Search Results for author:
Found 16 papers, 7 papers with code
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity.
Not All Steps are Equal: Efficient Generation with Progressive Diffusion Models
In the initial stage, a base denoising model is trained to encompass all timesteps.
Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models
We conduct a deep analysis of the three hierarchical features inherent in visual HOI detectors and propose a method for high-level relation extraction aimed at VL foundation models, which we call HO prompt-based learning.
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures
Diffusion models have recently exhibited remarkable performance on synthetic data.
CoNe: Contrast Your Neighbours for Supervised Image Classification
Moreover, to further boost the performance, we propose ``distributional consistency" as a more informative regularization to enable similar instances to have a similar probability distribution.
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection
Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets.
Can GPT-4 Perform Neural Architecture Search?
We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures.
ScaleNet: Searching for the Model to Scale
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales.
CNN-based Local Vision Transformer for COVID-19 Diagnosis
Deep learning technology can be used as an assistive technology to help doctors quickly and accurately identify COVID-19 infections.
Searching for Network Width with Bilaterally Coupled Network
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
ViTAS: Vision Transformer Architecture Search
Vision transformers (ViTs) inherited the success of NLP but their structures have not been sufficiently investigated and optimized for visual tasks.
K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
The operation weight for each path is represented as a convex combination of items in a dictionary with a simplex code.
BCNet: Searching for Network Width with Bilaterally Coupled Network
In BCNet, each channel is fairly trained and responsible for the same amount of network widths, thus each network width can be evaluated more accurately.
Prioritized Architecture Sampling with Monto-Carlo Tree Search
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.
Locally Free Weight Sharing for Network Width Search
In this paper, to better evaluate each width, we propose a locally free weight sharing strategy (CafeNet) accordingly.
Data Agnostic Filter Gating for Efficient Deep Networks
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).
