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Found 19 papers, 11 papers with code
Mask Hierarchical Features For Self-Supervised Learning
We mask part of patches in the representation space and then utilize sparse visible patches to reconstruct high semantic image representation.
Fast-BEV: A Fast and Strong Bird's-Eye View Perception Baseline
Our Fast-BEV consists of five parts, We novelly propose (1) a lightweight deployment-friendly view transformation which fast transfers 2D image feature to 3D voxel space, (2) an multi-scale image encoder which leverages multi-scale information for better performance, (3) an efficient BEV encoder which is particularly designed to speed up on-vehicle inference.
Fast-BEV: Towards Real-time On-vehicle Bird's-Eye View Perception
Recently, the pure camera-based Bird's-Eye-View (BEV) perception removes expensive Lidar sensors, making it a feasible solution for economical autonomous driving.
Parallel Reasoning Network for Human-Object Interaction Detection
Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects.
BEVBert: Topo-Metric Map Pre-training for Language-guided Navigation
Inspired by the robotics community, we introduce hybrid topo-metric maps into VLN, where a topological map is used for long-term planning and a metric map for short-term reasoning.
R$^2$F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference
Document-level natural language inference (DOCNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents.
A Mixture of Surprises for Unsupervised Reinforcement Learning
However, both strategies rely on a strong assumption: the entropy of the environment's dynamics is either high or low.
Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization
Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model.
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
Second, to improve the robustness of binary models with contextual dependencies, we compute the contextual dynamic embeddings to determine the binarization thresholds in general binary convolutional blocks.
IMCI: Integrate Multi-view Contextual Information for Fact Extraction and Verification
Then we integrate the multi-view contextual information to encode the evidence sentences to handle the task.
Task-Balanced Distillation for Object Detection
To alleviate this problem, a novel Task-decoupled Feature Distillation (TFD) is proposed by flexibly balancing the contributions of classification and regression tasks.
MVP: Robust Multi-View Practice for Driving Action Localization
Due to the small data scale and unclear action boundary, the dataset presents a unique challenge to precisely localize all the different actions and classify their categories.
1st Place Solutions for RxR-Habitat Vision-and-Language Navigation Competition (CVPR 2022)
Our model consists of three modules: the candidate waypoints predictor (CWP), the history enhanced planner and the tryout controller.
SupMAE: Supervised Masked Autoencoders Are Efficient Vision Learners
The proposed Supervised MAE (SupMAE) only exploits a visible subset of image patches for classification, unlike the standard supervised pre-training where all image patches are used.
Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision
This is because researchers do not choose consistent training recipes and even use different data, hampering the fair comparison between different methods.
RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training
Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability.
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
To alleviate feature suppression, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE).
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
Recently, large-scale Contrastive Language-Image Pre-training (CLIP) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks.
