Search Results for author:
Found 19 papers, 11 papers with code
Procedural Image Programs for Representation Learning
Learning image representations using synthetic data allows training neural networks without some of the concerns associated with real images, such as privacy and bias.
MaSS: Multi-attribute Selective Suppression
The recent rapid advances in machine learning technologies largely depend on the vast richness of data available today, in terms of both the quantity and the rich content contained within.
Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data
It is thus better to tailor synthetic pre-training data to a specific downstream task, for best performance.
Generating Realistic Physical Adversarial Examplesby Patch Transformer Network
Physical adversarial attacks apply carefully crafted adversarial perturbations onto real objects to maliciously alter the prediction of object classifiers or detectors.
Dynamic Network Quantization for Efficient Video Inference
Deep convolutional networks have recently achieved great success in video recognition, yet their practical realization remains a challenge due to the large amount of computational resources required to achieve robust recognition.
Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data
As the base dataset and unlabeled dataset are from different domains, projecting the target images in the class-domain of the base dataset with a fixed pretrained model might be sub-optimal.
RegionViT: Regional-to-Local Attention for Vision Transformers
The regional-to-local attention includes two steps: first, the regional self-attention extract global information among all regional tokens and then the local self-attention exchanges the information among one regional token and the associated local tokens via self-attention.
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition
Specifically, given a video segment, a multi-modal policy network is used to decide what modalities should be used for processing by the recognition model, with the goal of improving both accuracy and efficiency.
Detector-Free Weakly Supervised Grounding by Separation
In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector.
Ranked #1 on
Phrase Grounding
on Visual Genome
CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification
To this end, we propose a dual-branch transformer to combine image patches (i. e., tokens in a transformer) of different sizes to produce stronger image features.
Ranked #419 on
Image Classification
on ImageNet
A Broad Study on the Transferability of Visual Representations with Contrastive Learning
Tremendous progress has been made in visual representation learning, notably with the recent success of self-supervised contrastive learning methods.
Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths
Second, to effectively transfer knowledge, we develop a dynamic block swapping method by randomly replacing the blocks in the lower-precision student network with the corresponding blocks in the higher-precision teacher network.
Large Scale Neural Architecture Search with Polyharmonic Splines
Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification.
Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
In recent years, a number of approaches based on 2D or 3D convolutional neural networks (CNN) have emerged for video action recognition, achieving state-of-the-art results on several large-scale benchmark datasets.
NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
The typical way of conducting large scale NAS is to search for an architectural building block on a small dataset (either using a proxy set from the large dataset or a completely different small scale dataset) and then transfer the block to a larger dataset.
More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation
Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets.
Ranked #78 on
Action Recognition
on Something-Something V2
(using extra training data)
Efficient Fusion of Sparse and Complementary Convolutions
We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions.
Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
The proposed approach demonstrates improvement of model efficiency and performance on both object recognition and speech recognition tasks, using popular architectures including ResNet and ResNeXt.
NISP: Pruning Networks using Neuron Importance Score Propagation
In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification, for a pruned network to retrain its predictive power.
