Search Results for author:
Found 44 papers, 18 papers with code
Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining
We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic.
SRCN3D: Sparse R-CNN 3D Surround-View Camera Object Detection and Tracking for Autonomous Driving
This paper proposes Sparse R-CNN 3D (SRCN3D), a novel two-stage fully-convolutional mapping pipeline for surround-view camera detection and tracking.
A Benchmark and Asymmetrical-Similarity Learning for Practical Image Copy Detection
Moreover, this paper further reveals a unique difficulty for solving the hard negative problem in ICD, i. e., there is a fundamental conflict between current metric learning and ICD.
Accelerating Frank-Wolfe via Averaging Step Directions
The Frank-Wolfe method is a popular method in sparse constrained optimization, due to its fast per-iteration complexity.
Learning for Expressive Task-Related Sentence Representations
To achieve this goal, we first propose a new objective which explicitly points out the label token space in the input, and predicts categories of labels via an added [MASK] token.
Bridging the Source-to-target Gap for Cross-domain Person Re-Identification with Intermediate Domains
Instead of directly aligning the source and target domains against each other, we propose to align the source and target domains against their intermediate domains for a smooth knowledge transfer.
Results and findings of the 2021 Image Similarity Challenge
The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods.
Action Keypoint Network for Efficient Video Recognition
An effective approach is to select informative content from the holistic video, yielding a popular family of dynamic video recognition methods.
Learning Memory-Augmented Unidirectional Metrics for Cross-Modality Person Re-Identification
These two unidirectional metrics (IR image to RGB proxy and RGB image to IR proxy) jointly alleviate the relay effect and benefit cross-modality association.
Cross-Modality Person Re-identification
Person Re-Identification
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection
How to align the source and target domains is critical to the CDWSOD accuracy.
Ranked #1 on
Weakly Supervised Object Detection
on Clipart1k
Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval
By combining two fundamental learning approaches in DML, e. g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR.
Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
MFA basically considers three parallel information fusion strategies, i. e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion.
Synthetic-to-Real Translation
Unsupervised Domain Adaptation
+1
Bag of Tricks and A Strong baseline for Image Copy Detection
In this paper, a bag of tricks and a strong baseline are proposed for image copy detection.
D$^2$LV: A Data-Driven and Local-Verification Approach for Image Copy Detection
In this paper, a data-driven and local-verification (D$^2$LV) approach is proposed to compete for Image Similarity Challenge: Matching Track at NeurIPS'21.
Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework
Model smoothing is of central importance for obtaining a reliable teacher model in the student-teacher framework, where the teacher generates surrogate supervision signals to train the student.
Switch to Generalize: Domain-Switch Learning for Cross-Domain Few-Shot Classification
We hold a hypothesis, i. e., if a deep model is capable to fast generalize itself to different domains (using very few samples) during training, it will maintain such domain generalization capacity for testing.
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
To ensure these two properties to better characterize appropriate intermediate domains, we enforce the bridge losses on intermediate domains' prediction space and feature space, and enforce a diversity loss on the two domain factors.
Domain Adaptive Person Re-Identification
Person Re-Identification
Screening for a Reweighted Penalized Conditional Gradient Method
We couple this with a screening rule which is safe in the convex case, converging to the true support at a rate $O(1/(\delta^2))$ where $\delta \geq 0$ measures how close the problem is to degeneracy.
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
In this paper, we consider approximate Frank-Wolfe (FW) algorithms to solve convex optimization problems over graph-structured support sets where the linear minimization oracle (LMO) cannot be efficiently obtained in general.
Learning View Selection for 3D Scenes
Efficient 3D space sampling to represent an underlying3D object/scene is essential for 3D vision, robotics, and be-yond.
Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales
%We argue that such flexibility is also important for deep metric learning, because different visual concepts indeed correspond to different semantic scales.
Latent Group Structured Multi-task Learning
In multi-task learning (MTL), we improve the performance of key machine learning algorithms by training various tasks jointly.
Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory
With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, \emph{i. e.}, deep image classification and deep metric learning (on long-tailed data).
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.
Embodied Self-supervised Learning by Coordinated Sampling and Training
The proposed approach works in an analysis-by-synthesis manner to learn an inference network by iteratively sampling and training.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
If a sample belongs to a tail class, the corresponding feature cloud will have relatively large distribution range, in compensation to its lack of diversity.
Safe Screening for the Generalized Conditional Gradient Method
The conditional gradient method (CGM) has been widely used for fast sparse approximation, having a low per iteration computational cost for structured sparse regularizers.
Vehicle Re-identification with Viewpoint-aware Metric Learning
This paper considers vehicle re-identification (re-ID) problem.
K-Best Transformation Synchronization
In this paper, we introduce the problem of K-best transformation synchronization for the purpose of multiple scan matching.
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data
We propose a neural network based approach for extracting models from dynamic data using ordinary and partial differential equations.
Ranked #10 on
Image Classification
on Fashion-MNIST
Learning low-dimensional state embeddings and metastable clusters from time series data
This paper studies how to find compact state embeddings from high-dimensional Markov state trajectories, where the transition kernel has a small intrinsic rank.
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
The visibility awareness allows VPM to extract region-level features and compare two images with focus on their shared regions (which are visible on both images).
Ranked #14 on
Person Re-Identification
on Market-1501-C
Joint Map and Symmetry Synchronization
Most existing techniques in map computation (e. g., in the form of feature or dense correspondences) assume that the underlying map between an object pair is unique.
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning
Performance of distributed optimization and learning systems is bottlenecked by "straggler" nodes and slow communication links, which significantly delay computation.
Wide Compression: Tensor Ring Nets
Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications.
Deep Unsupervised Clustering Using Mixture of Autoencoders
Unsupervised clustering is one of the most fundamental challenges in machine learning.
Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency.
Ranked #3 on
Person Re-Identification
on UAV-Human
Straggler Mitigation in Distributed Optimization Through Data Encoding
Slow running or straggler tasks can significantly reduce computation speed in distributed computation.
A Simple Approach to Learn Polysemous Word Embeddings
Evaluating these methods is also problematic, as rigorous quantitative evaluations in this space is limited, especially when compared with single-sense embeddings.
SVDNet for Pedestrian Retrieval
This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (re-ID).
Ranked #13 on
Person Re-Identification
on CUHK03 detected
Dynamic Word Embeddings for Evolving Semantic Discovery
Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution.
Probabilistic Reconstruction in Compressed Sensing: Algorithms, Phase Diagrams, and Threshold Achieving Matrices
We further develop the asymptotic analysis of the corresponding phase diagrams with and without measurement noise, for different distribution of signals, and discuss the best possible reconstruction performances regardless of the algorithm.
Statistical Mechanics Information Theory Information Theory
Statistical physics-based reconstruction in compressed sensing
Compressed sensing is triggering a major evolution in signal acquisition.
Statistical Mechanics Information Theory Information Theory
