Search Results for author:
Found 27 papers, 20 papers with code
Neural Architecture Design for GPU-Efficient Networks
To address this issue, we propose a general principle for designing GPU-efficient networks based on extensive empirical studies.
DAMO-YOLO : A Report on Real-Time Object Detection Design
Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios.
Ranked #33 on
Real-Time Object Detection
on MS COCO
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation.
Ranked #1 on
Person Search
on PRW
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition
Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.
Ranked #2 on
Neural Architecture Search
on ImageNet
Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition
To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures.
Neural Architecture Search
Vocal Bursts Intensity Prediction
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS).
Ranked #88 on
Object Detection
on COCO minival
Entropy-Driven Mixed-Precision Quantization for Deep Network Design
Deploying deep convolutional neural networks on Internet-of-Things (IoT) devices is challenging due to the limited computational resources, such as limited SRAM memory and Flash storage.
DeepMAD: Mathematical Architecture Design for Deep Convolutional Neural Network
To this end, a novel framework termed Mathematical Architecture Design for Deep CNN (DeepMAD) is proposed to design high-performance CNN models in a principled way.
Ranked #1 on
Neural Architecture Search
on ImageNet
Maximizing Spatio-Temporal Entropy of Deep 3D CNNs for Efficient Video Recognition
In this work, we propose to automatically design efficient 3D CNN architectures via a novel training-free neural architecture search approach tailored for 3D CNNs considering the model complexity.
Ranked #84 on
Action Recognition
on Something-Something V2
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning.
Ranked #2 on
Text-To-SQL
on spider
Entroformer: A Transformer-based Entropy Model for Learned Image Compression
One critical component in lossy deep image compression is the entropy model, which predicts the probability distribution of the quantized latent representation in the encoding and decoding modules.
GiraffeDet: A Heavy-Neck Paradigm for Object Detection
This heavy-backbone design paradigm is mostly due to the historical legacy when transferring image recognition models to object detection rather than an end-to-end optimized design for object detection.
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images.
Learning Accurate Entropy Model with Global Reference for Image Compression
In this work, we propose a novel Global Reference Model for image compression to effectively leverage both the local and the global context information, leading to an enhanced compression rate.
Spatiotemporal Entropy Model is All You Need for Learned Video Compression
The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffering from its complex structure and error propagation problem.
Interpolation variable rate image compression
Compression standards have been used to reduce the cost of image storage and transmission for decades.
TAGPerson: A Target-Aware Generation Pipeline for Person Re-identification
In TAGPerson, we extract information from target scenes and use them to control our parameterized rendering process to generate target-aware synthetic images, which would hold a smaller gap to the real images in the target domain.
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty
Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again.
Robust Graph Structure Learning via Multiple Statistical Tests
A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges.
Learning to Cluster Faces via Transformer
In this paper, we repurpose the well-known Transformer and introduce a Face Transformer for supervised face clustering.
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection
In this way, an obvious gap can be produced between the distributions of normal and abnormal data in the target domain, therefore enabling the anomaly detection in the domain.
Fine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models.
Exploring the Quality of GAN Generated Images for Person Re-Identification
Recently, GAN based method has demonstrated strong effectiveness in generating augmentation data for person re-identification (ReID), on account of its ability to bridge the gap between domains and enrich the data variety in feature space.
ZenDet: Revisiting Efficient Object Detection Backbones from Zero-Shot Neural Architecture Search
Recent researches attempt to reduce this cost by optimizing the backbone architecture with the help of Neural Architecture Search (NAS).
NAS-Bench-Zero: A Large Scale Dataset for Understanding Zero-Shot Neural Architecture Search
Based on these new discoveries, we propose i) a novel hybrid zero-shot proxy which outperforms existing ones by a large margin and is transferable among popular search spaces; ii) a new index for better measuring the true performance of ZS-NAS proxies in constrained NAS.
Hierarchical Cross Contrastive Learning of Visual Representations
In this work, we propose a novel approach termed Hierarchical Cross Contrastive Learning(HCCL) to further distill the information mismatched by the conventional contrastive loss.
Fuxi-DA: A Generalized Deep Learning Data Assimilation Framework for Assimilating Satellite Observations
Data assimilation (DA), as an indispensable component within contemporary Numerical Weather Prediction (NWP) systems, plays a crucial role in generating the analysis that significantly impacts forecast performance.
