Search Results for author:
Found 38 papers, 23 papers with code
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
Although each declares to achieve superior performance, fair comparisons are lacking due to the inconsistent choices of the source/target datasets and feature extractors.
Ranked #1 on
Cross-Domain Facial Expression Recognition
on Source: AFE, Target: CK+, JAFFE, SFEW2.0, FER2013, ExpW
Cross-Domain Facial Expression Recognition
Domain Adaptation
+3
CLIP-Count: Towards Text-Guided Zero-Shot Object Counting
Specifically, we propose CLIP-Count, the first end-to-end pipeline that estimates density maps for open-vocabulary objects with text guidance in a zero-shot manner.
Ranked #3 on
Cross-Part Crowd Counting
on ShanghaiTech A
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference.
Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting
Extensive experiments conducted on the RGBT-CC benchmark demonstrate the effectiveness of our framework for RGBT crowd counting.
Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction
To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.
Efficient Crowd Counting via Structured Knowledge Transfer
Crowd counting is an application-oriented task and its inference efficiency is crucial for real-world applications.
STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning
Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms.
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal Numerical Reasoning
Therefore, we propose a Geometric Question Answering dataset GeoQA, containing 4, 998 geometric problems with corresponding annotated programs, which illustrate the solving process of the given problems.
Ranked #4 on
Mathematical Reasoning
on PGPS9K
Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR.
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
Towards a Unified View on Visual Parameter-Efficient Transfer Learning
Towards this goal, we propose a framework with a unified view of PETL called visual-PETL (V-PETL) to investigate the effects of different PETL techniques, data scales of downstream domains, positions of trainable parameters, and other aspects affecting the trade-off.
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning
To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and proposes a novel video self-supervised learning framework named Temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy.
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation
In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e. g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership.
Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training
During inference, instead of changing the motion generator, our method reformulates the input text into a masked motion as the prompt for the motion generator to ``reconstruct'' the motion.
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation.
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting
Here we propose a scale-sensitive generalized loss to tackle this problem.
Ranked #5 on
Object Counting
on FSC147
An Ensemble Framework for Explainable Geospatial Machine Learning Models
Analyzing spatial varying effect is pivotal in geographic analysis.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
MoPE: Parameter-Efficient and Scalable Multimodal Fusion via Mixture of Prompt Experts
Building upon this disentanglement, we introduce the mixture of prompt experts (MoPE) technique to enhance expressiveness.
Road Network Guided Fine-Grained Urban Traffic Flow Inference
Furthermore, we take the road network feature as a query to capture the long-range spatial distribution of traffic flow with a transformer architecture.
DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback
1) The travel time of a vehicle is delayed feedback on the effectiveness of TSC policy at each traffic intersection since it is obtained after the vehicle has left the road network.
Conditional Prompt Tuning for Multimodal Fusion
We show that the representation of one modality can effectively guide the prompting of another modality for parameter-efficient multimodal fusion.
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Our DISC framework is capable of uniformly highlighting the objects-of-interest from complex background while preserving well object details.
Crowd Counting using Deep Recurrent Spatial-Aware Network
Crowd counting from unconstrained scene images is a crucial task in many real-world applications like urban surveillance and management, but it is greatly challenged by the camera's perspective that causes huge appearance variations in people's scales and rotations.
Attentive Crowd Flow Machines
Traffic flow prediction is crucial for urban traffic management and public safety.
Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning
In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings.
Contextualized Spatial-Temporal Network for Taxi Origin-Destination Demand Prediction
Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs.
Investigation of wind pressures on tall building under interference effects using machine learning techniques
This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming.
Crowd Counting with Deep Structured Scale Integration Network
Automatic estimation of the number of people in unconstrained crowded scenes is a challenging task and one major difficulty stems from the huge scale variation of people.
Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction
Land remote sensing analysis is a crucial research in earth science.
Unconstrained Face Sketch Synthesis via Perception-Adaptive Network and A New Benchmark
Face sketch generation has attracted much attention in the field of visual computing.
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning
Our proposed method mainly leverages the intra-modality encoding and cross-modality co-occurrence encoding for fully representation modeling.
Prompt-Matched Semantic Segmentation
Previous methods usually fine-tuned the entire networks for each specific dataset, which will be burdensome to store massive parameters of these networks.
Urban Regional Function Guided Traffic Flow Prediction
The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest.
Visual Tuning
With the surprising development of pre-trained visual foundation models, visual tuning jumped out of the standard modus operandi that fine-tunes the whole pre-trained model or just the fully connected layer.
Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer
In this work, we propose a novel end-to-end wind power forecasting model named Hierarchical Spatial-Temporal Transformer Network (HSTTN) to address the long-term WPF problems.
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
In particular, to fully exploit the periodic information, we also improve the intensity function calculation of the point process with a periodic gated mechanism.
SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting
In the localization stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query tensor to predict the confidence, location, and size of each potential object.
