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Found 30 papers, 8 papers with code
CTAP for Chinese:A Linguistic Complexity Feature Automatic Calculation Platform
The construct of linguistic complexity has been widely used in language learning research.
Immersion and Invariance-based Disturbance Observer and Its Application to Safe Control
When the disturbance input matrix is nonlinear, existing disturbance observer design methods rely on the solvability of a partial differential equation or the existence of an output function with a uniformly well-defined disturbance relative degree, which can pose significant limitations.
Revisiting Prompt Engineering via Declarative Crowdsourcing
Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone.
Improving Automatic Parallel Training via Balanced Memory Workload Optimization
Transformer models have emerged as the leading approach for achieving state-of-the-art performance across various application domains, serving as the foundation for advanced large-scale deep learning (DL) models.
Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation
The SES method is designed specifically for sequence labeling tasks.
Adaptive Safety-Critical Control for a Class of Nonlinear Systems with Parametric Uncertainties: A Control Barrier Function Approach
This paper presents a novel approach for the safe control design of systems with parametric uncertainties in both drift terms and control-input matrices.
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism
Transformer models have achieved state-of-the-art performance on various domains of applications and gradually becomes the foundations of the advanced large deep learning (DL) models.
SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene
We present a 3D generative model for general natural scenes.
DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection
Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking Framework for Definition Modeling
This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1 Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack, achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and French.
The Tree Loss: Improving Generalization with Many Classes
We introduce the tree loss as a drop-in replacement for the cross entropy loss.
PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection
Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.
Disturbance Observer-based Robust Control Barrier Functions
This work presents a safe control design approach that integrates the disturbance observer (DOB) and the control barrier function (CBF) for systems with external disturbances.
Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation
Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation
We show that with the help of a content-rich discrete visual codebook from VQ-VAE, the discrete diffusion model can also generate high fidelity images with global context, which compensates for the deficiency of the classical autoregressive model along pixel space.
One to Transfer All: A Universal Transfer Framework for Vision Foundation Model with Few Data
Transferring with few data in a general way to thousands of downstream tasks is becoming a trend of the foundation model's application.
INTERN: A New Learning Paradigm Towards General Vision
Enormous waves of technological innovations over the past several years, marked by the advances in AI technologies, are profoundly reshaping the industry and the society.
Hierarchical Segment-based Optimization for SLAM
This paper presents a hierarchical segment-based optimization method for Simultaneous Localization and Mapping (SLAM) system.
Neural Implicit 3D Shapes from Single Images with Spatial Patterns
However, the image features for describing 3D point samplings of implicit functions are less effective when significant variations of occlusions, views, and appearances exist from the image.
Fixing the Teacher-Student Knowledge Discrepancy in Distillation
To solve this problem, in this paper, we propose a novel student-dependent distillation method, knowledge consistent distillation, which makes teacher's knowledge more consistent with the student and provides the best suitable knowledge to different student networks for distillation.
Differentiable Network Adaption with Elastic Search Space
In this paper we propose a novel network adaption method called Differentiable Network Adaption (DNA), which can adapt an existing network to a specific computation budget by adjusting the width and depth in a differentiable manner.
A Collaborative Visual SLAM Framework for Service Robots
We present a collaborative visual simultaneous localization and mapping (SLAM) framework for service robots.
Multi-scale Network Architecture Search for Object Detection
Many commonly-used detection frameworks aim to handle the multi-scale object detection problem.
DMCP: Differentiable Markov Channel Pruning for Neural Networks
In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process.
TiSASRec: Time Interval Aware Self-Attention for Sequential Recommendation
Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently.
Single image reflection removal via learning with multi-image constraints
Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass.
DeGNN: Characterizing and Improving Graph Neural Networks with Graph Decomposition
Despite the wide application of Graph Convolutional Network (GCN), one major limitation is that it does not benefit from the increasing depth and suffers from the oversmoothing problem.
Skin Lesion Segmentation Using Atrous Convolution via DeepLab v3
As melanoma diagnoses increase across the US, automated efforts to identify malignant lesions become increasingly of interest to the research community.
Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy
Fully convolutional neural network (FCN) has been dominating the game of face detection task for a few years with its congenital capability of sliding-window-searching with shared kernels, which boiled down all the redundant calculation, and most recent state-of-the-art methods such as Faster-RCNN, SSD, YOLO and FPN use FCN as their backbone.
Flow-Guided Feature Aggregation for Video Object Detection
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
Ranked #22 on
Video Object Detection
on ImageNet VID
