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Found 27 papers, 10 papers with code
Supervised Edge Attention Network for Accurate Image Instance Segmentation
In this task, many works segment instance based on a bounding box from the box head, which means the quality of the detection also affects the completeness of the mask.
Can multi-label classification networks know what they don't know?
Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels.
Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona
In this paper, we describe approaches for developing Emily, an emotion-affective open-domain chatbot.
Efficient and Systematic Partitioning of Large and Deep Neural Networks for Parallelization
We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy.
Freeing Hybrid Distributed AI Training Configuration
Hybrid Parallelism (HP), which applies different parallel strategies on different parts of DNNs, is more efficient but requires advanced configurations.
CONet: Channel Optimization for Convolutional Neural Networks
To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN.
Step-Wise Hierarchical Alignment Network for Image-Text Matching
Image-text matching plays a central role in bridging the semantic gap between vision and language.
Can multi-label classification networks know what they don’t know?
Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels.
Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning
Machine Learning (ML) has been embraced as a powerful tool by the financial industry, with notable applications spreading in various domains including investment management.
Hierarchical Cost Analysis for Distributed DL
In order to formalize the behaviors of the HP in distributed DL and quantitatively evaluate the cost caused by HP, we are studying Bridging DL composed by a double-level execution model associated with a symbolic cost model.
Strictly Decentralized Adaptive Estimation of External Fields using Reproducing Kernels
Approximations of the evolution of the ideal local estimate $\hat{g}^i_t$ of agent $i$ is constructed solely using observations made by agent $i$ on a fine time scale.
Energy-based Out-of-distribution Detection for Multi-label Classification
Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels.
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning
Query reformulation aims to alter noisy or ambiguous text sequences into coherent ones closer to natural language questions.
Context-Aware Graph Convolution Network for Target Re-identification
Most existing re-identification methods focus on learning robust and discriminative features with deep convolution networks.
Frame Aggregation and Multi-Modal Fusion Framework for Video-Based Person Recognition
To tackle the challenges above, we propose a novel Frame Aggregation and Multi-Modal Fusion (FAMF) framework for video-based person recognition, which aggregates face features and incorporates them with multi-modal information to identify persons in videos.
Learning Risk Preferences from Investment Portfolios Using Inverse Optimization
Our approach allows the learner to continuously estimate real-time risk preferences using concurrent observed portfolios and market price data.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching.
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.
Ranked #11 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL).
Continuous-Time Mean-Variance Portfolio Selection: A Reinforcement Learning Framework
We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL).
Saliency-Guided Attention Network for Image-Sentence Matching
Concretely, the saliency detector provides the visual saliency information as the guidance for the two attention modules.
Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings
Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic.
Exploration versus exploitation in reinforcement learning: a stochastic control approach
We carry out a complete analysis of the problem in the linear--quadratic (LQ) setting and deduce that the optimal feedback control distribution for balancing exploitation and exploration is Gaussian.
Parameter-Free Spatial Attention Network for Person Re-Identification
We propose a novel architecture for Person Re-Identification, based on a novel parameter-free spatial attention layer introducing spatial relations among the feature map activations back to the model.
Ranked #17 on
Person Re-Identification
on DukeMTMC-reID
Multi-task Sparse Learning with Beta Process Prior for Action Recognition
In this paper, we formulate human action recognition as a novel Multi-Task Sparse Learning(MTSL) framework which aims to construct a test sample with multiple features from as few bases as possible.
