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08/21/2023
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
Multi-view 3D detection based on BEV (bird-eye-view) has recently achiev...
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08/14/2023
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Text evaluation has historically posed significant challenges, often dem...
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05/26/2023
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning
Learned optimizers are a crucial component of meta-learning. Recent adva...
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05/21/2023
Integer or Floating Point? New Outlooks for Low-Bit Quantization on Large Language Models
Efficient deployment of large language models (LLMs) necessitates low-bi...
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04/03/2023
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
The goal of open-vocabulary detection is to identify novel objects based...
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03/24/2023
MoWE: Mixture of Weather Experts for Multiple Adverse Weather Removal
Currently, most adverse weather removal tasks are handled independently,...
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03/14/2023
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
Masked Autoencoders learn strong visual representations and achieve stat...
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02/08/2023
Q-Diffusion: Quantizing Diffusion Models
Diffusion models have achieved great success in synthesizing diverse and...
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12/06/2022
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
Mixed-precision quantization has been widely applied on deep neural netw...
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11/29/2022
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
The complicated architecture and high training cost of vision transforme...
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11/21/2022
PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning
Contrastive Language-Image Pre-training (CLIP) has shown promising open-...
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09/27/2022
Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models
Transformer architecture has become the fundamental element of the wides...
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09/15/2022
BadRes: Reveal the Backdoors through Residual Connection
Generally, residual connections are indispensable network components in ...
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07/05/2022
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning
Current point-cloud detection methods have difficulty detecting the open...
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06/20/2022
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
Domain adaptive text classification is a challenging problem for the lar...
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10/27/2021
2nd Place Solution for VisDA 2021 Challenge – Universally Domain Adaptive Image Recognition
The Visual Domain Adaptation (VisDA) 2021 Challenge calls for unsupervis...
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07/23/2021
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
Deep learning-based 3D object detection has achieved unprecedented succe...
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07/06/2021
Contrastive Multimodal Fusion with TupleInfoNCE
This paper proposes a method for representation learning of multimodal d...
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06/11/2021
Online Continual Adaptation with Active Self-Training
Models trained with offline data often suffer from continual distributio...
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03/23/2021
Self-Supervised Pretraining Improves Self-Supervised Pretraining
While self-supervised pretraining has proven beneficial for many compute...
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12/24/2020
P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding
Self-supervised representation learning is a critical problem in compute...
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12/14/2020
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Many real-world applications require the prediction of long sequence tim...
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12/05/2020
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
Contrastive learning (CL) has been successful as a powerful representati...
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11/30/2020
Annotation-Efficient Untrimmed Video Action Recognition
Deep learning has achieved great success in recognizing video actions, b...
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10/30/2020
Cross-Domain Sentiment Classification With Contrastive Learning and Mutual Information Maximization
Contrastive learning (CL) has been successful as a powerful representati...
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10/09/2020
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
The success of supervised learning hinges on the assumption that the tra...
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09/01/2020
A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
Large-scale labeled training datasets have enabled deep neural networks ...
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08/27/2020
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
The divergence between labeled training data and unlabeled testing data ...
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08/07/2020
Revisiting Mid-Level Patterns for Distant-Domain Few-Shot Recognition
Cross-domain few-shot learning (FSL) is proposed recently to transfer kn...
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07/14/2020
TCGM: An Information-Theoretic Framework for Semi-Supervised Multi-Modality Learning
Fusing data from multiple modalities provides more information to train ...
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06/23/2020
Rethinking Distributional Matching Based Domain Adaptation
Domain adaptation (DA) is a technique that transfers predictive models t...
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05/12/2020
Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
Few-shot learning (FSL) aims at recognizing novel classes given only few...
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11/26/2019
Decoupling Features and Coordinates for Few-shot RGB Relocalization
Cross-scene model adaption is a crucial feature for camera relocalizatio...
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11/22/2019
Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain ...
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09/28/2019
Generalized Zero-shot ICD Coding
The International Classification of Diseases (ICD) is a list of classifi...
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07/12/2019
Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) is a challenging class of vision a...
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10/14/2018
Hierarchical Attention Networks for Knowledge Base Completion via Joint Adversarial Training
Knowledge Base (KB) completion, which aims to determine missing relation...
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10/28/2017
Topology Adaptive Graph Convolutional Networks
Convolution acts as a local feature extractor in convolutional neural ne...
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07/29/2017
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
In this paper, we develop deep spatio-temporal neural networks to sequen...
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05/26/2017
Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
While domain adaptation has been actively researched in recent years, mo...
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03/17/2017
