Search Results for author:
Found 51 papers, 35 papers with code
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network
Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.
GFF: Gated Fully Fusion for Semantic Segmentation
Semantic segmentation generates comprehensive understanding of scenes through densely predicting the category for each pixel.
Ranked #29 on
Semantic Segmentation
on Cityscapes test
Reprojection R-CNN: A Fast and Accurate Object Detector for 360° Images
Specifically, we adapt the terminologies of the traditional object detection task to the omnidirectional scenarios, and propose a novel two-stage object detector, i. e., Reprojection R-CNN by combining both ERP and perspective projection.
Dual Graph Convolutional Network for Semantic Segmentation
Exploiting long-range contextual information is key for pixel-wise prediction tasks such as semantic segmentation.
Ranked #32 on
Semantic Segmentation
on Cityscapes test
Global Aggregation then Local Distribution in Fully Convolutional Networks
GALD is end-to-end trainable and can be easily plugged into existing FCNs with various global aggregation modules for a wide range of vision tasks, and consistently improves the performance of state-of-the-art object detection and instance segmentation approaches.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC 2007
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
For these applications, graph embedding is crucial as it provides vector representations of the graph.
TextNAS: A Neural Architecture Search Space tailored for Text Representation
Learning text representation is crucial for text classification and other language related tasks.
Semantic Flow for Fast and Accurate Scene Parsing
A common practice to improve the performance is to attain high resolution feature maps with strong semantic representation.
Ranked #2 on
Real-Time Semantic Segmentation
on Cityscapes test
Improving BERT with Self-Supervised Attention
One of the most popular paradigms of applying large pre-trained NLP models such as BERT is to fine-tune it on a smaller dataset.
LadaBERT: Lightweight Adaptation of BERT through Hybrid Model Compression
BERT is a cutting-edge language representation model pre-trained by a large corpus, which achieves superior performances on various natural language understanding tasks.
Improving Semantic Segmentation via Decoupled Body and Edge Supervision
Our insight is that appealing performance of semantic segmentation requires \textit{explicitly} modeling the object \textit{body} and \textit{edge}, which correspond to the high and low frequency of the image.
Boundary Content Graph Neural Network for Temporal Action Proposal Generation
To address this issue, we propose a novel Boundary Content Graph Neural Network (BC-GNN) to model the insightful relations between the boundary and action content of temporal proposals by the graph neural networks.
Ranked #25 on
Temporal Action Localization
on ActivityNet-1.3
Multivariate Time-series Anomaly Detection via Graph Attention Network
Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications.
AutoADR: Automatic Model Design for Ad Relevance
We add the model designed by AutoADR as a sub-model into the production Ad Relevance model.
Towards Efficient Scene Understanding via Squeeze Reasoning
Instead of propagating information on the spatial map, we first learn to squeeze the input feature into a channel-wise global vector and perform reasoning within the single vector where the computation cost can be significantly reduced.
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction
Instead, we model their dependencies via a chain of prediction models that take previous attention maps as input to predict the attention maps of a new layer through convolutional neural networks.
Evolving Attention with Residual Convolutions
In this paper, we propose a novel and generic mechanism based on evolving attention to improve the performance of transformers.
Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees
Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information.
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Experimental results on three different aerial segmentation datasets suggest that the proposed method is more effective and efficient than state-of-the-art general semantic segmentation methods.
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.
Enhanced Boundary Learning for Glass-like Object Segmentation
We use these two modules to design a decoder that generates accurate and clean segmentation results, especially on the object contours.
Ranked #20 on
Thermal Image Segmentation
on RGB-T-Glass-Segmentation
End-to-End Video Object Detection with Spatial-Temporal Transformers
Recently, DETR and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors.
Fast and Accurate Scene Parsing via Bi-direction Alignment Networks
Motivated by this, we propose a novel network by aligning two-path information into each other through a learned flow field.
Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation
Representation of semantic context and local details is the essential issue for building modern semantic segmentation models.
