Search Results for author:
Found 24 papers, 10 papers with code
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration
Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training.
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation.
DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection
This paper reveals that the recently developed Diffusion Model is a scalable data engine for object detection.
DLIP: Distilling Language-Image Pre-training
Furthermore, DLIP succeeds in retaining more than 95% of the performance with 22. 4% parameters and 24. 8% FLOPs compared to the teacher model and accelerates inference speed by 2. 7x.
AlignDet: Aligning Pre-training and Fine-tuning in Object Detection
To this end, we propose AlignDet, a unified pre-training framework that can be adapted to various existing detectors to alleviate the discrepancies.
Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models
A first-frame conditioning strategy is proposed to facilitate the model to generate videos transferred from the image domain as well as arbitrary-length videos in an auto-regressive manner.
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation
Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios.
Ranked #4 on
Open Vocabulary Panoptic Segmentation
on ADE20K
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
In particular, we first formulate the oscillation in PTQ and prove the problem is caused by the difference in module capacity.
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search
Our improved backbone network can reduce the computational effort while improving the accuracy of the object detection network.
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic Segmentation
Consequently, we offer the first attempt to provide lightweight SSSS models via a novel multi-granularity distillation (MGD) scheme, where multi-granularity is captured from three aspects: i) complementary teacher structure; ii) labeled-unlabeled data cooperative distillation; iii) hierarchical and multi-levels loss setting.
Knowledge Distillation
Semi-Supervised Semantic Segmentation
Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios
Then, Next Hybrid Strategy (NHS) is designed to stack NCB and NTB in an efficient hybrid paradigm, which boosts performance in various downstream tasks.
Ranked #258 on
Image Classification
on ImageNet
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation
Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-quality image-annotation pairs.
MoCoViT: Mobile Convolutional Vision Transformer
Recently, Transformer networks have achieved impressive results on a variety of vision tasks.
TRT-ViT: TensorRT-oriented Vision Transformer
We revisit the existing excellent Transformers from the perspective of practical application.
SepViT: Separable Vision Transformer
Vision Transformers have witnessed prevailing success in a series of vision tasks.
ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer
The vanilla self-attention mechanism inherently relies on pre-defined and steadfast computational dimensions.
Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation
Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label.
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
In this work, we revisit the role of discriminator in GAN compression and design a novel generator-discriminator cooperative compression scheme for GAN compression, termed GCC.
Online Multi-Granularity Distillation for GAN Compression
It reveals that OMGD provides a feasible solution for the deployment of real-time image translation on resource-constrained devices.
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions that can demonstrate a real-time performance on smartphones and IoT platforms.
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
In this way, PAD-NAS can automatically design the operations for each layer and achieve a trade-off between search space quality and model diversity.
An Empirical Study of Propagation-based Methods for Video Object Segmentation
While propagation-based approaches have achieved state-of-the-art performance for video object segmentation, the literature lacks a fair comparison of different methods using the same settings.
Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
Currently, owing to the ubiquity of mobile devices, online handwritten Chinese character recognition (HCCR) has become one of the suitable choice for feeding input to cell phones and tablet devices.
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
We design a nine-layer CNN for HCCR consisting of 3, 755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0. 21% drop in accuracy.
