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Found 33 papers, 17 papers with code
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Existing solutions tackle this problem by imposing a policy constraint on the policy improvement objective in both offline and online learning.
Balancing Logit Variation for Long-tailed Semantic Segmentation
In this way, we manage to close the gap between the feature areas of different categories, resulting in a more balanced representation.
ZeroPose: CAD-Model-based Zero-Shot Pose Estimation
In this paper, we present a CAD model-based zero-shot pose estimation pipeline called ZeroPose.
Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation
To this end, we propose T2S-DA, which we interpret as a form of pulling Target to Source for Domain Adaptation, encouraging the model in learning similar cross-domain features.
SeqCo-DETR: Sequence Consistency Training for Self-Supervised Object Detection with Transformers
To this end, we propose SeqCo-DETR, a novel Sequence Consistency-based self-supervised method for object DEtection with TRansformers.
Beyond Triplet: Leveraging the Most Data for Multimodal Machine Translation
Therefore, this paper correspondingly establishes new methods and new datasets for MMT.
Exploring Stochastic Autoregressive Image Modeling for Visual Representation
By introducing stochastic prediction and the parallel encoder-decoder, SAIM significantly improve the performance of autoregressive image modeling.
MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working.
Geo6D: Geometric Constraints Learning for 6D Pose Estimation
Numerous 6D pose estimation methods have been proposed that employ end-to-end regression to directly estimate the target pose parameters.
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation.
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks
Obj2Seq is able to flexibly determine input categories to satisfy customized requirements, and be easily extended to different visual tasks.
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Self-training has shown great potential in semi-supervised learning.
Uni6Dv2: Noise Elimination for 6D Pose Estimation
Uni6D is the first 6D pose estimation approach to employ a unified backbone network to extract features from both RGB and depth images.
Benchmarking Unsupervised Anomaly Detection and Localization
From the time the MVTec AD dataset was proposed to the present, new research methods that are constantly being proposed push its precision to saturation.
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose Estimation
They use a 2D CNN for RGB images and a per-pixel point cloud network for depth data, as well as a fusion network for feature fusion.
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2. 5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability.
Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus
The punctuation restoration task aims to correctly punctuate the output transcriptions of automatic speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
However, current methods can not effectively map image features to a tractable base distribution and ignore the relationship between local and global features which are important to identify anomalies.
Ranked #25 on
Anomaly Detection
on MVTec AD
Unsupervised Anomaly Detection
Weakly Supervised Defect Detection
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization
To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage.
Ranked #59 on
Anomaly Detection
on MVTec AD
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison
More concretely, we employ a Local-Net and Global-Net to extract features from any individual patch and its surrounding respectively.
Language Tags Matter for Zero-Shot Neural Machine Translation
Language tag (LT) strategies are often adopted to indicate the translation directions in MNMT.
MST: Masked Self-Supervised Transformer for Visual Representation
More importantly, the masked tokens together with the remaining tokens are further recovered by a global image decoder, which preserves the spatial information of the image and is more friendly to the downstream dense prediction tasks.
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind.
Learning Language Specific Sub-network for Multilingual Machine Translation
These jointly trained models often suffer from performance degradation on rich-resource language pairs.
The Volctrans Machine Translation System for WMT20
This paper describes our VolcTrans system on WMT20 shared news translation task.
Multimodal Categorization of Crisis Events in Social Media
Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time.
Advances in Collaborative Filtering and Ranking
In this dissertation, we cover some recent advances in collaborative filtering and ranking.
SSE-PT: Sequential Recommendation Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed for better use of the temporal ordering of items that each user has engaged with.
Ranked #1 on
Recommendation Systems
on MovieLens 1M
(nDCG@10 metric)
Temporal Collaborative Ranking Via Personalized Transformer
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed us to make better use of the temporal ordering of items that each user has engaged with.
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
In this paper, we propose using Graph DNA, a novel Deep Neighborhood Aware graph encoding algorithm, for exploiting deeper neighborhood information.
Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers
We find that when used along with widely-used regularization methods such as weight decay and dropout, our proposed SSE can further reduce overfitting, which often leads to more favorable generalization results.
SQL-Rank: A Listwise Approach to Collaborative Ranking
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion.
