23 citations · 67 across the 14 of their papers we have counts for
14 papers
Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models
Wenhao Li, Xiu Su, Yu Han +3
Diffusion models have demonstrated remarkable efficacy in various generative tasks with the predictive prowess of denoising model. Currently, diffusion models employ a uniform deno…
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
Yichao Cao, Qingfei Tang, Xiu Su +4
Human-object interaction (HOI) detection aims to comprehend the intricate relationships between humans and objects, predicting triplets, and serving as th…
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures
Wenhao Li, Xiu Su, Shan You +3
Diffusion models have recently exhibited remarkable performance on synthetic data. After a diffusion path is selected, a base model, such as UNet, operates as a denoising autoencod…
CoNe: Contrast Your Neighbours for Supervised Image Classification
Mingkai Zheng, Shan You, Lang Huang +5
Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping…
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection
Yichao Cao, Qingfei Tang, Feng Yang +4
Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and object…
Can GPT-4 Perform Neural Architecture Search?
Mingkai Zheng, Xiu Su, Shan You +4
We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textb…
ScaleNet: Searching for the Model to Scale
Jiyang Xie, Xiu Su, Shan You +3
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales. Current methods either simply resort…
Searching for Network Width with Bilaterally Coupled Network
Xiu Su, Shan You, Jiyang Xie +4
Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
Xiu Su, Shan You, Mingkai Zheng +4
In one-shot weight sharing for NAS, the weights of each operation (at each layer) are supposed to be identical for all architectures (paths) in the supernet. However, this rules ou…
ViTAS: Vision Transformer Architecture Search
Xiu Su, Shan You, Jiyang Xie +6
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks. One of the simplest solutions i…
BCNet: Searching for Network Width with Bilaterally Coupled Network
Xiu Su, Shan You, Fei Wang +3
Searching for a more compact network width recently serves as an effective way of channel pruning for the deployment of convolutional neural networks (CNNs) under hardware constrai…
Prioritized Architecture Sampling with Monto-Carlo Tree Search
Xiu Su, Tao Huang, Yanxi Li +5
One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. Ho…
Locally Free Weight Sharing for Network Width Search
Xiu Su, Shan You, Tao Huang +4
Searching for network width is an effective way to slim deep neural networks with hardware budgets. With this aim, a one-shot supernet is usually leveraged as a performance evaluat…
Data Agnostic Filter Gating for Efficient Deep Networks
Xiu Su, Shan You, Tao Huang +5
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Curren…