goal, aim...
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09/19/2022
Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving
Aiming towards a holistic understanding of multiple downstream tasks sim...
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07/18/2022
Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
To bridge the gap between supervised semantic segmentation and real-worl...
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05/26/2022
Task-Customized Self-Supervised Pre-training with Scalable Dynamic Routing
Self-supervised learning (SSL), especially contrastive methods, has rais...
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04/09/2022
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and Generation
Existing text-guided image manipulation methods aim to modify the appear...
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03/18/2022
Laneformer: Object-aware Row-Column Transformers for Lane Detection
We present Laneformer, a conceptually simple yet powerful transformer-ba...
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03/15/2022
CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving
Contemporary deep-learning object detection methods for autonomous drivi...
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02/14/2022
Wukong: 100 Million Large-scale Chinese Cross-modal Pre-training Dataset and A Foundation Framework
This paper presents a large-scale Chinese cross-modal dataset for benchm...
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01/10/2022
GhostNets on Heterogeneous Devices via Cheap Operations
Deploying convolutional neural networks (CNNs) on mobile devices is diff...
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12/27/2021
An Empirical Study of Adder Neural Networks for Object Detection
Adder neural networks (AdderNets) have shown impressive performance on i...
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11/09/2021
FILIP: Fine-grained Interactive Language-Image Pre-Training
Unsupervised large-scale vision-language pre-training has shown promisin...
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10/22/2021
SOFT: Softmax-free Transformer with Linear Complexity
Vision transformers (ViTs) have pushed the state-of-the-art for various ...
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09/20/2021
Learning Versatile Convolution Filters for Efficient Visual Recognition
This paper introduces versatile filters to construct efficient convoluti...
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09/06/2021
Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection
We present a flexible and high-performance framework, named Pyramid R-CN...
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09/06/2021
Voxel Transformer for 3D Object Detection
We present Voxel Transformer (VoTr), a novel and effective voxel-based T...
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07/31/2021
Greedy Network Enlarging
Recent studies on deep convolutional neural networks present a simple pa...
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07/13/2021
CMT: Convolutional Neural Networks Meet Vision Transformers
Vision transformers have been successfully applied to image recognition ...
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07/07/2021
S^3: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks
Shift neural networks reduce computation complexity by removing expensiv...
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06/21/2021
SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving
Aiming at facilitating a real-world, ever-evolving and scalable autonomo...
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06/21/2021
One Million Scenes for Autonomous Driving: ONCE Dataset
Current perception models in autonomous driving have become notorious fo...
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05/12/2021
Winograd Algorithm for AdderNet
Adder neural network (AdderNet) is a new kind of deep model that replace...
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03/26/2021
Distilling Object Detectors via Decoupled Features
Knowledge distillation is a widely used paradigm for inheriting informat...
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03/15/2021
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution
Deep learning based methods, especially convolutional neural networks (C...
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03/01/2021
Learning Frequency Domain Approximation for Binary Neural Networks
Binary neural networks (BNNs) represent original full-precision weights ...
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02/27/2021
Transformer in Transformer
Transformer is a type of self-attention-based neural networks originally...
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01/25/2021
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
Convolutional neural networks (CNN) have been widely used for boosting t...
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01/21/2021
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
Modern single image super-resolution (SISR) system based on convolutiona...
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12/23/2020
A Survey on Visual Transformer
Transformer is a type of deep neural network mainly based on self-attent...
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12/01/2020
Pre-Trained Image Processing Transformer
As the computing power of modern hardware is increasing strongly, pre-tr...
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10/21/2020
SCOP: Scientific Control for Reliable Neural Network Pruning
This paper proposes a reliable neural network pruning algorithm by setti...
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10/10/2020
Training Binary Neural Networks through Learning with Noisy Supervision
This paper formalizes the binarization operations over neural networks f...
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09/28/2020
Kernel Based Progressive Distillation for Adder Neural Networks
Adder Neural Networks (ANNs) which only contain additions bring us a new...
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09/18/2020
AdderSR: Towards Energy Efficient Image Super-Resolution
This paper studies the single image super-resolution problem using adder...
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09/18/2020
Searching for Low-Bit Weights in Quantized Neural Networks
Quantized neural networks with low-bit weights and activations are attra...
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07/21/2020
Video Super-resolution with Temporal Group Attention
Video super-resolution, which aims at producing a high-resolution video ...
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07/16/2020
Multi-Task Pruning for Semantic Segmentation Networks
This paper focuses on channel pruning for semantic segmentation networks...
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07/10/2020
Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
Video style transfer techniques inspire many exciting applications on mo...
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05/29/2020
HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
Neural Architecture Search (NAS) refers to automatically design the arch...
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05/14/2020
A Semi-Supervised Assessor of Neural Architectures
Neural architecture search (NAS) aims to automatically design deep neura...
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03/07/2020
Distilling portable Generative Adversarial Networks for Image Translation
Despite Generative Adversarial Networks (GANs) have been widely used in ...
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02/23/2020
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
Deep neural networks often consist of a great number of trainable parame...
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02/03/2020
Widening and Squeezing: Towards Accurate and Efficient QNNs
Quantization neural networks (QNNs) are very attractive to the industry ...
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12/31/2019
AdderNet: Do We Really Need Multiplications in Deep Learning?
Compared with cheap addition operation, multiplication operation is of m...
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11/27/2019
GhostNet: More Features from Cheap Operations
Deploying convolutional neural networks (CNNs) on embedded devices is di...
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10/07/2019
Unsupervised Image Super-Resolution with an Indirect Supervised Path
The task of single image super-resolution (SISR) aims at reconstructing ...
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09/30/2019