BoundarySqueeze: Image Segmentation as Boundary Squeezing
This module is used to squeeze the object boundary from both inner and outer directions, which contributes to precise mask representation.
TS2Vec: Towards Universal Representation of Time Series
Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps.
Customizing Graph Neural Networks using Path Reweighting
Specifically, the proposed CustomGNN can automatically learn the high-level semantics for specific downstream tasks to highlight semantically relevant paths as well to filter out task-irrelevant noises in a graph.
Global Aggregation then Local Distribution for Scene Parsing
Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation.
Improving Video Instance Segmentation via Temporal Pyramid Routing
To incorporate both temporal and scale information, we propose a Temporal Pyramid Routing (TPR) strategy to conditionally align and conduct pixel-level aggregation from a feature pyramid pair of two adjacent frames.
Competence-based Curriculum Learning for Multilingual Machine Translation
Therefore, we focus on balancing the learning competencies of different languages and propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M.
Graph Pointer Neural Networks
On the one hand, multi-hop-based approaches do not explicitly distinguish relevant nodes from a large number of multi-hop neighborhoods, leading to a severe over-smoothing problem.
PolyphonicFormer: Unified Query Learning for Depth-aware Video Panoptic Segmentation
The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously.
TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers
Detection Transformer (DETR) and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors.
Ranked #4 on
Video Object Detection
on ImageNet VID
(using extra training data)
Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation
We hope this simple, yet effective method can serve as a new, flexible baseline in unified video segmentation design.
Ranked #1 on
Video Panoptic Segmentation
on KITTI-STEP
(using extra training data)
Fashionformer: A simple, Effective and Unified Baseline for Human Fashion Segmentation and Recognition
This focus on joint human fashion segmentation and attribute recognition.
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part Segmentation
To the best of our knowledge, we are the first to solve the PPS problem via \textit{a unified and end-to-end transformer model.
SFNet: Faster, Accurate, and Domain Agnostic Semantic Segmentation via Semantic Flow
In this paper, we focus on exploring effective methods for faster, accurate, and domain agnostic semantic segmentation.
Towards Robust Referring Image Segmentation
It considers the negative sentence inputs besides the regular positive text inputs.
Convolution-enhanced Evolving Attention Networks
To the best of our knowledge, this is the first work that explicitly models the layer-wise evolution of attention maps.
Label-Efficient Interactive Time-Series Anomaly Detection
Time-series anomaly detection is an important task and has been widely applied in the industry.
Betrayed by Captions: Joint Caption Grounding and Generation for Open Vocabulary Instance Segmentation
Experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS) demonstrate the superiority of the CGG.
PanopticPartFormer++: A Unified and Decoupled View for Panoptic Part Segmentation
Third, inspired by Mask2Former, based on our meta-architecture, we propose Panoptic-PartFormer++ and design a new part-whole cross-attention scheme to boost part segmentation qualities further.
Towards Open Vocabulary Learning: A Survey
To our knowledge, this is the first comprehensive literature review of open vocabulary learning.
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges.
DST-Det: Simple Dynamic Self-Training for Open-Vocabulary Object Detection
We refer to this approach as the self-training strategy, which enhances recall and accuracy for novel classes without requiring extra annotations, datasets, and re-training.
Towards Language-Driven Video Inpainting via Multimodal Large Language Models
We introduce a new task -- language-driven video inpainting, which uses natural language instructions to guide the inpainting process.
RAP-SAM: Towards Real-Time All-Purpose Segment Anything
Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation.
Explore In-Context Segmentation via Latent Diffusion Models
In-context segmentation has drawn more attention with the introduction of vision foundation models.
VG4D: Vision-Language Model Goes 4D Video Recognition
By transferring the knowledge of the VLM to the 4D encoder and combining the VLM, our VG4D achieves improved recognition performance.
Enhancing Self-Attention with Knowledge-Assisted Attention Maps
Large-scale pre-trained language models have attracted extensive attentions in the research community and shown promising results on various tasks of natural language processing.
