research
∙
08/07/2023
Multi-Label Self-Supervised Learning with Scene Images
Self-supervised learning (SSL) methods targeting scene images have seen ...
share
research
∙
07/20/2023
Quantized Feature Distillation for Network Quantization
Neural network quantization aims to accelerate and trim full-precision n...
research
∙
06/07/2023
Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images
Most self-supervised learning (SSL) methods often work on curated datase...
research
∙
05/27/2023
Instance-based Max-margin for Practical Few-shot Recognition
In order to mimic the human few-shot learning (FSL) ability better and t...
research
∙
03/07/2023
No One Left Behind: Improving the Worst Categories in Long-Tailed Learning
Unlike the case when using a balanced training dataset, the per-class re...
research
∙
03/02/2023
Practical Network Acceleration with Tiny Sets: Hypothesis, Theory, and Algorithm
Due to data privacy issues, accelerating networks with tiny training set...
research
∙
07/12/2022
Synergistic Self-supervised and Quantization Learning
With the success of self-supervised learning (SSL), it has become a main...
research
∙
03/13/2022
Worst Case Matters for Few-Shot Recognition
Few-shot recognition learns a recognition model with very few (e.g., 1 o...
research
∙
02/18/2022
R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning
Most recent semi-supervised deep learning (deep SSL) methods used a simi...
research
∙
02/17/2022
PENCIL: Deep Learning with Noisy Labels
Deep learning has achieved excellent performance in various computer vis...
research
∙
02/16/2022
ActionFormer: Localizing Moments of Actions with Transformers
Self-attention based Transformer models have demonstrated impressive res...
research
∙
02/16/2022
Practical Network Acceleration with Tiny Sets
Network compression is effective in accelerating the inference of deep n...
research
∙
01/26/2022
Training Vision Transformers with Only 2040 Images
Vision Transformers (ViTs) is emerging as an alternative to convolutiona...
research
∙
01/07/2022
Compressing Models with Few Samples: Mimicking then Replacing
Few-sample compression aims to compress a big redundant model into a sma...
research
∙
11/30/2021
A Unified Pruning Framework for Vision Transformers
Recently, vision transformer (ViT) and its variants have achieved promis...
research
∙
11/11/2021
Fine-Grained Image Analysis with Deep Learning: A Survey
Fine-grained image analysis (FGIA) is a longstanding and fundamental pro...
research
∙
08/05/2021
Residual Attention: A Simple but Effective Method for Multi-Label Recognition
Multi-label image recognition is a challenging computer vision task of p...
research
∙
08/05/2021
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning o...
research
∙
08/03/2021
Weakly Supervised Foreground Learning for Weakly Supervised Localization and Detection
Modern deep learning models require large amounts of accurately annotate...
research
∙
06/17/2021
A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications
This paper starts by revealing a surprising finding: without any learnin...
research
∙
06/08/2021
Salvage of Supervision in Weakly Supervised Detection
Weakly supervised object detection (WSOD) has recently attracted much at...
research
∙
03/28/2021
Friends and Foes in Learning from Noisy Labels
Learning from examples with noisy labels has attracted increasing attent...
research
∙
03/28/2021
Distilling Virtual Examples for Long-tailed Recognition
In this paper, we tackle the long-tailed visual recognition problem from...
research
∙
03/25/2021
Rethinking Self-Supervised Learning: Small is Beautiful
Self-supervised learning (SSL), in particular contrastive learning, has ...
research
∙
01/12/2021
Mixup Without Hesitation
Mixup linearly interpolates pairs of examples to form new samples, which...
research
∙
11/03/2020
In Defense of Feature Mimicking for Knowledge Distillation
Knowledge distillation (KD) is a popular method to train efficient netwo...
research
∙
07/03/2020
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
Facial attributes (e.g., age and attractiveness) estimation performance ...
research
∙
02/26/2020
Rethinking the Route Towards Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects wi...
research
∙
11/19/2019
Neural Network Pruning with Residual-Connections and Limited-Data
Filter level pruning is an effective method to accelerate the inference ...
research
∙
11/18/2019
Neural Forest Learning
We propose Neural Forest Learning (NFL), a novel deep learning based ran...
research
∙
08/09/2019
Repetitive Reprediction Deep Decipher for Semi-Supervised Learning
Most recent semi-supervised deep learning (deep SSL) methods used a simi...
research
∙
07/06/2019
Deep Learning for Fine-Grained Image Analysis: A Survey
Computer vision (CV) is the process of using machines to understand and ...
research
∙
06/17/2019
Towards Real-Time Action Recognition on Mobile Devices Using Deep Models
Action recognition is a vital task in computer vision, and many methods ...
research
∙
03/19/2019
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Deep learning has achieved excellent performance in various computer vis...
research
∙
12/13/2018
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
Semi-Supervised Learning (SSL) has been proved to be an effective way to...
research
∙
12/11/2018
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Vehicle re-identification is an important problem and becomes desirable ...
research
∙
05/23/2018
AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference
Channel pruning is an important family of methods to speedup deep model'...
research
∙
05/11/2018
Piecewise classifier mappings: Learning fine-grained learners for novel categories with few examples
Humans are capable of learning a new fine-grained concept with very litt...
research
∙
04/17/2018
Vortex Pooling: Improving Context Representation in Semantic Segmentation
Semantic segmentation is a fundamental task in computer vision, which ca...
research
∙
03/08/2018
Learning Effective Binary Visual Representations with Deep Networks
Although traditionally binary visual representations are mainly designed...
research
∙
11/20/2017
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
Action recognition is an important yet challenging task in computer visi...
research
∙
09/22/2017
Code Attention: Translating Code to Comments by Exploiting Domain Features
Appropriate comments of code snippets provide insight for code functiona...
research
∙
07/20/2017
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Object detection aims at high speed and accuracy simultaneously. However...
research
∙
07/20/2017
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Reusable model design becomes desirable with the rapid expansion of comp...
research
∙
07/20/2017
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
We propose an efficient and unified framework, namely ThiNet, to simulta...
research
∙
07/20/2017
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
The difficulty of image recognition has gradually increased from general...
research
∙
06/19/2017
An Entropy-based Pruning Method for CNN Compression
This paper aims to simultaneously accelerate and compress off-the-shelf ...
research
∙
05/08/2017
Deep Descriptor Transforming for Image Co-Localization
Reusable model design becomes desirable with the rapid expansion of mach...
research
∙
03/20/2017
Learning Correspondence Structures for Person Re-identification
This paper addresses the problem of handling spatial misalignments due t...
research
∙
11/06/2016