Search Results for author:
Found 16 papers, 7 papers with code
FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
Due to this, previous neural architecture search (NAS) methods are computationally expensive.
Ranked #890 on
Image Classification
on ImageNet
ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
We formulate platform-aware NN architecture search in an optimization framework and propose a novel algorithm to search for optimal architectures aided by efficient accuracy and resource (latency and/or energy) predictors.
Adaptive Graph Representation Learning for Video Person Re-identification
While correlations between parts are ignored in the previous methods, to leverage the relations of different parts, we propose an innovative adaptive graph representation learning scheme for video person Re-ID, which enables the contextual interactions between relevant regional features.
Ranked #3 on
Person Re-Identification
on PRID2011
Graph Representation Learning
Video-Based Person Re-Identification
Panoptic Scene Graph Generation with Semantics-Prototype Learning
To promise consistency and accuracy during the transfer process, we propose to measure the invariance of representations in each predicate class, and learn unbiased prototypes of predicates with different intensities.
Ranked #3 on
Panoptic Scene Graph Generation
on PSG Dataset
MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification
As a challenging task, unsupervised person ReID aims to match the same identity with query images which does not require any labeled information.
Progressive Target-Styled Feature Augmentation for Unsupervised Domain Adaptation on Point Clouds
Unlike previous works that focus on feature extractor adaptation, our PTSFA approach focuses on classifier adaptation.
Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
In contrast, we propose a unified generative and discriminative approach in the framework of amortized variational inference.
Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images
As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images.
Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications
The application of deep learning techniques resulted in remarkable improvement of machine learning models.
An Enhanced Ad Event-Prediction Method Based on Feature Engineering
In digital advertising, Click-Through Rate (CTR) and Conversion Rate (CVR) are very important metrics for evaluating ad performance.
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
F3A-GAN: Facial Flow for Face Animation with Generative Adversarial Networks
Formulated as a conditional generation problem, face animation aims at synthesizing continuous face images from a single source image driven by a set of conditional face motion.
D3T-GAN: Data-Dependent Domain Transfer GANs for Few-shot Image Generation
To transfer knowledge between discriminators, we design a multi-level discriminant knowledge distillation from the source discriminator to the target discriminator on both the real and fake samples.
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE).
Ranked #25 on
Relation Extraction
on DocRED
MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding
In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module.
HeightFormer: Explicit Height Modeling without Extra Data for Camera-only 3D Object Detection in Bird's Eye View
In this work, we propose to explicitly model heights in the BEV space, which needs no extra data like LiDAR and can fit arbitrary camera rigs and types compared to modeling depths.
