Search Results for author:
Found 27 papers, 8 papers with code
MST-compression: Compressing and Accelerating Binary Neural Networks with Minimum Spanning Tree
Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations and weights.
Faster Segment Anything: Towards Lightweight SAM for Mobile Applications
Concretely, we distill the knowledge from the heavy image encoder (ViT-H in the original SAM) to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM.
A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering
This is an ongoing project and we intend to update the manuscript on a regular basis.
Generative AI meets 3D: A Survey on Text-to-3D in AIGC Era
AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC.
Attack-SAM: Towards Attacking Segment Anything Model With Adversarial Examples
Beyond the basic goal of mask removal, we further investigate and find that it is possible to generate any desired mask by the adversarial attack.
Segment Anything Model (SAM) Meets Glass: Mirror and Transparent Objects Cannot Be Easily Detected
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks.
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey.
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
Analyzing Effects of Mixed Sample Data Augmentation on Model Interpretability
Data augmentation strategies are actively used when training deep neural networks (DNNs).
A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI
This work conducts a survey on audio diffusion model, which is complementary to existing surveys that either lack the recent progress of diffusion-based speech synthesis or highlight an overall picture of applying diffusion model in multiple fields.
A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization
We evaluate our paradigm on a diverse model zoo consisting of 35 models for various OoD tasks and demonstrate: (i) model ranking is better correlated with fine-tuning ranking than previous methods and up to 9859x faster than brute-force fine-tuning; (ii) OoD generalization after model ensemble with feature selection outperforms the state-of-the-art methods and the accuracy on most challenging task DomainNet is improved from 46. 5\% to 50. 6\%.
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
First, we theoretically show the transferability of robustness from an adversarially trained teacher model to a student model with the help of mixup augmentation.
Tr-NAS: Memory-Efficient Neural Architecture Search with Transferred Blocks
To remedy this problem, we propose a new paradigm for NAS that effectively reduces the use of memory while maintaining high performance.
Adversarial Robustness for Unsupervised Domain Adaptation
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
Distilling Global and Local Logits With Densely Connected Relations
In prevalent knowledge distillation, logits in most image recognition models are computed by global average pooling, then used to learn to encode the high-level and task-relevant knowledge.
Ranked #25 on
Knowledge Distillation
on ImageNet
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Multi-Attention Based Ultra Lightweight Image Super-Resolution
MAFFSRN consists of proposed feature fusion groups (FFGs) that serve as a feature extraction block.
Towards an Adversarially Robust Normalization Approach
In this paper, we investigate how BatchNorm causes this vulnerability and proposed new normalization that is robust to adversarial attacks.
SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization
We argue that such random selection strategies of the patches may not necessarily represent sufficient information about the corresponding object and thereby mixing the labels according to that uninformative patch enables the model to learn unexpected feature representation.
Hybrid Weight Representation: A Quantization Method Represented with Ternary and Sparse-Large Weights
To solve this problem, we propose a hybrid weight representation (HWR) method which produces a network consisting of two types of weights, i. e., ternary weights (TW) and sparse-large weights (SLW).
A NEW POINTWISE CONVOLUTION IN DEEP NEURAL NETWORKS THROUGH EXTREMELY FAST AND NON PARAMETRIC TRANSFORMS
Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.
An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight Hypothesis
Based on SVWH, we propose a second ILWP and quantization method which quantize the predicted residuals between the weights in adjacent convolution layers.
Hybrid Residual Attention Network for Single Image Super Resolution
To address these issues, we present a binarized feature fusion (BFF) structure that utilizes the extracted features from residual groups (RG) in an effective way.
New pointwise convolution in Deep Neural Networks through Extremely Fast and Non Parametric Transforms
Some conventional transforms such as Discrete Walsh-Hadamard Transform (DWHT) and Discrete Cosine Transform (DCT) have been widely used as feature extractors in image processing but rarely applied in neural networks.
Efficient and Scalable View Generation from a Single Image using Fully Convolutional Networks
We present two FCN architectures for SIVG.
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks
Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3. 5 million frames of sharp and gradual transitions.
